In Wireless Sensor Networks (WSNs), localization is one of the most important technologies since it plays a critical role in many applications, e.g., target tracking. If the users cannot obtain the accurate location information, the related applications cannot be accomplished. The main idea in most localization methods is that some deployed nodes (landmarks) with known coordinates (e.g., GPS-equipped nodes) transmit beacons with their coordinates in order to help other nodes localize themselves. In general, the main localization algorithms are classified into two categories: range-based and range-free. In this paper, we reclassify the localization algorithms with a new perspective based on the mobility state of landmarks and unknown nodes, and present a detailed analysis of the representative localization algorithms. Moreover, we compare the existing localization algorithms and analyze the future research directions for the localization algorithms in WSNs.

Localization algorithms of Wireless Sensor Networks: a survey

Due to their low cost, various range-free localization techniques are widely applied to estimate device locations in wireless sensor networks (WSNs), especially where other communication signals, such as those from a global positioning system, are absent. Among range-free techniques, the centroid algorithm has gained popularity because of its simplicity and low computational cost, rendering it suitable for power-sensitive sensor nodes or nodes. WSN topologies are unpredictable because sensor nodes are often deployed at arbitrary locations, making it difficult for sufficient numbers of known (anchor) nodes to cover all unknown nodes. Consequently, both centroid algorithm and its enhanced versions [e.g., weighted centroid (WC) algorithms] yield relatively high localization inaccuracy. In this paper, to successfully form anchor-node triangles, we propose a low-cost technique to determine the number of virtual anchor nodes or virtual nodes together with their positions. Specifically, we develop and provide proof for accurate approximate unknown node sides. We also improve localization accuracy by adding virtual nodes that collaborate with physical anchor nodes to provide the necessary coverage of the unknown nodes. We address estimations of unknown node locations by applying a fuzzy-based centroid localization method to prioritize anchor nodes by assigning different fine-tuned weighted factors. The results show that the proposed algorithm outperforms state-of-the-art fuzzy-based localization techniques for WC algorithms.

Fuzzy Weighted Centroid Localization With Virtual Node Approximation in Wireless Sensor Networks

With exponential growth in the deployment of Internet of Things (IoT) devices, many new innovative and real-life applications are being developed. IoT supports such applications with the help of resource-constrained fixed as well as mobile nodes. These nodes can be placed in anything from vehicles to the human body to smart homes to smart factories. Mobility of the nodes enhances the network coverage and connectivity. One of the crucial requirements in IoT systems is the accurate and fast localization of its nodes with high energy efficiency and low cost. The localization process has several challenges. These challenges keep changing depending on the location and movement of nodes such as outdoor, indoor, with or without obstacles and so on. The performance of localization techniques greatly depends on the scenarios and conditions from which the nodes are traversing. Precise localization of nodes is very much required in many unique applications. Although several localization techniques and algorithms are available, there are still many challenges for the precise and efficient localization of the nodes. This paper classifies and discusses various state-of-the-art techniques proposed for IoT node localization in detail. It includes the different approaches such as centralized, distributed, iterative, ranged based, range free, device-based, device-free and their subtypes. Furthermore, the different performance metrics that can be used for localization, comparison of the different techniques, some prominent applications in smart cities and future directions are also covered.

Survey of Localization for Internet of Things Nodes: Approaches, Challenges and Open Issues

Initially, Low Power Wide Area Networks (LPWAN) were designed for communicating small sensor readings in a metropolitan area. With the rapidly increasing number of Internet of Things (IoT) devices, however, there is a growing need to localize these devices with LPWAN. Given the extreme battery lifetime requirements of IoT-enabled devices, we want to avoid satellite-based solutions. We use a publicly available outdoor LoRaWAN data set to evaluate Received Signal Strength fingerprint-based and range-based location estimation algorithms in terms of accuracy and computational performance. On the one hand, a fingerprint-based approach leads to 50% more accurate results when compared to a range-based approach, resulting in mean location estimation errors of 340m and 700m, respectively. On the other hand, in a range-based approach, no training database is required and the system can be deployed instantly in any environment with network coverage. Thus, when choosing a localization algorithm for any IoT application, a trade-off between accuracy, deployment cost and computational performance should be taken into account.

