Handcrafted Local Features are Convolutional Neural Networks

Image and video classification research has made great progress through the development of handcrafted local features and learning based features. These two architectures were proposed roughly at the same time and have flourished at overlapping stages of history. However, they are typically viewed as distinct approaches. In this paper, we emphasize their structural similarities and show how such a unified view helps us in designing features that balance efficiency and effectiveness. As an example, we study the problem of designing efficient video feature learning algorithms for action recognition. We approach this problem by first showing that local handcrafted features and Convolutional Neural Networks (CNNs) share the same convolution-pooling network structure. We then propose a two-stream Convolutional ISA (ConvISA) that adopts the convolution-pooling structure of the state-of-the-art handcrafted video feature with greater modeling capacities and a cost-effective training algorithm. Through custom designed network structures for pixels and optical flow, our method also reflects distinctive characteristics of these two data sources. Our experimental results on standard action recognition benchmarks show that by focusing on the structure of CNNs, rather than end-to-end training methods, we are able to design an efficient and powerful video feature learning algorithm.

INTRODUCTION
Despite a long history of prior work, image and video classification remains challenging. Recent progress on this problem mainly relies on the improvements of features, which can be broadly categorized into two classes: 1) local handcrafted approaches (Schmid & Mohr (1997); Lowe (2004); Wang et al. (2011)) that include handcrafted features and their corresponding encoding methods (Lazebnik et al. (2006); Perronnin et al. (2010)), and 2) learning based approaches that are mainly represented by CNNs for image recognition (LeCun et al. (1998); Krizhevsky et al. (2012); Simonyan & Zisserman (2014)). These two groups of methods have strong connections in both functionality and history but are typically treated as unrelated methodologies.
In a ground-breaking work, Schmid & Mohr (1997) proposed to use local features for general image retrieval. Since then, handcrafted features have become very popular and numerous improvements have sprung up (Lowe (2004); Fei-Fei & Perona (2005); Perronnin et al. (2010); Wang et al. (2011). They have been the best practices for image and video classification until recently. In particular, SIFT based features with Fisher Vector encoding were still the best ingredients for PASCAL VOC challenges 2012 (Everingham et al. (2014)). The reason behind this popularity is that handcrafted features do not rely on any labeled data and have very efficient training algorithms. The main problem of these methods, however, is that their modeling capacities are limited by the fixed transformations (filters) that stay the same for different sources of data. For example, HOG (Dalal & Triggs (2005)) and HOF (Laptev (2005)) use the same set of gradient and binning filters despite different inputs.
Roughly at the same time of Schmid & Mohr (1997)'s work, LeCun et al. (1998) published the first modern CNN architecture (LeNet-5) for handwritten character recognition. The three key aspects of this CNN framework are the convolution-pooling architecture of the model, the operational functions that implement this architecture, and the objective function that is optimized to learn the parameters of these functions. All three contributes to the effectiveness of the features obtained from the framework. Of the three, the latter two, namely the operational functions and the optimization criterion have the greater influence on the data requirements to learn the model and the computational expense of the learning. In fact, these were the major bottleneck for extending LetNet-5 from dealing with simple image processing tasks like digital recognition to processing more complex ones such as object recognition from natural images.
However, the first aspect, namely the convolution-pooling architecture too has a significant influence on the effectiveness of the features. Indeed, as we show in Section 3, state-of-the-art handcrafted features for image and video classification do implicitly employ this architecture.
We therefore retain only the first aspect, namely the conovlutional-pooling architecture, but replace the operational functions and optimization objective with simpler, easier to compute variants. As a case in point, we design a cost-effective video feature learning method based on the conovlutionalpooling architecture. Video feature learning is chosen because it often involves larger data size and more difficult labeling than image feature learning. For these kind of tasks, CNNs face the same problems as they had for object recognition a decade ago.
For restricting the computational complexity and staying label-free, we adopt the architecture of Improved Dense Trajectory (IDT) (Wang et al. (2013)), a state-of-the-art local video feature. To avoid the modeling capacity limitation of handcrafted transformation, we replace them with a Convolutional ISA model. Our proposed architecture, termed two-stream ConvISA, has the following attractive properties: • It has the convolution-pooling architecture that shares among handcrafted and CNN approaches. • Compared to handcrafted approaches, it has much more powerful modeling capabilities and can be adapted to different data sources. • Compared to end-to-end CNN approaches, it avoids the costly label collection and model training.
We conduct experiments on benchmark action datasets of HMDB51 and UCF101, as in Simonyan & Zisserman (2014). Table 1 compares the model training time and accuracy of our method to the two-stream CNN (Simonyan & Zisserman (2014)), a state-of-the-art CNN approach. Two-stream CNNs are trained in a multi-task learning manner by utilizing both UCF101 and HMDB51 datasets. Hence their results are not directly comparable to ours, but we put them here for reference purposes.
In terms of training time, our approach is much more efficient than two-stream CNNs by several orders of magnitude. Two-stream CNNs needs about 1 day to train a model on 4 Titan GPUs while two-stream ConvISA only needs about 2 hours on 1 CPU. With regard to accuracy, our method outperforms two-stream CNNs on HMDB51 and has similar results on UCF101 despite the fact that it was trained on less data and does not need any labels to train the feature extraction module.
In the remainder of this paper, we first provide more background information about video features with an emphasis on recent attempts at learning with deep neural networks. We then describe the relationship between handcrafted features and CNN-based features in detail, followed by the descriptions of our two-stream ConvISA algorithm. After that, we conduct experiments and show more detailed comparisons of our method to the state-of-the-art methods. Further discussions including potential improvements are provided at the end.

RELATED WORK
Features and encoding are the major sources of breakthroughs in conventional video representations. Among them the trajectory based approaches (Wang et al. (2013); Jiang et al. (2012)), especially the Dense Trajectory (DT) and IDT (Wang et al. (2011;2013)), are the basis of current state-of-the-art handcrafted algorithms. These trajectory-based methods are designed to address the flaws of imageextended video features. Their superior performance validates the need for a unique representation of motion features.
There have been many works attempting to improve IDT due to its popularity. Peng et al. (2014) enhanced the performance of IDT by increasing codebook sizes and fusing multiple coding methods. Sapienza et al. (2014) explored ways to sub-sample and generate vocabularies for DT features. Hoai & Zisserman (2014) achieved state-of-the-art performance on several action recognition datasets through applying three techniques including data augmentation, modeling score distribution over video subsequences, and capturing the relationship among action classes. Fernando et al. (2015) modeled the evolution of appearance in the video and achieved state-of-the-art results on the Holly-wood2 dataset. Lan et al. (2015a) proposed to extract features from videos with multiple playback speeds to achieve speed invariances. However, none of them dealt with the fact that IDT relies on very simple handcrafted descriptors. In contrast, data-driven approaches have demonstrated their modeling power over image recognition (Krizhevsky et al. (2012)) and are gradually replacing traditional handcrafted methods.
Motivated by the success of CNNs, researchers are working intensely towards developing CNN equivalents for learning video features. Several accomplishments have been reported from using CNNs for action recognition in videos (Wu et al. (2015); Varadarajan et al. (2015)). Karpathy et al. (2014) trained deep CNNs through one million weakly labeled YouTube videos and reported moderate success while using it as a feature extractor. Simonyan & Zisserman (2014) demonstrated a result competitive to IDT (Wang et al. (2013)) through training deep CNNs using both sampled frames and stacked optical flows. Tran et al. (2014) developed C3D to extend 2D convolution to 3D convolution. Ng et al. (2015) proposed to to build a LSTM on top of CNNs to capture longer-term relationship among frames. Wang et al. (2015) use the outputs of two-stream CNNs as features to replace HOG and achieve state-of-the-art results on HMDB51 and UCF101 datasets. All of the above relied on a large amount of labels which are expensive to get and generally perform worse than handcrafted features among small datasets.
There has been limited number of works on unsupervised methods for learning video features. Among them the Independent Component Analysis (ICA) (Hurri & Hyvärinen (2003)) was the first approach to learn representations of videos in an unsupervised way. Le et al. (2011) addressed the issue using stacked ConvISA. Srivastava et al. (2015) applied unsupervised feature learning through long-short term memory. Since these methods rely purely on pixel data, they struggled to capture motion information and generally performed no better than state-of-the-art handcrafted methods. Our previous work (Lan et al. (2015b)) tried to use stacked ConvISA to learn filters for optical flow data, but also could not outperform handcrafted methods due to the fact that stacked ConvISA does not have a good network structure for optical-flow feature learning.
There are also several attempts at connecting the traditional feature encoding pipeline to the neural network frameworks. Sydorov et al. (2014) studied the structure similarities between Fisher vectors and neural networks and proposed to jointly optimize Fisher vectors and the classifier. Richard & Gall (2015) converted the kMeans-based BoW model into an equivalent recurrent neural network and trained the BoW model and classifier together. Both above approaches focus on the end-to-end training of CNNs and again require labels and significantly increase the model training time. Instead, we emphasize the convoltional-pooling structure of CNNs rather than their training methods.

