Wireless Body Area Network (WBAN) is usually composed of nodes for contacting the body and coordinator for collecting the body data from the nodes. In this setup, the nodes are under constraint of the energy resource while the coordinator can be recharged and has relatively larger energy resource than the nodes. Therefore, the architecture mechanism of the networks must not allow the nodes to consume much energy. Primarily, Medium Access Control (MAC) protocols should be carefully designed to consider this issue, because the MAC layer has the key of the energy efficiency phenomenon (e.g., idle listening). Under these characteristics, we propose a new MAC protocol to satisfy the higher energy efficiency of nodes than coordinator by designing the asymmetrically energy-balanced model between nodes and coordinator. The proposed scheme loads the unavoidable energy consumption into the coordinator instead of the nodes to extend their lifetime. Additionally, the scheme also provides prioritization for the emergency data transmission with differentiated Quality of Service (QoS). For the evaluations, IEEE 802.15.6 was used for comparison.
In indoor environments, estimating localization using a received signal strength indicator (RSSI) is difficult because of the noise from signals reflected and refracted by walls and obstacles. In this study, we used a denoising autoencoder (DAE) to remove noise in the RSSI of Bluetooth Low Energy (BLE) signals to improve localization performance. In addition, it is known that the signal of an RSSI can be exponentially aggravated when the noise is increased proportionally to the square of the distance increment. Based on the problem, to effectively remove the noise by adapting this characteristic, we proposed adaptive noise generation schemes to train the DAE model to reflect the characteristics in which the signal-to-noise ratio (SNR) considerably increases as the distance between the terminal and beacon increases. We compared the model’s performance with that of Gaussian noise and other localization algorithms. The results showed an accuracy of 72.6%, a 10.2% improvement over the model with Gaussian noise. Furthermore, our model outperformed the Kalman filter in terms of denoising.
Although numerous schemes, including learning-based approaches, have attempted to determine a solution for location recognition in indoor environments using RSSI, they suffer from the severe instability of RSSI. Compared with the solutions obtained by recurrent-approached neural networks, various state-of-the-art solutions have been obtained using the convolutional neural network (CNN) approach based on feature extraction considering indoor conditions. Complying with such a stream, this study presents the image transformation scheme for the reasonable outcomes in CNN, obtained from practical RSSI with artificial Gaussian noise injection. Additionally, it presents an appropriate learning model with consideration of the characteristics of time series data. For the evaluation, a testbed is constructed, the practical raw RSSI is applied after the learning process, and the performance is evaluated with results of about 46.2% enhancement compared to the method employing only CNN.
The IEEE 802.15.4 standard is recognized as one of the most successful for short-range low-rate wireless communications and is used in Internet of Things (IoT) applications. To improve the performance of wireless networks, interest in protocols that rely on interaction between different layers has increased. Cross-layer design has become an issue in wireless communication systems as it can improve the capacity of wireless networks by optimizing cooperation between multiple layers that constitute network systems. Power efficiency and network scalability must be addressed to spread IoT. In multi-hop networks, many devices share wireless media and are geographically distributed; consequently, efficient medium access control (MAC) and routing protocols are required to mitigate interference and improve reliability. Cross-layer design is a novel network design approach to support flexible layer techniques in IoT. We propose a cross-layer protocol for the MAC layer and routing layer to satisfy the requirements of various networks. The proposed scheme enables scalable and reliable mesh networking using the IEEE 802.15.4 standard and provides robust connectivity and efficient path discovery procedures. It also proposes a novel address-allocation technique to improve address-allocation methods that cannot support large sensor networks. Simulation results indicate that the proposed scheme could improve reliability and reduce end-to-end delay.
A smart grid is a next-generation intelligent power grid that can maximize energy efficiency by monitoring power information in real time and by controlling the flow of power by introducing IT communication technology to the existing power grid. In order to apply a wireless communication network to a smart grid, it is necessary to be able to efficiently process large amounts of power-related data while enabling a high level of reliability and quality of service (QoS) support. In addition, international standards-based design is essential considering compatibility and scalability. The IEEE 802.15.4 standard is considered to be the most powerful communication method for processing data through the smart grid AMI. To reduce the energy consumption, as the duty cycle of the superframe increases, the probability of the congestion increases. However, this binary exponential algorithm in IEEE 802.15.4 standard does not account for the application of traffic characteristics that essentially negatively affect the smart grid network performances in terms of packet delivery ratio and time delay. Therefore, in this paper, we propose a new transmission scheme to reduce performance degradation by excessive collisions in the content access period (CAP), when data transmission is performed in IEEE 802.15.4 applied to smart grids. In addition, we investigated the main research topics required when applying wireless networking technology to smart grids and suggested improvement measures. Simulation results showed that the proposed scheme increased the data delivery rate and reduced the latency, and it was confirmed that reliability was improved.
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