Performance analysis of erasure coding based data transfer in Underwater Acoustic Sensor Networks

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.

Constrained by the interior properties of underwater acoustic channels such as limited bandwidth, time-varying multipath propagation, and large propagation delay, the design of MAC protocols for underwater acoustic sensor networks (UWSNs) poses significant challenges. Existing MAC protocols for UWSNs exhibit very low performance in channel utilization, throughput and end-to-end delay, as well as energy consumption since 1) they are based on the conventional stop-and-wait (SW) transmission scheme, and 2) fewer has error-control mechanism at data link layer. In this paper, we assume that the UWSN has a multi-hop topology, where there exist a sink node and some common sensor nodes; all the nodes adopt Time Division Multiple Access (TDMA) to access channel. First, we classify all the nodes into different groups according to the numbers of hops between these nodes themselves and the sink node; the nodes belonging to different groups adopt TDMA frames with different starting time. We then propose a new MAC protocol by incorporating the multi-channel technique and the juggling-like stop-and-wait (JSW) transmission scheme, which is called the JM-MAC. Finally, simulation results are presented to highlight its good performance in terms of channel utilization, throughput and delay.

Constrained by the interior properties of underwater acoustic channels such as limited bandwidth, time-varying multipath propagation, and large propagation delay, the design of multiple access (MAC) protocols for underwater acoustic sensor networks (UWSN) poses significant challenges. Existing MAC protocols for UWSNs exhibit very low performance in channel utilization, throughput and end-to-end delay, as well as energy consumption since 1) they are based on the conventional stop-and-wait (SW) transmission scheme, and 2) fewer has error-control mechanism at the data link layer. In this paper, we assume the UWSN has a multi-hop flat topology, where there exist a sink node and some other common sensor nodes; all the nodes adopt Time Division Multiple Access (TDMA) to access channel. First, we classify all the nodes into different groups according to the numbers of hops between these nodes themselves and the sink node; the nodes belonging to different groups adopt TDMA frames with different starting time. We then propose a new MAC protocol by incorporating the multi-channel technique and the juggling-like stop-and-wait (JSW) transmission scheme (which was tailored for underwater acoustic communications), which is called the JB-MAC. Finally, simulation results are presented to highlight its good performance in terms of channel utilization, throughput and delay.

Network coding (NC) and erasure coding (EC) share a common principle of encoding incoming data packets at network intermediate nodes so that destination nodes can reconstruct the original data packets using a sufficient number of encoded data packets collected at the destinations. While NC in general reduces the number of packets sent through the network, EC introduces redundancy into the packet streams to recover from lost packets. Many research papers in NC assume loss-free packet transmission, and to address this issue, end-to-end packet loss recovery through EC has been proposed. In this paper, we investigate hop-by-hop packet loss recovery through EC so as to avoid the accumulation of lost packets throughout the network using flow oriented EC. Specifically, we explore combined NC and EC at intermediate nodes in the networks to allow for improved packet loss recovery compared to end-to-end EC. Different implementations trading the packet processing complexity at the intermediate and destination nodes are considered. This paper first examines the effects of data loss in NC and then the improvements achievable with the proposed method are presented.

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 network is an emerging technique consisting of sensor nodes, and AUVs all working together to sense various phenomenon, converts the sensed information into digital data, store the digital data and communicate to the base stations through the intermediate nodes. Also Underwater Acoustic Sensor Networks are playing a main role in ocean applications. Unfortunately the efficiency of underwater Acoustic Sensor Networks is inferior to that of terrestrial sensor networks due to the long propagation delay, narrow bandwidth and high error rates. Also battery life and storage capacity of node is limited. Many routing protocols are proposed to improve the efficiency of Under Water Acoustic Sensor Networks. However their improvement is not enough, so there is a need of suitable routing protocol that consider all these limitations and makes communication in underwater network viable. In this paper, we propose a protocol called Reliable Routing Optimized Cluster Head (RROCH) protocol, a network coding approach for multihop topologies. We used performance metrics like packet delivery ratio, energy consumption, end-to-end delay and throughput of sensor nodes. LEACH, HMR-LEACH, LEACH-M are compared for their performance at different traffic conditions, number of nodes and depth. By analyzing our simulation results we found that RROCH protocol may be used for denser network with low traffic and HMR- LEACH protocol is suitable for higher traffic with less number of nodes.

In underwater acoustic sensor networks (UANs), reliable packet delivery is critical in data collection and monitoring of the oceans. It primarily relies on the design of routing protocols to guarantee network durability and connectivity. However, utilizing routing design to achieve enhanced network longevity and reliable packet forwarding is challenging due to the complex underwater environment, unstable link connectivity, high transmission power, and high propagation latency. Thus, we propose a novel routing strategy called the longevity-oriented and reliable forwarding percolation (LRP) routing protocol. The goal of LRP is to ensure reliability by exploring multipath percolation-based routing and extend network longevity by energy control and residual energy optimization. Network reliability can be estimated using a built-in calculation model, and the source node controls energy and records the residual energy to extend the network lifetime. Utilizing an optimization of the network reliability requirement and residual energy, we develop a routing strategy to select the activated link set and node set for each hop in an energy-saving and reliable way. Moreover, a recursive approach is used to avoid the occurrence of void regions. Simulation results exhibit the effectiveness of the power control and routing strategy and demonstrate its superiority over the benchmarks in terms of network longevity and reliability.

