I have given a keynote speech at the IEEE MASSAP Workshop at ICC 2015 in London. The talk is on wireless massive and ultra-reliable communications, which are seen as two new modes that will be featured in 5G. There is, of course, a technical part in the talk, but there is also a part which argues why the research on wireless and communication theory is still vital. The slides can be found here:
Petar Popovski has been leading the team that created the Best Readings in Smart Grid Communications. The list of best readings can be found here:
The list has been divided into six topics:
- General Survey-Type and Big-Picture Books and Papers
- Communications and Networks to Enable the Smart Grid
- Cyber Security and Privacy
- Architectures, Control and Operation for the Smart Grid, Microgrids and Distributed Resources
- Demand Response and Dynamic Pricing
- Data Management and Grid Analytics
We were part of a team that recently wrote an article “Five Disruptive Technology Directions for 5G”. The article was featured in MIT Technology Review and highlights the following figure. Although the figure is conceptual and with no ambition to provide precise numbers, it is interesting to see the reasoning behind the regions. The x-axis is represents the number of users in a cell and the y-axis represents the data rate per device.
- R1 – Describes the operating region of today’s wireless wide-area cellular systems. As the number of users increases in the system, the data rate per user decreases.
- R2 – It is the region targeted by the wireless research efforts that aim to increase the data rates, such as mmWave, network densification, massive MIMO, etc. Note that the upper part of this region decreases as the number of users increases, but slower compared to R1. This aims to highlight that many of the new broadband high-spectral efficiency techniques are inherently designed to serve efficiently multiple users, such as: interference alignment, wireless network coding, improved spatial reuse in mmWave wireless, etc.
- R3 – It is the region of massive M2M communication with relatively low data rate and therefore termed lowband communication. The required data rate is able to continuously decrease with the the increase of the number of connected devices, but not go abruptly to zero as the system becomes more congested. It is assumed that each device transmits sporadically and the y-axis represents the average data rate of the device. The declining becomes progressively faster due to the fact that short packets, sent by many devices, are affected by the signaling overhead. To see why this region represents a research challenge, we can think of this as follows: it is relatively easy to send 10 Mbps from each of the 10 devices, but it is much more difficult to send 10 kbps from each of the 10000 devices. The problem is that in the latter case the overhead of the protocols, collision waste, etc. start to dominate the performance.
- R4 – It is the region of lowband communication, but very low latency or ultra-high reliability. These features are not plotted on the figure. This region represents the connectivity required in e. g. car-to-car communication (low latency, high reliability), public safety, critical control of small-scale M2M installations, etc. To see why this region represents a research challenge, we can think of this as follows: it is relatively easy to send support 10 Mbps to a device during 95% of the time, but it is very difficult to guarantee 10 kbps during e. g . 99.999% of the time. If the 5G wireless connections are able to deliver such a reliability of a stringent latency requirement, then the number of new services will proliferate, as people and industries will start to trust the wireless connection as being a “true” cable replacement.
- R5 – region that cannot be reached due to fundamental limits of physics, information theory and networking theory.
We believe that a concise description of what 5G will be is given by the following:
Instead of being only a broadband “4G, but much faster” system, 5G will also include lowband communications, represented by the regions R3 and R4.
In the emerging area of M2M communications, Automatic Meter Reading (AMR) is a showcase application: a large number of meters use sophisticated wireless networking for two-way communication with a central controller/data collector. The same holds for other smart grid applications, such as Automated Demand Response (ADR), substation and distributed energy resources automation/monitoring/control, and Wide Area Measurement System (WAMS), which all could be categorized within M2M communications.
The usage of wireless techniques for M2M communication has been made possible due to the level of maturity attained by the wireless technologies: small, inexpensive embedded devices have significant computational power and operate at very low power levels. M2M communication has significantly different requirements from, e. g. human -centric services (download, web browsing, video streaming), where large data volumes are sent and high data rate is required. In majority of the scenarios, M2M communication is based on intermittent transmission/reception of small data portions and pose requirements that are different from the ones according to which the common wireless protocols are designed. Some of the most important requirements are the following:
• Transmission from a massive number of devices and maintenance of a large number of active connections;
• Ability to send a small amount of data while decreasing the overhead percentage;
• Real-time communication with low latency;
• Certain connections that carry critical control data require a high degree of reliability, such that a connection should be kept alive more than 99.XX % of the time.
These requirements become more challenging when one considers the forecasts that state that by 2020 there will be 50 billion M2M connected wireless devices , spanning a wide application range: smart grid, smart metering, control/ monitoring of homes and industry, e-health, etc. While there are many ongoing standardization activities , M2M communication solutions have started to be deployed through the existing cellular interfaces, such as GSM and LTE; in fact, cellular networks are and will continue to be short to medium term enablers for M2M applications, due to their ubiquitous coverage and well understood and developed business/engineering platforms .
