A Monitoring System of the Heating System Based on IoT and DDS

International Journal on Communications (IJC) Volume 2 Issue 4, December 2013

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A Monitoring System of the Heating System Based on IoT and DDS Li Yeli, Cheng Bo, Chen Junliang The State Key Lab of Networking and Switching Beijing University of Posts and Telecommunications Beijing 100876, China liyeli2013@126.com Abstract As an energy driven industry, heating system’s energy efficiency has always been a social focus. By using some emerging information technology, an effective solution has been proposed to improve the energy efficiency of the heating system. In this paper, a Monitoring System was designed and implemented by means of Internet of Things and DDS, the latter of which helps us to implement the data distribution of all of the data collected by the Internet of Things timely. It should be much helpful for heating system enterprises to improve the energy efficiency.

Keywords Internet of Things; DDS; Monitoring; Energy Efficiency

Introduction Internet of Things refers to uniquely identifiable objects and their virtual representations in an Internet-like structure. Since 1999 when the concept of Internet of Things was first put forward, the international community has been widely agreed that Internet of Things is the third wave of information industry in the world wide after computer and internet.

In addition, energy efficiency in industrial processes is becoming more and more relevant nowadays. With the help of the Internet of Things, the optimization of energy-efficient drive systems can be dealt with by real-time monitoring and remote control.

Heating system as one of the energy-efficient drive systems, its energy conservation has always been a social focus. Energy conservation is no doubt of great significance for sustainable development. With Internet of Things, a new effective solution can be developed to improve the energy-conservation of the heating system.

On one hand, using the technology of Internet of Things to set up a comprehensive and effective energy monitor and control system monitoring personnel will

be able to obtain real-time information of the heating system, such as the temperature of the boilers, the temperature of the water in the pipeline and so on.

Further, with all the gathered information the monitoring personnel will be able to adjust the parameters of the heating system in reason and timely, which will improve the energy efficiency.

The monitoring system of the heating system proposed in this paper is a new information system which uses the Internet of Things. The whole system contains Data Source Layer, Server Layer, Data Distribution Layer and Graphic Client Layer. Using Internet of Things, the Server Layer automatically gets all kinds of data from the Data Source Layer with sensors underground. The Server Layer will interact with each other and communicate with the Graphic Client Layer through the Data Distribution Layer. The Server layer and the Data Distribution Layer are the core layers of our system. One key issue with our system is to delivery kinds of data according to different QoS requirements.

For example, when we distribute data from the sub-servers to different graphic clients, one of the clients may want the data to be sent secondly while the others may only need the data to be sent minutely for the same data source. Some of the data must be sent reliably while others may not. As a result, DDS was selected to implement the Data Distribution Layer and will be explained in detail later. The Data Distribution layer was built based on the Open Splice community version which is the open-source implementation of the Object Management Group's (OMG) Data Distribution Service (DDS) for Real-Time Systems messaging middleware standard by the Prism Tech Company.

The rest of this paper was organized as follows: related work was described in section II. Section III presented the architecture of the Monitoring System of the Heating System. In section IV, the implementation of the Server Layer and the Data Distribution Layer of the

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International Journal on Communications (IJC) Volume 2 Issue 4, December 2013

Monitoring System of the Heating System were displayed in detail. Some experiments of testing features of the system were in section V. Conclusion was made in section VI. Related Work The development of Internet of Things has many years in history. In 1999 Professor Neil Gershenfeld of MIT, published his book “When Things Start to Think”, where he described the principle of the “Internet of Things” that is: The radio frequency identification (RFID), infrared sensors, global positioning systems, laser scanners and other information sensing device, according to the agreed protocol, to any article connected to the Internet up to information exchange and communication, in order to achieve intelligent identify, locate, track, monitor and manage a network.

November 17, 2005, in Tunis World Summit on the Information Society (WSIS), the International Telecommunication Union released the "ITU Internet Report 2005: Internet of Things", pointing out that the ubiquitous "Internet of things" communications era dawns, all the objects from tires to toothbrushes, from housing to the tissue can be exchanged via the Internet initiative. At present, many international communities are spending lots of effort studying it and many countries are paying more attention to the spread application of the Internet of Things. In April 2008, the U.S. National Intelligence Council published the “Six Technologies with Potential Impacts on U. S Interests out to 2015” in which the Internet of Things was identified as one of the six technologies as most likely to enhance or degrade US national power out to 2025 from 102 potentially disruptive technologies. What’s more, there have already been some successful applications based on the Internet of Things. Wei Chen proposed the application of Internet of Things for Electric Fire Control. Shen Bin, Zhang Guiqing proposed the “management system for building equipment internet of things” which not only can acquire equipment operation parameters, environmental data and person location information, but also can publish these data to the Interne. Jiahuan Wan, Xiuwan Chen, Jing Liu proposed the monitoring system of moving goods in the Internet of Things based on Compass. Lou ping, Quan Liu, Zude Zhou proposed the agile supply chain management over the Internet of Things.

