About this Journal Submit a Manuscript Table of Contents
Advances in Mechanical Engineering
Volume 2013 (2013), Article ID 913638, 8 pages
Research Article

IPv6-Based Smart Metering Network for Monitoring Building Electricity

School of Automation Science and Electrical Engineering, Beihang University, Xueyuan Road 37, Haidian District, Beijing 100191, China

Received 8 April 2013; Revised 3 June 2013; Accepted 4 June 2013

Academic Editor: Yong Tao

Copyright © 2013 Dong Xu and Jingmeng Liu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A smart electricity monitoring system of building is presented using ZigBee and internet to establish the network. This system consists of three hardware layers: the host PC, the router, and the sensor nodes. A hierarchical ant colony algorithm is developed for data transmission among the wireless sensor nodes. The wireless communication protocol is also designed based on IPv6 protocol on IEEE 802.15.4 wireless network. All-IP approach and peer-to-peer mode are integrated to optimize the network building. Each node measures the power, current, and voltage and transmits them to the host PC through the router. The host software is designed for building test characteristics, having a tree hierarchy and a friendly interface for the user. The reliability and accuracy of this monitoring system are verified in the experiment and application.

1. Introduction

Environmental problems caused by human energy consumption are the major international issue faced by all countries worldwide [1]. The struggle between global warming and human beings is well recognized by the international society. Electricity is the main form of energy. As it is generated from fossil fuels, electricity represents a significant portion of carbon emission [2]. From the view of the distribution of power consumption recently, the proportion of urban residents and commercial electricity are very important. So how to save this part of energy consumption is an important issue to protect the environment [3, 4].

Monitoring the electricity condition of residents, especially the power consumption situation of some big buildings, is an effective method for saving energy. This approach was limited by technology level before. In last decade, it has been growing rapidly for several reasons, such as the improvement of microelectronics technology, development in wireless sensor, and the networking protocol. A wireless sensor network (WSN) consists of a large number of lightweight sensor nodes having limited battery life, computational capabilities, storage, and bandwidth [5]. WSN is carried out, thanks to the development of embedded system, system on chip, wireless communications, and low-power technology [6]. WSN has brought a revolution in the field of information perception for its low power, low cost, and the advantages of distribution and self-organization [7]. The wireless sensor network applications, which are low data rate detection and control systems, do not ask for real-time data transmission and do not require high bandwidth, but often require very low power consumption [8].

To meet the requirements of WSN, ZigBee is a high awareness of the wireless sensor networks in some short-range wireless communications technology [9, 10]. Since it has advantages including low power, low cost, automatic dynamic network, and high security, ZigBee is very suitable for wireless sensor networks. ZigBee protocol meets the OSI seven-layer protocol model, that from the bottom to the top is composed of the physical layer, data link layer, network layer, application support sublayer, and application layer, but also has a security module to provide network security features [11]. The main advantages of ZigBee are its low cost and low energy consumption characteristics. These advantages make ZigBee ideal for monitoring, data collection, and analyzing in various smart grid applications [12].

The sensor nodes of metering and monitoring system sense a change in the environmental or physical quantity and transmit this data to the base station. Hierarchy-based multipath routing protocols are used for nodes that build a hierarchical relationship in order to discover efficient multiple paths [5, 13]. Ant colony algorithm is mostly used to find the optimal path in a large number of nodes [14]. The base station is usually a powerful machine like a PC. Internet Protocol version 6 (IPv6) is needed for the PC in the network [15]. IPv6 is a version of the internet protocol (IP) that is designed to succeed Internet Protocol version 4 (IPv4) and is also the basic technology of internet [16]. IPv6 simplifies aspects of address assignment and network renumbering when changing internet connection providers [17]. Network security is also integrated into the design of the IPv6 architecture, and the IPv6 specification mandates support for a fundamental interoperability requirement [18]. However, the WSN architecture is different from the IPv6 network architecture. Achieving all-IP communication between WSN and IPv6 networks needs further researches [19, 20].

