CHAPTER 1

1.      Introduction

Automobiles are considered to be the major mode of transportation by millions of world’s inhabitants. With the growing population rate, the transportation mechanism has become complex. The increasing demand of transport has made the communication between vehicles a necessity with high priority of safety and entertainment. The widespread use of transport has caused traffic saturation, congestion and increased probability of accidents(Jain, 2018). These facts have motivated the engineers and mechanics to develop application that aid vehicle conductors in taking decisions about routes while providing safety to all of its occupants. These applications can also aid the vehicle operators in avoiding traffic jams by choosing the less congested trajectory(Mezher, Paredes, Urquiza-Aguiar, Moreira, & Igartua, 2015). This will also help in increasing vehicle’s efficiency thereby contributing in reduction of environmental pollution. Vehicle Ad-hoc Network (VANET) is a type of application that allows the vehicle operators in gaining these advantages.

VANET is not a new topic in communication networking area, yet it has continued to provide new research challenges and problems. VANET is a type of Mobile Ad-hoc Network (MANET) which is defined as a network that connects autonomous nodes composed of mobile devices or other mobile pieces arranged in several ways(K. D. Singh, Rawat, & Bonnin, 2014). These nodes work under a top-down network administration setting. MANETs are categorized under VANET, InVANET and iMANET. VANET has the main aim of aiding vehicles group in maintaining smooth communication using any central base controller or station(Alves & Wille, 2018). In VANET vehicles, the messages are transmitted and received through a wireless VANET based application on intelligent transportation system. Using the promised future potential of VANET, it has been chosen to achieve a smart buses system(Mezher et al., 2015). The major aim of this project is to build on an IoT based system that will help in reducing waiting passenger time at bus stations and reduce the traffic congestion. VANET will allow the passengers to track the incoming buses through GPS location while the drivers will get notifications over available number of passengers at upcoming bus station. The main contribution of this paper is to present current state-of-the art in VANET technology that can be used in designing of automated intelligent buses system. Many VANETs have been proposed by other researchers, yet this study aims at suggesting a novel solution to achieve reduction in waiting time, traffic congestion and safety maximization. A detailed study of the networking architecture along with different topologies and network modelling will be presented in this paper. This paper will also discuss routing algorithms, designing, software designing and security system of VANET technology to be used in intelligent buses system model.

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With the development of wireless networking, VANET has received much attention on data delivery services. Especially intelligent transportations system(ITS) has already been using VANET for collecting traffic statistical data, routing data and identification of vehicle’s current locations(Yang & Bagrodia, 2009). Traffic Control Centers (TCC) also used VANET for collecting and maintains the vehicle information without allowing access over location of vehicles to others(Nafi & Khan, 2012). VANET allows TCC to support safety and non-safety related applications as well that allows in preventing traffic congestion, detection of gunshots and accident alerts.

1.1       General System and Background

The new era of Internet of Things (IoT) has driven the evolution of conventional VANETs into the Internet of Vehicles (IoV). Through IoV, the vehicles are able to interact real-time with each other(Gerla, Lee, Pau, & Lee, 2014). Whereas, IoV also allows the real-time interaction between roads and vehicles and vehicles and cities through mobile-communication technology, smart-terminal devices, vehicle navigation systems and communication platforms over which driving instruction controlling system is managed(Sakiz & Sen, 2017).

The term VANET refers to the wireless vehicular network that has a dynamic topology, limited bandwidth and limited security node. Due to these features, it becomes essential for VANETs to have a data delivery service so that reliable communication can be ensured regardless of nodes mobility, minimization of bandwidth consumption and secure level of communication(Dow, Hsuan, Lee, Lee, & Huang, 2010). VANETs, in building intelligent transportation system requires software for gaining destination routing data, securing communication and selecting the destination. The data routing can be ensured through building routing protocols that determines the pathway between two vehicles while the destination selection can be done through transmission method(Sousa et al., 2010). The best path selected for the vehicle is done based on “cost” of each path incurred for reaching the desired destination.

Global positioning system  (GPS), navigation systems and environmental awareness software are trending in the modern vehicles now days(Leung, 2010). These features allow the vehicles in preventing collisions, integrating wireless access systems and improving the performance of vehicles. The VANETs provide two types of communications for the vehicles i.e. vehicle to vehicle  (V2V) and vehicle to infrastructure  (V2I)  (see figure 1)(Aadil, Rizwan, & Akram, 2011). In V2V communication, the VANETs are formed amongst the vehicles so that information like safety provisions is communicated whereas in V2I, the information is exchanged between roadside units and onboard units of the vehicles  (see figure 2)(Rehman, Khan, Zia, & Zheng, 2013).

Figure 1: Taxonomy of VANET Communication

Source:(Aadil et al., 2011)

Figure 2: VANETs

Source: (Rehman et al., 2013)

VANETs use the vehicles as mobile nodes in order to create a mobile ad-hoc network. The vehicles using VANETs move in predefined road paths where vehicles communicate either with other vehicles (V2V) or with the fixed equipment situated next to road called road-side unit (RSU)(Reena, 2015). The RSUs can be either the toll plazas or bus stations. V2V communication is complex as compared to V2I. In the later, the communication is done through centralized servers available on internet based on various technologies and networks. Currently efforts are being made by the cellular operators for enhancing overall networking capacity so that support can be given for V2I communications(Yousefi, Mousavi, & Fathy, 2006). Vehicles to road-side units (V2R) include communication with short-distanced units like toll plazas, bus stations etc. (see figure 2 above). However, the communication in V2V is done ranging between 300 m to 1 km in free spaces(Rehman et al., 2013). Currently the connectivity process is not standardized in VANETs thus requiring development of new Dedicated Short-Range Communication (DSRC) standards for V2V and V2R communications. The new DSRC systems will allow point-to-point communication at short range. Several standards and technologies like 2G/3G/4G are available for enhancing the vehicular communication amongst other vehicles and road-side units(Jain, 2018). However, the challenge is to develop technologies and standards that are interoperable and provide good coverage along with security at lower cost.

