Search Results (314)

The Internet of Things (IoT) has a decentralized smart home ecosystem, as each protocol has its own gateway infrastructure needs. This study advances gateway convergence by proposing and rigorously evaluating a scalable architectural framework for future smart-home infrastructure. Specifically, this paper provides a detailed analysis of a proposed integrated multi-protocol gateway design that supports 18 of the most widely used IoT communication protocols simultaneously. It is a one-device implementation combining wireless technologies, including short-range radios (Sub-1 GHz, 2.4 GHz), LPWANs (Long Power Wide Area Networks), cellular (LTE, Long-Term Evolution), and wired (Ethernet, KNX). Using the ns-3 network simulator, this paper shows that this architecture is practical in a simulated smart-home environment with a large number of interconnected devices distributed across various zones. The results demonstrate substantial reductions in energy consumption and operational complexity, without compromising quality of service across heterogeneous communication technologies. Full article

Delivery services have drawn much attention and become of topmost significance in urban areas by presenting online food delivery selections for a diversity of dishes from a wide range of restaurants, decreasing both travel and waiting times. Customer data analysis acts as a cornerstone in corporate strategy, allowing enterprises to gather and interpret user feedback and helping them to make informed decisions that drive future business development. However, major knowledge gaps remain due to the scarcity of literature review studies on these delivery services, hindering a complete understanding of customer satisfaction in this sector. Furthermore, there has been little systematic research on managerial response tactics to online consumer complaints and negative reviews. Researchers have contributed by applying artificial intelligence, including deep learning and machine learning models, to analyze customer sentiment and understand customer brand perceptions. This study presents a Hybrid Deep Neural Network Model for Customer Satisfaction Analysis (HDNNM-CSA), with the aim of developing an efficient model which is capable of accurately classifying customer satisfaction levels in delivery apps based on textual responses provided by customer experience managers. To achieve this, the model initially pre-processes text data using text cleaning, emoji removal, normalization, tokenization, stop word removal, and stemming to clean and standardize the unstructured text data for further analysis. Following this, term frequency–inverse document frequency-based word embedding is utilized to transform the pre-processed text into meaningful feature representations. Lastly, an ensemble architecture involving bidirectional long short-term memory, temporal convolutional, and graph convolutional networks is deployed to classify customer satisfaction levels with managers’ responses. A series of experimental analyses are performed, and the results are examined for numerous features. A comparative analysis demonstrates the enhanced performance of the HDNNM-CSA technique with respect to existing approaches. Full article

Large-scale smart agriculture requires reliable and energy-efficient wireless connectivity to support distributed environmental sensing across wide rural areas. However, existing low-power wide-area network (LPWAN) technologies often face limitations in scalability, reliability, or infrastructure dependency when deployed in large agricultural fields. This study presents the design and experimental evaluation of a hierarchical sensor network architecture that integrates LoRaMESH for multi-hop sensing communication and Wi-Fi HaLow as a sub-GHz backhaul for data aggregation and cloud connectivity. In the proposed system, LoRaMESH forms intra-cluster sensor networks using a lightweight controlled flooding protocol, while Wi-Fi HaLow provides long-range IP-based connectivity between cluster gateways and a central access point. A real-world deployment covering approximately $2.5\mathrm{km}\times 1\mathrm{km}$ of agricultural area was implemented to evaluate the performance of the proposed architecture. Experimental results show that the LoRaMESH network achieves packet delivery ratios above $90\%$ across one to three hops, with average end-to-end delays between $10.6$ s and $13.3$ s. The Wi-Fi HaLow backhaul demonstrates high reliability within short to medium distances, reaching $99.5\%$ packet delivery ratio at 50 m and $89.68\%$ at 200 m. Energy measurements further indicate that the sensor nodes consume only $21.19\mathsf{\mu }\mathrm{A}$ in sleep mode, enabling long-term battery-powered operation suitable for agricultural monitoring applications. These results indicate that the proposed hierarchical architecture is a feasible connectivity option for the tested large-scale agricultural sensing scenario. Because no side-by-side LoRaWAN or NB-IoT benchmark was conducted on the same testbed, the results should be interpreted as a field validation of the proposed architecture rather than as a direct experimental demonstration of superiority over alternative LPWAN systems. Full article

