Localization Algorithm For Sensor Networks Research Articles

Localization Algorithms in Wireless Sensor Networks: Current Approaches and Future Challenges

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.

On accuracy of region based localization algorithms for wireless sensor networks

Although many localization algorithms have been proposed, few efforts have been devoted to theoretical analysis on accuracy of these algorithms. For range-based localization problems, the Cramér–Rao lower Bound (CRB) provides an algorithm-independent method to analytically compute a tight lower bound on the square of sensors’ localization errors. However, for range-free localization algorithms there are little similar work. In this paper, based on geometric properties, we theoretically analyze bounds on accuracy for Region Based Localization (RBL) algorithms which can be classified as one type of range-free localization algorithms. Assume a sensor node p can lie at any point with equal probability in a deployment region R whose size is s. If in a RBL algorithm R is partitioned into k arbitrary subregions, then the expected localization error of p in worst case is bounded below by s k 2 3 π and the expected localization error square of p in worst case is bounded below by s k 1 2 π . The bounds are not theoretically tight; however our simulation results show that the gaps between these bounds and corresponding achievable values are small. The obtained results can be used to bound localization accuracy of RBL algorithms in a wireless sensor network. (The localization accuracy is defined as the average localization error of all nodes in the network.) Our simulation results show that the derived bound can effectively reflect the best localization accuracy RBL algorithms can achieve in randomly deployed sensor networks with enough large number of sensors. We also investigate the key factors that impact localization accuracy in RBL algorithms. With these results, we show by examples with simulation results how to set up guidelines in the design of RBL algorithms in order to achieve high localization accuracy.

Localization algorithm in wireless sensor networks based on semi-supervised manifold learning and its application

Localization of mobile nodes in wireless sensor network gets more and more important, because many applications need to locate the source of incoming measurements as precise as possible. Many previous approaches to the location-estimation problem need know the theories and experiential signal propagation model and collect a large number of labeled samples. So, these approaches are coarse localization because of the inaccurate model, and to obtain such data requires great effort. In this paper, a semi-supervised manifold learning is used to estimate the locations of mobile nodes in a wireless sensor network. The algorithm is used to compute a subspace mapping function between the signal space and the physical space by using a small amount of labeled data and a large amount of unlabeled data. This mapping function can be used online to determine the location of mobile nodes in a sensor network based on the signals received. We use independent development nodes to setup the network in metallurgical industry environment, outdoor and indoor. Experimental results show that we can achieve a higher accuracy with much less calibration effort as compared with RADAR localization systems.

A Fletcher–Reeves Conjugate Gradient Neural-Network-Based Localization Algorithm for Wireless Sensor Networks

Multihop connectivity-based algorithms have been receiving increased attention in recent times for localization in wireless sensor networks (WSNs). This paper proposes the development of a Fletcher-Reeves update-based conjugate gradient (CG) multilayered feedforward neural network for multihop connectivity-based localization of a large number of sensor nodes in a 2-D sensor network on the basis of information gathered from beacon nodes. The neural-network-based system employs a classification scheme where the location of a sensor is simultaneously estimated in both the x- and y-directions. The usefulness of the proposed scheme is demonstrated by employing the scheme for three case studies, with varied environments, where it could consistently show better performance than two popular recently proposed schemes.

A Novel Iterative Multilateral Localization Algorithm for Wireless Sensor Networks

In many applications of wireless sensor networks, location is very important information. It can be used to identify the location at which sensor readings originate, in routing and data storage protocols based on geographical areas and so on. Location information can come from manual setting or GPS device. However, manual setting requires huge cost of human time, and GPS setting requires expensive device cost. Both approaches are not applicable to localization task of large scale wireless sensor networks. In this paper, we propose an accurate and efficient localization algorithm, called iterative multilateral localization algorithm based on time rounds. This Algorithm uses time round mechanism and anchor nodes triangle placement scheme to reduce error accumulation caused by iteratively localization. And it also reduces location errors and prevents abnormal phenomena caused by trilateral localization through limiting the minimum number of neighboring beacon nodes used in different localizing time rounds. Experimental results reveal that this algorithm has high localization accuracy, even if in large range errors, it can achieve good result.

