Smart Distribution of Bio-signal Processing Tasks in M-Health
The past few years have witnessed a rapid advancement of mobile healthcare systems. However, in the mobile computing environment, the resource fluctuations, stringent application requirements and user mobility have severely hindered the performance and re
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Abstract. The past few years have witnessed a rapid advancement of mobile healthcare systems. However, in the mobile computing environment, the resource fluctuations, stringent application requirements and user mobility have severely hindered the performance and reliability of the healthcare service delivery. The current approaches to solve this resource supply and service demand mismatch problem either limit the adaptation to an isolated node or require significant user's involvement. Given the distributed processing paradigm of mhealth system, we propose that a new adaptation approach could be dynamically redistributing processing tasks across distributed nodes. This PhD research addresses two main issues to validate this approach: (1) computation of a suitable assignment of tasks at compile-time or run-time; and (2) dynamic distribution of tasks across the nodes according to this new assignment at run-time.
1 Introduction Telemedicine has been receiving more and more attentions due to its ability to tackle the aging society problem, improve the quality of diagnosis and treatment and reduce the medical cost [1]. Being part of telemedicine, mobile healthcare (m-health) emerges with the fast adoption of advanced mobile technology into our daily life. Several m-health systems have been developed for mobile network environments [2-5], in which an m-health platform is introduced including a patient body-area network and some back-end healthcare service facilities. On top of the platform, multiple tele-monitoring applications can be operated to provide long term medical services to patients. However, like other applications running in a mobile environment, the usability of m-health applications are seriously affected by the fluctuations of context and scarcity of the environment’s resources, e.g. network bandwidth, battery power and computational power of handhelds, etc [5, 6]. From a technical point of view, to solve this application demand and resource mismatch, an appropriate adaptation mechanism can be embedded into the system. There are three approaches on building this adaptation mechanism [7]: (1) adjusting task - this is to automatically change task behavior to use less of a scarce resource, e.g. scalable video transmission over wireless network; (2) reserving resource - this is to ask the environment to guarantee a certain level of a resource, e.g. QoS management and reservation techniques; (3) informing user - this R. Meersman, Z. Tari, P. Herrero et al. (Eds.): OTM 2007 Ws, Part I, LNCS 4805, pp. 284 – 293, 2007. © Springer-Verlag Berlin Heidelberg 2007
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is to suggest a corrective action to the user. The second approach assumes that it is possible to reserve sufficient resources for the task, which is sometimes unrealistic, e.g. the drop of network bandwidth could be so significant that the required data transmission quality just cannot be met. The third approach could avoid the mismatch by giving the patient suggestions or warnings, e.g.
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