Search Results (1,483)

Wireless sensor networks (WSNs) consist of distributed nodes to monitor various physical and environmental parameters. The sensor nodes (SNs) are usually resource constrained such as power source, communication, and computation capacity. In WSN, energy consumption varies depending on the distance between sender and receiver SNs. Communication among SNs having long distance requires significantly additional energy that negatively affects network longevity. To address these issues, WSNs are deployed using multi-hop routing. Using multi-hop routing solves various problems like reduced communication and communication cost but finding an optimal cluster head (CH) and route remain an issue. An optimal CH reduces energy consumption and maintains reliable data transmission throughout the network. To improve the performance of multi-hop routing in WSN, we propose a model that combines Multi-Objective Particle Swarm Optimization (MOPSO) and a Decision Tree for dynamic CH selection. The proposed model consists of two phases, namely, the offline phase and the online phase. In the offline phase, various network scenarios with node densities, initial energy levels, and BS positions are simulated, required features are collected, and MOPSO is applied to the collected features to generate a Pareto front of optimal CH nodes to optimize energy efficiency, coverage, and load balancing. Each node is labeled as selected CH or not by the MOPSO, and the labelled dataset is then used to train a Decision Tree classifier, which generates a lightweight and interpretable model for CH prediction. In the online phase, the trained model is used in the deployed network to quickly and adaptively select CHs using features of each node and classifying them as a CH or non-CH. The predicted nodes broadcast the information and manage the intra-cluster communication, data aggregation, and routing to the base station. CH selection is re-initiated based on residual energy drop below a threshold, load saturation, and coverage degradation. The simulation results demonstrate that the proposed model outperforms protocols such as LEACH, HEED, and standard PSO regarding energy efficiency and network lifetime, making it highly suitable for applications in green computing, environmental monitoring, precision agriculture, healthcare, and industrial IoT. Full article

This work presents the development and evaluation of a low-cost and low-power IoT system for monitoring slope instabilities, rockfalls, and landslides using LoRa communication. The prototype integrates commercial ESP32-based hardware with an SX1276 transceiver, a triaxial MEMS accelerometer, and a GPS module for real-time tilt and location measurements. A tilt-estimation expression was derived from accelerometer data, enabling adaptation to different terrain inclinations. Laboratory tests were performed to validate the stability and accuracy of the inclination measurement, followed by outdoor LoRa range tests under mixed line-of-sight conditions. A lightweight dashboard was implemented for real-time visualization of GPS position, signal quality, and tilt data. The results show reliable tilt detection, consistent long-range communication, and low power consumption, highlighting the potential of the proposed prototype as a scalable and energy-efficient tool for geotechnical monitoring. Full article

The escalating global demand for energy-efficient and sustainable built environments has catalyzed the advancement of Building Energy Management Systems (BEMSs), particularly through their integration with cutting-edge technologies. This review presents a comprehensive and critical synthesis of the convergence between BEMSs and enabling tools such as the Internet of Things (IoT), wireless sensor networks (WSNs), and artificial intelligence (AI)-based decision-making architectures. Drawing upon 89 peer-reviewed publications spanning from 2019 to 2025, the study systematically categorizes recent developments in HVAC optimization, occupancy-driven lighting control, predictive maintenance, and fault detection systems. It further investigates the role of communication protocols (e.g., ZigBee, LoRaWAN), machine learning-based energy forecasting, and multi-agent control mechanisms within residential, commercial, and institutional building contexts. Findings across multiple case studies indicate that hybrid AI–IoT systems have achieved energy efficiency improvements ranging from 20% to 40%, depending on building typology and control granularity. Nevertheless, the widespread adoption of such intelligent BEMSs is hindered by critical challenges, including data security vulnerabilities, lack of standardized interoperability frameworks, and the complexity of integrating heterogeneous legacy infrastructure. Additionally, there remain pronounced gaps in the literature related to real-time adaptive control strategies, trust-aware federated learning, and seamless interoperability with smart grid platforms. By offering a rigorous and forward-looking review of current technologies and implementation barriers, this paper aims to serve as a strategic roadmap for researchers, system designers, and policymakers seeking to deploy the next generation of intelligent, sustainable, and scalable building energy management solutions. Full article

