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Machine to Machine (M2M) Communications Basics

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What do you mean by M2M communication?

Machine to machine (M2M) is a broad label that can be used to describe any technology that enables networked devices to exchange information and perform actions without the manual assistance of humans. M2M technologies transfer data on the condition of physical assets and devices to a remote central location for effective monitoring and control.

For LTE:

At first Long Term Evolution (LTE) seems to have everything that machine-to-machine (M2M) users don't need: a hefty hardware price premium in a sector that's notoriously price-sensitive, band fragmentation that adds cost and complexity, spotty coverage for applications that often are mission-critical and far more bandwidth than most applications will ever need. That assumption is correct, but it also overlooks all of the reasons why so many operators, vendors and end users are already exploring LTE for M2M – and, in a few cases, using it.

LTE M2M is a roadmap item at this point rather than a commercial offering. So to encourage them to speak freely about their strategies and market outlook, Heavy Reading Insider offered many of them the option of using their input on background. They also had the option of attribution when discussing topics that were already public or that they were comfortable disclosing for the first time.

M2M

M2M Applications:

The cellular based M2M solutions provide easier installation and provisioning targetted mainly for short term deployments. M2M communication could be carried over mobile networks (e.g. GSM-GPRS, CDMA EVDO networks). In the M2M communication, the role of mobile network is largely confined to serve as a transport network.

M2M devices vary from highly-mobile vehicles communicating in real-time, to im-mobile meter-reading appliances that send small amounts of data at random instants.

It covers the communications between the M2M Gateway(s) and M2M application(s), e.g. xDSL, LTE, WiMAX, and WLAN.

Other than cellular M2M is widely adopted in energy,transport,real estate and agriculture sectors. As mentioned previously smart meter utilizes the energy efficiently and hence bring down CO2 emissions. Hence M2M helps in lowering the effect of global warming.

In the transport sector M2M helps by providing information regarding best optimized routes to trucks,ships,trains and planes so that wastage of fuel can be avoided. This also helps reduce CO2 emissions by cutting the distance of the travel.

M2M helps in building and home management by conserving energy for various systems viz. cooling, lighting,heating,ventillation and other electronic appliances. It also provides security for the home or building owner with the M2M compliant security enabled devices.

In agriculture sector M2M provides solutions to monitor cattle health and grazing style, soil monitoring,smart farming and smart watering. This helps grow large amount of crops with lesser resources and hence save money for the farmers.

Use of M2M:

M2M communications, for instance, can be used to more efficiently monitor the condition of critical public infrastructure, such as water treatment facilities or bridges, with less human intervention. It can help businesses maintain inventory or make it easier for scientists to conduct research. Because it relies on common technology, it also could help a homeowner maintain the perfect lawn or create a shopping list at a button's touch.

Network Accessibility:

The possibility of connecting anything from anywhere brings new scenarios and exciting prospects.
Depending on acceptable latency, mission criticality, time-of-use pricing, peak data rates, security needs,
battery life, and distance requirements – there is a deluge of protocols and networks accessible for use.
For short-haul communication in home networking, one might evaluate ZigBee, wM-Bus, Power Line
Communication (PLC), WiFi, Z-Wave, LonWorks and for long-haul it could be PLC, Ethernet Broadband,
Ethernet IP, WiFi/WiMax, 2G/3G/4G cellular or even satellite communication.

Affordability:
Prices of end devices, platforms, network operators, and connectivity costs are all declining in recent
times. As these continue to drop, M2M solutions will become more mainstream and established.

Challenges in M2M Solutions

Organizations are currently facing many challenges in implementing end-to-end M2M solutions – primarily
due to the large number of technologies, services, transport mediums, stakeholders involved in a
deployment. Some of the challenges can be understood by looking at a Smart Home scenario:

  • Crowded Technologies
  • Data Collection
  • Storage
  • Analytics
  • User Interfaces

Summary:
Machine based interactions are witnessing a growth trajectory across industries. The challenges are many
wherein a number of interested parties need to come together for an implementation. Domain knowledge
and technical knowhow are the key requirements since an M2M solution touches upon core processes.

posted Jun 13, 2014 by Yogeshwar Thakur

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Related Articles

This article describes about Peak to Average Power Ratio or PAPR basics, its importance in wireless system and PAPR reduction techniques.

Description:

In Modulation schemes such as OFDM transmitter time domain signal will have higher PAPR, which leads to various distortions in the transmitter chain and degradation of system performance i.e. BER/PER. Also requires highly linear power amplifier which increases cost of the system. PAPR is ratio of peak power to average power of time domain complex baseband signal which is to be transmitted. We will discuss PAPR reduction techniques below.

PAPR Reduction Techniques:

Following section describes on PAPR reduction techniques viz. Clipping, Peak Windowing, Scrambling, Block Coding, Selected Mapping (SLM) and Partial Transmit Sequences (PTS).

Clipping:
The simple method is to clip the transmitted signal, which is modeled as multiplication or convolution of signal having high PAPR with window function. Undesired widening of the signal due to clipping is limited using filter technique. Various window functions such as cosine, Kaiser and Hamming are available.
Pros:Simple
Cons:Causes interference/distortion/out band emission which degrades the system performance. Filtering after clipping can reduce the distortion/emission but may also cause some peak re-growth.

Peak Windowing:
Here large signal peak of the signal is multiplied with a Gaussian shaped window. Suitable window function is selected for multiplication from Cosine, Kaiser and Hamming windows.
Pros: Simple
Cons:Both BER and out of band radiation is increased.

Scrambling:
This technique utilize scrambling technique to polarities of the subcarriers which removes the correlation among the subcarriers irrespective of the user codes used. PAPR will be greatly reduced as it will spread the information over larger band.
Pros:About 5 dB of PAPR is achieved using this technique in certain cases.
Cons:Gives poor performance when number of subcarriers increase.

Block Coding:
Find out the code words with minimum PAPR from a given set of code words. Map the input data blocks to these selected code words.
All block codes provide a low PAPR which is typically below 3 dB for small number of carriers.
Pros: Lowest PAPR
Cons: Low data rate and Significant overhead
Block Coding Sequences, most commonly used are Walsh, Gold, Orthogonal Gold, and Zadoff-Chu sequences. Zadoff Chu is used in LTE.

Selected Mapping (SLM):
The concept here is any one single data vector of the transmitter signal can have multiple representations. Out of these lowest PAPR time domain vector is selected for transmission.
Pros:Lowest PAPR
Cons: Complexity issue as SLM scheme needs multiple IFFT operation.

Partial Transmit Sequence (PTS):
This method is similar to SLM, but divides the frequency vector into smaller blocks before applying the phase transformations.
Pros: Lowest PAPR and Little redundancy.
Cons: Increased system complexity.

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