Improved area estimates for localization in wireless sensor networks

The paper describes a concept of software tool for development of nodes localization in large wireless sensor network. The basic module is the event driven simulator that enables loading position of wireless network nodes, description of the behavior of nodes, description of the environment and modeling of wave propagation in defined space. Node localization and deployment problem in wireless sensor network are supported by implemented algorithms.

In this paper, a distributed algorithm for localization and tracking in wireless sensor network is designed and implemented. Combining the fingerprint approach and subgradient optimization, the proposed algorithm, called golf algorithm, uses different strategies to attain fast movement for tracking a moving object and a fine tune to approach a stationary object, like a drive and a putt on the putting green in the golf. Based on the WLS objective function, a recursive form for both the fingerprint approach and subgradient optimization is derived and can be accomplished by anchor nodes in collaboration. From simulation results, we can see the proposed algorithm has better estimation performance than the conventional decentralized algorithms to localize a stationary or moving target in large wireless sensor networks. In addition, its hardware architecture is designed. The CORDIC operation is employed to compute the vector norm. Parallel processing is used to handle the calculation of fingerprint data. The tracking trajectories of the floating-point program and fixed-point hardware implementation are shown to be quite similar with small finite precision effect, which verifies the feasibility of this hardware solution.

This chapter describes one of the major challenges in technology advancement of Wireless Sensor Networks (WSNs), i.e., Localization in WSNs. In recent years, sensor node localization is an emerging research area in WSNs. The sensor data become useless, if we do not know the location of the reporting node. Coordinates determination of the sensor node is a challenging problem and it is referred as localization problem. The nodes which has unknown coordinates is termed as target Nodes. Various localization methods can be utilized to find out the location of sensor nodes, those coordinates are not known in a system/network. Efficient WSN localization can be treated as multi-dimensional optimization problem which can be addressed through population based stochastic techniques, which involves the minimization of a function of differences between Euclidean and measured distance between sensor nodes. In this chapter various connectivity, range information and mobility based localization algorithms have been discussed. For optimizing the results of these algorithms, various computational intelligence (CI) based optimizing algorithms like Particle Swarm Optimization, Biogeography Based Optimization, Firefly Algorithm and Genetic Algorithm have been discussed. A choice between these algorithm is influenced by the localization accuracy expected to be and convergence rate.

Large wireless sensor networks can contain hundreds or thousands of sensor nodes. Due to wireless sensor network’s properties of low-energy-efficiency, large-scale, low cost, and lossy nature, the development of efficient routing protocols for these large and dense wireless sensor networks is an interesting research topic. This research focuses on the design and implementation of protocols for dense and wireless sensor networks. More specifically, we propose to combine an underlying topology with Xmesh, the multihop routing strategy of Crossbow Technology’s motes. In an effort to limit the path lengths, we propose to impose an underlying connectivity graph for Xmesh. The underlying connectivity graph is a virtual topology of the network, hence the name “topology-based routing.” Instead of being always forwarded to the node with the best link quality among all neighbors, a packet is being routed according to the shortest path routing of the underlying graph which guarantees a bounded path length. Cayley graphs from the Borel subgroup have been known as the densest degree-4 graphs and all Cayley graphs are vertex transitive. In this work, we propose a topology-based routing for Xmesh with Cayley graphs as the underlying topology. We show that, indeed, by imposing a Cayley graph as an underlying graph, the average path lengths between nodes is smaller and that the averaged power consumed is less than the original Xmesh.

We propose a novel approach to the source localization and tracking problem in wireless sensor networks. By applying minimax approximation and semidefinite relaxation, we transform the traditionally nonlinear and nonconvex problem into convex optimization problems for two different source localization models involving measured distance and received signal strength. Based on the problem transformation, we develop a fast low-complexity semidefinite programming (SDP) algorithm for two different source localization models. Our algorithm can either be used to estimate the source location or be used to initialize the original nonconvex maximum likelihood algorithm.

Sensor localization in wireless sensor networks is an important component of many applications. Previous work has demonstrated how localization can be achieved using various methods. In this paper we focus on achieving fine-grained localization that does not require external infrastructure, specialized hardware support, or excessive sensor resources. We use a real sensor network and provide measurements on the actual system. We adopt a localization approach that relies on acoustic sounds and clock synchronization. The contribution of our work is achieving consistent sound pulse detection at each sensor and precise range estimation using a high-precision clock synchronization implementation. We first describe our technique and then we evaluate our approach using a real setup. Our results show that our approach achieves an average clock synchronization accuracy of 5μs. We verify this accuracy using an external global clock via an interrupt mechanism. Our sound detection technique is able to consistently identify sound pulses up to 10m distances in indoor environments. Combining the two techniques, we find that our localization method results in accurate range estimation with an average error of 11cm in distances up to 7m and in consistent range estimation up to 10m in various indoor environments.

There has been a growing interest in the applications of wireless sensor networks in unattended environments. In such applications, sensor nodes are usually deployed randomly in an area of interest. Knowledge of accurate node location is essential in order to correlate the gathered data to the origin of the sensed phenomena and assure the relevance of the reported information. In this paper, we present an efficient anchor-free protocol for localization in wireless sensor networks. Each node discovers its neighbors that are within its transmission range and estimates their ranges. Our algorithm fuses local range measurements in order to form a network wide unified coordinate systems while minimizing the overhead incurred at the deployed sensors. Scalability is achieved through grouping sensors into clusters. Simulation results show that the proposed protocol achieves precise localization of sensors and maintains consistent error margins.

