Martingale Theory-Based Optimal Task Allocation in Heterogeneous Vehicular Networks

Heterogeneous Vehicular Networks (HetVNETs) are an evolution of the traditional Vehicular Networks (VANETs) that integrate long-term and short-term communications technologies to deliver strategic services for roads and smart cities (for example, Intelligent Transport, Safety Notification, Multimedia Content, and others). Despite the recent evolution of HetVNETs, points related to security, data integrity and privacy are still open issues, since there is no standard to achieve trust among the network nodes. A promising approach to achieve trust is the usage of a reputation system, that monitors the behavior of network nodes to determine its trustworthiness. However, this de-termination is complex, because the uncertainty coming from the HetVNETs features, such as fast mobile nodes, self-organization, unstable communication links and frequently changing topology. Within this context, this paper presents a reputation system for HetVNETs, called TrustV, to perform a faster and reliable determination of network nodes trustworthiness based on game theory, as well as the long-term and short-term communication technologies of HetVNETs. The game theory approach allows TrustV to overcome the uncertain issues of HetVNETs, since the modeling of the cooperation as a game can identify selfish and malicious nodes during the time. The results suggest that the TrustV achieves a very high cooperation rate between vehicles at Het VNETs, even as it is capable to detect selfish and malicious nodes as well as black hole and grey hole attacks.

In radar networks, research on the task allocation strategy for multi-target imaging is essential, especially under limited radar resources. From the targets' perspective, the goal is to acquire enough resources to obtain high-quality inverse synthetic aperture radar (ISAR) images. Then there is competition among targets for radar resources. Therefore, the task allocation optimization problem is considered as a cooperative game, specifically a non-transferable coalitional game when the sparse ISAR imaging algorithm is adopted. A stable coalition structure, which allows cooperation among targets to achieve satisfactory image resolution using the least amount of time, represents an optimal task allocation scheme. Then, the corresponding algorithm based on coalition-forming rules is proposed. Through the framework of cooperative game theory, a stable network structure (i.e., the task allocation strategy) is formed while minimizing the radar time resource cost. Finally, experiments confirm that the proposed algorithm can achieve an optimal task allocation strategy with a lower computation time cost.

This brief examines recent developments in the Heterogeneous Vehicular NETworks (HETVNETs), integrating cellular networks with Dedicated Short-Range Communication (DSRC) for meeting the communications requirements of the Intelligent Transport System (ITS)services. Along with a review of recent literature, a unified framework of the HetVNET is presented. The brief focuses on introducing efficient MAC mechanisms for vehicular communications, including channel access protocols, broadcast/multicast protocols, the location-based channel congestion control scheme and the content-based resource allocation scheme. The cooperative communication between vehicles is discussed. This brief concludes with a discussion on future research directions, and provides the readers with useful insights into the future designs in the HetVNETs, to motivate new ideas for performance improvements in vehicular networks.

Internet of Vehicles (IoV) is an emerging concept that is believed to help realize the vision of intelligent transportation systems (ITSs). IoV has become an important research area of impactful applications in recent years due to the rapid advancements in vehicular technologies, high throughput satellite communication, the Internet of Things, and cyber-physical systems. IoV enables the integration of smart vehicles with the Internet and system components attributing to their environments, such as public infrastructures, sensors, computing nodes, pedestrians, and other vehicles. By allowing the development of a common information exchange platform between vehicles and heterogeneous vehicular networks, this integration aims to create a better environment and public space for the people as well as to enhance safety for all road users. Being a participatory data exchange and storage, the underlying information exchange platform of IoV needs to be secure, transparent, and immutable in order to achieve the intended objectives of ITS. In this connection, the adoption of blockchain as a system platform for supporting the information exchange needs of IoV has been explored. Due to their decentralized and immutable nature, IoV applications enabled by blockchain are believed to have a number of desirable properties, such as decentralization, security, transparency, immutability, and automation. In this article, we present a contemporary survey on the latest advancement in blockchain for IoV. Particularly, we highlight the different application scenarios of IoV after carefully reviewing the recent literature. We also investigate several key challenges where blockchain is applied in IoV. Furthermore, we present the future opportunities and explore further research directions of IoV as a key enabler of ITS.

Intelligent Transportation Systems (ITS) are nowadays considered very important applications of smart cities. One of the most important technologies that are utilized to support ITS is Vehicular Ad-hoc Networks (VANETs). In VANETs, vehicles communicate with each other (V2V) or with the infrastructure (Roadside Units) (V2I). Roadside Units (RSUs) collect data from vehicles in the coverage area and send it to cloud servers through the Internet. Cloud servers have high performance computational and storage capabilities that ITS applications require for data processing. However, due to the real-time requirements of the ITS applications, cloud approach alone cannot be guaranteed to satisfy the strict time constraints due to long latency access of the centralized cloud server. Fog Computing is an emerging approach that extends the services of cloud computing to the edge of the network. Fog Computing can be utilized in VANETs through deployment of fog nodes into RSUs. One of the major challenges is identifying the optimum number, locations and computational capabilities of the RSUs particularly in urban regions where obstacles exist heavily inside the coverage area of the RSUs. In this paper, we consider the optimization problem of fog-based RSU placement where the objective is to maximize the achieved level of service quality in a cost-effective way. The problem is formulated as a Satisfiability Modulo Theories (SMT) problem and solved using Microsoft Z3. The proposed approach is able to generate a set of solutions as Pareto front. We obtained data from OpenStreetMap for Cairo city. Our approach outperforms other solutions in the literature in terms of cost.

