Fay Arjomandi

Siavash Alamouti, Fay Arjomandi, Michel Burger et al.

As we transition from the mobile internet to the 'Cognitive Internet,' a significant shift occurs in how we engage with technology and intelligence. We contend that the Cognitive Internet goes beyond the Cognitive Internet of Things (Cognitive IoT), enabling connected objects to independently acquire knowledge and understanding. Unlike the Mobile Internet and Cognitive IoT, the Cognitive Internet integrates collaborative intelligence throughout the network, blending the cognitive IoT realm with system-wide collaboration and human intelligence. This integrated intelligence facilitates interactions between devices, services, entities, and individuals across diverse domains while preserving decision-making autonomy and accommodating various identities. The paper delves into the foundational elements, distinct characteristics, benefits, and industrial impact of the 'Cognitive Internet' paradigm. It highlights the importance of adaptable AI infrastructures and hybrid edge cloud (HEC) platforms in enabling this shift. This evolution brings forth cognitive services, a Knowledge as a Service (KaaS) economy, enhanced decision-making autonomy, sustainable digital progress, advancements in data management, processing techniques, and a stronger emphasis on privacy. In essence, this paper serves as a crucial resource for understanding and leveraging the transformative potential of HEC for Cognitive Internet. Supported by case studies, forward-looking perspectives, and real-world applications, it provides comprehensive insights into this emerging paradigm.

Siavash M. Alamouti, Fay Arjomandi

Modern multi-agent systems ranging from sensor networks monitoring critical infrastructure to crowdsourcing platforms aggregating human intelligence can suffer significant performance degradation due to systematic biases that vary with environmental conditions. Current approaches either ignore these biases, leading to suboptimal decisions, or require expensive calibration procedures that are often infeasible in practice. This performance gap has real consequences: inaccurate environmental monitoring, unreliable financial predictions, and flawed aggregation of human judgments. This paper addresses the fundamental question: when can we learn and correct for these unknown biases to recover near-optimal performance, and when is such learning futile? We develop a theoretical framework that decomposes biases into learnable systematic components and irreducible stochastic components, introducing the concept of learnability ratio as the fraction of bias variance predictable from observable covariates. This ratio determines whether bias learning is worthwhile for a given system. We prove that the achievable performance improvement is fundamentally bounded by this learnability ratio, providing system designers with quantitative guidance on when to invest in bias learning versus simpler approaches. We present the Adaptive Bias Learning and Optimal Combining (ABLOC) algorithm, which iteratively learns bias-correcting transformations while optimizing combination weights through closedform solutions, guaranteeing convergence to these theoretical bounds. Experimental validation demonstrates that systems with high learnability ratios can recover significant performance (we achieved 40%-70% of theoretical maximum improvement in our examples), while those with low learnability show minimal benefit, validating our diagnostic criteria for practical deployment decisions.