This paper presents a smart metering network system based on the IPv6 network protocol and ZigBee protocol for residential power measurement. Because wireless sensor nodes are located in the AC power outlets distributing throughout the whole building, the routers are used for data changing between host PC and the sensor nodes. The hierarchical ant colony algorithm is designed for data transmit ion among the wireless sensor nodes.

This paper is organized in six sections. The system structure is presented in the second section. Then, the wireless sensor node is developed in the third section. In Section 4, the software on host PC is designed. Section 5 illustrates the IPv6 network and the hierarchical ant colony algorithm based on ZigBee. Simulation and experiment results that show the validity of the presented are demonstrated approaches in Section 6.

2. System Structure

Figure 1 shows the system structure of the smart metering network. This monitoring system is composed of three hardware layers, which, from top to bottom, are the host PC, the router, and the wireless sensor nodes. Each layer has its software. The network protocol based on hierarchical ant colony algorithm is presented to improve the efficiency of data transmission among the wireless sensors. Between the host PC and the sensors, IPv6 protocol is achieved through the router.

Figure 1: The network structure of system.

The host PC is used for data analysis and power control. The software in the PC is implemented on the QT environment based on Linux operation system. First, the host software intelligently networks all the sensor nodes in the building. Then, according to the requirements of the user, it completes the configuration of each node. Each sensor node, based on the received configuration information, begins to upload data, to real-time protect the power, and to regularly maintain the system network.

The routers are set up between the host software and sensor nodes for data conversion between different protocols. Using a router in this structure also improves the stability of the network and transmission distance.

In the wireless sensor, the embedded software is developed based on an embedded operating system, Contiki is a multitasking open source operating system with a support of TCP/IP networks including IPv6. Contiki needs only a small amount of memory that only 2 kb of RAM and 40 kb of ROM are needed to provide multi-tasking environment and built-in TCP/IP support. It can dynamically load the upper application at runtime and achieve interprocess communication through the use of information events of lightweight process model. Because of these advantages, this operating system is very suitable to develop applications of sensor nodes.

The composition details of the smart metering network will be presented in the following.

3. Wireless Sensor Nodes

The sensor node consists of voltage and current sensors, ZigBee communication, power conversion, and relay control. The hardware structure of the node is shown in Figure 2. The node is set up by receiving the information from the host software that includes the sampling frequency, filter parameters, and power control parameters. After that, it measures the RMS of the voltage and current, the instantaneous values of voltage and current, active power, reactive power, and apparent power. Then, these data are transmitted to the host software according to the communication protocol.

Figure 2: The block diagram of wireless sensor node.

All the functions of the sensor node are implemented in the microcontroller called STM32W. It has advantages such as supporting for wireless transmission protocol, convenient interface with the measuring sensor, powerful computing capabilities for data processing, on-chip program and data memory to save the board space, and IO port for power control. It has a 32-bit Cortex-M3 microprocessor, Flash and RAM memory, and peripherals to design 802.15.4-based systems [21].

3.1. Electricity Measurement

One main function of the sensor is to measure the voltage, current, and power. As the microcontroller does not have a measure model, an energy metering IC named ADE7753 is chosen for this purpose. In addition to RMS calculation and active and apparent power information, the ADE7753 also accumulates the signed reactive energy. It has two fully differential voltage input channels, each of which has a PGA (programmable-gain amplifier) with possible gain selections of 1, 2, 4, 8, and 16. It also contains an on-chip power supply monitor continuously monitoring the analog supply [22]. This is useful to ensure correct operation during power up and down.

The voltage and current sensing circuits of the sensor node are designed according to the measurement signal interface characteristics of ADE7753. For voltage signal acquisition, a 1 MΩ resistor and a 1.2 kΩ resistor are connected in series, so that the voltage dropped on this 1.2 kΩ resistor is the sampling signal. Considering the sampling accuracy and system size and cost, current sampling uses the method of shunt resistor. A 5 mΩ resistor is set in the live ware to transform the output current into voltage drop to obtain the differential voltage signal for sampling.

