## 2 Related Work
### Context Models  
Context modeling has received much attention from researchers, maintaining an essential role in the context life cycle. Many surveys of context-modeling methods can be found in the literature [6; 9; 12; 39]. The current overview does not focus on the different modeling methods, for which the reader may refer to the aforementioned citations, but rather focuses on the details of context representation. [PERSON] [40] uses a key-value technique to map context data. Actors, devices and abstract devices are entities that expose events, which have a positive or negative distinction. [PERSON] and [PERSON] [11] propose a model that includes the entities _person, device, activity_ and _place_ as core classes, while their method of choice is a graphical language called CML. Each entity has relations to other entities with a certainty parameter attributed. CONON [41] is one of the first attempts to represent context, using formal ontologies. _Computational entity, person, location_ and _activity_ are subclasses of the main class of _context entity_. Upper-level as well as domain-specific ontologies are utilized to specialize from generic to actual ecosystems. ContextUML [42] models context, using atomic and complex context classes to represent low-level and high-level data. Context acquisition is not modeled but abstract context resources are utilized. _Object, service_ and _mechanism_ are core classes of the ContextUML model. CARE [43] categorizes context providers as users, network operators and service providers. Each entity falls under one of these categories and utilizes a profile manager to declare policies and derive high-level context data. SeCoMan [44] is a CA framework suitable to represent privacy and security. As such, the model is centered around the location and privileges related to persons. It follows a two-layer architecture, with the first layer capturing a representation of generic ecosystems tagged with privacy conditions and the second layer capturing the specifics of the applied ecosystem (a supermarket). [PERSON] [45] proposes a model where context is the main class, while location, user profile, environmental parameter and other contextual data are subclasses. Furthermore, a meta-model is defined, which relates context to rules and contextual elements. ECOPPA [46] is specialized in physical activity applications, thus activity assumes an important role in the model. _Computational entity, person, location_ and _environmental entity_ are associated with _activity_. The distinction between the upper level and domain level, found in other models, is also present in ECOPPA. Persuasion service is an interesting addition, which represents the recommendation service of the CA application. [PERSON] [47] proposes a meta-ontology, which includes six main classes: _user, device, activity, time, location_ and _service_. Each class is further analyzed into subclasses, and relations between the main classes are specified. An interesting design choice is that time is represented as a class rather than as a class property. [PERSON] et al. [48] propose a meta-context model, which efficiently models uncertainty. _User, actuator, environment_ and _service_ are all subclasses of thing_, which is the core class of the model. Furthermore, context is categorized as internal, external or boundary. MSSN-Onto [49] proposes an ontology that models multimedia sensory data, providing data interoperability in identifying events applied in various domains. The ontology captures different aspects of context, such as the observation aspect that includes _sensor_, _system_, _procedure_ and _observation_ classes. The modeling of sensor and data-specific contexts includes _scalar sensor_, _media sensor_, _multimedia data_ and _media segment_ classes. HSSN ontology [50] extends the semantic sensor network in order to apply data, sensor and platform diversity. _Mobile sensor_ and _static sensor_ are added as child concepts to _sensor_.