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.
Consequently, the future success of M2M, as an integral part of the IoT, will largely be determined by the successful global adoption of IPv6.
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). 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.
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. These networks also allow an array of new business opportunities and connections between consumers and producers in terms of the products being sold.
M2M was originally used for automation and instrumentation but now has been also used to refer to telematics 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.
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. 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.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.
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.
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. Many companies 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.
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. 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.
Application of M2M networks 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. 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.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, 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 after validated in a wind turbine fleet of three distributed wind farms. 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 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.