Fuzzy Based Trust Estimation for Congestion Control in Wireless Sensor Networks

Zarei, Mani; Rahmani, Amir Masoud; Sasan, Avesta; Teshnehlab, Mohammad

doi:10.1109/incos.2009.16

Cited by 19 publications

(17 citation statements)

References 9 publications

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“…TRANS [5] defines a set of routing protocols prepared with trust organization. In the fuzzy c-means clustering protocol [6], Zarei et al have recommended a fuzzy logic-based trust approximation system for congestion controller in wireless sensor networks. Fairness Congestion Control for a dis Trustful protocol [7] is essentially an adaptation of FCC, in which the Threshold Trust rate is used for choice making.…”

Section: Related Research Workmentioning

confidence: 99%

Performance Enhancement Heterogeneous Based EECP for Bandwidth Utilization

Kaur¹,

Mishra²

2017

Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering

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Abstract-Using the increase of user made on distributed network scheme, traffic congestion is one of the necessary circumstances. Distributed network consists of various networks, processors and intermediary strategies that overwork the changes or routers with high traffic and it is because of the project fault in the circulated networking architecture. Even though several researchers address the congestion recognition method, its avoidance and modification in their investigation are solid to be explored for any effective solution for this problem. Due to huge network congestion user will face the network speed problem, real congestion control methods are desirable, and mainly to solve "bursty" transportation of today's for actual extraordinary speed networks. Subsequently dawn 90's many systems have been proposed. This paper concentrates on heterogeneity based congestion mechanism patterns on the basis of certain key performance metrics. Mainly in this work we will judge the performance of Delay, congestion rate, throughput and channel capacity EECP based solution for a steady state against these key performance metrics.Index Terms-Heterogeneity EECP, RCM, Delay, congestion rate, throughput and channel capacity etc.

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Section: Related Research Workmentioning

confidence: 99%

Performance Enhancement Heterogeneous Based EECP for Bandwidth Utilization

Kaur¹,

Mishra²

2017

Proceedings of the Second International Conference on Research in Intelligent and Computing in Engineering

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“…Trust of node upon node j is calculated by the following equation T ij = w1*N ae + W2*P TR +W3*P L ...........(1) W1+W2+W3 where w1,w2,w3 are the corresponding weights used for N ae ,P TR ,P L such that w1,w2,w3 E [0,1]. A predefined trust threshold value (T TH ) is set on the basis of the application of the sensor networks [18]. If T ij > T TH , the link between the node i and j is called a trustworthy link.…”

Section: Trust Based Congestion Aware Ant Colony Optimizationmentioning

confidence: 99%

Performance Analysis of TBC-ACO Routing Protocol with Existing Routing Protocols of Wireless Sensor Networks

Radhika

Haritha²

2017

IJARCSSE

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I. INTRODUCTIONIn 21st Century, Wireless Sensor Networks [1] has gained huge amount of attention of researchers across the world with regard to tons of theoretical, practical and implementation challenges. The research progress in Wireless Sensor Networks has explored diverse novel applications enabled by large-scale networks comprising of thousands of sensor nodes capable of sensing environmental information, processing it in efficient manner and transmitting the information back to base locations for further analysis. Wireless Sensor Networks comprise of large number of low-cost, low power and multi-functional capable wireless sensor nodes [2,3] for sensing different information's, equipped with wireless communication transmission mediums and computational capabilities. In sensor nodes, different types of sensors in form of mechanical, biological, chemical, optical can be attached to acquire real-time data from environment and transmit it back after processing. The sensor nodes forming Wireless Sensor Networks [4,5] have limitation in terms of low memory, less processing speeds. A radio is integrated on nodes to transmit the information back to sink node. The nodes are powered using battery to act as main power source. As the batteries are the only and primary power source, it becomes difficult to replace and charge the batteries once nodes are deployed which remains the foremost challenge in front of researchers to enhance the Energy Efficiency of sensor nodes. The characteristics of Wireless Sensor Network [1,6,3] are Deployment of sensor nodes; Less power; less computation capability; Limited memory; Unreliability. Therefore, the characteristics open wide range of challenges for the development and real-time implementations of wireless sensor network in the world. Sensor nodes once deployed in the real world are grouped into clusters, and each cluster has a node named as -Cluster Head‖. All the nodes in the cluster forward their sensed data to the cluster head and it is the duty of Cluster Head to route the information back to Sink Node (termed as -Base Station‖) via multi-hop wireless communication as shown in Fig. 1. With regard to various limitations cum shortcomings, Wireless Sensor Networks requires a collection of network protocols for implementing network control and other managerial functions in terms of synchronization, localization, security, routing, clustering etc. Various routing protocols proposed till date for routing the packets among nodes suffer from energy-constraints. Researchers [2,5] have combined various allied field like Neuro-computing, evolutionary computing, fuzzy logic computing, reinforcement learning and swarm intelligence based techniques in WSN. The ultimate goal is to improvise the Routing Efficiency in Wireless Sensor Networks. Researchers have successfully deployed Swarm Intelligence based techniques to address varied challenges existing in area of Wireless Sensor Networks and able to successfully deploy WSN in varied applications. The aim of this research paper is to expl...

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“…In case of high congestion level, FCC performs rate adaptation and also it uses a back-pressure technique to inform neighboring upstream nodes to send fewer number of packets. Zarei et al propose a Fuzzy-Based Trust Estimation (FTE) as a congestion control scheme to find the corrupt or malicious sensors [26]. After finding these nodes, FTE isolates them to remove the traffic overhead arising from their packets.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy-based congestion control for wireless multimedia sensor networks

Sonmez

İncel

Isik

et al. 2014

J Wireless Com Network

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Congestion is a challenging problem for sensor networks because it causes the waste of communication and reduces energy efficiency. Compared to traditional wireless sensor networks, the probability of congestion occurrence in wireless multimedia sensor networks is higher due to the high volume of data arising from multimedia streaming. In this article, problems for multimedia transmission over wireless multimedia sensor networks are examined and sensor fuzzy-based image transmission (SUIT); a new progressive image transport protocol is proposed as a solution. SUIT provides fuzzy logic-based congestion estimation and an efficient congestion mitigation technique which decreases the image quality on-the-fly to an acceptable level. In case of congestion, SUIT drops some packets of the frames in a smart way and thus transmits frames to the sink with lower, but acceptable quality. In this way, SUIT improves the continuity of the video streaming. We evaluate the performance of SUIT by comparing it with two different competitors. The first one is an example transport protocol, namely Fuzzy Logic-Based Congestion Estimation. The second one is a buffer occupancy-based congestion control mechanism which is commonly used in previous studies. According to the simulation results, SUIT provides better energy consumption, frame delivery, frame loss and frame latency performance than its competitors.

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Fuzzy Based Trust Estimation for Congestion Control in Wireless Sensor Networks

Cited by 19 publications

References 9 publications

Performance Enhancement Heterogeneous Based EECP for Bandwidth Utilization

Performance Enhancement Heterogeneous Based EECP for Bandwidth Utilization

Performance Analysis of TBC-ACO Routing Protocol with Existing Routing Protocols of Wireless Sensor Networks

Fuzzy-based congestion control for wireless multimedia sensor networks

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