Link Quality Analysis from field deployments of ZigBee home automation mesh networks

Transcription

1 Link Quality Analysis from field deployments of ZigBee home automation mesh networks Jiao Wang 1, Claudio Borean 2 DET 1, Politecnico of Turin 1, China 1 ; Research&Prototyping 2, Telecom Italia 2, Italy 2, DET 2 enricowang86@gmail.com 1 ; claudio.borean@telecomitalia.it 2 ; ABSTRACT This paper describes the results of the field testing of several installations of a ZigBee Home Automation Profile system based on hardware platforms supporting ZigBee radios. ZigBee performance are analyzed and discussed in the paper together with the software developed to perform the system performance evaluation. Based on the collected information, analysis, data processing, and comparison of different devices performances are presented, including a review on how the different devices in the network use the mesh capabilities and the effect of different methods to calculate the link quality identifier () operated by different vendors. Specifically, the performances of two vendors have been analyzed and compared, as well as the statistics on the in bad weather conditions. KEYWORDS: WSN, HOME AUTOMATION, ZIGBEE,, MESH NETWORKS CONCLUSION Through an accurate in-home testing, we did the processing and analysis of real data collected on the field. In the ZigBee network tested, the mesh was widely used by each node which guaranteed a good coverage of the residential environments. Freescale and Ember (Now Silicon Labs) are two main vendors in the field of ZigBee chipsets and in this paper it has been described how their different ways to calculate the, which might impact a performance in real environment. PER-based way to calculate the appears to be more consistent with the real behavior of devices in the field, although this way performs unstable when there are interferences (for example, generated in perturbed weather conditions such as the electromagnetic field produced by thunder and lightning) and the results of this can affect the whole ZigBee network. RSSI-based way to calculate the can withstand the occasional interference generated in perturbed weather conditions better. However this method might neglect some neighborhood relationship, for example in case two nodes are far away (i.e. RSSI is low) but the PER is still very good. As a general statement, PER based devices perform better in good weather and RSSI-based devices perform better in bad weather. It has been already identified the future work about enriching the database with simultaneous measurements of the different hardware platforms in the same house at the same time, as well as about extending campaigns of measurement by changing the metrics in the stack and testing networks with mixed hardware configuration to check the potential unbalancing effect on the mesh. At the same time, the study should proceed trying to find a way to integrate the PER-based way and RSSI-based way to calculate and solve the problem that discussed in the paper. Quick Response Code Website: Access this article online Citation ISSN Jiao Wang, Claudio Borean Link Quality Analysis, International Journal of Research in Wireless Systems (IJRWS), Vol. 2, No. 2, pp , June, 2013 ISSN: International Journal of Research in Wireless Systems (IJRWS), Volume 2, Issue 2, June (2013) 16

