Fuzzy based Data Fusion for Energy Efficient Internet of Things

Fuzzy based Data Fusion for Energy Efficient Internet of Things

Madan Mohan Agarwal, Mahesh Chandra Govil, Madhavi Sinha, Saurabh Gupta
Copyright: © 2019 |Pages: 13
DOI: 10.4018/IJGHPC.2019070103
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Internet of Things will serve communities across the different domains of life. The resource of embedded devices and objects working under IoT implementation are constrained in wireless networks. Thus, building a scheme to make full use of energy is key issue for such networks. To achieve energy efficiency, an effective Fuzzy-based network data Fusion Light Weight Protocol (FLWP) is proposed in this article. The innovations of FLWP are as follows: 1) the simulated network's data fusion through fuzzy controller and optimize the energy efficiency of smart tech layer of internet of things (Energy IoT); 2) The optimized reactive route is dynamically adjusted based on fuzzy based prediction accurately from the number of routes provided by base protocol. If the selection accuracy is high, the performance enhances the network quality; 3) FLWP takes full advantage of energy to further enhance target tracking performance by properly selecting reactive routes in the network. Authors evaluated the efficiency of FLWP with simulation-based experiments. FLWP scheme improves the energy efficiency.
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1. Introduction

A number of different standardization bodies and groups are actively working on creating more interoperable protocols stacks and open standards for the Internet of things. As research move from the HTTP, TCP, IP stack to the IoT specific protocol stack. Researchers are suddenly confronted with an acronym soup of protocols from the wireless protocols like Zigbee, RFID, Bluetooth and BACnet to next generation protocol standards such as 802.15.4e, 6LoWPAN, RPL, CoAP, LEACH (Tyagi & Kumar, 2013) etc. The realization of cost reduction to achieve green networking is the research objective of this paper. Many efficient schemes for WSN have been proposed in the recent past such as hierarchy (Lee et al., 2012), ad hoc (Li et al., 2015) and exact (Majumder et al., 2012) ones, but these studies have not selected the routes between the objects in consideration of an energy efficient IoT. In this article, authors have investigated the cost-effective fusion scheme (Fuzzy Logic Based).

Much of the literature shows that energy consumption and resources utilization in the internet of things network are highly coupled. Consequently, some of the literature aims to decrease resources utilization in order to save energy, while others try to reach a balance between resource utilization and energy consumption. (Hsu et al., 2014) present an energy-aware task consolidation (ETC) technique that minimizes the energy consumption while others (Wang et al., 2016) explore an alternative VM placement approach to minimize energy consumption during the provision of data-intensive services with a global QoS guarantee in NCDCs. authors use an improved particle swarm optimization algorithm to develop an optimal VM placement approach involving a tradeoff between energy consumption and global QoS guarantee for data-intensive services. (Maddikunta & Madda, 2017) proposed a hybrid method of Gravitational Search Algorithm GSA and Artificial Bee Colony ABC algorithm to accomplish the efficient cluster head selection that exhibits high energy efficiency that improves the life time of IoT nodes. (Sarvesh et al., 2017) discussed the node placement technique and routing mechanism was effectively integrated in single network architecture to prolong the lifetime of IoT network. This proposed network architecture, sensor node and relay node are deployed, sensor nodes are responsible for collecting the environmental data and relay nodes are responsible for data aggregation and path computation which energy efficient and reliable path computation is done to reduce number of re transmissions. (Sun et al., 2017) is constructed the service network by encapsulating value added services such as spatial and temporal-constraints, energy efficiency, and the configurability of IoT services when necessary. (Reddy & Badda, 2017) proposed a hybrid algorithm which combined the Artificial Bee Colony (ABC) and Genetic Algorithm (GA) to manage the clustering heads that improves the lifetime of IoT objects.

Further, some mobile things of protocol are required to manage internet of things through light weight routing protocol for ad hoc networks. The ad hoc network protocol like AODV (Perkins et al., 2003), DSR, AOMDV, DSDV, etc., can be standardized for ad hoc environment. These protocols are infrastructure less and self-configured. These are light weight routing protocol because each device of things handles the receiving, processing, forwarding, and controlling operation (Tarkoma et al., 2014). The energy constraint is the serious problem in mobile of things network.

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