Abstract
Energy efficiency is one of the main challenges in developing Wireless Sensor Networks (WSNs). Since communication has the largest share in energy consumption, efficient routing is an effective solution to this problem. Hierarchical clustering algorithms are a common approach to routing. This technique splits nodes into groups in order to avoid long-range communication which is delegated to the cluster head (CH). In this paper, we present a new clustering algorithm that selects CHs using the grey wolf optimizer (GWO). GWO is a recent swarm intelligence algorithm based on the behavior of grey wolves that shows impressive characteristics and competitive results. To select CHs, the solutions are rated based on the predicted energy consumption and current residual energy of each node. In order to improve energy efficiency, the proposed protocol uses the same clustering in multiple consecutive rounds. This allows the protocol to save the energy that would be required to reform the clustering. We also present a new dual-hop routing algorithm for CHs that are far from the base station and prove that the presented method ensures minimum and most balanced energy consumption while remaining nodes use single-hop communication. The performance of the protocol is evaluated in several different scenarios and it is shown that the proposed protocol improves network lifetime in comparison to a number of recent similar protocols.
Highlights
Wireless sensor networks (WSNs) are emerging low-cost and versatile solutions that enable controlled monitoring of the environment
We propose a novel solution to this optimization problem using the grey wolf optimizer and a suitable way to present the clustering problem as an optimization problem
Network lifetime is represented as the round at which the first node dies (FND), the round at which half of the nodes are dead (HND) and the last round (LND)
Summary
Wireless sensor networks (WSNs) are emerging low-cost and versatile solutions that enable controlled monitoring of the environment. Instead of relying on the inter-cluster and intra-cluster distances to approximate energy consumption and assign fitness value to the solution, we introduce a single term fitness function that is directly related to the energy consumed by the network This has two advantages: i) assigning fitness values to solutions more accurately, and ii) eliminating the need to weight several terms in the fitness function which leads to fewer parameters for the protocol. The proposed protocol enables the BS to approximate how many rounds each node can operate using the current clustering and use this information to skip cluster setup phase in some of the rounds This leads to a significant reduction in energy consumption due to skipping the exchange of control packets. 3) Proposing a dual hop routing method that ensures both minimum and most balanced energy consumption for any CH and its relay node
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