Abstract
Today’s sensor networks need robustness, security and efficiency with a high level of assurance. Error correction is an effective communicational technique that plays a critical role in maintaining robustness in informational transmission. The general way to tackle this problem is by using forward error correction (FEC) between two communication parties. However, by applying zero-error coding one can assure information fidelity while signals are transmitted in sensor networks. In this study, we investigate zero-error coding via both classical and quantum channels, which consist of n obfuscated symbols such as Shannon’s zero-error communication. As a contrast to the standard classical zero-error coding, which has a computational complexity of , a general approach is proposed herein to find zero-error codewords in the case of quantum channel. This method is based on a n-symbol obfuscation model and the matrix’s linear transformation, whose complexity dramatically decreases to . According to a comparison with classical zero-error coding, the quantum zero-error capacity of the proposed method has obvious advantages over its classical counterpart, as the zero-error capacity equals the rank of the quantum coefficient matrix. In particular, the channel capacity can reach n when the rank of coefficient matrix is full in the n-symbol multilateral obfuscation quantum channel, which cannot be reached in the classical case. Considering previous methods such as low density parity check code (LDPC), our work can provide a means of error-free communication through some typical channels. Especially in the quantum case, zero-error coding can reach both a high coding efficiency and large channel capacity, which can improve the robustness of communication in sensor networks.
Highlights
Technology (CICAEET), Jiangsu Engineering Center of Network Monitoring, School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China; Department of Computer Science and Engineering, Michigan State University, The Department of Computer Science and Application, Zhengzhou Institute of Aeronautical Industry
In order to ensure the correctness of informational transmission, sensor networks should maintain a high level of robustness, security and efficiency by deploying specially designed transmission mechanisms
Sensors 2019, 19, 5071 techniques have been applied to different types of sensor networks such as an efficient spider web-like transmission mechanism for emergency data in vehicular ad hoc networks [1], a spammer identification scheme based on the Gaussian mixture model that utilizes machine learning for industrial mobile networks [2] and a robustness optimization scheme to protect a class of scale-free wireless sensor networks from cyberattacks [3]
Summary
Sensor networks are groups of specialized transducers that have a communications infrastructure that is intended to record and monitor conditions at different locations in the Internet of Things (IoT). Since packets in wireless sensor networks are commonly broadcast over shared channels and forwarded over multiple hops, using FEC is preferable as it can reduce the need to retransmit data packets, thereby reducing the power consumed in the process [4] Another way is to use error correction coding to defend noises in the transmission process. The number of k states that are distinguishable with no error after passing through the channel is determined This definition reconsiders the clique problem in terms of the zero-error capacity of graphs redefines it using quantum information theory. We introduce the classical zero-error channel model and coding method to the error correction in sensor networks, followed by a presentation of the quantum zero-error channel model.
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