Design of IoT-Based Home Fire Detection System Equipped with a Data Logger

In Industrial Era 4.0, one of the solutions to secure a house from potential fires is to design a smart home. The smart home design described in this paper is the design of a home monitoring system for potential fires. Home fire early detection monitoring system is designed based on the IoT concept and is equipped with a data logger. Data loggers function as data logging and data recorders from time to time. The hardware system design consists of Arduino Mega2560, ESP8266, fire sensor, gas sensor and datalogger module as data storage. The software design that also functions as a system monitoring display is the Blynk application on Android smartphones. Fire sensor and gas sensor activities were recorded on the data logger module. The system test was carried out around the gas stove in the kitchen room. The system design results were displayed via the Blynk application on a smartphone. Fire detection system is a change in the Virtual Button from the initial color to red. While the monitoring display of the gas detection system is in the form of moving the Virtual Level when a gas leak is detected. Fire sensor activity recording data from the datalogger is displayed in the form of a graph of the relationship of the working voltage of the fire sensor versus time. While the data recording of gas sensor activity from the datalogger is displayed in the form of a graph of the relationship of gas concentration versus time. All system response results and graphic data are displayed in realtime through the Blynk application on a smartphone. In addition, system performance data records are stored on a micro SD card for further analysis. From the results of the design of this system is expected to be a reference of home monitoring technology from potential fire equipped with a data storage system.

Introduction
The presence of self-designed products especially home-based fire detection devices based on the concept of the Internet of Things (IoT) is a necessity that highly expected in the Industrial Revolution 4.0 era. The design of a smart home home fire detection system based on the IoT concept has succeeded in detecting the potential for fires through smartphones [10]. In the smart home design the system is connected online with a fire detection circuit. The system is able to detect potential fire emergencies and monitor in real time the kitchen room. The detection device prototype is assembled on a single board IOP Publishing doi: 10.1088/1757-899X/1125/1/012079 2 consisting of a fire sensor, gas sensor, ESP8266 as a Wifi connectivity module and a router/ modem. Research related to IoT design by utilizing Arduino Mega2560, ESP8266, various sensors and Blynk applications on smartphones has been conducted by Juwariyah T., et al [6]. The results of the IoT design research was monitoring the early detection of potential fire can be displayed through the Blynk application on a smartphone. The weakness of the detection system is that the system has not been equipped with a data logger which is a system of recording data or recording data from time to time. The existence of a data logger will facilitate the process of finding historical data recorded by sensors. The existence of the addition of a data logger to the detection system design further analysis of system performance can be carried out.

Literature Survey
Some studies related to smart home systems, among others, were conducted by M. Aluh Ashari [8], who conducted a study of "IoT Based Smart Home Systems Using NodeMCU". In that study, several home electronic devices such as home power plugs were controlled via smartphones. Another smart home study was conducted by Delebarre, C., et al regarding Wireless Low Cost CO 2 Monitoring System Design and Evaluation Using Non Dispersive Infrared Sensor [3]. Smart home study was conducted by Rajes Khana regarding the Design of an Internet-Based Home Security System with the Android Platform [9]. In this research a miniature house was built with a home security monitoring system with a PIR sensor to detect the presence of moving objects, an MQ-2 sensor to detect the presence of a gas leak and to control several devices related to a home security system such as lights and door lock solenoids to lock door. Monitoring and control is done through an application on an android device that is connected to the arduino UNO server and microcontroller. Firdaus, et al conducted research on "CO monitoring and early detection of LPG gas leaks in housing using WSN (wireless sensor network)" [5]. In this study CO gasses sensors and LPG gas sensors are monitored at several points (nodes). This means that sensors are monitored simultaneously. WSN is able to work well as long as the node receives radio waves emiition, the data received by the receiver matches the data sent by the transmitter. The GUI display monitoring system was designed using Visual Basic software on a PC.

Materials
The software and hardware components used in the system design are described below.

Arduino Mega2560 Microcontroller
The microcontroller is a microcontroller platform which is open development. Its easy to use and open source schemes (open source) make Arduino the right choice for anyone who wants to build electronics projects. At present various types of arduino have been produced such as Arduo Uno, Arduino Mega, Arduino pro mini, Arduino Yun and others. The Arduino Mega platform has pins that can be connected to other electronic circuits such that Arduino can control other electronic components, such as: LEDs, relays, wifii modules, GSM modules, power supply modules, and various types of sensors such as, gas sensors, fire sensors, smoke sensors, temperature sensors, humidity sensors, PIR, RFID and others [7].

ESP 8266
ESP8266is an embedded chip designed for wifi-based communication. This chip has a TTL and GPIO serial output. ESP8266 can be used alone (standalone) or combined with other controllers such as a microcontroller. ESP8266 has the ability to network that is complete and integrated both as a client and as an Access Point. There are so many ESP88266 firmware, an ESP8266 chip can be programmed with a special purpose as needed, for example the ability to communicate with the web using HTTPS ports [12]. The ESP8266 chip is enhanced by Tensilica's L106 Diamond series with a 32-bit processor. There are 3 ways to use ESP8266: as wifi access using AT command, which is usually used by Arduino for wifi IOP Publishing doi:10.1088/1757-899X/1125/1/012079 3 connections, as a stand-alone system using NodeMCU and using LUA language, as a stand-alone system using Arduino IDE that can already be connected with ESP8266. ESP 8266 can act as a client to a wifi router, so when configuration is needed, the name of the access point and password are also set, besides that ESP8266 can be used as an Access Point where ESP8266 can receive wifi access [2].

Data Logger Shield
The Data Logger Shield is a shield that is used to store data (data logging) where this shield is compatible with Arduino Uno, Duemilanove, Diecimila, Leonardo, Mega R3 / Mega R3 or higher but for ADK / MEGA R2 or lower is not supported. This shield is equipped with RTC (Real Time Clock) to maintain the time, even when the arduino microcontroller is unplugged. Backup battery lasted for many years.

Gas sensor
The MQ6 Gas sensor module serves to detect the concentration of flammable gases in the air. This sensor provides an analog voltage output. Based on the MQ6 sensor datasheet it can measure gas concentrations from 300-10000 ppm; operating at temperatures of -200C to 500C; consume 150mA current at 5V voltage. Concentrations of types of gas that can be detected include: LPG, butane, propane, methane, alcohol, hydrogen and smoke.

Flame Sensor
The flame sensor module functions to detect the presence of fire because it is sensitive to the presence of UV (ultraviolet) to IR (infrared) waves. The analog pin input voltage is 5 V while the digital pin input voltage is 3.3 V. The maximum distance to detect the presence of fire is 3 feet (± 90 cm).

Blynk
Blynk is one of the server service platforms used to support the Internet of Things projects. This platform is used in Android and iOS mobile user environments. The Blynk application can be downloaded on the Google play store. It supports a variety of hardware used for the Internet of Things project. Blynk is a digital dashboard with graphical interface facilities for making projects. It was created with the aim of remote control and hardware monitoring using internet or intranet data communication (LAN network). Blynk's ability to store data and display data visually using numbers, colors or graphics makes it easier for beginners to use in making projects of the Internet of Things. As shown in Figure 6 there are three main components of Blynk : Blynk Apps, Blynk Cloud Server and Blynk Library [1].

