Flexure-FET-Based Receiver with Competitive Binding for Interference Mitigation in Molecular Communication

Molecular communication (MC), a biologically inspired technology, enables applications in nanonetworks and the Internet of Everything (IoE), with great potential for intra-body systems such as drug delivery, health monitoring, and disease detection. This paper extends our prior work on the Flexure-FET MC receiver by integrating a competitive binding model to enhance performance in high-interference environments, where multiple molecular species coexist in the reception space. Previous studies have largely focused on ligand concentration estimation and detection, without fully addressing the effects of inter-species competition for receptor binding. Our proposed framework captures this competition, offering a more biologically accurate model for multitarget environments. By incorporating competition dynamics, the model improves understanding of MC behavior under interference. This approach enables fine-tuning of receptor responses by adjusting ligand concentrations and receptor affinities, thereby optimizing the performance of the Flexure-FET MC receiver. Comprehensive analysis shows that accounting for competitive binding is crucial for improving reliability and accuracy in complex MC systems. Factors such as signal-to-noise ratio (SNR), symbol error probability (SEP), interferer concentration, and receptor dynamics are shown to significantly affect performance. The proposed framework highlights the need to manage these factors effectively. Results demonstrate that modeling interference through competitive binding offers a realistic system perspective and allows tuning of receiver response, enabling robust detection in environments with multiple coexisting species.

Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things

This paper focuses on Zero-Trust Foundation Models (ZTFMs), a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems. By integrating core tenets, such as continuous verification, least privilege access (LPA), data confidentiality, and behavioral analytics into the design, training, and deployment of FMs, ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments. We present the first structured synthesis of ZTFMs, identifying their potential to transform conventional trust-based IoT architectures into resilient, self-defending ecosystems. Moreover, we propose a comprehensive technical framework, incorporating federated learning (FL), blockchain-based identity management, micro-segmentation, and trusted execution environments (TEEs) to support decentralized, verifiable intelligence at the network edge. In addition, we investigate emerging security threats unique to ZTFM-enabled systems and evaluate countermeasures, such as anomaly detection, adversarial training, and secure aggregation. Through this analysis, we highlight key open research challenges in terms of scalability, secure orchestration, interpretable threat attribution, and dynamic trust calibration. This survey lays a foundational roadmap for secure, intelligent, and trustworthy IoT infrastructures powered by FMs.

Semantic Learning for Molecular Communication in Internet of Bio-Nano Things

Molecular communication (MC) provides a foundational framework for information transmission in the Internet of Bio-Nano Things (IoBNT), where efficiency and reliability are crucial. However, the inherent limitations of molecular channels, such as low transmission rates, noise, and inter-symbol interference (ISI), limit their ability to support complex data transmission. This paper proposes an end-to-end semantic learning framework designed to optimize task-oriented molecular communication, with a focus on biomedical diagnostic tasks under resource-constrained conditions. The proposed framework employs a deep encoder-decoder architecture to efficiently extract, quantize, and decode semantic features, prioritizing task-relevant semantic information to enhance diagnostic classification performance. Additionally, a probabilistic channel network is introduced to approximate molecular propagation dynamics, enabling gradient-based optimization for end-to-end learning. Experimental results demonstrate that the proposed semantic framework improves diagnostic accuracy by at least 25% compared to conventional JPEG compression with LDPC coding methods under resource-constrained communication scenarios.

