Multidisciplinary and Historical Perspectives for Developing Intelligent and Resource-Efficient Systems

• Main Authors: Aarne Mammela, Jukka Riekki, Adrian Kotelba, Antti Anttonen
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Autonomous systems; fundamental limits; multidisciplinary view; resource efficiency
• Online Access: https://ieeexplore.ieee.org/document/8318568/

As communication and computation systems become more complex and target at higher performance, the fundamental limits of nature can be expected to constrain their development and optimization. This calls for intelligent use of basic resources, that is, materials, energy, information, time, frequency, and space. We present a multidisciplinary and historical review on the body of knowledge that can be applied in researching, such as intelligent and resource-efficient systems. We review general system theory, decision theory, control theory, computer science, and communication theory. While multidisciplinarity has been recognized as important, there are no earlier reviews covering all these five disciplines. Based on the review, we build a chronology of intelligent systems and identify connections between the disciplines. Optimization, decision-making, open- and closed-loop control, hierarchy, and degree of centralization turn out to be recurring themes in these disciplines, which have converged to similar solutions that are based on remote control, automation, autonomy, and self-organization. We use future wireless networks as an example to illustrate the open questions and how they can be addressed by applying multidisciplinary knowledge. This paper can help researchers to use knowledge outside their own field and avoid repeating the work done already. The resulting consolidated view can speed up research and is especially important when the fundamental limits of nature are approached and new insights are required to overcome the challenges. The general, long-standing problem to be tackled is multiobjective optimization with autonomous and distributed decision-making in an uncertain, dynamic, and nonlinear environment where the objectives are mutually conflicting.