Optimizing Smart Parking System by Using Fog Computing

Fog computing for vehicular Ad-hoc networks: paradigms, scenarios, and issues

Vehicular ad hoc networks (VANETs) have recently received considerable attentions for both safety and commercial applications. In this paper, we address popular content distribution (PCD) in VANETs, in which a popular file is downloaded by a group of on-board units (OBUs) passing by a roadside unit (RSU). Due to high speeds and channel fadings, the OBUs may not finish downloading the entire file from the RSU, but only possess several content pieces. In this paper, we propose a cooperative approach using coalitional graph game to establish a peer-to-peer (P2P) network among the OBUs, in which content pieces are exchanged to complete the file downloading process. Further, we adopt cognitive radio (CR) technique for implementing vehicle-to-vehicle (V2V) transmissions. Simulation results show that the proposed algorithm enjoys a better performance in various conditions, compared to the non-cooperative approach, where OBUs randomly response to content requests from other OBUs.

Finding a vacant place to park cars in the rush hour is time-consuming and even may be frustrating for the drivers. In some studies, vehicles are equipped with communicative tools called On Board Units (OBUs), which come along with roadside devices known as Roadside Units (RSUs), which allow the drivers to communicate each other and trace a vacant parking place easily. Previous systems work with connection of sensors all over the road and parking space and may result in spending much time to find a parking place, occupancy of the empty parking place until the car gets to the desired location, requirement for an additional hardware, wired network communication, and security issues. In this paper, we propose an exponential smoothing and multi-objective decision-making by using cloud-based methods to find the best parking place, taking into consideration of the park-cost for the driver. The proposed system uses the cellular base stations to eliminate the cost of the RSUs and the sensors. The results of our simulations via NS-2 network simulator confirm the efficiency of the proposed model.

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.

In vehicular ad hoc networks (VANETs), vehicles communicate with each other and with roadside units (RSUs) in order to enhance road safety, improve traffic management and provide infotainment services. Along with the growth of VANETs, some challenges are emerging. Although there are many research work on VANETs, cheating attacks are still not well resolved such as selective message relaying attack, faked information reporting attack and resource-consuming attack launched by selfish or malicious participants. To deal with this kind of attacks, we present two novel lightweight security mechanisms by equipped each vehicle's On-Board Unit (OBU) with a small elegant module called TrInc, which is a trusted hardware and composed of only a non-decreasing counter and a key. We observe that TrInc-based method not only can effectively resist against cheating attacks in safety- oriented, convenience-oriented, and commercial-oriented VANET applications, but also significantly defend various aspects of security and privacy in VANETs. Compared with previous works, our proposal features low communication and computation overhead, less memory requirements, and good network scalability.

Due to the high mobility of on-board units (OBUs) and the large-scale network topology frequency hopping-based anti-jamming techniques are not applicable in vehicular ad hoc networks (VANETs). In this paper, to improve the communication performance of VANETs against smart jamming, unmanned aerial vehicles (UAVs) are used. These unmanned aerial vehicles observe the ongoing OBU and UAV communication status and induce the UAV to use a specific relay strategy. The UAV relays the OBU message to another roadside unit (RSU) with a better radio transmission condition if the serving RSU is heavily jammed or interfered. The interactions between a UAV and a smart jammer are formulated using an anti-jamming UAV relay game. The UAV decides whether or not to relay the OBU message to another RSU based on the relay game, the jammer observes the UAV and the VANET strategy it chooses the jamming power accordingly. The proposed relay strategy can reduce the bit-error rate of the OBU message efficiently and increasing the benefit of the VANET Using Q-learning analysis.

