Hybrid MAC-based Multipoint Relay with Energy Awareness for System Data Sharing in Wireless Sensor Network
スポンサーリンク
概要
- 論文の詳細を見る
A hybrid MAC-based multipoint relay with energy awareness is proposed for a system data sharing protocol in a wireless sensor network (WSN), which is abbreviated to the HMAC-EA-MPR-SDS protocol. It adopts a system data sharing concept that combines broadcasting routing with data fusion so that data exchange between wireless sensor nodes can be easily realized in the real world. In order to reduce the redundant broadcasting in the conventional system data sharing protocols such as the on-demand dynamic multihop data sharing (On-Demand DMDS) protocol, we propose a multipoint relay with energy awareness (EA-MPR). In EA-MPR, a subset of nodes (called MPR nodes) is selected to forward the data to be shared throughout the network, and the power consumption of the MPR nodes can be efficiently balanced by energy awareness. Therefore, the network lifetime can be extended drastically. A hybrid MAC based on EA-MPR is introduced to improve the transmission delay and scalability of the time division multiple access (TDMA) method adopted in the conventional system data sharing protocols. It allocates a short-timeslot TDMA for each node to broadcast its data to be shared as well as provides a high-speed access method (token chain) for the MPR nodes to exchange data. The simulation results prove that HMAC-EA-MPR-SDS can reduce the redundant broadcasting and the corresponding power consumption in comparison with On-Demand DMDS. Furthermore, it can achieve a much shorter transmission delay and a higher network scalability than other conventional system data sharing protocols.
- 信号処理学会の論文
信号処理学会 | 論文
- A study on audio watermarking method based on the cochlear delay characteristics
- Estimation of fundamental frequency of reverberant speech by utilizing complex cepstrum analysis
- 反響音を有する畳み込み形混合過程に対するブラインドソースセパレーションの学習法
- A Model-Concept of the Selective Sound Segregation : A Prototype Model for Selective Segregation of Target Instrument Sound from the Mixed Sound of Various Instruments
- Study of Control Strategy Mimicking Speech Motor Learning for a Physiological Articulatory Model