- freely available
Sensors 2015, 15(8), 20570-20607; https://doi.org/10.3390/s150820570
2. Principles of Context Aware Environments
- “…the set of location, identities of nearby people and objects and changes to those objects.” 
- “…location, identities of the people around the user, the time of day, season, temperature and so forth.” 
- “…the combination of the user’s location, environment, identity and time.” 
- “…what is happening at this moment.” 
- “…the state of the application’s surroundings.” 
- “…just the aspects of a current situation.” 
- “…extending to model the activities and tasks that are taking place in a location.” 
- “…the set of circumstances surrounding it are of relevance to its completion.” 
- “…any information that can be used to characterize the situation of entities (i.e., whether a person, place, or object) are considered relevant to the interaction between a user and an application, including the user and the application. Context is typically the location, identity, and state of people, groups and computational and physical objects.” 
2.1.1. Context Categorization
|Context Categorization||Observation Aspects and Context Features||Examples|
|5W1H (Who, When, Where, What, Why and How)||Analysis of the environment from different points of view. Intuitive to understand.||Life assistance of elderly Peter, 8 o’clock, garden, picking fruit, apples are ripe, with a ladder.|
|Physical/Virtual||Differentiation according to sources: sensing devices (physical); user, context servers, etc. (virtual). Simple but ambiguity to identify the same context as it can be physical or virtual depending on different situations.||Rehabilitation Physical: heart rate|
Virtual: patient’s medical history from database
|Static/Dynamic||Observation over time: always equal (static) or adaptive to changes in the environment (dynamic). Intuitive to understand.||Plant inspection Static: the place where a tree grows|
Dynamic: the aspect of the tree due to the current season
|Direct/Indirect||Differentiation through obtainment complexity, indirect context is more complex to acquire and needs computation, inference etc. Simple to identify.||Birthday Direct: actual date is the birthday|
Indirect: which birthday is it and does this mean something (e.g., 50th Birthday)
|Sensed, Combined, Inferred and Learned||Refined differentiation of obtainment complexity by sub-categorizations. Ambiguity in identifying the complexity of obtainment, difficult to distinguish clear differences of sub-categorizations||Navigation Sensed: proximity to an object|
Combined: speed and direction of motion
Inferred: check distance (rules)
Learned: compare with similar situations
|Internal/External||Differentiation of sources from the user’s point of view. Ambiguity in classifying the same context.||Life assistance Internal: desire to get up from the bed|
External: it is the time to get up
|Primary/Secondary||Obtainment complexity similar to direct/indirect. Simple. Ambiguity in identifying the complexity of obtainment.||Health monitoring Primary: check blood pressure|
Secondary: comparison of historical blood pressure data
2.1.2. Context Features
Context Temporal Validity
2.2. Context Awareness and Its Categorizations
- “...the ability of computing devices to detect and sense, interpret and respond to aspects of a user’s local environment and the computing devices themselves.” 
- “...provide the maximum flexibility of service based on real time context.” 
- “...automatically provide information or take actions according to the user’s present context and need.” 
- “...if an application has the ability to monitor input from sensing devices and choose the suitable context according to user’s need or interests, then it can be labelled as a context-aware application.” 
- Observing from the user interaction based viewpoint, the level of context awareness increases as the demand for the invention from users reduces.
- The hardware’s point of view focuses on the acquisition of context data which could be independent (the system itself obtains all necessary data) or based on an external infrastructure of devices. Hereby, intermediate levels could exist, as e.g., context awareness achieved by self-contained hardware could be augmented by using other technologies or infrastructures. The work reported in  states a close relation between active context awareness and infrastructure-based context awareness because of some common characteristics. It is believed that the extra infrastructure support is needful for the realization of active context awareness. The prominent commonality is that both try to reduce or even eliminate unnecessary user intervention in order to make the performance as “intelligent” as possible.
- If the measurement by hardware is the only means to realize context awareness, then the corresponding achievement is limited to be in the hard level. Soft context awareness is achieved by means of applying operations, e.g., analysis, inference, and learning on context knowledge base. However, it is difficult to apply and use to examine the level of a real context aware middleware, as most architectures simultaneously depend on both hardware and knowledge repository (context history).
