Tag Archives: Machine-to-machine communications

Mesin ke mesin

Dari Wikipedia bahasa Indonesia, ensiklopedia bebas

Ilustrasi kerja mesin-ke-mesin.

Mesin-ke-mesin (bahasa Inggris: machine-to-machine, disingkat M2M) adalah sebuah istilah yang mengacu pada peranti keras (device/hardware) yang dapat terhubung dan berkomunikasi satu sama lain tanpa bantuan manusia. Dalam hal ini masing-masing perangkat dapat bertukar informasi atau melakukan suatu pekerjaan lewat hubungan sinyal nirkabel. Penggunaan teknologi M2M dalam kehidupan sehari-hari misalnya sms banking, mesin pendingin yang bisa menceritakan kondisinya sendiri, atau AC rumah yang dapat menyala otomatis apabila ada mobil masuk.Teknologi M2M pertama digunakan oleh telemetri, sebuah perangkat yang berfungsi mengawasi kondisi perangkat-perangkat keras lain dari jarak jauh. Komponen-komponen yang termasuk dalam sistem M2M di antaranya adalah sensor, RFID, Wi-Fi atau segala jenis teknologi bergerak dan seluler.

Teknologi M2M di Indonesia

Teknologi M2M di Indonesia dipopulerkan oleh operator telekomunikasi lokal seperti XL Axiata, Telkomsel, dan Indosat. Layanan M2M yang diterapkan oleh Telkomsel ditargetkan pada sektor perbankan, otomotif, dan rumah pintar. Dengan layanan ini, pengguna dapat menjalankan alat atau mesin lewat perangkat bergerak. Telkomsel telah menggarap M2M sejak tahun 2003 dan pada tahun 2014 telah mendapatkan 1,5 juta pelanggan. M2M juga digarap oleh Indosat sejak tahun 2010, dan pada tahun 2015 mengembangkan solusi M2M pada teknologi GPS, EDC Wireless, dan ATM Wireless. XL Axiata mengembangkan teknologi M2M bekerjasama dengan Ericsson. Sejak dibangun pertama kali pada tahun 2012, XL Axiata telah mengembangkan sekitar 5 jenis layanan M2M dengan total 92 ribu pelanggan yang didominasi oleh kalangan industri.

Machine to machine

From Wikipedia, the free encyclopedia

Machine to machine (commonly abbreviated as M2M) refers to direct communication between devices using any communications channel, including wired and wireless. Machine to machine communication can include industrial instrumentation, enabling a sensor or meter to communicate the information it records (such as temperature, inventory level, etc.) to application software that can use it (for example, adjusting an industrial process based on temperature or placing orders to replenish inventory). 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.

More recent machine to machine communication has changed into a system of networks that transmits data to personal appliances. The expansion of IP networks around the world has made machine to machine communication quicker and easier while using less power. These networks also allow new business opportunities for consumers and suppliers.

1 History
1.1 In the 2000s
1.2 In the 2010s
2 Applications
3 Networks in prognostics and health management
4 Open initiatives


Wired communication machines have been using signaling to exchange information since the early 20th century. Machine to machine has taken more sophisticated forms since the advent of computer networking automation and predates cellular communication. It has been utilized in applications such as telemetry, industrial, automation, SCADA.

SCADA system – Heat station
The PLC (in an industrial process) controls the flow of cooling water, the SCADA system allows any changes related to the alarm conditions and set points for the flow (such as high temperature, loss of flow, etc) to be recorded and displayed.

Machine to machine devices that combined telephony and computing were first conceptualized by Theodore Paraskevakos while working on his Caller ID system in 1968, later patented in the U.S. in 1973. This system, similar but distinct from the panel call indicator of the 1920s and automatic number identification of the 1940s, which communicated telephone numbers to machines, was the predecessor to what is now caller ID, which communicates numbers to people.

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.

In 1977, Paraskevakos started Metretek, Inc. in Melbourne, Florida to conduct commercial automatic 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. Motorola was contracted in 1978 to develop and produce the single chip, but the chip was too large for Motorola’s capabilities at that time. As a result, it became two separate chips (shown at right).

