Sensor Node Selection Research Articles

Cluster-based Dynamic Energy Management for Collaborative Target Tracking in Wireless Sensor Networks

A primary criterion of wireless sensor network is energy efficiency. Focused onthe energy problem of target tracking in wireless sensor networks, this paper proposes acluster-based dynamic energy management mechanism. Target tracking problem isformulated by the multi-sensor detection model as well as energy consumption model. Adistributed adaptive clustering approach is investigated to form a reasonable routingframework which has uniform cluster head distribution. Dijkstra’s algorithm is utilized toobtain optimal intra-cluster routing. Target position is predicted by particle filter. Thepredicted target position is adopted to estimate the idle interval of sensor nodes. Hence,dynamic awakening approach is exploited to prolong sleep time of sensor nodes so that theoperation energy consumption of wireless sensor network can be reduced. The sensornodes around the target wake up on time and act as sensing candidates. With the candidatesensor nodes and predicted target position, the optimal sensor node selection is considered.Binary particle swarm optimization is proposed to minimize the total energy consumptionduring collaborative sensing and data reporting. Experimental results verify that theproposed clustering approach establishes a low-energy communication structure while theenergy efficiency of wireless sensor networks is enhanced by cluster-based dynamic energymanagement.

Prediction-based Dynamic Energy Management in Wireless Sensor Networks

Energy consumption is a critical constraint in wireless sensor networks. Focusing on the energy efficiency problem of wireless sensor networks, this paper proposes a method of prediction-based dynamic energy management. A particle filter was introduced to predict a target state, which was adopted to awaken wireless sensor nodes so that their sleep time was prolonged. With the distributed computing capability of nodes, an optimization approach of distributed genetic algorithm and simulated annealing was proposed to minimize the energy consumption of measurement. Considering the application of target tracking, we implemented target position prediction, node sleep scheduling and optimal sensing node selection. Moreover, a routing scheme of forwarding nodes was presented to achieve extra energy conservation. Experimental results of target tracking verified that energy-efficiency is enhanced by prediction-based dynamic energy management.

Dynamic power optimization with target prediction for measurement in wireless sensor networks

Energy constraint is a critical problem for practical applications in wireless sensor networks.Focused on the energy efficiency problem of wireless sensor networks,a dynamic power optimization method is proposed with target prediction. Particle filter is introduced to predict target state,which is adopted to awaken wireless sensor nodes so that their sleep time is prolonged.With the distributed computing capability of nodes,distributed genetic algorithm and simulated anneal is proposed to optimize the energy consumption of measurement. Considering the application of target tracking,target position prediction,node sleep scheduling and optimal sensing node selection are implemented.Moreover,a routing scheme of for- warding nodes is presented to achieve extra energy conserva- tion.Experimental results of target tracking verified that en- ergy-efficiency is enhanced by dynamic power optimization with target prediction in wireless sensor networks.

QS-Sift: QoS and spatial correlation-based medium access control in wireless sensor networks

Recently, it has been argued that allowing only a part of sensor nodes to transmit their data can reduce energy consumption dramatically. In this paper, we investigate the methods of selecting sensor nodes in-depth. Numerical results show that the transmission of poor sensed data (e.g. low SNR) is unnecessary because the contribution of poor data to the given maximum distortion Quality of Service (QoS) is very small if not negative. Based on this observation, we introduce the QS-Sift protocol, which ensure that nodes with good sensed data (e.g. high SNR) get higher priority than those with poor data when contending for the shared channel. And poor data has less chance to be delivered. All these processing are performed on each node without any centralised mechanism. Extensive simulations have been done to verify the effectiveness of the proposed schema in terms of energy consumption and transmission delay under given QoS constraints.

Energy-Efficient Jointly Sensing Nodes Selection with Information Accuracy Constraint in Wireless Sensor Networks

Energy-Efficient Jointly Sensing Nodes Selection with Information Accuracy Constraint in Wireless Sensor Networks

A statistics-based sensor selection scheme for continuous probabilistic queries in sensor networks

A common approach to improve the reliability of query results based on error-prone sensors is to introduce redundant sensors. However, using multiple sensors to generate the value for a data item can be expensive, especially in wireless environments where continuous queries are executed. Moreover, some sensors may not be working properly and their readings need to be discarded. In this paper, we propose a statistical approach to decide which sensor nodes to be used to answer a query. In particular, we propose to solve the problem with the aid of continuous probabilistic query (CPQ), which is originally used to manage uncertain data and is associated with a probabilistic guarantee on the query result. Based on the historical data values from the sensor nodes, the query type, and the requirement on the query, we present methods to select an appropriate set of sensors and provide reliable answers for several common aggregate queries. Our statistics-based sensor node selection algorithm is demonstrated in a number of simulation experiments, which shows that a small number of sensor nodes can provide accurate and robust query results.

Selection and navigation of mobile sensor nodes using a sensor network

Hybrid sensor networks comprise of mobile and static sensor nodes set up for the purpose of collaboratively performing tasks like sensing a phenomenon or monitoring a region. In this paper, we present a novel approach for navigating a mobile sensor node (MSN) through such a hybrid sensor network. The static sensor nodes in the sensor network guide the MSN to the phenomenon. One or more MSNs are selected based on their proximity to the detected phenomenon. Navigation is accomplished using the concepts of credit based field setup and navigation force from static sensor nodes. Our approach does not require any prior maps of the environment, thus cutting down the cost of the overall system. The simulation results have verified the effectiveness of the proposed approach. In each of the simulation runs, the static sensor nodes were able to successfully guide the MSN towards the phenomenon.

A Distributed Coverage- and Connectivity-Centric Technique for Selecting Active Nodes in Wireless Sensor Networks

Due to their low cost and small form factors, a large number of sensor nodes can be deployed in redundant fashion in dense sensor networks. The availability of redundant nodes increases network lifetime as well as network fault tolerance. It is, however, undesirable to keep all the sensor nodes active at all times for sensing and communication. An excessive number of active nodes lead to higher energy consumption and it places more demand on the limited network bandwidth. We present an efficient technique for the selection of active sensor nodes in dense sensor networks. The active node selection procedure is aimed at providing the highest possible coverage of the sensor field, i.e., the surveillance area. It also assures network connectivity for routing and information dissemination. We first show that the coverage-centric active nodes selection problem is NP-complete. We then present a distributed approach based on the concept of a connected dominating set (CDS). We prove that the set of active nodes selected by our approach provides full coverage and connectivity. We also describe an optimal coverage-centric centralized approach based on integer linear programming. We present simulation results obtained using an ns2 implementation of the proposed technique.