Fuzzy-Logic-Based Energy Optimized Routing for Wireless Sensor Networks

Jiang, Haifeng; Sun, Yanjing; Sun, Renke; Xu, Hongli

doi:10.1155/2013/216561

Cited by 48 publications

(35 citation statements)

References 23 publications

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“…When selecting alternate paths, the algorithm may create congestion, overhead, and high path energy consumption. Jiang et al select the next hop by considering three metrics as hop distance, residual energy, and transmission distance using fuzzy theory to reduce the energy loss. In some way, the created path is not optimal when considering the fuzzy approach by means of forming fuzzy rules and objective functions, and the lack of load distribution leads to reduced lifetime.…”

Section: Related Workmentioning

confidence: 99%

LSAPSP: Load distribution‐based slot allocation and path establishment using optimized substance particle selection in sensor networks

Natarajan¹,

Subapriya

Thangavel³

2020

Int J Communication

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Summary Sensor node energy conservation is the primary design parameters in wireless sensor networks (WSNs). Energy efficiency in sensor networks directly prolongs the network lifetime. In the process of route discovery, each node cooperates to forward the data to the base station using multi‐hop routing. But, the nodes nearer to the base station are loaded more than the other nodes that lead to network portioning, packet loss and delay as a result nodes may completely loss its energy during the routing process. To rectify these issues, path establishment considers optimized substance particle selection, load distribution, and an efficient slot allocation scheme for data transmission between the sensor nodes in this paper. The selection of forwarders and conscious multi‐hop path is selected based on the route cost value that is derived directly by taking energy, node degree and distance as crucial metrics. Load distribution based slot allocation method ensures the balance of data traffic and residual energy of the node in areal‐time environment. The proposed LSAPSP simulation results show that our algorithm not only can balance the real‐time environment load and increase the network lifetime but also meet the needs of packet loss and delay.

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Section: Related Workmentioning

confidence: 99%

LSAPSP: Load distribution‐based slot allocation and path establishment using optimized substance particle selection in sensor networks

Natarajan¹,

Subapriya

Thangavel³

2020

Int J Communication

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“…In the state of the art literature, the use of FLDBS in developing a clustering-based routing algorithm handles more effectively the challenge of routing associated with an uncertain environment. Besides, it also optimizes the selection of routing metric used by enabling the combination and evaluation of diverse parameters in an efficient manner [23][24][25].…”

Section: Fuzzy Logic-based Decision Systems (Flbds)mentioning

confidence: 99%

Fuzzy Logic and Bio-Inspired Firefly Algorithm Based Routing Scheme in Intrabody Nanonetworks

Fahim

Javaid

et al. 2019

Sensors

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An intrabody nanonetwork (IBNN) is composed of nanoscale (NS) devices, implanted inside the human body for collecting diverse physiological information for diagnostic and treatment purposes. The unique constraints of these NS devices in terms of energy, storage and computational resources are the primary challenges in the effective designing of routing protocols in IBNNs. Our proposed work explicitly considers these limitations and introduces a novel energy-efficient routing scheme based on a fuzzy logic and bio-inspired firefly algorithm. Our proposed fuzzy logic-based correlation region selection and bio-inspired firefly algorithm based nano biosensors (NBSs) nomination jointly contribute to energy conservation by minimizing transmission of correlated spatial data. Our proposed fuzzy logic-based correlation region selection mechanism aims at selecting those correlated regions for data aggregation that are enriched in terms of energy and detected information. While, for the selection of NBSs, we proposed a new bio-inspired firefly algorithm fitness function. The fitness function considers the transmission history and residual energy of NBSs to avoid exhaustion of NBSs in transmitting invaluable information. We conduct extensive simulations using the Nano-SIM tool to validate the in-depth impact of our proposed scheme in saving energy resources, reducing end-to-end delay and improving packet delivery ratio. The detailed comparison of our proposed scheme with different scenarios and flooding scheme confirms the significance of the optimized selection of correlated regions and NBSs in improving network lifetime and packet delivery ratio while reducing the average end-to-end delay.Sensors 2020, 19, 5526 2 of 26 diagnosis purposes. Due to their small size and better electronic properties, these NBSs have the potential to operate inside the human body without interrupting cellular biological function. One of the types of these NBSs is surface plasmon resonance sensors, which have already been deployed for effectively diagnosing various types of cancers and cardiovascular diseases [9,10].The tremendous potential of IBNNs in revolutionizing healthcare structure is confined by several fundamental limitations including, Nanoscale (NS) communication challenges and inadequate resources of NBSs in terms of energy, storage and computation [9,11]. In the last few years, research communities focused their attention on addressing these primary challenges for realizing the broader scope of IBNNs. In the context of enabling NS communication, electromagnetic communication in the Terahertz Band (THz) has received significant consideration [10,12]. The immense opportunities brought by electromagnetic communication in THz band such as extremely high data communication speed are leading to the development of new electromagnetic-based communication schemes [13,14]. The development of novel schemes for IBNNs also requires an in-depth comprehension of the intense energy constraint of these NBSs for effective outcomes. The extreme energy c...

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“…The remaining battery power, the number of hops to the destination node, and the traffic loads are taken into consideration to determine the optimal routing path from the source node to the destination node. Similar in [30], to prolong the network lifetime, a fuzzy logic based energy-optimization routing protocol is proposed, in which the social welfare function is used to predict inequality of residual energy of neighbor nodes. The algorithm computes the degree of energy balance based on the energy inequality.…”

Section: Rlated Workmentioning

confidence: 99%

Cross-Layer and Reliable Opportunistic Routing Algorithm for Mobile Ad Hoc Networks

Li¹,

Martínez-Ortega²,

Díaz³

2018

IEEE Sensors J.

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For improving the efficiency and the reliability of the opportunistic routing algorithm, in this paper, we propose the cross-layer and reliable opportunistic routing algorithm (CBRT) for Mobile Ad Hoc Networks, which introduces the improved efficiency fuzzy logic and humoral regulation inspired topology control into the opportunistic routing algorithm. In CBRT, the inputs of the fuzzy logic system are the relative variance (rv) of the metrics rather than the values of the metrics, which reduces the number of fuzzy rules dramatically. Moreover, the number of fuzzy rules does not increase when the number of inputs increases. For reducing the control cost, in CBRT, the node degree in the candidate relays set is a range rather than a constant number. The nodes are divided into different categories based on their node degree in the candidate relays set. The nodes adjust their transmission range based on which categories that they belong to. Additionally, for investigating the effection of the node mobility on routing performance, we propose a link lifetime prediction algorithm which takes both the moving speed and moving direction into account. In CBRT, the source node determines the relaying priorities of the relaying nodes based on their utilities. The relaying node which the utility is large will have high priority to relay the data packet. By these innovations, the network performance in CBRT is much better than that in ExOR; however, the computation complexity is not increased in CBRT. 

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Fuzzy-Logic-Based Energy Optimized Routing for Wireless Sensor Networks

Cited by 48 publications

References 23 publications

LSAPSP: Load distribution‐based slot allocation and path establishment using optimized substance particle selection in sensor networks

LSAPSP: Load distribution‐based slot allocation and path establishment using optimized substance particle selection in sensor networks

Fuzzy Logic and Bio-Inspired Firefly Algorithm Based Routing Scheme in Intrabody Nanonetworks

Cross-Layer and Reliable Opportunistic Routing Algorithm for Mobile Ad Hoc Networks

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