THE CONVOLUTION-POOLING ARCHITECTURE
In this section we first define the convolution-pooling structure and then compare IDT with CNNbased video features. We highlight their structural similarities by showing that they are both features generated by deep convolution-pooling cascade with two key elements: convolution and pooling layers.
We define a convolution-pooling cascade as any single, iterative or recursive implementation of the following sequence of operations: where w ⊗ x is a three-dimensional convolution of a filter w with the N × M × T video blocks x and f () is any non-linear compressive operation. w ⊗ x, and as a consequence c(x) also have size N × M × T . g() is a pooling function that results in a shrinking of the argument and operates on any

HANDCRAFTED VIDEO FEATURES
A typical handcrafted video feature extraction procedure is often composed of two stages of convolution and pooling. The first stage purely relies on handcrafted filters and generates descriptors from local data. The second one often uses filters learned from unsupervised methods to encode the descriptors generated from the first stage and pool them together to get global features. For example, shown in Figure 1 is a schematic description of three HOG-based IDT descriptors, each of which contains two stages of convolution and pooling (marked by dashed red and green boxes, respectively) including a total of three convolution and two pooling operations. Among them, Conv1 uses two gradient filters as w and with: f (x) = x. Conv2 is an oriented soft binning, which can be approximated with w being the unit directional vectors and f being non-linear activation functions such as rectified linear unit (Krizhevsky et al. (2012)): f (x) = max(x, 0). Conv3 is KMeans-based BoW, which uses KMeans centroids as w and a softmax function (Richard & Gall (2015)) as f : where k is the kth centroid. Pool1 is a local sum pooling: where d is the pool size in space and time and x, y, t are the space and time locations where g() applied. Pool2 is a global sum pooling: x,y,t x x,y,t .
Using above key operators, the IDT-HOG Net represents the procedure of generating a KMeansbased bag of words (BoW) encoded HOG feature from stacked frames. At the first stage, the stacked frames are convolved with two gradient filters followed by 8 oriented binning filters and one spatiotemporal sum pooling. During the second stage, the descriptors from the first stage are convolved with K binning filters learned using KMeans and pooled together afterwards. The IDT-HOF Net and IDT-MBH Net represent the procedures of generating KMeans encoded HOF and MBH features, respectively, from stacked optical flows. IDT-MBH Net is similar to the IDT-HOG Net except taking optical flows as inputs instead of pixels. IDT-HOF Net removes Conv1 and using 9 oriented binning filters instead of 8. Note that although we use KMeans encoding as an example, other encoding methods such as Fisher Vector and VLAD have similar procedures (Sydorov et al. (2014); Richard & Gall (2015)). For simplicity, we leave out the feature detection step, which can be viewed as another convolution with binary activation function. The main strength of this pipeline is that it is computationally efficient because of the layer-wise training and does not need labels to train the feature extraction module due to the objection of reconstructing the data itself. Its limitations lie in the first stage of the structure (dashed red box) in which it uses fixed parameters and structures for different sources of data.

COMPARISON WITH CNN-BASED VIDEO FEATURES
Needless to say CNNs employ convolution-pooling architecture. In CNNs, the non-linear activation is generally given by f (x) = tanh(x), f (x) = (1 + e −x ) −1 or f (x) = max(x, 0). The pooling functions are local average or maximum pooling, for example, g x,y,t (x) = max j,l,m∈ [1,d] x xj,yl,tm , where d is the pool size in space and time. The parameters of the model are the filters w. These are learned by minimizing a loss function, typically defined by where h(w, ·) is the full convolution-pooling architecture that takes x as inputs. As can be noted above, the loss function requires the labels y of the training data.
Comparing the above two procedures, it is clear that their differences are not so much structural, but rather in how to get the network parameters. With this understanding, we try to answer the question of how to design a video feature that balances efficiency and effectiveness. At first, one might try performing end-to-end training on the network structure in Figure 1. However, this training again requires labels and large computational resources. In addition, results from Sydorov et al. (2014) and Richard & Gall (2015) show that directly applying end-to-end learning on the traditional handcrafted pipelines would not bring large performance gains. So instead we keep the stage-wise unsupervised training to avoid the costly labeling and training. We address the limitations of handcrafted features by proposing two-stream ConvISA to replace the handcrafted filters and designing different network structures for pixel and optical flow modeling.

TWO-STREAM CONVISA
In this section, we will describe two-stream ConvISA in detail and its structures for both appearance (pixel) and motion (optical flow) stream learning (Simonyan & Zisserman (2014)).
As illustrated in Figure 2, an ISA (Hyvärinen et al. (2009)) is a unsupervised feature learning method that can be described as a two-layered network within convolution-pooling architecture with: f (x) = x 2 and g(x) = √ x. A more formal and precise definition of ISA and its connection to group sparsity optimization can be found in Appendix. Figure 3 visualizes, in both original and frequency domains, some example filters learned from ISA and PCA models. As illustrated in Figure 3b and 3d , the ISA model learns more complex filters that capture higher-frequency information. Furthermore, the pooling operation makes ISA lose information. To compensate for this information loss and capture more low-frequency information, we combine the outputs of ISA with an equivalent number of top outputs from PCA. Our enhanced method, denoted by ISA+, significantly outperforms individual PCA or ISA model. Since the PCA preprocessing is a default requirement for ISA, this improvement almost free. To address the problem of handcrafted features having the same network structure for different data sources, we design different network structures for pixel and optical flow data. Our learned descriptor for appearance stream, dubbed LOP, is learned by directly applying a ConvISA model to a stack of video frames and implicitly learning temporal pooling. Our learned descriptor of motion stream, denoted as LOF, is trained by applying a ConvISA model to each individual optical flow frame and explicitly performing a temporal pooling afterwards. This difference of the network structure is because of our observation that pixel data has high temporal correlation while optical flow data often has much less temporal correlation due to the estimation error. As a result, it is much easier to learn temporally consistent appearance filters than temporally consistent motion filters. As shown in Figure 4, in which we show some example images and optical flow patches and the filters learned in both structures. From Figure 4a and 4b, it is clear that image patches are consistent across frames while optical flow patches have large temporal variation. Quantitatively, we estimate a 0.8014 temporal correlation for pixels sampled from HMDB51 tracklets while only 0.2808 for the same measurement on optical flow. As a result, the learned appearance filters (Figure 4e) are temporally consistent and the learned flow filters are quite chaotic (Figure 4f). Since temporally similar filters have the effect of pooling, these results show that temporal pooling is necessary for learning video features. To discriminate this implicit temporal pooling from the explicit temporal pooling for optical flow data, we call it temporal projection.  Figure 4c and 4d, we replicate the filters 5 times for visualization purpose.

EXPERIMENTS
In the following section, we first compare our learned descriptors to the state-of-the-art video descriptors. We then show that our ISA+ model performs significantly better than either one of the single models. After that, we demonstrate that custom designed network structures for pixel and optical flow data are necessary. We conduct experiments on benchmark action recognition datasets of HMDB51 and UCF101 datasets.

DATASETS
The HMDB51 dataset (Kuehne et al. (2011)) has 51 action classes and 6766 video clips extracted from digitized movies and YouTube. Kuehne et al. (2011) provides both original videos and stabilized ones. We only use original videos in this paper. As in Kuehne et al. (2011), Mean accuracy (MAcc) is used for evaluation.
The UCF101 dataset (Soomro et al. (2012)) has 101 action classes spanning over 13320 YouTube videos clips. We use the standard splits with training and testing videos provided by Soomro et al. (2012). We report MAcc as in the original papers.

EXPERIMENTAL SETTINGS
As in Wang et al. (2013), IDT features are extracted using 15 frame tracking, camera motion stabilization and RootSIFT normalization and described by Trajectory, HOG, HOF, MBH, LOP and LOF descriptors. Two-stream ConvISA models are trained on 200000 IDT trackets for each stream of data. For both PCA and ISA, we keep the filter size the same as in the handcrafted descriptors and use a pooling size of 10 for ISA. Another PCA is used to reduce the dimensionality of the resulting descriptors by a factor of two. For Fisher Vector encoding, we map the raw descriptors into a Gaussian Mixture Model with 256 Gaussians trained from a set of 256000 randomly sampled data points. After encoding, we attach the normalized space-time locaiton information to the encoded descriptors as suggested in Lan et al. (2015a). Power and 2 normalization are also used before concatenating different types of descriptors into a video based representation. For classification, we use a linear SVM classifier with a fixed C = 100 as recommended by Wang et al. (2013) and the one-versus-all approach is used for multi-class classification scenario.

PERFORMANCE COMPARISON OF INDIVIDUAL DESCRIPTORS
In Table 2, we compare our learned descriptors LOG and LOF to the state-of-the-art video descriptors including handcrafted descriptors from IDT and spatial and temporal CNNs from Simonyan & Zisserman (2014). On the appearance descriptors, an impressive performance improvement of more than 10% over HOG, from 42.0% to 52.4% is achieved by LOG on HMDB51. For UCF101, LOG also gets more than 7% improvement over HOG and Spatial CNNs despite the fact that Spatial CNNs utilize additional training data. The same trend can be observed on motion descriptors. LOF outperforms other state-of-the-art descriptors by more than 4% on HMDB51 and more than 3% on UCF101. These results demonstrate that, with the same structure, the performance of handcrafted pipeline can be significantly boosted by learned descriptors.