The growth of Internet of Things (IoT) applications is driving demand for Low-Power Wide-Area Networks (LPWANs) to support higher data rates with the same energy efficiency. While Long Range (LoRa) provides excellent noise immunity and receiver sensitivity, its data rate might be insufficient for some applications, including those real-time applications in which LoRa is required to have infrequent transmissions to maintain low power consumption. In this paper, a novel modulation is introduced to address these limitations by utilizing narrowband chirp to represent a data symbol with chirp slopes, called a multi-slope chirp signal. At the receiver, a new blind non-coherent detection technique is also presented to recover the proposed signal. The simulation results confirm that the proposed scheme can successfully transmit information at 2 to 4 bits per symbol, and when compared to LoRa SF 6, it reduces the Time-on-Air (ToA) by half and also achieves an improvement in spectral efficiency in the frequency domain. Full article

The rapid growth of large-scale Internet of Things (IoT) deployments in urban environments requires accurate and energy-efficient localization methods for low-power wireless devices. In long-range wide-area networks (LoRaWAN), traditional GPS-based positioning is often impractical due to energy consumption constraints and signal propagation challenges in urban areas. This study proposes a hybrid localization system that integrates weighted centroid localization (WCL) with a machine learning (ML) regression model to improve outdoor positioning accuracy. The proposed approach first estimates approximate transmitter coordinates using a physically grounded WCL method based on received signal strength indicator (RSSI) measurements. These initial estimates are subsequently refined by ML models trained to learn nonlinear residual corrections. In addition to random partitioning, a spatial data splitting strategy is proposed and evaluated using a publicly available LoRaWAN dataset. The experimental results demonstrate that the hybrid WCL framework combined with a multilayer perceptron (MLP) significantly outperforms other ML models. The proposed method achieves a mean localization error of 160.47 m and a median error of 73.78 m. Compared to the baseline model, the integration of WCL reduces the mean localization error by approximately 29%, highlighting the effectiveness of incorporating physically interpretable priors into localization models. Full article

Low-Power Wide-Area Networks (LPWANs) like LoRaWAN enable IoT applications with low-power and long-range characteristics. While LoRaWAN class B mode is server-initiated downlink communication-oriented, its uplink communication, especially in mobile scenarios, remains underexplored. This paper proposes two novel systems, Edge-based SmartSync and Gateway-based SmartSync, aiming to enhance uplink by leveraging class B synchronization. Edge-based SmartSync enables end devices to dynamically adjust the Spreading Factor (SF) based on real-time Received Signal Strength Indicator (RSSI) from beacons, achieving a significant improvement in terms of packet delivery and energy consumption. Gateway-based SmartSync ensures the fair distribution of end devices across a lower SF to further enhance the efficiency of the system. The beacon is reengineered to convey sensitivity limits to end devices. The systems were implemented in the OMNeT++ simulator over a 25 km² area with 100–1000 mobile devices and evaluated against a baseline using metrics like the Packet Delivery Ratio, collisions, and energy consumption. The obtained results show that both systems are capable of improving the delivery ratio by over 40% and reducing collisions by 80% compared to the baseline, with energy savings exceeding 35%. Proposed systems offer cost-effective, adaptable solutions, paving the way for more reliable IoT deployments. Full article

The increasing reliance of Internet of Things (IoT) applications on low-power wide-area network technologies, particularly Long Range Wide Area Network (LoRaWAN), has amplified the need for security monitoring approaches that go beyond attack-specific signatures and generic traffic anomalies. Existing solutions are often tailored to individual threat scenarios or rely on statistical indicators, which limits their ability to systematically capture protocol-level misuse in an interpretable manner. This paper addresses this gap by proposing a protocol-aware validation methodology based on a Digital Twin abstraction of LoRaWAN communication behavior. The Over-The-Air Activation (OTAA) procedure is modeled as a finite-state machine that encodes expected message sequences, timing constraints, and specification-driven state transitions. Observed network events are continuously evaluated against this formal state model, enabling the identification of protocol-level deviations indicative of anomalous or non-conformant behavior. Illustrative examples include replay behavior, timing inconsistencies, and integrity-related anomalies, although the framework is not limited to predefined attack categories. The results demonstrate that state machine-based Digital Twin provides a structured and extensible foundation for protocol-aware security validation and Security Operation Center (SOC)-oriented telemetry enrichment. In this sense, the presented approach represents a concrete step toward protocol-aware intrusion detection for LoRaWAN networks by establishing a state-synchronized semantic validation layer upon which higher-level detection mechanisms can be built. Full article

