Massive IoT including the large number of resource-constrained IoT devices has gained great attention. IoT devices generate enormous traffic, which causes network congestion. To manage network congestion, multi-channel-based algorithms are proposed. However, most of the existing multi-channel algorithms require strict synchronization, an extra overhead for negotiating channel assignment, which poses significant challenges to resource-constrained IoT devices. In this paper, a distributed channel selection algorithm utilizing the tug-of-war (TOW) dynamics is proposed for improving successful frame delivery of the whole network by letting IoT devices always select suitable channels for communication adaptively. The proposed TOW dynamics-based channel selection algorithm has a simple reinforcement learning procedure that only needs to receive the acknowledgment (ACK) frame for the learning procedure, while simply requiring minimal memory and computation capability. Thus, the proposed TOW dynamics-based algorithm can run on resource-constrained IoT devices. We prototype the proposed algorithm on an extremely resource-constrained single-board computer, which hereafter is called the cognitive-IoT prototype. Moreover, the cognitive-IoT prototype is densely deployed in a frequently-changing radio environment for evaluation experiments. The evaluation results show that the cognitive-IoT prototype accurately and adaptively makes decisions to select the suitable channel when the real environment regularly varies. Accordingly, the successful frame ratio of the network is improved.
We investigated methods of analyzing the noise power spectrum (NPS) measurement for medical liquid crystal displays (LCDs). Uniform images displayed on the LCDs were imaged with a high-performance digital camera equipped with a close-up lens, and then the NPSs were calculated from the image data by means of several analysis methods. In a method using the 2D fast Fourier transform (FFT) with a 256 x 256 pixels data segment (basic method), we examined the efficacy of a background trend correction (BTC) and a Hanning windowing process used for reducing the spectral estimation errors in the Fourier analysis. To improve the frequency resolution of the basic method, we examined two 2D FFT methods by using 512 x 512 and 1024 x 1024 pixel segments. In addition, we studied a 1D FFT method with 1024-point 1D noise profiles (1D method). In these three methods, the BTC by a second-order polynomial fit and Hanning windowing were commonly applied. A 3-mega-pixel (MP) and a 5-MP monochrome LCD were employed for evaluating the respective methods. Also, a prototype 5-MP LCD equipped with a new anti-reflection surface-coated panel was compared with the conventional 5-MP LCD. The Hanning windowing process was indispensable for avoiding the spectral leakage errors caused by the pixel structures of the LCD. Sufficient frequency resolution was obtained by the 2D FFT method with the 1024 x 1024 pixels segments and the 1D method. The method which provided the most reliable NPSs was the 1D method, with which the BTC was achieved successfully.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.