=Paper=
{{Paper
|id=None
|storemode=property
|title=Smart Lighting in Multipurpose Outdoor Environments: Energy Efficient Solution using Network of Cooperating Objects
|pdfUrl=https://ceur-ws.org/Vol-1002/paper1.pdf
|volume=Vol-1002
|dblpUrl=https://dblp.org/rec/conf/ipsn/FloreaFPLS13
}}
==Smart Lighting in Multipurpose Outdoor Environments: Energy Efficient Solution using Network of Cooperating Objects==
https://ceur-ws.org/Vol-1002/paper1.pdf
Smart Lighting in Multipurpose Outdoor Environments:
Energy Efficient Solution using Network of Cooperating
Objects.
Anna Florea1, Ahmed Farahat1, Corina Postelnicu1, Jose L. Martinez Lastra1, and
Francisco J. Azcondo Sánchez2
1
Tampere University of Technology, Tampere, Finland
{anna.florea, corina.postelnicu, jose.lastra}@tut.fi
2
University of Cantabria, Santander, Spain
javier.azcondo@unican.es
Abstract. The first applications for smart environments targeted well-scoped
spaces and appliances. These applications were strong drivers to advance Wire-
less Sensor Networks (WSN) and the Internet of Things (IoT). With the evolu-
tion of the technological base, more complex environments became the new
targets. The concept of cooperating objects (CO) enables further advancement
of IoT and helps to grasp the multiple aspects of these environments. This paper
describes smart lighting application for the multipurpose outdoor environment
at the university campus area implemented following the new paradigm. The
application is aiming efficient use of energy and future integration with associ-
ated industrial systems.
Keywords: cooperating objects, embedded devices, web services, energy effi-
ciency, smart lighting.
1 Introduction
Early smart applications targeted stand-alone appliances within small living and/or
work spaces. With the adoption of more mature technology, it became possible to
embed smart applications in more complex environments, exhibiting different levels
of demands and requirements depending on the target domain (e.g. cities [1], factories
[2], and lower level industrial environments [3]).
Smart applications target enhanced user experience while facilitating efficient use
of resources. There are common challenges across all the application domains that
complicate the achievement of the objectives. These challenges include, but are not
limited to: multitude of purposes for which the same environment may be used, big
amounts of users with different profiles, and dynamics of ambience.
WSN and IoT were successfully applied to implement the early applications for
smart environments [4, 5], and continued to evolve driven by newly appeared chal-
lenges. The concept of CO [6] bears on the same technological base, as IoT and WSN
and is perceived as foundation for the future IoT [7]. This approach enables creation
2 A. Florea et al.
of sustainable smart solutions for complex applications in such domains as Smart
Grid [8].
This paper describes an approach to implementation of smart applications in a mul-
tipurpose environment following the cooperating objects paradigm. The use case pre-
sented is a smart lighting application for outdoor docking environment at a university
campus aiming improved user experience and reduced energy consumption. In addi-
tion to primary objectives, the solution is intended for the integration with industrial
systems located inside the building. The paper is structured as follows: Section 2 pro-
vides the research background discussing the technological considerations for smart
lighting applications; Section 3 describes the implemented smart lighting solution;
Section 4 draws the conclusions and outlines the future work.
2 Technological Considerations for Smart Lighting
Applications
2.1 Illumination
Lighting conditions have strong impact on everyday life and individual work per-
formance [9, 10]. Illumination accounts for 5 to 10% of total energy consumption on
the planet [11], with lighting systems presenting huge potential for energy savings
[12]. It is therefore of crucial importance that smart lighting applications should aim
efficient resource usage. Most of the savings can be achieved via suitable (multiple
type) control strategies [13], that have proven so far more effective than simple per-
sonal, institution, occupancy and day lighting driven control [12].
Recommended illumination levels (as produced by the Illumination Engineering
Society) vary from 100 lux in the warehouse areas to 5000 lux for fine inspection
operations [14]. In multipurpose environments, compliance with levels tailored for the
specific needs of one working environment at hand is achievable via a control strategy
allowing to switch between pre-set lighting modes correlated to specific user needs.
As far as energy consumption of lighting solution is concerned there are a number
of aspects to be taken into account. The luminous intensity drops rapidly as distance
from the light source to the observer increases. Because of the non-linear nature of
this dependency, implications of different lighting modes on energy consumption are
not as straightforward as it may be initially expected [14]. Although there are simula-
tion tools available allowing to estimate the energy consumption of lighting applica-
tions, it has been found that simulations tend to overestimate savings [12]. Therefore,
real measurements are needed in order to evaluate the energy efficiency of a lighting
solution in place.
