Internet of Things-Based Smart Electricity Monitoring and Control System Using Usage DataRead the full article
Wireless Communications and Mobile Computing provides the R&D communities working in academia and the telecommunications and networking industries with a forum for sharing research and ideas in this fast moving field.
Chief Editor Dr Cai is an Associate Professor in the Department of Computer Science at Georgia State University, USA and an Associate Director at INSPIRE Center.
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Mobile Edge Computing Enabled Efficient Communication Based on Federated Learning in Internet of Medical Things
The rapid growth of the Internet of Medical Things (IoMT) has led to the ubiquitous home health diagnostic network. Excessive demand from patients leads to high cost, low latency, and communication overload. However, in the process of parameter updating, the communication cost of the system or network becomes very large due to iteration and many participants. Although edge computing can reduce latency to some extent, there are significant challenges in further reducing system latency. Federated learning is an emerging paradigm that has recently attracted great interest in academia and industry. The basic idea is to train a globally optimal machine learning model among all participating collaborators. In this paper, a gradient reduction algorithm based on federated random variance is proposed to reduce the number of iterations between the participant and the server from the perspective of the system while ensuring the accuracy, and the corresponding convergence analysis is given. Finally, the method is verified by linear regression and logistic regression. Experimental results show that the proposed method can significantly reduce the communication cost compared with the general stochastic gradient descent federated learning.
Artificial Intelligence Application in Cybersecurity and Cyberdefense
Devices are increasingly getting connected to the internet with the advances in technologies called the Internet of Things (IoT). The IoTs are the physical device in which are embedded with software, sensors, among other technologies. Linking and switching data resources with other devices, IoT has been recognized to be a trending research arena due to the world’s technological advancement. Every stage of technology avails several capacities, for instance, the IoT avails any device, anyone, any service, any technological path or any network, any place, and any context to be connected. The effective IoT applications permit public and private business organizations to regulate their assets, optimize the performance of the business, and develop new business models. In this study, we scrutinize the IoT progress as an approach to the technological upgrade through analyzing traits, architectures, applications, enabling technologies, and future challenges. To enable an aging society, and optimize different kinds of mobility and transportation, and helps to enhance the effectiveness of energy, along with the definition and characteristics of the IoT devices, the study examined the architecture of the IoT that includes the perception layer, transmission layer, application layer, and network management. It discusses the enabling technologies of the IoT that include application domain, middleware domain, network domain, and object domain. The study further evaluated the role of the IoT and its application in the everyday lives of the people by making smart cities, smart agriculture and waste management, retail and logistics, and smart environment. Besides the benefits, the IoT has demonstrated future technological challenges and is equally explained within the study.
Cluster Design and Optimization of SWIPT-Based MEC Networks with UAV Assistance
In recent years, service isolation and service miniaturization have become very popular. The large services are dismantled into multiple low-cost and simple small services to improve the scalability and disaster tolerance of the entire services. A service network composed of unmanned aerial vehicles (UAVs) and MEC servers is proposed in this paper, which aims at decoupling multiple services of the SWIPT-MEC network. In this network, UAVs take charge of energy transmission and calculation task scheduling and MEC servers are focused on task calculation. To meet the resource requirements of the ground nodes (GNs) in the network, we designed a distributed iterative algorithm to solve the resource allocation decision problem of GNs and used the modified expert bat algorithm to complete the UAV’s trajectory planning in a two-dimensional space. The results show that the algorithm can formulate a more fair resource allocation strategy, and its performance is improved by 7% compared with the traditional bat algorithm. In addition, the algorithm in this paper can also adapt to changes in task length and has a certain degree of stability.
LCHI: Low-Order Correlation and High-Order Interaction Integrated Model Oriented to Network Intrusion Detection
Network intrusion poses a severe threat to the Internet of Things (IoT). Thus, it is essential to study information security protection technology in IoT. Learning sophisticated feature interactions is critical in improving detection accuracy for network intrusion. Despite significant progress, existing methods seem to have a strong bias towards single low- or high-order feature interaction. Moreover, they always extract all possible low-order interactions indiscriminately, introducing too much noise. To address the above problems, we propose a low-order correlation and high-order interaction (LCHI) integrated feature extraction model. First, we selectively extract the beneficial low-order correlation between the same-type features by the multivariate correlation analysis (MCA) model and attention mechanism. Second, we extract the complicated high-order feature interaction by the deep neural network (DNN) model. Finally, we emphasize both the low- and high-order feature interactions and incorporate them. Our LCHI model seamlessly combines the linearity of MCA in modeling lower-order feature correlation and the nonlinearity of DNN in modeling higher-order feature interaction. Conceptually, our LCHI is more expressive than the previous models. We carry on a series of experiments on the public wireless and wired network intrusion detection datasets. The experimental results show that LCHI improves 1.06%, 2.46%, 3.74%, 0.25%, 1.17%, and 0.64% on the AWID, NSL-KDD, UNSW-NB15, CICIDS 2017, CICIDS 2018, and DAPT 2020 datasets, respectively.
Design and Implementation of Rural Community Elderly Culture Platform Based on Real-Time Social Media Data Mining
In the current big data environment, science and technology not only provide a new governance model for rural community governance but also put forward higher requirements for rural community governance level. Under the background of rural revitalization, promoting the construction of rural community cultural service system is not only an important choice to realize the equalization of urban and rural basic public services but also an important way to protect the cultural rights and interests of rural residents. On the basis of analyzing the real-time data of social media, this paper studies the design and implementation method of rural community culture platform and then puts forward the strategy of community public culture informatization construction under the background of aging. From a global perspective, all countries have their own ways and means to invest in public cultural services. Especially from the perspective of countries with better development of public cultural services, multichannel funding sources are an important indicator of the quality of cultural undertakings. With the development of China’s social economy, the rural endowment insurance system is becoming more and more perfect, and the basic living needs of the elderly are basically met.
A Survey: Nonorthogonal Multiple Access with Compressed Sensing Multiuser Detection for mMTC
One objective of the 5G communication system and beyond is to support massive machine type of communication (mMTC) to propel the fast growth of diverse Internet of Things use cases. The mMTC is aimed at providing connectivity to tens of billions of sensor nodes. The dramatic increase of sensor devices and massive connectivity impose critical challenges for the network to handle the enormous control signaling overhead with limited radio resources. Nonorthogonal multiple access (NOMA) is a new paradigm shift in the design of multiple user detection and multiple access. NOMA with compressive sensing-based multiuser detection is one of the promising candidates to address the challenges of mMTC. The survey article is aimed at providing an overview of the current state-of-art research work in various compressive sensing-based techniques that enable NOMA. We present characteristics of different algorithms and compare their pros and cons, thereby providing useful insights for researchers to make further contributions in NOMA using compressive sensing techniques. Nonorthogonal CDMA massive connectivity grant free medium access compressive sensing multiuser detection