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Cooperative communications have been demonstrated to be effective in combating the multiple fading effects in wireless networks, and improving the network performance in terms of adaptivity, reliability, data throughput, and network lifetime. In this paper, we investigate the use of cooperative communications for Quality of Service (QoS) provisioning in resource-constrained wireless sensor networks, and propose MRL-CC, a Multi-agent Reinforcement Learning based multi-hop mesh Cooperative Communication mechanism. In MRL-CC, a multi-hop mesh cooperative structure is constructed for reliable data disseminations. The cooperative mechanism that defines cooperative partner assignments, and coding and transmission schemes, is implemented using a multi-agent reinforcement learning algorithm. We compare the network performance of MRL-CC with MMCC, a Multi-hop Mesh structure based Cooperative Communication scheme, and investigate the impacts of network traffic load, interference, and sensor node's mobility on the network performance. Simulation results show that MRL-CC performs well in terms of a number of QoS metrics, and fits well in large-scale networks and highly dynamic environments.

Ultra wideband (UWB) is a promising technology for wireless body area networks (WBANs). The authors investigate the wave propagation in the frequency range of 1-6-GHz for non-line-of-sight (NLOS) channels from the front to back of a human body by considering different wave polarisations: perpendicular and tangential with respect to the body. Time domain electromagnetic (EM) simulation using the anatomical model of a human body assuming frequency-dependent tissue properties is conducted. Radiographs of the energy density and delay spread of the NLOS channels are provided. The energy fading of UWB signals for the on-body channel suggests that the optimisation of the receiving antenna position for efficient signal transmission can be useful. A significant enhancement of the on-body communication link is identified by exploiting the transceiver antennas at the perpendicular to body polarisations. The impacts of body environment reflections on the channel characteristics of different polarisations are studied. The results show that the perpendicular polarisation channel characteristics are less influenced by the body environment reflections and are more stable regarding energy density and delay spread than tangential polarisation. The frequency-dependent propagation loss of on-body UWB channels is derived, where the efficient communication bandwidths for both perpendicular and tangential wave polarisations are given.

Blood vessels are flow-induced diffusive molecular channels equipped with transport mechanisms across their walls for conveying substances between the organs in the body. Mathematical modeling of the blood vessel as a molecular transport channel can be used for the characterization of the underlying processes and higher-level functions in the circulatory system. Besides, the mathematical model can be utilized for designing and realizing nano-scale molecular communication systems for healthcare applications including drug delivery systems. In this paper, a continuous-time Markov chain framework is proposed to simply model active transport mechanisms e.g. transcytosis, across the single-layered endothelial cells building the inner vessel wall. Correspondingly, a general homogeneous boundary condition over the vessel wall is introduced. Coupled with the derived boundary condition, the flow-induced diffusion problem in an ideal vessel structure with a cylindrical shape is accurately formulated which takes into account variation in all three dimensions. The corresponding concentration Green's function is analytically derived in terms of a convergent infinite series. Particle-based simulation results confirm the proposed analysis. Also, the effects of system parameters on the concentration Green's function are examined.

A novel implantable and externally controllable stem-cell-based platform for the treatment of Glioblastoma brain cancer has been proposed to bring hope to patients who suffer from this devastating cancer type. Induced Neural Stem Cells (iNSCs), known to have potent therapeutic effects through exosomes-based molecular communication, play a pivotal role in this platform. Transplanted iNSCs demonstrate long-term survival and differentiation into neurons and glia which then fully functionally integrate with the existing neural network. Recent studies have shown that specific types of calcium channels in differentiated neurons and astrocytes are inhibited or activated upon cell depolarization leading to the increased intracellular calcium concentration levels which, in turn, interact with mobilization of multivesicular bodies and exosomal release. In order to provide a platform towards treating brain cancer with the optimum therapy dosage, we propose mathematical models to compute the therapeutic exosomal release rate that is modulated by cell stimulation patterns applied from the external wearable device. This study serves as an initial and required step in the evaluation of controlled exosomal secretion and release via induced stimulation with electromagnetic, optical and/or ultrasonic waves.

Propagation of radio-frequency (RF) signals inside human body is demanding to analyze as it is a highly complex medium consisting of different frequency-dependent lossy materials of varying thickness. Moreover, experimental analyses are also unfeasible because that requires probes to be placed inside a human body to collect the signals. This paper focuses on in-body to in-body implant communication for future multi-nodal capsule-like leadless cardiac pacemaker technology. The frequency range of 0.3 - 3 GHz is analyzed using very detailed numerical simulations of digital human models. The results show that the Industrial, Scientific, and Medical (ISM) radio band of the frequency range of 2.4 - 2.5 GHz is optimal, having the least attenuation of signals considering the size constraints of the implant antenna. Furthermore, the placement of an additional sub-cutaneous implant transceiver is studied. The analysis shows that the abdominal wall is the optimal position for the placement of the implant compared to shoulder and lateral side of the body. This result is further validated by an in-vivo experiment on an adult pig. The other novelty of the study is the investigation of the channel behavior based on ventricular blood volume of the heart to find out the appropriate timing of the transmission of signals between the implants. The results show that the attenuation of the signal increases with the increase in blood volume inside the heart. This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. © 2018 IEEE Transactions on Biomedical Engineering

