Consensus In Wireless Sensor Networks Research Articles

Asymmetric Mixing Matrix Optimization for Faster Average Consensus in Wireless Sensor Networks

Asymmetric Mixing Matrix Optimization for Faster Average Consensus in Wireless Sensor Networks

Multi-Dimensional Privacy-Preserving Average Consensus in Wireless Sensor Networks

This brief studies the privacy preserving average consensus (PPAC) of wireless sensor networks (WSNs). Note that most of the PPAC schemes only focus on the consensus of one-dimensional state, which is not suitable for the actual scenarios. In view of this, the multi-dimensional privacy-preserving average consensus (MPPAC) problem is considered in this brief, where the nodes are divided into two types, the sink nodes and the ordinary ones. A novel MPPAC algorithm is proposed by introducing the super-increasing sequence as well as the RSA algorithm, where the super-increasing sequence plays a key role in tackling the multi-dimensional measurement of the sensors, and the RSA algorithm realizes the privacy preserving average consensus among sink nodes. Simulation results illustrate the effectiveness of this proposed scheme.

Efficient distributed average consensus in wireless sensor networks

Computing the distributed average consensus in Wireless Sensor Networks (WSNs) is investigated in this article. This problem, which is both natural and important, plays a significant role in various application fields such as mobile agents and fleet vehicle coordination, network synchronization, distributed voting and decision, load balancing of divisible loads in distributed computing network systems, and so on. By and large, the average consensus’ objective is to have all nodes in the network converged to the average value of the initial nodes’ measurements based only on local nodes’ information states. In this paper, we introduce a fully distributed algorithm to average the sensed data within the network itself. The network may be large since we never broadcast over all its nodes. Unlike earlier works, when a node detects a load (scalar value) imbalance in its closed neighborhoods during the average process, instead of sending parsimonious amount of load values from highly loaded nodes to less loaded ones, we move a large amount of load values by involving parallel atomic transactions between mutually exclusive pairs of neighbors. This improves the global convergence time speedup with low-cost communication and minimal energy consumption. First, we give the convergence proof of the distributed consensus process, and next we provide some experimental results based on NS3 framework to assess the behavior of the proposed algorithm.

Distributed control for energy-efficient and fast consensus in wireless sensor networks

ABSTRACTThe paper proposes a distributed control of nodes transmission radii in energy-harvesting wireless sensor networks for simultaneously coping with energy consumption and consensus responsiveness requirement. The stability of the closed-loop network under the proposed control law is proved. Simulation validations show the effectiveness of the proposed approach in nominal scenario as well as in the presence of uncertain node power requirements and harvesting system supply.

Eavesdropping-Based Gossip Algorithms for Distributed Consensus in Wireless Sensor Networks

In this letter, we present an eavesdropping-based gossip algorithm (EBGA). In the novel algorithm, when a node unicasts its values to a randomly selected neighboring node, all other nodes, which eavesdrop these values, simultaneously update their state values. By exploiting the broadcast nature of wireless communications, this novel algorithm has similar performance to broadcast gossip algorithms. Although broadcast gossip algorithms have the fastest rate of convergence among all gossip algorithms, they either converge to a random value rather than the average consensus, or need out-degree information available for each node to guarantee convergence to the average consensus. Utilizing non-negative matrix theory and ergodicity coefficient, we have proved that this novel algorithm can converge to the average consensus without any assumption which is difficult to be realized in real networks.

Step-size sequence design for finite-time average consensus in secure wireless sensor networks

This paper concerns the study of average consensus in wireless sensor networks with aim of providing a way to reach consensus in a finite number of steps. In particular, we investigate the design of consensus protocols when, for security reasons for instance, the underlying graph is constrained to be strongly regular or distance regular. The proposed design method is based on parameters of the intersection array characterizing the underlying graph. With this protocol, at execution time, average consensus is achieved in a number of steps equal to the diameter of the graph, i.e. the smallest possible number of steps to achieve consensus.

A Chemistry-Inspired Framework for Achieving Consensus in Wireless Sensor Networks

The aim of this paper is to show how simple interaction mechanisms, inspired by chemical systems, can provide the basic tools to design and analyze a mathematical model for achieving consensus in wireless sensor networks, characterized by balanced directed graphs. The convergence and stability of the model are first proven by using new mathematical tools, which are borrowed directly from chemical theory, and then validated by means of simulation results, for different network topologies and number of sensors. The underlying chemical theory is also used to derive simple interaction rules that may account for practical issues, such as the estimation of the number of neighbors and the robustness against perturbations. Finally, the proposed chemical solution is validated under real-world conditions by means of a four-node hardware implementation where the exchange of information among nodes takes place in a distributed manner (with no need for any admission control and synchronism procedure), simply relying on the transmission of a pulse whose rate is proportional to the state of each sensor.

Convergence of Gossip Algorithms for Consensus in Wireless Sensor Networks with Intermittent Links and Mobile Nodes

We study the convergence of pairwise gossip algorithms and broadcast gossip algorithms for consensus with intermittent links and mobile nodes. By nonnegative matrix theory and ergodicity coefficient theory, we prove gossip algorithms surely converge as long as the graph is partitionally weakly connected which, in comparison with existing analysis, is the weakest condition and can be satisfied for most networks. In addition we characterize the supremum for the mean squared error of convergence as a function associated with the initial states and the number of nodes. Furthermore, on the condition that the graph is partitionally strongly connected, the rate of convergence is proved to be exponential and governed by the second largest eigenvalue of expected coefficient matrix. For partitionally strongly connected digraphs, simulation results illustrate that gossip algorithms actually converge, and broadcast gossip algorithms can converge faster than pairwise gossip algorithms at the cost of larger error of convergence.

Decentralized Average Consensus in Wireless Sensor Networks with Unreliable Communication Channels

This study proposes and evaluates an average consensus scheme for wireless sensor networks. For this, two communication error models, the fading signal error model and approximated fading signal error model, are introduced and incorporated into the proposed decentralized average consensus scheme, especially adapted to the constraints of wireless sensor networks. A mathematical analysis is introduced to derive the approximated fading signal model from the fading signal model and different simulation scenarios are introduced and their results analyzed to evaluate the performance of the proposed scheme and its effectiveness in meeting the needs of wireless sensor networks.