Abstract

Advances introduced to electronics and electromagnetics leverage the production of low-cost and small wireless sensors. Wireless sensor networks (WSNs) consist of large amount of sensors equipped with radio frequency capabilities. In WSNs, data routing algorithms can be classified based on the network architecture into flat, direct, and hierarchal algorithms. In hierarchal (clustering) protocols, network is divided into sub-networks in which a node acts as a cluster head, while the rest behave as member nodes. It is worth mentioning that the sensor nodes have limited processing, storage, bandwidth, and energy capabilities. Hence, providing energy-efficient clustering protocol is a substantial research subject for many researchers. Among proposed cluster-based protocols, low-energy adaptive clustering hierarchy (LEACH) and threshold LEACH (T-LEACH), as well as modified threshold-based cluster head replacement (MT-CHR) protocols are of a great interest as of being energy optimized. In this article, we propose two protocols to cluster a WSN through taking advantage of the shortcomings of these protocols (i.e., LEACH, T-LEACH, and MT-CHR), namely centralized density- and threshold-based cluster head replacement (C-DTB-CHR) and C-DTB-CHR with adaptive data distribution (C-DTB-CHR-ADD) protocols that mainly aim at optimizing energy through minimizing the number of re-clustering operations, precluding cluster heads nodes premature death, deactivating some nodes located at dense areas from cluster’s participation, as well as reducing long-distance communications. In particular, in C-DTB-CHR protocol, some nodes belong to dense clusters are put in the sleeping mode based on a certain node active probability, thereby reducing the communications with the cluster heads and consequently prolonging the network lifetime. Moreover, the base station is concerned about setting up the required clusters and accordingly informing sensor nodes along with their corresponding active probability. C-DTB-CHR-ADD protocol provides more energy optimization through adaptive data distribution where direct and multi-hoping communications are possible. Interestingly, our simulation results show impressive improvements over what are closely related in the literature in relation to network lifetime, utilization, and network performance degradation period.

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