This paper introduces a unified framework for activity detection in IRS-assisted IoT networks that simultaneously addresses three critical practical challenges: asynchronous transmissions, heterogeneous power levels used by devices to report their local observations, and signal blockage in dynamic environments. The system leverages an intelligent reflecting surface (IRS) to enhance detection reliability, with optional incorporation of a direct line-of-sight (LoS) path. Departing from conventional approaches that handle these challenges in isolation, we formulate a comprehensive detection problem as a binary hypothesis test and develop a structured hierarchy of four detectors: an optimal detector alongside three computationally efficient detectors designed for practical scenarios with different levels of prior knowledge about noise variance, channel state information, and device transmit powers. This hierarchical approach systematically bridges the gap between theoretical optimality and implementation practicality. For each detector, we derive closed-form expressions for both detection and false alarm probabilities, establishing theoretical performance benchmarks and revealing fundamental scaling laws. Extensive simulations validate our analytical results and extract actionable design guidelines by systematically evaluating the impact of key system parameters including the number of antennas, samples, users, and IRS elements on detection performance, providing valuable insights for 6G IoT system designers. The proposed framework effectively bridges theoretical optimality with implementation practicality, providing a scalable solution for IRS-assisted IoT networks in emerging 6G systems.

This paper introduces a unified learning framework for Byzantine-resilient spectrum sensing and secure transmission in intelligent reflecting surface (IRS)-assisted networks under channel state information (CSI) uncertainty. The sensing module employs robust Bayesian belief updates with adversary-resistant aggregation and consensus, guaranteeing reliable primary user (PU) detection even when a bounded fraction of users are malicious. Based on the sensing outcome, the transmission module formulates the downlink design as a sum mean-squared error (MSE) minimization problem under transmit-power and signal-leakage constraints, jointly optimizing the base station (BS) precoder, IRS configuration, and user equalizers. For partial or known CSI, we develop a lightweight alternating optimization algorithm with provable sublinear convergence. For unknown CSI, we integrate constrained Bayesian optimization (BO) within a geometry-aware, low-dimensional latent space. Simulations demonstrate that the proposed framework achieves a higher probability of detection at a fixed false-alarm rate under adversarial attacks compared to state-of-the-art schemes. It also yields substantial reductions in user MSE, strong suppression of eavesdropper signal power, and fast convergence. This work provides a practical, resilient solution for coherent sensing–communication coordination in emerging sixth-generation (6G) applications such as vehicular, unmanned aerial vehicle (UAV), and Internet of Things (IoT) networks.

We propose a new coded blockchain scheme suitable for the Internet-of-Things (IoT) network. In contrast to existing works for coded blockchains, especially blockchain-of-things, the proposed scheme is more realistic, practical, and secure while achieving high throughput. This is accomplished by: 1) modeling the variety of transactions using a reward model, based on which an optimization problem is solved to select transactions that are more accessible and cheaper computational-wise to be processed together; 2) a transaction-based and lightweight consensus algorithm that emphasizes on using the minimum possible number of miners for processing the transactions; and 3) employing the raptor codes with linear-time encoding and decoding which results in requiring lower storage to maintain the blockchain and having a higher throughput. We provide detailed analysis and simulation results on the proposed scheme and compare it with the state-of-the-art coded IoT blockchain schemes including Polyshard and LCB, to show the advantages of our proposed scheme in terms of security, storage, decentralization, and throughput.

In order to fully unlock the transformative power of distributed ledgers and blockchains, it is crucial to develop innovative consensus algorithms that can overcome the obstacles of security, scalability, and interoperability, which currently hinder their widespread adoption. This paper introduces HybridChain that combines the advantages of sharded blockchain and DAG distributed ledger, and a consensus algorithm that leverages decentralized learning. Our approach involves validators exchanging perceptions as votes to assess potential conflicts between transactions and the witness set, representing input transactions in the UTXO model. These perceptions collectively contribute to an intermediate belief regarding the validity of transactions. By integrating their beliefs with those of other validators, localized decisions are made to determine validity. Ultimately, a final consensus is achieved through a majority vote, ensuring precise and efficient validation of transactions. Our proposed approach is compared to the existing DAG-based scheme IOTA and the sharded blockchain Omniledger through extensive simulations. The results show that IOTA has high throughput and low latency but sacrifices accuracy and is vulnerable to orphanage attacks especially with low transaction rates. Omniledger achieves stable accuracy by increasing shards but has increased latency. In contrast, the proposed HybridChain exhibits fast, accurate, and secure transaction processing, and excellent scalability.

In this paper, we consider a cognitive radio system with some of secondary users (SUs) equipped with multiple antenna full-duplex transceivers. We design a hybrid system to sense and access the spectrum opportunistically such that the SU operates on both underlay and interweave hybrid methods while the primary user (PU) may be active or inactive, respectively, to perform the sensing as agile as possible. Moreover, we maximize the throughput by allocating the spectral resources assuming limited available energy during each time frame with constraints on the interference to the primary user. We show that this problem is convex and propose a simple solution algorithm for it. Our simulations confirm the accuracy of our analytical and theoretical results and illustrate that the total achieved throughput of full-duplex cognitive radio even by considering self-interference is maximized under the assumed conditions.