## 1 Introduction  
Currently, the importance of the complex systems paradigm is a growing phenomenon. In the traditional view, a complex system is a system with multiple parts interacting, usually on a feedback basis, and affecting the subsequent new properties and behaviors of that system. Such a system has an important component in the form of unavoidable uncertainty conditioned by the following reasons: (1) the inability to measure the initial conditions of the system with infinite precision [1], (2) the difficulty of determining the ideal/expected value of such a system, and (3) the difficulty of accurately determining the future state of the system. The main research object in this paper is the systemic uncertainty of the complex Internet of Things (IoT) sensor network as a subsystem of Industry 4.0 [2]. The high level of developmental dynamism of the IoT network determines the study of this area mainly in the future context--on the principle of feedforward correction capabilities. The unknown nature of the future introduces the study of uncertainty as a possible research topic for different technologies (in addition to ambiguity and ignorance) in the areas of foresight and anticipation [3]. According to [4], uncertainty of IoT sensor networks becomes an important aspect of their adoption, determining their: (1) suitability for the purpose of reproducing certain physical phenomena, (2) perceived quality, and (3) usefulness of the information they provide. According to [PERSON], the state of uncertainty of the future forces the use of anticipatory actions based on analyses of alternative options, also based on ignorance. Creating the future only on the basis of methods using past data is highly dangerous. It includes important but secondary aspects of the future [5]. The study mainly focuses on the area of systemic uncertainty, and not environmental uncertainty, because that systemic uncertainty is a controllable uncertainty from a social point of view. The overarching goal of this paper is to establish the desired state of anticipatory systemic uncertainty of IoT, the existence of which is determined by [4]: (1) epistemic uncertainty, (2) ethical uncertainty, and (3) utilitarian uncertainty. Epistemic uncertainty (also called endogenous uncertainty) is based on the internal knowledge (or ignorance, depending on the research perspective) of the system caused, for example, by limitations of the measurement equipment and in the social consensus--dependent on the observer/stakeholder of such a system. This uncertainty may be related to the knowledge of the state not only in the future, but also in the past and present. This uncertainty is susceptible to discovery and expansion of knowledge, and can be directly influenced by an individual [3; 4; 6; 7]. Ethical uncertainty arises from the observer's own attitude/trust toward other entities, treated as systems, people, or technical devices. Utilitarian uncertainty refers to a forward-looking perspective. It is based on the belief that there is knowledge that either does not exist or whose existence is not even anticipated [4]. According to [4], the most significant challenge, when managing IoT networks--in which uncertainty is an integral component--is to change uncertainty in accordance with the changes in social needs on which the functioning of IoT networks is based. A key challenge in this area is therefore the organization of the process of such changes. According to the author of this publication, the organization of the process of changes in social needs can take place through anticipatory foresight systems, based on selected research methods. Anticipatory foresight systems should be treated as the main representatives of future research based on social construct taking into account: (1) the aspect of uncertainty with the possibility of its modification, (2) the available, unavailable, and unobtainable knowledge, as well as the attitude to it, and (3) the futures in their different types.