Search Results (53)

We present a low-cost, fully reproducible software and hardware protocol for smartphone sensor battery cost tests. Our pipeline combines a rigorous hardware checklist and light-sealed enclosure, a software checklist for iOS devices, and a BatteryTest app to control sensor configurations and log battery state during tests. Methodologically, we applied this standardized protocol in 30 independent analyzed test runs using six iPhone 14 Pro and three iPhone 13 Pro devices, and compared battery-life outcomes across predefined sensor conditions (idle, TrueDepth, GPS, accelerometer, pedometer, gyroscope, and rear camera), sampling rates, and sensor-specific settings. Key findings include: (i) Baseline battery life was approximately 10% higher on the 14 Pro versus the 13 Pro models under idle conditions. Sensor activation substantially reduced battery life, with GPS and camera usage exhibiting the strongest impact. (ii) Software parameters matter: the sampling rate change from 27 s to 3 s led to significantly decreased battery life in several scenarios, while reducing the location accuracy in GPS tests increased battery life by up to 20 h on the 13 Pro devices. (iii) Cross-device-generation consistency is heterogeneous. The iPhone 14 Pro lasts up to 50% longer on GPS tests, yet drains about an hour faster than the 13 Pro in camera tests. This work introduces the first standardized, and fully reproducible protocol for quantifying sensor-specific battery consumption on iPhones, enabling consistent, comparable, and low-cost energy benchmarking across device generations. Full article

This paper presents the design and implementation of a low-profile remote monitoring and control system for water level management in storage tanks located in rural communities. The system was developed to ensure a reliable water supply, prevent spills, reduce electrical energy consumption, and mitigate theft and vandalism risks posed by a previously installed, highly exposed commercial system. The proposed system employs LoRa technology to transmit water level data from the storage tank to a receiver located 6 km from the water well. When the water level drops below a predefined threshold, the system transmits an activation signal through the LoRa network to start the well pump and trigger tank refilling. In addition, an SMS monitoring module enables users to remotely verify water level and pump operational status at any time. System notifications and operational data are automatically delivered via SMS to predefined phone numbers, enabling continuous supervision without requiring internet connectivity. The implementation of the proposed system thus provides an efficient and reliable solution for water resource management in rural environments, ensuring continuous water availability and preventing supply shortages. LoRa communication enables robust long-range data transmission, while SMS-based monitoring offers real-time operational awareness for end users. The system was validated through field testing in a pilot rural community, demonstrating operational robustness, improved water management efficiency, and measurable positive impacts on residents’ water service continuity. The low-profile physical design significantly reduced theft and vandalism incidents reported by the local water authority. Experimental results showed an average monthly reduction of 41.2% in electrical energy consumption while maintaining high system reliability, physical security, and real-time monitoring capability. Full article

Driver distraction, particularly mobile phone use while driving, remains one of the leading causes of road traffic incidents worldwide. In response to this issue and leveraging recent technological advances and increased access to intelligent systems, this research presents the development of an application running on an intelligent embedded architecture for the automatic detection of mobile phone use by drivers, integrating computer vision, inertial sensing, and edge computing. The system, based on the YOLOv8n model deployed on a Jetson Xavier NX 16Gb—Nvidia, combines real-time inference with an inertial activation mechanism and cloud storage via Firebase Firestore, enabling event capture and traceability through a lightweight web-based HMI interface. The proposed solution achieved an overall accuracy of 81%, an inference rate of 12.8 FPS, and an average power consumption of 8.4 W, demonstrating a balanced trade-off between performance, energy efficiency, and thermal stability. Experimental tests under diverse driving scenarios validated the effectiveness of the system, with its best performance recorded during daytime driving—83.3% correct detections—attributed to stable illumination and enhanced edge discriminability. These results confirm the feasibility of embedded artificial intelligence systems as effective tools for preventing driver distraction and advancing intelligent road safety. Full article

Continuous authentication allows devices to keep checking that the active user is still the rightful owner instead of relying on a single login. However, current methods can be tricked by forging faces, revealing personal data, or draining the battery. Additionally, the environment where the user plays a vital role in determining the user’s online security. Thanks to several security attacks, such as impersonation and replay, the user or the device can easily be compromised. We present a lightweight system that pairs face recognition with complex environmental sensing, i.e., the phone validates the user when the surrounding light or noise changes. A convolutional network turns each captured face into a 128-bit code, which is combined with a random “nonce” and protected by hashing. A camera–microphone module monitors light and sound to decide when to sample again, reducing unnecessary checks. We verified the protocol with formal security tools (Scyther v1.1.3.) and confirmed resistance to replay, interception, deepfake, and impersonation attacks. Across 2700 authentication cycles on a Snapdragon 778G testbed, the median decision time decreased from 61.2 ± 3.4 ms to 42.3 ± 2.1 ms (p < 0.01, paired t-test). Data usage per authentication cycle fell by an average of 24.7% ± 1.8%, and mean energy consumption per cycle decreased from 21.3 mJ to 19.8 mJ (∆ = 6.6 mJ, 95% CI: 5.9–7.2). These differences were consistent across varying lighting (≤50, 50–300, >300 lux) and noise conditions (30–55 dB SPL). These results show that smart-sensor-triggered face recognition can offer secure and energy-efficient continuous verification, supporting smart imaging and deep-learning-based face recognition. Full article