The monitoring system of the heating system proposed in this paper can grab the real-time information of the heating system from the Internet of Things and 110

distribute data to distributed clients and servers according to their different QoS requirements with the help of the DDS. The main QoS requirements include the real-time requirements, the reliability requirements and the throughput requirements. With all of the information and the help of the Graphic Client Layer, the monitoring staff will be able to adjust the parameters of the heating system in reason and timely to improve the energy efficiency.

Architecture of the Monitoring System The Monitoring System of the Heating System contains four layers: the Data Source Layer, the Server Layer, the Data Distribution Layer and the Graphic Client Layer. The architecture is shown in FIG 1.

FIG. 1 THE ARCHITECTURE OF THE MONITORING SYSTEM

Data Source The main function of the Data Source is to provide data.

There is only one kind of data source in this system: sensors. Sensors in the heating system are always stationary, while sensors in one boiler station are only responsible for collecting data of their own station and delivery them to the sub-server belong to their station by cable or wirelessly.

Data collected by the sensors may include such as temperature of the water out of the boiler, temperature of the water back into the boiler, temperature of the fire and so on. All of the data real-time at different time frequency.

the the the are

Server The server layer is one of the core layers of the system.

It contains two kinds of servers: the sub-server and the dictionary server. There is at least one sub-server deployed at one station. All of the sub-servers are connected with each other by Ethernet. The

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architecture of the sub-server is shown in FIG 2. There is only one dictionary server of the system.

FIG. 2 THE ARCHITECTURE OF THE SUB-SERVER

The sub-server has three main functions: store the historical data, receive data from the IoT timely and publish data to the graphic clients according to their requirements or receive data from the graphic clients.

With all of the considerations, every sub-server will contain a historical database to store all of the historical data of the station it belongs to, a data-receiver to receive data from the IoT, a data-publisher which is responsible for publishing data to the graphic clients and a data-subscriber which is responsible for receiving data from the graphic clients.

The dictionary server of the system has only one and important function. It keeps all of the sub-servers’ configuration information. Normally, the graphic client of one station may only need to call the historical data of its own station, but sometimes it may be interested of the other stations’ historical data. Since the historical data were stored distributedly at different stations and the graphic client only knows the database information of its own station. The graphic client will need to retrieve the dictionary server to get the sub-server information it needs. Besides, whenever any of the sub-servers update its database information, it should update themselves to the dictionary server. To simplify the implementation, we use a relational database to store the database information of the sub-servers.

Data Distribution The data distribution layer of the monitoring system is another core layer of the system, which is responsible for distributing all of the data across the system.

The data to be distributed could be divided into three kinds. One kind of the data is the negotiation data between the clients and the servers. This kind of data will be distributed when the graphic client informs the sub-server which data source it wants or doesn’t want to monitor and the time frequency of the data and the reliability of the data. Another kind of data is the pure data. This kind of data is of the maximum amount and

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carries the actual data of the heating system. The pure data is always distributed between the sub-servers and the graphic clients. The last kind of data will be distributed between the sub-servers and the dictionary server and carries the update data of the sub-servers.

While the negotiation data and the update data of the sub-servers must be distributed reliably, some of the pure data can be distributed unreliably. The pure data may be distributed per second while the negotiation data only occurs when the graphic clients change their requirements or offer some new requirements and the update data of the sub-servers only occurs when new kinds of data sources join the system and change the database information of the sub-servers. Most of the pure data needs to be real-time.

With all of the considerations above, the Data Distribution Layer must have the following features.

First of all, it must be reliable to satisfy the requirements of all of the three kinds of data. Second, it must be of high through put and real-time compatible to satisfy the requirements of the pure data. In this paper, the Data Distribution Layer was built based on DDS. Graphic Clients The graphic client layer of the monitoring system as the frontend of the system has three major functions.

First of all, since the monitor pages of different stations differ from each other, the graphic client contains a page editor so that the users can customize the monitor pages themselves. Secondly, the graphic client of the station contains a data receiver to collect data from the data distribution layer and then show them on the monitor pages. Finally, the graphic client contains a data publisher to send negotiation data to the data distribution layer.

Implementation of the Server Layer and the Data Distribution Layer Key Technologies 1) Data-Distribution Service for Real-Time Systems (DDS) The OMG Data Distribution Service (DDS) specification provides a Data- Centric PublishSubscribe (DCPS) communication standard for a range of distributed real-time and embedded computing environments, from small networked embedded systems up to large-scale information backbones.

