A Joint Framework of IMM-LSTM-C Tracking and IBPDO-Based Node Selection for Energy-Efficient Cooperative Tracking in Underwater Acoustic Sensor Networks.

In this paper, we address the problem of genetic algorithm optimization for jointly selecting the best group of candidate sensors and optimizing the quantization for target tracking in wireless sensor networks. We focus on a more challenging problem of how to effectively utilize quantized sensor measurement for target tracking in sensor networks by considering best group of candidate sensors selection problem. The main objective of this paper is twofold. Firstly, the quantization level and the group of candidate sensors selection are to be optimized in order to provide the required data of the target and to balance the energy dissipation in the wireless sensor network. Secondly, the target position is to be estimated using quantized variational filtering (QVF) algorithm. The optimization of quantization and sensor selection are based on the Fast and Elitist Multi-objective Genetic Algorithm (NSGA-II). The proposed multi-objective (MO) function defines the main parameters that may influence the relevance of the participation in cooperation for target tracking and the transmitting power between one sensor and the cluster head (CH). The proposed algorithm is designed to: i) avoid the problem lot of computing times and operation counts, and ii) reduce the communication cost and the estimation error, which leads to a significant reduction of energy consumption and an accurate target tracking. The computation of these criteria is based on the predictive information provided by the QVF algorithm. The simulation results show that the NSGA-II -based QVF algorithm outperforms the standard quantized variational filtering algorithm and the centralized quantized particle filter.

Target tracking in wireless sensor networks (WSN) has brought up new practical problems. The limited energy supply and bandwidth of WSN have put stringent constraints on the complexity and inter-node information exchange of the tracking algorithm. In this paper, we propose a binary variational algorithm outperforming existing target tracking algorithms such as Kalman and Particle filtering. The variational formulation allows an implicit compression of the exchanged statistics between leader nodes, enabling thus a distributed decision-making. Its binary extension further reduces the resource consumption by locally exchanging only few bits.

Underwater acoustic sensor network (UASN) is an emerging technology meant for underwater explorations. In underwater communication, the acoustic signal travels a longer distance with smaller bandwidth. Characteristics of the acoustic channel and the distance between underwater sensor nodes vary with time due to the variations in the water current. This affects the channel quality as well as the topology of the network. Connectivity issues in UASN cause transmission reliability challenges, in transferring the data sensed by various sensor nodes to the other end. Thus, data reliability and energy efficiency are the two critical issues in the case of UASN. The robust signal attenuation leads to the reception of erroneous data or data loss. In this paper, Recursive Luby transform (RLT) code-based Hybrid Automatic Repeat reQuest is proposed to overcome the data reliability issues and to obtain transmission efficiency. RLT is a form of Luby transform code with small degree of distribution. The proposed rateless code-based Hybrid ARQ scheme combines the RLT code and selective retransmission ARQ (RLTCH). The RLT code is adopted for its minimal complexity in encoding and decoding processes. The performance of RLTCH is evaluated by considering the underwater-specific channel characteristics. Simulation results of RLTCH for multi-hop UASN reveal that the proposed technique increase the network throughput with reduced energy consumption and end-to-end delay. Comparative performance studies were conducted to confirm the superiority of RLTCH with other schemes based on parameters such as throughput, packet arrival ratio, delay and energy consumption.

In Underwater Acoustic Sensor Network (UASN), routing and propagation delay is affected in each node by various water column environmental factors such as temperature, salinity, depth, gases, divergent and rotational wind. High sound velocity increases the transmission rate of the packets and the high dissolved gases in the water increases the sound velocity. High dissolved gases and sound velocity environment in the water column provides high transmission rates among UASN nodes. In this paper, the Modified Mackenzie Sound equation calculates the sound velocity in each node for energy-efficient routing. Golden Ratio Optimization Method (GROM) and Gaussian Process Regression (GPR) predicts propagation delay of each node in UASN using temperature, salinity, depth, dissolved gases dataset. Dissolved gases, rotational and divergent winds, and stress plays a major problem in UASN, which increases propagation delay and energy consumption. Predicted values from GPR and GROM leads to node selection and Corona Virus Optimization Algorithm (CVOA) routing is performed on the selected nodes. The proposed GPR-CVOA and GROM-CVOA algorithm solves the problem of propagation delay and consumes less energy in nodes, based on appropriate tolerant delays in transmitting packets among nodes during high rotational and divergent winds. From simulation results, CVOA Algorithm performs better than traditional DF and LION algorithms.

