Protocol for Controlling Congestion in Wireless Sensor Networks

Nikokheslat, Hossein Dabbagh; Ghaffari, Ali

doi:10.1007/s11277-017-3992-y

Cited by 31 publications

(11 citation statements)

References 21 publications

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“…This solution is only practical when the congestion is near to the source. If the congestion is close to the destination and hence far away from the source of the package, the problems of the end-to-end method persist [22].…”

Section: ) Route Based Methodsmentioning

confidence: 99%

An Enhanced Distributed Congestion Control Method for Classical 6LowPAN Protocols Using Fuzzy Decision System

et al. 2020

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The classical Internet of things routing and wireless sensor networks can provide more precise monitoring of the covered area due to the higher number of utilized nodes. Because of the limitations in shared transfer media, many nodes in the network are prone to the collision in simultaneous transmissions. Medium access control protocols are usually more practical in networks with low traffic, which are not subjected to external noise from adjacent frequencies. There are preventive, detection and control solutions to congestion management in the network which are all the focus of this study. In the congestion prevention phase, the proposed method chooses the next step of the path using the Fuzzy decision-making system to distribute network traffic via optimal paths. In the congestion detection phase, a dynamic approach to queue management was designed to detect congestion in the least amount of time and prevent the collision. In the congestion control phase, the back-pressure method was used based on the quality of the queue to decrease the probability of linking in the pathway from the pre-congested node. The main goals of this study are to balance energy consumption in network nodes, reducing the rate of lost packets and increasing quality of service in routing. Simulation results proved the proposed Congestion Control Fuzzy Decision Making (CCFDM) method was more capable in improving routing parameters as compared to recent algorithms. INDEX TERMS Internet of Things, wireless sensor network, congestion control, fuzzy decision making, and back-pressure.

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Section: ) Route Based Methodsmentioning

confidence: 99%

An Enhanced Distributed Congestion Control Method for Classical 6LowPAN Protocols Using Fuzzy Decision System

et al. 2020

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“…These approaches provide average delivery ratio, network lifetime and network traffic. Nikokheslat and Ghaffari 25 devised a method for controlling the congestion in WSNs. The congestion in WSNs is mainly controlled by reducing data traffic and increasing network resources.…”

Section: Related Workmentioning

confidence: 99%

A rendezvous point‐based data gathering in underwater wireless sensor networks for monitoring applications

Choudhary

Goyal

2022

Int J Communication

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Summary In underwater wireless sensor networks (UWSNs), most of the mobile sink data gathering scheme assumes that sensor nodes are static. Also, very limited set of works aim to eliminate the data redundancy during data gathering. Moreover, the data collection latency, error rate and load have to be considered in addition to the buffer space and energy level of the nodes. Here, a mobile sink data gathering technique is designed where rendezvous points (RPs) and sojourn times are selected. The RPs, where the mobile sinks stop to gather the data, are selected based on the node density, current traffic load and energy level of nodes. The proposed scheme, that is, adaptive distributed mobile sink data gathering (ADMSDG), eliminates data redundancy even by considering mobility of all the nodes including sink node. Here, multihop data gathering is also considered to address the energy hole problem near the sink based on similarity index. To examine the result, proposed scheme and other state‐of‐the‐art schemes are implemented in NS2.30 having the same environment. Obtained results show that when proposed scheme is experimented with respect to number of nodes, the proposed ADMSDG technique achieves 18% high data collection accuracy with 5% reduced data collection delay and 5% less energy consumption. Similarly, when proposed scheme is experimented with respect to transmission range, the proposed ADMSDG technique achieves 18% high data collection accuracy with 12% reduced data collection delay and 8% less energy consumption.

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“…Based on the pioneering work on the TC-based scheme Congestion Detection and Avoidance (CODA) [26], while applying backward congestion notification to the source node to adjust the sending rate to mitigate the effects of congestion, research has also been conducted on congestion prediction to avoid congestion. For example, M. Zawodniok et al [27] proposed Dynamic Predictive Congestion Control (DPCC).…”

Section: Related Workmentioning

confidence: 99%

Congestion Control and Prediction Schemes Using Fuzzy Logic System with Adaptive Membership Function in Wireless Sensor Networks

Aimtongkham

Nguyen

So-In

2018

Wireless Communications and Mobile Computing

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Network congestion is a key challenge in resource-constrained networks, particularly those with limited bandwidth to accommodate high-volume data transmission, which causes unfavorable quality of service, including effects such as packet loss and low throughput. This challenge is crucial in wireless sensor networks (WSNs) with restrictions and constraints, including limited computing power, memory, and transmission due to self-contained batteries, which limit sensor node lifetime. Determining a path to avoid congested routes can prolong the network. Thus, we present a path determination architecture for WSNs that takes congestion into account. The architecture is divided into 3 stages, excluding the final criteria for path determination: (1) initial path construction in a top-down hierarchical structure, (2) path derivation with energy-aware assisted routing, and (3) congestion prediction using exponential smoothing. With several factors, such as hop count, remaining energy, buffer occupancy, and forwarding rate, we apply fuzzy logic systems to determine proper weights among those factors in addition to optimizing the weight over the membership functions using a bat algorithm. The simulation results indicate the superior performance of the proposed method in terms of high throughput, low packet loss, balancing the overall energy consumption, and prolonging the network lifetime compared to state-of-the-art protocols.

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Protocol for Controlling Congestion in Wireless Sensor Networks

Cited by 31 publications

References 21 publications

An Enhanced Distributed Congestion Control Method for Classical 6LowPAN Protocols Using Fuzzy Decision System

An Enhanced Distributed Congestion Control Method for Classical 6LowPAN Protocols Using Fuzzy Decision System

A rendezvous point‐based data gathering in underwater wireless sensor networks for monitoring applications

Congestion Control and Prediction Schemes Using Fuzzy Logic System with Adaptive Membership Function in Wireless Sensor Networks

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