Enhancing Localization Efficiency and Accuracy in Wireless Sensor Networks

Abstract

Accuracy is the vital indicator in location estimation used in many scenarios, such as warehousing, tracking, monitoring, security surveillance, etc., in a wireless sensor network (WSN). The conventional range-free DV-Hop algorithm uses hop distance to estimate sensor node positions but has limitations in terms of accuracy. To address the issues of low accuracy and high energy consumption of DV-Hop-based localization in static WSNs, this paper proposes an enhanced DV-Hop algorithm for efficient and accurate localization with reduced energy consumption. The proposed method consists of three steps: first, the single-hop distance is corrected using the RSSI value for a specific radius; second, the average hop distance between unknown nodes and anchors is modified based on the difference between actual and estimated distances; and finally, the least-squares approach is used to estimate the location of each unknown node. The proposed algorithm, named Hop-correction and energy-efficient DV-Hop (HCEDV-Hop), is executed and evaluated in MATLAB to compare its performance with benchmark schemes. The results show that HCEDV-Hop improves localization accuracy by an average of 81.36%, 77.99%, 39.72%, and 9.96% compared to basic DV-Hop, WCL, improved DV-maxHop, and improved DV-Hop, respectively. In terms of message communication, the proposed algorithm reduces energy usage by 28% compared to DV-Hop and 17% compared to WCL.

Processing‐in‐memory based multilateration localization in wireless sensor networks using memristor crossbar arrays

“…We are aware that a lower RMSE score corresponds to greater accuracy 25 . Research indicates that an increase in the number of anchor points (reference nodes) is associated with enhanced localization accuracy 25–27 . Adding more anchor points results in a greater quantity of data points and wider spatial coverage, facilitating the cross‐referencing of data and reducing measurement errors and outliers.…”

“…25 Research indicates that an increase in the number of anchor points (reference nodes) is associated with enhanced localization accuracy. [25][26][27] Adding more anchor points results in a greater quantity of data points and wider spatial coverage, facilitating the cross-referencing of data and reducing measurement errors and outliers. This increased number of anchor points enhances the localization algorithm's resilience to environmental changes.…”

Improvement of amorphous localization algorithm in WSN using ALO and GWO

“…In Figures 10,11, and 12, the minimum, average, and maximum execution times of Amorphous-ALO and Amorphous-GWO are shown. From these figures, it can be observed that Amorphous-ALO is taking more time than Amorphous-GWO.…”

“…These dumb nodes don't know their position; to know it, they have to rely on anchors. The position identification of dumb nodes is essential for different position‐based applications 9–11 …”

Performance of Differential Evolution Algorithms for Indoor Area Positioning in Wireless Sensor Networks

“…This algorithm exhibits excellent performance in reducing localization errors, and because of its low sensitivity to the number of nodes, the CMWN-DV-Hop (a = 10) algorithm can be applied to largescale wireless sensor networks [27]. Moreover, Fawad, M. et al proposed an improved DV-Hop algorithm, named the Hop-correction and energy-efficient DV-Hop (HCEDV-Hop) algorithm, to achieve efficient and accurate localization while reducing energy consumption [28]. To obtain accurate localization results, it is necessary to manage the topology structure within the DV-Hop algorithm effectively.…”