Underwater acoustic sensor networks (UASNs) have demonstrated remarkable potential in marine environmental monitoring. However, current research on node deployment methods aiming at disaster scenarios is insufficient, and few studies have paid attention to the spatial distribution characteristics of marine disasters in underwater areas, making it difficult to achieve effective monitoring. In addition, UASNs encounter the challenge that the energy of nodes is restricted and arduous to recharge. In this paper, we propose a node deployment method based on improved snake optimizer (DMISO) for marine disasters. Firstly, on the basis of analyzing the spatial distribution characteristics of common marine disasters, a hierarchical deployment method based on analytic hierarchy process is proposed to meet the monitoring requirements of target areas. Then, in order to reduce energy consumption, an improved snake optimizer based on opposition-based learning is used to optimize the deployment location of nodes with the joint optimization objective of network coverage and total energy consumption. After that, a path allocation strategy is proposed to reallocate a reasonable target location for each node so as to equalize energy consumption across all nodes. The simulation results show that DMISO reduces energy consumption by 35.46% compared to UFOA. Coverage is improved by an average of 4.77%and 17.4%, compared to UFOA and random deployment. In addition, DMISO has significant advantages in equalizing energy consumption and can effectively extend the network lifetime.

Underwater acoustic sensor networks (UASNs) are used extensively in activities such as underwater data collection and water pollution detection. An UASN consists of acoustic sensors that use batteries as their power supply. Because of the complex underwater environments in which UASNs are employed, replacing these batteries is difficult. Prolonging the battery life of UASNs by reducing their energy consumption (improving their energy efficiency) is one of the means of mitigating this problem. This paper proposes an energy-balanced unequal layering clustering (EULC) algorithm that improves the energy efficiency of acoustic sensors. The EULC algorithm designs UASNs with unequal layering based on node depth, providing a solution to the “hot spot”issue through the construction of clusters of varying sizes within the same layer. Simulation results show that the EULC algorithm effectively balances the energy in UASN nodes and thereby prolongs network lifetime.

Energy awareness is a crucial component in the design of wireless sensor networks at all layers. This paper looks into efficient energy utilization of a target tracking sensor network by predicting a target's trajectory through experience. Whilst this is not new, the chief novelty comes in conserving energy through both dynamic spatial and temporal management of sensors and yet assuming minimal locality information. We present a novel target trajectory model adapted from the Gauss-Markov mobility model, formulate the tracking problem as a hierarchical Markov decision process (HMDP) and solve it through neuro-dynamic programming (NDP). Our HMTT (hierarchical MDP for target tracking) algorithm conserves energy by reducing the rate of sensing (temporal management) but maintains acceptable tracking accuracy through trajectory prediction (spatial management). Analysis and simulation results demonstrate its effectiveness in energy conservation and tracking accuracy against other known target tracking algorithms.

Underwater target positioning technology is the most important part of UnderWater Acoustic Sensor Network(called UWASN), and it is one of the most important research directions in this field with broad application prospects in commercial and military fields. Due to the complex and variability of underwater acoustic environment, the underwater acoustic sensor network has the characteristics of fluidity, sparse deployment and energy limitation, which brings certain challenges to underwater positioning technology. Aiming at the scenario that the node redundancy in the underwater acoustic sensor network leads to low positioning efficiency, this paper considers the sound velocity correction factor based on the traditional anchor node selection algorithm in this paper. Under the premise of ensuring certain positioning accuracy, considering the communication overhead, node residual energy, position suspiciousness, sound ray propagation bending characteristics and other factors, the anchor node optimization mechanism which uses the particle swarm algorithm to iterate out the optimal sensor combination for improving the accuracy of positioning is designed. The simulation results show that the proposed algorithm shows small calculation, fast convergence and high positioning accuracy. It can effectively improve the energy utilization of nodes, balance positioning performance as well as energy use efficiency, and optimize the positioning result of UWASN, which is well suited for underwater acoustic positioning scenarios.