While cellular is becoming more common, many machines still use landlines (POTS, DSL, cable) to connect to the IP network. The cellular M2M communications industry emerged in 1995 when Siemens set up a department inside its mobile phones business unit to develop and launch a GSM data module called “M1” 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. Machine to machine 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, machine to machine 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.

21st century machine to machine data modules have newer features and capabilities such as onboard global positioning (GPS) technology, flexible land grid array surface mounting, embedded machine to machine optimized smart cards (like phone SIMs) known as MIMs or machine to machine 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.

The hardware components of a machine to machine network are manufactured by a few key players. In 1998, Quake Global started designing and manufacturing machine to machine satellite and terrestrial modems. 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-neutral products.

In the 2000s

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 2004, Christopher Lowery, a UK telecoms entrepreneur, founded Wyless Group, one of the first Mobile Virtual Network Operators (MVNO) in the M2M space. Operations began in the UK and Lowery published several patents introducing new features in data protection & management, including Fixed IP Addressing combined with Platform Managed Connectivity over VPNs. The company expanded to the US in 2008 and became T-Mobile’s largest partners on both sides of the Atlantic.

In 2006, Machine-to-Machine Intelligence (M2Mi) Corp started work with NASA to develop automated machine to machine intelligence. Automated machine to machine 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.

In 2009, AT&T and Jasper Technologies, Inc. entered into an agreement to support the creation of machine to machine devices jointly. They have stated that they will be trying to drive further connectivity between consumer electronics and machine to machine wireless networks, which would create a boost in speed and overall power of such devices. 2009 also saw the introduction of real-time management of GSM and CDMA network services for machine to machine applications with the launch of the PRiSMPro™ Platform from machine to machine network provider KORE Telematics. The platform focused on making multi-network management a critical component for efficiency improvements and cost-savings in machine to machine device and network usage.

Also in 2009, Wyless Group introduced PORTHOS™, its multi-operator, multi-application, device agnostic Open Data Management Platform. The company introduced a new industry definition, Global Network Enabler, comprising customer-facing platform management of networks, devices and applications.

Also in 2009, the Norwegian incumbent Telenor concluded ten years of machine to machine research by setting up two entities serving the upper (services) and lower (connectivity) parts of the value-chain. Telenor Connexion in Sweden draws on Vodafone’s former research capabilities in subsidiary Europolitan and is in Europe’s market for services across such typical markets as logistics, fleet management, car safety, healthcare, and smart metering of electricity consumption. Telenor Objects has a similar role supplying connectivity to machine to machine networks across Europe. In the UK, Business MVNO Abica, commenced trials with Telehealth and Telecare applications which required secure data transit via Private APN and HSPA+/4G LTE connectivity with static IP address.

In the 2010s

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 machine to machine site, which will serve as a hub for developers in the field of machine to machine communication electronics. In January 2011, Aeris Communications, Inc. announced that it is providing machine to machine telematics services for Hyundai Motor Corporation. Partnerships like these make it easier, faster and more cost-efficient for businesses to use machine to machine. In June 2010, mobile messaging operator Tyntec announced the availability of its high-reliability SMS services for M2M applications.

In March 2011, machine to machine 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.

In April 2011, Ericsson acquired Telenor Connexion’s machine to machine platform, in an effort to get more technology and know-how in the growing sector.

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

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

A research study from the E-Plus Group shows that in 2010 2.3 million machine to machine 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. IBM and StormMQ chair this standards group and Machine-to-Machine Intelligence (M2Mi) Corp is the secretary.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.

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


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.

Commonplace consumer application

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.

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 machine to machine 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 machine to machine 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 machine to machine 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.

Networks in prognostics and health management

Machine to machine wireless networks 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 monitoringafter 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.

Fault detection for a horde of industrial robots experiences similar difficulties as lack of fault detection models and dynamic operating condition. Industrial robots are crucial in automotive manufacturing and 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.

