INTRODUCTION
Wireless Sensor Networks (WSNs) are distributed embedded systems composed of a large number of low- cost, low-power, multifunctional sensor nodes. The sensor nodes are small in size and communicate wirelessly in short distances. These tiny sensor nodes can perform sensing, data processing and communicating. They are densely deployed in the desired environment.
A sensor network consists of multiple detection stations called sensor nodes, each of which is small, lightweight and portable. Every sensor node is equipped with a transducer, microcomputer, transceiver and power source. The transducer generates electrical signals based on sensed physical effects and phenomena. The microcomputer processes and stores the sensor output. The transceiver, which can be hard-wired or wireless, receives commands from a central computer and transmits data to that computer. The power for each sensor node is derived from the electric utility or from a battery. Sensors use a signal of some sort, from the environment and convert it to readable form for purpose of information transfer. Each sensor node has multiple modalities for sensing the environment such as acoustic, seismic, light, temperature, etc. However, each sensor can sense only one modality at a time.
The sensor nodes in the target tracking WSN use collaboration with the neighboring nodes. This requires data exchange between sensor nodes over an ad hoc wireless network with no central coordination medium.
There are various phenomena in our environment to be sensed by the sensor nodes. Examples include enemy detection and tracking for military purpose, machine monitoring and inventory control system, remote sensing and environmental monitoring. The sensors are typically battery- powered and have limited wireless communication bandwidth. Therefore, energy efficient target tracking systems are needed for less consumption of important energy from sensors.
The key advantage of WSN is the ability to gather useful information from the physical environment and communicate the same to more powerful devices that can process it. The purpose of the wireless sensor networks is to translate the information provided by the environment into digital form, collect and provide it to other computers.
A dense deployment of many nodes covering the same location creates redundancy which provides greater fault tolerance. Again multiple nodes may send the same information to the sink wasting bandwidth and energy.
Sensor nodes either run on batteries or harvest for energy and once deployed they are unattended and expected to operate for a long time. Their energy resources are limited. Thus it is crucial to use it efficiently to extend the network lifetime and service the application.
The energy constraints are more fundamental than the limited processor bandwidth and memory in sensor networks. Energy constraints are unlikely to be solved in the near future with the slow progress in battery capacity and energy scavenging. Moreover, the unattended nature of sensor nodes and the hazardous sensing environment rules out the possibility of replacement. For these reasons, energy awareness becomes the key research challenge for sensor network protocol design. Several researchers have addressed energy conservation recently. In this paper, the system design and implementation architecture is proposed to increase sensor network lifetime for target tracking. The purpose of the system is to support the ability to track the position of moving targets in an energy efficient manner with low energy consumption for the sensing nodes in the network and to extend the life time of a sensor network. This system is the designed architecture of an energy efficient target tracking system using acoustic sensors and photoelectric sensors. It also uses sleep mode and active mode for each acoustic sensor to make these acoustic sensor nodes save their important energy.
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RELATED WORK
Mobile target tracking
Target Tracking Target detection, classification and tracking are the important applications in WSNs. In a target tracking system, a moving target such as a vehicle or a person which passes through wireless sensor network can be tracked using multiple modalities, acoustic, light, seismic, etc. of the sensors. There are centralized and distributed approaches for target tracking in WSN. In a centralized target tracking system, sensors in the sensing network detect the target and send the target signatures to the Base Station (BS) that is also a sensor connecting to a laptop or a processing unit. BS determines whether there is a target or not by using the target signatures sent from the sensing nodes and tracks if there is the target. There may be many sensor nodes transporting the target information to BS at the same time. Therefore, this centralized approach causes the data receiving sensor at BS to die easily because of the information overload.
For a distributed approach, the whole sensor network is divided into regions. There is one manager node in each region. The processing tasks are performed at the manager nodes, not only at base station. To facilitate collaborative data processing for target tracking in sensor networks, the cluster architecture is usually used in which sensors are organized into clusters. Within each cluster, there consists of a cluster head (CH) and several neighboring member sensors. In the conventional cluster architecture, clusters are formed statically at the time of network deployment. The attributes of each cluster, such as the size of a cluster, the area it covers, and the members it possesses, are static. The static clustering architecture offers the sensor networks to save the important energy of the sensors for finding cluster heads.
