Adaptive energy efficient sensor scheduling for wireless sensor networks

In this paper, we consider an adaptive energy efficient sensor scheduling mechanism. We consider a wireless sensor network where the sink sends queries form time to time, and sensors are equipped with one or more sensing components. Our goal is to design an adaptive sensor scheduling mechanism to choose sets of active sensors to work alternatively such that different types of queries are served, the global connectivity requirements can be met, and network lifetime is maximized. A connected dominating set (CDS) based localized mechanism is proposed. Initially, a basic backbone is constructed, then when a query is issued, new sensors are activated locally such that to meet the requirements of the query and global connectivity. When a query expires, some sensors return to sleep and the CDS is restored. Our simulation results show that the solution is effective and it improved network lifetime.     

Modelling and solving optimal placement problems in wireless sensor networks

In this work, the optimal sensor displacement problem in wireless sensor networks is addressed. It is assumed that a network, consisting of independent, collaborative and mobile nodes, is available. Starting from an initial configuration, the aim is to define a specific sensors displacement, which allows the network to achieve high performance, in terms of energy consumption and travelled distance. To mathematically represent the problem under study, different innovative optimization models are proposed and defined, by taking into account different performance objectives. An extensive computational phase is carried out in order to assess the behaviour of the developed models in terms of solution quality and computational effort. A comparison with distributed approaches is also given, by considering different scenarios.     

Optimization architecture for joint multi-path routing and scheduling in wireless mesh networks

In Wireless Mesh Networks (WMN), the optimal routing of data depends on the link capacities which are determined by link scheduling. The optimal performance of the network, therefore, can only be achieved by joint routing and scheduling optimization. Although the joint single-path routing and scheduling optimization problem has been extensively studied, its multi-path counterpart within wireless mesh networks has not yet been fully investigated. In this paper, we present an optimization architecture for joint multi-path QoS routing and the underlying wireless link scheduling in wireless mesh networks. By employing the contention matrix to represent the wireless link interference, we formulate a utility maximization problem for the joint multi-path routing and MAC scheduling and resolve it using the primal–dual method. Since the multi-path routing usually results in the non-strict concavity of the primal objective function, we first introduce the Proximal Optimization Algorithm to get around such difficulty. We then propose an algorithm to solve the routing subproblem and the scheduling subproblem via the dual decomposition. Simulations demonstrate the efficiency and correctness of our algorithm.     

A column generation based heuristic for sensor placement,activity scheduling and data routing in wireless sensor networks

A wireless sensor network is a network consisting of distributed autonomous electronic devices called sensors. In this work, we develop a mixed-integer linear programming model to maximize the network lifetime by optimally determining locations of sensors and sinks, sensor-to-sink data flows, and activity schedules of the deployed sensors subject to coverage, flow conservation, energy consumption and budget constraints. Since solving this model is difficult except for very small instances, we propose a heuristic method which works on a reformulation of the problem. In the first phase of this heuristic, the linear programming relaxation of the reformulation is solved by column generation. The second phase consists of constructing a feasible solution for the original problem using the columns obtained in the first phase. Computational experiments conducted on a set of test instances indicate that both the accuracy and the efficiency of the proposed heuristic is quite promising.     

Maximizing system lifetime in wireless sensor networks

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.     

Maximizing lifetime of a wireless sensor network via joint optimizing sink placement and sensor-to-sink routing

Wireless sensor networks typically contain hundreds of sensors. The sensors collect data and relay it to sinks through single hop or multiple hop paths. Sink deployment significantly influences the performance of a network. Since the energy capacity of each sensor is limited, optimizing sink deployment and sensor-to-sink routing is crucial. In this paper, this problem is modeled as a mixed integer optimization problem. Then, a novel layer-based diffusion particle swarm optimization method is proposed to solve this large-scaled optimization problem. In particular, two sensor-to-sink binding algorithms are combined as inner layer optimization to evaluate the fitness values of the solutions. Compared to existing methods that the sinks are selected from candidate positions, our method can achieve better performance since they can be placed freely within a geometrical plane. Several numerical examples are used to validate and demonstrate the performance of our method. The reported numerical results show that our method is superior to those existing. Furthermore, our method has good scalability which can be used to deploy a large-scaled sensor network.     

On approximation of dominating tree in wireless sensor networks

We study the following problem: Given a weighted graph G = (V, E, w) with \({w: E \rightarrow \mathbb{Z}^+}\) , the dominating tree (DT) problem asks us to find a minimum total edge weight tree T such that for every \({v \in V}\) , v is either in T or adjacent to a vertex in T. To the best of our knowledge, this problem has not been addressed in the literature. Solving the DT problem can yield a routing backbone for broadcast protocols since (1) each node does not have to construct their own broadcast tree, (2) utilize the virtual backbone to reduce the message overhead, and (3) the weight of backbone representing the energy consumption is minimized. We prove the hardness of this problem, including the inapproximability result and present an approximation algorithm together with an efficient heuristic. Finally, we verify the effectiveness of our proposal through simulation.     

Maximum lifetime coverage preserving scheduling algorithms in sensor networks

In wireless sensor networks, when each target is covered by multiple sensors, sensors can take turns to monitor the targets in order to extend the lifetime of the network. In this paper, we address how to improve network lifetime through optimal scheduling of sensor nodes. We present two algorithms to achieve the maximum lifetime while maintaining the required coverage: a linear programming-based exponential-time exact solution, and an approximation algorithm. Numerical simulation results from the approximation algorithm are compared to the exact solution and show a high degree of accuracy and efficiency.     

