Toward better City Management through Smart City implementation

Smart City is currently becoming a global issue in the context of better city management. This paper discusses the implementation of Smart City and its effect on better city management. The discussion is based on a literature study comprising best and empirical practices related to the implementation of the idea of Smart City in Indonesia. The discussion focuses on the elements of Smart City, support from structural aspects, infrastructure, supra-structure and its relationship with city management as well. This paper can be the description of the best practices of the implementation of Smart City toward better city management with the Program of Toward 100 Smart Cities in Indonesia and the issue of moving the capital of Indonesia in relation with the need for better City management and the implementation of Smart City.

Introduction
City management is currently becoming better because it is being supported by the concept of Smart City and its implementation. Many cities in the world have even admitted that they are able to function in a better and smarter way and that Smart City has become a global phenomenon with real staying power (Barlow & Bencheton, 2019). In the past city management used to be implemented in a conventional way. Today city management uses more sophisticated methods by applying technology, including information and communication technology. This is implemented in Indonesia by preparing smart cities through the Program Toward 100 Smart Cities organised by the Ministry of Communication and Information Technology in the years of 2017 -2019. This program has so far been successful in encouraging cities and regencies in Indonesia to become smarter. This paper will also discuss Smart City and its relationship with city management. The concept of Smart City is indicated to have been experiencing changes from conventional to smarter ways in managing cities.

The concept of Smart City and its implementation
Several definitions of Smart City relate it with technology (Stimmel, 2016; Barlow & Bencheton, 2019) and scientific data (Barlow & Bencheton, 2019). Technology and scientific data are important elements. However, how they implementation in Smart City is of primary importance as well (Barlow & Bencheton, 2019). The examples of Smart Cities applying scientific data are, in this case, to determine the size and location of pocket parks, playgrounds, sidewalk extensions, community gardens, pedestrian malls, bike paths, and traffic circles (Barlow & Bencheton, 2019). A Smart City is defined as a new urban environment designed to support works through information and communication technology (ICT) and forms of other physical infrastructures (Stimmel, 2016). Information and communication technology is expected to be able to minimise urban infrastructural loads, mainly transportation, and in which environmental impacts can also be minimised as a result of the increasing awareness and participation from society (Mendes, 2004). Smart technology leads to the application of smart parking, smart streetlights, and smart meters that are measuring electricity consumption (Stimmel, 2016). The use of ICT is able to improve the rate of livability, workability, and sustainability in a city by connecting and integrating various components from one government to another (G2G), from one government to its citizens (G2C), and from one government to businesses (G2B) (Knowledge@Wharton and Tata Consultancy, 2016). For the government, this is done in order to obtain updated evaluation on how a city can function well at any time, prepare technology needed, and anticipate and handle any arising problems.
To be a smart city is not only a matter of developing and making use of sophisticated technology but also the matter of reaching something smarter than before. In studying the concept of and designing Smart City, it is important to determine that the concept must be in line with the formulated vision and mission of the city (Brown, 2015). In formulating the vision and mission of a city, it is important that strong political leadership and full understanding from people (citizens, businesses, and organisations) are of primary importance to overcome local challenges (Letaifa, 2015).
The Indonesian Ministry of Communication and Informatics (Kemenkominfo) is presently undergoing the Program of 100 Smart City Movement in the country. In 2017, 25 cities and regencies were appointed to participate in the program. In 2018, 50 cities and regencies were following to participate in the program. Moreover, by the end of this year, 2019, the other 25 cities and regencies have participated in the program. Several considerations are taken into for the cities and regencies to belong to the movement toward 100 smart cities such as regional financial capability, green-city index, local government performance index and sustainable cities index (Bappenas RI and Kominfo.go.id). Meanwhile, for comparison, in India the cities that are chosen for the mission of being a smart city are based on the citizens living in slum areas, public services with high intensity, and the availability of the projects for urban infrastructure (Smith et al., 2019).
Each city and regency that has been appointed is in charge of composing the Master plan of Smart City. In composing the master plan, each city and regency receives assistance from experts and practitioners in this field from various universities and mentors from Kemenkominfo. In general, the content of the Master plan of Smart City consists of the Vision of Regional Smart City, the Development Strategy of Smart City, the Action Plan of Regional Smart City, and the Road Map of Development of Regional Smart City. In this case, the Vision of Smart City needs to be adapted to the regional vision even though it is not precisely the same and it needs to be adapted to the city conditions and problems.
In relation to the development of preparing its Smart City status, the following essential aspects need to be taken care of i.e. structure, infrastructure & suprastructure as a driver, culture (people's readiness) as a mediator, and nature (resources, life, ecosystem) as an enabler (Citiasia Inc. in Direktorat Jenderal APTIKA -2018). The structure consists of the availability of human resources, ability of bureaucracy, and ability of budget; infrastructure consists of the availability of physical facilities, ICT, and social aspects. Meanwhile, suprastructure consists of the readiness of policies (regional regulations), institutions, and implementation (Citiasia Inc. in Direktorat Jenderal APTIKA -2018).
There are six elements of a Smart City that become the study in the Master Plan of Smart City made by Kemenkominfo, namely Smart Governance, Smart Economy, Smart Branding, Smart Society, Smart Living, and Smart Environment. Each element's related activities is as follows, the elements of Smart Governance covers Public Services (related to improving performance of public services), Bureaucracy (related to improving performance of the government bureaucracy), and Public Policy (related to improving efficiency of public policies). Meanwhile, the elements of smart branding consist of tourism (related to the development of the ecosystem of tourism), businesses (related to the development of business competitiveness, this is TTI and creative industry), and city appearance (related to the setting of facial appearance of a city).
In addition, Smart Economy consists of industry (related to the management of primary, secondary, and tertiary industries), welfare (related to the improvement of social welfare), and transaction (related to the development of financial ecosystem). Besides, Smart Living consists of harmony (related to the harmonisation of the quality of life and the environment), health (related to the guarantee of health facilities and services), and mobility (related to the development of transportation and logistics). Next is Smart Society, consisting of community (related to the development of smart people), learning (related to the development of educational system), and security (related to the guarantee of security and safety). Last but not least, Smart Environment consists of protection (related to the environmental protection), waste (related to the garbage and waste sewage system), and energy (related to the development of competitive and sustainable energy).
In relation to the elements of Smart City as discussed above, Giffinger et al. (2007) proposes six points i.e. Smart Economy (competitiveness), Smart People (social and human capital), Smart Governance (participation), Smart Mobility (transport and ICT), Smart Environment (natural resources), and Smart Living (quality of live). In this case, smart mobility comprises local accessibility; (inter)national accessibility; availability of ICT-Infrastructure; sustainable, innovative and a proposition for including safe transportation system exists. In Indonesia, there is not much discussion on smart mobility because of the limited transportation system, particularly public transport. City mobility will affect land use, environment, health, and that will be more complicated than inter-city mobility (Blanco & Moudon, 2017). In addition, limited amount of funds for public transport will also lead to the rapidly increasing number of private vehicles and that traffic congestion and damaged roads will also increase (Blanco & Moudon, 2017). Therefore, problems could arise related, and the provision of public transport should be one of the top priorities that need to be implemented in smart cities for the sake of their city management. Giffinger et al. (2007) also emphasise on smart economy, but not smart branding. For cases of smart cities and regencies in Indonesia, smart branding is also of primary necessity to accelerate the implementation of Smart City through quick wins and to promote local uniqueness through local smart branding. In relation to the dimensions of smart economy, the strengthening of ICT use in ecommerce and the virtual space use in UKM (Usaha Kecil Menengah / Small and Middle Enterprises) activities will give benefits to increasing access for promoting products and market. The development of SMEs Digital Kampongs as parts of the implementation of Smart City through the strengthening of smart economy needs certainly to be supported by smart people and smart governance (Rachmawati et al., 2018). In its development, shopping should not be done by visiting directly the location of the SMEs. Instead, it can be accessed through virtual space, and therefore the mobility of people can be minimised as well as the traffic and congestion.

