Underwater Acoustic Sensor Networks Research Articles

A QoS-Aware Energy-Efficient Chimp Optimization Routing Protocol with Efficient Sensor Node Deployment Strategy in Underwater Acoustic Sensor Network

As an extension of wireless sensor networks in the underwater environment, Underwater Acoustic Sensor Networks (UASNs) have led to a broad consideration of academicians. One of the problems that lower UWSN effectiveness in terms of network lifespan is premature energy depletion. It might be caused by the network nodes using different amounts of energy. In UASNs, the effectiveness and dependability of data transfer remain so adverse because of the intricate underwater environment in diverse ocean applications like surveilling atypical submarine oil pipelines. Inspired by the significance of UASNs’ quality of service in several implementations, this study proffers a metaheuristic optimization algorithm (AG) called Chimp Optimization-based Routing Protocol (CH-ORP) for UASNs obtaining intricate features of underwater (UW) medium into concern like 3D changing topology, high propagation delay, node mobility, and density, and, also, cluster head nodes’ rotation mechanism. Initially, the entire network (NW) paradigm has been considered as a three-dimensional cube out of a grid point of view, and the three-dimensional cube has been split into several little cubes by employing Delaunay Triangulation. The optimization has been carried out for lessening node failure rate and NW energy consumption rate by ideally placing the sensor nodes in UW acoustic communication. The NW topology’s steadiness has been assured by the AG, and this optimizes the node redeployment scheme by computing the fitness function for all nodes. The proffered AG’s simulation substantiations have been performed for exhibiting the CH-ORP’s efficiency that executes finer when compared to the advanced methodologies concerning energy efficiency, reliability, and end-to-end delay. It has been found that the proposed Ch-ORP achieves 698 packets received with 28% of energy consumption, 156-sec Network delay, 257 packet loss, 97.23% of PDR, and 1256 Mbps of Network throughput.

Joint Slot Scheduling and Power Allocation for Throughput Maximization of Clustered UASNs

In the last decades, independent consideration of underwater acoustic medium access control (MAC) layer has received much attention for designing a reliable data transmission protocol. Although it can simplify the system design, it is often insufficient for the enhancement of overall system performance. The focus of this paper is on the cross-layer optimization to maximize the network throughput (NT) of clustered underwater acoustic sensor networks (UASNs) by jointly optimizing the sensor nodes’ slot scheduling and power allocation. The formulated problem is a mixed integer non-linear programming problem, which is NP-hard. An alternating-optimization-based centralized algorithm is proposed first to solve it, which can achieve the best NT performance but at the price of high complexity. Therefore, a multi-leader multi-follower Stackelberg game based distributed algorithm is also proposed to achieve a better tradeoff between system performance and complexity. Simulation results demonstrate that our proposed schemes considering the information causality constraint have a better NT performance than those without a “systematic” consideration over such joint optimization, like CMS-MAC algorithm.

LSLPR: A Layering and Source-Location-Privacy-Based Routing Protocol for Underwater Acoustic Sensor Networks

LSLPR: A Layering and Source-Location-Privacy-Based Routing Protocol for Underwater Acoustic Sensor Networks

Secured Clustering Multilevel Underwater Acoustic Networks with Dynamic Routing Discovery (SCDRD) Protocol

Abstract: Due to multiple rising automated industry and combat field applications, researchers have developed a growing interest in the area of Underwater Acoustic Sensor Networks (UASNs) over the previous decade. Extending the lifetime of the energy and the consumption of energy in UASNs are enormous challenges. One of the primary difficulties in UASNs is the development of energy-saving routing mechanisms. In recent days the security of the data collected is the main concern because of widely accessible wireless sensors, because these low-power devices are incompatible with complicated encryption methods. As a result, for multi-hop Underwater Acoustic Sensor Networks (UASNs) with non-replenish able energy resources, lifetime optimization and security are two opposing design concerns. This work introduces an Efficient Energy Clustering with Dynamic Routing Discovery protocol (SCDRD protocol), which addresses two competing difficulties by taking into account several characteristics such as probability random walking and the energy balance control mechanism. The suggested protocol detects that energy consumption in the existing network topology is disproportional to energy deployment, which has an impact on the lifetime of wireless sensor networks. Through a quantitative security analysis, this work proposes a non-uniform efficient deployment technique to handle existing energy resource optimization problems and addresses the security location privacy concern. In all circumstances, the proposed Protocol produces the efficient trade-off between the energy balance and the routing mechanisms, allowing sensor networks to last substantially longer. In addition, the deployment of energy in the non-uniform manner results in the increased network’s lifetime and overall packets sent. The results of proposed work is compared with existing cost-aware routing protocols and outperform the existing protocol with respect to various performance parameters like energy, throughput and delivery ratio.