3.2. Data Interface

The sampling data is read from the ADE7753 via the serial peripheral interface bus (SPI). In this full duplex data transmission, the microcontroller is the master, while ADE7753 is the slave. The serial clock for a data transfer is applied at the SCLK by the microcontroller. All data transfer operations are synchronized to the serial clock. Data is shifted into the ADE7753 at the DIN logic input on the falling edge of SCLK and shifted out at the DOUT logic output on a rising edge of SCLK. The SPI transfer mode based on interface timing with half-cycle delay can guarantee the stability of data transmission. The microcontroller reads data, including RMS, waveform, the power, and the peak value, from ADE7753 with the commands in Table 1.

Table 1: Data interface command.
3.3. Power Supply

Sensor nodes are used to measure the state of residential electricity consumption whose power supply voltage is AC 220 V. The circuit of sensor node includes a microcontroller STM32W and an energy measurement chip ADE7753, so these circuits require DC 5 V and 3.3 V power supplies. A reasonable approach is to get these DC powers from AC 220 V. There are two technical solutions, switching power and linear power, that can transform AC to DC power. The circuit of switching power is complex, has high cost, and has a strong electromagnetic interference to the measurement circuit. The voltage fluctuation of residential electricity is less than 10%. So a linear power supply can meet the system requirements of DC power to affect the stability of the operation of the microcontroller and, more importantly, to affect the measurement accuracy of the voltage, current, power, and other energy information. In the design process, when using an external DC power supply, the data is from 2 to 3 bits more accurate than that using the 220 V linear power circuit. The DC power filter, particularly the -filter, is an effective way to solve these problems.

3.4. Alarm and Control

The function of alarm and power on and off control is needed in this smart metering system. Because different appliances have different power requirements, the alarm of system is not the same measurement limit. Each wireless sensor node has received the settings from the host software to set the protection limit of the power of the node. The connected electrical equipment using power plug generally does not change, so this limit value preset method is used for the first time when the equipments are connected. When the node power is over the setting limit for a certain time such as 5 seconds, an alarm message is sent out. According to the value of the power, the node decides to take the initiative to cut off power supply immediately or after waiting for the power control commands. For the case of electric short circuit caused error, the node will judge the error according to the measured current value. If there is an error, it will cut off the power to prevent accidents and alarm this information at the same time.

A switch is required to control the power on and off. Taking into account the state of normal power supply, only the abnormal condition or operation of the command issued by the host software will cut off the power supply. For monitoring and controlling power supply of the residents, switching operation is not frequent; the switch relay is selected to meet the system requirements. In normal operation, the relay coil controlled by the transistor is off, and the normally closed contact is used as the power control switch. When relay coil is energized, the contact is off to cut off the power.

3.5. Embedded Software

The embedded software of sensor node is developed fully using the IPv6 network protocol support of the operating system. According to the characteristics of the metering network, the function of this embedded software is as follows: first, each node with automatic search and networking capabilities has the ability to dynamically create a metering network; second, as the main task is to measure the power, the node should ensure data accuracy though there may be outside interference in the case of processing; according to the protocol, there are also the tasks to achieve the state control and power switch control under the relevant command of the host system.

A simple description is given of the functions and work processes of this embedded software as follows. The node initialization function sensorInit(  ), including the ADE7753 initialization function embInit7753(  ), is the beginning of the software work. When the node is ready, the network initialization function embNetworkInit(  ) is to set up the metering network. After the networking process, according to the feedback of network state function embNetworkState(  ), two functions, embSendData(  ) and embReadData(  ), begin to receive data, send data, and analyze protocol. The function applicationTick(  ) contains the node applications, such as parsing command and control. For example, the functions readOperation(  ) and writeOperation(  ) are used to control ADE7753 and to read the measurement data.

4. Software on Host PC

All the data are transmitted to the PC for user management. The software in host PC should have the following functions: (1) data collection and setting; (2) energy analysis, including energy consumption statistics, limited power consumption control, and energy distribution; (3) monitoring device control and protection. The software is named as iSEnergy. The software logic is designed in different levels for hierarchical management. It should consider different rooms in the building and the outlets in the room. The main layers are as follows: (1) for a building, it is able to view the energy usage of the entire building and each room, and, it can analyze and control the energy usage of each room; (2) for a room, it is able to view the energy consumption of the room and each equipment, and it could also control each device in the room; (3) for a monitoring device, it is able to view and control the energy usage.