2 I. INTRODUCTION The wireless sensor network (WSN) is considered as one of the most important technologies which have a profound influence on our daily life. Especially with the progressing of technology in the recent decades, WSN is coming to our life. ious applications are coming out. One of the most significant and common example of WSN is Home automation. ZigBee is based on the IEEE standard for personal area networks where there is a specification for a suite of high level communication protocols using small, low-power digital radios. ZigBee is considered as one of the most promising wireless protocols in the field of home automation. ZigBee is targeted at applications that require a low data rate, long battery life, and reliable networking devices are often used in the form of mesh network in order to transmit data over a longer distance, using multi-hop to pass data through intermediate devices to reach the more distance ones [1]. This allows ZigBee networks to form ad-hoc structures, with no centralized control nodes with or high-power transmitter/receiver able to reach all of the devices. Any ZigBee device can be equipped with capabilities to operate in the mesh network [2]. The ZigBee protocol is indeed one of the most promising protocols thanks to open-standard, relatively high throughput, low power consumption, low cost, short latency and good scalability and so on [3]-[9]. following devices: microwave oven, fridge, TV, washing machine and dish washer as depicted in Fig. 2. Fig. 1. Data acquisition system overview II. DESCRIPTION OF THE TEST BEDS A. DESCRIPTION OF THE DATA The system used to acquire data for the performance analysis is described in Fig. 1. Firstly, we extracted the information of the existing ZigBee network through the GW (gateway) by connecting the remote GAL components and collecting this data from the PC. The GAL is a Gateway Abstraction Layer for fast application development in ZigBee Networks (gateway middleware running on the coordinator). The GAL enables fast development of different applications (e.g. elderly monitoring, energy management) using a set of APIs. These APIs reduces the complexity of the application development by managing directly several commands required for device discovery, service discovery and the actual application [10]- [12]. The GAL is accessed directly by using a software module named (Link Quality Indicator) Viewer, which composes the gathered information into a huge XML file that describes the ZigBee network topology and status. Next, the requisite information is extracted from the xml for further analysis of the network via a Matlab program. B. DESCRIPTION OF THE ENVIRONMENT We installed 8 nodes in the testing house: one Smart gateway, one Smart Jolly and one Smart Info. The other five nodes are Smart plugs. They are installed to monitor the Fig. 2. Positions of the devices in the mansard and the ground floor of the testing house C. DESCRIPTION OF THE DEVICES AND THE TOOLS In this section we describe the characteristics of the different devices. International Journal of Research in Wireless Systems (IJRWS), Volume 2, Issue 2, June (2013) 17

3 The Smart gateway collects information and manages the network; it also runs the GAL middleware which controls the ZigBee network. The Smart jolly is a smart plug not assigned to monitor a specific device but can be moved around. The Smart plug is a power socket with a relay and a power/energy meter, equipped with ZigBee radio to communicate with the other devices. The Smart info is the interface to the home meter [13], which operates a bridging between the utility network and the ZigBee home network. Smart gateway is the ZigBee gateway with an energy management application to measure power from appliance and control them. Smart plugs (the Jolly is just a smart plug which can be used to monitor different devices, when moved in the house) are power/energy meters for monitoring the consumption of household appliances. They also have a relay so they can be switched on and off. The smart info has a PLC (Powerline communication) interface to communicate with the home meter and get the global in-home consumption and send this information through ZigBee. The radio transmitter/receiver and the ZigBee stack are as follows. TABLE I. THE RADIO AND THE ZIGBEE STACK OF EACH DEVICE Smart gateway Radio TX/RX Freescale ZigBee Stack Freescale Beestack Smart Jolly Freescale Freescale Beestack Smart plugs Ember EM350 Ember ZNet Smart Info STMRCO ST350 Ember ZNet The tools that we used in the field test and further are: Gal, viewer and Matlab. The tests were carried out as follows: The GAL middleware is loaded into the microprocessor of the ZigBee gateway and is leveraged on the stack running on a separate ZigBee network processor in the device. The viewer interfaces with the GAL. It gets the information related to the network topology and values and populates the XML files. The Matlab tool is used to read the XML file and extract the requisite information such as homework ID, node ID, time, and so on. The XML file comprises a set of information of different home networks. It also records the samples of every node in each network every five minutes since the GAL populates that information whilst periodically scanning the network status. But sometimes there might be holes between the consecutive samples of the same node, which means that the interval between the former sample and the latter of the same node maybe more than five minutes. There are a lot of factors that can influence this wireless network such as power-off or IP disconnections between the gateway and viewer such as ADSL network disconnections. However in order to perform an accurate analysis a complete record which can cover almost the 24 hours in a day is needed. So a complete XML file was selected which can be very huge (sometimes more than 10MB). In case it was too large for Matlab to deal with, hence the XML file had to be fragmented appropriately. The main focus is on the relationship between every two nodes. It is a wireless network and the samples are transmitted to the viewer running on a PC through VPN (Virtual Private Network). In order to get sufficient samples, it needs to be verified that the link between two nodes is relatively stable (the threshold was then set to 192 samples, which means 66% of the day there is a link between these two nodes). The graphs are plotted making the link quality visible to perform further analysis and comparison. In a ZigBee network, there is a unique ID number for each device. In this test, there are eight devices. Consider the gateway as an example with a unique ID of (hexadecimal 50:C2: DD: C3: 38:6C: EA) which is very long and can be easily confused with the other devices. To simplify this, an easy mapping of the real ID of the device is carried out to give it a shorter number. Table II indicates the relationship amongst the real ID number, the simplified number and the name of the device. III. PROCESSING number indicates the quality of the link. The range of is from 0 to 255 and the number is an integer. The higher the number the better the link. We draw the among the node links in the unit of a day. The behavior for the asymmetric link between node and node 2 as well as the symmetric link between node 3 and node 5 are shown in Fig. 3. In fig. 4 we show all the links within a day. Indeed, there are many other links, including the asymmetric ones and the symmetric ones. International Journal of Research in Wireless Systems (IJRWS), Volume 2, Issue 2, June (2013) 18

