Task Allocation within Mesh Networks: Influence of Architecture and Algorithms

Postawka, Aleksandra; Koszałka, Iwona Poźniak

doi:10.1007/978-3-319-08422-0_127

Search citation statements

Order By: Relevance

Paper Sections

Select...

Related Work1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2023

Publication Types

Select...

Article1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A graph based framework is defined here for dynamically assigning tasks to set(s) of robots. For mesh networks, an interesting research [23] stresses the importance of architectures and algorithms, when considering the introduction of two new allocation algorithms. We presented the above as just a (very) small part of what researchers focus on when discussing task allocation.…”

Section: Related Workmentioning

confidence: 99%

Multi-constrained network occupancy optimization

Halilovic

Zaimovic

Seceleanu

et al. 2023

ComSIS

View full text Add to dashboard Cite

The greater the number of devices on a network, the higher load in the network, the more chance of a collision occurring, and the longer it takes to transmit a message. The size of load can be identified by measuring the network occupancy, hence it is desirable to minimize the latter. In this paper, we present an approach for network occupancy minimization by optimizing the packing process while satisfying multiple constraints. We formulate the minimization problem as a bin packing problem and we implement a modification of the Best-Fit Decreasing algorithm to find the optimal solution. The approach considers grouping signals that are sent to different destinations in the same package. The analysis is done on a medium-sized plant model, and different topologies are tested. The results show that the proposed solution lowers the network occupancy compared to a reference case.

show abstract

Section: Related Workmentioning

confidence: 99%