- Based on the different models used to express context, context awareness can be grouped into three levels: location aware, medium and semantic. The investigation of  on the history of context aware systems shows that the development went over three generations which happen to represent one level, respectively. The first level is provided merely according to user's location. In the 2nd generation, more kinds of context information are employed to enable systems to know more about the environment. However, due to ambiguity, only a medium level of awareness is reached here. Later, common semantic technologies (e.g., ontology) were adopted to unambiguously represent context structures and their relationships in the 3rd generation. Here, context awareness can take advantage of semantic techniques to ensure formality, flexibility, interoperability and scalability.
|Classification Aspect||Context Awareness Levels||Description|
|User interaction||Personalized||Interaction is available for users to set preferences|
|Passive||Execution of appropriate options is subject to users’ decisions|
|Active||Execution of appropriate options acts autonomously|
|Acquisition hardware||Self-contained||Context awareness is achieved by independent hardware without any external support|
|Infrastructure-based||External systems or infrastructures provide additional support to realize context awareness|
|Information acquisition tool||Hard||Context awareness is obtained solely by hardware|
|Soft||Knowledge inference based on context repository is applied to achieve context awareness|
|Context model||Location aware||Context awareness is limited to location awareness|
|Medium-level||Various context is used but not unambiguously managed|
|Semantic||Semantic technologies are employed to enhance context awareness|
2.3. Context Lifecycle
2.3.1. Context Acquisition
2.3.2. Context Modelling
Key-Value Context Modelling
Markup Context Modelling
Graphical Context Modelling
Logic-Based Context Modelling
Multidisciplinary Context Modelling
Domain-Focused Context Modelling
User-Centric Context Modelling
Ontology-Based Context Modelling
Chemistry Inspired Context Modelling
|Context Modelling Technique||Advantages||Disadvantages||Applicability|
|Key-value||Simple; Ease of use; Flexible||Lack of standards; Useless when big in size; Cannot represent relationships; Difficult to retrieve information; Lack of validation tools; Lack of scalability; Only exact matching.||Adequate to model limited context in simple and self-independent applications which do not need to share data with other applications. Examples: [39,49]|
|Markup||Structured; Some validation tools are available; Flexible.||Lack of standards; Problems in capturing relationships; Timeless; Dependencies; Inconsistency checking; Reasoning and Uncertainty.||Efficient as mode of data transfer about shallow context over network; Applications in which levels of information are few. Examples: [50,51]|
|Graphical||Rich expressiveness; Relationships are allowed; Validation is possible through constraints; Different standards and implementations are available.||Interoperability is unsolved; Configuration must be required; A generic and well-developed standard is needed.||Particularly applicable to derive an ER-model which is useful as structuring instrument for a relational database. Examples: [52,53]|
|Object-oriented||Relationships are allowed; Some development tools are available; Can be fused by using programming languages.||Lack of standards; Lack of validation; Hard to retrieve information; Reasoning is not supported.||Suitable to be used in code-based (high-level programming languages) applications with high computational capability. Examples: [40,54]|
|Logic-based||Rich expressiveness; Support reasoning; Consistency check; Simplicity; Processing tools are available.||Lack of standards; Lack of validation.||Suitable for applications in which high-level information is needed and developers are willing to specify constraints. Examples: [41,55,56]|
|Multidisciplinary||Comprehensive understanding for context based on multiple disciplines; The division of context is concrete.||Too complex; Still at the first stage; Interoperability is unsolved.||Tailored to applications in which key human and social issues should be identified. Examples: |
|Domain-focused||Expressive; Flexible; Structured.||Lack of standards; Lack of validation.||Suitable to single domain-focused applications. Examples: |
|User-centric||Express context in an organized way; Scalability; Allow reasoning.||Lack of standards; Complex to use; Lack of validation; Lack of formality.||Suits applications focused on perspectives of users; data expression is in an intuitive manner. Examples: |
|Ontology-based||Support reasoning; Rich expressiveness; Relationships are allowed; Strong validation; Processing tools available; Mature standards; Interoperability.||Representation can be complicated; It will be complex to retrieve context information; Unable to address uncertainty.||Suitable to applications which highly need to exchange information with others; Sufficient knowledge engineering skills are available. Examples: [45,57,58,59]|
|Chemistry inspired||Medium expressivity to represent many kinds of context; Support for triggering services autonomously; Cross-domain inspired.||Lack of standards; Lack of validation; Not dynamic and scalable; In a nascent stage.||It is possible to apply this model to applications which require spontaneous interaction and composition of information. Examples: |
2.3.3. Context Reasoning
Probabilistic Logic-Based Reasoning
2.3.4. Context Distribution
2.3.5. Context Visualization
3. Survey on Context Aware Middleware Architectures
3.1. Technical Considerations for Context Aware Middleware
- The stand-alone architecture is the simplest and least powerful in which only a fixed amount of context is processed in an independent module. Besides, context sharing is not allowed.
