Tractable Policy Management Framework DAIS ITA for IoT Geeth de - - PowerPoint PPT Presentation

DAIS ITA

Distributed Analytics and Information Science International Technology Alliance

dais-ita.org

Tractable Policy Management Framework for IoT

SPIE DSS 2017 – Conference 10190 Ground/Air Multisensor Interoperability, Integration, and Networking for Persistent ISR VIII

Geeth de Mel, IBM UK

Geeth de Mel, IBM United Kingdom Ltd., UK Emre Goynugur, Murat Sensoy, Ozyegin University, Turkey Seraphin Calo, IBM Thomas J. Watson Research Center, USA

Overview: What is IoT?

Explosion in IoT

3

?

• Why Policies
• End users want to program their own devices (e.g. IFTTT)
• Each user has different preferences
• Policies as soft constraints
• It is not trivial to learn some rules through user behaviors
• it is forbidden to play music in room X. why?
• Learning takes time, rules are instant

Adaptive Interaction Governance for IoT

• Features of an IoT Policy Framework
• Expressiveness
• Efficient Reasoning over a large amount
• f data
• Automatic conflict detection
• Automatic conflict resolution
• Value added is from the transformations done by services

CONSTRAINTS

Traditional Policy Frameworks: Issues w.r.t IoT

Policy Creation Tool Policy Management Tool

Policy Repository

Policy Decision Point Policy Enforcement Point

System Logs

Policy Audit Tool

User

Requester

• Bulky
• Designed for IT systems
• Verbose
• Human oriented specification
• Lack of transparency
• Explicit rules for each situation
• Rigid
• E.g., Pace maker
• Responsibility issues
• E.g., Driverless car
• Policy Refinement

6

Contextual A new Paradigm Iterative & Stateful Interactive Adaptive

• A semantic policy language based on

OWL-QL

• Efficiency, expressiveness, and conflict

detection

• Model devices as a set of services
• Television: an Internet browser, a display, a

speaker and so forth.

• Define high-level policies w.r.t. ontology

and refine them to device/service level

• Use an AI planner to minimize the

violation costs

Tractable Policy Management Framework for IoT

Prohibitions Obligations

χ : ρ ?d : Device(?d) N P α Baby(?b) ∧ Sleeping(?b) ∧ inFlat(?b, ?f ) ∧ inFlat(?d, ?f ) a : ϕ ?a : MakeSound(?a) e Awake(?b) c 10.0 χ : ρ ?d : Doorbell(?x) N O α SomeoneAtDoor(?e) ∧ producedBy(?e, ?x)∧ belongsToFlat(?x, ?f ) ∧ hasResident(?f , ?p) ∧ Adult(?p) a : ϕ ?a : NotifyWithSound(?a) ∧ hasTarget(?a, ?p) e c 4.0

What is an Ontology?

“formal, explicit specification of a shared conceptualisation”

• T. Gruber (1993)

• A semantic policy language based on

OWL-QL

• Efficiency, expressiveness, and conflict

detection

• Model devices as a set of services
• Television: an Internet browser, a display, a

speaker and so forth.

• Define high-level policies w.r.t. ontology

and refine them to device/service level

• Use an AI planner to minimize the

violation costs

Tractable Policy Management Framework for IoT

Prohibitions Obligations

χ : ρ ?d : Device(?d) N P α Baby(?b) ∧ Sleeping(?b) ∧ inFlat(?b, ?f ) ∧ inFlat(?d, ?f ) a : ϕ ?a : MakeSound(?a) e Awake(?b) c 10.0 χ : ρ ?d : Doorbell(?x) N O α SomeoneAtDoor(?e) ∧ producedBy(?e, ?x)∧ belongsToFlat(?x, ?f ) ∧ hasResident(?f , ?p) ∧ Adult(?p) a : ϕ ?a : NotifyWithSound(?a) ∧ hasTarget(?a, ?p) e c 4.0

Reasoning about Policies for IoT

Policy Conflicts

• Two policies conflict if all three conditions hold true:
• Both policies are refined to the same addressee
• One is an obligation and the other is a prohibition
• Activation conditions hold true in a consistent world state
• Conflict detection is a query containment problem
• Apply query freezing to reduce it to query answering

Architecture

Policy Conflict Resolution

Find alternative ways to fulfill an obligation

Planning

• Suitable to integrate with a knowledge base
• known information is our domain
• current state is the initial state
• Convert device services into domain actions
• Embed ontology information into the domain file
• e.g. reasoning, consistency check
• Violated policy costs are added to the plan cost
• Preference1- and Cost2- based Planning

• IoT systems should be autonomous and intelligent
• Need a scalable, efficient, and expressive policy framework
• E.g., OWL-QL knowledge base
• Devices should collaborate (also with web services)
• Automated detection and resolution of conflicts is essential
• Reformulate conflict resolution as a planning problem
• Future Work
• Violation costs may be learned from user history
• Stream data and its impact on planning

12

Conclusions

Any Questions?

Acknowledgement

This research was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence under Agreement Number W911NF-16-3-0001. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence

• r the U.K. Government. The U.S. and U.K. Governments are authorized to

reproduce and distribute reprints for Government purposes notwithstanding any copy-right notation hereon.

• Dr. Şensoy thanks the U.S. Army Research Laboratory for its support under grant

W911NF-14-1-0199 and The Scientific and Technological Research Council of Turkey (TUBITAK) for its support under grant 113E238.