The global view of the DDS infrastructure is shown in

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FIG 3, in which most important DDS entities are involved.

TABLE 1 TOPICS

Topic

Negotiation

Pure_data

FIG. 3 THE GLOBAL VIEW OF THE DDS INFRASTRUCTURE

Global Data Space (GDS). The most important abstraction of DDS is the fully distributed GDS, as it stores all the data published in the domain. With GDS, the system can be scalable and avoid single point of failure.

Data writers/readers and publishers/subscribers. DDS uses data writer to publish data into the GDS and uses data reader to read data from the GDS. A publisher is a factory to create and manage data writers while a subscriber is a factory to create and manage data readers.

Topics. A topic contains a unique name, a data type and a set of QoS and it connects data writers and data readers. The data writer and data reader can only communicate if the data reader subscribes to the same topic as the data writer has published. The data type is usually defined by an OMG Interface Definition Language (IDL) structure.

QoS.DDS provides a rich set of QoS with which users can configure and control the local and end-to-end properties of DDS entities to meet the application requirements.

When implementing the monitoring system, we focus on all of the entities mentioned above. The data publisher of the server layer and the graphic client layer is implemented as the publisher and the data writer of DDS. The data receiver of the server layer and the graphic client layer is implemented as the subscriber and data reader of DDS. Three kinds of topics were provided according to the data to be distributed and their QoS requirements.

Topic Implementation Three kinds of topics were implemented: negotiation, pure_data, update_message. The information of the topics is shown in Table 1.

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Update_mes sage

Type Long data_souce_id; Int time_frequency; Boolean enable; Long data_source_id; Double value; Long timestamp; Long data_source_id; String server_ip; Boolean enable;

QoS Reliability(TRUE) Druability(PERSISTENT)

Reliability(TRUE)

Reliability(TRUE) Druability(PERSISTENT)

2) Negotiation. The negotiation topic is published by the graphic client layer and subscribed by the server layer. We attached a special type and some QoS policies to the negotiation topic. The topic type implies that any negotiation topic sample publised should be of the special type. The special type is difined by IDL and then compiled to the Java class. As it can be seen from Table 1 that any negotiation topic sample must contain a data_source id property to tell the subscriber which data source the client is intrested in. The sample must contain a time_frequency property to tell the subscriber the client’s real-time requirement of the corresponding data source. Also, the sample must contain anenability property to imply if the publisher does or doesn’t want to monitor the data source. The most important QoS requirement of the negotiation topic is that the RELIABILITY must be true. Since the subscribers maybe created after the pulishers, the druability of the topic must be PERSISTENT. 3) Pure_data. The pure_data topic is published by the server layer and subscribed by the graphic clients layer. Again, the pure_data also has a special topic type. The data_source_id property implies the data source of the pure_data topic sample. The value peoperty of double type carries the actual sensor data of the corresponding data source. With the help of the timestamp property, we will know the exactly time of the value. And, the main QoS policies of the pure_data topic is the reliabilty QoS policy.

4) Update_message. The updata_message topic is published by the sub-server and subscribed by the dictionary server. The topic type of the update_message includes the data_source_id property, the server_ip property recording the IP address of the corresponding data source’s sub-server and the enability property. The enability prerpty will tell the dictionary server if the sub-server records the data

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source or not. The QoS policy of the update_message is that the reliability of the topic must be true. Like the negotiation topic, the durabilty of the topic must be persistent.

The Subscriber and the Publisher Implementation 5) Publisher and Subscriber of the Negotiation Topic.

The publishers of the negotiation topic are created when the client needs to monitor the data of new data sources or when it wants to disable the monitoring of the subscribed data sources. After publishing data, the publisher will be destroyed to save resources. The subscribers of the negotiation topic are created whenever the sub-server is running. Since the publishers may be created and data are sent before the server runs, the durability of the negotiation topic is set to be PERSISTENT. Whenever the subscribers receive any negotiation topic samples, it will analyze the properties of the sample and react to different property values. The flow chart of the topic sample processing is shown in Fig 4.

