Recently, Molecular Communication (MC) has been recognized as an enabling technology for nanonetworks where MC is envisioned to enable nanorobots to achieve sophisticated and complex tasks in the human body for promising medical applications. Many MC methods that can be applied in the human body have been proposed and modeled in the literature. In this paper, we propose a new method and system for Molecular Communication in the Body, denoted MoSiMe and MoCoBo, respectively. The method takes advantage of how the absorption, distribution, metabolization, and excretion (ADME) bodily processes affects drugs, referred to as Pharmacokinetics (PK) in pharmacology, to enable MC between any points of the human body regardless of their distance even if they are in different parts of the body and separated by tissues and fluids. The architecture, design and components of the MoCoBo system and MoSiMe method are described and different transmitter designs, including a novel passive transmitter design for the first time in telecommunications, are introduced. An analytical model for the body channel is derived and validated with respect to existing human and animal tests. The model captures the ADME bodily processes that affect the kinetics of substances administered to the body. Additionally, an experimental MoCoBo proof of concept platform, capable of sending and receiving a stream of bits between a transmitter and a receiver, is built and validated against clinical trials, animal tests and analytical models. The introduced platform can also be utilized to test modulation techniques and designs for new MoCoBo transmitters and receivers.

This paper presents an intelligent, sensor-driven framework that integrates emerging technologies to deliver smart, reliable, and accessible transit assistance for visually impaired people (VIP). The proposed system leverages Internet of Things (IoT), Internet of Devices (IoD), GPS, and crowdsensing to collect multimodal data, comprising audio, video, and environmental signals, used to characterize and respond to users’ real-time mobility needs. A cloud-based architecture performs centralized data fusion and decision-making using artificial intelligence (AI) and machine learning (ML) algorithms, enabling rapid interpretation of sensor inputs and generation of personalized navigation guidance. The framework is implemented through a mobile application that coordinates data exchange between edge devices and cloud services, providing context-aware navigation, obstacle alerts, and two-way communication with transit operators. Unlike existing assistive mobility solutions, which rely primarily on static location services and lack cross-sensor integration, the proposed system introduces a unified AI-enabled sensing layer that supports dynamic adaptation to complex urban environments. The results demonstrate the framework’s potential to enhance autonomy, safety, and situational awareness for VIPs, offering a scalable foundation for inclusive smart transportation systems.