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The growing popularity of inexpensive IoT (Internet of Things) sensor networks makes their uncertainty an important aspect of their adoption. The uncertainty determines their fitness for purpose, their perceived quality and the usefulness of information they provide. Nevertheless, neither the theory nor the industrial practice of uncertainty offer a coherent answer on how to address uncertainty of networks of this type and their components. The primary objective of this paper is to facilitate the discussion of what progress should be made regarding the theory and the practice of uncertainty of IoT sensor networks to satisfy current needs. This paper provides a structured overview of uncertainty, specifically focusing on IoT sensor networks. It positions IoT sensor networks as contrasted with professional measurement and control networks and presents their conceptual sociotechnical reference model. The reference model advises on the taxonomy of uncertainty proposed in this paper that demonstrates semantic differences between various views on uncertainty. This model also allows for identifying key challenges that should be addressed to improve the theory and practice of uncertainty in IoT sensor networks.

Trust has been attracting a significant attention, both in its sociological and technical aspect. Distrust, the necessary counter-partner of trust, has not been a primary research target. This situation has led to significant shortage of research discussion about distrust. The paper discusses distrust from the perspective of the complexity-based model of confidence to demonstrate how trust and distrust can be interpreted and how they can coexist within the model.

Due to their growing number and increasing autonomy, drones and drone swarms are equipped with sophisticated algorithms that help them achieve mission objectives. Such algorithms vary in their quality such that their comparison requires a metric that would allow for their correct assessment. The novelty of this paper lies in analysing, defining and applying the construct of cross-entropy, known from thermodynamics and information theory, to swarms. It can be used as a synthetic measure of the robustness of algorithms that can control swarms in the case of obstacles and unforeseen problems. Based on this, robustness may be an important aspect of the overall quality. This paper presents the necessary formalisation and applies it to a few examples, based on generalised unexpected behaviour and the results of collision avoidance algorithms used to react to obstacles.

The aim of the article is to build a rule-based model (RMFDN) for increasing the energy efficiency of Smart Cities’ digital transformation processes. The problem that arises during the implementation of digital transformation processes concerns the measures that should be assigned to estimate the duration of the digital transformation. Previous studies of digital transformation have been based on the analysis of design processes based on key performance indicators (KPIs), their place and role in the digital transformation processes, and their monitoring with the use of information architecture. The analysis of the digital transformation processes of cities into Smart Cities shows that they seem inappropriate to the complexity and uncertainty of the digital transformation carried out. The new approach presented in the article is based on three key aspects: rule-based description of the state of digital transformation processes enabling their energy assessment, introducing energy maturity capsules to describe the state of these processes and application of measures based on project negentropy increments for maturity capsules.