Using IP for Smart Objects and ‘The Internet of Things’

Previously, we discussed Contiki, an operating system (OS) for Internet Protocol (IP) based wireless sensor networks (WSNs) and embedded devices. In this blog we will examine the use of IP for smart objects and ‘The Internet of Things’ in more detail.

A smart object is a small computing device that will also have a sensor or actuator as well as a means of communication – mostly, but not always, wireless communication. Smarts objects are embedded in other larger objects and systems including industrial machinery, car engines and lighting and heating systems. Smart objects are the technical drivers behind the smart ecosystems that are emerging in many diverse environments and applications and are the enablers of innovations in monitoring and automation for homes and industrial plants as well as the smart grid and energy efficiency systems. The potential application domains for smart objects is vast – from ad-hoc emergency response systems to smart transport infrastructure for urban areas.

The key property of a smart object is its capacity for bidirectional communication. Diverse measurements can be taken of environmental characteristics such as humidity, temperature, wind speed, pressure, vibration and energy consumption. In addition to measurements, the sensors in smart objects can also perform detection of elements in an environment such as pollutants or the presence of a chemical.

Smart objects are generally low-powered battery operated devices and will have a CPU and memory. Their form factor is small and the goal is that their price should be low – this was not always the case with wireless sensors but prices have lowered in recent years.

There are many protocols available for enabling wireless communication in a network of smart objects. Zigbee (http://www.zigbee.org) is a low-cost and low-power radio communications protocol based on the 802.15.4 standard for Wireless Personal Area Networks (WPANs). 6LowPAN (http://www.6lowpan.org/) also uses the 802.15.4 standard but uses IP with the aim of achieving the wireless ‘Internet of Things’. 6LoWPAN stands for IP version 6 (IPv6) over Low Power Wireless Personal Area Networks. The IPSO Alliance (http://www.ipso-alliance.org) promotes this use of IP for smart objects and, indeed, there are many good arguments for using the existing IP protocol.

IP is an well-established open standard and is media independent which means it can run over low-powered radios such as 802.11 WiFi, long range 3G (Third Generation) telephony as well as ethernet among others. Best effort or reliable transmission is available using UDP (User Datagram Protocol) or TCP (Transmission Control Protocol) respectively. Recent implementations such as uIP have shown that IP is a lightweight protocol and its versatility means it supports any type of application and in turn is supported by a diverse range of devices from high-end servers to smart phones. This versatility has led to IP’s ubiquity and over its comparatively long life its has showm itself to be stable (after all, it is used for the Internet), manageable (using protocols such as Dynamic Host Configuration Protocols – DHCP, Domain Name System – DNS and Simple Network Management Protocol – SNMP) and provides end-to-end communication.

These cogent arguments are made by the IPSO Alliance to advance the case for using IP in smart ecosystems. IPv6 is used in particular as it contains a number of optimisations for constrained devices, a category in which most smart objects will find themselves due to their unavoidable processing and memory limitations. Stateless compression of IPv6 headers is possible which means that smart objects can communicate with their neighbour in this compressed form. Packets can be fragmented and neighbouring smart object nodes can be discovered. The Internet Engineering Task Force (IETF) has defined these properties in the 6LoWPAN adaptation layer.

IPv6 was designed to counter the ever dwindling number of unallocated IP addresses. Its key feature is that it expands the IP address space from 32 to 128 bits. The development of IPv6 was vital as the number of computers will ultimately be far outstripped by networked devices and smart objects. A number of features has also been introduced to improve performance. The size of the Maximum Transmission Unit (MTU) has been increased from 576 to 1280 bytes and fragmentation is now implemented at the endpoints rather than in intermediate routers. A scoped multicast for broadcasting to all hosts with a multicast group is also included.

Despite these improvements, supporting IPv6 over LoWPAN networks is not a straightforward task as the frames used in LoWPAN are approximately one-tenth of the size of IPv6’s minimum MTU which makes fragmentation and compression mandatory. Furthermore, the 802.15.4 standard upon which LoWPAN is based is low-power and low-throughput and is prone to failures and interference. Finally, a LoWPAN network is a mesh network of short-range connections which is contrary to the expected typical IPv6 architecture. It is for these reasons that the 6LoWPAN working group was established by the IETF. 6LoWPAN defines how IPv6 communication is transported in 802.15.4 frames. In addition to the optimisations in fragmentation and header compression described earlier, 6LoWPAN supports layer-2 forwarding between the link-level addresses of the end of an IP hop.

Despite the development of the 6LoWPAN format specification, implementation was not considered by the IETF working group. There are also a number of issues that must be considered for LoWPAN environments. There are typically a large number of devices that are embedded in the environment or infrastructure and so cannot be moved to improve communication. Multi-hop routing is therefore used to extend range and avoid the many obstacles that can be present in an environment. Link quality can also be variable due to interference and environmental factors. Effective routing is thus a necessity in such a situation. Moreover, certain applications may require device mobility. In many cases, the devices may not be moving a particularly long distance but do respresent a change in the LoWPAN’s topology. Power management is not comprehensively defined in the 802.15.4 specification but must be considered for routing in commercial applications.

One issue to note when considering IPv6 over LoWPAN is Mesh-Under (Layer 2) and Route-Over (Layer 3) Forwarding.  With mesh-under the forwarding and routing of packets within the LoWPAN is carried out at the adaptation layer and no IP-routing takes place at the network layer.  Route-over performs routing at the IP layer and each smart object node serves as an IP router.

The number of smart objects in a LoWPAN is potentially vast. Each of these objects will require an address. Again, the IPSO Alliance argues that the easiest mechanism for doing this assignment and connecting these nodes to each other is to use IPv6.  IPv6’s Neighbour Discovery (ND) Protocol is used to resolve and automatically configure addresses as well as to discover routers and determine the reachability of neighbours. The IETF 6LoWPAN working group is currently working on  ND optimisations for 6LoWPAN environments. The considerations the working group has to take into account include the continously changing wireless link. the often lengthy sleep mode of the networked devices, the battery and processing constraints of the devices and the often mobile nature of the nodes. In essence, the 6LoWPAN ND Protocol extends the standard IPv6 ND Protocol and has added functionality to take mobility and fault tolerance into account. The final protocol will support both Rroute-Over and Mesh-Under networks.

There are a number of alternatives to using 802.15.4 for smart object communications. The use of low-power Wi-Fi and Broadband over Powerlines are other possibilities.The latter is particularly attractive as it uses the existing electricity network which means that a computing device can be networked by plugging it into any electrical outlet. No matter the physical medium used for transmission, however, IP would appear to be the appropriate choice for carrying the data generated by smart objects. Otherwise, the consequent loss of standardisation between LoWPANs would necessitate protocol translation gateways to enable different networks to communicate with each other. Given the predicted explosion in smart objects and networks in the coming years, this is an expensive and unattractive proposition from both a technical and financial perspective.

