Underwater acoustic sensor networks (UASNs) have become more and more important in ocean exploration applications, such as ocean monitoring, pollution detection, ocean resource management, underwater device maintenance, etc. In underwater acoustic sensor networks, since the routing protocol guarantees reliable and effective data transmission from the source node to the destination node, routing protocol design is an attractive topic for researchers. There are many routing algorithms have been proposed in recent years. To present the current state of development of UASN routing protocols, we review herein the UASN routing protocol designs reported in recent years. In this paper, all the routing protocols have been classified into different groups according to their characteristics and routing algorithms, such as the non-cross-layer design routing protocol, the traditional cross-layer design routing protocol, and the intelligent algorithm based routing protocol. This is also the first paper that introduces intelligent algorithm-based UASN routing protocols. In addition, in this paper, we investigate the development trends of UASN routing protocols, which can provide researchers with clear and direct insights for further research.

https://www.mdpi.com/1424-8220/16/3/414/pdf

A Survey on Underwater Acoustic Sensor Network Routing Protocols

Wireless sensor networks (WSNs) consist of large number of small sized sensor nodes, whose main task is to sense the desired phenomena in a particular region of interest. These networks have large number of applications such as habitat monitoring, disaster management, security and military etc. Sensor nodes are very small in size and have limited processing capability as these nodes have very low battery power. WSNs are also prone to failure, due to low battery power constraint. Data aggregation is an energy efficient technique in WSNs. Due to high node density in sensor networks same data is sensed by many nodes, which results in redundancy. This redundancy can be eliminated by using data aggregation approach while routing packets from source nodes to base station. Researchers still face trouble to select an efficient and appropriate data aggregation technique from the existing literature of WSNs. This research work depicts a broad methodical literature analysis of data aggregation in the area of WSNs in specific. In this survey, standard methodical literature analysis technique is used based on a complete collection of 123 research papers out of large collection of 932 research papers published in 20 foremost workshops, symposiums, conferences and 17 prominent journals. The current status of data aggregation in WSNs is distributed into various categories. Methodical analysis of data aggregation in WSNs is presented which includes techniques, tools, methodology and challenges in data aggregation. The literature covered fifteen types of data aggregation techniques in WSNs. Detailed analysis of this research work will help researchers to find the important characteristics of data aggregation techniques and will also help to select the most suitable technique for data aggregation. Research issues and future research directions have also been suggested in this research literature.

Data Aggregation in Wireless Sensor Networks: Previous Research, Current Status and Future Directions

Underwater acoustic sensor networks (UASNs) have become a popular research topic, with research challenges focused on underwater communication techniques. By incorporating long end-to-end latency, high energy consumption and dynamic network topology in UASNs, many intelligent routing protocols have been proposed to solve the problem. However, shortcomings still exist, and comprehensive routing protocols are urgently needed. In this paper, we propose an adaptive Deep Q-Network-based energy- and latency-aware routing protocol (DQELR) to prolong network lifetimes in UASNs. In the DQELR, a Deep Q-Network algorithm with both off-policy and on-policy methods is adopted to make globally optimal routing decisions. Based on both the energy and depth states of nodes at different communication stages, nodes with the maximum Q-value can be selected as forwarders adaptively considering both energy and latency. A hybrid of the broadcast and unicast communication mechanisms is also designed to reduce network overhead. In addition, network topology changes can be addressed through an on-policy method that makes a new routing decision when the current route becomes corrupted. With less energy consumption and strict latency limitations, the DQELR can prolong network lifetimes in UASNs. Simulation results show that the DQELR can achieve a superior network lifetime with better latency and energy efficiency performances relative to other general schemes applied in UASNs.

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DQELR: An Adaptive Deep Q-Network-Based Energy- and Latency-Aware Routing Protocol Design for Underwater Acoustic Sensor Networks

Wireless sensor networks (WSNs) are typically used with dynamic conditions of task-related environments for sensing(monitoring) and gathering of raw sensor data for subsequent forwarding to a base station. In order to deploy WSNs in real environments, a variety of technical challenges must be addressed. With traditional techniques developed for a specific task, it is hard to react in dynamic situations beyond the scope of the intended task. As a solution to this problem, machine learning (ML) techniques that are able to handle dynamic situations with successful learning process have been applied lately in WSNs. Particularly, deep learning (DL) techniques, a class of ML techniques characterized by the use of deep neural network, are used for WSNs to extract higher level features from raw sensor data. A range of benefits obtained from ML techniques applied to WSNs can be described as reduced computational complexity, increased feasibility in finding optimal solutions, increased energy efficiency, etc. On the other hand, it is found from our survey that large training time and large dataset to get acceptable performance are accompanied with large energy consumption which is not favorable for resource-restrained WSNs. Reviews on the applications of ML techniques in WSNs appeared in the literature. However, few reviews have dealt with the applications of DL techniques in WSNs. In this review, recent developments of ML techniques for WSNs are presented with much emphasis on DL techniques. The DL techniques developed for various applications in WSNs are addressed together with their respective deep neural network architectures.