The host software and wireless sensor nodes communicate using IPv6 wireless network. Relative to the ZigBee protocol, the use of IPv6 is simpler for system control and can achieve point-to-point access between the nodes.

Taking into account the cross-platform software features, iSEnergy is written using Qt, an open source software library. Qt is a well-known GUI Linux platform graphics library, with rich interface functions and stable system architecture. iSEnergy is divided into several functional modules as follows:(a)network communicating module: monitoring the IPv6 network communications between devices; packing and unpacking work based on user-defined data protocol;(b)database communicating module: storing the data of the database; monitoring data saved for the statistics and analysis;(c)user interface module: core of the system module responsible for interacting with users and other data to communicate between modules.

The main program starting process is shown in Figure 3. The running start interface, the main interface, the configuration interface, and the monitoring interface are shown in Figure 4.

Figure 3: The program starting process of host software.
Figure 4: The interfaces of iSEnergy.

5. Network of System

5.1. IPv6 Network Protocol

ZigBee is the protocol of wireless sensors. IEEE802.15.4 is responsible for the physical layer and media access control layer standard of this protocol. The IPv6 network cannot be directly built on IEEE802.15.4 network; this problem should be solved in the network communication protocol. There are two methods, all-IP approach and peer-to-peer mode, that can achieve ZigBee wireless sensor nodes access in an IPv6 network.

The all-IP approach is an address-centric way for data-centric sensor nodes to solve communication problems, so it will reduce efficiency and increase the power consumption of communication nodes. If individually visiting each node in the case, these nodes should have a global unique IP address. The all-IP approach provides strong support for the entire communication protocol.

Peer-to-peer mode achieves the interconnection between the inside and outside networks by setting a particular gateway to convert the protocol between WSN and IPv6 protocol in the same layer. In accordance with the work in different layers, the gateway can be divided into application-level gateway and network address translation gateway. The drawbacks are low user transparency, difficulty in using a variety of services offered by WSN, and the difference between the network protocols.

The smart metering network is designed by integrating these two modes. Each sensor node supports the IPv6 protocol, but also designed a gateway to solve transmission distance limitations of ZigBee. The gateway node as the interface of internal network and the user terminal can temporarily store and forward the data. Because the sensor node supports IPv6 protocol, the network with each node having its own address has good transparency.

5.2. Wireless Protocol

An intelligent algorithm to find the optimal path of the wireless sensor network is an important direction for wireless sensor networks. A hierarchical ant colony algorithm is presented as the communication protocol of the wireless sensor network. As shown in Figure 5, the 1st and 2nd nodes are in the first layer, both of which are on transmission range of the 3rd node. So, if data is translated from the 3rd node to the sink node, there are two most efficient paths off-line optimization goal is to find out the lowest energy consumption path. Therefore, the distance which could substitute the energy consumption between two nodes in the simulation is calculated. Through the distance of each path, the optimum next node can be found out. If it is busy, the other node could be selected.

Figure 5: The nodes in the wireless network.

Ant colony algorithm is used to find the optimal path in a large number of nodes. Because timely reaction to the electricity state of the household appliances is important, the efficiency is put in the first place instead of the energy consumption. Firstly, the clustering method is used to identify the efficient paths. And then the ant colony algorithm is used to find the optimal path in these paths.

The algorithm is approached as follows.

Step 1. Report the “dead center” which cannot interact with other nodes so that the users can modify their places or other activities.

Step 2. Let the sink node be level zero and initialize the ants. The ants take the sink node as current position and search through the range of the movement of the ants to find the first layer nodes. The next optimization nodes which exist in all first layer nodes are the position of sink are saved.

Step 3. Initialize the ants, and let them take every first node as current position. Then search through the range of the movement of the ants to find the second layer nodes. When finding them, (1) these nodes must exist in the range of the movement of the ants; (2) the nodes should not belong to the set of the nodes of the first layer. Finding the following nodes is the same as the second layer nodes: (1) the nodes must be in the range of the movement of the ants; (2) the nodes should not belong to higher layer nodes.

