Maria Bibi, Antonio F. Skarmeta, Shouket Zaman Khan
Global climate change and increased anthropogenic activities are responsible for outbreaks of invasive and endemic pests, diseases, and pathogens. Forest pests pose serious threats to the growth, productivity, and resilience of natural forests and green urban spaces globally. To cope with the hazardous impacts of trunk-boring pests on forest degradation, it is crucial to detect their infestations at early growth stages. However, the borer larvae’s hidden lifestyle and delayed apparent symptoms lead to widespread infestation, resulting in large-scale tree mortality. The development of smart systems is critical to protecting natural forests and forest plantations from the fatal impacts of boring pests. Applications of artificial intelligence (AI), Internet of Things (IoT), and remote sensing (RS) technologies are transforming traditional pest management strategies by developing more sustainable and robust solutions. The integration of these cutting-edge technologies is paving the way for early pest detection, identification, and outbreak prediction by implementing on-site decision support systems (DSS) and remote monitoring of agricultural fields and large-scale forests. Advanced IoT acoustic sensors can record vibrational signals of boring pest larvae activities inside the tree trunk to confirm pest infestations at early stages, assisting forest management stakeholders in taking preventive measures to suppress the pests’ outbreaks in a timely manner. Auditory signals recorded by piezoelectric sensors are affected by external environmental noise. To improve pest detection ability, different AI-based signal enhancement algorithms are deployed to suppress noisy signals and process enhanced signals. Enormously improved RS technologies can monitor dynamic structural changes in forests by capturing multispectral images through satellites and drones equipped with high-resolution cameras, and then the RS data are incorporated into advanced AI algorithms to analyze the pests and disease-induced stress in the forest ecosystem to develop smart forest protection systems. The integration of X-ray imaging and deep learning models is another achievement in the non-destructive management of trunk-boring pests.
Omar Sabri, Bassam Al-Shargabi, Abdelrahman Abuarqoub
et al.
The rapid proliferation of Internet of Things (IoT) devices in healthcare, from wearable sensors to implantable medical devices, has revolutionised patient monitoring, personalised treatment, and remote care delivery. However, the resource-constrained nature of IoT devices, coupled with the sensitivity of medical data, presents critical security challenges. Traditional encryption methods, while robust, are computationally intensive and unsuitable for IoT environments, leaving sensitive patient information vulnerable to cyber threats. Addressing this gap, lightweight encryption methods have emerged as a pivotal solution to balance security with the limited processing power, memory, and energy resources of IoT devices. This paper explores lightweight encryption methods tailored for IoT healthcare applications, evaluating their effectiveness in securing sensitive data while operating under resource constraints. A comparative analysis is conducted on encryption techniques such as AES-128, LEA, Ascon, GIFT, HIGHT, PRINCE, and RC5-32/12/16, based on key performance metrics including block size, key size, encryption and decryption speeds, throughput, and security levels. The findings highlight that AES-128, LEA, ASCON, and GIFT are best suited for high-sensitivity healthcare data due to their strong security features, while HIGHT and PRINCE provide balanced protection for medium-sensitivity applications. RC5-32/12/16, on the other hand, prioritises efficiency over comprehensive security, making it suitable for low-risk scenarios where computational overhead must be minimised. The paper underscores the significant trade-offs between efficiency, security, and resource consumption, emphasising the need for careful selection of encryption methods based on the specific requirements of IoT healthcare environments. Additionally, the paper highlights the growing demand for lightweight encryption methods that balance energy efficiency with robust protection against cyber threats. These insights offer valuable guidance for researchers and practitioners seeking to enhance the security of IoT-based healthcare systems while ensuring optimal performance in resource-constrained settings.