TEMPORAL PROJECTION VERSUS TEMPORAL POOLING
In Table 4, we compare the results of temporal projection and temporal pooling. As evidenced by the results of both datasets, for appearance modeling, it is better to do temporal projection than temporal pooling, and for motion modeling, temporal pooling performs much better than temporal projection. Furthermore, if we compare the single frame image filters (Figure 4c) to the filters learned using 5 frame stacks (Figure 4e) , we can see that adding temporal variation can help to learn more complex filters. A potential improvement, therefore, is to explicitly enforce temporal coherence for optical flow filter learning and learn the temporal pooling for optical flow data.

CONCLUSIONS
Contrary to the current trend of learning video features using end-to-end deep CNNs, which is computationally intensive and label demanding, we propose in this paper to revisit the traditional local feature pipeline and combine the merits of both hancrafted and CNN approaches. As an example, we present a video feature learning algorithm that has better performance, lower computational expensive than current state-of-the-art methods and does not require labels. We show that filters learned in an unsupervised fashion , when incorporates in a convolution-pooling structure, can outperform supervised end-to-end networks. This result serves as a reminder that the design choices in handcrafted features may still have many useful properties which could be potentially incorporated into future deep action recognition networks. Future work would be explicitly enforcing temporal consistency for optical flow feature learning and developing a deeper and better unsupervised learning method. We would also like to explore end-to-end fine-tuning given the unsupervised learned networks, which is less expensive than training from scratch. 7 APPENDIX ISA can be described as a two-layered network. Specifically, let matrix W ∈ R m×n and matrix V ∈ R d×m denote the parameters of the first and second layers of ISA respectively. n is the dimension of the inputs and d is the dimension of outputs. m is the number of latent variables between the first layer and the second layer. Typically d ≤ m ≤ n. The matrix W is learned from data with orthogonal constraint W W = I. Therefore we call W the projection matrix. The matrix V is given by the network structure to group the output variables of the first layer. V ij = 1 if the j-th output variable of the first layer is in the i-th group, otherwise V ij = 0. Therefore we call V the grouping matrix. Given an input pattern X t ∈ R n , the activation of i-th output unit of the second layer is p i (X t ; W, V ) defined by ISA enforces the activation of the output unit to be sparse. To achieve the sparse activation, it minimizes the following loss function defined on T training instances: s.t. W W = I .
Another way to interpret ISA is from sparse coding framework. Let G = [G 1 , G 2 , · · · , G d ] denote the variable group indexes defined by V , that is, j ∈ G i if and only if V i,j = 1. |G i | defines group size, which is generally the same across groups.
As in group LASSO (Friedman et al. (2010)), for any vector a ∈ R m , we defined the group 1 -norm a G,1 as We can write p i (X t ; W, V ) as p i (X t ; W, V ) = W X t G,1 .
Denote α t = W X t , since W W = I, we have where W † is the Moore-Penrose pseudo inverse of W . Eq. (2) can be re-formulated as a sparse coding method that Based on Eq.
(3), ISA is essentially searching a group-sparse representation α t of the input signal X t . The matrix W † is the dictionaries of sparse coding. The orthogonal constraint of W † makes the learned components maximally independent.


A Secure User Authentication Scheme against Smart-Card Loss Attack for Wireless Sensor Networks Using Symmetric Key Techniques

User authentication in wireless sensor networks (WSNs) is a critical security issue due to their unattended and hostile deployment in the field. In order to protect the security of real-time data query from an external user, many two-factor (password and smart-card) user authentication schemes are proposed. However, most of them are insecure against various attacks. This paper summarizes attacks and security requirements for two-factor user authentication in WSNs. Based on security requirements, a user authentication and session key establishment scheme is also proposed, and this scheme can resist smart-card loss attack merely by using symmetric key techniques. Security and performance analysis demonstrate that, compared to the existing schemes, the proposed approach is more secure and highly efficient.

Introduction
Wireless sensor networks (WSNs) have emerged as a field of interest in communication technologies over the years. They usually consist of a large number of autonomous sensor nodes, which are generally deployed in unattended environments. Each sensor node has limited storage capacity and computational resource and a small communication module to communicate with the outside world over an ad hoc wireless network. Generally speaking, most of the queries in WSN applications are issued by the points of base stations or gateway-(GW-) nodes of the network. However, one can foresee that there should be great needs to access the realtime data inside WSN; where real-time data from the sensor nodes may no longer be accessed through the GW-node only, instead, the data are to be accessed directly by the external party (user) when demanded. If the data in WSN were made available to the user on demand, then authentication of the user should be ensured before the users are allowed to access the data.
According to the background above and actual application demand, in 2009, Das [1] proposed an efficient two-factor user authentication scheme firstly based on password and smart-card. He claimed that his scheme is secure against many types of attacks, such as many logged-in users with the same login identity, stolen-verifier, guessing, replay, and impersonation attacks. Since then, more and more two-factor user authentication schemes have been proposed. Nyang and Lee [2] pointed out that Das's scheme was not practical and vulnerable to an offline password guessing attack by insiders and node compromise attacks, and his scheme did not meet other security issues, that is, encryption and authenticity verification of query responses. Further, they proposed an enhanced two-factor user authentication protocol for WSNs, which overcomes the security flaws of Das's scheme with some additional security services. However, their scheme [2] also does not care about mutual authentication and is without password updates. In 2010, Khan and Alghathbar [3] pointed out that the scheme of Das [1] was insecure and cannot resist many other security attacks, such as gatewaynodes bypass attacks. To overcome the security weaknesses of Das's scheme, Khan and Alghathbar [4] proposed an improved two-factor user authentication in WSNs, which provided protection against insider attacks and GW-node 2 International Journal of Distributed Sensor Networks bypass attacks at the same time; it introduced a password change phase for users. There are many other cases about improvement and enhancement. For example, He et al. [5] proposed an enhanced scheme based on Das's scheme [1]. Their scheme keeps the merits of the original protocol and can resist the security weaknesses such as vulnerabilities to insider attack and the impersonation attack. Vaidya et al. [6] showed that Das's scheme [1] and Khan-Alghathbar's scheme [4] have flaws and remain vulnerable to various attacks such as smart-card loss attacks. In order to overcome security weaknesses of both schemes, they proposed an improved two-factor user authentication which is resilient to smartcard loss attacks as well as other common types of attacks. Fan et al. [7] proposed a simple Denial-of-Service resistant user authentication scheme for two-tiered WSN, which is efficient. Their scheme can establish a session key between the user and a master node (cluster node) in the sensor network. In the same year, Chen and Shih [8] proposed a robust protocol for WSNs and achieved mutual authentication. Later biometricbased user authentication in WSNs has drawn some research attention. A biometric-based user authentication scheme for WSNs has been proposed by Yuan [9]. However, there also exist many flaws in Yuan's scheme. To improve the performance and the security, Yeh et al. [10] proposed the first user authentication protocol for WSNs by using the elliptic curve cryptography (ECC).
However, such schemes are still used by method of "attack-improve. " Namely, even though the two-factor user authentication protocol has been proposed, it was secure until others found its vulnerabilities and improved it. This "attack-improve" method is a never-ending cycle. For the two-factor user authentication in WSNs, there is no summary on security vulnerabilities and abilities of adversary, and nobody concludes the security requirements. What is more, there is no appropriate formal model to examine the security of the user authentication scheme for WSNs. In this paper, we will classify the summary for the attacks and security requirements, but it is still an open question of how to present a formal definition of the user authentication under the WSN setting and design the scheme, which can be reduced to satisfy the definition assuming the minimal cryptographic assumption.
What is worth mentioning is Sun's scheme proposed in [11]. Sun's scheme is an improved scheme based on K-A's scheme [4]. The security of the user authentication session in Sun's scheme is reduced by the model of Bellare and Rogaway [12]. However, Sun's scheme is still insecure in smart-card loss case discussed in [11]. Namely, adversary can implement offline password guessing attack after obtaining user's secret parameters in smart-card. Moreover, the model of Bellare and Rogaway used by Sun is not a formal model corresponding to two-factor user authentication in WSNs. Sun pointed out that it is still an open question whether to construct a secure user authentication scheme merely by using symmetric key techniques.
Our Contributions. In this paper, we summarize all attacks in existing protocols and assort them into several classes. It is the first time that four types of security requirements have been proposed for two-factor user authentication in WSNs. For those attacks and security requirements, we propose a secure user authentication scheme against smart-card loss attack for WSNs by using symmetric key techniques. The proposed scheme perfectly solves the open question which was pointed out in [11] and has a high efficiency.
Organization. The remainder of this paper is organized as follows. In Section 2, we list some notations which are used throughout the paper and summarized attacks for two-factor user authentication in WSN. In Section 3, we proposed security requirements aiming at security vulnerabilities discussed in the previous section. Our proposed scheme is described in Section 4. Its security analysis and performance analysis are discussed in Section 5. Finally, in Section 6, we conclude the paper with a brief summary and outline our future work.

