The spatial correlation between the main and eavesdropping channels impacts the effectiveness of beamforming (BF) technology in securing the Internet of Things (IoT) communication system. This study proposes an IRS-assisted secure BF scheme (IRS-SBS) for a multiuser IoT system under imperfect channel state information (CSI). The security issue, constrained by channel spatial correlation, is formulated as a nonconvex optimization problem with probabilistic constraints. To address this, we introduce a novel actor-critic algorithm combined with a generative diffusion model (AC-GDM). This approach jointly optimizes the base station (BS) precoding matrix and the intelligent reflecting surface (IRS) phase shift matrix, subject to constraints on transmit power and phase shifts. The AC-GDM algorithm utilizes a denoising process to recover the optimal BF solution from the Gaussian noise inherent in the multiuser wireless channel environment. Simulation results demonstrate that the minimum achievable secrecy rate of the IRS-SBS outperforms the artificial noise (AN) scheme and the BF scheme by approximately 1.9576 and 2.3596 bps/Hz, respectively. These results validate the effectiveness of IRS-SBS in significantly mitigating the impact of channel spatial correlation on the security of IoT communication systems.

The booming crowdsourced delivery leverages networks of local, part time couriers to meet growing customer demands for increasingly speedy shipping. By conducting city-wide low-cost delivery networks, crowd-sourced enabled instant delivery platforms have fought to figure out a way to offer instant shipping at low price. Nevertheless, limited ubiquitous computing infrastructures impede the discovery of equilibrium between cost reduction and sufficient shipping capacity. In this article, we present our endeavor towards understanding and designing a dynamic pricing framework based on a large-scale food-delivery platform. To reduce delivery fees in capacity overload regions and resolve the decline of service quality caused by insufficient shipping capacity, we address the challenge of quantifying the inter-regional shipping capacity disparities by accordingly applying a variety of techniques, including cooperative game theory, probability analysis and address entity recognition. In particular, we provide an$O(n^{2})$algorithm for the cooperative game based cost allocation, of which the exponential computational complexity$O(n^{2}2^{n})$is generally considered a major challenge in real-world applications. City-wide experiments in Nanjing and Shenzhen illustrate that we achieve a 10% decrease in delivery fees under overload shipping capacity and a 3% increase in completion rates in customer locations with poor service quality.

Intrusion detection is an efficient method to detect the malicious node in the MANET. This paper presents the improvements of the existing data analysis and pattern-matching algorithm based active set, which develop a more effective BER.