The majority of consumer IoT devices lack mechanisms for administrators to monitor and control them, hindering tailored security policies. A key challenge is identifying whether a new device, especially a streaming IoT camera, has joined the network. We present zCamInspector, a system for identifying known IoT cameras with supervised classifiers (zCamClassifier) and detecting zero-day cameras with one-class classifiers (zCamDetector). We analyzed ~40GB of traffic across three datasets: Set I (six commercial IoT cameras), Set II (five open-source IoT cameras, ~1.5GB), and Set III (four conferencing and two video-sharing applications as non-IoT traffic). From each, 62 flow-based features were extracted using CICFlowmeter. zCamInspector employs seven supervised models (ET, DT, RF, KNN, XGB, LKSVM, GNB) and four one-class models (OCSVM, SGDOCSVM, IF, DeepSVDD). Results show that XGB identifies IoT cameras with >99% accuracy and false negatives as low as 0.3%, outperforming state-of-the-art methods. For zero-day detection, accuracies reached 93.20% (OCSVM), 96.55% (SGDOCSVM), 78.65% (IF), and 92.16% (DeepSVDD). When all devices were treated as zero-day, DeepSVDD performed best with mean training/testing accuracies of 96.03%/74.51%. zCamInspector also achieved >95% accuracy for specific devices, such as Spy Clock cameras, demonstrating its robustness for identifying and detecting zero-day IoT cameras in diverse network environments.
Sistem monitoring kesehatan seperti suhu tubuh, detak jantung, dan saturasi oksigen merupakan aspek yang sangat vital dalam mendeteksi gejala awal dari berbagai penyakit seseorang terutama dalam kondisi pasca-pandemi COVID-19. Namun, permasalahan yang muncul adalah adanya keterbatasan dalam sistem monitoring konvensional yang bersifat manual dan tidak real-time yang seringkali menghambat efisiensi dalam proses memonitoring kesehatan, khususnya di lingkungan rumah sakit, puskesmas, atau pemantauan individu di rumah. Untuk itu, tujuan penelitian ini adalah merancang dan mengembangkan sistem monitoring kesehatan yang mampu mengukur tanda-tanda vital sign secara real-time dan terintegrasi dengan web server agar data dapat diakses secara jarak jauh melalui smartphone. Penerapan metode yang digunakan dalam penelitian ini mencakup perancangan perangkat keras menggunakan sensor MLX90614 untuk suhu tubuh, sensor MAX30100 untuk denyut jantung dan saturasi oksigen, serta mikrokontroler ESP32-CAM yang memiliki konektivitas Wi-Fi dan kamera. Data yang diperoleh dari sensor dikirimkan secara otomatis ke web server berbasis protokol HTTP dan ditampilkan dalam antarmuka berbasis web yang ramah pengguna. Kebaruan dari penelitian ini terletak pada integrasi sensor vital sign dengan ESP32-CAM berbasis IoT yang tidak hanya mengirimkan data biometrik secara real-time, tetapi juga memungkinkan pengawasan visual melalui kamera bawaan, yang sangat berguna untuk verifikasi pasien dalam konteks monitoring jarak jauh. Hasil pengujian sistem menunjukkan bahwa perangkat mampu membaca dan mengirimkan data dengan tingkat akurasi rata-rata suhu tubuh sebesar 99,34 %, detak jantung sebesar 87,89 %, dan saturasi oksigen sebesar 99,13%.
Kata Kunci - monitoring, suhu, detak jantung, saturasi oksigen, web server.
Edwin Villagran, John Javier Espitia, Fabián Andrés Velázquez
et al.
Protected agriculture increasingly requires solutions that reduce energy consumption and environmental impacts while maintaining stable microclimatic conditions. The integration of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) with solar technologies has emerged as a pathway toward autonomous and energy-efficient greenhouses and solar dryers. This study analyzes the scientific and technological evolution of this convergence using a mixed review approach bibliometric and systematic, following PRISMA 2020 guidelines. From Scopus records (2012–2025), 115 documents were screened and 79 met the inclusion criteria. Bibliometric results reveal accelerated growth since 2019, led by Engineering, Computer Science, and Energy, with China, India, Saudi Arabia, and the United Kingdom as dominant contributors. Thematic analysis identifies four major research fronts: (i) thermal modeling and energy efficiency, (ii) predictive control and microclimate automation, (iii) integration of photovoltaic–thermal (PV/T) systems and phase change materials (PCMs), and (iv) sustainability and agrivoltaics. Systematic evidence shows that AI, ML, and DL based models improve solar forecasting, microclimate regulation, and energy optimization; model predictive control (MPC), deep reinforcement learning (DRL), and energy management systems (EMS) enhance operational efficiency; and PV/T–PCM hybrids strengthen heat recovery and storage. Remaining gaps include long-term validation, metric standardization, and cross-context comparability. Overall, the field is advancing toward near-zero-energy greenhouses powered by Internet of Things (IoT), AI, and solar energy, enabling resilient, efficient, and decarbonized agro-energy systems.
