S.S. Dhillon

Bio: S.S. Dhillon is an academic researcher from Duke University. The author has contributed to research in topics: Wireless sensor network & Probabilistic logic. The author has an hindex of 2, co-authored 2 publications receiving 934 citations.

Sensor placement for effective coverage and surveillance in distributed sensor networks

Abstract: We present two algorithms for the efficient placement of sensors in a sensor field. The proposed approach is aimed at optimizing the number of sensors and determining their placement to support distributed sensor networks. The optimization framework is inherently probabilistic due to the uncertainty associated with sensor detections. The proposed algorithms address coverage optimization under the constraints of imprecise detections and terrain properties. These algorithms are targeted at average coverage as well as at maximizing the coverage of the most vulnerable grid points. The issue of preferential coverage of grid points (based on relative measures of security and tactical importance) is also modeled. Experimental results for an example sensor field with obstacles demonstrate the application of our approach.

Sensor placement for grid coverage under imprecise detections

Abstract: We present a resource-bounded optimization framework for sensor resource management under the constraints of sufficient grid coverage of the sensor field We offer a unique "minimalistic" view of distributed sensor networks in which sensors transmit/report a minimum amount of sensed data The proposed theory is aimed at optimizing the number of sensors and determine their placement to support such minimalistic sensor networks We represent the sensor field as a grid (two- or three-dimensional) of points The optimization framework is inherently probabilistic due to the uncertainty associated with sensor detections The proposed algorithm addresses coverage optimization under constraints of imprecise detections and terrain properties The issue of preferential coverage of grid points (based on relative measures of security and tactical importance) is also modeled Experimental results for an example sensor field with obstacles demonstrate the application of our approach

Sensor deployment and target localization based on virtual forces

Abstract: The effectiveness of cluster-based distributed sensor networks depends to a large extent on the coverage provided by the sensor deployment. We propose a virtual force algorithm (VFA) as a sensor deployment strategy to enhance the coverage after an initial random placement of sensors. For a given number of sensors, the VFA algorithm attempts to maximize the sensor field coverage. A judicious combination of attractive and repulsive forces is used to determine virtual motion paths and the rate of movement for the randomly-placed sensors. Once the effective sensor positions are identified, a one-time movement with energy consideration incorporated is carried out, i.e., the sensors are redeployed to these positions. We also propose a novel probabilistic target localization algorithm that is executed by the cluster head. The localization results are used by the cluster head to query only a few sensors (out of those that report the presence of a target) for more detailed information. Simulation results are presented to demonstrate the effectiveness of the proposed approach.

Movement-assisted sensor deployment

Abstract: Sensor deployment is an important issue in designing sensor networks. We design and evaluate distributed self-deployment protocols for mobile sensors. After discovering a coverage hole, the proposed protocols calculate the target positions of the sensors where they should move. We use Voronoi diagrams to discover the coverage holes and design three movement-assisted sensor deployment protocols, VEC (vector-based), VOR (Voronoi-based), and minimax based on the principle of moving sensors from densely deployed areas to sparsely deployed areas. Simulation results show that our protocols can provide high coverage within a short deploying time and limited movement.

Strategies and techniques for node placement in wireless sensor networks: A survey

Abstract: The major challenge in designing wireless sensor networks (WSNs) is the support of the functional, such as data latency, and the non-functional, such as data integrity, requirements while coping with the computation, energy and communication constraints. Careful node placement can be a very effective optimization means for achieving the desired design goals. In this paper, we report on the current state of the research on optimized node placement in WSNs. We highlight the issues, identify the various objectives and enumerate the different models and formulations. We categorize the placement strategies into static and dynamic depending on whether the optimization is performed at the time of deployment or while the network is operational, respectively. We further classify the published techniques based on the role that the node plays in the network and the primary performance objective considered. The paper also highlights open problems in this area of research.

Information fusion for wireless sensor networks: Methods, models, and classifications

Abstract: Wireless sensor networks produce a large amount of data that needs to be processed, delivered, and assessed according to the application objectives. The way these data are manipulated by the sensor nodes is a fundamental issue. Information fusion arises as a response to process data gathered by sensor nodes and benefits from their processing capability. By exploiting the synergy among the available data, information fusion techniques can reduce the amount of data traffic, filter noisy measurements, and make predictions and inferences about a monitored entity. In this work, we survey the current state-of-the-art of information fusion by presenting the known methods, algorithms, architectures, and models of information fusion, and discuss their applicability in the context of wireless sensor networks.