Zhisheng Yan

MAC protocol in wireless body area networks for E-health: challenges and a context-aware design

Wireless Body Area Networks (WBAN) is a promising low power technology that enables the communications between body area sensor nodes and a central coordinator. It targets at many applications in e-Health services. In WBAN, different data sources generate time-varying traffic. Large traffic volume may result in intolerant latency and thus it is extremely important that the most significant data can always be delivered in a real-time fashion. Besides, data transmission may suffer from deep fading and packets loss due to the dynamic on-body channel induced by movements and surrounding environment. Hence, energy-efficient medium access control (MAC) is crucially needed to allocate transmission bandwidth and to ensure reliable transmission considering WBAN contexts, i.e., time-varying human and environment conditions. To improve both efficiency and reliability, we investigate the challenges in the development of WBAN MAC design. Furthermore, based on the traffic nature and channel status, we introduce a context-aware MAC protocol to meet time-varying requirements of WBAN. We have demonstrated that the proposed protocol is able to reduce latency, energy consumption, and packet loss rate, as well as to achieve a reasonable trade-off between efficiency and reliability.

Medium Access Control for Wireless Body Area Networks with QoS Provisioning and Energy Efficient Design

With the promising applications in e-Health and entertainment services, wireless body area network (WBAN) has attracted significant interest. One critical challenge for WBAN is to track and maintain the quality of service (QoS), e.g., delivery probability and latency, under the dynamic environment dictated by human mobility. Another important issue is to ensure the energy efficiency within such a resource-constrained network. In this paper, a new medium access control (MAC) protocol is proposed to tackle these two important challenges. We adopt a TDMA-based protocol and dynamically adjust the transmission order and transmission duration of the nodes based on channel status and application context of WBAN. The slot allocation is optimized by minimizing energy consumption of the nodes, subject to the delivery probability and throughput constraints. Moreover, we design a new synchronization scheme to reduce the synchronization overhead. Through developing an analytical model, we analyze how the protocol can adapt to different latency requirements in the healthcare monitoring service. Simulations results show that the proposed protocol outperforms CA-MAC and IEEE 802.15.6 MAC in terms of QoS and energy efficiency under extensive conditions. It also demonstrates more effective performance in highly heterogeneous WBAN.

A context aware MAC protocol for medical Wireless Body Area Network

During long-term medical monitoring in Wireless Body Area Networks (WBAN), network requirements (i.e. traffic loads and latency) of various data sources may be different at different time. High traffic loads may lead to data overload and unacceptable latency, which makes potential danger of patients undiagnosed. It is important that real-time transmission of life-critical data can be always guaranteed. To address this problem, a context-aware MAC protocol is presented in this paper. According to analysis of collected life parameters, the protocol can switch between normal state and emergency state. As a result, data rate and duty cycle of sensor nodes are dynamically changed to meet the requirement of latency and traffic loads in a contexta-ware way. To save the power consumption, a TDMA-based MAC frame structure is used. Moreover, a novel optional synchronization scheme is proposed to decrease the overhead caused by traditional TDMA synchronization scheme. Simulation results show significant improvements of our design on latency and power consumption.

CA-MAC: A Hybrid context-aware MAC protocol for wireless body area networks

In wireless body area network (WBAN), various data sources are generated by different type of sensors and transmitted to the master node, which may result in temporally different traffic loads. With time-varying channel caused by frequent body movement, transmitted data may also experience deep fading and packet loss. To meet the complex transmission requirements in WBAN, a traffic-aware and channel-aware MAC protocol is required. In this paper, we introduce CA-MAC, a context-aware MAC protocol using a hybrid of contention-based and TDMA-based approaches. A dynamic control mechanism is proposed to address fading channel by adaptively modifying MAC frame structure. Schedule-based and polling-based techniques are also used to manage periodic, bursty, and emergency traffic requirement. Simulation results confirm the advantage of CA-MAC on reliability over existing TDMA-based approach.

Prediction-based dynamic relay transmission scheme for Wireless Body Area Networks

To support long-term pervasive healthcare services, communications in Wireless Body Area Networks (WBANs) need to be both reliable and energy-efficient. As a cooperative transmission method, relay transmission scheme works effectively in resisting shadowing effect and improving reliability in WBANs. However, the extra energy consumption introduced by relay transmission is very high, which can shorten the lifetime of the whole network. In this paper, temporal and spatial correlation models for on-body channels are first presented to better characterize the slow fading effect of on-body channels. Then a prediction-based dynamic relay transmission (PDRT) scheme that makes full use of the correlation characteristics of on-body channels is proposed. In the PDRT scheme, “when to relay” and “who to relay” are decided in an optimal way based on the last known channel states. Moreover, neither extra signaling procedure nor dedicated channel sensing period is needed. Simulation results show that the PDRT scheme achieves significant performance improvement in energy efficiency, as well as ensuring the transmission reliability.

