In this paper, a flexible role-based architecture for Body Sensor Networks (BSNs) is introduced. The proposed non-layered context-aware architecture is application-oriented and able to incorporate future applications. Particular applications have certain requirements. Functional units (roles) instead of protocol layers are designed to perform the required tasks for applications to work properly. The role data of an application is inserted in the role headers of the container and is available for other applications with the same basic, specific or particular roles. Furthermore, the performance of Automatic Repeat Request (ARQ), Forward Error Correction (FEC) block codes and FEC convolutional codes with respect to the throughput efficiency has also been analyzed for a BSN following the proposed role-based architecture. The numerical results show that the proposed role-based architecture outperforms the traditional layered architecture with respect to the throughput efficiency for all error control schemes. FEC block codes are able to maintain a high throughput efficiency over longer distances because the hop length extension technique is applied.
A Body Sensor Network (BSN) is formed by multiple sensors placed at the human body to monitor the vital signs of a person (Omeni, Wong, Burdett, & Toumazou, 2008). The physiological states of the person are being sensed, sampled and processed by specific implant and body surface sensors, as show in Fig. 1. Implant sensors are located inside the human body, whereas body surface sensors are placed at the human skin or at most two centimeters away. The vital signs are sent towards the gateway, which transmits them to a monitoring station. Finally, they are forwarded towards Internet for further analysis.BSNs support a wide variety of applications. In this paper, different application scenarios for body sensor networks have been identified. We distinguish between the healthcare scenario, the emergency or life-critical scenario, the entertainment scenario, the sports scenario and the military scenario. Furthermore, a flexible role-based architecture for BSNs is introduced. The proposed non-layered context-aware architecture is application-oriented and able to incorporate future applications. Particular applications (e.g. forwarding of vital signs towards a health center as routine or in case of emergency) have certain requirements. Functional units (roles) instead of protocol layers are designed to perform the required tasks for applications to work properly. The different role types have been classified. Packets are built with the data payload and the metadata required for each specific role. The role header fields of the most important common basic roles are described. An application example has been introduced to compare the traditional layered-network model with the proposed architecture. Furthermore, the performance of Automatic Repeat Request (ARQ), Forward Error Correction (FEC) block codes and FEC convolutional codes with respect to the throughput efficiency has also been analyzed for a BSN following the role-based architecture. The numerical results show that the proposed role-based architecture outperforms the traditional layered architecture with respect to the throughput efficiency for all error control schemes. FEC block codes are able to maintain a high throughput efficiency over longer distances because the hop length extension technique is applied.
To the best of our knowledge, this is the first paper that discusses the role-based architecture for BSNs and analyses packet size optimization based on the throughput efficiency for the proposed clean-slate architecture. The paper is structured as follows. In Section 2, we analyze different application scenarios for BSNs. In Section 3, we discuss the related work. In Section 4, we introduce our role-based architecture proposal. In Section 5, the role selection is described. In Section 6, the channel model for BSNs is explained. In Section 7, the optimal packet size for each error control scheme to optimize the throughput is derived. In Section 8, our numerical results are shown. Finally, the paper is concluded in Section 9.
In this paper, different application scenarios for BSNs have been identified. Furthermore, a role-based architecture for BSNs is introduced. Communication is organized using functional units (roles) instead of protocol layers. The common basic context-aware role is essential to identify the individual and interpret his/her health-related data properly.
Unnecessary information of the traditional layered-network model is suppressed. The role data of an application is inserted in the role headers of the container and is available for other applications with the same common basic, specific or particular roles. This way, the transmission of repeated information is avoided, the network load is diminished and congestion is reduced or eliminated.
The performance of ARQ and FEC codes with respect to the throughput efficiency in BSNs that follow our clean-slate approach has been analyzed. The optimal packet size has been determined for certain radio and channel parameters by maximizing the throughput efficiency. The numerical results show that the proposed role-based architecture outperforms the traditional layered architecture with respect to the throughput efficiency for all error control schemes. FEC block codes show the best results. They are able to maintain high throughput efficiency over longer distances because the hop length extension technique is applied.
An interesting future work research is to evaluate the throughput efficiency for BSNs when the proposed role-based architecture is extended with new roles, which satisfy the requirements of future applications.
