Related Works of Context-Aware Computing

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].

Taxonomy

• Application: Navigation, Shopping Assistant, Conference Assistant
• Context: User Location, User activity, Device placement,
• Sensor: GPS, Accelerometer, Gyroscope, Camera, Magnetometer
• Solution: Machine learning,

Comments

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 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 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.

Ref.

[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

Patents

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