Context-Based Network Estimation for Energy-Efficient Ubiquitous Wireless Connectivity

Abstract

Abstract-Context information brings new opportunities for efficient and effective system resource management of mobile devices. In this work, we focus on the use of context information to achieve energy-efficient, ubiquitous wireless connectivity. Our field-collected data show that the energy cost of network interfaces poses a great challenge to ubiquitous connectivity, despite decent availability of cellular networks. We propose to leverage the complementary strengths of Wi-Fi and cellular interfaces by automatically selecting the most efficient one based on context information. We formulate the selection of wireless interfaces as a statistical decision problem. The challenge is to accurately estimate Wi-Fi network conditions without powering up the network interface. We explore the use of different context information, including time, history, cellular network conditions, and device motion, to statistically estimate Wi-Fi network conditions with negligible overhead. We evaluate several context-based algorithms for the estimation and prediction of current and future network conditions. Simulations using field-collected traces show that our network estimation algorithms can improve the average battery lifetime of a commercial mobile phone for an ECG reporting application by 40 percent, very close to the estimated theoretical upper bound of 42 percent. Furthermore, our most effective algorithm can predict Wi-Fi availability for one and ten hours into the future with 95 and 90 percent accuracy, respectively.Index Terms-Network architecture and design, local and wide-area networks.

Functional Context Awareness: Measuring and Utilizing the Context Dependency of Mobile Usage

“…Others have exhibited framework systems in view of context data, e.g. evaluating and anticipating remote system conditions [10], system steering [35], battery administration [8], [36], and energy proficient GPS obligation cycling [12], [13]. Further, Eagle and Pentland have demonstrated that gadget use examples are in reality organized and unsurprising [37].…”

“…For instance, the work in [10] predicts system conditions to pick the best system interface, yet accept system use is pre-known, despite the fact that it relies on upon the conduct of applications, services, and the client. As another case, the creators of [8] demonstrate that battery utilization is context subordinate.…”

“…Context has additionally been widely misused to give improved framework proficiency and execution, for example, for energy administration [8], [9] and system choice [10]. These outlines misuse the context dependency of mobile use and mobile assets for particular purposes, and show noteworthy, evaluated, execution picks up.…”

“…[10]. To address the energy challenge, they frequently utilize impromptu plans along one or a greater amount of these lines: decreasing the recurrence of getting to exorbitant context [12], [13], [14], keeping away from them by and large [10], [12], [13], or substituting them with other context [15], [16], [17], [18].…”

Energy Consumption Reduction via Context-Aware Mobile Video Pre-fetching

“…In [6], [7] pre-fetching is scheduled based on predictions of WiFi availability and cellular signal strength, respectively, achieving up to 60% energy savings. In another work [8] pre-fetching is based on predicting what data is needed and when it will be used, by observing the user behavior and availability of WiFi connectivity, power & signal strength at different locations, thus achieving up to 70% savings.…”