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Machine to machine

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Machine to Machine (M2M) refers to technologies that allow both wireless and wired systems to communicate with other devices of the same type.[1][2] M2M is a broad term as it does not pinpoint specific wireless or wired networking, information and communications technology. This broad term is particularly used by business executives. M2M is considered an integral part of the Internet of Things (IoT) and brings several benefits to industry and business [3] in general as it has a wide range of applications such as industrial automation, logistics, Smart Grid, Smart Cities, health, defense etc. mostly for monitoring but also for control purposes.

In order to support the rapid new development and the worldwide adoption of the Internet of Things as well as the continued growth of M2M technology and its large scale applications in the future, a global adoption and deployment of the Internet Protocol Version 6 (IPv6) are required because all of the sensors and machine-readable identifiers needed to make the Internet of Things a reality will need an extremely large address space. Even if the current supply of IPv4 addresses were not to be exhausted soon, the size of IPv4 itself is not large enough to support the future requirement of IoT.[4] [5] [6] [7]

Consequently, the future success of M2M, as an integral part of the IoT, will largely be determined by the successful global adoption of IPv6.[4] [5] [6]

M2M can include the case of industrial instrumentation - comprising a device (such as a sensor or meter) to capture an event (such as temperature, inventory level, etc.) that is relayed through a network (wireless, wired or hybrid) to an application (software program) that translates the captured event into meaningful information (for example, items need to be restocked).[8] Such communication was originally accomplished by having a remote network of machines relay information back to a central hub for analysis, which would then be rerouted into a system like a personal computer.[9]

However, modern M2M communication has expanded beyond a one-to-one connection and changed into a system of networks that transmits data to personal appliances. The expansion of IP networks across the world has made it far easier for M2M communication to take place and has lessened the amount of power and time necessary for information to be communicated between machines.[10] These networks also allow an array of new business opportunities and connections between consumers and producers in terms of the products being sold.[11]

M2M was originally used for automation and instrumentation but now has been also used to refer to telematics applications.

History

M2M has existed in different forms since the advent of computer networking automation[12] and predates cellular communication. It has been utilized in applications such as telemetry, industrial, automation, SCADA.

M2M devices that combined telephony and computing were first conceptualized by Theodore G. Paraskevakos while working on his caller line identification system in 1968, later patented in the U.S. in 1973. This system was the predecessor to what is now Caller ID

The first caller identification receiver
Processing Chips

After several attempts and experiments, he realized that in order for the telephone to be able to read the caller's telephone number, it must possess intelligence so he developed the method in which the caller's number is transmitted to the called receiver's device. His portable transmitter and receiver were reduced to practice in 1971 in a Boeing facility in Huntsville, Alabama, representing the world’s first working prototypes of caller identification devices (shown at right). They were installed at Peoples’ Telephone Company in Leesburg, Alabama and in Athens, Greece where they were demonstrated to several telephone companies with great success. This method was the basis for modern-day Caller ID technology. He was also the first to introduce the concepts of intelligence, data processing and visual display screens into telephones which gave rise to the "Smartphone." [13]

In 1977, Paraskevakos started Metretek, Inc. in Melbourne, FL to conduct commercial remote meter reading and load management for electrical services which led to the “Smart Grid” and “Smart Meter.” To achieve mass appeal, Paraskevakos sought to reduce the size of the transmitter and the time of transmission through telephone lines by creating a single chip processing and transmission method. Although Motorola was contracted in 1978 to develop and produce the single chip, the size of the chip was too large for Motorola's capabilities at that time. As a result, it produced in two separate chips (shown at right).

