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The LoRaWAN Link Layer specification [1] is a communication protocol for the Internet of Things. It targets low power, long range, low cost communication using unlicensed spectrum. Network topology is collaborative, which reduces a lot protocol signalling compared to a cellular network. The device is connected to a network server, and protocol overhead is limited to 13 bytes for any data frame. There are three classes of operation. Class A is optimized for low power operation of end-devices, while class B and class C offer reduced downlink latency. The protocol specification offers several mechanisms to adjust the link layer parameters: adaptive data rate, adaptive power control, variable repetition rate, and channel selection.

Fuzzing network servers is a technical challenge, since the behavior of the target server depends on its state over a sequence of multiple messages. Existing solutions are costly and difficult to use, as they rely on manually-customized artifacts such as protocol models, protocol parsers, and learning frameworks. The aim of this work is to develop a greybox fuzzer (StateAFL) for network servers that only relies on lightweight analysis of the target program, with no manual customization, in a similar way to what the AFL fuzzer achieved for stateless programs. The proposed fuzzer instruments the target server at compile-time, to insert probes on memory allocations and network I/O operations. At run-time, it infers the current protocol state of the target server by taking snapshots of long-lived memory areas, and by applying a fuzzy hashing algorithm (Locality-Sensitive Hashing) to map memory contents to a unique state identifier. The fuzzer incrementally builds a protocol state machine for guiding fuzzing. We implemented and released StateAFL as open-source software. As a basis for reproducible experimentation, we integrated StateAFL with a large set of network servers for popular protocols, with no manual customization to accomodate for the protocol. The experimental results show that the fuzzer can be applied with no manual customization on a large set of network servers for popular protocols, and that it can achieve comparable, or even better code coverage and bug detection than customized fuzzing. Moreover, our qualitative analysis shows that states inferred from memory better reflect the server behavior than only using response codes from messages.

Quantum computers, besides offering substantial computational speedups, are also expected to preserve the privacy of a computation. We present an experimental demonstration of blind quantum computing in which the input, computation, and output all remain unknown to the computer. We exploit the conceptual framework of measurement-based quantum computation that enables a client to delegate a computation to a quantum server. Various blind delegated computations, including one- and two-qubit gates and the Deutsch and Grover quantum algorithms, are demonstrated. The client only needs to be able to prepare and transmit individual photonic qubits. Our demonstration is crucial for unconditionally secure quantum cloud computing and might become a key ingredient for real-life applications, especially when considering the challenges of making powerful quantum computers widely available.

The business system server under the IT automation operation and maintenance platform generates massive data samples, based on which the threshold can be set to realize the allocation and management of hardware resources. The traditional threshold selection method is to determine an appropriate threshold based on human experience. If the threshold is too high, it will not play its due role. But if the threshold is too low, it will frequently produce false positives. To solve this problem, an adaptive threshold method based on Chebyshev inequality theory combined with kernel density estimation is proposed to determine monitoring indexes, and a new dynamic implicit threshold model is established to analyse the data generated by the business system server for real-time monitoring and alarm processing. Through the experimental study on the CPU utilization data of the power grid server, lower missing and false positive rate are obtained, which verifies the feasibility and effectiveness of the proposed method.

Breast cancer has become the most common cancer in the world. Early diagnosis and treatment can greatly improve the survival rate of breast cancer patients. Computer diagnostic technology based on convolutional neural networks (CNNs) can assist in detecting breast cancer based on medical images, effectively improving detection accuracy. Hyperparameters in CNN will affect model performance, so hyperparameter tuning is necessary for model training. However, traditional tuning methods can get stuck in local minimums. Therefore, the weights and biases of artificial neural networks are usually trained using global optimization algorithms. Our research introduces cat swarm optimization (CSO) to construct a cat swarm optimization-guided convolutional neural network (CSO-CNN). The model can quickly obtain the optimal combination of hyperparameters and stably get closer to the global optimal. The statistical results of CSO-CNN obtained a sensitivity of 93.50% ± 2.42%, a specificity of 92.20% ± 3.29%, a precision of 92.35% ± 3.01%, an accuracy of 92.85% ± 2.49%, an F1-score of 92.91% ± 2.44%, Matthews correlation coefficient of 85.74% ± 4.94%, and Fowlkes-Mallows index was 92.92% ± 2.43%. Our CSO-CNN algorithm is superior to five state-of-the-art methods. In addition, we tested the CSO-CNN algorithm on the local computer to simulate the mobile environment and confirmed that the algorithm can be transplanted to the network servers.