Arduino IDE
The Arduino integrated development environment (IDE) is an Arduino board programming language application that can operate on Windows, macOS, or Linux. Arduino IDE is written in functions from C to C++. Programs written using Arduino IDE are called sketches.

Proposed Architecture
The block diagram of the fire detection system design is presented in Figure 1.

Algorithm
The algorithm of home fire detection system is as shown in Figure 2. The algorithm is coded in Arduino IDE.An algorithm that explains how the system works is presented in Figure 2. Based on the algorithm, it appears that the initial sensor condition is HIGH meaning that when there is no stimulation of fire or gas the highest voltage sensor has the highest value. When there is a fire or gas stimulation, the voltage of the sensor drops in value. This is in accordance with the concept of physics related to the law of conservation of energy, which means that when the sensor does work in the sense of responding to stimuli, the potential difference in the conductor ends on the sensor decreases. In other words when the sensor is working (positive) the electric potential energy decreases (negative) represented by the voltage difference. Figure 3 presents the results of the IoT hardware prototype home fire detection monitoring monitoring system. As shown in Figure 10, the main control is on the Arduino Mega 2560 board. Meanwhile, the ESP 8266 board functions as a client of the WiFi Router that Arduino Mega2560 uses for WiFi access through the AT command. One of the advantages of Arduino Mega 2560 is the availability of many analog pins, making it suitable for further development designs. This means that if in the future more analog sensors will be added to complete the design of the fire detection system or other smart home systems, there will not be too many pin configurations. Another advantage of the Arduino Mega 2560 is that the board provides many other pins for system development such as the availability of an additional three pairs of ports for serial communication (Tx/Rx). In the IoT prototype as shown in Figure 3 the fire sensor is connected to digital pin number 22 and the gasses sensor is connected to analog pin number 15 on the Arduino Mega 2560 board. The pin number determination is determined according to the hardware placement configuration conditions, in addition to that the wiring looks more presentable. Under different hardware configuration conditions the Arduino Mega 2560 pins may also change. The thing to note is that the fire sensor is connected to the DIGITAL PIN and the MQ6 gas sensor is connected to the ANALOG PIN.

. The Results of System Performance Data Collection
Cases of house fires often occur starting from the kitchen. The fire occurred due to the presence of LPG gas which was not detected and triggered by sparks from the gas stove. The chronology of the fire case is simulated with an inverse approach, in the sense that the process of taking fire data takes precedence for reasons of work safety. Retrieval of system performance data is done separately. First, the fire sensor performance data collection is presented as shown in Figure 5.a. Meanwhile, Figure 5.b presents the display of the detection system GUI through the Blynk application on a smartphone. In the Virtual Button application it lights up red, indicating the performance of the fire sensor when the candle flame (for example a fire flame) lights up. The results of the system response to the retrieval of data variations in the distance of the fire source from the fire sensor produces an average response time of (3.3 ± 1.0) seconds. System response time indicates the length of time the system responds and processes light stimuli in the form of a decrease in sensor voltage when responding to a stimulus. Figure 5.b appears the Virtual Button is red which indicates the system detects fire. On the right side of the Virtual Button appears Virtual Level moves to the left which means the fire sensor voltage drops. When fire sensor detects a fire the fire sensor voltage is in the LOW logic condition as explained earlier in the algorithm work system. Meanwhile, the graph of the flame sensor working voltage vs. time function is displayed continuously during data retrieval. From the graph, it appears that during the fire sensor working voltage recorded by the datalogger for one day shows the graph rises and falls in the LOW voltage condition range. Different things are shown in Figure 6 when the fire sensor does not detect fire, the graph as long as the data collection is always constant, which is in the HIGH voltage condition.  Figure 6.a shows the process of collecting gas sensor performance data. Retrieval of gas detection data is done by placing an open plastic bottle both ends. One end of the bottle is placed around the gas stove burner. The plastic bottle functions as a tube or pipe that can hold and contain gas, while the other end of the bottle's mouth is attached to the MQ6 gas sensor. In this way the gas is expected to be confined and when the gas concentration in the pipeline has passed the tolerance threshold set by the system algorithm, the system will respond to the presence of the gas. The results of the system response appear in the form of the Blynk application, especially the display of the Virtual Button changes color and the virtual level experiences an increase in gas concentration as shown in Figure 6.b. The graphs of the values of the gas concentration over the time recorded by the data logger during data collection appear to have increased linearly.

Conclusion
Smart home in the form of an early detection system for potential home fires can be designed and built based on the IoT concept. The system design is an integration of hardware and software assembly consisting of Arduino Mega 2560, ESP8266, datalogger, fire sensor, gas sensor, Arduino IDE and the Blynk application on an Android smartphone. The results of the system design are able to work and show the system's response to the presence of fire and the presence of gas leaks in the kitchen. The system design results are displayed via the Blynk application on a smartphone. The system performance towards the presence of fire is indicated by changing the detection of fire in the form of changing the Virtual Button from the initial color to red. The display of a gas detection system in the form of a Virtual Level moves when a gas leak is detected. Fire sensor activity recording data from the datalogger is displayed in the form of a graph of the relationship of the working voltage of the fire sensor versus time.
While the data recording of gas sensor activity from the datalogger is displayed in the form of a graph of the relationship of gas concentration versus time. All system response results and graphic data are displayed in realtime through the Blynk application on a smartphone. In addition, record system performance data stored on the micro SD card for further analysis. From the results of the design of this system is expected to be used as a reference technology for home monitoring of potential fire equipped with a data storage system.


s2fieldsofstudy:
Computer Science
Business

text:
Industry 4.0-A Breakthrough in artificial Intelligence the Internet of Things and Big Data towards the next digital revolution for high business outcome and delivery

The assembling ventures are presently changing from large scale manufacturing to redid creation. The quick headways in assembling advances and applications in the ventures help in expanding efficiency and increased flexibility for smooth production. Industry 4.0 is the new term coined by the German Scientist and it is the most needed upgrading strategy for the present industrial revolution. It caters the need for the on demand technology where the sectors like 3D printing, Big data, block model, Artificial Intelligence, Machine learning with Internet of things play a big role in all the industrial areas today. Today need like on demand delivery can be achieved through this 4.0 standard thereby making high revenue for the organization’s business and customers as well. This paper insists the need for all the industries to upgrade themselves to adopt the Industry standard 4.0. It includes a deep survey on the industry 4.0standard and it significance in different disciplines of technology. It gives an opportunity and the viable scope for the industries to move forward and take their business a head following the practices in this standard. Thus this paper proposes a new business model which will give a phenomenal scope in the above said verticals.

