Software and Manuals

Hardware Security Board

The Hardware Security Board consists of a physical hardware device which provides the link between sensors (data generators) and the COSMOS environment/platform. The Hardware Security Board can be either attached to one sensor or can be a hub for an entire collection of sensors (e.g. temperature, pressure, humidity, surveillance cameras, etc.).

Inference and Prediction

Inference and Prediction functional component is responsible for providing high-level knowledge from raw IoT data using different pattern recognition techniques. In this context, we have explored several supervised machine learning techniques including different variants of Support Vector Machines and K- Nearest Neighbor, in addition to statistical techniques such as Hidden Markov Model (HMM).

Data Pre-Processing

Thing's data pre-processing functional component provides several functions at Things and platform level which are summarized below:


Storlets are computational objects that run inside the object store system. Conceptually, they can be thought of the object store equivalent of database store procedures. The basic idea behind storlets of performing the computation near the storage is saving on the network bandwidth required to bring the data to the computation.

Data Mapper

The Data Mapper functional component is responsible for the ingestion of IoT data flowing on the COSMOS Message Bus into the cloud storage, annotating them with enriching metadata.

Situational Awareness

The large volume of data which is made available in an IoT environment does not necessarily mean applications can take effective decisions directly or make correct interpretations. For example, a single vehicle reporting a low speed is not always an indication of a traffic jam.


The role of Registry component is to provide the adequate functionality for the retrieval of Things. This Registry is in fact a semantic Registry and is backed by the core COSMOS ontology.


The main functionality of the Planner component is “solving” problems. As such, its nature is depended on the reasoning approach that COSMOS follows. We developed an ontology-based Case-Base Reasoning Planner and adopted the Flat Memory and the Category Exemplar model. When the Planner “senses” a situation or accepts a query, it means that there is a new problem to solve.


Privelets component is an essential part of the COSMOS chain. Things are able to communicate with each other as well as with COSMOS platform and thus expose information linked to the end-user and his/her environment.

µCEP Engine

The µCEP (micro Complex Event Processing) Engine is provided in different flavours. You can download ready-to-use binary files for several platforms at our COSMOS GitHub repositories, or simpler, just deploy a Docker container and start using it. Alternatively, you can clone the source code and compile it yourself.