Traffic-Aware Efficient Mapping of Wireless Body Area Networks to Health Cloud Service Providers in Critical Emergency Situations using ns2

In a post-disaster situation, increased concentration of patients in an area increases the traffic load of the network significantly, which degrades its performance with respect to mapping cost and network throughput. Therefore, to manage the increased traffic load and to provide ubiquitous medical services, we propose a disease-centric health-care management system using wireless body area networks (WBAN) in the presence of multiple health-cloud service providers (H-CSP). The theory of Social Network Analysis (SNA) is adopted to optimize the computational complexity and the traffic load of the network in an area, considering different disease types and the criticality indices of the WBANs. In such a scenario, Disease-centric Patient Group (DPG) formation among coexisting WBANs ensures optimized traffic load and reduced computational complexity. However, the formation of DPG alone is not sufficient to provide Quality-of-Service (QoS) to each WBAN. Therefore, to address these issues, we formulate a pricing model for the efficient mapping of critical WBANs from a DPG to a H-CSP to optimize the expected packet delivery delay and the network throughput. Consequently, to identify the critical WBANs from a DPG, we design a decision parameter based on an assortment of selection parameters. The performance of the Efficient Healthcare Management (HCM) scheme is analyzed based on distinct measures such as cost effectiveness, service delay, and throughput. Simulation results exhibit significant improvement in the network performance over the existing schemes.


a. WBANs are used to monitor physiological parameters of acommunity of people. They produces huge volumes of medicaldata packets. To store, analyze, and process such data,cloud computing provides adjustable storage and processinginfrastructure to analyze the data streams generated in WBANsfor both online and offline algorithms.
b. In this domain, Giancarlo et al. proposed a SaaS-based approachfor building a community of WBANs to support cloudassistedWBAN applications, named BodyCloud. Body-Cloud is an application-level infrastructure to integrate cloudand medical resources having multi-tier. 
c. Similarly, Fortino et al.deployed cloud-assisted WBANs and identified the importantissues, which are required to be solved for advancementand execution in advanced healthcare. This present system isoverviewed and wrapped based on the necessities of creatingefficient cloud-assisted WBAN architecture. 
d. Consequently, Quwaider et al.proposed a novel cloudlet-based for productivedata aggregation in WBANs. In this work, the authorsfocused on a large-scale data-generating WBANs to be availableto the end-user or to the service provider in a reliable manner.Additionally, Zhang et al. proposed adaptive map-reducedframework to scale the capability of cloud resources for realtimeapplications. 


The proposed system uses two approaches

(a) disease-centricrelation estimation among WBANs, and 
(b) cloud computing,

for modelling the optimization problem and solution approach,respectively. For easier understanding of the problem formulation,we discuss the basics of the proposed system in this
A) Disease-centric relation estimation among WBANs:

We elaborate the preliminaries of the disease-centric relationestimation approach for cloud-assisted WBANs.
Disease-centric relation among WBANs is obtainedbased on similar disease types, which is calculated using the n encounter matrix. Due to the mobility of WBANs, a WBAN Bi comesin contact with another WBAN

B) cloud computing:

Cloud computing infrastructure supports ubiquitous and elasticresource provision to the real-life applications.Therefore, to store and analyze huge data generated from the 




Operating System           :   Linux
Simulation Tool                            :    NS2
Documentation               :    Ms-Office


CPU type                                  :    Intel Pentium 4
Clock speed                              :    3.0 GHz
Ram size                                   :    512 MB
Hard disk capacity                    :    80 GB
Monitor type                             :    15 Inch color monitor
Keyboard type                          :     Internet keyboard
CD -drive type                          :     52xmax


T. Hayajneh, G. Almashaqbeh, S. Ullah, and A. V. Vasilakos, “A Survey of Wireless Technologies Coexistence in WBAN: Analysis and Open Research Issues,” Wireless Networks, vol. 20, no. 8, pp. 2165–2199, 2014.

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