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Software energy profilers are the tools to measure the energy consumption of mobile devices, applications running on those devices, and various hardware components. They adopt different modeling and measurement techniques. In this article, we aim to review a wide range of such energy profilers for mobile devices. First, we introduce the terminologies and describe the power modeling and measurement methodologies applied in model-based energy profiling. Next, we classify the profilers according to their implementation and deployment strategies, and compare the profiling capabilities and performance between different types. Finally, we point out their limitations and the corresponding challenges.

Nearly all bitrate adaptive video content delivered today is streamed using protocols that run a purely client based adaptation logic. The resulting lack of coordination may lead to suboptimal user experience and resource utilization. As a response, approaches that include the network and servers in the adaptation process are emerging. In this article, we present an optimized solution for network assisted adaptation specifically targeted to mobile streaming in multi-access edge computing (MEC) environments. Due to NP-Hardness of the problem, we have designed a heuristic-based algorithm with minimum need for parameter tuning and having relatively low complexity. We then study the performance of this solution against two popular client-based solutions, namely Buffer-Based Adaptation (BBA) and Rate-Based Adaptation (RBA), as well as to another network assisted solution. Our objective is two fold: First, we want to demonstrate the efficiency of our solution and second to quantify the benefits of network-assisted adaptation over the client-based approaches in mobile edge computing scenarios. The results from our simulations reveal that the network assisted adaptation clearly outperforms the purely client-based DASH heuristics in some of the metrics, not all of them, particularly, in situations when the achievable throughput is moderately high or the link quality of the mobile clients does not differ from each other substantially. Index Terms-Server and network assisted DASH, multi-access edge computing (MEC), quality of experience, fairness, load balancing, integer nonlinear programming (INLP), greedy scheduling algorithm

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Road safety is one of the most important applications of vehicular networks. However, improving pedestrian safety via vehicle-to-pedestrian (V2P) wireless communication has not been extensively addressed. In this paper, our vision is to propose a method which enables development of V2P road safety applications via wireless communication and only utilizing the existing infrastructure and devices. As pedestrians' smartphones do not support the IEEE 802.11p amendment which is customized for vehicular networking, we have initiated an approach that utilizes cellular technologies. Study shows potential of utilizing 3G and LTE for highly mobile entities of vehicular network applications. In addition, some vehicles are already equipped with cellular connectivity but otherwise the driver's smartphone is used as an alternative. However, smartphone limited battery life is a bottleneck in realization of such pedestrian safety system. To tackle the energy limitation in smartphones, we employ an adaptive multi-level approach which operates in an energy-saving mode in risk-free situations but switches to normal mode as it detects a risky situation. Based on our evaluation and analysis, this adaptive approach considerably saves electrical energy and thus makes the cellular-based road-safety system practical.

Previous studies have shown that a significant part of the overall energy consumption of battery-powered mobile devices is caused by network data transmission. Power models that describe the power consumption behavior of the network data transmission are therefore an essential tool in estimating the battery lifetime and in minimizing the energy usage of mobile devices. In this paper, we present a simple and practical power model for data transmission over an 802.11g WLAN and show its accuracy against physical data measured from three popular mobile platforms, Maemo, Android and Symbian. Our model estimates the energy usage based on the data transmission flow characteristics which are easily available on all the platforms without modifications to lowlevel software components or hardware. Based on our measurements and experimentation on real networks we conclude that our model is easy to apply and of adequate accuracy.

Abstract-The growing popularity of mobile internet services, characterized by heavy network transmission, intensive computation and an always-on display, poses a great challenge to the battery lifetime of mobile devices. To manage the power consumption in an efficient way, it is essential to understand how the power is consumed at the system level and to be able to estimate the power consumption during runtime. Although the power modeling of each hardware component has been studied separately, there is no general solution at present of combining them into a system-level power model. In this paper we present a methodology for building a system-level power model without power measurement at the component level. We develop a linear regression model with nonnegative coefficients, which describes the aggregate power consumption of the processors, the wireless network interface and the display. Based on statistics and expert knowledge, we select three hardware performance counters, three network transmission parameters and one display parameter as regression variables. The power estimation, based on our model, exhibits 2.62% median error on real mobile internet services.