Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> B. Adaptive and Opportunistic Sensory Sampling <s> Proactively providing services to mobile individuals is essential for emerging ubiquitous applications. The major challenge in providing users with proactive services lies in continuously monitoring their contexts based on numerous sensors. The context monitoring with rich sensors imposes heavy workloads on mobile devices with limited computing and battery power. We present SeeMon, a scalable and energy-efficient context monitoring framework for sensor-rich, resource-limited mobile environments. Running on a personal mobile device, SeeMon effectively performs context monitoring involving numerous sensors and applications. On top of SeeMon, multiple applications on the device can proactively understand users' contexts and react appropriately. This paper proposes a novel context monitoring approach that provides efficient processing and sensor control mechanisms. We implement and test a prototype system on two mobile devices: a UMPC and a wearable device with a diverse set of sensors. Example applications are also developed based on the implemented system. Experimental results show that SeeMon achieves a high level of scalability and energy efficiency. <s> BIB001 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> B. Adaptive and Opportunistic Sensory Sampling <s> A large number of context-inference applications run on o-the-shelf smart-phones and infer context from the data acquired by means of the sensors embedded in these devices. The use of ecient and eective sampling technique is of key importance for these applications. Aggressive sampling can ensure a more ne-grained and accurate reconstruction of context information but, at the same time, continuous querying of sensor data might lead to rapid battery depletion. In this paper, we present an adaptive sensor sampling methodology which relies on dynamic selection of sampling functions depending on history of context events. We also report on the experimental evaluation of a set of functions that control the rate at which the data are sensed from the Bluetooth device, accelerometer, and microphone sensors and we show that a dynamic adaptation mechanism provides a better trade-os compared to simpler function based rate control methods. Furthermore, we show that the suitability of these mechanisms varies for each of the sensors, and the accuracy and energy consumption values stabilize after reaching a certain level. <s> BIB002 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> B. Adaptive and Opportunistic Sensory Sampling <s> Mobile device based sensing provides a vast variety of user contextual information. To continuously acquire, classify and reason any context through built-in sensors cause mobile device batteries to deplete rapidly. Therefore, an urgent need has arisen to apply power saving approaches at the low level sensor operations while inferring user relevant context. In this paper, an adaptive sensing strategy is proposed to fill the mentioned deficiency within the realm of Human Activity Recognition (HAR). The strategy aims at inferring any user relevant postural activities through smartphone accelerometer by providing two intuitive power saving methods, which assign adaptive pairs of sampling frequencies and duty cycles during sensor operations. The proposed methods show from 20% up to 50% efficiency enhancement in power consumption caused by the accelerometer with respect to overall 15% accuracy decrease in context inference depending on the degree of user activity dynamics. <s> BIB003
To address power efficiency in context-awareness, efficient sensor management systems infusing low level sensory operations need to be considered. An example method could be illustrated in Fig. 7 . The first stage starts with dynamically selecting a sufficient number of sensors  , BIB001 , called "Dynamic Sensor Selection", while a context-aware application is running. Thereby, sensors can be put in an order according to their power consumption levels and application relevance depending on an interested context. In addition, the best energy saving algorithm would be the one that manipulates the frequentness of sensory sampling intervals. In this regard, different duty cycling approaches would be the next stage of the ladder by tuning the wave form to power a sensor for a desired power efficiency. Besides that, adaptively changing sensor sampling periods can also be the final stage to achieve a certain level of power efficiency BIB002 . By adjusting sampling periods in sensory operations as needed, the total number of sampling occurrences either increases or decreases. As a result, relevant power consumption will adjust accordingly. Above of all, an adaptive sensor management mechanism/ system to assign a mixture pair of duty cycles and sampling periods simultaneously would be a cure of consuming less power while running context-aware applications accurately enough BIB003 . However, intervening sensory operations to achieve power efficiency may jeopardize the accuracy of context-aware services, and thereby it creates a tradeoff between power consumption caused and accuracy provided by these services. In this sense, optimization of physical sensor hardware operations during data acquisition needs to be created in a collective way by employing sufficient number of sensor sets to characterize contextual information, creating work division among chosen sensors, scheduling time-variant duty cycles and setting adaptively changing sampling periods.