AC arc faults are one of the most important causes of residential electrical wiring fires, which may produce extremely high temperatures and easily ignite surrounding combustible materials. The global interest in machine learning-based methods for arc fault diagnosis applications is increasing due to continuous challenges in efficiency and accuracy. In this paper, a temporal domain visualization convolutional neural network (TDV-CNN) methodology is proposed. The current transformer and high-speed data acquisition system are used to collect the current of a series of arc faults, then the signal is filtered by a digital filter and converted into a gray image in time sequence before being fed into TDV-CNN. Five different electric loads were selected for experimental validation with various signal characteristics, including vacuum cleaner, fluorescent lamp, dimmer, heater, and desktop computer. The experimental results confirm that the classification accuracy of the five loads’ work states in the ten categories could reach 98.7% or even higher by adjusting parameters perfectly. The methodology is believed to be reliable for series arc detection with relatively high accuracy and also has important potential applications in other fault diagnosis fields.

Fog-based MANET (Mobile Ad hoc networks) is a novel paradigm of a mobile ad hoc network with the advantages of both mobility and fog computing. Meanwhile, as traditional routing protocol, ad hoc on-demand distance vector (AODV) routing protocol has been applied widely in fog-based MANET. Currently, how to improve the transmission performance and enhance security are the two major aspects in AODV’s research field. However, the researches on joint energy efficiency and security seem to be seldom considered. In this paper, we propose a source anonymity-based lightweight secure AODV (SAL-SAODV) routing protocol to meet the above requirements. In SAL-SAODV protocol, source anonymous and secure transmitting schemes are proposed and applied. The scheme involves the following three parts: the source anonymity algorithm is employed to achieve the source node, without being tracked and located; the improved secure scheme based on the polynomial of CRC-4 is applied to substitute the RSA digital signature of SAODV and guarantee the data integrity, in addition to reducing the computation and energy consumption; the random delayed transmitting scheme (RDTM) is implemented to separate the check code and transmitted data, and achieve tamper-proof results. The simulation results show that the comprehensive performance of the proposed SAL-SAODV is a trade-off of the transmission performance, energy efficiency, and security, and better than AODV and SAODV.

No abstract

With the wide utilization of wireless sensor networks(WSN), higher reliability and stability are being pursued gradually. In most cases, the communication capability for sensor network is influenced by the complex environmental conditions, the open characteristics of channels, the energy limitations of nodes, as well as network protocol design issues, ultimately leading to the high possibility of network failure. As a result, a timely and accurate fault diagnosis is of much significance for a network to ensure the stable operation and execution efficiency. This article firstly demonstrates the diagnostic process on the following three aspects, including the collection for network fault information, fault detection, and diagnosis process. In addition, the features of commonly used technologies are also analyzed and compared in order to identify their application scope respectively. Finally, this paper makes the summary for the possible development trends and future research directions of fault diagnosis.

With the rapid development of sensor technology for automated driving applications, the fusion, analysis, and application of multimodal data have become the main focus of different scenarios, especially in the development of mobile edge computing technology that provides more efficient algorithms for realizing the various application scenarios. In the present paper, the vehicle status and operation data were acquired by vehicle-borne and roadside units of electronic registration identification of motor vehicles. In addition, a motion model and an identification system for the single-vehicle lane-change process were established by mobile edge computing and self-organizing feature mapping. Two scenarios were modeled and tested: lane change with no vehicles in the target lane and lane change with vehicles in the target lane. It was found that the proposed method successfully identified the stochastic parameters in the process of driving trajectory simulation, and the standard deviation between simulation and the measured results obeyed a normal distribution. The proposed methods can provide significant practical information for improving the data processing efficiency in automated driving applications, for solving single-vehicle lane-change applications, and for promoting the formation of a closed loop from sensing to service.

Neighbour discovery is a process that new devices gather the information from their neighbours when they initially join in a wireless sensor network. During this process, energy utilisation efficiency serves as an essential evaluated factor to concern. This paper proposes a novel asynchronous multi-channel neighbour discovery method for the energy-sensitive time division multiple access (TDMA) wireless sensor network. Two aspects are designed to enhance the efficiency of the energy utilisation, including minimising the listening time of the first frame, which is formulated as a linear programming problem and solved using the CPLEX tool, as well as predicting the time slot and position of the beacon frame based on the attained information from the frames and the process dormancy when a beacon frame is impossible to come out. Our simulation results show that the proposed methods effectively reduce the energy consumption during neighbour discovery process in a TDMA-based wireless sensor network.

No abstract

No abstract