Zhe Peng

Make smartphones last a day: Pre-processing based computer vision application offloading

The benefit of offloading applications from smart-phones to cloud servers is undermined by the significant energy consumption in data transmission. Most previous approaches attempt to improve the energy efficiency only by choosing a more energy efficient network. However, we find that for computer vision applications, pre-processing the data before offloading can also substantially lower the energy consumption in data transmission at the cost of lower result accuracy. In this paper, we propose a novel online decision making approach to determining the pre-processing level for either higher result accuracy or better energy efficiency in a mobile environment. Different from previous work that maximizes the energy efficiency, our work takes the energy consumption as a constraint. Since people usually charge their smartphones daily, it is unnecessary to extend the battery life to last more than a day. Under both the energy and time constraints, we attempt to solve the problem of maximizing the result accuracy in an online way. Our real-world evaluation shows that the implemented prototype of our approach achieves a near-optimal accuracy for application execution results (nearly 99% correct detection rate for face detection), and sufficiently satisfies the energy constraint.

Smartphone-assisted energy efficient data communication for wearable devices

In dynamically changing network environments, no single data communication approaches for wearable devices are guaranteed to yield the best performance-cost ratio. To illustrate how different approaches perform in different environments, we conduct a theoretical analysis to four basic approaches that rely on either Wi-Fi, or smartphone-tethered cellular network, or both, to transmit data on wearable devices. In order to achieve energy efficient data communication on wearable devices (and associated smartphones), we propose a Lyapunov based on-line approach designation mechanism that dynamically chooses an appropriate data communication approach based on data transmission queue, estimated network conditions and the device moving speed. Due to the property of Lyapunov optimization framework, the proposed mechanism is able to minimize the power consumption of data communication for wearable devices (and associated smartphones) while meeting the delay time constraint. Moreover, it requires no prior knowledge of future network conditions and data request arrivals. Our trace-driven simulations demonstrate that our on-line designation mechanism delivers very close performance to the mechanism that can foresee the future, leaving very little space for further improvement.

SCoP: Smartphone energy saving by merging push services in Fog computing

Energy saving solutions on smartphone devices can greatly extend a smartphones lasting time. However, todays push services require keep-alive connections to notify users of incoming messages, which cause costly energy consuming and drain a smartphones battery quickly in cellular communications. Most keep-alive connections force smartphones to frequently send heartbeat packets that create additional energy-consuming radio-tails. No previous work has addressed the high-energy consumption of keep-alive connections in smartphones push services. In this paper, we propose Single Connection Proxy (SCoP) system based on fog computing to merge multiple keep-alive connections into one, and push messages in an energy-saving way. The new design of SCoP can satisfy a predefined message delay constraint and minimize the smartphone energy consumption for both real-time and delay-tolerant apps. SCoP is transparent to both smartphones and push servers, which does not need any changes on todays push service framework. Theoretical analysis shows that, given the Poisson distribution of incoming messages, SCoP can reduce the energy consumption by up to 50%. We implement SCoP system, including both the local proxy on the smartphone and remote proxy on the “Fog”. Experimental results show that the proposed system consumes 30% less energy than the current push service for real-time apps, and 60% less energy for delay-tolerant apps.

Smartphone-assisted smooth live video broadcast on wearable cameras

Wearable cameras require connecting to cellular-capable devices (e.g., smartphones) so as to provide live broadcast services for worldwide users when Wi-Fi is unavailable. However, the constantly changing cellular network conditions may substantially slow down the upload of recorded videos. In this paper, we consider the scenario where wearable cameras upload live videos to remote distribution servers under cellular networks, aiming at maximizing the quality of uploaded videos while meeting the delay requirements. To attain the goal, we propose a dynamic video coding approach that utilizes dynamic video recording resolution adjustment on wearable cameras and Lyapunov based video preprocessing on smartphones. Our proposed resolution adjustment algorithm adapts to network condition changes, and reduces the overheads of video preprocessing. Due to the property of Lyapunov optimization framework, our proposed video preprocessing algorithm delivers near-optimal video quality while meeting the upload delay requirements. Our evaluation results show that our approach achieves up to 50% reduction in power consumption on smartphones and up to 60% reduction in average delay, at the cost of slightly compromised video quality.

An efficient learning-based approach to multi-objective route planning in a smart city

Route planning is an important service in the map navigation. However, most of commercial map applications provide an optimal path that only minimize a single metric such as distance, time or other costs, while ignoring a critical criterion: safety. When citizens or travellers walk in a city, they may prefer to find a safe walking route to avoid the potential crime risk and to have a short distance, which can be formulated as a multi-objective optimization problem. Many previous methods are proposed to solve the multi-objective route planning, however, most of them are not efficient or optimized in a large-scale road network. In this paper, we propose a reinforcement learning based Multi-Objective Hyper-Heuristic (MOHH) approach to route planning in a smart city. We conduct experiments on the safety index map constructed based on the historical urban data of the New York city. Comprehensive experimental results show that the proposed approach is almost 34 and 1.4 times faster than the exact multi-objective optimization algorithm and the NSGA-II algorithm respectively. Moreover, it can obtain more than 80% Pareto optimal solutions in a large-scale road network.

U-safety: Urban safety analysis in a smart city

Information about urban safety, e.g., the safety index of a position, is of great importance to protect humans and support safe walking route planning. Despite some research on urban safety analysis, the accuracy and granularity of safety index inference are both very limited. The problem of analyzing urban safety to predict safety index throughout a city has not been sufficiently studied and remains open. In this paper, we propose U-Safety, an urban safety analysis system to infer safety index by leveraging multiple cross-domain urban data. We first extract spatially-related and temporally-related features from various urban data, including urban map, housing rent and density, population, positions of police stations, point of interests (POIs), crime event records, and taxi GPS trajectories. Then, these features are feeded into a sparse auto-encoder (SAE) model to obtain the final discriminative feature representation. Finally, we design a new co-training-based learning method, which consists of two separated classifiers, to calculate safety index accurately. We implement U-Safety and conduct extensive experiments based on real data sources obtained in New York City. The evaluation results demonstrate the advantages of U-Safety over other methods.