The Internet of Things (IoT) is an ecosystem of ever-increasing complexity; it’s the next wave of innovation that will humanize every object in our life. IoT is bringing more and more devices (things) into the digital fold every day, which will likely make IoT a multi-trillion dollars’ industry in the near future. To understand the scale of interest in the Internet of Things (IoT) just check how many conferences, articles, and studies conducted about IoT recently, this interest has hit fever pitch point last year as many companies see big opportunity and believe that IoT holds the promise to expand and improve businesses processes and accelerate growth.
However, the rapid evolution of the IoT market has caused an explosion in the number and variety of #IoT solutions, which created real challenges as the industry evolves, mainly, the urgent need for a reliable IoT model to perform common tasks such as sensing, processing, storage, and communicating. Developing that model will never be an easy task by any stretch of the imagination; there are many hurdles and challenges facing a real reliable IoT model.
One of the crucial functions of using IoT solutions is to take advantage of IoT analytics to exploit the information collected by "things" in many ways — for example, to understand customer behavior, to deliver services, to improve products, and to identify and intercept business moments. IoT demands new analytic approaches as data volumes increase through 2021 to astronomical levels, the needs of the IoT analytics may diverge further from traditional analytics.
There are many challenges facing IoT Analytics including; Data Structures, Combing Multi Data Formats, The Need to Balance Scale and Speed, Analytics at the Edge, and IoT Analytics and AI.
Most sensors send out data with a time stamp and most of the data is "boring" with nothing happening for much of the time. However once in a while, something serious happens and needs to be attended to. While static alerts based on thresholds are a good starting point for analyzing this data, they cannot help us advance to diagnostic or predictive or prescriptive phases. There may be relationships between data pieces collected at specific intervals of times. In other words, classic time series challenges.
While time series data have established techniques and processes for handling, the insights that would really matter cannot come from sensor data alone. There are usually strong correlations between sensor data and other unstructured data. For example, a series of control unit fault codes may result in a specific service action that is recorded by a mechanic. Similarly, a set of temperature readings may be accompanied by a sudden change in the macroscopic shape of a part that can be captured by an image or change in the audible frequency of a spinning shaft. We would need to develop techniques where structured data must be effectively combined with unstructured data or what we call Dark Data.
Most of the serious analysis for IoT will happen in the cloud, a data center, or more likely a hybrid cloud and server-based environment. That is because, despite the elasticity and scalability of the cloud, it may not be suited for scenarios requiring large amounts of data to be processed in real time.
For example, moving 1 terabyte over a 10Gbps network takes 13 minutes, which is fine for batch processing and management of historical data but is not practical for analyzing real-time event streams, a recent example is data transmitted by autonomous cars especially in critical situations that required a split second decision.
At the same time, because different aspects of IoT analytics may need to scale more than others, the analysis algorithm implemented should support flexibility whether the algorithm is deployed in the edge, data center, or cloud.
