IEMS: Indoor Environment Monitoring System using ZigBee Wireless Sensor Network Suman Sankar Bhunia
Sarbani Roy
Nandini Mukherjee
School of Mobile Computing & Communication, Jadavpur University Kolkata, West Bengal, India
Department of Computer Science & Engineering, Jadavpur University Kolkata, West Bengal, India
Department of Computer Science & Engineering, Jadavpur University Kolkata, West Bengal, India
[email protected]
[email protected]
[email protected]
used in military, environment, health, home and other commercial areas [2]. Monitoring a specific area for collection of different parameters is a common application of WSNs. In area monitoring, the WSN is deployed over a large geographical region where some phenomena are to be monitored.
ABSTRACT As far the global warming or climatic change is concerned not only emissions but also wastage of energy in buildings are responsible. So proper indoor management is the need of the hour but manually it seems to be an impossible task to do. This paper presents a framework for Indoor Environment Monitoring System (IEMS) to serve this purpose. Wireless sensor network (WSN) is deployed in every nook and hook of the targeted building. Nodes sense environmental parameters and transfer data by Zigbee radio protocol wirelessly. Utilizing the sensor web enablement (SWE) this framework eases the surveillance effectively.
Environmental monitoring is one such application area that is attracting researchers around the world in the wake of global warming. But very few of them have paid attention to indoor environment monitoring. However, it is very much a crucial aspect regarding climate change as unplanned consumption of power or wastage of power in turn escalates the emissions from the power generation utilities. If indoor environmental parameters like light, humidity, temperature, pressure can be measured we can decide upon the power usage requirement in particular space.
Categories and Subject Descriptors C.2.1 [Computer-Communication Networks]: Network Architecture and Design – Distributed networks, Network topology, Wireless communication.
This paper focuses on the design and implementation of an indoor environment monitoring framework, called IEMS. The framework has been implemented in the School of Mobile Computing & Communication (SMCC) building of Jadavpur University. As the calmness is desired in the building, the noise parameters are also taken into consideration.
General Terms Measurement, Design, Experimentation.
Keywords Wireless sensor network, Environment monitoring, Zigbee, Sensor web enablement, Sensor middleware.
WSN can use communication technologies such as Bluetooth, ZigBee. Although, transmission rate of Bluetooth is better than ZigBee, ZigBee has lower power consumption [12]. Therefore, ZigBee is used in our framework for communication among WSN nodes scattered in the building.
1. INTRODUCTION There are diverse applications of wireless sensor networks (WSNs) in the real world, typically invoking some kind of monitoring, tracking, or controlling activities. In an application, a WSN is scattered in a region where it is meant to collect data through its sensor nodes. WSN nodes have the ability of sensing, actuating and communicating the gathered data. Besides, a wireless network has advantages in terms of high accuracy, fault tolerance, flexibility, cost, autonomy and robustness compared to wired networks. Thus, WSN has the potential to become a significant enabling technology in many sectors. WSN is widely
The rest of the paper is organized as follows. Section 2 presents the architecture of the proposed Indoor Environment Monitoring system (IEMS). Implementation of IEMS has been discussed in section 3. Section 4 gives an overview of related works. Finally, in Section 5 we conclude the paper with direction of future work.
2. IEMS ARCHITECTURE We have designed IEMS with a layered architecture as shown in Figure 1. This section briefly describes different layers of the proposed system. The lowest layer consists of sensing devices with requisite sensors and hardware for communication, data storage and power supply. Operating system occupies the next layer. TinyOS, the most popular operating environment for WSN, is operational in this layer. The third layer contains sensor middleware. Here Global Sensor Network (GSN) is used as middleware [1]. On top of it, the application and service provider layer resides to accomplish sensor web enablement (SWE) [4]. Following is a description about each layer.
