IEEE internet of things magazine 

About: IEEE internet of things magazine is an academic journal published by Institute of Electrical and Electronics Engineers. The journal publishes majorly in the area(s): Computer science & The Internet. It has an ISSN identifier of 2576-3180. Over the lifetime, 147 publications have been published receiving 158 citations. The journal is also known as: IoTM & Internet of things magazine.

Federated Learning for the Internet of Things: Applications, Challenges, and Opportunities

Abstract: Billions of IoT devices will be deployed in the near future, taking advantage of faster Internet speed and the possibility of orders of magnitude more endpoints brought by 5G/6G. With the growth of IoT devices, vast quantities of data that may contain users' private information will be generated. The high communication and storage costs, mixed with privacy concerns, will increasingly challenge the traditional eco-system of centralized over-the-cloud learning and processing for IoT platforms. Federated learning (FL) has emerged as the most promising alternative approach to this problem. In FL, training data-driven machine learning models is an act of collaboration between multiple clients without requiring the data to be brought to a central point, hence alleviating communication and storage costs and providing a great degree of user-level privacy. However, there are still some challenges existing in the real FL system implementation on IoT networks. In this article, we discuss the opportunities and challenges of FL in IoT platforms, as well as how it can enable diverse IoT applications. In particular, we identify and discuss seven critical challenges of FL in IoT platforms and highlight some recent promising approaches toward addressing them.

Digital Twin Envisioned Secure Air-Ground Integrated Networks: A Blockchain-Based Approach

Abstract: The wide use of unmanned aerial vehicles provides a promising paradigm for improving air-ground services and applications (e.g., urban sensing, disaster relief) in air-ground integrated networks (AGINs). Digital twin (DT), which is an emerging technology that utilizes data, models, and intelligent algorithms to integrate cyber physical networks and digital virtual models, provides a real-time and dynamic simulation platform for strategy optimization and decision making in AGINs. Due to the openness and massive connectivity of AGINs, the security and reliability services in this system become an important issue. In this article, we investigate the DT envisioned secure federated aerial learning for AGINs via an aerial blockchain approach. Specifically, we propose a layered framework of DT envisioned AGINs, which comprises the construction segment, communication segment, aggregation segment, analysis segment, and operation segment. Based on this framework, we offer the applications of the proposed DT envisioned AGINs. To guarantee the security of data transmission in AGINs, we investigate the aerial blockchain-based approach for ensuring data security. Furthermore, we provide a case study of DT envisioned secure federated aerial computing in AGINs to validate the effectiveness of the proposed approach through designing the aerial blockchain and training model.

Deep Learning Feature Fusion Approach for an Intrusion Detection System in SDN-Based IoT Networks

Abstract: A survey of the literature shows that the number of IoT attacks are gradually growing over the years due to the growing trend of Internet-enabled devices. Software defined networking (SDN) is a promising advanced computer network technology that supports IoT. A network intrusion detection system is an essential component in the SDN-IoT network environment to detect attacks and classify the attacks into their categories. Following, this work proposes a deep-learning-based approach that detects attacks and classifies them into their attack categories. The model extracts the internal feature representations from the gated recurrent unit (GRU) deep learning layers; further, the optimal features were extracted using kernel principal component analysis (kernel-PCA). Next, features were fused together, and attack detection and its classification is done using the fully connected network. The proposed feature fused GRU network has achieved better performance than the GRU model and other well-known classical machine-learning-based models. The proposed method can be used in real time to effectively monitor the network traffic in the SDN-IoT environment to proactively alert about possible attacks and classify them into their attack categories.

Drone Charging Stations Deployment in Rural Areas for Better Wireless Coverage: Challenges and Solutions

Abstract: While fifth-generation (5G) cellular is meant to deliver gigabit peak data speeds, low latency, and connection to billions of devices, and 6G is already on the way, half of the world population living in rural areas are still facing challenges connecting to the internet. Compared to urban areas, users in rural areas are greatly impacted by low income, high cost of backhaul connectivity, limited resources, extreme weather, and natural geographical limitations. Therefore, how to connect the rural areas and the difficulties of providing connectivity draw great attention. This article first provides a brief discussion about existing technologies and strategies for enhancing the network coverage in rural areas, and their advantages, limitations, and cost. Next, we mainly focus on the UAV-assisted network in resource-limited regions. Considering the limitation of the onboard battery of UAVs and the electricity supply scarcity in some rural regions, we investigate the possibility and performance enhancement of the deployment of renewable energy (RE) charging stations. We outline three practical scenarios, and use simulation results to demonstrate that RE charging stations can be a possible solution to address the limited onboard battery of UAVs in rural areas, especially when they can harvest and store enough energy. Finally, future works and challenges are discussed.

Artificial Intelligence in Beyond 5G and 6G Reliable Communications

Abstract: The rapid increase in heterogeneous data traffic with the ongoing development of self-organizing and self-sustaining networks exposes the limitations of the fifth generation (5G) system, which was originally aimed at enabling the realization of the Internet of Everything. This study presents flexible design agreements of beyond 5G (B5G) from the current 3GPP study and proposes an intelligent network architecture for the 5G and |B5G paradigm to ensure that the network is self-sustained and self-organized. The key idea is to use machine learning (ML) to dynamically schedule flexible transmission time intervals at the slot level to optimize network performance. This study also provides an overview of the queuing model of the medium access control layer and presents how ML-enabled scheduling plays an important role in reducing queuing latency and providing reliable services of the B5G network.