A Two-Tier Distributed Fuzzy Logic Based Protocol for Efficient Data Aggregation in Multihop Wireless Sensor Networks

Sert, Seyyit Alper; Alchihabi, Abdullah; Yazıcı, Adnan

doi:10.1109/tfuzz.2018.2841369

Cited by 102 publications

(77 citation statements)

References 19 publications

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“…In order to enhance the data aggregation efficiency in a multi-hop wireless sensor network, Sert et al [46] introduced a two-tier distributed fuzzy logic-based protocol (TTDFP). In tier I, TTDFP selects the optimum cluster head through an energy-based competition of provisional leaders using a probabilistic model.…”

Section: Fuzzy Logic-based Routing Algorithmsmentioning

confidence: 99%

“…FEARM [43] utilized different parameters such as link quality, energy consumption, physical distance, and available buffer, whereas FLGR [44] did not employ physical distance parameter. On the other hand, new and colony optimization routing [45], TTDFP [46], and LPO [47] focused on identifying cluster head based on different parameters. The main forwarding strategies in most algorithms have been designed to select the efficient sensor node that is closer to the sink node with minimum route cost.…”

Section: Fuzzy Logic-based Routing Algorithmsmentioning

confidence: 99%

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On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

et al. 2019

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Wireless sensor networks have recently been widely used in several applications and scenarios, especially because they have the ability and flexibility for establishing a scalable and reliable wireless network. Cooperative multi-robotic systems (CMRS) are one example of these applications where establishing a wireless network between robots is essential and paramount to their operation. Further, these robots can utilize their mobility to provide sensing functionality for areas that are not covered by the static sensor. This can be achieved by equipping the robots with specific sensors to sense the area of interest (AoI) and report the sensed data to a remote monitoring center for further processing and decision-making. However, the nodes that form the sensor network have limited energy, and, as such, efficient algorithms in clusters’ formation, packets’ routing, and energy and mobility management are paramount. In this paper, a literature survey is presented containing the most related works that have been proposed to solve these challenges utilizing fuzzy logic. Most of the literature work attempted to utilize a de-centralized approach, where certain input parameters such as the residual energy, communication link quality, network congestion status, the nodes’ distance to the sink node and its location with respect to the other nodes, and the data and their sampling rate are all used as inputs to the fuzzy logic controller. These input parameters are used to determine several performance vital factors such as the cluster formation and its cluster head, best route to the sink node, optimal power management policies in terms of sleep/awake times needed to maximize the network lifetime, nodes’ mobility management policies to maintain network connectivity, and best route in terms of packet loss and delay.

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Section: Fuzzy Logic-based Routing Algorithmsmentioning

confidence: 99%

Section: Fuzzy Logic-based Routing Algorithmsmentioning

confidence: 99%

On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

et al. 2019

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“…TTDFP was proposed in [32] and is a fuzzy logic-based algorithm that includes two tiers. The first tier is a fuzzy clustering phase, which considers the remaining node energy, relative connectivity, and distance to the BS as the fuzzy inputs to adjust the competition radius for provisional CHs and subsequently uses the SA approach [33] to optimize the maximum competition radius and the threshold value.…”

Section: Related Workmentioning

confidence: 99%

An Unequal Clustering Algorithm for Wireless Sensor Networks Based on Interval Type-2 TSK Fuzzy Logic Theory

2020

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Clustering is an effective method for reducing energy consumption in wireless sensor networks (WSNs). In a multihop clustered network scenario, each sensor node transmits data to its own cluster head (CH), and the CH aggregates the data from its member nodes and forwards it to the base station (BS) via other CHs. However, the "hot spot" problem is prone to occur in clustered WSNs because CHs closer to the BS have heavier intercluster forwarding tasks. To address this problem, this paper proposes an unequal clustering algorithm based on interval type-2 TSK fuzzy logic theory (UCT2TSK). The relative distance to the BS (RDB), residual energy (RE), and node density (ND) are considered as the inputs of an interval type-2 fuzzy logic system (FLS). Through fuzzy reasoning, outputs are acquired that can be used to optimize the CHs and determine the cluster sizes. Simulation results verify that UCT2TSK can effectively balance energy consumption and enhance energy efficiency because it has better performance in network lifetime and network throughput than other classical and recent clustering algorithms.

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“…The number of alive nodes decrease when the number of round increases. Here, the existing algorithms of SSO, GA, and TTDFP 31 are compared with the proposed GIFSS-SSOGA algorithm. The existing algorithm of SSO got a low number of alive nodes than other existing algorithms.…”

Section: Performance Analysismentioning

confidence: 99%

A fuzzy multicriteria decision‐making‐based CH selection and hybrid routing protocol for WSN

Sreedharan

Pete

2020

Int J Communication

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Summary One of the famous approaches to decision making is named as multicriteria decision making (MCDM). In order to solve the MCDM issues, a better way is provided by the fuzzy logic. Expendability, cost, maintenance, availability of software, and performance characteristics are such problems considered by the decision. The precise estimation of the pertinent data is one of the vital phases in DM systems. This paper presents a fuzzy MCDM‐based cluster head (CH) selection and hybrid routing protocol to solve the most common issues. In this research article, the generalized intuitionistic fuzzy soft set (GIFSS) approach is utilized to select the optimal CH, and hybrid shark smell optimization (SSO), and a genetic algorithm (GA) is introduced for the effective routing. Initially, the wireless sensor network (WSN) system and energy models are designed, and then the nodes are grouped into several clusters. Next, based on the GIFSS, the CH nodes are selected, and finally, an effective routing is placed based on the hybrid optimizations. The implementation is performed on the NS2 platform, and the performances are evaluated by packet delivery ratio (PDR), delay, packet loss ratio (PLR), network lifetime, bit error rate (BER), energy consumption, throughput, and jitter. The existing approaches named energy centers examining using particle swarm optimization (EC‐PSO), variable dimension‐based PSO (VD‐PSO), energy‐efficient PSO‐based CH selection (PSO‐ECHS), low‐energy adaptive clustering hierarchy‐sugeno fuzzy (LEACH‐SF), SSO, and GA are compared with the proposed strategy. According to the implemented outcomes, it displays the proposed strategy and gives improved outcomes than the others.

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A Two-Tier Distributed Fuzzy Logic Based Protocol for Efficient Data Aggregation in Multihop Wireless Sensor Networks

Cited by 102 publications

References 19 publications

On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

On the Potential of Fuzzy Logic for Solving the Challenges of Cooperative Multi-Robotic Wireless Sensor Networks

An Unequal Clustering Algorithm for Wireless Sensor Networks Based on Interval Type-2 TSK Fuzzy Logic Theory

A fuzzy multicriteria decision‐making‐based CH selection and hybrid routing protocol for WSN

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