BR2OM: RSSI-based Refinement and Optimization Mechanism for Wireless Sensor Networks

There are many RSSI-based localization algorithms for wireless sensor networks (WSN). T his paper proposes a RSSI-based refinement of the optimization mechanism (BR 2 O M) based on multi-dimensional scaling (MDS). The basic idea of the algorithm is that establishing sub-areas for localization to ensure the processing of localization can be completed successfully. The sub-area is divided by cluster which is a tree structure. At the same time, the mechanism filters the Received Signal Strength Indicator (RSSI) based on th e link quality indication ( LQI ) to reduce the error number of RSSI. The RSSI is c orrect ed and refin ed by using cosine rule and the limitation of communication range . T he relationship of relative position among blind node s is obtained by adopt ing classical MDS algorithm . The relative position is finally converted into the global coordinate. The experiment result shows that the algorithm has many advantages, such as high accuracy, simple condition , and moderate cost . The algorithm satisf ies the requirement of large-scale network applications and also is valua ble to the positioning refined problems under circumstance of the non-line-of-sight.

Localization algorithm in wireless sensor networks with network mobility

This paper describes a localization algorithm necessary for building small-sized network's position reporting system using wireless sensor network. In existing sensor networks, much of the localization algorithm is devoted to making position estimations by learning a minimum of 3 position values of anchor node that's aware of absolute position value under the environment where sensor nodes are fixed. The proposed algorithm has an indefinite traveling direction and has shown in performance analysis that it is possible to estimate positions even with a small number of anchors. Also, by using the implemented board, although practicality has been proven with implementation of realistic distance measurement through distance measurement using RSSI (Received Signal Strength Indication) and traveling distance measurement using acceleration sensor.

A three dimensional localization algorithm for underwater acoustic sensor networks

Although many localization protocols have been proposed for terrestrial sensor networks in recent years, the unique characteristics of the underwater acoustic communication channel, such as high and variable propagation delay and the three dimensional volume of the environment make it necessary to design and develop new localization algorithms. In this paper, a localization algorithm called three-dimensional underwater localization (3DUL) is introduced. 3DUL achieves networkwide robust 3D localization by using a distributed and iterative algorithm. Most importantly, 3DUL exploits only three surface buoys for localization initially. The sensor nodes leverage the low speed of sound to accurately determine the inter-node distances. Performance evaluations show that 3DUL algorithm provides high accuracy in underwater localization, which does not degrade with network size.

Distributed localization algorithm for wireless sensor network based on multidimensional scaling and the shortest path distance correction

Sensor localization is crucial for the configuration and applications of wireless sensor network (WSN). A novel distributed localization algorithm, MDS-DC was proposed for wireless sensor network based on multidimensional scaling (MDS) and the shortest path distance correction. In MDS-DC, several local positioning regions with reasonable distribution were firstly constructed by an adaptive search algorithm, which ensures the mergence between the local relative maps of the adjacent local position regions and can reduce the number of common nodes in the network. Then, based on the relationships between the estimated distances and actual distances of anchors, the distance estimation vectors of sensors around anchors were corrected in each local positioning region. During the computations of the local relative coordinates, an iterative process, which is the combination of classical MDS algorithm and SMACOF algorithm, was applied. Finally, the global relative positions or absolute positions of sensors were obtained through merging the relative maps of all local positioning regions. Simulation results show that MDS-DC has better performances in positioning precision, energy efficiency and robustness to range error, which can meet the requirements of applications for sensor localization in WSN.

Cluster-based Three-dimensional Localization Algorithm for Large Scale Wireless Sensor Networks

In wireless sensor networks, three-dimensional localization is important for applications. It becomes a challenge with the scale of network getting large. This paper proposes a three-dimensional localization algorithm for large scale WSN on the basis of cluster. Focusing on the MDS-based localization, it adopts the cluster structure and the global coordinate system to represent the whole network logically, and reduces the influence of range measurement errors through decreasing the probability of multi-hop. With the combination of variable power of nodes and the triangle principle, the range measurement errors can be corrected. Through the comparison of three different computations in the algorithm of simulations, correction effects are presented. To address the proposed algorithm, CBLALS, more convincible, the comparison of CBLALS and DV-Distance (3D) is taken. The result shows that the positioning accuracy of CBLALS is much better than the one of DV-Distance (3D). With the increasing of the range measurement error, the positioning error of CBLALS varies gently, and could be controlled within 55% while the range measurement error is 30%.