The identified challenges may be addressed by considering LEDs over convention-
al light sources and by implementing smart lighting control customized for the specif-
ics of the environment. Numerous lighting solutions targeting energy efficient per-
formance were developed in previous decade, actively exploiting low consuming light
sources, control techniques targeting low energy consumption [15] and the combina-
tion of both [16]. The challenges of adopting the best practices of smart lighting solu-
Smart Lighting in Multipurpose Outdoor Environments 3
tions are related to the fact, that each application of this kind must be tailored to the
needs of the dedicated users and consider peculiarities of the specific environment.
On the other hand most of the recent solutions rest on same core architectural para-
digms, discussed in the following section.
2.2 Architectural paradigms
WSN technology is used for many different applications, including structural
health care monitoring, habitat monitoring, fire detection or ambient intelligence [2].
A WSN or WASN (wireless sensor and actuator network) is composed of a set of
nodes distributed over an area of interest. The nodes are able to sense, process, drive,
store and communicate. The network produces large amounts of raw data which then
sent to the central server via sink nodes. Some variations of the concept were pro-
posed by the research community looking to enhance either the autonomy of the net-
work (Autonomic Sensor Networks [17]) or data processing and reuse through dy-
namic tagging of semantic information (Semantic Sensor Networks [18]).
Leveraging RFID and WSN, the IoT aims to break the border between physical
and virtual reality through the creation of objects with a virtual representation, which
can be integrated into a network of a global scale to interact with each other.
A generic definition is formulated in [19] as follows: “The main tenet of the IoT is
extension of Internet into physical world, to involve interaction with a physical entity
in the ambient environment”. The entity may be an entity, a device (the means of
integration of the entity with the virtual world), a resource (the software component),
or service (defines standardized interfaces and processes for interaction with entities).
There are many definitions of the IoT proposed [20], with a definition focus shift-
ing in time from the objects themselves to their communication capabilities. The no-
tion of “cooperative IoT” can also be found in the literature [8]. Despite the focus
shift, there are three core features mentioned across all definitions: (1) global scale of
the application, (2) big amounts of devices, (3) heterogeneity of the devices.
Succeeding the IoT, the notion of cooperating objects emerged initially defined at
the abstract level in [21] in the following way: “… a Cooperating Object is a single
entity or a collection of entities consisting of: Sensors, controllers (information pro-
cessors), actuators or cooperating objects that communicate with each other and are
able to achieve, more or less autonomously, a common goal”. While the components
of an object are provided in the definition above, the term cooperation does require
further clarification.
In [6] cooperation is defined as “the ability of individual entities or objects to use
communication as well as dynamic and loose federation to jointly strive to reach a
common goal, which will typically be a goal in sensing or control”. A similar expla-
nation of cooperation is given in [8].
Dynamic cooperation relying on complex messaging patterns with nested messag-
ing threads is highlighted as the minimum technology needs to make object integra-
tion combining both visions a reality [22, 23].
The heterogeneity of devices is resolved by using semantic web service (SWS)
middleware for in embedded devices [24]. This enables CO to be used for complex
4 A. Florea et al.
cross-domain applications, e.g. smart grid enabling smart houses to communicate with
energy providers, marketplaces, alternative energy sources, etc. [8].
WSN and the IoT are paradigms that provide tools and methods for implementa-
tions of the solutions for complex smart environments. The approaches are often used
side by side complementing each other in order to fulfill all the needs of the uncon-
ventional use-cases. This becomes possible due to the similarity of the technological
base, which converges into the notion of CO.
3 Case Study: Smart Lighting Application
The solution described in this paper was designed to provide appropriate illumina-
tion for the multipurpose outdoor environment in a specific utilisation mode using
low amounts of electrical power.
The section is split in five parts, dedicated to the description of the testbed (Section
3.1.), analysis of the utilisation modes of the area (Section 3.2), description of the
designed architecture (Section 3.3), implementation and testing (Section 3.4), and the
opportunities for integration with other industrial applications (Section 3.5).
3.1 Target Environment
The proposed solution is intended for the backyard area auxiliary to one of the
buildings of Tampere University of Technology (Tampere, Finland) showed in Fig. 1.
The area is used for a variety of purposes, including:
§ Students and personnel everyday access to the building via two entrance
doors.