Introduction
Industry 4.0 has been characterized as a name for the latest thing of computerization and information trade in assembling advances, including digital actual frameworks, the Internet of things, distributed computing and intellectual registering and making the brilliant plant. Industry 4.0 is making it simpler for organizations to work together and divide information between clients, producers, providers and different gatherings in store network. It improves efficiency and seriousness, empowers the change to acomputerized economy, and gives occasions to accomplish monetary development and maintainability. In current occasions of covid, a few innovations are helping everybody in battling with it. The technologies like Artificial Intelligence and Machine Learning incomparable requesting innovations at this moment [1]. Such advancements help to construct some extraordinary gadgets which learn all aloneand work like us. Not just it helps us in battling with covid yet changed a few ventures. Artificial intelligence and ML are impressive supporters of the modern transformation 4.0.AI has decreased crafted by labourers and made work more problem free. Labourers accomplish need to work physically [2]. With little orders, the machine can do the errand for them. In eighteenth century, the industry 1.0 started using steam fueled and robotization of creation. It was massively advantageous as far as assembling a bigger number of different merchandise and making a superior way of life for a few. The material business, specifically, was changed by industrialization, as was transportation. As creation abilities expanded, business likewise developed from singular cabin proprietors dealing with their own and possibly their neighbors requirements toassociations with proprietors, directors and workers serving clients [3].

Industry 2.0
By the beginning of the 20th century, power transformed into the fundamental and plentiful source.It was very easy to use then other natural resources. In fact, many pioneer organizations use this to implement their machines work by themselves simply by using the commands supplied by the human. This paves way to force the singular machines and sustained for a long term. These new force sources pushed new motors, specifically, inward burning motors not very disparate from the one you may discover in your vehicle. The ignition of fuel creates heat that controls the motor. This period likewise saw the improvement of different organization programs that made it possible to fabricate the capability and ampleness of amassing workplaces [3]. Division of work, where each worker does a piece of the outright work, extended productivity. The products which are produced in bulk manner utilizes the typical sequence construction model.

Industry 3.0
It started with the main PC time. These early PCs were frequently basic, cumbersome and staggeringly huge comparative with the processing power they had the option to give, however they laid the preparation for a present reality that one is unable to envision without PC innovation. The creation andassembling of Silicon Valley products, the semiconductor and Integrated circuit chips, are easily accustomed to the singular machines in the industry which is completely atomized. It is also very easyto enhance further functionality by the supplant admin [4]. This period likewise brought forth the advancement of computer programs in the frameworks to benefit organizations in building the electronic gadgets.

Industry 4.0
Industry 4.0 focus more on the, Internet of things and big data. The scientific methods that use different mathematical formulas are easily handled by the different frameworks and libraries that are suitable for the multiple domain industries other than IT industry. Their roles are to gather data, process it usingthe above said methods (Slicing, dissecting) and using it to report generation for better understanding by the management of the organization. The improvement of unique innovation has been an essential driver of the development to Industry 4.0. A portion of the projects initially created during the later phases of the twentieth century, for example, fabricating execution frameworks, shop floor control anditem life cycle the board, were farsighted ideas that did not have the innovation expected to accustom their full usage [4] [5]. At present, Industry 4.0 can help this project arrive at their maximum capacity.

Demands of Industry 4.0:
The unpredictability of utilizing AI and the counterfeit business is immense, and computerization in the business needs to work together with various experts for building redid arrangements. Industry 4.0 includes different advances and stages [6]. That should be designed. A portion of the means are following.
x Accumulation of information x Chronological information collection x Live information utilizing sensors x Emphasis Engineering 4. AI impact in industry 4.0:

Prescient Quality and Yield
Prescient Quality and Yield utilizes Industrial Artificial Intelligence to uncover the shrouded reasons for large numbers of the enduring creation misfortunes makers face consistently. This is done by means of ceaseless, multivariate investigation, utilizing Machine Learning calculations that are interestingly prepared to personally see every individual creation measure. The particular AI/Machine Learning strategy utilized here is named "Supervised Learning", where the algorithm is prepared to distinguish patterns constantly in the data. Automated suggestions and cautions would then be able to be created to advise creation groups and cycle designers of an up and coming issue, and consistently share significant information on the best way to forestall the misfortunes before they occur.

Prescient Maintenance
Prescient support is quite possibly the most fundamental and notable uses of Industrial AI. Rather than performing upkeep as indicated by a foreordained timetable, prescient support utilizes to anticipate the following disappointment of a segment / machine /framework and afterward makes staff aware of perform centred support methodology to forestall the disappointment, yet not very early in order to squander vacation pointlessly. Once more, prescient upkeep frameworks depend on Machine Learning procedures to detail their forecasts (yet an alternate classification -unsupervised learning, as opposed to supervised). The preferences are various and can altogether lessen costs while wiping out the requirement for arranged personal time by and large. By seizing a disappointment with an AI Algorithm, frameworks can proceed to function without superfluous interferences. At the point when support is required, it's engaged -specialists are educated regarding the parts that need investigation, fix and substitution; which apparatuses to utilize, and which techniques to follow. Prescient support likewise prompts a more extended Remaining Useful Life (RUL) of hardware and gear since optional harm is forestalled while more modest workforces are expected to perform upkeep methodology

Generative plan
Makers can likewise utilize man-made consciousness in the plan stage. With an unmistakably characterized configuration brief as information, planners and architects can utilize an AI algorithm, by and large alluded to as generative plan programming, to investigate all the potential setups of an answer. The brief can incorporate limitations and definitions for material kinds, creation techniques, time imperatives and spending constraints. The arrangement of arrangements produced by the calculation would then be able to be tried utilizing AI. The testing stage gives extra data about which thoughts/plan choices worked, and which didn't. Thusly, extra upgrades can be made until an ideal arrangement is found.

Market adaption:
Computerized reasoning saturates the whole Industry 4.0 environment and isn't simply restricted to the creation floor. One illustration of this is the utilization of AI calculations to upgrade the production network of assembling tasks and to help them better react to, and foresee, changes on the lookout. To build assessments of market interest, a calculation can consider request designs ordered by date, area, financial credits, macroeconomic conduct, political status, climate examples and that's just the beginning. This is momentous for producers who can utilize this data to advance stock control, staffing, energy utilization, crude materials, and settle on better monetary choices with deference as well organization's system.

Technologies in fourth modern transformation:
Industry 4.0 advances, spreading over mobile computing to distributed computing, have gone through immense improvement in the most recent decade and are presently fit to be utilized as monetarily accessible, interconnected frameworks inside assembling. It holds the way to getting to ongoing outcomes and information that will launch the business into new degrees of lean accomplishments. The idea of Industry 4.0 in any case, is anything but a basic one. It wraps numerous innovations and is utilized in a wide range of settings. There are five pieces that characterize Industry 4.0 at its core.

Smart Factory
The term portrays a profoundly digitalized and associated climate where hardware and gear can improve measures through robotization and self-enhancement. The advantages additionally reach out past the actual creation of merchandise and into capacities like arranging, inventory network co-ordinations, and even item improvement. As manufacturing plants advance considering the information transformation, organizations need to reconsider how they handle everything from mechanization methodologies to labor force improvement strategies. En-route, producers will require modernized devices, including vigorous, adaptable endeavor asset arranging frameworks as an information and value-based spine, that assist them with adjusting they work toward a brilliant plant future. The definitive factor in planning a Smart Factory practically speaking is that all components of the industrial facilityapparatuses, items and plant innovation -are furnished with incorporated figuring power. Thusly, the information can be caught, prepared and sent.