Indeed, in the past few years it has been observed an increase in the number of networked machines connected to cellular networks, like deployment of cellular-based wireless smart meters . Some of those deployments are very large, such as Hydro- Quebec in Canada , with about 3.8 million devices that periodically send only a few bytes (KW/h consumption for instance). Another example is happening in Spain and Portugal, where Endesa, the largest Iberian operator, will replace a total of 13 million electric meters with smart meters by 2018 . Since neither GSM nor LTE are originally designed to support massive M2M communication, there are ongoing research and standardization activities to modify those interfaces, notably LTE, in order to support the M2M traffic characteristics .
The adequate provision of M2M applications brings many challenges to cellular networks; the foremost being the support of the massive simultaneous transmission of low data rate messages. This led 3GPP to initiate a study item that concluded with the proposal of several key adaptations to the 3GPP cellular networks architecture, which will allow to both handle M2M traffic, denoted as Machine-Type Communications (MTC) within 3GPP , , and reduce the impact on human centric communications. The foreseen changes in order to support M2M traffic should happen both in the access and core network, and alleviate the radio and signaling network congestions that could lead to large delays, packet loss and, in the extreme case, service unavailability. Of particular interest are enhanced load control mechanisms in the radio access network, which include: access class barring , ; orthogonal resources ; dynamic resources allocation ; back-off; slotted access; pull-based.
Another recent standardization activity, spurred foremost by M2M applications, is within the scope of IEEE, where 802.11ah task group is developing a WLAN standard tailored for Wi-Fi-enabled devices to get guaranteed access for short and massive data transmissions . The standard is still in its preliminary stages and it’s future operation is centering on the following principles: operating frequencies below 1GHz, BPSK, QPSK modulations and 16/256 QAM, while channel access should be group based, supporting up to 6000 devices simultaneously.
Finally, a potential, light weight solution for gathering of smart metering data is usage of Wireless M Bus technology . However, this standard essentially foresees only uplink transmissions of metered data and lacks feedback control link, as well as capabilities of autonomous and adaptable operation in changing networking scenarios.
 Q. D. Vo, J. P. Choi, H. M. Chang, and W. C. Lee, “Green perspective cognitive radio-based m2m communications for smart meters,” in Information and Communication Technology Convergence (ICTC), 2010 International Conference on. IEEE, 2010, pp. 382–383.
 L. X. D. Niyato and P. Wang, “Machine-to-machine communications for home energy management system in smart grid,” IEEE Communications Magazine, vol. 49, no. 4, pp. 53–59, 2011.
 David Boswarthick, Omar Elloumi, Olivier Hersent, Eds,“M2M Communications: A Systems Approach”, Wiley, 2012.
 Sierra Wireless Product Webpage., Accessed in October 2012, http://www.sierrawireless.com/en/Solutions/customer stories/EDMI.aspx.
 Quebec Press Release Smart Metering., Accessed in December 2012, http://www.rogers.com/web/link/showNewsDetail?fromWhere=linkInRSSXml&rssBusiUnit=W&NewsID=1906177072.
 Endesa Press Release Smart Metering., Accessed in January 2012, http://www.endesasmartgrid.com/index.php/en/smart-homes/smartmetering.
 3GPP, “Service Requirements for Machine-Type Communications (Stage 1),” 3rd Generation Partnership Project (3GPP), TS 22.368, June 2010. [Online]. Available: http://www.3gpp.org/ftp/Specs/html-info/23368.htm
 3GPP TR 37.868 V11.0, Study on RAN Improvements for Machine-type Communications, October 2011.
 M.-Y. Cheng, G.-Y. Lin, H.-Y. Wei, and A.-C. Hsu, “Overload control for machine-type-communications in lte-advanced system,” IEEE Communications Magazine, vol. 50, pp. 38 –45, June 2012.
 S.-Y. Lien, T.-H. Liau, C.-Y. Kao, and K.-C. Chen, “Cooperative access class barring for machine-to-machine communications,” IEEE Transactions on Wireless Communications, vol. 11, January 2012.
 J.-P. Cheng, C. han Lee, and T.-M. Lin, “Prioritized random access with dynamic access barring for ran overload in 3gpp lte-a networks,”in GLOBECOM Workshops, 2011 IEEE, pp. 368 –372, December 2011.
 K.-D. Lee, S. Kim, and B. Yi, “Throughput comparison of random access methods for m2m service over lte networks,” in GLOBECOM Workshops, 2011 IEEE, pp. 373 –377, December 2011.