Aspects of city management
Asian cities face rapid transformation that makes the management of the urban environment a challenging task that concerns everybody and for which both regional and national policies are needed. Many Asian cities are experiencing various urban environmental problems such as the management of air quality and urban transportation, solid waste, water supply, and sanitation (Imuraa et al., 2005). Therefore, the right management of a city is necessary.
The United Nations reveal that by 2050 two-thirds of the world population of 9 billion people or so will have lived in cities. Many cities have been working hard to provide basic services for their citizens such as clean water, drinking water, residences, and education and health services. In addition, waste system and sanitation are also essential parts that need to be taken care of in order to create a clean and healthy environment.
Therefore, it is important to pay attention to the following elements of urban management to support urban development, namely: Public private sector relationship; Urban Government Structure and Function; Internal Organization and Management Process; Administrative Leadership; Sub-municipal Organization; Strategic Planning and Management; Performance Management; Urban Government Finance; Taxation; Capital Funding; Urban Government Staffing (Davey, 1993). In order to achieve this, four tasks have been identified: 1) Providing essential infrastructure to the efficient operation of cities; 2) Providing services that develop human resources, improve productivity, and raise the standard of living of urban residents; 3) Regulating private activities that affect community welfare and the health and safety of the urban population; and 4) Providing services and facilities that support productive activities and allow private enterprise to operate efficiently in urban areas (Rondinelli 1990in Davey, 1993. To provide services for urban citizens translates into roads, traffic light, rainwater drainage, well-managed water supply distributed to homes and communal facilities, liquid waste sewage system, solid waste collection and discard, educational facilities, health treatment facilities (hospital, health clinic, physician, midwifery, dental clinic, and ambulance) (Davey, 1993).
In this ICT and Smart City era, urban management has experienced many changes in providing city services and also in handling its management. A Smart City integrates technology, systems, infrastructure, services, and ability into organic network that is complicated enough as a challenge that needs to be faced (Nam & Pardo, 2011) especially for urban future. Therefore, discussion on Smart City and its role in city management in a smart way is an important part that needs to be taken into consideration in cities of various countries and by urban managers. In this case, it is necessary for better urban management.

Smart City and its roles in Smart City Management
In the application of Smart City, one of the important steps is to identify the needs and priorities of city problems. In this case, it is necessary to identify what have become serious city problems. Therefore, a would-be smart city is expected to be able to solve the problems through the planning and implementation of the concept of Smart City. Thus the role of all stakeholders to implement the plans is indeed essential. It is crucial to identify the problems that become the priority to tackle. An effective management resource is intelligent management and is expected by an urban manager that a city can be well managed to achieve higher living quality of its residents (Stimmel, 2016). Smart City provides its citizens with the ability and resources they need for their happiness, fulfilment, and prosperity (Barlow & Bencheton, 2019). Thus, to achieve Smart City it is important to involve its citizens to work hand to make a city a better place to live in.
Nevertheless, the role of the government is of primary importance in the efforts of establishing city infrastructure to facilitate the citizens in their activities. In urban development, it is necessary to provide adequate infrastructure because it is a supporting factor in implementing an integrated city with interconnections among sectors. In this case, it is, for example, necessary to integrate and interconnect sectors such as energy, economy, and demography. In relation to the revolution of current urban energy, energy supply needs to be sufficiently provided with the availability of energy resources from renewable energy already implemented in cities (Etezadzadeh, 2016). It is important to discuss this issue further since the availability of effective and efficient infrastructure is really important to obtain Smart City. Moreover there has been registering a phenomenon of growing discrepancy between infrastructure demand and offer as well as significant urban environmental services in Asia (Imuraa et al., 2005).
In general, several public services that are needed in planning smart city and they are the ones that cover sectors of government, health, education, taxation, complaints from people, permits, as well planning. Those services are expected to make people's activities easy. The application of those public services is supposed to include contents that fit 1) inter probability or a system that is adaptive to both the present and coming times, 2) portability or a service system that is movable, 3) assurance or a system with safety guarantee for its users, 4) availability or a service system that is able to help public services, 5) usability or a service system that can be used in line with its function, 6) adaptability or a system that is adaptive to its users and environment (Dubai Government, 2017) Smart City is seen as a holistic view of all human activities in many places, including government, schools, hospitals, infrastructure, resources, businesses, and society (Knowledge@Wharton and Tata Consultancy, 2016). Its fulfilment and development need to be adapted to what it really needs. Therefore, the implementation of Smart City will be much better if it is correlated with the six elements of smart city as discussed above. Each element is closely relevant to urban management.
However, the elements of smart living, smart society, and smart environment are more predominantly related to urban management. As it has been stated above, the availability of efficient infrastructure is really important to run in a city. An example of this is among others the availability of efficient urban transport. Being efficient in this case can be seen from the side of both the provision by the government and the benefits to people. Therefore, transportation becomes the element of achieving smart living. The same case is the need for services that are able to improve human resources, productivity, and living standard of urban citizens. Therefore, all these combine to increase the achievement of smart living and that social welfare, people's health, and the safety of urban citizens can also be achieved.
The provision of services for urban citizens comprises road, traffic lights, rainwater drainage, well-managed water supply distributed to homes and communal facilities, liquid waste sewage system, and solid waste collection and discard. Everything related to waste (garbage and waste management) and energy (creating competitive and sustainable energy) exists in the context of achieving smart environment.
Several cities, as an example, have made an application to monitor garbage landfills and garbage-transporting process. Meanwhile, in relation to energy efficiency, several cities have also applied smart road light systems; by introducing automation in turning on and off the road lights and voltage adaptor as well. The provision of educational facilities also falls in the context of achieving smart society. Meanwhile, the provision of health services (hospital, health clinic, physician, midwifery, dental clinic, and ambulance service) is part of achieving smart living.
In Indonesia, the Ministry of Communication and Informatics has provided the call-centre service 112 for emergencies like fire and ambulance aid. This kind of service is intended for citizens when they are in emergency situation, just like 911 in the USA. This program is almost the same as the call centre 119 made by the Ministry of Health. In the future, both will be integrated (Medistiara, 2017). Several hospitals and even public health centres have applied online for the admission in the online medicine service, and provide online information of ward availability. In addition, another online service regarding health is available as well as online health service in case there is an accident in homes.
City management needs to pay attention to policies, plans, programs, and practices to make sure that the population growth is adapted to access to basic infrastructure, people's homes, and other facilities (Davey, 1993). In relation to this, the master plan of smart city needs to cover all the elements of Smart City, including smart living and smart environment. Action plans and road maps that reveal long-term plans (10 years), middle-term plans (5 years) and short-term plans (1 year) as well as quick wins (quick plan for the benefits to public) must also be included in the master plan of smart city. The roadmap composition also needs to include feasible ideas as well as planning how to implement them. A roadmap is of great help in giving guidelines for important steps that need to be taken to realise Smart City. It takes years to create a smart city (Davey, 1993). Thus this plan must be in line with the regional Long and Middle-term Development Plan.
It is likely to reconsider the definition of Smart City in a more comprehensive way as Branchi et al. (2014) once described a smart city as a space for coexistence based on the available technologies which can ensure that its residents thrive and develop while managing the city's economic, social and environmental sustainability. This definition has encouraged many cities to be smart for better urban management.

The issue of moving the capital in Indonesia: The need for better City Management and the implementation of Smart City
The Indonesian Government has been working hard to move its capital Jakarta. Several regions have been studied. The last study shows that some parts of the Regency of Penajam Paser Utara and Kutai Kartanegara, East Kalimantan, have the biggest possibility to be the capital. The issue of moving the capital is based on the fact that city management is quite hard to implement in Jakarta, considering that the number of people living in Jakarta has reached up to 10,467,630, according to the 2018 data at the Statistics Central Bureau. With its current population density, Jakarta functions as the centre for government, business, finance, trade, and services. High urbanisation has made Jakarta more and more crowded, and in situations like these problems are inevitable, such as high pollution and traffic congestion due to the limited availability of public transport, and therefore the use of private vehicles keeps increasing. The same case is the need for housing which is not balanced between supply and demand, and therefore a certain group of people cannot afford to own a house. Consequently, slum area in some places is unavoidable. The availability of green-open spaces is even far from enough.
In a new place where the capital will be located, it is expected that the space uses can be balanced, i.e. there will be a balance between offices, housing for officers, educational facilities, trade and services, and adequate green-open spaces as well. The dimensional side of smart environment is the main priority when searching for the new capital. The same case is the supporting infrastructure to facilitate smart mobility available by providing digital-based public transport. Also, digital-based public services in administration sector that will help realise smart governance needs to be available. More importantly, to enable the people to be smart due to the application of ICT in various aspects of their life is an endeavour for smart society, smart people, and smart living to come true. However achieving smart economy should not be the main purpose for the development of the new capital because it can lead to the forming of busy economic activities in the new capital.
The function of the new capital should be directed more to the aspects of nonbusiness centre and trade. The limitation of this function is of primary necessity considering that the multi-functions of the new capital will consequently lead to providing facilities for the fast-growing number of people because this will, in turn, attract more and more people (Takyi, 2016). Constructing business centres in the era of information and communication technology should not be a strategic primary aim of the new capital (Rachmawati et al., 2015). Instead, it can be decentralised in suburb areas or even in rural areas (Rachmawati et al., 2015). However, in relation to smart branding, city appearance needs to be taken into priority and realised as parts of the efforts to realize the beautiful appearance of the city.