A typical mission consists of several deadline-driven, inter-dependent tasks which need to adhere to specific resource constraints. This makes Optimal Task Allocation (OTA) of a particular mission a daunting task, even for simple and less-challenging environments. The inherent challenges of Mission Critical Environment (MCE) (e.g., Disconnected, intermittent, and limited communication among nodes, high error rate, node mobility, etc.) make OTA even harder to accomplish. To address this problem, earlier, we proposed an automated approach, namely, Centralized Optimal Task Allocation Algorithm (COTAA). While COTAA performs OTA in an efficient manner, it is based on some assumptions (e.g., nodes must follow publish/subscribe-based communication model, there is no inter-dependency among tasks, and static central unit is solely responsible for task allocation) that make COTAA applicable only to specific MCEs (e.g., post-disaster recovery) and do not work well for other MCEs such as group-based UAV (Unmanned Aerial Vehicle) operation, robot-driven mission, etc. In this paper, we propose another novel automated approach, Decentralized Optimal Task Allocation Algorithm (DOTAA), which relaxes the above-mentioned assumptions and performs OTA in an efficient manner. In DOTAA, we have exploited the concept of application-layer hash and bidding approach to perform the OTA for larger classes of MCEs. We have also evaluated our solution using ns-2 simulator and our results show that DOTAA outperforms COTAA in terms of scalability, task allocation time, and bandwidth consumption.

Intelligent Transportation Systems (ITS) play a vital role in the development of smart cities. They enable various road safety and efficiency applications such as optimized traffic management, collision avoidance, and pollution control through the collection and evaluation of traffic data from Road Side Units (RSUs) and connected vehicles in real time. However, these systems are highly vulnerable to data corruption attacks which can seriously influence their decision-making abilities. Traditional attack detection schemes do not account for attackers’ sophisticated and evolving strategies and ignore the ITS’s constraints on security resources. In this paper, we devise a security game model that allows the defense mechanism deployed in the ITS to optimize the distribution of available resources for attack detection while considering mixed attack strategies, according to which the attacker targets multiple RSUs in a distributed fashion. In our security game, the utility of the ITS is quantified in terms of detection rate, attack damage, and the relevance of the information transmitted by the RSUs. The proposed approach will enable the ITS to mitigate the impact of attacks and increase its resiliency. The results show that our approach reduces the attack impact by at least 20% compared to the one that fairly allocates security resources to RSUs indifferently to attackers’ strategies.

This paper embarks the first study on exploiting the synergy between vehicular caching and network coding for enhancing the bandwidth efficiency of data broadcasting in heterogeneous vehicular networks by presenting a service architecture that exercises the software defined network concept. In particular, we consider the scenario where vehicles request a set of information and they could be served via heterogeneous wireless interfaces, such as roadside units and base stations (BSs). We formulate a novel problem of coding-assisted broadcast scheduling (CBS), aiming at maximizing the broadcast efficiency for the limited BS bandwidth by exploring the synergistic effect between vehicular caching and network coding. We prove the NP-hardness of the CBS problem by constructing a polynomial-time reduction from the simultaneous matrix completion problem. To efficiently solve the CBS problem, we employ memetic computing, which is a nature inspired computational paradigm for tackling complex problems. Specifically, we propose a memetic algorithm, which consists of a binary vector representation for encoding solutions, a fitness function for solution evaluation, a set of operators for offspring generation, a local search method for solution enhancement, and a repair operator for fixing infeasible solutions. Finally, we build the simulation model and give a comprehensive performance evaluation to demonstrate the superiority of the proposed solution.

