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Energy-Balanced Location-Aided Routing Protocol for E-Health Systems
( Haoru Su ),( Sam Nguyen-xuan ),( Heungwoo Nam ),( Sunshin An ) 한국정보처리학회 2011 한국정보처리학회 학술대회논문집 Vol.18 No.2
E-Health is one of the most promising applications of wireless sensor networks. This paper describes a prototype for e-Health systems. Based on the system, we propose the energy-balanced location-aided routing protocol. The location and energy information of the neighbor Coordinators is collected and stored in the neighbor discovery procedure. And then the Coordinator selects the most suitable neighbor to forward the data. The simulation results show that the proposed protocol has better performance than the three other routing protocols.
Multi-Dimensional Channel Management Mechanism to Avoid Reader Collision in Dense RFID Networks
SU, Haoru,AN, Sunshin The Institute of Electronics, Information and Comm 2011 IEICE transactions on information and systems Vol.94 No.11
<P>To solve the RFID reader collision problem, a Multi-dimensional Channel Management (MCM) mechanism is proposed. A reader selects an idle channel which has the maximum distance with the used channels. A backoff scheme is used before channel acquisition. The simulation results show MCM has better performance than other mechanisms.</P>
A Learning-based Power Control Scheme for Edge-based eHealth IoT Systems
( Haoru Su ),( Xiaoming Yuan ),( Yujie Tang ),( Rui Tian ),( Enchang Sun ),( Hairong Yan ) 한국인터넷정보학회 2021 KSII Transactions on Internet and Information Syst Vol.15 No.12
The Internet of Things (IoT) eHealth systems composed by Wireless Body Area Network (WBAN) has emerged recently. Sensor nodes are placed around or in the human body to collect physiological data. WBAN has many different applications, for instance health monitoring. Since the limitation of the size of the battery, besides speed, reliability, and accuracy; design of WBAN protocols should consider the energy efficiency and time delay. To solve these problems, this paper adopt the end-edge-cloud orchestrated network architecture and propose a transmission based on reinforcement algorithm. The priority of sensing data is classified according to certain application. System utility function is modeled according to the channel factors, the energy utility, and successful transmission conditions. The optimization problem is mapped to Q-learning model. Following this online power control protocol, the energy level of both the senor to coordinator, and coordinator to edge server can be modified according to the current channel condition. The network performance is evaluated by simulation. The results show that the proposed power control protocol has higher system energy efficiency, delivery ratio, and throughput.