Quantum Key Distribution (QKD) provides secure communication by leveraging quantum mechanics, with the BB84 protocol being one of its most widely adopted implementations. However, the classical post-processing steps in BB84, such as sifting, error correction, and key verification, often result in significant communication overhead, limiting its efficiency and scalability. In this work, we propose three key optimizations for BB84: (1) PRNG-based predetermined key bit positioning, which eliminates redundant bit exchanges during sifting, (2) hash-based subsequence comparison, enabling lightweight and efficient key verification, and (3) adaptive basis reconciliation, which minimizes the communication costs associated with basis matching. The proposed optimizations achieve a 50% reduction in communication overhead for large key sizes compared to traditional QKD protocols, as demonstrated through rigorous performance analysis. While the focus of this work is on the BB84 protocol, these optimizations are also directly applicable to a broader class of Discrete-Variable QKD (DV-QKD) protocols, such as six-state, B92, and E91, which share a fundamentally similar post-processing structure. This generality highlights the modularity and adaptability of the proposed methods across diverse QKD implementations. The proposed optimizations enhance post-processing efficiency and scalability, enabling practical deployment in bandwidth-limited environments like IoT networks, secure financial systems, and defense communications, thereby supporting broader adoption of quantum communication systems.
Telecommunication, Transportation and communications
Abstract Due to the COVID-19 pandemic, there is a necessity to implement the latest technologies that will help provide safe, real-time, and predictive medical services. The current pandemic alert apps, including BlueDot, Aarogya Setu, and the JHU Dashboard, are based on centralized reporting and manual updates, which reduce their scalability, security, and predictions. In an effort to address these gaps, this paper proposes a novel concept that integrates blockchain, artificial intelligence (AI), and digital twins to decentralize pandemic alerting and monitoring. Within the proposed COVID-DT model, blockchain enables the tamper-free, decentralized sharing of data across healthcare stakeholders. BiLSTM networks improve predictive accuracy over time, and DTs generate dynamic digital imprints for use in ongoing monitoring and simulation of outbreaks. An implementation was designed based on Raspberry Pi edge devices, IoT sensors, and Hyperledger Fabric, and simulated in MATLAB. Findings show predictive accuracy of 97.7, lower latency time of 4.3 min using 12 worker nodes, consistent message delivery (approximately 80%), and a cost of communication of less than 700 bytes with an error of 10 percent. These results demonstrate the scalability, low power, and cybersecurity of this model. The COVID-DT framework provides a safe, efficient, and interoperable backbone for the management of future pandemics, surpassing current centralized systems.
The deployment of smart-city applications has increased the number of Internet of Things (IoT) devices connected to a network cloud. Thanks to its flexibility in matching data publishers and subscribers, broker-based data communication could be a solution for such IoT data delivery, and MQTT is one of the widely used messaging protocols in this class. While MQTT by default does not differentiate message flows by size, it is observed that transient local network congestion may cause size-dependent latency additions, and that the accumulation of large message copies in the cloud broker could run out of the broker memory. In response, in the scope of cloud-edge messaging, this research article presents problem analysis, system design and implementation, and empirical and analytical performance evaluation. The article introduces three message scheduling policies for subscribers deployed at network edge, and a memory allocation scheme for MQTT broker deployed at network cloud. The proposed design has been implemented based on Eclipse Mosquitto, an open-source MQTT broker implementation. Empirical and analytical validations have demonstrated the performance of the proposed design in latency mitigation, and the result also shows that, empirically, the proposed design may save the run-time broker memory footprint by about 75%. Applicability of the proposed design to other messaging services are discussed by the end of the 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.
Narges Rashvand, Kenneth Witham, Gabriel Maldonado
et al.
Automatic modulation recognition (AMR) is vital for accurately identifying modulation types within incoming signals, a critical task for optimizing operations within edge devices in IoT ecosystems. This paper presents an innovative approach that leverages Transformer networks, initially designed for natural language processing, to address the challenges of efficient AMR. Our Transformer network architecture is designed with the mindset of real-time edge computing on IoT devices. Four tokenization techniques are proposed and explored for creating proper embeddings of RF signals, specifically focusing on overcoming the limitations related to the model size often encountered in IoT scenarios. Extensive experiments reveal that our proposed method outperformed advanced deep learning techniques, achieving the highest recognition accuracy. Notably, our model achieved an accuracy of 65.75 on the RML2016 and 65.80 on the CSPB.ML.2018+ dataset.
