IoT

The proliferation of mobile devices has driven significant growth in adaptive mobile applications (AMAs) that dynamically adjust their behavior based on contextual changes. While existing research has extensively studied individual adaptive systems, limited attention has been given to cooperative adaptation—where multiple AMAs coordinate their adaptive behaviors within shared mobile ecosystems. This systematic literature review addresses this research gap by analyzing 95 peer-reviewed studies published between 2010 and 2025 to characterize the current state of cooperative adaptation in mobile applications. Following established systematic review protocols, we searched six academic databases and applied rigorous inclusion/exclusion criteria to identify relevant studies. Our analysis reveals eight critical dimensions of cooperative adaptation: Monitor–Analyze–Plan–Execute–Knowledge (MAPE-K) structure, application domain, adaptation goals, context management, adaptation triggers, aspect considerations, coordination mechanisms, and cooperation levels. The findings indicate that 63.2% of studies demonstrate some form of cooperative behavior, ranging from basic context sharing to sophisticated conflict resolution mechanisms. However, only 7.4% of studies explicitly address high-level cooperative adaptation involving global goal optimization or comprehensive conflict resolution. Energy efficiency (21.1%) and usability (33.7%) emerge as the most frequently addressed adaptation goals, with Android platforms dominating the research landscape (36.8%). The review identifies significant gaps in comprehensive lifecycle support, standardized evaluation methodologies, and theoretical frameworks for multi-application cooperation. These findings establish a foundation for advancing research in cooperative adaptive mobile systems and provide a classification framework to guide future investigations in this emerging domain. Full article

In the 21st century, Urbanization, population growth, and climate change have created significant problems in water resource management. Recent advancements in technologies such as Internet of Things (IoT), Edge Computing (EC), Artificial Intelligence (AI), and Big Data Analytics (BDA) are changing the operations of the water resource management systems. In this study, we present a systematic review, highlighting the contributions of these technologies in water management systems. More specifically, we highlight the IoT and EC water monitoring systems that enable real-time sensing of water quality and consumption. In addition, AI methods for anomaly detection and predictive maintenance are reviewed, focusing on water demand forecasting. BDA methods are also discussed, highlighting their ability to integrate data from different data sources, such as sensors and historical data. Additionally, a discussion is provided of how Water management systems could enhance sustainability, resilience, and efficiency by combining big data, IoT, EC, and AI. Lastly, future directions are outlined regarding how state-of-the-art technologies may further support efficient water resources management. Full article

The rapid expansion of Internet of Things (IoT) systems in critical infrastructures has raised significant concerns regarding network security and reliability. In particular, RPL (Routing Protocol for Low-Power and Lossy Networks), widely adopted in IoT communications, remains vulnerable to topological manipulation attacks such as Decreased Rank, Increased Rank, and the less-explored Worst Parent Selection (WPS). While several RPL security approaches address rank manipulation attacks, most assume static topologies and offer limited support for mobility. Moreover, trust-based routing and hybrid IDS (Intrusion Detection System) approaches are seldom integrated, which limits detection reliability under mobility. This study introduces a unified IDS framework that combines mobility awareness with trust-based decision-making to detect multiple rank-based attacks. We evaluate two lightweight, rule-based IDS architectures: a fully distributed model and a hybrid model supported by designated monitoring nodes. A trust-based mechanism is incorporated into both architectures, and their performance is assessed under static and mobile scenarios. Results show that while the distributed IDS provides rapid local responsiveness, the hybrid IDS maintains more stable latency and packet delivery under mobility. Additionally, incorporating trust metrics reduces false alerts and improves detection reliability while preserving low latency and energy usage, supporting time-sensitive applications such as healthcare monitoring. Full article

Zero-day intrusions on IoT endpoints demand defenses that curtail attacker impact and persistence after breach. This article presents Fine-Grained Runtime Containment Agent (FG-RCA), a lightweight post-exploitation containment system that learns least-privilege behavior from execution and enforces it in the kernel via eBPF with Linux Security Modules (LSM). In a learn phase, LSM/eBPF probes stream security-relevant events to a Rust agent that synthesizes policies per device role. In an enforce phase, policies are compiled into eBPF maps and evaluated at an extended hook set spanning process execution (bprm_check_security), file access (file_open), network egress and exfiltration (socket_connect, socket_sendmsg), privilege use (capable), process injection (ptrace_access_check), tamper/anti-forensics (inode_unlink). Policies bind to kernel-truth identities—inode, device, mount intrusion detection system (IDS), executable SHA-256, and cgroup/namespace identifiers—rather than paths, mitigating time-of-check to time-of-use (TOCTOU) and aliasing. Operational safeguards include Ed25519-signed policies, atomic rollback, and shadow mode logging events to enable policy evolution. Evaluation on embedded Linux demonstrates containment with low overhead. Full article

