Imagine a world where everything you use can capture your interactions and transmit that information to servers somewhere on the web. Imagine that toasters, washers and dryers, cars, refrigerators, phones, digital watches, televisions, blenders, coffee makers, game consoles, smart meters, etc., record your usage, actions and preferences and provide that information to their home servers. This is not an implausible idea since there are already more than a few devices (cable and satellite sets, game consoles) that already behave just like this. But in our not-too-distant future all our powered devices will follow that model. The Internet of Things (IoT) is about physical objects reaching the Internet on their own. Using technologies such as RFID, sensor networks, short-range wireless communications, and LANs, physical objects become intelligent devices that will make periodic calls to their data centers to report on their status or transmit the latest set of locally acquired data. This model will gradually change the way we see and interact with the physical things around us and offer great new opportunities for manufacturers, distributors, service providers, retailers and users. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essayFor example, it is not difficult to imagine that in the future home appliances will schedule their own service calls, anticipating real-time measures to remedy a problem by issuing or transmitting user input and usage data. Similarly, the property-casualty insurance industry could use information gathered from a person's automotive IoT to more accurately evaluate and price a particular policy and prevent fraudulent claims by examining data provided by people's driving habits. insured (Progressive Insurance introduced the Snapshot device that plugs into the car's diagnostic port and tracks a person's driving). Using insights gleaned from IoT data, product design could be aided by examining user interaction patterns, supply management and distribution logistics could be optimized by examining adoption rates – how quickly a device goes from manufacturer to distributor, to retailer and finally to connection to a home server from the home of end users – and the provision of healthcare could be made more efficient and effective by making common portable devices, such as mobile phones or digital watches, health monitoring capable of transmitting information on the wearer's vital signs. Welcome to the world of the Internet of Things. The data challenge It is expected that by 2020, approximately 50 to 100 billion things will be connected to the Internet. These smart objects will use common connectivity devices to connect to the Internet and exchange messages with arrays of dedicated servers, potentially generating a staggering 35 ZB/year of data. In case you were wondering, a zettabyte (ZB) of storage capacity is 10 to the power of 21 bytes. Just to give you a better perspective, as of 2009, the entire World Wide Web is estimated to contain nearly 500 exabytes of data, or half of a zettabyt. The IoT will produce a lot of data. IoT data will not be any different from Big Data data which is heterogeneous, dissimilar, unstructured and noisy. But even more remarkable will be the growth rate of IoT data. Currently, the amount of data generated by social media, transactions, and government and corporate entities is growing faster thanallow IT resources. Add to this challenge the volume of data that is expected to be generated by the IoT, and it becomes clear that traditional data storage and processing solutions could hardly be applied to acquire, validate and analyze these volumes of data. IoT data will be granular in nature and will contain information about locations, temperatures, patterns and behaviors. In the world of IoT, the challenge will be to find ways to analyze and exploit this information quickly and in near real time. It should come as no surprise that organizations that are able to make business decisions using this data will have a strategic advantage over their competition. But, as mentioned above, doing so requires a solid IT infrastructure, and that won't come cheap. IoT data, like Big Data, is unstructured and therein lies one of the most significant challenges. To address this problem effectively it is important that manufacturers, distributors, service providers and resellers agree on a simple, generic and textual format for constructing and describing IoT data, similar to the XML markup language. This investment in standardization will affect the entire IoT/Big Data processing pipeline – data acquisition, extraction and cleansing, integration and aggregation, and finally analytics – as some existing tools could be used to clean and transform this data more quickly and cost-effectively in a better format. suitable for analysis applications. Technological Challenge Due to the exponential growth rate of IoT data, the need for an IT infrastructure that can balance performance, energy efficiency and cost becomes important. To successfully adapt to the data growth patterns expected by the IoT, IT departments must prepare for large-scale computing environments with thousands of computer clusters capable of supporting scalable, predictable facilities that process large data sets. This new computing environment is best built in the cloud, where sharing very large and expensive clusters has become economical. Another advantage of cloud computing is its modular architecture where horizontal scalability can be achieved quickly and easily. A central challenge in IoT data processing is the limitations inherent to conventional computing resources. Largely due to power limitations, processor clock speeds have stalled and instead processors are being built with larger numbers of cores. As a result, application developers now have to worry about parallelism within a node, as well as between nodes. Since this architecture is very different (multiple processor cache and memory shared between cores), techniques for inter-node processing algorithms do not work for intra-node parallelism. Therefore, application developers must reevaluate how they design, build and deploy data processing applications. Another technology challenge is traditional I/O systems that for decades have been designed and optimized for sequential I/O performance rather than random access. But with the advent of solid state drives this performance limitation is disappearing and hard disk drives are being replaced by the new generation of I/O systems which in turn require IT departments to rethink how they design and implement systems database for processing large amounts of data. An IoT/Big Data IT infrastructure requires large investments and it is even more important for IT departments to better manage their operations and resources. User-driven application optimization will fail to achieve your goals.