Localization Control to Locate Mobile Sensors (original) (raw)

MAINT: Localization of Mobile Sensors with Energy Control

2015

Localization is an important issue for Wireless Sensor Networks (WSN). A mobile sensor may change its position rapidly and thus require localization calls frequently. A localization may require network wide information and increase traffic over the network. It dissipates valuable energy for message communication. Thus localization is very costly. The control of the number of localization calls may save energy consumption, as it is rather expensive. To reduce the frequency of localization calls for a mobile sensor, we propose a technique that involves \textit{Mobility Aware Interpolation} (MAINT) for position estimation. It controls the number of localizations which gives much better result than the existing localization control schemes using mobility aware extrapolation. The proposed method involves very low arithmetic computation overheads. We find analytical expressions for the expected error in position estimation. A parameter, the time interval, has been introduced to externally...

A SURVEY ON ERROR CONTROL OF LOCALIZATION METHODS AVAILABLE IN WIRELESS SENSOR NETWORKS

IJRCAR, 2013

This paper focuses on the various algorithms used for solving the errors in localization for wireless sensor networks. The major problem in the wireless sensor network is localization. Iterative localization algorithm is used to find out the exact position of the sensor nodes. Localization results are easy to measure the errors like range error and localization error. While using GPS, it gives a trivial solution and higher cost in some environments. The existing work on localization is divided into range based and range free localization. The key issue in localization is error and accuracy. Based on the survey taken on various localization algorithm is focused to overcome the errors like range and localization.

An Enhanced Localization Scheme for Mobile Sensor Networks

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

An Efficient Localization Algorithm for Wireless Sensor Networks

2016

Localization within a Wireless Sensor Network consists of defining the position of a given set of sensors by satisfying some non-functional requirements such as (1) efficient energy consumption, (2) low communication or computation overhead, (3) no, or limited, use of particular hardware components, (4) fast localization, (5) robustness, and (6) low localization error. Although there are several algorithms and techniques available in literature, localization is viewed as an open issue because none of the current solutions are able to jointly satisfy all the previous requirements. An algorithm called ROCRSSI appears to be a suitable solution; however, it is affected by several inefficiencies that limit its effectiveness in real case scenarios. This paper proposes a refined version of this algorithm, called ROCRSSI++, which resolves such inefficiencies using and storing information gathered by the sensors in a more efficient manner. Several experiments on actual devices have been perf...

An experimental evaluation of localization methods used in wireless sensor networks

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.

Locating Nodes in Mobile Sensor Networks More Accurately and Faster

2008 5th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, 2008

Localization in mobile sensor networks is more challenging than in static sensor networks because mobility increases the uncertainty of nodes' positions. Most existing localization algorithms in mobile sensor networks use Sequential Monte Carlo (SMC) methods due to their simplicity in implementation. However, SMC methods are very time-consuming because they need to keep sampling and filtering until enough samples are obtained for representing the posterior distribution of a moving node's position. In this paper, we propose a localization algorithm that can reduce the computation cost of obtaining the samples and improve the location accuracy. A simple bounding-box method is used to reduce the scope of searching the candidate samples. Inaccurate position estimations of the common neighbor nodes is used to reduce the scope of finding the valid samples and thus improve the accuracy of the obtaineed location information. Our simulation results show that, comparing with existing algorithms, our algorithm can reduce the total computation cost and increase the location accuracy. In addition, our algorithm shows several other benefits: 1) it enables each determined node to know its maximum location error, 2) it achieves higher location accuracy under higher density of common nodes, and 3) even when there are only a few anchor nodes, most nodes can still get position estimations.

Localization Algorithms for Mobile Wireless Sensor Networks: A Review and Future Scope

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 wireless sensor networks

2000

A fundamental problem in wireless sensor networks is localization -the determination of the geographical locations of sensors. Most existing localization algorithms were designed to work well either in networks of static sensors or networks in which all sensors are mobile. In this paper, we propose two localization algorithms, MSL and MSL*, that work well when any number of sensors are static or mobile. MSL and MSL* are range-free algorithms -they do not require that sensors are equipped with hardware to measure signal strengths, angles of arrival of signals or distances to other sensors. We present simulation results to demonstrate that MSL and MSL* outperform existing algorithms in terms of localization error in very different mobility conditions. MSL* outperforms MSL in most scenarios, but incurs a higher communication cost. MSL outperforms MSL* when there is significant irregularity in the radio range. We also point out some problems with a well known lower bound for the error in any range-free localization algorithm in static sensor networks.

Improved Accuracy Distribution Localization in Wireless Sensor Networks

International Journal of Computer Science and Mobile Computing, 2014

Localization system is important when there is an uncertainty of the exact location of some fixed or mobile devices. In this paper, the problem of localization system to estimate the position of randomly deployed nodes of a wireless sensor network (WSN) is addressed, a propose algorithm named Improved Accuracy Distribution localization for wireless sensor networks (IADLoc) is issued, it is used to minimize the error rate of localization without any additional hardware cost and minimum energy consumption and also is decentralized implementation. The IADLoc is a range free and range based localization algorithm that uses both type of antenna (directional and omni-directional), it allows sensors determine their location based on the region of intersection (ROI) when the beacon nodes send the information to the sink node and the latter sends this information to the sensors relying on the antenna. Keywords-Improved Accuracy Distribution Localization, antenna: directional and omni-directional; wireless sensor network, beacon nodes I. INTRODUCTION A wireless sensor network (WSN) is a network composed of a large number of sensor nodes, which are deployed in the monitoring field. With the rapid development of WSNs, providing development tools such as simulation environment before deploying real nodes in physical environments is getting more important. A well simulated environment [1] can help developers to build their prototype models to know the interactions and the behaviour of each node. In addition, most of WSN applications deploy large number of nodes in the simulated environment [2]. Localization is one of technologies in WSNs, it is used to estimate the position or coordinates of sensor nodes and sink nodes, which also called beacon nodes (locator or anchor) [3]. The localization algorithms can be divided into two categories: range-based algorithms and range-free algorithms. The ranged-based algorithms need to measure absolute point-to-point distance estimates or angle estimates in order to calculate the locations of unknown nodes. Range-free algorithms use the information of anchor nodes or the connectivity of WSN to estimate a rough coordinate as the actual one of unknown nodes [4].

Autonomous localization method in wireless sensor networks

2005

In wireless sensor networks, localization systems use data from sensors which receive signals from moving targets, measure RSSI, and translate RSSI into the distance between sensor and target. We consider a localization system that gives an error measurement model of distance and introduce a relationship between the number of data and accuracy. Extending the lifetime of a system is needed to save the energy of sensors and collect the necessary data. In this paper, we propose an efficient data collecting technique to get the accuracy required for the applications while saving energy. We verify that our proposal can efficiently collect necessary data to get accuracy in cases of random sensor placement.