Security Vulnerabilities/Attacks Summary
In this section, we describe all security vulnerabilities in previous works, except common types of attacks such as replay attack or stolen-verifier attack. We just focus on twofactor user authentication in WSNs. Some notations and symbols used throughout this paper are summarized in Notation.
Before summarizing the attacks, it is assumed that an adversary may have full control over the network with following capabilities.
(i) An adversary may intercept all the messages at any time.
(ii) An adversary may intercept, delete or modify, and inset any message over the public network.
(iii) An adversary may either hack passwords or steal user's smart-card and utilize secrets stored in smartcard, but not all at the same time.
(iv) An adversary may compromise the sensor node and extract all parameters stored in it.
Strictly speaking, almost all previous schemes are no longer safe after considering ability of adversary mentioned above, although they claimed that their schemes can resist lots of attacks. We will discuss this situation in the following paper.

Privileged-Inside Attack.
In registration phase, a user's password, that is, PW , is transmitted directly to the registration center in plaintext. However, in real word, it is common knowledge that many users use the same passwords in different applications or servers for their convenience of remembering long passwords and using them easily whenever required. Therefore, it would appear that the system administrator or privileged-insider of GW-node may try to use PW to impersonate the user to access other GW-nodes where could be a registered user.
For insider attack, some researchers have developed various defense approaches. He et al. [5] suggested that user can transmit ℎ( ⊕ PW ) instead of PW to GW-node in registration phase, where is a high-entropy random number selected by and not revealed to the GW-node. In addition, Sun's scheme [11] suggested that only submits his/her ID to the GW-node until receiving the smart-card which includes the initial password selected by GW-node; is able to change the initial password immediately by using the password change operation.

User Impersonation Attack.
User impersonation attack can be caused by many other security flaws such as the privileged-inside attack discussed in Section 2.1, other legal users being malicious, or the parameters in the smart-card being extracted by attackers.
In registration phase, many users will receive the unique and personalized smart-card from the identical GW-node. However, the secret parameters which are generated by the GW-node and related to smart-card, such as and , are kept unchanged for different users in some proposed scheme. In other words, different users and identical GW-nodes share the same secret parameters; namely, every entity does not have the secret parameter or key belonging to it. If the smartcard is stolen or the secret parameters are compromised, then the whole sensor network will be vulnerable to the user impersonation attack. For example, Li et al. [13] showed that an attacker who has registered as a user of GW-node can restore ℎ( ) = ⊕ ℎ(ID ‖ PW ) from his/her own password and smart-card in Das's scheme [1]. Note that can be extracted from 's smart-card directly. Therefore, the attacker is able to impersonate another legal user or forge nonregistered user to log in GW-node legally without being noticed.
We suggest that GW-node distinguishes different users by different secret parameters. For example, GW-node can compute ℎ(ID ‖ ) instead of in registration phase, take a secure billing service introduced in Li's scheme [13] which stores a password/verifier table, or add biometric keys in register/login phase, and so forth. However, these tricks are at the expense of anonymity. Namely, we can see from the tricks that if the user's identity ID or biometric keys are added to register/login phase, the vulnerable schemes are robust against user impersonation attack. Therefore, to accomplish secure aims, the schemes do not have the anonymity. In order to avoid anonymity loss, it is essential to design a secure user authentication scheme with anonymity for WSNs.

Guessing Attack.
Guessing attack is a crucial concern in any password-based system. The attacker can recover ℎ(PW ‖ * * * ) through privileged-inside attack, node compromise attack, or smart-card loss attack, where " * * * " is an acquired formula. Thus, the attacker can guess 's password in a relatively small dictionary containing all the words about . For example, in Das's scheme [1], the attacker who has registered as a legal user of GW-node can compute ℎ(ID ‖ PW ) = DID ⊕ ℎ(ID ‖ PW ) ⊕ DID [2], or an adversary can obtain any user's ℎ(ID ‖ PW ) = ℎ( ‖ )⊕ DID after the secret parameter stored in designated sensor node is compromised through node compromise attack [14]. Nyang and Lee [2] suggested that the prevention of the offline password guessing attack is using ℎ(ID ‖ PW ‖ ) instead of ℎ(ID ‖ PW ). The other common improvement method is sending user's identity ID and ℎ( ⊕ PW ) to the GW-node in registration phase; then enters into his/her smart-card after receiving the personalized smart-card from GW-node. They claimed that their methods can resist offline password guessing attack. However, they would not achieve that goal if smart-card loss attack was taken into account (see Section 2.6).
In fact, the improvements discussed above are not a solution, which people fail to realize. Symmetric key techniques and offline validation are widely used in login phase of "smart-card-password" two-factor user authentication schemes in WSN at present. Namely, inserts its smartcard to a terminal in login phase and keys ID and PW . Then the smart-card uses secret parameters and PW to compute some formulas. The correctness of these formulas will determine whether users log in to the scheme successfully, while it is independent of GW-node. Technically, there is an unavoidable loophole; that is, if the smart-card is stolen and secret parameters such as and are leaked, the scheme will not be safe anymore. This problem exists in almost two-factor user authentication scheme. For example, in He's scheme [5], , , are stored in 's smart-card, where = ℎ( ) = ℎ( ⊕ ℎ(ID ‖ ℎ( ⊕ PW ))). If the attacker obtains these parameters and user's ID , he/she also can attack by guessing. Therefore, designing a genuine twofactor user authentication scheme that can defeat guessing attack is meaningful for the application.

Node Compromise
Attack. Node compromise attack refers to a series of attacks caused by a malicious or captured sensor node. These attacks include guessing attack by obtaining the hash of password, impersonation of other sensor nodes by using secret parameters in captured sensor node, and GW-node bypassing attack. GW-node bypassing attack [3] means that the attacker can compute the legal messages to gain the trust of other sensor nodes by bypassing GW-node. The basic cause of above vulnerabilities is that several sensor nodes and GW-node share the same secret parameters such as .
To avoid node compromise attack, Huang et al. [14] suggested using ℎ( ‖ SID ) instead of as a shared key between sensor node and GW-node. ℎ( ‖ SID ) is stored in sensor node beforehand, where is generated securely by the GW-node. Even if sensor node is captured, the attacker cannot recover the value of and the shared key ℎ( ‖ SID ). Therefore, sensor node that had been captured has no influence on other sensor nodes and GW-node.

GW-Node Impersonation Attack.
There are at least two situations where the attack occurred. The first situation is GW-node bypassing attack, namely, adversary steals the secret shared key of GW-node from a captured sensor node to impersonate GW-node (GW-node bypassing attack can be regarded as GW-node impersonation attack). The second situation is "smart-card loss attack. " That means adversary steals secret parameters from smart-card, or a malicious legitimate user recovers secret parameters from their own smart-card and impersonates GW-node. In many schemes, smart-card would store numerous sensitive information such as ℎ(PW ), , or ℎ( ) directly. The other core reason is still that different users and identical GW-nodes share the same secret parameters such as and , or there is another issues like is poor in design. According to Vaidya's suggestion [6], the GW-node impersonation attack caused by smartcard loss attack can be avoided by adding various validation conditions. However, Vaidya's suggestion cannot solve the inherent problem.
By analyzing the second situation, one way to avoid the GW-node impersonation attack is to let GW-node distinguish different users by different secret parameters. The methods are similar to user impersonation attack (see Section 2.2). However, it is a fact that all these tricks are at the sacrifice of anonymity. Therefore, to accomplish secure aims, the schemes do not have anonymity. In order to avoid anonymity loss, it is essential to design a secure user authentication scheme with anonymity for WSNs.

Smart-Card Loss
Attack. Smart-card loss attack means that a variety of attacks are caused by information leakage in smart-card. If the compromised information in smartcard includes secret parameters associated with GW-node, "GW-node impersonation attack" and "GW-node bypassing attack" will happen. If it involves user's identity and password, "password guessing attacks" will happen.
As for the appearance of guessing attack related to smartcard, adversary can recover user's password from offline validation formulas through guessing attack. For example, in He's scheme [5], = ℎ( ) = ℎ( ⊕ ℎ(ID ‖ ℎ( ⊕ PW ))) and , , are all stored in smart-card (see Section 2.3). If these parameters in smart-card are compromised, adversary can easily carry out the offline password guessing attack.
For the guessing attack caused by smart-card loss attack, Sun et al. [11] pointed out that public key techniques in validation formulas of smart-card can solve this situation. However, the WSN applications cannot burden the high implementation costs of the public key algorithms. Actually, symmetric key techniques and offline validation are widely adopted in login phase of "smart-card-password" two-factor user authentication schemes in WSN at present. In the smartcard loss attack, it is still an open question whether there is a secure user authentication scheme merely by using symmetric key techniques. Therefore, designing a genuine two-factor user authentication scheme which can resist guessing attack is meaningful for the application.

