arXiv subjects

Lin Yao

Publications and source records attributed to Lin Yao.

58 records · Page 4Linked to original sources

arXiv2010

Decentralized Inter-User Interference Suppression in Body Sensor Networks with Non-cooperative Game

Body Sensor Networks (BSNs) provide continuous health monitoring and analysis of physiological parameters. A high degree of Quality-of-Service (QoS) for BSN is extremely required. Inter-user interference is introduced by the simultaneous communication of BSNs congregating in the same area. In this paper, a decentralized inter-user interference suppression algorithm for BSN, namely DISG, is proposed. Each BSN measures the SINR from other BSNs and then adaptively selects the suitable channel and transmission power. By utilizing non-cooperative game theory and no regret learning algorithm, DISG provides an adaptive inter-user interference suppression strategy. The correctness and effectiveness of DISG is theoretically proved, and the experimental results show that DISG can reduce the effect of inter-user interference effectively.

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arXiv2010

Dynamical Jumping Real-Time Fault-Tolerant Routing Protocol for Wireless Sensor Networks

In time-critical wireless sensor network (WSN) applications, a high degree of reliability is commonly required. A dynamical jumping real-time fault-tolerant routing protocol (DMRF) is proposed in this paper. Each node utilizes the remaining transmission time of the data packets and the state of the forwarding candidate node set to dynamically choose the next hop. Once node failure, network congestion or void region occurs, the transmission mode will switch to jumping transmission mode, which can reduce the transmission time delay, guaranteeing the data packets to be sent to the destination node within the specified time limit. By using feedback mechanism, each node dynamically adjusts the jumping probabilities to increase the ratio of successful transmission. Simulation results show that DMRF can not only efficiently reduce the effects of failure nodes, congestion and void region, but also yield higher ratio of successful transmission, smaller transmission delay and reduced number of control packets.

cs.NI↗

arXiv2010

Localization Technologies for Indoor Human Tracking

The proliferation of wireless localization technologies provides a promising future for serving human beings in indoor scenarios. Their applications include real-time tracking, activity recognition, health care, navigation, emergence detection, and target-of-interest monitoring, among others. Additionally, indoor localization technologies address the inefficiency of GPS (Global Positioning System) inside buildings. Since people spend most of their time in indoor environments, indoor tracking service is in great public demand. Based on this observation, this paper aims to provide a better understanding of state-of-the-art technologies and stimulate new research efforts in this field. For these purposes, existing localization technologies that can be used for tracking individuals in indoor environments are reviewed, along with some further discussions.

cs.NI↗

arXiv2009

Prediction-Based Data Transmission for Energy Conservation in Wireless Body Sensors

Wireless body sensors are becoming popular in healthcare applications. Since they are either worn or implanted into human body, these sensors must be very small in size and light in weight. The energy consequently becomes an extremely scarce resource, and energy conservation turns into a first class design issue for body sensor networks (BSNs). This paper deals with this issue by taking into account the unique characteristics of BSNs in contrast to conventional wireless sensor networks (WSNs) for e.g. environment monitoring. A prediction-based data transmission approach suitable for BSNs is presented, which combines a dual prediction framework and a low-complexity prediction algorithm that takes advantage of PID (proportional-integral-derivative) control. Both the framework and the algorithm are generic, making the proposed approach widely applicable. The effectiveness of the approach is verified through simulations using real-world health monitoring datasets.

cs.NI↗