In wireless body area networks (WBANs), the deep channel fading between the nodes and the hub significantly impairs the reliability of end-to-end signal transmission. However, some nodes in WBANs necessitate high-priority data transmission with stringent latency and accuracy requirements. Retransmission is ineffective against channel fading and can result in increased communication overhead and extended transmission delays. This paper proposes an opportunistic relay strategy tailored to the characteristics of WBAN nodes with varying priorities. This strategy converts suitable low-priority nodes as relays during high-priority time slots to forward high-priority data to the hub when deep fading occurs. The relay can be opportunistically selected based on the channel condition and the power usage. With Lyanupov optimization, we maximize the transmission reliability while ensuring the extra power consumption of low-priority nodes are acceptable. Subsequently, simulations are conducted in the Network Simulator 3 (NS3) to validate the proposed relay selection strategy, showing that the proposed strategy effectively improves the reliability from 90.2 % to 99.1 % with an additional power consumption of 30%.

Neural interfaces play a crucial role in biomedical engineering since they serve as pathways of communication linking the brain and computers. Integrating wireless neural interfaces with a Media Access Control (MAC) protocol presents an efficient method for recording and analyzing neural activities simultaneously from multiple sensor nodes. However, current wireless networks, such as Wireless Sensor Networks (WSNs) and Wireless Body Area Networks (WBANs), are not directly applicable to neural interface networks, due to the high complexity and weak priority access support. This paper proposes a MAC protocol called NI-MAC that not only includes priority access but also simplifies the superframe structure and frame formats. Both random access and managed access are included in the superframe design. Analysis of transmission latency and energy consumption shows the improvement over IEEE 802.15.4 and IEEE 802.15.6. With NS3 simulator, we compare the proposed NI-MAC with existing protocols and demonstrate that: transmission latency of high-priority data can achieve 12ms, while under IEEE 802.15.4 it is 22ms; as the number of access nodes increases, energy consumption is reduced by up to 50% compared to IEEE 802.15.6.

Neural interfaces are essential in biomedical engineering as they establish communication pathways between the brain and computers. Integration of wireless neural interfaces with random access protocols offers an efficient method for recording and analyzing neural activities. However, current access protocols, such as IEEE 802.15.4 and IEEE 802.15.6, do not appropriately prioritize data types in neural interface networks. This paper proposes a random access protocol that distinguishes access priorities for different data types. We provide analytical models for the proposed random access algorithm, and accordingly by optimizing parameters, such as the Exclusive Access Phase (EAP) duration in the superframe, the reliability and latency performance is improved. The performance of the proposed random access protocol is evaluated using NS3 simulator. Results indicate that the theoretical analysis matches experimental results, with a transmission successful rate over 95% and a latency below 50ms when there are 15 nodes with average arrival interval no less than 100ms.

At the request for quick measurement of airborne lidar, the high-density data collected from selected areas was reduced to different percentage of its original density. And the digital elevation models (DEMs) was generated from the ground points extracting from varying point density data sets. Then the correlation between DEMs generated from lower resolution ground data and those from original ground data were analyzed and compared. It is found that point density data set of at least 0.6 points per square meter is necessary to generate an accurate digital elevation model for the test of the measured urban environment. The conclusion is important for quick data acquirement and quick scanning of disaster area after the occurrence of natural disasters.