The Internet of Bodies (IoB) is a network formed by wearable, implantable, ingestible, and injectable smart devices to collect physiological, behavioral, and structural information from the human body. Thus, the IoB technology can revolutionize the quality of human life by using these context-rich data in myriad smart-health applications. Radio frequency (RF) transceivers have been typically preferred due to their availability and maturity. However, for most RF standards (e.g. Bluetooth Low Energy), the highly radiative omnidirectional RF propagation (even at the lowest settings) reaches tens of meters of coverage, thereby reducing energy efficiency, causing interference and co-existence issues, and raising privacy and security concerns. On the other hand, body channel communication (BCC) confines low-power and low-frequency (10 kHz-100 MHz) signals to the human body, leading to more secure and efficient communications. Since energy efficiency is one of the critical design parameters of IoB networks, this paper focuses on energy-efficient orthogonal body channel access (OBA) and non-orthogonal body channel access (NOBA) schemes with and without cooperation. To this aim, three main BCC topologies are presented: point-to-point channel, medium access channel, and broadcast channel. These topologies are then used as building blocks to create IoB networks relying on OBA and NOBA schemes for downlink (DL) and uplink (UL) traffic. For all schemes and traffic directions, optimal transmit power and phase time allocations are derived in closed-form, which is essential to reduce energy consumption by eliminating computational power. The closed-form expressions are further leveraged to obtain maximum network size as a function of data rate requirement, bandwidth, and hardware parameters.
|Original language||English (US)|
|Number of pages||1|
|Journal||IEEE Internet of Things Journal|
|State||Published - Dec 20 2022|
Bibliographical noteKAUST Repository Item: Exported on 2022-12-23
Acknowledgements: The authors gratefully acknowledge financial support for this work from the KAUST and the Smart Health Initiative (SHI) at KAUST. We would like to thank Qi Huang from CCSL group for his help in preparing Fig. 2