Network Lifetime Maximization Problem Research Articles

Partial coverage optimization under network connectivity constraints in heterogeneous sensor networks

The maximum network lifetime problem (MLP) in heterogeneous wireless sensor networks under connectivity and coverage constraints is addressed in this paper. Two main variants of the studied problem are considered. The first variant is α-coverage where a portion ((1 - α) percent) of the targets are allowed to be left uncovered. The second one is called β-coverage or β-constraint where each target has a minimum coverage rate β during the network’s lifetime service. When the two variants are considered at the same time, the problem is called αβ−Connected Maximum Lifetime Problem (αβ−CMLP) where we consider both global (whole targets) resp. local (individual target) monitoring level thresholds to improve the coverage quality of the deployed WSN. Unlike earlier works devoted to only α coverage, we deal with both local (α) and global (β) coverage leveling thresholds under network connectivity constraint. One approach to optimize the network’s lifetime is to divide the sensor nodes into Non-Disjoint subsets of sensors, or cover sets, and to schedule these covers with variable activation time periods, so that the global time lifespan of the network is optimized. To this end, we provide both exact and heuristic approaches in this study. First, a novel mathematical Mixed Integer Linear Programming (MILP) is presented to solve the αβ-coverage with network connectivity requirement to optimality. Unfortunately, due to the NP-Completeness of the addressed problem, the MILP’s resolution becomes impracticable for large optimization problems. To remedy this and to cope with large instances, we propose a new exact approach based on column generation able to achieve optimal solutions in reasonable time. In addition, since the CG’s subproblem resolution is also an NP-Hard problem, a new dedicated Heuristic (DH) is designed to solve the CG’s subproblem in polynomial time complexity. Moreover, we propose an exact ILP formulation for the CG’s subproblem if the DH heuristic fails to compute an attractive solution in each iteration of the CG computation process. Finally, several experiments are performed to evaluate the performances of our proposals.

Maximum Network Lifetime Problem with Time Slots and coverage constraints: heuristic approaches

Maximum Network Lifetime Problem with Time Slots and coverage constraints: heuristic approaches

A Lagrangean relaxation approach to lifetime maximization of directional sensor networks

AbstractWe consider the directional sensor network lifetime maximization problem (DSLMP). Given a set of directional sensor and target locations, the problem consists in assigning, at each time unit of a given time horizon, the action radius, the aperture angle, and the orientation direction to all sensors. The objective is to maximize the number of time units when all targets are covered, under certain constraints on sensor available energy. We present a mixed integer nonlinear programming formulation and tackle it by Lagrangean decomposition and subgradient optimization. The algorithm is equipped with a repairing heuristics aimed at finding good‐quality feasible solutions to DSLMP. The results of the application of the proposed approach to a number of problem instances are also reported.

Distributed on-demand clustering algorithm for lifetime optimization in wireless sensor networks

Wireless Sensor Networks (WSNs) play a significant role in Internet of Things (IoT) to provide cost effective solutions for various IoT applications, e.g., wildlife habitat monitoring, but are often highly resource constrained. Hence, preserving energy (or, battery power) of sensor nodes and maximizing the lifetime of WSNs is extremely important. To maximize the lifetime of WSNs, clustering is commonly considered as one of the efficient technique. In a cluster, the role of individual sensor nodes changes to minimize energy consumption, thereby prolonging network lifetime. This paper addresses the problem of lifetime maximization in WSNs by devising a novel clustering algorithm where clusters are formed dynamically. Specifically, we first analyze the network lifetime maximization problem by balancing the energy consumption among cluster heads. Based on the analysis, we provide an optimal clustering technique, in which the cluster radius is computed using alternating direction method of multiplier. Next, we propose a novel On-demand, oPTImal Clustering (OPTIC) algorithm for WSNs. Our cluster head election procedure is not periodic, but adaptive based on the dynamism of the occurrence of events. This on-demand execution of OPTIC aims to significantly reduce computation and message overheads. Experimental results demonstrate that OPTIC improves the energy balance by more than 18% and network lifetime by more than 19% compared to a non-clustering and two clustering solutions in the state-of-the-art.

A genetic approach for the maximum network lifetime problem with additional operating time slot constraints

The maximum network lifetime problem is a well-known and challenging optimization problem which has been addressed successfully with several approaches in the last years. It essentially consists in finding an optimal schedule for sensors activities in a wireless sensor network (WSN) aiming at maximizing the total amount of time during which the WSN is able to perform its monitoring task. In this paper, we consider a new scenario in which, in order to monitor some locations in a geographical area, the sensors need to be active for a fixed amount of time, defined as operating time slot. For this new scenario, we derive an upper bound on the maximum lifetime and propose a genetic algorithm for finding a near-optimal node activity schedule. The performance evaluation results obtained on numerous benchmark instances show the effectiveness of the proposed approach.