An Operating System for ‘The Internet of Things’

TinyOS (http://www.tinyos.net) is the original and probably best well known operating system (OS) for the sensor nodes that make up Wireless Sensor Networks (WSNs). Sun Microsystems has also developed Java based sensors using the Squawk Virtual Machine (See https://spots.dev.java.net/). However, there is another operating system that we must consider not only for WSNs but also for embedded devices generally and what is sometimes referred to as ‘The Internet of Things’.

The Contiki Operating System (http://www.sics.se/contiki) is an open source multitasking operating system for memory-constrained networked embedded devices and wireless sensor nodes. It is a well established OS. Version 1.0 was released in March 2003. The latest edition, Version 2.3, was released in June 2009. It is free to download under BSD license. It was developed jointly by several research and commercial organisations including the Swedish Institute for Computer Science (SICS), the Technical University of Munich, SAP, Cisco and the semiconductor manufacturer ATMEL.

Contiki has been ported to many of the vast range of sensor products available including Crossbow’s TelosB and MICAZ sensor motes as well as this vendor’s Environmental Sensor Bus (ESB)  (See http://www.xbow.com). Atmel’s AVR Raven sensors (See http://www.atmel.com) and Sentilla’s JCreate prototyping platform (http://www.sentilla.com) are also supported.

The key feature of the Contiki Operating System to note is that the OS provides Internet Protocol (IP) Communication, both for IP version 4 and version 6 (known as IPv4 and IPv6 respectively). This has major implications as Contiki makes IP-based sensor networks possible. Contiki’s uIP embedded TCP/IP stack was released in 2001 and is the world’s smallest IP stack taking up 5k ROM for  IPv4 and 11k ROM for IPv6. uIPv6 is the only certified IP stack (certification was achieved in October 2008) for embedded systems while uIPv4 is used in diverse applications including container tracking systems, oil pipelines, car engines and satellites.

One could argue that TCP/IP is unsuited for sensor networks because of the extreme communication conditions that sensor networks exhibit. However, there are a number of techniques which can be used to make IP-based Sensor Networks feasible. Spatial IP Address assignment can provide semi-unique addresses to sensor nodes. Application overlay networking lets distributed applications run on top of the network and decide how to process the packets. Distributed TCP caching lets sensor nodes assist each other so that neighbouring motes are able to retransmit segments that are lost due to errors on the radio channel. Finally, header compression reduces the header overhead to only a few bytes for messages carrying sensor data.

In addition to these benefits, Contiki also provides a low-power MAC (Media Access Control) layer and preemptive threads that run on top of events. Contiki provides support for both multi-threading and what are termed ‘protothreads’. Protothreads are lightweight stackless variants of the threading used in computer programming and are designed for severely memory constrained systems such as wireless sensor nodes or other small embedded systems.

From the perspective of the programmer, Contiki is written in the C programming language so knowledge of C is a more than sufficient starting point for getting started with the OS. Loadable modules are used to implement the different facets of the OS. The build system will also be very familiar to C programmers who regularly program on LINUX or UNIX platforms as applications can be compiled for different hardware or simulation platforms by using the standard ‘make’ command with different parameters.

In addition to uIP, Contiki also provides a lightweight communication stack for low-powered radios called Rime. Rime provides a number of communication primitives from best-effort local area broadcast to reliable multi-hop bulk data flooding.

IP-based sensor networks enabled by Contiki have been deployed in diverse projects. Examples include remote water monitoring in the Baltic Sea, locating vehicles and people in road tunnel fires (this is part of the Runes project. Runes stands for Reconfigurable Ubiquitous Networked Embedded Systems. See http://www.ist-runes.org/ for more details.) and in surveillance and intrusion detection applications. Commercial users include ABB, BMW, Cisco, NASA and General Electric.

Give the open and ubiquitous nature of IP, it is an ideal candidate for networking pervasive devices. Contiki and uIP have played a major role in demonstrating the potential of using IP in these diverse, often constrained networked devices. What are termed ‘Smart Objects’ are evolving all the time. Roughly, Smart Objects can be categorised as sensors, actuators and smart meters. These devices are the enablers of the smart grid, smart cities, home vnd building automation, asset tracking and utility metering. Connecting these smart objects is a major issue and IP would appear to be by far the most suitable protocol for what is termed ‘The Internet of Things’. To promote the use of IP for the Internet of Things Cisco, Ericsson,  SAP and others founded the IPSO Alliance (The IP for Smart Objects Alliance) in October 2008 (See http://www.ipso-alliance.org). IPSO seeks to extend the use of IP for resource-contrained devices over a wide range of radio technologies.

Extending IP to low-power, wireless personal area networks (LoWPANs) that typically make up a smart object network was once considered impractical and many vendors used proprietary protocols. However, 6LoWPAN has altered this perception as efficient IPv6 communication over IEEE 802.15.4 LoWPANs is facilitated. It can be argued that uIP was the pioneer for protocols such 6LoWPAN and was the first technology to make the Internet of Things possible.

Contiki is not the only WSN OS that offers IP Networking. TinyOS was relatively late in offering IP networking but this capability finally became available in 2007. IPv6 networking is now possible using TinyOS but the OS is not yet certified as IPv6 ready. A 6LoWPAN and IPv6 stack has been implemented. IP-based sensor networks and smart objects will become ever more prevalent in the coming years so an implementation was vital for TinyOS to remain relevant.

Building Management Systems

Figure 1: A Building Management System is key enabler of a ‘Smart’ or ‘Intelligent’ Building.

A Building Management System (BMS) (also known as a Building Automation System or BAS) is a control system that is used to control and monitor equipment so as to manage lighting, ventilation, heat, power, security and fire detection.The application domain of a BMS is very broad in scope and can include boiler room, pump and plant control, underfloor heating control, heat recovery systems and general process control. A BMS consists of hardware and software. Hardware will be used to control the equipment while instructions to these hardware components will be issued from a software system.

The common controls for equipment would be manual switches, time clocks or temperature switches. The purpose of a BMS is to automate these operations as much as is practicable. A BMS can be used for all kinds of buildings but would be most common in a manufacturing plant. Generally, the BMS will store a number of pre-set requirements for the building and controls the connected equipment to meet these requirements.

The hardware requirements for a BMS are diverse. Sensors including wireless sensors can be used to monitor temperature, humidity and even whether a room is occupied. Smart Meters can be used to monitor the use of resources such as gas, electricity and water. These devices can be controlled using Vertoda or can be interfaced with actuators to control mechanical moving parts. Heating, Ventilation and Air Conditioning (HVAC)  systems are generally controlled by SCADA (Supervisory Control and Data Acquisition) systems for coordinating operations. Distributed Control System (DCS) are complementary to SCADA systems in that they control operations and processes using a PLC (Programmable Logic Controller). Indeed, a Building Management System can be seen as a  particular example of DCS technology in action.