 M. J. Anthony Lo, Yee Wei Law and M. Kucharzak, “Enhanced lte advanced random-access mechanism for massive machine-to-machine (m2m) communications,” in 27th World Wireless Research Forum (WWRF) Meeting, 2011.
 S. Aust, R. V. Prasad, and I. G. M. M. Niemegeers, “IEEE 802.11ah: Advantages in standards and further challenges for sub 1 GHz Wi-Fi”, In 2012 IEEE International Conference on Communications ICC, December 2012
 EN 13757, “Communication systems for meters and remote reading of meters.” Part 4: Wireless meter readout (Radio meter reading for operation in the 868 MHz to 870 MHz SRD band), 2005.
CS, NP, GCM
As highlighted in the previous blog post, there is a new emerging standard in the M2M arena based on the IEEE 802.11 standards family. This standard is being developed under the IEEE 802.11ah group, and aims to define the physical (PHY) and medium access control (MAC) layers that operate at radio frequencies below 1 GHz. One of the goals of this standard is to ensure that the transmission ranges up to 1 km and that the data rates per user are above 100 kbit/s.
The standard is currently being drafted, but some essential details about this new standard are already available, which we will highlight in this blog post. It is important to emphasize that although the IEEE 802.11ah standard will define operations below 1 GHz, it will not use the TV white space bands (54-698 MHz in the US), which are targeted instead by IEEE 802.11af.
The PHY transmission in IEEE 802.11ah is an OFDM based waveform consisting of a total of 64 tones/sub-carriers (including tones allocated as pilot, guard and DC), which are spaced by 31.25 kHz. The modulations supported include BPSK, QPSK and 16 to 256 QAM. It will support multi user MIMO and single user beam forming.
In  is stated that stations will support the reception of 1 MHz and 2 MHz PHY transmissions. The channelization (i.e. operating frequency) depends on the region. In Europe it will be within 863-868 MHz, allowing either five 1 MHz channels or two 2 MHz channels. While in the US the available band will be within 902-928 MHz, allowing either twenty-six 1MHz channels or thirteen 2MHz channels. In Japan, the available band is within 916.5-927.5 MHz, with eleven 1MHz channels. In China the available band will be within 755-787 MHz, with thirty-two 1 MHz channels. South Korea and Singapore also have specific channelizations that can be found in .
The MAC layer will include a power saving mechanism and an alternative approach to perform channel access, which will allow an access point to support thousands of stations, as required for M2M applications. The channel access also supports a mode of operation where only a restricted number of stations can transmit.
There are several use cases for this standard , which include:
- Sensor Networks – where the IEEE 802.11ah is used as the communication medium for the transmission of short-burst data messages from sensors, which include smart metering;
- Backhaul networks for sensors – where the IEEE 802.11ah can be used to create the backhaul of mesh networks created by IEEE 802.15.4 networks;
- Extended Wi-Fi range for cellular traffic off-loading – where the IEEE 802.11ah can be used to off-load traffic from a cellular network. The caveat is that the performance should be at least comparable with the one from the cellular network;
- M2M communications – Whereas current systems are optimized more for human-to-human (H2H) communications, IEEE 802.11ah standard will mainly consider sensing applications.
- Rural communication – Wireless communication in rural areas has led to some effort that is also titled as bridging the digital divide. Large potential is given by sub 1 GHz due to the wider supported range.
In future blog posts, we will follow up with the standardization activities in IEEE 802.11ah.
The best way to reduce energy consumption of wireless device is to turn it on only when necessary. This is easy to realize if the transmitter and receiver know the exact timing of their communications, that is, if a complete rendez-vous can be accomplished between them. But of course, this is not the case most of the time since the traffic pattern over communications network is bursty and unpredictable: the receiver does not know when the transmitter wants to send packets to it.
Then, the transmitter can somehow poke a sleeping receiver when it needs to communicate. This is called wake-up signaling, and a lot of studies have been (and are being) done for sensor networks where the energy-efficiency of devices is one of the most important requirements. In general, the wake-up signaling is done through secondary channel. Here, the primary channel is the channel for data transmission/reception which consumes relatively high amount of energy. On the other hand, the secondary channel is only for sending wake-up message, which is realized by very simple and low-power radio. When there is no communications demand, only secondary channel is active, and radio interface for primary channel is completely turned off. Since the secondary radio consumes little amount of energy, we can significantly reduce the energy consumed in an idle state. That is, the gap of energy consumption between primary and secondary channels is exploited for energy saving.