- In the layered manner, different responsibilities are allocated to multiple layers. However, the interaction and dependence among all layers raises concerns.
- As the core of the centralized middleware architecture, a central server is endowed with crucial computational capabilities and rich storage capacity. Communication exists between the central server and other devices for exchanging information. The prominent limitation is once the central server fails, the entire architecture will be influenced dramatically.
- The distributed architecture is widely applied and it enables developers to design middleware in a more flexible manner. Different components hold distributed responsibilities and they are independent of each other. However, every component involved in the distributed architecture needs to cope with the stress of computation and storage.
Security & Privacy
Cloud-Based Big Data Analytics
3.2. Context Aware Middleware Architectures
3.2.1. Context Aware Middleware for Pervasive Elderly Homecare (CAMPH)
- Physical Space Layer: Each Physical Space (PS) may contain physical entities such as sensors, actuators and computing devices. At this layer, data is modelled as a pair of attribute-value. PSs with high relevance, such as having similar attributes, are sorted into the same class named context domain or context space.
- Context Data Management Layer: Main components allocated in this layer are Context data/events database, Query processing, Context space management, and Context reasoning. A hierarchical reasoning scheme, in which low-level reasoning performs on single PS data while high-level reasoning makes inference from context domain, is applied. Besides, SQL-based context query interface is available to acquire context data or subscribe to event notifications.
- Service Management Layer: Context data is fully utilized to enable context aware service organization and discovery.
- Application Layer: Different homecare applications can invoke and orchestrate context aware services or make requests for context data directly in this layer.
3.2.5. Context Awareness for Internet of Things (CA4IOT)
- SDAL. The main components located in this layer are sensor wrappers, wrapper repository, wrapper generator, sensor device definition (SDD) local repository and SDD cloud repository. This layer is responsible for acquiring a variety of context data.
- CSDL. This layer is in charge of discovering context and semantic. Relevant components are context and semantic discoverers, context and semantic discoverer generator, and context and semantic discoverers repository.
- CPRL. A collection of important functions are distributed in this layer like processing data, reasoning high-level context, fusing context, knowledge generating and storing.
- DSCDL. Users can make requests via multi-model interfaces. Local repository can interact with repositories which reside in the cloud or open linked data to provide better answers for those queries by means of big data analytics.
- The programming layer. It is responsible for constructing and reconfiguring context aware applications by adopting the instructions from the decision layer.
- The knowledge layer. Three ontologies including Context Model, Tasklet Model, and Service Model are proposed to represent semantics of knowledge which is necessarily required by CAMPUS to make adaptation decisions. The knowledge could be the requirements desired by target service, the properties of the available tasklets, the context requirements imposed by tasklets, and the properties of run-time context.
- The decision layer. Decision maker uses a multi-stage normative decision model, which includes preprocessing, screening and choice, to choose the best tasklet alternatives for a given task. The automated adaptation decisions will be forwarded to the programming layer.
3.2.7. Context-Aware Services Framework (CASF)
- Physical sensor layer. It can only recognize sensor data. Physical sensors are the only context information source.
- Public context layer. Two types of context providers in terms of complexity of context information process are located in this layer. A basic context provider only processes sensor data from physical sensors while a combined context provider can make use of information from both sensors and other context providers. All context information generated in this layer is served based on web services so that openness and interoperability are achieved. By using a proposed context ontology and OWL-S, context providers are able to be constructed in web services.
- Context service layer. Context information is consumed in this layer so that context aware services can be generated and provided to users.
3.2.8. Semantic Web-Based Context Management (SeCoMan)
- Application. Different applications reside on top of SeCoMan in order to offer desired services for users.
