Machine-to-machine (M2M) Communications

Architecture, Performance and Applications

Carles Anton-Haro,Mischa Dohler

• Editore: Woodhead Publishing
• Anno: 2014
• ISBN:
  9781782421108
• Argomento: Technology & Engineering
• Categoria: Signals & Signal Processing

Part one of Machine-to-Machine (M2M) Communications covers machine-to-machine systems, architecture and components. Part two assesses performance management techniques for M2M communications. Part three looks at M2M applications, services, and standardization.

Machine-to-machine communications refers to autonomous communication between devices or machines. This book serves as a key resource in M2M, which is set to grow significantly and is expected to generate a huge amount of additional data traffic and new revenue streams, underpinning key areas of the economy such as the smart grid, networked homes, healthcare and transportation.

• Examines the opportunities in M2M for businesses
• Analyses the optimisation and development of M2M communications
• Chapters cover aspects of access, scheduling, mobility and security protocols within M2M communications

1

Introduction to machine-to-machine (M2M) communications

C. Antón-Haro¹; M. Dohler^{2 1} Centre Tecnològic de Telecomunicacions de Catalunya (CTTC), Barcelona, Spain
² King's College London (KCL), London, UK and Worldsensing, Barcelona, Spain

Abstract

This introductory chapter, by the editors, highlights fundamental concepts related to machine-to-machine (M2M) communications. To this end, the concept and ecosystem of M2M are introduced, with emphasis on connectivity technologies and data flows. Then, the market opportunity is explored where challenges and opportunities are also highlighted. Thereupon, examples of commercial rollouts and deployments, as well as experimentations, are discussed. The standards ecosystem is then explored in great detail, where some references to early research projects are also given. Finally, the rationale of the book is provided with some executive summaries to each chapter included.

Keywords

Introduction

Machine-to-machine

Data

Connectivity

M2M market

Deployment

Standards

Standards development organization (SDO).

Acknowledgment

This work is partially supported by the FP7 project NEWCOM# (318306) funded by the European Commission.

1.1 Introducing machine-to-machine

We have witnessed the fixed Internet emerging with virtually every computer being connected today; we are currently witnessing the emergence of the mobile Internet with the exponential explosion of smart phones, tablets, and netbooks. However, both will be dwarfed by the anticipated emergence of the Internet of Things (IoT), in which everyday objects are able to connect to the Internet, tweet, or be queried. While the impact onto economies and societies around the world is undisputed, the technologies facilitating such a ubiquitous connectivity have struggled so far and have only recently commenced to take shape.

1.1.1 Machine-to-machine and the big data opportunity

A cornerstone to this connectivity landscape is and will be machine-to-machine (M2M). M2M generally refers to information and communications technologies (ICT) able to measure, deliver, digest, and react upon information in an autonomous fashion, i.e., with no or really minimal human interaction during deployment, configuration, operation, and maintenance phases.

Flagship examples of M2M technologies are telemetry readings of status of oil and brakes of cars on the move, health state measurements of blood pressure and heartbeat of the elderly, monitoring of corrosion state of oil or gas pipelines, occupancy measurements of parking in cities, remote metering of water consumption, and real-time monitoring of critical parts of a piece of machinery.

While machines do not excel humans in writing poetry, they are definitely industry favorites when it comes to (i) repetitive jobs, like delivering water meter data once a day, and (ii) time-critical jobs with decisions taken within a few milliseconds based on the input of an enormous amount of data, like the real-time monitoring of rotating machinery parts.

M2M is all about big data, notably about (i) real-time, (ii) scalable, (iii) ubiquitous, (iv) reliable, and (v) heterogeneous big data, and thus associated opportunities. These technical properties are instrumental to the ecosystem:

• Indeed, real time allows making optimal and timely decisions based on a large amount of prior collected historical data. The trend is to move away from decision making based on long-term averages to decisions based on real-time or short-term averages, making a real difference to the large amount of nonergodic industrial processes.

• Scalability implies that all involved stakeholders, i.e., technology providers, service companies, and finally end user in a given industry vertical, can scale up the use and deployment where and when needed without jeopardizing prior deployments. Wireless is instrumental when it comes to scalability!

• Ubiquitous deployment and use are important since it allows reaching this critical mass required to make technologies survive long term. A sparse or punctual use of specific M2M technologies makes sense in the bootstrapping phase of the market but is unsustainable in the long term.