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2.2 Operating System: TinyOS TinyOS is an open source operating system developed for Berkeley motes using NesC programming language. Benefiting from the component-based and event-driven structure of NesC, it facilitates the development and implementation of wireless sensor network significantly [11]. A few important concepts of TinyOS are clarified below. Component: Applications written on NesC are composed of components which are of two different types: - Modules: implementation of application behaviour - Configurations: components wired together Interface: Interfaces attached to each component are considered as the only way of communication between components. Components may use or provide interfaces. Events: Events run in response to a hardware interrupt or component signal. One event can preempt another event. Events follow LIFO semantics. Tasks: Tasks are atomic in nature with respect to other tasks. Tasks are single threaded. Command: Interfaces are composed of command and event. A command is called by the user of interface and implemented by the provider of the interface. For further information on developing applications using TinyOS can be found in [13].
Figure 1. Layered Architecture of IEMS
2.1 Sensing Devices Sensors can collect physical data from the environment, although they cannot carry out the entire activity by themselves. A sensor has to be powered and a mechanism should be in place for collecting, communicating the sensed data. So the sensors are mounted on another device which is called mote. Basically, motes are processing units which get input from sensors or sensor board and process the input data before propagating the same. There are various models of motes of different makes. For this work only Crossbow made Iris motes are used.
2.3 Middleware: Global Sensor Network As a middleware we prefer to use Global Sensor Network (GSN) [1]. It provides a reusable software platform for processing of data streams generated by wireless sensor networks. GSN acquires data, filters it with enriched SQL syntax, runs customisable algorithms on the results of the query, and outputs the generated data. GSN can be configured to acquire data from various data sources. The high number of data sources in GSN allows it to work for sophisticated data processing scenarios. GSN offers advanced data filtering functionalities through an enhanced SQL syntax.
It is to be noted that just the sensor mounted motes cannot propagate data to the server. Thus, in order to retrieve data, motes are required to be bridged with a computer. To solve this bridging problem a device named as gateway or interface board is connected to the computer. Gateway or base station acts as sink node of a wireless sensor network. It interfaces the whole sensor network with the server. So, it is also called an interface board. Serial and USB interface boards from Crossbow, i.e., MIB510 and MIB520 respectively are used for this work.
2.4 Sensor Web Enablement Web enablement is necessary for accessing an application realtime through the Internet. The term "Sensor Web" was first used by Kevin Delin of NASA in 1997, which is a type of sensor network that is well suited for environmental monitoring. Open Geospatial Consortium Inc. (OGC) focused on developing standards to enable the discovery, exchange and processing of sensor observations, as well as the tasking of sensor systems just like HTTP and HTML enabled exchange of information on the web. Sensor Web Enablement standards that have been built and prototyped by members of the OGC include the following specifications [4]: - Observations & Measurements (O&M) - Sensor Model Language (SensorML) - Transducer Model Language (TransducerML or TML) - Sensor Observations Service (SOS) - Sensor Planning Service (SPS) - Sensor Alert Service (SAS) - Web Notification Services (WNS)
The main job of sensing is done by the sensors. Usually sensors with different sensing capabilities are clubbed onto a single board to ease the tasks of the developers. These boards are mounted on motes and output sensed data, e.g., light, air temperature, humidity, barometric pressure, noise etc. The resource constrained sensing devices need to communicate wirelessly. So a proper radio communication mode has to be adopted. ZigBee is a low-cost wireless mesh networking technology having characteristics of less distance and low speed. ZigBee is named from the zigzag flying of bees that forms a mesh network among flowers [7]. The main advantages of ZigBee technology lie in the following aspects [12]: (i) Self configuration (ii) Reliability (iii) Easy deployment (iv) Long battery life (v) Low cost, and (vi) Security. ZigBee wireless standard is based on IEEE 802.15.4 which defines the physical layer (PHY) and media access control (MAC) layer. The ZigBee Alliance builds on this foundation by providing the network and security layer and the framework for the application layer [14].
