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Joint Application Admission Control and Network Slicing in Virtual Sensor Networks

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

TL;DR

The paper tackles joint Application Admission Control and Wireless Sensor Network Slicing (AAC-SNS) in a single shared WSN infrastructure, aiming to maximize admitted applications while reconfiguring resources under node and network constraints. It develops a comprehensive mathematical programming framework that covers static offline optimization and a dynamic online setting with application arrivals, complemented by a greedy heuristic for scalable deployment decisions. The model incorporates coverage, routing, bandwidth, and energy constraints with detailed interference-aware and DODAG-based routing considerations, and offers three energy-aware objective strategies (Total, Max-min, Mixed) to guide deployments. Empirical evaluations on realistic multimedia WSNs show that the dynamic strategies, particularly the Mixed approach, and the heuristic can achieve near-optimal admission performance while remaining computationally tractable, demonstrating the practical viability of virtualization and network slicing in wireless sensor networks.

Abstract

We focus on the problem of managing a shared physical wireless sensor network where a single network infrastructure provider leases the physical resources of the networks to application providers to run/deploy specific applications/services. In this scenario, we solve jointly the problems of Application Admission Control (AAC), that is, whether to admit the application/service to the physical network, and wireless Sensor Network Slicing (SNS), that is, to allocate the required physical resources to the admitted applications in a transparent and effective way. We propose a mathematical programming framework to model the joint AAC-SNS problem which is then leveraged to design effective solution algorithms. The proposed framework is thoroughly evaluated on realistic wireless sensor networks infrastructures.

Joint Application Admission Control and Network Slicing in Virtual Sensor Networks

TL;DR

The paper tackles joint Application Admission Control and Wireless Sensor Network Slicing (AAC-SNS) in a single shared WSN infrastructure, aiming to maximize admitted applications while reconfiguring resources under node and network constraints. It develops a comprehensive mathematical programming framework that covers static offline optimization and a dynamic online setting with application arrivals, complemented by a greedy heuristic for scalable deployment decisions. The model incorporates coverage, routing, bandwidth, and energy constraints with detailed interference-aware and DODAG-based routing considerations, and offers three energy-aware objective strategies (Total, Max-min, Mixed) to guide deployments. Empirical evaluations on realistic multimedia WSNs show that the dynamic strategies, particularly the Mixed approach, and the heuristic can achieve near-optimal admission performance while remaining computationally tractable, demonstrating the practical viability of virtualization and network slicing in wireless sensor networks.

Abstract

We focus on the problem of managing a shared physical wireless sensor network where a single network infrastructure provider leases the physical resources of the networks to application providers to run/deploy specific applications/services. In this scenario, we solve jointly the problems of Application Admission Control (AAC), that is, whether to admit the application/service to the physical network, and wireless Sensor Network Slicing (SNS), that is, to allocate the required physical resources to the admitted applications in a transparent and effective way. We propose a mathematical programming framework to model the joint AAC-SNS problem which is then leveraged to design effective solution algorithms. The proposed framework is thoroughly evaluated on realistic wireless sensor networks infrastructures.
Paper Structure (19 sections, 44 equations, 6 figures, 4 tables, 3 algorithms)

This paper contains 19 sections, 44 equations, 6 figures, 4 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. Problem Statement and System Model
  4. General optimization framework
  5. Constraints on coverage and on resources of the sensors
  6. Routing constraints
  7. Bandwidth constraints
  8. Energy constraints
  9. Optimization model for dynamic resource allocation
  10. Constraints on coverage and on resources of the sensors
  11. Routing and bandwidth constraints
  12. Energy constraints
  13. Objective function
  14. Heuristic algorithm for dynamic resource allocation
  15. Performance Evaluation
  16. ...and 4 more sections

Figures (6)

  • Figure 1: Impact of moving cost. a) Deployed applications. b) Number of movements. c) Number of activations. $\varphi_i$ = 10 J $\forall i \in S$.
  • Figure 2: Impact of activation cost. a) Deployed applications. b) Number of movements c) Number of activations. $\delta_{ij}$ = 10 J $\forall i \in S, \forall j \in A$.
  • Figure 3: Temporal evolution of system performance. a) Deployed applications. b) Overall residual energy. $\varphi_i$ = 10 J. $\delta_{ij}$ = 10 J. $\forall i \in S, \forall j \in A$
  • Figure 4: Cumulative distribution function of the per node residual energy at time 360000.
  • Figure 5: Performance evaluation of the heuristic algorithm vs the dynamic scheme. (a) Deployed applications (b) Computation Time (in log scale)
  • ...and 1 more figures