Underwater Wireless Sensor Nodes Research Articles

A Novel Hybridized Cluster-Based Geographical Opportunistic Routing Protocol for Effective Data Routing in Underwater Wireless Sensor Networks

Underwater wireless sensor nodes comprise hundreds to thousands of battery-operated sensor nodes with limited bandwidth. These networks are employed to transmit the data with enhanced quality of service (QoS). However, efficient data routing is the most challenging obstacle in many underwater applications. To solve the issues in underwater sensor nodes, the hybridized cluster-based geographical opportunistic routing protocol with distance vector establishment has been proposed to transmit the data efficiently. Primarily, the proposed methodology finds out the shortest path with minimal hop count whereas the void node can be updated with infinite hop count. Thereafter, the sleep/wake scheduling and waiting mechanism and periodic beaconing algorithm are incorporated into the proposed model to attain a higher packet delivery ratio with minimal energy consumption. This proper scheduling and optimal cluster routing enhance the continuous data transmission in underwater applications. The simulation result reveals that the proposed method achieves better energy efficiency and higher network lifetime when compared with the existing clustering methods.

A Study of the Accuracy of Positioning in a Network of Wireless Underwater Sensors Depending on the Genetic Algorithm

Wireless Sensors Networks (WSNs) are a scientific revolution in wireless communications and embedded systems. WSN is based on the idea of abandoning the human factor, which was often an obstacle because it was not possible to be in the places where these networks are placed, especially if the collection of information required a long time, Underwater wireless sensor nodes can be deployed for monitoring, exploration, and for disaster protection, and this is what is called Underwater Wireless Sensor Networks (UWSNs). nIn this paper, we will study how the parameters of the genetic algorithm change when locating sensors under water, Including the error rate, the number of nodes in the network and the time taken to implement.

Underwater Wireless Sensor Network Performance Analysis Using Diverse Routing Protocols

The planet is the most water-rich place because the oceans cover more than 75% of its land area. Because of the unique activities that occur in the depths, we know very little about oceans. Underwater wireless sensors are tools that can continuously transmit data to one of the source sensors while monitoring and recording their surroundings’ physical and environmental parameters. An Underwater Wireless Sensor Network (UWSN) is the name given to the network created by collecting these underwater wireless sensors. This particular technology has a random path loss model due to the time-varying nature of channel parameters. Data transmission between underwater wireless sensor nodes requires a careful selection of routing protocols. By changing the number of nodes in the model and the maximum speed of each node, performance parameters, such as average transmission delay, average jitter, percentage of utilization, and power used in transmit and receive modes, are explored. This paper focuses on UWSN performance analysis, comparing various routing protocols. A network path using the source-tree adaptive routing-least overhead routing approach (STAR-LORA) Protocol exhibits 85.3% lower jitter than conventional routing protocols. Interestingly, the fisheye routing protocol achieves a 91.4% higher utilization percentage than its counterparts. The results obtained using the QualNet 7.1 simulator suggest the suitability of routing protocols in UWSN.

An underwater piezoelectric energy harvester based on magnetic coupling adaptable to low-speed water flow

The rapid development of the fifth-generation mobile networks (5G) and the Internet of Things (IoT) is inseparable from a large number of miniature, low-cost, and low-power sensors and actuators. To solve the problem of self-powered underwater wireless sensor nodes (WSNs), this paper proposes a magnetically coupled piezoelectric energy harvester (MPEH) based on fluid-induced vibration (FIV) to achieve high-efficiency performance in low-velocity water environments. The results show that compared with the non-magnetic PEH, the initial vibration velocity, velocity bandwidth, and maximum output voltage of the magnetic attraction coupling piezoelectric energy harvester (MAPEH) are increased by 18.42%, 41.38%, and 5.79% respectively. Besides, it is also shown that the vertical distance between the magnets, the diameter, and the mass of the vibrating column have a significant effect on the vibration characteristics, water velocity bandwidth, and performance of the magnetic repulsion coupled piezoelectric energy harvester (MRPEH). The initial vibration velocity of the MRPEH is reduced by 42.1%, and when the water speed is 0.2 m/s, its output voltage is 12.21 times that of the classical configuration. This proposed structure can provide a reference for the optimization of underwater FIV-based PEH, and also lay a foundation for further interconnection of underwater self-powered sensors.

