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Improved Quality of Service in IEEE 802.15.4 Mesh Networks

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International Workshop on Wireless and Industrial Automation San Francisco, California, March 7, 2005 Page 1 of 6 Improved Quality of Service in IEEE 802.15.4 Mesh Networks Jay Werb Michael Newman Victor Berry Scott Lamb [email protected] [email protected] [email protected] [email protected] Sensicast Systems 220-3 Reservoir Street Needham, MA 02494 Phone 781-453-2555 Daniel Sexton Michael Lapinski Phone 518-387-4121 Phone 518-387-6690 Email: [email protected] Email: [email protected] GE Global Research One Research Circle Niskayuna, NY 12309 In collaboration under a Department of Energy grant for the Industries of the Future, General Electric and Sensicast Systems have studied performance of 2.4 GHz IEEE 802.15.4 radio transceivers in factory environments, with particular attention to jamming from 802.11 and multipath fading. Temporal and frequency variations in link quality are explored. The implications for network reliability and protocol design are discussed. I. INTRODUCTION Wireless sensors for industrial applications are expected to open large opportunities for data collection where it has traditionally been considered technically impossible or cost prohibitive. To overcome installation and acceptance barriers a wide variety of requirements must be satisfied. Some of these barriers include cost and reliability. Short-range wireless technologies such as IEEE 802.15.4 [1] combined with mesh networking techniques are being widely considered as the answer to both cost and reliability in industrial settings. However RF communications, particularly indoors, is well known to be unpredictable. Wireless Mesh Sensor Networks are being deployed today in various monitoring and control applications. Some radio network designs, such as ZigBee, presume that radio connectivity is reasonably consistent over time. Others take the opposite approach of presuming that links are entirely unreliable, and build large degrees of physical redundancy into the network in the hope that a collection of redundant but unreliable individual links will result in a reliable overall system. Surprisingly little work has been done in the middle ground, of endeavoring to understand the root cause of link failure in real-world factory environments and applying this knowledge in the design of protocols that adaptively detect and use workable radio channels. Proper understanding of the channel characteristics is needed in order to determine adequate design margins to minimize the installation effort or the amount of physical network reconfiguration required as the environment around the network changes. One approach would simply be to over-configure the network by increasing the node density with additional mesh routing nodes. However this can cause issues with additional installation cost, network maintenance and decreased network capacity. A better approach would be to just slightly over-configure the network by understanding the appropriate required design margins. Of course success in this approach requires collection and analysis of a statistically relevant and representative set of Radio Frequency (RF) channels and environments. In our work on the Department of Energy grant we have attempted to better understand what effects are present in the RF environment in industrial facilities. As we wish to employ standards when possible, and need an international solution in fielded products, we decided to focus our attention on the performance of 2.4 GHz IEEE 802.15.4 physical radios in this environment. We hope to gain insight into the characteristics required of a mesh network as well as the suitable design margins required. II. BACKGROUND In our project we needed to make several important technology and design decisions early so that we could launch critical product development activities. One of these decisions was the choice of a physical radio technology. There were several options available to us. To make an initial decision we first took measurements of a readily accessible and representative harsh indoor environment. We took an initial channel measurement using a network analyzer and twoInternational Workshop on Wireless and Industrial Automation San Francisco, California, March 7, 2005 Page 2 of 6 antennas spaced 25 feet apart. An image of the facility is shown in Figure 1 and the schematic of the test equipment in shown in Figure 2. Initial measurements of this environment as shown in Figure 3 illustrates that in the commonly used 2.40 – 2.83 GHz radio band there is both significant frequency selective fading as well as flat fading depending on the area of interest. The dark points represent the actual measurement whereas the lighter points represent the fading as averaged over a 2MHz bandwidth. Figure 1: Initial Test Environment Figure 2: Initial Test Configuration Figure 3: Channel Fading – Line of sight channel Examination of Figure 3 shows deep fading at certain frequencies. The 2 MHz bandwidth plot is intended to approximate the bandwidth of IEEE 802.15.4, and clearly shows nulls on the order of 15 dB even with spreading. It is also interesting to note that the fades are frequency-specific. This and similar data led us to suspect that link quality could be substantially improved with a protocol that utilizes multiple frequencies. With this observation we took two parallel tracks. First, we tested the fundamental performance of IEEE 802.15.4 radios in factory environments to be sure we understand the root causes of link failure. Second, we implemented a first system based on 802.15.4 radios, with a design that incorporates channel diversity, path diversity, temporal diversity, and increased transmit power. Our strategy is summarized in Figure 4. Static Multipath Time Variant Multipath Static InterferenceTime Variant Interference Spatial Diversity     Frequency Diversity ☺☺☺☺ ☺☺☺☺ ☺☺☺☺ ☺☺☺☺ Temporal Diversity  ☺☺☺☺ Transmit Power     Overall Risk Coverage ☺☺☺☺ ☺☺☺☺ ☺☺☺☺ ☺☺☺☺ Figure 4: Cost/Benefit of Risk Mitigation Strategies Key: ☺=Good =Fair =Minimal =None As shown in Figure 4, we identified four general causes of reduced link quality in a factory, and four strategies that together would help overcome these issues.


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