Localization In Mobile Sensor Networks Research Articles

Barycentric Coordinate-Based Distributed Localization for Mobile Sensor Networks Under Denial-of-Service Attacks

Barycentric Coordinate-Based Distributed Localization for Mobile Sensor Networks Under Denial-of-Service Attacks

Localization for V2X communication with noisy distance measurement

Mobile sensor network localization is a growing research topic after IEEE 802.15.4 specified the procedure of low-rate wireless personal area networks (LR-WPANs), which further helps localize vehicles in the automobile industry. This paper presents a new localization scheme based on flying anchors deployed in vehicular infrastructure. The mobile anchor nodes follow a random C-shaped trajectory. A global positioning system (GPS) is attached to each anchor node, transmitting beacons with ID and location to all other vehicles in a network. Distance calculation is facilitated through link quality induction, employing the centroid method to compute localization error. Mobile anchor localization, particularly when employing a C-shaped trajectory commonly adopted by various topologies, consistently yields optimal positioning outcomes. However, this approach can be susceptible to the impact of noisy measurements, potentially reducing overall localization performance. To overcome this problem, we proposed a framework based on extended Kalman filtering (EKF), which is used to refine the coordinates of the vehicles. To compute the lower bounding of the vehicular node, an analytical framework is also proposed to enhance the localization error accuracy. Simulation results show that the EKF framework provides better positioning accuracy compared to the existing C-shaped solution, irrespective of noise statistics, topology selection, and anchor node density. With the help of the Extended Kalman Filter (EKF) framework, we achieved a comprehensive localization error of 0.99 m, accompanied by a standard deviation of 0.47.

Range-Based Coordinate Alignment for Cooperative Mobile Sensor Network Localization

This article studies a coordinate alignment problem for cooperative mobile sensor network localization with range-based measurements. The network consists of target nodes, each of which has only access position information in a local fixed coordinate frame, and anchor nodes with GPS position information. To localize target nodes, we aim to align their coordinate frames, which leads to a nonconvex optimization problem over a rotation group $\text{SO}(3)$ . Then, we reformulate it as an optimization problem with a convex objective function over spherical surfaces. We explicitly design both iterative and recursive algorithms for localizing a target node with an anchor node, and extend to the case with multiple target nodes. Finally, the advantages of our algorithms against the literature are validated via simulations.

Enabling accurate range free localization for mo-bile sensor networks

The Existing localization algorithm was Sequential Monte Carlo (SMC) method for mobile sensor networks. In this paper, we propose an energy efficient algorithm called novel localization, which can achieve high localization in a better way. In existing algorithm, high Localization is achieved by getting more number of beacon nodes as input which improves the computational cost and decreases the sampling efficiency to a higher rate. But in the Proposed algorithm, we achieve high Localization accuracy with less number of beacon nodes itself thus the computational cost will be less and the sampling efficiency will be high. Our algorithm uses the approximate calculated position information of sensor nodes to increase the localization accuracy. The existing algorithms doesn’t have high localization accuracy when nodes move very fast, so we have propose a new algorithm, novel localization which is executed based on spped of nodes. The novel algorithm improves the Localization accuracy also when the node move fast.

Vision-Based Coordinated Localization for Mobile Sensor Networks

In this paper, we propose a coordinated localization algorithm for mobile sensor networks with camera sensors to operate under Global Positioning System (GPS) denied areas or indoor environments. Mobile robots are partitioned into two groups. One group moves within the field of views of remaining stationary robots. The moving robots are tracked by stationary robots and their trajectories are used as spatiotemporal features. From these spatiotemporal features, relative poses of robots are computed using multiview geometry and a group of robots is localized with respect to the reference coordinate based on the proposed multirobot localization. Once poses of all robots are recovered, a group of robots moves from one location to another while maintaining the formation of robots for coordinated localization under the proposed multirobot navigation strategy. By taking the advantage of a multiagent system, we can reliably localize robots over time as they perform a group task. In experiment, we demonstrate that the proposed method consistently achieves a localization error rate of 0.37% or less for trajectories of length between $715~{\rm cm}$ and $890~{\rm cm}$ using an inexpensive off-the-shelf robotic platform.

Secure Node Localization in Mobile Sensor Networks

Secure node localization in wireless sensor networks (WSN) has become an important research topic. Although, Global Positioning System (GPS) based node localization has got significant attention from researchers, GPS-free node localization trend is evolving in recent times. GPS-free node localization in mobile sensor networks can be constructed in two ways: Beacon based (BB) and Without Beacon based (WBB). The BB approach has been studied extensively under adversarial model and many algorithms based on BB approach have been proposed in literature in order to localize nodes in a secure manner. In contrast, WBB approach for node localization under adversarial model has not received substantial attention from researchers. In this paper, the authors discuss WBB approach for node localization under adversarial model. The authors discuss static and dynamic key settings for node localization using WBB approach. The authors present an improved protocol for node localization in mobile sensor networks, aiming at minimizing the impact of node capture threats. The authors consider the LEAP (Localized Encryption and Authentication Protocol) (Zhu, Setia, & Jajodia, 2003) and the LOCK (Localized Combinatorial Keying) (Eltoweissy, Moharrum, & Mukkamala, 2006) as the building blocks of their proposed scheme. The authors show that the improved protocol provides effective node localization in a secure manner with minimal node capture threats.