Lifetime Maximization in Mobile Edge Computing Networks

Mobile edge computing has emerged as a promising technology to augment the computational capabilities of mobile devices. For a multi-user network in which its users periodically compute their tasks with the help of an edge cloud, we investigate the network lifetime maximization problem based on present user task information. We pursue this objective via a minimum energy efficiency maximization (MEEM) strategy that jointly optimizes the fraction of user task computations offloaded to the cloud and the respective allocation of edge computing and network communication resources across the users. We also investigate the network lifetime maximization problem for the case when the user task information is available for all future time slots, as well. This setting represents an upper bound for the MEEM strategy. Optimal solutions for both investigated strategies are formulated via feasibility testing and geometric programming. We show that MEEM can achieve a 70% lifetime improvement over the state-of-the-art and 460% lifetime improvement over the case of local user task computation only. We also show that for a high value of the maximum tolerable delay for completing the computation tasks of the users, MEEM achieves the globally optimal network lifetime performance. Finally, we show that MEEM achieves a significant reduction (3X) in variation of enabled network lifetime over diverse network topologies, relative to the state-of-the-art.

Genetic algorithm‐based meta‐heuristic for target coverage problem

In wireless sensor networks (WSNs), network lifetime and energy consumption are two important parameters which directly impacts each other. In order to enhance the global network lifetime, one should need to utilise the available sensors' energy in an optimise way. There are several approaches discussed in the literature to maximise the network lifetime for well-known target coverage problem in WSN. The target coverage problem is presented as a maximum network lifetime problem (MLP) and solved heuristically using various approaches. In this study, the authors propose a genetic algorithm (GA)-based meta-heuristic to solve the above said MLP. The GA is a non-linear optimisation solution method which is proven to be better as compared to the column generation or approximation schemes.

NDSC based methods for maximizing the lifespan of randomly deployed wireless sensor networks for infrastructures monitoring

Abstract This paper addresses the problem of wireless sensor network (WSN) lifetime maximization, under limited available energy constraint. The investigations have shown that the valid amount of energy was not used by the existing disjoint set covers (DSC) based scheduling method for WSNs lifetime maximization, because of the DSC constraints. Instead, we suggest in this paper to schedule non-disjoint sets covers (NDSC) for maximizing WSNs lifetime. Thus, we have formulated this problem, using the integer linear programming (ILP) mathematical model, then we developed an approach based on genetic algorithm GA to find the maximal lifespan. As main contributions, we investigated and designed a new method using the NDSC instead of the DSC. This approach removes the latter’s constraint and gives the opportunity to a sensor to participate in more than one cover, and thereby improves significantly the WSNs’ lifetime. We proposed an exact method and a genetic algorithms (GA) for the NDSC efficient scheduling for the WSN lifetime maximization. The exact method lies on the integer linear programming (ILP). For the GA based heuristics, we used a specific arrangement of chromosomes combining several crossover and mutation strategies for encoding the solutions. We provided experimental results for different instances involving sensors with non-identical amount of initial energy and power consumption. In addition, we provided comparative analysis results between the solutions obtained by our both methods and the existing methods based on DSC. The comparisons of the run times and the solutions’ quality revealed the dominance of the solutions yielded by our methods based on the NDSC compared to those based on the DSC.

Selective α-Coverage Based Heuristic in Wireless Sensor Networks

In order to fulfill the coverage requirement, Maximum Network Lifetime Problem (MLP) is to maximize the network lifetime while providing full coverage over a predefined set of targets. There is a variant of coverage problem called α-coverage, where a fraction of targets allowed to be left uncovered to provide partial coverage. This variant of coverage is presented by Maximum Network α-Lifetime Problem (α-MLP) that is the special case of classical MLP. During α-coverage, there has been no choice over targets which are going to be left uncovered, instead, a random set of targets are left uncovered. If we can wisely select the set of targets which are going to be left uncovered, then, the network lifetime can be prolonged further. In this paper, we propose an energy-efficient heuristic to solve α-MLP. Our heuristic not only supports partial coverage, but, it also put a valuable observation on the targets which are left uncovered. Our simulation outcomes show that the network lifetime achieved by our heuristic is far better than those achieved with many existing heuristics for α-coverage.