The increasing variability of residential demand, combined with the expansion of distributed generation and electric vehicles, has introduced new challenges to the stability of Smart Grids (SGs). Centralized management models lack the flexibility required to operate under these conditions, reinforcing the need for scalable and data-driven architectures. This study proposes an energy management solution based on microservices, supported by hybrid communication in Low Power Wide Area Networks (LPWAN), integrating Long Range Wide Area Network (LoRaWAN) and LoRaMESH to enhance connectivity, local resilience, and reliability in data acquisition for Internet of Things (IoT) and Demand Response (DR) applications. A prototype composed of a Smart Meter (SM), a Data Aggregation Point (DAP), and a Concentrator (CON) was evaluated in a controlled environment, achieving Packet Delivery Rates above 97%, an average RSSI of −92 dBm, and a Signal-to-Noise Ratio close to 9 dB, validating the robustness of the hybrid communication. At a larger scale, data from 5567 households in the Low Carbon London (LCL) project were used to generate representative Load Profiles (LPs) through seven aggregation and clustering techniques, consistently identifying the 18:00–21:00 interval as the critical peak, with demand reaching up to 42% above the daily average. Fourteen load shifting algorithms were evaluated, and the Hybrid Adaptive Algorithm based on Intention and Resilience (HAAIR), proposed in this work, achieved the best overall performance with a 1.83% peak reduction, US$65.40 in cost savings, a reduction of 60 kg of CO₂, a Comfort Loss Index of 0.04, resilience of 9.5, and reliability of 0.98. The results demonstrate that the integration of hybrid LPWAN communication, modular microservice-based architecture, and adaptive DR strategies driven by Artificial Intelligence (AI) represents a promising pathway toward scalable, resilient, and energy-efficient SGs. Full article

Advancement of precision agriculture increasingly relies on cost-effective and scalable technologies for real-time environmental management, particularly in greenhouse environments where vertical and spatial microclimate heterogeneity influences crop performance. This study presents the design, implementation, and experimental validation of an Android-based smartphone application edge supervisory monitoring system integrated with multi-layer wireless sensing and control nodes for real-time monitoring in a smart greenhouse. The system combined multi-layer wireless sensor nodes, wireless control nodes, a Long-Range Wide Area Network (LoRaWAN) gateway, Message Queuing Telemetry Transport (MQTT) communication, and a cloud-synchronized smartphone-based supervisory interface for visualizing environmental data, detecting defined abnormal events, and controlling actuators remotely. For feasibility tests, 54 sensing nodes and 12 actuator nodes were deployed across three vertical layers in two sections, measuring temperature, humidity, CO₂ concentration, and light intensity. Abnormality was defined as environmental threshold violations, statistical signal deviations, actuator power inconsistencies, and communication timeout events. Experimental results revealed vertical and spatial environmental variability across greenhouse sections, while real-time time-series and 3D spatial maps enabled the rapid detection of abnormal conditions. The rule-based abnormality detection engine identified out-of-range environmental values and sensor-related inconsistencies and generated immediate notifications. Smartphone profiling revealed that display and system-level processes accounted for energy consumption, with battery power reaching a peak of 3.5 W and application CPU utilization ranging from 40% to 70% during active monitoring. The results demonstrate system-level feasibility, responsiveness, and scalability under commercial greenhouse workloads, supporting future integration of predictive control and energy-efficient operation. Full article

Temperature and humidity monitoring in grain-carton warehousing is essential for quality assurance, yet fixed wiring is difficult under frequent stacking and battery-powered tags require routine maintenance. This study proposes a distributed passive monitoring sensing system that combines high-efficiency light energy harvesting with low-power long-range wide-area network (LoRa) communication. The key novelty is a carton-oriented separated architecture: an external photovoltaic harvester is wired to internal sensing/communication modules, mitigating stack-induced shading and enabling reliable operation for sensors embedded inside densely stacked cartons; an occlusion-tolerant multi-tag reporting strategy is further adopted. The tag integrates (i) an energy management module based on the bq25570 with a monocrystalline light cell and energy storage for low-light/intermittent illumination, (ii) a LoRa transceiver optimized for long-range and occlusion-tolerant data delivery, and (iii) a temperature–humidity sensing module for reliable microenvironment measurements. A hardware layout with an external photovoltaic panel and internal core modules mitigates carton-induced shading, while low-power scheduling and a lightweight protocol ensure robust sensing and transmission. Experiments show that the energy management module achieves > 60% charging efficiency at a 1.3 V input. After penetrating three layers of grain cartons, the LoRa link maintains a stable range of 500–800 m with ≤1% packet loss under concurrent multi-tag transmission. The measurement errors are within ±1 °C and ±3% relative humidity (RH) in the experimental setup. The proposed system eliminates fixed bus wiring and routine battery replacement, offering a scalable solution that enables maintenance-free monitoring in densely stacked warehousing environments. Full article