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source id so that all of the pure_data topic samples are of the sepeical source id. Then the processing will end. f) Minus one from the subscribers counter of the corresponding data source publisher. As mentioned in c), when the enability property value of the negotioation topic sample is false, we need to minus one from the counter. g) React according to the value changing of the counter. If the counter is changed from one to zero which implies that no graphic client is interesting of the data source anymore, the processing goes to h).Otherwise, the sub-server ends the processing. h) Destroy a pure_data topic publisher. When the counter equals zero, we should destroy the corresponding publisher to save resources. Then the processing will end.

a) Find data source from the data source id list. First of all, the subscribers of all of the sub-servers will receive the topic sample. Since different data sources are deployed at different sub-servers, only one of the sub-servers will find the data source id of the topic sample from the data source id list it contains. Those which don’t find the data souce id from the list will end the processing. b) React according to the enability property value of the topic sample. The sub-server which has the special data source id will then extract the enabilily property value of the topic sample. If the enability property value is true, it will go to c). If the enability propertyvalue is false, it will go to f) c) Add one to the subscribers counter of the corresponding data souce publisher. We maintain a hashmap to record the data source id as the key and the counter of the subscribers of the data souce pure_data topic publisher as the value. If the enale property value is true, we add 1 to the counter. d) React according to the value changing of the counter. If the counter is changed from zero to one, meaning that a new pure_data topic publisher of the corresponding data source needs to be created and the processing goes to e). Otherwise, the sub-server ends the processing. e) Create a new pure_data topic publiser. When creating the publisher, we tell the pulisher the data

FIG. 4 THE FLOW CHART OF THE TOPIC SAMPLE PROCESSING

6) Publisher and Subscriber of the Pure_data Topic.

The publishers of the pure_data topics are created and destroyed on the sub-servers as mentioned above.

Every publisher only publishes pure_data topic samples of one source id at the largest time frequency.

The subscribers are created on the graphic clients when the clients are started or begin to monitor a new data source and are destroyed when the graphic clients shut down or stop monitoring an existing data source.

Every subscriber may receive pure_topic samples which contain the data source id the subscriber is not interested in. To solve the problem, we use content_filter_topic and anon_data_available listener.

The contet_filter_topic is built based on the pure_data

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topic and attaches to a SQL query which will only extract the topic sample of the special data source id.

The listener will be triggered when the topic samples are available and then the timestamp property value of each of the samples is extracted. If the topic sample is the first one, the listener will record the timestamp and return the sample to the upper layer to show on the screen. Otherwise, the listener will calculate the difference between the new timestamp and the old one.

If the difference satisfies the time frequency requirement, it will update the old timestamp and return the sample to the upper layer to show on the screen.

7) Publisher and Subscriber of the Update_message Topic When the sub-server’s data source list changes, it needs to inform the dictionary server. We use the update_message to transfer update information between the sub-server and the dictionary server. The publishers are created on the sub-server when the sub-server wants to update the data source list and destroyed after publishing data. The subscribers are created on the dictionary server when it starts. Like the negotiation topic, the durability QoS must be persistent since the subscriber may be created after the publisher.

When the subscriber receives any update_meassage topic samples, it will analyze each of the samples. First of all, it will check if the enability property value. If the value turns out to be true, then it will update the Data_source_IP table and set the corresponding record’s IP filed to be the IP property value of the topic sample and the enability field of the corresponding records to be true. Otherwise, it will set the corresponding record’s IP filed to be the IP property value of the topic sample and the enability field of the corresponding records to be false.

Experiments We have deployed our system on four connected PCs. One acts as the sub-server, two others act as the graphic clients and the last one acts as the dictionary server. They are connected by the Ethernet with the focus on the distribution of the negotiation topic. Other topic distribution is similar to the negotiation topic.

The Distribution of the Negotiation Topic The main purpose of this part of test is to verify the correctness of negotiation topic distribution, including two aspects: build the publisher and the subscriber, publish and receive data correctly.

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Firstly, we started the graphic clients. Each of the clients then reads the configuration file which contains the data source list it needs to monitor. One record of the file is“sensor_1|true|1”. After that, the subscriber of the clients published topic samples according to the configuration file.

Secondly, we started the sub-server and created the negotiation topic subscriber on the sub-server. Even though, the subscriber was created after the publisher, it received the topic samples published before. The data source list on the sub-server is shown in FIG 5.

FIG. 5 THE DATA SOURCE LIST OF THE SUB-SERVER

Conclusions The development of the Internet of Things brings us a new way to improve the energy efficiency of the heating system. DDS is a suitable middleware standard for real-time systems which can be utilized to set up a monitor system to obtain information timely. In this context, a Monitoring Information System was designed and implemented based on DDS and IoT. This system should be much helpful for energy efficiency in industrial processes.

ACKNOWLEDGMENT

This research was supported by the National Grand Fundamental Research 973 Program of China under Grant No. 2011CB302506, 2011CB302704; National Key Technology Research and Development Program of China Research on the mobile community cultural service aggregation supporting technology" (Grant No. 2012BAH94F02); National High-tech R&D Program of China (863 Program) under Grant No. 2013AA102301; National Natural Science Foundation of China under Grant No. 61001118, 61132001); Program for New Century Excellent Talents in University (Grant No. NCET-11-0592); Project of New Generation Broad band Wireless Network under Grant No. 2011ZX03002-002-01.

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