This study develops a multidimensional classification of Latin American and Caribbean countries based on a multidimensional set of economic, social, technological, and environmental indicators. This study develops a multidimensional assessment of the performance of Latin American and Caribbean countries, taking into account the following indicators for the period 2017–2022: education expenditure (% of GDP), health expenditure (% of GDP), GDP per capita (constant USD), CO₂ emissions per capita (metric tons), energy consumption per capita (kWh), internet users (% of population), mobile phone subscriptions (per 100 inhabitants), and the Global Innovation Index (GII). Initially, through the application of principal component analysis (PCA), the objective was to reduce the complexity of the data set and reveal the main structural dimensions. Subsequently, cluster analysis was used to classify countries according to shared development patterns. To achieve this, the average of the indicators for the 2017–2022 period was used as a basis, which enabled the reduction in short-term distortions and the capture of structural trends. The results reveal the existence of distinct groups, with countries with higher levels of digital connectivity, investment in human capital, and economic dynamism experiencing more favorable development conditions. Full article

Efficient vehicle driving generally intends to reduce fuel consumption, emissions of harmful substances, and accident rates based on energy-efficient driving patterns as a set of parameters defining optimal vehicle and route characteristics, together with specific ways of driving a vehicle that the particular driver applies. To gain environmental friendliness in driving, two main approaches can be outlined: optimal route planning and driver training based on the principles of ecological driving. The latter can be supported by using software for real-time, efficient vehicle driving recommendations. In order to develop the principles of ecological driving as well as generate relevant real-time recommendations, it is necessary to identify the specific parameters required to analyze driver behavior and vehicle performance, determine the corresponding energy consumption, and understand the influence of route and environmental conditions on overall efficient vehicle driving. These tasks require a large amount of data, often obtained from heterogeneous sources, which, when publicly available, are complex for consolidation, transmission, and processing, not to mention the complexity of the data model itself. This study provides a thorough review of the current data sources and techniques for efficient vehicle driving analysis, focusing on the availability and relevance of dataset sources and repositories. The categorization of parameters and data processing techniques enabling efficient vehicle driving analysis is carried out according to efficiency types such as driver’s efficiency, resource consumption efficiency, and route planning efficiency. For each type of efficiency, we provide a list of contextual groups and features, identifying the dataset containing the necessary feature, making it possible not only to determine the parameters defining, for example, driver efficiency, but also locate the corresponding dataset serving as a stepping stone for researchers and practitioners to join the community investigating efficient vehicle driving analysis. We also discuss future trends and perspectives, identifying alternative data sources for efficient vehicle driving analysis, and focus on data collection issues revealed by the practical use case of collecting data from mobile phone sensors. Full article

End-of-life (EoL) mobile phones represent a valuable reservoir of critical raw materials at higher concentrations compared to primary ores. This review emphasizes the critical need to transition from single-material recovery approaches to comprehensive, holistic strategies for recycling EoL mobile phones. In response to the call for sustainable techniques with reduced energy consumption and pollutant emissions, biohydrometallurgy emerges as a promising solution. The present work intends to review the most relevant studies focusing on the exploitation of microbial consortia in bioleaching and biorecovery processes. All living organisms need macro- and micronutrients for their metabolic functionalities, including some of the elements contained in mobile phones. By exploring the interactions between microbial communities and the diverse elements found in mobile phones, this paper establishes a microbial-centric perspective by connecting each element of each layer to their role in the microbial cell system. A special focus is dedicated to the concepts of ecodesign and modularity as key requirements in electronics to potentially increase selectivity of microbial consortia in the bioleaching process. By bridging microbial science with sustainable design, this review proposes an innovative roadmap to optimize metal recovery, aligning with the principles of the circular economy and advancing scalable biotechnological solutions for electronic waste management. Full article

The pervasive use of screens, averaging nearly 7 h per day globally between mobile phones, computers, notebooks and TVs, has sparked a growing desire to minimize reflections from ambient lighting and enhance readability in harsh lighting conditions, without the need to increase screen brightness. This demand highlights a significant need for advanced anti-glare (AG) technologies, to increase comfort and eventually reduce energy consumption of the devices. Currently used production technologies are limited in their texture designs, which can lead to suboptimal performance of the anti-glare texture. To overcome this design limitation and improve the performance of the anti-glare feature, this work reports a new, cost-effective, high-volume production method that enables much needed design freedom over a large area. This is achieved by combining mastering via large-area Laser Beam Lithography (LBL) and replication by Nanoimprint Lithography (NIL) processes. The environmental impact of the production method, such as regards material consumption, are considered, and the full cycle from design to final imprint is discussed. Full article

