With improvements in the area of Internet of Things (IoT), surveillance systems have recently become more accessible. At the same time, optimizing the energy requirements of smart sensors, especially for data transmission, has always been very important and the energy efficiency of IoT systems has been the subject of numerous studies. For environmental monitoring scenarios, it is possible to extract more accurate information using smart multimedia sensors. However, multimedia data transmission is an expensive operation. In this study, a novel hierarchical approach is presented for the detection of forest fires. The proposed framework introduces a new approach in which multimedia and scalar sensors are used hierarchically to minimize the transmission of visual data. A lightweight deep learning model is also developed for devices at the edge of the network to improve detection accuracy and reduce the traffic between the edge devices and the sink. The framework is evaluated using a real testbed, network simulations, and 10-fold cross-validation in terms of energy efficiency and detection accuracy. Based on the results of our experiments, the validation accuracy of the proposed system is 98.28%, and the energy saving is 29.94%. The proposed deep learning model’s validation accuracy is very close to the accuracy of the best performing architectures when the existing studies and lightweight architectures are considered. In terms of suitability for edge computing, the proposed approach is superior to the existing ones with reduced computational requirements and model size.

New technologies such as mobile phones, social media and artificial intelligence, have significant impacts on every aspect of education, where digital connectivity is the foundation to support the way people learn. Current Internet and pre-5G cellular communication networks can deliver visual and auditory data, which enable distance/virtual learning. However, remote physical interaction between students and learning facilities, which is an essential part of a new education paradigm i.e., Education 4.0, is still missing. The 5G cellular network with excellent latency and reliability performance would be a game changer by enabling students to feel the physical objects and control them remotely. In this paper, we identify and discuss the unique opportunities the 5G networks can bring to Education 4.0, their technical challenges and potential solutions. We also showcase our Education 4.0 prototype of remote lab.

Prediction has recently been considered as a promising approach to meet low-latency and high-reliability requirements in long-distance haptic communications. However, most of the existing methods did not take features of tasks and the relationship between prediction and communication into account. In this paper, we propose a task-oriented prediction and communication co-design framework, where the reliability of the system depends on prediction errors and packet losses in communications. The goal is to minimize the required radio resources subject to the low-latency and high-reliability requirements of various tasks. Specifically, we consider the just noticeable difference (JND) as a performance metric for the haptic communication system. We collect experiment data from a real-world teleoperation testbed and use time-series generative adversarial networks (TimeGAN) to generate a large amount of synthetic data. This allows us to obtain the relationship between the JND threshold, prediction horizon, and the overall reliability including communication reliability and prediction reliability. We take 5G New Radio as an example to demonstrate the proposed framework and optimize bandwidth allocation and data rates of devices. Our numerical and experimental results show that the proposed framework can reduce wireless resource consumption up to 77.80% compared with a task-agnostic benchmark.

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Increasing insensitivity demands on initiation trains in missile systems necessitates utilizing high voltage initiators. Exploding Foil Initiator (EFI), also known as SlapperDetonator, is the well-known high voltage detonator that can only be initiated under the action of large electrical currents. EFI is strictly immune to Electrostatic Discharge (ESD), Electromagnetic Interference (EMI) and Radio Frequency Interference (RFI) effects due to its specific high current pulse need. Besides, an EFI can detonate a durable secondary high explosive like HNS-IV which is compliant to MIL -STD -1316. This paper mentions the effects of barrel length, bridge copper thickness and flyer plate thickness on the electrical performances of EFI striplines and average velocities of flyer plates experimentally. Furthermore, a numerical study is performed, which both predicts the electrical performances of EFI striplines and average velocities of flyer plates under specified parameters to have a better view of the integrity between theoretical and experimental results. NomenclatureA = constant for velocity calculations C = capacitance of the capacitor in Capacitor Discharge Circuit (CDC) C p = specific heat capacity CF = constant for velocity calculations E = energy I = time dependent current L = inductance L cdc = inductance of Capacitor Discharge Circuit (CDC) L sl = inductance of EFI stripline L t = total Inductance of EFI System M b = mass of the metallic bridge M f = mass of the flyer plate M tamp = mass of the tamper n = polytropic gas exponent Q 0 = initial electrical charge P e = pressure pulse on the explosive R = resistance R b = resistance of bridge R b0 = initial bridge resistance R cdc = resistance of Capacitor Discharge Circuit (CDC) 2 R cvr = resistance of Current Viewing Resistor (CVR) R d = total dynamic resistance of EFI System R sl = resistance of stripline R sw = resistance of spark gap switch R t = total resistance of EFI System T = temperature t = time t p = duration of the pressure pulse T 0 = initial temperature T b = burst temperature V = discharge voltage V 0 = initial voltage of the capacitor V f = final velocity of the flyer plate α = first temperature coefficient of resistance χ = displacement of the flyer plate = velocity of the flyer plate = acceleration of the flyer plate