Development an accurate and stable range-free localization scheme for anisotropic wireless sensor networks

With the high-speed development of wireless radio technology, numerous sensor nodes are integrated into wireless sensor networks, which has promoted plentiful location-based applications that are successfully applied in various fields, such as monitoring natural disasters and post-disaster rescue. Location information is an integral part of wireless sensor networks, without location information, all received data will lose meaning. However, the current localization scheme is based on equipped GPS on every node, which is not cost-efficient and not suitable for large-scale wireless sensor networks and outdoor environments. To address this problem, research scholars have proposed a rangefree localization scheme which only depends on network connectivity. Nevertheless, as the representative range-free localization scheme, Distance Vector-Hop (DV-Hop) localization algorithm demonstrates extremely poor localization accuracy under anisotropic wireless sensor networks. The previous works assumed that the network environment is evenly and uniformly distributed, ignored anisotropic factors in a real setting. Besides, most research academics improved the localization accuracy to a certain degree, but at expense of high communication overhead and computational complexity, which cannot meet the requirements of high-precision applications for anisotropic wireless sensor networks. Hence, finding a fast, accurate, and strong solution to solve the range-free localization problem is still a big challenge. Accordingly, this study aspires to bridge the research gap by exploring a new DV-Hop algorithm to build a fast, costefficient, strong range-free localization scheme. This study developed an optimized variation of the DV-Hop localization algorithm for anisotropic wireless sensor networks. To address the poor localization accuracy problem in irregular C-shaped network topology, it adopts an efficient Grew Wolf Optimizer instead of the least-squares method. The dynamic communication range is introduced to refine hop between anchor nodes, and new parameters are recommended to optimize network protocol to balance energy cost in the initial step. Besides, the weighted coefficient and centroid algorithm is employed to reduce cumulative error by hop count and cut down computational complexity. The developed localization framework is separately validated and evaluated each optimized step under various evaluation criteria, in terms of accuracy, stability, and cost, etc. The results of EGWO-DV-Hop demonstrated superior localization accuracy under both topologies, the average localization error dropped up to 87.79% comparing with basic DV-Hop under C-shaped topology. The developed enhanced DWGWO-DVHop localization algorithm illustrated a favorable result with high accuracy and strong stability. The overall localization error is around 1.5m under C-shaped topology, while the traditional DV-Hop algorithm is large than 20m. Generally, the average localization error went down up to 93.35%, compared with DV-Hop. The localization accuracy and robustness of comparison indicated that the developed DWGWO-DV-Hop algorithm super outperforms the other classical range-free methods. It has the potential significance to be guided and applied in practical location-based applications for anisotropic wireless sensor networks

Coverage Protocols for Wireless Sensor Networks: Review and Future Directions

The coverage problem in wireless sensor networks (WSNs) can be generally defined as a measure of how effectively a network field is monitored by its sensor nodes. This problem has attracted a lot of interest over the years and as a result, many coverage protocols were proposed. In this survey, we first propose a taxonomy for classifying coverage protocols in WSNs. Then, we classify the coverage protocols into three categories (i.e. coverage aware deployment protocols, sleep scheduling protocols for flat networks, and cluster-based sleep scheduling protocols) based on the network stage where the coverage is optimized. For each category, relevant protocols are thoroughly reviewed and classified based on the adopted coverage techniques. Finally, we discuss open issues (and recommend future directions to resolve them) associated with the design of realistic coverage protocols. Issues such as realistic sensing models, realistic energy consumption models, realistic connectivity models and sensor localization are covered

A survey of localization in wireless sensor network

Localization is one of the key techniques in wireless sensor network. The location estimation methods can be classified into target/source localization and node self-localization. In target localization, we mainly introduce the energy-based method. Then we investigate the node self-localization methods. Since the widespread adoption of the wireless sensor network, the localization methods are different in various applications. And there are several challenges in some special scenarios. In this paper, we present a comprehensive survey of these challenges: localization in non-line-of-sight, node selection criteria for localization in energy-constrained network, scheduling the sensor node to optimize the tradeoff between localization performance and energy consumption, cooperative node localization, and localization algorithm in heterogeneous network. Finally, we introduce the evaluation criteria for localization in wireless sensor network

