Abstract
Wireless sensor networks (WSN) empower applications for critical decision-making through collaborative computing, communications, and distributed sensing. However, they face several challenges due to their peculiar use in a wide variety of applications. One of the inherent challenges with any battery operated sensor is the efficient consumption of energy and its effect on network lifetime. In this paper, we introduce a novel grid-based hybrid network deployment (GHND) framework which ensures energy efficiency and load balancing in wireless sensor networks. This research is particularly focused on the merge and split technique to achieve even distribution of sensor nodes across the grid. Low density neighboring zones are merged together whereas high density zones are strategically split to achieve optimum balance. Extensive simulations reveal that the proposed method outperforms state-of-the-art techniques in terms of load balancing, network lifetime, and total energy consumption.
Highlights
Wireless sensor network with its sole purpose of data collection, processing, and communicating to other nodes in the network is extensively used for diverse sets of applications such as surveillance, weather forecasting, forest fire detection, smart homes, and health care and other biomedical applications
To start with the simulation setup, we have made few assumptions such as the following: (1) all the sensor nodes and base station (BS) are static after deployment; (2) BS is located outside the field boundary and is known to every node in the network; (3) sensor nodes have the information of their location and initial energy; (4) nodes already have their unique IDs
We have proposed a grid-based hybrid network deployment framework for load balancing to ensure energy efficiency in Wireless sensor networks (WSN)
Summary
Wireless sensor network with its sole purpose of data collection, processing, and communicating to other nodes in the network is extensively used for diverse sets of applications such as surveillance, weather forecasting, forest fire detection, smart homes, and health care and other biomedical applications. The decision of selecting cluster head (CH) per grid is usually done by the nodes themselves which makes it suitable for large scale networks. Different energy efficient cluster-based and grid-based algorithms have been proposed such as LEACH [8], PEGASIS [9], CBDAS [10], and GBDD [11] but still load balancing and energy efficiency are open issues because of the randomized nature of WSN. The iterative process of cluster formation and CH reselection requires transmitting continuous control messages which results in extensive energy consumption of the nodes and leads to poor performance of the network. This paper focuses on a technique that can ensure load balancing and intelligent selection and reselection of zone head (ZH) to maximize network lifetime.
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