We have been applying the concept of wake-up signaling to reduce the energy wastefully consumed by WiFi routers in a research project funded by Japanese government. The active duration of WiFi routers is much shorter than the idle duration. For example, WiFi router at your home is automatically powered-on/off according to your communications demands. A very simple wake-up receiver, which operates with non-coherent on-off-keying (OOK) detection, is installed into WiFi router. The energy gap between WiFi and such a simple receiver is so large that we can have a huge gain in terms of energy saving. But, one problem was the need for WiFi station (e.g. your laptop or smatphone) to have an additional device to transmit a wake-up signal.
Our solution to this problem was to reuse WiFi transmitter already installed into WiFi station. The simple, OOK wake-up receiver at WiFi router is designed to be able to detect the length of WiFi frames observed over 2.4 GHz ISM band. The WiFi station embeds information (e.g. wake-up ID) into the length of transmitted WiFi frames (you can imagine Morse code where the length of WiFi frame corresponds to dot and dash). The wake-up receiver turns on WiFi router if the detected length matches with its registered ID. The detailed information on wake-up mechanisms and receiver can be found in  and .
Basically, we have realized information exchange between WiFi transmitter and a very simple, low-cost, and low-power receiver which has completely different physical layer from WiFi. The layering concept has been developed to offer communications capabilities between devices having a common communications protocols. We have shown that, in a particular setting and scenario, communications between devices implementing different protocols are possible and useful. We are now seeking for scenarios in M2M where this type of communications and device can be exploited.
 Y. Kondo, H. Yomo, S. Tang, M. Iwai, T. Tanaka, H. Tsusui, and S. Obana,” Energy-efficient WLAN with on-demand AP wake-up using IEEE 802.11 frame length modulation,” Elsevier Computer Communications, Vol. 35, Issue 14, pp. 1725–1735, August 2012. http://www.sciencedirect.com/science/article/pii/S0140366412001478
 H. Yomo, Y. Kondo, N. Miyamoto, S. Tang, M. Iwai, and T. Ito, “Receiver Design for Realizing On-Demand WiFi Wake-up using WLAN Signals,” in Proc. of IEEE Globecom 2012, Dec. 2012. http://arxiv.org/abs/1209.6186
Two of our researchers (German and Nuno) are at the 3rd ETSI Workshop presenting a poster about our ongoing work in enhancing the capacity of GPRS and LTE in the Radio Access Network.
There are more than 220 participants attending the workshop.
German and Nuno will post a summary of the events in the workshop in the end of the day.
If you are around come and visit them in the poster section during the coffee breaks.
Current cellular mobile networks are designed for human communication, and therefore are optimized for the traffic characteristics of human-based communication applications, i.e. communication with a certain session length, data volume, interaction frequency and patterns.
As the number of Machine-to-Machine (M2M) type devices increases so does the motivation to optimize existing cellular networks. While the features of the traffic generated by M2M devices are varied and application specific, the connected devices will be a mix of sensors and actuators. It is expected that a large majority will be sensors; therefore a bulk of the generated traffic will be in the uplink direction, i.e. from the M2M devices towards the network.
According to a recent market research study, in the next 10 years it is expected that the amount of M2M devices connections will reach 2.1 billion and that from these 61% will be due to the utilities market .
There are two interesting points about the characteristics of the traffic generated by this kind of application: The first is that the amount of data reported is generated sporadically and in small amounts, i.e. only a few bytes at a time; The second is that due to the expected density of the devices and the observed correlation between the reporting times , then the network will often observe a traffic pattern similar to a botnet, i.e. the traffic will arrive in batches. These two points will have an adverse effect on current cellular networks.
Currently, if one of these M2M devices wants to send a report to the network it will most likely use as carrier a SMS message. For this to happen, first the device needs to be connected to the network, which entails significant signaling overhead , then there is also the overhead associated to the transmission of a SMS message. Therefore, in the case of batch arrivals this will easily lead to the network to become overloaded due to signaling, while only transmitting a limited amount of data.
The network overload can occur at the radio access network, i.e. from a multitude of devices towards a base station, where in this case the base station gets overloaded, or globally where several base stations convey the traffic to a core network node. The ultimate consequence is the induction of signaling congestion and high computational load in the network.
In future blog posts we will describe what are the ongoing efforts to optimize the cellular networks to handle the characteristics of this particular type of M2M traffic.
 2.1 billion M2M devices in 2021, http://www.analysysmason.com/About-Us/News/Press-releases1/M2M-forecast-2012-PR-Jun2012/
 M. Zubair Shafiq et al, “A First Look at Cellular Machine-to-Machine Traffic – Large Scale Measurement and Characterization” in Proceedings of the International Conference on Measurement and Modeling of Computer Systems (SIGMETRICS), London, United Kingdom, June 2012.
