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Competent QoS-aware and energy efficient protocols for body sensor networks

Authors
  • Boudargham, Nadine
Publication Date
Jun 29, 2020
Source
HAL-INRIA
Keywords
Language
English
License
Unknown
External links

Abstract

Body Sensor Networks (BSNs) are formed of medical sensors that gather physiological and activity data from the human body and its environment, and send them wirelessly to a personal device like Personal Digital Assistant (PDA) or a smartphone that acts as a gateway to health care. Collaborative Body Sensor Networks (CBSNs) are collection of BSNs that move in a given area and collaborate, interact and exchange data between each other to identify group activity, and monitor the status of single and multiple persons.In both BSN and CBNS networks, sending data with the highest Quality of Service (QoS) and performance metrics is crucial since the data sent affects people’s life. For instance, the sensed physiological data should be sent reliably and with minimal delay to take appropriate actions before it is too late, and the energy consumption of nodes should be preserved as they have limited capacities and they are expected to serve for a long period of time. The QoS in BSNs and CBSNs largely depends on the choice of the Medium Access Control (MAC) protocols, the adopted routing schemes, and the efficient and accuracy of anomaly detection.The current MAC, routing and anomaly detection schemes proposed for BSNs and CBSNs in the literature present many limitations and open the door toward more research and propositions in these areas. Thus this thesis work focuses on three main axes. The first axe consists in studying and designing new and robust MAC algorithms able to address BSNs and CBSNs' challenges. Standard MAC protocols are compared in high traffic BSNs and a new MAC protocol is proposed for such environments; then an emergency aware MAC scheme is presented to address the dynamic traffic requirements of BSN in ensuring delivery of emergency data within strict delay requirements, and energy efficiency of nodes during regular observations; moreover, a traffic and mobility aware MAC scheme is proposed for CBSNs to address both traffic and mobility requirements for these networks.The second axe consists in proposing a thorough and efficient routing scheme suitable for BSNs and CBSNs. First, different routing models are compared for CBSNs and a new routing scheme is proposed in the aim of reducing the delay of data delivery, and increasing the network throughput and the energy efficiency of nodes. The proposed scheme is then adapted to BSN's requirements to become a solid solution for the challenges faced by this network. The third axe involves proposing an adaptive sampling approach that guarantees high accuracy in the detection of emergency cases, while ensuring at the same time high energy efficiency of the sensors.In the three axes, the performance of the proposed schemes is qualitatively compared to existing algorithms in the literature; then simulations are carried a posteriori with respect to different performance metrics and under different scenarios to assess their efficiency and ability to face BSNs and CBSNs' challenges.Simulation results demonstrate that the proposed MAC, routing and anomaly detection schemes outperform the existing algorithms, and present strong solutions that satisfy BSNs and CBSNs' requirements.

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