Auction-based adaptive sensor activation algorithm for target tracking in wireless sensor networks

Target-tracking algorithms typically organize the network into a logical structure (e.g., tree, cluster, or faces) to enable data fusion and reduce communication costs. These algorithms often predict the target’s future position. In addition to using position forecasts for decision making, we can also use such information to save energy by activating only the set of sensors nearby the target’s trajectory. In this work, we survey of the state of the art of target-tracking techniques in sensor networks. We identify three different formulations for the target-tracking problem and classify the target-tracking algorithms based on common characteristics. Furthermore, for the sake of a better understanding of the target-tracking process, we organize this process in six components: target detection, node cooperation, position computation, future-position estimation, energy management, and target recovery. Each component has different solutions that affect the target-tracking performance.

Target Tracking for Sensor Networks: A Survey

A considerable number of papers in the literature provide solutions for fixed-gain filters, such as the $\alpha$ - $\beta$ filter, that minimize the mean squared error under various dynamic models. This note demonstrates that those specialized models are unnecessary as the tools to explicitly find optimal fixed gains for arbitrary observable systems are already in the literature. The results produced by this method agree with published results for specialized algorithms. The solution of this letter also obviates the need for special “aeolotropic” filter design. Code implementing the algorithm and simulations is available in Matlab on IEEEXplore.

A General Solution to Optimal Fixed-Gain ( $\alpha$ - $\beta$ - $\gamma$ etc.) Filters

Target tracking is an important application of sensor networks, particularly interesting for ecology applications related to wildlife monitoring. In this context, understanding the territorial occupation of animals is fundamental for understanding their habits. In this work, we propose the PRATIQUE--a prediction-based clustering algorithm for tracking targets considering a discrete sensor field divided into cells. This approach is based on two hierarchical levels: static clusters at the first level and dynamic clusters at the second level. This hybrid scheme reduces the cost of communication and ensures that all data generated by an event be delivered to a single node. We use Kalman, Alpha-Beta, or Particle Filters in order to predict the target's position. Prediction is used to prepare the set of nodes that will detect the next event, thereby reducing the message overhead during the tracking task. Results show that prediction errors are close to one cell.

A prediction-based clustering algorithm for tracking targets in quantized areas for wireless sensor networks

Real-time multimedia applications require quality of service (QoS) provisioning in terms of bounds on delay and packet loss along with soft bandwidth guarantees. The shared nature of the wireless communication medium results in interference. Interference combined with the overheads, associated with a medium access control (MAC) protocol, and the implementation of a networking protocol stack limit the available bandwidth in IEEE 802.15.4-based networks and can result in congestion, even if the transmission rates of nodes are well below the maximum bandwidth supported by an underlying communication technology. Congestion degrades the performance of admitted real-time multimedia flow(s). Therefore, in this paper, we experimentally derive the IEEE 802.15.4 channel capacity using an unslotted CSMA-CA MAC protocol. We experimentally derive channel capacity for two cases, that is, when the CSMA-CA protocol is working without ACKs and when it is working with ACKs. Moreover, for both cases, we plot the relationship of offered data load with delay and packet loss rate. Simulation results demonstrate that the parameters that affect the choice of a CSMA-CA MAC layer protocol are end-to-end delay and packet loss requirements of a real-time multimedia flow, data load within the interference range of transmitters along the forwarding path, and length of the forwarding path.

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Contiki-Based IEEE 802.15.4 Channel Capacity Estimation and Suitability of Its CSMA-CA MAC Layer Protocol for Real-Time Multimedia Applications

This paper presents the throughput results obtained from a Wireless Sensor Network testbed, with single and multiple sources in different network deployments and routing architectures. The experimental testbed deployed at IIT Madras consisted of commercially available Crossbow TelosB and MicaZ nodes and a custom-built sensor node based on the DigiNet Xbee chip, with all nodes implementing the Zigbee standard. The networks were deployed in uniform grid topologies in three different deployments with up to 228 nodes. The main aim of the experiments is to analyze the throughput and packet delivery ratio observed with single and multiple sources. The experimental results show that delivery ratio reduces with increase in data rate due to collisions and help characterise the network capacity limits.

