A New Highly-Synchronized Wireless Mesh Network Model In Use By The Electric Company To Switch To Automatic Meter Reading: Case Study Leor Hardy and Marius Gafen, Virtual Extension, Israel
Abstract—In this paper we introduce the concept of a highly synchronised wireless mesh model, leading to a very reliable and coordination-free network. The reliability improvement is caused by the spatial diversity of the propagation paths - the reception of multiple packets’ copies over many paths, some of them possibly faded or disrupted. To prove the inherently coordination-free capability, the paper studies the proper operation of the full network immediately after installing the meters by personnel who was not trained for the task and the short (less than a day) deployment time. The architecture of the system and the results of the field test are discussed, as they lead to a high probability that changes in the site are inherently handled by the system without human intervention. The model, evaluations and results reported concern a commercial pilot of 100 nodes, conducted by the Israel Electric Company in a dense urban area. Keywords — wireless mesh networks; automatic meter reading; spatial diversity; time-synchronization; flooding.
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I. INTRODUCTION
HE main goals for most AMR/AMI (Automatic Meter Reading) systems and AMI (Automatic Metering Infrastructure). [1] is to enable the reception of the required data from the electricity meters, without the need for physical connection or access, and to enable the remote control of the circuitry attached to the electricity meter by a control centre application. Since AMR/AMI systems can determine real-time energy consumption of the customers accurately, they are important for electric utilities, as their use can reduce operational costs and enable flexible management systems based on real-time energy consumption monitoring. Furthermore, AMR/AMI systems can be used to report real-time tampering with the meters, and also to disconnect or reconnect users remotely. Hence, AMR/AMI systems provide an alternative real-time intelligent metering system for electric utilities with the potential to improve business performance and the technical reliability of various electric utility operations. II. ISRAEL ELECTRIC COMPANY PROJECT The Israel Electric Company (IEC) decided to investigate into a WSN (Wireless Sensor Network) based system to Manuscript received January 7, 2008. L. Hardy and M. Gafen are with (+972-3-7321207; fax: +972-3-5731629; e-mail:
[email protected]).
Virtual
Extension,
Israel
enhance the performance of its operations by enabling reliable wireless automatic meter reading and real-time monitoring. Traditionally, IEC uses manual electricity meter reading, a procedure which requires physical presence and visual inspection of the utility meters. The main reason for the IEC decision to implement a wireless collection of electric utility meter data was the potential of a cost-effective way of gathering energy consumption data for the billing system, using an AMR/AMI system capable of reducing the electric utility operational costs, by eliminating the need for human readers. An additional benefit was the expected added value in terms of new services such as remote reduction of a customer’s service, pre-paid services, real-time price signals, and control of customers’ applications. The IEC defined a specific set of requirements to be implemented in the metering network towards its way of implementing AMR/AMI, which supports both existing functionalities and future operational requirements: 1) Remote billing – by transmitting the read-out through the network to the gateway to the IEC control centre, to be used for calculating the billing and for management purposes. Each network reading is initiated by the control centre at a maximum reading rate of once every 30 minutes, with a minimum reading rate of once every 24 hours and 250 meters maximum meters per cell. 2) Remote connection and reduction of service. The required activity, which is a command, rather than a control function, can be initiated by the control centre and should be implemented at the meter level within seconds. 3) Overload monitoring – by transmitting the meters readout to the control centre, where it could have been either registered or used for activating the remote disconnection of the customer, thus serving as a remote fuse capability. 4) Tamper monitoring – by transmitting to the control centre the change of the status of tampering detectors located within the meter. 5) Simple and low-cost deployment and maintenance, for the meters to be installed, removed, added, replaced and maintained by regular staff who does not have to be trained for this purpose. 6) High resiliency – in particular when the RF propagation conditions are changing, such as adding or removing