4 TABLE II. MAPPING BETWEEN THE DEVICE AND THE SIMPLIFIED NUMBER ZigBee MAC address ( in hexadecimal) ZigBee MAC address ( in decimal) Simplified number Place where the device plugged in Class of the device 50C2DDC338 6CEA DE D6F0000F0F D6F0000F1F C D6F0000F0F0 6A D6F0000F0F2 A D6F0000F0F1 1B E102001BD Smart gateway Smart Jolly Microwave oven Fridge TV Washing machine Dish washer Smart Info Smart gateway Smart Jolly Smart plugs Smart plugs Smart plugs Smart plugs Smart plugs Smart Info 90 between Node 1 and 2 with average Date 19/06/2012 Time Fig. 4. All the links on a given day (19th of June, 2012) between Node 3 and 5 with average237 between Node 5 and 3 with average Date 19/06/2012 Time Fig. 3. Asymmetric link between 1 and 2 as well as symmetric link between 3 and 5 on the 19th of June, 2012 For all these links, information of the ZigBee network of everyday is obtained and the database is formed. IV. ANALYSIS A. ANALYSIS ON THE MESH USAGE Mesh usage (MU) is a factor that we have defined in order to describe how the application leverages on the mesh. The following table describes the MU of each node of a given day. MU describes how well one node uses the mesh. MU is defined as the ratio between the maximum number of links one node could and the number of links it actually has. Take node 1 on Fig. 5 as an example. The MU of node 1 is 7/2 (since there are 8 nodes overall, then 7 possible links and 2 links are actually used by the node). The higher the value of MU of one node, the more the node depends on the mesh network, since it does not have direct links with the other nodes. International Journal of Research in Wireless Systems (IJRWS), Volume 2, Issue 2, June (2013) 19

5 We analyzed the statistics of the MU of each node of every node in our database. The result is that, MU of each node is quite stable which means every node uses the mesh in a quite good way: neither depends on the mesh too much nor independent from the mesh. The map below describes the situation of the house. The network is identified as number #207. The following tables illustrate the mapping in the first two weeks and the second two weeks. TABLE III. THE MESH USAGE OF EACH NODE ON A GIVEN DAY (19TH OF JUNE, 2012) Node MU 1 7 / / / / / / / / 2 B. ANALYSIS ON THE CALCULATION OF BY DIFFERENT VENDORS Whilst analyzing the data in the database it was found that there are some links that are always good but other links that are not. In order to make it easier to analyze and to verify the reliability of the preliminary result, we made a comparison in another house. In this house, two series of completely different devices were used under the same situations as the aforementioned test without changing anything. Each type of device was running for two weeks. In the first two weeks we used the devices with Ember (EM) (Now Silicon Labs) ZigBee stack. In the second two weeks, we changed the devices by the ones using Freescale (FS) ZigBee stack. Finally, a comparison based on the data tested in these days was made. Two series of devices ran for two weeks and the analysis was based on the data obtained. The devices using EM ZigBee stack was running from the 14th of August, The devices using FS ZigBee stack was running from the 28th of August, The results within the same week are almost the same. It is reasonable because the devices were homogeneous. Hence, two typical days in different weeks are chosen to do the comparison. ZigBee Stack ZigBee Stack Fig. 5. Map of network #207 TABLE IV. MAPPING THE FIRST WEEK Real ID number (ZigBee MAC address in decimal) Simplifi ed number Device type FS Gateway EM TV EM Washing machine EM Hair dryer TABLE V. MAPPING THE SECOND WEEK Real ID number (ZigBee MAC address in decimal) Simplified number Device type FS Gateway FS TV FS Washing machine FS Hair dryer Since the two series of devices are from two different mesh networks in each week, only the links that both the two networks have in common are used to do the analysis. This is the link between the washing machine and the TV. The comparison (between the results of 16/08/2012 and 28/08/2012) is shown in Fig.6. International Journal of Research in Wireless Systems (IJRWS), Volume 2, Issue 2, June (2013) 20

