A Simple and Efficient RSS-AOA Based Localization with Heterogeneous Anchor Nodes

Accurate and reliable localization is crucial for various wireless communication applications. Numerous studies have proposed accurate localization methods using hybrid received signal strength (RSS) and angle of arrival (AOA) measurements. However, these studies typically assume identical measurement noise distributions for different anchor nodes, which may not accurately reflect real-world scenarios with varying noise distributions. In this paper, we propose a simple and efficient localization method based on hybrid RSS-AOA measurements that accounts for the varying measurement noises of different nodes. We derive a closed-form estimator for the target location based on the linear weighted least squares (LWLS) algorithm, with each LWLS equation weight being the inverse of its residual variance. Due to the unknown variances of LWLS equation residuals, we employ a two-stage LWLS method for estimation. The proposed method is computationally efficient, adaptable to different types of wireless communication systems and environments, and provides more accurate and reliable localization results compared to existing RSS-AOA localization techniques. Additionally, we derive the Cramer-Rao Lower Bound (CRLB) for the RSS-AOA signal sequences used in the proposed method. Simulation results demonstrate the superiority of the proposed method

Accurate RSS-Based Localization Using an Opposition-Based Learning Simulated Annealing Algorithm

Wireless sensor networks require accurate target localization, often achieved through received signal strength (RSS) localization estimation based on maximum likelihood (ML). However, ML-based algorithms can suffer from issues such as low diversity, slow convergence, and local optima, which can significantly affect localization performance. In this paper, we propose a novel localization algorithm that combines opposition-based learning (OBL) and simulated annealing algorithm (SAA) to address these challenges. The algorithm begins by generating an initial solution randomly, which serves as the starting point for the SAA. Subsequently, OBL is employed to generate an opposing initial solution, effectively providing an alternative initial solution. The SAA is then executed independently on both the original and opposing initial solutions, optimizing each towards a potential optimal solution. The final solution is selected as the more effective of the two outcomes from the SAA, thereby reducing the likelihood of the algorithm becoming trapped in local optima. Simulation results indicate that the proposed algorithm consistently outperforms existing algorithms in terms of localization accuracy, demonstrating the effectiveness of our approach

RSS-Based Target Localization in Underwater Acoustic Sensor Networks via Convex Relaxation

The received signal strength (RSS) based target localization problem in underwater acoustic wireless sensor networks (UWSNs) is considered. Two cases with respect to target transmit power are considered. For the first case, under the assumption that the reference of the target transmit power is known, we derive a novel weighted least squares (WLS) estimator by using an approximation to the RSS expressions, and then transform the originally non-convex problem into a mixed semi-definite programming/second-order cone programming (SD/SOCP) problem for reaching an efficient solution. For the second case, there is no knowledge on the target transmit power, and we treat the reference power as an additional unknown parameter. In this case, we formulate a WLS estimator by using a further approximation, and present an iterative ML and mixed SD/SOCP algorithm for solving the derived WLS problem. For both cases, we also derive the closed form expressions of the Cramer–Rao Lower Bounds (CRLBs) on root mean square error (RMSE). Computer simulation results show the superior performance of the proposed methods over the existing ones in the underwater acoustic environment

Target Localization in Underwater Acoustic Sensor Networks Using RSS Measurements

This paper addresses the target localization problems based on received signal strength (RSS) measurements in underwater acoustic wireless sensor network (UWSN). Firstly, the problems based on the maximum likelihood (ML) criterion for estimating target localization in cases of both known and unknown transmit power are respectively derived, and fast implementation algorithms are proposed by transforming the non-convex problems into a generalized trust region subproblem (GTRS) frameworks. A three-step procedure is also provided to enhance the estimation accuracy in the unknown target transmit power case. Furthermore, the Cramer–Rao lower bounds (CRLBs) in both cases are derived. Computer simulation results show the superior performance of the proposed methods in the underwater environment