On the role of symmetry in solving maximum lifetime problem in two-dimensional sensor networks

We analyze continuous and discrete symmetries of the maximum lifetime problem in two dimensional sensor networks.We show how a symmetry of the network and invariance of the problem under a given transformation group G can be utilized to simplify its solution. We prove that for a G-invariant maximum lifetime problem there exists a G-invariant solution.Constraints which follow from the G-invariance allows us to reduce the problem and its solution to the subset of the sensor network. The subset we call an optimal fundamental region of network with respect to the action of the symmetry group G. We analyze in detail solutions of the maximum network lifetime problem invariant under a group of isometry transformations of a two dimensional Euclidean plane.

A hybrid exact approach for maximizing lifetime in sensor networks with complete and partial coverage constraints

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 the literature, to solve both MLP and α-MLP.

Cross-Layer Network Lifetime Maximization in Interference-Limited WSNs

In wireless sensor networks (WSNs), the network lifetime (NL) is a crucial metric since the sensor nodes usually rely on limited energy supply. In this paper, we consider the joint optimal design of the physical, medium access control (MAC), and network layers to maximize the NL of the energy-constrained WSN. The problem of NL maximization can be formulated as a nonlinear optimization problem encompassing the routing flow, link scheduling, transmission rate, and power allocation operations for all active time slots (TSs). The resultant nonconvex rate constraint is relaxed by employing an approximation of the signal-to-interference-plus-noise ratio (SINR), which transforms the problem to a convex one. Hence, the resultant dual problem may be solved to obtain the optimal solution to the relaxed problem with a zero duality gap. Therefore, the problem is formulated in its Lagrangian form, and the Karush–Kuhn–Tucker (KKT) optimality conditions are employed for deriving analytical expressions of the globally optimal transmission rate and power allocation variables for the network topology considered. The nonlinear Gauss–Seidel algorithm is adopted for iteratively updating the rate and power allocation variables using these expressions until convergence is attained. Furthermore, the gradient method is applied for updating the dual variables in each iteration. Using this approach, the maximum NL, the energy dissipation per node, the average transmission power per link, and the lifetime of all nodes in the network are evaluated for a given source rate and fixed link schedule under different channel conditions.

Exact approaches for lifetime maximization in connectivity constrained wireless multi-role sensor networks

In this paper, we consider the duty scheduling of sensor activities in wireless sensor networks to maximize the lifetime. We address full target coverage problems contemplating sensors used for sensing data and transmit it to the base station through multi-hop communication as well as sensors used only for communication purposes. Subsets of sensors (also called covers) are generated. Those covers are able to satisfy the coverage requirements as well as the connection to the base station. Thus, maximum lifetime can be obtained by identifying the optimal covers and allocate them an operation time. The problem is solved through a column generation approach decomposed in a master problem used to allocate the optimal time interval during which covers are used and in a pricing subproblem used to identify the covers leading to maximum lifetime. Additionally, Branch-and-Cut based on Benders’ decomposition and constraint programming approaches are used to solve the pricing subproblem. The approach is tested on randomly generated instances. The computational results demonstrate the efficiency of the proposed approach to solve the maximum network lifetime problem in wireless sensor networks with up to 500 sensors.

Cross-Layer Optimization Scheme Using Cooperative Diversity for Reliable Data Transfer in Wireless Sensor Networks

Cooperative diversity has been shown to provide significant performance gains in wireless networks where communication is impeded by channel fading. In resource constraint networks, the advantages of cooperation can be further exploited by optimally allocating the energy and bandwidth resources among users in a cross-layer way. In this paper, we investigate the problem of transmission power minimization and network lifetime maximization using cooperative diversity for wireless sensor networks, under the constraint of a target end-to-end transmission reliability and a given transmission rate. By utilizing a cross-layer optimization scheme, distributive algorithms which jointly consider routing, relay selection, and power allocation strategies are proposed for the reliability constraint wireless sensor networks. We demonstrate through simulations that the proposed cross-layer cooperative strategies achieve significant energy savings and prolong the network lifetime considerably.

A column generation approach to extend lifetime in wireless sensor networks with coverage and connectivity constraints

This paper addresses the maximum network lifetime problem in wireless sensor networks with connectivity and coverage constraints. In this problem, the purpose is to schedule the activity of a set of wireless sensors, keeping them connected while network lifetime is maximized. Two cases are considered. First, the full coverage of the targets is required, and second only a fraction of the targets has to be covered at any instant of time. An exact approach based on column generation and boosted by GRASP and VNS is proposed to address both of these problems. Finally, a multiphase framework combining these two approaches is built by sequentially using these two heuristics at each iteration of the column generation algorithm. The results show that our proposals are able to tackle the problem efficiently and that combining the two heuristic approaches improves the results significantly.

