Over the last decade, the Internet of Things (IoT) has caused widespread disruption in every sphere of our life. The evolution of IoT is not influenced by advancement in the unique technology segment; Instead, a series of emerging technologies and innovation trends have converged together to create an unified experience of the ubiquitous world. The emergence of Edge computing, the 5G/ 6G revolution, and cloud computing have introduced a set of architectural patterns to minimize latency, network bandwidth requirements and allow systems to scale beyond the limit. In the world of ‘new normal, the endless opportunities with both business and social transformations will weave IoT applications into our everyday life with billions of sensors seamlessly interacting with each other. Big Data and advanced analytics have transformed the massive volume of sensory signals and multimedia feeds into actionable insights and new revenue streams across the digital value chains.
A rapid expansion in exposing pervasive channels and deploying intelligent automation have brought critical challenges towards the future of digital transformation in the 21st century. The promising applications of AI and ML are mostly executed within the centralized cloud ecosystem, far from the point of action. Such intelligence is not designed to gain situational awareness from within the operating landscape. Harnessing the benefit of capturing and analyzing temporal data and timely interpretation of sensory events within the active window of the operational cycles are emerging as the key imperatives to gain strategic advantage and address cybersecurity concerns. As the diversity of sensors and applications grow exponentially, structured intelligence or pre-built rule-based automation in the edge runtime will not be efficient and extensible to elevate process automation and autonomic functions.
The application of edge-centric design is not quite new in the context of industrial automation. Purpose-based control systems are designed to log exceptions and logic-driven switching functions to manage the enterprise assets. The contemporary design of edge architecture demands autonomous operations and supports intuitive workflow with limited or no human interventions. The hypes around the self-driving car, robotic surgery, remote patient monitoring, intelligent home energy automation, intelligent grid surveillance, smart city infrastructure, etc., are relying on edge nodes to mimic expert thinking and conscious acts like a human. The usual AI/ ML models are augmenting actionable insights with large-scale computing processing a significant volume of historical data.
The compute-intensive iterative training enhances the model performance over time. Such capabilities are usually as an offline process to perform deeper analysis and extract predictive/ prescriptive trends. Within the current and future implementation scope of edge intelligence, we witness three critical issues or changes that needed to be addressed:
- Limitations of predefined rules/ bespoke validations can be addressed through extensible and intuitive AI/ ML models capable of extracting actionable insights from discrete sensory feeds/ events. However, such capabilities establish strong coupling with distant cloud infrastructure.
- Inability to develop situational awareness that would require an adaptive knowledge enrichment capability by aggregating inbound data and events in real-time. Such context awareness is extremely critical to scale autonomous operations, inhibit catastrophic failures and develop edge-to-edge collaboration with minimum dependency on the cloud.
- By extrapolating point 2, the edge runtime system should minimize the exposure of the distributed IoT assets to cybersecurity threats by shortening the response time or take preventive action. In addition, the pattern of intrusive request/ command and ransomware attacks should be recognized at the point of action.
Thus, the emerging IoT systems intend to inherit a new breed of intelligence that can sense, decide, and act on physical events within the shortest possible window of the event lifecycle. And the convergence of Cognitive AI with distributed IoT systems promises a one-stop solution to address the strategic imperatives of the intelligent edge-native platforms and achieve the Industry 4.0 benchmark of next-generation IoT systems.
The cognitive models are designed to replicate human behavior and reasoning patterns to comprehend predictive and prescriptive trends. The semantic-based learning of the cognitive AI models has a minimum dependency on the historical data and, intracule incremental knowledge development capabilities over time. Such runtime is a perfect fit for any intuitive operation at the edge node constrained with limited processing and persistence capabilities. The systematic process of gaining operational maturity and responsive automation can’t be achieved through re-training the models over time. Enhancement of cognitive maturity should be achieved through gradual intake of information and performing point-in-time analysis.
Moreover, the process of developing cognitive intelligence, perceptions, and neuromotor capabilities is primarily accomplished through an adaptive process of unsupervised learning. The capabilities around self-learning, weighing context and analyzing conflicting evidence through Cognitive AI boost operational intelligence, scalability, and extensibility of an intelligent edge runtime for IoT systems.
The progressive trends of Mobile edge computing and Cloudlets are diffusing edge-based intelligence in connected and more controlled enterprise systems. However, within the diversity of pervasive cyber-physical ecosystems, the autonomy of the discrete edge nodes would require gain in operational intelligence with minimum supervision.
The emerging innovation in cognitive computational intelligence is revealing a great potential to introduce a contemporary soft computing-based algorithm, architectural rethinking, and progressive system design of the next generation of IoT systems. The cognitive IoT Systems crush the strong partition between the silos and interdependencies of software and hardware subsystems. The flexibility of the edge-native AI component is flexible enough to recognize the changes in the physical environment and dynamically adjust the analytical outcomes in real-time. As a result, the interaction between human-machine or machine to machine becomes more dynamic, interoperable, and contextual to the time and scope of any operation.
As we shift our focus to the new paradigm, the real-time pattern recognition and anomaly detection capabilities of cognitive edge runtime lowers the vulnerability of distributed IoT applications and networks significantly. Such protective intelligence can complement the less secure existing automation and also, be part of future implementations. Thus, the convergence of Cognitive AI with edge computing will continue to disrupt the innovation of IoT systems by overcoming the inherent limitations of emerging edge-oriented design patterns. The bond between two emerging technology trends will continue to get stronger due to the rapid evolution of increasing computing and storage power on embedded devices.