Exact approaches for lifetime maximization in connectivity constrained wireless multi-role sensor networks (original) (raw)
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The aim of the Connected Maximum Lifetime Problem is to define a schedule for the activation intervals of the sensors deployed inside a region of interest, such that at all times the activated sensors can monitor a set of interesting target locations and route the collected information to a central base station, while maximizing the total amount of time over which the sensor network can be operational. Complete or partial coverage of the targets are taken into account. To optimally solve the problem, we propose a column generation approach which makes use of an appropriately designed genetic algorithm to overcome the difficulty of solving the subproblem to optimality in each iteration. Moreover, we also devise a heuristic by stopping the column generation procedure as soon as the columns found by the genetic algorithm do not improve the incumbent solution. Comparisons with previous approaches proposed in the literature show our algorithms to be highly competitive, both in terms of solution quality and computational time.
A Column Generation based Heuristic for Maximum Lifetime Coverage in Wireless Sensor Networks
Several studies in recent years have considered many strategies for increasing sensor network lifetime. We focus on a centralised management scheme where a large number of sensors are randomly deployed in a region of interest to monitor a set of targets and we propose an adaptive scheduling by dividing sensors into non-disjoint cover sets, each cover set being active in different period of time. In this paper, we design a column generation (CG) method based heuristic for efficiently solving the maximum lifetime coverage problem. We first model the problem with a linear programming (LP) formulation for non-disjoint cover sets where the objective is to maximise the sum of activation times of cover sets, with respect the sensor's battery lifetime. As the number of cover sets may be exponential to the number of sensors and targets, an initial set of cover sets is constructed and other cover sets are generated through the resolution of an auxiliary problem formulated as a integer pro...
Journal of Network and Computer Applications, 2015
In this paper we face the problem of maximizing the amount of time over which a set of target points, located in a given geographic region, can be monitored by means of a wireless sensor network. The problem is well known in the literature as Maximum Network Lifetime Problem (MLP). In the last few years the problem and a number of variants have been tackled with success by means of different resolution approaches, including exact approaches based on column generation techniques. In this work we propose an exact approach which combines a column generation approach with a genetic algorithm aimed at solving efficiently its separation problem. The genetic algorithm is specifically aimed at the Maximum Network α-Lifetime Problem (α-MLP), a variant of MLP in which a given fraction of targets is allowed to be left uncovered at all times; however, since α-MLP is a generalization of MLP, it can be used to solve the classical problem as well. The computational results, obtained on the benchmark instances, show that our approach overcomes the algorithms, available in literature, to solve both MLP and α-MLP.
European Journal of Operational Research, 2012
Wireless sensor networks involve many different real-world contexts, such as monitoring and control tasks for traffic, surveillance, military and environmental applications, among others. Usually, these applications consider the use of a large number of low-cost sensing devices to monitor the activities occurring in a certain set of target locations. We want to individuate a set of covers (that is, subsets of sensors that can cover the whole set of targets) and appropriate activation times for each of them in order to maximize the total amount of time in which the monitoring activity can be performed (network lifetime), under the constraint given by the limited power of the battery contained in each sensor. A variant of this problem considers that each sensor can be activated in a certain number of alternative power levels, which determine different sensing ranges and power consumptions. We present some heuristic approaches and an exact approach based on the Column Generation technique. An extensive experimental phase proves the advantage in terms of solution quality of using adjustable sensing ranges with respect to the classical single range scheme.
An exact approach for maximizing the lifetime of sensor networks with adjustable sensing ranges
Computers & Operations Research, 2012
This paper addresses the problem of target coverage for wireless sensor networks, where the sensing range of sensors can vary, thereby saving energy when only close targets need to be monitored. Two versions of this problem are addressed. In the first version, sensing ranges are supposed to be continuously adjustable (up to the maximum sensing range). In the second version, sensing ranges have to be chosen among a set of predefined values common to all sensors. An exact approach based on a column generation algorithm is proposed for solving these problems. The use of a genetic algorithm within the column generation scheme significantly decreases computation time, which results in an efficient exact approach.
A Column Generation based Heuristic to extend Lifetime in Wireless Sensor Network
Sensors & Transducers, 2012
Lifetime Optimization has received a lot of interest in wireless sensor networks. In our study we propose an energy-aware centralized method by organizing the nodes in non-disjoint cover sets where each cover set is capable of monitoring all the targets of the region of interest and by activate these cover sets successively. We first model the problem with a linear programming (LP) formulation for non-disjoint cover sets where the objective is to maximize the total work time of all cover sets, with respect the sensor's battery lifetime. As the number of cover sets may be huge, exponential to the number of sensors and targets, we develop a resolution method based on a column generation (CG) process. This method requires the resolution of an auxiliary problem formulated as an integer programming (IP) problem. We propose a heuristic for addressing the auxiliary problem which produces very good solutions in lower computational times compared to an exact resolution as shown in the si...
