Which distributed averaging algorithm should I choose for my sensor network

Average consensus and gossip algorithms have recently received significant attention, mainly because they constitute simple and robust algorithms for distributed information processing over networks. Inspired by heat diffusion, they compute the average of sensor networks measurements by iterating local averages until a desired level of convergence. Confronted with the diversity of these algorithms, the engineer may be puzzled in his choice for one of them. As an answer to his/her need, we develop precise mathematical metrics, easy to use in practice, to characterize the convergence speed and the cost (time, message passing, energy...) of each of the algorithms. In contrast to other works focusing on time-invariant scenarios, we evaluate these metrics for ergodic time-varying networks. Our study is based on Oseledec’s theorem, which gives an almost-sure description of the convergence speed of the algorithms of interest. We further provide upper bounds on the convergence speed. Finally, we use these tools to make some experimental observations illustrating the behavior of the convergence speed with respect to network topology and reliability in both average consensus and gossip algorithms. A. Problem statement. I

A Centrality-Based Security Game for Multi-Hop Networks

We formulate a network security problem as a zero-sum game between an attacker who tries to disrupt a network by disabling one or more nodes, and the nodes of the network who must allocate limited resources in defense of the network. The utility of the zero-sum game can be one of several network performance metrics that correspond to node centrality measures. We first present a fast centralized algorithm that uses a monotone property of the utility function to compute saddle-point equilibrium strategies for the case of single-node attacks and single- or multiple-node defense. We then extend the approach to the distributed setting by computing the necessary quantities using a finite-time distributed averaging algorithm. For simultaneous attacks to multiple nodes the computational complexity becomes quite high, so we propose a method to approximate the saddle-point equilibrium strategies based on a sequential simplification, which performs well in simulations

INDIGO: An In Situ Distributed Gossip Framework for Sensor Networks

Abstract—With the onset of Cyber-Physical Systems (CPS), distributed algorithms on Wireless Sensor Networks(WSNs) have been receiving renewed attention. The distributed consensus problem is a well studied problem having a myriad of applications which can be accomplished using asynchronous distributed gossip algorithms on Wireless Sensor Networks(WSN). However, a practical realization of gossip algorithms for WSNs is found lacking in the current state of the art. In this paper, we propose the design, development and analysis of a novel in-situ distributed gossip framework called INDIGO. A key aspect of INDIGO is its ability to execute on a generic system platform as well as on a hardware oriented testbed platform in a seamless manner allowing easy portability of existing algorithms. We evaluate the performance of INDIGO with respect to the distributed consensus problem as well as the distributed optimization problem. We also present a data driven analysis of the effect, certain operating parameters like sleep time and wait time have on the performance of the framework and empirically attempt to determine a sweet spot. The results obtained from various experiments on INDIGO validate its efficacy, reliability and robustness and demonstrate its utility as a framework for the evaluation and implementation of asynchronous distributed algorithms

Weight Optimization for Consensus Algorithms with Correlated Switching Topology

We design the weights in consensus algorithms with spatially correlated random topologies. These arise with: 1) networks with spatially correlated random link failures and 2) networks with randomized averaging protocols. We show that the weight optimization problem is convex for both symmetric and asymmetric random graphs. With symmetric random networks, we choose the consensus mean squared error (MSE) convergence rate as optimization criterion and explicitly express this rate as a function of the link formation probabilities, the link formation spatial correlations, and the consensus weights. We prove that the MSE convergence rate is a convex, nonsmooth function of the weights, enabling global optimization of the weights for arbitrary link formation probabilities and link correlation structures. We extend our results to the case of asymmetric random links. We adopt as optimization criterion the mean squared deviation (MSdev) of the nodes states from the current average state. We prove that MSdev is a convex function of the weights. Simulations show that significant performance gain is achieved with our weight design method when compared with methods available in the literature.Comment: 30 pages, 5 figures, submitted to IEEE Transactions On Signal Processin

Distributed Consensus in Networks

Distributed algorithms have gained a lot of attention during recent years. Their application in industry, particularly in wireless sensor networks has motivated researchers to try to design them in order to be less resource-consuming (e.g. memory and power), faster, and more reliable. There have been numerous distributed algorithms for different types of problems in the context of distributed algorithms. We are interested in a fundamental coordination problem namely the majority consensus problem. In the majority consensus problem nodes try to find the opinion of the majority in a network of interest. As our first contribution and motivated by the distributed binary consensus problem in [1] we propose a distributed algorithm for multivalued consensus in complete graphs. As our second contribution we propose an algorithm for the optimization of the binary interval consensus algorithm pioneered by Ben ezit et al in [2]. Finally we use binary interval consensus algorithm to design a framework for error-free consensus in dynamic networks using which nodes can leave or join the network during or after the consensus process.Open Acces