Priority-based transmission rate control with a fuzzy logical controller in wireless multimedia sensor networks

Chen, Young-Long; Lai, Hung-Pin

doi:10.1016/j.camwa.2011.09.034

Cited by 23 publications

(14 citation statements)

References 15 publications

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“…The existing work on EWPBRC [3] and FEWPBRC [1] consider only incoming and outgoing data rate for predicting the level of congestion in each sensor node. Omitting buffer occupancy leads to buffer overflow.…”

Section: Resultsmentioning

confidence: 99%

“…It proposes Priority based Rate Adjustment (PRA) to control congestion. Fuzzy Logic Controller with the Exponential Weight of Priority-Based Rate Control (FEWPBRC) algorithm [1] uses both input and output data rate for assessing the congestion level and it applies fuzzy logic to determine the output rate of sink node. This approach provides service discrimination among different categories of data types.…”

Section: Related Workmentioning

confidence: 99%

“…CODA [11] and Fusion [14] use the broadcast nature of WMSN to give feedback about congestion to neighboring nodes even though there is no guarantee for broadcasted messages. PCCP [12] [13], EWPBRC [3] and FEWPBRC [1] ignore buffer occupancy in detecting the inception of congestion that results in repeated buffer overflow. CCF [9] provides only equal fairness among nodes.…”

Section: Related Workmentioning

confidence: 99%

“…Wireless multimedia sensor network is a collection of a huge number of wireless sensors outfitted with devices which are capable of sensing and retrieving images, audio/video streams and scalar data [1]. And the importance of the traffic generated by these sensors is different; it is needed that preference must be given to packets carrying important data by providing more network resources than other packets to achieve diverse levels of service [2].…”

mentioning

confidence: 99%

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Probabilistic QOS Aware Congestion Control in Wireless Multimedia Sensor Networks

Mayandi¹,

Pillai²

2016

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The general problem faced in the field of Wireless Multimedia Sensor Networks (WMSNs) is congestion. The most common method in the area of WMSNs to minimize congestion is traffic control. Quality Of Service (QOS) is widely used in WMSNs to guarantee preferential service for critical applications by controlling end-to-end delay, reducing data loss and by providing adequate bandwidth. The present work is on Probabilistic QOS Aware Congestion Control (PQACC) which employs probabilistic method based congestion prediction and priority based data transmission rate adjustment, where inelastic real-time traffic and elastic non-real-time traffic are treated separately. Using the present PQACC approach, average throughput, average source-to-sink delay and average packet loss probability are improved by 9%, 10.33% and 16.03% compared to EWPBRC and achieved 5.97%, 7.05% and 11.69% improvement compared to FEWPBRC. Simulation result reveals that, congestion is effectively predicted, controlled and provides necessary level of QOS in terms of delay, throughput and packet loss, hence making this approach possible in mission critical applications.

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Section: Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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Probabilistic QOS Aware Congestion Control in Wireless Multimedia Sensor Networks

Mayandi¹,

Pillai²

2016

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“…In these algorithms, the decision to drop packets is based on length of the queue. The past research was done to improve scheduler performance using various methods of linear as well as fuzzy logic control based techniques like priority based transmission rate control [8], priority index based control [14], number of hops and channel capacity based control [15], bandwidth requirement and throughput based control [16], differentiated services based control [2] etc. However, this past research work on packet scheduling algorithms mainly focused on the parameters of network layer in which the scope of optimization is limited because it does not truly reflects the congestion happening in lower layers viz.…”

Section: Physical Layer Packet Loss Ratiomentioning

confidence: 99%

Optimization of quality of service parameters for efficient channel allocation

Saxena

Singhai

Raghuvanshi

2018

IJET

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The bandwidth-intensive network applications with aggressive quality of service (QoS) requirements requires fast and efficient networks. The wireless network performance is impacted due to multitude of data transport at uneven transmission rates on various channels and line losses leading to congestion. It is a big challenge to achieve the required QoS by managing delay, jitter, bandwidth and packet loss parame-ters on a network. This paper highlights the major causes affecting QoS and proposes an optimization technique which allocates the channel dynamically by integrating all the parameters affecting QoS across network layer, medium access control (MAC) layer and physical layer. The proposed algorithm utilizes the feedback parameters namely queueing delay, packet priority and timeout, MAC layer contention delay and packet loss ratio as inputs and a closed loop processing control for the scheduler based on fuzzy logic control (FLC). Hence, the algo-rithm is more realistic and considers the line conditions. The simulation results show that the proposed algorithm is faster and utilizes the overall network more efficiently.

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Energy efficient resource allocation algorithms combining PSO with FLC and Taguchi method in hybrid opportunistic networks

Chen,

Lin,

Chen

et al. 2023

Applied Soft Computing

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Priority-based transmission rate control with a fuzzy logical controller in wireless multimedia sensor networks

Cited by 23 publications

References 15 publications

Probabilistic QOS Aware Congestion Control in Wireless Multimedia Sensor Networks

Probabilistic QOS Aware Congestion Control in Wireless Multimedia Sensor Networks

Optimization of quality of service parameters for efficient channel allocation

Energy efficient resource allocation algorithms combining PSO with FLC and Taguchi method in hybrid opportunistic networks

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