• Reliability, often overlooked, means the industry customer gets sensor readings that can be relied on because the system had been calibrated, tested, and certified. A prominent example is the vertical of urban parking, where Google's crowdsourced platform to get parking occupancy had been discontinued since stakeholders in this space preferred to get reliable occupancy messages 24/7 from certified sensors installed at each parking spot, rather than unreliable crowdsourced data, even when offered for free.

• Finally, the big data dream can only be materialized if heterogeneous data, i.e., data from different verticals, are combined to give unprecedented insights into behavior, trends, and opportunities and then also act upon them. In fact, when referring to “big” in big data, it is less about quantity of data (i.e., large volumes of terabytes of information) but rather the quality of data (i.e., different information streams giving a new comprehension of the field, where one stream could be only a single bit).

The data being delivered by M2M systems are often referred to as the oil of the twenty-first century. It might be coincidental but the M2M data flow resembles the oil processing flow in that it is composed of data (i) upstream, (ii) mash-up, and (iii) downstream:

• Upstream: Here, data are being collected from sensors in the field that deliver the data wirelessly to a gateway via single-hop or mesh networking technologies. The data traverse the Internet and reach dedicated servers or cloud platforms, where they are stored and profiled for further analytics.

• Downstream: Here, business intelligence is applied to the data and intelligent decisions are taken. These are pushed back down to the customer, by either controlling some actuators, displaying some information/alerts in control/service platforms, or simply informing users about issues pertaining to this specific M2M application. Human–computer interfaces (HCI) or even computer–computer interfaces (beyond the typical APIs) will be instrumental in ensuring the offtake of M2M technologies.

• Mash-up: The data processing and business intelligent platforms are likely the most important constituent of emerging M2M systems. Data flows from machines, humans, and the Internet in general will feed intelligent analytics engines, which are able to find trends, optimize processes, and thus make the industry vertical more efficient and effective.

These three constituents are depicted in Figure 1.1.

Figure 1.1 Upstream (top), downstream (middle), and mash-up (bottom) components for typical industrial M2M turnkey solutions.

1.1.2 Machine-to-machine technology landscape

As for wireless M2M technologies, the ecosystem has so far relied on ZigBee-like and 2G/3G cellular technologies; however, new players are entering the space, such as low-power Wi-Fi, Bluetooth low energy, and proprietary cellular systems. The pros and cons of these are as follows:

• ZigBee-like technologies: IEEE 802.15.4 and derivatives were (and still are) perceived as the holy grail for wireless sensor networking and M2M usage. Indeed, with the latest IEEE 802.15.4e amendments, it has become a very energy-efficient technology, even in the case of multi-hop mesh. However, in its very nature of providing fairly high data rates over short distances, it is against the essence of M2M, which mainly requires very low data rates over large distances. The need for frequent repeaters/gateways and skilled engineers to handle connectivity/radio planning and the lack of a global critical mass when it comes to coverage and deployment/adoption have prevented the predicted growth and will likely be the demise of the technology itself. ZigBee and derivatives have never made the jump from being a technology to being a turnkey solution, i.e., a system that is easy to use to customers whose core business is not dealing with dimensioning wireless. However, this community has achieved something that none of the others have yet: they have penetrated the control community and were able, despite clearly being technically inferior compared to any of the below systems, to become the certified choice for wireless SCADA-like systems (see, e.g., WirelessHART, ISA100.11a, and Wireless M-Bus).

• Low-power Wi-Fi: An interesting contender in the M2M space is emerging in the form of a well-tuned Wi-Fi system. Wi-Fi enjoys an enormous popularity in both the business-to-consumer (B2C) and B2B markets, with more than 2bn access points installed and a truly vibrant standardization ecosystem active. There is Wi-Fi coverage virtually anywhere where it is worth taking M2M data. Interestingly, it consumes significantly less energy when transmitting information [1]. This is mainly because data are transmitted in a single hop only when needed, thus requiring the radio to be switched on only at events or regular alive beacons. In contrast, ZigBee-like technologies need to listen periodically for neighbors to transmit data; this consumes in the end more energy than a simple single-hop network. It is thus to no surprise that chip manufacturing giants, like Broadcom [2], have decided to ditch the IEEE 802.1...