Closing remarks
Considering that more and more cities have declared themselves as smart cities, it is expected that in the future the achievement of well-managed cities will be higher. Part of this achievement is supported by the use of ICT as an aspect of smart city-supporting infrastructure. Besides, the application of structural aspects (human resources and budgeting) and supra-structure (institutions and policies) are also important pillars that should support it. Strategic innovations are expected to develop so that the achievement of Smart City will be higher and that city management will also be even better. Indonesia has been one of the examples for best practices in the movement toward 100 smart cities, cities which enable them to manage all aspects of urban life in a better way and that supports a plurality of aspects from smart governance to all the rest that urban residents need. For the success of future city management, the development of the new Indonesian capital should begin with smart city management, particularly on the aspects of smart environment and smart living.


Broadcasting in VANET: State of the Art

Vehicular ad-hoc network (VANET) is in the offing as the most promising technology to resolve all kinds of traffic problems such as those related to traffic congestion, road mishaps, and address upbringing issue such as those pertaining to noxious emissions. In VANET, relevant information should be disseminated with a minimum bandwidth usage thus requiring an efficient and dependable multi-hop data broadcast protocol. Broadcasting play an important role in VANET to distribution of information from vehicle to all neighboring vehicle for some specific purpose. It reduces collision, contention, redundant messages and hidden node problem. It also improves the message reliability. Due to limited range of radio communication in VANET, all the vehicles may not be able to receive the broadcast data in a single hop. So multi-hop communication is needed. This article investigates and compares the various multihop broadcasting protocols

INTRODUCTION
Traffic problems have been increasing alarmingly, and most of it is of recurring type such as congestion, accidents, environment related etc. This has led to a lot of research in the area of Intelligent transport systems (ITS) which aims to build a safe and an effective traffic environment using reliable real-time traffic information. Vehicular ad-hoc network (VANET), a part of ITS, is proving valuable for providing reliable real-time traffic information. VANET, being a subset of MANET, is essentially deployed for exchange of information from one vehicle to another vehicle (V2V), from a vehicle to an infrastructure (V2I) and from a vehicle to broadband (V2B). It consists of many nodes (vehicles) and stationary road side info-station that communicate with each other. The elements of VANET are always in a state of dynamic self-configuration. Vehicles or Mobile Stations are supposed to be moving randomly and thus have a dynamic topology. The VANET network can either be autonomous or can be connected to some other WAN such as the Internet. The information obtained from the moving nodes (vehicles) can be used to infer their future trajectory which in turn can lead to the build-up of the traffic data. In-vehicle communication are communications entirely exchanged within the vehicle itself. These communications generally take place between the various electronic devices in the vehicle i.e. between the individual electronic modules. Sensing systems oversee the motorist condition, whether weary or asleep. Vehicle-to-vehicle (V2V) are communications (wireless transmission) that take place between vehicles. Road information can be shared in real time. This exchange of information in real time can help to prevent accidents and traffic jams. Vehicle-to-road infrastructure (V2I) communications takes place between vehicles and existing roadside infrastructure. Some examples of V2I communication includes real-time updates on traffic and weather from the infrastructure to the driver. Vehicle-to-broadband (V2B) communications takes place between vehicles and broadband cloud structures using the cellular mobile network. The advantages of V2B are manifold such as remote traffic information, infotainment, etc. V2B communications are helpful for live assistance of drivers, tracking or vehicles, etc.
In VANET, relevant information should be disseminated with a minimum bandwidth usage to obviate the risk of mishap, avert precarious situations and extenuate such issues. VANET provide a variety of interesting applications from safety to comfort. Many of these applications rely on broadcasting. Broadcasting play an important role in VANET to dissemination of information from vehicle to all neighboring vehicle in the network for some specific purpose. In broadcasting there is no signal destination vehicle compare to the traditional vehicle to vehicle or vehicle to infrastructure communication. Instead the information is broadcasted from one signal source to all vehicles inside a certain geographic region.

II.BROADCASTING IN VANET
The type of Broadcast Technology used greatly affects the Network performance. In the beginning, the most straightforward approach for implementing broadcasting were all based on the "flooding methods," a technique which was successfully deployed by the then telecommunications industry. By these methods, a node will communicate information to another node, which in turn would communicate the same information to another node and so on until every node in the network had access to that information. These methods worked well for small networks. For bigger network, these methods result in dispensable messages and much collisions. "Simple flooding" sometimes also referred as "blind flooding," is plagued with the "storm" phenomenon associated with broadcasting, whereby a large number of redundant messages are exchanged. A variety of techniques exists to resolve the "storm," the prominent being the Urban Multi-Volume Broadcast protocol (IEEE 802.11) which authorizes the outlying nodes to accept and transmit data for the purpose of reducing the "storm" and notify others on the real transmitters and themselves. The protocol also utilizes ACKs (acknowledgement data) as an assurance of message delivery.
The IEEE-802.11protocols are an optional RTS/CTS handshake followed by an acknowledgement to delivers a unicast packet. Broadcast messages cannot use the RTS/CTS exchange because it would flood the network. For Examples, if all the receivers transmit a CTS in response to a RTS then many collisions can occur at the same time. If RTS/CTS mechanism is not used, then hidden terminal problem will occur. So the best way is multi hop broadcast using relaying. In the next section, broadcasting based MAC protocols are presented and then a comparative analysis on the protocols are designed.

III.EXISTING BRODCASTING SCHEMES
VANET broadcast protocol should be designed for dense traffic condition, sparse traffic condition and regular traffic condition on city and highway scenario. Survey of related literature reveals that lots of broadcasting protocols for VANET has been proposed but still a full-fledged protocol is not designed which can be useful in all the traffic condition. Impairment of radio signals increases with the spacing between nodes and broadcasting schemes need to account for this, specifically in low density networks. There are obstacles like buildings in urban areas that frequently block signal transmission. Message dissemination using broadcasting is divided into two categories-single hop and multi-hop schemes. Mostly dissemination schemes are multihop. Multi hop schemes are again classified into two categories-restrictive schemes and promiscuous schemes (Sanguesa et al, 2016). Restrictive scheme is used to address the problems of broadcast storms. Promiscuous scheme considers the VANET"s characteristics such has fragmentation and frequent partition. So the techniques such as Store-and Forward is used to disseminate information. Figure 2 shows the taxonomy of broadcast protocols. According to the characteristics of the protocol and the techniques used for establishing if a node is allowed to retransmit messages, the multi-hop schemes can be categorized as those based on: a) beacon, b) topology, c) distance, d) flooding, e) probabilistic, and f) store and forward techniques. Figure 3 shows the types of multi-hop broadcast protocols. Beacon Based Scheme Beacons are periodic messages generated by vehicles about their position, speed etc. This information is used toimprove the knowledge about the surrounding area. ATB, CDS, RTAD, DV-CAST, and NSF.

Topology Based Schemes
In VANET, the communications between the vehicle are many topology aware broadcast techniques which utilizes topology data to boost message dissemination process. Examples of such protocols are: eSBR, CLBP, eMDR, NJL, NJL, DV-CAST, RTAD, and JSF. Distance based Scheme greatly affected by the rapidly changing topology. There are The methodology requires that the information is rebroadcasted depending on the distance between sender and receiver vehicles. Schemes like TLO, SBS, eSBR, eMDR, and FDPD are distance based schemes.

