Exposure-Length Trade-off Optimization for Obstacle-Aware Intrusion Paths in Heterogeneous Wireless Sensor Networks

Nguyen Thi My Binh; Ho Viet Duc Luong; Nguyen Tuan Nghia; Nguyen Quang Huy; Dang Trong Hop

cs.NENov 1, 2026

Exposure-Length Trade-off Optimization for Obstacle-Aware Intrusion Paths in Heterogeneous Wireless Sensor Networks

Authors: Nguyen Thi My Binh, Ho Viet Duc Luong, Nguyen Tuan Nghia, Nguyen Quang Huy, Dang Trong Hop

Organizations: Hanoi University of Industry, 298 Cau Dien Street, Hanoi, 100000, Vietnam · Hanoi University of Science and Technology, 1 Dai Co Viet, Hanoi, 100000, Vietnam

Abstract

Barrier coverage is a key paradigm for intrusion detection and surveillance in IoT-enabled heterogeneous wireless sensor networks, where monitoring quality depends not only on the existence of sensing barriers but also on the detectability of feasible crossing paths. In this paper, we study the obstacle-aware maximal exposure path planning problem in heterogeneous sensor fields under truncated omni-directional sensing and line-of-sight occlusion. Unlike conventional maximal exposure formulations that focus only on exposure maximization, the considered problem seeks side-to-side feasible paths that simultaneously maximize accumulated sensing exposure and minimize path length in obstacle-constrained environments. To address this bi-objective problem, we develop OG-MOEA/D, a problem-specific MOEAD method that combines a fixed-grid path representation with obstacle-aware feasibility checking and adaptive repair. The standard decomposition, neighborhood update, SBX, and polynomial mutation mechanisms are inherited from MOEA/D rather than claimed as new. Extensive simulations on networks with varying sensor densities and obstacle layouts show that OG-MOEA/D consistently improves hypervolume over generations and produces well-distributed Pareto fronts. Direct obstacle-constrained comparisons with MOEA/D and NSGA-II over 20 independent runs show higher HV and lower IGD for OG-MOEA/D in all four tested sensor-network sizes. The results should be interpreted as controlled simulation evidence for surveillance-strength evaluation in synthetic two-dimensional heterogeneous WSN scenarios, rather than as a model of realistic evasive intrusion behavior or a complete real-world deployment.

Figures & tables

Panel of Figure 1 — Figure 1: Illustration of truncated sensing with obstacle-induced occlusion. The blue disk represents the nominal sensing region of sensor $s$ . Point $T_{1}$ is within range and visible to the sensor, while point $T_{2}$ is within the nominal radius but lies behind the obstacle and therefore receives zero sensing contribution according to ( 6 ).

Figure 2: Hypervolume evolution over generations for different sensor densities.

Panel of Figure 3 — Figure 3: Pareto Front evolution over generations for different sensor densities.

Panel of Figure 4 — Figure 4: Representative visual evolution of the discovered paths in the early and middle stages of optimization across different sensor densities.

Panel of Figure 5 — Figure 5: Representative visual evolution of the discovered paths in the late stage of optimization across different sensor densities.

Topology	Algorithm	Best Exposure	Runtime (s)
50 Sensors	MIGA	70.69	1016.2
50 Sensors	OG-MOEA/D	73.97	663.0
100 Sensors	MIGA	116.35	2694.2
100 Sensors	OG-MOEA/D	116.12	2283.0
150 Sensors	MIGA	171.83	3798.8
150 Sensors	OG-MOEA/D	171.91	2758.9

Table 1: Comparison of solution quality (exposure) and computational cost (runtime) between OG-MOEA/D and MIGA.

Topology	Metric	OG-MOEA/D	MOEA/D	NSGA-II
50 Sensors	HV	85090.4 $\pm$ 7898.8	53663.5 $\pm$ 5991.3 (+)	72818.9 $\pm$ 2117.8 (+)
50 Sensors	IGD	0.141 $\pm$ 0.037	0.339 $\pm$ 0.018 (+)	0.314 $\pm$ 0.018 (+)
100 Sensors	HV	139134.7 $\pm$ 13622.6	91111.4 $\pm$ 7838.6 (+)	107386.0 $\pm$ 5350.7 (+)
100 Sensors	IGD	0.102 $\pm$ 0.032	0.278 $\pm$ 0.031 (+)	0.259 $\pm$ 0.018 (+)
150 Sensors	HV	187614.6 $\pm$ 11643.7	134939.9 $\pm$ 7871.5 (+)	157929.9 $\pm$ 3830.8 (+)
150 Sensors	IGD	0.085 $\pm$ 0.035	0.233 $\pm$ 0.019 (+)	0.191 $\pm$ 0.012 (+)

Table 2: Multiobjective performance across obstacle-constrained sensor-network topologies over 20 independent runs.