One-Dimensional Localization Algorithm Based on Signal Strength Ratio

Localization algorithm in wireless sensor networks has attracted significant interest in recent years. The RSSI approach is considered as the cheapest solution allowing coarse-grain, yet effective for many applications in outdoor environments. However, when environments change, there exists a big localization error using RSSI without a reconstructing data set. In order to solve the problem of reestablishing the data set when the environments change, a novel one-dimensional localization algorithm based on received signal strength ratio (1-DIM-RSSR) is proposed for the one-dimensional wireless sensor network applications, such as road surveillance and pipeline transmission. While synthetically considering accuracy, power consumption, localization time, and device costs, we can only use coordinate x to locate the nodes by defining y as 0. Our algorithm is based on the theory as follows: the values of Signal Strength vary from one environment to another. When environments change, even the distance between the transmitter and the receiver remains unchanged, but there is still at least 10dBm's difference. On the contrary, the ratio of the received signal strength from two base stations varies little in the condition of the unchanged distance, and thus it avoids reconstructing the dataset when environments change. 1-DIM-RSSR comprises of two phases: the data set construction phase and the localization phase. In the first phase, N reference nodes are uniformly distributed between the two base stations BS1 and BS2. We collect the signal strength RRi1 and RRi1 from BS1 and BS2 respectively and get signal strength ratio Ri = RRi1/RRi2 at each point, where i ϵ [1, N] corresponding to the N references nodes. In the localization phase, we record the signal strength ratio Rʹ of mobile nodes and pick the one that best matches the observed signal strength ratio. Our idea is to compute the distance | Ri - Rʹ| at a fixed set of locations, and then pick the location that minimizes the distance. Simulation and experiment results show that localization precision of the proposed algorithm compared with the RSSI algorithm is improved when the environments change. The proposed method is easy to operate, and need not reconstruct the dataset when the environments change. So this method holds the promise for many applications such as monitoring product lines, road surveillance, and pipeline transmission. Meanwhile, since our algorithm is based on 1-D localization, how to extend this conception to 2-D or even 3-D localization will be our ongoing work.

Localization in time and space for wireless sensor networks: An efficient and lightweight algorithm

In many applications that use Wireless Sensor Networks (WSNs), detected events need to be localized in both time and space. As a result, sensor nodes need to have precisely synchronized clocks as well as to be localized in a common spatial reference system. While synchronization and localization algorithms have been proposed to solve these problems independently, in this work we propose to combine both synchronization and localization into a single problem that we refer to as the time–space localization problem. We then propose a novel and efficient time–space localization algorithm for wireless sensor networks which we refer to as the Lightness algorithm. Our proposed algorithm not only takes advantage of the additional hardware resources required by the positioning mechanism in order to improve the performance and scalability of synchronization, but also benefits from the additional communication needed by the synchronization mechanism in order to decrease positioning errors. We also present an extensive set of experiments to evaluate the performance of our algorithm. Our results indicate clearly that our proposed scheme is scalable while keeping a low synchronization error and a low communication overhead. Our results also indicate that the additional packets needed to compute clocks’ drift have the ability to decrease the positioning errors to almost one third of the initial positioning.

Extended Monte Carlo localization algorithm for mobile sensor networks

A real-world localization system for wireless sensor networks that adapts for mobility and irregular radio propagation model is considered. The traditional range-based techniques and recent range-free localization schemes are not well competent for localization in mobile sensor networks, while the probabilistic approach of Bayesian filtering with particle-based density representations provides a comprehensive solution to such localization problem. Monte Carlo localization is a Bayesian filtering method that approximates the mobile node’s location by a set of weighted particles. In this paper, an enhanced Monte Carlo localization algorithm—Extended Monte Carlo Localization (Ext-MCL) is proposed, i.e., the traditional Monte Carlo localization algorithm is improved and extended to make it suitable for the practical wireless network environment where the radio propagation model is irregular. Simulation results show the proposal gets better localization accuracy and higher localizable node number than previously proposed Monte Carlo localization schemes not only for ideal radio model, but also for irregular one.

A robust localization algorithm in wireless sensor networks

Most of the state-of-the-art localization algorithms in wireless sensor networks (WSNs) are vulnerable to various kinds of location attacks, whereas secure localization schemes proposed so far are too complex to apply to power constrainedWSNs. This paper provides a distributed robust localization algorithm called Bilateration that employs a unified way to deal with all kinds of location attacks as well as other kinds of information distortion caused by node malfunction or abnormal environmental noise. Bilateration directly calculates two candidate positions for every two heard anchors, and then uses the average of a maximum set of close-by candidate positions as the location estimation. The basic idea behind Bilateration is that candidate positions calculated from reasonable (i.e., error bounded) anchor positions and distance measurements tend to be close to each other, whereas candidate positions calculated from false anchor positions or distance measurements are highly unlikely to be close to each other if false information are not collaborated. By using ilateration instead of classical multilateration to compute location estimation, Bilateration requires much lower computational complexity, yet still retains the same localization accuracy. This paper also evaluates and compares Bilateration with three multilateration-based localization algorithms, and the simulation results show that Bilateration achieves the best comprehensive performance and is more suitable to real wireless sensor networks.

