What are the Tactical Issues in IoT ?

The Internet of Things (IoT) is poised to impact virtually every aspect of society.  The following areas are the major four sectors.

  • Consumer IoT
  • Industrial IoT
  • Enterprise IoT
  • Public Sector IoT

There are many strategic issues to consider with respect to planning and organizing a given industry sector or company for IoT.  These issues include direct financial impact on business as well as indirect areas such as risk management, customer relations, product life-cycle management, and more.

iottacticalplanning

In contrast, some issues are of a more tactical nature, but no less important challenges for managers and engineers to solve.  The following represents a non-exhaustive list of IoT Tactical Considerations:

  • Value of Use Cases in IoT: Many potential future IoT solutions involve assets that are so inexpensive that the cost associated with embedded computing is simply not justified (yet). There needs to be compelling use cases, and associated ROI, as a means of justification.  These are important IoT Business Value issues.
  • Who owns Data in IoT: This is an area that requires resolution in terms of enterprise/company vs. personnel data. Does the individual own data associated with their actions, behaviors, etc.?  Is the data shared or does that company really own it as they would like to claim?  These are all important IoT Data Management issues.
  • Privacy and Security: Privacy as we know it may need to be redefined.  One big privacy issue is “who owns data” (see above).  While closely related, privacy and security are NOT exactly the same thing.  There is a need to consider IoT Identity Management, AAA, and Preference Management.  These are all important IoT Orchestration and Mediation considerations.
  • Data Governance: Related to the above, data governance is an essential component of data collection, use, and distribution that can ensure a balance exists between the needs of the individual and the needs of the business.  These are issues that require decisions impacting individual businesses, industry vertical and cross-industry policy considerations, and effective incorporation of various Data Technologies.
  • Optimal Storage Solutions: Large amounts of storage (in the Cloud) for all the data generated by IoT and Big Data tools and fast analytics processing systems.  These are all important Cloud Computing issues.
  • Sensor Data Management: Sensors are used in ICT for detection of changes in the physical and/or logical relationship of one object to another(s) and/or the environment. Physical changes include temperature, light, moisture, pressure, sound, and motion.  Sensors will gather an enormous amount of IoT data, most of which will be of the unstructured (big) data variety.   To optimize decsions in this area, it is important to consider best practices in the area of Industrial Convergence.
  • Interoperability: Every engineer knows that interoperability is important, especially as it pertains to inter-system communications and/or interfacing different technologies.  For example, different sensors need to be able to talk to each other and/or talk more efficiently.  Many interoperability issues can be solved through use of IoT Application Programming Interfaces (API).
  • Distributed Computing, Storage, Sensing and Control: We are embracing the incidence of ubiquitously connected smart devices (wearable computing, smart metering, smart home, smart city, connected vehicles and large-scale wireless sensor networks), which are currently becoming the main factor of computing. Whereas the evolution of ICT has taken us from mainframes, to PCs, and back to the cloud (e.g. from centralized to distributed to centralized computing), clearly distributed computing will have an increasingly important role in the digital economy.  These issues impact many areas including Distributed Computing.
  • Self-configuring and Adaptive Systems: By its nature, IoT is autonomous, meaning there is little or no human intervention. This can be a blessing and a curse if there are not adaptive systems in place to both configure/reconfigure systems (such as needed in the case of a fault or major change) as well as to allow for preferences (from individuals, companies, and other communities of interest) to supersede previous programmatic logic.  One of the key technologies that must be considered here is Artificial Intelligence (AI).
  • Open Standards: Everyone says that want open standards, but the timing of them is very important. The industry does not want standards to be open too soon, which would stifle innovation from companies seeking differentiation.  However, they cannot come too late either, as production systems will not scale without open interfaces, programming environments, data sharing, etc.  This is an important consideration area as part of IoT Operations planning.
  • Power Issues: This includes battery life, energy harvesting, and related issues such as device-to-device communication with only one device needing power and others using RFID and other methods.
  • Energy Harvesting: Related to power issues, energy harvesting can be a solution to provision of power for small devices associated with the IoT ecosystem. Methods of energy harvesting include: Radio Waves, Thermal Gradients, Mechanical Vibrations, and Specialized Photocells

More Information

For more information about the above, see the Mind Commerce Knowledge Center

About Mind Commerce

Analysis of telecom and ICT infrastructure, technologies, and applications.
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