Approximation for a scheduling problem with application in wireless networks

A network of many sensors and a base station that are deployed over a region is considered. Each sensor has a transmission range, an interference range and a carrier sensing range, which are r, αr and βr, respectively. In this paper, we study the minimum latency conflict-aware many-to-one data aggregation scheduling problem: Given locations of sensors along with a base station, a subset of all sensors, and parameters r, α and β, to find a schedule in which the data of each sensor in the subset can be transmitted to the base station with no conflicts, such that the latency is minimized. We designe an algorithm based on maximal independent sets, which has a latency bound of (a + 19b)R + Δb ? a + 5 time slots, where a and b are two constant integers relying on α and β, Δ is the maximum degree of network topology, and R is the trivial lower bound of latency. Here Δ contributes to an additive factor instead of a multiplicative one, thus our algorithm is nearly a constant (a + 19b)-ratio.     

Optimal scheduling and power allocation in wireless networks with heavy traffic

This paper addresses the problem of joint transmit power allocation and time slot scheduling in a wireless communication system with time varying traffic. The system is handled by a single base station transmitting over time varying channels. This may be the case in practice of a hybrid TDMA-CDMA (Time Division Multiple Access-Code Division Multiple Access) system. The operating time horizon is divided into time slots; a fixed amount of power is available at each time slot. The users share each time slot and the power available at this time slot with the objective of minimizing the expected total queue length. The problem is reformulated, via a heavy traffic approximation, as the optimal control of a reflected diffusion in the positive orthant. We establish a closed form solution for the obtained control problem. The main feature that makes it possible is an astute choice of some auxiliary weighting matrices in the cost rate. The proposed solution relies also on the knowledge of the covariance matrix of the non-standard multi-dimensional Wiener process which is the driving process in the reflected diffusion. We then compute this covariance matrix given the stationary distribution of the multi-dimensional channel process. Further stochastic analysis is provided: the cost variance, and the Fokker–Planck equation for the distribution density of the queue length.     

Hybrid approximation for minimum-cost target coverage in wireless sensor networks

This paper presents a hybrid approximation scheme for the Max-SNP-complete minimum-cost target coverage problem in wireless sensor networks. LP-rounding and set-cover selection are polynomial-time approximations for this problem. Our hybrid scheme combines these two methods using a crafted convex combination. We show that the hybrid scheme with appropriately chosen coefficients produces much better approximations than either of the two methods used alone. We show that, through a large number of numerical experiments, the hybrid scheme never exceeds an approximation ratio of 1.14, providing up to 14.86% improvement over the best approximations previously known.     

Modeling and optimization of energy efficient routing in wireless sensor networks

Wireless sensor networks (WSNs) have important applications in remote environmental monitoring and target tracking. The development of WSNs in recent years has been facilitated by the availability of sensors that are smaller, less expensive, and more intelligent. The design of a WSN depends significantly on its desired applications and must take into account factors such as the environment, the design objectives of the application, the associated costs, the necessary hardware, and any applicable system constraints. In this study, we propose mathematical models for a routing protocol (network design) under particular resource restrictions within a wireless sensor network. We consider two types of constraints: the distance between the linking sensors and the energy used by the sensors. The proposed models aim to identify energy-efficient paths that minimize the energy consumption of the network from the source sensor to the base station. The computational results show that the presented models can be used efficiently and applied to other network design contexts with resource restrictions (e.g., to multi-level supply chain networks).     

Approximation algorithms for minimum broadcast schedule problem in wireless sensor networks

A wireless sensor network usually consists of a large number of sensor nodes deployed in a field. One of the major communication operations is to broadcast a message from one node to the rest of the others. In this paper, we adopt the conflict-free communication model and study how to compute a transmission schedule that determines when and where a node should forward the message so that all nodes could receive the message in minimum time. We give two approximation algorithms for this NP-hard problem that have better theoretically guaranteed performances than the existing algorithms. The proposed approach could be applied to some other similar problems.     

Key pre-distribution in a non-uniform rectangular grid for wireless sensor networks

A deterministic key pre-distribution scheme is proposed in the paper. The distribution of keys to the nodes precedes a virtual arrangement of the nodes into a non-uniform rectangular grid structure. Distribution of keys is based on projective planes and pairwise connectivity. With small memory requirements, the nodes induce a network which offers a trade-off between connectivity and resilience. The impact of resilience and connectivity can be controlled by choosing the number of rows and columns suitably. Another significant aspect of the proposed scheme is that the path between any two nodes is not unique, which leads to a well-connected network.     

Approximation algorithm for minimum power partial multi-coverage in wireless sensor networks

Journal of Global Optimization - In this paper, we consider the wireless sensor network in which the power of each sensor is adjustable. Given a set of sensors and a set of targets, we study a...     

Improved algorithm for broadcast scheduling of minimal latency in wireless ad hoc networks

A wide range of applications for wireless ad hoc networks are time-critical and impose stringent requirement on the communication latency. One of the key communication operations is to broadcast a message from a source node. This paper studies the minimum latency broadcast scheduling problem in wireless ad hoc networks under collision-free transmission model. The previously best known algorithm for this NP-hard problem produces a broadcast schedule whose latency is at least 648（rmax/rmin）^2 times that of the optimal schedule, where rmax and rmin are the maximum and minimum transmission ranges of nodes in a network, respectively. We significantly improve this result by proposing a new scheduling algorithm whose approximation performance ratio is at most （1 ＋ 2rmax/rmin）^2＋32, Moreover, under the proposed scheduling each node just needs to forward a message at most once.