As the 4th industrial revolution using information becomes an issue, wireless communication technologies such as the Internet of Things have been spotlighted. Therefore, much research is needed to satisfy the technological demands for the future society. A LPWA (low power wide area) in the wireless communication environment enables low-power, long-distance communication to meet various application requirements that conventional wireless communications have been difficult to meet. We propose a method to consume the minimum transmission power relative to the maximum data rate with the target of LoRaWAN among LPWA networks. Reinforcement learning is adopted to find the appropriate parameter values for the minimum transmission power. With deep reinforcement learning, we address the LoRaWAN problem with the goal of optimizing the distribution of network resources such as spreading factor, transmission power, and channel. By creating a number of deep reinforcement learning agents that match the terminal nodes in the network server, the optimal transmission parameters are provided to the terminal nodes. The simulation results show that the proposed method is about 15% better than the existing ADR (adaptive data rate) MAX of LoRaWAN in terms of throughput relative to energy transmission.

PurposeWith the rapid growth of the Internet of Things (IoT) market and requirement, low power wide area (LPWA) technologies have become popular. In various LPWA technologies, Narrow Band IoT (NB-IoT) and long range (LoRa) are two main leading competitive technologies. Compared with NB-IoT networks, which are mainly built and managed by mobile network operators, LoRa wide area networks (LoRaWAN) are mainly operated by private companies or organizations, which suggests two issues: trust of the private network operators and lack of network coverage. This study aims to propose a conceptual architecture design of a blockchain built-in solution for LoRaWAN network servers to solve these two issues for LoRaWAN IoT solution.Design/methodology/approachThe study proposed modeling, model analysis and architecture design.FindingsThe proposed solution uses the blockchain technology to build an open, trusted, decentralized and tamper-proof system, which provides the indisputable mechanism to verify that the data of a transaction has existed at a specific time in the network.Originality/valueTo the best of our knowledge, this is the first work that integrates blockchain technology and LoRaWAN IoT technology.

The \"New Engineering\" has put forward new requirements for the teaching of engineering courses such as computer science and technology, and Internet of Things engineering. Reform the entire teaching process of the Linux operating system course based on the OBE concept for the integration of new engineering disciplines, organically combining basic commands of the Linux operating system, network server configuration, Shell script programming, and Python language programming, integrating comprehensive examples of information security and the Internet of Things, and exploring the application-oriented engineering talent training mode for Linux operating system operation and development under the background of new engineering disciplines. The teaching performance of this course has been evaluated as excellent for three consecutive semesters, indicating that the teaching reform of the course has achieved the expected goals.

Mobile health (M-Health) system is the remote form of Wireless Body Area Networks (WBAN), which can be used for collecting patient’s health data in real-time with mobile devices, and storing it to the network servers. The data can be accessed by doctors to monitor, diagnosed and treat patients through a variety of techniques and technologies. The main advantage of the M-Health system is the ease of time-independent communication from physically distant places that enhances the quality of healthcare services at a reduced cost. Furthermore, to provide faster access to the treatment of patients, an M-Health system can be integrated with the internet of things (IoT) to offer preventive or proactive healthcare services by connecting devices and persons. However, its equally great drawback lies in transmitting and receiving the health information wirelessly through an open wireless medium that offers different security and privacy violation threats. We aim to address such a deficiency, and thus a new scheme called an efficient and provable secure certificate-based combined signature, encryption and signcryption (CBCSES) scheme, has been proposed in this article. The scheme not only obtains encryption and signcryption but also provides encryption or signature model alone when needed. To show the effectiveness of the proposed scheme, detailed security analyses, i.e. indistinguishable under adaptive chosen-ciphertext attacks (IND-CBCSES-CCA) and unforgeable under adaptive chosen message attacks (EUF-CBCSES-CMA), and the comparisons with relevant existing schemes are carried out. The results obtained authenticate the superiority of our scheme in terms of both computation and communication costs with enhanced security.

Internet of things phenomenon has brought up distinctive technologies that are using wireless communication and appearing in smart city applications. Long range (LoRa) modulation technique has pulled up the market and forced the announcement of LoRa wide area network (LoRaWAN) standard in 2021 by ITU-T with Y.4480 standard code. LoRaWAN is a medium access control protocol using low power wide area network approaches with the aim of long-range coverage and management of many end devices. LoRaWAN networks are emerging all over the world with some existing optimization, planning and network allocation problems that need to be overcome. This paper focuses on comparative analysis and interpretation of measurements performed in a LoRaWAN network deployed in an 18-floor building with a LoRaWAN gateway on the roof. The research covers results of comparative measurements between end device and Adeunis field test device (AFTD) for received signal strength indicator (RSSI), signal-to-noise ratio (SNR) and spreading factor (SF). End devices have been randomly selected from 18th, 12th, 6th and 1st floors and their daily performance data have been gathered through the network server. AFTD has been used to get 100 sample measurements for each floor. Maximum and average RSSI values obtained from end device measurements are higher than ones measured with AFTD except the case in the 18th floor. Excluding the maximum SNR values at the 1st and the 18th floors, all SNR values measured with AFTD are higher than ones obtained from end device measurements. SF measurements show that higher SF values are more likely to be used with increasing distances to the gateway as expected from the theoretical background.