Smell of Source: Learning-Based Odor Source Localization with Molecular Communication

Odor source localization is a fundamental challenge in molecular communication, environmental monitoring, disaster response, industrial safety, and robotics. In this study, we investigate three major approaches: Bayesian filtering, machine learning (ML) models, and physics-informed neural networks (PINNs) with the aim of odor source localization in a single-source, single-molecule case. By considering the source-sensor architecture as a transmitter-receiver model we explore source localization under the scope of molecular communication. Synthetic datasets are generated using a 2D advection-diffusion PDE solver to evaluate each method under varying conditions, including sensor noise and sparse measurements. Our experiments demonstrate that \textbf{Physics-Informed Neural Networks (PINNs)} achieve the lowest localization error of \(\mathbf{0.89 \times 10^{-6}}\) m, outperforming \textbf{machine learning (ML) inversion} (\(\mathbf{1.48 \times 10^{-6}}\) m) and \textbf{Kalman filtering} (\(\mathbf{1.62 \times 10^{-6}}\) m). The \textbf{reinforcement learning (RL)} approach, while achieving a localization error of \(\mathbf{3.01 \times 10^{-6}}\) m, offers an inference time of \(\mathbf{0.147}\) s, highlighting the trade-off between accuracy and computational efficiency among different methodologies.

Martian Dust Storm Detection with THz Opportunistic Integrated Sensing and Communication in the Internet of Space (IoS)

This paper presents the Mars Dust Storm Detector (MDSD), a system that leverages the THz Opportunistic Integrated Sensing and Communications (OISAC) signals between Mars surface assets (rovers and landers) to extract environmental information, particularly dust storm properties. The MDSD system utilizes the multi-parameter sensitivity of THz signal attenuation between Martian communication devices to provide rich, real-time data on storm intensity, particle characteristics, and potentially even electrification state. This approach, incorporating HITRAN spectroscopic data and Martian-specific atmospheric parameters, allows for accurate modeling and analysis. The system's ability to repurpose THz ISAC signals for environmental sensing demonstrates an efficient use of resources in the challenging Martian environment, utilizing communication infrastructure to enhance our understanding of Mars' atmospheric dynamics. The system's performance is evaluated through extensive simulations under various Node Density Factors (NDFs), comparing different interpolation algorithms for dust storm intensity mapping. Results demonstrate that linear interpolation achieves superior accuracy (correlation >0.90) at high NDFs, while nearest-neighbor and IDW algorithms maintain complete spatial coverage in sparse networks. Error analysis identifies dust particle size uncertainty as the primary contributor to estimation errors, though the system shows resilience to Martian atmospheric variations. This work extends the opportunistic use of ISAC technology to planetary exploration, contributing to both Mars atmospheric monitoring capabilities and ISAC applications in the Internet of Space (IoS).

A Novel Framework of Horizontal-Vertical Hybrid Federated Learning for EdgeIoT

This letter puts forth a new hybrid horizontal-vertical federated learning (HoVeFL) for mobile edge computing-enabled Internet of Things (EdgeIoT). In this framework, certain EdgeIoT devices train local models using the same data samples but analyze disparate data features, while the others focus on the same features using non-independent and identically distributed (non-IID) data samples. Thus, even though the data features are consistent, the data samples vary across devices. The proposed HoVeFL formulates the training of local and global models to minimize the global loss function. Performance evaluations on CIFAR-10 and SVHN datasets reveal that the testing loss of HoVeFL with 12 horizontal FL devices and six vertical FL devices is 5.5% and 25.2% higher, respectively, compared to a setup with six horizontal FL devices and 12 vertical FL devices.

DebriSense: Terahertz-based Integrated Sensing and Communications (ISAC) for Debris Detection and Classification in the Internet of Space (IoS)

The proliferation of Low Earth Orbit (LEO) satellite constellations has intensified the challenge of space debris management. This paper introduces DebriSense-THz, a novel Terahertz-Enabled Debris Sensing system for LEO satellites that leverages Integrated Sensing and Communications (ISAC) technology. We present a comprehensive THz channel model for LEO environments, incorporating debris interactions such as reflection, scattering, and diffraction. The DebriSense-THz architecture employs machine learning techniques for debris detection and classification using Channel State Information (CSI) features. Performance evaluation across different frequencies (30 GHz-5 THz), MIMO configurations, debris densities, and SNR levels demonstrates significant improvements in debris detection and classification accuracy (95-99% at 5 THz compared to 62-81% at 30 GHz). Higher SNR configurations enhance sensing performance, particularly at higher frequencies. The system shows robust performance across various debris densities and MIMO size in the THz range, with a noted trade-off between communication reliability and sensing accuracy at lower frequencies. DebriSense-THz represents a significant advance in space situational awareness, paving the way for more effective debris mitigation strategies in increasingly congested LEO environments.