Currently Underwater Acoustic Sensor Network (UASN) is playing a vital role in exploration and monitoring operations in underwater environments that are difficult to reach. Underwater communication mainly relies on acoustic waves, which limits the performance of UASN due to its limited bandwidth, large propagation delays, and high path loss. In this paper, we present a comprehensive mathematical model for underwater sensor network communication compared to available mathematical models in the literature. We also investigate the tradeoffs between energy consumptions, end-to-end delay, and number of hops of UASN. The model obtained in this paper is used to determine the number of hops in energy efficient UASNs for underwater time-critical missions.

Recently, underwater acoustic sensor networks (UASNs) have been proposed to explore underwater environments for scientific, commercial, and military purposes. However, long propagation delays, high transmission losses, packet drops, and limited bandwidth in underwater propagation environments make realization of reliable and energy-efficient communication a challenging task for UASNs. To prolong the lifetime of battery-limited UASNs, two critical factors (i.e., packet size and transmission power) play vital roles. At one hand, larger packets are vulnerable to packet errors, while smaller packets are more resilient to such errors. In general, using smaller packets to avoid bit errors might be a good option. However, when small packets are used, more frames should be transmitted due to the packet fragmentation, and hence, network overhead and energy consumption increases. On the other hand, increasing transmission power reduces frame errors, but this would result in unnecessary energy consumption in the network. To this end, the packet size and transmission power should be jointly considered to improve the network lifetime. In this study, an optimization framework via an integer linear programming (ILP) has been proposed to maximize the network lifetime by joint optimization of the transmission power and packet size. In addition, a realistic link-layer energy consumption model is designed by employing the physical layer characteristics of UASNs. Extensive numerical analysis through the optimization model has been also performed to investigate the tradeoffs caused by the transmission power and packet size quantitatively.

UWASN (UnderWater Acoustic Sensor Network) is a kind of WSN (Wireless Sensor Network) consisting of underwater acoustic sensor nodes, which can be deployed for real-time warship monitoring, oceanographic data collection, environmental monitoring, disaster prevention, etc. The design of a reliable data transfer protocol for UWASN is challenging [1] owing to the specific characteristics of acoustic channels: high error rates, high energy consumption, limited bandwidth, low transmission speed, and long and unstable packet delivery delay.

Marine environmental monitoring, marine resource development, and maritime rights and interests protection had put great demands on underwater acoustic sensor networks. At the same time, it was found that the configuration and optimization of the underwater acoustic sensor network had important influence on the network performance and service quality in the uncertain marine environment. According to the characteristics of dynamic evolution of network topology and combining with different practical application scenarios, this article studied and discussed the model description, configuration mechanism, and optimization strategy of underwater acoustic sensor network. First, the classical underwater acoustic sensor network models were divided and discussed from three aspects: dimension, sensor types, and dynamic and static states. The characteristics of the six models are compared and analyzed. Second, according to the characteristics of six different models, the configuration mechanism and optimization strategy are discussed and classified. The configuration mechanism of models was studied from the aspects about determinism, self-adaptation, and group intelligence. The optimization strategy of models was discussed from the aspects about coverage, connectivity, energy consumption, time delay, data quality, and so on, and the relations and differences between different methods were compared and analyzed. Finally, the five future researches direction were expected, in order to provide a clear idea for further research in this field.

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 much attention due to various aquatic applications. However, UASNs have many specific characteristics, such as high propagation delay, high packet error rate, and low bandwidth, which bring challenges to network congestion control. Furthermore, the point-to-point congestion control algorithms cannot guarantee the end-to-end optimal performance. Therefore, congestion avoidance is an important issue to be considered when designing routing protocols for UASNs. In addition, since the sensor nodes deployed underwater are powered by batteries, which are hard to be replaced or recharged, energy limitation should be taken into account as well. In this paper, we propose a reinforcement-learning-based congestion-avoided routing (RCAR) protocol to reduce the end-to-end delay and energy consumption. With the application of reinforcement learning, the protocol converges to the optimal route from the source node to the surface sink by exploring hop-by-hop. In RCAR, a reward function in reinforcement learning is defined in which congestion and energy are both considered for adequate routing decision. To accelerate the convergence of the algorithm, we introduce a dynamic virtual routing pipe with variable radius, which is related to the average residual energy of the neighbors of the sender node. Moreover, in the cross-layer-information-based RCAR protocol, an information update method based on a handshake in the MAC layer is proposed, which guarantees the optimal routing decision. The simulation results show that the proposed RCAR protocol outperforms hop-by-hop vector-based forwarding protocol (HHVBF), QELAR, and GEDAR in terms of convergence speed, energy efficiency, and end-to-end delay.