Secure localization algorithms for wireless sensor networks

In the military and emergency preparedness class of applications, wireless sensor networks have a number of desirable characteristics, such as being autonomous systems that can be deployed in a remote - possibly hostile - environment and can perform tasks like battlefield surveillance or enemy tracking, as well as monitor the security of military facilities. One of the main challenges in this kind of application is security. Due to their key role in WSNs and also their fragility, localization systems can be the target of an attack that could compromise the entire functioning of a WSN and lead to incorrect military plans and decision making, among other problems. In this article we show how current localization systems are vulnerable to these security attacks, and how existing techniques can be used to prevent or impede these attacks in WSNs.

A Distributed AOA Based Localization Algorithm for Wireless Sensor Networks

In this paper we propose a distributed algorithm for solving the positioning problem in ad-hoc wireless networks. The method is based on the capability of the nodes to measure the angle of arrival (AOA) of the signals they produce. The main features of the distributed algorithm are simplicity, asynchronous operations (i.e. no global coordination among nodes is required), ability to operate in disconnected networks. Moreover each node can join the computation at any time. Numerical results, obtained by simulating several scenarios, show that the algorithm can reach a good level of convergence even when the number of communications is limited.

A Combined Localization and Geographic Routing Algorithm for Rapidly-Deployed Wireless Sensor Networks

We consider a network of randomly-deployed, low-cost sensors and a single powerful sink node. A partition-based localization algorithm is proposed in which the sink node imparts sector-based location information through the phased-array transmission of a series of beacons. One-hop neighbor information is used by each sensor in a series of sector-partitioning routines to identify the sub-sector in which it resides. A geographic routing algorithm is then proposed which uses the sector-based localization results as the basis for a routing algorithm. Simulation results indicate that the performance of our localization algorithm improves with node density, and that the performance of the combined localization and routing algorithm in the presence of severe measurement noise is comparable with that provided by the same routing algorithm using perfect location information.

Co-designed anchor-free localization and location-based routing algorithm for rapidly-deployed wireless sensor networks

We consider a wireless sensor network consisting of a single sink node at the center of a field of randomly distributed sensors. A simple anchor-free localization algorithm is proposed, in which the sink node imparts radial location information through the phased-array transmission of a series of beacons. Each individual sensor uses the knowledge of received beacons as well as information from neighbors to continue partitioning current sectors and identifying sub-sectors in which it resides until an accuracy requirement is satisfied. An energy-preserving routing algorithm is then proposed which uses the localization results as the basis for selecting relay nodes. We present the localization algorithm, analyze its partition errors as well as its impact on communication energy consumption, and then present the location-based routing algorithm. Simulation results indicate that the performance of our co-designed localization and routing algorithm in the presence of severe noise is as good as that provided by a shortest-path routing algorithm under ideal assumptions; therefore, our co-designing approach can achieve high performance with lightweight algorithms.

Concentric Anchor Beacon Localization Algorithm for Wireless Sensor Networks

Many applications in wireless sensor networks require sensor nodes to obtain their absolute or relative geographical positions. Although various localization algorithms have been recently proposed, most of them require nodes to be equipped with range-determining hardware to obtain distance information. In this paper, we propose a concentric anchor beacon (CAB) localization algorithm for wireless sensor networks. CAB is a range-free approach and uses a small number of anchor nodes. Each anchor emits beacons at different power levels. From the information received by each beacon heard, nodes can determine in which annular ring they are located within each anchor. Each node uses the approximated center of intersection of the rings as its position estimate. We also propose two heuristics, namely CAB with equal area and CAB with equal width, to determine the transmitting power levels of the beacons. Simulation results show that the estimation error is reduced by half when anchors transmit beacons at two different power levels instead of at a single power level. CAB also gives a lower estimation error than some other range-free localization schemes (e.g., centroid and approximated point-in-triangulation) when the anchor-to-node range ratio is less than 4.

Monte Carlo localization for mobile wireless sensor networks

Localization is crucial to many applications in wireless sensor networks. In this article, we propose a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo localization algorithm. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. To do so, we constrain the area from which samples are drawn by building a box that covers the region where anchors’ radio ranges overlap. This box is the region of the deployment area where the sensor node is localized. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73% (average 30%), for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55% (average 22%). Finally, the processing time is reduced by 93% for a similar localization accuracy.