3D Underwater Sensor Networks Research Articles

Optimum energy efficient multi‐hop protocol for homogenous and heterogeneous 3D underwater acoustic sensor networks

SummaryInformation transmission is extremely challenging in underwater acoustic sensor networks (UASNs) because, in the acoustic channel, the packet loss is high compared to other channels due to low bandwidth and a long propagation delay. So, designing an efficient energy algorithm is a big challenge in the acoustic channel. In this paper, first, a cluster head (CH) election fitness function based on a hybrid particle swarm optimization and gravitational search algorithm (PSO‐GSA) is proposed for a three‐dimensional (3D) underwater sensor network (UASN). The proposed algorithm includes CH election, CH load, and neighbor CH distance for multi‐hop transmission (MH‐PSOGSA). Then, based on the proposed algorithm, a relay‐based traffic‐aware energy‐efficient routing protocol (R‐TAEERP) with a weight function is proposed to offload the heavy consumption of the CHs. MATLAB simulations were accomplished to compare the performance of MH‐PSOGSA and R‐TAEERP with the existing representative protocols. The heterogeneity in energy and traffic has been considered. The simulation results show better performance of the proposed algorithm than other existing representative protocols in terms of energy consumption, the performance of the stable region, and the lifetime of the network.

FSE2R: An Improved Collision-Avoidance-based Energy Efficient Route Selection Protocol in USN

The 3D Underwater Sensor Network (USNs) has become the most optimistic medium for tracking and monitoring underwater environment. Energy and collision are two most critical factors in USNs for both sparse and dense regions. Due to harsh ocean environment, it is a challenge to design a reliable energy efficient with collision free protocol. Diversity in link qualities may cause collision and frequent communication lead to energy loss; that effects the network performance. To overcome these challenges a novel protocol Forwarder Selection Energy Efficient Routing (FSE2R) is proposed. Our proposal’s key idea is based on computation of node distance from the sink, Residual Energy (RE) of each node and Signal to Interference Noise Ratio (SINR). The node distance from sink and RE is computed for reliable forwarder node selection and SINR is used for analysis of collision. The novel proposal compares with existing protocols like H2AB, DEEP, and E2LR to achieve Quality of Service (QoS) in terms of throughput, packet delivery ratio and energy consumption. The comparative analysis shows that FSE2R gives on an average 30% less energy consumption, 24.62% better PDR and 48.31% less end-to-end delay compared to other protocols.

Adaptive Fuzzy Game-Based Energy-Efficient Localization in 3D Underwater Sensor Networks

Numerous applications in 3D underwater sensor networks (UWSNs), such as pollution detection, disaster prevention, animal monitoring, navigation assistance, and submarines tracking, heavily rely on accurate localization techniques. However, due to the limited batteries of sensor nodes and the difficulty for energy harvesting in UWSNs, it is challenging to localize sensor nodes successfully within a short sensor node lifetime in an unspecified underwater environment. Therefore, we propose the Adaptive Energy-Efficient Localization Algorithm (Adaptive EELA) to enable energy-efficient node localization while adapting to the dynamic environment changes. Adaptive EELA takes a fuzzy game-theoretic approach, whereby the Stackelberg game is used to model the interactions among sensor and anchor nodes in UWSNs and employs the adaptive neuro-fuzzy method to set the appropriate utility functions. We prove that a socially optimal Stackelberg–Nash equilibrium is achieved in Adaptive EELA. Through extensive numerical simulations under various environmental scenarios, the evaluation results show that our proposed algorithm accomplishes a significant energy reduction, e.g., 66% lower compared to baselines, while achieving a desired performance level in terms of localization coverage, error, and delay.

A Dynamic Hierarchical Clustering Data Gathering Algorithm Based on Multiple Criteria Decision Making for 3D Underwater Sensor Networks

Data gathering is the basis of monitoring applications in an underwater sensor network, and excellent network coverage and data transmission reliability are the guarantees for the quality of monitoring tasks. However, the energy consumption of the nodes is too fast due to the heavy load of the cluster heads closer to the sink when data is transmitted between cluster heads (CHs) and the sink by multihop, which leads to an energy hole problem in an underwater sensor network of clustering technology. Aiming to address this problem, we propose a dynamic hierarchical clustering data gathering algorithm based on multiple criteria decision making (DHCDGA) in a 3D underwater sensor network. Firstly, the entire monitoring network is divided into many layers. For selecting a cluster head in each layer, multiple criteria decision making of an intuitionistic fuzzy Analytic Hierarchy Process (AHP) and hierarchical fuzzy integration is adopted. Furthermore, a sorting algorithm is used to form a clustering topology algorithm to solve the problem that there is the only node in one cluster. Then, an energy-balanced routing algorithm between clusters is proposed according to the residual energy of the node, the depth, and the number of neighbor nodes. Finally, the simulation results show that DHCDGA can not only effectively balance the energy consumption of the network and prolong the network lifetime but also improve network coverage and data gathering reliability.

