Priority-based bandwidth allocation in wireless sensor networks

Marin, Andrea; Rossi, Sabina

doi:10.4108/eai.14-12-2015.2262651

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…In detail, we show how the proposed algorithm can be applied to a model of a real system. To this end, the algorithm is used to verify the ρ-reversibility of the Markov chain underlying the analytical model used for the performance evaluation of the Fair Allocation Control Window (FACW) protocol [11].…”

Section: Resultsmentioning

confidence: 99%

Efficient Computation of Renaming Functions for ρ-reversible Discrete and Continuous Time Markov Chains

Sottana

Piazza

Albarelli

2017

Proceedings of the 11th EAI International Conference on Performance Evaluation Methodologies and Tools

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With the introduction of ρ-reversibility, the basic notion of reversible Markov chain has been relaxed by allowing a wider range of scenarios. Specifically, the reversibility properties are not just sought on the chain itself, but also on all the possible topology-preserving renamings of its state space. Such renamings, called Renaming Functions, exhibit many interesting properties which can be exploited in different contexts. Unfortunately, finding a renaming function for a Markov chain is a very computationally intensive task. Using a naive approach it could require to check for all the possible state space permutations, which is unfeasible for all but the most trivial chains. As a matter of fact, we prove that the corresponding decision problem is polynomially equivalent to Graph Isomorphism. Nevertheless, we introduce an algorithm that, exploiting some necessary conditions for ρ-reversibility, is able to efficiently prune the search space and then verify the remaining renaming candidates. The correctness of the method is theoretically demonstrated and its practical effectiveness is shown over a significant set of discrete and continuous ρ-reversible Markov chains.

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Section: Resultsmentioning

confidence: 99%

Efficient Computation of Renaming Functions for ρ-reversible Discrete and Continuous Time Markov Chains

Sottana

Piazza

Albarelli

2017

Proceedings of the 11th EAI International Conference on Performance Evaluation Methodologies and Tools

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“…This work is an extension of the article proposed in Marin and Rossi (2016b). With respect to the conference version, we have included here all the proofs of the theorems and propositions and the analysis of robustness of the model by resorting to the stochastic simulation.…”

Section: Introductionmentioning

confidence: 99%

A Product-Form Model for the Performance Evaluation of a Bandwidth Allocation Strategy in WSNs

Marin

Rossi

Burato

et al. 2018

ACM Trans. Model. Comput. Simul.

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Wireless Sensor Networks (WSNs) are important examples of Collective Adaptive System, which consist of a set of motes that are spatially distributed in an indoor or outdoor space. Each mote monitors its surrounding conditions, such as humidity, intensity of light, temperature, and vibrations, but also collects complex information, such as images or small videos, and cooperates with the whole set of motes forming the WSN to allow the routing process. The traffic in the WSN consists of packets that contain the data harvested by the motes and can be classified according to the type of information that they carry. One pivotal problem in WSNs is the bandwidth allocation among the motes. The problem is known to be challenging due to the reduced computational capacity of the motes, their energy consumption constraints, and the fully decentralised network architecture. In this article, we study a novel algorithm to allocate the WSN bandwidth among the motes by taking into account the type of traffic they aim to send. Under the assumption of a mesh network and Poisson distributed harvested packets, we propose an analytical model for its performance evaluation that allows a designer to study the optimal configuration parameters. Although the Markov chain underlying the model is not reversible, we show it to be ρ-reversible under a certain renaming of states. By an extensive set of simulations, we show that the analytical model accurately approximates the performance of networks that do not satisfy the assumptions. The algorithm is studied with respect to the achieved throughput and fairness. We show that it provides a good approximation of the max-min fairness requirements.

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Probabilistic neural network based efficient bandwidth allocation in wireless sensor networks

Dhinnesh¹,

Sabapathi²

2021

J Ambient Intell Human Comput

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Priority-based bandwidth allocation in wireless sensor networks

Cited by 4 publications

References 18 publications

Efficient Computation of Renaming Functions for ρ-reversible Discrete and Continuous Time Markov Chains

Efficient Computation of Renaming Functions for ρ-reversible Discrete and Continuous Time Markov Chains

A Product-Form Model for the Performance Evaluation of a Bandwidth Allocation Strategy in WSNs

Probabilistic neural network based efficient bandwidth allocation in wireless sensor networks

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