The PLC or actuator is just one example of an equipment controller that can be used by a BMS. System and terminal controllers are also available. In essence, these devices send control signals to the equipment to modify their calibrations or turn themselves on or off. Software is required to interface with these PLCs remotely and in many cases with non-PLC sensors and meters as well. One can see, therefore, that the diverse equipment and consequent standards means that a BMS is a complex platform from a software perspective that will require many monitoring and control components  for different equipment, buildings and conditions.

Generally, special protocols are used for BMS but this is beginning to change. BACNet (http://www.bacnet.org) is a data communication protocol used for building automation and control networks that was developed by the American Society of Heating, Refrigerating and Air-Conditioning Engineers. Lonworks is a platform created by Echelon (http://www.echelon.com) for enabling the creation of networks using devices that transport their data over diverse media such as twisted pair wire, power lines, fiber optics and RF (Radio Frequency). Modbus (http://www.modbus.org) is a open communications standard that is often used in SCADA systems. While these standards are quite specific in their intent we are beginning to see web protocols such as SOAP (Simple Object Access Protocol) and XML (eXtensible Markup Language) being used by BMS. As explored in a previous blog, different protocols are used to capture smart meter data while Zigbee is the prominent communications protocol used by Wireless Sensor Networks (WSNs).

The key tasks of a BMS is monitoring and controlling equipment within a building or plant and providing reporting functionality on the performance of this equipment. A BMS can be used to detect equipment and plant faults, dirty or used filters as well as abnormal occurrences. A BMS can also provide details of energy consumption by different equipment and by different processes and operations that take place within the building. The results of energy saving initiatives can also be monitored with the BMS as can the consumption of gas, electricity and water. Measurements can be taken of different environmental conditions within the building such as temperature, humidity and air quality. Occupancy patterns of a building can also be monitored by a BMS and can assist in operational decision making regarding equipment.

Using the diverse hardware and communication technologies available a BMS can control all equipment within a building. Loads such as chilled water pumps can be turned off when not required. Lights and lifts can be turned off when the building is unoccupied. Peripheral controls such as occupancy sensors can relay this data to the BMS which in turn can send a control message to other equipment. A BMS can also play a key role in energy management by enforcing policies to reduce energy use during times of low occupancy e.g. automatically turning lights off when a room is unoccupied, turning off the air air conditioning overnight etc.

A BMS can be integrated into one unifed system with CCTV, Access Control, Energy Management and other voice and data systems to provide an overall system for managing building-wide operations. Data can be shared between these systems for security purposes and for detecting events such as fire in an isolated part of the building. A BMS is then a key component in what is often termed an Intelligent or Smart Building.

Given the myriad of equipment that needs to be monitored and controlled, many software systems can be required to make up one overall monitoring system. Vertoda can provide data capture and management for smart meters and wireless sensors among others. The latter devices in many cases are cheaper and more flexible alternatives to traditional sensors. The data captured by Vertoda is available to the other components of the Smart Building including the BMS. The fault management, Business Intelligence/Reporting and device management functionality of Vertoda can all be integrated into one unified solution for managing and controlling a plant or building with the consequent financial, security and energy saving benefits outlined above.

Smart Meters 101 Part 3 – Management & Issues

We have previously examined the types of smart meters that are available i.e. electricity, water and gas. The next question to examine is how this vast new pool of data can be managed by a utility or indeed by an organisation that has installed smart meters on their premises themselves.

The Information Systems used to manage smart meters are typically referred to as Meter Data Management Systems (MDMS) . MDMS capture data from multiple metering systems and can then supply that data to the other Information Systems within the organisation for purposes such as billing, maintenance, forecasting and customer service. Like Enterprise Systems, MDMS have a central database that stores all customer, meter and reading data. The different Information Systems that require this data can interact with the MDMS using an Application Programming Interface (API). Alternatively, the data can be pushed from the MDMS using data export functionality.

It would be impossible for organisations and utilities to reap the cost and consumption saving benefits that an energy management strategy enabled by smart meters would provide without using an MDMS.  MDMS can manage outages by recording start and restore times and organise outage events by meter, grid or customer. MDMS can provide fault management functionality and notify other Information Systems such as Outage Management Systems of alerts. The pool of data provide by an MDMS can be combined with other data such as weather information to predict how a grid might perform under extreme weather conditions and can serve as a basis for developing distribution planning and reliability strategies. And, of course, as smart meters provide data of energy consumption the data captured by MDMS ultimately is used to measure revenues for the utility.

MDMS are a key component of what are referred to as Advanced Metering Infrastructure (AMI) systems.  AMI refers to the entire chain of technologies that enable the smart grid. These include not only the MDMS but also the smart meter equipment and the communications used to relay the data readings to the MDMS. AMI gives the utility company control over all aspects of the smart grid.

Vertoda can act as an MDMS as it provides device organisation, fault management and Business Intelligence capabilities to capture data from smart meters and transform that data into meaningful information that the utility organisations can use for business decision making. This information is then available for the ERP, Business Intelligence and other Information Systems of the organisation. Furthermore, given that Vertoda can manage devices such as RFID, Wireless Sensors and GPS in addition to smart meters our system can act as comprehensive middleware to manage all the devices used to enable smart infrastructure.

There are a number of issues which must be considered in relation to smart meters and the smart grid. We have previously noted that the security of smart meters is a problem as the vast majority of devices do not encrypt or digitally sign the data they generate. The possible solutions for smart meter security would be to encrypt or sign the data at the device level or to transfer the data through a secure communications link. This is a vital issue as without security there is nothing to prevent consumers being maliciously disconnected or having their consumption data hacked and modified.

A related concern is that of privacy. Some claim that meters record readings so often that power flows can be interpreted to reveal consumption behaviour and even use of individual appliances. Some even fear that consumers will in the future be cited for excessive use of resources through the monitoring of their smart meter. Many US states such as the Colorado Public Utilities Commission have initiated inquiries into the privacy implications of the use of smart meters. The Dutch parliament recently rejected a bill that would have compelled all citizens to have a smart meter installed in their home. In Britain, The Department for Energy and Climate Change (DECC) says that there is theoretically scope for using the smart metering communications infrastructure to enable a variety of other services such as the monitoring of vulnerable householders by health authorities or social services departments.

The invasiveness of technology is always a concern and is not confined to smart metering. Finding a solution that would alleviate concerns will be difficult to achieve. However, one step in the right direction would be to encrypt the data in transit and secure the raw consumption data in the database it is stored in so that the ‘public’ view of the information is the billing information generated using this raw data. Vertoda can offer a solution to secure the smart meter data both in transit and in storage.