Parallel Attack/No Protection against Forgery
Attack. This attack works in many existing schemes. With Das's scheme [1], for example, a malicious legal user of the system * can embed a synchronized Trojan virus into the other legal user 's system and log in to the WSN as at certain moment. For the detailed description of parallel attack, refer to [8,10,15].
In Chen-Shih's scheme [8], the authors added the random nonce to DID and to neutralize this attack. However, Yoo et al. [15] found this improvement remains vulnerable. The reason is that secret parameters used by different users are uniform. For instance, ℎ( ) exists in both DID and DID * , so it can be offset by "⊕. " In order to resolve the problems resulting from parallel attack, we can consider changing the traditional thinking mode of generating DID or sacrificing anonymity to achieve discrepancy of parameters for different users.

Security Requirements
Sastry and Wagner [16] investigated several issues regarding IEEE 802.15.4 [17], such as ACL management problems (i.e., the same key in multiple ACL entries, loss of ACL state due to power interruptions, key management problems, and insufficient integrity protection), and provided some solutions for these problems. However, the requirements for security authentication protocol in WSNs need to be considered by more appropriate methods in order to resolve the application layer issues, such as mutual authentication, impersonation, replay, parallel session, sinkhole, and wormhole attacks as well as other kinds of sensor node attacks which are described in detail in Section 2.
This section is aimed at discussing the security requirements for two-factor user authentication in WSNs. The proposed security requirements will cover all the known attacks in two-factor user authentication schemes.

Authentication Security.
Authentication security includes not only the mutual authentication between user and GWnode, but also mutual authentication between GW-node and sensor node in WSNs. The main idea of authentication security may be summed up in one sentence: only by stealing all parameters of a participant can adversary impersonate the participant.
The essential idea of mutual authentication mainly has three aspects. As for the user, if the adversary wants to impersonate the user , he/she must obtain the 's password PW and 's smart-card simultaneously. Similarly, even if 's PW and smart-card are compromised, it is difficult to impersonate the GW-node or sensor node. As for the GWnode, we assume that the GW-node is secure, except for privileged-inside attack. Namely, the adversary cannot obtain all parameters of the GW-node directly. However, even if all secret values of every and sensor nodes are compromised, it is still difficult to impersonate the GW-node. As for the sensor node , it is impossible to impersonate the sensor node unless is captured immediately. If other sensors are captured or all secret parameters of other participants in current system are compromised, it is still difficult for attackers to impersonate . The three points above almost cover all attacks in Section 2, such as privileged-inside attack, user impersonation attack, node compromised attack, GWnode impersonation attack, smart-card loss attack, and parallel attack.

Session Key Security Requirement.
It is necessary to establish a session key for translating sensitive data between legal users and sensor nodes. Therefore, semantic security and forward security of session key should be needed in the two-factor user authentication system. More narrowly, semantic security of session keys is guaranteed if an active attacker cannot get any information about the session keys International Journal of Distributed Sensor Networks 5 shared between the legitimate parties involved. Specifically, the attacker cannot distinguish a real session key from a random one, chosen from the same key space, in the security definition; for more details, see [12]. Forward security of session key means that if long-term secret keys of all the participants are compromised, the secrecy of previously established session keys should not be affected. In the twofactor (password and smart-card) user authentication scheme for WSNs, long-term secret keys refer to the participants' passwords, secret keys in smart-card, and nodes' master keys.

Password Security Requirement.
Password security requirement can divide into two aspects: offline dictionary attack security and online dictionary attack security. Offline dictionary attack security is also called guessing attack and it means that there is no successful attacker as follows: the attacker intercepts and stores the message and then chooses a candidate password of dictionary in offline mode by using the interception of messages to test whether the candidate password is correct. The process repeats until another candidate password is guessed correctly. One of online dictionary attack securities is to resist undetectable online dictionary attack, and the other is to resist computer program online dictionary attack. This is how we define password security requirement: except if legal users' secret values in smart-card are compromised, the external attackers and internal participants will not be unable to carry out the offline dictionary attack on these legal users. Namely, it means that it can resist offline password guessing attack discussed in Section 2.3. In addition, adversary may be able to carry out the online attack; however, once the online dictionary attack occurred, the server can detect it rapidly. Namely, it means that it can resist undetectable online dictionary attack and computer program online dictionary attack.

Password Updating Security Requirement.
A passwordbased user authentication scheme should provide users with a password updating facility so that a user can update his/her password freely.

Proposed Scheme
To resist the attacks discussed in Section 2 and meet security requirements discussed in Section 3, we propose a secure user authentication scheme against smart-card loss attack for WSNs by using symmetric key techniques in this section. There are three phases in our scheme: the registration phase, the password updating phase, and the authentication phase.

Registration Phase.
In this phase, user has to submit an identity and password to the GW-node in a secure way. Then, the GW-node issues a license to . The detailed steps are depicted as follows.
Step-R1. chooses his/her identifier ID and password PW , generates a random number , and computes PW = ℎ(PW ‖ ). Then sends ID and PW to the GW-node over a secure channel.
Step-R3. After receiving the smart-card, the user inputs into it and finishes the registration.

Password Updating Phase.
The password updating phase is needed whenever user wants to update his old password PW . The detailed process is as follows.
Step-P1. User inserts his/her smart-card into the terminal and enters his/her identity ID , old password PW , and new password PW * and requests to change the password.
Step-P2. 's smart-card validates the entered parameter ID using the stored value. If ID is correct, then is allowed to change the password. Otherwise, the password change phase is terminated.

Authentication Phase.
The authentication phase is invoked when wants to perform some query or to access data from the network. The phase is further divided into login and verification phases. Figure 1 shows both the login phase and the verification phase.
(1) Login Phase. inserts her/his smart-card to a terminal and keys ID and PW . The smart-card validates ID with the stored ones. If the entered ID is correct, the smart-card performs the following operations.
(2) Verification Phase. Upon receiving the login request {ID , , 1 } at time 1 , the GW-node authenticates and creates a session key between and sensor node by the following steps.
Step-V1. Validate 1 . If ( 1 − 1 ) ≤ Δ then the GW-node proceeds to the next step, or else abort, where Δ denotes the expected time interval for the transmission delay.
Step-V2. The GW-node computes = ℎ 1 (ℎ(ID ‖ ) ‖ 1 ) and ( ) = {ID , 1 , 1 }, where ( * ) means that " * " is decrypted by the symmetric key . Then the GW-node checks whether the decrypted messages ID and 1 are equal to the ones received. If it is true, the GW-node establishes trust on the and proceeds to the next step; otherwise abort.
Step-V6. Upon receiving the message from the sensor node , the GW-node first validates 3 in a similar line of Step-V1. GW-node computes * = ℎ 1 (ℎ( ‖ SID ) ‖ SK ‖ ID ‖ SID ‖ 3 ) and then checks whether * = . If it is true, the GW-node establishes trust on the sensor node or else rejects it.
Step-V7. The GW-node computes = (ID ‖ SID ‖ SK ‖ Step-V8. Upon receiving the message from the GW-node, first validates 4 in a similar line of Step-V1. computes ( ) = {ID , SID , SK, 1 , 4 }; then checks whether the decrypted messages ID , 1 , and 4 are equal to the previous ones. If it is true, user establishes trust on the GW-node and establishes a session key SK with the sensor node ; then he/she can enjoy the data from the WSN; otherwise stop accessing to the sensor network.

Scheme Analysis
In this section, based on adversary's capabilities assumptions in Section 2, we carry out a security analysis of our scheme. Then efficiency analysis is followed (computational cost and communication cost). Finally, we give a performance comparison with existing schemes.

