Due to their on-body and ubiquitous nature, wearables can generate a wide range of unique sensor data creating countless opportunities for deep learning tasks. We propose DeepWear, a deep learning (DL) framework for wearable devices to improve the performance and reduce the energy footprint. DeepWear strategically offloads DL tasks from a wearable device to its paired handheld device through local network connectivity such as Bluetooth. Compared to the remote-cloud-based offloading, DeepWear requires no Internet connectivity, consumes less energy, and is robust to privacy breach. DeepWear provides various novel techniques such as context-aware offloading, strategic model partition, and pipelining support to efficiently utilize the processing capacity from nearby paired handhelds. Deployed as a user-space library, DeepWear offers developer-friendly APIs that are as simple as those in traditional DL libraries such as TensorFlow. We have implemented DeepWear on the Android OS and evaluated it on COTS smartphones and smartwatches with real DL models. DeepWear brings up to 5.08× and 23.0× execution speedup, as well as 53.5 and 85.5 percent energy saving compared to wearable-only and handheld-only strategies, respectively.

Deep-learning (DL) is receiving huge attention as enabling techniques for emerging mobile and IoT applications. It is a common practice to conduct DNN model-based inference using cloud services due to their high computation and memory cost. However, such a cloud-offloaded inference raises serious privacy concerns. Malicious external attackers or untrustworthy internal administrators of clouds may leak highly sensitive and private data such as image, voice and textual data. In this paper, we propose Occlumency, a novel cloud-driven solution designed to protect user privacy without compromising the benefit of using powerful cloud resources. Occlumency leverages secure SGX enclave to preserve the confidentiality and the integrity of user data throughout the entire DL inference process. DL inference in SGX enclave, however, impose a severe performance degradation due to limited physical memory space and inefficient page swapping. We designed a suite of novel techniques to accelerate DL inference inside the enclave with a limited memory size and implemented Occlumency based on Caffe. Our experiment with various DNN models shows that Occlumency improves inference speed by 3.6x compared to the baseline DL inference in SGX and achieves a secure DL inference within 72% of latency overhead compared to inference in the native environment.

High-end mobile GPUs are now becoming an integral part of mobile devices. However, a mobile GPU constitutes a major portion of power consumption on the devices, and mobile games top as the most popular class of graphics applications. This paper presents the design and implementation of RAVEN, a novel, on-the-fly frame rate scaling system for mobile gaming applications. RAVEN utilizes human visual perception of graphics change to opportunistically achieve power saving without degrading user experiences. The system develops a light-weight frame comparison technique to measure and predict perception-aware frame similarity. It also builds a low resolution virtual display which clones the device screen for performing similarity measurement at a low-power cost. It is able to work on an existing commercial smartphone and support applications from app stores without any modifications. It has been implemented on Nexus 5X, and its performance has been measured with 13 games. The system effectively reduces the overall power consumption of mobile devices while maintaining satisfactory user experiences. The power consumption is reduced by 21.78% on aver-age and up to 34.74%.

A mobile GPU constitutes the majority of power consumption on a mobile device and mobile games top as the most popular class of graphics applications. In this demo, we present FROG, a novel frame rate optimization system for mobile gaming applications. FROG makes use of human visual perception to graphics and regulates application's frame rendering process on-the-fly for maximizing power saving without degrading the user experience. The system works on an existing commercial smartphone and support the legacy gaming applications from app stores without requiring any changes from applications

Numerous complains have been made by Android users who severely suffer from the sluggish response when interacting with their devices. However, very few studies have been conducted to understand the user-perceived latency or mitigate the UI-lagging problem. In this paper, we conduct the first systematic measurement study to quantify the user-perceived latency using typical interaction-intensive Android apps in running with and without background workloads. We reveal the insufficiency of Android system in ensuring the performance of foreground apps and therefore design a new system to address the insufficiency accordingly. We develop a lightweight tracker to accurately identify all delay-critical threads that contribute to the slow response of user interactions. We then build a resource manager that can efficiently schedule various system resources including CPU, I/O, and GPU, for optimizing the performance of these threads. We implement the proposed system on commercial smartphones and conduct comprehensive experiments to evaluate our implementation. Evaluation results show that our system is able to significantly reduce the user-perceived latency of foreground apps in running with aggressive background workloads, up to 10x, while incurring negligible system overhead of less than 3.1 percent CPU and 7 MB memory.

In this paper, we propose a new class of message routing scheme for a distributed network which can scale up to the needs of a very large number of mobile users. We leverage the work in the GeoKad paper which uses the Distributed Geographic Table, allowing node retrieval from resources that are close to any given region. The paper addresses the problem regarding efficient message passing and peer misses. We propose a new routing scheme that addresses this kind of limitation. In fact, the problem can be solved in two ways, either by preserving the state of peer and further use a prediction algorithm or by better message routing. The proposed scheme is based on swarm intelligence (SI) and its attribute of exploit and explore. We leverage the statistical properties of time-varying network connectivity for opportunistic message forwarding. Considering the present 3G or Wi-Fi environment this approach can also help in optimizing energy as it can prevent query over flooding which is mostly encountered in such network scenarios.

In this paper, we propose an in-situ Crowd-aware Sensing Platform, called "CrowdMon", which envisions the cooperation among mobile users in highly crowded urban areas such as metro and square. CrowdMon establishes a spontaneous connection from co-located users in a semantic proximity and enables them to share contextual information such as location, ambient music, and mood of places. To the best of our knowledge, CrowdMon is the first attempt to support crowd-aware services at a platform level. We show interesting use cases of CrowdMon and an initial system design to realize the crowd-based context sharing.