Fuzzy based Data Fusion for Energy Efficient Internet of Things

Agarwal, Madan Mohan; Govil, Mahesh Chandra; Sinha, Manish Kumar; Gupta, Saurabh

doi:10.4018/ijghpc.2019070103

Cited by 7 publications

(3 citation statements)

References 23 publications

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“…Also, to achieve energy efficiency in the IoT environment, an effective fuzzy‐based network data fusion lightweight protocol has been proposed by Agarwal et al 70 The protocol is based on a modified version of the AODV protocol (AOMDV). During the discovery phase of the AOMDV protocol, multiple paths are discovered and the path‐list is buffered at the node.…”

Section: Review Of Energy‐efficient Data Fusion Approaches In Iotmentioning

confidence: 99%

A roadmap towards energy‐efficient data fusion methods in the Internet of Things

Pourghebleh

Hekmati

Davoudnia

et al. 2022

Concurrency and Computation

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Nowadays, with the rapid progress of Internet-based and distributed systems such as cloud computing, peer-to-peer networking, and Internet of Things (IoT), significant improvements in almost every engineering and commercial field have been made. On the basis of IoT, smart cities are formed utilizing intelligent information processing, universal connectivity, ubiquitous sensing, and real-time monitoring. Energy conservation is one of the significant issues in current IoT development due to the poor battery endurance of IoT objects. Over the last years, with smart cities' explosive growth, a huge range of studies regarding energy efficiency have been done. The diversity of sparse data and multi-sourcing is utilized in developing IoT scenarios. In order to use efficiently of these data to improve the IoT services, data fusion plays an important role. It saves network resources, improves data transmission efficiency, and extracts useful information from raw data. To the best of our knowledge, there is still a lack of a comprehensive and systematic study about surveying and analyzing the available energy-efficient data fusion techniques in the IoT. Thus, this article aims to address this gap using a systematic method.

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Section: Review Of Energy‐efficient Data Fusion Approaches In Iotmentioning

confidence: 99%

A roadmap towards energy‐efficient data fusion methods in the Internet of Things

Pourghebleh

Hekmati

Davoudnia

et al. 2022

Concurrency and Computation

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“…Madan Mohan Agarwal et al [25] proposed a fuzzy-based data fusion technique for IoT (FLWP). FLWP is proposed to maximize the lifespan of the network.…”

Section: Related Workmentioning

confidence: 99%

“…It achieves not able performance by means of reliability and by avoiding the packet retransmission. However, C-RPL packet loss occurs, as it takes more time to select the cluster parent.Madan Mohan Agarwal et al [25] proposed a fuzzy-based data fusion technique for IoT (FLWP). FLWP is proposed to maximize the lifespan of the network.…”

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confidence: 99%

Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things

Sankar¹,

Balasubramaniyam²,

Luhach

et al. 2019

Sensors

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Energy conservation is one of the most critical problems in Internet of Things (IoT). It can be achieved in several ways, one of which is to select the optimal route for data transfer. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for IoT. The RPL changes its path frequently while transmitting the data from source to the destination, due to high data traffic in dense networks. Hence, it creates data traffic across the nodes in the networks. To solve this issue, we propose Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) for IoT. It has two processes, namely parent selection and data aggregation. The process of parent selection uses routing metric residual energy (RER) to choose the best possible parent for data transmission. The data aggregation process uses the compressed sensing (CS) theory in the parent node to combine data packets from the child nodes. Finally, the aggregated data transmits from a downward parent to the sink. The sink node collects all the aggregated data and it performs the reconstruction operation to get the original data of the participant node. The simulation is carried out using the Contiki COOJA simulator. EDADA-RPL’s performance is compared to RPL and LA-RPL. The EDADA-RPL offers good performance in terms of network lifetime, delay, and packet delivery ratio.

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Fuzzy Based Energy Efficient Routing for IoT: Traffic Delay Optimization

Reetha,

Pandeeswari

2024

Int J Communication

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Energy conservation and optimized traffic delay are crucial challenges in Internet of Things (IoT) systems, particularly in wireless sensor networks (WSNs). This study presents a novel approach to address these issues through a fuzzy‐based routing protocol. Traditional methods often struggle to effectively enhance routing protocols with optimized packet size control. Researchers have proposed a combination of machine learning and evolutionary techniques to overcome this limitation and enhance energy efficiency in WSNs. The new approach called MOSPFNN (Multi‐Objective Spider Prey‐localized Fuzzy Neural Network) leverages fuzzy logic control (FLC) to select optimal paths for traffic‐aware multipath routing. Additionally, a new meta‐heuristic algorithm of SP optimizer is employed to develop an energy‐efficient spatial routing protocol with superior convergence and minimal local optima. The proposed protocol incorporates optimal FNN for congestion monitoring and traffic‐aware routing. Simulation results validate that new protocol outperforms existing methods in terms of average end‐to‐end delay and packet delivery ratio (PDR). Using MOSPFNN, traffic with different priority levels can achieve a successful PDR rate of over 92% and network lifetime of above 2% than previous network within a reasonable timeframe. This research contributes to advancing energy‐efficient and traffic delay‐aware routing protocols in IoT systems.

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Fuzzy based Data Fusion for Energy Efficient Internet of Things

Cited by 7 publications

References 23 publications

A roadmap towards energy‐efficient data fusion methods in the Internet of Things

A roadmap towards energy‐efficient data fusion methods in the Internet of Things

Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things

Fuzzy Based Energy Efficient Routing for IoT: Traffic Delay Optimization

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