Figure 6: Hypervolume convergence of OG-MOEA/D, MOEA/D, and NSGA-II over 100 generations across four obstacle-constrained sensor-network configurations.

Figure 7: Parameter sensitivity of OG-MOEA/D on HV and IGD using Friedman mean ranks over five independent seeds.

Panel of Figure 8 — Figure 8: Grid-resolution sensitivity of OG-MOEA/D on the 50-sensor topology. Error bars show one standard deviation over five independent runs. The tested grid-point counts $n=\{201,81,41,21,11,6\}$ correspond to $\Delta x=\{2,5,10,20,40,80\}$ for $W=400$ .

Wireless Sensor Networks

See all 12 papers

Learning to Optimize UAV Path Planning for Data Sensing in Wireless Sensor Networks
Sep 15, 2026Sijie Ma, Zeyuan Ma, Weijia Cao +4Robotic Data Collection Metaheuristic Optimization
South China University of Technology · South China Normal University · Information Research Institute, CAS +2
Adaptive search with path-constrained mobile sensors using multi-arm bandits
Oct 29, 2026Abraham P. Vinod, Parth K. Thaker, Gautam Dasarathy +1Adaptive Sensing Multi-Robot Systems
Mitsubishi Electric Research Laboratories, Cambridge, MA, USA · Arizona State University, Tempe, AZ, USA
Bayesian Networks for Path-Based Sensors: Gathering Information and Path Planning in Communication Denied Environments
May 15, 2026Alkesh K. Srivastava, George P. Kontoudis, Donald Sofge +1Bayesian Networks Informative Path Planning
University of Maryland, College Park, MD, US. · Temple University, Philadelphia, PA, US. · Colorado School of Mines, Golden, CO, US. +1

Sep 15, 2026J/K move · Enter open · S save

Learning to Optimize UAV Path Planning for Data Sensing in Wireless Sensor Networks

Sijie Ma, Zeyuan Ma, Weijia Cao, Yue-Jiao Gong+3

South China University of Technology · South China Normal University · Information Research Institute, CAS+2

Robotic Data Collection Metaheuristic Optimization Learning to Optimize Motion Planning Wireless Sensor Networks

Open paper AskPDF arXiv

UAVs have emerged as highly flexible platforms for data sensing in Wireless Sensor Networks (WSNs). Path planning for UAVs in such tasks plays a key role to assure remote sensing effectiveness and friendly energy consumption. However, existing approaches show two key limitations: i) they are primarily hand-crafted with certain design biases that harm adaptation on unseen tasks. ii) they predominantly assume idealized spatial complexities of actual environments through simplified simulation, causing them to underperform during real-world deployment. In this paper, we propose a novel learning-assisted planning framework, termed Landscape-Aware Meta Differential Evolution (LAMDE), to tackle the mentioned limitations. The major contributions come from the following aspects. We first re-formulate such UAV path planning problem to embrace challenging constraints. To efficiently navigate this highly constrained space, we propose a bi-level learning to optimize approach, where the meta-level is a trainable algorithm configuration policy that meta-learns an adaptable planning strategy for low-level planning algorithm. To address the potential training data scarcity and distribution shift in real-world environments, we introduce a landscape-aware automatic augmentation scheme that enriches training data. At the low-level, a Differential Evolution algorithm is deployed for solving the path planning tasks. To enhance the solving flexibility, we further design a variable-length encoding strategy that dynamically prunes redundant hover points and optimizes continuous flight parameters concurrently within a unified search space. Based on all proposed designs, we meta-train LAMDE and compare it with representative baselines. Comprehensive experiments demonstrate that LAMDE achieves state-of-the-art performance on the tested complex UAV path planning tasks in WSN data collection scenarios.

Abstract

Wireless Sensor Networks

Explore similar work

Learning to Optimize UAV Path Planning for Data Sensing in Wireless Sensor Networks

Adaptive search with path-constrained mobile sensors using multi-arm bandits

Bayesian Networks for Path-Based Sensors: Gathering Information and Path Planning in Communication Denied Environments