We have alluded above to the transmission of smart meter data. This is another difficulty for smart meter users as currently there is a lack of standardisation in data transmission. One mechanism by which data transmission can take place is by using mobile network systems such as GSM (Global System for Mobile Communications) using SMS/text message, GPRS (General Packet Radio Service) , CDMA (Code Division Multiple Access) or 3G (Third Generation). Of course, this assumes that the smart meter is in a ‘good cell area’ and is subject to vulnerabilities in the mobile transmission of unencrypted data. Radio transmission in a licensed or unlicensed band is another possibility but has wireless security issues. With RF Mesh (the RF stands for radio frequency), each smart meter essentially acts as a router and forwards the data to an Access Point or hub which will then send the data to a utility using a mobile network or land line. The other alternative is to configure the smart meter to communicate with a radio tower which will in turn forward the data to the utility’s IT systems.

Power line transmission is another option. Broadband over powerline, also known as power line networking, enables Internet connections with the home through existing electricity transmission and distribution lines. A power line modem has recently being developed by On Semiconductor (http://www.onsemi.com) for this purpose. For the utility company, the most logical option would seem to be the transfer of data from the smart meter through the power line as the infrastructure is already in place and it would be a standard mechanism for transferring data. The lack of a standard mechanism by meter vendors for data transfer is a challenge for utilities as otherwise they have to capture data from diverse mobile and radio systems, a functionality that can be provided by Vertoda.

The development of data transmission standards is still evolving. The Spanish utility, Iberdola (http://www.iberdola.es), has initiated the PRIME project to develop an open standard for powerline communication. Echelon (http://www.echelon.com) have developed a similar standard. Power line transmission and the use of mobile networks appears to be more popular in Europe than the US where RF Mesh and tower-based smart meter communications appear to be more prevalent.

Similarly there’s a lack of standardisation in terms of access points. Access Points can communicate using radio, mobile or power line communications. Some smart meter vendors offer data concentrators which forward data to the utility through an IP network.

Within the home, communication will also take place between the smart meter and the consuming appliances. Zigbee started to gain traction in smart meter to appliance communication in 2007 and has been adopted by many smart meter vendors. WIFI and WIMAX are other possiblities for wireless communication within the home but Zigbee appears to be the most likely standard given its adoption by so many leading vendors.

Unsurprisingly, there are many vendors of smart meters including EnergyICT (http://www.energyict.com), Iskraemeco (http://www.iskraemeco.co.uk), Echelon (http://www.echelon.com), GE (http://www.ge.com) and Landis & Gyr (http://www.landisandgyr.com). Most of the leading software companies have strategies in place for the smart grid. Google has developed their own power meter, SAP and Oracle offer solutions in this area and Microsoft and IBM have ‘green’ strategies in place that take the smart grid into account. The value proposition offered by Vertoda in the smart meter arena is one of data and security management. Vertoda can both acts as middleware and provide the security framework required for encrypting both data in transit and in storage.

Smart Meters 101 Part 2 – Gas & Electricity Meters

Previously we have discussed water smart meters and the benefits that can be derived for both water utilities and the customer. Smart meters can have also been developed to measure gas and electricity consumption. Smart metering has been comparatively slow to penetrate gas suppliers however ,principally because of the characteristics and nature of gas. Utilities cannot restrict the flow through a meter (and thus limit load) as this would affect the pressure of the gas delivered to an appliance and consequently its operation and safety. Remotely reconnecting gas powered appliances would be hazardous as gas could flow out into a building from appliances that have been left on after an outage or disconnection.

Figure 1: Gas Smart Meters provide more detailed data for the Consumer & Utility Company

Gas smart meters are being used however, and the benefits of gas  smart meters are clearly defined. Gas smart meters can be polled following outages instead of utility employees having to go door to door to reenable service. The information provided by gas smart meters is also much richer and can help utilities to explain consumption patterns to their customers, for instance, the impact of cold weather on natural gas usage and the consequences for the monthly bill. A type of demand response is also possible using gas smart meters as utilities can charge customers different prices for using gas at different times of the day.

A recent report by British Gas estimates that gas smart meters can cut energy bills by about a third – UK£400 per year on average. A field trial of 50,000 smart meters undertaken by British Gas shows not only a reduction in bills but also a 40% drop in billing enquiries. 85% of triallists found that smart meters were easy to use and the display of cost information on screen encouraged households to turn off appliances when they are not being used. One issue to note however is that only 44% of participants felt that smart meters were reducing their energy consumption.

The British Gas trial deployed both gas and electricity smart meters and it is the latter which are the most well known type of smart meter. Electricity smart meters have benefits both for the utility and the customer. Demand Response which exposes customers to peak electricity prices and encourages them to modify their electricity consumption accordingly would not be possible without electricity smart meters. As there is a communications link between the smart meter and the utility, the location of outages can be identified quickly and enable the utility company to verify that outages have been resolved at every meter location. Given the richer pool of data provided by electricity smart meters, grid engineers can pinpoint bottlenecks and inefficiencies in the network by analysing the periodic data generated by smart meters. Forecasting and load balancing can also improve using this new data resource.

Figure 2: Electricity Smart Meters benefit Consumers, Utility Companies and the Environment

As well as reducing cost for the consumer by encouraging the better management of consumption, some government bodies require utlities to reimburse customers for electricity they produce on-site and feed into the grid. This is known as net metering as the utility will subtract the amount of electricity produced from the amount of the electricity taken from the grid. The consumer then pays or receives the net amount. This is becoming feasible as more and more consumers in the coming years will have their own small renewable energy facilities such as a wind turbine, solar power or home fuel cells. Net metering programs are underway in Ontario and British Columbia in Canada and are being planned as part of Amsterdam’s smart city initiative.

Of course, the chief driver for electricity smart meters is an environmental one. Being provided with information on their consumption should encourage consumers to manage and reduce their energy use. In other words, consumers are incentivised financially to reduce their electricity consumption. Energy consumption that is more spread out rather than a series of peaks and troughs means more efficient operations for the utility and less peaks and stress for infrastructure. In theory, this should also means less infrastructure being built in terms of transmission lines and new plants and distributed electricity generation using wind and solar power.  Furthermore, the previously discussed Demand Response can reduce emissions and the finer level of data generated by smart meters facilitates the calculation of a consumer’s environmental footprint. Smart meters and smart grids should also lead to smart appliances that can sense overload on the grid and reduce their own power usage accordingly.

We have now discussed the main types of smart meter. In the next article we will discuss current issues in smart metering, Meter Data Management (MDM) and the implications of smart meters for IT.