Counter Based Scheme
This technique is an improvement over flooding. Here the number of receiving nodes are reduced by means of a counter and a threshold value. Probabilistic scheme These methods are based on probability theory. In accordance with the settings of the transmitting nodes, appropriate probabilistic distributions are worked out to establish the probability of transmitting the message. FDPD, SBS, APAL, p-persistence approaches, LISCast, CPB and EEAPD are under this category.

Store and Forward Scheme
In this scheme, a vehicle car stores the message and waits to rebroadcast until a given criterion is fulfilled. A vehicle usually waits to rebroadcast the message until a new neighbor is found. Schemes like DV-CAST, UV-CAST, SCB, NSF, and JSF, fall under the store/forward approach.   Authors of [8] has already presented a comparative study of broadcast protocols proposed till 2016. This paper present the comparative study of the protocols proposed after 2016 till date. There are five such protocols which is discussed in this section. LISCast [4] stands for local topology information sensing technology-based on broadcast protocol. It aims to resolve the "storm" phenomenon while attaining a faster response. These protocols capitalize on probabilistic forwarding schemes to restrict superfluity. According to the beacon, LISCast utilizes a beacon for raw data collection from neighboring nodes (their numbers and spread), from which it seeks outs characteristic information such as maximal forwarding distance, effective candidate number, and overall traffic density. LISCast operates in conjuction with a roadside unit and producing a bit of redundant data but does not always work the best in the sparse networks, because the characteristic information provided by BSM (Basic Safety Message) is not so precise. LISCast can readily adapt to a dynamic topology by optimizing the parameters of delay, redundancy, and broadcast efficiency while achieving a high level of transmission reliability. It can also maximize the coverage of each hop, realizing the rapid dissemination of emergency messages. LISCast is best suited for high density networks as it reduces end-to-end delay. Further, it surpasses other protocols on performance parameters such as forwarding adequacy and redundancy.

Effective and Efficient Adaptive Probability Data Dissemination (EEAPD)
EEAPD is a probability-based protocol blended with a delay methodology founded on the measure of number of track sections and linear separation from the source node. EEAPD is formulated to work best in high density networks with the intention of maximizing productivity while at the same time minimizing the network bandwidth. Like the LISCast, EEAPD aims to resolve the "storm" phenomenon while attaining a faster response. Most significantly, EEADP estimates the waiting time period based on the measure of linear separation from node. EADP takes into account the number of track sections (slots), message directionality, and node density. Moreover, it can probabilistically resolve a pertinent node to make a re-broadcast message with the least delay period. EADP considers the suitability of nodes which can re-broadcast an emergency packets based on the number of track sections. EADP is a versatile probabilitybased protocol very well suited for safety-based applications which in-turn requires superior degree of efficiency and effectiveness. Such applications are primarily concerned with ensuring passenger"s safety. The two principal characteristics of EADP are: a) beaconless; i.e. irrespective of the network density, there is no periodic exchange of safety messages, and b) it is best suited for safety-based applications; its performance has been positively ascertained in a pragmatic environment [5]. AddP -Adaptive Data Dissemination Protocoloffers highly efficient and credible data dissemination. In AddP, the beacon frequency is actively adapted not only to reduce the number of beacons but also the number of transmitted messages. This is accomplished with the help of beacon congestion control algorithm adapted for high density networks. AddP implements a node selection scheme, for the purpose of rebroadcasting, premised on network density and separation from neighboring elements. AddP is both reliable and efficient (low overheads). The two principal characteristics of AddP are: a) to propogate message alerts to most of the participating nodes with reduced latency, and b) to resolve the "storm" phenomenon while attaining a faster response with fewer disconnection, and the issue of the hidden nodes [1].

Cluster-based medium access control (CB-MAC)
The CB-MAC protocol was developed for all types of communications (safety or non-safety data), the difference being that safety transmission must occur quickly with recommended delays not exceeding 100ms [4][5][6]. Further, the CB-MAC is aimed towards improving the credibility of communications together with a higher throughput and lower latency. The protocol describes the mechanism for cluster configuration. The methods for egress or ingress of nodes from a cluster is also specified. The process of merging of different cluster is also set out. Moreover, the format for the conventional control packets have been revised and fresh control packets has been created to enable cluster-based communications. Safety messages cannot employ the RTS/CTS scheme (IEEE 802.11) because it is ineffective in broadcast mode. However, the RTS/CTS scheme is used for non-safety communications in order to alleviate the issues of hidden nodes. The Markov chain model forms the basis of the CB-MAC protocol which circumvents the hidden node problem by through RTS/CTS and simultaneously achieves maximum resource utilization [2].
SEAD (Simple and Efficient Adaptive Data Dissemination protocol) The protocol was developed to cope with the "storm" phenomenon associated with broadcasts in a scalable vehicular network. The protocol is well suited for all types of applications (safety or ordinary data) and provides a resilient message transmission scheme. The scheme can further be used to configure the protocol to realize a desired performance (by way of packet delivery ratio, etc.) in accordance with the application"s stipulations. SEAD imparts very small end-to-end period for communications while maintaining a superior packet transmission with insignificant packet drops and thus does not significantly loads the network. SEAD was conceived to be compliant with all classes of applications and can be easily tailored for a specific application through a native "alpha" parameter. SEAD achieves a high level of efficacy for transmission of safety messages as substantiated through simulators. SEAD has excelled S1PD in enhancing the packet delivery ratio (PDR) with a very small end-to-end period and is low on bandwidth expenditure [7].

IV. COMPARATIVE ANALYSIS
A summary report is tabulated below (Table 1) which compares the various protocols discussed in this paper in terms of standard network parameters namely; reliability, efficiency, supported application class, scheme type, mode of transmission, and the objective of the protocol. A meticulous review of these protocols will aid in identifying their benefits and drawbacks and will thus prompt for refinement in existing protocols and/or formulation of entirely new ones.  Amongst all the standard waiting methodology protocols utilizing the probabilistic approach, LISCast remains the best suited for high density networks as it reduces end-toend delay. Further, it surpasses other protocols on performance parameters such as forwarding adequacy and redundancy. A downside of LISCast is that it may not perform satisfactorily in scantly populated networks primarily because the BSMs intelligence is not definitive [4]. The simulation study presented in [5] shows that EEAPD is comparatively superior to both SEAD and AddP as it has a very small end-to-end time period with enhanced packet delivery ratio irrespective of the network density. This is because there is no periodic exchange of safety messages. More significantly, EEADP can be viably implemented under pragmatic scenario such as V2V architecture within an established number of track sections. Most of the proposed protocol till date are efficiently used in highway scenarios. Some of them can be used in urban areas also. But still protocols are not tested for semi urban area. They are not efficiently working in sparse traffic condition where vehicular connectivity problem frequently occurs. Under probability-based broadcasting methodologies, every node in the system has a chance to participate in communications based on some specific odds assigned to them. Hence, all the nodes have a certain chance to participate in the broadcast operation. This is what makes the probability-based mechanisms more resilient to breaches and failures. Unlike the deterministic techniques, the probabilistic approach remains largely immune to journeying nodes. It is precisely for these reasons that most of the newer broadcasting techniques are using probabilistic approach.

V. CONCLUSIONS
In most of the VANET applications, multi hop broadcasting is required. Uncontrolled or blind retransmission in a multihop communication causes congestion and delays in a network. Various researchers have proposed various multihop broadcasting protocols for VANET. This paper presents the need of broadcasting, taxonomy of broadcasting protocols and a comparative study of multi hop broadcasting protocol. We have compared the all multihop broadcasting protocols proposed in last four years.