Machine Learning for Advanced Wireless Sensor Networks: A Review

The Energy hole problem, a common phenomenon in wireless sensor networks, significantly decreases the lifetime of any deployed network. Some of the popular techniques to minimize such problems are using mobile sinks instead of static sinks, extending the transmission range dynamically, and deploying redundant sensor nodes near the base station/sink. The major drawback to these techniques are that energy holes may still be created at some point due to their static nature of deployment, despite having the overall residual energy very high. In this research work, we adopt a new approach by dividing the whole network into equiangular wedges and merging a wedge with its neighboring wedge dynamically whenever individual residual energy of all member nodes of a wedge fall below a threshold value. We also propose an efficient Head Node (HN) selection scheme to reduce the transmission energy needed for forwarding data packets among Head Nodes. Simulation results show that WEMER, our proposed WEdge MERging based scheme, provides significantly higher lifetime and better energy efficiency compared to state-of-the-art Power-Efficient Gathering in Sensor Information Systems (PEGASIS) and contemporary Concentric Clustering Scheme (CCS), and Multilayer Cluster Designing Algorithm (MCDA).

https://www.mdpi.com/1424-8220/20/1/277/pdf

Minimizing the Energy Hole Problem in Wireless Sensor Networks: A Wedge Merging Approach

One of the major restrictions for the application areas in wireless sensor network is the threat of limited energy resources. The sensor nodes are equipped with ir-rechargable batteries as the source of power. To maximize the entire network life time it is required to optimize the energy usage for the sensor nodes in WSNs. It is already established that clustering is an efficient routing technique requires less energy consumption to transmit data to the base station with compare to the direct communication techniques. Efficiency of clustering technique is directly dependent on formation of the cluster and cluster head selection techniques. The uneven distribution of the sensors among the clusters will cause energy leakage problem for some cluster heads and may cause premature deaths to them. In this paper, we present a novel cluster based routing algorithm that ensures to form clusters with even load as well as select cluster heads in a better way using fuzzy based environment so that optimal data transmission on single or multi hop environment is maintained. The simulation results more than 40% prolonged life time for the sensor nodes.

Cluster based routing algorithm with evenly load distribution for large scale networks

The application areas of wireless sensor network often suffered from the threat of limited energy resources. The potentiality of the applications is directly dependent on the number of active sensor nodes are available within the sensor network. Maximizing the lifetime of the sensor nodes is the ultimate way to improve the lifetime of the entire sensor network. Considering mobile sink node raises a new aspect to the wireless sensor network applications in large scale networking. It improves the performance in case of target detection, can pretend the energy hole problem but throws a new challenge of updating the sink's location information as the sink node is being propagated continuously and the frequent location updates requires heavy energy dissipation for the sensor nodes to maintain the routing path. In this paper we introduce a novel cluster based routing protocol which is aimed to optimize the nodes energy usage with considering the mobility for the sink node to pretend the common energy-hole problem.

Energy efficient routing and bypassing energy-hole through mobile sink in WSN

Detecting re-entry of a moving object inside an irregular region is one of the most fundamental challenges of Wireless Sensor Networks. The performance of an effective and efficient sensor network for detecting re-entry of moving object is highly related to the proper configuration and design of the network and effective communication between the sensors deployed. For an irregular space of random shape and size, it is practically very difficult to detect an objectpsilas trajectory within the sensing boundary since boundary is not regular. In this paper we have focused on the conversion of a physically random space (concave) to a logically convex hull so that sensing boundary is well defined and then we have detected the network cuts and pockets within that convex hull to detect the status of the moving object. Here we have adopted the technique of using multiple time consecutive frames from two or more sensors currently sensing the object and exploits the time intensity variations with their geometric diversity among the reentry object and their observer sensors.

Detecting re-entry of a moving object in an irregular space

The wireless sensor network is consisting of a large number of micro chips with limited energy capacities that are capable of gathering data in different kinds of environments. Clustering sensors into small groups and each group is controlled by a cluster head, so that sensors transmit data to cluster-heads only and then cluster-heads send the aggregated information to the base station, may save some energy. In this paper, we propose an optimal energy-adaptive algorithm which is motivated from the LEACH. We have tried to modify the LEACH's random cluster-head election protocol for ensuring the balanced energy dissipation over the whole network to increase the network lifetime. The modified duty cycle for the sensor nodes has been proposed and to ensure this we optimize TDMA through sleep wakeup based decentralized MAC protocol. Simulation based on power consumption results that our algorithm outperforms LEACH around 30%.

https://ieeexplore.ieee.org/iel7/6570846/6575920/06575983.pdf

Energy efficient optimization in the LEACH architecture

In Wireless Sensor Networks deploying a sensor node ensuring full coverage with connectivity is a real challenge. It is extremely difficult to find an optimal node deployment strategy that can minimize cost, and communicate on overhead and provide a high degree of coverage with network connectivity. In this paper we have made a comparative study to reach an optimum deployment solution using a layered deployment model. We have also presented the trade-offs between optimal coverage and optimal connectivity topology and their mathematical foundations. The simulation results verify the mathematical calculations.

Chandan Kr. Bhattacharyya

Papers

Cluster based routing algorithm with evenly load distribution for large scale networks

Energy efficient routing and bypassing energy-hole through mobile sink in WSN

Detecting re-entry of a moving object in an irregular space

Energy efficient optimization in the LEACH architecture

Coverage and connectivity in Wireless Sensor Networks: Their trade-offs