In recent years, computation offloading, through which applications on a mobile device can offload their computations onto more resource-rich clouds, has emerged as a promising technique to reduce battery consumption as well as augment the devices' limited computation and memory capabilities. In order for computation offloading to be energy-efficient, an accurate estimate of battery consumption is required to decide between local processing and computation offloading. In this paper, we propose a novel technique for estimating battery consumption without requiring detailed information about the mobile application's internal structure or its execution behavior. In our approach, the relationship is derived between variables that affect battery consumption (i.e., the input to the application, the transmitted data, and resource status) and the actual consumed energy from the application's past run history. We evaluated the performance of the proposed technique using two different types of mobile applications over different wireless network environments such as 3G, Wi-Fi, and LTE. The experimental results show that our technique can provide tolerable estimation accuracy and thus make correct decisions between local processing and computation offloading.

In emerging wearable sensor systems, it is crucial to save energy because these systems are severely energy-constrained. For making the sensors in these systems energy efficient, transmission power control (TPC) is widely used, and thus far, many TPC algorithms have been proposed in the literature. However, these TPC algorithms do not always work well in all wireless body channel conditions, which are capriciously varied due to diverse sensor environments such as sensor placements, body movements, and body locations. In this paper, we propose a simple TPC algorithm that quickly and stably approaches the optimal transmission power level and works well in all wearable sensor environments. We experimentally evaluated the proposed TPC algorithm and proved that it works well under all wireless body channel conditions.

IP Datacast over DVB-H has been adopted as a core technology to build complete end-to-end mobile broadcast TV systems. In order for this technology to be successful in the market, provisioning of acceptable QoE (Quality of Experience) to the users, as well as a wide range of business models to the service providers, is essential. In this paper, we analyze the channel zapping time, which is an important metric to measure QoE for mobile broadcast TV services. In particular, we clarify primary components that determine the channel zapping time for protected services in IP Datacast over DVB-H. Our analysis is based on the data gathered during the trial service of the OMA-BCAST Smartcard profile in Singapore, Asia. Based on the analysis, we show that a significant reduction in channel zapping time can be achieved by optimizing the transmission parameters related to the key derivation time and the synchronization time between the content stream and the key stream.