This study presents a novel low-cost and low-power prepaid water meter system that combines tokenization and LC sensors to monitor water consumption accurately with mobile application via Bluetooth Low Energy (BLE) connectivity compared to conventional meters. Water meters play a vital role in monitoring water usage in Indonesia. Postpaid billing methods that rely on manual data recording are a source of concern due to potential inaccuracies caused by human error. This study presents the development of a prepaid water meter system that integrates LC sensors, BLE connectivity, a tokenization mechanism, and a mobile application to address this issue. The system offers a cost-effective solution by utilizing BLE + Global System for Mobile (GSM) from the user’s mobile phone. Using the design thinking methodology, the mobile application for the prepaid water meter achieved a usability testing score of 80. The load testing results for the back-end server, conducted with a sample size of 515 users, revealed a back-end latency of 1.973 milliseconds and an error rate of 8.74%. Furthermore, the LC sensors integrated into the PWM device showed an average error rate of 1.33%. The power consumption during each work cycle was measured at 129 mA and each battery is expected to last six years. Overall, with simple LC sensors, this system can precisely measure water usage. Full article

Discrepancies between laboratory vehicle performance and real-world traffic conditions have been reported in numerous studies. In response, emission and fuel regulatory frameworks started incorporating real-world traffic evaluations and vehicle monitoring using portable emissions measurement systems (PEMS) and on-board diagnostic (OBD) data. However, in regions with technical and economic constraints, such as Latin America, the use of PEMS is often limited, highlighting the need for low-cost methodologies to assess vehicle performance. OBD interfaces provide extensive vehicle and engine operational data in this context, offering a valuable alternative for analyzing vehicle performance in real-world conditions. This study proposes a straightforward methodology for assessing vehicle fuel efficiency and carbon dioxide (CO₂) emissions under real-world traffic conditions using OBD data. An experimental campaign was conducted with three gasoline-powered passenger vehicles representative of the Ecuadorian fleet, operating as urban taxis in Ibarra, Ecuador. This methodology employs an OBD interface paired with a mobile phone data logging application to capture vehicle kinematics, engine parameters, and fuel consumption. These data were used to develop engine maps and assess vehicle performance using the vehicle-specific power (VSP) approach based on the energy required for vehicle propulsion. Additionally, VSP analysis combined with OBD data facilitated the development of an energy-emission model to characterize fuel consumption and CO₂ emissions for the tested vehicles. The results demonstrate that OBD systems effectively monitor vehicle performance in real-world conditions, offering crucial insights for improving urban transportation sustainability. Consequently, OBD data serve as a critical resource for research supporting decarbonization efforts in Latin America. Full article

Indoor location services often use Bluetooth low energy (BLE) devices for their low energy consumption and easy implementation. Applications like device monitoring, ranging, and asset tracking utilize the received signal strength (RSS) of the BLE signal to estimate the proximity of a device from the receiver. However, in multipath environments, RSS-based solutions may not provide an accurate estimation. In such environments, receivers with antenna arrays are used to calculate the difference in time of flight (ToF) and therefore calculate the direction of arrival (DoA) of the Bluetooth signal. Other techniques like triangulation have also been used, such as having multiple transmitters or receivers as a network of sensors. To find a lost item, devices like Tile^© use an onboard beeper to notify users of their presence. In this paper, we present a system that uses a single-measurement device and describe the method of measurement to estimate the location of a BLE device using RSS. A BLE device is configured as an Eddystone beacon for periodic transmission of advertising packets with RSS information. We developed a smartphone application to read RSS information from the beacon, designed an algorithm to estimate the DoA, and used the phone’s internal sensors to evaluate the DoA with respect to true north. The proposed measurement method allows for asset tracking by iterative measurements that provide the direction of the beacon and take the user closer at every step. The receiver application is easily deployable on a smartphone, and the algorithm provides direction of the beacon within a 30° range, as suggested by the provided results. Full article