Bus-VANETs allow the buses driving along the road to gain information from RSUs through V2I communication platform(Jiang & Du, 2013). However, the buses share the critical information like available seats, selected route and number of on-board passengers with other buses on the same route through V2V communication. The routing protocols of Bus-VANETs can be found in several studies where the data packets are used by exploitation of longer transmission range and driving route that is most predictable(Ho, 2011). Bus-VANETs allow the bus drivers to offload data from cellular networks where images, audios or videos can be downloaded or uploaded through RSU rather than through cellular bands while passing through them. Some of the studies have explored the RSU’s role in IoV system and pointed it out to be an essential component that provides sensing, processing, communication and computing capabilities to vehicles(Sakiz & Sen, 2017). RSUs are the physical bodies that could be equipped with cameras, speed checkers or even Wi-Fi outputs. RSUs are most likely to be digital billboards, motion detecting outlets, smart bus stops and toll collection boxes. Smart bus stop is a RSU that could be connected directly or indirectly with cloud  (internet) for storing, passing, computing, communicating and processing information regarding bus routes, passenger counts, vacant bus seats, estimated arrival time and number of further stops(Zhou, Dai, & Li, 2013).

In VANETs, the RSUs use DSRC for communicating with peer RSUs (one smart bus stop to the other smart bus stop) and with buses on the road. RSUs in Bus-VANET are usually connected either directly or indirectly with the internet(Zhou et al., 2013). In the event of indirect internet connection, the RSUs communicate through DSRC or wired connections that connect others internet enabled RSUs. Whereas, internet technologies like 6LowPAN, Wi-Max, Wi-Fi, 2G/3G/4G and DSRC can be used for transmitting and communicating important information between RSUs and vehicles(Iqbal, 2018). In the RSUs, the application layer is considered to be the most comprehensive layer that provides cloud-based applications and services. Some of the major services and applications that can be provided by RSUs (smart bus stations) include infotainment applications (media sharing, internet sharing etc.), bus routes, maps for navigation, information about nearby restaurants, warnings and safety messages, expected time of bus arrival, number of vacant seats in coming bus, number of passengers in the bus and expected time of next bus arrival(Chou, Tseng, & Yang, 2013).

In the intelligent transport systems based on VANET, the information about bus can be broadcasted with the RSUs and other peer buses within the range of the RSUs(Nafi & Khan, 2012). The information presentation on the smart bus stops (RSU) can be used by the passengers as well as by the drivers. For instance, if there is no vacant seat for the passengers in the upcoming bus, the bus driver will be notified to not to stop over the bus station and carry on to travels unless there are vacant seats. This could only be done by the drivers by having full information about non-availability and availability of the seats and number of passengers standing at bus stop  (see figure 3 below)(Ho, 2011). The information will be broadcasted by the RSU (smart bus stop) to all the buses and RSUs within the range. In traditional public transport system, the absence of proper information would be translated into wastage of fuel, frustration amongst passengers due to overloading, wastage of time and inefficient route timing. In order to avoid this wastage, VANETs system can be used that that the passengers could get information about capacity of bus, location of bus, number of passengers on-board and timings of the arrival of the bus. In case of theft, accidents or other violent activity (like gun shots), the VANETs system will broadcast the warnings and safety announcement for passengers and other buses on the route likewise(Leung, 2010).

1.2       Challenges

Along with many benefits of VANET, there are several challenges and problems facing the application of this system. Lack of infrastructure and additional responsibilities over vehicles pose new threats to successful integration and implementation of the system(S. Singh & Agrawal, 2014). This is because several vehicles become participants of the network while managing and controlling the communication over the network. In this paper, we have focused on the scenario where multiple smart bus stations (RSUs) are cooperated for providing information related to buses on routes via V2I communication system using cloud platform, IoT device and touch bus stop display. However, due to limited coverage of the RSUs, the buses might not be able to retrieve the needed information through V2I communication platforms(Chahal, Harit, Mishra, Sangaiah, & Zheng, 2017). With V2V communication process, the buses on different routes can also share their cached information with other bus drivers on the same route. However, it is challenging for designing an efficient mechanism system that can be used for data dissemination in such complex environment(Mekki, Jabri, Rachedi, & Jemaa, 2017).

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The V2V communication amongst buses is challenging due to several reasons. Firstly, as the urban bus system is a little complicated in which the bus routes are dependent upon road topology that makes it difficult to estimate the encounter probability of two buses(Jiang & Du, 2015). Secondly, for making scheduling decision, exploitation of the synergy between the encounter probability and requested information is required(Jiang & Du, 2013). Such an effort is considered to be non-trivial. Thirdly, the vehicles in VANETs tend to move in high velocities during the data transmission process, it might lead towards loss of packet and cause link failures. The rates of packet loss can be as much as 20% as the buses/cars move(Xu, Wu, Xu, & Sun, 2012). Moreover, transmitting information in audios and videos can be very challenging due to short contact duration time amongst vehicles or between the vehicles and RSUs.