Improved Metaheuristics-Based Clustering with Multihop Routing Protocol for Underwater Wireless Sensor Networks.

Underwater wireless sensor networks (UWSNs) comprise numerous underwater wireless sensor nodes dispersed in the marine environment, which find applicability in several areas like data collection, navigation, resource investigation, surveillance, and disaster prediction. Because of the usage of restricted battery capacity and the difficulty in replacing or charging the inbuilt batteries, energy efficiency becomes a challenging issue in the design of UWSN. Earlier studies reported that clustering and routing are considered effective ways of attaining energy efficacy in the UWSN. Clustering and routing processes can be treated as nondeterministic polynomial-time (NP) hard optimization problems, and they can be addressed by the use of metaheuristics. This study introduces an improved metaheuristics-based clustering with multihop routing protocol for underwater wireless sensor networks, named the IMCMR-UWSN technique. The major aim of the IMCMR-UWSN technique is to choose cluster heads (CHs) and optimal routes to a destination. The IMCMR-UWSN technique incorporates two major processes, namely the chaotic krill head algorithm (CKHA)-based clustering and self-adaptive glow worm swarm optimization algorithm (SA-GSO)-based multihop routing. The CKHA technique selects CHs and organizes clusters based on different parameters such as residual energy, intra-cluster distance, and inter-cluster distance. Similarly, the SA-GSO algorithm derives a fitness function involving four parameters, namely residual energy, delay, distance, and trust. Utilization of the IMCMR-UWSN technique helps to significantly boost the energy efficiency and lifetime of the UWSN. To ensure the improved performance of the IMCMR-UWSN technique, a series of simulations were carried out, and the comparative results reported the supremacy of the IMCMR-UWSN technique in terms of different measures.

Coefficient Permuted Adaptive Block Compressed Sensing for Camera Enabled Underwater Wireless Sensor Nodes

Underwater wireless sensor network (UWSN) comprises of large number of sensors and underwater vehicles deployed collaboratively to perform data collection, interpretation and processing. These sensors and vehicles have been equipped with cameras to capture a visual picture of underwater targets and precious resources in recent times. These cameras in the sensors generate large volumes of data as they are continuously involved in surveillance of the aquatic environment. Transmitting or storing the data as a whole drains the power of the battery-operated nodes. Therefore it is necessary to reduce the data to save energy and improve the lifetime of the sensors. To serve this purpose, block compressed sensing (BCS) based image compression can be used. However, BCS has two significant issues: low sampling efficiency for poorly sparsed real-time underwater images and fixing the samples chosen from image blocks. The former can be overcome by permuting and evenly distributing the transform coefficients to all blocks. Similarly, the latter can be overcome by adopting adaptive block compressed sensing (ABCS). A combination of coefficient permutation and ABCS, namely coefficient permuted adaptive block compressed sensing (CP-ABCS), is proposed for better image reconstruction with fewer samples. This proposed approach operates in the processor of the sensors to compress data within the nodes and then transmit the reduced data. It has improved PSNR of 4-8dB, SSIM of 0.1-0.3 and space-saving (SS) of 5-10% compared with other literature schemes. Also, it has used only significantly fewer samples of about 10-20% for reconstruction.

Application of NSGA-II to Obtain the Charging Current-Time Tradeoff Curve in Battery Based Underwater Wireless Sensor Nodes.

In this paper, a novel application of the Nondominated Sorting Genetic Algorithm II (NSGA II) is presented for obtaining the charging current–time tradeoff curve in battery based underwater wireless sensor nodes. The selection of the optimal charging current and times is a common optimization problem. A high charging current ensures a fast charging time. However, it increases the maximum power consumption and also the cost and complexity of the power supply sources. This research studies the tradeoff curve between charging currents and times in detail. The design exploration methodology is based on a two nested loop search strategy. The external loop determines the optimal design solutions which fulfill the designers’ requirements using parameters like the sensor node measurement period, power consumption, and battery voltages. The inner loop executes a local search within working ranges using an evolutionary multi-objective strategy. The experiments proposed are used to obtain the charging current–time tradeoff curve and to exhibit the accuracy of the optimal design solutions. The exploration methodology presented is compared with a bisection search strategy. From the results, it can be concluded that our approach is at least four times better in terms of computational effort than a bisection search strategy. In terms of power consumption, the presented methodology reduced the required power at least 3.3 dB in worst case scenarios tested.