In transportation environment, Intelligent Transportation System (ITS) is being used in various stages of every transport mode (road, rail, air & sea). The aim of intelligent transportation system is to provide the On-Road Vehicles (ORVs) with reliable Internet access connectivity, emergency notification and other safety information via infrastructure-based Road Side Units (RSUs). In the past decade, intelligent transportation system has been started in many countries where RSUs are provided with all type of communication services to connect, compute, communicate and share the information between its neighboring RSUs and on-road vehicles. However, there are certain diffcuilties in which the information cannot be delivered reliably such as Line Of Sight (LOS) communication with on-road vehicles, packet loss, latency, retransmission overhead and multiple target tracking of objects over time-to-time etc. Thus, the intelligent transportation system becomes a complex network of entities. Modern intelligent transportation systems use relay-RSU nodes (i.e., small sized Unmanned Aerial Vehicles) also referred as RRSU node network deployed between satellites and on-road vehicles as they provide many advanages like coverage, reliability and information sharing without depending more on knowledge provided by satellites. In this paper, a novel Aerial Intelligent Relay-Road Side Unit (AIR-RSU) framework has been proposed to determine the network connectivity status level and analyze the communication link stability among RRSUs, and between RRSUs and on-road vehicles for every time instants Ti based on certain performance metrics discussed in this paper. The proposed AIR-RSU framework holds the precise status/information about the network connectivity needed in the transportation environment carefully and effectively accounting the frequent changes in positions. The performance of the proposed AIR-RSU framework is evaluated using rectangle waypoint mobility model for on-road vehicles and steady state random waypoint mobility model for RRSU nodes, which measures the network connectivity status and the performance metrics periodically. Furthermore, a comparative analysis is done to both individual nodes and entire network in terms of network load.

How to guarantee the data rate and latency requirement for an application with limited energy is an open issue in wireless virtualized sensor networks. In this paper, we integrate the wireless energy transfer technology into the wireless virtualized sensor network and focus on the stochastic performance guarantee. Firstly, a joint task and resource allocation optimization problem are formulated. In order to characterize the stochastic latency of data transmission, effective capacity theory is resorted to study the relationship between network latency violation probability and the transmission capability of each node. The performance under the FDMA mode and that under the TDMA mode are first proved to be identical. We then propose a bisection search approach to ascertain the optimal task allocation with the objective to minimize the application latency violation probability. Furthermore, a one-dimensional searching scheme is proposed to find out the optimal energy harvesting time in each time block. The effectiveness of the proposed scheme is finally validated by extensive numerical simulations. Particularly, the proposed scheme is able to lower the latency violation probability by 11.6 times and 4600 times while comparing with the proportional task allocation scheme and the equal task allocation scheme, respectively.

Vehicular communication is a crucial component of intelligent transportation systems (ITS) upon which many services are based starting with relaying traffic conditions up to enabling autonomous driving. Using a backhaul link, a road side unit (RSU) is assumed to fetch content from the Internet to serve the requests of incoming vehicles while they are within its range. Unless an effective caching mechanism is employed at the RSU, popular content might be unnecessarily retrieved from the network backhaul repeatedly especially in scenarios with large number of vehicles. This incurs significant capital and operating expenditures to RSU operators due to backhaul leasing and utilization. In this work, we explore cost-effective solutions for RSU operators by leveraging the content that is readily available on vehicles within the RSU's coverage range to intelligently cache popular content and serve newly arriving vehicles. However, due to the limited RSUs storage capacity and continuous flow of contents, the problem of establishing an RSU caching strategy to maximize the serving rate of incoming vehicles in an energy-efficient manner is challenging. Thus, we propose a technique based on deep reinforcement learning and demonstrate its effectiveness compared to the state-of-the-art.

Innovative soft computing-enabled cloud optimization for next-generation IoT in digital twins

Predict and prevent from misbehaving intruders in heterogeneous vehicular networks

The focus of this chapter is twofold: information dissemination from infrastructure nodes deployed along the roads, the so-called Road-Side Units (RSUs), to passing-by vehicles, and content downloading by vehicular users through nearby RSUs. In particular, in order to ensure good performance for both content dissemination and downloading, the presented study addresses the problem of RSU deployment and reviews previous work that has dealt with such an issue. The RSU deployment problem is then formulated as an optimization problem, where the number of vehicles that come in contact with any RSU is maximized, possibly considering a minimum contact time to be guaranteed. Since such optimization problems turn out to be NP-hard, heuristics are proposed to efficiently approximate the optimal solution. The RSU deployment obtained through such heuristics is then used to investigate the performance of content dissemination and downloading through ns2 simulations. Simulation tests are carried out under various real-world vehicular environments, including a realistic mobility model, and considering that the IEEE 802.11p standard is used at the physical and medium access control layers. The performance obtained in realistic conditions is discussed with respect to the results obtained under the same RSU deployment, but in ideal conditions and protocol message exchange. Based on the obtained results, some useful hints on the network system design are provided.

An intelligent transportation System (ITS) is an unconventional application that aims to provide traffic congestion information and emergency information to other vehicles. In ITS, the compilation and distribution of traffic event information by vehicles are of the most extreme significance. To establish an effective data-sharing network in ITS, the vehicles need to share the data in a protected form in real-time. Conventional ITS system includes data streaming from On-Board Unit (OBU) sensors through Road Side Units (RSU) to centralized cloud servers. Nevertheless, it is still a challenge to distribute essential event information in a selected area under the dynamic ITS environment and in the existence of a malicious vehicle. The current fundamental issues which emerge include privacy concerns due to third party management of cloud servers, single point of failure which are a bottleneck in data flow among the vehicles.