There is increasing interest in improving the reliability of short-range wireless links in dense IoT deployments, where BLE is widely used due to its low power consumption and robust GFSK modulation. For this purpose, this work presents a novel Orthogonal Space-Time (OST) scheme for transmission and detection of BLE signals while preserving the BLE GFSK waveform and modulation constraints. The proposed signal processing system integrates advanced OST coding techniques with nonlinear GFSK modulation to achieve high-quality communication while maintaining phase continuity. This implies that the standard BLE GFSK modulator and demodulator blocks can be reused, with additional processing introduced only in the multi-antenna encoder and combiner. A detailed theoretical analysis demonstrates the feasibility of employing the Rayleigh fading channel model in BLE communications and establishes the BER performance bounds for various MIMO configurations. Simulations confirm the advantages of the proposed OST-GFSK signal processing scheme, maintaining a consistent performance when compared with OST linear modulation approaches under Rayleigh fading channels. As a result, the proposed IoT-enabling technology integrates the advantages of widely used OST linear modulation with nonlinear GFSK modulation required for BLE. Full article

The integrity of certification processes in the agrifood and fishing industries is essential for combating fraud, ensuring food safety, and meeting rising consumer expectations for transparency and sustainability. Yet, current certification systems remain fragmented, and they are vulnerable to tampering and highly dependent on manual or centralized procedures. This study addresses these gaps by providing a comprehensive survey that systematically classifies blockchain-based certification technologies and methodologies applied to the fisheries sector. The survey examines how the blockchain enhances trust through immutable record-keeping, smart contracts, and decentralized verification mechanisms, ensuring authenticity and accountability across the supply chain. Special attention is given to case studies and implementations that focus on ensuring food safety, verifying sustainability claims, and fostering consumer trust through transparent labeling. Furthermore, the paper identifies technological barriers, such as scalability and interoperability, and puts forward a collection of functional and non-functional requirements for holistic blockchain implementation. By providing a detailed overview of current trends and gaps, this study aims to guide researchers, industry stakeholders, and policymakers in adopting and optimizing blockchain technologies for certification. The findings highlight the potential of blockchain to innovate certification systems, easing the way for more resilient, sustainable, and consumer-centric agrifood and fishing industries. Full article

The proliferation of Internet of Things (IoT) devices in financial markets has created distributed ecosystems where cryptocurrency exchanges, trading platforms, and market data providers operate as autonomous edge nodes generating massive volumes of sensitive financial data. Collaborative machine learning across these distributed financial IoT nodes faces fundamental challenges: institutions possess valuable proprietary data but cannot share it directly due to competitive concerns, regulatory constraints, and trust management requirements in decentralized networks. This study presents a privacy-preserving federated learning framework tailored for distributed financial IoT systems, combining differential privacy with Shamir secret sharing to enable secure collaborative intelligence across blockchain-based cryptocurrency trading networks. We implement per-layer gradient clipping and Rényi differential privacy composition to minimize utility loss while maintaining formal privacy guarantees in edge computing scenarios. Using 5.6 million orderbook observations from 11 cryptocurrency pairs collected across distributed exchange nodes, we evaluate three data partitioning strategies simulating realistic heterogeneity patterns in financial IoT deployments. Our experiments reveal that federated edge learning imposes 9–15 percentage point accuracy degradation compared to centralized cloud processing, driven primarily by data distribution heterogeneity across autonomous nodes. Critically, adding differential privacy (ε = 3.0) and cryptographic secret sharing increases this degradation by less than 0.3 percentage points when mechanisms are calibrated appropriately for edge devices. The framework achieves 62–66.5% direction accuracy on cryptocurrency price movements, with confidence-based execution generating 71–137 basis points average profit per trade. These results demonstrate the practical viability of privacy-preserving collaborative intelligence for distributed financial IoT while identifying that the federated optimization gap dominates privacy mechanism costs. Our findings offer architectural insights for designing trustworthy distributed systems in blockchain-enabled financial IoT ecosystems. 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