Underwater Wireless Sensor Networks: An Energy-Efficient Clustering Routing Protocol Based on Data Fusion and Genetic Algorithms

Due to the limited battery energy of underwater wireless sensor nodes and the difficulty in replacing or recharging the battery underwater, it is of great significance to improve the energy efficiency of underwater wireless sensor networks (UWSNs). We propose a novel energy-efficient clustering routing protocol based on data fusion and genetic algorithms (GAs) for UWSNs. In the clustering routing protocol, the cluster head node (CHN) gathers the data from cluster member nodes (CMNs), aggregates the data through an improved back propagation neural network (BPNN), and transmits the aggregated data to a sink node (SN) through a multi-hop scheme. The effective multi-hop transmission path between the CHN and the SN is determined through the enhanced GA, thereby improving transmission efficiency and reducing energy consumption. This paper presents the GA based on a specific encoding scheme, a particular crossover operation, and an enhanced mutation operation. Additionally, the BPNN employed for data fusion is improved by adopting an optimized momentum method, which can reduce energy consumption through the elimination of data redundancy and the decrease of the amount of transferred data. Moreover, we introduce an optimized CHN selecting scheme considering residual energy and positions of nodes. The experiments demonstrate that our proposed protocol outperforms its competitors in terms of the energy expenditure, the network lifespan, and the packet loss rate.

Wireless information and power transfer for underwater acoustic time‐reversed NOMA

The acoustic signal transmission over the underwater channel has a limited sum rate and it consumes high power due to the properties of the underwater environment. This study attempts to use the non-orthogonal multiple access (NOMA) technologies for underwater communications. NOMA can be an attractive candidate for underwater communication due to its high spectral efficiency, resistance for carrier frequency offset, and efficient energy consumption. To cope with the hard-recharging capability of the underwater wireless sensor nodes caused by the ocean environment, this study proposes a novel transmission scheme called time-reversed NOMA (TR-NOMA) for underwater communication. In the proposed TR-NOMA, a single-input multiple-output NOMA scheme with a passive-time reversal technique is proposed to reduce the time–frequency dispersion of the underwater acoustic channels. Consequently, simultaneous wireless information and power transfer (SWIPT) can be applied for underwater TR-NOMA. In this study, a SWIPT-NOMA is postposed to harvest energy in downlink transmission from the transmitted signal. The bit error rate (BER) and the outage probability are used to characterise the performance of the proposed TR-NOMA scheme and simulation results show how the proposed TR-NOMA significantly outperforms the conventional NOMA schemes. Additionally, a mathematical framework for the average BER of TR-NOMA is delineated.

Cascading Model in Underwater Wireless Sensors using Routing Policy for State Transitions

Underwater wireless sensors communicate information from different depth of water column to sink positioned on the surface of water. Monotonically increasing data traffic occurs as it reaches surface of water column causing an imbalance of routing and resulting in congested nodes. Underwater wireless sensor node can be either in normal, overloaded state, congested state. The purpose of this work is achieving control over normal and isolated states. The congested node use the permutation of links and it observe the transition probabilities for routing. Then it determines the state of node based on the immediate reward and the action to be taken within the time episode. Thus nodes are isolated and recovered after the time episode. Improved estimate in forwarding scenario of overloaded and isolating nodes is observed via simulation results in NS2 simulator.

Analyzing lifetime of energy harvesting underwater wireless sensor nodes

SummaryUnderwater Wireless Sensor Networks (UWSNs) are utilized to monitor underwater environments that pose many challenges to researchers. One of the key complications of UWSNs is the difficulty of changing node batteries after their energy is depleted. This study aims to diminish the issues related to battery replacement by improving node lifetime. For this goal, three energy harvesting devices (turbine harvester, piezoelectric harvester, and hydrophone harvester) are analyzed to quantitate their impacts on node lifetime. In addition, two different power management schemes (schedule‐driven and event‐driven power management schemes) are combined with energy harvesters for further lifetime improvement. Performance evaluations via simulations show that energy harvesting methods joined by power management schemes can improve node lifetime substantially when actual conditions of Istanbul Bosporus Strait are considered. In this respect, turbine harvester makes the biggest impact and provides lifetime beyond 2000 days for most cases, while piezoelectric harvester can perform the same only for low duty cycle or event arrival values at short transmission ranges.

