A Link Level Load-Aware Queue Scheduling algorithm on MAC layer for wireless mesh networks

He, Wei; Yang, Shanlin; Teng, Da; Hu, Yun

doi:10.1109/wcsp.2009.5371607

Cited by 3 publications

(4 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The start and end time of this process is set by the user in the script using a special command. The command handler plans to start a special timer for the start time of the process, and the timer and end time are written to the timer parameters [15]. To account for the parameters e and L of the mobile robot in the assembly model was added a special object that counts the number of packets transmitted / sent / forwarded node and the residual energy of the node [16].…”

Section: Implementation Of the Methods For Detecting Abnormal Behaviormentioning

confidence: 99%

Probabilistic Method for Anomalies Detection Based on the Analysis of Cyber Parameters in a Group of Mobile Robots

Basan¹,

Basan²,

Makarevich³

2018

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

This article is devoted to the issues of ensuring the security of a group of mobile robots in the implementation of attacks aimed at the property of accessibility of information and the availability of network nodes. The article presents a method for detecting an abnormal behavior of a network node based on the analysis by the group members of the parameters: residual energy and network load. Analysis of the behavior of individual robots relative to general behavior using probabilistic methods avoids the problem of creating a reference distribution for describing the behavior of a node, as well as creating a signature database for detecting anomalies. The developed method demonstrates high detection rate of denial of service attack and distributed denial of service attack with the number of malicious nodes not exceeding or slightly exceeding the number of trusted nodes. It also provides detection of the Sybil attack.

show abstract

Section: Implementation Of the Methods For Detecting Abnormal Behaviormentioning

confidence: 99%

Probabilistic Method for Anomalies Detection Based on the Analysis of Cyber Parameters in a Group of Mobile Robots

Basan¹,

Basan²,

Makarevich³

2018

Adv. sci. technol. eng. syst. j.

View full text Add to dashboard Cite

show abstract

“…However, in reality, there are factors in these lower layers causing congestion, delays and packet losses. The proposed algorithm considers the parameters of MAC layer and physical layer in addition to those of network layer which are MAC layer contention delay [3] and physical layer packet loss ratio [4]. MAC layer contention delay [3] refers to the time taken for a wireless node to gain access to a shared physical communication channel for a packet transmission.…”

Section: Related Workmentioning

confidence: 99%

“…This is represented by physical layer packet loss ratio [4]. Thus, the proposed algorithm represents more realistic overall network and line conditions by considering the parameters of lower layers namely MAC layer contention delay [3] and physical layer packet loss ratio [4,9] in addition to the network layer parameters namely queuing delay [5], packet priority [21] and packet timeout [14] and hence delivers enhanced QoS performance.…”

Section: Related Workmentioning

confidence: 99%

Optimization of quality of service parameters for efficient channel allocation

Saxena

Singhai

Raghuvanshi

2018

IJET

View full text Add to dashboard Cite

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.

show abstract

“…The main task of link scheduling is to establish a link between the stations and select the appropriate paths for job transferring. He et al [8] proposed a loadbased scheduling algorithm to optimize the link scheduling between stations so as to achieve the load balance of each station and reduce the degree of paths congestion. Pinheiro et al [9] studied link scheduling and path selection by fuzzy control.…”

Section: Introductionmentioning

confidence: 99%

Online Self-Organizing Network Control with Time Averaged Weighted Throughput Objective

Zhang

Ли

Yan

2018

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

We study an online multisource multisink queueing network control problem characterized with self-organizing network structure and self-organizing job routing. We decompose the self-organizing queueing network control problem into a series of interrelated Markov Decision Processes and construct a control decision model for them based on the coupled reinforcement learning (RL) architecture. To maximize the mean time averaged weighted throughput of the jobs through the network, we propose a reinforcement learning algorithm with time averaged reward to deal with the control decision model and obtain a control policy integrating the jobs routing selection strategy and the jobs sequencing strategy. Computational experiments verify the learning ability and the effectiveness of the proposed reinforcement learning algorithm applied in the investigated self-organizing network control problem.

show abstract

A Link Level Load-Aware Queue Scheduling algorithm on MAC layer for wireless mesh networks

Cited by 3 publications

References 7 publications

Probabilistic Method for Anomalies Detection Based on the Analysis of Cyber Parameters in a Group of Mobile Robots

Probabilistic Method for Anomalies Detection Based on the Analysis of Cyber Parameters in a Group of Mobile Robots

Optimization of quality of service parameters for efficient channel allocation

Online Self-Organizing Network Control with Time Averaged Weighted Throughput Objective

Contact Info

Product

Resources

About