Remote patient monitoring is a promising and transformative pillar of healthcare. However, deploying such systems at a scale—across thousands of patients and Internet of Medical Things (IoMT) devices—demands robust, low-latency, and scalable storage systems. This research examines the application of Fog Computing for remote patient monitoring in IoMT settings, where a large volume of data, low latency, and secure management of confidential healthcare information are essential. We propose a four-layer IoMT–Fog–Cloud architecture in which Fog nodes, equipped with graph-based databases (Neo4j), conduct local processing, filtering, and integration of heterogeneous health data before transmitting it to cloud servers. To assess the viability of our approach, we implemented a containerised Fog node and simulated multiple patient-device networks using a real-world dataset. System performance was evaluated using 11 scenarios with varying numbers of devices and data transmission frequencies. Performance metrics include CPU load, memory footprint, and query latency. The results demonstrate that Neo4j can efficiently ingest and query millions of health observations with an acceptable latency of less than 500 ms, even in extreme scenarios involving more than 12,000 devices transmitting data every 50 ms. The resource consumption remained well below the critical thresholds, highlighting the suitability of the proposed approach for Fog nodes. Combining Fog computing and Neo4j is a novel approach that meets the latency and real-time data ingestion requirements of IoMT environments. Therefore, it is suitable for supporting delay-sensitive monitoring programmes, where rapid detection of anomalies is critical (e.g., a prompt response to cardiac emergencies or early detection of respiratory deterioration in patients with chronic obstructive pulmonary disease), even at a large scale. Full article

The increasing availability of IoT-enabled mobility data and intelligent transportation systems in Smart Cities demands efficient and interpretable models for destination prediction. This study presents a comparative analysis between Markov Chains and Hidden Markov Models applied to urban mobility trajectories, evaluated through mean precision values. To ensure methodological rigor, the Smart Sampling with Data Filtering (SSDF) method was developed, integrating trajectory segmentation, spatial tessellation, frequency aggregation, and 10-fold cross-validation. Using data from 23 vehicles in the Vehicle Energy Dataset (VED) and a filtering threshold based on trajectory recurrence, the results show that the HMM achieved 61% precision versus 59% for Markov Chains (p = 0.0248). Incorporating day-of-week contextual information led to statistically significant precision improvements in 78.3% of cases for precision (95.7% for recall, 87.0% for F1-score). The remaining 21.7% indicate that model selection should balance model complexity and precision-efficiency trade-off. The proposed SSDF method establishes a replicable foundation for evaluating probabilistic models in IoT-based mobility systems, contributing to scalable, explainable, and sustainable Smart City transportation analytics. Full article

The proliferation of Internet of Medical Things (IoMT) devices in healthcare requires robust intrusion detection systems to protect sensitive data and ensure patient safety. While existing neural network-based Intrusion Detection Systems have shown considerable effectiveness, significant challenges persist—particularly class imbalance and high data dimensionality. Although various approaches have been proposed to mitigate these issues, their actual impact on detection accuracy remains insufficiently explored. This study investigates advanced Artificial Neural Network (ANN) architectures and preprocessing strategies for intrusion detection in IoMT environments, addressing critical challenges of feature dimensionality and class imbalance. Leveraging the WUSTL-EHMS-2020 dataset—a specialized dataset specifically designed for IoMT cybersecurity research—this research systematically examines the performance of multiple neural network designs. Our research implements and evaluates five distinct ANN architectures: the Standard Feedforward Network, the Enhanced Channel ANN, Dual-Branch Addition and Concatenation ANNs, and the Shortcut Connection ANN. To mitigate the class imbalance challenge, we compare three balancing approaches: the Synthetic Minority Over-sampling Technique (SMOTE), Hybrid Over-Under Sampling, and the Weighted Cross-Entropy Loss Function. Performance analysis reveals nuanced insights across different architectures and balancing strategies. SMOTE-based models achieved average AUC scores ranging from 0.8491 to 0.8766. Hybrid sampling strategies improved performance, with AUC increasing to 0.8750. The weighted cross-entropy loss function demonstrated the most consistent performance. The most significant finding emerges from the Dual-Branch ANN with addition operations and a weighted loss function, which achieved 0.9403 Accuracy, 0.8786 AUC, a 0.8716 F1-Score, 0.8650 Precision, and 0.8786 Recall. Compared to the related work’s baseline, it demonstrates a substantial increase in F1 Score by 8.45% and an improvement of 18.67% in AUC and Recall, highlighting the model’s superiority at identifying potential security threats and minimizing false negatives. Full article