Low-Power Wide-Area Network (LPWAN) technologies play a central role in large-scale wireless sensor network (WSN) deployments, where energy efficiency, coverage and reliability dominate over throughput. Among them, Long Range (LoRa) technology has emerged as a widely adopted physical-layer solution due to its ability to operate at extremely low signal-to-noise ratios (SNRs). While multi-antenna techniques can potentially enhance link performance, their applicability in LoRa-type systems is constrained by low-SNR operation, strict energy budgets and the quality of channel state information (CSI). This paper presents a systematic and progressively structured evaluation of multiple-input multiple-output (MIMO) techniques in LoRa-type systems under representative operating conditions. A multi-stage simulation framework, implemented using the Vienna SLS v2.0 (Q3) simulator and adapted to LoRa-like waveforms, is employed to isolate the impact of large-scale propagation, small-scale fading, antenna configuration and CSI quality. The analysis starts from a system-level coverage baseline and advances to link-level evaluations of diversity-oriented MIMO schemes and spatial multiplexing configurations under both ideal and imperfect CSI. The results demonstrate that spatial diversity techniques are well aligned with the operational characteristics of LoRa links, offering robust performance in low-SNR regimes and under limited CSI accuracy. In contrast, spatial multiplexing exhibits higher sensitivity to channel estimation errors, with its practical benefits becoming apparent primarily when evaluated using throughput-oriented metrics such as packet error rate and normalized goodput. Overall, the study highlights the fundamental trade-off between reliability and capacity in LoRa MIMO systems and provides design-oriented insights for wireless sensor network deployments. Full article

Enzyme-free flexible glucose sensors hold great promise in the field of wearable health monitoring. However, their performance is limited by the balance between the catalytic interface activity and stability. This paper reports a strategy for interface gradient roughening of screen-printed carbon electrodes (SPCE) via controllable electrochemical exfoliation (EE). It systematically reveals the inherent relationships among the degree of EE treatment, electrode morphology, surface chemistry, and electrochemical performance. On this basis, the deposition of gold nanoparticles (AuNPs) with high density and uniform distribution is achieved, and a high-performance flexible enzyme-free glucose sensor is constructed. The study finds that EE treatment can significantly increase the true surface area of the electrode and introduce abundant oxygen-containing functional groups, thus effectively reducing the charge transfer resistance. Nevertheless, excessive exfoliation leads to the degradation of the conductive network, indicating the existence of a critical “performance window”. The EE-SPCE optimized with 150 cycles has both a high active area and good electrical conductivity, providing an ideal deposition substrate for AuNPs, increasing their distribution density by approximately 158% and reducing the average particle size to 125 nm. The fabricated AuNPs/EE-SPCE sensor exhibits excellent performance in glucose detection: it has a high sensitivity of 550.766 μA·mM⁻¹·cm⁻² in the range of 0.1–3 mM, a detection limit of 0.0998 mM, a wide linear range, excellent selectivity, long-term stability, and good mechanical flexibility. This research not only develops an efficient and scalable method for constructing flexible sensing interfaces but also clarifies the trade-off relationship among “roughening–conductivity–catalytic performance” at the mechanistic level, providing an important theoretical basis and a general strategy for rationally designing high-performance flexible electrochemical devices. Full article