While cellular is becoming more common, sizable numbers of machines still use land lines (POTS, DSL, cable) to connect to the IP network. The cellular M2M communications industry emerged in 1995 when Siemens set up a dedicated department inside its mobile phones business unit to develop and launch a GSM data module called “M1”[14] based on the Siemens mobile phone S6 for M2M industrial applications, enabling machines to communicate over wireless networks. In October 2000, the modules department formed a separate business unit inside Siemens called "Wireless Modules" which in June 2008 became a standalone company called Cinterion Wireless Modules. The first M1 module was used for early point of sale (POS) terminals, in vehicle telematics, remote monitoring and tracking and tracing applications. M2M technology was first embraced by early implementers such as GM and Hughes Electronics Corporation who realized the benefits and future potential of the technology. By 1997, M2M wireless technology became more prevalent and sophisticated as ruggedized modules were developed and launched for the specific needs of different vertical markets such as automotive telematics. Today, M2M data modules are extremely sophisticated and come with an array of features and capabilities such as onboard global positioning (GPS) technology, flexible land grid array surface mounting, embedded M2M optimized smart cards (like phone SIMs) known as MIMs or M2M identification modules, and embedded Java, an important enabling technology to accelerate the Internet of Things (IOT). Another example of an early use is OnStar's system of communication.[15]

The hardware components of a machine to machine network are manufactured by a few key players. In 1998, Quake Global started designing and manufacturing M2M satellite and terrestrial modems.[16] Initially relying heavily on ORBCOMM network for its satellite communication services, Quake Global expanded its telecommunication product offerings by engaging both satellite and terrestrial networks, which gave Quake Global an edge in offering network agnostic[17] products.

In 2004, Digi International began producing wireless gateways and routers. Shortly after in 2006, Digi purchased Max Stream, the manufacturer of XBee radios. These hardware components allowed users to connect machines no matter how remote their location. Since then, Digi has partnered with several companies to connect hundreds of thousands of devices around the world.

In 2006, Machine-to-Machine Intelligence (M2Mi) Corp started work with NASA to develop automated machine-to-machine intelligence. Automated M2M intelligence enables a wide variety of mechanisms including wired or wireless tools, sensors, devices, server computers, robots, spacecraft and grid systems to communicate and exchange information efficiently.[18]

In 2009, AT&T and Jasper Technologies, Inc. entered into an agreement to support the creation of M2M devices jointly. They have stated that they will be trying to drive further connectivity between consumer electronics and M2M wireless networks, which would create a boost in speed and overall power of such devices.[19] 2009 also saw the introduction of real-time management of GSM and CDMA network services for M2M applications with the launch of the PRiSMPro™ Platform from M2M network provider KORE Telematics. The platform focused on making multi-network management a critical component for efficiency improvements and cost-savings in M2M device and network usage.[20] Also in 2009, the Norwegian incumbent Telenor concluded ten years of M2M research by setting up two entities serving the upper (services) and lower (connectivity) parts of the value-chain. Telenor Connexion[21] in Sweden draws on Vodafone's former research capabilities in subsidy Europolitan and is a market leader in Europe's market for services across such typical markets as logistics, fleet management, car safety, healthcare, and smart metering of electricity consumption.[22] Telenor Objects has a similar role supplying connectivity to M2M networks across Europe. Telefonica set up a business branch of Telefónica Digital specialized in M2M with global solutions for managed connectivity, transport and utilities and sustainability [23] In the UK, Business MVNO Abica, commenced trials with Telehealth and Telecare applications which required secure data transit via Private APN and HSPA+ connectivity.

In early 2010 in the U.S., AT&T, KPN, Rogers, Telcel / America Movil and Jasper Technologies, Inc. began to work together in the creation of a M2M site, which will serve as a hub for developers in the field of M2M communication electronics.[24] In February 2010, Vodafone, Verizon Wireless and nPhase (a joint partnership of Qualcomm and Verizon) announced their strategic alliance to provide global M2M solutions that would offer their customers an easy way to roll out M2M solutions across Europe and the US.[25] In March 2010, Sprint and Axeda Corporation announced their strategic alliance for global M2M solutions.[26] In January 2011, Aeris Communications, Inc. announced that it is providing M2M telematics services for Hyundai Motor Corporation.[27] Partnerships like these make it easier, faster and more cost-efficient for businesses to use M2M. In June 2010, mobile messaging operator tyntec announced the availability of its high-reliability SMS services for M2M applications.

