Adaptive Multichannel Data Dissemination: Support of Dynamic Traffic Awareness and Push–Pull Time Balance

Abstract

Data dissemination by the use of periodic broadcast is a viable scalable mechanism due to the explosion of information services in wireless networks. Many research works have exploited the data broadcast methodologies, however, upon the premise that the broadcast traffic is static on a single broadcast channel. In practice, the broadcast traffic changes dynamically and multiple broadcast channels can be utilized for efficient data dissemination. Accordingly, in this paper we devise an adaptive multichannel data-dissemination mechanism, including two components: the multichannel traffic-awareness and the deterministic balance search techniques. The multichannel traffic-awareness technique performs a cyclic estimation of dynamic access frequency distribution in response to traffic dynamics. With the measured estimation, the deterministic balance search technique adjusts channel allocation and item classification to further minimize and balance the push access and the pull response time. The experimental results show that the proposed mechanism is able to attain the result of channel partition and item classification very close to the optimum and, thus, minimize the mean access time in both push and pull channels simultaneously. Therefore, the reliability of broadcast contents is guaranteed.

Towards Realizable, Low-Cost Broadcast Systems for Dynamic Environments

“…Thus for (11) Since higher values of indicate more major and local spacing deviations ( is fixed, and (7) represents a geometric progression), we conclude through (11) that slight, distributed, global spacing deviations should be preferred over sharp deviations of distinct page speeds.…”

“…A great deal of work has been done based on this model, studying data prefetching, caching [8] and indexing [9], [10], hybrid data broadcasting [11], as well as scheduling strategies and noise interference [12], [13].…”

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

“…The simulation results presented in this section are obtained with the following values to the parameters: P = Table 1 summarizes the characteristics of the networks that have been simulated while Figs. 5,6,7,8,9,10,11,12,13,14 depict the simulation results of these networks. Figure 5 depicts the response time of network N 1 as the number of groups varies.…”

“…Finally, hybrid systems (e.g. [13][14][15]) try to combine the benefits of the pure-push and pure-pull approaches. In this paper, we will focus on the adaptive push-based approach of [9].…”

Energy‐ and latency‐efficient broadcasting mechanism supporting long‐term evolution e‐multimedia broadcast/multicast service transmission

“…The technique was to iteratively change the broadcasting patterns to broadcast the frequently accessed data. A number of hybrid approaches that combine the broadcast and an on-demand service were also proposed [17][18][19]. Nicopolitidis et al [17] estimated the demand for each item by the incoming requests for items that will not appear in the broadcast in the near future, and stated that, without requests for such broadcast missed items, the demand estimation is impossible.…”