Security Analysis
Authentication Security. Our scheme provides authentication security, where all entities can authenticate each other.
International Journal of Distributed Sensor Networks 7 Further, no one can impersonate the participant unless the adversary steals all parameters of the participant. More details are as follows.
We assume that the GW-node is secure; namely, adversary cannot get the values of and from GW-node directly. Our scheme is passing PW = ℎ(PW ‖ ) instead of the plain password to resist the privileged-insider attack. Furthermore, if all secret values of every and sensor nodes are compromised, it is still difficult to impersonate the GWnode. Because the compromised value of ℎ( ‖ SID ) in cannot affect and smart-card just stores = ℎ(ID ‖ ) ⊕ PW about GW-node, attackers cannot recover from it.
It is also difficult to impersonate except that is captured immediately because the secret values are unique in different sensor nodes by binding with sensor nodes' identity. Moreover, the compromised sensor node cannot affect other participants including the users and GW-node.
In our scheme, we solved the open question which was pointed out in [11]; namely, our scheme is secure merely by using symmetric key techniques when the smart-card is stolen. If the adversary wants to impersonate user , he/she must obtain the user's password PW and smart-card simultaneously. This is because that smart-card only stores { , , ℎ(⋅)} and uses online validation which is different from Sun's scheme [11]. It is known that offline validation with symmetric cryptography could cause guessing attack that we have discussed in Section 2.6. Moreover, offline password guessing attack also exists in online validation (Sun pointed out that his proposed scheme is insecure in smart-card loss case [11]). But in our scheme, = ℎ(ID ‖ ) ⊕ PW stored in smart-card is used to generate symmetric cryptography key = ℎ 1 (( ⊕ PW ) ‖ 1 ), and = (ID ‖ 1 ‖ 1 ) is transmitted to GW-node, where 1 ∈ {0, 1} . It is deserved to note that the function of the random number 1 prevents exhaust attacks, but without freshness. In this way, adversary cannot recover user's password PW from smartcard through offline password guessing attack. What is more, our scheme only uses symmetric key techniques. Similarly, even if 's PW and smart-card are both compromised, it is difficult to impersonate the GW-node or sensor node.
Based on the analysis above, our scheme meets authentication security. This means that our scheme can resist all attacks in Section 2, such as user impersonation attack, node compromised attack, GW-node impersonation attack, smartcard loss attack, and parallel attack.
Session Key Security Requirement. Our proposed scheme establishes a session key SK = ℎ 1 (ID ‖ ℎ( ‖ SID ) ‖ 2 ) for translating sensitive data between legal users and sensor nodes. Obviously, the session key SK has semantic security and forward security.
Password Security Requirement. In accordance with the above analysis, the proposed scheme can resist privileged-inside attack. In addition, according to the analysis above, the external attackers and internal participants are unable to carry out the offline dictionary attack on legal users even if legal users' secret values in smart-card are compromised. Online dictionary attack also can be avoided. So, our scheme meets password security requirement.
Password Updating Security Requirement. A password updating facility is provided for users in our scheme (see Section 4.2).
From Table 1, it is easy to see that the proposed scheme has more security functionality as compared with other existing protocols for two-factor user authentication in WSNs. In Table 1, "usual" denotes the common attacks such as replay attack or stolen-verifier attack, "RA" denotes relay attack, "PIA" denotes privileged-insider attack, "UIA" denotes user impersonation attack, "GA" denotes guess attack, "NCA" denotes node compromise attack, "GIA" denotes GW-node impersonation attack, "SRA" denotes smart-card loss attack, and "PA" denotes parallel attack. "Y" denotes the scheme that can resist this attack and "N" denotes the scheme that cannot resist this attack.

Performance Analysis.
In this subsection, we compare our protocol with related ones in terms of computational cost in the registration/authentication phase and communication cost in the message exchange phase. Because these two phases are the main procedures of an authentication protocol, let us define "H" as performing one-way hash function, "Pub/Pri" as public/private-key computation, "Se/Sd" as symmetric key encryption/decryption, "MAC" as message authentication code (MAC) function computation, "PM/PA" as elliptic curve point multiplication/addition computation, "E" as elliptic curve polynomial computation, and "NME" as the numbers of message exchanges in authentication phase. The result is shown in Table 2.
Computational Cost. The computational cost for user registration is onetime job for certain period of time. The user and the GW-node in our scheme only require 1 hash operation, respectively. And computational cost for user authentication is prime concern. Our sensor node requires only 3 hash operations with ensuring security. Although its computation cost just requires one or two more hash operations than some schemes [1, 4-6, 9, 11, 14], it is highly efficienct compared with other schemes [10,18,19]. It is also desirable that the user and the GW-node for user authentication require symmetric encryption operation Se/Sd, and our scheme does not require Pub/Pri [9], PA/PM, E [10,18], and MAC [19], which need additional storage and computations. Sequently, all operations added to protect query responses can be implemented using a block cipher such as Advanced Encryption Standard (AES) [20]. Also, note that most of the recently developed nodes in WSNs, for example, TMote, TelosB, and Micaz, already have a built-in AES module and, thus, no additional hardware is required [21]. Therefore, our scheme is wellsuited to the resource-limited sensor node. Compared with previous schemes which have too many vulnerabilities on security, our proposal achieves stronger security without loss of immense effectiveness. In conclusion, our scheme is also practical for the real world applications in enhancing the security over wireless communications.   [14] Y N N N Y N N N N Khan and Alghathbar's [4] Y Communication Cost. In WSN, without constant supply of power, sensor nodes are resource-limited. In addition, the most energy-intensive element of sensor is wireless communication module, namely, receiving, sending, and so on. Therefore, the number of message exchanges is crucial. Our scheme only needs four message exchanges to achieve all security features; however, many schemes which need less number of message exchanges have various vulnerabilities in security. In Sun's scheme [11], it uses nonce instead of timestamp to reduce the trouble of synchronized time clocks, but it needs one additional message and also has security flaws. Obviously, compared with Sun's scheme our scheme is well-suited to the resource-limited sensor node. Considering computational cost and communication cost, it is clear that the proposed scheme not only is an efficient scheme with high reliability, but also is practical for real-time applications.

Conclusion and Future Work
In real-time, access control is an imperative requirement for WSNs to protect the data access from unauthorized parties. However, there is no appropriate formal model to examine the security of the two-factor user authentication scheme for WSNs. In addition, previous schemes failed to resist the smart-card loss attack merely by using symmetric key techniques. Therefore, designing a genuine two-factor user authentication scheme which can resist guessing attack caused by smart-card loss attack is meaningful for the application.
In this regard, we summarize the main attacks and security requirements for two-factor user authentication in WSNs in this paper. A user authentication and session key establishment scheme based on security requirements is also proposed, which can resist smart-card loss attack merely by using symmetric key techniques. The proposed scheme solves the open question pointed out in [11]. We have shown a security analysis and performance analysis of our proposal and carried out a comparison with existing schemes. Analysis shows that the proposed scheme is more secure and highly efficient. Therefore, it is very suited to WSNs environments.
We highlight two areas for our future work. For the attacks and security requirements discussed in this paper, there is no appropriate formal model to examine the security of the two-factor user authentication scheme for WSNs. One direction of our future work will be to present a formal definition of the two-factor user authentication scheme under WSNs setting and construct new scheme in the given definition. Moreover, the proposed scheme cannot provide the user privacy. That is, the adversary can trace the target user by observing the user authentication session. Thus, the other future work is to devise the anonymous user authentication scheme for WSNs.

Notations and Symbols
: Ac e r t a i nu s e r ID : 's identifier PW : 's password DID : 's dynamic login identifier GW-node: Gateway-node of WSN : The symmetric key known to only GW-node : The secret parameter generated securely by the GW-node, which is shared with the GW-node and : A high-entropy random number selected by user, not revealed to the GW-node : Ac e r t a i ns e n s o rn o d e SID : 's identifier : The shared key between the GW-node and sensor node which is generated securely by the GW-node and stored in sensor node ℎ( ‖ SID ): The secret key instead of stored in sensor node, which is shared with the GW-node and sensor node, where is secret parameter generated securely by the GW-node : Th et i m e s t a m p s ,s u c ha s 1 and 2 : Cryptographic random numbers or the nuances, such as 1 and 2 ℎ(⋅), ℎ 1 (⋅): The cryptographic hash functions ‖: The concatenation operator ⊕: Th eb i t w i s ee x c l u s i v eo ro p e r a t o r .


s2fieldsofstudy:
Computer Science
Engineering

text:
Equipped-Human Reference Architecture

The successful application of most modern complex systems depends on human components as part of their system architectures. While the complexity of these systems is further complicated due to those human elements, most of these complex system designs may still be well-engineered using traditional systems engineering approaches and methodologies. Reference architectures (RA) are applied in this paper to demonstrate their use for defining systems that are human centered. That is, systems where the human is not the proverbial cog in the architecture rather where the human is the core emphasis of the design. This paper proposes a construct for Equipped-Human Reference Architecture (EHRA) as a useful systems engineering tool for addressing the complexity inherent in developing human centric systems. We assert that well conceived and constructed EHRA could provide a reusable and evolving set of architecture guidelines and constraints that define the complete equipped-human design space. Additionally, well-structured EHRA should be extensible to provide usable systems engineering design tools in order to aid equipped-human systems (EHS) developers when they are designing and evolving specific EHS solution architectures. © 2015 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of Stevens Institute of Technology.

Introduction
Human operated systems are by their very nature complex. However, human-centered systems present designers with engineering challenges that are extremely complex and not well understood. We assert that traditional systems engineering architecting approaches are not robust enough to aid human-systems engineers in managing the added inherent complexity of designing and implementing human-base architectures. This paper introduces and uses Equipped-Human Reference Architecture (EHRA) as a systems engineering approach for managing complexity in human-based system architectures and solutions across the equipped-human design space. The paper begins by reviewing some of the available literature on the subject of reference architecture (RA) followed by a definition of EHRA in the context in which it is presented. Next, a description of the conceptual elements that constitute EHRA are presented and described. After defining the requisite structure of EHRA, a proof-of-concept, Unified Modelling Language (UML) model will be proposed that presents EHRA as a useful systems engineering tool. The proof-of-concept model EHRA will then be used to illustrate the development of EHS solution architecture: police officer.