In March 2011, M2M network service provider KORE Wireless teamed with Vodafone Group and Iridium Communications Inc., respectively, to make KORE Global Connect network services available via cellular and satellite connectivity in more than 180 countries, with a single point for billing, support, logistics and relationship management. Later that year, KORE acquired Australia-based Mach Communications Pty Ltd. in response to increased M2M demand within Asia-Pacific markets.[28][29]

In April 2011, Ericsson Acquires M2M Platform Telenor Connexion's machine-to-machine (M2M) platform, in an effort to get more technology and know-how in the growing sector.[30]

In August 2011, Ericsson announced that they have successfully completed the asset purchase agreement to acquire Telenor Connexion’s M2M (machine-to-machine) technology platform.[31]

Cloud connectivity is becoming a significant piece of the M2M Solution as cellular and wireless connection speeds increase. M2M solutions providers now offer Platforms as a Service (PaaS), which simplify machine networks by allowing users to manage deployments remotely. Device Cloud by Etherios is a PaaS that can integrated into the Salesforce.com platform and offers API's that can be used to develop a custom application.

According to the independent wireless analyst firm Berg Insight, the number of cellular network connections worldwide used for M2M communication was 47.7 million in 2008. The company forecasts that the number of M2M connections will grow to 187 million by 2014.[32]

A research study from the E-Plus Group[33] shows that in 2010 2.3 million M2M smart cards will be in the German market. According to the study, this figure will rise in 2013 to over 5 million smart cards. The main growth driver is segment “tracking and tracing” with an expected average growth rate of 30 percent. The fastest growing M2M segment in Germany, with an average annual growth of 47 percent, will be the consumer electronics segment.

In April 2013, OASIS MQTT standards group is formed with the goal of working on a lightweight publish/subscribe reliable messaging transport protocol suitable for communication in M2M/IoT contexts.[34] IBM and StormMQ chair this standards group and Machine-to-Machine Intelligence (M2Mi) Corp is the secretary.[35] In May 2014, the committee published the MQTT and NIST Cybersecurity Framework Version 1.0 committee note to provide guidance for organizations wishing to deploy MQTT in a way consistent with the NIST Framework for Improving Critical Infrastructure Cybersecurity.[36]

In May 2013, M2M network service providers KORE Telematics, Oracle, Deutsche Telekom, Digi International, ORBCOMM and Telit formed the International M2M Council (IMC). The first trade organization to service the entire M2M ecosystem, the IMC aims at making M2M ubiquitous by helping companies instill and manage the communication between machines.[37][38]

Applications

Wireless networks that are all interconnected can serve to improve production and efficiency in various areas, including machinery that works on building cars and on letting the developers of products know when certain products need to be taken in for maintenance and for what reason. Such information serves to streamline products that consumers buy and works to keep them all working at highest efficiency.[11]

Another application is to use wireless technology to monitor systems, such as utility meters. This would allow the owner of the meter to know if certain elements have been tampered with, which serves as a quality method to stop fraud.[citation needed] In Quebec, Rogers will connect Hydro Quebec's central system with up to 600 Smart Meter collectors, which aggregate data relayed from the province's 3.8-million Smart Meters.[citation needed] In the UK, Telefonica won on a €1.78 billion ($2.4 billion) smart-meter contract to provide connectivity services over a period of 15 years in the central and southern regions of the country. The contract is the industry’s biggest deal yet.[39]

A third application is to use wireless networks to update digital billboards. This allows advertisers to display different messages based on time of day or day-of-week, and allows quick global changes for messages, such as pricing changes for gasoline.[citation needed]

The industrial M2M market is undergoing a fast transformation as enterprises are increasingly realizing the value of connecting geographically dispersed people, devices, sensors and machines to corporate networks. Today, industries such as oil and gas, precision agriculture, military, government, smart cities/municipalities, manufacturing, and public utilities, among others, utilize M2M technologies for a myriad of applications. Companies such as FreeWave Technologies, Inc. and others have enabled complex and efficient data networking technologies to provide capabilities such as high-speed data transmission, mobile mesh networking, and 3G/4G cellular backhaul. These features allow large-scale operations to develop secure communication networks in difficult areas such as remote, hard-to-reach locations.[40]