 Annex B.4 3GPP TR 37.868 V11.0.0
This is a research blog dedicated the communication technologies that are related to Machine-to-Machine (M2M) communication. We will primarily provide information related to our research project MassM2M, but also analysis of the relevant literature, technology trends, and research ideas.
What is M2M?
Probably today you have used M2M communication if you paid by a credit card. The terminal (a machine) in the shop connects to a server (another machine) in order to approve the transaction. M2M is about communication between devices, objects, things, which is different from Human-to-Machine (H2M) (e. g. “Googling”) or Human-to-Human (H2H) (e. g. “Skyping”).
Techno-economical forecasts indicate that in the coming years M2M communication will become massive, connecting tens of billions devices. Wireless chips have grown in capability and power efficiency, while shrinking in size and cost. It becomes affordable to embed wireless chips in many diverse objects and make them “digitally visible”, similar to the way a person is digitally visible through Facebook.
M2M becomes massive also in terms of diversity across applications. Wireless M2M networks are instrumental to manage the complexity of tracking, fleet, and asset management. The industrial sector can widely apply M2M in monitoring and control of processes and equipment. Radio Frequency Identification (RFID) in the retail industry is an example where M2M enables real-time visibility of the individual items. The M2M showcase is the smart grid: the evolved power grid where a rich information flow is used to balance the electricity production (e. g. windmills), distribution, storage, and consumption (e. g. large industrial capacities).
M2M has a large transformational power to make the processes more efficient by saving time, costs, and energy. But the present optimism about M2M is also fueled by its promise for new business models and the large innovation potential. The companies that use M2M, such as the industrial sector, can introduce novel features in their products and deploy new, information-intensive services. The companies that provide M2M services, such as the telecom operators, see them as important alternatives to the flat-data-rate-like services.
Skeptics may question the upcoming M2M revolution, pointing, for example, that RFID has been around for years, but failing to become massive. This is true; but it was also true that the phrase “smart phone” had been around from 1992, while it lifted off with the iPhone in 2007. Recall that the mobile phone started with voice as a single application at its focus, only to become our assistant and chief entertainer. M2M communication has started slowly with a wide range of applications, so our expectations should be very high for the upcoming billion connected devices.
Research on M2M
M2M services are already up and running in the networks of many mobile operators. Furthermore, there is are very active standardization processes related to M2M in different bodies, such as ETSI or 3GPP. So, is there a need/space for carrying out fundamental research on M2M communication technologies?
Our answer is (clearly) yes. Specifically, there are two (expected or predicted?) features of M2M communication that allow one to pose new and interesting research questions:
- The “Massive” feature: Technology predictions say that by 2020 there will be 50 billion connected wireless devices, spanning a wide application range: smart grid, metering, control/monitoring of homes and industry, e-health, etc. Wireless networks need a revolutionary reengineering to be able to embrace the massive number of devices. In many cases M2M traffic will feature short data packets, where the useful data is comparable in size to the signaling overhead used to send that packet. To- day the networks can efficiently carry large data from few devices; the problem is how to carry few bytes from a large set of devices (machines). In a nutshell, sending 100000 bytes from one device is very different form sending 1 byte from 100000 different devices; the latter will clearly consume much more resources for signalling/coordination.
• The requirement for dependability. M2M communication becomes vital for various control, monitoring, and industrial processes, where it is critical to keep the wireless link alive during 99.99+% of the time. This is in a stark contrast to many existing systems, such as WiFi, which works fine around 95% of the time, but offers zero data rate under harsh receiving conditions. Increased dependability means that the wireless link is available almost all the time and, under harsh conditions, it can scale down the data rate in order to maintain reliable connection.
Other M2M issues that pose interesting research questions are security and device management. They are only peripherally related to our research, but it has to be noted that they also essential for wide adoption of wireless M2M deployments.
Regarding our research approach, we have two different tracks. In one track we investigate protocols and algorithms for rather generic communication systems. An example is Frameless ALOHA, where we are exploring a new concept for massive random access. In the second track we adapt our research context to a particular system, such as LTE, see the article on Code-Expanded Access in LTE. Although it may seem far from the cutting-edge research, we are very interested in the GSM system. We believe that the GSM networks should not be put out of use, but rather to be re-engineered and dedicated to M2M traffic. Even the research on GSM can become fundamental if we understand the GSM protocols and structure as design constraints for new protocols and signalling schemes that should be built on top of it. Our initial activities related to dependable wireless communication have been chiefly related to the university spinoff Wisecan.