Node Selection Algorithm for Underwater Acoustic Sensor Network Based on Particle Swarm Optimization

In underwater acoustic sensor networks (UASN), the main challenging issues are bandwidth, higher propagation delay, and heavy packet loss during data transmission. The issues can be solved through efficient routing algorithms. Due to the complexity and variability of the 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 has proposed the deep learning-high dynamic biased track (DL-HDBT) algorithm. The DL-HDBT combines the deep learning and hybrid dynamic biased tracking algorithm. Deep learning (DL) helps in the identification of the best relay nodes in the network and traffic-congested nodes are tracked using a high dynamic bias track. The routing protocol has been implemented in the ns2-AqaSim simulator and testbed for measurement of the performance metrics of the UASN. The simulation results showed that the novel routing method throughput has increased by 17%, 35%, and 57% when compared with SUN, VBF and DF method. It can effectively improve the throughput of nodes, balance positioning performance as well as energy use efficiency, and optimize the positioning result of UWASN.

A time synchronization algorithm for hidden mobile node (HMB), which can only receive signals, joining an existing synchronized underwater Acoustic (UWA) sensor network (UASN) is proposed. In order to obtain the location of the HMB or communicate with it, the local time of the HMB should synchronize with the UASN. However, the propagation delay in UWA channels could not be ignored compared to electromagnetic radio channels. In the proposed algorithm, the HMB do the uniform linear motion in a certain direction, and the clock drift could be solved. After the derivation of the solution equations and the MATLAB simulation, it was proved that this algorithm is useful to reduce time error and improve the node localization accuracy in specific underwater circumstance.

Localization is considered as an important research concept for underwater acoustic sensor networks (UASNs). It performs significant role in diverse routing methods, estimating the node position and node recovery. In UASNs, localization methods have different characteristics compared with the terrestrial networks. The challenges involved in UASNs are varying water temperature and pressure, time synchronization of beacon nodes, complicated ocean currents, and positioning of nodes. To overcome these challenges, a virtual node is deployed using the Nelder–Mead algorithm with the static localization method. In this study, two types of localization methods namely static and dynamic methods are considered and a virtual node is deployed in a static localization manner. Since anchor nodes cannot communicate to the entire network for localization additionally, virtual nodes are deployed to measure the received signal strength indicator and error ratio for effective transmission. In addition “GPS node” is equipped with a ship for easy deployment without communication overhead. The simulation result justifies that static localization for an autonomous underwater sensor networks perform with better coverage rate without time synchronization and acoustic transmission overhead.

Underwater communication uses sonar waves instead of electromagnetic waves, results in low data rate as compared to ground communication. The constraints of underwater communication are large propagation delay, low bandwidth and low data rate results in degradation efficiency. As ultra-wide band (UWB) signals have high penetration capability through the obstacle, so underwater communication channel can be modified as UWB Saleh-Valenzuela (S-V) model. Here, an underwater positioning scheme is proposed with the use of UWB channel. Theoretical performance analysis of positioning scheme in underwater acoustic sensor network is simulated using MATLAB. The applications of this technique can be applied to various underwater acoustic (UWA) sensor networks (SNs).

The autonomous underwater vehicle (AUV) aided mobile data collection is an effective method for reducing the energy consumption of the underwater acoustic (UWA) sensor networks. In this paper, we propose an AUV-aided path-planning scheme using cooperative transmission mechanism for a medium-scale UWA sensor network. In the proposed scheme, we analyze not only the energy consumption, but also the task duration and path-planning cost comprehensively for practical applications. We analyze four different path-planning schemes in terms of energy consumption of UWA sensor nodes and travel cost of AUV. The simulation results show that the lawn mower path-planning scheme has lower energy consumption of UWA sensor networks. But the circle path-planning scheme has lower working time and path energy consumption of AUV. Therefore, in view of different needs, we should make a comprehensive selection.

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.