Smart Meters 101 Part 1: Overview & Water Smart Meters

Smart Meters and the smart grid are beginning to be talked about in the mainstream press as more and more electricity companies roll out smart meter projects. Smart Meter project are underway or imminent throughout the world in countries such as Victoria, Australia, (http://bit.ly/363G7a), Italy (http://bit.ly/1avyko) and Brazil (http://bit.ly/1YOLTn). Smart meters are typically defined as a new type of electricity meter. These types of smart meters provide two-way communication to both the consumer and the electricity utility of consumption by time of use so the days of the meter reader calling to a person’s house are fast coming to an end.. Smart Meters are replacing this old Automatic Reading (AMR) system and provide more detailed information. However, smart meters are often incorrectly assumed to refer solely to electricity measurement. The question then is what other kinds of energy usage can be measured by smart meters and what are the issues and challenges facing the deployment of smart meters not only for electricity utilties but also for organisations and domestic households? Over the next few weeks, we will examine these issues.

In essence, smart meters are designed to tell customers how much energy they are consuming on a real time basis. This data includes not only how much energy they’re consuming but also the monetary cost of this consumption and the impact the consumption is having on greenhouse gas emissions. A smart meter can’t pinpoint which appliances are consuming most energy or the cost of energy at different times of the day but a smart meter is a necessary component for facilitating Demand Response and other techniques and systems that can provide this information. Smart meters are deployed throughout a geographical area and relay consumption data back to the utility provider. This network of meters is what makes up a ’smart grid’. With a smart grid, utility companies have the capability to measure consumption on an per-area or per-premises basis.

Typically smart meters take periodic measurement of consumption during specific time periods and communicate this data to a utility or a smart meter management company, usually on a daily basis. These meters are also known as interval meters. Some meters may also provide an easy to read display to help consumers reduce consumption and monitor compliance with local laws and regulations. There are different types of smart meters for measuring different types of consumption. Currently, smart meters are provided for the measurement of water, gas and electricity consumption.

A water smart meter measures the consumption of water in a household or organisation. The data provided by this type of meter indicates the amount of water being used as well as its flow patterns and can be used to track, predict and change trends in water supply demand for organisations. For organisations and individuals in certain industries such as agriculture, water usage is a key metric and a managed water system can enable the early detection of leaks, the reduction of waste water and the early warning of fluctuating rates of water usage. While the measurement intervals of smart meters are usually either 10 or 14 minutes for electricity companies intervals of 30 to 60 minutes are more common for water utilities.

Water smart meters provide a rich pool of data to the utility company. Chief among this data are details of leaks at consumer premises.Many algorithms can be used to detect leaks but one simple rule of thumb is to ascertain where hourly usage ever drops to zero. If it doesn’t, it’s likely that there is a water leak. In many jurisdictions, consumers may be fined for not repairing a water leak so timely notification of such data would be likely to win customer goodwill. Leak or indeed theft of water from a mains can also be detected by comparing the day’s consumption for the users of the mains (e.g. residences on a street) with readings from the water mains serving the street. A water smart grid infrastructure can also detect a drop in pressure for a water main. This usually denotes a break. By pinpointing the location and extent of a water main break in a timely fashion, utilities can respond to problems in their network and service their customers in a more satisfactory fashion.

In certain parts of the world drought is a major issue. In such cases, it is essential to identify outdoor watering or non-essential water use during daylight hours. Up to now, it has been difficult to enforce restrictions on such behaviour but individual smart meters that would detect such over-consumption and violation of restrictions make compliance much easier to enforce.

While smart meter programs are expensive to implement, they do provide consumers with better service at a cheaper cost once they are installed. For example, it is possible to determine final meter readings and issue final bills for customers leaving an area. Water flow can be remotely disconnected or restricted where appropriate. This means that utilities no longer have to send service engineers to customers who have requested a disconnect or are being disconnected for non-payment. Meters can also be tested remotely to check that they are working. Similarly, theft of a meter can be remotely detected.

Like electricity and gas meters, water smart meters ensure that bills are based on actual readings rather than on estimates. This minimises disagreements and conflict with the consumer and the water utility, reduces calls querying and/or complaining about bills to the utility and improves customer satisfaction. Water smart meters can also help control electricity costs for the utility itself. Many utilities pump water to a high point during off peak hours when electricity prices are lower. To avoid turning on these pumps during periods of high electricity prices the utility can develop water rates that track electricity rates. This reduces peak water consumption.

Figure 1: Smart Meters are a key component of Water Conservation Programs

In the case of water, the most important benefit of smart meters are the role they can play in conservation. Water conservation programs are being undertaken in many parts of the world and smart meters are enabling these programs. The real-time data provided by water smart meters allow residents and businesses to identify where most water is being used and where behaviour modification or the installation of a water efficient fixture may be appropriate. Water conservation programs have been undertaken on a trial basis by the New South Wales government in April this year and in Dubai International Academic City. The cornerstone of any water conservation program is understanding where, when, how and why water is used. Such data can only be measured using a smart meter. Leaks should be identified when they occur and water consumption should be actively managed. This management requires the data that smart meters provide.

One issue that should also be considered is the granularity of measurement. Often, one water smart meter records the water consumption for a premises even if this premises is a large site. To obtain a better quality of data, individual it may be necessary to also install meters at high use areas like cooling towers, iririgation systems, food preparation areas or at a rainwater supply tank. This finer level of measurement will lead to a more effective water management system where will not only enable overuse at a business premises be detected on a timely basis but also the exact location of the problem.

The management of water smart meters is a key issue as the data generated is used for fault and operations management as well as billing purposes. As we will discuss in the next few weeks, the lack of standardisation is hindering the capture of this data. Vertoda can not only capture data from smart water meters but also transform that data into meaningful information for reporting leakages and providing details of revenue generated as well as acting as a bridge between the water network and the software and Information Systems of the utility.

Demand Response

One technique that is gaining recognition in the area of green energy and the more effective management of the Electricity Grid is Demand Response (DR). The goal of DR is to manage consumer consumption of electicity in response to supply conditions. An effective DR policy will result in consumers reducing their electricity consumption at critical times or in response to rising prices. DR differs from Dynamic Demand as the latter entails the use of devices which passively switch themselves off when the grid is overloaded. Devices with DR switches by contrast respond to explicit requests to switch off. DR can involve the reduction of power used and differs from energy efficiency schemes which implies the using of less energy to perform a particular task.

We know therefore that DR is a distinctive technique. The overriding rationale of DR would appear to be of societal benefit rather than of economic benefit for the utility companies. By managing consumer consumption, DR improves the reliability of the electricity grid and reduces peak electricity prices by decreasing peak demand. In essence, it is a set of actions that are taken to reduce load on the grid when congestion that threatens the supply-demand balance occurs or when market conditions raise the price of the electricity supply.