The vehicular ad-hoc network (VANET) is a promising technology in near future. VANET allows vehicles to communicate with each other and with roadside units (RSUs). Wireless Access in Vehicular Environment (WAVE), defined by IEEE, provides the Internet connectivity for vehicles as well as vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. RSUs can form a wireless mesh network (WMN) as a backbone infrastructure to enhance the performance of VANET. However, WAVE supports only one-hop communications for IP packets in VANET, and existing researches do not consider the compatibility between VANET and WMN infrastructure formed by RSUs. We propose the extended WMN (ExWMN) which has the feasibility to extend WMN to VANET domain. In ExWMN, the vehicles can send IP packets to an access router by using the multi-hop capability of WMN. ExWMN uses the addressing scheme of WMN for the extended VANET domain and exploits geographical routing protocols instead of existing routing protocols of WMN due to VANET characteristics such as mobility and dynamically changing network topologies. ExWMN supports the compatibility between the extended VANET and a WMN infrastructure due to RSUs, which provide a bridging function between them. This paper presents the architecture and the detailed procedures of the ExWMN.

Intelligent transportation system (ITS) is becoming an important part of our society. Vehicular ad hoc networks (VANETs), as one of the most fundamental technologies in ITS, have attracted great interests in both industry and academia. Due to ever-increasing data demands in VANETs, it is challenging to meet the requirements of ITS, especially for safety-critical applications. Fog computing is an emerging paradigm with the vision of supporting low communication latency, enhancing quality of service, and improving efficiency of network. Meanwhile, collision warning (CW) is critical for enhancing driving safety, which has stringent timing requirement. This work is dedicated to addressing safety issues in VANETs by designing and implementing a fog computing based collision warning system. Distinguishing from conventional cloud-based solutions, where vehicles have to communicate with servers resided in the backbone network via cellular network, the fog computing based system offloads communication and computation workloads to the infrastructures which are closer to end users (i.e., vehicles). In particular, the road-side unit (RSU) with dedicated short range communication (DSRC) interface and local computation unit is acted as the fog node, and it is installed at the road intersection. Then, vehicles approaching to the intersection will periodically report their status including positions, speed, driving direction and acceleration, to the RSU via DSRC. Based on the collected real-time status of surrounding vehicles, a collision prediction algorithm is designed and implemented in the computation unit of the fog node. The warning message will be triggered if potential collision is detected, and it will be transmitted to corresponding vehicles. For performance evaluation, we have implemented the system in realistic VANETs scenario. In addition, we have also implemented a cloud computing based CW system for performance comparison, where the LTE interface is adopted for transmitting messages to the cloud server, where the collision prediction algorithm is executed. Based on the field testing, we have demonstrated the superiority of the fog computing based CW system in terms of enhancing driving safety by providing real-time communication and distribute computation capabilities.

In Vehicular Ad Hoc Networks (VANETs), the Road-Side-Units (RSUs) that acts as base station for serving vehicles may experience significant traffic and congestion during peak hours due to the density of vehicles. To solve this problem, we suggest a coordinated strategy including Unmanned-Aerial-Vehicles (UAVs) on-demand use to supplement and service RSUs and other vehicles. Better resource allocation strategies are required to handle this issue because RSUs and UAVs may share the same set of channels, which will lead to interference. In this research, we propose a UAV-Assisted Protocol (UAVa) to improve network performance, especially throughput. The platform for resource allocation between UAVs and RSUs established by our suggested protocol is cooperative and well-coordinated. The criterion of channel allocation is based on the probability that a Line-of-Sight (LoS) link will exist between the vehicle and the UAV/RSU. The UAV serves the car if the LoS probability is high; otherwise, the RSU offers the service. Signals are successfully decoded to reduce interference if their Signal-to-Interference-plus-Noise-Ratio (SINR) is higher than a set SINR threshold. In contrast, other signals are subject to a back-off timer. We show through simulations that including UAVs is more effective and provides a reliable communication system in VANETs. By implementing a probabilistic access mechanism, our coordinated vehicular network efficiently lessens the stress on RSUs while on the load to UAVs, especially in locations with high traffic density. The LoS connectivity and optimized channel allocation offer better quality of service through RSU and UAVs in this cooperative environment.