buildings in the neighbourhood, or changes within the buildings where the meters are installed, such as adding floors, as well as adding or removing walls. From previous experience, IEC knew that one of the most difficult requirements of such an AMR/AMI is a reliable high-performance data communication network, and one of the main goals was that of addressing this requirement. III. A NEW WIRELESS MESH NETWORK MODEL A. Introduction Real-life WSNs (Wireless Mesh Networks) are required to achieve a stringent performance set. In many cases, this set of performances includes simplicity of deployment and maintenance, as well as high-resiliency to changes in the surroundings. To achieve such a performance set, each of the modern networks adheres to a specific set of techniques, from the multitude developed over the last years. The WSN model described in this paper makes use of several such techniques, coalesced in a new and unique way, for meeting the performance set required by the wireless mesh network used for Automatic Meter Reading by an Electric Company. B. The system model The Wireless Mesh Network model described in this paper combines in an original way several techniques described in the literature, namely flooding [6]-[10], cooperative communications [2] – [4] and OLA [5] with the added element of synchronisation. The basic network cell consists of a set of nodes and one coordinator (with the possibility of an alternate coordinator for increased reliability), and uses mesh topology. The coordinator is a specialized node, which functions both as a communication gateway between the control centre (central electrical utility company location) and the network, as well as a simple network cell manager with receiving, transmitting and storage capability, which: 1) Receives from the control centre a node table and stores it 2) Receives from the control centre the payload commands and stores them, before forwarding (transmitting) them to the network 3) Transmits payload operational data to the end points of the network cell. 4) Receives the payload data from the nodes’ network and stores it, before forwarding (transmitting) the payload to the control centre, upon the control centre’s request 5) Updates the stored node table with the status of the nodes (functioning / non-functioning) and reports changes to the control centre All the nodes of the system, with the exception of the coordinator, are homogenous and function both as an interface between the sensor (Electricity Meter) and the network, as well as a network receiving and transmitting member. Their functions are:
1) Receive from the Meter operational data and store it 2) Receive transmissions, decode, store and execute the network data, according to the rules detailed bellow 3) Transmit (forward) the operational data to the Meter 4) Transmit operational data according to the rules detailed bellow The transmissions use synchronous GFSK modulation / demodulation techniques and frequency hopping in the unlicensed 870/915 MHz frequency bands.
Figure 1 — a network cell block diagram The communication between the coordinator and the control centre, as well as between the node and the Meter were mentioned in order to offer a complete description of the case study and are not included in the scope of this paper, hence from this point on, the paper will focus on the operation of the network only. C. The node rules The following rules govern transmissions received by a node: 1) Transmissions not belonging to the node cell are ignored 2) Transmissions belonging to the node cell, but not addressed to the specific node, are retransmitted at the next frame 3) Transmissions belonging to the node cell and addressed to the specific node. These transmissions can in turn be of three types: 1) A network command from the coordinator to the node; upon receiving this command, the node initializes the next frame transmission, for sending its payload to the coordinator 2) Other network commands from the coordinator to the node - executed according the each command rule 3) The payload, which is forwarded to the Meter D. The communication protocol The basic communication protocol is TDMA, with the smallest structure named sub-frame and carrying the following data: 1) The addresses of the origin and destination , 2) A small set of network command and control data, 3) The payload - various operational data. Several identical frames are combined in a frame. Nsf –
