Wireless Body Area Networks (WBANs) have developed as an effective solution for a wide range of healthcare, military and sports applications. Most of the proposed works studied efficient data collection from individual and traditional WBANs. Cloud computing is a new computing model that is continuously evolving and spreading. This paper presents a novel cloudlet-based efficient data collection system in WBANs. The goal is to have a large scale of monitored data of WBANs to be available at the end user or to the service provider in reliable manner. A prototype of WBANs, including Virtual Machine (VM) and Virtualized Cloudlet (VC) has been proposed for simulation characterizing efficient data collection in WBANs. Using the prototype system, we provide a scalable storage and processing infrastructure for large scale WBANs system. This infrastructure will be efficiently able to handle the large size of data generated by the WBANs system, by storing these data and performing analysis operations on it. The proposed model is fully supporting for WBANs system mobility using cost effective communication technologies of WiFi and cellular which are supported by WBANs and VC systems. This is in contrast of many of available mHealth solutions that is limited for high cost communication technology, such as 3G and LTE. Performance of the proposed prototype is evaluated via an extended version of CloudSim simulator. It is shown that the average power consumption and delay of the collected data is tremendously decreased by increasing the number of VMs and VCs.
1.1. Wireless body area networks
Wireless Body Area Networks (WBAN) comprises of a group of communicating sensor nodes. These sensor nodes can be implanted or wearable, which can monitor different vital body parameters and gather a lot of body information [1–5]. These devices, communicating through wireless technologies, can transmit data from the body to a home base station from where the data can be forwarded to a hospital, clinic, or a service provider in real-time manner. The WBAN technology is still in its primitive stage and is being widely researched. The technology, once accepted and adopted, is expected to be a breakthrough invention in many healthcare applications, leading to concepts like telemedicine and mobile health monitoring.
Initial applications of WBANs are expected to appear primarily in the healthcare domain, especially for continuous monitoring and logging vital parameters of patients suffering from chronic diseases such as diabetes, asthma and heart attacks, as well as in elder care monitoring. Other emerging applications of this technology include military, sports, gaming, social computing, entertainment and security. Extending the technology to new areas could also assist communication by seamless exchanges of information between individuals, or between individual and machines.
Wearable system in the real-time health monitoring is the most important skill in moving to more efficient and proactive health service. These systems permit persons to monitor the changes in their own vital signs. Then, these systems send responses to the service provider to maintain a standard healthiness position. On the other hand, a telemedical system can be integrated with the wearable systems to provide watchful health recruits when there is a change in the life-threatening. Furthermore, the proposed systems can be used for health monitoring of patients in ambulatory settings . For example, they can be used as a part of a analytic technique, optimal maintenance of a chronic condition, a supervised recovery from an acute event or surgical procedure, to monitor adherence to treatment guidelines (e.g., regular cardiovascular exercise), or to monitor effects of drug therapy.
The multiple WBAN sensor nodes are capable of sampling, processing, and communicating one or more vital signs like heart rate, blood pressure, oxygen saturation, breathing rate, diabetes, body temperature, ECG and activity, or environmental parameters like location, temperature, humidity, light, movement, proximity and direction. Typically, these sensors are implanted or placed strategically on the human body as tiny patches or hidden in users’ clothes allowing ubiquitous health monitoring in their native environment for extended periods of time.
1.2. Cloud computing
Cloud computing is a new computing paradigm that is continuously evolving and spreading. Empowered by hardware virtualization technology, parallel computing, distributed computing, and web services, cloud computing present a huge revolution in the information and communication technology . Cloud computing can be defined as ‘‘a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, servers, storage, applications and services) that can be rapidly provisioned and released with minimal management effort or service provider interaction’’ . There are several examples for emerging cloud computing infrastructures and platforms such as Microsoft Azure , Amazon EC2, Google App Engine, and other on premises cloud, i.e. private cloud . Furthermore, cloud computing helps companies to improve the IT services, development of applications to achieve unlimited scalability, automaticity on demand services of the IT infrastructure, and increasing their revenues . Cloud Computing service models include: Software as a Service (SaaS), Platform as a Service (PaaS), and Infrastructure as a Service (IaaS). Clients of cloud computing might be users in other Clouds, organizations, enterprises, or might be a single user .
1.3. Cloudlet and wireless body area networks
The huge amount of data collected by WBAN nodes demands scalable, on-demand, powerful, and secure storage and processing infrastructure. Cloud computing is playing a significant role in achieving the aforementioned objectives. The cloud computing environment links different devices ranging from miniaturized sensor nodes to high-performance supercomputers for delivering people-centric and context-centric services to the individuals and industries. The possible integration of WBANs with cloud computing will introduce viable and hybrid platform that must be able to process the huge amount of data collected from multiple WBANs. This WBAN-cloud model will enable end users to globally access the processing and storage infrastructure at economical costs. On the other hand, since WBANs forward useful and life-critical information to the cloud, which may operate in distributed and hostile environments, novel security mechanisms are required to prevent malicious interactions to the storage infrastructure. Both the cloud providers and the users must take strong security measures to protect the storage infrastructure.
A cloudlet is a new architectural element that arises from the convergence of mobile computing and cloud computing. It represents the middle level of a 3-level hierarchy; WBAN, cloudlet and cloud. A cloudlet can be viewed as a data center in a box whose goal is to bring the cloud capabilities closer to the users. This paper discuses a novel model that exploit cloudlet based computing for supporting large scale data collection in WBANs. The model brings a high computing capacity of cloud system closer to the WBANs users. This will help WBANs users to be connected directly to cloud resources using cheaper communication technologies.
1.4. Objectives and contributions
The main goal of this paper is to develop a large scale WBANs system in the presence of cloudlet-based data collection model. The objective is to minimize end-to-end packet cost by dynamically choosing data collection to the cloud by using cloudlet based system. The goal is to have a large monitored data of WBANs to be available to the end user or to the service provider in reliable manner. While reducing packet-to-cloud energy, the proposed work also attempt to minimize the endto-end packet delay by choosing cloudlet so that the overall delay is minimized, thus leading to have the monitored data in the cloud in real time mode. Note that in the absence of network congestions in low data-rate WBANs, the storage delays due to data collection manner are usually much larger compared to the congestion delay.