Across the Baltic Sea region, areas situated in climate-sensitive water zones are increasingly exposed to environmental and socio-economic challenges. Gdańsk, Poland, is a prominent example where the rising threat of climate-related hazards, particularly connected with flooding, coincides with growing demand for resilient and adaptive housing solutions. Located in the Vistula Delta, the city’s vulnerability is heightened by its low-lying terrain, polder-based land systems, and extensive waterfronts. These geographic conditions underscore the urgent need for flexible, climate-responsive design strategies that support long-term adaptation while safeguarding the urban fabric and the well-being of local communities. This study provides evidence-based guidance for adaptive housing solutions tailored to Gdańsk’s waterfronts. It draws on successful architectural and urban interventions across the Baltic Sea region, selected for their environmental, social, and cultural relevance, to inform development approaches that strengthen resilience and social cohesion. To achieve this, an exploratory case study methodology was employed, supported by desk research and qualitative content analysis of strategic planning documents, academic literature, and project reports. A structured five-step framework, comprising project identification, document selection, qualitative assessment, data extraction, and analysis, was applied to examine three adaptive housing projects: Hammarby Sjöstad (Stockholm), Kalasataman Huvilat (Helsinki), and Urban Rigger (Copenhagen). Findings indicate measurable differences across nine sustainability indicators (1–5 scale): Hammarby Sjöstad excels in environmental integration (5/5 in carbon reduction and renewable energy), Kalasataman Huvilat demonstrates strong modular and human-scaled adaptability (3–5/5 across social and housing flexibility), and Urban Rigger leads in climate adaptability and material efficiency (4–5/5). Key adaptive measures include flexible spatial design, integrated environmental management, and community engagement. The study concludes with practical recommendations for local planning guidelines. The guidelines developed through the Gdańsk case study show strong potential for broader application in cities facing similar challenges. Although rooted in Gdańsk’s specific conditions, the model’s principles are transferable and adaptable, making the framework relevant to water sensitivity, flexible housing, and inclusive, resilient urban strategies. It offers transversal value to both urban scholars and practitioners in planning, policy, and community development. Full article

Low Power Wide Area Networks (LPWAN) have emerged as essential connectivity solutions for the Internet of Things (IoT), addressing requirements for long range, energy efficient communication that traditional wireless technologies cannot meet. With LPWAN connections projected to grow at 26% compound annual growth rate until 2027, understanding real-world performance is crucial for technology selection. This review examines four leading LPWAN technologies—LoRaWAN, Sigfox, Narrowband IoT (NB-IoT), and LTE-M. This review analyzes 20 peer reviewed studies from 2015–2025 reporting real-world deployment metrics across power consumption, range, data rate, scalability, availability, and security. Across these studies, practical performance diverges from vendor specifications. In the cited rural and urban LoRaWAN deployments LoRaWAN achieves 2+ year battery life and 11 km rural range but suffers collision limitations above 1000 devices per gateway. Sigfox demonstrates exceptional range (280 km record) with minimal power consumption but remains constrained by 12 byte payloads and security vulnerabilities. NB-IoT provides robust performance with 96–100% packet delivery ratios at −127 dBm on the tested commercial networks, and supports tens of thousands devices per cell, though mobility increases energy consumption. In the cited trials LTE-M offers highest throughput and sub 200 ms latency but fails beyond −113 dBm where NB-IoT maintains connectivity. NB-IoT emerges optimal for large scale stationary deployments, while LTE-M suits high throughput mobile applications. Full article

