Pervasive computing systems such as smart spaces typically combine multiple embedded and/or mobile sensing, computing and interaction devices. A variety of distributed computing approaches are used to integrate these devices to support coordinated applications. This paper describes how simple user descriptions of (primarily) physical aspects of such a system can be combined with information from system introspection to make the system and its log recordings more understandable to potential users, as well as supporting easier configuration and monitoring, and allowing the expression of certain kinds of system behaviour that are otherwise hard to achieve

De Roure, David

Frey, Jeremy

Glover, Kevin

Greenhalgh, Chris

Humble, Jan

Page, Kevin

Robinson, Jamie

Wilson, Steve

English

Southampton (e-Prints Soton)

3 combines information derived from introspection of the running  (software)  system  with  user-provided descriptions  of  the  hardware  in  use  and  the  physical context. Work on the PCM was initially motivated by the conjecture that pervasive systems such as this might be  easier  to  understand  by  having  first-class representations of the physical and hardware elements of  the  system  as  well  as  the  digital  and  software elements. Although this prototype is built specifically as a client for ECT, the same principles could be applied to other middleware for pervasive computing. This  section  describes  the  architecture  and  general operation  of  the  PCM;  the  following  section  gives examples  of  its  use  and  capabilities.  The  PCM  is implemented  in  Java  using  the  Eclipse  Rich  Client Platform  (RCP),  i.e.  it  is  a  “heavyweight”  desktop application. Its architecture is as shown in figure 2 and described  below.  It  is  implemented  using  the  JENA  open source RDF framework for Java.  The  internal  world  model  is  defined  using  a relatively  simple  OWL  ontology  and  maintained  in  a JENA RDF model (figure 2, centre). The graphical user interface  consists  of  a  number  of  Eclipse/RCP  views which  display  different  subsets  and representations of this common model, e.g. a “physical” view (figure 1, top right), a software component view, a properties view (figure 1, bottom centre). Changes in the JENA model trigger events which cause the views to be updated (a typical model-view-controller approach).  The system connects to a running ECT system (or can start a new one), and continuously introspects the running system to find: all computers that are part of the system (such as that presented by the computer icon in figure  1),  all  software  components  that  are  currently running,  all  software  components  that  can  be  created and  all  links  currently  established  between  software components. This introspection is a standard capability of ECT. This information is mapped to the ontology and published and maintained in the common model, which therefore is kept up to date with the state of the software system.  Through a simple drag and drop interface the user can  identify  physical  ‘things’  that  are  currently  to  be considered as part of the system (figure 1, left, top & bottom,  and  the  ‘user-described’  items  in  the  main view).  These  can  include:  sensors  such  as  the  PIR sensor, devices such as the data capture board, locations such as the lab or other significant entities. Internally, these are cloned from the ‘possible things’ RDF model to the common model.  Linked to the common model is a forward-chaining rule  engine  (the  standard  RETE  engine  provided  in JENA). As the model changes – due to user action or changes in the software system – these rules can fire to add or remove inferences to or from the current model. Figure 1. Screen image of the Physical Configuration Manager showing a view of a chemistry laboratory. 

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Combining System Introspection with User-Provided Description to Support Configuration and Understanding of Pervasive systems

Combining System Introspection with User-Provided Description to Support Configuration and Understanding of Pervasive systems

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