Literature Review
Reviews of the available academic and professional literature related to reference architecture (RA) were conducted in preparation of this paper. While source literature on the topic of RA is rich, there is clearly not a single accepted ontology that describes RA for all systems engineering applications. RA specifically related to EHS is sparse, if non-existent.
The earliest definitions of RA were found in Engineering Systems Monograph: The Influence of Architecture in Engineering Systems, by Crawley and de Weck et al. where they defined architecture as "a necessary but incompletely understood property of complex engineering systems 1 ", and architecting as, "a necessary but incompletely understood step in creating them 2 ." In 2008, Cloutier and Muller et al. provided a detailed description of RA emphasizing architectural consistency and by defining key guiding principles for business reference architecture 3 .
Much later, in 2013, Muller (alone), proposed a process for developing and maintaining reference architecture entitled, A Reference Architecture Primer, in which he stated that RA, "captures the essence of the architecture of a collection of systems 4 ", and that the, "purpose of a Reference Architecture is to provide guidance for the development of architectures for new versions of the system or extended systems and product families 5 ." All the academic studies propose a process for developing and maintaining reference architecture, which the researchers have used for design and development of the EHRA.
The DoD has developed a Reference Architecture Description in which the purpose of RA is described as, "guidance and direction to the DoD enterprise on the better use of Reference Architectures for guiding and constraining architecture descriptions, developments and usages for current and future capabilities 6 ." In recent years, the United States Navy, Air Force and Army have all adopted RA approaches for developing combat systems. The Air Force developed the Joint Strike Fighter (F-35) with the RA approach of reducing costs and minimizing design risk through the reuse of common components such as the airframe, avionics, communications and weapon systems 7 . More recently, the United States Navy has adopted some of the modularity principles of RA in the design of Littoral Combat Ships. The Navy states that, "the underlying strength of the LCS lies in its innovative design approach, applying modularity for operational flexibility 8 ." Currently, researchers at the Army Natick Soldier Research, Development, and Engineering Center (NSRDEC), in Natick, MA, are developing a systems engineering approach that will enable Soldier System designers to use RA as a means of engineering Soldier Systems. The Army's approach is to, "create an analytical model-based capability through which changes in Soldier as a System (SaaS) inputs (loads, technology/equipment, physiological & cognitive state, stress levels, training, TTPs) may be assessed to predict changes in SaaS and small unit performance in operationally relevant environments. The capability includes the architecture, system models, quantitative analysis tools, supporting infrastructure such as databases, human performance sensors, analysis methods, and comprehensive assessment capabilities 9 ." The NSRDEC approach was the impetus for the exploration of EHRA in this paper.
The Systems and Software Engineering-Architecture Description (ISO/IEC/IEEE 42010) defines a Conceptual Model of Architecture Description. The ISO/IEC/IEEE 42010:2011 Systems and Software Engineering Architecture Description specifies the, "creation, analysis and sustainment of architectures of systems through the use of architecture descriptions 10 . While the IOS/IEC/IEEE 42010 definition does not explicitly define RA, many of the specifications and constructs defined within the description are common to the key elements of RA described by other sources.
Raytheon has defined RA for the architecturally based Command and Control Systems that they have developed for their customers known as the Raytheon Mission Architecture Program (RayMAP). Raytheon states that, "RayMAP is made up of several elements that collectively provide the foundation for solid, disciplined architecture capabilities. RA is being used to promote commonality, reuse, interoperability, increased responsiveness and affordability in system and enterprises 11 ." In 2002, IBM began promoting their Rational Unified Process ® (RUP ® ) which encouraged the adoption of reference architecture, not only to aid in design, but also as an integral part of corporation's business strategy model 12 . The Oracle IM Reference Architecture is a "classically abstract architecture" designed to aid corporations in developing IT infrastructures and processes that allow the company to fully exploit the information that is contained in large data sets. In an article entitled, Inputs of a Big Data Reference Architecture, the author, Sunil S. Ranka, supports Oracles approach for using reference architecture as a starting point for designing and building big data IT exploitation systems 13 .
Mike Rollings, the Enterprise Architectures Research Director at the Burton Group, asserts that, "making architecture relevant in an organization requires more than the application of brute force. It involves relentlessly pursuing business outcomes and applying both influence and integrated governance. This is the only way to improve the decisions influenced by architecture 14 ." WIKIBOOKS explores RA as a construct in the design of a new type of production system, which is capable of self-expansion that they call a Seed Factory. The following excerpt from this publication provides the most accurate description of RA.
[RA is] a high level design for a class of shared solutions, exhibiting their key concepts, relationships, and features. It is a starting point for later stages of design and becomes customized and more detailed for specific applications. A reference design saves effort because each application does not have to repeat the early stages of development. At the architecture level it is used to provide common language and understanding for Stakeholders, parties with an interest in a project. It is also used to identify technology risks and readiness level (TRL), and make early estimates of cost and schedule. It includes system level goals, design principles, an architecture description, high level interactions between elements and the system environment, general element requirements, and element descriptions. Supporting data for reference architecture shows the reasoning for how it was arrived at. It includes data sources, analyses to support concept selection, and tracking from goals to lower elements. Reference architectures should be updated as the system concepts and specific applications become more detailed 15 .

Common RA Concepts
While no single definition of RA would appear to exist, there are several key, common characteristics of RA that are consistently stated throughout the literature. From this review, we propose that the highest-level objective for any RA should be in defining an architecture that describes and bounds the design space of possible solutions architectures. For this paper that design space is defined as any EHS. Additionally, there would appear to be agreement that RA should be expressed as abstractly as possible while also capturing the core elements that are common to any possible solution architecture contained within a design space. RA must capture the key operational and environmental context in which the specific derived solution architectures are intended for use.
RA should be modular and, thus, reusable for designing, or redesigning, disparate solution architectures within a given design space. Next, RA should be capable of evolving by capturing lessons learned from predecessor system solutions and being capable of representing that captured knowledge as usable guidelines and constraints for use by future solution architecture designers.
Finally, RA should provide useful design tools that aid system designers in developing new solution architectures and/or in extending existing solution architectures within the design space of the RA. All these studies recommend development of a RA and give insight on the RA properties and development process. In this paper we use these core principles to define, develop and apply EHRA to the EHS design space.

Definitions
For the purposes of this paper, Equipped-Human Reference Architecture (EHRA) is an abstract representation of the common objects, design guidelines and constraints, which represent the equipped-human design space from which specific Equipped Human Solution (EHS) architectures are derived. Equipped-Human System Solution Architecture (EHS solution architecture) represents a specific instance of EHS system design that is engineered following the principles of the traditional systems engineering Vee lifecycle model.

Elements of the Equipped-Human Design Space
EHS solution architecture describes a human who has been equipped to perform some task(s) in some operational environment or a group of humans who have been equipped to perform some task(s) in some operational environment. The five abstract elements of the equipped-human design space are illustrated in Figure 1 and constitute the Human-Equipment-Task (H-E-T) Framework 16 that defines the design space for EHS. Note that the H-E-T framework is also applicable to groups of similarly equipped-humans such as a fire fighting company or a soldier squad who have been trained to perform combined missions. The five central elements of the Equipped-Human (EH) design space are the human, his/her equipment, the task they are being equipped to perform, the environment in which the task is executed and the EHS objective. It is difficult to think about the EHS and design of the first three elements separately. The equipment depends on the task to be performed, and the environment. The task that can be performed depends on the human's abilities. Each of these elements is interrelated and comprises an integrated EHS. The environment cannot, of course, be "designed" or changed in any sense, but a choice can sometimes be made about the environment in which to conduct a given task. In previous work it has been demonstrated that the interrelationships between these elements server to define the measures of performance (MOPs), measures of effectiveness (MOEs) and technical performance measures (TPMs) of any EHS.
Different human class types can be distinguished by their capabilities, training, and experience (behaviour). For the development of the proposed EHRA in this paper, capabilities, training and experience will be replaced with the terms knowledge, skills and abilities, respectively.
The equipment that comprises the EHS would include that which the equipped-human wears or carries, and also various transport equipment, communications equipment and other equipment necessary to complete his/ her specified tasks. For the development of the proposed EHRA in this paper, equipment types are defined by the characteristics of function, properties and capabilities. The mission will specify tasks that the EHS is intended to address.
The physical environment includes terrain, temperature, moisture level, time of day, water sources, and other conditions define the range of environments a given EHS must operate in to carry out their task. The physical environment can be characterized as urban, remote, sea, air, land, space, etc. Additionally there are operational environment considerations that are human centered. These operational variables include social behaviour aspects of the human entity that must be aligned with the task objectives. Development and Engineering Center (NSRDEC). The illustration depicts how EHRA provides systems engineering guidance for adapting the abstract concepts contained within the H-E-T framework (human, equipment and task) into a Human as a System (HaaS) architectural that provides guidelines, which systems engineers may use, or reuse, to build actual EHS solution architectures. At the highest-level of the EHRA Super Vee Model, the H-E-T framework defines the fundamental abstract blueprint, or strategy, encompassed in all EHS solution architectures. The second-level of the EHRA Super Vee model describes the Systems of Systems (SoS) architectural guidelines and constraints, or EHRA, that are defined in terms of the HaaS architectural construct, or human as an equipment chassis. The third-level of the EHRA Super Vee model describes how EHS system developers use the EHRA HaaS architecture to design specific EHS solution architectures. The EHS solution architecture is the specification and design that allows the systems engineer to build, or extend, specific EHS solution architectures that are derived through the application of the higher-level EHRA HaaS guidelines.