Cross Layer Optimization for Lifetime Maximization in Wireless Sensor Network Based on Particle Swarm Optimization

Network lifetime is a critical metric in the design of energy-constrained wireless sensor networks. In this paper, we consider the joint cross layer optimization of the physical layer, medium access control layer and routing layer to maximize network lifetime of a multi-sources and single-sink wireless sensor network with energy constraints. We focus on synchronous small-scale sensor network with interference-free link scheduling and practical MPSK link transmission scheme. As the network lifetime maximization problem is a constrained non-convex optimization problem that is difficult to be solved, and the particle swarm optimization algorithm is a good intelligent algorithm, we employ it to solve the problem mentioned above effectively in this paper. The penalty function technique is brought in to work out the constrained optimization problem by converting it to an unconstrained optimization problem. Simulation results show the effectiveness of the proposed algorithm in energy saving and network lifetime maximization, and the particle swarm optimization can solve the network lifetime maximization problem fast and efficiently.

On the use of multiple sinks to extend the lifetime in connected wireless sensor networks

Abstract This paper addresses the maximum network lifetime problem in wireless sensor networks. In this problem, the purpose is to schedule a set of wireless sensors, keeping them connected and guaranteeing that all targets are covered, while network lifetime is maximized. Two variants of this problem are considered; in the first case it is assumed that a single sink (base station) is available, the second case considers the presence of several sinks. To solve the problem, a hybrid Column Generation-GRASP heuristic is proposed. The method is shown to be able to find optimal or near optimal solutions efficiently in both cases.

Load balancing techniques for lifetime maximizing in wireless sensor networks

Energy consumption has been the focus of many studies on Wireless Sensor Networks (WSN). It is well recognized that energy is a strictly limited resource in WSNs. This limitation constrains the operation of the sensor nodes and somehow compromises the long term network performance as well as network activities. Indeed, the purpose of all application scenarios is to have sensor nodes deployed, unattended, for several months or years.This paper presents the lifetime maximization problem in “many-to-one” and “mostly-off” wireless sensor networks. In such network pattern, all sensor nodes generate and send packets to a single sink via multi-hop transmissions. We noticed, in our previous experimental studies, that since the entire sensor data has to be forwarded to a base station via multi-hop routing, the traffic pattern is highly non-uniform, putting a high burden on the sensor nodes close to the base station.In this paper, we propose some strategies that balance the energy consumption of these nodes and ensure maximum network lifetime by balancing the traffic load as equally as possible. First, we formalize the network lifetime maximization problem then we derive an optimal load balancing solution. Subsequently, we propose a heuristic to approximate the optimal solution and we compare both optimal and heuristic solutions with most common strategies such as shortest-path and equiproportional routing. We conclude that through the results of this work, combining load balancing with transmission power control outperforms the traditional routing schemes in terms of network lifetime maximization.

Joint Contention and Sleep Control for Lifetime Maximization in Wireless Sensor Networks

In this paper, we propose a joint control scheme to maximize network lifetime in wireless sensor networks with joint contention and sleep control. For the contention resolution of sensor nodes in the network, we consider the p-persistent contention protocol. Also, the sleep control is introduced with the probability of turning off the transceiver of a sensor node. Using the probabilities of contention and sleep, we formulate the network lifetime maximization problem to satisfy the throughput requirement and each link's signal to noise interference ratio (SINR) requirement. Finally, we demonstrate that the proposed scheme improves the lifetime of wireless sensor networks compared to the existing schemes.

Optimization Algorithm for Energy-efficient Wireless Sensor Network Design Problem

Wireless sensor networks hold promise as a way of monitoring the structural health of railway structures, especially those that are underground or otherwise difficult to access. In this paper, effective algorithms for designing wireless sensor networks are investigated. The authors review optimization problems arising in railway system and highlight the importance of minimizing the total cost of the wireless sensor network in a general communication environment while maximizing the network lifetime in a poor communication environment. A life span algorithm for the network lifetime maximization problem and a Lagrangian heuristic algorithm for the total cost minimization problem are proposed. The effectiveness of these algorithms was verified using randomly generated data and data acquired from a real wireless sensor network. The results indicate that the proposed algorithms can be effectively used in the design of wireless sensor networks for railway structural health monitoring.