Integrated Performance Evaluation of the Smart Body Area Networks Physical Layer for Future Medical and Healthcare IoT

Abstract

This paper performs integrated performance evaluation, including preamble detection in the Smart Body Area Networks (SmartBAN) physical layer (PHY). The system specifications for a PHY and media access control layer (MAC) in SmartBAN, which is a standard for medical and health care advanced by the European Telecommunications Standards Institute (ETSI), were issued in April 2015. In the PHY, the packet structure has a two-octet preamble, which is used, e.g., for timing synchronization. However, it is considered that the current preamble structure is not appropriate for handling medical and healthcare data that are required to have high reliability because of the too simple structure. Therefore, we propose adding a start frame delimiter (SFD) to correctly detect the header position. Computer simulations indicate that preambles with an SFD consisting of an orthogonal maximal length sequence (M-sequence) perform better than SmartBAN and similar approaches, particularly when transmitting over the IEEE model CM3. In addition, the packet error ratio (PER) and energy efficiency are evaluated in an integrated manner while taking preamble detection into consideration. The numerical results from computer simulations indicated the best performance with respect to PER was achieved using a preamble with orthogonal M-sequences of 4 octets. However, for energy efficiency, better results were obtained using a preamble with orthogonal M-sequences of 2 octets. Additionally, the theoretical analysis found the optimum length of the PHY packet to achieve the maximum energy efficiency with PER less than 10−2.

Toward an Advanced Human Monitoring System Based on a Smart Body Area Network for Industry Use

“…In this instance, the preamble structure was mentioned when the multilevel PSK was applied. The modified preamble structure was proposed in [32]. The reason for this was that the preamble structure shown in Figure 1 was too simple to perform highly reliable communication dealing with medical and healthcare information.…”

“…In this equation, L in f o is the information bit length in the PSDU, E link is the energy consumption of the communication link and P succ is the transmission success ratio. P succ can be expressed as follows [32]:…”

“…As a channel model, this research assumed the additive white Gaussian noise (AWGN) channel and IEEE model CM3, which is a channel model of a wearable WBAN [32]. Similarly, the IEEE model CM3 was applied to the path loss model.…”

Knowledge Discovery of Hospital Medical Technology Based on Partial Ordered Structure Diagrams

“…When people choose a hospital for treatment, they often need to manually query the medical technologies of each hospital and then make a detailed comparison, which causes inconvenience for people to choose a hospital for treatment (Alabdulkarim et al, 2019). In addition, the massive medical technology data in medicine has various types of characteristics, with strong professionalism and also ambiguity, which makes it difficult for non-medical professionals to mine the tules covered in medical technology data (Takabayashi et al, 2019). Therefore, this paper introduces partially ordered structure theory into the knowledge discovery of medical technology data, constructs the APOSD and the OPOSD according to the formal context formed between the hospital and medical technology, and uses the branch relationship and the group structure relationship.…”

Complex Pignistic Transformation-Based Evidential Distance for Multisource Information Fusion of Medical Diagnosis in the IoT

“…Therefore, the emergence of the IoT becomes a milestone in the field of the digital medical domain. Medical diagnosis under the smart IoT environment can send physiological information and medical signals through communication networks to monitoring systems for analyzing and diagnosing with the aid of artificial intelligence techniques [ 5 , 6 , 7 ]. Consequently, it is beneficial for improving clinical medical services and lowering management costs, so that it can help to make better lifestyle and disease prevention plans and personalized medical services for patients [ 8 , 9 ].…”