This article presents a systematic methodology for the design and secure implementation of smart homes, emphasizing structured steps from planning and analysis to deployment. The proposed methodology prioritizes cybersecurity, system flexibility, and usability to meet evolving user needs. The study demonstrates how centralized management, process automation, and open-source technologies can enhance user comfort, optimize energy consumption, and reduce operational costs. Special attention is given to the integration of various communication protocols, detailed security measures, and testing processes, ensuring reliable, scalable, and resilient smart home solutions.

The article presents a proposal of an algorithm for predicting the energy potential of a photovoltaic system. After a thorough study of real data on the amount of energy produced for the year 2022 from the photovoltaic system and meteorological data for the monitored period, a methodology for the design of a predictive solution algorithm was proposed, and subsequently the algorithm itself was developed to predict the amount of energy produced depending on meteorological data.

The indoor positioning task is an exciting problem because of the practical applications and the scientific challenges. This work emphasises the impact of signal source selection on the effectiveness of positioning models relying on Wi-Fi received signal strength data by eliminating unstable signal sources such as mobile access points. Two localisation algorithms, based on k-Nearest Neighbour and Random Forest, were applied to data from three academic buildings to test the effectiveness of various access points (APs) selection filters. Additionally, new hierarchical selection models were proposed. The methods divide building into zones to improve the characteristics of the positioning error by a local selection of APs. Hierarchical methods reduced the median number of AP to 67 in comparison to over 100 selected by existing methods, obtaining a statistically similar median error of 6.01 m.

Summary In the recent time, Wi‐Fi–based indoor positioning attracts a high number of researchers worldwide because of its relatively easy implementation on widely available smart phones. Indoor positioning services can potentially open new markets for service providers and, thus, allow new ways to increase their income. A number of Wi‐Fi–enabled devices have grown significantly as well as coverage of buildings with Wi‐Fi signals, which can be considered ubiquitous nowadays. Recently, there has been a vast number of novel algorithms proposed for Wi‐Fi signals; however, they were mostly tested using only one type of device. In this paper, we will investigate the feasibility of Wi‐Fi–based positioning on different devices and highlight some of the problems that researchers have to face during the proposal of novel positioning algorithms. We have tested different devices in both laboratory and real conditions to see how they perform and which parameters can have a negative impact on the performance of the positioning system.

The positioning of users using their smartphones represents interesting service for various areas. Position of users can represent valuable information for various service providers. In industry 4.0 smart devices such as smartphone or tablet can help users to react faster in case of emergency. It can help to reduce reaction times for service workers and improve users’ orientation in the environment, or delegate work to the nearest available employee. In previous work we have described modular positioning system that is able to automatically select optimal positioning module based on available radio signals. In this paper we will focus on estimation of crossing between environments e.g. indoor and outdoor. The proposed algorithm is crucial for implementation of seamless positioning. Since both environments have specific demands not only on positioning solutions but also on maps and navigation algorithms. The proposed algorithm does not require deployment of new infrastructure and can be used on widely available smartphones. The algorithm was tested in real conditions by different users and using various smartphone devices to prove its performance under various conditions.

Summary In the recent time indoor positioning becomes an extremely hot topic between researchers worldwide. This is mainly because of lack of available positioning solutions in the indoor environment and possibility to offer novel services based on location of users. Such services can also open new markets for service providers and thus increase their income. In this paper we will focus on application of optimization algorithms on performance of hybrid indoor positioning system, which utilize radio signals from both Global System for Mobile communication and Wi‐Fi networks simultaneously. The system is based on fingerprinting framework, which is widely used for indoor positioning because of its performance in strong multipath environment. Tests of the optimization algorithms were performed via simulations that were performed using simulation model created in Matlab environment. Copyright © 2016 John Wiley & Sons, Ltd.

Commonly used Global Navigation Satellite Systems (GNSS) are inappropriate as Location Based Services (LBS) in indoor environment. Therefore research teams are developing different systems, which can be used as a suitable alternative. One of options is to use Inertial Navigation System (INS) which consists of inertial sensors and mathematic procedures. This concept has been known for a long time, but with arrival of Microelectro Mechanical System (MEMS) INS found wide use. Smartphones with inertial sensors, such as accelerometers and gyroscopes, allow us to use them as input devices for Pedestrian Dead Reckoning (PDR). In this paper we present PDR by using smartphone sensors. They can be classified as low-cost Inertial Measurement Unit (IMU), and have been compared with more precise and expensive Xsens IMU. Accuracy of inertial sensors has increased in the past few years, but they still cannot alone provide proper accuracy because of many negative effects, such as heading drift due to gyroscope bias. Particle Filter (PF) has been successfully used with map constraints to increase the accuracy of proposed location system. Presented results show that low-cost smartphone IMU combined with PF can be applicable as proper navigation system.

Most of outdoor localization systems is based on range based measurements. Thus, the mobile terminal needs calculate its distance from the reference station. It can be represented by a base station or an access point. Performance of this approach is highly affected by signal fluctuations due to multipath propagation, especially in dense urban areas. In this paper, we propose and evaluate the feasibility of implementation positioning system in outdoor environment. Proposed localization system is based on finger-printing approach and utilizes signals from the IEEE 802.11a network. IEEE 802.11a is not traditional platform for mobile positioning, but it is widely implemented in recent years and would be utilized for this purpose. We analyze the influence of different factors on the positioning accuracy and provide some recommendation to implementation of the system based on the IEEE 802.11a.

This paper deals with optimization of the Rank Based Fingerprinting (RBF) algorithm, which was previously proposed by the author. Results achieved in the real world experiments shown that RBF algorithm can achieve more accurate position estimate compared to some of traditional fingerprinting algorithms. Accuracy of the RBF algorithm seems to be less affected by change of device and small signal fluctuations since algorithm is based on assumption that bias and scale of measured RSS data will not affect rank of the APs. Optimization algorithm that significantly reduces computational complexity of the RBF algorithm will be introduced. Function of the algorithm was investigated in the simulations and real world experiments. Achieved results show that proposed optimization algorithm can significantly decrease computation complexity, especially when radio map database is high enough. Achieved results show that proposed optimization algorithm allows use of the RBF algorithm in applications, where the position estimate must be calculated in a very short time e.g. tracking and navigation applications.

A novel Received Signal Strength (RSS) rank based fingerprinting algorithm for indoor positioning is presented. Because RSS rank is invariant to bias and scaling, the algorithm provides the same accuracy for any receiver device, without the need for RSS calibration. Similarity measures to compare ranked vectors are introduced and their impact on positioning accuracy is investigated in experiments. Experimental results shown that proposed algorithm can achieve better accuracy than commonly used NN and WKNN fingerprinting algorithms.