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Location Management in LTE Networks using Multi-Objective Particle Swarm Optimization

Long-term evolution (LTE) and LTE-advance (LTE-A) are widely used efficient network technologies serving billions of users, since they are featured with high spectrum efficiency, less latency, and higher bandwidth. Despite remarkable advantages offered by these technologies, signaling overhead remains a major issue in accessing the network. In particular, the load of signaling is mainly attributed to location management. This paper proposes an efficient approach for minimizing the total signaling overhead of location management in LTE networks using multi-objective particle swarm optimization (MOPSO). Tracking area update (TAU) and paging are considered to be the main elements of the signaling overhead of optimal location management in LTE. In addition, the total inter-list handover contributes significantly to the total signaling overhead. However, the total signaling cost of TAU and paging is adversely related to the total inter-list handover. Two cost functions should be minimized, the first is the total signaling cost of TAU and paging and the second is the total signaling overhead. The trade-off between these two objectives can be circumvented by MOPSO, which alleviates the total signaling overhead. A set of non-dominated solutions on the Pareto-optimal front is defined and the best compromise solution. The proposed algorithm results feasible compromise solution, minimizing the signaling overhead and the consumption of the power battery of a user. The efficacy and the robustness of the proposed algorithm have been proven using large scale environment problem illustrative example. The location management in LTE networks using MOPSO best compromise solution has been compared to a mixed integer non-linear programming (MINLP) algorithm. Location management mobility management entity MME pooling clustering SON Distributed Centralized pooling scheme fuzzy implementation setup LP-CPLEX

to higher requirements on networks, since signaling overhead directly affects the cellular performance. High levels of signaling overhead result in increased computational cost and more power consumption [1]. This could take a form of high load and data traffic in the core network which harms the cellular network reliability. The development in signaling overhead may tackle the problem of data traffic by 50% [4]. In addition, LTE and LTE-A have a flat IP-based heterogeneous architecture that increases the signaling overhead [1]. Accordingly, the research focus was shifted from signaling overhead to handling greater amount of data and increasing speed connectivity. In fact, several aspects could lead to signaling overhead, however, it can be primarily attributed to user mobility.
Signaling overhead is triggered by transmission or reception of data between mobile networks and user equipment (UEs). The performance of signaling overhead can be evaluated by two factors, namely, paging and tracking area update (TAU). From one side, paging is normally actuated by mobility management entity (MME) placing an idle-user within the tracking area (TA) of the system. TA is commonly termed routing area or location area in other applications. TA is a virtual area, which combines a group of cells located within a particular area [1]. The main drawback of TA is the service interruption which occurs when a particular cell gets reassigned to a different TA. In addition, the edge users located between two or more tracking areas (TAs) are subject to ping-pong effect. Tracking area list (TAL) is the state-of-the-art extension of TA proposed by the 3rd Generation Partnership Project (3GPP). Several TAs can be combined to form TAL. Thus, the structure of TA and TAL is nearly similar. However, TAL is characterized by greater flexibility and diversity of cells. MME defines the location of a user via his/her most recent registered location. Accordingly, MME is updated by the last TAL. From the other side, TAU is initiated by the user to the MME, this procedure includes reporting an update. This update governs transferring the user from a cell in one TAL to another TAL. TAL has a prominent role of minimizing the total signaling overhead generated by TAU and paging. Thus, the design of TAL could allow to adapt various TAs and reduce the ping-pong effect. Once the mobile user switches into an idle mode, the signals generated by TAU and paging are diminished through TA or, in other words, via TAL. Both TAU and paging are employed to track the location of the user, and subsequently give a continuous update to the evolved packet core (EPC). Consequently, the total signaling cost of TAU and paging have a major signaling impact on EPC, allowing them to play a vital role for the location management between UEs. An important issue to consider is that the inter-list handover resides on three factors: UEs, probability of the user to move between cells, and the cost of inter-MME. A careful look at the aforementioned discussion reveals a bottleneck effect between the total signaling cost of TAU and paging from one side, and the total cost of the inter-list handover from the other side [5,6]. Thus, the location management addressed in this work is NP-hard, and locating approximate solution is NP-hard. Therefore, this work handles two main factors of optimal location management in LTE, the first factor is the total signaling cost of TAU and paging, and the second factor is the total inter-list handover. The ultimate objective is to minimize the total signaling overhead which is a result of paging, TAU and inter-list handover. The problem is tackled by optimizing TAL, which is successfully achieved by minimizing the total signaling cost of TAU and paging, defined as the first objective, and the total inter-list handover -the second objective, consequently attenuating the total signaling overhead. It should be remarked that TAU and paging are inversely related to the total inter-list handover. In an effort to circumvent the abovementioned challenge, the problem can be divided into twolayers. The two-layered approach can be carried out by mixed integer non-linear programming (MINLP) [7] which divides the problem into two sub-problems. The main disadvantage of this approach using MINLP is that one objective is favored over the other. By consequence, the final solution obtained by MINLP confines the solution on the Pareto-optimal front. Unfortunately, the solution obtained will not guarantee the best compromise solution.
Thus, the main focus is two optimize the two objectives simultaneously. According to the conflict between the aforementioned two objectives, the trade-off can be achieved through a best compromise among a set of non-dominated solutions on the Pareto-optimal front using multi-objective particle swarm optimization (MOPSO) [8]. In this regard, the signaling overhead problem, which is mainly attributed to the total signaling of paging, TAU and inter-list handover is studied for an arbitrary network. For demonstration and numerical results, a large scale network is considered. Numerical results show the robustness for the proposed algorithm of finding the best compromise solution by solving the trade-off between objective one, which is the total signaling cost of TAU and paging, and objective two, which is the total interlist handover. Also, the results guarantee the minimization of the signaling overhead problem to lower levels. In addition, the proposed algorithm significantly reduces the power consumption of the user's device. The contributions of this study can be summarized as follows: 1) The location management problem has been reformulated and approached as a multi-objective optimization problem.
2) The problem is solved as a minimization problem considering the two above-mentioned objectives using MOPSO. 3) A fuzzy based mechanism to extract the best compromise solution has been implemented. 4) The superiority of the solution obtained by MOPSO over MINLP has been proven.
The rest of the paper is organized as follows: Related work and an overview of mobility management techniques is presented in Section II. Section III defines the problem, states the system model and formulates the problem. The description of MOPSO, the related flow chart, and MOPSO implementation is presented in Section IV. Numerical results and the performance of the proposed deployment algorithm compared to MINLP are illustrated in Section V. Section VI includes the summary of the work and the concluding remarks.

II. RELATED WORK
Location management is utilized by various technologies and is popular among many researchers [9,10]. Actually, location management has motivated scholars to investigate the signaling overhead problem. TAU and paging are essential parts of location management [9,11]. Number of stereotype techniques aimed to attenuate the signaling overhead, for instance (velocity-based, timer-based, and movement-based [11][12][13]). The control-plane components, for instance, service gateway (SG) or MME, have been employed to regulate the signaling load [14][15][16]. Two schemes of MME pooling, namely the centralized and distributed MME pooling schemes have been investigated measuring the load in signaling from user mobility [14]. However, the study in [14] did not examine the structure of TA and TAL. As introduced in [15] a pool of SGs assists the TAs in terminating the intermittent connections once UE exits one TA and enters another. The minimization of relocation frequency has been studied through relocating the data by SGs to support high-mobility users [16]. The shortcoming of the approach proposed in [15,16] is that it targets only non-idle users, which could weaken the service quality through SG re-allocation.
The signaling overhead problem is mainly attributed to TAL assignment. The TAL assignment research can be divided into two categories: static and dynamic TAL configuration. TAL design using conventional optimization techniques has been introduced through case studies using a linear programming (LP) CPLEX (LP-CPLEX) optimizer [17,18]. The results in [17,18] showed significantly better results in terms of signaling overhead compared to the static configuration of TAL. The solution proposed in [17,18] has been extended in a new case study with less complex protocol of LP-CPLEX optimizer [19]. Recently, centralized and distributed MME pooling scheme has been employed to attempt addressing the signaling overhead problem [6]. The work in [6] used MINLP optimizer to solve the trade-off between the total signaling cost of TAU and paging and the total inter-list handover. It was found that the centralized MME pooling scheme outperforms the distributed MME pooling scheme in terms of minimum total signaling overhead. It is important to recall that the location management in LTE networks is NP-hard and finding appropriate solution is NP-hard. Therefore, a critical drawback of the study in [6] is that the algorithm used favored one objective over the other one, thus the solution obtained is far from being the best compromise solution.
Particle swarm optimization (PSO) is a global search technique which considers the natural behavior of bird swarming [20]. It has been used effectively to solve: the problem of resource allocation and maximization of the system throughput while keeping the minimum user rate requirement [21], sensor deployment in designing sensor networks [22], near optimal deployment of sensors with Voronoi diagram evaluating the cost function [23], and optimal tuning of fuzzy feedback filter [24]. PSO algorithm introduced in [20,25] is applicable for single objective maximization problems. The multi-objective version of PSO is termed MOPSO. Due to the fact that a single optimal solution is no longer available in multiobjective context, MOPSO is concerned with selecting the best compromise solution from a set of non-dominated solutions [8,[26][27][28]. Moreover, evolutionary techniques have been efficient of solving several recent problems, operational aircraft maintenance routing [29][30][31], directional steering [32,33], and optimization of membership functions of a fuzzy logic controller utilized in control applications [24,28,34,35]. In the next section, the problem of location management in LTE networks is formulated to be subsequently solved by MOPSO.