An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network

Coverage maintenance is a bottleneck restricting the development of underwater acoustic sensor networks (UASNs). Since the energy of the nodes is limited, the coverage of UASNs may gradually decrease as the network operates. Thus, energy-saving coverage control is crucial for UASNs. To solve the above problems, this paper proposes a coverage-control strategy (referred to as ESACC) that establishes a sleep–wake scheduling mechanism based on the redundancy of deployment nodes. The strategy has two main parts: (1) Node sleep scheduling based on a memetic algorithm. To ensure network monitoring performance, only some nodes are scheduled to work, with redundant nodes in a low-power hibernation state, reducing energy consumption and prolonging the network lifetime. The goal of node scheduling is to find a minimum set of nodes that can cover the monitoring area, and a memetic algorithm can solve this problem. (2) Wake-up scheme. During network operation, sleeping nodes are woken to cover the dead nodes and maintain high coverage. This scheme not only reduces the network energy consumption but takes into account the monitoring coverage of the network. The experimental data show that ESACC performs better than current algorithms, and can improve the network life cycle while ensuring high coverage

Event-Driven Sensor Deployment in an Underwater Environment Using a Distributed Hybrid Fish Swarm Optimization Algorithm

In open and complex underwater environments, targets to be monitored are highly dynamic and exhibit great uncertainty. To optimize monitoring target coverage, the development of a method for adjusting sensor positions based on environments and targets is of crucial importance. In this paper, we propose a distributed hybrid fish swarm optimization algorithm (DHFSOA) based on the influence of water flow and the operation of an artificial fish swarm system to improve the coverage efficacy of the event set and to avoid blind movements of sensor nodes. First, by simulating the behavior of foraging fish, sensor nodes autonomously tend to cover events, with congestion control being used to match node distribution density to event distribution density. Second, the construction of an information pool is used to achieve information-sharing between nodes within the network connection range, to increase the nodes’ field of vision, and to enhance their global search abilities. Finally, we conduct extensive simulation experiments to evaluate network performance in different deployment environments. The results show that the proposed DHFSOA performs well in terms of coverage efficacy, energy efficiency, and convergence rate of the event set

RSS-Based Target Localization in Underwater Acoustic Sensor Networks via Convex Relaxation

The received signal strength (RSS) based target localization problem in underwater acoustic wireless sensor networks (UWSNs) is considered. Two cases with respect to target transmit power are considered. For the first case, under the assumption that the reference of the target transmit power is known, we derive a novel weighted least squares (WLS) estimator by using an approximation to the RSS expressions, and then transform the originally non-convex problem into a mixed semi-definite programming/second-order cone programming (SD/SOCP) problem for reaching an efficient solution. For the second case, there is no knowledge on the target transmit power, and we treat the reference power as an additional unknown parameter. In this case, we formulate a WLS estimator by using a further approximation, and present an iterative ML and mixed SD/SOCP algorithm for solving the derived WLS problem. For both cases, we also derive the closed form expressions of the Cramer−Rao Lower Bounds (CRLBs) on root mean square error (RMSE). Computer simulation results show the superior performance of the proposed methods over the existing ones in the underwater acoustic environment

Target Localization in Underwater Acoustic Sensor Networks Using RSS Measurements

This paper addresses the target localization problems based on received signal strength (RSS) measurements in underwater acoustic wireless sensor network (UWSN). Firstly, the problems based on the maximum likelihood (ML) criterion for estimating target localization in cases of both known and unknown transmit power are respectively derived, and fast implementation algorithms are proposed by transforming the non-convex problems into a generalized trust region subproblem (GTRS) frameworks. A three-step procedure is also provided to enhance the estimation accuracy in the unknown target transmit power case. Furthermore, the Cramer–Rao lower bounds (CRLBs) in both cases are derived. Computer simulation results show the superior performance of the proposed methods in the underwater environment

Target Localization in Underwater Acoustic Sensor Networks Using RSS Measurements

This paper addresses the target localization problems based on received signal strength (RSS) measurements in underwater acoustic wireless sensor network (UWSN). Firstly, the problems based on the maximum likelihood (ML) criterion for estimating target localization in cases of both known and unknown transmit power are respectively derived, and fast implementation algorithms are proposed by transforming the non-convex problems into a generalized trust region subproblem (GTRS) frameworks. A three-step procedure is also provided to enhance the estimation accuracy in the unknown target transmit power case. Furthermore, the Cramer–Rao lower bounds (CRLBs) in both cases are derived. Computer simulation results show the superior performance of the proposed methods in the underwater environment