RSUs are the smart sensing, processing, communicating, computing and storing devices that provide the required services without having human to interact(Zhang, Liu, Leung, Chu, & Jin, 2015). However, these RSUs like smart bus stops with self-operating capabilities bring in light the ethical concerns when it comes to information gathering, dissemination, decision making and communication. Currently, no significant effort has been made towards designing of the ethical code of conduct for RSUs in the IoV systems due to several complexities(Iqbal, 2018). Firstly, the architectural design of RSUs is usually complicated that include concerns like security, trust management, privacy, gateway, web servicing layers, EDGE computing and traditional layers. Secondly, the security and privacy also play crucial role in development of the VANETs system(Iqbal, 2018). Due to being exposed to an open environment, the security concerns can arise including social engineering, masquerading, Denial of Service, eavesdropping etc.(Iqbal, 2018). The information transmitted about number of passengers in coming bus and possible routes can be crucial in events like robbery, theft and other violent crimes. The information can be used for breaching of privacy and data modification. These ethical concerns can cause the application of VANETs for public transport to be inefficient(Busanelli, 2014).

Other than the above challenges, the smooth deployment of VANET requires further challenges to be addressed first. These challenges relate to mobility & dynamic network topology and routing issues. Due to the high mobility (like 100 to 200 km/h) of the vehicles make the overall topology of VANETs very challenging(Vegni, Biagi, & Cusani, 2013). The density of vehicles varies from being sparse to dense that cause fragmented problems in VANET. High speed of the vehicles can also result in network unreachability that can degrade the performance of the VANET application. Furthermore, the high speed of the vehicles can also topple the signals and cause fast fading.  Apart from topology challenges, the routing issues can also pose a threat to efficient deployment of VANETs. The conventional routing protocols do not suit the VANET system due to its specific networking characteristics including frequent disconnections and fluctuating network topology(Chahal et al., 2017).

Since VANETs are different from MANETs due to its localization, various network nodes, hard delay constraints and rapid topology change characteristics, so there are different challenges faced by VANETs than MANETs. The major challenges include frequent neighbourhood change due to higher rate of mobility, increasing channel load, irregular connectivity issues and packet loss due to hidden terminal problems(Iqbal, 2018). In VANETs, the critical information is required to be disseminated as quickly as possible, however it is a challenge to send the critical information within the given time frame due to limited transmission range of DSRC. The failure to timely and accurately disseminate the critical information to RSUs or other vehicles can lead to collateral damage to the neighboring vehicles and the passengers(Mekki et al., 2017). However, the DSRC based VANETs are considered to be traditional with wired connectivity amongst RSUs. This has caused the surge in demand for cloud-based connectivity of VANETs amongst vehicles (V2V) and with the RSUs(Mekki et al., 2017).

Power management in VANET is another challenge that is concerned with the power of transmission of the messages(Jakubiak & Koucheryavy, 2008). Whenever the power is too high, the transmission can get disrupted while affecting other transmissions at distant nodes due to high interferences. It is recommended to use lesser transmission power wherever the network is denser. In order to maximize overall throughput of transmission and minimize the interferences, the adjustment of transmission power must be done in the VANETs(Jakubiak & Koucheryavy, 2008). Apart from transmission power, other major challenges include efficient routing, varying density over time, fading wireless channel and huge size of VANETs. Previously, the routing of VANET development was based on single ad hoc routing method or traditional ad hoc topology. However, these methods are not sufficient enough to meet the different varieties of ad hoc networks. With the passage of time, the need for developing new protocols arose for achieving successful communication amongst vehicles and with RSUs(Jakubiak & Koucheryavy, 2008).

The cloud based VANETs in future are also prone to face challenges including intermittent connectivity, high mobility and location awareness, heterogeneous vehicle management, high latency, security and low bandwidth(Aadil et al., 2011). In order to maintain intermittent connectivity, the packet data loss needs to be avoided in vehicular networks and RSUs. Similarly, for coping up with the emergency situations, each vehicle connected via VANET requires location awareness and high mobility(Rehman et al., 2013). Moreover, management of heterogeneous vehicles and the connections between them sporadically is considered as the future challenge for VANETs based on cloud internet.

1.3       Scope and Research Questions

The study in this thesis is in response to an increasingly technological and connected world. Scientific discoveries, engineering achievements, and social dynamics have led to the evolution of human societies. In the last two decades, ways in which people interacted and lived have changed, mainly because of the new technologies.

Although VANET and BusNET have been an active research area for years, to date there has been no actual real-life implementation of them due to a lack of necessary roadside infrastructures. Furthermore, in the case of BusNET, there has been a lack of research on how the bus stops can be designed as smart infrastructure to apply the VANET and to add value for the commuters and public transport providers.

To this end, our first two objectives are to find a solution to overcome this infrastructure problem and then design an effective, smart framework called B2BANET. The final objective is to implement the proposed framework in Saudi’s NEOM City to validate its feasibility.

To achieve the above objectives, we explore the following IoT and VANET related technologies in the thesis:

• Sensing technology.
• Wireless technology.
• Computational technology.

Due to the space and time constraints, the scope of our research is confined to applying the above technologies to public bus transportation in a smart city. In a nutshell, the roadside infrastructure (bus stops) is replaced with our proposed node architecture in a multi-level wireless sensor network (WSN); for details, please see Chapter 4.