Multi-node leadless pacemaker system overcomes the main limitations related to lead complications of the conventional cardiac pacemaker and will thus replace them in the near future. The multiple nodes of the technology require the development of low-power, low data-rate and energy-efficient communication framework for device synchronization and bi-directional communication between them. Moreover, the nodes need to communicate with the outer peripheral devices for data telemetry, control and remote monitoring. This paper focuses on evaluation of different energy-efficient modulation schemes at 433 MHz for bi-directional communication between the nodes using homogeneous liquid phantom model of human heart and living animal experiments. In this paper, we have analyzed three simple, low-budget modulation schemes - On Off-Keying (OOK), Frequency Shift-Keying (FSK), and Gaussian Frequency Shift-Keying (GFSK). The analysis is done based on the total transmitter power required to achieve a reliable communication indicated by the minimum threshold values of bit-error rate and packet-error rate. The experiments have been conducted for three common implant communication scenarios - in-body to in-body, in-body to on-body and in-body to off-body links. For conducting the experiments, we have designed the experimental setup with electronic components and fabricated antennas. The results have shown that GFSK has the best performance among the other modulation techniques based on the total transmitter power. We also investigated higher order of the same modulation schemes - 4-FSK and 4-GFSK. The results showed that GFSK performed much better than 4-FSK and 4-GFSK. This research will be carried forward to build the entire radio frequency communication framework for the multi-node pacemaker technology. Open Access CC-BY

To achieve full cooperative diversity gains while still obtaining spectral and energy efficiency, cooperative communications with relay selection schemes, i.e., only the best relay is selected from multiple relaying candidates to cooperate with the communication, have been extensively studied in recent research. In this paper, we review the recent research on optimal relay assignment for cooperative communications, and investigate the use of cooperative communications with adaptive relay selection for soft QoS provisioning in resource-constrained wireless sensor networks. We propose EEARS, an energy-efficient adaptive relay selection scheme, which is based on a multi-agent reinforcement learning framework. In EEARS, optimal relays, in terms of outage probability, spectral and energy efficiency, are selected distributedly from multiple relaying candidates to participate in the communication, without the needs of prior knowledge of the wireless network model and centralized control. Simulation results show that EEARS fits well in dynamic environments, and is effective in improving the satisfying level of soft QoS provisioning for WSNs, i.e., increasing the spectral and energy efficiency, and reducing the amount of time that QoS violation occurs, by exploiting spatial and time diversities. Furthermore, compared with schemes using fixed transmitting power, EEARS can achieve a higher energy efficiency by varying the transmission power level according to wireless channel conditions.

We develop a battery-free communication system for a wireless video capsule endoscope with potential video streaming of a rate of up to 15 Mbps. We apply our innovative approach using backscatter for implants, a RADAR approach that remotely reads the information from the deep implants, such as the video capsule endoscope. We use the 5G deployed network with edge computing and slicing to transmit the video data from the capsule to a high-performance computing platform in a secure way with guaranteed end-to-end latency to perform polyp detection and localization. This way, the energy-intensive inference using deep learning neural networks for polyp detection and localization can be completed in the edge where control signals are sent back to the pill to increase the spatial and temporal resolution of the video, to obtain high-quality images for further analysis. The paper provides a system-level description, demonstrating the capsule data streaming to the network.

International audience; Colonoscopy is the gold standard for colon cancer screening though still some polyps are missed, thus preventing early disease detection and treatment. Several computational systems have been proposed to assist polyp detection during colonoscopy but so far without consistent evaluation. The lack of publicly available annotated databases has made it difficult to compare methods and to assess if they achieve performance levels acceptable for clinical use. The Automatic Polyp Detection sub-challenge, conducted as part of the Endoscopic Vision Challenge (http://endovis.grand-challenge.org) at the international conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2015, was an effort to address this need. In this paper, we report the results of this comparative evaluation of polyp detection methods, as well as describe additional experiments to further explore differences between methods. We define performance metrics and provide evaluation databases that allow comparison of multiple methodologies. Results show that convolutional neural networks (CNNs) are the state of the art. Nevertheless it is also demonstrated that combining different methodologies can lead to an improved overall performance.

Ultra wideband (UWB) technology has big potential for applications in wireless body area networks (WBANs). The inherent characteristics of UWB signals make them suitable for the wireless interface of medical sensors. In particular, implanted medical wireless sensors for monitoring physiological parameters, automatic drug provision, etc. can benefit greatly from this ultra low power (ULP) interface. As with any other wireless technology, accurate knowledge of the channel is necessary for the proper design of communication systems. Only a few models that describe the radio propagation inside the human body have been published. Moreover, there is no comprehensive UWB in-body propagation model that includes the frequency-dependent attenuation. Hence, this paper extends a statistical model for UWB propagation channels inside the human chest in the 1–6 GHz frequency range by including the frequency-dependent attenuation. This is done by modeling the spectrum shape of distorted pulses at different depths inside the human chest. The distortion of the pulse was obtained through numerical simulations using a voxel representation of the human body. We propose a mathematical expression for the spectrum shape of the distorted pulses that act as a window function to reproduce the effects of frequency-dependent attenuation.

The performance of several decorrelating transforms is evaluated in the JPEG-2000 baseline system. The transforms considered are based on uniform parallel 2-channel and 8-channel filter banks and discrete cosine transform (DCT) building blocks. Through the analysis of lossy compression results, a system employing a combination of 8- channel and 2-channel filter banks is found to perform best overall. Also, the performance of several reversible transforms is evaluated in the same coding framework. Based on lossy and lossless compression results, a reversible version of the Cohen-Daubechies-Feauveau (2,2) wavelet transform was found to be most effective.