Optimal base station selection for anycast routing in wireless sensor networks

Energy constraints have a significant impact on the design and operation of wireless sensor networks. This paper investigates the base station (BS) selection (or anycast) problem in wireless sensor networks. A wireless sensor network having multiple BSs (data sink nodes) is considered. Each source node must send all its locally generated data to only one of the BSs. To maximize network lifetime, it is essential to optimally match each source node to a particular BS and find an optimal routing solution. A polynomial time heuristic is proposed for optimal BS selection and anycast via a sequential fixing procedure. Through extensive simulation results, it is shown that this algorithm has excellent performance behavior and provides a near-optimal solution.     

Fault-tolerant monitor placement for out-of-band wireless sensor network monitoring

Monitoring a sensor network to quickly detect faults is important for maintaining the health of the network. Out-of-band monitoring, i.e., deploying dedicated monitors and transmitting monitoring traffic using a separate channel, does not require instrumenting sensor nodes, and hence is flexible (can be added on top of any application) and energy conserving (not consuming resources of the sensor nodes). In this paper, we study fault-tolerant out-of-band monitoring for wireless sensor networks. Our goal is to place a minimum number of monitors in a sensor network so that all sensor nodes are monitored by k distinct monitors, and each monitor serves no more than w sensor nodes. We prove that this problem is NP-hard. For small-scale network, we formulate the problem as an Integer Linear Programming (ILP) problem, and obtain the optimal solution. For large-scale network, the ILP is not applicable, and we propose two algorithms to solve it. The first one is a ln(kn) approximation algorithm, where n is the number of sensor nodes. The second is a simple heuristic scheme that has much shorter running time. We evaluate our algorithms using extensive simulation. In small-scale networks, the latter two algorithms provide results close to the optimal solution from the ILP for relatively dense networks. In large-scale networks, the performance of these two algorithms are similar, and for relatively dense networks, the number of monitors required by both algorithms is close to a lower bound.     

Network Lifetime Maximization for Estimation in Multihop Wireless Sensor Networks

We consider the distributed estimation by a network consisting of a fusion center and a set of sensor nodes, where the goal is to maximize the network lifetime, defined as the estimation task cycles accomplished before the network becomes nonfunctional. In energy-limited wireless sensor networks, both local quantization and multihop transmission are essential to save transmission energy and thus prolong the network lifetime. The network lifetime optimization problem includes three components: i) optimizing source coding at each sensor node, ii) optimizing source throughput of each sensor node, and iii) optimizing multihop routing path. Fortunately, source coding optimization can be decoupled from source throughput and multihop routing path optimization, and is solved by introducing a concept of equivalent 1-bit MSE function. Based on the optimal source coding, the source throughput and multihop routing path optimization is formulated as a linear programming (LP) problem, which suggests a new notion of character-based routing. The proposed algorithm is optimal and the simulation results show that a significant gain is achieved by the proposed algorithm compared with heuristic methods.     

An optimal algorithm for solving partial target coverage problem in wireless sensor networks

This paper deals with the partial target coverage problem in wireless sensor networks under a novel coverage model. The most commonly used method in previous literature on the target coverage problem is to divide continuous time into discrete slots of different lengths, each of which is dominated by a subset of sensors while setting all the other sensors into the sleep state to save energy. This method, however, suffers from shortcomings such as high computational complexity and no performance bound. We showed that the partial target coverage problem can be optimally solved in polynomial time. First, we built a linear programming formulation, which considers the total time that a sensor spends on covering targets, in order to obtain a lifetime upper bound. Based on the information derived in previous formulation, we developed a sensor assignment algorithm to seek an optimal schedule meeting the lifetime upper bound. A formal proof of optimality was provided. We compared the proposed algorithm with the well‐known column generation algorithm and showed that the proposed algorithm significantly improves performance in terms of computational time. Experiments were conducted to study the impact of different network parameters on the network lifetime, and their results led us to several interesting insights. Copyright © 2011 John Wiley & Sons, Ltd.     

Base Station Location -Aware Optimization Model of the Lifetime of Wireless Sensor Networks

Recently, wireless sensor networks (WSNs) have been progressively applied in various fields and areas. However, its limited energy resources is indisputably one of the weakest point that strongly affects the network’s lifetime. A WSN consists of a sensor node set and a base station. The initial energy of each sensor node will be depleted continuously during data transmission to the base station either directly or through intermediate nodes, depending on the distance between sending and receiving nodes. This paper consider determining an optimal base station location such that the energy consumption is kept lowest, maximizing the network’s lifetime and propose a nonlinear programming model for this optimizing problem. Our proposed method for solving this problem is to combine methods mentioned in [1] respectively named the centroid, the smallest total distances, the smallest total squared distances and two greedy methods. Then an improved greedy method using a LP tool provided in Gusek library is presented. Finally, all of the above methods are compared with the optimized solution over 30 randomly created data sets. The experimental results show that a relevant location for the base station is essential.     

