The Impact of the Adversary's Eavesdropping Stations on the Location Privacy Level in Internet of Vehicles

The Internet of Vehicles (IoV) has got the interest of different research bodies as a promising technology. IoV is mainly developed to reduce the number of crashes by enabling vehicles to sense the environment and spread their locations to the neighborhood via safety-beacons to enhance the system functioning. Nevertheless, a bunch of security and privacy threats is looming; by exploiting the spatio-data included in these beacons. A lot of privacy schemes were developed to cope with the problem like CAPS, CPN, RSP, and SLOW. The schemes provide a certain level of location privacy yet the strength of the adversary, e.g., the number of eavesdropping stations has not been fully considered. In this paper, we aim at investigating the effect of the adversary’s eavesdropping stations number and position on the overall system functioning via privacy and QoS metrics. We also show the performances of these schemes in a manhattan-grid model which gives a comparison between the used schemes. The results show that both the number and the emplacement of the eavesdropping stations have a real negative impact on the achieved location privacy of the IoV users

A Safety-Aware Location Privacy-Preserving IoV Scheme with Road Congestion-Estimation in Mobile Edge Computing

By leveraging the conventional Vehicular Ad-hoc Networks (VANETs), the Internet of Vehicles (IoV) paradigm has attracted the attention of different research and development bodies. However, IoV deployment is still at stake as many security and privacy issues are looming; location tracking using overheard safety messages is a good example of such issues. In the context of location privacy, many schemes have been deployed to mitigate the adversary’s exploiting abilities. The most appealing schemes are those using the silent period feature, since they provide an acceptable level of privacy. Unfortunately, the cost of silent periods in most schemes is the trade-off between privacy and safety, as these schemes do not consider the timing of silent periods from the perspective of safety. In this paper, and by exploiting the nature of public transport and role vehicles (overseers), we propose a novel location privacy scheme, called OVR, that uses the silent period feature by letting the overseers ensure safety and allowing other vehicles to enter into silence mode, thus enhancing their location privacy. This scheme is inspired by the well-known war strategy “Give up a Pawn to Save a Chariot”. Additionally, the scheme does support road congestion estimation in real time by enabling the estimation locally on their On-Board Units that act as mobile edge servers and deliver these data to a static edge server that is implemented at the cell tower or road-side unit level, which boosts the connectivity and reduces network latencies. When OVR is compared with other schemes in urban and highway models, the overall results show its beneficial use

WHISPER: A Location Privacy-Preserving Scheme Using Transmission Range Changing for Internet of Vehicles

Internet of Vehicles (IoV) has the potential to enhance road-safety with environment sensing features provided by embedded devices and sensors. This benignant feature also raises privacy issues as vehicles announce their fine-grained whereabouts mainly for safety requirements, adversaries can leverage this to track and identify users. Various privacy-preserving schemes have been designed and evaluated, for example, mix-zone, encryption, group forming, and silent-period-based techniques. However, they all suffer inherent limitations. In this paper, we review these limitations and propose WHISPER, a safety-aware location privacy-preserving scheme that adjusts the transmission range of vehicles in order to prevent continuous location monitoring. We detail the set of protocols used by WHISPER, then we compare it against other privacy-preserving schemes. The results show that WHISPER outperformed the other schemes by providing better location privacy levels while still fulfilling road-safety requirements

Between Location Protection and Overthrowing: A Contrariness Framework Study for Smart Vehicles

The file attached to this record is the author's final peer reviewed version.Internet of Vehicles (IoV) capabilities can be used to decrease the number of accidents by sharing information among entities like the location of the Smart Cars (SC). This information is not encrypted due to several real-time communications requirements. Many methods were proposed by the literature to withhold the attacker from exploiting such a privacy gap, affecting negatively at the same time other application layers like safety, comfort, and road-congestion. In this article, we provide a holistic overview of the effects of existing techniques on both privacy and other application layers both from the attacker and the defender point of vie

SAMA: Security-Aware Monitoring Approach for Location Abusing and UAV GPS-Spoofing attacks on Internet of Vehicles

The quick revolution on the wireless communication technologies had opened the gate towards promising implementations; Vehicular-Ad-hoc Networks (VANETs) and the safety-enhancing applications provided by the Internet of Vehicles (IoV) paradigm are one of them. By periodically broadcasting safety-beacons, vehicles can ensure a better safety-driving experience since beacons contain fine-grained location that is sent to the neighborhood. Nevertheless, some attacks basing on falsify or encrypt location-related data are threatening the road-safety considerably. In this paper, and by assuming a GPS-spoofing attack originated from Unmanned-Aircraft-Vehicles (UAV) system, we provide a Security-Aware Monitoring Approach (SAMA) that protects vehicles against such location abusing by allowing the Law-Side Authority (LSA) to monitor the potential malicious or tricked vehicles. SAMA is Implemented using the triangulation concept via Received-Signal-Strength-Indicator (RSSI) in conjunction with C++ map and multimap data-structures. The performances of SAMA are evaluated in terms of location-estimation precision and beacons collection per type

APOLLO: A Proximity-Oriented, Low-Layer Orchestration Algorithm for Resources Optimization in Mist Computing

The fusion of satellite technologies with the Internet of Things (IoT) has propelled the evolution of mobile computing, ushering in novel communication paradigms and data management strategies. Within this landscape, the efficient management of computationally intensive tasks in satellite-enabled mist computing environments emerges as a critical challenge. These tasks, spanning from optimizing satellite communication to facilitating blockchain-based IoT processes, necessitate substantial computational resources and timely execution. To address this challenge, we introduce APOLLO, a novel low-layer orchestration algorithm explicitly tailored for satellite mist computing environments. APOLLO leverages proximity-driven decision-making and load balancing to optimize task deployment and performance. We assess APOLLO’s efficacy across various configurations of mist layer devices while employing a round-robin principle for equitable task distribution among the close low-layer satellites. Our findings underscore APOLLO’s promising outcomes in terms of reduced energy consumption, minimized end-to-end delay, and optimized network resource utilization, particularly in targeted scenarios. However, the evaluation also reveals avenues for refinement, notably in CPU utilization and slightly low tasks success rates. Our work contributes substantial insights into advancing task orchestration in satellite-enabled mist computing with more focus on energy and end-to-end sensitive applications, paving the way for more efficient, reliable, and sustainable satellite communication systems