Floating structures are increasingly recognized as crucial infrastructure for deep-sea energy exploitation, offshore communities, and maritime hub facilities in recent years. Understanding their energy consumption characteristics under varying meteorological conditions is essential for ensuring operational efficiency and resilience. This study investigates the influencing factors and variation patterns of energy use in floating structures under normal and typhoon environments. Three representative scenarios with different scales and functions were developed based on a bionic hexagon-shaped floating unit, and their respective energy demands were defined. A systematic sensitivity analysis was conducted using DeST with Typical Meteorological Year data and field observations from Super Typhoon Yagi (No. 2411) at Qionghai Station. Results indicate that, according to sensitivity analysis using the dynamic “intraday fluctuation + daily quantile” threshold, dry-bulb temperature and specific humidity are the dominant factors influencing floating-structure energy consumption, contributing 31.1% and 7.8% increases, respectively—significantly higher than other parameters. Under typhoon conditions, total energy consumption rose slightly relative to the TMY baseline, by 0.12%, 0.49%, and 0.95% across the three scenarios, with diurnal variations within ±5%. This study provides a quantitative basis for optimizing energy storage design and enhancing the resilience of floating structures to extreme meteorological events. Full article

Efficient routing remains the linchpin for achieving sustainable performance in Wireless Sensor Networks (WSNs) within the Internet of Things (IoT). However, traditional routing mechanisms increasingly struggle to cope with the growing complexity of network architectures, frequent changes in topology, and the dynamic behavior of mobile nodes. These issues contribute to data congestion, uneven energy consumption, and potential communication breakdowns, underscoring the urgency for optimized routing strategies. In this paper, we present a comprehensive review of over 100 studies of spanning conventional and AI-enhanced energy-efficient routing techniques. It covers diverse approaches, including metaheuristics, machine learning, reinforcement learning, and AI-based cross-layer methods aimed at improving the performance of WSN-IoT systems. The key limitations of existing solutions are discussed along with performance metrics such as scalability, energy efficiency, throughput, and packet delivery. We also highlight various research challenges and provide research directions for future exploration. By synthesizing current trends and gaps, we provide researchers and practitioners with a structured foundation for advancing intelligent, energy-conscious routing in next-generation IoT-enabled WSNs. Full article

In response to the coexistence of multi-objective conflicts and environmental complexity in the renewal of contemporary urban industrial heritage, this study develops a simulation and decision-making methodology for architectural and environmental renewal based on a digital twin framework. Using the Xi’an Old Steel Plant Industrial Heritage Park as a case study, a community-scale digital twin model integrating multiple dimensions—architecture, environment, population, and energy systems—was constructed to enable dynamic integration of multi-source data and cross-scale response analysis. The proposed methodology comprises four core components: (1) integration of multi-source baseline datasets—including typical meteorological year data, industry standards, and open geospatial information—through BIM, GIS, and parametric modeling, to establish a unified data environment for methodological validation; (2) development of a high-performance dynamic simulation system integrating ENVI-met for microclimate and thermal comfort modeling, EnergyPlus for building energy and carbon emission assessment, and AnyLogic for multi-agent spatial behavior simulation; (3) establishment of a comprehensive performance evaluation model based on Multi-Criteria Decision Analysis (MCDA) and the Analytic Hierarchy Process (AHP); (4) implementation of a visual interactive platform for design feedback and scheme optimization. The results demonstrate that under parameter-calibrated simulation conditions, the digital twin system accurately reflects environmental variations and crowd behavioral dynamics within the industrial heritage site. Under the optimized renewal scheme, the annual carbon emissions of the park decrease relative to the baseline scenario, while the Universal Thermal Climate Index (UTCI) and spatial vitality index both show significant improvement. The findings confirm that digital twin-driven design interventions can substantially enhance environmental performance, energy efficiency, and social vitality in industrial heritage renewal. This approach marks a shift from experience-driven to evidence-based design, providing a replicable technological pathway and decision-support framework for the intelligent, adaptive, and sustainable renewal of post-industrial urban spaces. The digital twin framework proposed in this study establishes a validated paradigm for model coupling and decision-making processes, laying a methodological foundation for future integration of comprehensive real-world data and dynamic precision mapping. Full article