Big Data:
Industry 4.0 is the combination of this present reality with the virtual world. This advanced transformation is set apart by innovation that exploits Big Data and Artificial Intelligence (AI) to sustain programmed learning frameworks. Makers in the present commercial center look to accomplish business insight through the gathering, investigation and sharing of information across all key useful spaces to accomplish creation greatness. The interconnection among frameworks and PCs and the capacity to investigate a lot of information have made conceivable the presence of savvy machines that can settle on educated choices with no human intervention. The Internet of Things (IoT) has been associating components for a long time, however the worth extricated from the information through the Big Data has taken the term to another level: The Internet of Systems.
The measure of data delivered by IoT and the present assembling frameworks should be converted into noteworthy thoughts. That is the reason Big Data groups the data gathered and makes applicable determinations that help improve organizations' activities in the accompanying manners:

Improving warehouse Process:
The Sensors and portable devices can improve operational productivity by recognizing human mistakes, performing quality controls and indicating perfect creation or assembly routes.

Exclusion of bottlenecks:
Big Data distinguishes factors that can influence execution, at no additional expense, directing producers in recognizing the issue.

Predictive Demand:
Visualization is more exact and expressive forecasts on account of the perception of movement through internal analysis (client inclinations) and external analysis (patterns and outer occasions) beyond ancient data. This permits the organization to adjust/upgrade its item portfolio.

Predictive Maintenance:
Data took care of sensors recognize potential disappointments in the activity of hardware before it turns into a breakdown. The framework sends a caution to the equipment so it can respond as expected.

IoT:
Industry 4.0 and IoT, characterized as smart, interconnected hardware and items self-ruling conveying and streamlining along the whole worth chain, are key components of this computerized insurgency. The data stored on the cloud are recorded by the tools which are typically gadgets that have actuators or traditional devices. The data collection and the decision making are done by the interface legacy devices and frameworks connected to the internet provisioned by the IoT. The development of machines, procedures and schemes are integrated by the IoT in the modern setting.

Interoperability:
In manufacturing field, interoperability speaks to an attributes of a manufacturing method wherein its parts are equipped for trading data with each other, utilizing the data that has been exchanged. It is the association of Cyber Physical System, humanoid and smart factory collaborative with one another through the IoT [9]. In doing as such, producing accomplices cansuccessfully share data, blunder free.

Cyber Physical System:
The algorithms control the instrument or a computer system called Cyber Physical System. The Physical environment and embedded systems are its combination. National Science Foundation (US) defines "The collective factors like responsive, robust, connection, distribution and coordination makes the Emerging CPS". The usability, security, safety, resiliency, adaptability, capability are the today's system which will be far lesser than the CPS of tomorrow. Advanced Manufacturing, next generation air traffic management, smart medical technologies, smart buildings, smart transportation and smart electric grid are the areas of CPS application examples. The advancements of the applications in the engineering industry involve CPS's continuous developments.

Conclusion:
The survey on Industry 4.0 helps the industry support the latest business model and adopts the best practices to implement the on demand delivery for the clients. The business standard of this model given in this paper tells the application of this industry 4.0 standard on the recent emerging areas like big data, artificial intelligence, internet of things and to enhance the business and delivery on those areas. This paper also focuses on services and the solutions that are offered by this model, scale up the business practices to the international standard, emphasizing the importance for the business to adopt to the recent technologies. This paper can be enhanced to the other emerging areas which were not depicted here as future work.


Vulnerabilities caused by faulty settings of web application components

Any web application intrinsically consists of many components (bases, frameworks, loaders). Errors occurring in these components can have multiple implications and even a minor component can reduce the overall security of the application. The paper will be useful for both information security experts and casual users.

Introduction
Any web application operates over several different layers of the OSI model with different components to operate at different layers, and of course, vulnerability is likely to exist within different layers [1,2].
It is important that any web application consists of several structural parts.

Figure 1. Web app configuration
This includes the client side (can be a mobile or desktop computer), network devices that connect computers to the server side (firewall or server filter), web services, an application server that implements the business logic (application server is not an obligatory part since it controls workflows,

Figure 2. App as seen by the programmer
This is a fairly large number of components, with part of the application server code at the top, the frameworks responsible for running the application -coming below, database components and query conversion -at the very bottom, and applications that work separately with certain objects (e.g. only with databases or conversion queries).

Invalid differentiation of rights
Invalid differentiation of access rights is a fairly common problem that may occur. Any application requires some kind of administration and these interfaces should not be accessible by anyone other than the administrator. First, it should be clear which interfaces are present in a particular application server, since this is not always obvious [6,7].
How do access-rights problems come up?
• Firstly, the application has to be managed somehow, and this should be done by a limited number of people.
• Secondly, the application must have access to system resources, yet limited.
• There are two major problems with invalid differentiation of rights: • Interface accessibility; • Invalid user access rights that are used to run components. If possible, all rights granted to a component should be minimized. A vulnerability is often caused by a situation when the administrator downloading a software installer simply clicks "next" and accepts all possible options, without realizing which function the application should be allowed to perform and which should be disabled.
Accordingly, this practice of accepting default settings often leads to vulnerabilities. How it all ends up:

An example of vulnerability in the form of interface accessibility
Accessibility of management interfaces, including virtually dead network ports, URLs.

Figure 4. Network ports
The example shows network scanning results, displaying the open ports for SSH remote management, as well as the port for the MySQL database management. With proper security, it is impossible to gain access from a desktop computer in a non-administrator network (access should be provided to a specific range of IP addresses).

Over-granting of privileges
An example of over-granted privileges is the following entry in the password file (on Linux systems): www-data:x:33:33:www-data:/var/www/:bin/sh Where www-data is the user the server is running with, a /bin/sh is the console permissions. A casual user the server is managing should not be permitted to use the server console. However, in a situation with incorrectly granted privileges, care should be taken when restricting rights, since in some cases user rights disabled may result in a server failure at large.
One example of a misconfigured application is the display of errors that occur when working with the server. In particular, this is the stack trace output in JAVA applications. Disclosure of information in error messages (framework, PL, DBMS, server).
From the errors, you can determine which methods are used to transfer data, usernames, finding the web directory. It is therefore essential that the error output is switched off.
There are systems for collaborative code development (git, svn) that, once misused, often cause a vulnerability. For example, an administrator, without checking for a vulnerability, simply copies the code in its entirety and runs it on the server. Often, fragments of such code contain access to the root directories of the server on which they are run. This slavish copying makes the root directory accessible from the Internet. Having access to the root directory does not make it difficult to access the files that the server itself runs on.

Figure 5. Index directory
Responsibility for such vulnerabilities lies with both the developer who left the vulnerability and the administrator who allowed the vulnerability to be exploited.
For git, you can use the git -clean command, which removes unneeded directories. Vulnerabilities are also found on client hardware, concerning, in particular, the use of autocompletion (i.e. in login/password fields) and caching of the data used, the lack of protection against XSS, clickjacking (creating a copy of the site with a transparent page to superimpose attacker's forms to intercept data).
Typical web browser problems: • Auto completion and caching; • Lack of security flags or headers (protection against client-side attacks: Clickjacking, XSS); • Invalid setting of cookie scope. Another example is the accessibility of cookies from the outside: Set-Cookie: …; Path=/; Domain: .bank.com In the above example, cookies are available from a wide range of domains (the dot before bank specifies a whole range of subdomain names).
Other quite common problems are the presence of files generated during development in system directories (local copies of working scenarios, standard non-protected directories) [8]. An important aspect is proper configuration of the server, because quite often the developer or server administrator leaves the MultiView mode (Apache httpd) enabled or the indexing of directories that lets an attacker find out the contents of the server (file names, directory names, etc.). To make matters worse, the silliest mistake is to have default users.
However, one of the most hazardous vulnerabilities is to integrate developer mode, which makes it easier for developers to create and immediately displays errors. It can lead to the disclosure of information of all kinds. Through this vulnerability, it is easy to get access to the system files, see the login/password pairs used in debugging (most probably they will be valid, since not every administrator thinks about deleting debug information, hoping for the developer's common sense).