Wireless sensor networks have important military and civilian applications where determining the geographical location of a specific event is crucial. High-accuracy event localization is a challenging task that depends on the type of event to track. Hence, this work proposes a general approach for achieving event localization in wireless sensor networks, keeping independence from the kind of signals emitted by such events. The proposed approach estimates the distance between an event and a sensor node according to the intensity of the signal perceived by that sensor. In addition, lateration and fingerprinting techniques are implemented to determine the location of particular events. In order to evaluate our proposal, acoustic event localization is implemented in a real wireless sensor network based on the Arduino hardware. Results from this specific implementation show an event localization error of approximately 0.2 m inside a 2×2 m area.

SummaryIndoor localization in wireless sensor networks is a challenging task. Static localization and moving target monitoring are addressed using ensemble learning method and received signal strength indicator. The suggested strategy combines several regression trees to have better performance. This solution has been experimentally evaluated using real measurements in an office room. The performance results have been analyzed through a comparison with learning‐based localization algorithms currently available in the literature. The analysis shows that the adopted solution is simple in term of computation, accurate and robust to environmental variation.

Recent works show that the Hilbert curve path for mobile-beacon-based localization in sensor networks has better accuracy than other approaches, such as randomized and circular paths. In this paper, the modified Hilbert curve path is introduced, and its theoretical lower bound error, compared with the basic Hilbert curve path, has been shown using Cramér–Rao lower bound analysis. It is shown that the proposed approach has a more uniform localization accuracy across all the nodes in the environment than the basic Hilbert approach. The simulation results show that this approach, combined with inter-node communication, provides better and close to uniform accuracy compared with previously proposed approaches.

Large scale wireless sensor networks, consisting of thousands of nodes spread over a large geographical area, are being planned for a number of monitoring and control applications. The individual nodes in such networks would be severely limited in their capabilities - they will have limited memory and CPU resources and would typically run on batteries. A suitable routing protocol for large wireless sensor networks should scale well with the size of the network in terms of the control packet, memory and processing overhead. Dynamic link/node characteristics, such as PHY/MAC loss rates and battery levels, in such networks require continuous monitoring of the quality of existing routes and frequent discovery of new routes. The routing protocol should allow distribution of traffic load through out the network to avoid artificial congestion, and hence high MAC level loss rates, in parts of the network. The existing routing protocols do not meet all the criteria mentioned above. Hence, we propose a new routing protocol meeting the criteria mentioned above. The proposed protocol, called AntSens, may be considered as belonging to the category of ant routing protocols that are based on the manner ants discover and remember the best routes to the food location.

In wireless sensor networks (WSNs), localization is one of the most important topics because the location information is typically useful for many applications.The primary data used in a localization process include the locations of anchor nodes and the distances between neighboring nodes.However, these data may contain outliers that deviate from their true values.The existence of the outliers might make the estimated positions not accurate.Thus, it is important to detect and handle outliers in order to achieve high localization accuracy.In this paper, we survey the existing outlier detection techniques for localization in wireless sensor networks.We provide taxonomy for classifying outlier detection techniques in WSNs localization based on different features.In addition, we present comparisons of these techniques.Finally, we discuss the future research directions in this area.

Sensor localization is a fundamental and crucial issue for wireless sensor networks operation and management. Accurate self-localization capability is highly desirable in wireless sensor network. A fundamental problem in distance-based sensor network localization is whether a given sensor network is uniquely localizable or not. Flip ambiguity is a main problem that can make the sensor network not uniquely localized. It causes large errors in the location estimates. This paper proposes a method in which the localization is done through two steps. During the first step, tabu search (TS) is used to obtain an accurate estimation of the nodes' location. During the second step, simulated annealing algorithm (SAA) is used to refine the location estimates of those nodes that are likely to have flip ambiguity problem. The simulation results confirm that the proposed algorithm has better performance than that of the existing algorithms.

Machine learning has been successfully used for target localization in wireless sensor networks (WSNs) due to its accurate and robust estimation against highly nonlinear and noisy sensor measurement. For efficient and adaptive learning, this paper introduces online semi-supervised support vector regression (OSS-SVR). The first advantage of the proposed algorithm is that, based on semi-supervised learning framework, it can reduce the requirement on the amount of the labeled training data, maintaining accurate estimation. Second, with an extension to online learning, the proposed OSS-SVR automatically tracks changes of the system to be learned, such as varied noise characteristics. We compare the proposed algorithm with semi-supervised manifold learning, an online Gaussian process and online semi-supervised colocalization. The algorithms are evaluated for estimating the unknown location of a mobile robot in a WSN. The experimental results show that the proposed algorithm is more accurate under the smaller amount of labeled training data and is robust to varying noise. Moreover, the suggested algorithm performs fast computation, maintaining the best localization performance in comparison with the other methods.

Localization is one of the most important research subjects in terms of the wireless sensor networks (WSNs), because most of the information measured and distributed by the sensors are useful when sensors locations are well known. In this paper, a range free localization algorithm for sensor positioning is proposed. It is based on sensor distributed network model, coverage radius, hop count between each sensor and anchor, and improvement in the minimum mean square error, which computes coefficients for distance estimation between sensors and anchors. In the proposed method, the best coefficient for each hop count is computed with offline processing and Monte Carlo method. Then, these coefficients are stored in each sensor database and they are used for localization in the practical environment. Unlike some existent positioning methods, in this algorithm, it is not required to compute distance estimation coefficient by sensors. In the proposed method, it is assumed that all sensors use the omnidirectional antenna for their usual information transmission. High precision in geographical coordinate determining, less traffic load, and especially good performance in the homogeneous and non-homogeneous environment are the most important features of this algorithm. Simulation results show that the proposed algorithm has a good position determination and reduced traffic load for WSNs, as compared with some existent positioning schemes. Indeed, the proposed method improves localization precision and reduces traffic load simultaneously.