III. PROBLEM FORMULATION
This study aims to minimize the two main contributing factors to the total signaling overhead problem, namely, the total signaling cost of TAU and paging and the total interlist handover. This can be accomplished by defining the optimal deployment of overlapping TALs. The total signaling overhead problem can be tackled by one of the two popular schemes: centralized pooling scheme [5,14] or distributed pooling scheme [14]. Both schemes associate the distribution of TAL with MME. The main difference between centralized scheme and distributed one is the method of calculating TAU. According to the result of comparison between the two schemes, distributed scheme requires extra signal load from MME relocation, thus, it can be stated that centralized scheme is more efficient than the distributed scheme [5,6]. Therefore, the problem considered in this section utilizes a centralized MME pooling scheme [5,14]. Both schemes are illustrated in Figure 1, where the upper portion depicts the centralized MME pooling scheme, while the lower portion shows the distributed one.

A. Important Notations
Table I presents math and network notation that will be used throughout the paper. Table II lists notation related to the multi-objective particle swarm optimization algorithm. For any N ∈ N, n ∈ [1, N ] indicates that n is an integer number between 1 and N . z ∈ {0, 1} denotes a binary number or more simple z ∈ Z. Throughout the paper,

B. Preliminaries and Design Hypothesis
The cells are allocated within every TAL through the centralized MME pooling scheme. Every TA refers to a single cell, hence, TA and cell will be used interchangeably. The centralized MME pooling scheme is depicted in the upper portion of Figure 1. The model includes two layers of assignments.   : Position of parameter j within particle i, ∀i, j ∈ N v i,j : Velocity of parameter j within particle i, ∀i, j ∈ N jth parameter of ith best global particle, ∀i, j ∈ N P : Number of parameters within 1 particle, P ∈ N The first layer considers the Cells-to-TALs/MMEs assignment associated with the core network. The second layer presents the TALs/MMEs to UEs assignment, which relates to every cell within the system. The first layer of the system evaluates the UEs mobility pattern among the cells, then it allocates the cells in TALs/MMEs with the goal of attenuating the total signaling overhead caused by TAU and paging. In the second layer, a number of TALs is positioned at a portion of UEs termed σ l k . The selection of σ l k defines the usage portion of every single TAL/MME per cell which leads to interference of TALs through the cells. This technique guarantees a broader assortment of cells in the list.
1) Cell/TA-to-TAL/MME Assignment: The allocation of cell/TA-to-TAL/MME in the centralized pooling scheme is the main objective of the presented model. Every TAL refers to a certain one-to-one MME basis. The allocation of Cellsto-TAL/MME is given through a binary decision variable C l k,n ∈ Z for all l, k, n ∈ N, k, n ∈ [1, N ] and l ∈ [1, L]. In order to proceed with the model and problem formulation, it is necessary to present the following example. Example 1. As an illustrative example of C l k,n ∈ Z allocation in the search space, define C l k,n to be a cell-to-TAL assignment. For one list (l = 1), define the maximum number of cells/TAs to ben = 3 with N = 4 MMEs or lists. Accordingly, the total number of possible combinations in matrixL 1 = C l is 3×4, where C l k,n is a binary decision created in the system. One can specify the decision variables in list 1 as Equation (1) in Example 1 shows thatL 1 is symmetric with Also, it demonstrates that number of nonzero rows/columns has to be 3. In addition, number of ones in any given nonzero row/column has to be 3 with one zero row/column. Therefore, a general cell-to-TAL assignment with L lists and N MMEs or lists can be expressed bȳ The assignment of the maximum number of cells within a list has to satisfy the following expression where MX denotes maximum number of TAs in a list. Also, according to the above-mentioned discussion and Example 1, the cell/TA assignment in every TAL takes the following form C l k,n = C l n,k , ∀l ∈ [1, L], and k, n, k = n ∈ [1, N ] (4) 2) TAL-to-UE: According to the fact that any TAL associated with MME could be assigned to more than one cell, the assigned cell has not to violate the MME load of any nonzero n column, which could be achieved by where σ l k denotes the percentage of usage of list l associated with cell k.

C. Total Signaling of TAU and paging
The signaling cost of TAU and paging is a fundamental consideration when placing an idle-user in a cellular network [11,13]. In fact, the total signaling overhead created by TAU is a result of a user movement from a cell in one TAL to a different TAL. Whereas, the total signaling overhead created by paging is due to a necessity to locate a user via the core network. Since the total signaling overhead cost is inversely related to the size of TAL, there is negative correlation between the total signaling created by TAU and paging, and the size of TAL. This is the case due to the fact that the greater the number of cells within a certain list the lower is the probability for a user to move between lists.

D. Problem Description and Cost Functions
This subsection presents the centralized MME pooling problem [5,6,14]. The objective is to minimize the signaling overhead, which can be accomplished by minimizing two objective functions, namely, the total signaling cost of TAU and paging, and the total inter-list handover. The total signaling cost of TAU of cell k for any n nonzero column is equivalent to with UE k being the total number of UEs served by cell k, U C being the TAU cost of UE moving from one list to another, H C being the MME cost relocation over handover process, and O l M defining whether list l belongs to MME M or not. The total cost of paging of cell k for any n nonzero column can be evaluated by where G a denotes the arrival rate of paging and G C refers to the paging cost of equipment of a particular user. Recall the constraints in Equations (2) C l k,n = C l n,k , ∀l ∈ [1, L], k, n, k = n ∈ [1, N ] where Equations (11), (12), and (13) refer to boundary constraints. Accordingly, the total signaling cost of TAU and paging of the ith element is defined by and the first objective function is given by Hence, the total signaling overhead introduced by TAU and paging could be minimized through the minimization of J 1 in Equation (15). On the other side, the total inter-list handover of the kth element for any n nonzero column is with the second objective function being defined by such that the minimization of total signaling overhead could be achieved by diminishing the total inter-MME reallocation cost, which is defined by J 2 in Equation (17). The fluid flow model is popular and frequently employed to resemble the mobility behavior of a user in a system [5]. The fluid flow model presents the UEs traffic outflow rate of an enclosed area. In this specific case enclosed area refers to a single cell. For a given cell k, let P M denote its perimeter, ρ k refer to UE density of the cell, and v be the UE average velocity. Hence, the average of cell crossings with respect to time is defined by implying that the cells are hexagonal-shaped with length L H , which means that P M = 6L H . According to the above-mentioned discussion and the problem statement, the two objectives J 1 and J 2 are inversely related. A weighting scheme can be applied to the two conflicting objectives [24]. However, there is no guarantee that the obtained solution would be the best compromise solution [28].

E. Overview of model decomposition using MINLP
The problem formulation of the model mentioned above is NP-hard, thus, the problem can be divided into two sub-problems. One sub-problem may consider the cell-to-TAL/MME assignment which is allocated periodically to minimize the inter-list handover rate of UEs from one cell to another. This sub-problem mainly includes Equations (9), (10), (16) and (17). The second sub-problem could assume the set of TALs/MMEs given and, thereby, using the given data one can find the optimum usage ratio for each TAL/MME. The other sub-problem mainly includes Equations (11), (13), (6), (7), (14) and (15). For a full description of the previously mentioned two sub-problems using MINLP visit [6,7]. A scrutiny look at the final solution obtained by MINLP, one can find that the solution is confined on the Paretooptimal front. However, one objective is strictly favored the optimum solution over the other one, which is a significant weakness. Thus, the best compromise solution cannot be achieved using this method. Consequently, a random artificial search technique such as optimization algorithms based on the social behavior of animals could help in approaching a set of solutions on the Pareto-optimal of the NP-hard problem [36]. In that case, the two above-mentioned sub-problems would be solved simultaneously through the optimization algorithm and the best compromise solution would be defined.