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.     

Maximizing Lifetime for Data Aggregation in Wireless Sensor Networks

This paper studies energy efficient routing for data aggregation in wireless sensor networks. Our goal is to maximize the lifetime of the network, given the energy constraint on each sensor node. Using linear programming (LP) formulation, we model this problem as a multicommodity flow problem, where a commodity represents the data generated from a sensor node and delivered to a base station. A fast approximate algorithm is presented, which is able to compute (1−ε)-approximation to the optimal lifetime for any ε > 0. Then along this baseline, we further study several advanced topics. First, we design an algorithm, which utilizes the unique characteristic of data aggregation, and is proved to reduce the running time of the fastest existing algorithm by a factor of K, K being the number of commodities. Second, we extend our algorithm to accommodate the same problem in the setting of multiple base stations, and study its impact on network lifetime improvement. All algorithms are evaluated through both solid theoretical analysis and extensive simulation results. Yuan Xue received her B.S. in Computer Science from Harbin Institute of Technology, China in 1994 and her M.S. and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 2002, and 2005. Currently she is an assistant professor at the Department of Electrical Engineering and Computer Science of Vanderbilt University. Her research interests include wireless and sensor networks, mobile systems, and network security. Yi Cui received his B.S. and M.S. degrees in 1997 and 1999, from Department of Computer Science, Tsinghua University, China, and his Ph.D. degree in 2005 from the Department of Computer Science, University of Illinois at Urbana-Champaign. Since then, he has been with the Department of Electrical Engineering and Computer Science at Vanderbilt University, where he is currently an assistant professor. His research interests include overlay network, peer-to-peer system, multimedia system, and wireless sensor network. Klara Nahrstedt (M ' 94) received her A.B., M.Sc degrees in mathematics from the Humboldt University, Berlin, Germany, and Ph.D in computer science from the University of Pennsylvania. She is an associate professor at the University of Illinois at Urbana-Champaign, Computer Science Department where she does research on Quality of Service(QoS)-aware systems with emphasis on end-to-end resource management, routing and middleware issues for distributed multimedia systems. She is the coauthor of the widely used multimedia book ‘Multimedia:Computing, Communications and Applications’ published by Prentice Hall, and the recipient of the Early NSF Career Award, the Junior Xerox Award and the IEEE Communication Society Leonard Abraham Award for Research Achievements, and the Ralph and Catherine Fisher Professorship Chair. Since June 2001 she serves as the editor-in-chief of the ACM/Springer Multimedia System Journal. An erratum to this article is available at .     

无线传感器网络中的最优感知节点分布及分布优化算法

无线传感器网络中，感知节点的合理分布以及网络拓扑的动态调整对于更加有效地进行信息收集以及提高网络的生存期限都具有重要的作用。为此，针对传感器网络的初始规划提出了一种基于遗传算法的最优分布。仿真结果表明，算法能够针对特定的目标区域获得较好的节点分布。在最佳分布的基础上，结合传感器网络的拓扑管理和节点定位，引入了一种有效的传感器网络拓扑和节点分布优化方法，为传感器网络的拓扑性能管理提供了有效的算法保证。     

Polytype target coverage scheme for heterogeneous wireless sensor networks using linear programming

Sensing coverage is one of fundamental problems in wireless sensor networks. In this paper, we investigate the polytype target coverage problem in heterogeneous wireless sensor networks where each sensor is equipped with multiple sensing units and each type of sensing unit can sense an attribute of multiple targets. How to schedule multiple sensing units of a sensor to cover multiple targets becomes a new challenging problem. This problem is formulated as an integer linear programming problem for maximizing the network lifetime. We propose a novel energy‐efficient target coverage algorithm to solve this problem based on clustering architecture. Being aware of the coverage capability and residual energy of sensor nodes, the clusterhead node in each cluster schedules the appropriate sensing units of sensor nodes that are in the active status to cover multiple targets in an optimal way. Extensive simulations have been carried out to validate the effectiveness of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.     

