Trust-aware energy-efficient stable clustering approach using fuzzy type-2 Cuckoo search optimization algorithm for wire
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Trust-aware energy-efficient stable clustering approach using fuzzy type-2 Cuckoo search optimization algorithm for wireless sensor networks Nitin Mittal1
•
Simrandeep Singh1 • Urvinder Singh2 • Rohit Salgotra2
Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract With the advancement of communication and sensor technologies, it has become possible to develop low-cost circuitry to sense and transmit the state of surroundings. Wireless networks of such circuitry, namely wireless sensor networks (WSNs), can be used in a multitude of applications like healthcare, intelligent sectors, environmental sensing, and military defense. The crucial problem of WSN is the reliable exchange of data between different sensors and efficient communication with the data collection center. Clustering is the most appropriate approach to prolong the performance parameters of WSN. To overcome the limitations in clustering algorithms such as reduced cluster head (CH) lifetime; an effective CH selection algorithm, optimized routing protocol, and trust management are required to design an effective WSN solution. In this paper, a Cuckoo search optimization algorithm using a fuzzy type-2 logic-based clustering strategy is suggested to extend the level of confidence and hence network lifespan. In intra-cluster communication, a threshold-based data transmission algorithm is used and a multi-hop routing scheme for inter-cluster communication is employed to decrease dissipated energy from CHs far away from BS. Simulation outcomes indicate that the proposed strategy outperforms other communication techniques in the context of the successful elimination of malicious nodes along with energy consumption, stability period, and network lifetime. Keywords CS WSN Network lifetime Stability period Fuzzy logic Trust-aware
1 Introduction Advances in sensor technology popularize battery-powered wireless sensor networks (WSNs) in many industrial areas including vehicle traffic monitoring, smart factories, IoT, and public safety networks, etc. [1, 2]. WSNs are applied in many fields such as in health-care, environmental sensing, industrial monitoring [3, 4], and vehicle to vehicle communication [5–7]. A WSN is comprised of a base station (BS) and several distributed sensor nodes which, through the sensing of certain physical parameters, communicate
& Nitin Mittal [email protected]; [email protected] 1
Department of Electronics and Communication Engineering, Chandigarh University, Mohali, Punjab 140413, India
2
Department of Electronics and Communication Engineering, Thapar University, Patiala, Punjab 147004, India
with the environment. The BS is tasked with receiving, processing, and providing data to the end-user for decision making [2]. Nodes in WSN rely on their on-board, limited, non-rechargeable, and non-changeable batteries. Additionally, sensor nodes are limited in storage, memory, and CPU processing capabilities [3]. As sensor nodes and BS use wireles
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