Semantic-based Smart Homes: a Multi-Agent Approach

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1 Semantic-based Smart Homes: a Multi-Agent Approach Giuseppe Loseto, Floriano Scioscia, Michele Ruta, Eugenio Di Sciascio Politecnico di Bari, BARI, Italy

2 Outline Home and Building Automation (HBA): state of the art Standards and appliances Ambient Intelligence Knowledge-based HBA: framework and approach Semantic enhancement to EIB/KNX standard Agent Framework Case Study Logic-based negotiation for energy efficiency Conclusion and future work 2 of 17

3 Home and Building Automation: state of the art Goal Increase comfort and building efficiency Decrease waste and maintenance costs Integration of different home systems Most important HBA standards ZigBee (HA Profile) LonWorks X-10 EIB/KNX Low Cost Widespread Ethernet support (KNXnet/IP protocol) 3 of 17

4 Ambient Intelligence Classic Domotics Static and not flexible architectures Limited interoperability Reduced functionalities and scenarios User-driven interaction (low autonomicity) Agent-based Domotics Flexible and scalable Concurrency, cooperation, negotiation enabled Services and resources accessible via agent-oriented frameworks Semantic-based Domotics Improved interoperability Rich description of user/service profiles Decentralized architecture supporting autonomous device-driven interactions 4 of 17

5 Proposed approach Goal A knowledge-based agent framework for HBA: semantic annotation of user profiles, device settings and appliance behaviors w.r.t. an OWL-DL ontology modelling typical home environments home self-configuration through collaboration of autonomous smart agents, capable to provide services and address complex requests Technological Solutions A. Semantic-based enhancement of EIB/KNX protocol standard [Ruta et al., IEEE TII, 7(4), 2011]: integration of a semantic micro-layer preserving a full backward compatibility advanced service and resource discovery support B. Logic-based negotiation process to: negotiate on available home and energy resources through a user-transparent and device-driven interaction; discover the (set of) elementary services that maximize the overall utility and cover the user/device request support non-expert users in selecting home configurations ranked w.r.t. a global utility 5 of 17

6 Framework Architecture [Ruta et al., IEEE TII, 7(4), 2011] KNX NETWORK EIB/KNX BUS KNX ROUTER Device Manager LAN Client Manager Mobile Matchmaker CENTRAL UNIT IP BACKBONE NETWORK MOBILE CLIENTS 6 of 17

7 Agent Framework Device Interface Agents support semantic-based enhancements in case of legacy appliances User Agents running on a mobile client, address a request toward the home environment, describing needs and preferences of the user Smart Device Agents encapsulate their status and properties in a semantic annotation and send semantic-based requests to the home agent for negotiating an environmental profile Home agent acts as a mediator in a negotiation round between the user agent and each available device agent 7 of 17

8 Semantic-enhanced communication If request is originated by a mobile agent, processing starts from time t2 [Ragone et. al., JWSR, 4(3), 2007] 8 of 17

9 Logic-based Negotiation Protocol Integration of knowledge representation and reasoning techniques originally devised for the Semantic Web Ontology Languages (OWL, DIG, RDF) Inference Services Semantic Matchmaking Theoretical framework based on Description Logics (DLs) Negotiation protocol [Ragone et al., EC-Web, 2009], originally devised for e- marketplace scenarios, revised for buildings energy systems multi-issue incomplete information rational agents 9 of 17

10 Negotiation Process 1. Home Agent (mediator) splits one-to-many negotiation (requester agent VS device agents) in several concurrent one-to-one negotiations 2. The negotiation follows an alternating offers pattern with minimum concession The goal is to remove conflicting preferences between B and S i Agents take turns in making concessions (requester moves first) In each round, agent drops the conflicting preference with minimal utility 3. The process ends with: conflict deal, the global utility of an agent is lower than its disagreement threshold; agreement, there is nothing more to negotiate on and the global utility of each agent is greater than its disagreement threshold. 10 of 17

11 Case Study: Energy Management in Smart Homes An OWL-DL ontology specifies classes and properties needed to characterize a typical home environment with energy constraints. Thing Energy Sources Device Service Characteristic Activity Weather Eolic Generator Appliance Blind Regulation General Status Eat Outdoor Brightness Photovoltaics HVAC Light Level Regulation Physical Status Sleep Wheather Conditions Biomass Lighting Temperature Regulation Psychic Status Cook Wind Speed Safety Relax 11 of 17