Faulty settings
Another common problem is faulty settings in the configuration file of the web server for virtual hosts (a mechanism that allows a server with one IP address to contain several different domains). The http v 1.1 protocol requires a mandatory "host" field in the client request header, which specifies the domain name of the virtual server.
When it comes to an application server, there is such a thing as "three-tier application architecture". The two-tier architecture consists of a client while on the server side there is a database as the second tier. The three-tier architecture also has an application server. No one ever thinks about it, despite having great potential for an attack within the network (having a local connection), because it has broad functionality, usually has many management interfaces, open ports and a lot of default accounts). When attacking a company's local network, the application server and the corresponding management consoles are the first to search for, wherefrom the default system management records are derived. An example application server is IBM WebSphere that has a standard 9060 port that hosts the console (Fig. 13).

Conclusion
Based on the information presented, you can conclude that correct app settings is the key to successful protection of the system as such. The most crucial setting errors are those in the differentiation of access permissions and over-granted privileges.


Robust Multi-view Registration of Point Sets with Laplacian Mixture Model

Point set registration is an essential step in many computer vision applications, such as 3D reconstruction and SLAM. Although there exist many registration algorithms for different purposes, however, this topic is still challenging due to the increasing complexity of various real-world scenarios, such as heavy noise and outlier contamination. In this paper, we propose a novel probabilistic generative method to simultaneously align multiple point sets based on the heavy-tailed Laplacian distribution. The proposed method assumes each data point is generated by a Laplacian Mixture Model (LMM), where its centers are determined by the corresponding points in other point sets. Different from the previous Gaussian Mixture Model (GMM) based method, which minimizes the quadratic distance between points and centers of Gaussian probability density, LMM minimizes the sparsity-induced L1 distance, thereby it is more robust against noise and outliers. We adopt Expectation-Maximization (EM) framework to solve LMM parameters and rigid transformations. We approximate the L1 optimization as a linear programming problem by exponential mapping in Lie algebra, which can be effectively solved through the interior point method. To improve efficiency, we also solve the L1 optimization by Alternating Direction Multiplier Method (ADMM). We demonstrate the advantages of our method by comparing it with representative state-of-the-art approaches on benchmark challenging data sets, in terms of robustness and accuracy.

Introduction
Point set registration is a fundamental problem that has wide applications in computer vision [26], robotics [25], computer graphics [12], medical image analysis [19] and so on. With the advent of sensors such as LiDAR (Light Detection and Ranging) and depth cameras, it becomes relatively easy to capture realworld 3D scene data. However, one usually attains partial point clouds at once due to the 3D nature of the objects. To accurately reconstruct the 3D model, it is necessary to align multiple point clouds acquired from multi views of the object into a unified coordinate system.
Point set registration has been extensively studied in literature, and many efforts are devoted to the pair-wise (two sets) registration problem. Among these methods, the most classical one is the Iterative Closest Point (ICP) algorithm [18], in which the registration is decomposed as the alternative implementation of point correspondence and transformation estimation. Given a set of data points, the rigid transformation of ICP is solved by minimizing the summation of the squared distance of the closest point pairs. However, ICP tends to get trapped in a local optimum because of the hard assignment scheme, improper initialization, occlusions, and the interference of noise and outliers.
In contrast to the hard assignment scheme of ICP, previous works also explore the use of statistical models for registration, such as GMM [13], which replaces the previous binary assignment with probability. These methods formulate point set registration as a probability density estimation, where the mean of each component is initialized as the point location. In principle, probability registration can provide better estimation for convergence and geometric matching, thereby they are further extended for multi-view registration [8]. Most existing probability methods rely on GMM for registration, nevertheless, Gaussian distribution minimizes the quadratic distance between the data points and their means, which makes GMM susceptible to noise with heavy tail and sensitive to outliers [10].
In this paper, to address the aforementioned problems, we propose a novel and robust multi-view registration method based on the LMM composed by the Laplacian distribution. Due to the heavy-tail property and the sparsity-induced L 1 norm, LMM is more robust against outliers. We formulate point set registration as a likelihood estimation problem, which can be solved by EM framework. To handle the L 1 optimization, we customize the rotation estimation to a linear programming problem by exponential mapping and name it as linear programming approximation (LPA). Moreover, inspired by [4], we also use ADMM to solve L 1 optimization, which has higher efficiency without significant accuracy decreasing. We test and compare the proposed methods (including LPA and ADMM) with representative state-of-the-art approaches in terms of accuracy and robustness, and the results indicate that our methods outperform previous ones with higher accuracy and are more robust against noise and outliers.
In a nutshell, the main contributions of this paper are twofold as follows: -We propose a novel multi-view registration method for point clouds based on the sparsity-induced Laplacian mixture model. Due to the L 1 norm of Laplacian distribution, the proposed method is more robust against noise and outliers; -To handle L 1 optimization, we customize it as a linear programming problem via exponential mapping, which can be effectively solved by the interior point method. For efficiency, we further deduce it into the ADMM framework, thereby it can also be solved by the ADMM algorithm.

Related Work
We briefly review the related work of point set registration from the perspective of pair-wise registration and multi-view registration.

Pair-wise Registration
The objective of pair-wise registration is to align two point sets by solving a transformation matrix. The most popular algorithm of pair-wise registration is ICP [18], which composes two iterative steps: 1) correspondence step in which the point correspondence between two point sets is established, and 2) transformation step in which the transformation matrix based on the current correspondence is updated. ICP takes the least-squares estimator as the objective function, thereby the closed-form solution in each step can be attained through singular value decomposition (SVD). However, as the Gauss-Markov theorem [21] pointed, the least-squares estimator is sensitive to outliers. Moreover, ICP requires good initialization and fails to handle non-overlapping point sets. To improve performance, many variants of ICP have been proposed. Fitzgibbon [9] adopts M-estimator for registration error minimization and solves the problem by non-linear Levenberg-Marquardt optimization, thereby proper initialization is required. Chetverikov et al. [6] propose a trim scheme named TrICP to automatically remove non-overlapping regions for accurate registration. For efficiency, Rusu et al. [22] introduce Fast Point Feature Histograms (FPFH) to describe the local geometry around a point to reduce the computational complexity. Lei et al. [15] compute eigenvalues and normals from multiple scales and take them as local descriptors to speed up matching. However, descriptor-based methods [15,11] are susceptible to point sets with noise and low overlapping.
Recently, deep learning-based methods such as [1] are also proposed to estimate the transformation matrix. Nevertheless, the lack of comprehensive registration datasets results in a hard time for the learning methods to grasp all shape variations. Alternatively, probability registration methods adopt GMM to represent data points, such as Robust Point Matching [7], Coherent Point Drift [17], and GMM-Reg [13]. These methods either simultaneously model the source and the target point sets by GMM, and the transformation matrix is solved by minimizing the discrepancy between the two GMMs, or singly model the source point set as GMM, and then evoke maximum likelihood estimation to fit the target point set. However, GMM fails to attain accurate registration results under the contamination of noise with a heavy tail. Moreover, since GMM minimizes the quadratic distance between the points and its means, it suffers from severe outliers. In contrast, we introduce Laplacian mixture models to model data distribution, which adopt the L 1 norm for error evaluation, thereby it is more robust against outliers for registration.

