-Ambient Intelligence aims at autonomic coordination domotic solutions. An enhancement to ISO/IEC 14543-3 and control of appliances and subsystems located in a given standard, a.k.a. EIB/KNX (European Installation Bus/Konnex) environment. Home and Building Automation (HBA) complies [3], has been devised. Particularly, a context-aware multitwhiethustherisapnadraadsitgamticbsuett iotfisopbearsaetdioonnalasnceenxaprliicoist. iTnhteisrapcatpioenr wpritoh- agent framework for building automation is proposed, which poses a more flexible multi-agent approach, leveraging semantic- supports semantic annotation of both user profiles and device based resource discovery and orchestration in HBA. Backward- capabilities. The integration of a semantic micro-layer within compatible enhancements to EIB/KNX domotic standard allow KNX protocol stack enables novel decision support features to support the semantic characterization of user profiles and in HBA, while preserving full backward compatibility. sdueivtiacbelefuhnocmtioensaelritviiecse,s/sfounecntiaobnlainligt:ies(i)acnceogrodtiinagtiotno iomf ptlhiceitmaonsdt Machine-understandable metadata characterize home enviexplicit user needs, (ii) device-driven interaction for adapting the ronment, appliances and user profiles and preferences. Annoenvironment to context evolution. A power management problem tations are expressed in ontological formalisms derived from in HBA is presented as a case study to better clarify the proposal Description Logics (DLs) [4]: particularly DIG [5] has been and assess its effectiveness. adopted, being a more compact equivalent of OWL-DL1. As EIBIn/KdeNxX,TeSremmsa-ntAicmWbieebn,t MIunltteil-lAiggeenncte,SyBstueimldsi.ng Automation, opposed to both static configuration approaches of standard HBA technologies and user-driven service selection of most I. INTRODUCTION ruesseera-trrcahnsppraorpenotsaalnsd, tdheevifcrea-mdreiwveonrkintwereacptrioesne.nTtohtehries aeinma,btlhees Ambient Intelligence (AmI) [1] refers to a research vision adopted multi-agent system allows requests coming from users built upon advances in sensors networks, pervasive computing and/or devices being collected by a home mediator which acts and artificial intelligence, that make a given environment as a broker between users and home appliances. Each request capable of being sensitive and responsive by recognizing user is treated as a one-to-many negotiation among sender agent needs and self-adapting accordingly. Devices communicate and various device agents. Such a complex process is divided and interact autonomously, without direct human intervention, in concurrent one-to-one negotiations between the home agent also making decisions based on multiple factors, including user and each device agent. Services/resources so selected are used presence and preferences. They are coordinated by intelligent to cover sender requirements to the best possible extent. systems acting as supervisors, devoted to manage available The remainder of the paper is organized as follows. Section resources in order to meet assigned requirements. II outlines the proposed framework architecture and enhanceHome and Building Automation (HBA) technologies should ments to KNX standard. A case study is reported in Section adhere to AmI fundamentals, but current systems are still III, while relevant related work is discussed in Section IV. far from that vision, being unable to grant such flexibility Final remarks are in Section V. and autonomy. They now basically enable a static set of operational scenarios pre-defined during implementation and II. SEMANTIC-BASED HOME AUTOMATION require explicit interaction with the user. On the contrary, an Multi-Agent Systems (MAS) are very helpful in HBA due to advanced and flexible management of information about users, their ubiquitous and distributed nature. Hereafter the reference devices and resources/services in a given ambient is needed. framework and the related infrastructure are reported. Really smart HBA infrastructures, autonomously controlling A. Knowledge-based domotic and agent framework and adapting building appliances, could be conceived borrowing frameworks and approaches from artificial intelligence As shown in Figure 1, the adopted MAS comprises a studies in pervasive computing field, also adapting theory and mediator as well as user and device agents referred to home solutions of agent-based software design [2]. appliances -including energy-providing systems (e.g., photoThis paper proposes the exploitation of Knowledge Rep- voltaic collectors). The number of connected resources and resentation (KR) technologies and automated reasoning tech- agents may vary unpredictably -a new user, device or energy niques, originally implementing the Semantic Web paradigm 1OWL Web Ontology Language, W3C Recommendation, February 10th and properly adapted, to overcome restrictions of common 2004, available at http://www.w3.org/TR/owlfeatures/
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The user agent, running on a mobile client, addresses requests toward the home manager, describing the needs and preferences of the user. Each device agent can expose one or more services (i.e., functional profiles). The agent-based architecture and EIB/KNX protocol enhancements allow also device agents to issue requests to the home system in order to supports automatic reconfiguration and adaptation to changing conditions. Figure 2 refers to the agent modeling scheme.
Smart Device Agents are thought to be embedded within advanced devices (i.e., home appliances with some computational capabilities and memory availability). They encapsulate device status and properties in a semantic annotation to be provided during discovery operated by other agents or to be issued in semantic-based requests toward the home agent. Such requests are generated after a sensor data gathering phase or when the internal status changes. The goal is to negotiate a home configuration better fitting a possible new situation.
