Future buildings will be smart to support personalized people comfort and building energy efficiency as well as safety, emergency, and context-aware information exchange scenarios. In this work we propose a decentralized and embedded architecture based on agents and wireless sensor and actuator networks for enabling efficient and effective management of buildings. The main purpose of the agent-based architecture, which is currently implemented in MAPS, is to efficiently support distributed and coordinated sensing and actuation operations. The high modularity of the proposed architecture allows for easy adaptation of higher-level application-specific agents that can therefore exploit the architecture to implement intelligent building management policies.
tradeoff energy saving and inhabitants’ preferences where preferences can be learnt and predicted through an unsupervised online real-time learning algorithm (analyzing inhabitants’ behavior). MASBO agents reside on a server and constantly monitor data from sensors and eventually actuate some commands. MASBO works as an enhancement to an existing building automation system by adding learning, reasoning and autonomous capabilities. The responsibility of controlling sensors and actuators, and keeping a requested environmental value constant is not addressed by MASBO.
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However, all the aforementioned contributions do not provide agents embedded in
the sensor and actuator devices that would introduce intelligence decentralization and
improve system efficiency. This is due to the exploitation of conventional sensing and
actuation systems that do not offer distributed computing devices for sensing and
actuation. To overcome this limitation, wireless sensor and actuator networks
(WSAN) [
In this paper we propose a decentralized and embedded management architecture
for intelligent buildings that is based on WSANs and overcomes the limitations of the
aforementioned solutions [
The agent-based architecture (see Fig. 1) for decentralized and embedded building management is composed of coordinator agents (CAs), which run in the basestations, and sensor agents (SAs), which are executed in the sensor/actuator nodes. Specifically, the architecture relies on a multi-basestation approach to allow for large buildings composed of multiple floors and diversified environments. Thus, the architecture is purposely hybrid: hierarchical and peer-to-peer. Interaction between CAs is peer-to-peer whereas interaction between coordinator agents and their related SAs (or SA cluster) is usually master/slave. Moreover, SAs of the same cluster coordinate to dynamically form up a multi-hop ad-hoc network rooted at the master CA.
CA1
M/S Interaction
High-level P2P Coordination
CAI
M/S Interaction
CAn
M/S
Interaction SA1,1
SA1,n1
SAi,1
SAi,ni
SAn,1
SAn,nn Multi-hop SA Cluster1
Multi-hop SA Clusteri
Multi-hop SA Clustern ─ Heterogeneous Platform Support incorporates a set of adapters that allow interfacing the system with different type of sensor/actuator platforms. An adapter is linked to a specific hardware device able to communicate with a specific sensor platform in the network. ─ Network Management allows to fully manage a WSAN cluster. This layer supports packet coding/decoding according to the BMF application-level protocol and packet transmission/reception to/from the WSAN cluster.
Moreover, this layer supports device discovery within the cluster. ─ Group Organization provides group-based programming of sensors and actuators, tracking of nodes and groups in the system, and management of node configurations and group compositions. Node organization in groups is specifically defined to capture the morphology of buildings. Nodes belong to groups depending on their physical (location) or logical (operation type) characteristics. ─ Request Scheduling allows the support for higher-level application-specific requests. Through this layer, a CA can ask for the execution of specific tasks to single or multiple SAs or groups of SAs. Moreover, this layer keeps track of the requests submitted to the system, waits for data from the nodes and passes them to the requesting applications. A request is formalized through the following tuple: R = <Obj, Act, R, LT>, where Obj is a specific sensor or actuator belonging to a node, Act is the action to be executed on Obj, R is the frequency of each executed Act, LT is the length of time over which these actions are to be reiterated. Moreover, a request can target a single node or a group of nodes having Obj. ─ Inter-CA Coordination offers efficient mechanisms for coordination between CAs. Specifically, CAs cooperate for submitting queries and retrieving data spanning multiple SA clusters.
SA is designed around the following layers:
─ Hardware Sensor Platform allows to access the hardware sensor/actuator
platform. In particular, the layer facilitates the configuration of the platform
specific drivers and the use of the radio.
─ Network Management manages the node communication with the reference CA
and among the cluster nodes according to the BMF application protocol.
─ Sensing and Actuation Management allows to acquire data from sensors and
execute actions on actuators. In particular, this layer allows to address different
types of sensors/actuators in a platform independent way.
─ Node Management is the core of the SA and allows to coordinate all the layers
for task execution. In particular, it handles events from the lower layers every
time that a network packet arrives or data from sensor/actuator are available,
and from the upper layers every time that data are processed or a stored request
has to be executed.
─ Dynamic Group Management provides group management functionalities to the
SA. A node can belong to several groups at the same time and its membership
can be dynamically updated on the basis of requests from CAs.
─ In-node Signal Processing allows the SA to execute signal processing functions
on data acquired from sensors [
Currently the agent-based architecture is implemented in JADE [
In this paper we have proposed an agent-based architecture for flexible, efficient and embedded sensing and actuation in buildings. Specifically, the distributed software architecture is embedded into both WSANs and more capable computing devices (e.g. PCs, smartphones, plug computers). The proposed architecture can be seen as basic middleware for developing intelligent building management systems to achieve the Smart Building concept. Currently the proposed architecture is exploited to monitor the space occupation and energy expenditure in computer laboratories for students to analyze energy consumption patterns with respect to users’ behavior so as to semi-automatically implement behavior policies. On-going work is devoted to the design of a higher-level agent-based architecture for Smart Buildings atop the proposed architecture to trade off inhabitants’ personal comfort and building energy expenditure.
Acknowledgments. This work has been partially supported by CONET, the Cooperating Objects Network of Excellence, funded by the European Commission under FP7 with contract number FP7-2007-2-224053.