Step 4. By finishing every step, we could get the following purposes. Every node can save information including which layer does this node belong to and the next nodes’ IP. Then we can choose the optimal path to interact with the rememorized next node. If the better node which we choose is busy in working or breaking down, we should choose another next node to interact with the next one. Then, this node becomes the current node to interact with the next one. Finally, it reaches to the sink node.

By finishing the above steps, the path has reached the highest transmission efficiency.

6. Experiment and Application

6.1. Simulation and Experiment

In the simulation, a theoretical model is built with the parameters. The location is used to substitute IP. The transmission distance is the distance between two nodes. For the transmission efficiency : between two nodes, although the distance is different, but relative to its transmission in terms of speed, time difference of distance is negligible, so the transmission efficiency is not affected by the two-node distance, but by the number of nodes. For the two-way interaction, the actual wireless sensor is bidirectional.

The conditions of the optimal path are as follows: firstly, the transmission must be successful; secondly, the transmission efficiency should be excellent; finally, the transmission energy consumption should be low.

The high efficiency of transmission path after the stratification in the system of a total of 15 nodes is shown in Figure 6. The starting point is node 4. The data reach to the sink node passing through node 2 and node 10. In this path, node 4 belongs to the third layer, node 2 belongs to the second layer, and node 10 belongs to the first layer.

Figure 6: The path after stratified.

After optimization by the ant colony algorithm, the path is shown in Figure 7. The starting point is node 4, passing through node 2 and node 15, to reach the sink node, in which node 4 belongs to the third layer, node 2 belongs to the second layer, the node 15 belongs to the first layer. Compared with the first path, this path is not only efficient, but also has low power.

Figure 7: The optimized path of WSN.

In the testing experiment, the smart metering network is installed in a building, a simple environment with less interference. Under the condition that the antenna is installed and that there are no obstructions, the maximum transmission distance is not more than 70 meters to ensure the normal communication between router node and end node. When obstructions exist, in order to ensure the reliability of wireless communications and to analogize the system installed in the building environment, it is more reliable in the case that the distance between nodes does not exceed 20 meters.

By the installation space limitations, if the antenna is not used, the reliable transmission distance between nodes is 10 meters. When the transmission distance is ensured, the ZigBee router node can achieve good control of the terminal nodes, and the terminal nodes can feed back information in time. Because the system communication delay is very short, usually for the dozens of subtle, it meets the measurement requirements very well.

The measurement accuracy, as well as the stability of the network of the metering system, is an important factor for application. Therefore, we tested and calibrated the accuracy and linearity of the voltage, current, and power measurement of sensor node. Take the current measurement, for example, the results of the experiment are given below. A sliding rheostat is connected to an autotransformer, so we get that a circuit can continuously adjust the supply voltage and operating current.

In the current measurement experiment, the currents were adjusted to 0 A, 2.5 A, 5 A, 7.5 A, and 10 A, and the data acquisition time interval is 1 s. When the current is 0 A, the test result is not zero, so the mean of the measured data is taken as the zero-off-set calibration. The current measurement results after this calibration are shown in Figure 8 with good accuracy. Figure 9 shows the discreetness of the current data when it is 5 A. The data range is between 4.98 A and 5.04 A, and the data fluctuation is small. Figure 10 shows the relationship of the means of collected current data. It can be seen that the various measurements have good linearity. The experiment shows that the deviation of the sensor node data collection ranges within 0.8 percent; after mean filtering, data accuracy is higher than 0.1 percent, so the accuracy can well meet the energy measurement precision.

Figure 8: Current measurement data.
Figure 9: Fluctuation data.
Figure 10: Linearity data.
6.2. Application Results

The smart metering network designed in this paper is exquisite and stores the electrical data of the electrical equipment, buildings, rooms, and the entire building. A different analysis model of the range of electricity consumption is set up to monitor the state of electricity. This metering network is installed in the Electrical and Electronic Center of Beihang University to monitor electrical conditions in a student laboratory.

The host software is installed on a computer connected to the Internet and a router is connected to this host computer through the Internet. The sensor node is installed in the installation box in the wall, which will not bring any impact to the experimental environment. The sensor node connects to the host computer through a router with Zigbee wireless network.

