A Novel Adaptive Framework for Wireless Push Systems Based on Distributed Learning Automata

Abstract

A novel adaptive scheme for wireless push systems is presented in this paper. In this wireless environment two entities play the most important role: the server side and the client side that is connected to the system. The server side is responsible to broadcast an item per transmission in order to satisfy the clients' requests. The performance of the server side depends on item selections. Hence, the server broadcasts an item and the clients are satisfied if the transmitted item was the desired one. In this work, a set of learning automata try to estimate the client demands in a distributed manner. More specifically, an autonomous learning automaton is utilized on each client group, since the clients are gathered into groups based on their location. The output of each automaton is combined in order to produce a well-performed transmission schedule. Concurrently, a round robin phase is adopted, giving the opportunity to the non-popular items to be transmitted. In this manner, the various client demands are treated fairly. The introduced technique is compared with a centralized adaptive scheme and the results indicate that the proposed scheduling framework outperforms the centralized one, in terms of response time and fairness.

Using estimation of distribution algorithm to coordinate decentralized learning automata for meta-task scheduling

“…In order to accelerate the convergence speed, variable structure stochastic automata (VSSA) are proposed and they have been proved to be the fastest converging LAs, whose transition and output matrices are time-varying. They have been widely employed to systems that have time-varying environments, e.g., channel assignment [20], wireless and wired networks [21] [22], dominating set problem [23] and tutorial-like system [24]. Normally, only one LA is used to construct the learning model of related problems.…”

Learning Automata-Based Cross Layer Framework with Context Awareness for Wireless Systems

“…computing and adaptable computing have put more stress on existing communication infrastructure [1][2][3][4][5][6][7]. Intelligent and real-time data acquisition is must for providing pervasive connectivity based applications.…”

“…The effective deployment of pervasive connectivity based applications such as smart home, smart grid, smart surveillance, smart transport, smart farming and smart care, etc. depends heavily on data acquisition system and communication systems [1][2][3][4][5][6][7].…”

“…The computing cost could be identified as processing time, overhead in data reading as well as in data writing, delay due to task switching and idle CPU cycles. The communication cost could be classified in few simpler terms such as overall bandwidth consumption, interference due to noise, the amount of data retransmission and overhead due to the control data such as acknowledgement, retransmission request and cyclic redundancy codes [2][3][4][5][6][7]. Finally, the capital cost could be categorized as the cost of hardware, software, implementation and maintenance.…”

“…The advantages brought by the ICT have been offset to some extent due to few intrinsic factors such as computing and communication cost [1][2][3][4][5][6][7][8]. Energy awareness indulges in issues pertaining to higher consumption of energy.…”