Throughput of Underwater Wireless Sensor Nodes with Energy Harvesting Capabilities Using RF and Optical Links

Throughput of Underwater Wireless Sensor Nodes with Energy Harvesting Capabilities Using RF and Optical Links

Underwater Wireless Sensor Network Deployment Based on Chaotic Particle Swarm Optimization Algorithm

The application of particle swarm optimization algorithm in underwater wireless sensor node deployment strategy was studied. The chaotic particle swarm optimization algorithm was proposed. Set up a function variance to determine whether the particles enter the premature state. Then chaos searched and took the chaotic search optimal solution vector as the global optimization solution to guide the particles quickly jump out of local optimum, accelerate the convergence speed and to overcome the premature problem of standard particle swarm algorithm brought. The network coverage was the fitness function to optimize the deployment of nodes question. The chaotic particle swarm optimization algorithm was used to wireless sensor network node deployment strategy. The experimental results showed that this method could effectively improve the convergence speed and increase network coverage.

Multi-source Energy Harvesting System for Underwater Wireless Sensor Networks

Due to increase in its wide range of applications, underwater wireless sensor nodes are gaining importance and there is need to develop reliable underwater sensor networks. Major hindrance to performance of underwater networks is power supply to nodes. The ultimate aim in designing energy harvesters is to provide continuous power supply for node system in adequate environmental energy conditions. To achieve continual, reliable and efficient power supply for underwater sensors, we propose multi source energy harvester system which manages energies from piezoelectric harvesting and microbial fuel cell. Analytical expressions are obtained for proposed system and are validated using extensive simulations.

Interacting multiple model filter-based distributed target tracking algorithm in underwater wireless sensor networks

This paper deals with the problem of accurately tracking a single target, which has various trajectories, moving through the environment of underwater wireless sensor networks (UWSNs). This paper addresses the issues of estimating the states of the target, improving energy efficiency by using a distributed architecture. Each underwater wireless sensor node composing the UWSNs is battery-powered, so the energy conservation problem is a critical issue. This paper provides algorithms increasing the energy efficiency of each sensor node by using the proposed Wake-Up/Sleep (WUS) scheme. An interacting multiple model (IMM) filter is applied to the proposed distributed architecture in order to cope with a target maneuver. Simulation results illustrate the performance of the proposed tracking filter according to the various target maneuver patterns.

Coverage Holes Recovery Algorithm Based On Nodes Balance Distance Of Underwater Wireless Sensor Network

Abstract Underwater wireless sensor network nodes deployment optimization problem is studied and underwater wireless sensor nodes deployment determines its capability and lifetime. Underwater wireless sensor network if no wireless sensor node is available in the area due to used up energy or any other reasons, the area which is not detected by any wireless sensor node forms coverage holes. The coverage holes recovery algorithm aiming at the coverage holes in wireless sensor network is designed in this article. The nodes movement is divided into several processes, in each movement process according to the balance distance and location relations move nodes to separate the aggregate nodes and achieve the maximum coverage of the monitoring area. Because of gradually increasing the balance distance between nodes, in each movement process the nodes moving distance is small and reduce the sum of the nodes movement distance. The simulation and experimental results show that this recovery algorithm achieves the goal of the nodes reasonable distribution with improving the network coverage and reducing the nodes movement distance thus extends the lifetime of the network in the initial deployment phase and coverage holes recovery phase.

센서노드 선정기법 기반 수중 무선센서망 분산형 표적추적필터

This paper deals with the problem of accurately tracking a single target moving through UWSNs (Underwater Wireless Sensor Networks) by employing underwater acoustic sensors. This paper addresses the issues of estimating the states of the target, and improving energy efficiency by applying a Kalman filter in a distributed architecture. Each underwater wireless sensor nodes composing the UWSNs is battery-powered, so the energy conservation problem is a critical issue. This paper provides an algorithm which increases the energy efficiency of each sensor node through WuS (Waked-up/Sleeping) and VM (Valid Measurement) selecting schemes. Simulation results illustrate the performance of the distributed tracking filter.