EHRA Super Vee Model
While there are many RA model representations available in the literature, most are too specific and narrowly focused to be adopted for use in describing EHS. As illustrated in Figure 3, the closest RA model approximation to that of the EHRA HaaS Super Vee Model is provided by Cloutier and Muller et al 19

Proof-of-Concept Model
While the EHRA Super Vee Model defines the conceptual application of EHRA for developing EHS solution architectures it does not provide a real systems engineering tool for developing systems. For any RA to be of use to systems engineers, the RA must not only be instantiable, but the RA must also be extensible. Therefore, there must be a mechanism for system developers to add, or extend, specific solutions architecture elements to the RA, which in turn, will aid other systems engineers in defining and building actual EHS solution architectures based on the guidelines provided by the RA. Figure 4 is an illustration of the initial EHRA HaaS UML model that was developed to describe EHRA, during research performed preceding this paper 21 . The EHRA UML HaaS Model attempts to capture the core system components and guidelines that are necessary to define the baseline equipped-human described in the H-E-T framework. Extending the EHRA HaaS UML Model though the use of subclasses allows EHS developers to instantiate specific EHS solution architectures throughout the EHS design space. In general, the EHRA UML Model depicts an architecture that is simple and balanced.

EHRA HaaS UML Model
There are many possible methods for representing RA. These methods include sophisticated relational databases, detailed process diagrams and even simple drawings. Cardwell advocates for the use of Unified Modeling Language (UML) when developing reference architectures 22 .
The EHRA UML HaaS Model, illustrated in Figure 4, attempts to capture the core system components and guidelines that are necessary to define the baseline equipped-human described in the H-E-T framework. Extending the EHRA HaaS UML Model though the use of subclasses allows EHS developers to instantiate specific EHS solution architectures throughout the EHS design space. In general, the EHRA HaaS UML Model depicts an architecture that is simple yet balanced. The UML architecture is abstract enough to capture the concepts described in the H-E-T framework but specific enough to be extended to actual EHS solution architectures through the addition of subclasses that may subsequently be instantiated for actual EHS solution architectures.

EHS Solution Architecture
A well-recognized EHS solution architecture is that of a police officer. Police officers are individual humans who have been equipped and trained to respond to a multitude of different tasks related to law enforcement and public safety. Possible EHS solution architecture: police officers are defined by the example sub-classing, illustrated by the bright blue coloring in Figure 5. A solution architecture that is similar to that of the police officer is the solution architecture: firefighter. Each EHS is trained and similarly equipped to respond to emergency situations. Thus both EHS solution architectures share common solution architecture systems components such as uniforms, radios and CPR training. However, it is unlikely that the solution architecture: firefighter should be equipped with a firearm or that the solution architecture: policeman would ever be trained in operating a fire hydrant and hose. Because of the commonality at the higher level of the EHRA (e.g., emergency response training; hostile environment conditions; etc.), EHS developers can take advantage of these reusable aspects from other EHS when designing similar EHS solution architectures. Regardless of the specific EHS being developed, all EHS share the common system components defined in the EHRA HaaS UML Model, which should be reusable by all EHS developers when developing new EHS solution architectures.

Discussion
Ideally, EHRA provides many advantages for developers of EHS solution architectures. EHRA is intended to provide reusable guidelines and constraints that capture EHS patterns, and allows all developers of EHS to learn and profit from the experience of predecessor design efforts. The ability to reuse past EHS design information can aid system developers in scoping the cost and schedule for developing new EHS solution architectures by focusing systems engineering resources on the areas of the EHS design elements that are new and represent the greatest risk to engineering EHS. Because EHRA provides reusable, time-tested modules for developing EHS solution architectures, the resulting EHS solution architectures should be of high quality and should be extremely dependable and maintainable until changes in human training; equipment technology and/or new missions require a replacement EHS solution architecture to be developed. Additionally, since EHRA should continue to provide current EHS design space engineering information to systems engineers, the resulting in EHS solution architectures that are developed should be very well understood, robust and scalable.
Finally, the use of EHRA may present EHS systems developers with emergent systems engineering techniques that could not be realize using more traditional methods. Practically, the promise of ideal EHRA is still far from being a working, dependable systems engineering tool. However, in the past three years remarkable strides have been made in developing more constrained, specific EHS RA. For example, the NSRDEC has been developing Soldier System Reference Architecture, using the constructs describe in this paper, for engineering Soldier Systems that, focus less on equipment, and provides equal weighting to the soldier's performance and the soldier's mission. EHRA is a higher-level abstraction of the NSRDEC's approach to engineering soldier systems engineering approach.
While the H-E-T Framework provides a novel first step for characterizing EHS, the current lack of understanding about how human will react in a given environment and the scientific community's inability to quantitatively model these emergent human qualities limits the scope of EHRA development and presents the greatest systems engineering challenges for developers of all EHS solution architectures. The EHRA HaaS UML model presented in this paper represents an initial attempt at representing EHRA in a form that is extendable and useable by systems engineering practitioners for developing EHS solution architectures. The model works well to encapsulate the abstract H-E-T concepts into a reusable EHRA HaaS architecture from which actual EHS solution architectures can be derived through extension of the EHRA class structure. The UML model is abstract and is not overly complicated. While there are still ambiguities present in the H-E-T framework with respect to the EHS operational environment, the model structure captures the system context well in relation to operational considerations. The current EHRA UML model may be used to instantiate new EHS solution architectures as demonstrated in the EHS solution architecture: police officer. Finally, the current EHRA UML model is simple, but not too simple.
However, this initial model does have deficiencies. Firstly, the model does not provide EHS system developers with design aids for assessing and measuring EHS solution architecture performance. Additionally the UML model fails to provide a robust mechanism for capturing and representing predecessor EHS development information to EHS system developers. In fact, there is strong evidence that EHRA may not be achievable in the construct of a single systems engineering tool. Rather, it may be necessary and advantageous to have a set of systems engineering tools, such as UML models, databases, and requirements management schema, which when combined present EHS system developers with a robust and complete set of tools that represent more specific EHRA. In the final analysis, the EHS design space may be too broad to be represented by a single EHRA. However, the ultimate value of EHRA may be found to be in its use as a validation tool for more specific EHS RA, such as Soldier Systems Reference Architecture or as Police Officer Systems Reference Architecture.

Summary and Conclusion
In summary, EHRA is an evolving systems engineering construct that could be used by EHS system developers to inform the design, development, deployment, assessment and sustainment of a solution specific complex system architectures that fall within design space of a EHS. EHRA should capture the fundamental concepts of the complete EHS design space as expressed in the H-E-T framework. EHRA should also provide mechanisms to iteratively update and re-present those concepts, so that they can be used, and reused, by different EHS systems developers to build and deploy specific EHS solution architectures. In this paper, the application of EHRA to the systems engineering methodology has been ideally expressed using the EHRA Super Vee model. A practical EHRA HaaS UML model has been presented in this paper that may be used by systems engineers to instantiate EHS solution architectures within the EHS design space.
As an abstraction, the H-E-T framework defines the needs for all EHS. As a model, the EHRA represents EHS as HaaS architecture. As a practical systems engineering tool, the EHRA HaaS architecture provides system developers with a set of general, reusable and evolving systems engineering guidelines and constraints from which any EHS solution architecture may be realized. While there are many practical approaches that may be used to construct RA, this paper presents an EHRA UML modeling approach that is both usable, and reusable, by systems engineering practitioners and is extensible for EHRA developers and maintainers.
In conclusion, EHRA should provide EHS developers with a reusable, modular set of guidelines and constraints for engineering EHS solution architectures across the spectrum of the EHS design space. In order to provide those capabilities to EHS systems engineers, the EHRA construct must encompass the following characteristics: • EHRA should be simple, but not too simple, capturing and representing only those aspects of EHS that are common and necessary to all EHS. • EHRA should capture the operational and environmental context for a given EHS solution architecture.
• EHRA should be in a form that is usable and suitable for practitioners of systems engineering.
• EHRA should provide EHS developers with the necessary design aids for engineering EHS.
• EHRA should be extensible to predecessor EHS solution architectures and instantiatable for the development of new EHS solution architecture within the EHS design space. • EHRA should evolve with changes to the EHS design space as new EHS solution architectures are built to address new missions and/or to incorporate novel technologies so that future EHS developers can reuse the components and information gained through those systems engineering advances in future EHS implementations.
The EHRA HaaS UML model presented in this report demonstrates an initial approach for how EHRA may be represented in the form of a UML model as a useful systems engineering tool, which, in turn, may be used by EHS system developers to instantiate different possible EHS solution architectures across the spectrum of the EHS design space. While additional research and development are still necessary, we believe that the EHRA HaaS UML Model presented in this report represents an important first step for understanding how EHRA may be structured in order to provide a practical and useful system engineering tools for current and future developers of EHS solution architectures.