Optimization of lifetime in sensor networks (original) (raw)
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Heuristic Solutions for the Lifetime Problem of Wireless Sensor Networks
Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2016
In [5, 7, 8] an analytical model of the lifetime problem of wireless sensor networks is developed. The solution given by the model is not practical for WSNs. Each time, there is a change in a sensor network, the solution needs to be recalculated. Also, it is difficult to build ILP solvers inside the small sensors. Furthermore, when the number of sensor nodes and CHs increases, it quickly becomes infeasible to calculate an optimum solution. As the analytical model is not able to be used to solve complicated networks, heuristic solutions are then examined that can compute the solutions for large sensor networks. Finally, the simulation results of the heuristic solutions are presented and discussed.
Review of lifetime optimization techniques in Wireless Sensor Networks
2015
In the recent years, the technology of wireless sensor networks has gained a lot of importance. Wireless sensor networks are a special case of ad-hoc wireless networks. A wireless sensor network is a collection of sensor nodes that communicate through wireless links to work together to carry out functions. The sensor nodes’ basic function is to monitor the physical and environmental changes in terms of pressure, humidity, temperature etc. (referred as sensing). Sensor nodes have processing, communication and sensing capabilities. WSNs are used in number of diverse scenarios including area monitoring, health care monitoring, air pollution monitoring, natural disaster prevention, industrial health monitoring, calamity prevention etc. Sensor nodes have small batteries with limited power. Large number of sensor nodes in WSN makes it impractical to replace sensor node batteries. Thus the life time of sensor nodes is an important attribute of a wireless sensor network. The life time of a ...
A Survey of Network Lifetime Maximization Techniques in Wireless Sensor Networks
Emerging technologies, such as the Internet of things, smart applications, smart grids and machine-to-machine networks stimulate the deployment of autonomous, self-configuring, large-scale wireless sensor networks (WSNs). Efficient energy utilization is crucially important in order to maintain a fully operational network for the longest period of time possible. Therefore, network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance in terms of extending the flawless operation of battery-constrained WSNs. In this paper, we review the recent developments in WSNs, including their applications, design constraints and lifetime estimation models. Commencing with the portrayal of rich variety definitions of NL design objective used for WSNs, the family of NL maximization techniques is introduced and some design guidelines with examples are provided to show the potential improvements of the different design criteria.
A Randomized Algorithm for Wireless Sensor Network Lifetime Optimization
Proceedings of the 18th ACM International Symposium on QoS and Security for Wireless and Mobile Networks on 18th ACM International Symposium on QoS and Security for Wireless and Mobile Networks
A wireless sensor network consists of a set of sensors and a monitored set of targets (or an area). Typically, there are much more sensors than the targets, but their operation time is limited by the battery capacity. The sensors may be randomly deployed, especially in hard-to-reach areas, such as mountains, forests, battlefields, etc. In this work, we tackle the Maximum-Lifetime Problem, which aims at maximizing the network lifetime by successively activating and deactivating the subsets of sensors ensuring expected minimum coverage rate. To solve the problem, we propose and evaluate a randomized heuristic algorithm for the maximization of network lifetime while satisfying the coverage requirement. The conducted experiments show that the algorithm is competitive with the stateof-the-art approach in terms of obtained schedule lengths. CCS CONCEPTS • Networks → Network algorithms; • Theory of computation → Design and analysis of algorithms.
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.
Network-Lifetime Maximization of Wireless Sensor Networks
Network lifetime (NL) maximization techniques have attracted a lot of research attention owing to their importance for extending the duration of the operations in the battery-constrained wireless sensor networks (WSNs). In this paper, we consider a two-stage NL maximization technique conceived for a fully-connected WSN, where the NL is strictly dependent on the source node's (SN) battery level, since we can transmit information generated at the SN to the destination node (DN) via alternative routes, each having a specific route lifetime (RL) value. During the first stage, the RL of the alternative routes spanning from the SN to the DN is evaluated, where the RL is defined as the earliest time, at which a sensor node lying in the route fully drains its battery charge. The second stage involves the summation of these RL values, until the SN's battery is fully depleted, which constitutes the lifetime of the WSN considered. Each alternative route is evaluated using cross-layer optimization of the power allocation, scheduling and routing operations for the sake of NL maximization for a predetermined per-link target signal-to-interference-plus-noise ratio values. Therefore, we propose the optimal but excessive-complexity algorithm, namely, the exhaustive search algorithm (ESA) and a near-optimal single objective genetic algorithm (SOGA) exhibiting a reduced complexity in a fully connected WSN. We demonstrate that in a high-complexity WSN, the SOGA is capable of approaching the ESA's NL within a tiny margin of 3.02% at a 2.56 times reduced complexity. We also show that our NL maximization approach is powerful in terms of prolonging the NL while striking a tradeoff between the NL and the quality of service requirements.
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.
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.