the number of the sub-frames in a frame, corresponds to the maximum number of hops between the coordinator and the farthest reaching node in the cell, plus a guard number, to compensate for the worsening of the RF propagation conditions. For the system described in this case study Nsf is fixed. However, the model can be enhanced for dynamically adjusting the Nsf in more dynamical environments or in environments where the number of necessary hops is difficult to estimate in advance. Such enhancements are beyond the scope of this paper, to be subsequently developed in additional studies. For propagating the message from the initialling node to the destination node, the nodes use the flooding technique described above. All the nodes that receive a transmission forward exactly the same transmission in the sub frame that follows immediately the preceding sub frame, according to the cooperative communication technique The repeating process repeats itself Nsf times and then stops. The data is collected from the nodes by polling, initiated by the coordinator. For collecting the data from a specific node, the model uses a cycle of two frames – one from the coordinator to a network node, interrogating the specific node and one from the specific node, for sending its data to the coordinator. Tsf x Nsf
Tsf x Nsf
coordinator to nodes
Tr
node to coordinator
Tr t
Figure 2 — collecting data from one node For collecting a complete set of data from the whole network cell, the coordinator interrogates all the Nn nodes, in an order of interrogation which is fixed for the system described in this case study. The coordinator stores the collected data, to be subsequently forwarded to the control centre upon request. Thus, if the time of a sub-frame is Tsf, the number of nodes in the network cell is Nn and Tr is a rest time, then the time of collecting a complete set of data from the whole network cell Tssf, called super-frame, is: (1) Tssf= ((Tsf x Nsf) + Tr ) x Nn x 2 Similar to the OLA, this model is implemented in the physical layer, thus practically eliminating the routing overheads of the higher-layer interventions (MAC and Network layer) and exhibiting the efficiency demonstrated in the literature [11]. Here the similarity ends. Unlike OLA, this model uses a synchronous mode of propagation, using an original synchronising at the frame and at the bit-level. Instead of operating under the OLA “integrate-and-fire” model, a “synchronise-and-fire” model is used, thus achieving the capability to control the avalanche effect and to turn the network to be predictable and manageable. The synchronisation is done also at the physical layer, using the transmissions to re-synchronise the nodes at the beginning of each frame, according to the principle of collaborative
transmission [12]. The synchronisation enables a variable cycle length of transmissions, fitting to the specific case requirements (in cases where the power saving constrains are critical, the synchronisation enables the systems to be in the very low power consumption mode for most of the time). This cycle length (D) is one of the network parameters sent from the coordinator to the nodes and which can be in turn controlled by the control centre. IV. THE MODEL IMPLEMENTATION – PERFORMANCE AND RESULTS
An AMR/AMI system based on the WMN model presented in this paper was installed during August 2005 in a neighbourhood of multi-storied, multi-tenant buildings chosen by the IEC to represent a typical environment. The system consisted of a 1-year pilot and commercial use since then of one coordinator and 100 nodes. The coordinator was installed on an IEC pillar at street level and connected to IEC control centre by Motorola – the main contractor of the project, with the connection using a Motorola point-topoint radio. The meters were installed by the regular IEC meters’ staff at 10 eight-story and four-story buildings, with the farthest away building distanced 220 yards from the coordinator, designated as the maximum range requirement for IEC. All meters are connected to wireless cards, using 100 mW transmitters in the 916 MHz unlicensed frequency range and acting as a nodes. With 100 nodes (Nn=100), a sub-frame time Tsf= 10 msec, the number of hops Nsf set to 20, and the rest time set to 800 msec, the super-frame time of collecting a complete set of data from the whole network cell Tssf is 200 seconds. The specified maximum sample rate was once every 30 minutes. Hence the cell size can be increased to 900 Meters per cell without any change in the parameters, well beyond the requirements of the case study. The simplicity and the speed of the deployment were beyond expectations, with no incidents noted during the installation. After the installation of the coordinator and the deployment of the last nodes/meters, the system started to work in a matter of minutes, with no adjustments and with all the meters responding as expected. Since the deployment (August 2005) and until the time of writing this paper (December 2007), IEC has thoroughly tested the system, checking routinely once a week all the readings and ad-hoc during the week, sometimes several times a day. The network has performed according to the specifications without any incident, despite construction and street works done in the area, with one exception, of a meter which ceased working because of a mechanical failure. The problem was detected on the same day by one of IEC control centre ad-hoc tests, as the wireless card continued to work, and the IEC meters’ staff replaced the meter.