As a time-frequency doubly selective channel, severe multipath, Doppler, as well as the large time-delay characteristics of the underwater acoustic (UWA) channel pose significant challenge to the research and design of UWA communication and network systems. To mitigate these negative factors, inherent sparsity contained in the UWA channel has been extensively investigated to improve UWA communication via a sparsity exploitation receiver. While the performance of the UWA sensor network is highly dependent on that of the physical layer, there are few investigations reported on exploiting channel sparsity from the viewpoint of UWA networking. In this article, a UWA sensor network adopting the sparsity exploitation physical layer is evaluated based on the network simulator 3 (NS-3) simulation tool. The simulation time-varying channel is generated by incorporating the Bellhop channel model with the statistical characteristics extracted from experimental shallow water channels. Three types of physical layers, i.e., those do not adopt sparse exploitation, adopting compressed sensing (CS), as well as the dynamic CS (DCS) technique, are employed for evaluation and comparison of network behavior under different media access control (MAC) protocols. The evaluation results verify the effectiveness of sparsity exploitation in improving UWA sensor network performance in the presence of time variations, while giving a quantitative comparison between enhancement achieved by the CS and DCS sparsity exploitation.

Underwater Acoustic Sensor Network (UASN) has become increasingly important, with numerous applications emerging from various areas such as commercial, environmental-research and defense. This paper provides a comprehensive view of current state-of-the-art in UASN by analyzing the research done by various communities. It briefly states the basics of underwater acoustic communication and cites advances in research and development at various layers of networking modules, namely physical, data link, network, transport and application layer. It also covers interesting new concepts of cross-layer protocol stack design along with requirement of network management protocols pertaining to UASN. Finally, various hardware, software tools and test-beds developed by prominent universities/research organizations are described. We also briefly provide information about the test-bed set-up at our laboratory at BITS -Pilani K K Birla Goa Campus.

With the widespread use of Underwater Acoustic Sensor Networks (UASNs) in marine monitoring, disaster prevention and other fields. In recent years, researchers have gradually paid more attention to the security issues of UASNs. Because of the development environment is inaccessible and severely hostile, UASNs are vulnerable to various security threats, such as selective forwarding attacks and DoS attacks [1]. In recent years, the trust model has been recognized as an effective way to defend against the internal attacks in the terrestrial networks. However, it is not feasible to apply these trust models directly in UASNs, because the underwater environment has the characteristics that signal attenuation and the communication quality of channel will change with the environment. The communication quality of the underwater link is unstable, and the poor quality of the link will have a negative impact on the trust value of the nodes. In this paper, we propose a new trust model for stable and accurate node trust evaluation in UASNs. A fast link quality assessment method which is suitable for UASNs with limited node energy and computing ability was adopted. Based on the analysis of the effects of various internal attacks, three types of trust evidence were selected to accurately calculate the trust value of nodes.

Localization is an essential and major issue for underwater acoustic sensor networks (UASNs). Almost all the applications in UASNs are closely related to the locations of sensors. In this paper, we propose a multi‐anchor nodes collaborative localization (MANCL) algorithm, a three‐dimensional (3D) localization scheme using anchor nodes and upgrade anchor nodes within two hops for UASNs. The MANCL algorithm divides the whole localization process into four sub‐processes: unknown node localization process, iterative location estimation process, improved 3D Euclidean distance estimation process, and 3D DV‐hop distance estimation process based on two‐hop anchor nodes. In the third sub‐process, we propose a communication mechanism and a vote mechanism to determine the temporary coordinates of unknown nodes. In the fourth sub‐process, we use two‐hop anchor nodes to help localize unknown nodes. We also evaluate and compare the proposed algorithm with a large‐scale localization algorithm through simulations. Results show that the proposed MANCL algorithm can perform better with regard to localization ratio, average localization error, and energy consumption in UASNs. Copyright © 2014 John Wiley & Sons, Ltd.

