On efficient deployment of sensors on planar grid

One practical goal of sensor deployment in the design of distributed sensor systems is to achieve an optimal monitoring and surveillance of a target region. The optimality of a sensor deployment scheme is a tradeoff between implementation cost and coverage quality levels. In this paper, we consider a probabilistic sensing model that provides different sensing capabilities in terms of coverage range and detection quality with different costs. A sensor deployment problem for a planar grid region is formulated as a combinatorial optimization problem with the objective of maximizing the overall detection probability within a given deployment cost. This problem is shown to be NP-complete and an approximate solution is proposed based on a two-dimensional genetic algorithm. The solution is obtained by the specific choices of genetic encoding, fitness function, and genetic operators such as crossover, mutation, translocation for this problem. Simulation results of various problem sizes are presented to show the benefits of this method as well as its comparative performance with a greedy sensor placement method

Clinical features, laboratory and molecular findings of children with leukocyte adhesion deficiency type-III from a single center in India

Leukocyte adhesion deficiency (LAD) Type-III is caused by homozygous mutations in FERMT3 causing Kindlin-3 deficiency. Here we describe three children with molecularly proven LAD-III presenting with neonatal onset mucocutaneous bleeding, infections and persistent neutrophilic leukocytosis. CD18 and CD11a expression on neutrophils was normal in all three, thus ruling out LAD-I. All three had normal platelet glycoprotein expression. Platelet aggregation studies in P2 showed an abnormality similar to Glanzmann thrombasthenia. This article aims to highlight clinical and laboratory clues to the diagnosis of LAD-III, aiding prompt administration of prophylaxis and curative therapy of haematopoietic stem cell transplant

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

Abstract—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. Index Terms—Genetic algorithms, mobile agents, distributed sensor networks.