Semantic Modelling of Plans and Execution Traces for Enhancing Transparency of IoT Systems

Milan Markovic, Daniel Garijo, Peter Edwards, Wamberto Vasconcelos

Research output: Chapter in Book/Report/Conference proceedingPublished conference contribution

9 Citations (Scopus)
11 Downloads (Pure)

Abstract

Transparency of IoT systems is an essential requirement for enhancing user’s trust towards such systems. Provenance mechanisms documenting the execution of IoT systems are often cited as an enabler of such transparency. However,
provenance records often lack detailed descriptions of a system’s expected behaviour. Plan specifications describe the steps needed to achieve a certain goal by a human or an automated system. Once plans reach a certain level of complexity, they are typically decomposed in different levels of abstraction. However, this decomposition makes it difficult to relate high level abstract plans
to their granular execution traces. This paper introduces EPPlan, a vocabulary for linking the different levels of granularity of a plan with their respective provenance traces. EP-Plan also provides the means to describe plan metadata such as constraints, policies, rationales, and expected participating agents associated with a plan.
Original languageEnglish
Title of host publicationProceedings of the 6th IEEE International Conference on Internet of Things
Subtitle of host publicationSystems, Management and Security
PublisherIEEE Explore
Pages110-115
Number of pages6
ISBN (Electronic)978-1-7281-2949-5
ISBN (Print)978-1-7281-2950-1
DOIs
Publication statusPublished - 23 Dec 2019
EventThe 6th IEEE International Conference on Internet of Things 2019: Systems, Management and Security 2019 - Granada, Spain
Duration: 22 Oct 201925 Oct 2019
Conference number: 6th
https://ieeexplore.ieee.org/xpl/conhome/8924808/proceeding

Conference

ConferenceThe 6th IEEE International Conference on Internet of Things 2019
Abbreviated title(IOTSMS 2019)
Country/TerritorySpain
CityGranada
Period22/10/1925/10/19
Internet address

Bibliographical note

The work described here was funded by the award made by the RCUK Digital Economy programme to the University of Aberdeen (EP/N028074/1), a SICSA PECE travel award, the Defense Advanced Research Projects Agency with award W911NF-18-1-0027, the SIMPLEX program with award W911NF-15-1-0555 and from the National Institutes of Health under awards 1U01CA196387 and 1R01GM117097.

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