Page 22

EETE JUNE 2013

M2M Comunications Data management for a modern M2M framework By Sasan Montaseri our world is surrounded by electronic and wireless devices. Managing all the different types of machines we have to deal with every day has encouraged technologists to have them connected so they are able to communicate with each other and perform tasks independently. This technology, which is known as Machine-to-Machine (M2M), is the science of enabling devices to manage and communicate data so they require the least human presence or manual programming. M2M data communication and data management is increasingly gaining attention and soon, according to various reports, more and more devices need to connect, communicate and distribute their data to each other and over the Internet. There are various reports, including one from Cisco explaining that there will be over 10 billion connected devices by 2016. Each machine will produce, share, receive and analyze data, and data management persists in becoming an important component for M2M ecosystem. Some of key components for an M2M ecosystem include: Sensors: to detect any input data in the form of light, heat, motion, signal, and so on, and can transform it into humanreadable output. RFID: a technology incorporating the radio frequency portion of the electromagnetic spectrum to identify data relating to any living or non-living thing. Wi-Fi: some type of wireless technology in which electromagnetic waves data carry signals in telecommunications. Autonomic computing system: a self-managing computing model that would control the functioning of computer applications and systems without input from the user, which can function while being invisible to the user. Before M2M communication is made possible, machines need to be equipped with a suitable database framework so they can collect, analyze and share data in a scalable fashion. Machine intelligence – IQ –has a direct relation to its ability to understand and analyze data. One scenario for such a deployment is when every machine (node) accumulates and shares certain data with other machines (nodes) so any node can do data “analysis” and “processing” from their collected data. In a network gateway scenario, other devices are connected to each other and to the cloud, which may include a relational back-end database. Data interoperability and standards are also important. Data management capabilities - such as asynchronous or synchronous replication to distribute data, SSL to secure data, and the conventions and data structure necessary to govern how machines should exchange data - play an important role for M2M ecosystems. For example, a home automation application can monitor the cycle of a washing-machine, schedule a coffee machine to finish brewing at a specific time, and check that lights are off when away from home. Residents need to access and control information on these devices through multiple interfaces, whether mounted in the home or on a personal smart phone or tablet. A database for such devices must provide a framework for communication within the M2M infrastructure. Managing big data A sharp increase is expected in machine communication and data exchange as more machines are connected to the Internet and other networks to share information with other machines. M2M communication is on the rise: millions of machines are being connected to collect and accumulate a large amount of data. Known as the Internet of Things (IoT), this network connects embedded data in a variety of systems such as medical devices, automotive software, mobile devices, and tablets. However, these machines often encounter serious data management challenges in which a large amount of data is collected and accumulated at different locations. As these devices become part of a networked and connected ecosystem, the complexity of data management and system connectivity makes it difficult to find and query required data without configuring data distribution to replicate between machines or synchronize with a back-end RDBMS product. Embedded ecosystems are generally composed of many machines that divide up a big data problem. Some data is private to each machine, while other data is shared with select peers; so the database should use replication and synchronization to efficiently distribute local data with other machines and back-end databases according to the manufacturer’s business logic and policies. Any number of peers can participate in replication, allowing an ecosystem to scale up as more machines are added. Data produced on a machine or retrieved from other machines should be locally queried for rapid decision-making, Sasan Montaseri is the founder of ITTIA – www.ittia.com – He can be reached at sasan@ittia.com 22 Electronic Engineering Times Europe June 2013 www.electronics-eetimes.com


EETE JUNE 2013
To see the actual publication please follow the link above