Modeling and Optimization Approaches for Satellite Broadcast Scheduling Problem

Author(s):  
Sezgin Kilic ◽  
Omer Ozkan
2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.


2003 ◽  
Vol 2 (2) ◽  
pp. 277-283 ◽  
Author(s):  
S. Salcedo-Sanz ◽  
C. Bousono-Calzon ◽  
A.R. Figueiras-Vidal

2014 ◽  
Vol 25 (03) ◽  
pp. 331-342 ◽  
Author(s):  
NHAT LAM ◽  
MIN KYUNG AN ◽  
DUNG T. HUYNH ◽  
TRAC NGUYEN

Our work is to study the Minimum Latency Broadcast Scheduling problem in the geometric SINR model with power control. With power control, sensor nodes have the ability to adjust transmitting power. While existing works studied the problem assuming a uniform power assignment or allowing unlimited power levels, we investigate the problem with a more realistic power assignment model where the maximum power level is bounded. To the best of our knowledge, no existing work formally proved the NP-hardness, though many researchers have been assuming that this fact holds true. In this paper, we provide a solid proof for this result.


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