In this paper, we present a new enhancement for an emergency and disaster relief system called Critical and Rescue Operations using Wearable Wireless sensors networks (CROW ${}^2$ ). We address the reliability challenges in setting up a wireless autonomous communication system in order to offload data from the disaster area (rescuers, trapped victims, civilians, media, etc.) back to a command center. The proposed system connects deployed rescuers to extended networks and the Internet. CROW ${}^2$ is an end-to-end system that runs the recently-proposed Optimized Routing Approach for Critical and Emergency Networks (ORACE-Net) routing protocol. The system integrates heterogeneous wireless devices (Raspberry Pi, smart phones, sensors) and various communicating technologies (WiFi IEEE 802.11n, Bluetooth IEEE 802.15.1) to enable end-to-end network connectivity, which is monitored by a cloud Internet-of-Things platform. First, we present the CROW ${}^2$ generic system architecture, which is adaptable to various technologies integration at different levels (i.e., on-body, body-to-body, off-body). Second, we implement the ORACE-Net protocol on heterogeneous devices including Android-based smart phones and Linux-based Raspberry Pi devices. These devices act as on-body coordinators to collect information from on-body sensors. The collected data is then pushed to the command center thanks to multi-hop device-to-device communication. Third, the overall CROW ${}^2$ system performance is evaluated according to relevant metrics including end-to-end link quality estimation, throughput and end-to-end delay. As a proof-of-concept, we validate the system architecture through deployment and extracted experimental results. Finally, we highlight motion detection and links’ unavailability prevention based on the recorded data where the main factors (i.e., interference and noise) that affect the performance are analyzed.

Wearable wireless networks (WWNs) offer innovative ways to connect humans and/or objects anywhere, anytime, within an infinite variety of applications. WWNs include three levels of communications: on-body, body-to-body and off-body communication. Successful communication in on-body and body-to-body networks is often challenging due to ultra-low power consumption, processing and storage capabilities, which have a significant impact on the achievable throughput and packet reception ratio as well as latency. Consequently, all these factors make it difficult to opt for an appropriate technology to optimize communication performance, which predominantly depends on the given application. In particular, this work emphasizes the impact of coarse-grain factors (such as dynamic and diverse mobility, radio-link and signal propagation, interference management, data dissemination schemes, and routing approaches) directly affecting the communication performance in WWNs. Experiments have been performed on a real testbed to investigate the connectivity behavior on two wireless communication levels: on-body and body-to-body. It is concluded that by considering the impact of above-mentioned factors, the general perception of using specific technologies may not be correct. Indeed, for on-body communication, by using the IEEE 802.15.6 standard (which is specifically designed for on-body communication), it is observed that while operating at low transmission power under realistic conditions, the connectivity can be significantly low, thus, the transmission power has to be tuned carefully. Similarly, for body-to-body communication in an indoor environment, WiFi IEEE 802.11n also has a high threshold of end-to-end disconnections beyond two hops (approximatively 25 m). Therefore, these facts promote the use of novel technologies such as 802.11ac, NarrowBand-IoT (NB-IoT) etc. as possible candidates for body-to-body communications as a part of the Internet of humans concept.

In this paper, we present an accurate and realistic simulation for body area networks (BAN) and body-to-body networks (BBN) using deterministic and semi-deterministic approaches. First, in the semi-deterministic approach, a real-time measurement campaign is performed, which is further characterized through statistical analysis. It is able to generate link-correlated and time-varying realistic traces (i.e., with consistent mobility patterns) for on-body and body-to-body shadowing and fading, including body orientations and rotations, by means of stochastic channel models. The full deterministic approach is particularly targeted to enhance IEEE 802.15.6 proposed channel models by introducing space and time variations (i.e., dynamic distances) through biomechanical modeling. In addition, it helps to accurately model the radio link by identifying the link types and corresponding path loss factors for line of sight (LOS) and non-line of sight (NLOS). This approach is particularly important for links that vary over time due to mobility. It is also important to add that the communication and protocol stack, including the physical (PHY), medium access control (MAC) and networking models, is developed for BAN and BBN, and the IEEE 802.15.6 compliance standard is provided as a benchmark for future research works of the community. Finally, the two approaches are compared in terms of the successful packet delivery ratio, packet delay and energy efficiency. The results show that the semi-deterministic approach is the best option; however, for the diversity of the mobility patterns and scenarios applicable, biomechanical modeling and the deterministic approach are better choices.

This paper addresses a reliability concern of the emerging wearable applications under dynamic and realistic conditions. We propose a new joint throughput and channel aware (TCA) dynamic scheduling algorithm for IEEE 802.15.6 standard to enhance the system performance while exploiting m-periodic scheduled access mechanism. First, various mobility patterns are generated with special emphasis on space and time varying links and their performance which are most vulnerable under dynamic environment. A deterministic pathloss values (as an estimate of the channel) are obtained from a set of training sequence which is generated using motion capture system and bio-mechanical modeling. Consequently, signal to noise (SNR), bit error rate (BER) and packet error rate (PER) are calculated. The TCA algorithm during the first phase uses this estimated PER to select the potential nodes for a time slot. Whereas in the second phase, based on the nodes priority and the data packets availability among the potential candidates, finally a slot is assigned to one node. This process is repeated by the coordinating node until the end of the super frame. This scheme has a significant gain over a reference scheme (i.e., without above adaptation). On average, 20-to-55 percent extra packets are received, along with 1-to-5 joules of energy savings though at the cost of higher delay ranging from 20-to-200 ms while operating at low power levels (i.e., 0 dBm, -5 dBm, -10 dBm).