Benchmarking RSS-based localization algorithms with LoRaWAN

A weighted centroid algorithm of no height influence is proposed considering the influence of access point(AP) deployment height on positioning accuracy. Firstly, the received signal strength indication(RSSI) signal are collected and processed by Gaussian fitting and Kalman filtering, and the distance between terminal(such as phone) and AP is obtained according to indoor signal propagation model. Then, the vertical distance is eliminated by geometric method, and the plane distance between the AP and the terminal is obtained. Finally, the position coordinate is calculated by the weighted centroid algorithm of no height influence. For a multi-storey building, a Floor number recognition method is proposed, the two dimensional(2D) planar positioning and the Floor number recognition method are combined to construct a near three dimensional(N-3D) indoor location algorithm. In the laboratory building, the floor number was recognised with the above methods and the recognition rate is 100%. To do positioning experiments in the large classroom, the results showed that: compared to the traditional positioning algorithm, the improved algorithm have a certain increase on the accuracy and stability.

Research on WiFi Indoor Location Algorithm Based on RSSI Ranging

Localization is essential in the field of wireless sensor networks (WSNs). Compared to other non-interactive localization algorithms, the weighted centroid localization scheme only uses the information of the received signal strength (RSS), which makes it simple to implement and robust to variation in the propagation environment. In this paper, we propose an improved weighted centroid localization algorithm based on RSSI for an indoor localization. Real experimental results show that IWCL-RSSI is better than WCL (weighted centroid localization) in terms of the localization accuracy.

http://ieeexplore.ieee.org/iel7/7020226/7029843/07029859.pdf

Weighted centroid localization for wireless sensor networks

Localization algorithm is an important and challenging topic in today's wireless sensor networks (WSNs). In order to improve the localization accuracy, a weighted centroid localization algorithm based on least square to predict the location of any sensor in a WSNs is proposed in this paper. The proposed algorithm proposes a Least-Square-based weight model which can reasonably weigh the proportion of each anchor node in the unknown node. In the weight model, we utilize least square method to compute the weight. Then, we increase the weight of anchor nodes closer to the unknown node, introduce the parameter k into the proposed likelihood model, and we determine the optimal value of the parameter k through our experiments. Experimental results show that the proposed weighted centroid algorithm is better than WCL (Weighted Centroid Localization) and AMWCL-RSSI (anchor-optimized modified weighted centroid localization based on RSSI) in terms of the localization accuracy.

Weighted Centroid Localization algorithm based on least square for wireless sensor networks

Min-Max localization algorithm is usually used by wireless sensor nodes because of the reason that it considers few conditions and have a coarse localization which can determine a blind node within a certain range. In this paper, we propose improved Extended Min-Max (improved E-Min-Max) algorithm for localization in wireless sensor networks (WSNs). Experimental results show that improved E-Min-Max algorithm outperforms E-Min-Max algorithm in the localization accuracy.

An improved E-Min-Max localization algorithm in wireless sensor networks

Mobile edge computing (MEC) is a promising technique to support the emerging delay-sensitive and compute-intensive applications for user equipment (UE) by means of computation offloading. However, designing a computation offloading algorithm for the MEC network to meet the restrictive requirements towards system latency and energy consumption remains challenging. In this paper, we propose a joint user-association, task-partition, and resource-allocation (JUTAR) algorithm to solve the computation offloading problem. In particular, we first build an optimization function for the computation offloading problem. Then, we utilize the user association and smooth approximation to simplify the objective function. Finally, we employ the particle swarm algorithm (PSA) to find the optimal solution. The proposed JUTAR algorithm achieves a better system performance compared with the state-of-the-art (SOA) computation offloading algorithm due to the joint optimization of the user association, task partition, and resource allocation for computation offloading. Numerical results show that, compared with the SOA algorithm, the proposed JUTAR achieves about 21% system performance gain in the MEC network with 100 pieces of UE.

https://www.mdpi.com/1424-8220/23/3/1601/pdf?version=1675383324

JUTAR: Joint User-Association, Task-Partition, and Resource-Allocation Algorithm for MEC Networks

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Weighted centroid localization for wireless sensor networks

Weighted Centroid Localization algorithm based on least square for wireless sensor networks

An improved E-Min-Max localization algorithm in wireless sensor networks

JUTAR: Joint User-Association, Task-Partition, and Resource-Allocation Algorithm for MEC Networks