12 Case Study: Solve Concept Covering Problem REQUEST + CONTEXT ON OFF Request Covering algorithm 1. Find incompatible active services 2. Is the request already completely covered? (Concept Abduction) [Colucci et. al., IJEC, 12(2), 2007] 3. Skip inactive services contrasting with currently active ones 4. The negotiation protocol via Concept Covering [Ragone et. al., JWSR, 4(3), 2007] allows to select one or more inactive functionalities, whose combination cover request features The algorithm returns: the set of services to be activated; the (possibly empty) set of ones to be disabled; a description of the uncovered request, if present. 12 of 17

13 Case Study: Negotiation Example 1/4 B : semantic annotated user profile S : semantic description of services B: User Request S 1 : Heat Pump i β i u(β i ) i σ 1,i u(σ 1,i ) 1 issuggestedforsensation.cold issuggestedforsensation.cold = 0 available kwh = 0 available kwh = 10 outside Temperature outside Temperature 0.2 t β outside Temperature 0.2 u 1 : 0.8 * 1 = 0.8 t S1 0.6 B: User Request i β i u(β i ) 1 issuggestedforsensation.cold = 0 available kwh = 10 outside Temperature 0.2 t β 0.8 u 2 : 0.8 * 0.7 = 0.56 S 2 : Heater at half power i σ 2,i u(σ 2,i ) 1 issuggestedforsensation.cold available kwh outside Temperature 0.3 t S of 17

14 Case Study: Negotiation Example 2/4 B : semantic annotated user profile S : semantic description of services B: User Request S 3 : Heater at full power i β i u(β i ) i σ 3,i u(σ 1,i ) 1 issuggestedforsensation.cold issuggestedforsensation.cold = 0 available kwh available kwh = 10 outside Temperature outside Temperature 0.2 t β 0.8 t S3 0.6 u 3 : 0.8 * 0.8 = 0.64 Heat Pump Heater at half power Heater at full power Scenario # Heat Pump is more efficient with a temperature of 10 and no stored kwh. 14 of 17

15 Case Study: Negotiation Example 3/4 B : semantic annotated user profile S : semantic description of services B: User Request S 1 : Heat Pump i β i u(β i ) i σ 1,i u(σ 1,i ) 1 issuggestedforsensation.cold issuggestedforsensation.cold = 4 available kwh = 0 available kwh = 10 outside Temperature outside Temperature 0.2 t β outside Temperature 0.2 u 1 : 0.8 * 0.8 = 0.64 t S1 0.6 B: User Request i β i u(β i ) 1 issuggestedforsensation.cold = 4 available kwh = 10 outside Temperature 0.2 t β 0.8 u 2 : 0.8 * 1 = 0.8 S 2 : Heater at half power i σ 2,i u(σ 2,i ) 1 issuggestedforsensation.cold available kwh outside Temperature 0.3 t S of 17

16 Case Study: Negotiation Example 4/4 B : semantic annotated user profile S : semantic description of services B: User Request S 3 : Heater at full power i β i u(β i ) i σ 3,i u(σ 1,i ) 1 issuggestedforsensation.cold issuggestedforsensation.cold = 4 available kwh available kwh = 10 outside Temperature outside Temperature 0.2 t β 0.8 t S3 0.6 u 3 : 0.8 * 0.8 = 0.64 Heat Pump Heater at half power Heater at full power Scenario # Heat Pump is more efficient but we have 4 kwh to spend (self-produced, coming from renewable sources). Then we prefer Heater at half power. 16 of 17

17 Conclusion and future work Main contribution Distributed knowledge-based agent framework for HBA Non-standard inferences to support non exact matches and reveal conflicting information between request and resources about energy constraints Logic-based bilateral negotiation protocol applied to buildings energy systems Support for non-expert users in selecting home configurations maximizing both user comfort and home efficiency Future work directions Improve the mobile user agent with automatic user profiling Involve additional domotic protocols in standard enhancement Extend the framework toward a Smart Grid vision with a Home-to-Home negotiation Carry out a simulation campaign to fully evaluate the approach within a Neighborhood Area Network (NAN) 17 of 17

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