System Support For Energy Efficient Mobile Computing

Mobile devices are developed rapidly and they have been an integrated part of our daily life. With the blooming of Internet of Things, mobile computing will become more and more important. However, the battery drain problem is a critical issue that hurts user experience. High performance devices require more power support, while the battery capacity only increases 5% per year on average. Researchers are working on kinds of energy saving approaches. For examples, hardware components provide different power state to save idle power; operating systems provide power management APIs to better control power dissipation. However, the system energy efficiency is still low that cannot reach users’ expectation. To improve energy efficiency, we studied how to provide system support for mobile computing in four different aspects. First, we focused on the influence of user behavior on system energy consumption. We monitored and analyzed users’ application usages information. From the results, we built battery prediction model to estimate the battery time based on user behavior and hardware components’ usage. By adjusting user behavior, we can at most double the battery time. To understand why different applications can cause such huge energy difference, we built a power profiler Bugu to figure out where does the power go. Bugu analyzes power and event information for applications, it has high accuracy and low overhead. We analyzed almost 100 mobile applications’ power behavior and several implications are derived to save energy of applications and systems. In addition, to understand the energy behavior of modern hardware architectures, we analyzed the energy consumption and performance of heterogeneous platforms and compared them with homogeneous platforms. The results show that heterogeneous platforms indeed have great potential for energy saving which mostly comes from idle and low workload situations. However, a wrong scheduling decision may cause up to 30% more energy consumption. Scheduling becomes the key point for energy efficient computing. At last, as the increased power density leads to high device temperature, we investigated the thermal management system and developed an ambient temperature aware thermal control policy Falcon. It can save 4.85% total system power and more adaptive in various environments compared with the default approach. Finally, we discussed several potential directions for future research in this field

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Transparent resource sharing framework for internet services on handheld devices

Handheld devices have limited processing power and a short battery lifetime. As a result, computationally intensive applications cannot run appropriately or cause the device to run out of battery too early. Additionally, Internet-based service providers targeting these mobile devices lack information to estimate the remaining battery autonomy and have no view on the availability of idle resources in the neighborhood of the handheld device. These battery-related issues create an opportunity for Internet providers to broaden their role and start managing energy aspects of battery-driven mobile devices inside the home. In this paper, we propose an energy-aware resource-sharing framework that enables Internet access providers to delegate (a part of) a client application from a handheld device to idle resources in the LAN, in a transparent way for the end-user. The key component is the resource sharing service, hosted on the LAN gateway, which can be remotely queried and managed by the Internet access provider. The service includes a battery model to predict the remaining battery lifetime. We describe the concept of resource-sharing-as-a-service that allows users of handheld devices to subscribe to the resource sharing service. In a proof-of-concept, we evaluate the delay to offload a client application to an idle computer and study the impact on battery autonomy as a function of the CPU cycles that can be offloaded

Resource Allocation in Wireless Networks with RF Energy Harvesting and Transfer

Radio frequency (RF) energy harvesting and transfer techniques have recently become alternative methods to power the next generation of wireless networks. As this emerging technology enables proactive replenishment of wireless devices, it is advantageous in supporting applications with quality-of-service (QoS) requirement. This article focuses on the resource allocation issues in wireless networks with RF energy harvesting capability, referred to as RF energy harvesting networks (RF-EHNs). First, we present an overview of the RF-EHNs, followed by a review of a variety of issues regarding resource allocation. Then, we present a case study of designing in the receiver operation policy, which is of paramount importance in the RF-EHNs. We focus on QoS support and service differentiation, which have not been addressed by previous literatures. Furthermore, we outline some open research directions.Comment: To appear in IEEE Networ

Energy Harvesting Wireless Communications: A Review of Recent Advances

This article summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed as well as models for energy consumption at the nodes.Comment: To appear in the IEEE Journal of Selected Areas in Communications (Special Issue: Wireless Communications Powered by Energy Harvesting and Wireless Energy Transfer