Multi-view registration
Multi-view registration aims to simultaneously register multiple point sets from different views, which is often solved through sequential pair-wise registration. Transformation parameters are sequentially updated by ICP or probability methods if a new point set is added. Except for the drawbacks from pair-wise registration, these methods also suffer from error accumulation and propagation. Bergevin et al. [3] propose a star-network and sequentially put one point set in the center of it, then pair-wise registration by ICP is implemented to align the central point set with the other ones. In contrast, Williams et al. [24] simultaneously compute the correspondence between all point sets, which is timeconsuming. Mateo et al. [16] introduce the Bayesian perspective to assign different weights for different correspondences, with the detection of false correspondences. Although the registration accuracy is improved, it needs to compute many variables.
Additionally, information theoretic measures are also customized for multiview registration task. Wang et al. [23] first represent point sets by cumulative distribution function (CDF), and then minimize the Jensen-Shannon divergence between CDFs for multi-view registration. Later, Chen et al. [5] use the Havrda-Charvat divergence to evaluate the differences between CDFs, compared with previous Jensen-Shannon divergence, this method is more efficient. Nevertheless, information theory based approaches are still with low efficiency.
Recently, GMM is generalized for multi-view registration by [8] named JRMPC, which assumes data points are generated by a central GMM, and then casts the registration task as a clustering process. By this, global information of point sets is combined to avoid the error accumulation. Zhu et al. [27] propose a method named EMPMR, which assumes that each data point is generated from a GMM whose Gaussian centroids are composed of corresponding points from other point sets. Nevertheless, GMM suffers from heavy-tail noise, and is sensitive to severe outliers due to the L 2 norm. Based on JMRPC, TMM [20] achieves better robustness by replacing the Gaussian distribution with a t-distribution, however, its time consumption is relatively considerable. To address these drawbacks, we propose a novel and robust multi-view registration method based on the sparsityinduced L 1 norm, which is effectively solved by LPA or ADMM.

Multivariate Laplacian Distribution
Suppose x ∈ R d is a random variable following the multivariate Laplacian distribution, with the probability density function as where µ and b represent the mean and the scale parameter of the d-dimensional Laplacian distribution, respectively, and · 1 denotes the sparsity-induced L 1 norm, summarizing the absolute values of all elements of a vector. Previous work has shown that the Gaussian distribution is sensitive to heavy outliers because of its short tails [10,2], while the Laplacian distribution has heavier tails.

Laplacian Mixture Model
The task of multi-view registration is to align the multiple point sets in X to the same center frame. To this end, we solve the rotation matrix R i and the translation vector t i for each X i , and represent the transformed coordinates asx il = R i x il + t i . Due to the influence of noise, it is hard to hope that corresponding points in different frames will have the same coordinate after alignment. In contrast, we assume the multi-view point sets will constitute multiple clusters.
Suppose each data pointx il is generated from a unique LMM, where the Laplacian centers are composed of the corresponding points in other frames after alignment. However, it is difficult to directly get the accurate correspondence relationship between different point sets. Here we adopt the nearest neighbor search based on the kd-tree to approximate the corresponding points. In specific, for pointx of the center frame, we denote c j (x) as the nearest neighbor ofx from the j-th point set, which can be defined as: For simplicity, we use isotropic covariance b and equal membership probability for all LMM components. Thereby, the LMM of x ∈ X i is defined as Note that different from previous GMM based methods, in which a uniform distribution has to be added to account for noise and outliers, there is no need for our LMM based method, since Laplacian distribution has a heavy tail and is sparse enough to accommodate outliers. We adopt the maximum likelihood estimation (MLE) to solve unknown parameters. The log-likelihood function of observed data points is , b represents all parameters. It is intractable to directly solve the maximum likelihood solution for such a complex model. Instead, we adopt the effective EM framework by introducing latent variables for solving, as presented in the following.

Registration by EM Algorithm
To estimate the parameters by EM algorithm, we first define a set of latent variables where Z il = j means thatx il is generated from the j-th component of the LMM. Given all observed data X , model parameters can be estimated by maximizing the expectation of the loglikelihood function: Since we regard each Laplacian component equal, the prior probability P (Z; Θ) is a constant term. In addition, P (X | Z; Θ) can be derived from Eq. (3), namely, After ignoring constant terms, the objective function of Eq. (5) is reformulated as where α ilj = P (Z il = j | x il ; Θ) denotes the posterior and d denotes the data dimension (d = 3 in our case). Therefore, the multi-view registration problem is cast into a constrained optimization problem as follows: In order to maximize f (Θ) by EM algorithm, we alternatively perform E-step and M-step as follows, after which, the transformation parameters of multi-view registration are attained.

E-Step
Given data point set X and currently estimated parameters Θ (k) , E-step calculates the distribution of latent variable Z, which can be divided into two steps. The first step is to establish point correspondence. For point x il in i-th point set, it is initially transformed into the center frame with new coordinatê x i . Then the corresponding point in j-th frame can be approximated by the nearest neighbour ofx The second step updates α (k+1) ilj = P (Z il = j | x il ; Θ (k) ), which represents the posterior probability of point x il generated from j-th component of LMM. According to Bayesian formula, we have where β ilj denotes the probability density of Laplacian distribution:

M-Step
M-step estimates the parameters Θ (k+1) by maximizing the expectation f (Θ) with current values α , b}, we first solve transformation parameters {R i , t i } and then solve the variance scale b. Transformation parameters are estimated by The above equation is a weighted least absolute value (WLAV) problem with SO(3) constraint. Although there is lack of a closed-form solution, we propose two methods to iteratively solve it in Section 4.3. After updating transformation parameters, we take the partial derivative of f (Θ) with respect to b and equate it to zero, then the update of b (k+1) is where N i denotes the cardinality of the i-th point set. As can be seen, b presents the weighted average of the deviations.

WLAV subproblem
Previous GMM based approaches usually need to solve a weighted least square problem, and the closed-form solution can be attained through SVD. However, due to the L 1 norm optimization, WLSV problem requires iterative solving. In this work, we propose two methods to solve this kind of problem, namely, LPA based and ADMM based methods, as presented in the following.

