Target Tracking in Wireless Sensor Network

Wireless Sensor Networks (WSNs) consist of small nodes with sensing, computation, and wireless communications capabilities. In this paper target tracking using dynamic clustering technique has been presented. The dynamic clustering mechanism proposed performs the clustering along the route of the target movement with minimum numbers of sensor nodes to track the target object. The sensors detecting the object need to transmit the sensing data and identification. Sensors forming clusters are termed as core sensors. Within each cluster, the core sensors are selected based on the estimated signal strength since the nodes closer to the targets having larger measurements have a higher probability of becoming core sensors. The core sensors are used to compute the location of a target based on the locations of the neighbouring nodes. These core sensors send this information to the corresponding Cluster Head (CH), using which the target localization is processed. The position of moving object is detected by object moving algorithm. The location is sent to sink from CH node. Target tracking is used in traffic tracking and vehicle tracking. DOI: 10.17762/ijritcc2321-8169.16047

Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize

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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)

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Markov decision processes with applications in wireless sensor networks: A survey

Ministry of Education, Singapore under its Academic Research Funding Tier