Abstract
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.
Highlights
Sensors enabled Internet of Things (IoT) networks have been regarded as reasonable data collection and control applications over various network communication infrastructures through smart sensors called IoT nodes [1,2,3]
Sensors-enabled IoT networks are comprised of various smart sensor nodes (RFID enabled) that assemble facts from the encompassing conditions and convey the data to the static base station (BS) or overload the data to cloud applications where users download the data for processing [4,5,6]
We propose a novel adaptive neuro-fuzzy clustering algorithm (ANFCA) using both fuzzy logic and a neural network to address the problem of leaning rate of membership function, balancing the load, and minimizing the energy consumption to improve the lifetime of the sensor-enabled IoT
Summary
Sensors enabled Internet of Things (IoT) networks have been regarded as reasonable data collection and control applications over various network communication infrastructures through smart sensors called IoT nodes [1,2,3]. The neural network itself maps the weight functions or membership functions according to the problem up to an acceptable error rate of the system, which makes the system more efficient in terms of performance It leads to the idea of augmenting the learning algorithm and generalization capability into the fuzzy system. A hybrid system based on two different soft-computing techniques—adaptive neural networks and fuzzy inference systems—is proposed to optimize the number of cluster heads that evenly distribute the load among sensors in a network. We refer to this hybrid system as an adaptive neuro fuzzy clustering algorithm (ANFCA).
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