Abstract
Wireless sensor networks (WSNs) are the cornerstone of the current Internet of Things era. They have limited resources and features, a smaller packet size than other types of networks, and dynamic multi-hop transmission. WSNs can monitor a particular area of interest and are used in many different applications. For example, during the COVID-19 pandemic, WSNs have been used to measure social distancing/contact tracing among people. However, the major challenge faced by WSN protocols is limited battery energy. Therefore, the whole WSN area is divided into odd clusters using k-means++ clustering to make a majority rule decision to reduce the amount of additional data sent to the base station (or sink) and achieve node energy-saving efficiency. This study proposes an energy-efficient binarized data aggregation (EEBDA) scheme, by which, through a threshold value, the collected sensing data are asserted with binary values. Subsequently, the corresponding cluster head (CH), according to the Hamming weight and the final majority decision, is calculated and sent to the base station (BS). The EEBDA is based on each cluster and divides the entire WSN area into four quadrants. All CHs construct a data-relay transmission link in the same quadrant; the binary value is transferred from the CHs to the sink. The EEBDA adopts a CH rotation scheme to aggregate the data based on the majority results in the cluster. The simulation results demonstrate that the EEBDA can reduce redundant data transmissions, average the energy consumption of nodes in the cluster, and obtain a better network lifetime when compared to the LEACH, LEACH-C, and DEEC algorithms.
Highlights
Accepted: 7 September 2021In recent years, wireless sensor networks (WSNs) have emerged as a topic of interest to most scholars because of the increasingly mature and advanced technology of microelectro-mechanical systems (MEMS) and communication batteries, as well as improved communication technology and related application software [1,2]
The simulation results demonstrate that the efficient binarized data aggregation (EEBDA) can reduce redundant data transmissions, average the energy consumption of nodes in the cluster, and obtain a better network lifetime when compared to the Low-energy adaptive clustering hierarchy (LEACH), LEACH-C, and distributed energy-efficient clustering (DEEC)
According to the aforementioned simulated results, the evidence of spatial correlation chain-clustering in the EEBDA binarized data aggregation mechanism is superior to LEACH, LEACH-C, and DEEC protocols in terms of the residual energy of sensor nodes and the network lifetime
Summary
Wireless sensor networks (WSNs) have emerged as a topic of interest to most scholars because of the increasingly mature and advanced technology of microelectro-mechanical systems (MEMS) and communication batteries, as well as improved communication technology and related application software [1,2]. In addition to reducing the redundant transmission of data, the reporting nodes can save energy and extend the lifetime of the entire network [5]. The calculation method for defining the k value of k-means++ to ensure that the distance between nodes in the cluster is less than the threshold value between transmission and reception in the first-order radio model and provides further energy-saving efficiency and extends the overall network lifetime. The CH transmits the majority binary value to the hop until it reaches the BS by the chain This is a proven method of reducing CH energy consumption. The remainder of this paper is organized as follows: Section 2 describes related research examining the energy consumption, energy efficiency, cluster head selection, and cluster formation in a WSN used, and applying the Hamming weight to count the number of non-zero bits to obtain the majority result.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