Telematics and in-vehicle entertainment is an area of focus for M2M developers. Recent examples include Ford Motor Company, which has teamed with AT&T to wirelessly connect Ford Focus Electric with an embedded wireless connection and dedicated app that includes the ability for the owner to monitor and control vehicle charge settings, plan single- or multiple-stop journeys, locate charging stations, pre-heat or cool the car.[citation needed] In 2011, Audi partnered with T-Mobile and RACO Wireless to offer Audi Connect. Audi Connect allows users access to news, weather, and fuel prices while turning the vehicle into a secure mobile Wi-Fi hotspot, allowing passengers access to the Internet.[41]

Application of Machine to Machine Network in Prognostics and Health Management

Machine to machine wireless network can serve to improve the production and efficiency of machines, to enhance the reliability and safety of complex systems, and to promote the life-cycle management for key assets and products. By applying Prognostic and Health Management (PHM) techniques in machine networks, the following goals can be achieved or improved:

  • Near-zero downtime performance of machines and system;
  • Health Management of a fleet of similar machines.

The application of intelligent analysis tools and Device-to-Business (D2B)TM informatics platform form the basis of e-maintenance machine network that can lead to near-zero downtime performance of machines and systems.[42] The e-maintenance machine network provides integration between the factory floor system and e-business system, and thus enables the real time decision making in terms of near-zero downtime, reducing uncertainties and improved system performance.[43] In addition, with the help of highly interconnected machine networks and advance intelligent analysis tools, several novel maintenance types are made possible nowadays. For instance, the distant maintenance without dispatching engineers on-site, the online maintenance without shutting down the operating machines or systems, and the predictive maintenance before a machine failure become catastrophic. All these benefits of e-maintenance machine network add up improve the maintenance efficiency and transparency significantly.

As described in,[44] The framework of e-maintenance machine network consists of sensors, data acquisition system, communication network, analytic agents, decision-making support knowledge base, information synchronization interface and e-business system for decision making. Initially, the sensors, controllers and operators with data acquisition are used to collect the raw data from equipment and send it out to Data Transformation Layer automatically via internet or intranet. The Data Transform Layer then employs signal processing tools and feature extraction methods to convert the raw data into useful information. This converted information often carries rich information about the reliability and availability of machines or system and is more agreeable for intelligent analysis tools to perform subsequent process. The Synchronization Module and Intelligent Tools comprise the major processing power of the e-maintenance machine network and provide optimization, prediction, clustering, classification, bench-marking and so on. The results from this module can then be synchronized and shared with the e-business system on for decision making. In real application, the synchronization module will provide connection with other departments at the decision making level, like Enterprise Resource Planning (ERP), Customer Relation Management (CRM) and Supply Chain Management (SCM).

Another application of Machine-to-Machine network is in the health management for a fleet of similar machines using clustering approach. This method was introduced to address the challenge of developing fault detection models for applications with non-stationary operating regimes or with incomplete data. The overall methodology consists of two stages: 1) Fleet Clustering to group similar machines for sound comparison; 2) Local Cluster Fault Detection to evaluate the similarity of individual machines to the fleet features. The purpose of fleet clustering is to aggregate working units with similar configurations or working conditions into a group for sound comparison and subsequently create local fault detection models when global models cannot be established. Within the framework of peer to peer comparison methodology, the machine to machine network is crucial to ensure the instantaneous information share between different working units and thus form the basis of fleet level health management technology.

The fleet level health management using clustering approach was patented for its application in wind turbine health monitoring [45] after validated in a wind turbine fleet of three distributed wind farms.[46] Different with other industrial devices with fixed or static regimes, wind turbine’s operating condition is greatly dictated by wind speed and other ambient factors. Even though the multi-modeling methodology can be applicable in this scenario, the number of wind turbines in a wind farm is almost infinite and may not present itself as a practical solution. Instead, by leveraging on data generated from other similar turbines in the network, this problem can be properly solved and local fault detection models can be effective built. The results of wind turbine fleet level health management reported in [45][47] demonstrated the effectiveness of applying a cluster-based fault detection methodology in the wind turbine networks. Similar with a group of wind turbine, the fault detection for a horde of industrial robots is also experiencing difficulties as lack of fault detection models and dynamic operating condition. Industrial robots are crucial part in the current automotive manufacturing facilities and are designed to perform different tasks as welding, material handling, painting, etc. In this scenario, robotic maintenance becomes critical to ensure continuous production and avoid downtime. Historically, the fault detection models for all the industrial robots are trained similarly. Critical model parameters like training samples, components, and alarming limits are set the same for all the units regardless of their different functionalities. Even though these identical fault detection models can effectively identify faults sometimes, numerous false alarms discourage users from trusting the reliability of the system. However, within a machine network, industrial robots with similar tasks or working regimes can be group together; the abnormal units in a cluster can then be prioritized for maintenance via training based or instantaneous comparison. This peer to peer comparison methodology inside a machine network could improve the fault detection accuracy significantly.[46]

