=Paper=
{{Paper
|id=None
|storemode=property
|title=Decentralized and Embedded Management for Smart Buildings
|pdfUrl=https://ceur-ws.org/Vol-752/03-07fortinoetal.pdf
|volume=Vol-752
|dblpUrl=https://dblp.org/rec/conf/asa/FortinoG11
}}
==Decentralized and Embedded Management for Smart Buildings==
https://ceur-ws.org/Vol-752/03-07fortinoetal.pdf
PROCEEDINGS OF THE WORKSHOP ON APPLICATIONS OF SOFTWARE AGENTS
ISBN 978-86-7031-188-6, pp. 3 - 7, 2011
Decentralized and Embedded Management for Smart
Buildings
Giancarlo Fortino and Antonio Guerrieri
DEIS – University of Calabria,
Via P. Bucci, cubo 41c, Rende (CS), 87036, Italy
g.fortino@unical.it, aguerrieri@deis.unical.it
Abstract. 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.
Keywords: Multi-agent systems, Building Management Systems, Wireless
Sensor and Actuator Networks, MAPS.
1 Motivations and Related Work
Nowadays, due to advances in communication and computing technologies, the
need to have high comfort levels together with an optimization of the energy
consumption is becoming important for inhabitants of buildings. Moreover, buildings
should also support their inhabitants with automatic emergency and safety procedures
as well as context aware information services. To meet all these requirements, future
buildings have to incorporate diversified forms of intelligence [1].
We believe that agent-based computing [2] can be exploited to implement the
concept of intelligent buildings due to the agent features of autonomy, proactiveness,
reactiveness, learnability, mobility and social ability. Specifically agents can
continuously monitor building indoors and their living inhabitants to gather useful
data from people and environment and can cooperatively achieve even conflicting
specific goals such as personalized people comfort and building energy efficiency.
A few research efforts based on agents have been to date proposed to design and
implement intelligent building systems. In [3] the authors present the MASBO (Multi-
Agent System for Building cOntrol) architecture that aims to provide a set of software
agents to support both on-line and off-line applications for intelligent work
environments. MASBO is used to develop a multi-agent system (MAS) able to
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PROCEEDINGS OF THE WORKSHOP ON APPLICATIONS OF SOFTWARE AGENTS, 2011
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.
In [4] the authors propose a working solution to the problem of thermal resource
distribution in a building using a market-based MAS. Computational agents
representing individual temperature controllers bid to buy or sell cool or warm air.
The agents, running in a monolithic process on a workstation, are able to distribute
the thermal resources so that all the building offices have an equitable temperature
distribution. Temperature sensors and air flow actuators are all accessible directly
through distributed hardware modules via a network connection.
In [5] the authors describe a MAS that monitors and controls an office building in
order to provide added values like energy saving together with the delivery of energy.
The developed system is distributed in the sense that some agents are located on
PDAs and others run on the Bluetooth access points (workstations) that communicate
with the PDAs. The system makes use of the existing power lines for communication
between the agents and the sensing and actuation system controlling lights, heating,
ventilation, etc.
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) [6] can be adopted. WSANs represent a viable and more flexible solution to
traditional building monitoring and actuating systems (BMAS), which require
retrofitting the whole building and therefore are difficult to implement in existing
structures. In contrast, WSAN-based solutions for monitoring buildings and
controlling equipment, such as electrical devices, heating, ventilation and cooling
(HVAC), can be installed in existing structures with minimal effort. This should
enable monitoring of structure conditions, and space and energy (electricity, gas,
water) usage while facilitating the design of techniques for intelligent device
actuation.
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 [3][4][5]. In particular, the aim of our architecture is to
optimize and fully decentralize the sensing and actuation operations through
distributed cooperative agents both embedded in sensor/actuator devices and running
on more capable coordinators (PC, plug computers, PDA/smartphones). The proposed
architecture can be easily programmed to support a wide range of building
management applications integrating comfort, energy efficiency, emergency, safety,
and context-aware information exchange aspects.
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PROCEEDINGS OF THE WORKSHOP ON APPLICATIONS OF SOFTWARE AGENTS, 2011
2 Agent-Based Architecture
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.
CA's Layers
Request Scheduling
High-level P2P Coordination
Group Organization
Network M anagement
CA1 CAI CAn
Heterogeneous Platform Support
M/S M/S M/S SA's Layers
Interaction Interaction Interaction DynamicGroup In-node Signal M ulti Request
M anagement Processing Scheduling
SA1,1 SA1,n1 SAi,1 SAi,ni SAn,1 SAn,nn
Node M anagement
Sensing and Actuation
Network M anagement
M anagement
M ulti-hop SA Cluster1 M ulti-hop SA Clusteri M ulti-hop SA Clustern
Hardware Sensor Platform
Fig. 1. Agent-based architecture for decentralized and embedded management of buildings
based on wireless sensor and actuator networks.
On the right side of Fig. 1 the main functionalities of CA and SA are shown
according to a layered organization that is partially derived from the Building
Management Framework (BMF) [7].
CA includes the following layers:
─ 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
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PROCEEDINGS OF THE WORKSHOP ON APPLICATIONS OF SOFTWARE AGENTS, 2011
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 = , 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 [8]. It can compute simple aggregation functions
(e.g. mean, min, max, variance, R.M.S.) and more complex user-defined
functions on buffers of acquired data.
─ Multi Request Scheduling allows the scheduling of sensing and actuation
requests. In particular, it stores the requests from CAs and schedules them
according to their execution rate.
Currently the agent-based architecture is implemented in JADE [9] at basestation
side and MAPS [10] at sensor side.
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PROCEEDINGS OF THE WORKSHOP ON APPLICATIONS OF SOFTWARE AGENTS, 2011
3 Conclusions and Future Work
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.
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