On optimal resource allocation in virtual sensor networks

Delgado, Carmen; Gállego, José Ramón; Canales, María; Ortín, Jorge; Bousnina, Sonda; Cesana, Matteo

doi:10.1016/j.adhoc.2016.04.004

Cited by 30 publications

(18 citation statements)

References 33 publications

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“…We also consider the case of running the algorithm twice (one with each definition of v ji ) and taking the best result (GreedyMax). We compare the results with the optimum solution (Optimum) given in [3] obtained by formalizing the resource allocation problem as a MILP and solving it via commercial solvers; optimality gap and computation time are the main performance metrics used in the evaluation.…”

Section: Simulations and Performance Evaluationmentioning

confidence: 99%

“…This energy consumption is left as a function of a b and s g . 1 1 Detailed expressions for this energy consumption can be found in [3]. (lines 7-8 of Algorithm 1).…”

Section: Greedy Algorithmmentioning

confidence: 99%

“…Namely, we consider a wireless sensor network composed of heterogeneous physical resources in terms of sensor node hardware capabilities and communication protocols and we propose a simple yet effective algorithm to optimally deploy multiple applications while accounting for the constraints posed by the physical infrastructure. The proposed algorithm is then validated against the optimal solution to the resource allocation problem obtained in our previous work [3], which formalized the resource allocation problem as a Mixed Integer Linear Programming (MILP) and solved it with commercial solvers. The performance evaluation shows that the proposed algorithm is extremely fast when coping also with medium/large network scenarios, still providing solutions which are only 5% away from the optimal ones.…”

Section: Introductionmentioning

confidence: 99%

“…Related works on applying greedy-based heuristic to wireless networks are commented in Section V, while concluding remarks are finally given in Section VI II. REFERENCE SCENARIO AND PROBLEM STATEMENT We consider the same reference system as in [3] with a wireless sensor network composed of a set S = {s 1 , s 2 , . .…”

Section: Introductionmentioning

confidence: 99%

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A greedy approach for resource allocation in Virtual Sensor Networks

et al. 2017

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Abstract-Virtual Sensor Networks (VSNs) envision the creation of general purpose wireless sensor networks which can be easily adapted and configured to support multifold applications with heterogeneous requirements, in contrast with the classical approach of wireless sensor networks vertically optimized on one specific task/service. The very heart of VSNs' vision is the capability to dynamically allocate shared physical resources (processing power, bandwidth, storage) to multiple incoming applications. In this context, we tackle the problem of optimally allocating shared resources in VSNs by proposing an efficient greedy heuristic that aims to maximize the total revenue out of the deployment of multiple concurrent applications while considering the inherent limitations of the shared physical resources. The proposed heuristic is tested on realistic network instances with notable performances in terms of execution time while keeping the gap with respect to the optimal solution limited (below 5%in the tested environments).

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Section: Simulations and Performance Evaluationmentioning

confidence: 99%

“…This energy consumption is left as a function of a b and s g . 1 1 Detailed expressions for this energy consumption can be found in [3]. (lines 7-8 of Algorithm 1).…”

Section: Greedy Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A greedy approach for resource allocation in Virtual Sensor Networks

et al. 2017

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“…The authors of [4] propose an optimization framework to prolong network lifetime by properly scheduling the tasks in a shared/virtual sensor network. In previous works [5], [6] we have proposed a mathematical programming framework to optimally allocate in a static scenario the shared physical resources of the general purpose WSN to multiple concurrent applications. In these works the whole set of applications was known in advance and constant during the analysis time period.…”

Section: Introductionmentioning

confidence: 99%

Energy-aware dynamic resource allocation in virtual sensor networks

Delgado

Canales

Ortín

et al. 2017

2017 14th IEEE Annual Consumer Communications &Amp; Networking Conference (CCNC)

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Abstract-Sensor network virtualization enables the possibility of sharing common physical resources to multiple stakeholder applications. This paper focuses on addressing the dynamic adaptation of already assigned virtual sensor network resources to respond to time varying application demands. We propose an optimization framework that dynamically allocate applications into sensor nodes while accounting for the characteristics and limitations of the wireless sensor environment. It takes also into account the additional energy consumption related to activating new nodes and/or moving already active applications. Different objective functions related to the available energy in the nodes are analyzed. The proposed framework is evaluated by simulation considering realistic parameters from actual sensor nodes and deployed applications to assess the efficiency of the proposals.

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Cloud Fog Architectures in 6G Networks

Yosuf

Alahmadi

El-Gorashi

et al. 2021

Computer Communications and Networks

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prior to the advent of the cloud, storage and processing services were accommodated by specialized hardware, however, this approach introduced a number of challenges in terms of scalability, energy efficiency, and cost. Then came the concept of cloud computing, where to some extent, the issue of massive storage and computation was dealt with by centralized data centers that are accessed via the core network. The cloud has remained with us thus far, however, this has introduced further challenges among which, latency and energy efficiency are of the pinnacle. With the increase in embedded devices' intelligence came the concept of the Fog. The availability of massive numbers of storage and computational devices at the edge of the network, where some are owned and deployed by the end-users themselves but most by service operators. This means that cloud services are pushed further out from the core towards the edge of the network, hence reduced latency is achieved. Fog nodes are massively distributed in the network, some benefit from wired connections, and others are connected via wireless links. The question of where to allocate services remains an important task and requires extensive attention. This chapter introduces and evaluates cloud fog architectures in 6G networks paying special attention to latency, energy efficiency, scalability, and the trade-offs between distributed and centralized processing resources.

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On optimal resource allocation in virtual sensor networks

Cited by 30 publications

References 33 publications

A greedy approach for resource allocation in Virtual Sensor Networks

A greedy approach for resource allocation in Virtual Sensor Networks

Energy-aware dynamic resource allocation in virtual sensor networks

Cloud Fog Architectures in 6G Networks

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