A Reinforcement-Learning-Based Opportunistic Routing Protocol for Energy-Efficient and Void-Avoided UASNs

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.

Mobile multimedia ad hoc services run on dynamic topologies due to node mobility or failures and wireless channel impairments. A robust routing service must adapt to topology changes with the aim of recovering or maintaining the video quality level and reducing the impact of the user's experience. In those scenarios, beacon-less Opportunistic Routing (OR) increases the robustness by supporting routing decisions in a completely distributed manner based on protocol-specific characteristics. However, the existing beacon-less OR approaches do not efficiently combine multiple metrics for forwarding selection, which cause higher packet loss rate, and consequently reduce the video quality level. In this paper, we assess the robustness and reliability of our recently developed OR protocol under node failures, called cross-layer Link quality and Geographical-aware OR protocol (LinGO). Simulation results show that LinGO achieves multimedia dissemination with QoE support and robustness in scenarios with dynamic topologies.

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.

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.

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.

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.

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.

UASN (Underwater Acoustic Sensor Network) has intrinsic impediments, since it is utilized and utilizes acoustic signs to impart in the sea-going world. Examples include long delays in propagation, limited bandwidth, high transmitting energy costs, very high attenuation in the signal, expensive implementation and battery replacement etc. The UASN routing schemes must therefore take account of these features to achieve balance energy, prevent void hole and boost network life. One of the significant issue in routing is the presence of void node. A void node is a node that does not have any forwarder node. The presence of void may cause the bundle conveyance in the steering time which prompts information misfortune. The gap during steering influences the network execution regarding proliferation delay, vitality utilization and network lifetime, and so forth. So with the objective to remove the void node in the networks, this work presents an energy efficient optimal path routing for void avoidance in underwater acoustic sensor networks. This work uses the concept of gray wolf optimization algorithm to calculate the fitness function and that fitness function is used to select the best forwarder node in the networks. This work only consider the vertical directions which further reduces the end to end delay. The proposed work has been simulated on MATLAB and performances are evaluated in terms of broadcast copies of data, energy tax, and packet delivery ratio, number of dead nodes, network lifetime and delay.

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).

This paper proposes cooperative routing algorithms to improve the performance of underwater acoustic sensor networks (UW-ASNs). With the consideration of the poor acoustic link quality, a cross-layer design of physical layer and network layer is described for selecting simultaneously routing relays (RR) for forwarding data on routing paths and cooperative relays (CR) for one-hop cooperative communications. In the networks, sources that have data to transmit independently selects their own relays (i.e., both RR and CR) among their neighbors based on their link quality indicators (i.e., signal-to-noise ratio (SNR), time of arrival (ToA)) and their physical distances represented by hop count (HC) to the destination. Exploiting packet receiving or overhearing in the networks, these parameters are averaged and updated frequently to adapt to the unreliable and varying characteristics of acoustic channel over the time. By this way, the "best" relays with the "best" estimated measurements are selected reliably. Our simulation results show that the proposed algorithms improve the network performance in terms of end-to-end delay, energy consumption and packet delivery ratio.

The Underwater Acoustic Sensor Network (UASN) is a large network in which the vicinity of a transmitting node is made up of numerous operational sensor nodes. The communication process may be substantially disrupted due to the underwater acoustic channel’s time-varying and space-varying features. As a result, the underwater acoustic communication system faces the problems of reducing interference and enhancing communication effectiveness and quality through adaptive modulation. To overcome this issue, this paper intends to propose a model for optimal path selection and secured data transmission in UASN via Long Short-Term Memory (LSTM) based energy prediction. The proposed model of transmitting the secured data in UASN through the optimal path involves two major phases. Initially, the nodes are selected under the consideration of constraints like energy, distance and link quality in terms of throughput. Moreover, the energy is predicted with the aid of LSTM and the optimal path is selected with the proposed hybrid optimization algorithm termed as Pelican Updated Chimp Optimization Algorithm (PUCOA), which is the combination of two algorithms including the Pelican Optimization Algorithm (POA) and Chimp Optimization Algorithm (COA). Further, the data is transmitted via the optimal path securely by encrypting the data with the proposed improved blowfish algorithm (IBFA). At last, the developed LSTM+PUCOA model is validated with standard benchmark models and it proves that the performance of the proposed LSTM+PUCOA model attains 90.85% of accuracy, 92.78% of precision, 91.78% of specificity, 89.79% of sensitivity, 7.21% of FPR, 89.76% of F1 score, 89.77% of MCC, 10.20% of FNR, 92.45% of NPV, and 10.22% of FDR for Learning percentage 70.

Underwater wireless sensor networks (UWSNs) are capable of providing facilities for the wide range of aquatic applications. However, due to the adverse environment, UWSNs face huge challenges and issues i.e., limited bandwidth, node mobility, higher propagation delay, high manufacturer and deployment costs etc. In this paper, we propose two techniques: the geographic and opportunistic routing via transmission range (T-GEDAR) and the geographic and opportunistic routing via the backward transmission (B-GEDAR). Firstly, in the absence of forwarder node, we increase the transmission range to determine the forwarder node. Because of this, we can send packets to the sink; Secondly, when the forwarder node is unavailable in adjustable transmission range. Then, the B-GEDAR is used for determining the forwarder node so that the packet delivery ratio (PDR) can be increased effectively. This is because, our simulation results perform better network performance in terms of an energy efficiency, PDR, and the fraction of void nodes.

The Internet of Things (IoT) and cloud computing are well developed and applied in different services. Among these services, some of them take latency and overhead as the metrics to evaluate the quality of services. This paper introduces how an opportunistic routing (OR) protocol reduces latency and overhead when it is applied in wireless sensor network (WSN) application among lightweight devices. OR mitigates the delay problem and balances the energy consumption and delay of the nodes in WSNs. However, OR, with the nature of broadcasting, can easily cause heavy overhead such as redundant data and control packets in the forwarding process. In the current solution, the number of candidates (potential forwarders) is not limited by the dynamics of forwarding process. Also, the overhead of establishing the candidate sets is high due to global information calculation. Therefore, a low‐delay OR protocol with reducing overhead which designs the dynamic candidate area (DCA) to establish candidate sets is first proposed in this paper. The main work is as follows. (a) The duty cycle of a node is adjusted according to the distance between the node and sink node in the initialized network. (b) A method to establish dynamic candidate sets is proposed based on the adaptive duty cycle. Before and during the forwarding process, the dynamic candidate area is adjusted in time. (c) According to the feature of the candidate area, the corresponding routing metric is proposed to complete the cooperative communication among candidates. Through further theoretical analysis and simulations, the results indicate that this protocol achieves better performance in terms of transmission delay, data and control overhead, and network lifetime compared to the state‐of‐the‐art solutions.

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.

TOT: Trust aware opportunistic transmission in cognitive radio Social Internet of Things