Mycorrhizal Fungi and Plant Symbiosis for Energy Harvesting in the Internet of Plants

Biological entities in nature have developed sophisticated communication methods over millennia to facilitate cooperation. Among these entities, plants are some of the most intricate communicators. They interact with each other through various communication modalities, creating networks that enable the exchange of information and nutrients. In this paper, we explore this collective behavior and its components. We then introduce the concept of agent plants, outlining their architecture and detailing the tasks of each unit. Additionally, we investigate the mycorrhizal fungi-plant symbiosis to extract glucose for energy harvesting. We propose an architecture that converts the chemical energy stored in these glucose molecules into electrical energy. We conduct comprehensive analyses of the proposed architecture to validate its effectiveness.

Sustainable and Precision Agriculture with the Internet of Everything (IoE)

The accelerated pace of global population growth underscores the crucial role of the agricultural sector in mitigating food scarcity, as well as in supporting livelihoods through employment opportunities and bolstering national economies. This sector faces several critical challenges, including resource depletion, socioeconomic issues, gaps in technology and innovation, and the impact of climate change. The introduction of mechanization has significantly transformed agriculture by enhancing sustainability and increasing the productivity of crops. Recently, traditional farming methods have been supplemented with advanced technologies steering the industry towards precision agriculture. The convergence of these advanced technologies has facilitated the automation of various tasks such as water management, crop monitoring, disease management, and harvesting. The concept of Internet of Everything (IoE) has gained traction due to its holistic approach towards integrating various IoT specializations, called IoXs where X referring to a specific domain. This includes areas like the Internet of Sensors (IoS), Internet of Vehicles (IoV), Internet of Energy (IoEn), Internet of Space Things (IoST), Industrial Internet of Things (IIoT), and Internet of Drones (IoD). This paper explores the potential of the Internet of Everything (IoE) in revolutionizing agricultural systems. The focus is on assessing the impact of cutting-edge and novel technologies, such as 6G, molecular communication (MC), Internet of Nano Things (IoNT), Internet of Bio-Nano Things (IoBNT), Internet of Fungus, and designer phages, in significantly improving agricultural yield, efficiency, and productivity. Additionally, the potential of these technologies is evaluated in terms of their applicability, associated challenges, and future research directions within the realm of precision agriculture.

Bacterial Communications and Computing in Internet of Everything (IoE)

Concurrent with advancements in molecular communication (MC), bacterial communication is emerging as a key area of interest. Given the frequent use of bacteria in various MC models, it is essential to have a thorough grasp of their intrinsic communication, signaling, and engineering techniques. Although it is crucial to have a strong understanding of the communication background, the inherent biological variability of bacteria may introduce complexity. Thus, an in-depth understanding of bacteria and their communication is a must for improving and extending the models in which they are utilized. Furthermore, the emerging and evolving domain of bacterial computing provides an exciting opportunity for advancing applications in areas such as environmental monitoring and biological computing networks. By integrating the communication and sensing capabilities, bacterial computing offers a promising framework for enhancing the adaptability and responsiveness of bacteria. This paper provides a comprehensive review of bacterial communication and computing, illustrating their application and the link with the concept of the Internet of Everything (IoE). Through the analysis of these biological systems, we reach a deeper insight on how the small-scale interactions may contribute to the major concept of universal interconnectedness; thus, we make the knowledge to flow and communication stronger between different fields. The discussion include the identification of the different bacterial mechanisms that could revolutionize the traditional communication systems. Thus, this paper offers valuable insights into previously unaddressed aspects of bacterial behavior, suggesting novel avenues for future research and aiming to advance understanding and application of bacterial sensing, communication and computing in MC models.