6 It can be clearly seen that the on the same link by using two series of devices is quite different and the difference is very large. This phenomenon illustrates that there are different ways of different vendors to calculate the. between Node 3 and 2 with average254 in blue between Node 3 and 2 with average58 in red 300 very low. Using the EM method, the between them is perfect while using the FS method, the is very poor even if the PER is limited. In reality, these two nodes can communicate with each other very well. V. VS. OCCASIONAL INTERFERENCE DUE TO BAD WEATHER is: Date 16/08/2012 in blue Date 28/08/2012 in red Fig. 6. Comparison between two days on the same link Freescale vendor and its stack calculate based on the RSSI (Received Signal Strength Indicator). RSSI is measured in dbm or mw [14]. The relationship between and the RSSI is linear and it =255*(RSSI+81)/91 Obviously, the distance and multipath can affect RSSI a lot. Ember vendor and its stack to calculate based on the PER (Packet Error Rate) [15]. The higher, the lower PER. And there is a relationship between them based on the experience, which is shown in TABLE VI. In ZigBee, 1 packet has 128 bytes. TABLE VI. EMBER VENDOR CALCULATE BASED ON PER Errors per Byte (or more) 0 number calculated in different ways will have different results. EM method appears to be more consistent with the real behavior of devices in the field. Consider that there are two nodes that are far away from each other with the PER is still A. COMPARISON OF THE TWO CONTINUOUS DAYS Whilst testing and analyzing the data, it was found out that there is a relationship with the occasional interference due to bad weather and the. The EM devices had a stronger dependence on the weather while the FS devices did not. A typical example was chosen with two continuous days with completely different weather: 22/06/ Good weather 23/06/ Rainy and thunderstorms We focus on the EM links. The result is that: over 1/3 of the links of the day 23 performed worse than the ones of the day 22 with the mean value difference around 10 ( number). between Node 5 and 6 with average250 in blue between Node 5 and 6 with average239 in red X: Y: 242 X: Y: 183 X: Y: 173 X: Y: 131 X: Y: Date 22/06/2012 in blue Date 23/06/2012 in red Fig. 7. Comparison on the same link in different weathers of Ember devices Fig.7 is the comparison of the two days on the same link, which is the link between 5 (TV) and 6 (washing machine). The range of of 22 nd of June, 2012 is 10, with the maximum value of 252, minimum value 242 and mean value 250. While in the 23 rd of June, 2012, the range of is from 72 to 254 and the mean value is 239. In table VII, the mean value differences between good weather and bad weather on the links are 10 to 15. But the standard deviation and variance differences are huge. This means that, there are a lot of fluctuations of in bad weather, which can also be seen in the figure. This can be explained, because: For FS: depends on the RSSI, but bad weather does not affect the received power level too much. International Journal of Research in Wireless Systems (IJRWS), Volume 2, Issue 2, June (2013) 21

