Efficient algorithms to solve Broadcast Scheduling problem in WiMAX mesh networks

Raja, Gunasekaran; Siddharth, S.; Krishnaraj, P.; Kalaiarasan, M.; Uthariaraj, V. Rhymend

doi:10.1016/j.comcom.2010.03.016

Cited by 21 publications

(17 citation statements)

References 12 publications

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“…The schedule obtained in our methodology has 8 (M ) time slots and the utilization of the whole network (ρ) is 0.167 (20/120) which is the same to the optimal solution using CPLEX. The same result is obtained in earlier studies [1], [9], [11], [14]. In other earlier studies [8], [12], the value of M obtained is the same as our result i.e., M = 8, but the utilization obtained is 0.15 which is less than the result obtained in our methodology.…”

Section: Simulation Results and Comparison With Other Methodscontrasting

confidence: 56%

Binary Particle Swarm Optimization for TDMA Broadcast Scheduling Problem

Kim

Byeon

Liu

et al. 2012

2012 Third International Conference on Innovations in Bio-Inspired Computing and Applications

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Abstract-This paper presents binary particle swarm optimization (BPSO) for finding an optimum conflict-free transmission schedule for a broadcast radio network. This is known as Broadcast Scheduling Problem (BSP) and shown as an NP-complete problem in earlier studies. Because of this NP-complete nature, earlier studies used genetic algorithms, mean field annealing, neural networks, factor graph and sum product algorithm, and sequential vertex coloring algorithm to obtain the solution. We are able to achieve better results using our proposed BPSO. The results are obtained using our methodology is compared with CPLEX and all the other earlier solution methods.Keywords-Broadcast scheduling problem (BSP); packet radio network, binary particle swarm optimization (BPSO); timedivision multiple access (TDMA); optimum transmission schedule

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Section: Simulation Results and Comparison With Other Methodscontrasting

confidence: 56%

Binary Particle Swarm Optimization for TDMA Broadcast Scheduling Problem

Kim

Byeon

Liu

et al. 2012

2012 Third International Conference on Innovations in Bio-Inspired Computing and Applications

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“…A series of simulations are carried to evaluate the performance of IGA to solve broadcast scheduling problem, in comparison with finite state approach [1], GA [3], GA with collision free set (GACFS) [4], Mean field annealing [13] and competent permutation genetic algorithm [14]. The performance of the GA and IGA were tested for a large number of times, using Ad hoc networks of different sizes.…”

Section: Solving Bsp Using Iga: Simulation and Resultsmentioning

confidence: 99%

“…40 nodes with 66 edges and maximum degree of 7 is analyzed in problem instance # 2 to this the channel utilization is increased moderately and the time delay is reduced by IGA. The results of other algorithm G-HNCNN, GACFS, FSMA, CPGA and MFA are given in [7], [4], [1], [14] and [13]. The algorithm GACFS and CPGA have not calculated the average time delay, which is represented by hyphen in Table 4.…”

Section: Optimum Schedule Performance With Other Methodsmentioning

confidence: 99%

“…Two benchmark problems discussed by [13] are solved using IGA and results are compared with other algorithms such as, the finite state machine based algorithm FSMA [1], co-evolutionary genetic algorithm for collision free set GACFS [4], gradual hysteretic noisy chaotic neural network G-HNCNN [7], Mean field annealing algorithm MFA [13] and the competent permutation genetic algorithm CPGA [14] is shown in Table 4. 30 nodes with 70 edges is analyzed in problem instance # 1 the channel utilization is largely improved compared to other algorithms similarly the time delay is also reduced reasonably by IGA compared to other methods.…”

Section: Optimum Schedule Performance With Other Methodsmentioning

confidence: 99%

“…Gunasekaran et al, [4] proposed two different algorithms for spatial reuse in WiMAX networks. First, a dynamic programming (DP) method is adopted to produce maximal collision free set of nodes, but suffered from high memory requirements.…”

confidence: 99%

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Broadcast Scheduling Problem in TDMA Ad Hoc Networks using Immune Genetic Algorithm

Arivudainambi

Rekha

2012

INT J COMPUT COMMUN

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Abstract:In this paper, a new efficient immune genetic algorithm (IGA) is proposed for broadcast scheduling problem in TDMA Ad hoc network. Broadcast scheduling is a primary issue in wireless ad hoc networks. The objective of a broadcast schedule is to deliver a message from a given source to all other nodes in a minimum amount of time. Broadcast scheduling avoids packet collisions by allowing the nodes transmission that does not make interference of a time division multiple access (TDMA) ad hoc network. It also improves the transmission utilization by assigning one transmission time slot to one or more non-conflicting nodes such a way that every node transmits at least once in each TDMA frame. An optimum transmission schedule could minimize the length of a TDMA frame while maximizing the total number of transmissions. The aim of this paper is to increase the number of transmissions in fixed Ad hoc network with time division multiple access (TDMA) method, with in a reduced time slot. The results of IGA are compared to the recently reported algorithms. The simulation result indicates that IGA performs better even for a larger network.

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Artificial intelligence‐empowered vision‐based self driver assistance system for internet of autonomous vehicles

Rawlley

Gupta

2022

Trans Emerging Tel Tech

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Artificial intelligence (AI) and edge computing have truly advanced in vehicular networks encouraging assessment of real-time traffic conditions using kinetic information of autonomous vehicles with the help of road side units (RSUs). However, regardless of numerous improvements in sensor fusion technologies the existing vision/LIDAR-based systems have found severe difficulties during perception on roads. In addition, the inter-vehicular communications are hampered due to inefficient RSU placement techniques causing high-latency issues during transmission of messages. Therefore, this article presents an AI-driven vision-based self driver assistance system (VSDAS) comprising a joint RSU deployment mechanism that utilizes enhanced memetic architecture-based optimal RSU placement (MARP) method and an object detection model that implements an improved Haar-cascade object detection algorithm for speedy identification of object. We have designed two varieties of genetic algorithm (GA) to solve optimal placement problem of RSUs: genetic architecture-based with random restart hill climbing (GARRH) and the other is MARP for efficient placement of RSUs. After our experimental results, we see that the MARP algorithm shows best possible RSU locations over different generations achieving significantly better fitness scores than the GAHRC and GA ascribing to its local search process. In addition, Haar-cascade achieves better mean average precision than local binary pattern and histogram of oriented gradients by selecting key frames. The experimental outcomes of our model reveals that the proposed enhanced memetic algorithm reduces the transmission delay to a greater extent. Additionally, the outcomes of our investigations on two public datasets (KITTI and Panasonic) showed that our improved algorithm clearly enhances the object detection performance.

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Efficient algorithms to solve Broadcast Scheduling problem in WiMAX mesh networks

Cited by 21 publications

References 12 publications

Binary Particle Swarm Optimization for TDMA Broadcast Scheduling Problem

Binary Particle Swarm Optimization for TDMA Broadcast Scheduling Problem

Broadcast Scheduling Problem in TDMA Ad Hoc Networks using Immune Genetic Algorithm

Artificial intelligence‐empowered vision‐based self driver assistance system for internet of autonomous vehicles

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