Efficient localization for mobile sensor networks based on constraint rules optimized Monte Carlo method

Wireless sensor networks (WSNs) have been widely used in many fields. The issue of node localization is a fundamental problem in WSNs. And it is the basis and prerequisite for many applications. Due to the mobility of the sensor nodes, it is more challenging to locate nodes in the mobile WSNs than in the static ones. The existing localization schemes for mobile WSNs are almost based on the Sequential Monte Carlo (SMC) localization method. The SMC-based schemes may suffer from low sampling efficiency resulted from a large sampling area, which makes them difficult to achieve high localization accuracy and efficiency. Some schemes try to reduce the sampling area by further employing position relationship with neighbor common nodes, while we have found that the movements of the neighbor beacon nodes have not been fully exploited. Addressing this issue, in this paper, some new constraint rules are developed and some existing constraint rules are optimized with the consideration of the moving distance and direction of neighbor beacons. A series of distance constraint conditions are further created, by which, the scope/size of the sampling area can be further reduced, and the samples can be filtered more accurately. The performance of our algorithm is evaluated by extensive simulation experiments. The simulation results show that the localization error and computation cost of our proposed algorithm are lower than those of the existing ones, even when the speed of the sensor nodes is relative high.

Toward Accurate Mobile Sensor Network Localization in Noisy Environments

The node localization problem in mobile sensor networks has received significant attention. Recently, particle filters adapted from robotics have produced good localization accuracies in conventional settings. In spite of these successes, state-of-the-art solutions suffer significantly when used in challenging indoor and mobile environments characterized by a high degree of radio signal irregularity. New solutions are needed to address these challenges. We propose a fuzzy logic-based approach for mobile node localization in challenging environments. Localization is formulated as a fuzzy multilateration problem. For sparse networks with few available anchors, we propose a fuzzy grid-prediction scheme. The fuzzy logic-based localization scheme is implemented in a simulator and compared to state-of-the-art solutions. Extensive simulation results demonstrate improvements in the localization accuracy from 20 to 40 percent when the radio irregularity is high. A hardware implementation running on Epic motes and transported by iRobot mobile hosts confirms simulation results and extends them to the real world.

Accurate and Efficient Node Localization for Mobile Sensor Networks

In this paper, we propose a range-free cooperative localization algorithm for mobile sensor networks by combining hop-distance measurements with particle filtering. In the hop-distance measurement step, we design a differential-error correction scheme to reduce the positioning error accumulated over multiple hops. We also introduce a backoff-based broadcast mechanism in our localization algorithm. It efficiently suppresses redundant broadcasts and reduces message overhead. The proposed localization method has fast convergence with small location estimation error. We verify our algorithm in various scenarios and compare it with conventional localization methods. Simulation results show that our proposed method has similar or superior performance when compared to other state-of-the-art localization algorithms.

A Distributed Method to Localization for Mobile Sensor Networks based on the convex hull

There has been recently a trend of exploiting the heterogeneity in WSNs and the mobility of either the sensor nodes or the sink nodes to facilitate data dissemination in WSNs. Recently, there has been much focus on mobile sensor networks, and we have even seen the development of small-profile sensing devices that are able to control their own movement. Although it has been shown that mobility alleviates several issues relating to sensor network coverage and connectivity, many challenges remain. Among these, the need for position estimation is perhaps the most important. Not only is localization required to understand sensor data in a spatial context, but also for navigation, a key feature of mobile sensors. This paper concerns the localization problem in the case where all nodes in the network (anchors and others sensors) are mobile. We propose the technique following the capabilities of nodes. Thus, each node obtains either an exact position or an approximate position with the knowledge of the maximal error born. Also, we adapt the periods where nodes invoke their localization. Simulation results show the performances of our method in term of accuracy and determinate the technique the more adapted related to the network configurations.

Accurate and Energy-Efficient Range-Free Localization for Mobile Sensor Networks

Existing localization algorithms for mobile sensor networks are usually based on the Sequential Monte Carlo (SMC) method. They either suffer from low sampling efficiency or require high beacon density to achieve high localization accuracy. Although papers can be found for solving the above problems separately, there is no solution which addresses both issues. In this paper, we propose an energy efficient algorithm, called WMCL, which can achieve both high sampling efficiency and high localization accuracy in various scenarios. In existing algorithms, a technique called bounding-box is used to improve the sampling efficiency by reducing the scope from which the candidate samples are selected. WMCL can further reduce the size of a sensor node's bounding-box by a factor of up to 87 percent and, consequently, improve the sampling efficiency by a factor of up to 95 percent. The improvement in sampling efficiency dramatically reduces the computational cost. Our algorithm uses the estimated position information of sensor nodes to improve localization accuracy. Compared with algorithms adopting similar methods, WMCL can achieve similar localization accuracy with less communication cost and computational cost. Our work has additional advantages. First, most existing SMC-based localization algorithms cannot be used in static sensor networks but WMCL can work well, even without the need of experimentally tuning parameters as required in existing algorithms like MSL*. Second, existing algorithms have low localization accuracy when nodes move very fast. We propose a new algorithm in which WMCL is iteratively executed with different assumptions on nodes' speed. The new algorithm dramatically improves localization accuracy when nodes move very fast. We have evaluated the performance of our algorithm both theoretically and through extensive simulations. We have also validated the performance results of our algorithm by implementing it in real deployed static sensor networks. To the best of our knowledge, we are the first to implement SMC-based localization algorithms for wireless sensor networks in real environment.

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.

Sequential Monte Carlo localization in mobile sensor networks

Node localization in wireless sensor networks is essential to many applications such as routing protocol, target tracking and environment surveillance. Many localization schemes have been proposed ...