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The Future of IoT

University of Michigan, Ann Arbor 1003
(University of Michigan, Ann Arbor)



The Internet of Things (IoT) phenomenon - ubiquitous connected things providing key physical data and further processing of that data in the cloud to deliver business insights - presents a huge opportunity for many players in all businesses and industries. Many companies are organizing themselves to focus on IoT and the connectivity of their future products and services.

The following trends explore the impact of many technologies on IoT and predict what is next for IoT.  

  • 5G Networks will continue to fuel IoT growth. With the deployment of wireless 5G that open the door for more devices, and data traffic. You can add to this trend the increasing adoption of edge computing which will make it easier for business to process data faster and close to the points of action. Major wireless carriers will continue to roll out 5G networks over next few years. 5G promises greater speed and the ability connect more smart devices at the same time. Faster networks mean the data accumulated by your smart devices will be gathered, analyzed and managed to a higher degree. That will fuel innovation at companies that make IoT devices and boost consumer demand for new products. 5G’s arrival will also open the door to new privacy and security concerns. In time, more 5G IoT devices will connect directly to the 5G network than via a Wi-Fi router. This trend will make those devices more vulnerable to direct attack. For home users, it will become more difficult to monitor all IoT devices, because they will bypass a central router. On a broader scale, the increased reliance on cloud-based storage will give attackers new targets to attempt to breach.
  • Artificial Intelligence (AI) a big player in IoT. Making the most of data, and even understanding on a basic level how modern infrastructure functions, requires computer assistance through AI. AI will help IoT data analysis in the data areas. Machine learning is a type of AI that helps computers “learn” without someone having to program them. The computers are programmed in a way that focuses on data that they receive. This new data can then help the machine “learn” what your preferences are and adjust itself accordingly. For instance, when a video website suggests a movie you might like, it’s likely learned your preferences based on your past choices. AI is the fundamental ingredient needed to make sense of the vast amount of data collected these days, and increase its value for the business. AI will help IoT data analysis in the following areas: data preparation, data discovery, visualization of streaming data, time series accuracy of data, predictive and advance analytics, and real-time geospatial and location (logistical data). 
  • Digital assistant devices, including HomePod, Alexa, Siri, and Google Assistant, are the future hubs for the next phase of smart devices, and companies are trying to establish “their hubs” with consumers, to make it easier for them to keep adding devices with less struggle and no frustrations.
  • A real expansion of smart IoT. IoT is all about connectivity and processing, nothing will be a better example than smart cities. Smart sensors around the neighborhood will record everything from walking routes, shared car use, building occupancy, sewage flow, and temperature choice 24/7 with the goal of creating a place that’s comfortable, convenient, safe, and clean for those who live there. Once the model is perfected, it could be the model for other smart neighborhoods and eventually smart cities.
  • The rise of industrial IoT and digital twin technology. An amalgamation of technologies is pushing this new techno-industrial revolution, and IoT plays a big part in making manufacturing more efficient, less risky, and more profitable. Industrial IoT brings enhanced efficiency and productivity through data integration and analysis in a way that isn’t possible without an interconnected manufacturing process. Another notion that is gaining popularity is “digital twin” technology. Through its use, organizations can create a clear picture of how their IoT devices are interacting with the manufacturing process. This gives keen businesses insight into how the life cycle of their machines operates and allows them to predict changes that may be needed ahead of time. According to a Gartner survey, 48% of smart manufacturing adopters have made plans to make use of the digital twin concept.
  • IoT focus on security using blockchain. The current centralized architecture of IoT is one of the main reasons for the vulnerability of IoT networks. With billions of devices connected and more to be added, IoT is a big target for cyber-attacks, which makes security extremely important. Blockchain offers new hope for IoT security for several reasons. First, blockchain is public, everyone participating in the network of nodes of the blockchain network can see the blocks and the transactions stored and approves them, although users can still have private keys to control transactions. Second, blockchain is decentralized, so there is no single authority that can approve the transactions eliminating Single Point of Failure (SPOF) weakness. Third and most importantly, it’s secure—the database can only be extended and previous records cannot be changed. In the coming years, manufacturers will recognize the benefits of having blockchain technology embedded in all devices and compete for labels like “Blockchain Certified”.
  • Standardization still a problem. Standardization is one of the biggest challenges facing the growth of IoT. To understand the difficulty of standardization, we need to deal with all three categories in the standardization process: Platform, Connectivity, and Applications. In the case of the platform, we deal with UX/UI and analytic tools, while connectivity deals with customer’s contact points with devices, and last, applications are the home of the applications which control, collect and analyze data. All three categories are inter-related and we need them all, missing one will break that model and stall the standardization process. There is no way to solve the problem of fragmentation without a strong push by organizations like IEEE or government regulations to have common standards for IoT devices.
  • More social, legal, and ethical issues. IoT devices represent a largely unregulated new technology. Security and privacy concerns will drive legislation and regulatory activity. IoT will inevitably find itself facing social and legal questions in the near future. This is particularly relevant for data collected by these devices, which may soon find itself falling under the umbrella of the General Data Protection Regulation (GDPR). This regulation regarding the handling of personal data and privacy in the European Union, the GDPR extends its reach beyond the European region. Any business that wants to successfully operate within the EU will need to comply with the guidelines laid out in its 88-page document. Security issues are essential when it comes to the legal regulation of personal data. Development teams can ensure the required level of security and compliance on various levels, including data encryption, active consent, various means of verification and other mechanisms. Their goal is to collect data legitimately and keep its accessibility, processing, and storage to a minimum that is dictated by the software product.



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