Toward Optimal Distributed Node Scheduling in a Multihop Wireless Network Through Local Voting

Vergados, Dimitrios J.; Amelina, Natalia; Jiang, Yuming; Kralevska, Katina; Granichin, Oleg

doi:10.1109/twc.2017.2767045

Cited by 36 publications

(31 citation statements)

References 57 publications

(60 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An effective and reasonable hub planning is a major challenge in multi-jump remote networks. A conveyed hub booking algorithm, called Local Voting [7]. The thought originates from finding that the delay or shortest delivery time is gotten when the heap is stabled all through the network.fairness Average delay, maximum delay by local voting has exhibited better performance when compared to other scheduling algorithms from the literature.…”

Section: Literature Surveymentioning

confidence: 99%

Semi-Equalizing Load in Multi-hop Wireless Networks

2019

IJITEE

View full text Add to dashboard Cite

Scheduling transmissions in a well-organized and fair manner in multi hop wireless network [MWN] is very crucial and challenging .For semi equalizing the load a distributed node scheduling algorithm is used through slot reallocation based on local information swap . The algorithm helps to find the delay or shortest delivery time is achieved when the load is semi-equalized throughout the network. We have simulated the Local voting algorithm and found that the system converges asymptotically toward the optimal schedule. In this paper we propose a congestion free scheme to schedule the node transmissions conflict free. The proposed algorithm achieves better performance than the other distributed algorithms in terms of fairness, average delay, and maximum delay in simulation results.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Semi-Equalizing Load in Multi-hop Wireless Networks

2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…We start with consensus algorithms, based on the principle of iterative averaging (similar dynamics appear in the literature under different names, e.g. local voting [Amelina et al, 2015, Vergados et al, 2018, Laplacian flows [Bullo, 2018, Proskurnikov andCao, 2017a] or rendezvous algorithm [Lin et al, 2007a]).…”

Section: Consensus Via Iterative Averagingmentioning

confidence: 99%

“…The relevant results can be found in [Amelina et al, 2015, Vergados et al, 2018, Granichin and Khantuleva, 2017.…”

Section: Instead Of the Maximal Deviationmentioning

confidence: 99%

Evolution of clusters in large-scale dynamical networks

Proskurnikov

Granichin

2018

CAP

View full text Add to dashboard Cite

Recent tremendous progress in electronics, complexity theory and network science provides new opportunities for intellectual control of complex large-scale systems operating in turbulent environment via networks of interconnected miniature devices, serving as actuators, sensors and data processors. Actual dynamics of the resulting control systems are too sophisticated to be examined controlled by traditional methods, which primarily deal with ordinary differential equations. However, their complexity can be dramatically reduced by fast processes, organizing the elementary units of the system (called agents) into relatively small number of clusters. The clusters emerge and deteriorate in response to changes in the environment, and the processes of their formation and destruction are very short in time. During the periods of the clusters’ existence, the system’s dynamics is essentially low-dimensional due to synchronization between the agents in each cluster. An enormously complicated system is thus reduced to a finite-dimensional model with time-varying structure of the state vector. The low-dimensionality of the reduced model allows to control it by using classical methods, e.g. model-predictive or adaptive control. This philosophy of complex systems control is illustrated on an experimental setup, called the “airplane with feathers”. The wings of this airplane are equipped with arrays of microsensors, microcomputers, and microactuators (“feathers”). The feathers self-organize into clusters by using a multi-agent consensus protocol; the aim of this coordination is to reduce the perturbing forces, affecting the airplane in a turbulent flow.

show abstract

“…In this paper, we propose a distributed link scheduling algorithm called Local Voting (LV) that provides simultaneously low end-to-end latency and high reliability but on a significantly lower energy cost compared to existing algorithms in the literature. LV stems from the finding that the shortest delivery time is obtained when the load is equalized throughout the network [2], [16].The performance of LV is studied through extensive simulation results in the 6TiSCH simulator [18].…”

Section: Introductionmentioning

confidence: 99%

A Load Balancing Algorithm for Resource Allocation in IEEE 802.15.4e Networks

Kralevska¹,

Vergados²,

Jiang³

et al. 2018

2018 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)

Self Cite

View full text Add to dashboard Cite

The recently created IETF 6TiSCH working group combines the high reliability and low-energy consumption of IEEE 802.15.4e Time Slotted Channel Hopping with IPv6 for industrial Internet of Things. We propose a distributed link scheduling algorithm, called Local Voting, for 6TiSCH networks that adapts the schedule to the network conditions. The algorithm tries to equalize the link load (defined as the ratio of the queue length over the number of allocated cells) through cell reallocation. Local Voting calculates the number of cells to be added or released by the 6TiSCH Operation Sublayer (6top). Compared to a representative algorithm from the literature, Local Voting provides simultaneously high reliability and low end-to-end latency while consuming significantly less energy. Its performance has been examined and compared to On-thefly algorithm in 6TiSCH simulator by modeling an industrial environment with 50 sensors.

show abstract

Toward Optimal Distributed Node Scheduling in a Multihop Wireless Network Through Local Voting

Cited by 36 publications

References 57 publications

Semi-Equalizing Load in Multi-hop Wireless Networks

Semi-Equalizing Load in Multi-hop Wireless Networks

Evolution of clusters in large-scale dynamical networks

A Load Balancing Algorithm for Resource Allocation in IEEE 802.15.4e Networks

Contact Info

Product

Resources

About