Routing Protocol For Underwater Networks Research Articles

Survey of AI-driven routing protocols in underwater acoustic networks for enhanced communication efficiency

The high-speed growth of undersea communication networks requires sophisticated routing protocols to deal with challenging underwater conditions including large latencies, limited bandwidths and varying topologies. In this paper, we examine the use of Artificial Intelligence (AI), Machine Learning (ML), Reinforcement Learning (RL) and fuzzy logic to optimize routing protocols for underwater networks. We provide a comprehensive survey of existing AI-based approaches, emphasizing their novelties and constraints underwater.To assess the efficiency of these AI-based routing protocols, we carry out extensive simulations across various underwater environments where metrics such as packet delivery ratio, energy consumption, end-to-end delay, and computational efficiency are focused on. The results reveal that AI-aided protocols excel over conventional methods particularly in situations involving complex environmental dynamics as well as resources limitation.However, there are practical implementation issues which must be solved before the real-world application of AI-based routing such as hardware constraints, concerns on energy usage , and scalability. This study provides valuable insights into the integration of AI technologies into underwater communication networks, paving the way for more reliable and efficient underwater operations. Our findings contribute to the growing body of knowledge in this field and offer a foundation for future advancements in underwater communication technologies.

EEARP - an Efficient and Energy Aware Routing Protocol for Underwater Wireless Sensor Networks

Recently, Underwater Wireless Sensor Networks (UWSNs) have attracted significant attention from both academia and industry to explore the vast underwater environment. Since UWSNs suffer from long propagation delay, low bandwidth, and high error rate, providing an efficient routing protocol is challenging. This paper proposes EEARP, a series of new routing protocols for underwater networks to improve the performance of existing DBR. The DBR does not consider energy. It only receives data packets from upper depth node and forwards the packets to lower depth nodes. Moreover, this greedy behavior of DBR causes void area problem. EEARP creates a directed acyclic graph rooted at a sink. Each node receives information like depth, energy, and the number of parents from its parents. When forwarding, each node sends a data packet to one or more of its parent nodes. We implemented EEARP in NS2 simulator and evaluated its performance under different scenarios. Results confirm that EEARP outperforms DBR in terms of energy saving, network lifetime, end-to-end delay and packet loss ratio.

A Vector-Based Routing Protocol in Underwater Wireless Sensor Networks

One major concern shared by many researchers about underwater wireless sensor networks (UWSNs), with respect to the limitations and particularities of underwater environment, is the problem of routing. These limitations include three-dimensional topology, limited bandwidth, node movement, long delay, limited energy, and construction costs. The new routing protocols for underwater networks have been developed on the basis of voracious routing systems. The main problem with UWSNs is finding an efficient route between the source and the target to send more packets to the target with lower levels of energy consumption. In this research, by improving VBF algorithm, which is dependent on the radius of the routing pipe, an algorithm is introduced which considers pipe radius as a function of the environment’s dimensions and of the range and the number of nodes. Consequently, by changing one of these parameters, the radius of the routing pipe changes. However, to control the energy consumed by the nodes, there exists a function that, if the recipient node’s energy to receive the packet is much lower than that of the sender node, the proposed method reduces the size of the routing pipe’s radius to lessen its chance of being selected as the guiding node so that other nodes are able to have the chance of getting the packet’s guiding node. The proposed algorithm has been compared with VBVA, HHVBF, and VBF protocols; the simulation results obtained from NS-2 simulator indicate that the proposed protocol could cut back on energy consumption, especially in networks with high number of nodes, by relying on changing the width of the routing pipe in proportion to network density. It was also successful in delivering more packets in non-dense networks.

Fair and Throughput-Optimal Routing in Multimodal Underwater Networks

While acoustic communications are still considered the most prominent technology to communicate under water, other technologies are being developed based, e.g., on optical and radio-frequency electromagnetic waves. Each technology has its own advantages and drawbacks: for example, acoustic signals achieve long communication ranges at order-of-kbit/s rates, whereas optical signals offer order-of-Mbit/s transmission rates, but only over short ranges. Such diversity can be leveraged by multimodal systems, which integrate different technologies and provide the intelligence required to decide which one should be used at any given time. In this paper, we address a fundamental part of this intelligence by proposing optimal multimodal routing (OMR), a novel routing protocol for underwater networks of multimodal nodes. OMR makes distributed decisions about the flow in each link and over each technology, at any given time, in order to advance a packet toward its destination; in doing so, it prevents bottlenecks and allocates resources fairly to different nodes. We analyze the performance of OMR via simulations and in a field experiment. The results show that OMR successfully leverages all technologies to deliver data, even in the presence of imperfect topology information. To permit the reproduction of our results, we share our simulation code.

SPARCO: Stochastic Performance Analysis with Reliability and Cooperation for Underwater Wireless Sensor Networks

Reliability is a key factor for application-oriented Underwater Sensor Networks (UWSNs) which are utilized for gaining certain objectives and a demand always exists for efficient data routing mechanisms. Cooperative routing is a promising technique which utilizes the broadcast feature of wireless medium and forwards data with cooperation using sensor nodes as relays. Here, we present a cooperation-based routing protocol for underwater networks to enhance their performance called Stochastic Performance Analysis with Reliability and Cooperation (SPARCO). Cooperative communication is explored in order to design an energy-efficient routing scheme for UWSNs. Each node of the network is assumed to be consisting of a single omnidirectional antenna and multiple nodes cooperatively forward their transmissions taking advantage of spatial diversity to reduce energy consumption. Both multihop and single-hop schemes are exploited which contribute to lowering of path-losses present in the channels connecting nodes and forwarding of data. Simulations demonstrate that SPARCO protocol functions better regarding end-to-end delay, network lifetime, and energy consumption comparative to noncooperative routing protocol—improved Adaptive Mobility of Courier nodes in Threshold-optimized Depth-based routing (iAMCTD). The performance is also compared with three cooperation-based routing protocols for UWSN: Cognitive Cooperation (Cog-Coop), Cooperative Depth-Based Routing (CoDBR), and Cooperative Partner Node Selection Criteria for Cooperative Routing (Coop Re and dth).

Utilizing kinematics and selective sweeping in reinforcement learning-based routing algorithms for underwater networks

Effective utilization of mobile ad hoc underwater distributed networks is challenging due to high system costs and the harsh environment characterized by low bandwidth, large latency, high energy consumption, and node mobility. This work addresses the routing issue, which is critical in successfully establishing and utilizing an underwater network. In particular, it focuses on reinforcement learning (RL)-based routing algorithms, which possess the ability to explore the network environment and adapt routing decisions to the constantly changing topology of the network due to node mobility and energy usage. This paper presents a routing algorithm based on Q-learning, one of the RL approaches, with additional Kinematic and Sweeping features, therefore referred to as QKS. These two additional features are introduced to address the potential slow convergence associated with pure RL algorithms. The results of a detailed packet-level simulation have been obtained using the NS-2 open-source network simulator with underwater modeling additions. The energy efficiency, convergence, and delivery performance of QKS are compared with two other routing protocols for underwater networks, a basic flooding approach (ICRP (Liang, 2007)) and a basic Q-learning implementation (QELAR (Hu, 2010)), using simulations of networks with both fixed and mobile nodes.