In addition to the above issues, other research questions based on the challenges expounded in Section 1.2 to be investigated include:

• How could our designed node architecture sense, process, communicate, compute and store data in a secure, private and effective manner?
• Bus routes depend on road topology. How could our proposed model estimate the encounter probability of buses for making scheduling decision?

The highlight research Questions are the essential of my research

• What is the impact of using VANET technology to the communities and the government?
• How the level of Collaboration, Communication and Interaction would help in applying Smart transportation in smart cities:
• Does smart application would fill the gap between technology and costumer to lead people to use more public transportation than private.
• Does increasing the number of services from the provider would raise the number of people using public transportation
• How applying VANET technology efficient to Increase number of people using public transportation in Saudi Arabia smart city?
• How could our proposed routing protocol B2BANET (s) be able to disseminate critical information within a given timeframe (QoS) due to limited transmitted range of DSRC

CHAPTER 2

2.      Literature Review

Issues of Public Transportation in Saudi Arabia:

Saudi Arabia can be described as quite a conservative society. The deep culture creates an environment that defines most of the decisions made daily. Simple decisions like the choice of the mode of transport are embedded heavily on such perspectives. The effect, therefore, provides an environment for defining such elements effectively. Preliminary studies reveal that people tend to give up on public transport if they experience delays in their daily trips. (Masoumi 2019, p. 38). Such delays are usually connected to the actions of the transit company or the not covering the needed destination. The tendency for coming back to the public transport option is therefore diminished over time. This research will analysing the reasons behind the slow uptake of public transport in Saudi Arabia.

Saudi Arabia has a unique cultural standing. The majority of the members of the society are recipients of a high income. The result is seen in the improved need for privacy. The public transport system cannot assure them of the high level of privacy that can be instrumental in driving their actions (Alotaibi 2017, p. 13). The current bus conditions play a major role in pushing prospective users away. Enough campaigns need to be carried out to highlight the advantages of the public transport system that are in tandem with the prevailing cultural standing. A typical Saudi family is comparatively large. The privacy requirement for families of such sizes indicates the lack of preference for public transport means. Gender segregation practiced in the Kingdom also increases the preference for private transport (Alotaibi 2017, p. 13). Some residents suggested that in case the local authorities were willing to enhance the usage of public transport. They need to ensure separate compartments for men, women, and children. Such a suggestion can, however, be quite expensive to implement.       

Majority of the people in Saudi Arabia associated public transport with poverty. The high rates of income do not serve to improve perception. It means that most of them can afford a private car, thus diminishing the need for public transport (Alotaibi and Potoglou 2017, p 8). The means is therefore left for low key worker groups mostly comprised of expatriates. The participants in the study indicated that the vehicles used in public transport are not as comfortable. They also indicated that the cleanliness levels are not satisfactory, thus limiting the uptake level in the long run (Aljoufie 2016, p. 536). The agency running the public transport system should, therefore, focus on finding lasting solutions to the identified problems to change the perceptions. However, with the raise of the fuel price in the country since 2017 led the government to effort on improving it and more people to think switching to the public transport when it would be sufficient. Moreover, it is clear that the new vision is going towards implementing a sufficient public transportation as this would have several worthy benefits to the country such as reduction the air-pollution, less traffic, more jobs and good transportation systems.

  The efficiency of the Transport System

Now a days, the suggestion of banning private cars in the city of Riyadh has been attributed to the increased need to look into the efficiency of the transport system. The city requires to implement necessary physical planning strategies that can be used in informing the change processes in society (Algunaibet 2017, p. 6). The elaborate processes are supposed to be made towards ensuring that the transport system becomes efficient. Parking controls can be used to discourage the residents from going into the city with their private cars.

In conclusion, the Saudi government should make the necessary investments in improving the public transport system. The decisions can include strategies meant to change the cultural and social perceptions of society. Improving access to bus stops is important in encouraging the female section of the demographic to use public transport.

In early 2000s, much of the research was based on analysing the lower level aspects of the communication between vehicles. The main research challenges posed in the 2000s included changing topology of VANET, high mobility of nodes and the connectivity dependency on location(Jain, 2018). However, much sooner, the research community realized that by bringing internet access to drivers and passengers could enable the development of myriad of vehicular applications that could change the landscape of traffic information systems. Given the internet sludge in recent years, the message routing and communication through VANETs has become an attractive and promising area of research(Jain, 2018). Our research background and the related studies overall with several traditional research areas including geographic routing, DTN, intelligent transportation system and VANET. The figure 4 below indicates the taxonomy of related work and research areas matching our research aim.

Source: (Park & Kim, 2015)

In VANETs, many studies have been conducted for studying data dissemination between vehicles (V2V) and with RSUs (Wang et al., 2015; Zhao et al., 2015; Zhu, Li and Saad, 2015; Liu et al., 2016; Liu et al., 2014; Ye, Roy and Wang, 2012; Dai et al., 2012). These studies have focused on enhancing the efficiency of the data dissemination through cooperation of the vehicles. Due to the increasing complexity of the public traffic network in cities, Bus-VANET has recently attracted much attention in research. Some of the major studies (Luo et al., 2010; Li et al., 2010; Huang et al., 2013) considered bus relays for forwarding important data items while focusing much on routing schemes designing. Luo et al. (2010) pointed out that for improving network connectivity, the fixed routing of buses can be used. Li et al. (2010) also developed a relay-node-selection protocol with which suitable bus could be selected as a relay so that the data forwarding cost could be reduced. Another study by Zhang et al. (2014) designed geo-cast routing scheme (Vela) for mining the bus routes and predicting travelling time of buses and estimate their encounter probability. With such an application, it was easier for the passengers and drivers to extract information on traffic conditions, number of buses commuting in the nearby area and patterns of individual vehicles.