A surveillance system, which tracks mobile targets, is one of the most important applications of wireless sensor networks. When nodes operate in a duty cycling mode, tracking performance can be improved if the target motion can be predicted and nodes along the trajectory can be proactively awakened. However, this will negatively influence the energy efficiency and constrain the benefits of duty cycling. In this paper, we present a Probability-based Prediction and Sleep Scheduling protocol (PPSS) to improve energy efficiency of proactive wake up. We start with designing a target prediction method based on both kinematics and probability. Based on the prediction results, PPSS then precisely selects the nodes to awaken and reduces their active time, so as to enhance energy efficiency with limited tracking performance loss. We evaluated the efficiency of PPSS with both simulation-based and implementation-based experiments. The experimental results show that compared to MCTA algorithm, PPSS improves energy efficiency by 25-45 percent (simulation based) and 16.9 percent (implementation based), only at the expense of an increase of 5-15 percent on the detection delay (simulation based) and 4.1 percent on the escape distance percentage (implementation based), respectively.

Due to the aquatic nature of communication in the underwater world, the underwater acoustic sensor network (UASN) is commonly used. However, it has inherent limitations, such as limited bandwidth, high transmission energy, long propagation delays, void regions, and expensive battery replacement. Improving network lifetime (NL) is the primary objective since replacing batteries in UWSN is very expensive and challenging. NL is improved by having a high packet delivery ratio (PDR), reduced dead nodes, and reduced energy consumption (EC). If two more node batteries are depleted, they become dead nodes, causing partitions on the network and resulting in a void region problem. Void regions occur when a node has no forwarder node to forward data packets toward the sink node. Void nodes affect the routing techniques’ overall performance regarding end‐to‐end delay (EED), data loss, and EC. So, the primary objective of this work is to avoid void regions. For the same, this paper proposes a void hole detection algorithm. The algorithm selects the best next hop node based on the fitness function calculated by the gray wolf optimization (GWO) algorithm, considering only the vertical directions despite horizontal directions, further reducing the EED. The proposed approach is simulated using MATLAB, and the evaluation is based on data broadcast copies, PDR, EC, dead node number (DNN), average operational time (AOT), NL, and EED. The paper has presented a comparison with weighting depth and forwarding area division depth‐based routing (WDFAD‐DBR) routing protocol for underwater acoustic sensor network (UASN) and energy and depth variance‐based opportunistic void avoidance scheme (EDOVS) for UASN. WDFAD‐DBR avoids void holes by selecting forwarding nodes and taking the weighting sum of depth differences of two hops; in comparison, EDOVS considers not only the depth parameters but also the normalized residual energy. The proposed paper contributes to developing an energy‐efficient routing algorithm that removes void nodes by selecting the appropriate forwarder node in void regions based on the GWO algorithm. The proposed work increases the network lifetime by avoiding void regions and balancing the EC. The simulation results show that the proposed algorithm gains more than 20% PDR, less EC, and 60% less broadcasted copies of data packets and has more NL than the WDFAD‐DBR, and 10% improved PDR and lesser broadcasted copies were sent than EDOVS along with the enhanced NL, by varying the transmission range the proposed algorithm showing the better performance in terms of EC, PDR, and DNN along with the values of 61.6, 0.97, and 35 (scenario node 60 and transmission range 600 m) and 64.1, 0.89, and 25, respectively, by variable network size.

Underwater Acoustic Sensor Networks (UASNs) are essential for offshore engineering, military surveillance, environmental monitoring, and oceanographic exploration. However, challenges including high latency, congested networks, low bandwidth, and energy limitations make it difficult to communicate effectively in UASNs. In order to tackle these problems, this study suggests a new Energy‐Efficient Double‐Level Deep Reinforcement Learning with Chaotic Chimp Optimization (E2D2RL‐ChCo) model that is intended to improve localization precision, reduce packet loss, and effectively handle network congestion. The proposed E2D2RL‐ChCo model uses transformer‐based Markov Decision Processes (MDPs) for node localization at the top level and congestion‐aware routing at the bottom level to guarantee efficient task scheduling and energy‐efficient data transfer. By integrating Chaotic Chimp Optimization (ChCo), learning parameters are adjusted to enhance convergence and solution quality. Simulation results demonstrate up to 33.8% reduction in energy consumption, 61.5% decrease in end‐to‐end delay, and over 52% improvement in successful packet delivery compared to baseline models. The proposed model also improves localization error to 0.0075, outperforming existing strategies. This work fills gaps in existing literature by integrating deep reinforcement learning and metaheuristics to optimize both localization and congestion in real‐time UASN scenarios. This approach enables more sustainable and reliable UASN operations, paving the way for enhanced performance in real‐world aquatic environments.

