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Sensors in and around the environment becoming ubiquitous has ushered in the concept of smart animal agriculture which has the potential to greatly improve animal health and productivity using the concepts of remote health monitoring which is a necessity in times when there is a great demand for animal products. The data from in and around animals gathered from sensors dwelling in animal agriculture settings have made farms a part of the Internet of Things space. This has led to active research in developing efficient communication methodologies for farm networks. This study focuses on the first hop of any such farm network where the data from inside the body of the animals is to be communicated to a node dwelling outside the body of the animal. In this paper, we use novel experimental methods to calculate the channel loss of signal at sub-GHz frequencies of 100 - 900 MHz to characterize the in-body to out-of-body communication channel in large animals. A first-of-its-kind 3D bovine modeling is done with computer vision techniques for detailed morphological features of the animal body is used to perform Finite Element Method based Electromagnetic simulations. The results of the simulations are experimentally validated to come up with a complete channel modeling methodology for in-body to out-of-body animal body communication. The experimentally validated 3D bovine model is made available publicly on https://github.com/SparcLab/Bovine-FEM-Model.git GitHub. The results illustrate that an in-body to out-of-body communication channel is realizable from the rumen to the collar of ruminants with $\leq {90}~{\rm dB}$ path loss at sub-GHz frequencies ( $100-900~MHz$ ) making communication feasible. The developed methodology has been illustrated for ruminants but can also be used for other related in-body to out-of-body studies. Using the developed channel modeling technique, an efficient communication architecture can be formed for in-body to out-of-body communication in animals which paves the way for the design and development of future smart animal agriculture systems. 

Recent advances in audio-visual augmented reality (AR) and virtual reality (VR) demands 1) high speed (>10Mbps) data transfer among wearable devices around the human body with 2) low transceiver (TRX) power consumption for longer lifetime, especially as communication energy/b is often orders of magnitude higher than computation energy/switching. While WiFi can transmit compressed video (HD 30fps, compressed @6-12Mbps), it consumes 50-to-400mW power. Bluetooth, on the other hand, is not designed for video transfer. New mm-Wave links can support the required bandwidth but do not support ultra-low-power (<1mW). In recent years, Human-Body Communication (HBC) [1]–[6] has emerged as a promising low-power alternative to traditional wireless communication. However, previous implementations of HBC transmitters (Tx) suffer from a large plate-to-plate capacitance (C p , between signal electrode and local ground of the transmitter) which results in a power consumption of aC p V2f (Fig. 16.6.1) in voltage-mode (VM) HBC. The recently proposed Resonant HBC [6] tries to overcome this problem by resonating C p with a parallel inductor (L). However, the operating frequency is usually < a few 10's of MHz for low-power Electro-Quasistatic (EQS) operation, resulting in a large/bulky inductor. Moreover, the resonant LC p circuit has a large settling time (≈5Q 2 RC P , where R is the effective series resistance of the inductor) for EQS frequencies which will limit the maximum symbol rate to <1MSps for a 21MHz carrier (the IEEE 802.15.6 standard for HBC), making resonant HBC infeasible for> 10Mb/s applications. 

Continuous real-time health monitoring in animals is essential for ensuring animal welfare. In ruminants like cows, rumen health is closely intertwined with overall animal health. Therefore, in-situ monitoring of rumen health is critical. However, this demands in-body to out-of-body communication of sensor data. In this paper, we devise a method of channel modeling for a cow using experiments and FEM based simulations at 400 MHz. This technique can be further employed across all frequencies to characterize the communication channel for the development of a channel architecture that efficiently exploits its properties. 

To solve the challenge of powering and communication in a brain implant with low end-end energy loss, we present Bi-Phasic Quasi-static Brain Communication (BP-QBC), achieving < 60dB worst-case channel loss, and ~41X lower power w.r.t. traditional Galvanic body channel communication (G-BCC) at a carrier frequency of 1MHz (~6X lower power than G-BCC at 10MHz) by blocking DC current paths through the brain tissue. An additional 16X improvement in net energy-efficiency (pJ/b) is achieved through compressive sensing (CS), allowing a scalable (6kbps-10Mbps) duty-cycled uplink (UL) from the implant to an external wearable, while reducing the active power consumption to 0.52μW at 10Mbps, i.e. within the range of harvested body-coupled power in the downlink (DL), with externally applied electric currents < 1/5th of ICNIRP safety limits. BP-QBC eliminates the need for sub-cranial interrogators, utilizing quasi-static electrical signals for end-to-end BCC, avoiding transduction losses. 

Applications like Connected Healthcare through physiological signal monitoring and Secure Authentication using wearable keys can benefit greatly from battery-less operation. Low power communication along with energy harvesting is critical to sustain such perpetual battery-less operation. Previous studies have used techniques such as Tribo-Electric, Piezo-Electric, RF energy harvesting for Body Area Network devices, but they are restricted to on-body node placements. Human body channel is known to be a promising alternative to wireless radio wave communication for low power operation [1-4], through Human Body Communication, as well as very recently as a medium for power transfer through body coupled power transfer [5]. However, channel length (L) dependency of the received power makes it inefficient for L>40cm. There have also been a few studies for low power communication through the human body, but none of them could provide sustainable battery-less operation. In this paper, we utilize Resonant Electro Quasi-Static Human Body Communication (Res-EQS HBC) with Maximum Resonance Power Tracking (MRPT) to enable channel length independent whole-body communication and powering (Fig.1). We design the first system to simultaneously transfer Power and Data between a HUB device and a wearable through the human body to enable battery-less operation. Measurement results show 240uW, 28uW and 5uW power transfer through the body in a MachineMachine (large devices with strong ground connection) Tabletop (small devices kept on a table, as in [5]) and Wearable-Wearable (small form factor battery operated devices) scenario respectively, independent of body channel length, while enabling communication with a power consumption of only 2.19uW. This enables >25x more power transfer with >100x more efficiency compared to [5] for Tabletop and 100cm Body distance by utilizing the benefits of EQS. The MRPT loop automatically tracks device and posture dependent resonance point changes to maximize power transfer in all cases.