Multi Sensor Fusion Model for Detecting Movements of a Target in Wireless Sensor Networks

Abstract
Target tracking is one of the key applications of wireless sensor networks (WSNs). Existing work mostly requires organizing groups of sensor nodes with measurements of a target’s movements or accurate distance measurements from the nodes to the intention, and predicting those activities. These are, however, often not easy to precisely achieve in practice, more than ever in the case of impulsive environments, sensor faults, etc. To explore efficient use of mobile sensors to address the limitations of static WSNs in target detection, in proposed system proposes a data fusion model that enables static and mobile sensors to effectively collaborate in target exposure. An optimal sensor movement scheduling algorithm is developed to minimize the total moving distance of sensors while achieving a set of spatiotemporal performance requirements including high detection probability, low method false alarm rate and enclosed detection delay. The effectiveness of pproposed approach is validated by extensive simulations based on real data traces collected by 23 sensor nodes.
I.Introduction
A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical or ecological conditions, such as temperature, resonance, vibration, pressure, activity or pollutants and together pass their data through the network to a main locality. The more contemporary networks are bi-directional, enabling also to control the movement of the sensors. The enlargement of wireless sensor networks was stimulated by military applications such as battlefield surveillance; today such networks are used in many industrial and consumer applications, such as developed process monitor and control
Deploying wireless sensor networks (WSNs) for mission critical applications (such as intruder detection and tracking) often faces the fundamental challenge of meeting stringent spatial and temporal performance requirements imposed by users. In case of a surveillance application may require any intruder to be detected with a high probability (e.g., > 90%), a low false alarm rate (e.g., < 1%), and a bounded delay (e.g., 20s). Due to the limited capability and unreliable nature of low-power sensor nodes, over-provisioning of sensing coverage seems to be the only choice for a static sensor network to meet such stringent performance requirements. However, over-provisioning only works up to the point where the reality meets the original expectation about the characteristics of physical phenomena and environments. If a new on-demand task arise after deployment and its requirements exceed the statically designed network facility, the task could not be accomplished. For instance, in a battlefield monitoring scenario, sensor failures in a small region may lead to a perimeter breach and the sensor nodes deployed in other regions become useless. Tracking framework, called Face Track, which employ the nodes of a spatial region bordering a target, called a face. Instead of predicting the target location separately in a face, estimate the target’s moving toward another face. Introduce an edge detection algorithm to generate each face further in such a way that the nodes can prepare to the lead of the target’s moving, which greatly helps tracking the goal in a timely fashion and recovering from special cases, e.g., sensor fault, loss of tracking. Also, develop an optimal selection algorithm to select which sensors of faces to query and to forward the tracking data. The challenge is to determine how to perceive the target in a WSN efficiently. the performance of variable brink lengths of the polygon versus adjustable transmission power levels in a WSN for target detection and its energy cost in the WSNs; the impact of the target’s dynamic movements, brink detection, and real-time polygon forwarding in target tracking. In this, propose a data-fusion centric target detection model that features effective collaboration between static and mobile sensors.
The proposed system derives an optimal sensor movement scheduling algorithm that minimizes the total moving distance of sensors under a set of spatiotemporal performance requirements including (1) bounded revealing delay, (2) maximum target detection probability, and (3) low conduct extensive simulations based on real data traces collected by 23 sensors in the SensIT vehicle detection experiments. The results show that a small number of mobile sensors can significan1tlhe detection performance of a network.0 Moreover; the proposed movement scheduling algorithm can achieve satisfactory performance in a range of realistic scenarios.