There are two types of DR. The first category of DR is triggered in response to emergencies and is referred to as interruptible DR. This DR category is activiated by utilities in cases of local system emeergices. The second category is prices responsive and is activated by utilties in the case of high wholesale electricity prices. DR is typically managed as a program consisting of participating (and volunteering) customers. Under the energy-only DR payment option, participants receive payment only for curtailment during a DR event. With capacity-payment DR, on the other hand, participants receive regular payment or a discount for being available for DR events.

Savings estimates for implementing DR for energy conservation and curtailment are in the billions of dollars. Customers benefit from availing of energy at cheaper prices. The question regarding DR is why utilty companies would implement it if it ostensibly reduces profits. Is DR something that will be regulatory driven rather than adopted voluntarily by utility companies?

If one looks beyond market price the benefits of DR can be seen for utility companies. Emergency DR programs are designed to relieve grid reliability issues. Extremely high demand for electricity, shortages of generation and significant transmission contraints can all be alleviated by DR. This ensures a smoother operation for utility companies and reduced maintenance issues and operational fire fighting.

Unsurprisingly, there is a high level of research activity in the area of demand response. The Pier Demand Response Research Center (http://drrc.lbl.gov/pier-drrc.html) at the Lawrence Berkeley National Laboratory in California are undertaking projects for the development of demand responsive technologies in buildings and the use of Critical Peak Pricing (CPP) as a form of price responsive demand response. The demand response and smart grid coalition (DRSG – see http://www.drsgcoalition.org/), a trade association for companies that provide products and services in the areas of demand response, smart meters and smart grid technologies, have developed policy recommendations advocating the use of DR as part of any solution to America’s environmental problems. DRSG count IBM, Oracle and the Zigbee Alliance among their many members so, clearly, DR has the attention of significant industry players.

DR is gaining credence among utility companies, particularly in the US. Pacific Gas and Electric Company (http://www.pge.com/demandresponse/) have a demand response program that offers incentive for business owners who curtail their facility’s energy use during times of peak demand while Puget Sound Energy (http://www.pse.com) in Washington state began a residential DR pilot program in October 2009 to evaluate how water heating and central air conditioning customers can voluntarily manage their electric demand during peak periods.

Demand Reponse, then, can be a key driver in the development of a green energy strategy for a country. As well as societal benefits both the consumer and the utility companies can benefit from the adoption of a DR policy.

Smart Economies, Smart Homes, Smart Cities & the Smart Planet

The adjective ’smart’ has been applied to many different entities in recent years. Terms such as smart home, smart grid and smart meter have now been joined by other, often vaguer, concepts such as the smart city, the smart economy and the smart planet. But what do these terms mean? And what role can technology play in enabling these ’smart’ concepts?

The term smart economy is frequently used by governments and can be a very loose term. In essence, a smart economy, like a real economy, has many components. It can be defined as using technology to deliver the infrastructure an economy needs. Initiatives that would be common in a ’smart economy’ include smart meters and grids to provide electricity in a ‘greener’ way, using Cloud Computing to deliver services and having environmentally friendly data centres that leverage virtualization and other cloud computing techniques. Allied to this is the development of communications infrastructure to support remote access to Cloud Computing services and Data Centres.

Clearly, the technology required to achieve any smart economy aspiration are many and varied. Smart Meters are clearly mandatory for any smart grid plans but, in addition, the data produced by smart grids needs to be converted into information that can be used by utility companies to better manage and monitor the energy being generated. Smart Grids are not limited of course to electricity as smart meters are available for measuring gas and water consumption. This more granular measurement of resource consumption is a fundamental tenet of any smart economy. Cloud Computing services such as Software As A Service (SaaS) will be supported by software companies. The evolution of Cloud Computing services could mean that we eventually come full circle back to the old Terminal-Host style architecture for computing where a typical user only needs a computer with an Internet connection to avail of application software. Infrastructure As A Service (IaaS) is currently being provided by services such as Amazon Elastic Compute Cloud (EC2) (See http://aws.amazon.com/ec2/) where you can effectively rent a server. From a social and economic point of view, Cloud Computing services could potentially result in more and more service enterprises being created to provide software, hardware and development platforms over the Internet while the hardware requirements of a typical user will potentially decrease so that only a PC and Internet connection is required to do the majority of work. This sharing of computer infrastructure and reduction of application installations is indeed ’smarter’ and, in the case of the former, ‘greener’.

Smart Homes is another term that can be very broadly defined. The vision of a smart home was first promulgated by the HomeRF working group. This group was disbanded in 2003 when Wi-Fi 802.11b became commonly available in the home and support for the competing Bluetooth protocol became commonplace in devices such as mobile phones (See http://en.wikipedia.org/wiki/HomeRF for further details about HomeRF). The growth of wireless networking protocols and their common availability has advanced the possibility of the smart home. Smart homes will be able to manage energy and, in conjunction with installed smart meters, can measure resource consumption (electricity, gas and water). In addition to energy management, smart homes can also be remotely managed. Security systems and home appliances can all be controlled remotely by the home owner inside and outside the home. Many major corporations are undertaking smart home projects. Ericsson Turkey (http://www.ericsson.com/developer/sub/articles/other_articles/090831_IMS) have developed a system in partnership with Done (http://www.donetr.com) which uses Ericsson’s Connected Home Gateway (HIGA) to turn equipment on and off. This system also allows more sophisticated levels of control such as automatically turning off the gas supply and electricity when fires are detected by video cameras installed in the home.

Nokia has also unveiled its Home Control Centre which combines technologies such as wireless networks, CCTV and mobile devices to facilitiate the remote control of household appliances through any computing device that has a web browser. The Home Control Centre, which is due to be released in 2010 and has been spun off to the independent There Corporation in May this year, will initially provide solution in the area of home security. Currently named ThereGate, the product is a technology independent open LINUX-based platform that supports the most common smart home technologies. All devices for monitoring energy and security are managed under one system that can be accessed from a mobile phone or web browser.  Further details are provided at There Corporation’s website (http://therecorporation.com/).

The question we must now ask is what technologies facilitiate the development of smart homes. The Z-Wave wireless communication proprietary standard (http://www.z-wave.com) for home appliances enables remote communication and control of these devices. The Zigbee set of communication standards (http://www.zigbee.org) is used for communication between low powered radio devices such as wireless sensors. Just like the aim of the Bluetooth protocol is to eliminate unnecessary cables the goal of Zigbee, from a home automation perspective, is to eliminate unnessary remote controls. Protocols like Zigbee and Z-Wave can be used  along with Wi-Fi and Bluetooth to enable the development of Home Area Networks (HANs). In addition to radio protocols the Internet Protocol (IP) can also play its part in the development of the smart home. IP can be used for security cameras and is also being used for wireless sensors. The Zigbee Alliance adopted Internet Protocol standards earlier this year and different manufacturers are developing sensors with embedded IP.  In 2008 Arch Rock (http://www.archrock.com/) introduced the first commercial implementation of the Internet Engineering Task Force’s (IETF) 6LoWPAN standard for IP version 6 communication over low power devices such as sensors.

