Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Jointly Optimizing Power Allocation and Device Association for Robust IoT Networks under Infeasible Circumstances

Nguyen Xuan Tung, Trinh Van Chien, Dinh Thai Hoang, Won Joo Hwang

TL;DR

This work proposes a novel framework to enhance IoT system robustness by solving two problems, comprising maximizing the number of satisfied IoT devices and jointly maximizing both the number of satisfied devices and total network throughput, using a modified branch-and-bound (BB)-based method to solve the first problem.

Abstract

Jointly optimizing power allocation and device association is crucial in Internet-of-Things (IoT) networks to ensure devices achieve their data throughput requirements. Device association, which assigns IoT devices to specific access points (APs), critically impacts resource allocation. Many existing works often assume all data throughput requirements are satisfied, which is impractical given resource limitations and diverse demands. When requirements cannot be met, the system becomes infeasible, causing congestion and degraded performance. To address this problem, we propose a novel framework to enhance IoT system robustness by solving two problems, comprising maximizing the number of satisfied IoT devices and jointly maximizing both the number of satisfied devices and total network throughput. These objectives often conflict under infeasible circumstances, necessitating a careful balance. We thus propose a modified branch-and-bound (BB)-based method to solve the first problem. An iterative algorithm is proposed for the second problem that gradually increases the number of satisfied IoT devices and improves the total network throughput. We employ a logarithmic approximation for a lower bound on data throughput and design a fixed-point algorithm for power allocation, followed by a coalition game-based method for device association. Numerical results demonstrate the efficiency of the proposed algorithm, serving fewer devices than the BB-based method but with faster running time and higher total throughput.

Jointly Optimizing Power Allocation and Device Association for Robust IoT Networks under Infeasible Circumstances

TL;DR

This work proposes a novel framework to enhance IoT system robustness by solving two problems, comprising maximizing the number of satisfied IoT devices and jointly maximizing both the number of satisfied devices and total network throughput, using a modified branch-and-bound (BB)-based method to solve the first problem.

Abstract

Jointly optimizing power allocation and device association is crucial in Internet-of-Things (IoT) networks to ensure devices achieve their data throughput requirements. Device association, which assigns IoT devices to specific access points (APs), critically impacts resource allocation. Many existing works often assume all data throughput requirements are satisfied, which is impractical given resource limitations and diverse demands. When requirements cannot be met, the system becomes infeasible, causing congestion and degraded performance. To address this problem, we propose a novel framework to enhance IoT system robustness by solving two problems, comprising maximizing the number of satisfied IoT devices and jointly maximizing both the number of satisfied devices and total network throughput. These objectives often conflict under infeasible circumstances, necessitating a careful balance. We thus propose a modified branch-and-bound (BB)-based method to solve the first problem. An iterative algorithm is proposed for the second problem that gradually increases the number of satisfied IoT devices and improves the total network throughput. We employ a logarithmic approximation for a lower bound on data throughput and design a fixed-point algorithm for power allocation, followed by a coalition game-based method for device association. Numerical results demonstrate the efficiency of the proposed algorithm, serving fewer devices than the BB-based method but with faster running time and higher total throughput.

Paper Structure

This paper contains 22 sections, 5 theorems, 50 equations, 8 figures, 4 tables, 5 algorithms.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Models and Optimization Problem Formulation
  4. Signal Model
  5. Problem Formulation
  6. Conventional joint power allocation and device association optimization problem
  7. The number of satisfied IoT devices maximization problem and the dual-objective problem for the power allocation and the AP association under infeasible circumstances
  8. Proposed Algorithm
  9. The number of satisfied IoT devices optimization
  10. Proposed solution to the dual-objective optimization problem in \ref{['DualProblem_v1']}
  11. Total Data Throughput Maximization Problem With The Quality of Service Constraints
  12. Power allocation with equal data throughput constraints
  13. AP association
  14. Alternative Algorithm
  15. Complexity Analysis
  16. ...and 7 more sections

Key Result

Lemma 1

For a given number of APs and the limited power budget at each AP, there always exists a solution to problem ProblemMaxQ.

Figures (8)

  • Figure 1: The considered model of a downlink IoT network with the participation of multiple APs and IoT devices.
  • Figure 2: An example coordinates of $5$ APs and $15$ IoT devices.
  • Figure 3: The achievable rate of IoT IoT devices with different solutions when the system has 8 IoT IoT devices and 3 APs.
  • Figure 4: The convergence in the average number of satisfied IoT devices when the system serves 15 IoT devices with the minimum data throughput $0.5$ (bits/s/Hz) by 5 APs.
  • Figure 5: The convergence in the total network throughput when the system serves 15 IoT devices with the minimum data throughput $0.5$ (bits/s/Hz) by 5 APs.
  • ...and 3 more figures

Theorems & Definitions (15)

  • Remark 1
  • Lemma 1
  • proof
  • Lemma 2
  • proof
  • Remark 2
  • Remark 3
  • Lemma 3
  • proof
  • Remark 4
  • ...and 5 more