A cloud supported model for efficient community health awareness

The needs for efficient and scalable community health awareness model become a crucial issue in today's health care applications. Many health care service providers need to provide their services for long terms, in real time and interactively. Many of these applications are based on the emerging Wireless Body Area networks (WBANs) technology. WBANs have developed as an effective solution for a wide range of healthcare, military, sports, general health and social applications. On the other hand, handling data in a large scale (currently known as Big Data) requires an efficient collection and processing model with scalable computing and storage capacity. Therefore, a new computing paradigm is needed such as Cloud Computing and Internet of Things (IoT). In this paper we present a novel cloud supported model for efficient community health awareness in the presence of a large scale WBANs data generation. The objective is to process this big data in order to detect the abnormal data using MapReduce infrastructure and user defined functions with minimum processing delay. The goal is to have a large monitored data of WBANs to be available to the end user or to the decision maker in reliable manner. While reducing data packet processing energy, the proposed work is minimizing the data processing delay by choosing cloudlet or local cloud model and MapReduce infrastructure. So, the overall delay is minimized, thus leading to detect the abnormal data in the cloud in real time mode. In this paper we present a multi-layer computing model composed of Local Cloud (LC) layer and Enterprise Cloud (EP) layer that aim to process the collected data from Monitored Subjects (MSs) in a large scale to generate useful facts, observations or to find abnormal phenomena within the monitored data. Performance results show that integrating the MapReduce capabilities with cloud computing model will reduce the processing delay. The proposed MapReduce infrastructure has also been applied in lower layer, such as LC in order to reduce the amount of communications and processing delay. Performance results show that applying MapReduce infrastructure in lower tire will significantly decrease the overall processing delay.

[1]  Alessandro Tognetti,et al.  CAPTURE AND CLASSIFICATION OF BODY POSTURE AND GESTURE USING WEARABLE KINESTHETIC SYSTEMS , 2005 .

[2]  Mohamed F. Younis,et al.  Efficient aggregation of delay-constrained data in wireless sensor networks , 2005, The 3rd ACS/IEEE International Conference onComputer Systems and Applications, 2005..

[3]  Brent Waters,et al.  Ciphertext-Policy Attribute-Based Encryption , 2007, 2007 IEEE Symposium on Security and Privacy (SP '07).

[4]  Yuguang Fang,et al.  CAM: Cloud-Assisted Privacy Preserving Mobile Health Monitoring , 2013, IEEE Transactions on Information Forensics and Security.

[5]  Jorge Werner,et al.  A Cloud Computing Solution for Patient's Data Collection in Health Care Institutions , 2010, 2010 Second International Conference on eHealth, Telemedicine, and Social Medicine.

[6]  Emil Jovanov,et al.  Guest Editorial Introduction to the Special Section on M-Health: Beyond Seamless Mobility and Global Wireless Health-Care Connectivity , 2004, IEEE Transactions on Information Technology in Biomedicine.

[7]  Mahmoud Al-Ayyoub,et al.  CloudExp: A comprehensive cloud computing experimental framework , 2014, Simul. Model. Pract. Theory.

[8]  Fei Yuan,et al.  Data Density Correlation Degree Clustering Method for Data Aggregation in WSN , 2014, IEEE Sensors Journal.

[9]  Aleksandar Milenkovic,et al.  Journal of Neuroengineering and Rehabilitation Open Access a Wireless Body Area Network of Intelligent Motion Sensors for Computer Assisted Physical Rehabilitation , 2005 .

[10]  E. Jovanov,et al.  A WBAN System for Ambulatory Monitoring of Physical Activity and Health Status: Applications and Challenges , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[11]  Mohammed Feham,et al.  A New Architecture of a Ubiquitous Health Monitoring System: A Prototype Of Cloud Mobile Health Monitoring System , 2012, ArXiv.

[12]  Subir Biswas,et al.  Conversation Monitoring via Low-cost Speaker Diarization using Wearable Wireless Sensors , 2012 .

[13]  Yanpei Chen,et al.  Interactive Analytical Processing in Big Data Systems: A Cross-Industry Study of MapReduce Workloads , 2012, Proc. VLDB Endow..

[14]  Subir Biswas,et al.  Remote monitoring of soldier safety through body posture identification using wearable sensor networks , 2008, SPIE Defense + Commercial Sensing.

[15]  Tulika Mitra,et al.  Timing Analysis of Body Area Network Applications , 2007, WCET.

[16]  C. Siva Ram Murthy,et al.  Interoperability of Wi-Fi hotspots and cellular networks , 2004, WMASH '04.

[17]  Xiaohua Jia,et al.  Energy efficient real-time data aggregation in wireless sensor networks , 2006, IWCMC '06.

[18]  Yaser Jararweh,et al.  Cloudlet-based for big data collection in body area networks , 2013, 8th International Conference for Internet Technology and Secured Transactions (ICITST-2013).

[19]  M. Lang,et al.  Impulse UWB Radio System Architecture for Body Area Networks , 2007, 2007 16th IST Mobile and Wireless Communications Summit.

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

[21]  Subir Biswas,et al.  Physical Context Detection using Wearable Wireless Sensor Networks , 2008 .

[22]  Yaser Jararweh,et al.  Resource Efficient Mobile Computing Using Cloudlet Infrastructure , 2013, 2013 IEEE 9th International Conference on Mobile Ad-hoc and Sensor Networks.

[23]  Radha Poovendran,et al.  Minimizing Energy Consumption in Body Sensor Networks via Convex Optimization , 2010, 2010 International Conference on Body Sensor Networks.

[24]  Yon Dohn Chung,et al.  Parallel data processing with MapReduce: a survey , 2012, SGMD.

[25]  Ming Li,et al.  Data security and privacy in wireless body area networks , 2010, IEEE Wireless Communications.

[26]  Christine Jardak,et al.  Parallel processing of data from very large-scale wireless sensor networks , 2010, HPDC '10.

[27]  IstepanianR. S.H.,et al.  Guest Editorial Introduction to the Special Section on M-Health , 2004 .

[28]  Subir Biswas,et al.  Body posture identification using hidden Markov model with a wearable sensor network , 2008, BODYNETS.

[29]  Sushil Jajodia,et al.  Secure Data Aggregation in Wireless Sensor Networks: Filtering out the Attacker's Impact , 2014, IEEE Transactions on Information Forensics and Security.

[30]  Joel J. P. C. Rodrigues,et al.  Analysis of Cloud-Based Solutions on EHRs Systems in Different Scenarios , 2012, Journal of Medical Systems.

[31]  Subir Biswas,et al.  Transmission power assignment with postural position inference for on-body wireless communication links , 2010, TECS.

[32]  Joshua R. Smith,et al.  Power consumption analysis of Bluetooth Low Energy, ZigBee and ANT sensor nodes in a cyclic sleep scenario , 2013, 2013 IEEE International Wireless Symposium (IWS).

[33]  Abraham O. Fapojuwo,et al.  A centralized energy-efficient routing protocol for wireless sensor networks , 2005, IEEE Communications Magazine.

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