As a result of ever-growing energy demands, motor vehicles are among the largest contributors to overall energy consumption. This has led researchers to focus on fuel consumption, which has important implications for the environment, the economy, and geopolitical stability. This article presents a comprehensive analysis of various fuel consumption modeling methods, with the aim of identifying parameters that significantly influence fuel consumption. The scientific novelty of this article lies in its use of low-cost technology, i.e., an OBD-II interface paired with a mobile phone, combined with modern mathematical modeling methods to create an accurate model of the fuel consumption of a vehicle. A vehicle test drive was performed, during which variations in selected parameters were recorded. Based on the obtained data, a model of the vehicle’s fuel consumption was built using three forecasting methods: a multivariate regression model, decision trees, and neural networks. The results show that the multivariate regression model obtained the lowest MSE, MAR, and MRSE coefficients, indicating that this was the best forecasting method among those tested. Sufficient forecast error results were obtained using neural networks, with increases of approximately 73%, 10%, and 131% in MSE, MAE, and MRAE, respectively, compared to regression results. The worst results were obtained with the decision tree model, with increases of approximately 163%, 21%, and 92% in MSE, MAE, and MRAE compared to the regression results. Full article

The transportation demands of people are increasing day by day depending on the population, and the number of vehicles in traffic is causing various problems. To meet the energy needs of vehicles, there is a huge burden on countries in terms of fossil fuels. In addition, the use of fossil fuels in vehicles has a serious impact on environmental pollution. Various studies have been carried out to prevent unnecessary fuel consumption and emissions. Behavior of drivers, who are important components of traffic, are carefully examined in the context of this subject. Driver distraction causes various environmental problems as well as traffic safety issues. In this study, the negative situations that arise as a result of drivers waiting at traffic lights dealing with their mobile phones are discussed. Roadside observations are made for drivers at considered intersections in Erzurum Province, Turkey. As a result of these observations, delays at selected intersections due to mobile phone use are calculated. Unnecessary fuel consumption and emissions due to delays are also analyzed. An annual fuel consumption of approximately 177.025 L and emissions of 0.294 (kg) NO_X and 251.68 (kg) CO₂ occur at only selected intersections. In addition, a second roadside observation is made in order to analyze driver behavior and the most preferred type of mobile phone usage is determined. It is seen that drivers mostly exhibit the “Talking” and “Touchscreen” action classes. Considering the economic conditions and environmental pollution sensitivities of countries, attempts have been made to raise awareness about fuel consumption and emissions at traffic lights. Full article

The rise of Internet of Things (IoT) technology has moved the digital world in a new direction and is considered the third wave of the information industry. To meet the current growing demand for food, the agricultural industry should adopt updated technologies and smart agriculture based on the IoT which will strongly enable farmers to reduce waste and increase productivity. This research presents a novel system for the application of IoT technology in agricultural soil measurements, which consists of multiple sensors (temperature and moisture), a micro-processor, a microcomputer, a cloud platform, and a mobile phone application. The wireless sensors can collect and transmit soil information in real time with a high speed, while the mobile phone app uses the cloud platform as a monitoring center. A low power consumption is specified in the hardware and software, and a modular power supply and time-saving algorithm are adopted to improve the energy effectiveness of the nodes. Meanwhile, a novel soil information prediction strategy was explored based on the deep Q network (DQN) reinforcement learning algorithm. Following the weighted combination of a bidirectional long short-term memory, online sequential extreme learning machine, and parallel extreme machine learning, the DQN Bi-OS-P prediction model was obtained. The proposed data acquisition system achieved a long-term stable and reliable collection of time-series soil data with equal intervals and provided an accurate dataset for the precise diagnosis of soil information. The RMSE, MAE, and MAPE of the DQN Bi-OS-P were all reduced, and the R2 was improved by 0.1% when compared to other methods. This research successfully implemented the smart soil system and experimentally showed that the time error between the value displayed on the mobile phone app and its exact acquisition moment was no more than 3 s, proving that mobile applications can be effectively used for the real-time monitoring of soil quality and conditions in wireless multi-sensing based on the Internet of Things. Full article

Information Technology’s development increases day by day, making life easier in terms of work and progress. In these developments, knowledge management is becoming mandatory in all the developing sectors. However, the conventional model for growth analysis in organizations is tedious as data are maintained in ledgers, making the process time consuming. Media Ecology, a new trending technology, overcomes this drawback by being integrated with artificial intelligence. Various sectors implement this integrated technology. The marketing strategy of Huawei Technologies Co. Ltd. is analyzed in this research to examine the advantages of Media Ecology Technology in integration with artificial intelligence and a Knowledge Management Model. This combined model supports sensor technology by considering each medium, the data processing zone, and user location as nodes. A Q-R hybrid simulation methodology is implemented to analyze the data collected through Media Ecology. The proposed method is compared with the inventory model, and the results show that the proposed system provides increased profit to the organization. Paying complete attention to Artificial intelligence without the help of lightweight deep learning models is impossible. Thus, lightweight deep models have been introduced in most situations, such as healthcare management, maintenance systems, and controlling a few IoT devices. With the support of high-power consumption as computational energy, it adapts to lightweight devices such as mobile phones. One common expectation from the deep learning concept is to develop an optimal structure in case time management. Full article