FLCEER: Fuzzy Logic Cluster-Based Energy Efficient Routing Protocol for Underwater Acoustic Sensor Network

FLCEER: Fuzzy Logic Cluster-Based Energy Efficient Routing Protocol for Underwater Acoustic Sensor Network

Sathishkumar Natesan (School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India) and Rajakumar Krishnan (School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India)
DOI: 10.4018/IJITWE.2020070105
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Abstract

Underwater acoustic sensor networks (UASN) play a crucial role in various applications such as tsunami detection, surveillance of the ocean by the defense department, monitoring offshore oil, and identifying gas basins underwater. UASNs can be one of the supporting infrastructures for the Internet of Things (IoT). UASNs have the problems of long latency, high bit error rate, and low bandwidth. These pose various challenges such as high consumption of energy, low reliability, low packet retransmission, and high delay for UASNs. To overcome the shortcomings mentioned above, various approaches are suggested. This article proposes a multi-layer fuzzy logic cluster-based energy-efficient routing protocol for UASNs. It splits the network area into equal sized rings. The priority number (PRN) is utilized for all underwater cluster heads (UCHs). Based on the highest PRN, the UCH starts communicating among UCHs. Here, the PRN makes the task very selective avoiding collisions and also reducing propagation delays. The cluster formation is done by sending a message to all underwater cluster members (UCMs) and the selection of UCH and UCM are done. Each has a threshold value. The intra-ring clustering process splits a ring into equal-sized clusters. Additionally, inter-cluster routing applies the fuzzy logic metrics to choose the optimum data route in transferring the data from the underwater cluster head (UCH) to the sink node (SN). It is tested using Aqua-Sim simulation which is based on NS2. It is compared with an existing protocol such as multi-layer cluster energy efficient (MLCEE), depth-based routing (DBR), energy efficient DBR (EEDBR). The results prove that it has improved energy efficiency, packet delivery ratio, throughput, and the network's lifetime.
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Introduction

Underwater Acoustic Sensor Networks (UASNs) is an upcoming area of interest for investigators. It is globally popular with wide applications such as identifying gas reservoirs and monitoring them offshore, studying the extent of pollution, surveillance of ocean by the defense department and such others. The proposed system is designed to prevent collisions and reduce propagation delays. It is expected to form inter and intracluster routing using fuzzy logic metrics. Such a system utilizes a single layer approach without employing a handshaking protocol. UASN and Terrestrial Wireless Sensor Networks (TWSN) face the challenges of long latency, high bit error rate and low bandwidth. It will cause several hardships to UASN such as reduced packet retransmission rate, high energy consumption and low reliability (Goyal, Dave & Verma, 2016; Kumar & Kumar, 2020).

The sensors are located at various points in the ocean, to gather information and provide a delay-sensitive routing to the sink node. This is useful for industrial computing and to do additional processing. It progressively controls the traffic. In particular when the features of spatiotemporal ones of the UASNs are measured. Thus, a framework of networks through strategies for routing or intelligent traffic engineering is necessary (Lin et al., 2019). Improving the lifetime of the network is an important task in wireless sensor networks (WSNs). The clustering of the network can increase the lifespan of WSNs. It can play an important role in the simplification of intra-domain routing. Only a small number of sensor nodes are connected with the cluster model and the randomly selected cluster heads (CHs). The primary role of CH is in aggregating the data obtained from the sensor node and send them to the sink node (base station) (Murugaanandam & Ganapathy, 2019).

While different protocols are suggested to perform routing, sub-aquatic communication, issues concerning energy efficacy are necessary for the UASN. To get the best out of those issues, investigation related to UASN is still necessary for examining and improving the performance of routing (Gomathi & Manickam, 2018). Recently, UASN has contributed significantly to both industry and academia in discovering underwater natural resources as well as scientific data collection in Aquatic Sensor Network (ASN) environments. The salient features of UASN are in overcoming the deficiencies like high propagation delay, low bandwidth, floating node mobility, poor energy efficiency. The ground based WSN which is a conventional type is not that efficient. The need of the hour is power-efficient protocols in the communications field which effectively employs UASNs (Huang et al., 2011).

Node clustering facilitates the network to attain efficient distribution of workload, energy efficiency, accomplish data aggregation, and maintain a connected hierarchy. Alternatively, the energy that is required for communication is reduced by multi-hop communication. Here, the relay load is distributed with the help of multi-path communication and such communication increases the network reliability (Adhikary & Mallick, 2017). The talented tools for discovering underwater natural resources as well as gathering technical data from the aquatic environment are considered for UASN. In UASNs, while sensor nodes transmit signals, obstacles on the way must be avoided by the design of a protocol for routing which effectively takes signal around them. This is compulsory. Furthermore, batteries are naturally used to run the sensor nodes. These batteries are costly to replace, thereby constituting a limiting factor in the lifetime of the UASN (Khan et al., 2019).

To come up with a routing scheme for the UASN that is energy-efficient, the author proposed a cooperative communication. An omnidirectional antenna alone is armed for each node of the network as well as a node with multiple coordinates while attracting the benefit of spatial variety. This investigation is restricted possibly at the relay node itself to do Amplify-and-Forward (AF) technique. While for the node at the receiving side, a Fixed Ratio Combining (FRC) policy is utilized (Ahmed et al., 2017). Sensor nodes in WSN’s batteries have a small lifetime and its energy should be sensibly applied. The sensor nodes that are far from the sink node take up more energy. It increases more and more as the transmitting distance still increases. The selection of a proper Cluster Head (CH) will extend the life of the network (Mary & Gnandurai, 2017).

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