§ Load/unload of material /equipment to/from trucks via two additional ded-
icated doors.
§ Parking purposes (there are several parking spaces in the area).
The zone is illuminated with four lamps, which are turned on and off following the
work time schedule and security guidelines (i.e. some of the lamps are on during the
night time to provide minimum illumination to the area); furthermore, the lamps are
always on during the darkest period of winter.
The existing operational pattern fulfils the basic need for lighting, but does not
consider such important aspects as current utilisation mode of the area and nature of
the environment hosted by the building. As it was previously mentioned, there are
different types of actors attending the area: students, research and support personnel,
and vehicles of various scales. Each of the actors has own purpose when visiting the
area, thus lighting conditions tailored for particular utilisation scenario could facilitate
the goal achievement and offer better user experience to the users of the area. The part
of the building facing the area considerably differs from average study blocks, being
more similar to industrial environment, rather than administrative building. The area
is actively used as a docking station, and preparations for load and unload operations
could become easier if the lighting was automatically adjusted to the activity (i.e.
proper lamps were turned to the need intensity to illuminate the working area).
Smart Lighting in Multipurpose Outdoor Environments 5
Fig. 1. Views of the testbed area: view to the front wall with access doors and legacy lamps
mounted and a view to the parking area.
In addition to the abovementioned problems, the existing lighting installation lacks
energy efficiency due to the type of lamps used, applied control strategy and lack of
dimming capabilities. These obstacles are easily overcome by migration to LED
lamps with ballast offering dimming functionality, which is expected to turn into even
bigger savings as cold climate prevents overheating of the diodes.
3.2 Defining the representation of the area state
The key to the improved user experience lays in the knowledge on the current sta-
tus of the area. Several criteria were considered during the study of the presented
multipurpose environment. The most descriptive parameters, selected for the imple-
mentation are:
─ Users present, indicating both the fact of presence and the category of users;
─ Weather conditions, focusing on the climate dimensions influencing the visibility;
─ Illumination level provided by the natural conditions.
Table 1. Profile dimensions.
Weather conditions
Illumination
Users present Clear Mist Rain Snow
level
Personnel P111 P112 P113 P114
Truck P121 P122 P123 P124
Above threshold
Both P131 P132 P133 P134
None P141 P142 P143 P144
Below threshold …
The notion of profile (P) was introduced in order to combine multiple criteria in
one parameter, to uniquely identify the superposition of the dimensions as shown in
Table 1. Each profile is marked with unique identifier Pijk where indexes stand for
6 A. Florea et al.
one of the alternative values of the profile dimensions (e.g. P111 corresponds to a situa-
tion when there are people in the area, the sky is clear and it is bright outside).
Each profile is mapped to a specific lighting scene (S) - a collection of operating
modes to be assigned to each lamp on-site in order to provide the desired lighting
conditions. The range of operating modes varies depending on the lamp and may
consist of either “on” and “off” modes, or include a set of intermediate stages if dim-
ming features are available.
Table 2. Profile to Lighting Scene mapping.
Lamp operating modes
P (% of total power) S
L1 L2 L3 L4
P111 0 0 0 0 S1
… … …
P244 30 50 50 0 Sm
Table 2. illustrates mapping between profiles and lighting scenes. It is important to
realize, that total amount of lighting scenes is smaller than the amount of profiles,
because same combination of lamp operating modes may apply to more than one
profile.Therefore the approach results in a reasonable number of lighting scenes to be
set up. The provided tabular representations were used as input for the algorithm de-
sign, helping to identify possible changes in future related to changes in amounts of
lamps and profile dimensions, as hard-coded implementation prevents the scalability
of the solution.
3.3 Architecture
The application was designed to serve the two purposes: provide users of the area
with lighting conditions adjusted to their needs, provide detailed information about
the energy consumed by the installation. The first objective can be easily achieved
through sensing of the environmental conditions and user detection and consequent
mapping of the detected profile to the required lighting scene. The second objective
puts requirement for synchronization of the measurements recordings with the profile
changes and raises the question about the degree of granularity of energy measure-
ments. Considering the need to investigate the energy consumption patterns and ob-
tain detailed information about the performance of the updated lighting system, it has
been decided to measure consumption of each individual lamp block installed.