The communication and networking standards are well established for the smart home but what about the hardware?Many components such as IP Cameras and CCTV have existed for a long time but the sensors and actuators required to control devices are continuously evolving. The market for these devices is a growing one. OnWorld predicts that the Wireless Sensor Network (WSN) market for smart homes will be US$6 billion in 2012. (See http://hiddenwires.co.uk/resourcesnews2009/news20090722-05.html). The key drivers here are energy and health management. Low-powered WSNs can be used in conjunction with smart grids to enable a utility company’s smart meter to communicate with a consumer’s network. In addition, WSNs can also be used to control lighting and heating in a home thus better managing energy consumption. WSNs along with Bluetooth are also being used in the area of home-based healthcare enabling the elderly and people with health problems to be remotely monitored by medical professionals. OnWorld also predicts that the aforementioned growth of IP-based WSNs will enable the home network to connect to the Cloud thus enabling the development of new products and services for the smart home. One possibility here would be an expert system that collects data from home appliances and issue recommendations regarding energy usage and possible savings.

In addition to the emerging technologies in the wireless arena it may be possible to use an existing technology that is already available in many homes – broadband. A recent GreentechMedia article ( http://www.greentechmedia.com/articles/read/the-smart-home-thats-tuned-to-the-weather/) has described how a Californian startup called EcoFactor uses a broadband gateway to control temperature in the home. Local weather forecasts are also analysed to determine an appropriate heating and cooling strategy over a 24 hour period. This demonstates how many homes will not have to wait to install wireless devices to become smart homes – they can potentially use their existing broadband connection.

The common thread thus far for the terms smart economy and smart home is that of energy management. Energy management also plays a key role in the Smart City. Many cities are implementing plans to become smart cities. One example is Amsterdam in Holland (See http://www.businessweek.com/globalbiz/content/jun2009/gb2009068_275981.htm). Energy management is the key driver for Amsterdam’s smart city initiative. Households are installing energy saving systems as well as solar panels and household wind turbines to enable them to sell energy back to the city. 300 power hookups to recharge electric cars have also been deployed around the city. The local utility Alliander is also in the process of developing a smart grid. Between now and 2012 up to €1.1 billion is expected to invested in this inital stage of making Amsterdam a smart city. The goal of the project is to cut emissions by 40% by 2025 as well as boosting the economy through private and public investment. More details are available at http://amsterdamsmartcity.com/.

Figure 1: Taipei is aiming to become a smart city.

Smart cities initiatives, then, can be viewed as energy management and emission reduction programs at a macro level while the micro implementation of such programs is carried out at the level of the smart home. In other words the technologies used to enable smart homes also enables smart cities.

Unsurprisingly, major industy players such as IBM and Cisco are involved in the development of smart city infrastructure. Currently, the emphasis for smart cities is on energy management but there are other initiatives in areas such as traffic management. Instelligent computing systems have been deployed in cities such as Singapore, Brisbane and Stockholm to reduce traffic congestion and pollution. One mechanism used to reduce congestion is a congestion tax. This tax has been applied in Stockholm, Oslo, London and Singapore. The aim of most congestion taxes is not just to reduce congestion but also to encourage ancilliary benefits such as alleviating environmental damage and improving public transport. In Stockholm, for example, proceeds from the congestion tax are to be used to build a ring road for the city.

To make a congestion charge system viable technology must play a role. In conjunction with IBM, Stockholm implemented a free-flow roadside system that uses laser and camera technology to detect, identify and charge vehicles. The results of Stockholm’s smart traffic system have been tangible and dramatic. Traffic has reduced by 25% and public transport timetables had to be rewritten as buses were getting to their destinations faster because of reduced traffic jams. More people are using the Stockholm public transport system and greenhouse gases such as carbon dioxide have reduced by 40% in the inner city. (See http://www.ibm.com/podcasts/howitworks/040207/index.shtml for more details.)

Another facet to smart traffic systems is enabling traffic signal controllers to act as a system. Usually, traffic lights are independent with limited coordination in sequencing. Using smart systems, real-time feedback can be provided so that, for example, a light can be turned from red to green for an approaching vehicle if an intersection has no other traffic. Smart systems can also enable the rereouting of traffic if an accident occurs or in the case of adverse weather conditions.

Smart cities aren’t just about energy and traffic management programs. They can be used to improve coordination across local government agencies as well. Services such as patient care can be dramatically improved if agencies use integrated information systems rather than multiple unintegrated IS. The US city of Albuquerque has reported a 2000% improvement in efficiency in sharing information across government agencies. The benefits have ranged from better and more timely information for citizens and improved services and public safety.

The scope of smart cities is an ambitious one. Cities from Incheon in Korea to Bordeaux in France have plans to become smart cities. Indeed, in some cases smart cities are being built from the ground up. For example, the city of Masdar in the United Arab Emirates has been designed as a smart city and relies entirely on renewable energy resources.

The prefix ’smart’ then principally implies the delivery of energy management programs using computing hardware and software systems. The principal hardware involved that will be used to achieve a smart entity be it a home, city or indeed economy are wireless sensors and smart meters. We are effectively considering better home development and urban planning when we consider the concept of ’smartness’.

Figure 2: The Smart Planet is a cornerstone of IBM’s strategy.

The final term we will consider is that of the Smart Planet. This vision is principally being driven by IBM (See http://www.ibm.com/ibm/ideasfromibm/us/smartplanet/index.shtml?sa_campaign=message/ideas/leadspace/all/planetflash). The vision of a smart planet is one where natural resources such as water are managed using software and sensors and computing driven solutions are available across entire ecosystems such as supply chains, healthcare networks and cities. The smart planet strategy is much more than a marketing gimmick.  The big idea behind the strategy is that many of the physical systems in the world – electrical grids, transportation systems, buildings, factories and rivers – can be managed more efficiently if they’re monitored by sensors.  The performance of these systems can then be analysed and improved. Sensors, RFID and GPS (Global Positioning System) can all be used to enable the management of these systems. IBM cites diverse examples of its strategy in action. Volkswagen is using IBM software and RFID technology to better manage its manufacturing operations while the San Francisco Public Utilities commission is using IBM software to reduce water pollution. (More details can be found at http://www.businessweek.com/globalbiz/blog/globespotting/archives/2009/07/ibms_smart_plan.html). Other examples include the use of IBM software and sensors by the Irish environmental protection agency to collect data across beaches and lakes to measure water quality and the use of sensors in Galway Bay in Ireland to collect data for the evaluation of weather and environmental conditions for the local fishing industry.

