In another survey, we only focus on single-source context sensing without a complete application solution. We need to broaden the scope and try to find more related works on multi-source sensors with complete upper layer application.
In this survey, we focus on context-aware computing, which was an individual research field.
- Context: any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves [IEEEIoTJ18-Survey].
- Context-aware computing
- Context lifecycle: Context Acquisition -> Context Modeling -> Context Reasoning -> Context Dissemination
- Context modeling (Context representation):
- A context model is to define and store context data in a machine-processable form.
- Context modeling is defined as the context representation that provides assistance in the under standing of properties, relationship, and details of context
- Models: Key-Value models, Logic based models, Ontology based models
- Context reasoning:
- The inference (reasoning) process can be used to derive new facts based on existing rules in the systems.
- Context reasoning can be defined as a method of deducing new knowledge, and understanding better, based on the available context. It can also be explained as a process of giving high-level context deductions from a set of contexts [IEEECST14-Survey].
- Application: Navigation, Shopping Assistant, Conference Assistant
- Context: User Location, User activity, Device placement,
- Sensor: GPS, Accelerometer, Gyroscope, Camera, Magnetometer
- Solution: Machine learning,
- Context-Aware Mobile Computing Survey, Dartmouth00-Survey
- Highly cited survey paper, context are defined and classified. Many applications are shown.
- Too early, most applications are based on simply location.
- Context: Location, time, activity, light.
- Applications: Shopping Assistant, Conference Assistant, etc.
- A survey on context-aware systems, IJAHUC07-Survey
- Highly cited survey paper
- Sensor classification: Physical sensors, Virtual sensors, Logical sensors.
- Context attributes: Context type, Context value, Timestamp, Source, Confidence.
- Context-Aware Computing in IoT Survey, IEEECST14-Survey
- A highly cited survey paper, categorization of context (primary/secondary),
- Design principles for context awareness framework (middleware)
- Comprehensive easy to learn and easy to use API
- Automatic context life cycle management
- Extended, rich, and comprehensive modeling
- Monitoring and detect event
- Subsection for “Context Modeling” methods:
- Ontology based Modeling, the most popular method.
- Subsection for “Context Reasoning” steps:
- Context pre-processing
- Sensor data fusion
- Context inference: Generation of high-level context information using lower-level context.
- Subsection for “Context Reasoning” methods:
- Supervised learning
- Unsupervised learning
- Fuzzy logic
- Ontology based
- Probabilistic logic
- Context-aware navigation, Sensors14-Navigation
- Taxonomy: Navigation, User activity, Device placement, GPS, Accelerometer, Gyroscope, Camera, Magnetometer, Machine learning
- Context-aware computing in manufacturing, IJCIM16-Concept
- Context-aware computing in Industry, a specific implementation.
- Context-Aware Computing, Learning, and Big Data, IEEEIoTJ18-Survey
- Recent survey, good summary of IoT research trends and IoT platforms.
- Context life cycle: Context Acquisition, Context Modelling, Context Reasoning, Context Distribution.
- Context-aware communication and computing, Springer18-Book
- Book in 2018, shows the states-of-the-art.
[Dartmouth00-Survey] Chen, G., & Kotz, D. (2000). A survey of context-aware mobile computing research. Dartmouth Computer Science Technical Report TR2000-381.
[IJAHUC07-Survey] Baldauf, M., Dustdar, S., & Rosenberg, F. (2007). A survey on context-aware systems. International Journal of Ad Hoc and Ubiquitous Computing, 2(4), 263-277.
[IEEECST14-Survey] Perera, C., Zaslavsky, A., Christen, P., & Georgakopoulos, D. (2014). Context-aware computing for the internet of things: A survey. IEEE communications surveys & tutorials, 16(1), 414-454.
[Sensors14-Navigation] Saeedi, S., Moussa, A., & El-Sheimy, N. (2014). Context-aware personal navigation using embedded sensor fusion in smartphones. Sensors, 14(4), 5742-5767.
[IJCIM16-Concept] Alexopoulos, K., Makris, S., Xanthakis, V., Sipsas, K., & Chryssolouris, G. (2016). A concept for context-aware computing in manufacturing: the white goods case. International Journal of Computer Integrated Manufacturing, 29(8), 839-849.
[IEEEIoTJ18-Survey] Sezer, O. B., Dogdu, E., & Ozbayoglu, A. M. (2018). Context-aware computing, learning, and big data in internet of things: a survey. IEEE Internet of Things Journal, 5(1), 1-27.
[Springer18-Book] Temdee, P., & Prasad, R. (2018). Context-aware communication and computing: Applications for smart environment. Springer International Publishing.
Industry Progress (Commercial SDK)
- Skyhook: Cooperative partner of Apple in localization. Massive dataset on GPS/Wi-Fi/Cell-Tower/BLE.
Open Source Project
- SensingKit: Multi-Platform Mobile Sensing Framework for Large-Scale Experiments
- The Context Toolkit : Toolkit for context-aware applications
- Aware : Context Instrumentation Framework
- The Pennyworth Project : Creating Context-Aware Tools for Everyday Use
- CARSKit : Java-based context-aware recommendation engine
- Microsoft: Integration of location logs, GPS signals, and spatial resources for identifying user activities, goals, and context