LPA Method
We first transform the sub-problem (11) into a canonical form: where P = [p 1 , . . . , p n ] ∈ R 3×n and Q = [q 1 , . . . , q n ] ∈ R 3×n represent the source point set and the target point set respectively, while w i represents the weight. By exponential mapping, the rotation matrix R can be written as: where [r] × represents the skew-symmetric matrix of r ∈ R 3 , expressed as To eliminate the SO(3) constraint, we linearly approximate the rotation matrix R by neglecting the higher-order terms of (14). Then the deviation can be approximated as Then the objective function can be reformulated as: where x denotes [r, t] T . Let u = |Ax − b|, the original WLAS problem is transformed into a linear programming problem: which can be efficiently solved by interior point methods [14]. Moreover, we deduce another method to solve L 1 optimization based on the ADMM in the following. We name the two optimizations as Ours-LPA and Ours-ADMM, respectively, and compare them with previous approaches in Section 5. (13) can be reformulated as:

ADMM Method Considering the constraint
Then the augmented Lagrangian function of ADMM is where we replace w i (Rp i + t − q i ) by s i to simplify the formula. Due to the space limit, we directly present the iterative steps as where sub-problem (21) can be solved efficiently by the following shrinkage operator: while sub-problem (22) can be solved by SVD.

Experiments
In this section, we compare the performance of the proposed method with three representative state-of-the-art approaches for multi-view registration, namely, JRMPC [8], TMM [20], and EMPMR [27]. The implementation of all compared methods is publicly available. All experiments in the following are performed on a laptop with a 6-core 2.2GHz Intel CPU and 16GB RAM.

Data Sets and Evaluation Measure
We use six 3D data sets from the Stanford 3D Scanning Repository 5 (Bunny, Buddha, Dragon, and Armadillo) and the AIM@SHAPE Repository 6 (Bimba and Olivier hand) for test. To quantitatively evaluate the registration performance of different methods, we compute the error by where denote the ground truth and the estimated rigid transformation, respectively. Table 1. Statistics of registration errors of all compared methods, where the red and blue fonts indicate the best and the second-best performance for each metric. The proposed method Ours-LPA attains the overall best performance.

Methods
Armadillo

Comparison Results
We first downsample each data set to 4,000 points, and crop them along the xyplane to generate the missing overlapping, then we rotate them around the x, y and z-axes with the rotation angle uniformly distributed between [−20 • , 20 • ]. Moreover, we add Gaussian noise to each data point with the signal-to-noise ratio (SNR) equal to 70dB and 30% outliers following the uniform distribution.
The comparison results are reported in Table 1. We color the best in red and the second-best in blue for each metric. As observed, compared with initial errors, TMM has larger rotation deviations for all data sets, JRMPC and EMPMR also suffer from rotation estimation, such as the Bunny and the Armadillo data sets. In contrast, our proposed methods including Ours-ADMM and Ours-LPA achieve fewer rotation errors than initial cases for all data sets. Moreover, the proposed Ours-LPA attains the highest accuracy for rotation estimation than all competitors. EMPMR has the overall lowest translation error, but its rotation errors are relatively large. Our proposed method Ours-LPA attains the second highest accuracy for translation estimation, and the deviations are small enough. Thereby, the proposed method Ours-LPA achieves the overall best performance. We present several test examples in Fig. 1.
We further report the time consumption of different methods in Table 2. As observed, due to the closed-form solution of EMPMR based on the L 2 norm, it consumes relatively less time. Our proposed method Ours-ADMM has the second fastest speed, and its time consumption is quite close to EMPMR. Moreover, from Table 1, we find that Ours-ADMM has fewer rotation errors than EMPMR, meanwhile with acceptable translation deviations. Ours-LPA consumes relatively more time than Ours-ADMM, however, it is still faster than TMM and JRMPC. Therefore, to register point sets with high efficiency and comparable accuracy, we suggest Ours-ADMM for use.

Robustness against Outliers
Subsequently, we evaluate the robustness of the proposed method against outliers. We adopt the bunny data set for this purpose. We add 1% − 80% outliers to the point cloud. Besides, we contaminate the point cloud with 70dB Gaussian noise. Several examples are illustrated in Fig. 2. The test results are reported in Table 3. As can be seen, with outlier increasing, the proposed method Ours-LPA exhibits higher robustness than compared ones. EMPMR has fewer deviations at low outlier contamination, but it suffers from severe outliers (more than 30%). JRMPC also shows certain robustness against outliers, whereas its performance is unstable. In contrast, Ours-ADMM attains the quite stable performance for all outlier tests, and it has the a very similar registration accuracy to the first two winners. TMM has the largest deviations than the others, indicating its weakness in handling outliers.

Robustness against Noise
We also test the performance of the proposed methods in term of noise. To this end, we first contaminate the point cloud with 30% outliers, and then increase the noise intensity by decreasing the signal-to-noise ratio. The test results are reported in Table 4. As observed, the proposed method Ours-LPA achieves the overall best performance, and its registration errors are even 1,000 times fewer than TMM. JRMPC and Ours-ADMM attain similar results, thereby they have the second-best performance. EMPMR shows shortcomings for noise, especially for low SNR noise.

Conclusion
We have presented a novel and robust multi-view registration method for point clouds based on the Laplacian mixture models (LMM). We adopt Laplacian distribution to represent each data point, and then cast the multi-view registration task as a density estimation problem, which can be efficiently solved through the expectation-maximization framework. Due to the heavy tail and the sparsity-induced L 1 norm, LMM is more robust against GMM. To solve the L 1 problem, we deduce our objective function into two optimization paradigms, namely, linear programming and ADMM. We test the proposed methods on challenging data sets with contamination of noise and outliers, and compare it with three representative state-of-the-art approaches, and results demonstrate the salient advantages of the proposed method: more robust against noise and outliers, as well as higher accuracy.


Cloud Computing Deployment Models: A Comparative Study

Cloud computing has become the embraced in the computer world. Cloud implementation is the process of creating a virtual computing environment. Deployment in the cloud provides organizations with flexible and scalable virtual computing resources. A cloud deployment model is the type of architecture in which a cloud system is deployed. These models differ in terms of administration, ownership, access control, and security protocols. This paper describes the different types of cloud computing service models and deployment models; it also gives us a comparative study of various clouds using many factors. The comparison is simply based on various factors such as reliability, cost, data control, workload, performance, and many other cloud

I. INTRODUCTION
The demand for cloud computing has led to different types of cloud deployment models. Cloud computing is also known as the fifth utility (along with water, electricity, gas, and telephone) that is available based on user demand. Cloud computing is based on pay as per use of model. In this, a cloud computing model provides an on-demand online computing service as required by the user. [1] With all the new cloud options and the phrase "as a service" seemingly added to everything imaginable, it helps to take a step back and look at the differences between the main types of cloud deployment and the different types of services. cloud computing. Cloud deployment describes how a cloud platform is deployed, how it is hosted, and who has access to it. All cloud computing deployments operate on the same principle by virtualizing the computing power of servers into segmented, software-driven applications that provide compute and storage capabilities.
[2] Like all clouds, they have different characteristics like storage capacities, billing systems, and different methods of providing the services of other clouds. The recent problem is that people do not know which cloud is suitable according to their requirements; they cannot choose the right cloud for their services among the different clouds managed by different cloud providers [3]. So, to facilitate these kinds of situations, this paper helps define the comparison of some of the most popular clouds, taking in mind. It's all the important aspects that can help a normal customer, business and academic organizations to choose the particular cloud from according to your needs. Briefly, this paper presents a comprehensive analysis of cloud computing, explaining its services and deployment models, identifying various features of interest, and comparing them with different deployment models.

