Distributed Linear Summing in Wireless Sensor Networks

2019 International Conference on Applied Electronics (AE)

2019

Self Cite

The exact information about the network size is crucial for the proper functioning of many distributed algorithms. In this paper, we analyze the average consensus algorithm for a distributed network size estimation bounded by the stopping criterion proposed for the wireless sensor networks. We analyze its four initial configurations over random geometric graphs of different connectivity under various parameters of the implemented stopping criterion. The performance is evaluated by the mean square error and the convergence rate expressed as the iteration number for the consensus. Finally, the results obtained under various conditions are compared to find the best performing configuration of both the average consensus algorithm and the implemented stopping criterion. Also, the results are compared to the distributed summing functionality.

“…Here, W is the weight matrix, whose elements affect AC in several aspects (e.g., the convergence rate, the robustness, the convergence of the algorithm, etc.) [8].…”

Section: A Modeling Of Ac Over Wsnsmentioning

confidence: 99%

“…As mentioned earlier, we analyze the Constant weights, whose weight matrix can be defined as follows [8]:…”

Section: A Modeling Of Ac Over Wsnsmentioning

confidence: 99%

Section: A Modeling Of Ac Over Wsnsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Network Size Estimation Executed by Average Consensus Bounded by Stopping Criterion for Wireless Sensor Networks

Kenyeres

2019 International Conference on Applied Electronics (AE)

2019

Self Cite

“…Furthermore, x(k) is a column vector variant over the iterations gathering the inner states of all the sensor nodes at the k th iteration. Subsequently, each sensor is able to estimate the sum of all inner states as follows [13]:…”

Section: Theoretical Background a Model Of Dlcdsas And Wsnsmentioning

confidence: 99%

On Comparative Study of Deterministic Linear Consensus-based Algorithms for Distributed Summing

Kenyeres

2019 International Conference on Applied Electronics (AE)

2019

Self Cite

Application of data aggregation mechanisms is supposed to ensure high confidence of measurements and low energy demands in wireless sensor networks. Therefore, many modern applications utilize distributed algorithms for aggregate function estimation in order to minimize negative factors affecting the operation of the wireless sensor networks. This paper is concerned with deterministic linear consensus-based algorithms for distributed summing or more specifically, a comparative study of five frequently applied algorithms from this algorithm category over random graphs and random geometric graphs. The selected algorithms are examined using various methodologies and metrics.

Distributed Linear Summing in Wireless Sensor Networks with Implemented Stopping Criteria

Kenyeres¹,

Adv. sci. technol. eng. syst. j.

2020

Self Cite

Many real-life applications based on the wireless sensor networks are equipped with data aggregation mechanisms for suppressing or even overcoming negative environmental effects and data redundancy. In this paper, we present an extended analysis of the linear average consensus algorithm for distributed summing with bounded execution over wireless sensor networks. We compare a centralized and a fully-distributed stopping criterion proposed for the wireless sensor networks with a varied initial configuration over random geometric graphs with 200 vertices in order to identify the optimal initial configuration of both analyzed stopping criteria and the algorithm as well as finding out which stopping criterion ensures higher performance of the examined algorithm in terms of the estimation precision and the convergence rate expressed as the iteration number for the consensus.