Asynchronous Pattern-Designed Channel Access Protocol in Underwater Acoustic Wireless Sensor Networks

Due to the significant propagation delay in underwater sensor networks, conflict retransmission in channel access protocols comes at a high cost. This poses a challenge in scenarios where multiple sensor nodes generate data frames with strong temporal correlations, such as in disaster warning applications. Traditional channel allocation and timeout-based retransmission mechanisms lead to considerable access delays, making it difficult to meet the requirements. To tackle this issue, we propose the asynchronous pattern-designed random access (APDRA) protocol. This protocol enhances the access probability by designing retransmission time intervals for data frames based on pattern design. Additionally, we introduce a successive interference cancellation (SIC) mechanism at the receiver for decoding. This mechanism facilitates the transformation of the conventional method of discarding conflicted data frames into iterative decoding, thereby enhancing transmission efficiency. Via the utilization of simulations, we compare the APDRA protocol conventional underwater medium access control (MAC) protocols and existing retransmission mechanisms. The results demonstrate that the APDRA protocol has the ability to improve both the transmission success ratio (TSR) and reduces the access delay to some extent.

Optimal path selection and secured data transmission in underwater acoustic sensor networks: LSTM-based energy prediction.

The Underwater Acoustic Sensor Network (UASN) is a large network in which the vicinity of a transmitting node is made up of numerous operational sensor nodes. The communication process may be substantially disrupted due to the underwater acoustic channel's time-varying and space-varying features. As a result, the underwater acoustic communication system faces the problems of reducing interference and enhancing communication effectiveness and quality through adaptive modulation. To overcome this issue, this paper intends to propose a model for optimal path selection and secured data transmission in UASN via Long Short-Term Memory (LSTM) based energy prediction. The proposed model of transmitting the secured data in UASN through the optimal path involves two major phases. Initially, the nodes are selected under the consideration of constraints like energy, distance and link quality in terms of throughput. Moreover, the energy is predicted with the aid of LSTM and the optimal path is selected with the proposed hybrid optimization algorithm termed as Pelican Updated Chimp Optimization Algorithm (PUCOA), which is the combination of two algorithms including the Pelican Optimization Algorithm (POA) and Chimp Optimization Algorithm (COA). Further, the data is transmitted via the optimal path securely by encrypting the data with the proposed improved blowfish algorithm (IBFA). At last, the developed LSTM+PUCOA model is validated with standard benchmark models and it proves that the performance of the proposed LSTM+PUCOA model attains 90.85% of accuracy, 92.78% of precision, 91.78% of specificity, 89.79% of sensitivity, 7.21% of FPR, 89.76% of F1 score, 89.77% of MCC, 10.20% of FNR, 92.45% of NPV, and 10.22% of FDR for Learning percentage 70.

ASVMR: Adaptive Support-Vector-Machine-Based Routing Protocol in the Underwater Acoustic Sensor Network for Smart Ocean

The underwater acoustic sensor network (UASN) plays a crucial role in collecting real-time data from remote areas of the ocean. However, the deployment of UASN poses significant challenges due to the demanding environmental conditions and the considerable expenses associated with its implementation. Therefore, it is essential to design an appropriate routing protocol to effectively address the issues of packet delivery delay, routing void, and energy consumption. In this paper, an adaptive support vector machine (SVM)-based routing (ASVMR) protocol is proposed for the UASN to minimize end-to-end delay and prolong the network lifetime. The proposed protocol employs a distributed routing approach that dynamically optimizes the routing path in real time by considering four types of node state information. Moreover, the ASVMR protocol establishes a “routing vector” spanning from the current node to the sink node and selects a suitable pipe radius according to the packet delivery ratio (PDR). In addition, the ASVMR protocol incorporates future states of sensor nodes into the decision-making process, along with the adoption of a waiting time mechanism and routing void recovery mechanism. Extensive simulation results demonstrate that the proposed ASVMR protocol performs well in terms of the PDR, the hop count, the end-to-end delay, and the energy efficiency in dynamic underwater environments.