Emerging as the effective strategy of intelligent transportation system (ITS), vehicular ad hoc networks (VANETs) have the capacity of drastically improving the driving experience and road safety. In typical VANET scenarios, high mobility and volatility of vehicles result in dynamic topology of vehicular networks. That is, individual vehicle may pass through the effective domain of multiple neighboring road-side-units (RSUs) during a comparatively short time interval. Hence, efficient and low-latency cross-domain verification with all the successive RSUs is of significance. Recently, a lot of research on VANET authentication and key distribution was presented, while the critical cross-domain authentication (CDA) issue has not been properly addressed. Particularly, the existing CDA solutions mainly reply on the acquired confidential keying information from the neighboring entities (RSUs and vehicles), while too much trustworthiness is granted to the involved RSUs. Please note that the RSUs are distributively located and may be compromised or disabled by adversary, thus vital vehicle information may be revealed. Furthermore, frequent data interactions between RSUs and cloud server are always the major requisite so as to achieve mutual authentication with cross-domain vehicles, which leads to heavy bandwidth consumption and high latency. In this paper, we address the above VANET cross-domain authentication issue under the novel RSU edge networks assumption. Please note that RSUs are assumed to be semi-trustworthy entity in our design, where critical vehicular keying messages remain secrecy. Homomorphic encryption design is applied for all involved RSUs and vehicles. In this way, successive RSUs could efficiently verify the cross-domain vehicle with the transited certificate from the neighbor RSUs and vehicle itself, while the identity and secrets of each vehicle is hidden all the time. Afterwards, dynamic updating towards the anonymous vehicle identity is conducted upon validation, where conditional privacy preserving is available. Moreover, pairing-free mutual authentication method is used for efficiency consideration. Formal security analysis is given, proving that the HCDA mechanism yields desirable security properties on VANET cross domain authentication issue. Performance discussions demonstrate efficiency of the proposed HCDA scheme compared with the state-of-the-art.

Vehicular ad hoc networks have played a key role in intelligent transportation systems that considerably improve road safety and management. This new technology allows vehicles to communicate and share road information. However, malicious users may inject false emergency alerts into vehicular ad hoc networks, preventing nodes from accessing accurate road information. In order to assure the reliability and trustworthiness of information through the networks, assessing the credibility of nodes has become a critical task in vehicular ad hoc networks. A new scheme for malicious node detection is proposed in this work. Multiple factors are fed into a fuzzy logic model for evaluating the trust for each node. Vehicles are divided into clusters in our approach, and a road side unit manages each cluster. The road side unit assesses the credibility of nodes before accessing vehicular ad hoc networks. The road side unit evicts a malicious node based on trust value. Simulations are used to validate our technique. We demonstrate that our scheme can detect and evict all malicious nodes in the vehicular ad hoc network over time, lowering the ratio of malicious nodes. Furthermore, it has a positive impact on selfish node participation. The scheme increases the success rate of delivered data to the same level as the ideal cases when no selfish node is present.

With the development of the vehicular communications technology, roadside units (RSUs) placement problem has recently received considerable attentions in vehicular ad hoc networks (VANETs). This paper addresses the roadside units (RSUs) placement problem for traffic flow coverage requirement number in vehicular ad hoc networks (VANETs). Given a set of road segments, some traffic flows and a coverage requirement number n, the problem is how to find the optimal placement of RSUs, such that each traffic flow is covered by at least n RSUs. We first investigate full RSUs placement problem, which place all the RSUs on the given road segments totally. We prove that this problem is NP-hard. Then, we propose a greedy algorithm to solve the problem and theoretical analysis is given. We also study incremental RSUs placement problem, which place the partial RSUs based on the deployed RSUs on the given road segments. Based on the proposed greedy algorithm, we also propose a algorithm to solve the incremental RSUs placement problem. Extensive simulations have been conducted to show the performance of our proposed algorithms is superior to the other algorithms.