It is essential to assign the correct triage level to patients as soon as they arrive in the emergency department in order to save lives, especially during peak demand. However, many healthcare systems estimate the triage levels by manual eyes-on evaluation, which can be inconsistent and time consuming. This study creates a full Digital Twin-based architecture for patient monitoring and automated triage level recommendation using IoT sensors, AI, and cloud-based services. The system can monitor all patients’ vital signs through embedded sensors. The readings are used to update the Digital Twin instances that represent the present condition of the patients. This data is then used for triage prediction using a pretrained model that can predict the patients’ triage levels. The training of the model utilized the synthetic minority over-sampling technique, combined with Tomek links to lessen the degree of data imbalance. Additionally, Lagrange element optimization was applied to select those features of the most informative nature. The final triage level is predicted using the Tabular Prior-Data Fitted Network, a transformer-based model tailored for tabular data classification. This combination achieved an overall accuracy of 87.27%. The proposed system demonstrates the potential of integrating digital twins and AI to improve decision support in emergency healthcare environments. Full article

Internet of Things (IoT) devices can be used to provide safety, security, and other services that ensure that smart systems work as intended. However, the increasing complexity of the tasks is increasing the potential of performance loss when limited resources are not utilized appropriately. Distributed complex event processing (CEP) applications can be used to execute multiple unique tasks on sensor data. Since these operations can require a variety of data from multiple sensors across separate task steps, non-optimal code and data management can lead to increased processing delays. In this study, a mathematical model for optimizing critical path performance across multiple independent CEP flows is proposed. The model optimally assigns both where codes are executed at, as well as where their respective data should be placed at. The proposed solution is implemented within an open source library with the inclusion of operator placement heuristics from the literature. Approaches are tested within a realistic smart-street scenario. Consumer delays, algorithm runtimes, and delivery ratios within different time windows are reported. The results indicate that the proposed approach can reduce the delivery times for the critical CEP paths better than the heuristic solutions, with the downside of increased optimization runtimes. Full article

The Internet of Things (IoT) has rapidly evolved into a central component of today’s technological landscape, enabling seamless connectivity and communication among a vast array of devices. It underpins automation, real-time monitoring, and smart infrastructure, serving as a foundation for Industry 4.0 and paving the way toward Industry 5.0. Despite the potential of IoT systems to transform industries, these systems face a number of challenges, most notably the lack of processing power, storage space, and battery life. Whereas cloud and fog computing help to relieve computational and storage constraints, energy limitations remain a severe impediment to long-term autonomous operation. Among the threats that exploit this weakness, the Denial-of-Sleep (DoSl) attack is particularly problematic because it prevents nodes from entering low-power states, leading to battery depletion and degraded network performance. This research investigates machine-learning (ML) and deep-learning (DL) methods for identifying such energy-wasting behaviors to protect IoT energy resources. A dataset was generated in a simulated IoT environment under multiple DoSl attack conditions to validate the proposed approach. Several ML and DL models were trained and tested on this data to discover distinctive power-consumption patterns related to the attacks. The experimental results confirm that the proposed models can effectively detect anomalous behaviors associated with DoSl activity, demonstrating their potential for energy-aware threat detection in IoT networks. Specifically, the Random Forest and Decision Tree classifiers achieved accuracies of 98.57% and 97.86%, respectively, on the held-out 25% test set, while the Long Short-Term Memory (LSTM) model reached 97.92% accuracy under a chronological split, confirming effective temporal generalization. All evaluations were conducted in a simulated environment, and the paper also outlines potential pathways for future physical testbed deployment. Full article

The rapid expansion of IoT devices has introduced significant security challenges, with malware authors constantly evolving their techniques to exploit vulnerabilities in IoT networks. Despite this growing threat, progress in developing effective detection solutions remains limited. In this study, we present an ML-based framework for detecting and classifying network threats targeting IoT environments. Using the CTU-IoT-Malware-Capture 2023 dataset and the UNSW Bot-IoT dataset, we transformed the task into a structured multi-class classification problem to better reflect real-world detection challenges. Our primary contribution lies in demonstrating the effectiveness of post-training quantization on gradient-boosted models, specifically a Quantized XGB variant enhanced with histogram-based quantization. This approach significantly reduces model size and inference time without sacrificing accuracy. The proposed model achieved high classification accuracies of 99.93% and 99.99% on the two datasets, while the quantization step led to 1.42× and 3× improvements in inference speed, and reductions in model size by 3.61× and 2.71×, respectively, making it well-suited for deployment in resource-constrained IoT settings. This work demonstrates not only the effectiveness of gradient boosting in handling complex traffic data but also introduces an efficient optimization strategy for real-time IoT threat detection. Full article