Testbed based throughput analysis in a Wireless Sensor Network

The emerging field of wireless sensor networks (WSNs) allows novel methods of designing surveillance and tracking systems. Tracking moving targets in WSNs is a canonical application of practical importance to military as well as civilian fields. Since WSNs are highly constrained in computation, communication and energy resources, the application is required to be designed with minimal resource consumption. The collaborative and distributed approach in WSN is suited for effective utilization of scarce resources. In this paper, a Hierarchical Clustered network architecture has been considered for target tracking. A computationally simple predictive target tracking approach using α-β filter is proposed, evaluated and compared against a Kalman filter based approach.

Alpha-beta filter based target tracking in clustered wireless sensor networks

With an expeditious increase in the vehicle transport in the major cities, it has become more difficult to find the free parking space to park the vehicles. Constant rise in the number of vehicles on the roads has led to increase in the unwanted traffic, drivers frustration, delay in the transportation and increase in the noise and air pollution. Due to lack of proper knowledge of parking space, it has become unable to utilize most of the nearby parking space. This work proposes an innovative approach to guide the vehicle drivers to the parking space using Hierarchical Wireless Sensor Networks. A three sensors per parking slot based technique is used to distinguish the type of parked vehicles and to find the free parking slots in the parking area. Free, partially-occupied and occupied parking slot states information is stored at the Parking Server in the form of parking location and the parking area layout with parking slot states marking on it. Parking Server makes the parking information available to the vehicle drivers by the mean of web and mobile applications for guiding vehicles for parking. The results show that the parking detection with multiple passive sensor nodes per parking slot system is more energy efficient and vehicle parking detection is as accurate as the single active sensor per parking slot system in the existing parking designs.

M-parking: Vehicle parking guidance system using hierarchical Wireless Sensor Networks

Internet of Things (IoT) paradigm is gaining a mountainous attention because of its noteworthy impact on everyday life of the people. Billions of things are expected to be a part of IoT paradigm in the near future, providing enormous applications ranging from home automation, smart environment, health care, etc. One of the major hurdles for IoT to be effective with existing network architectures is a prolonged and seamless connectivity between the things and with the end user. Due to heterogeneity in the characteristics of Wireless Sensor Network (WSN), it has become a most prominent technology to bind the things with the Internet and with the end users. Wireless Sensor Networks are self healing and active in nature, that is, they can sense, instruct, request, respond and can easily be adopted to Wireless Sensor and Actuator Networks (WSAN). Addressing scheme in Wireless Sensor networks has to be foreseen if it has to be deployed in Internet of Things paradigm. This paper proposes a discrete cluster based addressing scheme on IEEE 802.15.4 standard for all the things included in a Wireless Sensor Network based IoT applications. Results have been evaluated with existing IPv6 based addressing and it is found that cluster based addressing scheme on the traditionally used IEEE 802.15.4 communication channel suits well over the end to end IPv6 communication channel.

A discrete addressing scheme for Wireless Sensor Networks based Internet of Things

Target tracking is a well studied application of wireless sensor networks. An interesting addition to this application is the presence of a mobile guided vehicle that is used to intercept the mobile target. The system provides the current (and sometimes predicted) position of the target to the guided vehicle, so that it can move towards the target. The objective is to reach the target in the shortest possible time. In this paper, two approaches, namely, \alpha-\beta predicted position based guidance and \alpha-\beta prediction proportional guidance, have been developed and evaluated for interception efficiency.

Sarang Deshpande

Papers

Testbed based throughput analysis in a Wireless Sensor Network

Alpha-beta filter based target tracking in clustered wireless sensor networks

M-parking: Vehicle parking guidance system using hierarchical Wireless Sensor Networks

A discrete addressing scheme for Wireless Sensor Networks based Internet of Things

On Guided Navigation in Target Tracking Sensor Networks Using Alpha-Beta Filters