Impact of Mobility on Consensus Building in the Leader-Follower Model

Kandalan, Roya Norouzi; Alla, Sindhu; Rezaeian, Nima Hassan

doi:10.1109/vtcfall.2019.8891121

Cited by 3 publications

(2 citation statements)

References 13 publications

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“…P 1 is the transition matrix corresponding to the first instant shown in Figure 4a and P 2 is the transition matrix corresponding to the second instant shown in Figure 4b. Experiments in Reference [4,31] demonstrate that making leaders mobile improves the connectivity of the network. As a result, the system reaches a consensus in less number of iterations.…”

Section: Mobility In Leader-follower Model: Results and Discussionmentioning

confidence: 99%

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Impact of Leadership and Mobility on Consensus-Building in Sensor Networks

Kandalan

Varanasi

Buckles

et al. 2020

Sensors

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Introducing leadership and mobility is known to benefit wireless sensor networks in terms of consensus-building and collective decision-making. However, these benefits are neither analytically proven nor quantified in the literature. This paper fills this gap by investigating the mobility dynamics in wireless sensor networks analytically. The results of the analytical investigation are presented as a set of theorems and their proofs. This paper also establishes a natural synergy between the leader-follower model and its bipartite graph representation. It demonstrates the advantages of the leader-follower model for consensus-building over others in terms of improved convergence rate. It presents a strategy for choosing leaders from among the agents participating in the consensus-building process using the well-known graph-coloring solution. Then, it shows how the leader-follower model helps improve the convergence rate of consensus-building. Finally, it shows that the convergence rate of the consensus-building process can be further improved by making the leaders mobile.

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Section: Mobility In Leader-follower Model: Results and Discussionmentioning

confidence: 99%

“…This section begins with an example to demonstrate that mobility accelerates the convergence rate of consensus in a leader-follower model even in a disconnected network [4,31]. Following this discussion, Theorem 2 proves this point for the class of networks with isolated agents.…”

Section: Impact Of Mobility On a Disconnected Graphmentioning

confidence: 91%

Impact of Leadership and Mobility on Consensus-Building in Sensor Networks

Kandalan

Varanasi

Buckles

et al. 2020

Sensors

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Mobility Accelerates Consensus Building in Sensor Networks

2020

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Mobility Prediction of Mobile Wireless Nodes

2022

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Artificial intelligence (AI) is a fundamental part of improving information technology systems. Essential AI techniques have revolutionized communication technology, such as mobility models and machine learning classification. Mobility models use a virtual testing methodology to evaluate new or updated products at a reasonable cost. Classifiers can be used with these models to achieve acceptable predictive accuracy. In this study, we analyzed the behavior of machine learning classification algorithms—more specifically decision tree (DT), logistic regression (LR), k-nearest neighbors (K-NN), latent Dirichlet allocation (LDA), Gaussian naive Bayes (GNB), and support vector machine (SVM)—when using different mobility models, such as random walk, random direction, Gauss–Markov, and recurrent self-similar Gauss–Markov (RSSGM). Subsequently, classifiers were applied in order to detect the most efficient mobility model over wireless nodes. Random mobility models (i.e., random direction and random walk) provided fluctuating accuracy values when machine learning classifiers were applied—resulting values ranged from 39% to 81%. The Gauss–Markov and RSSGM models achieved good prediction accuracy in scenarios using a different number of access points in a defined area. Gauss–Markov reached 89% with the LDA classifier, whereas RSSGM showed the greatest accuracy with all classifiers and through various samples (i.e., 2000, 5000, and 10,000 steps during the whole experiment). Finally, the decision tree classifier obtained better overall results, achieving 98% predictive accuracy for 5000 steps.

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Impact of Mobility on Consensus Building in the Leader-Follower Model

Cited by 3 publications

References 13 publications

Impact of Leadership and Mobility on Consensus-Building in Sensor Networks

Impact of Leadership and Mobility on Consensus-Building in Sensor Networks

Mobility Accelerates Consensus Building in Sensor Networks

Mobility Prediction of Mobile Wireless Nodes

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