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.

Key Words

  • 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,


Special Topics

  • 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 navigation

  • 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
      • Rules
      • 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 navigation

  • Context-aware computing in manufacturing, IJCIM16-Concept
    • Context-aware computing in Industry, a specific implementation.

Context aware navigation

  • 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 navigation

  • 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


  • Microsoft: Integration of location logs, GPS signals, and spatial resources for identifying user activities, goals, and context