Biologically inspired computing and networking represents an emerging research field that seeks the understanding of key principles, processes and mechanisms in biological systems and leverages the understanding to develop novel computing and networking mechanisms. This special issue features five selected recent research efforts in biologically inspired computing and networking.
The first paper entitled “Distributed Power Controller of Massive Wireless Body Area Networks Based on Deep Reinforcement Learning”, authored by P. He et al., addresses the energy saving issue of massive wireless body area networks (WBAN). A distributed power controller is developed based on deep Q-learning algorithm to mitigate the affection of inter-network interference. Simulation results demonstrate the significant improvement of proposed scheme in terms of energy efficiency.
In order to solve the problem of low classification accuracy of motor imagery caused by the differences between individual electroencephalogram (EEG) signals, the second paper, “A Personalized Feature Extraction and Classification Method for Motor Imagery Recognition” authored by J.G. Wang et al., proposes a personalized feature extraction method based on filter bank and elastic net and a personalized channel selection based on Deep Belief Network (DBN). According to individual differences, feature vectors and channels containing more classification information can be flexibly selected, avoiding manual adjustment of a specific frequency range for feature extraction and inputting all channels in traditional common spatial pattern (CSP) algorithm.
The third paper, entitled “Real-Time Monocular Obstacle Detection Based on Horizon Line and Saliency Estimation for Unmanned Surface Vehicle” authored by Z. Rui et al., presents a real-time monocular obstacle detection method based on horizon line and saliency estimation for USVs. Specifically, a novel semantic segmentation method based on Gaussian mixture model (GMM) and Markov random field (MRF) is designed to detect the horizon line, which outperforms existing methods based on edge or line features. Inspired by human visual attention mechanisms, an efficient saliency detection method based on background prior and contrast prior is presented to detect obstacles below the estimated horizon line. To reduce false positives caused by sun glitter, waves, and foam, the continuity of the adjacent frames is employed to filter the detected obstacles.
Because of the development of the IoT technologies, various industries have applied a large number of IoT devices. However, the Internet of Things usually does not have a sufficient computing power, so the simple data encryption standard (DES) with lower security is usually used as the main encryption method. The fourth paper, “A High Security Symmetric Key Generation by using Genetic Algorithm based on A Novel Similarity Model” authored by M.-Y. Tsai, proposes a DES encryption based on genetic algorithm with a novel fitness function, which greatly reduce the weakness of the original DES encountering weak keys.
Finally, the paper “Developing an Intelligent Agricultural System based on Long Short-Term Memory” authored by H.-T. Wu develops an intelligent agriculture system based on Long Short-Term Memory (LSTM). The system develops an Internet of Things (IoT) to monitor the environmental conditions of soil, sunlight, and temperature; additionally, the research combines the information from the Central Weather Bureau for predicting the timing for watering and notifying farmers about the suggested amount of pesticides and fertilizers.
These five research papers together highlight recent advances in the emerging interdisciplinary field of biologically inspired networking. Although the field of biologically inspired networking has a long history, further research is needed for practical application and development. We hope that the special issue stimulates further research in the field. To conclude the editorial remarks, we thank the Editor-in-Chief, Dr. Imrich Chlamtac for helping us organize the special issue. We also thank all authors and reviewers who contributed to this special issue.
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Chen, Y., Nakano, T., Lin, L. et al. Editorial: Biologically Inspired Computing and Networking. Mobile Netw Appl 26, 1344–1346 (2021). https://doi.org/10.1007/s11036-021-01768-8