Body Sensor Networking, Design and Algorithms. Saeid Sanei
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powered by many sources such as the national grid, wind turbine, and solar cells, for conventional BSNs, the consumable energy should be optimised and batteries with limited power (though rechargeable) used [31, 32]. On the other hand, with regards to data transmission, the nodes in a WSN often transfer the data with similar rates as long as the data modality is the same. This is, however, not the case for a BSN, as various sensors sample and transfer the data at rates appropriate to the underlying physiological variables under examination.
Another concern about the data type in BSNs is that the human body is nonhomogeneous and each part is modelled as an entirely nonlinear system. Also, the physiological signals are inherently highly nonstationary, i.e. their statistical properties vary over time. Therefore, accurate analysis of such data is significantly more challenging than for other types of data, and many linear signal processing methods, therefore, are likely to fail to capture and analyse the true features of the data.
Additionally, BSNs are generally meant for monitoring human physiological, biological, and motion data, which are related to user's personal safety and privacy as well as other ethical issues. Therefore, some means of QoS, privacy protection, integrity, prosperity, and security in archiving and real-time data transmission must be considered [33, 34].
In terms of data communication through conventional wireless systems, WBANs support a variety of real-time health monitoring and consumer electronics applications. The latest standardization of WBANs is the IEEE 802.15.6 standard [35] which aims to provide an international standard for low-power, short-range, and extremely reliable wireless communication within the surrounding area of the human body, supporting a vast range of data rates for different applications. The security association in this standard includes four elliptic curve-based key agreement protocols that are used for generating a master key.
The Federal Communications Commission (FCC) has approved the allocation of 40 MHz of spectrum bandwidth for medical BAN low-power, wide-area radio links at the 2360–2400 MHz band. This allows off-loading WBAN communication from the already saturated standard Wi-Fi spectrum to a standard band [36].
Apart from 2390–2400 MHz band which is not subject to registration or coordination and may be used in all areas including residential, the 2360–2390 MHz frequency range is available on a secondary basis. The FCC will expand the existing Medical Device Radiocommunication (MedRadio) Service in Part 95 of its rules. WBAN devices using this band can operate on a ‘licence-by-rule’ basis, which eliminates the need to apply for individual transmitter licences. Usage of the 2360–2390 MHz frequencies is restricted to indoor operation at healthcare facilities and subject to registration and site approval by coordinators to protect aeronautical telemetry primary usage [37].
1.3 BSN Architecture
The general architecture of a BSN is shown in Figure 1.1. Sensor nodes which are placed around and possibly inside the body collect physiological data and perform preliminary processing. The data are then gathered by a sink node and transmitted to a local PC or mobile system for personal or local use (such as alarming) or the base station of a public network to share with relevant bodies over the Internet. The recipient of the BSN data can be healthcare units, social welfare, emergency units within hospitals, or other experts in clinical, experimental, and sport departments. For any one of the above cases there are many design and technical challenges to tackle.
Figure 1.1 Overall architecture of a BSN.
A more detail architecture, which are discussed in the corresponding chapter of this book, involves various levels and modes of communications between the body-mounted sensors and the corresponding clinical or social agencies. Such data transfer systems inherently include in-house or short-range media (approximately 2–3 m), often called intra-BAN communication, between personal and public network (inter-BAN communication), and those entirely within public wireless communication system (beyond-BAN communication).
Figure 1.2 summarises the main research areas of BSNs. Some research has been in progress on how to design wearable sensors [38], fault diagnosis of the BSN and how to mitigate the faults and avoid their impacts [39], energy consumption and energy harvesting [40], and sensor deployment [41]. Without doubt, tremendous research in signal processing – particularly on denoising [42], artefact removal, feature detection, data decomposition, estimation, feature extraction [43], and data compression [44] – has been carried out intensively for various applications. Such valuable experiences can be directly exploited and integrated within the design of BSNs.
Data fusion, as another BSN direction of research, has been under vast development as new techniques in multimodal data recording, analysis, and multiagent distributed systems and networks have been introduced.
Moreover, machine learning techniques have powered up BSN research by developing new techniques in clustering, classification [45], anomaly detection, and decision making as well as many other approaches in big data analytics, to suit the corresponding data.
Figure 1.2 Main research areas in a BSN.
BSNs have been looked at through different angles by a growing number of scholars in sensor technology, data processing, and communications. Some researchers have combined situational awareness and data fusion technologies to enable human activity recognition [46, 47]. Others have tried to understand the data by developing sophisticated signal processing algorithms to deal with multichannel biomedical data [48, 49]. Indeed, the design and provision of supercomputers, availability of large memory clusters, and accessibility of the cloud have been crucial to the expansion of sensor networks. Moreover, new data processing and machine learning methods based on tensor factorisation, cooperative learning, graph theory, kernel-based classification, deep learning neural networks, and distributed systems together with pervasive computing have revolutionised the assessment of the information collected from multisensor networks, particularly when the dataset is large. Network communication, on the other hand, involves network topology design [50], channel characterization [51], channel access control [52, 53], routing algorithm design [54], lightweight communication protocols design, energy harvesting in a network, and many other issues related to short- and long-range communications. These key technologies must be considered and further developed for building a complete BSN system.
To enable long-term data collection from the human body, biocompatible sensors and devices need to be designed. This field of research brings new areas of engineering researchers in biotechnology, biomaterials, bioelectronics, and biomechanics together to develop practical sensors.
The demand for a green environment pushes for the optimization of energy harvesting and an effective solution to energy consumption together with enhancing the QoS. For WSNs the plethora of battery technologies available today enables system designers to tailor their energy storage devices to the needs of their applications. The latest lithium battery technologies allow optimization for any operating lifetime or environment. For applications with small temperature variation and short lifetime, lithium manganese dioxide (LiMnO2) batteries provide solid performance at cost-effective prices, while applications demanding large temperature ranges and multidecade lifetimes are satisfied with batteries based on lithium thionyl chloride (LiSOCL2) chemistry [7].
While batteries represent the preferred low-cost energy storage technology, energy harvesting/scavenging devices are beginning to emerge as viable battery replacements in some applications. For example, power can be generated from temperature differences through thermoelectric and pyroelectric effects, kinetic motion of piezoelectric