7 For EM: depends on the PER. Electromagnetic field produced by thunder and lightning makes interference which affects the PER much. TABLE VII. MEAN VALUE AND STANDARD VARIANCE ON THE OTHER EMBER LINKS BETWEEN GOOD WEATHER AND BAD WEATHER Date and links Link of 5 and 6 Link of 4 and 5 Link of 7 and 5 Link of 6 and 5 22/06/2012 Good weather 23/06/2012 Bad weather Mean Mean Mean Mean B. STATISTICS AND TOTAL COMPARISON In order to prove that the Ember devices have a stronger dependence on the weather and the data above is not an accidental phenomenon; statistics were computed on all the records from the database. Almost all the bad weather days were selected in Turin in the summer of 2012 and a comparison was done to get TABLE IX. In TABLE IX, things to be noted are: The record of the 22nd of June, 2012 was chosen as a reference because it is a good day while all the other records are of the days with heavy rain and thunderstorms. The focus is on the Ember links. Only the existing ones were chosen. The link of 3 and 5, 5 and 3, 7 and 3 always existed as well, but the differences on the between good weather and bad weather are not obvious. The abnormal behavior on the mean value and standard variance will be presented in red color. Results are equivalent to the abnormal links divided by the total links. The total number of links equals to 12. VI. LINK COST Freescale and Ember devices use different ways to calculate the and each way has its advantages and disadvantages. In order to integrate the two ways to calculate, link cost was proposed. As its name, link cost is considered as cost, the smaller the better. Link cost was divided into 1 to7 to express the degree of the cost. The relationship between the link cost and is now discussed for the two approaches. The Ember way to associate to link cost is like this: The value ranges from 0 to 255 with the maximum value representing the best possible link quality. The relationship between and link cost for purposes of route/neighbor maintenance in the stack is such that values of 200 map to the lowest costs of 1, 3 and 5, represent links with high error rates below 200. And the worst case cost, 7 is assigned. The value of 200 represents approximately 80% reliability of receiving the packet intact. A rolling average of values from multiple packets is used by the stack to calculate the incoming ZigBee link cost (1 to 7) for each neighbor [14]. The outgoing link cost for each neighbor is obtained from Link Status broadcasts. ZigBee defines the bi-directional link cost as the larger of incoming and outgoing costs. This is the value used for selecting routes through the network. The measurement is based on the chip error rate (similar to Bit Error Rate [BER]) of the current packet being received from the previous hop of the inbound route, so that it provides information specific to the link-layer connection to the neighboring device relaying the current packet to the local device. This data is also used by the stack to determine connectivity between neighboring nodes and to select parent devices when joining the network as a ZigBee end device. Routing metrics are cost values used by routers to determine the best path to a destination network. The Freescale way to associate to link cost as defined in ZigBee specification is described in TABLE VIII [15]: TABLE VIII. FS WAY TO ASSOCIATE THE TO THE LINK COST Link Cost > < 50 7 VII. CONCLUSIONS After an accurate in-home testing, the real data collected on the field was processed and analyzed. In the ZigBee network tested, the mesh was widely used by each node which guaranteed a good coverage of the residential environments. Freescale and Ember (Now Silicon Labs) are two main vendors in the field of ZigBee chipsets and in this paper it has been described how their different ways to calculate the might impact their performance in a real environment. International Journal of Research in Wireless Systems (IJRWS), Volume 2, Issue 2, June (2013) 22

8 TABLE IX. TABLE VIII STATISTICS ON THE EMBER LINKS Date and links 22/6/ /6/ /6/ /6/ /6/ /6/2012 1/8/2012 2/8/ /8/2012 1/9/ Abnormal links Mean null null null null Mean Mean Mean Mean Mean Mean Mean Mean Results 0% 25% 33.3% 33.3% 41.7% 33.3% 58.3% 41.7% 50% 33.3% International Journal of Research in Wireless Systems (IJRWS), Volume 2, Issue 2, June (2013) 23

