Healthcare facility is scarce for rural women in the developing world. The situation is worse for patients who are suffering from diseases that require long-term feedback-oriented monitoring such as breast cancer. Lack of motivation to go to the health centers on patients' side due to sociocultural barriers, financial restrictions and transportation hazards results in inadequate data for proper assessment. Fortunately, mobile phones have penetrated the masses even in rural communities of the developing countries. In this scenario, a mobile phone-based remote symptom monitoring system (RSMS) with inspirational videos can serve the purpose of both patients and doctors. Here, we present the findings of our field study conducted on 39 breast cancer patients in rural Bangladesh. Based on the results of extensive field studies, we have categorized the challenges faced by patients in different phases of the treatment process. As a solution, we have designed, developed and deployed e-ESAS-the first mobile-based RSMS in rural context. Along with the detail need assessment of such a system, we describe the evolution of e-ESAS and the deployment results. We have included the unique and useful design lessons that we learned as e-ESAS evolved through participatory design process. The findings show how e-ESAS addresses NOT THE PUBLISHED VERSION; this is the author's final, peer-reviewed manuscript. The published version may be accessed by following the link in the citation at the bottom of the page.

Location-based services are becoming increasingly popular with the proliferation of location aware devices. It is not possible to access location-based services and preserve privacy at the same time when the user provides exact location information. Cloaking or obfuscating location data is the only way to protect location-privacy. To do that, most of the systems use third party location anonymizer. In this paper, we propose a novel location privacy framework without any trusted third party (TTP). Most of the existing solutions attempt to satisfy k-anonymity. However, there are several drawbacks of using fixed and user defined k. In order to solve the problems our proposed solution aims to meet probabilistic k-anonymity. Based on historic data expected number of users present in a place is predicted which is used as probabilistic anonymity level. Thus we eliminate the use of any TTP which results into improvement of query-processing time and provides fewer query results for the user to process eventually minimizing the overall response time. Users' exact location information is not revealed in either communication or computation process.

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With the advancement of hand-held devices, wireless and sensor network pervasive computing has achieved a perfect momentum. Formerly, a requirement existed that was a serious impediment and threat to the mobility of users -the necessary presence of a fixed wired network. This has been resolved by the recent advances in wireless and mobile technologies, particularly Bluetooth and WiFi. The advancement of available, portable, low cost mobile devices (PDAs, cell phones, etc.) has resulted in the user's mobility at unprecedented levels. As these devices can communicate with one another, the combined capabilities can be leveraged to form a useful new set of tools. Presently, pervasive computing is being extended into the sophisticated healthcare sector with the promise of providing an easier and more efficient mode of communication between physicians and patients or between the physicians themselves. In this paper we provide the details of our application 'Healthcare Aide', which has been designed to provide not only more convenience for doctor -doctor, resident doctor -doctor, patient -doctor and nurse -doctor interaction but also a smooth pathway for real-time decision making. Our pervasive middleware MARKS (Middleware Adaptability for Resource Discovery, Knowledge Usability and Self-healing) provides the underlying support for this application in a completely transparent manner. In this paper, we have also presented our survey results from users' point of view along with performance analysis.Design und Einfü hrung eines virtuellen Assistenten fü r im Gesundheitswesen tä tige Personen durch den Einsatz von ubiquitä ren Computertechnologien.Die technologischen Fortschritte bei Pocketcomputern, drahtlosen Netzwerken (insbesondere Bluetooth und WiFi) und Sensoren ermö glichen die weitgehende Unterstü tzung der Mobilitä t von Benutzern -dort wo frü her Einschrä nkungen aufgrund fest verlegter Netzwerkkabel bestanden. Da Pocketcomputer immer leistungsfä higer und preiswerter werden und auch miteinander problemlos kommunizieren kö nnen, verspricht die Entwicklung entsprechender Applikationen Unterstü tzung im Bereich des Gesundheitswesens. In diesem Beitrag prä sentieren die Autoren Details zur Anwendung ,,Healthcare Aide'', die zur Interaktion und EchtzeitEntscheidungsfindung bei Arzt -Arzt, Patient -Arzt, Schwester -Arzt verwendet werden kann. Die Middleware MARKS stellt die technische Plattform fü r diese Anwendung dar. In diesem Beitrag werden darü ber hinaus Studienergebnisse aus der Sicht der Benutzer zusammen mit einer Performanzanalyse prä sentiert.Schlü sselwö rter: virtueller Assistent; Healthcare Aide; ubiquitä re Gesundheitsfü rsorge; Middleware MARKS IntroductionPervasive computing (Weiser, 1993) is the concept that incorporates computing in our working and living environment in such a way that the interaction between humans and computers becomes extremely natural, and the user can get many types of data in a totally transparent manner. Considering virtual reality, which builds an artificial world in the comp...

Cloud computing infrastructure helps users to minimize cost by outsourcing data and computation on-demand. Due to the varying user needs in terms of computation power, storage capacity, etc., cloud providers offer various machines to choose from, to maximize the intended need. In this paper, we disprove several common conceptions regarding the performance and cost of cloud by experimenting on instances of two different families (compute and storage optimized) of the most popular cloud platform, Amazon Elastic Compute Cloud (EC2). Our analysis shows the interesting finding that, for the machines of the same configuration, storage optimized instances have lower disk readwrite speed than compute optimized, which does not completely reflect the claim made by Amazon in all cases. Additionally, storage optimized instances have notable performance difference among them. We also identify that the I/O performance of same instance type varies over different time periods.