Lecture Notes in Computer Science 6701, 2011, pp. 607–619, 2011
Wireless sensor networks involve a large area of real-world contexts, such as national security, military and environmental control applications, traffic monitoring, among others. These applications generally consider the use of a large number of low-cost sensing devices to monitor the activities occurring in a certain set of target locations. One of the most important issue that is considered in this context is maximizing network lifetime, that is the amount of time in which this monitoring activity can be performed by opportunely switching the sensors from active to sleep mode. Indeed, the lifetime of the network can be maximized by individuating subset of sensors (i.e., covers) and switching among them. Two important aspects need to be taken into account among others: (i) coverage: each determined cover has to cover the entire set of targets, and (ii) connectivity: each cover should provide satisfactory network connectivity so that sensors can communicate for data gathering or data fusion (connected covers). In this paper we consider the problem of determining the maximum network lifetime to monitor all the targets by means of connected covers. We analyze the problem and propose an exact approach based on column generation and two heuristic approaches, namely a greedy algorithm and a GRASP algorithm, to solve it. We analyze the performance of the heuristic approaches by comparing the obtained solutions with those provided by the exact approach when available. Our preliminary experimental results show the proposed solution algorithms to be promising in terms of tradeoff between quality of solutions and computational effort.
Lifetime Maximization for Connected Target Coverage in Wireless Sensor Networks
IEEE/ACM Transactions on Networking, 2008
Recent advances in micro-electro-mechanical systems, digital electronics, and wireless communications have led to the emergence of wireless sensor networks (WSNs), which are comprised of a large number of sensors each with sensing, data processing and communication capabilities. As sensors are unattended low-cost devices, network lifetime is one of the most important and challenging issues in WSNs which defines how long the deployed WSN can function well. Maintaining coverage and connectivity are two fundamental requirements in a WSN. In this thesis, we consider the connected target coverage (CTC) problem with the objective of maximizing the network lifetime by scheduling sensors into multiple sets, each of which can maintain both target coverage and connectivity. We first model the CTC problem as a maximum cover tree (MCT) problem and prove that the MCT problem is NP-Complete. We determine an upper bound and a lower bound on the network lifetime for the MCT problem and then develop a (1 + w)H(M) approximation algorithm to solve it, where w is an arbitrarily small number, H(M) = 1≤i≤M
Maximizing lifetime in wireless sensor networks with multiple sensor families
Computers & Operations Research, 2015
Wireless sensor networks are generally composed of a large number of hardware devices of the same type, deployed over a region of interest in order to perform a monitoring activity on a set of target points. Nowadays, several different types of sensor devices exist, which are able to monitor different aspects of the region of interest (including sound, vibrations, proximity, chemical contaminants, among others) and may be deployed together in a heterogeneous network. In this work, we face the problem of maximizing the amount of time during which such a network can remain operational, while maintaining at all times a minimum coverage guarantee for all the different sensor types. Some global regularity conditions in order to guarantee a fair level of coverage for each sensor type to each target are also taken into account in a second variant of the proposed problem. For both problem variants we developed an exact approach, which is based on a column generation algorithm whose subproblem is either solved heuristically by means of a genetic algorithm or optimally by an appropriate ILP formulation. In our computational tests the proposed genetic algorithm is shown to be able to dramatically speed up the procedure, enabling the resolution of large-scale instances within reasonable computational times.
Maximizing system lifetime in wireless sensor networks
European Journal of Operational Research, 2007
One of the most critical issues in wireless sensor networks is represented by the limited availability of energy on network nodes; thus, making good use of energy is necessary to increase network lifetime. In this paper, we define network lifetime as the time spanning from the instant when the network starts functioning properly, i.e., satisfying the target level of coverage of the area of interest, until the same level of coverage cannot be guaranteed any more due to lack of energy in sensors. To maximize system lifetime, we propose to exploit sensor spatial redundancy by defining subsets of sensors active in different time periods, to allow sensors to save energy when inactive. Two approaches are presented to maximize network lifetime: the first one, based on column generation, must run in a centralized way, whereas the second one is based on a heuristic algorithm aiming at a distributed implementation. To assess their performance and provide guidance to network design, the two approaches are compared by varying several network parameters. The column generation based approach typically yields better solutions, but it may be difficult to implement in practice. Nevertheless it provides both a good benchmark against which heuristics may be compared and a modeling framework which can be extended to deal with additional features, such as reliability.