Fog computing extends cloud services to the network edge, enabling low-latency processing for Internet of Things (IoT) applications. However, this distributed approach is vulnerable to a wide range of attacks, necessitating advanced intrusion detection systems (IDSs) that operate under resource constraints. This study proposes integrating self-awareness (online learning and concept drift adaptation) into a lightweight RL (reinforcement learning)-based IDS for fog networks and quantitatively comparing it with non-RL static thresholds and bandit-based approaches in real time. Novel self-aware reinforcement learning (RL) models, the Hierarchical Adaptive Thompson Sampling–Reinforcement Learning (HATS-RL) model, and the Federated Hierarchical Adaptive Thompson Sampling–Reinforcement Learning (F-HATS-RL), were proposed for real-time intrusion detection in a fog network. These self-aware RL policies integrated online uncertainty estimation and concept-drift detection to adapt to evolving attacks. The RL models were benchmarked against the static threshold (ST) model and a widely adopted linear bandit (Linear Upper Confidence Bound/LinUCB). A realistic fog network simulator with heterogeneous nodes and streaming traffic, including multi-type attack bursts and gradual concept drift, was established. The models’ detection performance was compared using metrics including latency, energy consumption, detection accuracy, and the area under the precision–recall curve (AUPR) and the area under the receiver operating characteristic curve (AUROC). Notably, the federated self-aware agent (F-HATS-RL) achieved the best AUROC (0.933) and AUPR (0.857), with a latency of 0.27 ms and the lowest energy consumption of 0.0137 mJ, indicating its ability to detect intrusions in fog networks with minimal energy. The findings suggest that self-aware RL agents can detect traffic–dynamic attack methods and adapt accordingly, resulting in more stable long-term performance. By contrast, a static model’s accuracy degrades under drift. Full article

The rapid expansion of smart city infrastructures and Internet of Things (IoT) networks has led to extremely dense wireless deployments, driving unsustainable energy consumption and exacerbating environmental concerns. To improve sustainability in the long term, future wireless systems must fundamentally prioritize energy-efficient and autonomous operation. Integrated sensing and communication (ISAC) is emerging as a key enabler for next-generation systems by jointly supporting sensing and communication through shared spectrum, hardware, and signal processing resources. In IoT systems, sensing of target parameters, e.g., range, angle, velocity and identity, etc., form the basis of autonomous and environment-aware applications. However, this integration increases overall power consumption due to the added coordination overhead and the workload placed on shared hardware components. To this end, backscatter communication provides a low-power alternative that enables passive data transmission through energy harvesting and sharply reduces the need for active radio circuits. However, the coexistence of sensing and backscatter functions introduces mutual interference, which often requires large multiple-input multiple-output (MIMO) arrays for effective mitigation. Furthermore, sensing performance depends heavily on line-of-sight conditions, while backscatter links operate only over short ranges. Although increasing array size or transmit power can extend coverage, it imposes substantial energy and hardware costs and undermines sustainability goals. To address these limitations, cell-free MIMO is emerging as a promising candidate technology for next-generation systems. Cell-free MIMO relies on a dense deployment of distributed access points that cooperate to serve devices across a wide area. This cooperation enables effective beamforming and interference management, providing spatial diversity comparable to large, centralized antenna arrays without incurring their associated hardware or power costs. They also enable aggregation of weak double-hop reflections, reduced effective-illumination distances, multi-view sensing, and robustness to blockage, which is invaluable to backscatter communication. This perspective article introduces the foundations, challenges, and architectural considerations of cell-free backscatter-aided integrated sensing and communication (CF-BISAC) systems. By leveraging the advantages of battery-less backscatter IoT devices and the distributed nature of cell-free MIMO, CF-ISABC aims to maximize sensing and communication performance under strict energy constraints, contributing toward energy-aware ISAC systems capable of supporting high-density, low-power wireless applications. Full article

The process of building data analytics systems, including big data systems, is currently being investigated from various perspectives that generally focus on specific aspects, such as data security or privacy, to the detriment of an engineering perspective on systems development. To address this limitation, our proposal focuses on developing analytics systems through a reuse-based approach, including stages ranging from problem definition to results analysis by identifying variations and building reusable, context-based assets. This study presents the reuse process by constructing two case studies that address the water table level prediction problem in two different contexts: the irrigated period and the non-irrigated period in the same study area. The objective of this study is to demonstrate the influence of context on the performance of widely used predictive models for this problem, including long short-term memory (LSTM), artificial neural networks (ANNs), and support vector machines (SVMs), as well as the potential for reusing the developed analytics system. Additionally, we applied the permutation feature importance (PFI) to determine the contribution of individual variables to the prediction. The results confirm that the same problem hypotheses yield different performance in each case in terms of coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE). They also show that the best-performing predictive models differ for some of the hypotheses (ANN in one case and LSTM in another), supporting the assumption that context can influence model selection and performance. Reusing assets allows for more efficient evaluation of these alternatives during development time, resulting in analytics systems that are more closely aligned with reality, while also offering the advantages of software system composition. Full article