Acer et al. (2012) analysed the end-to-end data delivery issues arising in Bus-VANET application. The study focused on estimation of road traffic conditions, alighting problems and passengers boarding on the buses. Jiang and Du (2015) also designed two-tier VANET architecture for integrating the buses, traffic control centres and RSUs. With this new architecture, it was easier to determine the path of the buses via the corporation of the high-tier nodes. Considering the communication with RSUs (like smart bus stops), Abdrabou and Zhuang (2011) proposed a framework based on queuing theory so that messages can flow effectively and smoothly to RSUs through V2V communication.

Reis et al. (2011) also studied the overall role of the RSUs as relay nodes in improving communication on highways. The authors of the study also modelled and estimated the average time taken for propagating the packets to disconnect the nodes. Gerla et al. (2006) proposed the vehicular grid ad-hoc network for managing the emergency operations including natural disasters and terrorist attacks. Such type of network allowed access of internet to the bus drivers. The authors of the study also argued that the internet access is only possible due to presence of the vehicles in nearby neighbourhood.

Considering use of Wi-Fi networks by the passengers to get information about vehicles, many studies have been conducted. Marfia et al. (2007) conducted a study while exploiting the use of public Wi-Fi for providing the vehicular communication between RSUs and vehicles. The study mapped all public access points in Poland where the vehicles could use infrastructure for communicating with other vehicles so that traffic congestion could be reduced. Analytic and other simulation models were also utilized in order to optimize the networking and communications strategies. Similarly, Zhang et al. (2016) also proposed a communication model based on VANET that enabled RSUs to balance out the download and upload requests of data from the vehicles running on highway. The infrastructure for the vehicle communication used internet access and routers so that the critical information could be passed between the vehicles and RSUs at short time.

Wu et al. (2005) used the testbed method for exploiting the communication between infrastructure (like toll plazas and smart bus stops) and vehicles. The study indicated that store-carry forward approach of communication could enable data dissemination between vehicles. However, this method would result in data loss and delay in message communication. Ormont et al. (2008) also utilized the test-bed approach in Madison city for analysing the signals of Wi-Fi over the city. The study indicated that communication between vehicles and RSUs using VANET could be done through 3G cellular networks. The authors installed the test-bed monitors in two buses in the city route where each bus operated on different routes on single day. Internet access was provided to passengers using 3G cellular network. The internet access also helped the bus drivers to stay connected with traffic controllers and other buses on similar route so that any information regarding traffic congestion or accidents could be known beforehand. Authors of the study also argued that test-beds can provide map coverage and performance analysis of the buss’s routes at specific locations. Such a method can infer the mobility patterns and allow passengers to get information about bus routes efficiently.

According to Seet et al. (2004), the traditional transport system failed to provide the message facility between two vehicles. Moreover, Zhou et al. (2005) pointed out that it was not easy for the traditional transport system to track down the location and route of the buses. Due to these issues, the passengers waiting time was longer at the bus stops. Li et al. (2000) also analysed the deficiencies in the traditional transport system while pointing out that it failed to indicate the capacity of seats, number of onboard passengers, routing preferences and estimated arrival time. Blum, Eskandarian and Hoffman (2004) also commented that due to unknown number of vacant seats in the bus, the passengers used to travel while standing in the bus. However, with time passage, the evolution of intelligent transport system eliminated these drawbacks and problems. Khan, Khan and Shukla (2013) improvised time-orientation to be of utmost importance when designing the new intelligent transport system. According to Khan, Khan and Shukla (2013), by having complete information of the upcoming bus, the passengers and drivers both can save time and cut on fuel cost.

According to Suryavanshi & Koul (2015), wireless networks allow rapid communication between vehicles and the RSUs. The range of wireless signals can be extended for passing information regarding the vehicles to the other peer vehicles or RSUs. The author presented WiBro and WiMAX technologies that allowed communication between the traffic and public transit. According to Mahmood (2018), use of VANET applications in managing traffic and route of public transport has become a norm. The deployment of sensors is installed on the roads that represent the roads as grid. The author also argued that VANETs applications have taken a new shape with development of vehicular cloud computing (VCC) that is based on mobile cloud computing. VANET clouds are described by Mahmood (2018) to be of three types i.e. vehicles using clouds (VuC), hybrid vehicular clouds (HVC) and vehicular clouds (VCs). The VCC is used for conducting intensive computing that can be used for storage, routing and other applications development for smart city vehicles. VCs also aid the transport management centers in storing predefined information and communicating protocols efficiently. Cloud applications are used by the smart parking applications using three main layers i.e. sensor layer, communication layer and application layer. Cloud based message dissemination protocols are also used in VANET applications that allow discovering fresh and updated routs of the vehicles and transmit the data to RSUs (like smart bus stops and toll plazas) and traffic management centre. Mahmood (2018) also proposed various classifications of VANETs that could allow the construction of smart cities with smart transportation system through Internet of Things (IoT).

