Krishnendu Chakrabarty

Sensor deployment and target localization based on virtual forces

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.

Grid coverage for surveillance and target location in distributed sensor networks

We present novel grid coverage strategies for effective surveillance and target location in distributed sensor networks. We represent the sensor field as a grid (two or three-dimensional) of points (coordinates) and use the term target location to refer to the problem of locating a target at a grid point at any instant in time. We first present an integer linear programming (ILP) solution for minimizing the cost of sensors for complete coverage of the sensor field. We solve the ILP model using a representative public-domain solver and present a divide-and-conquer approach for solving large problem instances. We then use the framework of identifying codes to determine sensor placement for unique target location, We provide coding-theoretic bounds on the number of sensors and present methods for determining their placement in the sensor field. We also show that grid-based sensor placement for single targets provides asymptotically complete (unambiguous) location of multiple targets in the grid.

Sensor placement for effective coverage and surveillance in distributed sensor networks

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 deployment and target localization in distributed sensor networks

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 the new sensor locations that improve the coverage. Once the effective sensor positions are identified, a one-time movement with energy consideration incorporated is carried out, that is, 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.

Test wrapper and test access mechanism co-optimization for system-on-chip

Test access mechanisms (TAMs) and test wrappers are integral parts of a system-on-chip (SOC) test architecture. Prior research has concentrated on only one aspect of the TAM/wrapper design problem at a time, i.e., either optimizing the TAMs for a set of pre-designed wrappers, or optimizing the wrapper for a given TAM width. In this paper, we address a more general problem, that of carrying out TAM design and wrapper optimization in conjunction. We present an efficient algorithm to construct wrappers that reduce the testing time for cores. Our wrapper design algorithm improves on earlier approaches by also reducing the TAM width required to achieve these lower testing times. We present new mathematical models for TAM optimization that use the core testing time values calculated by our wrapper design algorithm. We further present a new enumerative method for TAM optimization that reduces execution time significantly when the number of TAMs being designed is small. Experimental results are presented for an academic SOC as well as an industrial SOC.

On a new class of codes for identifying vertices in graphs

We investigate a new class of codes for the optimal covering of vertices in an undirected graph G such that any vertex in G can be uniquely identified by examining the vertices that cover it. We define a ball of radius t centered on a vertex /spl upsi/ to be the set of vertices in G that are at distance at most t from /spl upsi/. The vertex /spl upsi/ is then said to cover itself and every other vertex in the ball with center /spl upsi/. Our formal problem statement is as follows: given an undirected graph G and an integer t/spl ges/1, find a (minimal) set C of vertices such that every vertex in G belongs to a unique set of balls of radius t centered at the vertices in C. The set of vertices thus obtained constitutes a code for vertex identification. We first develop topology-independent bounds on the size of C. We then develop methods for constructing C for several specific topologies such as binary cubes, nonbinary cubes, and trees. We also describe the identification of sets of vertices using covering codes that uniquely identify single vertices. We develop methods for constructing optimal topologies that yield identifying codes with a minimum number of codewords. Finally, we describe an application of the theory developed in this paper to fault diagnosis of multiprocessor systems.

Test Challenges for 3D Integrated Circuits

One of the challenges for 3D technology adoption is the insufficient understanding of 3D testing issues and the lack of DFT solutions. This article describes testing challenges for 3D ICs, including problems that are unique to 3D integration, and summarizes early research results in this area. Researchers are investigating various 3D IC manufacturing processes that are particularly relevant to testing and DFT. In terms of the process and the level of assembly that 3D ICs require, we can broadly classify the techniques as monolithic or as die stacking.

On computing mobile agent routes for data fusion in distributed sensor networks

The problem of computing a route for a mobile agent that incrementally fuses the data as it visits the nodes in a distributed sensor network is considered. The order of nodes visited along the route has a significant impact on the quality and cost of fused data, which, in turn, impacts the main objective of the sensor network, such as target classification or tracking. We present a simplified analytical model for a distributed sensor network and formulate the route computation problem in terms of maximizing an objective function, which is directly proportional to the received signal strength and inversely proportional to the path loss and energy consumption. We show this problem to be NP-complete and propose a genetic algorithm to compute an approximate solution by suitably employing a two-level encoding scheme and genetic operators tailored to the objective function. We present simulation results for networks with different node sizes and sensor distributions, which demonstrate the superior performance of our algorithm over two existing heuristics, namely, local closest first and global closest first methods.

A distributed coverage- and connectivity-centric technique for selecting active nodes in wireless sensor networks

Due to their low cost and small form factors, a large number of sensor nodes can be deployed in redundant fashion in dense sensor networks. The availability of redundant nodes increases network lifetime as well as network fault tolerance. It is, however, undesirable to keep all the sensor nodes active at all times for sensing and communication. An excessive number of active nodes lead to higher energy consumption and it places more demand on the limited network bandwidth. We present an efficient technique for the selection of active sensor nodes in dense sensor networks. The active node selection procedure is aimed at providing the highest possible coverage of the sensor field, i.e., the surveillance area. It also assures network connectivity for routing and information dissemination. We first show that the coverage-centric active nodes selection problem is NP-complete. We then present a distributed approach based on the concept of a connected dominating set (CDS). We prove that the set of active nodes selected by our approach provides full coverage and connectivity. We also describe an optimal coverage-centric centralized approach based on integer linear programming. We present simulation results obtained using an ns2 implementation of the proposed technique.

Multiresolution data integration using mobile agents in distributed sensor networks

We describe the use of the mobile agent paradigm to design an improved infrastructure for data integration in a distributed sensor network (DSN). We use the acronym MADSN to denote the proposed mobile-agent-based DSN. Instead of moving data to processing elements for data integration, as is typical of a client/server paradigm, MADSN moves the processing code to the data locations. This saves network bandwidth and provides an effective means for overcoming network latency, since large data transfers are avoided. Our major contributions are the use of mobile agent in DSN for distributed data integration and the evaluation of performance between DSN and MADSN approaches. We develop an enhanced multiresolution integration (MRI) algorithm where multiresolution analysis is applied at a local node before accumulating the overlap function by mobile agent. Compared to the MRI implementation in DSN, the enhanced integration algorithm saves up to 90% of the data transfer time. We develop objective functions to evaluate the performance between DSN and MADSN approaches. For a given set of network parameters, we analyze the conditions under which MADSN performs better than DSN and determine the condition under which MADSN reaches its optimum performance level.