Underwater Wireless Sensor Networks with RSSI-Based Advanced Efficiency-Driven Localization and Unprecedented Accuracy.

Deep-sea object localization by underwater acoustic sensor networks is a current research topic in the field of underwater communication and navigation. To find a deep-sea object using underwater wireless sensor networks (UWSNs), the sensors must first detect the signals sent by the object. The sensor readings are then used to approximate the object's position. A lot of parameters influence localization accuracy, including the number and location of sensors, the quality of received signals, and the algorithm used for localization. To determine position, the angle of arrival (AOA), time difference of arrival (TDoA), and received signal strength indicator (RSSI) are used. The UWSN requires precise and efficient localization algorithms because of the changing underwater environment. Time and position are required for sensor data, especially if the sensor is aware of its surroundings. This study describes a critical localization strategy for accomplishing this goal. Using beacon nodes, arrival distance validates sensor localization. We account for the fact that sensor nodes are not in perfect temporal sync and that sound speed changes based on the medium (water, air, etc.) in this section. Our simulations show that our system can achieve high localization accuracy by accounting for temporal synchronisation, measuring mean localization errors, and forecasting their variation. The suggested system localization has a lower mean estimation error (MEE) while using RSSI. This suggests that measurements based on RSSI provide more precision and accuracy during localization.

A Nonuniform Clustering Routing Algorithm Based on a Virtual Gravitational Potential Field in Underwater Acoustic Sensor Network

Abstract-Due to the harsh deployment environment of the underwater coustic sensor networks (UASNs), a reliable and energy-saving routing algorithm has always been an important challenge and a hot topic. A Non-uniform Clustering (NC) algorithm is designed first in which clusters are genetated according to different node densities. Based on NC, the backbone of the underwater acoustic sensor network is formed in UASNs. To guarantee the reliability of data transmission of the backbone network, a Non-uniform Clustering Routing algorithm based on Virtual Gravitational Potential Field (NC_RVGPF) is proposed. This algorithm (1) establishes a virtual gravitational potential field model to allow data transmission by three-dimensional underwater nodes, (2) designs the virtual gravitational potential energy by combining the transmission distance between the nodes, the residual energy of the nodes, and other parameters, and (3) selects the path of the highest average potential energy as being the optimal path of data transmission. The simulation results show that compared with the classical routing algorithm, NC_RVGPF algorithm has higher transmission efficiency, less energy consumption, and can more effectively extend the network’s lifetime.

A Coverage Optimization Algorithm for Underwater Acoustic Sensor Networks based on Dijkstra Method

A Coverage Optimization Algorithm for Underwater Acoustic Sensor Networks based on Dijkstra Method

Lightweight Differentiated Transmission Based on Fuzzy and Random Modeling in Underwater Acoustic Sensor Networks.

Energy-efficient and reliable underwater acoustic communication attracts a lot of research due to special marine communication conditions with limited resources in underwater acoustic sensor networks (UASNs). In their final analysis, the existing studies focus on controlling redundant communication and route void that greatly influence UASNs' comprehensive performances. Most of them consider directional or omnidirectional transmission for partial optimization aspects, which still have many extra data loads and performance losses. This paper analyzes the main issue sources causing redundant communication in UASNs, and proposes a lightweight differentiated transmission to suppress extra communication to the greatest extent as well as balance energy consumption. First, the layered model employs layer ID to limit the scale of the data packet header, which does not need depth or location information. Second, the layered model, fuzzy-based model, random modeling and directional-omnidirectional differentiated transmission mode comb out the forwarders step by step to decrease needless duplicated forwarding. Third, forwarders are decided by local computation in nodes, which avoids exchanging controlling information among nodes. Simulation results show that our method can efficiently reduce the network load and improve the performance in terms of energy consumption balance, network lifetime, data conflict and network congestion, and data packet delivery ratio.