The increasing deployment of underwater vehicles demands accurate and energy-efficient target tracking in sensor networks. However, existing approaches have largely addressed tracking accuracy and energy efficiency in isolation, and a system-level framework that jointly optimizes both remains lacking. To address this gap, this paper proposes a joint optimization framework with two main contributions. First, to improve tracking accuracy under complex maneuvering conditions, we develop an Interactive Multi-Model using Long Short-Term Memory Classification (IMM-LSTM-C) algorithm, which integrates multi-step model likelihoods into an LSTM network for precise motion classification, achieving a 7.1% accuracy improvement over IMM-BP. Second, to reduce network energy consumption while maintaining tracking performance, we introduce an Improved Binary Prairie Dog Optimization (IBPDO) algorithm for node selection, enhanced with Cauchy mutation and opposition-based learning. Simulation results show that IBPDO achieves 6.1-8.2% higher accuracy than BWOA and reduces energy consumption by 12% compared to LNS. Furthermore, the complete joint framework demonstrates synergistic effects, reducing tracking error by 19.3% and energy consumption by 15.4% over the IMM + LNS baseline. The proposed framework provides an effective balance between tracking accuracy and energy efficiency in underwater acoustic sensor networks.

In Underwater Acoustic Sensor Network (UWASN), node redeployment strategy is utilized to handle the reliable network coverage. The sensors deployed in the underwater are used to intellect the area and collected information is moved to the sink node. Node redeployment strategy is essential for the nodes which are placed outside the monitoring area in UWASN. In this paper, the node redeployment strategy is performed based on the hybrid Emperor Penguin Optimization (EPO) algorithm with Particle Search Algorithm (PSO) for better underwater acoustic communication and the proposed method is named as HEPSO. This hybridization is performed to reduce node failure rate and network energy consumption rate by optimally place the sensor nodes in underwater acoustic communication. The stability of the network topology is guaranteed by this algorithm and it enhances the node redeployment strategy by calculating the fitness function for each and every node. The implementation of the proposed algorithm is carried out by the MATLAB platform. The performance parameters like network coverage rate, network connectivity rate, network lifetime, number of nodes outside monitored space and total movement distance of nodes are evaluated and related with current methods like NRBSCT (Node Redeployment Based on Stratified Connected Tree) and MRNR (Moving Redundancy Nodes Redeployment) strategy.

In recent years, Underwater Acoustic Sensor Networks (UASN) has gained much attention from researchers because of its diverse applications. UASNs face several issues and challenges like limited bandwidth, high propagation delay, 3D topology, media access control, routing, resource utilization, and energy constraints. Unlike the nodes in terrestrial wireless sensor networks (TWSNs), UASNs suffer from energy constraints, severely affecting the network lifetime and throughput. Simulation of UASNs is a common aspect of researchers. It facilitates analysis of the working and performance of a UASN before it is implemented and deployed, which incurs substantial time and cost. Among the different simulation platforms available for simulating UASNs, UnetStack is one, which is an efficient and well-known tool available for simulating UASN, with significant benefits. But, the present UnetStack does not provide direct functionality for monitoring the energy of nodes during simulations, which is crucial. This paper presents the design and implementation of the residual energy model framework in UnetStack. Additionally, through the experimental simulations, the number of frames transmitted & received, and the depletion of node energy over time presented. Further, the implemented energy model framework able the researchers in the design of energy-aware routing protocols and load balancing methods.

Underwater Acoustic Sensor Network(UASN) is an emerging Marine information collection technology. When a mobile node (MN) joins a fixed UASN (the clock of the anchor nodes (ANs) in UASN is accurate and the location is known). After a period of sailing, clock drift would cause out-sync which affects the positioning, navigation and other services of MN. Thus, the development of elimination method for clock drift in UASN is imperative.

In this article, initially we propose an optimal packet size selection scheme for reduced channel time wastage for Non-Orthogonal Multiple Access (NOMA) in Underwater Acoustic Sensor Networks (UASNs). Existing conventional NOMA technique achieves the sum rate without considering the traffic generation in UASNs, which leads to wastage of resources due to unequal transmission times in paired transmission. In contrast, the proposed scheme overcomes this problem by making equal transmission time slots for both weak and strong users using optimal data packet sizes to avoid the wastage of resources. Further, we propose an optimal power allocation for weak and strong users with respect to the distance between transceiving nodes by using particle swarm optimization. The analytical results clearly show that the proposed scheme for NOMA in UASNs significantly improves the data rate performance in comparison with the existing conventional NOMA technique.