Open Initiatives

  • Eclipse machine to machine industry working group (open communication protocols, tools, and frameworks), the umbrella of various projects including Koneki, Eclipse SCADA
  • ITU-T Focus Group M2M (global standardization initiative for a common M2M service layer)[39]
  • 3GPP studies security aspects for machine to machine (M2M) equipment, in particular automatic SIM activation covering remote provisioning and change of subscription.[40]
  • Weightless – standard group focusing on using TV “white space” for M2M
  • XMPP (Jabber) protocol[41]
  • OASIS MQTT – standards group working on a lightweight publish/subscribe reliable messaging transport protocol suitable for communication in M2M/IoT contexts.[27]
  • Open Mobile Alliance (OMA_LWM2M) protocol[42]
  • RPMA (Ingenu)
  • Industrial Internet Consortium

The IoT Challenge: How Can Service Providers Own The Ecosystem?

BY DINESH DHANASEKHARAN. excelacom | FEB 03, 2016

This article first appeared in the Connect-World.

Machine-to-machine communications (M2M) and the Internet of Things (IoT) are completely changing business and consumer relationships in virtually every industry. Today’s era of connected devices allow businesses to reduce expenses via automation and virtualization, and create new revenue streams using product innovation, personalized offerings, and an optimized customer experience.

Our coffee machines, washing machines and printers now have the ability to sense when we are running low on supplies – and automatically re-order our favorite products. Business giants like Coca-Cola are capitalizing on real-time data analytics with smart vending machines, built to find optimal selling times and to schedule maintenance for each of their thousands of locations. Health companies and automotive companies are forming partnerships with technology juggernauts to create prototypes for smart contact lenses that can track blood sugar levels and enable self-driving cars.

This is only the beginning. Within the next five years, M2M communications and IoT solutions technology will advance exponentially – with IDC predicting a worldwide market value of US$7.1 trillion dollars by 2020. New business models and offerings will translate into new revenue streams across all industry types.

Opportunities for Communications and Media Providers

With M2M and IoT capabilities and technology, communications and media providers can gain an advantage in every segment of their business—residential, small business and enterprise:

  • Transportation and fleet management. Telematics systems can help track deliveries, automatically and accurately construct an estimated arrival time available for both the business and the consumer, and help map the quickest routes – increasing productivity, decreasing operations cost (e.g., saving money on gas) and heightening the customer experience.
  • Retail and Finance/Kiosk applications. M2M can provide both point of sale and vending solutions for the small business and enterprises.
  • Manufacturing. Remote monitoring solutions can help manage assets and containers, and track cargo to give the company complete transparency and allow ample time to solve any issues before they affect business.
  • Utilities. Smart meters and smart grid networks can help provide transparent and accurate energy usage for both the consumer and the business.
  • Healthcare and health monitoring. EKG body sensors, pedometers and more can sync and analyze traceable health information from mobile devices to a consumer’s computer as well as healthcare providers.
  • Security. Video surveillance and alarm system monitoring can help improve physical security for both the business and the consumer in real time.
  • Consumer services and appliance control. Smart appliances can help secure, send, receive and track consumer information and service difficulties in real time. Alerts in suspicious financial transactions or data usage can be automatically sent to the consumer – maximizing both safety and the customer experience.

While the benefits of M2M and IoT technology will, without a doubt, evolve the current communications and media industry, there are challenges that may prevent a smooth transition into the connected era. Managing a fragmented market, outdated legacy billing systems, unstandardized transactions and outdated support processes for M2M service delivery can impede progress and delay revenue opportunities if not dealt with in a cost-efficient manner. A controlled, phased approach will ensure a successful shift into this expanding market.

Challenge 1: The Fragmented Marketplace

For M2M and IoT to truly evolve our businesses and ultimately our way of life, these technologies must support and interact with each other. If a self-driving car will one day be able to place phone and video calls directly from the vehicle, how will communications and media providers work to support these features? If smart TVs begin to fully incorporate interactive advertisements – allowing for consumers to buy products directly from their TV sets – what partnerships and regulations will support these purchases? If all these connected devices continue to automatically collect more and more information about the consumer 24/7, how much of this data can be stored and shared with other connected companies?