Nature inspired discrete firefly algorithm for optimal mobile data gathering in wireless sensor networks

Nowadays wireless sensor networks enhance the life of human beings by helping them through several applications like precision agriculture, health monitoring, landslide detection, pollution control, etc. The built-in sensors on a sensor node are used to measure the various events like temperature, vibration, gas emission, etc., in the remotely deployed unmanned environment. The limited energy constraint of the sensor node causes a huge impact on the lifetime of the deployed network. The data transmitted by each sensor node cause significant energy consumption and it has to be efficiently used to improve the lifetime of the network. The energy consumption can be reduced significantly by incorporating mobility on a sink node. Thus the mobile data gathering can result in reduced energy consumption among all sensor nodes while transmitting their data. A special mobile sink node named as the mobile data transporter (MDT) is introduced in this paper to collect the information from the sensor nodes by visiting each of them and finally it sends them to the base station. The Data collection by the MDT is formulated as a discrete optimization problem which is termed as a data gathering tour problem. To reduce the distance traveled by the MDT during its tour, a nature-inspired heuristic discrete firefly algorithm is proposed in this paper to optimally collect the data from the sensor nodes. The proposed algorithm computes an optimal order to visit the sensor nodes by the MDT to collect their data with minimal travel distance. The proposed algorithm is compared with tree-based data collection approaches and ant colony optimization approach. The results demonstrate that the proposed algorithm outperform other approaches minimizing the tour length under different scenarios.     

Optimal Speed Control of Mobile Node for Data Collection in Sensor Networks

A data mule represents a mobile device that collects data in a sensor field by physically visiting the nodes in a sensor network. The data mule collects data when it is in the proximity of a sensor node. This can be an alternative to multihop forwarding of data when we can utilize node mobility in a sensor network. To be useful, a data mule approach needs to minimize data delivery latency. In this paper, we first formulate the problem of minimizing the latency in the data mule approach. The data mule scheduling (DMS) problem is a scheduling problem that has both location and time constraints. Then, for the 1D case of the DMS problem, we design an efficient heuristic algorithm that incorporates constraints on the data mule motion dynamics. We provide lower bounds of solutions to evaluate the quality of heuristic solutions. Through numerical experiments, we show that the heuristic algorithm runs fast and yields good solutions that are within 10 percent of the optimal solutions.     

A distributed reinforcement learning based sensor node scheduling algorithm for coverage and connectivity maintenance in wireless sensor network

The fundamental challenge for randomly deployed resource-constrained wireless sensor network is to enhance the network lifetime without compromising its performance metrics such as coverage rate and network connectivity. One way is to schedule the activities of sensor nodes and form scheduling rounds autonomously in such a way that each spatial point is covered by at least one sensor node and there must be at least one communication path from the sensor nodes to base station. This autonomous activity scheduling of the sensor nodes can be efficiently done with Reinforcement Learning (RL), a technique of machine learning because it does not require prior environment modeling. In this paper, a Nash Q-Learning based node scheduling algorithm for coverage and connectivity maintenance (CCM-RL) is proposed where each node autonomously learns its optimal action (active/hibernate/sleep/customize the sensing range) to maximize the coverage rate and maintain network connectivity. The learning algorithm resides inside each sensor node. The main objective of this algorithm is to enable the sensor nodes to learn their optimal action so that the total number of activated nodes in each scheduling round becomes minimum and preserves the criteria of coverage rate and network connectivity. The comparison of CCM-RL protocol with other protocols proves its accuracy and reliability. The simulative comparison shows that CCM-RL performs better in terms of an average number of active sensor nodes in one scheduling round, coverage rate, and energy consumption.

     

Maximum Lifetime Routing Problem in Duty-Cycling Sensor Networks

In order to extend the lifetime of a wireless sensor network, the energy consumption of individual sensor nodes need to be minimized. This can be achieved by minimizing the idle listening time with duty cycling mechanism and/or minimizing the number of communications per node. The nodes will have different relay loads for different routing strategies: therefore, the routing problem is important factor in minimization of the number of communications per node. In this paper, we investigate achievable network lifetime with a routing mechanism on top of an existing duty-cycling scheme. To this end, we formulated the routing problem for duty-cycling sensor network as a linear programming problem with the objective of maximizing the network lifetime. Using the developed linear programming formulation, we investigate the relationship between network lifetime and duty-cycling parameter for different data generation rates and determine the minimum duty-cycling parameter that meets the application requirements. To the best of our knowledge, this is the first mathematical programming formulation which addresses the maximum lifetime routing problem in duty-cycling sensor network. In order to illustrate the application of the analytical model, we solved the problem for different parameter settings.     

Analysis of Target Detection Probability in Randomly Deployed Sensor Networks

The main functionality of a surveillance wireless sensor network is to detect unauthorized traversals in a field. In this paper, we develop a formulation to determine the probability of detecting a randomly positioned target by a set of binary sensors to serve as the deployment quality measure. This formulation leads to a recipe to determine the number of sensors required to deliver a certain deployment quality level. The sensing- and communication-neighbor degrees which can be used as design criteria in a sensor network are defined and calculated. The model is verified by simulations whose outcomes closely match the analytical results.     