DESLR: Energy-efficient and secure layered routing based on channel-aware trust model for UASNs

Underwater acoustic sensor networks (UASNs) have garnered significant interest and have found widespread applications in smart ocean applications and auxiliary navigation. Nevertheless, UASNs encounter a multitude of challenges due to the constraints of the underwater environment, such as high energy consumption, secure transmission, and dynamic network topology in uncertain conditions. In this paper, we propose an energy-efficient and secure layered routing method (DESLR) for UASNs to enhance energy efficiency and improve transmission reliability. The DESLR establishes a channel-based trust model which considers factors of direct trust and indirect trust to describe the behaviors of nodes. In order to effectively identify malicious nodes, great consideration is given to the state of the acoustic channel’s reliability, as varying channel conditions can significantly impact packet transmission. An improved Ant Colony Optimization Algorithm (ACOA) is introduced to discover energy-efficient and low-latency routing paths by balancing energy consumption and routing distance. Additionally, DESLR utilizes a Layered Clustering method to group nodes into clusters and a two-layer fuzzy logic approach to select cluster heads (CH) that avoid hotspots and ensure uniform energy distribution. Simulation results demonstrate that DESLR can effectively detect defective nodes by analyzing anomalous behaviors and outperforms other related work in terms of energy consumption, average end-to-end delay, and packet loss.

Learning automaton-based energy-efficient and fault-tolerant topology evolution algorithm for underwater acoustic sensor network

With the development of Marine Internet of Things, underwater acoustic sensor network (UASN) has become a research focus. However, the energy of UASN nodes is limited, and the complex underwater environment is easy to cause the failure of UASN nodes, which will affect the normal operation of UASN. To address this issue, in this paper, an UASN energy-efficient and fault-tolerant topology is generated to prolong the node lifetime and improve the fault tolerance for node failure. Initially, the learning automaton is introduced and improved to optimize the transmission power of each node. Based on the optimized power of nodes, the node lifetime considering node load is analyzed. Then the preferential growth mechanism is improved to present the energy-efficient and fault-tolerant topology evolution algorithm. Consequently, the energy-efficient and fault-tolerant UASN topology is generated. In the simulation experiments, the selection criteria of parameters about transmission power and node load under different actual requirements is obtained, and the performance of generated topology is validated. Simulation results show that the fault tolerance and energy efficiency of generated topology in this paper outperforms the TCEB and PG-OSTCG topologies.

AUV-Aided joint time synchronization and localization of underwater target with propagation speed uncertainties

The accurate sensor localization is one of the most basic requirements for the Underwater Acoustic Sensor Networks (UASNs) in many applications. However, in harsh underwater environment, the existence of propagation speed variation with depth (i.e., stratification effect) and asynchronous clock generally affects the ranging estimation, and in turn significantly decreases localization accuracy. Taking into account these specific characteristics of UASNs, we first propose an efficient Autonomous Underwater Vehicle (AUV) aided joint localization and time synchronization solution for UASNs, subjected to stratification effect constraint, in which AUV acts as a mobile anchor and broadcasts beacon signals periodically to provide localization reference for unknown target sensors. Next, since the estimation accuracy of localization and synchronization is very sensitive to the accuracy of the AUV locations, the joint localization and synchronization method in the case of inaccurate AVU locations is considered. Then, Cramer Rao Lower Bound (CRLB) analyses are presented for both accurate and inaccurate AVU locations cases. Finally, numerical simulation results show that the proposed method in this paper outperforms the existing ones in terms of estimation errors.

AUV-Assisted Stratified Source Location Privacy Protection Scheme Based on Network Coding in UASNs

The position of the source is sensitive and critical information in underwater acoustic sensor networks (UASNs). In this study, a network coding-based scheme called the stratified source location privacy protection scheme (SSLP-NC) with autonomous underwater vehicle (AUV) is suggested for a strong adversary that can decode data. First, for the adversary with passive attacks, several fake source selection algorithms are suggested for two circumstances where the source is in the shallow and deep sea, respectively. Each node then utilizes a pseudo-random number generator to create sequences on a regular basis so that the key data can be delivered to the sink without interference. Then, for the adversary with active attack, the node encrypts the source and fake data using the preexisting pseudo-random number sequence as an encoding vector to thwart the adversary’s decryption. Further, this work develops a relay node selection approach for transmitting the encoded data, which increases the variety of the data transmission pathways. Finally, this study includes a hole avoidance strategy that uses nodes or an AUV to address the potential hole issue. The simulation demonstrates that the SSLP-NC successfully fends off an adversary that can decode data packets, and performs better than the EECOR and DBR-MAC algorithms in terms of network safe time and packet delivery rate.