In designing the smart transport system in smart cities, the work of Mahmood (2018) cannot be undermined. The author described the VANET-WSN architecture that is designed to be generic. This architectural model allows collaboration and coordination of VANET based vehicles with other systems. Such an architectural model also allows vehicle’s drivers to share important information with RSUs and other vehicles like accidents, traffic congestion or emergency situation like terrorist attack. This model is based on four layers i.e. users’ layer, data layer, communication layer and service layer. The users’ layer allowed the passengers, pedestrians and other vehicles to establish a request through onboard computers (installed on footpaths or smart bus stops). The data layer analyses and filters the data for specifying the communication type for next layering. It also stores important data temporarily. The communication layer consists of communication devices like VANETs, 3G/4G/5G, satellite, private networks etc that pass on routing protocols and fix security issues. The services layer consists of cloud computing and IoT services that allow free interaction with the vehicular sensor network. Use of this model was proposed by Mahmood (2018) for running a smart transportation system.

Hull et al. (2006) and Lee et al. (2006) proposed CarTel and MobEyes vehicular communication applications that helped in dealing with traffic congestion problems through V2V and V2I interactions. FleaNet was also proposed by Lee et al. (2006a) that is an application of VANET allowing RSUs to connect with pedestrians and vehicle owners through wireless devices. Other researches indicated development of ride-sharing systems and VANET applications for improving transportation efficiency (Hartwig, 2007; Fu et al., 2008; Lue and Colorni, 2009 and Chen & Regan, 2009).

Liu et al. (2010) proposed a query dissemination application using VANET that allowed passengers to call vehicles and request proper transportation through point-to-point and source routing queries. According to the study, the call centers and bus tracking systems are already using such software for giving quick response to queries of pedestrians and passengers. The application allows passengers to send out queries as GPSR in the electric map. The nodes in the map get notified of the query and if vehicles are matched with the request, the message is sent to the intermediate mode. If vehicles do not match the query, for instance if a passenger wants to go to south but the arriving bus is going north, then the requested query will be moved to next intermediate model until the message arriving at its destination is fully discarded.

Park & Kim (2013) designed the routing scheme of buses by using Hybrid model. However, they proposed Hybrid Routing approach to be of greater efficiency as it covered a greater number of buses and bus routes as compared to Hybrid model. Sede et al. (2008) designed the hybrid routing approach in which there is higher probability of contact of one bus with the others. However, Sede et al. (2008) pointed that this approach was generated only from V2V communication and thus it required improvement in terms of passengers’ desires for getting better network services. Dailey et al. (2000) and Welch (2006) advocated use of GPS system for obtaining the location of buses so that the arrival times could be transmitted to the passengers requesting buses at the smart bus stops.

It is important to note that the overall efficiency of the smart transport system relies on accuracy, speed and reliability of the information related to bus routes, location and passengers on board. However, these types of applications require prediction algorithms to be of high efficiency. According to Park & Kim (2013), the prediction algorithms are prone to potential prediction errors and uncertainty and cannot handle the inflow of data well. Feng et al. (2018) also conducted a study to propose a method for improving means of bus dispatch, improve the efficiency of bus operations, implement the smart bus-stop and design the overall intelligent bus positioning system by using the Internet of Things technology like RFID and Zigbee.

According to the study by Feng et al. (2018), the proposed system (Zigbee wireless network) allowed installation of touch screens at bus stops that could provide data on number of passengers waiting, arrival time of bus and other information like vacant seats. The study used Zigbee wireless network technology for improving the quality and efficiency of bus service as its enhanced communication between the vehicle terminals, platform systems and dispatch monitoring centre. By using this study, the future research could be presented for development of urban intelligent bus system at national level. The study results could be used for constructing intelligent bus system by using Internet of Things technology that promotes standardization of urban bus industry management, rationalization of command, integration of services and enhancement of the urban bus attractiveness. 

 

CHAPTER 3

3.1 Overview of Proposed Framework (B2BANET)

Our proposed framework is of a hybrid WSN that consists of mobile nodes (buses), stationary nodes (roadside bus stops) and the Internet infrastructure with cloud server as the sink. The roadside nodes also act as a cluster head, shown in Figure 3. Each bus is equipped with our proposed node architecture (see Section 3.2), which uploads real-time data, including GPS location, vehicle engine status, passenger information to a cloud platform. The cloud server can be accessed remotely by authorized or administrative personnel via a monitoring webpage. The user-friendly web-browser will provide complete visibility uploaded data in real time as well as instant activities reports and history logs. In a companion, a smart display is installed at bus stops to help passenger track the desired bus, as well as to notify the driver to pick them up at the stop.

The cloud platform (such as Amazon Web Service) provides a whole eco-system for our proposed B2BAANET, as shown in Figure 3.

Figure 4: Interior model for the B2BANET

3.2 Proposed Node Architecture

The proposed node architecture is based on a mini Linux single board computer with a GPS and 4G connectivity, which periodically sends data back to the cloud service. The cloud service ingests the data, processes and saves the useful information to the cloud database, such as location history, travel time and predicted arrival time. This data is then transferred to the bus stop so that the information can be displayed to the passenger.

We use the Linux-based node architecture because:

– It is a very powerful and versatile platform, which supports many programming languages, including C/C++, Python, HTML, and JavaScript, and the software libraries are widely available.

– The cost of such Linux boards and communication devices are affordable to provide a low-cost and quickly roll-out solution for the mobile (bus) and stationary (roadside) nodes in our proposed B2BANET.