V. CONCLUSION In this paper we describe a highly synchronised wireless mesh network whose implementation is being used for wireless automatic meter reading and real-time monitoring. The model uses a modified OLA (Opportunistic Large Arrays) flooding and cooperative communication technique, added by an original propagation mode, which is synchronous at the bit level. The system using this model was implemented successfully at the Israel Electric Company AMR/AMI pilot and has been operating according to the specifications without any incident. Finally, the paper mentions several enhancements for the model, to optimize in diverse applications with different wireless mesh network requirements. ACKNOWLEDGMENT The authors would like to thank Ofec Goll for his constructive comments. He is currently working at Virtual Extension as the software manager of the R&D team. REFERENCES [1] V.C. Gungor and F. Lambert, “A Survey on Communication Networks for Electric System Automation,” Computer Networks Journal (Elsevier), vol. 50, pp. 877-897, May 2006. [2] Y.-W. Hong, W.-J. Huang, F.-H. Chiu, and C.-C. J. Kuo, “Cooperative Communications in ResourceConstrained Wireless Networks,” IEEE Signal Processing Magazine, vol. 24, no. 3, pp. 47–57, May 2007. [3] A. Sendonaris, E. Erkip, and B. Aazhang, “User Cooperation Diversity–Part I: System Description and User Cooperation Diversity–Part II: Implementation Aspects and Performance Analysis,” IEEE Trans. Commun., vol. 51, no. 11, pp. 1927–1948, Nov. 2003. [4] J. N. Laneman, D. N. C. Tse, and G.W.Wornell, “Cooperative diversity in wireless networks: Efficient protocols and outage behavior,” Information Theory, IEEE Transactions on, Volume: 50, Issue: 12, pp 30623080, Dec. 2004. [5] Y.-W. Hong and A. Scaglione, “Cooperative transmission in wireless multi-hop ad hoc networks using opportunistic large arrays (OLA),” SPAWC 2003. 4th IEEE Workshop on Signal Processing Advances in Wireless Communications, 2003. [6] K. Obraczka, K. Viswanath, and G. Tsudik, “Flooding for reliable multicast in multi-hop ad hoc networks,” ACM Wireless Networks, vol. 7, pp. 627–634, Nov. 2001. [7] J. N. Al-Karaki and A. E. Kamal, “Routing Techniques in Wireless Sensor Networks: A Survey,” IEEE Wireless Commun. Mag., vol. 11, no. 6, pp. 6– 28, Dec. 2004.
[8] Sze-Yao Ni, Yu-Chee Tseng, Yuh-Shyan Chen, and Jang-Ping Sheu. “The broadcast storm problem in a mobile ad hoc network,” MobiCom ’99: Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking, pages 151–162, New York, NY, USA, 1999. ACM Press. [9] Shih-Hsien Wang, Ming-Chieh Chan, and Ting-Chao Hou. “Zone-based controlled flooding in mobile ad hoc networks,” Proc. of the International Conference on Wireless Networks, Communications and Mobile Computing, volume 1, pages 421–426 vol.1, 2005. [10] Brad Williams and Tracy Camp, “Comparison of broadcasting techniques for mobile ad hoc networks,” MobiHoc ’02: Proceedings of the 3rd ACM international symposium on Mobile ad hoc networking & computing, pages 194–205, New York, NY, USA, 2002. ACM Press. [11] C. E. Perkins and E. M. Royer, “Ad-hoc on-demand distance vector routing,” Proc. 2nd IEEE Workshop on Mobile Computing Systems and Applications (WMCSA ’99), pp. 90–100, New Orleans, La, USA, February 1999. [12] A. Krohn, M. Beigl, C. Decker, T. Riedel, “Syncob: Collaborative time synchronization in wireless sensor networks,” INSS. 4th International Conference on Networked Sensing Systems (2007), pp. 283-290, USA, June 2007. Leor Hardy received his B.Sc. (1984) in electrical engineering from the Technion Institute of Technology. Currently he leads the R&D team of Virtual Extension (Israel). Since 1984, he has participated in several R&D projects for TeleSciCOM, Nexus and Elisra, and served as a senior consultant to Alvarion and Metalink. Marius Gafen received his B.Sc. (1970) in electrical engineering from the Technion Institute of Technology. Currently he is a senior researcher with Virtual Extension. Since 1970, he participated and led several R&D projects for MoD and private hi-tech companies in Israel, including ArelNET, NSIcom, Coresma and Sonarics. His research interests include digital radio broadcasting and embedded real-time systems.