HCAR: A Hybrid-Coding-Aware Routing Protocol for Underwater Acoustic Sensor Networks

Because of the characteristics of the underwater acoustic sensor networks (UASNs), such as long propagation delay, narrow bandwidth, limited resources, and intermittent connectivity, the design of the routing protocol has always been the focus and difficulty in UASNs research. Most of the existing coding-based underwater routing protocols only use the error correction function of network coding to improve the reliability of data transmission, ignoring that the network coding can effectively optimize the performance of packet transmission. In this paper, we propose a hybrid coding-aware routing protocol (HCAR). HCAR introduces inter-flow network coding and designs a new routing framework that combines reactive routing with opportunistic routing. It exhaustively explores potential coding opportunities in the network and proactively creates coding opportunities during the entire routing process, which enables HCAR to take full advantage of the broadcast nature of the wireless medium, thereby effectively reducing the number of transmissions. In addition, we design a new encoding method that does not rely on opportunistic listening, and thus avoids introducing excessive delay and overhead. The simulation results show that HCAR can effectively optimize the routing problem of the networks with multiple source nodes and a single sink node, further reducing the transmission overhead while ensuring reliability. We also implemented the key mechanism of HCAR protocol to verify its feasibility through sea tests.

Node localization without underwater anchors for underwater acoustic sensor networks

Node localization without underwater anchors for underwater acoustic sensor networks

Dynamic layered routing protocols based on BP-NN for underwater acoustic sensor networks

Underwater acoustic sensor network (UASN) effectively improves the reliability of underwater long distance transmission through multi-hop transmission between multiple nodes. However, the limited energy of underwater acoustic nodes and large end-to-end transmission delay lead to uneven load and short lifetime of UASN. In this paper, we propose a dynamic layered routing protocol based on back propagation neural network (BP-NN), named as BPDLR, to address the above issues. In the proposed scheme, considering such factors as node topology, residual energy and communication environments, the UASN is vertically divided into the top layer, the middle layer and the bottom layer. The routing designs in each layer are different, and each of them is predicted by BP-NN training model for routing. In addition, we compare the proposed BPDLR protocol with the existing schemes such as static layered routing (SLR) protocol and depth-based routing (DBR) protocol in terms of network node survival, average end-to-end delay and packet loss rate. The simulation results show that, the proposed BPDLR protocol effectively alleviates the energy hole problem of UASN and extends the network lifetime.

A novel technique for any-cast transmission scheduling in underwater acoustic sensor networks

A novel technique for any-cast transmission scheduling in underwater acoustic sensor networks

A Centralized Control-Based Clustering Scheme for Energy Efficiency in Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UASNs) are among the most critical tools for underwater environment sensing and information acquisition. Since the complexity of the underwater environment renders battery replenishment and replacement more difficult and UASNs are mainly powered by batteries built into the underwater acoustic sensor nodes, the optimization of node energy consumption is particularly important for extending the network lifetime. Recently, energy consumption optimization solutions for UASNs have mainly focused on node clustering and multihop transmission. However, most schemes are implemented using distributed algorithms; in these types of approaches, decision information usually comes from only a subset of nodes, and it is difficult to make the most globally beneficial decision. Therefore, in this paper, the idea of centralized control is applied for the optimization of network energy consumption from a global perspective. Specifically, a centralized control-based clustering scheme (CCCS) for UASNs is proposed. In this scheme, a node density-based adaptive clustering approach is adopted, intracluster controllers are established within clusters, and the choices of both relay nodes and relay clusters are optimized to achieve energy balancing and route optimization. The performance achieved in simulations shows that compared to similar methods, CCCS is able to balance the node energy consumption and effectively increase the lifetime of a UASN.