Cross Layer Design for Optimizing Transmission Reliability, Energy Efficiency, and Lifetime in Body Sensor Networks

Chen, Xi; Xu, Yixuan; Liu, Anfeng

doi:10.3390/s17040900

Cited by 60 publications

(61 citation statements)

References 50 publications

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“…Chen et al in [18] proposed a novel Cross-Layer Design Optimization (CLDO) scheme. Indeed, the design of CLDO relies on the three lower layers (i.e., PHY, MAC and network layer).…”

Section: Related Workmentioning

confidence: 99%

Enhanced IoT-Based End-To-End Emergency and Disaster Relief System

Arbia

Alam

Kadri

et al. 2017

JSAN

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Abstract:In this paper, we present a new enhancement for an emergency and disaster relief system called Critical and Rescue Operations using Wearable Wireless sensors networks (CROW 2 ). We address the reliability challenges in setting up a wireless autonomous communication system in order to offload data from the disaster area (rescuers, trapped victims, civilians, media, etc.) back to a command center. The proposed system connects deployed rescuers to extended networks and the Internet. CROW 2 is an end-to-end system that runs the recently-proposed Optimized Routing Approach for Critical and Emergency Networks (ORACE-Net) routing protocol. The system integrates heterogeneous wireless devices (Raspberry Pi, smart phones, sensors) and various communicating technologies (WiFi IEEE 802.11n, Bluetooth IEEE 802.15.1) to enable end-to-end network connectivity, which is monitored by a cloud Internet-of-Things platform. First, we present the CROW 2 generic system architecture, which is adaptable to various technologies integration at different levels (i.e., on-body, body-to-body, off-body). Second, we implement the ORACE-Net protocol on heterogeneous devices including Android-based smart phones and Linux-based Raspberry Pi devices. These devices act as on-body coordinators to collect information from on-body sensors. The collected data is then pushed to the command center thanks to multi-hop device-to-device communication. Third, the overall CROW 2 system performance is evaluated according to relevant metrics including end-to-end link quality estimation, throughput and end-to-end delay. As a proof-of-concept, we validate the system architecture through deployment and extracted experimental results. Finally, we highlight motion detection and links' unavailability prevention based on the recorded data where the main factors (i.e., interference and noise) that affect the performance are analyzed.

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“…Chen et al in [18] proposed a novel Cross-Layer Design Optimization (CLDO) scheme. Indeed, the design of CLDO relies on the three lower layers (i.e., PHY, MAC and network layer).…”

Section: Related Workmentioning

confidence: 99%

Enhanced IoT-Based End-To-End Emergency and Disaster Relief System

Arbia

Alam

Kadri

et al. 2017

JSAN

View full text Add to dashboard Cite

show abstract

“…The earliest proposed schemes target to enhance the on-body devices transmission reliability and improve energy efficiency. Chen et al in [16] proposed a novel cross-layer design optimization (CLDO) scheme. Indeed, the design of CLDO relies on the three lower layers (i.e., PHY, MAC and network layer).…”

Section: Overview Of Key Wwn Applications and Implementationsmentioning

confidence: 99%

Communication Challenges in on-Body and Body-to-Body Wearable Wireless Networks—A Connectivity Perspective