The sensor scheduling for energy-efficient target tracking with high performance in wireless sensor networks (WSNs) is a dilemma problem. By analyzing the intrinsic relationship between tracking performance and energy consumption, we cast the scheduling problem of WSN as the optimal policy problem of partially observable Markov decision process (POMDP), and propose a dynamic cluster members scheduling (DCMS) algorithm to solve the tradeoff between tracking performance and energy consumption. First, we exploit an election method, based on the optimal mixed weights of the signal strength and the residual energy of node, to choose the cluster head node. Then, we seem each cluster members an agent, and model the scheduling problem of cluster members by POMDP. At last, a point-based online value iteration algorithm is presented to solve the DCMS to generate the collaboration strategy of sensor cluster members dynamically. The simulation results show that the proposed approach can improve the accuracy of target tracking, decrease the energy consumption of sensor nodes, and prolong the lifetime of sensor networks.

Multi-hop communication in Underwater Acoustic Sensor Network (UASN) brings new challenges in reliable data transmission. Recent work shows that data collection from underwater sensor nodes using Autonomous Underwater Vehicle (AUV) minimizes multi-hop data transmission, improving energy efficiency in UASN. However, due to continuous movements, AUV has limited communication time to collect data from sensor nodes in UASN. We therefore propose a novel underwater routing protocol, namely AEERP (AUV aided Energy Efficient Routing Protocol). In AEERP, an AUV collects data from gateway nodes. Among all the nodes, selected nodes play the role of gateway node for communication with AUV. Gateway nodes are selected based on RSSI values of the hello packet received from AUV. In addition, after consuming energy up to a certain threshold by a gateway node, another high energy node is selected as a gateway node. This adaptive selection of gateway node balances energy consumption increasing network lifetime. Mathematical analysis and NS-2 simulation show that AEERP performs better than Travel Salesperson Problem (TSP) in terms of energy cost and data delivery ratio.

In recent years, underwater acoustic sensor networks (UWSNs) are envisioned for different potential applications, ranging from long-term marine environmental monitoring, industrial instrumentation control, to military surveillance and security. Compared to wireless sensor networks (WSNs), energy-efficient data transmission becomes more critical in UWSNs due to non-rechargeable batteries of sensor nodes with limited amount of energies in long-term marine monitoring applications. Besides, in underwater acoustic communications, transmitting and receiving power levels dominate the energy consumption during the data transfer. Data packet retransmission caused by network deployment error increases the energy consumption and reduce the network lifetime. In this paper, we investigate a two-dimensional deployment strategy of UWSNs with a square grid topology. We present a mathematical model to study the deployment error of UWSNs. Based on this model, a parameter, i.e., α, is introduced to balance the network robustness and the energy consumption of sensor nodes. α is defined as the ratio of the transmission range of a sensor node to the distance between two closest adjacent sensor nodes. We report optimal values of α corresponding to this balance for different sizes of UWSNs.

As an extension of wireless sensor network in underwater environment, underwater acoustic sensor networks (UASNs) have caused widespread concern of academia. In UASNs, the efficiency and reliability of data transmission are very challenging due to the complex underwater environment in variety of ocean applications, such as monitoring abnormal submarine oil pipelines. Motivated by the importance of energy consumption in many deployments of UASNs, we therefore propose an energy-efficient data transmission scheme in this paper, called energy-efficiency grid routing based on 3D cubes (EGRCs) in UASNs, considering the complex properties of underwater medium, such as 3D changing topology, high propagation delay, node mobility and density, as well as rotation mechanism of cluster-head nodes. First, the whole network model is regarded as a 3D cube from the grid point of view, and this 3D cube is divided into many small cubes, where a cube is seen as a cluster. In the 3D cube, all the sensor nodes are duty-cycled in the media access control layer. Second, in order to make energy efficient and extend network lifetime, the EGRC shapes an energy consumption model considering residual energy and location of sensor nodes to select the optimal cluster-heads. Moreover, the EGRC utilizes residual energy, locations, and end-to-end delay for searching for the next-hop node to maintain the reliability of data transmission. Simulation validations of the proposed algorithm are carried out to show the effectiveness of EGRC, which performs better than the representative algorithms in terms of energy efficiency, reliability, and end-to-end delay.