3. IMPLEMENTATION OF IEMS This section discusses the design and implementation of the proposed system. Implementation of IEMS starts from the mote
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section 2, Zigbee is used for radio communication in 2.4 GHz ISM band. Other communication technologies use the same band also. So, nonoverlapping channels are used in order to avoid interference and subsequent performance degradation of the network [3]. Apart from XMesh routing protocol XServe [9] serves as the primary gateway between wireless mesh networks and enterprise applications. XServe provides services to route data to and from the mesh network with higher level services to parse, transform and process data as it flows between the mesh and the outside applications. So, XServe is installed onto the server to fetch raw data (in hexadecimal) from the base station which is attached to the server. Then XServe feeds the converted data to the application.
tier where the sensor motes are programmed and deployed for collection of data. The motes talk with each other and forward sensed data to the sink node over ZigBee. The sink node is attached with the server which runs the Xserve and GSN. Later, the server is connected to the internet to facilitate remote access of the data.
3.3 Middleware Accurate measurements from wireless sensors along with an appropriate interface to integrate these measurements into the IEMS are essential. Therefore, Global Sensor Network (GSN) middleware [1] is chosen. It acts as a local database as well as provides XML-based interface for IEMS. A web-based interface is provided to view real-time as well as historical sensor data and corresponding graphs may also be generated. Figure 3 shows a screenshot of IEMS web interface.
Figure 2. Conceptual framework of IEMS
3.1 Deployment of Nodes For sensing purposes the motes along with sensor boards are deployed in different locations of SMCC building. The main objective is to monitor each room in the building. Deployment is done in such a manner that every room (classrooms, seminar hall, laboratories, library, office, faculty cabins) is covered by at least two nodes to ensure efficient monitoring. In order to route data from different rooms to the sink node in the server room we deployed intermediate nodes with limited sensing capabilities in corridors and staircases. Washroom, reception areas are equipped with single node as these are low-priority areas in terms of monitoring. Primarily, nodes are mounted with environmental sensors for sensing light, temperature, humidity, barometric pressure. Noise sensors along with other sensors are deployed in the library. Few other nodes are deployed as intermediate nodes to reach data to base station i.e., sink node.
Figure 3. IEMS web interface screenshot Within the SWE framework O&M provides metadata to SOS and SPS whenever required by IEMS. Data are processed and stored along with the node id. Thus the office staff can monitor indoor environment at ease with the IEMS framework. He or she can take necessary steps accordingly to ensure efficient building management.
3.2 Data Acquisition Moteworks2.0 [9] is used for data communication within the framework, which is based on TinyOS 1.1 [13] and comes with Xmesh and Xserve. Xmesh [9] routing protocol is installed on nodes and it manages data collection and dissemination. Low power listening is enabled in nodes due to power constraint. In this mode all nodes are time synchronized. Typically, the nodes wake up for a very short interval (1msec) to see if the radio is detecting any signal over the noise background. If so, then the radio is kept on to receive the signal. Otherwise the nodes remain in sleep state. Thus the power consumption in the nodes is lowered. XmeshBase is installed on the base station which acts as a gateway or sink node of the WSN. Base station is programmed depending upon its type, which is serial port or USB interface. The base station sends heartbeat messages periodically to the nodes connected in the network. If it does not get a response from the concerned node within a predefined time it is assumed that somehow the radio link is disrupted and the network is reconfigured accordingly to avoid any data loss. As mentioned in
Another important aspect is the notification service. SAS and WNS of SWE framework provide support for implementation of this notification service in IEMS. A threshold value is set for every parameter which is sensed earlier using SOS or SPS. If the threshold is reached, a notification or alert message would be sent to concerned person(s) via e-mail as shown in Figure 4 and (or) a short message service (SMS) would be used to send the information to the concerned person's mobile phone. The notification system is empowered to prioritize the events and processes accordingly. Thus Sensor Web Enablement (SWE) [4] is utilized to achieve our goal.
