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Abstract-The rise in popularity of mobile applications creates a growing demand to deliver richer functionality to users executing on mobile devices with limited resources. The availability of cloud computing platforms has made available unlimited and scalable resource pools of computation and storage that can be used to enhance service quality for mobile applications. This paper exploits the observation that using local resources in close proximity to the user, i.e. local clouds, can increase the quality and performance of mobile applications. In contrast, public cloud offerings (e.g. Amazon Web Services) offer scalability at the cost of higher delays, higher power consumption and higher price on the mobile device. In this paper we introduce MAPCloud, a hybrid, tiered cloud architecture consisting of local and public clouds and show how it can be leveraged to increase both performance and scalability of mobile applications. We model the mobile application as a workÀow of tasks and aim to optimally decompose the set of tasks to execute on the mobile client and 2-tier cloud architecture considering multiple QoS factors such as power, price, and delay. Such an optimization is shown to be NP-Hard; we propose an ef¿cient simulated annealing based heuristic, called CRAM that is able to achieve about 84% of optimal solutions when the number of users is high. We evaluate CRAM and the 2-tier approach via implementation (on Android G2 devices and Amazon EC2, S3 and CloudFront) and extensive simulation using two rich mobile applications( Video-Content Augmented Reality and Image processing). Our results indicate that MAPCloud provides improved scalability as compared to local clouds, improved ef¿ciency (power/delay) (about 32% lower delays and power) and about 40% decrease in price in comparison to only using public cloud.

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This paper studies the optimal and fair service allocation for a variety of mobile applications (single or group and collaborative mobile applications) in mobile cloud computing. We exploit the observation that using tiered clouds, i.e. clouds at multiple levels (local and public) can increase the performance and scalability of mobile applications. We proposed a novel framework to model mobile applications as a location-time workflows (LTW) of tasks; here users mobility patterns are translated to mobile service usage patterns. We show that an optimal mapping of LTWs to tiered cloud resources considering multiple QoS goals such application delay, device power consumption and user cost/price is an NP-hard problem for both single and group-based applications. We propose an efficient heuristic algorithm called MuSIC that is able to perform well (73% of optimal, 30% better than simple strategies), and scale well to a large number of users while ensuring high mobile application QoS. We evaluate MuSIC and the 2-tier mobile cloud approach via implementation (on real world clouds) and extensive simulations using rich mobile applications like intensive signal processing, video streaming and multimedia file sharing applications. Our experimental and simulation results indicate that MuSIC supports scalable operation (100+ concurrent users executing complex workflows) while improving QoS. We observe about 25% lower delays and power (under fixed price constraints) and about 35% decrease in price (considering fixed delay) in comparison to only using the public cloud. Our studies also show that MuSIC performs quite well under different mobility patterns, e.g. random waypoint and Manhattan models.

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Abstract-Situation awareness in emergency response is critical. Knowing the status of the hazards, the rescue workers, and the building occupants can help the incident commander responding to emergencies in taking the right decisions which can save lives. Such situation awareness can be achieved by using the building sensing and communications infrastructures as well as having the rescue workers deploy their own. Current sensing and communications techniques are, nonetheless, not fault-free. In this paper we study, both in the lab and during emergency response drills, the nature of the different wireless networks, namely sensor networks and Wi-Fi networks, when transmitting different types of data. Based on our findings, we propose a series of practical and novel techniques that exploit the availability of different networks, the rescue workers' mobility, and the possibility of having rescue teams carry more than one sensor of the same type which increases the reliability factors highly in practice.

Benign prostatic hyperplasia (BPH) is prostate weighting f over 500g and is usually public in men older than fifty years.A case of 78-year-old man was referred to Sina hospital complaining of urinary frequency. His total prostate-specific antigen was 17.3 ng/mL and the volume of his prostate was measured at 350 mL by transrectal ultrasound. Simple prostatectomy was done and a huge adenoma was enucleated in an open retropubic manner weighting 1070g. “Giant BPH” is a rare pathology of the prostate gland. In this study, we report a successful enucleation of a giant BPH (1070 g) without any significant complications.