The exponential growth and widespread adoption of Internet of Things (IoT) devices have introduced many vulnerabilities. Attackers frequently exploit these flaws, necessitating advanced technological approaches to protect against emerging cyber threats. This paper introduces a novel approach utilizing hardware honeypots as an additional defensive layer against hardware vulnerabilities, particularly hardware Trojans (HTs). HTs pose significant risks to the security of modern integrated circuits (ICs), potentially causing operational failures, denial of service, or data leakage through intentional modifications. The proposed system was implemented on a Raspberry Pi and tested on an emulated HT circuit using a Field-Programmable Gate Array (FPGA). This approach leverages hardware honeypots to detect and mitigate HTs in the IoT devices. The results demonstrate that the system effectively detects and mitigates HTs without imposing additional complexity on the IoT devices. The Trojan-agnostic solution offers full customization to meet specific security needs, providing a flexible and robust layer of security. These findings provide valuable insights into enhancing the security of IoT devices against hardware-based cyber threats, thereby contributing to the overall resilience of IoT networks. This innovative approach offers a promising solution to address the growing security challenges in IoT environments.
Nowadays, attackers target Internet of Things (IoT) devices for security exploitation, and search engines for devices and services compromise user privacy, including IP addresses, open ports, device types, vendors, and products.Typically, application banners are used to recognize IoT device profiles during network measurement and reconnaissance. In this paper, we propose a novel approach to obfuscating IoT device banners (BANADV) based on adversarial examples. The key idea is to explore the susceptibility of fingerprinting techniques to a slight perturbation of an IoT device banner. By modifying device banners, BANADV disrupts the collection of IoT device profiles. To validate the efficacy of BANADV, we conduct a set of experiments. Our evaluation results show that adversarial examples can spoof state-of-the-art fingerprinting techniques, including learning- and matching-based approaches. We further provide a detailed analysis of the weakness of learning-based/matching-based fingerprints to carefully crafted samples. Overall, the innovations of BANADV lie in three aspects: (1) it utilizes an IoT-related semantic space and a visual similarity space to locate available manipulating perturbations of IoT banners; (2) it achieves at least 80\% success rate for spoofing IoT scanning techniques; and (3) it is the first to utilize adversarial examples of IoT banners in network measurement and reconnaissance.
In the wake of disruptive IoT technologies generating massive amounts of diverse data, Machine Learning (ML) will play a crucial role in bringing intelligence to Internet of Things (IoT) networks. This paper provides a comprehensive analysis of the current state of resource allocation within IoT networks, focusing specifically on two key categories: Low-Power IoT Networks and Mobile IoT Networks. We delve into the resource allocation strategies that are crucial for optimizing network performance and energy efficiency in these environments. Furthermore, the paper explores the transformative role of Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) in enhancing IoT functionalities. We highlight a range of applications and use cases where these advanced technologies can significantly improve decision-making and optimization processes. In addition to the opportunities presented by ML, DL, and RL, we also address the potential challenges that organizations may face when implementing these technologies in IoT settings. These challenges include crucial accuracy, low flexibility and adaptability, and high computational cost, etc. Finally, the paper identifies promising avenues for future research, emphasizing the need for innovative solutions to overcome existing hurdles and improve the integration of ML, DL, and RL into IoT networks. By providing this holistic perspective, we aim to contribute to the ongoing discourse on resource allocation strategies and the application of intelligent technologies in the IoT landscape.
In the rapidly evolving landscape of the IoT, the security of connected devices has become a paramount concern. This paper explores the concept of proactive threat hunting as a pivotal strategy for enhancing the security and sustainability of IoT systems. Proactive threat hunting is an alternative to traditional reactive security measures that analyses IoT networks continuously and in advance to find and eliminate threats before they occure. By improving the security posture of IoT devices this approach significantly contributes to extending IoT operational lifespan and reduces environmental impact. By integrating security metrics similar to the Common Vulnerability Scoring System (CVSS) into consumer platforms, this paper argues that proactive threat hunting can elevate user awareness about the security of IoT devices. This has the potential to impact consumer choices and encourage a security-conscious mindset in both the manufacturing and user communities. Through a comprehensive analysis, this study demonstrates how proactive threat hunting can contribute to the development of a more secure, sustainable, and user-aware IoT ecosystem.
