Wireless sensor networks (WSNs) are an emerging technology used in many applications in both the civilian and military domains. Typically, these networks are deployed in remote and hostile environments. They are vulnerable to various kinds of security attacks, of which sybil attacks are some of the most harmful. Thus, it is necessary to solve the problems related to sensor node constraints and the need for high WSN security. This paper proposes an energy trust system (ETS) for WSNs to effectively detect sybil attacks. It employs multi-level detection based on identity and position verification. Then, a trust algorithm is applied based on the energy of each sensor node. Data aggregation is also utilized to reduce communication overhead and save energy. We analyze the performance of the proposed system in terms of security and resource consumption using theoretical and simulation-based approaches. The simulation results show that the proposed ETS is effective and robust in detecting sybil attacks in terms of the true and false positive rates. By virtue of the application of multi-level detection, the proposed system achieves more than 70% detection at the first level, which significantly increases to 100% detection at the second level. Furthermore, this system reduces communication overhead, memory overhead, and energy consumption by eliminating the exchange of feedback and recommendation messages among sensor nodes.
Wireless Sensor Network (WSN) is an emerging technology that offers great promise for various applications. The sensing capabilities combined with relatively small processing power and wireless communication makes it one of the main technologies to be exploited in the future. Despite its attractive features, WSN is vulnerable to various security attacks. The constraints of WSN such as limited energy and memory make the security problem even more critical. One of the security issues of WSN is it is susceptible to sybil attack. In this attack, the adversary forges multiple entities to disrupt the entire network. This paper addresses the problem by developing a lightweight trust system using energy as a metric parameter for a hierarchical WSN. The performance evaluation of this system shows efficiency and scalability for detecting sybil attacks in terms of true and false positive detection in a heterogeneous WSN. Furthermore, this system reduces the communication overhead in the network by cancelling feedback and recommendations among sensor nodes (SNs).
The technological advances of smart cities have been progressively increasing to improve the quality of life to humans, especially in urban mobility. Parking appears to be a major issue, with residents needing to find a suitable parking space among many parking areas, resulting in time and fuel waste as well as environmental pollution. We propose in this paper a new automated system model that integrates reinforcement learning (RL), Q-learning, and image processing algorithms based on modified Internet of Spatial Things (IoST) architecture to optimize automated parking in smart cities. For demonstrating the efficiency of the proposed model, iFogSim simulation is used to reduce network usage and latency. Moreover, it deploys heterogeneous devices in multi layers and different scenarios. The experimental results show that the suggested system for automated car parking in fog-based placement-IoST network is feasible and effective. it minimizes latency and the total network usage compared to the cloud-based placement of the implemented system.
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