Target Tracking in Wireless Sensor Network: The Current State of Art

Target Tracking in Wireless Sensor Network: The Current State of Art

Madhuri Rao (Siksha ‘O' Anusandhan University, India) and Narendra Kumar Kamila (C. V. Raman College of Engineering, India)
DOI: 10.4018/978-1-5225-0501-3.ch017
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Abstract

Wireless Sensor nodes are being employed in various applications like in traffic control, battlefield, and habitat monitoring, emergency rescue, aerospace systems, healthcare systems and in intruder tracking recently. Tracking techniques differ in almost every application of Wireless Sensor Network (WSN), as WSN is itself application specific. The chapter aims to present the current state of art of the tracking techniques. It throws light on how mathematically target tracking is perceived and then explains tracking schemes and routing techniques based on tracking techniques. An insight of how to code localization techniques in matlab simulation tool is provided and analyzed. It further draws the attention of the readers to types of tracking scenarios. Some of the well established tracking techniques are also surveyed for the reader's benefit. The chapter presents with open research challenges that need to be addressed along with target tracking in wireless sensor networks.
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Introduction

It is believed that very soon the world will be seeing a sensor interface in almost all devices such as tablets, smart phone, vehicles, highways, heath managing devices, weaponry and etc. Wireless Sensor network by their very nature promise a huge scope is monitoring and tracking applications. With the help of long haul and remote sensors monitoring large geographical areas is only now possible. Monitoring greenhouse gas emissions using airborne sensors, processing global cyber events using cyber sensors distributed over the internet and space exploration using a network of telescopes are just a few possibilities. Qiang Liu et.al(2015) have in fact, developed a system that collects the state information of a target that is being tracked by the wireless sensor network and which sends this tracking information to a remote fusion center for generating global estimates. However, wireless sensor network has many constraints such as limited battery power, limited processing abilities, limited range of communication, limited bandwidth, limited sensing abilities and limited data storage capacities of its nodes. These very constraints attract researchers to embed these tiny devices as an interface in Systems built with global perspective. Wireless Sensor Network is a collection of such tiny constrained nodes with a localized region of control. However, when collaborated together by the control of a routing strategy or by demanding sink node, it brings added advantages over even traditional Open Systems Interconnection (OSI) Model of seven layer based network. Some of its enhanced features that make it comparatively better are: ease of use, cross layer design, mobility of nodes, scalability, heterogeneity, resilience and possibilities of energy harvesting. Cross layer design brings about optimization of the network; it also allows information to be exchanged between various layers in itself. With the advent of Micro Electro Mechanical Systems (MEMS) technology these devices are being built with more capabilities packed in even lesser volume. MEM enables this cross layer design implementation. Target tracking is one of the fundamental applications of Wireless Sensor Networks (WSN). Tracking techniques differ in almost every application of WSN, as WSN is itself application specific. Tracking a moving vehicle in Vehicle Monitoring System, tracking an intruder in a battlefield zone, tracking an endangered species in its habitat, or tracking the state of health of a bridge, all of these situations need different considerations. This is majorly because of the change in the topology of sensor network, secondly change in density of deployed nodes, change in the environment where the nodes are deployed, and change the physical properties of the sensor nodes. There are many metrics for measuring the quality of tracking, such as tracking error, missing rate, reaction time, etc. Its applications in various areas have unfolded the potential it has in its acceptability and in its widespread use. Its purpose of installation is mostly for tracking. Savic,V., et.al(2015) have proposed a model for tracking phenomena occurring in closed environments but the inherent features of wireless sensor network such as smaller size, smaller memory, smaller computational resources and very limited battery power, call for more research in sensor collaboration in sensing and tracking. Distributed computational techniques with use of minimal power resources are what WSN calls for. In most of the tracking applications researched so far, the WSN is homogenous. WSN may be homogenous with nodes having similar resources or be heterogeneous with certain nodes possessing additional resources such as more battery power. Tracking techniques differ accordingly, however aim to minimize tracking error, missing rate, reaction time while also consuming least battery power. Tracking error can be defined as the deviation between real trajectory and detected trajectory of the target object. Because the real trajectory of target is unknown, detected trajectory is usually regarded as the real trajectory.

Some of the major issues related to target tracking in wireless sensor network can be outlined as following:

  • 1.

    Tracking a mobile objects direction and speed

  • 2.

    Tracking variations in speed of a moving object

  • 3.

    Sensor node fault tolerance

  • 4.

    Identification and classification of multiple targets

  • 5.

    Tracking with mobile sensors

  • 6.

    Energy efficient missing target recovery

  • 7.

    Tracking precision

  • 8.

    Prediction accuracy

  • 9.

    Fault tolerant target tracking

  • 10.

    Minimize tracking error, missing rate, reaction time while also consuming least battery power.

  • 11.

    Tracking in confined and open environments with network topology changing dynamically

  • 12.

    Tracking and monitoring states of dynamic targets

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