Smart Environment

A smart environment is any design that utilizes knowledge of the environment’s context to aid accomplishing various tasks such as navigation, scheduling energy consumption,

notification of activities and evacuation planning. The term “smart” refer to the ability of the

to make decisions or to provide valuable information. The contextual information is defined as

features of information of any entity in the smart environment. For example, the location of

people, the gesture or posture of an individual, the states of certain objects such as doors or

computers, the events ongoing and the activities performed by occupants are all considered to be

contextual information of the smart environment. To collect contextual information, a typical

smart environment is equipped with various sensors, including those detect the density of the

gas, those detect whether there is an occupant inside the detection area, those detect various

states of the objects and those can measure the position of occupants directly. The data obtained

from these sensors are fused in a central control system for handling configurations of energy

supply, air conditioner or providing information related to the current context to the occupants.

Consider a scenario that a smart home application is capable of controlling the cooling system.

We assume the activity of occupants can be labeled as three different categories: sleeping, awake

and absent. From various sensors deployed in the home, the application uses some algorithm to

discover the activity of the occupants. For example, if the occupancy sensor in the bedroom

reports that there is no individual in there, then the application can conclude there is 90%

probability that the occupants are in the state of being awake or absent. By knowing this, the

smart home application turns off the cooling system in the bedroom to save energy consumption.

Moreover, by applying machine learning technology and providing a set of training data to the

smart home application, the application is able to predict when occupant will perform certain

activities. In the bedroom example mentioned above, it is possible for the smart environment

application learn from the data and discover that the occupant normally goes to bed at around

system at around 9:30PM so that when the occupant goes to bed, the air is already cooled and his

comfortableness is maximized.

The information gathered by sensors in a smart environment can be categorized based on

two different resolution criteria: spatial resolution and logical resolution. The spatial resolution

defines the spatial granularity of occupancy problem to be investigated. Under high resolution,

each individual occupant is considered to be the research subject while under low resolution, a

group of individuals in a specific area of the building are considered to be the research subject.

The logic resolution defines the logic granularity of occupancy problem to be investigated. Under high logic resolution, answers to questions such “where is A” are the outcome of the solution to the occupancy problem. Under low logic resolution, answers to question such as “what is happening in the building” are the outcome of the solution to the occupancy problem. The purpose of this dissertation is to build an estimation framework that is capable of estimating

the building occupancy at different spatial resolutions so as to handle different situations. On the

other hand, the possible extensions on integrating questions from different logical resolutions can

also be added. The different resolutions of occupancy problem are illustrated in figure 3.1 and

3.2 where 3.1 presents the spatial resolution of the occupancy problem and 3.2 presents the logic

resolution of the occupancy problem.

(a) Spatial high resolution (b) Spatial low resolution

Figure 3.2 Logic Resolution

Among various information that can be derived from sensors deployed in a smart

environment, the most important one is the location information of occupants, i.e, the occupancy

of the building. This is because occupancy directly related to many aspects of contextual

information of a building such as energy consumption and occupants’ activity. In terms of

energy consumption, occupancy directly informs which parts of the building are in need of

energy and which part of the building do not need energy at all so that the central control system

can effectively handle the energy supply. In terms of activity discovering, occupancy is the key

factors to recognize the ongoing activities of occupants. For instance, when the occupants are

holding a meeting in the building, the detected occupancy in the conference room will be

dramatically increased, in this case, the pattern of occupancy change directly indicates the

activity performed by occupants. Because of the importance of location information, in this

dissertation, we focus on deriving location information of occupants from sensors equipped in

the building. Other research topics related to smart environment such as activity discovery,

activity recognition and learning from data to building occupancy model are out of the scope of

we assume that the smart environment is equipped with binary proximity sensors, which reports

1 when one or more occupants are within its sensing area and reports 0 otherwise. These sensors

have low resolution in detecting the movement of occupants and have the following features in

terms of resolution, accuracy and ambiguity:

1. The sensors provide anonymous position information of occupant. Binary

proximity sensors do not provide measurement of occupant’s identity.

2. Location information obtained is ambiguous when there are multiple occupants in

the sensing area of a sensor. The binary proximity sensor does not have the capability of

identifying the number of occupants in its detection area.

3. Data collected are subject to sensor errors and environmental noise.

4. Sensors are deployed sparsely. The occupants’ locations become latent variables while being outside a sensor’s detection area.

These features make it impossible to directly measure occupants’ positions using binary

proximity sensors. To overcome the difficulties brought by these features, we build an agent-

based model to simulate the occupants’ movements and use particle filter algorithms to assimilate sensor data into the agent-based model dynamically. The data assimilation provides

estimations of occupants’ states in real time, which can be used to supply initial conditions for future simulations to improve simulation results.