Federated learning (FL) is emerging as a pivotal paradigm for environmental monitoring, enabling decentralized model training across edge devices without exposing raw data. This review provides the first structured synthesis of 361 peer-reviewed studies, offering a comprehensive overview of how FL has been implemented across environmental domains such as air and water quality, climate modeling, smart agriculture, and biodiversity assessment. We further provide comparative insights into model architectures, energy-aware strategies, and edge-device trade-offs, elucidating how system design choices influence model stability, scalability, and sustainability. The analysis traces the technological evolution of FL from communication-efficient prototypes to robust, context-aware deployments that integrate domain knowledge, physical modeling, and ethical considerations. Persistent challenges remain, including data heterogeneity, limited benchmarking, and inequitable access to computational infrastructure. Addressing these requires advances in hybrid physics–AI frameworks, privacy-preserving sensing, and participatory governance. Overall, this review positions FL not merely as a technical mechanism but as a socio-technical shift—one that aligns distributed intelligence with the complexity, uncertainty, and urgency of contemporary environmental science. Full article

Air pollution monitoring systems use distributed sensors that record dynamic environmental conditions, often producing large volumes of heterogeneous and stochastic data. Efficient aggregation of this data is essential for reducing communication overhead while maintaining the quality of information for decision making. In this paper, we propose an unsupervised learning approach for soft clustering of sensors in air pollution monitoring systems. Our method utilizes the Expectation–Maximization algorithm, which is an unsupervised machine learning method and probabilistic technique, to cluster sensors into distinct sets corresponding to normal and polluted zones. This clustering is driven by the need for a dynamic data transmission policy: sensors in polluted zones must intensify their operation for detailed monitoring, while sensors in clean zones can reduce reporting rates and transmit condensed data summaries to alleviate network load and conserve energy. The cluster membership probability enables a tunable trade-off between data redundancy and monitoring accuracy. The high efficiency of the proposed AI-based clustering is validated by the simulation results. Under common pollution scenarios and with adequate sample sizes, the EM algorithm exhibits a relative error below 5%. The presented approach provides a foundation for a wide range of intelligent and adaptive data aggregation protocols. Full article

The acquisition, processing, and monitoring of biomechanical variables in dynamic environments require sensor network architectures capable of handling high concurrency and large data volumes. This study aims to develop, validate, and deploy a robust asynchronous network architecture of Inertial Measurement Units (IMUs) utilizing Bluetooth Low Energy (BLE) 5.0 for real-time biomechanical signal acquisition, overcoming the range, speed, and stability limitations of prior implementations. A network of six IMUs was implemented, with communication managed by a hybrid Python 3.10–LabVIEW 2022 Q3 framework. This architecture ensures concurrent, asynchronous data acquisition while maintaining stable sensor interconnection through virtual port emulation. System evaluation demonstrated superior technical performance, exhibiting high acquisition efficiency (close to 100%) and data loss below ±2% across 75 assessments per sensor. These assessments were obtained by evaluating the posture of 25 participants during three postural experiments, with a maximum indoor range of 40 m and an outdoor range of 105 m, validating the system’s scalability and robustness for motion capture. The approach was applied in a case study using a Fuzzy Inference System (FIS) to assess the upper limb via the Rapid Upper Limb Assessment (RULA) method. The system successfully quantified the temporal distribution of injury risk bilaterally, overcoming the limitations of observational methods and providing objective metrics crucial for occupational health in seated tasks. Full article

In this paper, we investigate a UAV-assisted wireless powered communication network (WPCN) where UAVs act as access points (APs) to periodically serve a group of ground sensor nodes (SNs). Unlike fixed APs in traditional WPCNs, UAV-assisted WPCNs can leverage UAV mobility to maximize system throughput by optimizing the UAV trajectory and wireless resource allocation. However, due to the limited data buffer capacity of the SNs, UAVs may fail to provide timely services, leading to data overflow. Therefore, UAVs must offer efficient and timely services to the SNs. Our objective was to maximize the total energy efficiency of all ground SNs by jointly optimizing UAV transmit power, downlink (DL) wireless energy transfer (WET) time, uplink (UL) wireless information transfer (WIT) time, and SN transmit power under minimal quality-of-service (QoS) constraints. However, the formulated optimization problem is non-convex and difficult to solve directly. To address this, we applied fractional programming theory to transform the non-convex problem into a tractable form. Subsequently, a block coordinate descent-based algorithm was proposed to obtain a near-optimal resource allocation scheme. Extensive simulation results show that our proposed method achieved significantly better performance in terms of system throughput and energy efficiency compared to other benchmark solutions. Full article

