Localization Techniques for Mobile Wireless Sensor Networks

Wireless sensor networks (WSNs) have boomed in this last decade. They are involved in all aspects of our daily lives and make it easier. Despite the great strides in these networks, several problems arose and are remained open. The localization of sensor networks nodes is one of the most challenging problem that has attracted many researches. Consequently, a lot of work has been carried out to provide accurate location information of sensors and sensing field. In this paper, we present a taxonomy of the most important localization technologies proposed for WSNs. We survey different published localization algorithms in the literature while giving an up-to-date with the most recent schemes. Then we highlight their characteristics and describe metrics used for their classification. A complete comparison will be given and also directions of future research will be discussed.

Telecommunication Systems

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.

Wireless sensor networks (WSNs) have recently emerges as promising technology in wireless communication field and gained special attention by research groups. It uses small and cheap gadgets with low energy requirements and limited on board computing resourceswhich communicates with each other's or base stations without any pre-defined infrastructure. The property of being infrastructure less makes it suitable in distinctive application situations including remotemonitoring, disaster management, military applications and biomedical health observing devices. In many of these applications, node localization is unavoidably one of the important system parameters for example in target tracking if the nodes are not able to obtain the accurate location information, the related task cannot be performed.It is also helpful in routing, network coverage and quarry management of sensors. In general the localization techniques are ordered into two general classifications: range based and range free. In this paper, we discussed the various localization algorithms with their applicable areas, requirements and limitations. Moreover, on conclusion we compare these localization algorithms and analyze the future research directions for the localization algorithms in WSNs.

Indonesian Journal of Electrical Engineering and Computer Science, 2022

The problem of localization in wireless sensor networks has received considerable attention from researchers over the past decades. Several methods and algorithms have been proposed to solve this problem. The effectiveness of these algorithms depends on the accuracy of the estimated positions and the information required to calculate the coordinates. In this paper, we propose to evaluate four of the most commonly used localization methods in sensor networks. Our study considers a mathematical description of the studied methods in order to evaluate their complexity, and then a practical implementation on the simulation tool Cooja. We evaluate the performance of the studied methods as a function of the number of deployed sensor nodes and their degree of mobility in terms of several performance metrics. The objective is to reveal the most suitable localization method for a particular case of deployment. Improvement proposals are also provided to improve the most relevant localization method for the investigated study. This is an open access article under the CC BY-SA license.

The important function of a sensor network is to collect and forward data to destination. It is very important to know about the location of collected data. This kind of information can be obtained using localization technique in wireless sensor networks (WSNs). Localization is a way to determine the location of sensor nodes. Localization of sensor nodes is an interesting research area, and many works have been done so far. It is highly desirable to design low-cost, scalable, and efficient localization mechanisms for WSNs. In this paper, we discuss sensor node architecture and its applications, different localization techniques, and few possible future research directions.

International Journal of Computer Applications, 2015

The wireless sensor networks are prone to various connectivity, coverage and localization issues. All of these three terms are related to the neighbor formation, connectivity and convergence of the sensor networks. There are several existing solutions available against these problems. This paper proposes a survey on the techniques to overcome the issues of coverage, convergence and neighbor formation. The techniques surveyed under this survey are novel threedimensional localization DV-Hop algorithm (NTLDV-HOP), distance vector hop (DV-HOP), Received Signal Strength Indicator (RSSI) and the correction value based DV-HOP (RCDV) and correction value based DV-HOP (CDV). Under this survey, we have evaluated all of these techniques for their effectiveness in various situations on the basis of theoretical analysis. This theoretical analysis is based upon the critical evaluation of the existing WSN localization techniques mentioned above. These are termed as the best available techniques for the WSN localization, but having different natures and adaptable and effective in scenarios of their adaptation environment.

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.

Many applications in wireless sensor networks require sensor nodes to obtain their absolute geographical positions. There are various localization algorithms have been recently proposed. Localization – finding the position of individual sensor nodes-remains one of the most difficult research challenges. Many applications based on networked sensors require location awareness; a node must be able to find its location.This paper presents a reviewof some localization algorithms which used different techniques such as CAB, GRAHAM’S SCAN, QUERY MODEL, EVENT TRIGGERED DISTRIBUTED OPTIMIZATION, and BILATERATION.

Localization in mobile sensor networks is more challenging than in static sensor networks because mobility increases the uncertainty of nodes positions. The localization algorithms used in the Mobile sensor networks (MSN)are mainly based on Sequential Monte Carlo (SMC) method. The existing SMC based localization algorithms commonly rely on increasing beacon density in order to improve localization accuracy and suffers from low sampling efficiency and also sampling in those algorithms are static and have high energy consumption. Those algorithms cannot able to localize sensor nodes in some circumstances.The main reason for that is in some time slots the sensor node cannot hear any beaconnode. This results in localization failure. The Improved Monte Carlo Localization (IMCL) algorithm achieves high sampling efficiency, high localization accuracy even in the case when there is a low beacon density. This can be achieved using bounding box and weight computation techniques. This algorithm also uses time series forecasting and dynamic sampling method for solving the problem of localization failure. Simulation results showed that the proposed method has a better performance in sparse networks in comparison with previous existing method

2022

Recent advances in radio and embedded systems have enabled the proliferation of wireless sensor networks. Wireless sensor networks are tremendously being used in different environments to perform various monitoring tasks such as search, rescue, disaster relief, target tracking and a number of tasks in smart environments. In many such tasks, node localization is inherently one of the system parameters. Node localization is required to report the origin of events, assist group querying of sensors, routing and to answer questions on the network coverage. So, one of the fundamental challenges in wireless sensor network is node localization. This paper reviews different approaches of node localization discovery in wireless sensor networks. The overview of the schemes proposed by different scholars for the improvement of localization in wireless sensor networks is also presented. Future research directions and challenges for improving node localization in wireless sensor networks are also discussed.