There are other terms prefixed as ’smart’ such as smart infrastructure, smart business etc. The fundamental point is that anything that is classified as ’smart’ is enabled by computer hardware and software technology, sensor in particular being common to ’smart’ ecosystems.

The final question we will consider is where Vertoda fits into the concept of a smart home, smart city and indeed smart planet. Vertoda can capture data from both wireless sensors and smart meters and so is ideal for translating this new pool of data into meaningful information that users can act upon. For smart homes, Vertoda can be used to capture data from household appliances and can provide information about energy usage and securing the home. Vertoda is an open source system and so can be used with any vendor hardware and can aggregate all the data emanating form the smart appliances in a household. Similarly, Vertoda can capture and secure data from smart meters and supply meaningful information on energy consumption and revenue to a utility company’s Enterprise Systems and Databases. Vertoda can also secure and translate the information required for building, security and energy management programs that make up a smart city. As Vertoda can be used with any type of sensor or smart meter it can used in the many diverse ecosystems envisaged by IBM’s smart planet strategy.

Blogs on Wireless Sensor Networks

For information on Wireless Sensor Networks there are a number of good blogs available on the Internet including http://www.wsnblog.com, http://freaklabs.org http://www.blogcatalog.com/blog/sensor-networks and http://www.blogcatalog.com/blog/wireless-network-and-sensor-networks.

Complex Event Processing & Green IT

Complex event processing (CEP) is the analysis of event data in real-time to detect patterns and respond to these event patterns. The area of CEP is an emerging one but it is growing – so much so that Forrester Research has recently conducted a report on the industry (See http://www.destinationcrm.com/Articles/CRM-News/Daily-News/Forrester-Gives-a-Welcoming-Wave-to-Complex-Event-Processing-55492.aspx). It cites hedge-fund trading and algorithmic trading as common applications for CEP. Other CEP applications include credit card fraud detection, business activity monitoring, and security monitoring. The principles of CEP were first defined by Dr David Luckham in his book “The Power of Events”.

A complex event is inferred from a series of simple events. Within an organisation, events occur all the time at every level of the firm. The goal of CEP is to discover information contained in these events and analyse their collective impact. CEP will then respond to the occurrence of a particular complex event.

The website complexevents.com (http://complexevents.com) describes CEP as an emerging technology for building and managing information systems including Enterprise Application Integration, Network and Business Security, Regulatory Compliance, Activity Monitoring and Event-Driven Architectures. CEP enables organisations to keep track and react to the information being produced by their systems. The overriding goal of CEP is to make the information contained in events occurring within the organisation’s Information Systems available as well as to detect its impact on the organisation and act upon this information in real time. complexevents.com specifically refers to RFID as a technology that can provide information for CEP. CEP is enabled using techniques such as event stream processing, event relationship detection and complex pattern detection. Using CEP, event data can be filtered, aggregated, correlated and analysed. CEP is enabled using the Rapide programming language. Rapide is an event driven system simulation language and analysis toolset. It uses complex event patterns and event processing agents (EPAs) to model dynamic, hierarchical systems.  It is free to download at http://complexevents.com/stanford/rapide/.

A number of vendors offer CEP solutions. Oracle (http://www.oracle.com/technologies/soa/complex-event-processing.html) offers a CEP solution that uses real-time pattern matching to define and identify complex event patterns. It has been voted number 1 CEP provider by Waters Ranking. Microsoft announced their entry into the CEP market in May 2009 (See http://geekswithblogs.net/cyoung/archive/2009/05/11/microsoft_does_wombles-again.aspx) while IBM released their CEP software in May (http://searchdatamanagement.techtarget.com/news/article/0,289142,sid91_gci1356275,00.html). There are also some smaller vendors in the CEP space such as Aleri (http://www.aleri.com/) and Coral8 (http://www.coral8.com), both of whom recently merged.

CEP vendors are mostly focussing on delivering solutions for financial markets. There have also been some applications in Customer Relationship Management (CRM) and solutions that leverage RFID and sensors for industries such as Telecommunications and Utilities. The question we are going to address here is the role CEP could potentially play in the management and securing of devices such as sensors, smart meters and wind turbines.

Coral8’s platform offer solutions for the utilities industry so that is a good starting point for assessing the role CEP can play in Green IT. As Coral8 point out, their platform can be used to highlight problems in the delivery of service to consumers. CEP can play an even greater role in the smart grids being developed by electricity utilities as the whole aim of smart meters is to better assess consumption and spread demand for electricity. As a richer level of detail is being provided to utility companies, CEP can be used to analyse this data and identify critical patterns in electricity usage. In addition, the event correlation functionality of CEP can be used to manage the network as device utilisation can be monitored and alert conditions can be flagged when they occur. This equipment monitoring also facilitates predictive maintenance of equipment. 

CEP also gives real-time visibility of events that impact smart grids. Key Performance Indicators for smart grid performance can be detected as they occur and the flow of data across a utility company’s IT infrastructure – SCADA systems, ERP systems, Billing systems etc. – can be monitored as they occur. Early trials of smart meters are also presenting problems that CEP can assist. Data can be unreliable and intermittent and CEP can quickly detect the lack of data quality and/or integrity. The functionality of CEP gives a finer level of control over operations as well an enhancing business decision making.

Wind Farms can also benefit from using CEP in the management of their turbines. An individual wind turbine produces a wealth of data and events. From a maintenance perspective, single individual events that occur could represent a pattern that is in fact an alert condition or maintenance requirement. CEP can be used to analyse this stream of events that take place within an individual wind turbine. Furthermore, while data is made available using a SCADA  system and in many cases is accessible from an OPC (OLE Process Control) server, this data is relevant for other systems and software within the organisation. The amount of energy a wind turbine produces, for example, ultimately represents the revenue a company is making. CEP can trace the flow of this data across the Information Systems of a Wind Farm. Availability is another key metric for wind turbines. The time that a wind turbine is unavailable and consequently the amount of revenue being lost can be analysed using CEP and persistent unavailability patterns can be detected and analysed. This is a cardinal point as many Wind Farms have Service Level Agreements with their suppliers where the latter are penalised if a wind turbine is unavailable for more than an agreed period.

Sensors, and Wireless Sensor Network (WSN) technology in particular, can also leverage CEP to detect patterns in the vast amounts of data that can be potentially produced. RFID has already being cited as a technology that can enable CEP and WSNs can play a similar role. WSNs enable a context aware environment for equipment, environments and buildings and produce data that can analysed and transformed into meaningful information using CEP. This is not just applicable to Green IT but also for building, security and supply chain management applications among many others.

CEP then has great potential in many areas including Green IT. Given the rich data and events pools these new technologies provide, CEP should play a major role in analysing these data and events in the coming years.