An Efficient Anchor-Free Localization Algorithm for all Cluster Topologies in a Wireless Sensor Network

Abstract

A wireless sensor network is a type of network, which has many application domains such as military, civil security, industrial and environmental. Localization of fixed or mobile wireless sensors in wireless sensor networks is a delicate issue that has attracted the attention of many researchers. Indeed, a good estimation of the distances between different wireless sensors allows to derive their precise locations in the network. An ideal solution for locating these wireless sensors is to equip them with localization devices such as GPS. However, this solution is not an energy-efficient. Indeed, GPS is very energy-consuming, and the deployment environment is not easily accessible to humans. So, it is not possible to replace the batteries of these wireless sensors when they are discharged. Therefore, it is necessary to propose an energy-saving anchor-free localization algorithm. Despite the grouping of nodes into clusters (or sub-networks), existing anchor-free localization algorithms suffer from a low rate of node localization, low localization accuracy, and high energy consumption. To improve the proposed solutions in the literature, an Efficient Anchor Free Localization Algorithm (EAFLA) was proposed. Regardless the topology of each cluster, our algorithm allows localization of all wireless sensors with a very low localization error rate and consumes less energy.

An Accurate Anchor-Free Contextual Received Signal Strength Approach Localization in a Wireless Sensor Network

“…The final step was the global localization phase. The most recent work in [ 13 ] presented an efficient anchor-free algorithm (EAFLA), which grouped nodes into one-hop clusters and estimated distances between nodes within each cluster using RSS. For nodes not within one hop, the Kleincrock model [ 14 ] and Al-Kashi’s theorem (cosine law) were used to derive the positions of sensors in the network.…”

“…After proving the competence of the CRSSA in different and varying channel parameters and numbers of nodes, we evaluate its performance by comparing the NRMSE against state-of-the-art anchor-free localization techniques proposed in recent studies, namely, Shah et al [ 21 ], SFO [ 17 ], and EAFLA [ 13 ]. Shah et al employed a time division multiple access (TDMA) scheduling algorithm, where each node broadcasts a beacon message containing its unique node ID.…”

“…The EAFLA proposed in [ 13 ] begins by electing cluster heads and forming clusters according to the low-energy adaptive cluster hierarchical (LEACH) protocol. Distances between nodes are then estimated using a combination of the RSS signal transmission model and the Kleinrock and Sylvester model [ 14 ].…”

“…Estimate the inter-node distance between neighbor nodes i and k, dik ← using 13 Estimate the positions of nodes ( xi , ŷi ) using gradient-based optimization method with weighted residuals.…”

Unsupervised Learning-Based Exploration of Urban Rail Transit Passenger Flow and Travel Pattern Mining

“…This versatility not only paves the way for innovative applications but also poses significant implications for future technological advancements. By enabling the extraction of deep insights from vast, unstructured datasets, unsupervised learning methodologies are reshaping the way we analyze data and solve complex problems in various domains [19,20,21].…”