Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> G. Context-Aware Framework Designs <s> SenSay is a context-aware mobile phone that adapts to dynamically changing environmental and physiological states. In addition to manipulating ringer volume, vibration, and phone alerts, SenSay can provide remote callers with the ability to communicate the urgency of their calls, make call suggestions to users when they are idle, and provide the caller with feedback on the current status of the SenSay user. A number of sensors including accelerometers, light, and microphones are mounted at various points on the body to provide data about the user’s context. A decision module uses a set of rules to analyze the sensor data and manage a state machine composed of uninterruptible, idle, active and normal states. Results from our threshold analyses show a clear delineation can be made among several user states by examining sensor data trends. SenSay augments its contextual knowledge by tapping into applications such as electronic calendars, address books, and task lists. <s> BIB001 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> G. Context-Aware Framework Designs <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> BIB002 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> G. Context-Aware Framework Designs <s> In this paper, we propose a human daily activity recognition method by fusing the data from two wearable inertial sensors attached on one foot and the waist of the subject, respectively. We developed a multi-sensor fusion scheme for activity recognition. First, data from these two sensors are fused for coarse-grained classification in order to determine the type of the activity: zero displacement activity, transitional activity, and strong displacement activity. Second, a fine-grained classification module based on heuristic discrimination or hidden Markov models (HMMs) is applied to further distinguish the activities. We conducted experiments using a prototype wearable sensor system and the obtained results prove the effectiveness and accuracy of our algorithm. <s> BIB003 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> G. Context-Aware Framework Designs <s> We present Darwin, an enabling technology for mobile phone sensing that combines collaborative sensing and classification techniques to reason about human behavior and context on mobile phones. Darwin advances mobile phone sensing through the deployment of efficient but sophisticated machine learning techniques specifically designed to run directly on sensor-enabled mobile phones (i.e., smartphones). Darwin tackles three key sensing and inference challenges that are barriers to mass-scale adoption of mobile phone sensing applications: (i) the human-burden of training classifiers, (ii) the ability to perform reliably in different environments (e.g., indoor, outdoor) and (iii) the ability to scale to a large number of phones without jeopardizing the "phone experience" (e.g., usability and battery lifetime). Darwin is a collaborative reasoning framework built on three concepts: classifier/model evolution, model pooling, and collaborative inference. To the best of our knowledge Darwin is the first system that applies distributed machine learning techniques and collaborative inference concepts to mobile phones. We implement the Darwin system on the Nokia N97 and Apple iPhone. While Darwin represents a general framework applicable to a wide variety of emerging mobile sensing applications, we implement a speaker recognition application and an augmented reality application to evaluate the benefits of Darwin. We show experimental results from eight individuals carrying Nokia N97s and demonstrate that Darwin improves the reliability and scalability of the proof-of-concept speaker recognition application without additional burden to users. <s> BIB004 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> G. Context-Aware Framework Designs <s> Supporting continuous sensing applications on mobile phones is challenging because of the resource demands of long-term sensing, inference and communication algorithms. We present the design, implementation and evaluation of the Jigsaw continuous sensing engine, which balances the performance needs of the application and the resource demands of continuous sensing on the phone. Jigsaw comprises a set of sensing pipelines for the accelerometer, microphone and GPS sensors, which are built in a plug and play manner to support: i) resilient accelerometer data processing, which allows inferences to be robust to different phone hardware, orientation and body positions; ii) smart admission control and on-demand processing for the microphone and accelerometer data, which adaptively throttles the depth and sophistication of sensing pipelines when the input data is low quality or uninformative; and iii) adaptive pipeline processing, which judiciously triggers power hungry pipeline stages (e.g., sampling the GPS) taking into account the mobility and behavioral patterns of the user to drive down energy costs. We implement and evaluate Jigsaw on the Nokia N95 and the Apple iPhone, two popular smartphone platforms, to demonstrate its capability to recognize user activities and perform long term GPS tracking in an energy-efficient manner. <s> BIB005 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> G. Context-Aware Framework Designs <s> Location-based applications have become increasingly popular on smartphones over the past years. The active use of these applications can however cause device battery drain owing to their power-intensive location-sensing operations. This paper presents an adaptive location sensing framework that significantly improves the energy efficiency of smartphones running location-based applications. The underlying design principles of the proposed framework involve substitution, suppression, piggybacking, and adaptation of applications' location-sensing requests to conserve energy. We implement these design principles on Android-based smartphones as a middleware. Our evaluation results show that the design principles reduce the usage of the power-intensive GPS (Global Positioning System) by up to 98% and improve battery life by up to 75%. <s> BIB006 </s> Context-Awareness for Mobile Sensing: A Survey and Future Directions <s> G. Context-Aware Framework Designs <s> Human activity recognition is a challenging problem for context-aware systems and applications. Research in this field has mainly adopted techniques based on supervised learning algorithms, but these systems suffer from scalability issues with respect to the number of considered activities and contextual data. In this paper, we propose a solution based on the use of ontologies and ontological reasoning combined with statistical inferencing. Structured symbolic knowledge about the environment surrounding the user allows the recognition system to infer which activities among the candidates identified by statistical methods are more likely to be the actual activity that the user is performing. Ontological reasoning is also integrated with statistical methods to recognize complex activities that cannot be derived by statistical methods alone. The effectiveness of the proposed technique is supported by experiments with a complete implementation of the system using commercially available sensors and an Android-based handheld device as the host for the main activity recognition module. <s> BIB007
The pervasive mobile computing, which captures and evaluates sensory contextual information in order to infer user relevant actions/activities/behaviors, has been becoming a well established research domain. Most studies rely on recognition of user activities (especially posture detection) and definition of common user behaviors by proposing and implementing numerous context modeling systems. In addition, researchers have been aware of the need for computational power while trying to infer sensory context accurately enough. However, most works provides some partial answers to the tradeoff between context accuracy and battery power consumption. It is difficult to say that power saving methods have been significantly taken at the low-level physical sensory operations. Especially, there is not a generic framework that intends to apply adaptively changing dynamic sensor management strategies. In contrast, most works for creating a context-aware application emphasize either to set a minimum number of sensors or to maximize power efficiency by solely applying less complexity in computations and/or changing transferring methods of obtained context to the outer network services. From the standpoint of the creation of a generic framework design for context-aware middleware services, it would be notable to mention the following studies: • "EEMSS" in  uses hierarchical sensor management strategy by powering a minimum number of sensors and applying appropriate sensor duty cycles so that the proposed framework could recognize user states through smartphone sensors while improving device battery lifetime. Unfortunately, sensors employ fixed duty cycles whenever they are utilized, and also they are not adjustable to respond differently to variant user behaviors. Energy consumption is reduced by shutting down unnecessary sensors at any particular time. On the other hand, classification of sensory data is based on pre-defined test classification algorithms. • The hierarchical sensor management system is also studied by introducing "SeeMon" system in BIB002 which achieves energy efficiency and less computational complexity by only performing continuous detection of context recognitions when changes occur during context monitoring. The framework also employs a bidirectional feedback systems in computations to detect similar context recognitions in order to prevent from redundant power consumptions. • Similarly, "Sensay" in BIB001 is a context-aware mobile phone but as in form of an external sensor box which is mounted on the users' hip area. It receives many different sensory data, and eventually determines to dynamically change cell phone ring tone, alert type and un-interruptible user states. However, it classifies user states offline, and the system does not have energy efficiency. • "Darwin" studied in BIB004 proposes a system that combines classifier evolution, model pooling, and collaborative inference for mobile sensing applications. It is implemented for a speaker recognition application by using efficient but sophisticated machine learning techniques; however, there is no power saving method applied. • "Jigsaw" presented in BIB005 , a continuous sensing engine for mobile phone applications, balances the performance needs of an application and resource demands. The engine employs each sensor under a processing pipeline. It performs all the sensing and classification processing exclusively on the mobile phone. It also uses sensorspecific pipelines that have been designed to cope with individual challenges experienced by each sensor. Duty cycling techniques are attached to adaptive pipeline process if applicable to conserve battery life. • The study in BIB006 creates a general framework problem under an energy efficient location based sensing application. It is noted that there are four critical factors that affect energy efficiency in location-sensing through GPS. These factors are static use of location sensing mechanisms, absence of use of power-efficient sensors to optimize location-sensing, lack of sensing cooperation among multiple the similar applications, and unawareness of battery level. The framework solution is given by introducing substitution to find an alternative less power consuming location-sensing mechanism, suppression to use less power consuming sensor instead of GPS when user location is static, piggybacking to synchronize with other location-based applications to infer a collaborative location info, and adaptation to adjust system parameters such as time and distance longer when battery level is low. There are also some other studies proposed in BIB007 , BIB003 ,  in order to provide comprehensive solutions into creating a base framework for context-aware applications. Table VI analyzes some important frameworks by breaking down each study and comparing them in terms of learning paradigm, applied algorithm, power efficiency, processing method, interested context, input sensor, platform where implementation is carried out, and accuracy of framework outcome.