Bargaining Solution-Based Resource Allocation Scheme for Cloud-Assisted Wireless Body Area Networks

Wireless body area network (WBAN) is an emerging technology that has enormous potentials for continuous health monitoring of various diseases. For different medical and healthcare applications, the collected physiological data from multiple WBANs are further transmitted to the remote medical cloud servers. However, several technical issues and challenges are associated with the integration of WBANs and cloud computing services. In this study, we develop a new cloud-assisted WBAN control scheme to effectively use the limited system resource. By employing the main ideas of Generalized Gini and Choquet bargaining solutions, our approach unfolds into dual stages of bargaining processes while increasing the flexibility and adaptability. In particular, we consider the unique features of cloud-assisted WBAN applications and provide a generalized fair-efficient solution for the resource allocation problem. Numerical simulation results demonstrate that we can verify the superiority of our proposed scheme over the existing protocols. Lastly, major further challenges and future research directions about the cloud-assisted WBAN paradigm are summarized and discussed.

[1]  Xiaofei Wang,et al.  Cloud-enabled wireless body area networks for pervasive healthcare , 2013, IEEE Network.

[2]  Sungwook Kim,et al.  Game Theory Applications in Network Design , 2014 .

[3]  Majid Sarrafzadeh,et al.  The Advanced Health and Disaster Aid Network: A Light-Weight Wireless Medical System for Triage , 2007, IEEE Transactions on Biomedical Circuits and Systems.

[4]  Sagar Naik,et al.  A new fairness index for radio resource allocation in wireless networks , 2005, IEEE Wireless Communications and Networking Conference, 2005.

[5]  Yaser Jararweh,et al.  A cloud supported model for efficient community health awareness , 2016, Pervasive Mob. Comput..

[6]  Mohammad Mehedi Hassan,et al.  Resource Provisioning for Cloud-Assisted Body Area Network in a Smart Home Environment , 2017, IEEE Access.

[7]  José Manuel Zarzuelo,et al.  Inequality averse multi-utilitarian bargaining solutions , 2008, Int. J. Game Theory.

[8]  Sudip Misra,et al.  Traffic-Aware Efficient Mapping of Wireless Body Area Networks to Health Cloud Service Providers in Critical Emergency Situations , 2018, IEEE Transactions on Mobile Computing.

[9]  Sherali Zeadally,et al.  Certificateless Public Auditing Scheme for Cloud-Assisted Wireless Body Area Networks , 2018, IEEE Systems Journal.

[10]  Sudip Misra,et al.  Cost-Effective Mapping between Wireless Body Area Networks and Cloud Service Providers Based on Multi-Stage Bargaining , 2017, IEEE Transactions on Mobile Computing.

[11]  Generalized Ginis and cooperative bargaining solutions , 1994 .

[12]  Efe A. Ok,et al.  The Choquet Bargaining Solutions , 2000, Games Econ. Behav..

[13]  Bin Liu,et al.  Joint Power-Rate-Slot Resource Allocation in Energy Harvesting-Powered Wireless Body Area Networks , 2018, IEEE Transactions on Vehicular Technology.

[14]  Yaser Jararweh,et al.  Cloudlet-based Efficient Data Collection in Wireless Body Area Networks , 2015, Simul. Model. Pract. Theory.

[15]  Victor C. M. Leung,et al.  Enabling technologies for wireless body area networks: A survey and outlook , 2009, IEEE Communications Magazine.

[16]  Aleksandar Milenkovic,et al.  Wireless sensor networks for personal health monitoring: Issues and an implementation , 2006, Comput. Commun..

[17]  M. A. Hinojosa,et al.  Multi-Utilitarian Bargaining Solutions , 2007 .

[18]  Hamid Aghvami,et al.  Technologies and Challenges for Cognitive Radio Enabled Medical Wireless Body Area Networks , 2018, IEEE Access.

[19]  Yaser Jararweh,et al.  An efficient big data collection in Body Area Networks , 2014, 2014 5th International Conference on Information and Communication Systems (ICICS).