3D Localization and Visualization of Sensor Nodes and Data Sensing in Underwater Wireless Sensor Networks

Underwater sensor networks (USNs) have many challenges in terms of determining network information. One of the challenges is determining 3D position coordinates of the sensor nodes in real-time in the aquatic environment. In this work, experiments have been conducted by deploying sensor nodes underwater at a shallow depth. As a case study, sensors have been used to sense water temperature and transmit to anchor (sink) node in a multi-hop fashion. Hardware experiments have been successfully conducted for localization and data transmission and results stored in industrial cloud platform smartcore for any further requirement. NS-3 with Aqua-3D - an Aqua-Sim network simulator has the required modules to visualize 3D Underwater Sensor Network. Simulation results show that 3D network visualization and node localization is obtained accurately. The Aqua-3D results are compared with the GPS coordinate values for localization accuracy.

Efficient Routing Protocol Based on Reinforcement Learning for Magnetic Induction Underwater Sensor Networks

In recent research on 3D underwater wireless sensor network (UWSN), magnetic induction communication is a promising candidate, thanks to several unique features, such as small transmission delay, constant channel behavior, and adequate long communication range. However, designing a routing protocol that prolongs the network lifetime and reduces the transmission delay has been still a challenge for a 3D UWSN. In this paper, we propose an efficient routing protocol based on reinforcement learning, in particular, the Q-learning that aims to investigate the resource management in the hierarchical networks. Through defining the single hopping bonus metrics of distance and energy, we deduce the updating formula of the routing algorithm and derive the relationship between energy priority and distance priority. In addition, we set up a regulatory factor to adjust the proportion between energy saving and low delay, and thus, it can meet different needs. The simulation results show that the proposed routing approach outperforms the conventional protocol in extending the network lifetime and reducing the transmission delay.

Smooth 3D Dubins Curves Based Mobile Data Gathering in Sparse Underwater Sensor Networks.

Sensory data collection is one of the most important concerns in underwater sensor networks (USNs). Because full connectivity cannot be guaranteed, mobile data gathering with autonomous underwater vehicles (AUVs) is widely used in sparse three-dimensional (3D) USNs to solve energy-imbalance problems between different sensor nodes. AUVs with relatively abundant energy and storage can collect sensory data from one sensor node to transmit to another node, so as to avoid energy-intensive multi-hop transmission. As a result, movement control strategy and data collecting path planning for AUVs are very crucial for the performance of data acquisition. This paper proposes a smooth 3D Dubins curves based mobile data gathering mechanism to overcome the kinematic nonholonomic constraints of AUVs. The objective of our proposed method is to collect sensory data along smooth 3D Dubins paths, which are interpolated by continuous Bezier curves in the Z-axis from 2D Dubins curves. Extensive simulation results verify that the proposed method has a more efficient performance in terms of path smoothness and energy consumption; thus it is very suitable for mobile data collection in 3D underwater sensor networks.

Coverage enhancing of 3D underwater sensor networks based on improved fruit fly optimization algorithm

The coverage rate of the underwater sensor networks directly influences on the monitoring efficiency in underwater environment, and it can be effectively improved by adjusting the positions of the mobile nodes reasonably for a 3D underwater sensor network which consists of mobile nodes as underwater robots like Autonomous Underwater Vehicles. An optimal deployment method can quickly set up a reasonable topology of the sensor networks and achieve a higher efficiency for detecting or investigating. An optimal algorithm of coverage enhancing for 3D Underwater sensor networks based on improved Fruit Fly Optimization Algorithm (UFOA) is proposed in this paper. This method realizes the global optimal coverage based on foraging behavior of fruit flies, and it has the features of higher speed of convergence, few parameters to set up and stronger global searching ability. Simulation result indicates that the proposed UFOA method can significantly improve the effective coverage rate of the sensor networks compared with some widely studied PSO and IPSO algorithms.

A collision-analysis-based energy-efficient routing protocol in 3D underwater acoustic sensor networks

In recent years, three-dimensional (3D) underwater sensor networks (USNs) have received substantial attention as a promising tool for target tracking and remote monitoring under the seas. Energy consumption is crucial in USNs because it is nearly impossible to recharge the batteries of the sensors. Given the properties of intermittent link connectivity in USNs, the existing message-based and synchronization-based approaches cannot meet packet delivery requirements. In this paper, an analysis for the probability of collisions between any two transmissions in USNs is presented, in which the analyzed collision rate corresponds to the simulation and is demonstrated to be relaxed with a sufficient data processing rate in underwater networks. Based on this result, a tailored delay-aware energy-efficient routing protocol (DEEP) is proposed. DEEP is composed of an energy model with realistic parameters in which the available 3-dB bandwidth is derived with respect to the distances between nodes. DEEP involves an adaptable forwarding node selection mechanism, which incorporates the concept of energy efficiency and further reduces the collision rate. Simulations show that DEEP expends less energy for successful packet delivery compared with previous studies. Benefits from the higher bandwidth in USNs, DEEP reduces the collision occurrences and elevates the packet delivery ratio with less end-to-end delay time especially when the network conditions are unfavorable. These results confirm that DEEP effectively handles the challenges.