Open M2M initiatives

Further reading

  • Mark Fell. "Roadmap for the Emerging Internet of Things - Its Impact, Architecture and Future Governance" (PDF). Carré & Strauss, United Kingdom, 2014.
  • Mark Fell. "Manifesto for Smarter Intervention in Complex Systems" (PDF). Carré & Strauss, United Kingdom, 2013.
  • H. Wu, C. Zhu, R. J. La, X. Liu, and Y. Zhang. "FASA: Accelerated S-ALOHA using access history for event-driven M2M communications" (PDF). IEEE/ACM Transactions on Networking, 2013.{{cite web}}: CS1 maint: multiple names: authors list (link)

See also

References

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  9. ^ "Machine-to-Machine (M2M) Communications", MobileIN.
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  11. ^ a b "When Machines Speak", InfoWorld. Cite error: The named reference "Info" was defined multiple times with different content (see the help page).
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  14. ^ "Neue Produkte: GSM-Modul M1". computerwoche.de. Retrieved 2013-08-19.
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  18. ^ NASA and M2Mi Corp. to Develop 'Automated M2M Intelligence', October 11, 2006
  19. ^ "AT&T, Jasper Technologies, Inc. Join Forces to Connect New Categories of Consumer Electronics and Business Devices to Nation's Fastest Network", Jasper Technologies, Inc. Telematics devices as provided by companies like Ctrack allows data to be pushed from a vehicle or an asset using GSM and GPS to a server for use in a business intelligence application. Such information may include driver behaviour, asset condition and location.
  20. ^ "M2M Evolution". M2M Evolution. Retrieved 2014-01-21.
  21. ^ About us - Telenor Connexion. Retrieved October 20, 2010.
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  25. ^ Vodafone, Verizon Wireless and nPhase announce strategic alliance to provide global M2M solutions, Vodafone press release, 12 March 2010.
  26. ^ Sprint and Axeda Announce Alliance for Global M2M Solutions, Sprint press release
  27. ^ Telecommunications - Hyundai Selects Aeris Communications as Telecommunications Carrier, article in Telecommunications Community eNewsletter.
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  31. ^ Aug 24, 2011 (2011-08-24). "Ericsson completes acquisition of Telenor Connexion's M2M technology platform". m2mnow. Retrieved 2014-02-25.{{cite web}}: CS1 maint: numeric names: authors list (link)
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  34. ^ OASIS Members to Advance MQTT Standard for M2M/ IoT Reliable Messaging, April 2013
  35. ^ a b OASIS MQTT Standards Group
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  38. ^ "Connected World magazine | International M2M Council Looks for Vertical Opportunities". Connectedworldmag.com. 2013-05-29. Retrieved 2013-08-19.
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  42. ^ A. Muller, A. Crespo Marquez, and B. Iung, "On the concept of e-maintenance: review and current research," Reliability Engineering & System Safety, vol. 93, pp. 1165-1187, 2008.
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  45. ^ a b E. R. Lapira, H. Al-Atat, and J. Lee, "Turbine-To-Turbine Prognostics Technique For Wind Farms," ed: Google Patents, 2012.
  46. ^ a b E. R. Lapira, "Fault detection in a network of similar machines using clustering approach," 2012.
  47. ^ E. Lapira, D. Brisset, H. D. Ardakani, D. Siegel, and J. Lee, "Wind turbine performance assessment using multi-regime modeling approach," Renewable Energy, vol. 45, pp. 86-95, 2012.
  48. ^ "M2M Communications via XMPP" (PDF). Retrieved 2014-01-21.
  49. ^ OMA_LWM2M
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