KNX Device Interface Agents support semantic-based enhancements in case of legacy or elementary appliances (e.g., switches, lamps, and so on). In such cases, if semantic annotations are asked, the request will be replied by the agent. Conversely, if the home agent refers to standard KNX device properties, the request will be simply forwarded by the agent to the device.
The Home (Mediator) Agent has the responsibility of
making the domotic environment a first-class abstraction that
provides the surrounding conditions for agents to exist and
that mediates both the interaction among agents and the
access to resources [
Formally, a request (as well as each available home
service/resource) is expressed as a set of formulas B =
{ 1; 2; : : : ; n} (Si = { i;1; i;2; : : : ; i;m} for the i-th
service, respectively) in Description Logics. ALN (Attributive
Language with unqualified Number restrictions) was adopted
as reference language in the current system prototype and case
study. Each formula represents a preference, to which a utility
value is assigned by means of a function u : B → Q+ such
that ∑h u ( h) = 1 (u : S → Q+ s.t. ∑k u ( k) = 1,
respectively), i.e., utility values are normalized. Besides, each
agent sets a disagreement threshold t, that is the minimum
utility required to pursue a deal. The bilateral negotiation protocol
is of alternating offers with minimum concession type: if some
preferences in B and Si are in contrast, provider and requester
take turns in issuing counter-offers, each relaxing at every step
the preference with the lowest utility value. The process is
repeated until either an agreement is found (i.e., remaining
elements in B and Si are not in contrast) or the negotiation
fails because the residual utility of one of the agents has
gone below its disagreement threshold. The overall utility of
the agreement is then computed as the product of individual
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utilities. Due to space constraints, the reader is referred to
[
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Two Interface Objects –data structures KNX used to set
device properties– have been defined to manage structured and
machine-understandable semantic descriptions. To maintain a
full compatibility with original protocol and applications, new
objects are compliant with structural specification in [
According to this classification, GPD and SPD objects are
featured by properties with following identifiers:
- PID_OBJ_TYPE = 1 (0x01h): 16-bit mandatory field
indicating the object type;
- PID_OUUID = 77 (0x4Dh): 16-bit Ontology Universally
Unique Identifier (OUUID) marking the reference ontology
the device semantic annotation refers to [
B. Reference architecture
The communication architecture infrastructuring the above framework integrates an EIB/KNX bus and an IP network used as fast backbone. Nowadays IP is increasingly adopted in automation systems and particularly in HBA. Such a hybrid home network interconnects several KNX/IP routers and enables the communication among different KNX lines via IP. In this way, devices send and receive KNX group telegrams through multicast IP frames compliant with the EIBnet/IP routing protocol.
As depicted in Figure 6, the overall framework architecture consists of four main functional components: - Central Unit: which represents the system core and embeds Framework Architecture
EIB/KNX BUS
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a mobile client manager, a device manager and a micro
matchmaker (based on the one presented in [
Particularly, the agent running on the central unit allows to: (i) discover and orchestrate suitable home device functionalities compatible with users or context requirements via semaFntic-based inferences; (ii) rank in relevance order w.r.t. received requests the best services/resources to be activated; (iii) find possible inconsistencies between home current status and selected services or resources; (iv) inform about the matchmaking outcomes evidencing possible open issues and negotiation options. During start-up phase, the central unit also takes care of system configuration. It finds out all KNX routers connected to the home LAN through a discovery procedure defined in EIBnet/IP standard. For each router, a new bidirectional tunneling channel is established and the system is ready to accept further semantic requests.
Figure 7 shows a typical system interaction. Along with requests issued by a User Agent toward the Home Agent running in the central unit, the proposed system also allows a Device Agent to perform queries. In such case, devices exploit the previously described novel application layer service for conveying semantic requests in one or more KNX frames. Routers then forward them to the central unit over the IP
network. Instead, if the request comes from a mobile client, the centra l unit directly receives it via Bluetooth/Wi-Fi and processing starts at time t2 of the diagram in Figure 7. In either cas e, the Home Agent aims to find a set of suitable home functionalities for performing a semantic-based covering process. Given a request and several availa ble services –i.e., home appliances– the covering allows to compose services in order to satisfy the request to the best possible extent.
The orchestration process can be formalized as in what
follows:
1. A FUNCTIONALITY REQUEST message is sent to KNX
Router to discover available home appliances.
2. For each on-line device, the router sends a
PROPERTY REQUEST message to retrieve compressed semantic
annotations of exposed services/resources.
3. Data received from devices are then forwarded to the central
unit, decoded and temporarily stored in local memory.
4. Algorithm 1 is applied to request and service annotations.
An early compatibility check is performed in order to find
any active service/resource in conflict with the request. They
will be deactivated subsequently. Then a Concept Abduction
Problem (CAP) [
A case study referred to power management in home automation was developed to make evident the capabilities of the proposed agent-based framework. The home agent exploits a bilateral negotiation process whose final aim is to obtain a logic-based ranking of available services and resources according to current status of user, devices and home environment, seeking to maximize both comfort and energy efficiency.