Femtocell网络中博弈式频率复用算法

提出了一种适用于Femtocell网络的博弈式频率复用算法。在Femtocell网络中,首先Femtocell基站通过感知其无线环境选择临时子频带,然后通过对分簇后的Femtocell网络执行簇内协调和簇间博弈两步算法,消除相邻Femtocell之间的频谱冲突,使Femtocell网络合理地复用频谱资源。在Femtocell随机部署的网络中,该方法解决了Femtocell网络中的频谱冲突问题。仿真表明,采用该算法后,Femtocell网络的频谱冲突得到有效改善,Femtocell系统平均信道容量明显提高。     

Game-theoretical frequency reuse algorithm in Femtocell network

A game-theoretical frequency reuse algorithm has been proposed for Femtocell network.Firstly,each Femtocell tries to get an available sub-band by sensing its radio circumstance.Then,inner-cluster frequency collisions are avoided by implementing a negotiation mechanism.Lastly,each cluster is regarded as a game player to get the largest utility by using its optimal strategy.Despite Femtocell base stations are randomly installed by users,the proposed method performs well in Femtocell network.Simulation results show that frequency collisions are dramatically reduced and the average capacity of Femtocells is considerably enhanced.     

Decentralized detection in sensor networks

In this paper, we investigate a binary decentralized detection problem in which a network of wireless sensors provides relevant information about the state of nature to a fusion center. Each sensor transmits its data over a multiple access channel. Upon reception of the information, the fusion center attempts to accurately reconstruct the state of nature. We consider the scenario where the sensor network is constrained by the capacity of the wireless channel over which the sensors are transmitting, and we study the structure of an optimal sensor configuration. For the problem of detecting deterministic signals in additive Gaussian noise, we show that having a set of identical binary sensors is asymptotically optimal, as the number of observations per sensor goes to infinity. Thus, the gain offered by having more sensors exceeds the benefits of getting detailed information from each sensor. A thorough analysis of the Gaussian case is presented along with some extensions to other observation distributions.     

Deploying mobile nodes for maximal energy matching in WSNs

This paper proposes a self‐stabilizing distributed algorithm for deploying mobile nodes with loaded energy to the stationary nodes by considering the energy those stationary nodes need. The goal is to deploy mobile nodes to appropriate locations for energy supplements such that the network lifetime can be extended. The problem of maximizing the lifetime is NP‐hard. Therefore, it is unrealistic to search for an optimal solution in sensor networks. In this paper, we design several simple rules for mobile nodes and stationary nodes separately in order to find a feasible solution. Simple rules are especially suitable and necessary for low computability sensor networks. Our algorithm is simple and distributed. We prove that our method is stable and has good performance. Simulations show its efficiency too. Copyright © 2010 John Wiley & Sons, Ltd.     

Transactions Papers - Device Placement for Heterogeneous Wireless Sensor Networks: Minimum Cost with Lifetime Constraints

Device placement is a fundamental factor in determining the coverage, connectivity, cost and lifetime of a wireless sensor network (WSN). In this paper, we explore the problem of relay node placement in heterogeneous WSN. We formulate a generalized node placement optimization problem aimed at minimizing the network cost with constraints on lifetime and connectivity. Depending on the constraints, two representative scenarios of this problem are described. We characterize the first problem, where relay nodes are not energy constrained, as a minimum set covering problem. We further consider a more challenging scenario, where all nodes are energy limited. As an optimal solution to this problem is difficult to obtain, a two-phase approach is proposed, in which locally optimal design decisions are taken. The placement of the first phase relay nodes (FPRN), which are directly connected to sensor nodes (SN), is modeled as a minimum set covering problem. To ensure the relaying of the traffic from the FPRN to the base station, three heuristic schemes are proposed to place the second phase relay nodes (SPRN). Furthermore, a lower bound on the minimum number of SPRN required for connectivity is provided. The efficiency of our proposals is investigated by numerical examples.     

Microwave sensor for tangerine classification based on coupled-patch antennas

This paper deals with a microwave sensor for classifying tangerines by flavour using coupled-patch antennas. The operating frequency of the antennas is 2.45 GHz. The sensor determines the flavour of each tangerine by measuring the magnitudes of coupled signals of the antennas with the tangerine fruit at the centre. The sorting is carried out using an artificial neural network implemented on a field programmable gate array. The classification performance of the sensor is 95% accurate, so it has potential for use in sorting tangerines by flavour. In addition, the system uncertainty is analysed to determine optimal operating conditions.