The designed architecture is shown in Fig. 2. Smart lighting application . Due to
the small scale of the target area, only four proximity sensors (denoted as PS1-PS4 in
the figure) are needed for user detection. Sensors allow detecting the direction from
which a user is approaching the area as well as distinguishing between trucks and
people. Additionally a Temperature-Humidity-Light (THL) wireless sensor nodes are
needed to sense illumination level and weather conditions. The complete information
about weather conditions is formed by data from THL sensor and weather web ser-
Smart Lighting in Multipurpose Outdoor Environments 7
vice, which receives full weather profile of the location from a third party weather
service (Weather-Yahoo!1).
Fig. 2. Smart lighting application architecture
The command application is distributed over three embedded devices, supporting
SWS. The first node (denoted as PD in the figure) hosts the main application, receives
input data and communicates commands to the lamps. The other two devices (EA1
and EA2) are energy analyzers; they are intended to measure the individual energy
consumption of the lamps installed. The required amount of energy analyzers depends
on the amount of actuators (i.e. ballasts) and the required granularity of measure-
ments. Each actuator in turn may serve several lamps. Its capacity is limited by the
total power of the load attached.
Targeting detailed measurements of energy consumption, each of the lamps is
provided with a dedicated actuator. The main controller communicates with individu-
al luminaires via a gateway, which transforms the messages received via serial port
into native Digital Lighting Addressable Interface (DALI) messages, understood by
the lamps’ ballasts.
Finally, all three command devices feed the events reporting measurements and
status change for further archiving or use in adjacent systems.
3.4 Implementation and testing
Devices used for the pilot implementation, except the RS-232/DALI gateway, are
shown in Fig. 3. Devices hosting the command logic are three S1000 RTU modules:
1
http://developer.yahoo.com/weather/
8 A. Florea et al.
one with extension for wireless communication (PD) and two with E10 expansion
modules for monitoring of energy consumption (EA1 and EA2). The outputs of the
proximity sensors are wired to the digital inputs of the PD, and W-Z-THL sensors are
communicating the measurements via ZigBee PRO protocol. Each of the energy ana-
lyzer allows to measure energy consumption and related parameters for three phases.
In presented scenario, every phase is assigned to particular ballast and each analyzer
is in charge of two ballasts, helping to distribute evenly the processing load. The bal-
lasts are integrated in the luminaires and are located behind the light sources.
Fig. 3. Hardware components of the implementation: LED lamp with integrated ballast, prox-
imity sensor for outdoor use, wireless THL sensor node, two devices S1000 with wireless
communication and energy analyzer expansion modules.
The command functionality is realized through a set of distributed control and
monitoring applications. Programs run in S1000 nodes are implemented in Structured
Text (ST) language of IEC 61131-3 standard. The weather service is implemented in
Java programming language using the Spring framework. The application uses the
Weather-Yahoo! API to obtain weather information and interpret it in terms of visibil-
ity characteristics defined in Table 1. This information complements the values ob-
tained from THL sensors and helps their adequate interpretation.
Fig. 4. Sequence diagrams of possible operation scenarios
Smart Lighting in Multipurpose Outdoor Environments 9
Control and monitoring functionality is implemented in parallel and there are two
processes executed in parallel in the devices. Sequence diagram in Fig. 4 illustrates
messaging patterns of two possible scenarios: profile change and energy consumption
measurements.
When main application receives sensor data it identifies the corresponding profile.
In order to avoid big amounts of nested “IF” statements, the profile ID is computed
as function of tree profile variables. Then the associated lighting scene is identified. If
the computed scene is different from the current one, main application sends a series
of messages to the ballasts via gateway in order to set up the new scene. Then a noti-
fication sent to the energy analysers about the profile change. This message triggers
response messages from analysers, containing data on energy consumption. Main
application receives data from the analysers and composes a message to be sent to the
data acquisition application. It is important to obtain the energy performance infor-
mation from all the lamps when the profile changes. Therefore, when the first analys-
er receives the request from the main application, it updates own knowledge about the
profile and composes a message containing requested energy data. But, instead of
sending the data to the requesting device, it passes the request together with own reply
to the second analyser. The second device also updates its profile data and ads re-
quested energy information to the message received from the first device. Finally the
information is passed to the main application, where it is used to compose the mes-
sage to be sent to the adjacent systems via the Event Hub.
Besides the scenario described above, energy analysers perform regular measure-
ments of energy consumption and related parameters. The frequency of measurements
is dependent on the current profile. In order to reduce amount of traffic and detect
abnormal consumption patterns, measured data are sent to the data acquisition appli-
cation in the two following cases:
• The nominal time interval defined for the given profile has elapsed;
• The amount of total energy consumed has increased for a value bigger
than the threshold defined.