Accurate range-free localization for anisotropic wireless sensor networks

Journal ArticlePosition information plays a pivotal role in wireless sensor network (WSN) applications and protocol/ algorithm design. In recent years, range-free localization algorithms have drawn much research attention due to their low cost and applicability to large-scale WSNs. However, the application of range-free localization algorithms is restricted because of their dramatic accuracy degradation in practical anisotropic WSNs, which is mainly caused by large error of distance estimation. Distance estimation in the existing range-free algorithms usually relies on a unified per hop length (PHL) metric between nodes. But the PHL between different nodes might be greatly different in anisotropic WSNs, resulting in large error in distance estimation. We find that, although the PHL between different nodes might be greatly different, it exhibits significant locality; that is, nearby nodes share a similar PHL to anchors that know their positions in advance. Based on the locality of the PHL, a novel distance estimation approach is proposed in this article. Theoretical analyses show that the error of distance estimation in the proposed approach is only one-fourth of that in the state-of-the-art pattern-driven scheme (PDS). An anchor selection algorithm is also devised to further improve localization accuracy by mitigating the negative effects from the anchors that are poorly distributed in geometry. By combining the locality-based distance estimation and the anchor selection, a range-free localization algorithm named Selective Multilateration (SM) is proposed. Simulation results demonstrate that SM achieves localization accuracy higher than 0.3r, where r is the communication radius of nodes. Compared to the state-of-the-art solution, SM improves the distance estimation accuracy by up to 57% and improves localization accuracy by up to 52% consequently.This work is partially supported by the National Science Foundation of China (61103203, 61173169, 61332004, and 61420106009), the Hong Kong RGC General Research Fund (PolyU 5106/11E), the International Science & Technology Cooperation Program of China (2013DFB10070), and the EU FP7 QUICK project (PIRSES-GA-2013-612652)

Hybrid approach for localization in anisotropic sensor networks

In many real-world applications including agricultural, meterological, military applications, etc, localization techniques are widely used to estimate the geographic locations of sensor nodes based on the precision positions of a few anchors equipped with special hardware. Existing localization algorithms mainly try to improve their accuracy in position estimation by using various heuristic-based or mathematical techniques. Every node in the network follows the same technique to find its physical location. However, each individual method with its own strength can only outperform the others in some but not all nodes. Based on this observation, we develop a hybrid approach for the localization problem. Each node collects the same kind of information. By analysing the information, a node can decide what is the best localization algorithm to use. Different nodes can make their own decisions. Our simulation results reveal that the hybrid approach is effective that it outpeforms existing algorithms. To the best of our knowledge, our work presents the first effort in solving the absolute localization problem by adopting a hybrid approach. © 2006 IEEE.published_or_final_versio

HyBloc: Localization in Sensor Networks with Adverse Anchor Placement

To determine the geographical positions of sensors, numerous localization algorithms have been proposed in recent years. The positions of sensors are inferred from the connectivity between sensors and a set of nodes called anchors which know their precise locations. We investigate the effect of adverse placement and density of anchors on the accuracies of different algorithms. We develop an algorithm called HyBrid Localization (HyBloc) to provide reliable localization service with a limited number of clustered anchors. HyBloc is distributed in nature with reasonable message overhead. Through simulations, we demonstrate that HyBloc provides more accurate location estimates than some existing distributed algorithms when there are only a few anchors. HyBloc also performs well when anchors are clustered together

An iteratively Reweighted Least Square algorithm for RSS-based sensor network localization

In this article, we give a new algorithm for localization based on RSS measurement. There are many measurement methods for localizing the unknown nodes in a sensor network. RSS is the most popular one due to its simple and cheap hardware requirement. However, accurate algorithm based on RSS is needed to obtain the positions of unknown nodes. Recent algorithms such as MDS(Multi-Dimensional Scaling)-MAP, PDM (Proximity Distance Matrix) cannot give accurate results based on RSS as the RSS signals always have large variations. Besides, recent algorithms on sensor network localization ignore the received signal strength (RSS) and thus get a disappointing accuracy. This is because they are mostly focused on the difference between the estimated distance and the real distance. This paper introduces a target function - signal-based maximum likelihood (SML), which uses the maximum likelihood based on the directly measured RSS signal. Inspired by the SMACOF (Scaling by Majorizing A COmplicated Function) algorithm, an iteration surrogate algorithm named IRLS (Iteratively Reweighted Least Square) is introduced to solve the SML. From the simulation results, the IRLS algorithm can give accurate results for RSS positioning. When compared with other popular algorithms such as MDS-MAP, PDM, and SMACOF, the error (distance between the estimated position and the actual position) calculated by IRLS is less than all the other algorithms. In anisotropic network, IRLS also has good performance. © 2011 IEEE.published_or_final_versionThe 2011 IEEE International Conference on Mechatronics and Automation (ICMA 2011), Beijing, China, 7-10 August 2011. In Proceedings of ICMA, 2011, p. 1085-109

HEA-Loc: A robust localization algorithm for sensor networks of diversified topologies

In recent years, localization in a variety of Wireless Sensor Networks (WSNs) is a compelling but elusive goal. Several algorithms that use different methodologies have been proposed to achieve this goal. The performances of these algorithms depend on several factors, such as the sensor node placement, anchor deployment or network topology. In this paper, we propose a robust localization algorithm called Hybrid Efficient and Accurate Localization (HEA-Loc). HEA-Loc combines two techniques, Extended Kalman Filter (EKF) and Proximity-Distance Map (PDM) to improve localization accuracy. It is distributed in nature and works well in various scenarios as it is less susceptible to anchors deployment and the network topology. Furthermore, HEA-Loc has strong robustness and it can work well even the measurement errors are large. Simulation results show that HEA-Loc outperforms existing algorithms in both computational complexity and communication overhead. ©2010 IEEE.published_or_final_versionThe IEEE Wireless Communications and Networking Conference (WCNC 2010), Sydney, NSW., 18-21 April 2010. In Proceedings of WCNC, 2010, p. 1-