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Fachgespräch "Drahtlose Sensornetze", Universitt Stuttgart, 2006. [2] Akyildiz I.F., W. Su, Y. Sankarasubramaniamm, and E.Cayirci,“Wireless sensor networks: a survey,” Computer Networks, ElsevierScience B.V., Volume 38, Issue 4, pp.393–422, 2002. [3] “Avoiding RF Interference Between WiFi and Zigbee”, http://www.mobiusconsulting.com/papers/ZigBeeandWiFiInt erference.pdf (August 2010) [4] Botts M., Percivall G., Reed C., and Davidson J., “OGC Sensor Web Enablement: Overview And High Level Architecture”. Open Geospatial Consortium Inc. (OGC) 06050r2 White Paper, July 2006. [5] Choi Gi Heung; Gi Sang Choi; Joo Hyoung Jang; , "A framework for wireless sensor network in web-based monitoring and control of indoor air quality (IAQ) in subway stations," Computer Science and Information Technology, 2009. ICCSIT 2009. 2nd IEEE International Conference on , vol., no., pp.378-382, 8-11 Aug.2009. [6] Culler, D., Estrin, D., and Srivastava, M., “Overview of sensor networks”, IEEE Computer, volume 37, pp.41–49, 2004 [7] Ding Gang, Sahinoglu Z., Orlik P., Jinyun Zhang and Bhargava B., “Tree-Based Data Broadcast in IEEE 802.15.4 and ZigBee Networks”, published by the IEEE transactions on mobile computing, Volume 38, No. 11, pp.1561-1574, Nov. 2006. [8] Mainwaring, A., D. Culler, J. Polastre, R. Szewczyk, and J. Anderson, “Wireless sensor networks for habitat monitoring”, in the Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications, pp. 88-97, 2002. [9] “Moteworks getting started guide”, (August 2010), http://sce.uhcl.edu/yang/public/download/MoteWorks_Gettin g_Started_Guide.pdf [10] Lee, A.M.C.; Angeles, C.T.; Talampas, M.C.R.; Sison, L.G.; Soriano, M.N.; , "MotesArt: Wireless Sensor Network for Monitoring Relative Humidity and Temperature in an Art Gallery," Networking, Sensing and Control, 2008. ICNSC 2008. IEEE International Conference on , vol., no., pp.12631268, 6-8 April 2008 [11] Levis P., S. Madden, J. Polastre, R. Szewczyk, K. Whitehouse, A. Woo, D. Gay, J. Hill, M. Welsh, E. Brewer, and D. Culler, “TinyOS: An Operating System for Wireless Sensor Networks”, In Ambient Intelligence, SpringerVerlag, 2005. [12] Safaric S., Malaric K., “ZigBee wireless standard”, in the Proceedings of Multimedia Signal Processing and Communications, 48th International Symposium ELMAR2006, Zadar, Croatia, pp.259-262, June 2006. [13] Tinyos website: http://www.doc.tinyos.net/ (August 2010) [14] ZigBee Alliance Board of Directors, “ZigBee Specification”, pp:29-30,Jan.2008.
Figure 4. Email notification screenshot
4. RELATED WORK Since environmental monitoring is an important topic, significant research work is being done in this field using wireless sensor networks. The development and deployment of WSN testbeds for various practical applications, including environmental monitoring is discussed in [6]. Intel Research Laboratory and Berkeley University concentrated their research to monitor the environment of Great Duck Island in Maine [8]. Relative humidity and temperature monitoring in an art gallery is implemented using MotesArt [10]. A framework for web based monitoring of indoor air quality in subway stations is given in [5].
5. CONCLUSION & FUTURE WORK This paper presents a Zigbee based WSN framework which facilitates monitoring the indoor environment. Through implementation, it could be verified that the IEMS framework is reasonable and the software design is user-friendly. Also it was observed that data can be analyzed and monitored remotely over the web. The system has been tested for functionality and stability for several weeks at School of Mobile Computing & Communication. The framework ushers in a new edge in efficient indoor monitoring or building management. There are several ways to improve the system in future. One is to implement an IP based wireless sensor network for having command over every node and incorporate modularity. Another interesting avenue is to incorporate mobile sensor nodes in the IEMS (to monitor the corridors). Our future work will investigate the optimal deployment schemes also.
6. REFERENCES [1] Aberer Karl, Manfred Hauswirth, Ali Salehi, “Middleware support for the Internet of Things”, 5th GI/ITG KuVS
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