Context Aware Computing for The Internet of Things: A Survey

TLDR: This paper surveys context awareness from an IoT perspective and addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT.

Abstract: As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.

Figures

TABLE I IOT MIDDLEWARE COMPARISON [14]

Fig. 4. Relationship between sensor networks and IoT.

Fig. 3. Layered structure of a sensor network: These layers are identified based on the capabilities posed by the devices. In IoT, this layered architecture may have additional number of sub layers as it is expected to comprises large verity of in sensing capabilities.

TABLE III RELATIONSHIP BETWEEN DIFFERENT CONTEXT CATEGORIES

TABLE XII SUMMARIZED TAXONOMY USED IN TABLE XIII

TABLE V COMPARISON OF CONTEXT ACQUISITION METHODS BASED ON RESPONSIBILITY (PUSH, PULL)

Frequently asked questions

Q1. What are the contributions mentioned in the paper "Context aware computing for the internet of things: a survey" ?

In this paper, the authors survey context awareness from an IoT perspective. The authors present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then the authors provide an in-depth analysis of context life cycle. The authors evaluate a subset of projects ( 50 ) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade ( 2001-2011 ) based on their own taxonomy. 

Q2. What have the authors stated for future works in "Context aware computing for the internet of things: a survey" ?

Recent developments such as Transducer Electronic Data Sheet ( TEDS ) [ 221 ], Open Geospatial Consortium ( OGC ) Sensor Web Enablement related standards such as Sensor Markup Languages ( SensorML ) [ 133 ], sensor ontologies [ 143 ], and immature but promising efforts such as Sensor Device Definitions [ 224 ] show future directions to carry out the research work further, in order to tackle this challenge. Recent developments in semantic technologies [ 135 ], [ 143 ], [ 225 ] and linked data [ 226 ], [ 227 ] show future directions to carry out further research work. There are many types of context that can be used to enrich sensor data. Incorporating and integrating multiple techniques has shown promising success in the field. 

Q3. What is the key component of a context-aware framework?

Hydra identifies context reasoning rule engine, context storage, context querying, and event/action management as the key components of a context-aware framework. 

Q4. What are the challenges that need to be considered when developing sensor network middleware solutions?

Molla and Ahamed [8] identified ten challenges that need to be considered when developing sensor network middleware solutions: abstraction support, data fusion, resource constraints, dynamic topology, application knowledge, programming paradigm, adaptability, scalability, security, and QoS support. 

Q5. What are the components that can be dynamically composed according to the needs?

Components such as reasoning models, data fusion operators, knowledge bases, and context discovery components can be dynamically composed according to the needs. 

Q6. What are the main components of the android mobile operating system?

In addition, the android mobile operating system consists of a number of software sensors such as gravity, linear accelerometer, rotation vector, and orientation sensors. 

Q7. What are the common and basic information that needs to be captured in relation to context?

Some of the most common and basic information that needs to be captured in relation to context are context type, context value, time stamp, source, and confidence. 

Q8. What are the possible sources that can be used to collect evidence regarding the location of a user?

The possible sources that can be used to collect evidence regarding the location are GPS sensors, motion sensor, calendar, email, social networking services, chat clients, ambient sound (sound level, pattern), users nearby, camera sensors, etc. 

Q9. What are some of the major sensor network middleware approaches?

Some of the major sensor network middleware approaches are IrisNet, JWebDust, Hourglass, HiFi, Cougar, Impala, SINA, Mate, TinyDB, Smart Object, Agilla, TinyCubus, TinyLime, EnviroTrack, Mires, Hood, and Smart Messages. 

Q10. What is the purpose of object based modelling?

object based modelling is suitable to be used as an internal, nonshared, code based, run-time context modelling, manipulation, and storage mechanism. 

Q11. What are the techniques used to acquire context?

The techniques used to acquire context can be varied based on responsibility, frequency, context source, sensor type, and acquisition process. 

Q12. How can a consumer subscribe to a context management system?

Subscription (also called publish / subscribe): Context con-sumer can be allowed to subscribe with a context management system by describing the requirements. 

Q13. What are the popular context modelling techniques?

Their discussion is based on the six most popular context modelling techniques: key-value, markup schemes, graphical, object based, logic based, and ontology based modelling. 

Q14. What is the definition of a context attribute?

A context attribute has an identifier, a type and a value, and optionally a collection of properties describing specific characteristics [89]. 

Q15. What are the abbreviations used to denote the context reasoning techniques employed by the project?

The authors use the following abbreviations to denote the context reasoning techniques employed by the project: supervised learning (S), un-supervised learning (U), rules (R), fuzzy logic (F), ontology-based (O), and probabilistic reasoning (P). 

Q16. What is the importance of adding ontologies when necessary?

As an IoT solution will be used in many different domains, the ability to add ontologies (i.e. knowledge) when necessary is critical for wider adaptation. 

Q17. What is the importance of integrating context-aware capabilities in software and hardware?

in order to build a fully context-aware solution, the authors have to embed context-aware capabilities in both software and hardware layers. 

Q18. What are the abbreviations used to denote the context modelling techniques employed by the project?

The authors use the following abbreviations to denote the context modelling techniques employed by the project: key-value modelling (K), markup Schemes (M), graphical modelling (G), object oriented modelling (Ob), logic-based modelling (L), and ontology-based modelling (On).6) 

Q19. What is the difference between graphical and key-value modelling?

In terms of expressive richness, graphical modelling is better than markup and key-value modelling as it allows relationships to be captured into the context model. 

Q20. What is the method to tackle the problem of context awareness?

the best method to tackle the problem of context awareness it to combine multiple models in such a way that, as a whole, they reduce weaknesses by complementing each other.