The rapid expansion of the Internet of Things (IoT) has revolutionized various domains, offering significant benefits through enhanced interconnectivity and data exchange. However, the security challenges associated with IoT networks have become increasingly prominent owing to their inherent vulnerability. This paper provides an in-depth analysis of the network layer in IoT architectures, highlighting the potential risks posed by routing attacks, such as blackholes, wormholes, sinkholes, Sybil, and selective forwarding attacks. This study explores the unique challenges posed by the constrained resources, heterogeneity, and dynamic topology of IoT networks, which complicate the implementation of robust security measures. Various countermeasures, including trust-based mechanisms, Intrusion Detection Systems (IDS), and routing protocols, are evaluated for their effectiveness in mitigating these threats. This study also emphasizes the importance of considering misbehavior observation, trust management, and lightweight defense strategies in the design of secure IoT networks. These findings contribute to the development of comprehensive defense mechanisms tailored to the specific challenges of IoT environments.
Emmanuel Effah, Ousmane Thiare, Alexander M. Wyglinski
In this paper, we present a real-world hardware evaluation of a robust, affordable, location-independent, simple, and infrastructure-less cluster-based agricultural Internet of Things (CA-IoT) network based on a commercial off-the-shelf (COTS) Bluetooth Low-Energy (BLE) communication technique and Raspberry Pi module 3 B + (RPI 3 B +) to address global food insecurity caused by climate change and increasing global population via precision farming and greenhouses. Using an engineering design approach, an initial centralized agricultural IoT hardware test-bed was implemented with the aid of BLE, RPi 3 B +, DHT22, STEMMA soil moisture sensors, UM25 meters, and LoPy /low-power Wi-Fi modules, among other devices. This test-bed was adapted and modified after the proposed cluster-based architecture to evaluate the performance of CA-IoT networks. This study provides holistic account of our location-independent CA-IoT solution covering the design and deployment experiences that can serve as a reference document to the agricultural Internet of Things (Agri-IoT) community. Additionally, the proposed solution performed satisfactorily when tested under indoor and outdoor (on-farm) environmental conditions in the USA and Senegal. Unlike existing Agri-IoT test-beds, a sample performance evaluation showed that our context-relevant CA-IoT technology is simple to deploy and manage by inexperienced users and is energy-efficient, location-independent, robust, and task- and size-scalable to provide a rich set of measurements for both educational and commercial purposes.
The internet-of-things (IoT) refers to the growing field of interconnected pervasive computing devices and the networking that supports smart, embedded applications. The IoT has multiple human-computer interaction challenges due to its many formats and interlinked components, and central to these is the need to provide sensory information and situational context pertaining to users in a more human-friendly, easily understandable format. This work addresses this by applying mixed reality toward expressing the underlying behaviors and states internal to IoT devices and IoT-enabled objects. It extends the authors' previous research on IoT Avatars (mixed reality character representations of physical IoT devices), presenting a new head-mounted display framework and interconnection architecture. This contributes i) an exploration of mixed reality for smart spaces, ii) an approach toward expressive avatar behaviors using fuzzy inference, and iii) an early functional prototype of a hybrid physical and mixed reality IoT-enabled object. This approach is a step toward new information presentation, interaction, and engagement capabilities for smart devices and environments.
Sanchita Saha, Ashlesha Hota, Bikramjit Choudhury
et al.
In the technology driven era, automation and communication have attained new heights, which paved the way for an improved human lifestyle. The Internet of Things (IoT) is one of the key aspects of this domain that promises to connect real-world objects embedded with sensors, enable them to communicate, and aid in making informed decisions. However, the growth of technology for convenience has also attracted various attacks on privacy and confidentiality. Numerous classical public-key cryptography based security solutions have been adopted to evade the problems in group communication. Unfortunately, most of the solutions fail to meet computationally lightweight requirements. In this paper, we propose a lightweight NTRU (Nth Degree Truncated Polynomial Ring Units) and Secret Sharing Based Secure Group Communication Scheme that is suitable for low bandwidth communication over the Internet of Medical Things (IoMT), Vehicular Adhoc Networks (VANET) and Precision Agriculture. Theoretical analyses and illustrations demonstrate that the proposed scheme is superior in comparison to the existing schemes.