This paper presents a dynamic transmission power adjustment technique to enhance energy efficiency and extend the operational lifespan of mobile devices in fog computing environments. By monitoring the locations of surrounding access points and their own locations, mobile devices adapt their transmission power prior to task offloading. The technique employs four transmission power levels: 3.6mW (default) for access points beyond 265m, 2.7mW for distances between 210m and 265m, 1.8mW for distances between 150m and 210m, and 0.9m W for distances less than or equal to 150m. Two key metrics are evaluated: the consumed energy of mobile devices (in Joules) and the remaining operational devices over time. Additionally, the number of offloaded tasks for each transmission power level is analyzed. Simulation results demonstrate the effectiveness of dynamic transmission power adjustment in improving energy efficiency, prolonging device lifespan, and optimizing task offloading in fog computing environments. These findings contribute to the advancement of energy-efficient mobile computing and provide valuable insights for future optimizations.

Edge computing (EC) serves as a promising complement to cloud computing, yet the challenge of task offloading decision-making persists. While task offloading can extend device lifespan, it may introduce delays that surpass acceptable application thresholds. To address this challenge, we propose a machine learning (ML) technique that leverages key factors such as network latency, battery level, and device location to inform task offloading decisions. This novel ML technique, which is exclusively presented in this paper, holds potential for enhancing task offloading decision-making. Furthermore, the paper discusses future work, including testing and validating the proposed technique, as well as conducting an investigation into the impact of varying the number of mobile users within an edgeenabled environment.

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.

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.

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.

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 view.

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 are 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.

This paper presents a comprehensive survey of existing cyber security solutions for fog-based smart grid SCADA systems. We start by providing an overview of the architecture and the concept of fog-based smart grid SCADA systems and its main components. According to security requirements and vulnerabilities, we provide a classification of these solutions into four categories, including authentication solutions, privacy-preserving solutions, key management systems, and intrusion detection systems. For each category, we describe the essence of the methods and provide a classification with respect to security requirements. Therefore, according to the machine learning methods used by the intrusion detection system (IDS), we classify the IDS solutions into nine categories, including deep learning-based IDS, artificial neural networks-based IDS, support vector machine-based IDS, decision tree-based IDS, rule-based IDS, Bloom filter-based IDS, random forest-based IDS, random subspace learning-based IDS, and deterministic finite automaton-based IDS. The informal and formal security analysis techniques used by the cyber security solutions are tabulated and summarized. In addition, we provide a taxonomy of attacks tackled by privacy-preserving and authentication solutions in the form of tables. Based on the present study, several proposals for challenges and research issues such as detecting false data injection attacks are discussed at the end of the paper.

Internet of Vehicles (IoV) had remarkably enhanced the road -safety. Thanks to the environment sensing feature provided by the embedded devices and sensors. Nevertheless, this benignant feature had also introduced privacy issues; as vehicles spread their fine-grained locations at the aim of fulfilling safety requirements, adversaries can use this latter to track and identify the IoV users. Different privacy schemes and techniques are designed and evaluated like the mix-zone, encryption, groups forming, and silent-period based techniques. However, the majority do suffer from serious limitations inherited from the technique itself. In this paper, we propose a safety-friendly location privacy-preserving scheme, WHISPER, that adjusts the transmission range of vehicles on-the-fly in order to, occasionally, escape the continuous location tracking. We detail the protocols used by WHISPER, then we compare it against other privacy- preserving schemes using different metrics. The results show that WHISPER outperformed the other schemes after giving better location privacy levels while still keeping the road-safety fulfilled.