In this work, a regression method is implemented on a low-cost digital temperature sensor to improve the sensor’s accuracy; thus, following the EN12830 European standard. This standard defines that the maximum acceptable error regarding temperature monitoring devices should not exceed 1 °C for the refrigeration and freezer areas. The purpose of the proposed method is to improve the accuracy of a low-cost digital temperature sensor by correcting its nonlinear response using simple linear regression (SLR). In the experimental part of this study, the proposed method’s outcome (in a custom created dataset containing values taken from a refrigerator) is compared against the values taken from a sensor complying with the EN12830 standard. The experimental results confirmed that the proposed method reduced the mean absolute error (MAE) by 82% for the refrigeration area and 69% for the freezer area—resulting in the accuracy improvement of the low-cost digital temperature sensor. Moreover, it managed to achieve a lower generalization error on the test set when compared to three other machine learning algorithms (SVM, B-ELM, and OS-ELM).

Efficient shortest path algorithms are of key importance for routing and navigation systems. However, these applications are designed focusing on the requirements of motor vehicles, and therefore, finding paths in pedestrian sections of urban areas is not sufficiently supported. In addition, finding the shortest path is often not adequate for urban sidewalk routes, as users of these applications may also be interested in alternative routes that, although slightly longer, possess other desirable features and properties. According to the literature, the search for alternative routes is carried out mainly using the k-shortest paths (KSP) algorithm which represents an ordered list of all available alternatives. Even though various KSP algorithms have been proposed, to the best of our knowledge, there is no research addressing all issues inherent in a pedestrian navigation system. The purpose of this paper is to present a heuristic algorithm for graph datasets that implements a penalty-based method which, by increasing certain edge weights, effectively searches for the most accessible alternative paths in multi-route cases. To demonstrate how the algorithm works, we present experimental results on finding the most accessible paths in pedestrian sections of the historical center of Thessaloniki city.

In developed countries, companies active in the food retail sector consume about 20% of the total power required for cooling and are therefore ranked among them with the largest energy footprint after the industries. For this reason, refrigeration equipment manufacturers are increasingly focusing on methods that will reduce the power consumption required for its efficient operation. The solutions that are currently implemented mainly focus on interventions in the development phase of freezer units (better insulation, variable frequency compressors, etc.), which, however, do not adequately address the problem. This paper presents the development of an IoT-based technology device which, using machine learning techniques, aims to reduce the power consumption of refrigeration equipment and consecutively, the energy footprint of food retailers.

It has been shown in the literature that the lifetime of a wireless sensor network is heavily connected to the number of transmissions that network nodes have to undertake. Considering this finding, along with the effects of the energy hole problem where nodes closer to the sink node transmit more than the more distant ones, a node close to the sink node will be the one that transmits the most, while it will also be the node that will deplete its battery first. Taking into consideration that the failure of a single network node to operate, due to its battery being discharged, can lead to a network stopping its operation, the most energy-consuming node in the network will also be the one that will be responsible for the network’s termination. In this sense, the most energy-consuming node’s energy consumption optimization is the main case in this paper. More specifically, in this work, it is firstly shown that the energy consumption of a wireless sensor network is closely related to each network node’s traffic load, that is the transmissions of the packets that are created or forwarded by a node. The minimization of the most energy-consuming node’s energy consumption was studied here, while the implementation of a traffic-load-based algorithm is also proposed. Under the proposed algorithm, given a simple shortest path approach that assigns a parent (i.e., the next hop towards the sink node) in each network node and the knowledge it provides regarding the distance (in hops in this paper’s case) of network nodes from the sink node, the proposed algorithm exploits the shortest path’s results in order to discover, for all network nodes, neighbors that are of the same distance (from the sink node) with the initially assigned parent. Then, if such neighbors exist, all these neighbors are equally burdened with the parenting role. As a result, the traffic load is shared by all of them. To evaluate the proposed algorithm, simulation results are provided, showing that the goals set were achieved; thus, the network lifetime was prolonged. In addition, it is shown that under the algorithm, a fairer distribution of the traffic load takes place.

In recent years, accessibility has become a topic of great interest on a global scale across the scientific, business, and policy sectors. There are two primary reasons for this growing trend. Firstly, accessibility serves as a vital indicator reflecting the social performance of communities, and the public is increasingly aware of critical social issues such as accessibility. Secondly, accessibility is essential for the sustainable development of regions and civil settings, facilitating inclusion and business growth. In this regard, information and communications technologies can play a crucial role in facilitating the accessibility of spaces by disabled people. Numerous digital tools and smart applications are already available to serve this purpose. This study presents a novel digital tool called ‘Seek & Go’, a comprehensive aid application designed specifically for disabled individuals. The app features a GPS navigation system that caters to pedestrians with disabilities and unique accessibility requirements. The present study documents the models underlying the development of ‘Seek & Go’, discusses technical aspects of the application, and presents user experience insights.