148 research outputs found
Set It and Forget It: Approximating the Set Once Strip Cover Problem
We consider the Set Once Strip Cover problem, in which n wireless sensors are
deployed over a one-dimensional region. Each sensor has a fixed battery that
drains in inverse proportion to a radius that can be set just once, but
activated at any time. The problem is to find an assignment of radii and
activation times that maximizes the length of time during which the entire
region is covered. We show that this problem is NP-hard. Second, we show that
RoundRobin, the algorithm in which the sensors simply take turns covering the
entire region, has a tight approximation guarantee of 3/2 in both Set Once
Strip Cover and the more general Strip Cover problem, in which each radius may
be set finitely-many times. Moreover, we show that the more general class of
duty cycle algorithms, in which groups of sensors take turns covering the
entire region, can do no better. Finally, we give an optimal O(n^2 log n)-time
algorithm for the related Set Radius Strip Cover problem, in which all sensors
must be activated immediately.Comment: briefly announced at SPAA 201
Average Case Network Lifetime on an Interval with Adjustable Sensing Ranges
Given n sensors on an interval, each of which is equipped with an adjustable sensing radius and a unit battery charge that drains in inverse linear proportion to its radius, what schedule will maximize the lifetime of a network that covers the entire interval? Trivially, any reasonable algorithm is at least a 2-approximation for this Sensor Strip Cover problem, so we focus on developing an efficient algorithm that maximizes the expected network lifetime under a random uniform model of sensor distribution. We demonstrate one such algorithm that achieves an expected network lifetime within 12 % of the theoretical maximum. Most of the algorithms that we consider come from a particular family of RoundRobin coverage, in which sensors take turns covering predefined areas until their battery runs out
Maximizing Barrier Coverage Lifetime with Mobile Sensors
Sensor networks are ubiquitously used for detection and tracking and as a
result covering is one of the main tasks of such networks. We study the problem
of maximizing the coverage lifetime of a barrier by mobile sensors with limited
battery powers, where the coverage lifetime is the time until there is a
breakdown in coverage due to the death of a sensor. Sensors are first deployed
and then coverage commences. Energy is consumed in proportion to the distance
traveled for mobility, while for coverage, energy is consumed in direct
proportion to the radius of the sensor raised to a constant exponent. We study
two variants which are distinguished by whether the sensing radii are given as
part of the input or can be optimized, the fixed radii problem and the variable
radii problem. We design parametric search algorithms for both problems for the
case where the final order of the sensors is predetermined and for the case
where sensors are initially located at barrier endpoints. In contrast, we show
that the variable radii problem is strongly NP-hard and provide hardness of
approximation results for fixed radii for the case where all the sensors are
initially co-located at an internal point of the barrier
Efficiently Realizing Interval Sequences
We consider the problem of realizable interval-sequences. An interval
sequence comprises of integer intervals such that , and is said to be graphic/realizable if there exists a
graph with degree sequence, say, satisfying the condition
, for each . There is a characterisation
(also implying an verifying algorithm) known for realizability of
interval-sequences, which is a generalization of the Erdos-Gallai
characterisation for graphic sequences. However, given any realizable
interval-sequence, there is no known algorithm for computing a corresponding
graphic certificate in time.
In this paper, we provide an time algorithm for computing a
graphic sequence for any realizable interval sequence. In addition, when the
interval sequence is non-realizable, we show how to find a graphic sequence
having minimum deviation with respect to the given interval sequence, in the
same time. Finally, we consider variants of the problem such as computing the
most regular graphic sequence, and computing a minimum extension of a length
non-graphic sequence to a graphic one.Comment: 19 pages, 1 figur
The Generalized Microscopic Image Reconstruction Problem
This paper presents and studies a generalization of the microscopic image reconstruction problem (MIR) introduced by Frosini and Nivat [Andrea Frosini and Maurice Nivat, 2007; Nivat, 2002]. Consider a specimen for inspection, represented as a collection of points typically organized on a grid in the plane. Assume each point x has an associated physical value l_x, which we would like to determine. However, it might be that obtaining these values precisely (by a surgical probe) is difficult, risky, or impossible. The alternative is to employ aggregate measuring techniques (such as EM, CT, US or MRI), whereby each measurement is taken over a larger window, and the exact values at each point are subsequently extracted by computational methods.
In this paper we extend the MIR framework in a number of ways. First, we consider a generalized setting where the inspected object is represented by an arbitrary graph G, and the vector l in R^n assigns a value l_v to each node v. A probe centered at a vertex v will capture a window encompassing its entire neighborhood N[v], i.e., the outcome of a probe centered at v is P_v = sum_{w in N[v]} l_w. We give a criterion for the graphs for which the extended MIR problem can be solved by extracting the vector l from the collection of probes, P^- = {P_v | v in V}.
We then consider cases where such reconstruction is impossible (namely, graphs G for which the probe vector P is inconclusive, in the sense that there may be more than one vector l yielding P). Let us assume that surgical probes (whose outcome at vertex v is the exact value of l_v) are technically available to us (yet are expensive or risky, and must be used sparingly). We show that in such cases, it may still be possible to achieve reconstruction based on a combination of a collection of standard probes together with a suitable set of surgical probes. We aim at identifying the minimum number of surgical probes necessary for a unique reconstruction, depending on the graph topology. This is referred to as the Minimum Surgical Probing problem (MSP).
Besides providing a solution for the above problems for arbitrary graphs, we also explore the range of possible behaviors of the Minimum Surgical Probing problem by determining the number of surgical probes necessary in certain specific graph families, such as perfect k-ary trees, paths, cycles, grids, tori and tubes
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