Cloud-centric IoT frameworks remain dominant; however, they introduce major challenges related to data privacy, latency, and system resilience. Existing open-source solutions often lack standardized principles for scalable, local-first deployment and do not adequately integrate fault tolerance with hybrid automation logic. This study presents a practical and extensible local-first IoT architecture designed for full operational autonomy using open-source components. The proposed system features a modular, layered design that includes device, communication, data, management, service, security, and presentation layers. It integrates MQTT, Zigbee, REST, and WebSocket protocols to enable reliable publish–subscribe and request–response communication among heterogeneous devices. A hybrid automation model combines rule-based logic with lightweight data-driven routines for context-aware decision-making. The implementation uses Proxmox-based virtualization with Home Assistant as the core automation engine and operates entirely offline, ensuring privacy and continuity without cloud dependency. The architecture was deployed in a real-world office environment and evaluated under workload and fault-injection scenarios. Results demonstrate stable operation with MQTT throughput exceeding 360,000 messages without packet loss, automatic recovery from simulated failures within three minutes, and energy savings of approximately 28% compared to baseline manual control. Compared to established frameworks such as FIWARE and IoT-A, the proposed approach achieves enhanced modularity, local autonomy, and hybrid control capabilities, offering a reproducible model for privacy-sensitive smart environments. Full article

Subsoil compaction is a major constraint in sugarcane production, limiting yields and reducing resource-use efficiency. This study presents WireDepth, an innovative cloud-connected monitoring system that leverages edge computing and IoT technologies for real-time, spatially aware analysis and visualization of subsoiling depth. The system integrates ultrasonic, laser, inclinometer, and potentiometer sensors mounted on the subsoiler, with on-board microcontroller processing and dual wireless connectivity (LoRaWAN and NB-IoT/LTE-M) for robust data transmission. A cloud platform delivers advanced analytics, including 3D depth maps and operational efficiency metrics. System accuracy was assessed using 300 reference depth measurements, with Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) calculated per sensor. The inclinometer and potentiometer achieved the highest accuracy (MAPE of 0.92% and 0.84%, respectively), with no significant deviation from field measurements (paired t-tests, p > 0.05). Ultrasonic and laser sensors exhibited higher errors, particularly at shallow depths, due to soil debris interference. Correlation analysis confirmed a significant effect of depth on sensor accuracy, with laser sensors showing the strongest association (Pearson r = 0.457, p < 0.001). Field validation in commercial sugarcane fields demonstrated that WireDepth improves subsoiling precision, reduces energy waste, and supports sustainable production by enhancing soil structure and root development. These findings advance precision agriculture by offering a scalable, real-time solution for subsoiling management, with broad implications for yield improvement in compaction-affected systems. Full article

The exponential growth of Internet of Things (IoT) devices introduces significant security challenges due to their resource constraints and diverse attack surfaces. To address these issues, this paper proposes the Centralized Two-Tiered Tree-Based Intrusion Detection System (C2T-IDS), a lightweight framework designed for efficient and scalable threat detection in IoT networks. The system employs a hybrid edge-centralized architecture, where the first tier, deployed on edge gateways, performs real-time binary classification to detect anomalous traffic using optimized tree-based models. The second tier, hosted on a centralized server, conducts detailed multi-class classification to diagnose specific attack types using advanced ensemble methods. Evaluated on the realistic CIC-IoT-2023 dataset, C2T-IDS achieves a Macro F1-Score of up to 0.94 in detection and 0.80 in diagnosis, outperforming direct multi-class classification by 5–15%. With inference times as low as 6 milliseconds on edge devices, the framework demonstrates a practical balance between accuracy, efficiency, and deployability, offering a robust solution for securing resource-constrained IoT environments. Full article

Ammonia is a gas primarily produced for use in agriculture, refrigeration systems, chemical manufacturing, and power generation. Despite its benefits, improper management of ammonia poses significant risks to human health and the environment. Consequently, monitoring ammonia is essential for enhancing industrial safety and preventing leaks that can lead to environmental contamination. Given the abundance and diversity of studies on Internet of Things (IoT) systems for gas detection, the main objective of this paper is to systematically review the literature to identify emerging research trends and opportunities. This review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, focusing on sensor technologies, microcontrollers, communication technologies, IoT platforms, and applications. The main findings indicate that most studies employed sensors from the MQ family (particularly the MQ-135 and MQ-137), microcontrollers based on the Xtensa architecture (ESP32 and ESP8266) and ARM Cortex-A processors (Raspberry Pi 3B+/4), with Wi-Fi as the predominant communication technology, and Blynk and ThingSpeak as the primary cloud-based IoT platforms. The most frequent applications were agriculture and environmental monitoring. These findings highlight the growing maturity of IoT technologies in ammonia sensing, while also addressing challenges like sensor reliability, energy efficiency, and development of integrated solutions with Artificial Intelligence. Full article