- Context Management. As the core of the SeCoMan framework, it provides context aware supports for applications. Three kinds of actors with different rights to interact with SeCoMan are defined including Framework Administrator, Application Administrator, and Users. A set of predefined queries are allowed for applications to get information about indoor location of users and objects. Semantic rules are used to specify policies regarding restricted access to location information so that privacy is guaranteed.
- Plug-in. It provides SeCoMan with context information, which is especially focused on locations. In other words, the plug-in layer acts as an independent context source.
- AAL systems. This component, as the hardware architecture, includes different BSN (Body Sensor Network) foundations and monitoring systems for meeting different target user requirements.
- Context aggregator and providers (CAP). Raw data from AAL systems is converted and abstracted to high-level context by CAP. More specifically, context providers categorize sensor data into context based on pre-designed ontology. Afterwards, different context is integrated by context aggregator to provide complete information. In addition, reasoning mechanisms are applied to infer more useful information.
- Service providers. They are the producers of context aware services, such as applications.
- Context aware middleware (CaM). By utilizing existing knowledge and incoming context, CoM is able to indentify assistive services for the given context and trigger associated actions. CaM is the core component of CoCaMAAL with multiple key functions, such as context management, context storing, context retrieval, context manipulation, service mapping, self-adaptation, service discovery, and security service.
- Context data visualization. Proper interfaces (e.g., GUI) are available for users to visualize context data.
3.2.10. Big Data for Context Aware Monitoring (BDCaM)
|Middleware||Year||Architectural Style||Context Abstraction||Context Reasoning||Scalability||Cloud-Based Big Data Analytics|
|CAMPH||2009||Layered & Distributed||Key-value||Rule||×||×|
|Octopus||2011||Layered||Not specified||Not specified||×||×|
|CA4IoT||2012||Layered & Distributed||Ontology||Ontology and statistical||√||√|
|SeCoMan||2014||Layered||Ontology||Ontology and rule||×||×|
|FlexRFID||2015||Layered & Distributed||Markup||Rule||×||×|
|Middleware||Fault Tolerance||Interoperability||Service Discovery||Storage||Security & Privacy||Context Awareness Level|
4. Open Issues
- Security & Privacy are a must in any middleware architecture. Amounts of sensitive context are employed to characterize the situation so as to provide relevant services. Hence, the demand for enforcing security & privacy is increasing. Besides, as it is agreed that it will be a fashion to drive middleware architectures to be cloud-oriented, leveraging cloud resources will impose many challenges on security & privacy. e.g., it could be possible for any entity with internet connection to easily collect, access, visualize, archive, share, and search data or services from the cloud. It will not be an easy task to guarantee security & privacy, political or legal restrictions of data in cloud. Potential solutions could be restricting unauthorized manipulation, protecting privacy of information storage, ensuring the information security during processing or probably creating private clouds dedicated to public sectors.
- Increase the degree/level of context awareness. There has been a plethora of so-called context aware middleware proposals in recent years. However, they differ in real capabilities, which refer to context awareness level. The desired awareness means that middleware could adequately understand any change of current environment based on all available context information. The majority of current context aware middleware proposals only reach a very limited level of cognition and awareness for their involved circumstances.
- Standardization. It seems that it will not be feasible to formulate a single standard for a generalized context aware middleware due to the variety of domains and applications involved. However, considerable efforts have been put to build a generic domain-focused middleware solution . For example,  put forward a standardized middleware for the semantic web domain. Different middleware solutions adopt different standards compliant with needs from different domains. All these standards should be collected to form a complete standardization platform in which the selection of standard could be enabled to fit in a certain domain.
- Increase autonomy. Although context aware middleware architectures reduce the need of human intervention when they serve personalized applications, human intervention is still necessary and playing an important role in realizing context awareness. For instance, to infer more useful and higher-level context information, users have to define rules again and again when the involved surrounding changes. Real autonomy can be developed if the inference rules can evolve and change automatically according to the changing environment. Potential solutions can make use of big data related techniques like mining and learning algorithms.
- Lack of testing. It can be noted that most of middleware architectures included in this survey are still at the conceptual stage. Prototypes based on some of these middleware solutions have been built, but complete implementations (let alone actual usage) are missing. Besides, commercial or practical operation which goes beyond simple pilot projects is even a more difficult target. More attempts should be made to drive the move of these middleware solutions from theoretical research to tests and further to full-fledged deployment to actual environments.
Conflicts of Interest
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