Let us consider the following example scenarios taken from our case study. EIB/KNX-compatible equipment in Bob’s house includes: an air-source heat pump, an electrical heater, photovoltaic collectors with battery accumulator and a weather station measuring outside temperature. Bob comes home from work and feels cold. He inputs this information to the user agent on his smartphone, which issues a request to the home agent in order to provide heating. The home agent collects environmental information from device agents and associates it to the user profile, so that the request to be satisfied takes both user preferences and home status into account. Weather station reports that outside temperature is 10◦C, while photovoltaic accumulator reports that 4kW h energy is available in scenario A and 0kW h in scenario B. The proposed example can be formalized as follows with respect to an HBA ontology (expressly defined for the case study and not reported here due to lack of space).
Requests in scenarios A and B, named BA and BB respectively, are reported in Table I and Table II. They combine the user agent requirement for a service suggested in case of cold feeling and contextual information about temperature and energy availability, provided by the weather station and energy manager agents, respectively. Higher utility is assigned to the user preference, because user satisfaction is the primary goal of the related agent. Device agents make three service profiles available for activation: heat pump, electrical heater at half power, electrical heater at full power. They are named S1, S2 and S3 and their descriptions are reported in Table III, Table IV and Table V, respectively. Utility values in Table III model the fact that the heat pump is more beneficial when no self-produced electric power is available and for higher external temperatures (due to thermodynamics, coefficient of performance is higher when working at a lower temperature differential). Values in Table IV model the fact that the electrical heater is more beneficial when self-produced electric power is available, while those in Table V model the fact that using the heater at full power requires more electricity, but is more efficient at lower temperatures.
Let us consider scenario A. As explained in Section II, the home agent (i) receives the functionality request from the user agent, (ii) collects available service descriptions from device agents, (iii) checks compatibility between active functionalities and the request, (iv) solves the Concept Covering Problem in order to find functionalities that are suitable to cover (part of) the request, mediating negotiation to select the ones with highest utility, and (v) activates selected functionalities. In our example, no service is active at the request time, so step (iii) has no effect. The first negotiation round occurs between user agent with request BA and the first device agent, heat pump, with service S1. It can be noticed that constraints A;3 and 1;3 about temperature are in conflict, as well as constraints A;2 and 1;2 about available energy. Therefore negotiation is carried out as in what follows: 1. User agent discards A;2 (u = 0:8; u = 1). 2. Device agent discards 1;3 (u = 0:8; u = 0:8). No more conflicts exist and utility of both agents is above their thresholds, so an agreement is reached with overall utility u = u u = 0:64. Discarding environmental constraints can appear as inappropriate, since they model matters of fact, not modifiable preferences. This kind of situations can be taken into account by dividing every request and service profile in two sets of constraints, strict and a negotiable ones, with violation of any strict constraint immediately leading to a missed deal. In the current system prototype strict constraints are not implemented (although it is trivial to do so, with the framework already in place), but similar effects can be obtained by properly setting utility values and disagreement thresholds.
Negotiation is executed in the same way in all the other cases. Utility outcomes are summarized in Table VI. It can be noticed that, when solar power is available (scenario A), the heater is globally more beneficial than the heat pump, because no external resources are consumed, even though the heat pump is more efficient than the electrical heather from a thermodynamic standpoint. Conversely, when no self-produced power is available (scenario B), the heat pump is preferred. Nevertheless, it is useful to notice that for colder outside temperatures the utility of the heat pump would decrease and the heater might become the best option again.
The presented example is purposely simplified in order to make presentation of the proposed approach clear and short. In real scenarios, more articulated requests and service descriptions can be used. Benefits of the framework (enabling logicbased matchmaking and negotiation with support to approximate matches and service ranking) become even larger w.r.t. both standard home automation technologies, characterized by static profiles, and other state-of-the-art ontology-based agent infrastructures, which support only rule-based inferences and exact matches.
Ambient Intelligence aims to increase comfort of
living/working environments by efficiently exploiting available
services and resources. Flexible and adaptive discovery and
fruition of pervasive and embedded systems must be leveraged
for that. Therefore, mobile context-aware middlewares [
Unfortunately, the above agent-based solutions either
require direct user intervention or support only elementary
agent behaviors and basic interactions, lacking advanced
service/resource characterization, discovery and composition.
The exploitation of knowledge representation and reasoning
techniques and technologies is thought as a means to reach
higher levels of accuracy and controllability w.r.t. the above
approaches, resulting in an improvement of user comfort and
building efficiency. An agent system approach based on logic
reasoning was proposed in [
The paper presented a distributed multi-agent framework for home and building automation, based on a semantic enhancement of EIB/KNX standard exploiting knowledge representation and reasoning technologies. The proposed approach allows advanced, fine-grained resource/service discovery grounded on the formal annotation of user characteristics and device capabilities and leveraging logic-based negotiation. The devised framework has been realized in a prototypical testbed in order to verify both feasibility and effectiveness.
The authors acknowledge partial support of national project ERMES (Enhance Risk Management through Extended Sensors) - PON (2011-2014).