The node architecture consists of a main controller and various peripherals, including sensors and communication module. The overview of the system as shown below:

The main controller collects data from attached sensors and other peripherals, then sends back data to the cloud server via the communication modules. Optionally it can send and receive data from another unit on another mobile node (bus) via the local mesh network where the roadside node is the cluster head, mentioned previously.

The node architecture supports multiple communication module, including:

– GPS: provide real-time coordinates of the vehicle.

– WiFi/LAN and 3G/LTE modem: provide internet access to the vehicle where possible.

– Zigbee: establish inter-vehicle mesh network to exchange data.

A Linux single board computer can be installed in mobile and stationary nodes, using the Raspberry Pi A+ or the Forlinx FCU1101 (see Figure 6). These tiny micro Linux computers features the high processing power and capable of running complex data processing algorithm, thus it is suitable for the B2BANET.

	Raspberry Pi A+	Forlinx FCU1101
Processor	BCM2837B0 1.4GHz	i.MX6UL 528MHz
Memory	512MB RAM	256MB RAM
Interface	USB, UART, GPIO	RS485
Connectivity	WiFi, Bluetooth	WiFi, Zigbee, Bluetooth, Ethernet, 4G
Rating	0 to +50°C	-35 to +70°C
Cost	30 USD	100 USD

However, we choose the Raspberry Pi over FCU1101 because it has a better RAM and very popular to the IoT applications, thanks to the easy setup and vast amount of community support. Furthermore, the Raspberry Pi software libraries are increasingly developed and freely to use.

 Moreover, our proposed node architecture facilitates attachment of a GPS device to the Linux board and the data is transferred via the serial port for real-time location detection. A 3G module is also attached in our proposed node architecture to provide internet connectivity, via the Serial port of the Linux board, as shown in Figure 7.

• Software Platform

For B2BANET implementation, we use Python as the programming language for the Linux board because of the following advantages:

– Powerful scripting language with simple syntax and easy to program.

– The software library is widely developed and freely to use.

– Integration with IoT cloud platform is fully supported, including commonly used library such as JSON, MQTT and AWS IoT.

– The code can be run on most Linux based machines, from embedded devices to cloud servers, make it easy to test and deploy, thanks to highly scalable and reliable.

– Python is also well-known for easy integration of external peripheral, including sensors, serial port devices and cameras.

The device is programmed to periodically send data to the server every 5 to 30 seconds, which contains essential information of the vehicle. We have tested our node architecture and software platform and the test program is attached in the appendix. The data is encrypted and securely transferred to the server via a HTTPS connection to avoid data corruption and prevent attacker to insert false data into the system.

The sample data sent to the server is formatted as the following JSON message:

{ “status”: “OK”, “timestamp”: 1565757939, “coordinates”: { “lat”: -37.720851167, “lon”: 145.048513 }, “device_name”: “Smart-Bus-001”, “sensor_1”: … … }

The data is then stored in a database for analysing, as well as serving the web app to display the location of the vehicle. The device can also receive message from the cloud server in the same secure connection, such as notifications or requests.

The coordinates and other data from the vehicle can be used to display the real-time location and status to the end user, such as in the web app as shown below:

In the testing condition, the latency between the device and the web app is less than 1 second, including the time data is transferred from the device to the cloud and cloud to the web app.

Based on the historical data and real-time status, the cloud service can run the analysing program to estimate the arrival time of the bus to the next stop. This information is then sent to the display device at the bus stop, or to the mobile app. The cloud service can also be programmed to trigger alert and send notification to the authorities if an emergency or law violation is detected on the bus.

3.4 Proposed Roadside Unit (Smart Bus Stop)

The node architecture proposed in Section 3.2 is embedded into the proposed Smart Bus Stop.  The Smart Bus Stop enables the connection of people with the city infrastructure and significantly supports their engagement. Information about the city together with the map and history of the city and streets bring a new perspective for tourism and increased knowledge about the history. Timetables for all public transport lines are commonplace. Travel planning thus becomes a simple aid for effective travelling and support of the use of public transport as well as the reduction of the number of vehicles in the streets. The possibility to publish information about offices, schools and hospitals belong among the other great functions of this product. Since it is an information channel accessible to all the citizens, even children – setting of accessible and blocked sites is one of the inevitable requirements of today.

• Bus-to-Bus and Bus-to-Infrastructure Communication

The smart device installed in the bus can also be used to establish the inter-vehicle and bus stop cluster network using Zigbee mesh technologies, without sending and receiving data to the internet server. This technique enables real-time data can be shared between the vehicles without the need of the internet connection and serving as a backup connection when the internet connection fails.

In the mesh network, the network can be formed as the partial-mesh or full-mesh topology, which is node can act as the router and end device at the same time.

The proposed Zigbee module (in Figure 10) used for this mesh network is the popular XBee 3 module from Digi. Operates on the ISM 2.4 GHz, these wireless devices can establish a local network with the line-of-sight distance up to 3.2km, with the data rate up to 250 kbps. Furthermore, using mesh topology can extend the network whenever a new device is added to the network, hence increases the coverage and reliability of the network.