Towards Robust Routing in Three-Dimensional Underwater Wireless Sensor Networks

The areas of three-dimensional (3D) underwater wireless sensor networks (UWSNs) have attracted significant attention recently due to their applications in detecting and observing phenomena that cannot be adequately observed by means of two-dimensional UWSNs. However, designing routing protocols for UWSNs is a challenging task because path breakage occurs frequently due to uncertain node and link failures. In this paper, we present a Robust Routing Protocol (RRP) that aims to achieve robustness under harsh underwater conditions of 3D UWSNs. In RRP, each sensor has a data structure called backup bin, which allows to construct main backup links and auxiliary backup links for repairing failed links and ensuring normal data delivery. Moreover, sensor nodes can also enlarge their transmission range to build new links when the main routing path is not reachable through backup links. Simulation results show that the proposed protocol can reduce node and link failures' impact on metrics of packet delivery ratio, network throughput, energy consumption, and average end-to-end delay.

Coverage Optimization Algorithm Based on Sampling for 3D Underwater Sensor Networks

We discuss the problem of maximizing the sensor field coverage for a specific number of sensors while minimizing the distance traveled by the sensor nodes. Thus, we define the movement task as an optimization problem that involves the adjustment of sensor node positions in a coverage optimization mission. We propose a coverage optimization algorithm based on sampling to enhance the coverage of 3D underwater sensor networks. The proposed coverage optimization algorithm is inspired by the simple random sampling in probability theory. The main objective of this study is to lessen computation complexity by dimension reduction, which is composed of two detailed steps. First, the coverage problem in 3D space is converted into a 2D plane for heterogeneous networks via sampling plane in the target 3D space. Second, the optimization in the 2D plane is converted into an optimization in a line segment by using the line sampling method in the sample plane. We establish a quadratic programming mathematical model to optimize the line segment coverage according to the intersection between sensing circles and line segments while minimizing the moving distance of the nodes. The intersection among sensors is decreased to increase the coverage rate, while the effective sensor positions are identified. Simulation results show the effectiveness of the proposed approach.

A LOW-COST RANGE-FREE LOCALIZATION SCHEME FOR THREE-DIMENSIONAL UNDERWATER SENSOR NETWORKS

Localization is one of the fundamental tasks for underwater sensors networks (USNs). It is required for data tagging, target detection, route protocols, and so on. In this paper, we propose an efficient low-cost range-free localization scheme for 3D underwater sensor networks (3D-LRLS) without any additional hardware infrastructure. In our scheme, each anchor node has variable transmission power levels. At first, the power levels of each anchor are decided by the Delaunay triangulation for the network space. Then, ordinary sensors listen to the beacons sent from the anchor nodes. Based on the beacon messages, each node calculates its location individually by a low computational complexity method. The extensive simulation results demonstrate that 3D-LRLS is efficient in terms of both localization ratio and localization accuracy.

E-PULRP: Energy Optimized Path Unaware Layered Routing Protocol for Underwater Sensor Networks

Energy optimized Path Unaware Layered Routing Protocol (E-PULRP) for dense 3D Underwater Sensor Network (UWSN) is proposed and analysed in this paper. In the proposed E-PULRP, sensor nodes report events to a stationary sink node using on the fly routing. E-PULRP consists of a layering phase and communication phase. In the layering phase, a layering structure is presented wherein nodes occupy different layers in the form of concentric shells, around a sink node. The layer widths and transmission energy of nodes in each layer are chosen taking into consideration the probability of successful packet transmission and minimization of overall energy expenditure in packet transmission. During the communication phase, we propose a method to select intermediate relay nodes on the fly, for delivering packets from the source node to sink node. We develop a mathematical framework to analyse the energy optimization achieved by E-PULRP. We further obtain expressions for throughput, delay and derive performance bounds for node densities and packet forwarding probabilities, for given traffic conditions. A comparison is made between the results obtained based on simulations and analytical expressions. The energy efficiency is also demonstrated in comparison with existing routing protocol for underwater sensor networks.

3D Underwater Sensor Network Localization

We transform the 3D underwater sensor network (USN) localization problem into its 2D counterpart by employing sensor depth information and a simple projection technique. We first prove that a nondegenerative projection preserves network localizability. We then prove that given a network and a constant k, all of the geometric k-lateration localization methods are equivalent. Based on these results, we design a purely distributed bilateration localization scheme for 3D USNs termed as underwater sensor positioning (USP). Through extensive simulations, we show that USP has the following nice features: (1) improved localization capabilities over existing 3D methods, (2) low storage and computation requirements, (3) predictable and balanced communication overhead, and (4) robustness to errors from the underwater environment.