Reliable forecasts of dam releases are essential to anticipate downstream hydrological responses and to improve the operation of fluvial early warning systems. This study integrates an explicit release prediction module into a digital forecasting framework using the Lindoso–Touvedo hydropower cascade in northern Portugal as a case study. A data-driven approach couples short-term electricity price forecasts, obtained with a gated recurrent unit (GRU) neural network, with dam release forecasts generated by a Random Forest model and an LSTM model. The models (GRU and LSTM) were trained and validated on hourly data from November 2024 to April 2025 using a rolling 80/20 split. The GRU achieved R² = 0.93 and RMSE = 3.7 EUR/MWh for price prediction, while the resulting performance metrics confirm the high short-term skill of the LSTM model, with MAE = 4.23 m³ s⁻¹, RMSE = 9.96 m³ s⁻¹, and R² = 0.98. The surrogate Random Forest model reached R² = 0.91 and RMSE = 47 m³/s for 1 h discharge forecasts. Comparison tests confirmed the statistical advantage of the AI approach over empirical rules. Integrating the release forecasts into the Delft FEWS environment demonstrated the potential for real-time coupling between energy market information and hydrological forecasting. By improving forecast reliability and linking hydrological and energy domains, the framework supports safer communities, more efficient hydropower operation, and balanced river basin management, advancing the environmental, social, and economic pillars of sustainability and contributing to SDGs 7, 11, and 13. Full article

To meet the escalating data demands of 5G and beyond networks, densified Heterogeneous Networks (HetNets) provide a promising solution, deploying small base stations for improved spectral and energy efficiency. However, HetNets pose challenges, particularly in user association. This journal introduces the Downlink/Uplink Decoupling (DUDe) approach, which enhances uplink performance in HetNets by allowing different access points for uplink and downlink associations. We assess DUDe’s energy efficiency through extensive simulations across various scenarios, demonstrating substantial energy savings compared to centralized 5G systems. Our findings underscore the importance of energy-efficient design for reducing network operational costs and carbon footprint in 5G networks. In addition to energy efficiency gains, DUDe also offers improved resource allocation and network flexibility, making it a valuable solution for evolving wireless communication ecosystems. Full article

The Green Internet of Things (GIoTs) has emerged as a transformative paradigm for environmental conservation, enabling autonomous, self-sustaining sensor networks that operate without batteries and with minimal ecological footprint. This approach is especially critical for long-term mangrove monitoring in smart coastal cities, where conventional battery-powered systems are impractical due to frequent, costly, and environmentally disruptive replacements that hinder continuous data collection. This paper presents a self-sustaining GIoT sensing system for mangrove monitoring powered by sedimentary microbial fuel cells (SMFCs), enabling perpetual, battery-less, and zero-emission operation. A spatial dynamic energy management (DPM) strategy is implemented for the efficient integration of a microcontroller unit with a LoRa wireless communication transceiver and the SMFC harvested energy, ensuring a balanced self-sustained approach into a GIoT sensing network. Experimental results demonstrate an average power consumption of 190.45 μW per 14-byte data packet transmission, with each packet containing pH, electrical conductivity and ambient temperature measurements from the mangrove environment. Under the spatial DPM strategy, the network of four sensing nodes exhibited an energy consumption of 1.14 mWh. Given a harvested power density of 15.1 mW/m² from the SMFC, and utilizing a 0.1 F supercapacitor as an energy buffer, the system can support at least six consecutive data transmissions. These findings validate the feasibility of sustainable, low-power GIoT architectures for ecological monitoring. Full article