Enhancing the performance of mobile healthcare systems based on task-redistribution

Mobile healthcare (m-health) systems have attracted a great deal of attention due to their potential to improve the quality of diagnosis, reduce medical costs and help address the challenges posed by the aging society. A generic m- health service platform has been developed and specialized to deal with emergency settings such as epileptic seizure detection, cardiac care and trauma care. The m-health infrastructure and services can be in this case described as both mission-critical and safety critical. Dynamic context-aware adaptation mechanisms are required in order to meet the stringent requirements on such mission critical applications. The use of mobile devices means that attention must be paid to resource optimization and in emergency situations timeliness of response is also a critical factor. These factors lead to critical performance requirements on the system across several dimensions. This paper presents an m-health application scenario requiring rapid response and identifies the system performance measures that are key to the success of such m-health solutions. As an extension to the m-health service platform we propose an adaptive middleware framework based on dynamic task redistribution. In particular, we present a computational model to estimate the QoS of m- health system given a particular task assignment and further to select the optimal assignment.

[1]  Bin Yao,et al.  A taxonomy for describing matching and scheduling heuristics for mixed-machine heterogeneous computing systems , 1998, Proceedings Seventeenth IEEE Symposium on Reliable Distributed Systems (Cat. No.98CB36281).

[2]  Ing Widya,et al.  Context-Aware Optimal Assignment of a Chain-Like Processing Task onto Chain-Like Resources in M-Health , 2007, International Conference on Computational Science.

[3]  Michael G. Norman,et al.  Models of machines and computation for mapping in multicomputers , 1993, CSUR.

[4]  Carles Gomez,et al.  TCP/IP analysis and optimization over a precommercial live UMTS network , 2005, IEEE Wireless Communications and Networking Conference, 2005.

[5]  Ahmad Rahmati,et al.  Context-for-wireless: context-sensitive energy-efficient wireless data transfer , 2007, MobiSys '07.

[6]  Tien-Fu Chen,et al.  Branch-and-bound task allocation with task clustering-based pruning , 2004, J. Parallel Distributed Comput..

[7]  Kang G. Shin,et al.  Optimal Task Assignment in Homogeneous Networks , 1997, IEEE Trans. Parallel Distributed Syst..

[8]  Mahadev Satyanarayanan,et al.  A conceptual framework for network and client adaptation , 2000, Mob. Networks Appl..

[9]  Kishor S. Trivedi,et al.  Techniques for System Dependability Evaluation , 2000 .

[10]  Yang Xiao,et al.  Throughput and delay limits of IEEE 802.11 , 2002, IEEE Communications Letters.

[11]  Shahid H. Bokhari,et al.  Partitioning Problems in Parallel, Pipelined, and Distributed Computing , 1988, IEEE Trans. Computers.

[12]  K. Wac,et al.  Mobile patient monitoring: the MobiHealth system. , 2004, Studies in health technology and informatics.

[13]  Valerie M. Jones,et al.  Future Challenges and Recommendations , 2006 .

[14]  Ing Widya,et al.  Optimal Assignment of a Tree-Structured Context Reasoning Procedure onto a Host-Satellites System , 2007, 2007 IEEE International Parallel and Distributed Processing Symposium.

[15]  Ing Widya,et al.  QoC-based Optimization of End-to-End M-Health Data Delivery Services , 2006, 200614th IEEE International Workshop on Quality of Service.

[16]  Ishfaq Ahmad,et al.  Optimal task assignment in heterogeneous distributed computing systems , 1998, IEEE Concurr..

[17]  Tom H. F. Broens,et al.  Context Aware Body Area Networks for Telemedicine , 2007, PCM.