Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> A. Energy Awareness <s> Today's mobile phones come with a rich set of built-in sensors such as accelerometers, ambient light sensors, compasses, and pressure sensors, which can measure various phenomena on and around the phone. Gathering user context such as user activity, geographic location, and location type requires continuous sampling of sensor data. However, such sampling shortens a phone's battery life because of the associated energy overhead. This article examines the root causes of this energy overhead and shows that energy-efficient continuous sensing can be achieved through proper system design. <s> BIB001 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> A. Energy Awareness <s> Modern smartphones are increasingly performant and feature-rich but remain highly power-constrained. Effective energy management demands an informed policy, and to that end we present a detailed power analysis of the Samsung Galaxy S III smartphone. We measure power consumption by instrumentation at the circuit level, and present a breakdown by the major components including CPU, RAM, display, GPU, wireless radios, camera, GPS and environmental sensors. <s> BIB002
Because mobile devices operate on a finite supply of energy contained in their batteries, energy awareness is one of the key resource management issues in mobile sensing. Specifically, continuously capturing user context through sensors imposes heavy workload in physical and computational capacity aspects of the device working process, and it drains the battery power rapidly. To understand this issue better, an application example BIB001 can be examined. Accordingly, the accelerometer sensor builtin HTC Touch Pro is employed at a fixed sampling frequency. When the phone samples the accelerometer, overall power consumption on device increases by 370 mW; whereas, according to the data sheet of the accelerometer, it should consume less than 1 mW when active. Even if the accelerometer itself wastes very little power to operate its functionality, the phone with its main processor and other hardware components causes much more power consumption during the operation to accomplish a contextual sensory data extraction. Another example provided in BIB002 reports that today's smart devices are not durable to employ all sensors at the same time by giving an example of Samsung Galaxy SIII smartphone with a fully-charged battery. It is experimentally examined that the smartphone consumes 805 mW in idle mode, whereas the same device puts extra 573 mW power consumption while employing GPS connectivity. The experiment shows that constant sensor usage almost leads to reduce battery life by half. Given examples conclude that any sensor employment in mobile devices would draw more current from the device battery than it happens during a regular device run. Therefore, the mobile device battery will not last a long time to support device operations. Energy awareness can be integrated by creating energy profiles and energy estimation to present maps of power usage allowing the analysis of power draining caused by physical sensor hardware, computations in coding, and transmission of data. Using this analysis and having more statistics on behavioral characterization of mobile device infrastructure, context-aware applications can benefit more efficient process environment. Also, user intervene is important for securing energy awareness. In order to make power efficient decisions, users need to understand when power drain mostly occurs on their mobile devices. On the other hand, increasing usage of mobile device application features along with network connectivity all the time will force the need for enhancing energy efficiency. For instance, constant connectivity in social networking platforms require continuous sensing that affects on the device battery life. Cellular and Wi-Fi are the most widely used wireless transmission technologies. Due to the increase in mobile data traffic and everdemanding popularity of mobile applications, energy consumption on wireless mobile data transmission/communications grows rapidly. Especially, energy cost of wireless networking operations in addition to the expectations of future contextaware applications requiring more use of multimedia, image and video rendering, data compressions/decompressions, complex web service connections will be extremely high. Therefore, there are some actions needed to be taken to decrease energy consumption and to prolong battery lifetime as in the followings: • Radio optimization could reduce energy dissipation due to wireless communications by enhancing some radio parameters such coding, modulation schemes, power transmission and antenna direction. • Data reduction would be a solution to reduce the amount of data to be processed or transferred. Methods such as adaptive sampling, network coding and data compression could help removing unnecessary information in sensing task. • Energy-efficient routing aims at ensuring connectivity and coverage, and exploiting redundancy in topology control protocols by dynamically adapting network settings with respect to application needs. • Sensing scheduling, battery characterization and energy modeling are examined in detail in upcoming subsections.