9 Ember way to calculate the (based on the PER) appears to be more consistent with the real behavior of devices in the field. But this way it performs unstable when there are interferences (for example, generated in perturbed weather conditions such as the electromagnetic field produced by thunder and lightning) and the results of this can affect the whole ZigBee network. Freescale way to calculate the (based on the RSSI) can withstand the occasional interference generated in perturbed weather conditions better. However this method might neglect some neighborhood relationship, for example in case two nodes are far away (i.e. RSSI is low) but the PER is still very good. As a general statement, Ember devices perform better on good weather and Freescale devices perform better on bad weather. Link cost is actually the index used to integrate the two ways to calculate the. However it lightens the contradiction but it still cannot solve it. It has been already identified the future work about enriching the database with simultaneous measurements of the different hardware platforms in the same house at the same time, as well as about extending campaigns of measurement by changing the metrics in the stack and testing networks with mixed hardware configuration (EM/FS) to check the potential unbalancing effect on the mesh. At the same time, the study should proceed trying to find a way to integrate the Ember way and Freescale way to calculate and solve the problem that discussed above. ACKNOWLEDGMENT This work was partly supported by the Italian government under the project TESL@. This paper is supported by the Telecom Italia Lab of Turin, Italy. REFERENCES [1] ZigBee Alliance official website. Available: [2] Wikipedia website. Available: [3] Claudio Borean, Laboratorio Accreditato di Prova (LAP) Corso ZigBee LAP_v0.3.pdf, Telecom Italia. pp. 13. [4] Zhongmin Pei, Zhidong Deng, Bo Yang. Application-oriented Wireless Sensor Network. The National High Technology Research and Development Program of China under Grant No. 2006AA04Z208 and 2006AA040102, 2006, pp. 4. [5] Energy@home Technical Team, Energy@home Use Case, version 1.2. Available online. [6] Energy@home Technical Team, Energy@home Technical Specification v0.9. [7] ZigBee Alliance, ZigBee Cluster Library Specification, [8] ZigBee Alliance, ZigBee Specification, [9] BSI British Standards, document BS EN :2009, Household appliances interworking - Part 1: Functional specification, July [10] Claudio Borean, Claudio Pastrone, GAL: a Gateway Abstraction Layer for fast application development in ZigBee Networks white paper of EuZDC, [11] Kawamoto, R. Emori, T. Sakata, S. Furuhata, K. Yuasa, K. Hara, S. Alpha Systems Inc., Tokyo, DLNA-ZigBee Gateway Architecture and Energy Efficient Sensor Control for Home Networks, Mobile and Wireless Communications Summit, th IST. [12] IEEE, IEEE Standard for Information technology-- Telecommunications and information exchange between systems-- Local and metropolitan area networks-- Specific requirements Part 15.4: Wireless Medium Access Control (MAC) and Physical Layer (PHY) Specifications for Low Rate Wireless Personal Area Networks (LR-WPANs), [13] Claudio Borean, Andrea Ricci, and Gabriele Merlonghi, Energy@home: a User-Centric Energy Management System, Energy@home, white paper. [14] Freescale Specification, Available: [15] Ember Specification, Available: International Journal of Research in Wireless Systems (IJRWS), Volume 2, Issue 2, June (2013) 24

10 Short Biography with photograph Jiao Wang Jiao Wang is involved in this research and project during his internship in the WSN lab of Telecom Italia in Turin. He got his Telecommunication Engineering Master Degree from Politecnico di Torino. Claudio Borean Claudio Borean is involved in Smart grids research activities for "Innovation and industry relations" Department of Telecom Italia. He received an Electronic Engineering Master Degree from Politecnico di Torino and a "Master in Telecommunication" from ISGRR institute. He worked in several research projects about Next Generation Wireless LAN, RFID technologies and mobile services evolution, Wireless sensor networks and the Internet Of Things (IoT). He has been involved in ZigBee Alliance for new service implementations for mobile and residential applications. He is currently involved in the smart grids research activities, with special interest in home energy management systems. He has been member of the Technical Committee of Energy@Home since 2009 and he has been the Chairman of Telecom Applications working group of ZigBee Alliance since 2006, vice-chair of the ZigBee Retail group since 2010 and technical Editor of Home Automation group since He is Chairman of the Standardization committee of Energy@home. He is author of several patents and papers about wireless technologies. He is a PMI certified Project Manager Professional (PMP) since April International Journal of Research in Wireless Systems (IJRWS), Volume 2, Issue 2, June (2013) 25