Vehicular ad hoc network (VANET) has attracted more attention for providing safe and efficient traffic management in the modern roads. Performance of the VANET depends on proper routing protocol, mobility management, and efficient inter-vehicular communications. To ensure the optimal data forwarding and energy efficiency in VANET, a suitable network hierarchy is required. In this paper, we propose a new optical network-based clustering hierarchy (ONCH) to improve the quality of services of the VANET. In the proposed scheme, the road side units (RSUs) of a VANET will be divided into several small groups called clusters. In each cluster, an RSU will act as gateway node and each gateway node will be connected to the WiMAX base station through optical fiber. We have analyzed the performance of the proposed ONCH scheme by numerical simulations. The simulated results prove that the ONCH scheme of VANET provides higher energy efficiency than the conventional wireless network-based clustering hierarchy (WNCH) scheme of VANET.

Vehicular ad-hoc network (VANET) technology enables communication between vehicles, or vehicles and road-side units (RSUs) through wireless communication devices installed on the vehicles. One of the most important goals of VANETs is providing safety applications for passengers. In addition, VANETs provide comfort applications to users. Guaranteeing a reliable and stable routing protocol over VANETs is a very important step. The proposed research attempts to improve routing protocols that decrease the end-to-end delay to suit VANET communication characteristics. In addition, it proposes analysis of the end-to-end delay probability distribution. More specifically, we derive a closed-form expression for the probability distribution of the re-healing delay in a VANET conditioned on the distance between two VANET clusters. Furthermore, we propose a closed-form expression for the probability distribution of the unconditional re-healing delay. Moreover, we develop a mathematical model to calculate the probability distribution of the end-to-end delay. On the other hand, using Unmanned Aerial Vehicles (UAVs) or drones in wireless communications and Vehicular Ad-hoc Networks (VANETs) has started to attract attention. We propose a routing protocol that uses infrastructure drones for boosting VANET communications to achieve a minimum vehicle-to-drone packet delivery delay. In addition, we propose a closed-form expression for the probability distribution of the vehicle-to-drone packet delivery delay on a two-way highway. Moreover, based on that closed-form expression, we can calculate the minimum drone density (maximum separation distance between two adjacent drones) that stochastically limits the worst case of the vehicle-to-drone packet delivery delay. Furthermore, we propose a drones-active service (DAS) that is added to the location service in a VANET. This service dynamically and periodically obtains the required number of active drones based on the current highway connectivity state by obtaining the maximum distance between each two adjacent drones while satisfying a probabilistic constraint for vehicle-todrone packet delivery delay. Our analysis focuses on two-way highway VANET networks with low vehicular density. The simulation results show the accuracy of our analysis and reflect the relation between the drone density, vehicular density and speed, other VANET parameters, and the vehicle-to-drone packet delivery delay. In addition, we propose a new routing protocol called multi-copy intersection-based routing (MCIR) for vehicular ad-hoc networks (VANETs) in urban areas. MCIR is an intersectionbased routing protocol that forwards multiple copies of the packets in different road segments. Moreover, it is a beacon-less routing protocol with a carry-and-forward strategy. We show via simulation that the MCIR protocol is superior to other existing routing protocols, especially in low vehicular density scenarios. The results show that MCIR achieves a shorter end-to-end delay and a higher packet delivery ratio in urban VANET communications.

We discuss the problem of predicting the number of available parking spaces in a parking lot. The parking lot is modeled by a continuous-time Markov chain, following Caliskan, Barthels, Scheuermann, and Mauve. The parking lot regularly communicates the number of occupied spaces, capacity, arrival and parking rate through a vehicular ad-hoc network. The navigation system in the vehicle has to compute from this data the probability of an available parking space upon arrival. We derive a structural result that considerably simplifies the computation of the transition probabilities in the navigation system of the vehicle.