After all the sensors are powered to run, the electrical equipments are connected to the installation of the sensor node power box and would not change this relationship in this application. 30 sensor nodes were installed in an experiment laboratory, each of which monitored an electrical device in this application. The electrical equipment included computers, projectors, oscilloscopes, signal generators, soldering iron, electrical test platform, and so on. Sensor nodes worked 24 hours a day monitoring the power consumption. The host software was set to record the data of all the nodes in a week. During this week, the system runs stably, and 30 sensor nodes continuously worked to collect metering data and to communicate through the wireless network with the host.

Each sensor node is connected to a device. The sensors sample the data including voltage RMS, current RMS, and power consumption. On the host software, all of the devices are monitored and can be controlled at any time.

Figure 11 shows the power measurement curves of a projector, a soldering iron, and an oscilloscope. The projector, whose measured result is 310 W, consumed the most power in these electrical equipments. When using in the laboratory, the teachers who forget to turn off the projector after use will waste more energy. The power consumption of oscilloscope was about 80 W, while that of the iron was 30 W. The soldering iron was turned off when not used, and the scope worked a long time in the whole process.

Figure 11: Power curves of some equipments.

7. Conclusion

A smart metering network system is designed in this paper which contains three layers of hardware: the wireless sensor nodes, the router, and the host PC. The system has two levels of protocols. One is the IPv6 network that is between the PC and the sensor nodes, and the other is the hierarchical ant colony algorithm among the sensor nodes based on Zigbee. This system achieves intelligent building monitoring of all electrical equipments.

With no additional installation requirements, the sensor nodes are installed in the building walls in the terminal box. These sensor nodes, including ADE7753 as energy measurement core module, can take accurate real-time monitoring through circuit design and algorithm processing. The measurement accuracy of voltage, current, and power is more than one-thousandth. The microprocessor used in the sensor node integrates Zigbee communication module. So an IPv6 network, in which each node achieves a separate IP address, is established based on Zigbee protocol using the routing node to improve the transmission distance. The data transmission distance between sensor nodes is within 10 meters with no antenna installed, and this can meet the monitoring requirements for the building. The host software of smart monitoring network system is achieved in a PC, and it displays and analyzes power monitoring data with a friendly and intuitive interface acquisition. The smart system improves the safe use of electricity by the effective measures of active power control and automatic overload protection function. Experimental results verify the feasibility of the system, and the application in the office building of the Electric and Electronic Center of Beihang University obtains satisfactory results.

Conflict of Interests

The authors declared that they have no conflict of interests in this work.


This work is supported by the National Nature Science Foundation of China (under Grant 61203353).


