Green Computing in Sensors-Enabled Internet of Things: Neuro Fuzzy Logic-Based Load Balancing

Electronics

Vinod Kumar¹  Upasana Dohare²  Sushil Kumar²  Pankaj Kumar Kashyap²  Rupak Kharel³ 

[1] Department of Computer Engineering, Delhi Technological University (DTU), New Delhi 110042, India;School of Computer and Systems Sciences, Jawaharlal Nehru University, New Delhi 110067, India;School of Engineering, Manchester Metropolitan University, Manchester M1 5GD, UK;

关键词: fuzzy logic;    neural network;    load balancing;    supervised learning;    back-propagation learning;    clustering;   

DOI  :  10.3390/electronics8040384

来源: DOAJ

【 摘 要 】

Energy is a precious resource in the sensors-enabled Internet of Things (IoT). Unequal load on sensors deplete their energy quickly, which may interrupt the operations in the network. Further, a single artificial intelligence technique is not enough to solve the problem of load balancing and minimize energy consumption, because of the integration of ubiquitous smart-sensors-enabled IoT. In this paper, we present an adaptive neuro fuzzy clustering algorithm (ANFCA) to balance the load evenly among sensors. We synthesized fuzzy logic and a neural network to counterbalance the selection of the optimal number of cluster heads and even distribution of load among the sensors. We developed fuzzy rules, sets, and membership functions of an adaptive neuro fuzzy inference system to decide whether a sensor can play the role of a cluster head based on the parameters of residual energy, node distance to the base station, and node density. The proposed ANFCA outperformed the state-of-the-art algorithms in terms of node death rate percentage, number of remaining functioning nodes, average energy consumption, and standard deviation of residual energy.

【 授权许可】

Unknown