## 6 Related Work  
Since IoT comprises diverse technologies with varying capacities, there is a need to design appropriate architectures and mechanisms, such that information sharing and communication can be made more efficient. Credible work has been concluded in this regard, while others are still under research. Below, we discuss a few of them.  
The OpenIoT project [33] details an open source architecture that aims to connect Internet-enabled things with cloud computing, thus enabling ICT companies to offer sensor-based solutions. The architecture of [34] consists of three layers, namely application, virtualization and physical plane. The cloud computing capabilities are placed in the virtualization plane. Sensor middlewares are placed in the physical plane, which collects, filters and normalizes sensors data and communicates them to the cloud. The application plane contains utilities that enable users to control and monitor the sensor. These utilities also control requests made from connected services.  
A similar model is also produced in the IoT-A project [35]. However, the artifact, IoT Architectural Reference Model (ARM) [36], provides a more abstract and domain-specific description as opposed to detailing the technicalities. It consists of three major components. An IoT Reference Model stipulates the high abstraction level definitions, as well as information and communication models. These definitions and abstract models are driven by the business vision, scenarios and stakeholder requirements and serve as the second major component. Its IoT Reference Architecture provides a reference for developing IoT compliant architectures and mainly details various views, such as functional, deployment, operational and perspectives, such as security, resilience, performance and interoperability, derived from the scenarios and requirements.  
[PERSON] _et al._[37] proposed a self-adaptive architecture for smart spaces, which utilizes an information security measurement ontology (ISMO) to carry out the adaptation process. The model is inspired from IBM's MAPE-K control loop [38]. Though the authors provide a detailed view of the architecture, they did not explicate how user requirements and things' hardware capacities should be addressed while new security strategies are adapted to a given situation.  
Other adaptive security solutions and studies, for instance risk adaptable access control (RAdAC) [39],  
context-sensitive adaptive authentication [40], the RSA Adaptive Authentication platform [41], security event information management solutions, such as AlienVault Unified Security Management [42] and HP ArcSight [43], _etc._, either emphasize a single security service, e.g., authentication or confidentiality, or lack automated adaptation as an essential risk management component.  
EDAS is focused on events corresponding to any security-related service or activity, _i.e._, intrusion, confidentiality, energy, mobility, _etc._ Conceptually, EDAS can be related to the IoT-A ARM model, as it is driven by requirements and scenarios designed for IoT-enabled eHealth in remote patient monitoring settings. It is based on IoT-eHealth essentials that we have identified previously as functional, security and risk management requirements in [44]. Though designed primarily for IoT-eHealth, we suggest that EDAS, as an event-driven architecture, can be utilized for any IoT-enabled services where things can generate and communicate events. However, this proposition further needs to be investigated.