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Mobile Base Station Optimal Tour in Wide Area IoT Sensor Networks

Sachin Kadam

TL;DR

This paper introduces the Mobile Base Station Optimal Tour (MOT) problem, which seeks a minimum-cost, non-revisiting tour over a subset of candidate stops such that the union of their coverage regions ensures complete sensor data collection under a global sensor energy constraint.

Abstract

Wide-area IoT sensor networks require efficient data collection mechanisms when sensors are dispersed over large regions with limited communication infrastructure. Unmanned aerial vehicle (UAV)-mounted Mobile Base Stations (MBSs) provide a flexible solution; however, their limited onboard energy and the strict energy budgets of sensors necessitate carefully optimized tour planning. In this paper, we introduce the Mobile Base Station Optimal Tour (MOT) problem, which seeks a minimum-cost, non-revisiting tour over a subset of candidate stops such that the union of their coverage regions ensures complete sensor data collection under a global sensor energy constraint. The tour also avoids restricted areas. We formally model the MOT problem as a combinatorial optimization problem, which is NP-complete. Owing to its computational intractability, we develop a polynomial-time greedy heuristic that jointly considers travel cost and incremental coverage gain while avoiding restricted areas. Using simulations, we obtain tours with low cost, complete sensor coverage, and faster execution. Our proposed greedy algorithm outperforms state-of-the-art approaches in terms of a performance indicator defined as the product of tour length and algorithm execution time, achieving an improvement of 39.15%. The proposed framework provides both theoretical insight into the structural complexity of MBS-assisted data collection and a practical algorithmic solution for large-scale IoT deployments.

Mobile Base Station Optimal Tour in Wide Area IoT Sensor Networks

TL;DR

This paper introduces the Mobile Base Station Optimal Tour (MOT) problem, which seeks a minimum-cost, non-revisiting tour over a subset of candidate stops such that the union of their coverage regions ensures complete sensor data collection under a global sensor energy constraint.

Abstract

Wide-area IoT sensor networks require efficient data collection mechanisms when sensors are dispersed over large regions with limited communication infrastructure. Unmanned aerial vehicle (UAV)-mounted Mobile Base Stations (MBSs) provide a flexible solution; however, their limited onboard energy and the strict energy budgets of sensors necessitate carefully optimized tour planning. In this paper, we introduce the Mobile Base Station Optimal Tour (MOT) problem, which seeks a minimum-cost, non-revisiting tour over a subset of candidate stops such that the union of their coverage regions ensures complete sensor data collection under a global sensor energy constraint. The tour also avoids restricted areas. We formally model the MOT problem as a combinatorial optimization problem, which is NP-complete. Owing to its computational intractability, we develop a polynomial-time greedy heuristic that jointly considers travel cost and incremental coverage gain while avoiding restricted areas. Using simulations, we obtain tours with low cost, complete sensor coverage, and faster execution. Our proposed greedy algorithm outperforms state-of-the-art approaches in terms of a performance indicator defined as the product of tour length and algorithm execution time, achieving an improvement of 39.15%. The proposed framework provides both theoretical insight into the structural complexity of MBS-assisted data collection and a practical algorithmic solution for large-scale IoT deployments.
Paper Structure (7 sections, 10 equations, 2 figures, 1 table, 1 algorithm)

This paper contains 7 sections, 10 equations, 2 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. System Model
  4. MBS Optimal Tour (MOT) Problem
  5. Greedy Algorithm
  6. Simulation Results
  7. Conclusions and Future Work

Figures (2)

  • Figure 1: Illustration of a mobile base station (MBS) tour through a wide area region while avoiding restricted areas and stopping at multiple predetermined locations such that the combined coverage of these stops includes all deployed IoT sensors.
  • Figure 2: MBS data collection tour in a wireless IoT sensor network with a restricted area. Blue hollow circles represent IoT sensor nodes, green filled circles indicate selected MBS stops with numbers, black lines with arrows indicate the MBS tour path, and the red shaded region shows the restricted area. The MBS returns to its starting stop to form a closed-loop trajectory. This tour achieves full sensor coverage while minimizing the total cost of the tour with a constraint on the total energy of the network and restricted area avoidance. The MBS tour $0 \rightarrow 3 \rightarrow 8 \rightarrow 9 \rightarrow 1 \rightarrow 7 \rightarrow 4 \rightarrow 14 \rightarrow 16 \rightarrow 19 \rightarrow 23 \rightarrow 10 \rightarrow 26 \rightarrow 28 \rightarrow 18 \rightarrow 6 \rightarrow 11 \rightarrow 21 \rightarrow 0$ has a length of approximately $178m$, and the algorithm execution time is $0.12s$.

Theorems & Definitions (1)

  • Remark 1: Computational Complexity