Integrating renewables into the grid introduces price fluctuations that stem from their inherent volatility. To mitigate this, adopting an energy-aware production planning strategy benefits factories and the grid. By aligning production processes with periods of abundant renewable energy and lower costs, factories can actively reduce their energy expenditures while simultaneously smoothing grid strain. However, such a transition requires expensive retrofitting of production planning systems. This paper proposes an approach to transparently integrating energy awareness into an existing Manufacturing Execution System (MES), resulting in a cost reduction without the need to retrofit the MES.

The growing emphasis on environmental sustainability, driven by regulations such as Europe's Carbon Border Adjustment Mechanism (CBAM) and increased consumer awareness, is a notable trend. It is estimated that manufacturing energy consumption accounts for 24 % of global greenhouse gas emissions. As demand for renewable energy rises, current market mechanisms are unable to guarantee a carbon-free energy supply. This paper presents an approach that integrates energy availability and product carbon footprint (PCF) into production planning. The necessity for factories and energy suppliers to adapt their operations is highlighted. To ensure the optimal utilization of available energy resources, it is essential that factories implement production schedules that are responsive to fluctuations in supply. Energy suppliers must provide accurate long-term production profiles and employ anomaly detection for short-term adjustments. The proposed solution is designed to align energy consumption with renewable energy availability and PCF.

With technological revolutions like the Internet of Things and Industry 4.0 on the doorstep, localization of network nodes is more important than ever. Indoor localization systems mostly work with omnidirectional antennas for simplicity reasons. Using directional antennas has advantages, though, and devices with such antennas will be included in IoT systems someday. In this paper, the focus is on dynamic changes in the configuration of localization systems. Adding an antenna must be handled fast and efficiently. Frequent recalculations of the configuration could be necessary, and limited resources make a reduction of the computational effort desirable. In this work we look at possible methods to cut the computational effort while minimizing the penalty on accuracy. Since accuracy is of utmost importance for indoor localization, there is a fine line which is important to find.

With increasing deployment of wireless devices also localization gets a consequential boost in industrial and other IoT environments. Localization so far in most cases assumes omnidirectional antennas. This paper investigates a generalized Received Signal Strength (RSS) fingerprinting localization algorithm for antennas with arbitrary radiation patterns in order to include IoT devices with directional antennas in localization.Focus is set on the recursive algorithm which improves performance of the traditional fingerprinting methods. First a machine-learning aided analysis of the overlapping reception areas of the antennas is conducted for quickly determining possible initial positions of the nodes to be localized. Then, for reaching higher resolution, different up-scaling techniques and gradient search is applied. In particular, the localization accuracy vs. computational efficiency is investigated which is an important trade-off for many applications.

Common localization approaches are usually based on omni-directional antennas. There are various algorithmic methods, all of which assume strictly uniform radiation in all directions. However, in general, the output characteristics of these (nominally omnidirectional) antennas are not completely omnidirectional or even highly directional depending on the specific design. This article investigates the use of clustering algorithms to identify different areas of joint signal parameters that can be used for an RSS-based localization method. A set of clustering algorithms is investigated, and their properties to cluster RSS maps for localization applications are compared. In particular, their score for cluster quality and their execution speed relevant for deployment in embedded systems has been evaluated.