II. CLOUD COMPUTING SERVICES
The cloud architecture can be divided into four layers based on their functioning which show in Fig.1.

A. Infrastructure as a Service (IaaS)
IaaS is the most comprehensive and flexible type of cloud service. Essentially, it provides a fully virtualized computing infrastructure that is provisioned and managed over the Internet. An IaaS provider manages the physical edge of the infrastructure (servers, data storage space, etc.) in a data center, but allows customers to fully customize those virtualized resources to meet their specific needs. With IaaS, the customer can purchase, install, configure, and manage any software they need to use, including items such as operating systems, middleware, applications, business analytics, and development tools. IaaS eliminates the capital expense of building internal infrastructure.

B. Platform as a Service (PaaS)
PaaS provides the framework you need to create, test, deploy, manage, and update software products. It uses the same basic infrastructure as IaaS, but also includes the operating systems, middleware, development tools, and database management systems necessary to create software applications. PaaS is extremely useful for any company that develops web-based software and applications. Many of the tools required to develop for multiple platforms (computers, mobile devices, browsers, etc.) can be quite expensive. Customers can access the development tools using PaaS cloud service.

C. Function-as-a-Service (FaaS)
FaaS allows customers to react code reactively, without the need to allocate processing resources ahead of time. The cloud service provider handles the infrastructure, allowing the customer to focus strictly on the application of application codes. Functions are scaled automatically, making them excellent for adapting to dynamic workloads that vary in terms of resource consumption. Customers only pay for the resources they use, making FaaS the truest form of "pay-as-you-go" cloud computing. Most FaaS applications are quite simple and can be deployed very quickly. The cloud customer just needs to upload the complied function code and tell the platform how to provision resources when it executes.

D. Software as a Service (SaaS)
SaaS is a fully developed software solution ready to buy and use over the Internet by subscription. The SaaS provider manages the infrastructure, operating systems, middleware, and data necessary to deliver the program, ensuring that the software is available when and where customers need it. Many SaaS applications run directly through web browsers, eliminating the need for downloads or installations. SaaS applications allow businesses to get up and running quickly and scale operations quickly. You do not need to purchase or implement the hardware and software used to deliver your business services.

III. CLOUD COMPUTING DEPLOYMENT MODELS
There are six types of Deployment Models, from them five are main: Private Cloud, Public Cloud, Hybrid Cloud, Community Cloud, Virtual Private Cloud. Inter-Cloud is also a type of Deployment models and it has two types of clouds: Federated Clouds, Multi-clouds. In Fig. 2, it displays the uses of deployment models in Data center and its growth in present era and in future.

A. Private Cloud
The private cloud deployment model is also called as the internal or corporate model. A private cloud belongs to a specific organization. That organization controls the system and manages it centrally. While a third party (for example, a service provider) can host a private cloud server. Most companies choose to keep the hardware in their local data center. From there, an internal team can oversee and manage everything.

B. Public Cloud
The public cloud model is well-known cloud service. This type of cloud is a popular choice for web applications, file sharing, and non-confidential data storage. Public clouds are recommended for software development and collaborative projects. The service provider owns and operates all the hardware necessary to run a public cloud. Vendors keep the devices in massive data centres. The public cloud delivery model plays an important role in development and testing. Developers frequently use public cloud infrastructure for development and testing purposes. Its virtual environment is inexpensive and can be easily configured and quickly deployed, making it perfect for test environments. [1]

C. Hybrid Cloud
Hybrid clouds combine public clouds with private clouds. They are designed so that data and applications move smoothly with each other and the two platforms interact smoothly. It is the perfect solution for a business or organization that needs a bit of both, which are generally industry and size dependent.
[2] In essence, a hybrid cloud generally starts out as a private cloud which then extends the integration to use one or more public cloud services. This deployment model makes sense when companies have sensitive data that cannot be stored in the cloud or regulatory requirements that call for data protection, storage, and more. [6]

D. Community cloud
A cloud service that provides services to a community of users or organizations with shared interests or concerns. Organizations using this cloud service have shared missions, governance, security requirements, and policies. Cloud services can be hosted on the premises of the consumer organization, on the premises of the peer organization, at one provider, or a combination of these. This community cloud term is often used in marketing to explain the target consumers of the service, although the actual cloud could technically be a VPC, private or hybrid cloud model. [6]

E. Virtual private cloud (VPC)
A virtual private cloud (VPC) is a private cloud computing environment which is within a public cloud. Essentially, a VPC provisions logically isolated sections of a public cloud to provide a virtual private environment. Like all cloud environments, VPC resources are available on demand to scale as needed and are highly configurable.
[8] This implementation is a compromise between a public and a private model in terms of price and features.

F. Inter-Clouds
Inter-cloud or "cloud of cloud" is a term that refers to a theoretical model for cloud computing services based on the idea of combining many different individual clouds into one seamless mass in terms of on-demand operations.
Inter-cloud would simply ensure that a cloud could use resources beyond its reach, taking advantage of pre-existing contracts with other cloud providers. [9].
There are mainly two types of Inter-cloud:  Multi Cloud  Federated cloud (cloud federation)  Multi Cloud Multi-cloud is the use of two or more cloud computing services from several different cloud providers. A multi-cloud environment could be completely private, completely public, or a combination of both. Businesses use a multi-cloud environment to allocate computing resources and reduce the risk of downtime and data loss. They can also increase the computing power and storage available to businesses. Cloud innovations in recent years have led to a shift from single-user private clouds to multi-tenant public clouds and hybrid clouds. [10]

 Federated cloud (cloud federation)
A federated cloud is also called a cloud federation which is manage multiple internal and external cloud computing services for to meet business needs. A federation is the union of several smaller parties that perform a common action. [11]

IV. COMPARISON OF CLOUD DEPLOYMENT MODELS
Here is a comparative table that provides an overview of all six cloud deployment model based on many factors as listed below [1] [5] [7] [12].

V. CONCLUSION
Cloud computing has transformed the way businesses around the world do business in a way that many people are unaware of. Understanding the difference among various types of cloud computing and identifying which one is best suited for a growing business is tremendously important. This paper provides the knowledge of the introduction to cloud computing, its concepts, models and services. The paper also discusses the comparison of all cloud computing deployment models in table form. These clouds are compared against supported platforms, supported languages, storage capacity, services, and products. Fig. 3 shows Public cloud is the most popular general deployment option, with a usage share of over 61%. Traditional on-premises deployment, with just under half (49%) of shared use, ranks second. Hybrid cloud, which combines public cloud services with on-premises private cloud infrastructure, ranks third, with approximately 39% usage. The study encouraged respondents to choose from several of the five cloud deployment options. It shows a tenth (9%) selected all five, and almost a fifth (19%) selected four out of five. Among them two-thirds (64%) selected at least two cloud deployment options. The upshot is that while the public cloud is by far the most popular choice, most of the organizations surveyed employ a mix of cloud types. Interestingly, multi-cloud or the use of multiple cloud computing and storage services in a single homogeneous network architecture had the fewest users (24% of respondents).

Fig 3: Cloud Computing Technology Market Analysis by
Type [13]