CHAPTER 4

4.  Routing Protocols for B2BANET (Preliminary, incomplete, to be continued)

4.1 Limitations and challenges of the current routing topologies:

Recent researches have addressed the problems of the current routing topologies in VANETs, where the network is very dynamic, and the topology is constantly changed, due to the nature of the moving nodes in the network. On the other hand, large number of nodes requires carefully route planning and minimize relay nodes. Furthermore, more nodes in the wireless networks attract more spamming broadcast both in routing and data dissemination.

The current routing topologies are facing two main challenges:

– Link failures: as the vehicle is constantly moving, the link quality is constantly changed and may lead to the route failure, in which the data rate is decreased, or packets are lost. This problem can be addressed and minimized by introducing a dynamic link failure detection and re-routing to a more stable route.

– Large overhead: as the network is dynamic and the number of nodes can be expanded, large overhead can become a major problem, where more bandwidth is required and thus reduce the efficiency of the network.

Several methods have developed to solve these two main challenges, notably Ad Hoc On-Demand Distance Vector (AODV) and using fuzzy logic to find the best route for each node in the network. However, most of the calculations are merely based on the link quality (e.g. RSSI) or just with the little knowledge of the actual physical states (position, velocity, orientation…) of the device, therefore the reliability and repeatability of the result is not high.

4.2 Proposed new and improved routing topology:

In this thesis, an improved method of routing in BsBANET is developed and evaluated, where a physical state of the device is heavily considering when calculating the best route to communicate with other nodes and with the base station via the cluster head (bus stop). The physical states of the bus include:

– The GPS coordinates of the vehicle, and the distance between the vehicles as well as the distance to the bus stops, base stations and cell towers.

            – The velocity of the vehicle, as well as the rate of change of the distance between the vehicles and between vehicle and stations.

            – The orientation of the vehicle, this factor affects the utilization of the directional antenna, in which the position and direction of the antenna can be changed to provide a better link to another node.

The states of the vehicle provide more inputs to the best route-finding algorithm and can produce more consistent and reliable result compared to the traditional methods. This method requires development of the IoT device equipped to the vehicle to collect data used for calculation to find the best route.

Implementation of the improved VANET routing topology for B2BANET:

The routing procedure consists of two main processes:

1. Discovery process:

A node broadcast discovery packet to the neighbour nodes to check the links and get the status of the network. Rather than flooding the entire network with a “hello” message, it based on the current GPS coordinates and last known position of the nearby nodes to selectively send the message to them.

This approach reduces the unnecessary spamming message to the unrelated nodes as well as reduces network overhead. On the other hand, fast and reliable response from nearby nodes can be received to quickly calculate the best route.

In this process, physical states of the vehicle play an important role when selecting nearby nodes and best route, such as:

– Vehicles which their GPS coordinates closer to each other should have higher priority than further vehicles.

– Vehicles which travelling in the same direction or converging to the same destination should have higher priority than vehicle travelling in opposite direction or diverging apart.

The priorities and weighing factors can be determined by the fuzzy logic, which taking multiple inputs to produce the most favourable result.

1. Calculating the best route:

Once the responses from the neighbouring nodes are received, a Cuckoo search algorithm can be used to find the best route. In this algorithm, a group of “seeding eggs” are initialized with values from the responses of the nodes, after each iteration, the best route is chosen and protected in the next iteration. This algorithm follows three ideal rules:

– Each cuckoo lays one egg at a time and dumps its egg in a randomly chosen nest;

– The best nests with high-quality eggs will be carried over to the next generation;

– The number of available host nests is fixed, and the egg laid by a cuckoo is discovered by the host bird with a probability of [0…1].

With filtering helps from the selective neighbour nodes in the discovery process, the search optimization can be processed quicker and produce more consistent result.

4.3 Initial evaluation using simulation

The performance of the new topology can be simulated on a computer to compare with other topologies, as many software packages are available to use, including MATLAB and Java.

There are many factors to consider when comparing topologies efficiency:

– Packet delivery rate (PDR): is the ratio of the received messages compared to the total messages originated from the source node. The higher the better.

– End to end delay (EED): the time delay for the packet comes from the source node to the destination node, including discovery, buffering, queuing, processing, propagating… The lower the better.

– Throughput: is the total number of packets per time unit delivered to the node, usually calculated as bits per second. The higher the better.

– Routing overhead: the total control packet sent between nodes; lower routing overhead means better routing efficiency.

– Packet loss rate: the number of the packet that lost during transmission, the lower the better.

The initial simulation of Yahiabadi (2019) suggests that the selective neighbouring with GPS assisted when calculating best route has better results compared to the conventional routing topologies. In conclusion, this method is very promising to implement an efficient and smart VANET network.

Timeline: The timeline of the project is shown below:

Items/Months	1	2	3	4	5	6	7	8	9	10	11	12	13	14	15	16	17	18	19	20	21	22	23	24	25	26	27	28	29	30	31	32	33	34	35	36	37	38	39	40	41	42	43	44	45	46	47	48
Literature Review
Scoping
Construct Framework/Model
Build and test Prototype/Node Architecture
Plan System
Software Platform
Survey questioners
Analyse data
Write & publish
Built “smartness” into B2BANET
Incorporate efficient and safer service for public transportation using B2BANET
Build complete system
Evaluate & Optimise
Write thesis
Review thesis

 

Publications:

• Alharbi, N., and B. Soh. “Roles and Challenges of Network Sensors in Smart Cities.” IOP Conference Series: Earth and Environmental Science. Vol. 322. No. 1. IOP Publishing, 2019.
• Converting VANET to be IOV, (in progress)

Appendix

Code

Schematic

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