The current M2M and IoT market is disjointed and borderline chaotic; thousands of different companies are doing thousands of different things in thousands of different ways. Current research suggests that IoT will remain fragmented until at least 2018, as there will be no dominant ecosystem, and as a result, there will be a lack of industry standards for providers.

Today, we are in the “trial and error” phase of creating a unified connected device ecosystem, where errors are many times more probable. While networks within the ecosystem are still beginning to form, vendor, technology producer and service provider co-dependency may falter during the day-to-day operations of large IoT/M2M projects. For M2M and IoT initiatives to reach full potential, integrators must develop a plan to ensure the longevity of their products and connections.

Challenge 2: Outdated Legacy Billing Systems

With greater information and analytic capabilities comes a greater inefficiency to support and organize older billing systems. In legacy systems, information on purchases and customer preferences lagged, and Big Data was not supported.

But in today’s world of Big Data, the vast amounts of information being pushed through may not be able to support this structure. In addition, the amount of data utilized by emerging IoT technology is increasing at an exponential rate, and legacy billing models may not work in the future.

In addition to expanding billing systems on the business side, the systems must also be optimized from a technological standpoint. IoT is made up of billions of micro-transactions, which legacy billing systems are not equipped to handle in real time. Analytics of data patterns, consumer information and usage patterns needed to make crucial billing decisions must be automated.

Challenge 3: Unstandardized Transactions

While IoT and M2M technology is continuing to push for personalized and usage-based billing systems, a solely individual approach may be difficult to achieve. Currently, transactions are not standard in how they measure usage, so customization is generally needed for each customer. Data from IoT and M2M can occur from thousands of different channels and avenues, and properly capitalizing on usage individually is unsustainable. As with managing a fragmented marketplace, standardization is needed without completely erasing customization and stifling the growth of an optimized customer experience.

Challenge 4: Outdated Support Processes for M2M Service Delivery

The technological infrastructure needed to support IoT and M2M connections is greatly underprepared, and existing processing models, service catalogues and operation support processes are not optimized for efficient M2M service delivery. The new network and backend systems for connected devices must be able to process large amounts of information and deliver predictive analytics.

In addition, removing all legacy technology, reconfiguring information in formats specific to only legacy machines, and then purchasing and installing new technology is both time and cost extensive—and unrealistic. To maximize the return on M2M service delivery, new modular-type hardware and software must be integrated with the existing legacy technology.

Solutions: Next Steps for M2M and IoT Integration

To conquer the business, technological and operational challenges at hand, industries must devise a strategy to ensure longevity for both their networks and products.

  • In terms of unifying the fragmented market, companies must continue to join forces, test and administer hardware, software and processes that allow M2M and IoT technologies to get to market quicker and foster extended compatibility.
  • For tackling the outdated billing systems, legacy technology should be gradually updated through automation initiatives and adding specific required modules (not via full replacement), and by increasing data storage capacity with virtualization and cloud services – allowing for more effective data analysis.
  • For the business side, new combination models including both subscription and usage-based packages should be offered to allow for standardization, customization and future growth. Examples include personalized data plans with premium options, or “build your own package” options.
  • The balance for standardizing transactions while still maintaining customization is crucial in the communications and media industry. In this saturated market, differentiation is key and it’s important for providers to rely on predictive analytics to determine the most beneficial options for different groups of consumers.
  • Lastly, businesses must invest in upgrading existing processing models, service catalogues and operational support processes for M2M service delivery.

We’re already seeing this play out in at least one forward-thinking, top tier service provider in the North American market. This provider has created an integrated IoT lab and made it available to technology entrepreneurs, with space for storage, workstations, 3D printing and conference rooms. Internally, the provider has a dedicated IoT/M2M team that is committed to innovation and developing the company’s IoT and M2M business and technology strategy.

M2M and IoT are quickly becoming agents of innovation in today’s business practices, technological development, and in our everyday lives. While connected devices and networks have the potential to turn huge profits for industries in the next few years, a long-term strategy must be in place to support the changes happening now and prepare for the unknown revenue opportunities that the future still holds. For more information on how Excelacom can help you own the ecosystem and overcome the challenges of IoT and M2M, contact us at marketing @ excelacom com.