TY - JOUR
T1 - Green Computing in Sensors-Enabled Internet of Things
T2 - Neuro Fuzzy Logic-Based Load Balancing
AU - Kashyap, Pankaj Kumar
AU - Kumar, Sushil
AU - Dohare, Upasana
AU - Kumar, Vinod
AU - Kharel, Rupak
N1 - Funding Information:
Funding: The research was supported by Manchester University, New Delhi, India, under the grant UPE-II.
Funding Information:
Acknowledgments: The research was supported by Manchester Metropolitan University, UK and Jawaharlal Nehru University, New Delhi, India, under the grant UPE-II.
Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2019/4/1
Y1 - 2019/4/1
N2 - 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.
AB - 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.
KW - Back-propagation learning
KW - Clustering
KW - Fuzzy logic
KW - Load balancing
KW - Neural network
KW - Supervised learning
UR - http://www.scopus.com/inward/record.url?scp=85064659735&partnerID=8YFLogxK
U2 - 10.3390/electronics8040384
DO - 10.3390/electronics8040384
M3 - Article
AN - SCOPUS:85064659735
VL - 8
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
SN - 0039-0895
IS - 4
M1 - 384
ER -
