Over the past decade, there has been a remarkable acceleration in the evolution of smart cities and intelligent spaces, driven by breakthroughs in technologies such as the Internet of Things (IoT), edge–fog–cloud computing, and machine learning (ML)/artificial intelligence (AI). As society begins to harness the full potential of these smart environments, the horizon brightens with the promise of an immersive, interconnected 3D world. The forthcoming paradigm shift in how we live, work, and interact owes much to groundbreaking innovations in augmented reality (AR), virtual reality (VR), extended reality (XR), blockchain, and digital twins (DTs). However, realizing the expansive digital vista in our daily lives is challenging. Current limitations include an incomplete integration of pivotal techniques, daunting bandwidth requirements, and the critical need for near-instantaneous data transmission, all impeding the digital VR metaverse from fully manifesting as envisioned by its proponents. This paper seeks to delve deeply into the intricacies of the immersive, interconnected 3D realm, particularly in applications demanding high levels of intelligence. Specifically, this paper introduces the microverse, a task-oriented, edge-scale, pragmatic solution for smart cities. Unlike all-encompassing metaverses, each microverse instance serves a specific task as a manageable digital twin of an individual network slice. Each microverse enables on-site/near-site data processing, information fusion, and real-time decision-making within the edge–fog–cloud computing framework. The microverse concept is verified using smart public safety surveillance (SPSS) for smart communities as a case study, demonstrating its feasibility in practical smart city applications. The aim is to stimulate discussions and inspire fresh ideas in our community, guiding us as we navigate the evolving digital landscape of smart cities to embrace the potential of the metaverse.

While integrated sensing and communication has attracted great attention, integrated imaging and communication is still largely open. Microwave, millimeter-wave, and tera-Hz imaging needs to sample a huge number of antenna positions and is thus too complex and costly to mobile communication devices. To address this challenge, we propose a Lightweight Imaging Algorithm (LIA) to reduce the complexity of both imaging hardware and computation. Based on the synthetic aperture radar principle, LIA uses the iterative matrix inversion lemma to estimate image pixels efficiently. Simulations with both simulated and real-world data verified that LIA had superior performance. The algorithm is promising to compact handheld and mobile devices in integrated imaging and communication applications.

In this paper, we study both the jamming capability of the cognitive-radio-based jammers and the anti-jamming capability of the cognitive radio networks (CRN), by considering multiple uncooperative jammers and independent Rayleigh flat-fading propagations. A Markov model of CRN transmission is set up for the cross-layer analysis of the anti-jamming performance. The transitional probabilities are derived analytically by considering a smart jamming attack strategy. Average throughput expression is obtained and verified by simulations. The results indicate that CRN communications can be extremely susceptible to smart jamming attacks targeting the CRN spectrum sensing and channel switching procedures.

Physical-layer secure transmissions require that the legitimate parties Alice and Bob have certain signal advantages over the eavesdropper Eve. Unfortunately, it is still unknown what advantages can be guaranteed realistically in practice. This letter shows that hybrid massive MIMO can be exploited to the advantage of Alice for this purpose. Specifically, it allows Alice to use large antenna arrays that stealthy eavesdroppers can not afford in many practical situations. To realize this advantage, an efficient transmission scheme is developed with hybrid massive MIMO, random dumb antenna selection and channel reciprocity-based signal randomization techniques. It can secure the transmission against multiple stealthy eavesdroppers. Transmission security is analyzed assuming eavesdroppers with sufficient side knowledge and channel estimation capabilities. Simulations are conducted to verify the superior performance.

Over the past decade, there has been a remarkable acceleration in the evolution of smart cities and intelligent spaces, driven by breakthroughs in technologies such as the Internet of Things (IoT), edge-fog-cloud computing, and machine learning (ML)/Artificial Intelligence (AI). As society begins to harness the full potential of these smart environments, the horizon brightens with the promise of an immersive, interconnected 3D world. The forthcoming paradigm shift in how we live, work, and interact owes much to groundbreaking innovations in augmented reality (AR), virtual reality (VR), extended reality (XR), Blockchain, and Digital Twins (DT). However, realizing the expansive digital vista in our daily lives is challenging. Current limitations include an incomplete integration of pivotal techniques, daunting bandwidth requirements, and the critical need for near-instantaneous data transmission, all impeding the digital VR Metaverse from fully manifesting as envisioned by its proponents. This paper seeks to delve deeply into the intricacies of the immersive, interconnected 3D realm, particularly in applications demanding high levels of intelligence. Specifically, this paper introduces Microverse, a task-oriented, edge-scale, pragmatic solution for smart cities. Unlike an all-encompassing Metaverses, each Microverse instance serves a specific task as a manageable digital twin of an individual network slice. Each Microverse enables on-site/near-site data processing, information fusion, and real-time decision-making within the edge-fog-cloud computing framework. The Microverse concept is verified using smart public safety surveillance (SPSS) for smart communities as a case study, demonstrating its feasibility in practical smart city applications. The aim is to stimulate discussions and inspire fresh ideas in our community, guiding us as we navigate the evolving digital landscape of smart cities to embrace the potential of the Metaverse.

In black-box adversarial attacks, attackers query the deep neural network (DNN) and use the query results to optimize the adversarial samples iteratively. In this paper, we study the method of adding white noise to the DNN output to mitigate such attacks. One of our unique contributions is a theoretical analysis of gradient signal-to-noise ratio (SNR), which shows the trade-off between the defense noise level and the attack query cost. The attacker’s query count (QC) is derived mathematically as a function of noise standard deviation. This will guide the defender to find the appropriate noise level for mitigating attacks to the desired security level specified by QC and DNN performance loss. Our analysis shows that the added noise is drastically magnified by the small variation of DNN outputs, which makes the reconstructed gradient have an extremely low SNR. Adding slight white noise with a very small standard deviation, e.g., less than 0.01, is enough to increase QC by many orders of magnitude yet without introducing any noticeable classification accuracy reduction. Our experiments demonstrate that this method can effectively mitigate both soft-label and hard-label black-box attacks under realistic QC constraints. We also prove that this method outperforms many other defense methods and is robust to the attacker’s countermeasures.