  1. V. Cecchi, X. Yang, K. Miu, and C. O. Nwankpa, “Instrumentation and measurement of a power distribution system laboratory for meter placement and network reconfiguration studies,” IEEE Transactions on Instrumentation and Measurement, vol. 56, no. 4, pp. 1224–1230, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. S. S. Chavan, S. Jayaprakash, and V. J. Kumar, “An open standard protocol for networking of energy meters,” IEEE Transactions on Power Delivery, vol. 23, no. 4, pp. 1749–1753, 2008. View at Publisher · View at Google Scholar · View at Scopus
  3. C. Bennett and S. B. Wicker, “Decreased time delay and security enhancement recommendations for AMI smart meter networks,” in Proceedings of the Innovative Smart Grid Technologies Conference (ISGT '10), January 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. J. C. S. de Souza, M. B. Do Coutto Filho, M. T. Schilling, and C. de Capdeville, “Optimal metering systems for monitoring power networks under multiple topological scenarios,” IEEE Transactions on Power Systems, vol. 20, no. 4, pp. 1700–1708, 2005. View at Publisher · View at Google Scholar · View at Scopus
  5. K. W. Sha, J. Gehlot, and R. Greve, “Multipath routing techniques in wireless sensor networks: a survey,” Wireless Personal Communications, vol. 70, no. 2, pp. 807–829, 2013. View at Publisher · View at Google Scholar
  6. L. Cao, W. Jiang, and Z. Zhang, “Automatic meter reading system based on wireless mesh networks and SOPC technology,” in Proceedings of the 2nd International Conference on Intelligent Networks and Intelligent Systems (ICINIS '09), pp. 142–145, November 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. E. Valigi and E. Di marino, “Networks optimization with advanced meter infrastructure and smart meters,” in Proceedings of the 20th International Conference and Exhibition on Electricity Distribution (CIRED '09), June 2009. View at Publisher · View at Google Scholar · View at Scopus
  8. M. Bsoul, A. Al-Khasawneh, A. E. Abdallah, et al., “An energy-efficient threshold-based clustering protocol for wireless sensor networks,” Wireless Personal Communications, vol. 70, no. 1, pp. 99–112, 2013. View at Publisher · View at Google Scholar
  9. T. Maity and P. S. Das, “Intelligent online measurement and management of energy meter data through advanced wireless network,” in Proceedings of the International Conference on Devices and Communications (ICDeCom '11), February 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. H. Y. Tung, K. F. Tsang, and K. L. Lam, “ZigBee sensor network for advanced metering infrastructure,” in Proceedings of the International Conference on Consumer Electronics (ICCE '10), pp. 95–96, January 2010. View at Publisher · View at Google Scholar · View at Scopus
  11. N. C. F. Tse, W. H. Lau, and J. Y. C. Chan, “ZigBee based smart metering network for monitoring building integrated electric vehicle charging circuits,” in Proceedings of the IEEE PES General Meeting (PES '10), July 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. X. Jiang, S. Dawson-Haggerty, P. Dutta, and D. Culler, “Design and implementation of a high-fidelity AC metering network,” in Proceedings of the International Conference on Information Processing in Sensor Networks (IPSN '09), pp. 253–264, April 2009. View at Scopus
  13. G. Wood, “IPv6: making room for the world on the future Internet,” IEEE Internet Computing, vol. 15, no. 4, pp. 88–89, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Lee and G. L. Park, “Power load distribution for wireless sensor and actuator networks in smart grid buildings,” International Journal of Distributed Sensor Networks, vol. 2013, Article ID 372982, 8 pages, 2013. View at Publisher · View at Google Scholar
  15. J.-C. Liu and C.-D. Chung, “Distributed estimation in a wireless sensor network using hybrid MAC,” IEEE Transactions on Vehicular Technology, vol. 60, no. 7, pp. 3424–3435, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. S. S. Kolahi and P. Li, “Evaluating IPv6 in peer-to-peer 802.11n wireless LANs,” IEEE Internet Computing, vol. 15, no. 4, pp. 70–74, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. M. Mackay and C. Edwards, “A managed IPv6 transitioning architecture for large network deployments,” IEEE Internet Computing, vol. 13, no. 4, pp. 42–51, 2009. View at Publisher · View at Google Scholar · View at Scopus
  18. J.-H. Lee, “Cross-layered IPv6 neighbor discovery scheme over WLAN mesh networks,” IEEE Communications Letters, vol. 13, no. 12, pp. 992–994, 2009. View at Publisher · View at Google Scholar · View at Scopus
  19. C. Vogt and M. Zitterbart, “Efficient and scalable, end-to-end mobility support for reactive and proactive handoffs in IPv6,” IEEE Communications Magazine, vol. 44, no. 6, pp. 74–82, 2006. View at Publisher · View at Google Scholar · View at Scopus
  20. M. Park, J.-T. Kim, E.-H. Paik, and K.-R. Park, “Deployment strategy and performance evaluation of the IPv6 home network using the home server,” IEEE Transactions on Consumer Electronics, vol. 53, no. 1, pp. 114–119, 2007. View at Publisher · View at Google Scholar · View at Scopus
  21. X. N. Wang and H. Y. Qian, “Research on all-IP communication between wireless sensor networks and IPv6 networks,” Computer Standards & Interfaces, vol. 35, no. 4, pp. 403–414, 2013.
  22. S. Jianhua and H. Liping, Principles and Applications of STM32W Microcontroller With ZigBee Radio Frequency, Press of Beihang University, 2010.