optimize sensor placement

This work addresses the optimal deployment of underwater sensors for two-dimensional barrier coverage, aiming to maximize the detection probability of vessels traversing a surveillance region whose trajectories follow a Log-Gaussian Cox Line Process (LGCLP). To tackle the inherent complexity of optimizing over random line processes, we propose a novel line-to-point transformation framework that maps trajectory distributions into a transformed domain, thereby converting the geometric barrier coverage problem into a tractable and scalable optimization of a probabilistic detection function. Leveraging AIS historical trajectory data, we design a data-driven numerical algorithm to compute near-optimal sensor placements. Experimental evaluation on real maritime datasets demonstrates that our approach achieves a barrier-wide detection probability of 98.3%, significantly outperforming conventional grid-based and greedy deployment strategies.

This paper addresses the Omnidirectional Sensor Placement Problem (OSPP) in two-dimensional environments, aiming to minimize the number of sensors required to satisfy user-specified coverage requirements for visual path planning tasks such as robotic environmental inspection and target search. To handle three critical visibility models—unrestricted visibility, limited sensing range, and localization uncertainty—we propose a Hybrid Acceleration–Refinement (HAR) heuristic framework. HAR is the first to integrate multi-strategy placement outcomes, incorporate an acceleration mechanism based on convex decomposition and sampling-based preprocessing, and explicitly accommodate localization uncertainty. Experimental results demonstrate that HAR significantly reduces sensor count compared to conventional heuristics, improves sampling efficiency, and ensures robust full coverage under moderate levels of localization uncertainty.

To address insufficient coverage and resource wastage in wireless sensor networks (WSNs) for smart agriculture, this paper proposes a gradient-descent–Lagrangian-multiplier joint optimization method for sensor node deployment. Leveraging grid-based modeling, the approach integrates gradient descent with the Lagrangian multiplier method to simultaneously maximize coverage, minimize node count, and jointly optimize energy consumption and deployment cost. A novel Bluetooth-based short-range extension mechanism is introduced to enhance scalability. Experimental results demonstrate 98.5% coverage—3.5 percentage points higher than particle swarm optimization—while significantly reducing node count, deployment cost, and energy consumption compared to classical deterministic methods. This work establishes the first multi-objective joint optimization framework specifically tailored for agricultural WSNs, uniquely balancing practical applicability with theoretical rigor.

This work addresses the real-time optimization of mobile radar deployment in dynamic environments. Methodologically, it introduces an information-driven trajectory planning framework that uniquely couples Model Predictive Path Integral (MPPI) control with a physically grounded radar range measurement model incorporating actual radar parameters. The framework integrates Cubature Kalman Filter-based state estimation, information-geometric modeling, and Monte Carlo uncertainty quantification to enable kinematically feasible, dynamically consistent, and obstacle-aware online trajectory optimization. Experimental evaluation across 500 simulations demonstrates that the proposed approach reduces average root-mean-square error (RMSE) by 38–74% compared to static deployment and simplified models, while shrinking the upper tail of the 90% highest-density interval by 33–79%. These improvements significantly enhance both localization accuracy and robustness for dynamic targets.

This paper addresses the optimal sensor placement problem for static source localization under fusion of heterogeneous measurements—TDOA, RSS, AOA, and TOA. We establish a unified Cramér–Rao bound (CRB) analytical framework and, for the first time, derive and systematically compare the geometric observability constraints characterizing the optimal configurations for each measurement type. Leveraging the A-optimality criterion, we propose a hybrid-measurement-aware cooperative placement strategy, integrating geometric observability modeling with numerical optimization, validated via Monte Carlo simulations. Results demonstrate that the proposed strategy achieves mean-square error (MSE) performance approaching the theoretical CRB lower bound across diverse mixed-measurement combinations. In representative scenarios, it improves localization accuracy by 30%–50% over random or conventional placements, significantly enhancing information complementarity and robustness to measurement uncertainties and source geometry variations.

This study addresses the joint optimization of sensor placement and orientation scheduling to minimize the probability of undetected intruder traversal through a protected area. The problem is decomposed into a master–subproblem structure: orientation scheduling is formulated as a defender–attacker zero-sum game, whose Nash equilibrium is efficiently computed and used as the utility function for placement optimization. The work introduces a unified framework that, for the first time, integrates game-theoretic utility design with weak submodular optimization, offering theoretical guarantees on convergence and approximation optimality. Experimental results demonstrate that the proposed method achieves near-optimal detection performance while substantially reducing computational overhead compared to existing baselines.

This study addresses the unclear mechanisms by which configuration parameters influence topology quality and performance in tactical wireless networks. It systematically investigates the sensitivity of three parameter categories—structural constraints, technology choices, and modeling assumptions—by generating optimized topologies using a tabu search metaheuristic and assessing statistical significance through Friedman and Wilcoxon non-parametric tests. The findings reveal a fundamental distinction between parameters that substantially reshape network topology and those that merely modulate performance magnitude. Moreover, the work identifies scale-dependent technological transition phenomena and threshold effects induced by structural constraints. These insights yield actionable design principles for parameter tuning and topology optimization in mission-critical tactical networks.

This study addresses the capacitated p-location problem by introducing, for the first time, multi-scale area coverage constraints to ensure spatial fairness. To this end, the authors propose two models—C$p$LP-TC and its multi-scale extension C$p$LP-MTC—along with a strengthened integer linear programming (ILP) formulation, valid inequalities, and a parameter-free randomized sampling spatial voting (RSSV) heuristic, integrated with problem-size reduction techniques for efficient solution. Evaluation on an open-source benchmark dataset constructed from French administrative regions demonstrates that the approach effectively quantifies the trade-off between service efficiency and regional equity, offering scalable and reproducible decision support for fair facility location in real-world applications.

This study addresses the joint problem of docking station placement and resident AUV allocation for subsea pipeline leak response under spatial uncertainty. The authors propose a two-stage mixed-integer linear programming framework: the first stage minimizes the worst-case response time across all potential leak locations, while the second stage further optimizes the average response time under the maximum response time bound established in the first stage. This work is the first to integrate both worst-case and average response times into a unified optimization framework and introduces a cost–time Pareto analysis to balance system resilience and economic efficiency. A case study based on the Johan Sverdrup oil and gas field in Norway demonstrates that a small number of strategically located docking stations can achieve highly effective response coverage, explicitly quantifying the trade-off between deployment cost and response performance.