Formation of a cluster is triggered by certain events such as detection of an approaching target with acoustic sounds. Specifically, when a sensor with sufficient battery and computational power, detects with a high signal-to- noise ratio (SNR), certain signals of interest, it volunteers to act as a CH. On the other hand, a decentralized approach has to be used to ensure that for most of the time only one CH is active in the neighborhood of a target to be tracked. Sensors in the vicinity of the active CH are “invited” to become members of the cluster and will report their sensor data to the CH. In this manner, a cluster is only formed in the area of high event concentration. Sensors do not statically belong to a cluster, and may support different clusters at different times. Moreover, as only one cluster is active in the locality of a target, redundant data is suppressed.
Although dynamic clustering offers many benefits, it consumes the sensor’s essential energy for choosing the next cluster head and trying to form cluster with its neighbors until the target leaves the sensor networks. By focusing on energy conservation and tracking quality, static clustering architecture and distributed approach are adopted in this paper.
In a simple Leader-based Approach, there is only one leader at any time instant where a new measurement is taken. Belief is updated based on Bayesian filtering. The leader selects the new leader node from its neighborhood to handoff the tracking responsibility. Then the current leader communicates the current belief to the new leader. The benefits of this approach are minimum communication of measurements and good scalability with number of targets if the targets are well separated. However, there are also many potential issues with many redundant tracks without proper track initiation and management, difficulties in handling ambiguity with track collisions resulting from redundant tracks or target crossovers. And it uses minimum collaborative signal processing to improve localization and tracking performance.

Increasing Ray search protocol (IRS)
The basic principle of this protocol is to route the search packet along a set of trajectories called rays that maximizes the likelihood of discovering the target information by consuming least amount of energy. Low power optimization techniques developed for conventional ad hoc networks are not sufficient as they do not properly address particular features of embedded and sensor networks. It is not enough to reduce overall energy consumption, it is also important to maximize the lifetime of the entire network, that is, maintain full network connectivity for as long as possible. This paper considers an approach where the sensor nodes go into dormant state (sleep mode) and active state.
Many approaches have been proposed to efficiently utilize energy inWSNs. For example appropriate transmission ways were designed to save energy for multi-hop communication ad- hoc networks. Thus a fundamental problem in WSNs is to maximize the system lifetime under some constraints. Pan et al.proposed two algorithms to find the optimal locations of base stations in the two-tiered WSNs. Their approaches assumed the initial energy and the energy consumption parameters were not the same for all Application Nodes (ANs). If any of the above parameters were the same, their approaches could not work. Eberhart et al.also proposed an algorithm on particle swarm optimization (PSO) to find the Base-station locations for general power consumptions constraints.
Energy based-source localization is motivated by a simple observation that the sound level decreases between the sound source and the listener as distance becomes larger. By modeling the relation between sound level (energy) and distance from the sound source one may estimate the source location using multiple energy reading at different known sensor locations.

STATEMENT OF THE PROBLEM FORMULATION
Wireless sensor networks are deployed to detect changes in various environments. These environments have their constraints which may not allow human reach for maintenance purposes. There is therefore a need to reduce and or even eliminate human involvement in information gathering in event detection and tracking applications. The unattended nature of sensor nodes and the hazardous sensing environment prevents manual battery replacement. For these reasons, energy awareness becomes the key research challenge for sensor network protocol design.
This proposed architecture borrows a lot from the cluster-based approach where the sensor nodes go into two operation modes, the sleep mode and the active mode. The cluster based architecture failed to address the address the balancing of power consumption in the network as some sensor nodes will spend more energy than others within the same network.
The unbalanced nature of energy consumption is as a result of some sensor nodes being in the active mode more than others in the same network. This paper therefore proposes an approach which enables the sensor nodes to send information about their available energy to the base station. Here the nodes within target and which record a lot of energy consumption will be allowed to go into sleep mode while inviting the next nearest sensor node to take over. As a result the lifetime of the network will be increased since the energy spending of the entire sensor nodes will be the same at any one time. In addition, the redundancy of the information sent to the cluster head will be minimized since not all the nodes in the target vicinity will be in active mode, but selected according to their power levels.
REFERENCES
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