Recursive bayesian approaches for auto calibration in drift aware wireless sensor networks

The purpose for wireless sensor networks is to deploy low cost sensors with sufficient computing and communication capabilities to support networked sensing applications. Even when the sensors are properly calibrated at the time of their deployment, they develop drift in their readings leading to biased sensor measurements. Noting that a physical phenomenon in a certain area follows some spatio-temporal correlation, we assume that the sensors readings in that area are correlated. We also assume that the instantiations of drifts are uncorrelated. Based on these assumptions, and inspired by the resemblance of registration problem in radar target tracking with the bias error problem in wireless sensor networks, we follow a Bayesian framework to solve the Drift/Bias problem in wireless sensor networks. We present two methods for solving the drift problem in a densely deployed sensor network, one for smooth drifts and the other for unsmooth drifts. We also show that both methods successfully detect and correct sensor errors and extend the effective life time of the sensor network

Identifying Trustful Node Using Hybrid Trust Evaluation Method

A wireless sensor network referred as (WSN) is defined as a collection of distributed sensor nodes to work together for monitoring the physical and environmental conditions. Trust metrics in the wireless sensor networks is denoted as the important problem and it helps in solving the problem of access control, reliable communication, privacy and secure routing scheme. This paper establishes a new kind of approach to evaluate the trust value in WSN. The proposed algorithm and its evaluation made on the trust value of each node seen in the network depends on the metrics, trust attributesand trust parameters

An Efficient Lagrangean Relaxation-based Object Tracking Algorithm in Wireless Sensor Networks

In this paper we propose an energy-efficient object tracking algorithm in wireless sensor networks (WSNs). Such sensor networks have to be designed to achieve energy-efficient object tracking for any given arbitrary topology. We consider in particular the bi-directional moving objects with given frequencies for each pair of sensor nodes and link transmission cost. This problem is formulated as a 0/1 integer-programming problem. A Lagrangean relaxation-based (LR-based) heuristic algorithm is proposed for solving the optimization problem. Experimental results showed that the proposed algorithm achieves near optimization in energy-efficient object tracking. Furthermore, the algorithm is very efficient and scalable in terms of the solution time

Overlapping layers for prolonging network life time in multi-hop wireless sensor networks

Wireless sensor networks have been proposed as a practical solution for a wide range of applications due to their benefits of low cost, rapid deployment, self-organization capability, and cooperative data-processing. Many applications, such as military surveillance and habitat monitoring, require the deployment of large-scale sensor networks. A highly scalable and fault-tolerant network architecture, the Progressive Multi-hop Rotational Clustered (PMRC) structure has been proposed, which is suitable for constructing large-scale wireless sensor networks. However, similar to other multi-hop structures, the PMRC structure also suffers from the bottleneck problem; This thesis is focused on solving the bottleneck problem existing in the PMRC structure. First, the Overlapping Neighbor Layers (ONL) scheme is proposed to balance the energy consumption among cluster heads at different layers. Further, the Minimum Overlapping Neighbor Layers (MONL) scheme is proposed wherein the overlapped area between neighbor layers is gradually increased through network life time to achieve load balance and energy efficiency in the whole network area. Simulation results show that the MONL scheme significantly prolongs network life time and demonstrates steady performance on sensor networks with uniformly distributed sensor nodes. To further prolong the network life time, traffic-similar sensor nodes distribution combined with the MONL scheme is studied; The proposed overlapped layers schemes are proven to be effective in solving the bottleneck problem and prolonging network life time for PMRC-based networks. They can also be applied for other multi-hop cluster-based sensor networks. The traffic-similar nodes distribution concept can be applied in optimizing sensor network deployment to achieve desired network life time

Solving Isolated Nodes Problem in ZigBee Pro for Wireless Sensor Networks

Wireless sensor network based on the ZigBee protocol consists of many sensor devices. In some cases, the sensor nodes may turn to isolated node because random distribution, particularly when creating the network. In this research was suggested two cases to overcome on the isolated node problem, the first case had able to overcome this problem by distributing the isolated nodes on the router nodes that carry the least number of sensor nodes, it helps to minimize the computational overhead on router nodes too, while the second one is able to overcome this problem by calculating the distance between the isolated nodes and the routers and then adds these nodes to the nearest routers. Subsequently, this method helps to minimize the energy consumption. The results show our approach able to solve the problem of isolated nodes using these two methods and when compared between them turns out the second method is better In terms of energy consumption. In addition, we are able to make the network larger scale

Consensus and Products of Random Stochastic Matrices: Exact Rate for Convergence in Probability

Distributed consensus and other linear systems with system stochastic matrices $W_k$ emerge in various settings, like opinion formation in social networks, rendezvous of robots, and distributed inference in sensor networks. The matrices $W_k$ are often random, due to, e.g., random packet dropouts in wireless sensor networks. Key in analyzing the performance of such systems is studying convergence of matrix products $W_kW_{k-1}... W_1$. In this paper, we find the exact exponential rate $I$ for the convergence in probability of the product of such matrices when time $k$ grows large, under the assumption that the $W_k$'s are symmetric and independent identically distributed in time. Further, for commonly used random models like with gossip and link failure, we show that the rate $I$ is found by solving a min-cut problem and, hence, easily computable. Finally, we apply our results to optimally allocate the sensors' transmission power in consensus+innovations distributed detection

Overlapping Multi-hop Clustering for Wireless Sensor Networks

Clustering is a standard approach for achieving efficient and scalable performance in wireless sensor networks. Traditionally, clustering algorithms aim at generating a number of disjoint clusters that satisfy some criteria. In this paper, we formulate a novel clustering problem that aims at generating overlapping multi-hop clusters. Overlapping clusters are useful in many sensor network applications, including inter-cluster routing, node localization, and time synchronization protocols. We also propose a randomized, distributed multi-hop clustering algorithm (KOCA) for solving the overlapping clustering problem. KOCA aims at generating connected overlapping clusters that cover the entire sensor network with a specific average overlapping degree. Through analysis and simulation experiments we show how to select the different values of the parameters to achieve the clustering process objectives. Moreover, the results show that KOCA produces approximately equal-sized clusters, which allows distributing the load evenly over different clusters. In addition, KOCA is scalable; the clustering formation terminates in a constant time regardless of the network size