Elsevier

Information Sciences

Volume 284, 10 November 2014, Pages 142-156
Information Sciences

NDNC-BAN: Supporting rich media healthcare services via named data networking in cloud-assisted wireless body area networks

https://doi.org/10.1016/j.ins.2014.06.023Get rights and content

Abstract

Nowadays, Wireless Body Area Network (WBAN) is broadly utilized for health monitoring and remote medical care. When the physiological information collected in WBAN is distributed to cloud computing platform, a new healthcare service mode is enabled by “cloud-assisted WBAN”, where user’s body signals can be stored, processed, managed and analyzed over a long-term period. Though the provisioning of healthcare services is largely enhanced via cloud-enabled technologies, more challenging issues are raised due to the increased user’s requirements on quality of experience (QoE) in terms of user mobility, content delivery latency, and personalized interaction, etc. In order to tackle these challenges, this paper presents various solutions including: (1) a novel integration of WBAN with Long Term Evolution (LTE) to support high user mobility; (2) an efficient scheme to distribute contents by leveraging the emerging named data networking (NDN) technology, to support rich media healthcare content delivery without service interruption while achieving low cost and bandwidth saving; (3) the use of adaptive streaming to adjust suitable content size according to the dynamic bandwidth. The experimental results conducted by OPNET verify the viability of NDN and adaptive streaming to support the healthcare services involving the transmissions of rich media contents between WBAN and internet.

Introduction

Wireless Body Area Network (WBAN) or Body Area Network (BAN) contains a set of wearable body sensor nodes which are typically placed on or around human body, collecting data and sending it to access point through various wireless technologies, among which Zigbee is mostly used.

Though the network parameters are fine-tuned to meet particular quality of service (QoS) requirements in Zigbee based intra-BAN and inter-BAN [6], existing network architectures are hard to support high mobility for both patients and physicians due to the intrinsic features of low data rate and channel dynamics. Nowadays, this problem can be alleviated by using mobile device, such as iPhone/iPad, android phone, even a robot, as a personal server to interconnect intra-BAN with outside world. The mobile device should have at least two interfaces, i.e., one Zigbee interface to communicate with Zigbee-enabled body sensor nodes within intra-BAN, the other interface to forward body signals to external network.

Once the user’s health status is delivered to cloud platform, a new healthcare service mode can be supported by cloud-enabled technology [17]. The proposed architecture of cloud-assisted WBAN is shown in Fig. 1, where the basic data transmissions are divided into the following three stages:

  • User’s physiological parameters such as heart rate, respiratory rate, blood pressure, oxygen saturation, etc., will be collected by body sensors, and forwarded to cloud by mobile device in an on-demand fashion.

  • User’s physiological status is stored, processed, managed and analyzed over a long-term period in the cloud platform.

  • The physiological information stored in cloud platform can be distributed to hospital, doctor, nurse or immediately family members, etc.

Since cloud computing provides a low-cost approach to support extensive data storage and computing-intensive analysis of healthcare big data, the provisioning of healthcare services is largely enhanced via cloud-assisted WBAN. However, more challenging issues are raised due to the increased user’s requirements on quality of experience (QoE) in terms of user mobility, content delivery latency, and personalized interaction, etc. In order to illustrate these challenges, we give three scenarios where the communications and QoS are hard to be supported by the conventional WBAN architecture:

  • For the application of medial emergency handling (e.g., medical emergency response during earthquake), various medical personnel need to get patient’s comprehensive physiological data in a timely fashion for fast analysis and diagnosis. However, fetching patient’s data by traditional Client/Server (C/S) mechanism consumes lots of energy from the patient’s portable device by multiple transmissions of the same content [7], [10]. Due to the intrinsic resource-poor feature of the portable device (e.g., a mobile phone), the dissemination of the time-sensitive data streams suffers from undesired performance in terms of end-to-end delay, packet delivery ratio and throughput, etc. Named data networking (NDN) can alleviate such problems, and enable different rescue team members to obtain patient’s vital signals simultaneously at an earliest moment.

  • In 3D Telehealth Education Application as shown in Fig. 2, let’s assume that a physician is doing some surgery for a patient, while the whole process is recorded into 3D tutorial video under a permission with the patient before the operation. The video contents are continuously uploaded to a remote server associated with an online medical school, where the students can request the video contents for studying. In case that a certain number students are downloading the real-time video contents during the operation, NDN will offer opportunities to save bandwidth.

  • In the application of help-on-demand healthcare video delivery, for the first-time mother, the requested content can be tutorial video of breast feeding. For an autism patient, the requested data can be a personalized social media content. For a patient just moving out of hospital, the requested content can be a tutorial video to provide guidance of rehabilitation therapy, etc. Within a community, if the number of similar interests is large enough, NDN can amortize the cost of introducing the cache and exhibit its advantages extensively.

In order to address the issues raised in the above scenarios, we propose a novel architecture named by NDNC-BAN to support rich media healthcare services via NDN in cloud-assisted WBAN, and the following mechanisms are presented in this paper:

  • Support High User Mobility: we propose a novel integration of WBAN with Long Term Evolution (LTE). This paper considers data collected from body sensors are interested not only by physicians but also by a number of different end users at the same time in a real time fashion. Especially, we allow both patients and physicians to moves freely. Thus, due to the flexible bandwidth supported by LTE technology, diversity of the applications and services in healthcare system are developed. We propose that a smart-phone serving as the gateway of patient data can bring comfortableness for hospitalized patients mainly in the following points: (i) smart-phone can be a communication terminal for the patient while being able to appropriately process various physiological data, (ii) smart-phone can utilize the resources of the cellular network for long distance delivery for the sensed data, and (iii) patient’s mobility is guaranteed.

  • Support QoS for Multi-users to Request Healthcare Contents: When various individuals inquire the patient’s data, NDN technology is deployed to achieve resources saving, such as energy, bandwidth, etc.

  • Efficient Content Delivery from Cloud: We leverage the emerging NDN to design an efficient and robust scheme to deliver on-demand content from cloud, to support high quality help-on-demand healthcare video delivery without service interruption while achieving low cost and bandwidth saving. In addition, the technology of adaptive streaming is employed to adjust suitable resolution of the on-demand video based on the dynamic bandwidth.

  • Flexible and Personalized Interaction: The personalized health related data of elderly people will be stored and analyzed in the cloud-assisted healthcare system, which will properly provide expert-level services for end-users in terms of the mental status analysis, eating habits monitoring, early prevention of diseases, and other healthcare services. With the cloud-assisted analysis in terms of health and mental status, the analyzing result will be sent to last-mile mobile device of end user, facilitating flexible and personalized interaction.

In summary, the main contributions of this paper include: (1) present various challenging issues to support a rich media interaction between intra-BAN and beyond-BAN; (2) utilize caching and NDN technology to save energy and other resources to distribute rich media contents; (3) propose a novel application of NDN and adaptive streaming to enhance Quality of Service (QoS) of realtime transmission data from WBAN; (4) propose an approach to implement the integrated healthcare system which contains LTE, NDN and adaptive streaming.

The remainder of this paper is organized as follows. In Section 2, we present our network architecture. In Section 3, we introduce the implementation of NDN and adaptive streaming. Section 4 describes our simulation settings and performance metrics. Section 5 presents simulation results and verifies the proposed schemes. Finally, Section 6 concludes this paper.

Section snippets

The proposed network architecture

In this section, we first present the problems existing in typical Zigbee network for healthcare. Then, an improved network is proposed to address those problems.

Efficient content delivery via NDN and adaptive streaming

In this section, we present the implementation of NDN and adaptive streaming in the proposed network architecture. Fig. 5 illustrates the basic idea of efficient content delivery via NDN technology and adaptive streaming.

Simulation methodology

To evaluate the performance of the proposed schemes, we compare the following schemes using extensive simulation studies: (i) the scheme with NDN and adaptive streaming (denoted by NDN-Adapt); (ii) the scheme with C/S and adaptive streaming (denoted by CS-Adapt); and (iii) the scheme with C/S with fixed data rate, (denoted by CS-Only). We present our simulation settings and performance metrics in this section. The simulation results are discussed in Section 5.

NDN mechanism and the proposed

Performance evaluation

In NDN, Interest might be immediately satisfied by edge routers or Zigbee gateway. Therefore, in comparison with the pure C/S mode, NDN have three advantages: (i) reduced number of requested contents that need to be fetched to the Zigbee gateway, (ii) lower RTT, and (iii) smaller energy consumption of the Zigbee gateway.

In order to verify above advantages obtained from NDN, we run twice for each scenario with different settings in terms of simulation time. In the first simulation, the time is

Conclusion

This paper proposes a novel hybrid WBAN network architecture via NDN and adaptive streaming for remote health monitoring and QoS provisioning. To the best of our knowledge, there are no other research investigating the overlay of NDN on top of WBAN until now. In a dynamic and unstable wireless environment, NDN with adaptive streaming is a suitable solution to support the mobility of both patients and physicians. The simulation results have verified that the contents from WBANs can be

Acknowledgements

This work is supported by Youth 1000 Talent Program, the National Natural Science Foundation of China Project (Grant No. 61300224), and the International Science and Technology Collaboration Program (2014DFT10070) funded by China Ministry of Science and Technology (MOST).

References (17)

  • Named data networking, 2014....
  • Ns-3 based named data networking (ndn) simulator, 2014....
  • Opnet modeler, 2014....
  • Zigbee specification, 2014....
  • M. Chen
    (2004)
  • M. Chen et al.

    Body area networks: a survey

    Mobile Netw. Appl.

    (2011)
  • X. Ge et al.

    Energy efficiency analysis of miso-ofdm communication systems considering power and capacity constraints

    Mobile Netw. Appl.

    (2012)
  • B. Han, X. Wang, N. Choi, T. Kwon, Y. Choi, Amvs-ndn: Adaptive mobile video streaming and sharing in wireless named...
There are more references available in the full text version of this article.

Cited by (84)

  • Industry 4.0 and Health: Internet of Things, Big Data, and Cloud Computing for Healthcare 4.0

    2020, Journal of Industrial Information Integration
    Citation Excerpt :

    The adoption of Cloud technologies in the context of healthcare has been dubbed Healthcare as a Service (HaaS) [47,59]. Compared with the common drivers to the adoption of Cloud technologies in more general applications and in the IoT paradigm [37], HaaS applications share the same benefits from scalable, on-demand, and virtually infinite computation, storage, and networking resources; in addition to these, other aspects have been found to be important, such as: ease of data sharing, ease of data collection and integration, and in some cases enhanced performance, availability, reliability and security [60–63] (see Fig. 2). Moreover the technologies related to mobile and personal devices benefit from Cloud and Fog Computing for managing the growth in digital data and anywhere-and-anytime request for medical services [16,37,64].

  • Study on real-time wearable sport health device based on body sensor networks

    2020, Computer Communications
    Citation Excerpt :

    In a single-hop star network, each object has a central node responsible for data relay from the node to the base station and control from the base station to the node. The advantage of this communication method is that not all nodes need a large transmission power, and the harm to the human body is small, and the transmission distance is farther than the point-to-point method [25,26]. The network type multi-hop structure uses the human body as the transmission medium, and the data of each node jumps from the neighboring node to the central node through the optimal routing path.

  • The role of Information and Communication Technologies in healthcare: taxonomies, perspectives, and challenges

    2018, Journal of Network and Computer Applications
    Citation Excerpt :

    Several monitoring solutions gather clinical information (such as position, temperature, or breath frequency) via body sensors or mobile devices, and integrate it in the cloud, leveraging seemingly infinite storage, scalable processing capability as well as high service availability (Biswas et al., 2010; Rolim et al., 2010; Bourouis et al., 2012; Fortino et al., 2012; Shah et al., 2013; Ochian et al., 2014; Thilakanathan et al., 2014; Xu and Zhong, 2014; Hossain and Muhammad, 2015; Santos et al., 2016). Often, the cloud represents a flexible and affordable solution to overcome the constraints generated by either sensor technologies or mobile devices and therefore a growing number of proposals takes advantage of the cloud capabilities to remotely offload computation- or data-intensive tasks (Nkosi and Mekuria, 2010; Doukas and Maglogiannis, 2012; Wan et al., 2013; Wu et al., 2013; Chen, 2014; Cimler et al., 2014; Tong et al., 2014; Wang et al., 2014; Zhang et al., 2014) or to perform further processing activities and analyses (Thuemmler et al., 2013; Zhang et al., 2017). Depending on the type of sensors adopted, applications can be clusterized in in-body e on-body (Ullah et al., 2012).

View all citing articles on Scopus
View full text