QoS-driven scheduling approach using optimal slot allocation for Wireless Body Area Networks

Wireless Body Area Network (WBAN) is a promising type of networks that mainly targets at applications in ubiquitous communication and e-Health services. Different from other types of networks, one important challenge for WBAN is that its quality of service (QoS) requirement, in terms of delivery probability and data rate, will be time varying since human body is a highly dynamic physical environment. Another significant challenge for WBAN is that energy efficiency needs to be guaranteed in such a resource-limited network. In this paper, a QoS-driven scheduling approach is proposed to address these challenges. We model the WBAN channel as a Markov model as suggested by the emerging IEEE 802.15.6 BAN standard and propose a threshold-based scheme to adjust the transmission order of nodes. The number of slots for each node is optimally assigned according to the QoS requirement while minimizing the energy consumption of nodes. The results from extensive simulations show that the proposed approach can provide high QoS and energy efficiency under different network conditions, especially in highly heterogeneous ones in WBAN.

RnB: rate and brightness adaptation for rate-distortion-energy tradeoff in HTTP adaptive streaming over mobile devices

Video streaming is a prevalent mobile service that drains a significant amount of battery power. While various efforts have been made toward saving both video transfer and display energy, they are independently designed in an ad-hoc way and thereby can cause some non-apparent yet critical performance issues. To fill in this gap, this paper presents a fundamentally new design by jointly considering the end-to-end pipeline from the initial video encoding to the final mobile display. In essence, we shift the classic R-D tradeoff that has governed streaming system designs for decades to a fresh rate-distortion-energy (R-D-E) tradeoff specifically tailored for mobile devices. We present RnB, a video bitrate and display brightness adaptation platform that is standard-compliant, backward compatible, and device-neutral in order to achieve the proposed R-D-E tradeoff. RnB is empowered by some new discovery about the inherent relationship among bitrate, display brightness, and video quality as well as by an control-theoretic formulation to dynamically adapt the bitrate and scale the display brightness. Experimental results based on real-time implementation show that RnB can achieve an average of 19% energy reduction with final video quality comparable to conventional R-D based schemes.

Prius: Hybrid Edge Cloud and Client Adaptation for HTTP Adaptive Streaming in Cellular Networks

In this paper, we present Prius, a hybrid edge cloud and client adaptation framework for HTTP adaptive streaming (HAS) by taking advantage of the new capabilities empowered by recent advances in edge cloud computing. In particular, emerging edge clouds are capable of accessing an application layer and radio access networks (RANs) information in real time. Coupled with powerful computation support, an edge cloud-assisted strategy is expected to significantly enrich mobile services. Meanwhile, although HAS has established itself as the dominant technology for video streaming, one key challenge for adapting HAS to mobile cellular networks is in overcoming the inaccurate bandwidth estimation and unfair bitrate adaptation under the highly dynamic cellular links. Edge cloud-assisted HAS presents a new opportunity to resolve these issues and achieve systematic enhancement of quality of experience (QoE) and QoE fairness in cellular networks. To explore this new opportunity, Prius overlays a layer of adaptation intelligence at the edge cloud to finalize the adaptation decisions while considering the initial bandwidth-irrelevant bitrate selection at the clients. Prius is able to exploit RAN channel status, client device characteristics, and application-layer information in order to jointly adapt the bitrate of multiple clients. Prius also adopts a QoE continuum model to track the cumulative viewing experience and an exponential smoothing estimation to accurately estimate a future channel under different moving patterns. Extensive trace-driven simulation results show that Prius with hybrid edge cloud and client adaptation is promising under both slow and fast-moving environments. Furthermore, the Prius adaptation algorithm achieves a near-optimal performance that outperforms the exiting strategies.

Admission Control for Wireless Adaptive HTTP Streaming: An Evidence Theory Based Approach

In this research, we propose an evidence theory based admission control scheme for wireless cellular adaptive HTTP streaming systems. This novel scheme allows us to effectively address the uncertainty and inaccuracy in QoE management and network estimation, and seamlessly grant or deny the access requests. Specifically, based on recent work of QoE continuum model and QoE continuum driven adaptation algorithm, we utilize Dempster-Shafer evidence theory to assign proper degree of belief to admission, rejection and an uncertainty decision for each users evidence. We then can strategically combine the weighted evidence of multiple users and make the final decision. The evaluation results show that the proposed scheme can provide satisfactory QoE for both existing and new users while still achieving comparable bandwidth efficiency.

LARM: A Lifetime Aware Regression Model for Predicting YouTube Video Popularity

Online content popularity prediction provides substantial value to a broad range of applications in the end-to-end social media systems, from network resource allocation to targeted advertising. While using historical popularity can predict the near-term popularity with a reasonable accuracy, the bursty nature of online content popularity evolution makes it difficult to capture the correlation between historical data and future data in the long term. Although various existing efforts have been made toward long-term prediction, they need to accumulate a long enough historical data before the prediction and their model assumptions cannot be applied to the complex YouTube networks with inherent unpredictability. In this paper, we aim to achieve fast prediction of long-term video popularity in the complex YouTube networks. We propose LARM, a lifetime aware regression model, representing the first work that leverages content lifetime to compensate the insufficiency of historical data without assumptions of network structure. The proposed LARM is empowered by a lifetime metric that is both predictable via early-accessible features and adaptable to different observation intervals, as well as a set of specialized regression models to handle different classes of videos with different lifetime. We validate LARM on two YouTube data sets with hourly and daily observation intervals. Experimental results indicate that LARM outperforms several non-trivial baselines from the literature by up to 20% and 18% of prediction error reduction in the two data sets.