IV. MULTI-OBJECTIVE PARTICLE SWARM OPTIMIZATION A. Particle Swarm Optimization
Particle Swarm Optimization (PSO) is considered to be an evolutionary heuristic technique. This technique imitates the cognitive and social behavior of animals in their natural environment such as fish schooling and bird swarming [20,25]. In the very beginning, PSO is initiated by releasing an initial random population of particles into the space. In this paper, the population size is fixed. Every particle within the space is a potential solution or, in other words, a potential candidate. It is worth mentioning that particle and candidate will be used interchangeably. Each single particle has a set of parameters such that the set of parameters swarms in the space irregularly in a multi-dimensional space. The swarming of particles is aimed at determining an optimal solution. The position of a particle represents a possible solution including the optimal solution. The velocity of every particle has a major impact of heading the best potential solution or alternatively, the best candidate position. Additionally, the subsequent position and velocity of each particle depend on: current velocity, current position, current position of the neighboring particles, and best position of the neighboring particles. Let P ∈ N and N ∈ N denote number of parameters to be optimized and population size, respectively. For any ith candidate solution within the space, define the candidate solution (position) at iteration t ∈ N by X i = [x i,1 (t) , . . . , x i,P (t)] ∈ R P and the velocity of every candidate by V i = [v i,1 (t) , . . . , v i,P (t)] ∈ R P for all t = 1, 2, . . . , T and i = 1, 2, . . . , N . Accordingly, the velocity update of the jth parameter within the ith swarming particle is given by [20,25] where α (t) denotes an inertial factor to be continuously reduced with every iteration such that α (t) = 0.99α (t − 1), rand 1 and rand 2 are real random numbers between 0 and 1. k 1 and k 2 refer to weighting factors associated with personal and social influence, respectively. x i,j denotes a best local particle within the previous generation while x i,j refers to the best global particle within all previous generations, for all i = 1, 2, . . . , N and j = 1, 2, . . . , P. It should be noted that the iterative solution starts at t = 1 continues until the total number of iterations set by the user is reached. The position update of a parameter within the swarming particle associated with Equation (19) can be expressed by [20,25] The candidate solution X i is initialized between the minimum and maximum boundaries of the space, termed x min and x max , respectively, such that x i,j (t) ∈ x j min , x j max . On the other side, the velocity with N intv being the number of intervals.

B. Pareto-optimal front and clustering
For a multi-objective optimization problem, or to be more specific, two-objective optimization problem, the trade-off between any two solutions can follow two possible scenarios: either one dominates the other or none dominates the other. For instance, if X 1 dominates X 2 according to the first objective, while X 2 dominates X 1 based on the second objective, both solutions are considered non-dominated and are located on the Pareto-optimal front. All the solution positioned on the Pareto-optimal front are combined into one set. Number of solutions on the Pareto-optimal front could be extremely large. Clustering, however, can reduce the number of non-dominated solutions within a large set while keeping the desired characteristics of the trade-off. In MOPSO, the local set of nondominated solutions is denoted by S with S i = X i (1). After which, non-dominated solutions are to be added to form a large set S i . Clustering is performed based on the distance between two pairs. The pairs separated by large distance are retained while minimal distance pairs are combined into one cluster [37]. Therefore, the local non-dominated set can be resized to satisfy the user settings, for instance S i , has the size S L . The non-dominated global set is denoted by S and includes all the non-dominated solutions from the first to the last generation. In the same spirit, clustering is performed to keep pairs within large distance and combine pairs within small distance. In that case, the non-dominated global set will not exceed a predefined size, for example S has the size S G . It should be remarked that X i and X k belong to the sets S i and S , respectively. The steps of the clustering algorithm can be found in [8,26].
Individual distances between all the members of the local set S i , and the members of the global set S are measured with respect to the objective space. Let X i and X i denote members of S i and S , respectively. For the case when X i and X i give minimum distances, they are to be selected as local best and global best of the ith particle, respectively.

C. Extraction of the best compromise solution
Once the Pareto-optimal front is available, one solution should be selected as the best compromise solution. The fuzzy approach is implemented to identify the best compromise solution. Define M as a number of non-dominated objectives on the Pareto-optimal front and N Obj as a number of objectives. Let the set of objective functions on the Paretooptimal front beJ c = J c 1 ,J  (22) for m = 1, 2, . . . , M , and c = 1, 2, . . . , N Obj . In our case there are two objectives, thus N Obj = 2. Next, the best compromise solution is obtained bȳ Given the solution ofμ m as in Equation (23) for m = 1, 2, . . . , M , the best compromise solution isμ m that has the maximum value. Figure 2 illustrates the flow chart of MOPSO computational algorithm.

D. Implementation of the MOPSO algorithm
For simplicity, the implementation process of the MOPSO algorithm can be subdivided into two stages: Stage I: Recall Cell/TA-to-TAL/MME Assignment in Section III, specifically Equations (2), (3) and (4). There are N ∈ N cells in the system withn ∈ [1, N ) cells being distributed within the list andn < N : such thatL ∈ Z LN ×N denotes all lists and the associated superscript refers to the list number. Thus, the number of the cell-to-TAL combination is given by L×N . It should be noted thatL l is a symmetric matrix for l = 1, 2, . . . , L, such that L l = L l ∈ Z N ×N , with being the transpose of the matrix. There aren nonzero rows withinL l and the rest of rows (N −n) are equal to zero, while the dimension of each row is 1 × N . Let L l l ∈ Z 1×N be a nonzero row, such that l is an integer between 1 and N ( l ∈ N and l ∈ [1, N]). In fact,n numbers within L l l are equal to 1 and the rest are zeros. Also,n nonzero columns within the square matrix L l are identical, for l = 1, 2, . . . , L. Accordingly, number of variables to be optimized and allocated within one list is N . In the L lists in Equation (24), there are L × N binary parameters to be optimized and subsequently allocated such that Stage II: Consider TAL-to-UE in Section III, Equation (5). Each TAL is associated with a specific MME and their relationship can be expressed as N k=1 σ l k · C l k,n = 1, l = 1, 2, . . . , L with σ l = σ l 1 σ l 2 · · · σ l N ∈ R 1×N for l = 1, 2, . . . , 10. Therefore, in the L lists in Equation (26), there are another L × N real parameters to be optimized. The decision variable σ l has to match the L l l , for instance In order to obtain σ l , define the following auxiliary variablē σ l = σ l 1σ l 2 · · ·σ l N ∈ R 1×N withσ l k ∈ [0, 1] for all l = 1, 2, . . . , 10 and k = 1, 2, . . . , N . Hence, we optimize for X 2 such that After optimizing X 2 , σ l can be defined as follows where C 1 l ,k is a cell associated with the nonzero row L l l . Next, to satisfy the equality in Equation (26), one has This concludes the implementation process of the MOPSO algorithm. Therefore, Stage I and Stage II summarize the optimization process and number of parameters to be optimized. It is worth re-mentioning that X 1 and X 2 in Stage I and Stage II, respectively, would be solved by MOPSO algorithm simultaneously. In total, there are 2LN parameters to be optimized. Let X i denote the vector of parameters to be optimized with dimension 2LN × 1 for all i = 1, 2, . . . , N . One can find the complete vector (X i ) to be The total number of parameters to be optimized in one particle is P = 2LN with X 1 ∈ Z N ×1 and X 2 ∈ R N ×1 . A set of random particles is initiated such that every single particle has P = 2LN parameters, and there are N particles within one iteration/generation. Therefore, the number of objective functions obtained from one generation is 2LN . Velocity and position of the ith particle are defied according to Equations (19) and (20), respectively. All the remaining steps are detailed above.

