, such as location, deployment, and tracking, are fundamental issues, in that many applications rely on them for needed information. In this paper, we address one of the fundamental problems, namely coverage. Coverage in general, answers the questions about quality of service (surveillance) that can be provided

The budgeted maximum coverage problem is: given a collection S of sets with associated costs defined over a domain of weighted elements, and a budget L, find a subset of S 0 ` S such that the total cost of sets in S 0 does not exceed L, and the total weight of elements covered by S 0

One of the fundamental issues in sensor networks is the coverage problem, which reflects how well a sensor network is monitored or tracked by sensors. In this paper, we formulate this problem as a decision problem, whose goal is to determine whether every point in the service area of the sensor

We define a new problem called the Generalized Maximum Coverage Problem (GMC). GMC is an extension of the Budgeted Maximum Coverage Problem, and it has important applications in wireless OFDMA scheduling. We use a variation of the greedy algorithm to produce a ( 2e−1 e−1 +ɛ)-approximation for every

The objective of the coverage problem is to organize the monitoring of targets by sensors in an energy efficient manner so as to maximize the lifetime of coverage. We consider the coverage problem in a network of myopic sensors, such as video sensors and acoustic sensors, which are only able

Motivated by some problems in genome assembling, we investigate properties of spacings from absolutely continuous distributions. Several results on the asymptotic behavior of the maximal uniform and non-uniform k-spacings are presented. Applications of these results to the coverage problem

This paper considers the problem of deploying a mobile sensor network in an unknown environment. A mobile sensor network is composed of a distributed collection of nodes, each of which has sensing, computation, communication and locomotion capabilities. Such networks are capable of self

In this paper we consider the p-coverage problem on the real line. We first give a detailed description of an algorithm to solve the coverage problem without the upper bound p on the number of open facilities. Then we analyze how the structure of the optimal solution changes if the setup costs

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This paper shows that with B = {1, 2,..., n}, the smallest k such that (B × B) − {(j, j) | j ∈ B} = k⋃ (Ci × Di) i=1. This provides a simple is s(n), where s(n) is the smallest integer k such that n � ( k ⌊ k 2 ⌋ set-based formulation and a new proof of a result for boolean ranks [2] and biclique covering of bipartite graphs [1, 5], making these intricate results more accessible. Key words: