RCAR: A Reinforcement-Learning-Based Routing Protocol for Congestion-Avoided Underwater Acoustic Sensor Networks

Underwater acoustic sensor networks (UW-ASNs) have recently been proposed for exploring the underwater resources and gathering the scientific data from the aquatic environments. UW-ASNs are faced with different challenges, such as high propagation delay, low bandwidth, and high energy consumption. However, the most notable challenge is perhaps how to efficiently forward the packets to the surface sink by considering the energy constrained sensor devices. The opportunistic routing concept may provide an effective solution for the UW-ASNs by the cooperation of the relay nodes to forward the packets to the surface sink. In this paper, the energy consumption problem is addressed and an energy-efficient cooperative opportunistic routing (EECOR) protocol is proposed to forward the packets toward the surface sink. In the EECOR protocol, a forwarding relay set is firstly determined by the source node based on the local information of the forwarder and then, a fuzzy logic-based relay selection scheme is applied to select the best relay based on considering the energy consumption ratio and the packet delivery probability of the forwarder. In the UW-ASNs, most of the energy is wasted due to the collisions amongst sensor nodes during the packet transmission. To alleviate the packet collisions problem, we have designed a holding timer for each of the forwarder to schedule the packets transmission toward the surface sink. We have performed our extensive simulations of the EECOR protocol on the Aqua-sim platform and compared with existing routing protocols in terms of average packet delivery ratio, average end-to-end delay, average energy consumption, and average network lifetime.

Underwater acoustic sensor networks (UASNs) have been used for various scenarios, such as marine exploration, and development of marine resources. However, due to the challenging underwater environment, UASNs suffers from high propagation delay, low transmission reliability and energy limitation, which pose significant obstacles to the delivery efficiency of data packets. What's more, the sparse topology, the failure of node or link, and other factors can cause void holes, resulting in the considerable package retransmission and the low network reliability of UASNs. To this end, this paper proposed a reinforcement-learning-based opportunistic routing protocol (ROEVA) to reduce energy consumption as well as to improve the transmission reliability which also addresses the issue of void routing in underwater acoustic sensor networks. To seek optimal routing rules, a reward function based on reinforcement learning is proposed, where factors such as energy, delay, link quality, and depth information are all taken into account for appropriate routing decisions. Before forwarding the data packet, a two-hop availability checking function is defined, which can identify trap nodes and avoid routing holes. In addition, aiming to reduce packet redundancy and collisions, a waiting mechanism derived from opportunistic routing is proposed. According to the calculated Q-values, the waiting mechanism determines the priority list for packet forwarding. Evaluation results demonstrate that the proposed ROEVA protocol outperforms HHVBF, RCAR, QELAR and GEDAR in terms of energy efficiency, packet delivery ratio (PDR), average hop count, and end-to-end delay, which are analyzed by varying the number of nodes from 100 to 500.

In this paper we introduce R-CARP, a reputation based channel aware routing protocol for underwater acoustic sensor networks (UASNs). While many routing protocols have been proposed for UASNs, solutions to secure routing protocols from attacks such as sinkhole attack and selective forwarding are still overlooked. These routing attacks can dramatically disrupt network performance, especially in some application scenarios such as homeland security and critical infrastructure monitoring, where a high reliability on message delivery is required. Designing secure and reliable protocols for UASNs is particularly challenging due to acoustic modems unique characteristics such as low bandwidth and bit rate, high propagation delays and high energy consumption when in transmit mode. The aim of this work is therefore to propose R-CARP, a secure and reliable routing protocol tailored to such communication constrained environment. R-CARP is an improved version of CARP, the channel aware routing protocol presented in [5], enriched with a reputation based mechanism to contrast malicious node behavior. To secure R-CARP we employ BLS, a short digital signature algorithm, exploiting its aggregation property to reduce the additional communication overhead. By means of simulation based performance evaluation, we show that, under attack, R-CARP is effective at bypassing malicious nodes and outperforms CARP in terms of packet delivery ratio (PDR) and energy per bit (EPB) by a factor of up to 2, at the cost of a slight increment in terms of latency.

Underwater Acoustic Sensor Network (UASNs) are deployed in sea for exploration of underwater hidden natural resources. There are certain research issues regarding routing and MAC protocol in the deployment of UASNs. The main challenges may be inefficient network life time, low throughput, high propagation delay and unbalance energy consumption. In order to improve the results of the basic parameters of UASNs, we propose a new cross layer MAC protocol, which integrates network layer and MAC layer, called Fitness Function based Medium Access Control (FF-MAC) protocol for UASNs. We use fitness function for suitability of a node to be selected as forwarder. We also use handshaking mechanism and scheduling algorithm for fair share of channel in the network. The simulation results show that our proposed MAC improves latency and energy consumption compared with Improved Vector Based Forwarding (I-VBF).

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.

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.

Underwater acoustic sensor networks (UASNs) have attracted significant interest and been developed for a wide range of applications. Using acoustic signal for transmitting in underwater leads the effects on long propagation delay, low bandwidth, and low data rate. These characteristics result the high end-to-end delay from the sensor to a sink. In this paper, an important property of acoustic signal that the speed of acoustic signal varies with water depth has been considered to explore a new routing protocol in UASNs. This property indicates that the speed of acoustic signal becomes faster when water depth is below 1000 meters. It implies that the shortest end-to-end delay path may not be the shortest path directly form the source to the sink. Based on this property, a per-hop based routing protocol, named depth adaptive routing protocol (DARP), is proposed. DARP takes the acoustic speed in different water depth into consideration. DARP can find a shortest end-to-end delay path to transmit the data. The simulation results also verify that DARP actually reduces the end-to-end delay and outperforms other routing protocols in terms of end-to-end delay.

Underwater Acoustic Sensor Networks (UASNs) is a prominent field in communications due to several applications. UASNs enable underwater data collection and monitoring in different applications. UASNs face several challenges like node mobility, low bandwidth, high energy consumption, and routing. The complexity of the underwater routing is increased due to node mobility. Several underwater routing protocols exist in the literature; they determine next-hop based on different criteria. Some criteria to select next-hop are link quality, residual energy, hop-count, etc. Many underwater routing protocols either use hop-count or hop-count as one of the criteria to choose next-hop. Such routing protocols result in lower hop-count, resulting in smaller end-to-end delays. These routing protocols are instrumental in the delay-sensitive applications where the end-to-end delay is the primary requirement. However, maintaining up-to-date information of the hop-count of nodes is one of the major challenges due to frequent changes in underwater topology caused due to the water current. This survey paper focuses on underwater routing protocols based on hop-count in selecting the next-hop. It focuses on updating hop-count information in various hop-count-based underwater routing protocols.

The Underwater Acoustic Sensor Network (UASN) plays a dynamic role in various application extents like surveillance, military, terrestrial networks and industrial application. In UASN, each Underwater Sensor Node (USN) is deployed in many places. The multiple paths are always used for transmitting data from USN to the Sink Node (SN). Each sensor constitutes a set of small devices and a small battery. If data is received from multiple nodes at the same time then, there is a chance for a collision and also it will bring down network lifetime. In recent years, only a few research works are done in fuzzy based routing in UASN. For increasing the network’s lifetime, there is always a need for an optimum routing path in UASN with improved parameters related to propagation delay, energy and throughput. The present paper proposes the Fuzzy Logic Depth based Energy-Aware Routing (FLDEAR) path in UASN. A static USN was utilized in the proposed system. Each USN is assigned a Priority Number (PN) by the SN. The PN is used for avoiding collision in the proposed model. The simulation is conducted employing Aqua-Sim (AS) which is based on Network Simulator-2(NS2). The simulation results illustrate that this system outperforms the existing routing protocol such as protocols of Energy Efficient Cooperative Opportunistic Routing (EECOR), Fuzzy Depth Based Routing (FDBR) and Depth Based Routing (DBR) in terms of improved parameters related to energy, propagation delay and throughput. Finally, the lifetime of UASN is improved by 70–80% by the implementation of the proposed FLDEAR protocol.

A cross-layer protocol is proposed to deal with the problems of high latency, low bandwidth and high bit-error-rate (BER) in underwater acoustic sensor networks (UASNs). Nodes are organized as clusters based on depth and the nodes with same depth belong to same cluster and cluster head (CH) is chosen by the CH in high-level depth. At network layer, nodes in different depths send packet to their CH hop by hop and CH transmits the aggregated data to the one-depth higher CH till the data arrives at sink node in surface; At MAC layer, a CSMA/CA-based MAC protocol is used in each cluster while CHs use a pre-defined schedule to allocate the channel; At physical layer, nodes change the transmission power and frequency to decrease channel collision and energy consumption in a self-adaptive way. According to the simulation results, it brings benefits in improving transmission reliability and decreasing transmission delay.

As the utilization of Underwater Acoustic Sensor Networks (UASNs) in industrial and military operations increases, there is a requirement to guarantee the confidentiality and integrity of the data being transmitted. However, protecting communication in UASNs is very challenging as they are often deployed in unattended environments without physical protection. The slow propagation speed of underwater acoustic waves and low channel bandwidth are additional challenges affecting the design and implementation of security mechanisms. This paper outlines the main disadvantages of using conventional key-exchange protocols in resource-constrained environments such as UASNs, and proposes the utilization of implicit certificates and Hashed One-pass Menezes-QuVanstone (HOMQV) key-exchange protocol as an alternative. We implemented the proposed solution on several computing platforms and present the performance evaluation results in terms of computational time and communication overhead. Our analysis shows that using HOMQV in combination with Elliptic Curve Qu-Vanstone (ECQV) protocol for implicit certificate generation can significantly reduce communication and power consumption requirements compared to Diffie-Hellman (DH) protocol based on X.509 certificates.

The design of medium access control (MAC) protocols for Underwater Acoustic Sensor Networks (UASNs) pose many challenges due to low bandwidth and high propagation delay. In this paper, a new medium access control (MAC) protocol called Handshake Triggered Chained-Concurrent MAC (HTCC) is proposed for large-scale applications in UWSNs. The main idea of HTCC is to establish a chained concurrent transmission accomplishing spatial reuse. The novelties of HTCC lie in: firstly, the protocol allows multi-direction handshake with different nodes simultaneously; secondly, a random access mechanism is integrated with the handshake mechanism for improve channel utilization. Simulation results show that HTCC outperforms extended version of Slotted floor acquisition multiple accesses (Ext-sFAMA) in terms of network throughput, the RTS efficiency, as well as fairness in representative scenarios.

Considering the energy constraint for fixed sensor nodes and the unacceptable long propagation delay, especially for latency sensitive applications of underwater acoustic sensor networks, we propose a MAC protocol that is latency-optimized and energy-efficient scheme and combines the physical layer and the MAC layer to shorten transmission delay. On physical layer, we apply convolution coding and interleaver for transmitted information. Moreover, dynamic code rate is exploited at the receiver side to accelerate data reception rate. On MAC layer, unfixed frame length scheme is applied to reduce transmission delay, and to ensure the data successful transmission rate at the same time. Furthermore, we propose a network topology: an underwater acoustic sensor network with mobile agent. Through fully utilizing the supper capabilities on computation and mobility of autonomous underwater vehicles, the energy consumption for fixed sensor nodes can be extremely reduced, so that the lifetime of networks is extended.

Improving the energy efficiency of underwater acoustic sensor networks (UW-ASNs) is a crucial issue due to the reduced and nonrechargeable energy resource of the underwater sensor nodes. In this work, we address the energy sink hole problem in UW-ASNs while considering the unique and harsh characteristics of the underwater channel. Our goal is to determine the optimal deployment and routing settings that surmount the energy sink hole problem and hence maximize the network lifetime. We prove that sensors can evenly consume their initial battery power provided that first they adjust their transmission power when they transmit the route through traffic and second they are appropriately placed while deployed. Mainly, we propose a deployment scheme and the corresponding balanced routing strategy that lead to uniform energy consumption among all underwater sensors subject to a predefined reliability level at the sink. Specifically, we look for the optimal deployment settings especially in terms of nodes’ separation distances that help achieving uniform energy consumption in the network while satisfying the application requirement especially in terms of desired information reliability. Jointly, at the routing layer, we assume that each sensor is provided with the possibility of dynamically adjusting its transmission power up to a given number of levels N. To this goal, we mainly deal with two main cases: fixed and variable nodes separation distance. For the fixed case, we suppose that any two successive nodes in the network are equally spaced, and we strive for deriving the optimal distance as well as the optimal number of transmission power levels along with optimal load weight corresponding to every possible transmission power level for every sensor node. For the variable case, we deal with two subcases: first, we suppose that the distance separating successive nodes follows an arithmetic progression, and second, we assume that the distance separating successive nodes is following a geometric sequence. Note that for both cases, namely, fixed and variable, we succeed to determine the optimal distances separating successive nodes and the optimal number N of transmission power levels along with the corresponding optimal load weight that overcome the energy holes problem, and hence the network lifespan is maximized while respecting the desired reliability level.

Underwater Acoustic Sensor Networks (UASNs) is an important and ideal information-gathering paradigm for aqueous environments. However the harsh acoustic traffic condition makes the task of efficient transmission difficult. This paper presents the bit error rate guided time limited energy optimal algorithm (TLEO) for UASNs, which resides between the network layer and the MAC layer. The main feather of TLEO is the packet length is dynamically set to the optimal value according to the current bit error rate of the acoustic channel. To ensure the transmission in a time sensitive manner, this scheme affords limited waiting times for each node according to different channel conditions. Our simulations show 50%-70% reductions in transmission energy consumption and the scheme is more efficient when bit error rate is higher.