Research Area:  Internet of Things
Wireless sensor network (WSN) technology is considered to be an integral part of large-scale and efficient deployment of Internet-of-Things (IoT). More specifically, in mission-critical IoT applications, trust in the sensor data is becoming increasingly important. Sensor nodes have limited processing, storage, and communication capabilities, which make them susceptible to attacks and unreliable functioning. However, the limitations in the energy resources of the sensors are a major challenge in maximizing the network’s lifetime. Grouping the sensors into clusters was proposed to address such energy limitations. Many meta-heuristic clustering protocols have been proposed to maximize the network lifetime, which is an NP-hard problem. This problem is more complicated when considering the trust factor. The majority of existing clustering models were built to reduce the energy consumption in the network without considering the energy consumption required to detect untrusted nodes, and thus, it requires extra energy consumption to perform this task. This article proposes a clustering protocol with a trust model that detects the untrusted nodes through energy and data-trust. In addition, the proposed clustering protocol maximizes the network’s lifetime through the good characteristics of stochastic fractal search optimization. Finally, a novel fitness function is introduced to select the cluster-heads among the trusted nodes. The function is based on the following four parameters: 1) the remaining energy of the nodes; 2) the density of the nodes; 3) the distance between each node and the base-station; and 4) the network’s dissipated energy. When forming the clusters, the density of the cluster-heads is considered to balance the load of all of the cluster-heads. The experimental evaluation performed here affirms the efficacy of the proposed protocol in comparison with existing protocols.
Author(s) Name:  Safaa Hriez; Sufyan Almajali; Hany Elgala; Moussa Ayyash; Haythem Bany Salameh
Journal name:  IEEE Systems Journal ( Early Access )
Publisher name:  IEEE
Volume Information:  Page(s): 1 - 12
Paper Link:   https://ieeexplore.ieee.org/abstract/document/9400512