E. Computational flow using MOPSO
The computational flow of the proposed location management of LTE networks using MOPSO technique can be briefly summarized as follows: Step 1 (Initialization): initiate t = 1 and generate N random particles X i (1) ∈ R P ∀i = 1, 2, . . . , N where X i (1) = Evaluate the objective function of every particle. For every particle, generate a local best set S i (1) with X i (1) = X i (1) and S i (1) = X i (1) for all i = 1, 2, . . . , N . Search for non-dominated solutions and generate a non-dominated global set S (1) where the closest member of S (1) to X i (1) is marked as the global best X i (1) of the ith particle. Set the initial value of the inertia factor α (1).
Step 2 (iteration update): update the iteration number to t = t + 1.
Step 3 (Inertia factor update): update the inertia factor.
Step 4 (Velocity update): update the velocity V i (t) as defined in Equation (19) given the particle X i (t − 1), the local best X i (t − 1), and the global best X i (t − 1) for all i = 1, 2, . . . , N .
Step 5 (Position update): update the position X i (t) in accordance with Equation (20) given the particle X i (t − 1) and the velocity V i (t) for all i = 1, 2, . . . , N .
Step 6 (Non-dominated local set update): The updated position X i (t) is added to the set S i (t) for all i = 1, 2, . . . , N . Keep all non-dominated solutions and eliminate all dominated solutions within the local best set S i (t). If the size of S i (t) exceeds the predefined value S L , apply clustering and reduce the size to S L .
Step 7 (Non-dominated global set update): Set S (t) = S (t − 1). If any of the non-dominated solutions within S i (t) are non-dominated by S (t) and are not its members, add it to the set S (t). If the size of S (t) exceeds the predefined limit S G , apply clustering to reduce the size to S G .
Step 8 (Local best and global best update): The individual distances between members of S i and members of S (t) are evaluated with respect to the space of the objective function. The pair that gives minimum distance is selected as local best and global best of the ith particle, respectively.
Step 9 (Stopping criteria): If the maximum number of iterations defined by the user is reached, stop the program. Otherwise, go to Step 2.

V. PERFORMANCE EVALUATION AND SIMULATIONS
Optimization of the LTE networks location management using MOPSO is evaluated and compared to MINLP using MATLAB . In order to achieve reliable assessment, both algorithms, MOPSO and MINLP, have been implemented at different speed levels. The speed range has been subdivided into four levels: very slow speed stands for (0-8 m/s); slow speed indicated (8-16 m/s); normal speed should be interpreted as (16-25 m/s); and high speed implies speeds in the (25-33 m/s) range. The environment is assumed to have 30 cells with an average of 100 users are evenly distributed throughout the systems of cells. The users are allocated in a regular manner through the cells within the system. The system consists of 10 lists with 0.05 rate of paging. Clearly, the number of lists in the system equals the total number of MME. Table III outlines the full details of the model parameters associated with LTE networks.

A. MOPSO Set-up
The aim of the simulation section is to examine the convergence of MOPSO in comparison with MINLP. Recall Stage I subsection of the Cell/TA-to-TAL/MME Assignment (Section IV), more specifically, Equations (24), (3) and (4). As mentioned above, there are N = 30 cells in the system withn = 16 cells being distributed within the list. Thus, L l ∈ Z 30×30 denotes a complete list. Define L l l ∈ Z 30×1 to be a nonzero row, such that l is any integer between 1 and 30. L l l includes 16 cells with the value of 1 while the rest of the cells have the value of 0. For L = 10 lists in Equation (24), number of parameters to be optimized is 300 such that Each TAL is related to MME and the relation can be expressed by 30 k=1 σ l k · C l k,n = 1, l = 1, 2, . . . , 10 with σ l = σ l 1 σ l 2 · · · σ l 30 ∈ R 1×30 for l = 1, 2, . . . , 10. A new variableσ l is introduced such thatσ l = σ l 1σ l 2 · · ·σ l 30 ∈ R 1×30 . For 10 lists in Equation (26), one has 300 real parameters to be optimized, such that therefore, the decision σ l is defined by Therefore, there are 600 parameters to be optimized. Define vector X i as the total number of parameters to be optimized such that And the problem of optimization is defined as a minimization problem using MOPSO. The setting parameters of MOPSO are listed in Table IV with T = 400 being the total number of iterations. Also, it should be noted that the setting of parameters in Table IV were selected as a result of a set of trials. The inertia factor α (t) is calculated by α (t) = 0.99α (t − 1) and initiated at α (1) = 1.

B. MOPSO Total Signaling Cost
Using MOPSO, the total signaling of TAU and paging (J 1 ) and the total inter-list handover (J 2 ) are presented and compared to MINLP at four different speeds mentioned above.
The optimization algorithms, MOPSO and MINLP have been implemented and compared to assess: 1) The minimum value of the two objective functions J 1 and J 2 obtained by each algorithm. 2) The best compromise solution by MOPSO and the MINLP solution.
3) The totaling signaling overhead and average battery power consumption.
In order to evaluate the robustness of MOPSO algorithm in terms of its ability to approach the optimal solution, the algorithm has been implemented four times at each of the four speed levels considering different random initialization conditions of the X vector. The speed ranges examined are the following: [0,8], [8,16], [16,25] and [25,33] m/sec. For clarity, three out of the four trials have been presented. Additionally, the following color notation is used: red color demonstrates a best compromise solution, blue illustrates a solution on the Pareto-optimal front and magenta represents a candidate solution as well as search history obtained using MOPSO. Also, black refers to a solution obtained by MINLP. An illustration of the search history of a single trial of the speed [0,8] (m/sec) within the two-dimensional space is depicted in Figure 3 presenting all candidate solutions, Pareto-optimal front and the best compromise solution. Figure 4 demonstrate the Pareto-optimal front in blue color and the best compromise solutions in red color obtained by MOPSO plotted against MINLP in black color at speed ranges [8,16]. In spite of the fact that the solution generated by MINLP is located on the Pareto-optimal front as depicted in Figure 4, MINLP favored the objective function J 2 over J 1 . MOPSO, on the contrary, captured a set of solutions on the Pareto-optimal front with the trade-off represented by the best compromise solution.
The best compromise solution creates a balance between the inter-list handover which resides on UEs, probability of the user to travel between cells, and the cost of inter-MME, from one side, and the total signaling cost of TAU and paging which impact the EPC significantly by influencing the location management of UEs, on the other side. The significance of the proposed algorithm can be observed in terms of total signaling overhead and battery power consumption. The consumption estimation of the triggered TAU signal in a regular smartphone is considered 10 mW [5]. Figure 5 illustrates the total signaling overhead of MOPSO best compromise solution average values versus MINLP. MOPSO exhibited lower total signaling overhead than MINLP at various speed ranges as presented in Figure 5. As Figure 6 confirms that MOPSO has a significant advantage over MINLP with respect to battery power consumption. The average values of MOPSO best compromise solutions associated with battery power consumption versus MINLP are depicted in Figure 6. The overall quality of MOPSO convergence very close to the optimal solution is presented in terms of mean and standard deviation (STD). Table V lists the best compromise solutions of non-dominated objective functions recorded on the Paretooptimal front considering four trials at various speed ranges. Also, Table V outlines the mean and STD of the four trials at every speed range. The accuracy of convergence can be    evaluated by the percentage of STD with respect to the means, which is known as a relative standard deviation (RSD). RSD of J 1 is 0.7%, 0.7%, 1.3% and 1.26% and RSD of J 2 is 1.4%, 2.8%, 1.2% and 0.68% at the four speed ranges. Indeed, the RSD of J 1 and J 2 illustrates the remarkable performance of MOPSO when applied to the LTE networks location management. Likewise, the total power consumption has been evaluated in Table VI in terms of the mean and STD of the results obtained by MOPSO algorithm versus MINLP at different speeds. It can be noticed that MOPSO generated lower values of total power consumption than MINLP.

VI. CONCLUSION
Cellular networks are essential as they offer a variety of connectivity solutions. LTE and LTE-A provide reliable and fast wireless network service. Nonetheless, the total signaling overhead has been a concern, in particular with the rapid growth of more advanced cellular phones and phone applications. In fact, location management which allocate the idle user is directly related to the total signaling overhead. The total signaling overhead can be assessed by two elements, the total signaling cost of TAU and paging and the total inter-list handover. However, these two elements are adversely related. This study proposes a new formulation of the total signaling overhead as a true multi-objective problem where both conflicting objectives are optimized simultaneously. This novel approach is based on the multi-objective particle swarm optimization (MOPSO) which allows to minimize the total