With the development of technologies and the growing need for accurate positioning inside buildings, the localization method based on Received Signal Strength Indicator (RSSI) fingerprinting is becoming increasingly popular. Its popularity is explained by the relative simplicity of implementation, low cost and the ability to use existing wireless infrastructure. This review article covers all the key aspects of building such systems: from the wireless communication technology and the creation of a radiomap to data preprocessing methods and model training using machine learning (ML) and deep learning (DL) algorithms. Specific recommendations are provided for each stage that can be useful for both researchers and practicing engineers. Particular attention is paid to such important issues as RSSI signal instability, the impact of multipath propagation, differences between devices and system scalability issues. In conclusion, the review highlights the most promising areas for further research. For smart cities, the approaches and recommendations presented in the review contribute to the development of urban services by combining indoor positioning systems with IoT platforms for automation, transport and energy management.

Designing an efficient wireless sensor network (WSN) system is considered a challenging problem due to the limited energy supply per sensor node. In this paper, the performance of several bi-objective optimization algorithms in providing energy-efficient clustering solutions that can extend the lifetime of sensor nodes were investigated. Specifically, we considered the use of the Moth–Flame Optimization (MFO) algorithm and the Salp Swarm Algorithm (SSA), as well as the Whale Optimization Algorithm (WOA), in providing efficient cluster-head selection decisions. Compared to a reference scheme using the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, the simulation results showed that integrating the MFO, SSA or WOA algorithms into WSN clustering protocols could significantly extend the WSN lifetime, which improved the nodes’ residual energy, the number of alive nodes, the fitness function and the network throughput. The results also revealed that the MFO algorithm outperformed the other algorithms in terms of energy efficiency.

Accurate demand forecasting is crucially important to reduce inventory and backlogging cost. In this study, we analyze howpromos, holiday statements, price changes, stock availability and date-time features (weekdays, months etc.) affect thedemand by using several forecasting methods. Data sets were collected for the products of the global pharmaceuticalcompany providing services in Turkey. Actual daily sales data for 2016, 2017 and 2018 were used in the construction of thisdata set. In order to predict the next periods demand, we used four different models which are Holt Winters, RidgeRegression, Random Forest and Xgboost. We also ensemble those models to improve forecasting accuracy. Next, byweighting inversely proportional to the error rates of the models, binary, triple and quadruple combinations of the singlemodels were compared with themselves and the single models. Our numerical results show that the lowest forecasting errorrate was obtained in ensemble models. Particularly, the lowest error rate in individual models was obtained in Random Forestwith 15.7% RMSPE (Root Mean Square Percentage Error) value, and the lowest error rate was obtained with 10.7% RMSPEvalue in Holt Winters & Xgboost models combination. Results show that ensemble of several models can increase theforecasting accuracy.

Technological progress and digitalization have ushered in significant transformations in business strategies. At present, research is scarcely focused on the influence of the adoption of digital technologies (DTs) on establishing comprehensive relationships within the context of a circular economy (CE), and the supply chain (SC) framework to contribute to the Resource-Based View (RBV) theory. This study utilizes survey data collected from 235 manufacturing practitioners employed by Turkish manufacturing enterprises to explore a model elucidating the relationship between DTs adoption and sustainability performance (SP) through supply chain traceability (SCT), supply chain resilience (SCR), and circular economy practices (CEPs), based on 10R strategies. Through this linkage, this research accentuates that the exclusive integration of CEPs with digital technology solutions is insufficient for industrial enterprises to attain their long-term sustainability goals. It underscores the necessity of ensuring SCT and/or SCR in this context.