The abovementioned criteria are applied to each phase separately, as different
lamps connected to same analyzer may be set to different operating modes in certain
lighting scenes.
Fig. 5. Evaluation testbed.
10 A. Florea et al.
The control box, containing the S1000 modules, is located inside the building,
while the lamps and sensors are located outside, which complicates the balancing and
commissioning. The correctness of the logic and message flows has been verified in
the testbed as depicted in figure Fig. 5. User presence was simulated by changing the
status of digital inputs of the PH device and the light intensity was illustrated through
the amount of digital outputs turning on in the same device. Dedicated digital outputs
of energy analysers were used to track the message flows, both between the devices
and to the external applications.
3.5 Integration with Industrial Applications
Support of SWS at the device level enables direct integration of the solution with
the other applications requiring the information produced by the smart lighting appli-
cation. Possible integration scenarios are considered in this section.
During its execution, the designed smart lighting application produces data sent to
the data acquisition application for storing. However some of this information may be
used in other real-time monitoring and control applications.
As most of the data generated by the application relates to energy consumption of
the ballasts and their operating modes, it can be included in energy monitoring appli-
cations as a separate set of parameters as well as a component of a composite key
performance indicator (KPI) e.g. total energy consumption of the site. For the dis-
cussed case-study the site consists of the testbed depicted in Fig. 1 and the neighbour-
ing facilities of the Factory Automation Systems and Technology laboratory hosting
the production line (Fig. 6. ). The line consists of 10 manufacturing cells, each con-
taining at minimum one robot and a conveyor system. The line is capable of drawing
729 different layouts of mobile phones, using different combinations of frame, key-
board, and screen types. Cell 1 is in charge with determining whether incoming pal-
lets are occupied with finished products and they need unload to be performed on
them, or they need further circulation in the line. Quality inspection takes place also
here via a machine. The buffer is implemented at Cell 7.
Fig. 6. Production line.
Smart Lighting in Multipurpose Outdoor Environments 11
The integration becomes possible due to the availability of the Event Hub (see Fig.
2), receiving the WS messages from the command devices and directing them to the
subscribed applications. A client application was developed to receive the messages
from the smart lighting application and store it in the MySQL database (DB). It sub-
scribes to for the required messages from the hub, parses them following the infor-
mation on the system configuration contained in the dedicated XML file and stores
information in the database using Hibernate library to interface the DB.
From the perspective of the aims of the lighting application, integration with shop-
floor systems is required for truly holistic control strategy both in the manufacturing
site and related outdoor area, as well as improved user experience. Extending the
described setup to a bigger scale, data received from the proximity sensors may be
used to create notifications for personnel and machines about readiness of the docking
area for load and unload operations, avoiding centralised control and allowing emer-
gent behaviour of the system. Smart lighting application, in turn, could benefit from
receiving of information from the above mentioned applications or the line controllers
via the event hub. This opportunity enables implementation of light control scenarios
driven by the status of the production environment, e.g. setting up lighting scene re-
quired for loading and unloading operation as soon as both truck and line are ready
for the process to be started, avoiding influence of human factor in the environment
adjustment process which can be regulated by safety and security policies. It could
also save a lot of time for the personnel, especially when needed lighting conditions
are provided by a big amount of lamps with individual manual switches.
4 Conclusions and Future Work
The notion of CO relies on the technological base similar to one of IoT and WSN,
and comprises features allowing taking applications for smart environments to a new
level. It enables creation of sustainable smart solutions for such complex environ-
ments as smart grid, urban transportation systems, etc.
The paper presents an implementation of smart lighting application in a multipur-
pose environment following the CO’s vision. The use case presented is a smart light-
ing application for outdoor docking environment at a university campus. The infor-
mation about the status of the testbed is acquired via a set of wired and wireless sen-
sors and the core functionality is implemented in three networked embedded devices
featuring SWS middleware. The application evaluates status of the environment, and
manipulates the lamps’ ballasts in order to set up proper illumination. Additionally, it
measures the energy consumption of individual ballasts allowing evaluation of control
strategy from energy efficiency perspective.
Future work will concentrate on such incremental improvements of the solution as
fine-tuning of the lighting scenes and optimisation of control application, optimized
device and application configuration, as well as its further integration with tools for
holistic energy management.
12 A. Florea et al.
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