et al. 2017

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Wearable wireless networks (WWNs) offer innovative ways to connect humans and/or objects anywhere, anytime, within an infinite variety of applications. WWNs include three levels of communications: on-body, body-to-body and off-body communication. Successful communication in on-body and body-to-body networks is often challenging due to ultra-low power consumption, processing and storage capabilities, which have a significant impact on the achievable throughput and packet reception ratio as well as latency. Consequently, all these factors make it difficult to opt for an appropriate technology to optimize communication performance, which predominantly depends on the given application. In particular, this work emphasizes the impact of coarse-grain factors (such as dynamic and diverse mobility, radio-link and signal propagation, interference management, data dissemination schemes, and routing approaches) directly affecting the communication performance in WWNs. Experiments have been performed on a real testbed to investigate the connectivity behavior on two wireless communication levels: on-body and body-to-body. It is concluded that by considering the impact of above-mentioned factors, the general perception of using specific technologies may not be correct. Indeed, for on-body communication, by using the IEEE 802.15.6 standard (which is specifically designed for on-body communication), it is observed that while operating at low transmission power under realistic conditions, the connectivity can be significantly low, thus, the transmission power has to be tuned carefully. Similarly, for body-to-body communication in an indoor environment, WiFi IEEE 802.11n also has a high threshold of end-to-end disconnections beyond two hops (approximatively 25 m). Therefore, these facts promote the use of novel technologies such as 802.11ac, NarrowBand-IoT (NB-IoT) etc. as possible candidates for body-to-body communications as a part of the Internet of humans concept.

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“…The sensor nodes adopt sleep/wake periodic mode to save energy for lacking of energy [17]. In a unit cycle, the nodes sleep/wake work periodically.…”

Section: Network Parametersmentioning

confidence: 99%

“…The purpose is to achieve thata situation in which, the target enters the monitoring area, at least one node can monitor the target. Huang et al [17] studied the optimal placement of sensors with the goal of minimizing the number of installed devices, while ensuring coverage of target points; and wireless connectivity among sensors.…”

Section: Tracking Mobile Targetmentioning

confidence: 99%

Fast and Efficient Data Forwarding Scheme for Tracking Mobile Targets in Sensor Networks

Zhao

Liu

et al. 2017

Symmetry

Self Cite

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Abstract:Transferring emergent target tracking data to sinks is a major challenge in the Industrial Internet of Things (IIoT), because inefficient data transmission can cause significant personnel and property loss. For tracking a constantly moving mobile target, sensing data should be delivered to sinks continuously and quickly. Although there is some related research, the end to end tracking delay is still unsatisfying. In this paper, we propose a Fast and Efficient Data Forwarding (FEDF) scheme for tracking mobile targets in sensor networks to reduce tracking delay and maintain a long lifetime. Innovations of the FEDF scheme that differ from traditional scheme are as follows: firstly, we propose a scheme to transmit sensing data through a Quickly Reacted Routing (QRR) path which can reduce delay efficiently. Duty cycles of most nodes on a QRR path are set to 1, so that sleep delay of most nodes turn 0. In this way, end to end delay can be reduced significantly. Secondly, we propose a perfect method to build QRR path and optimize it, which can make QRR path work more efficiently. Target sensing data routing scheme in this paper belongs to a kind of trail-based routing scheme, so as the target moves, the routing path becomes increasingly long, reducing the working efficiency. We propose a QRR path optimization algorithm, in which the ratio of the routing path length to the optimal path is maintained at a smaller constant in the worst case. Thirdly, it has a long lifetime. In FEDF scheme duty cycles of nodes near sink in a QRR path are the same as that in traditional scheme, but duty cycles of nodes in an energy-rich area are 1. Therefore, not only is the rest energy of network fully made use of, but also the network lifetime stays relatively long. Finally, comprehensive performance analysis shows that the FEDF scheme can realize an optimal end to end delay and energy utilization at the same time, reduce end to end delay by 87.4%, improve network energy utilization by 2.65%, and ensure that network lifetime is not less than previous research.

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Cross Layer Design for Optimizing Transmission Reliability, Energy Efficiency, and Lifetime in Body Sensor Networks

Cited by 60 publications

References 50 publications

Enhanced IoT-Based End-To-End Emergency and Disaster Relief System

Enhanced IoT-Based End-To-End Emergency and Disaster Relief System

Communication Challenges in on-Body and Body-to-Body Wearable Wireless Networks—A Connectivity Perspective

Fast and Efficient Data Forwarding Scheme for Tracking Mobile Targets in Sensor Networks

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