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Efficient and economical renewable energy system adapted to the Algerian geothermal context

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.