With the rapid advancement of diffusion-based text-to-video generation, challenges surrounding video ownership verification and copyright protection have become increasingly urgent. Traditional digital watermarking techniques are typically handcrafted for specific types of distortions, while existing diffusion-based watermarking methods are primarily applied to image-level tasks. As a result, the effectiveness of these methods significantly diminishes when videos undergo complex transformations. To address this issue, we introduce RINGet, a robust watermarking framework for diffusion-based video generation. RINGet embeds user-defined keys into the initial latent variables in the Fourier domain while maintaining imperceptibility in the spatial domain through reversible Fourier transforms. The framework adopts a radius-based and ring-based segmentation strategies to improve robustness against rotation while preserve the quality of the generated videos. Moreover, to mitigate distribution shifts caused by watermark embedding, the key is partitioned into discrete segments and distributed across different initial latent variables. Extensive experiments demonstrate the superiority of the RINGet framework over traditional approaches in terms of robustness, particularly under severe perceptual-domain distortions, while preserving high video quality and inference efficiency.

Energy harvesting sensor nodes based on real nonvolatile processors are demonstrated to show the desirable characteristics of those systems, such as no battery, zero stand-by power, microsecond-scale sleep and wake-up time, high resilience to random power failures and fine-grained power management. Furthermore, we show its applications to a distributed moving object detection system, one of novel nonvolatile computing systems.

Cities have long been considered as complex entities with nonlinear and dynamic properties. Pervasive urban sensing and crowd sourcing have become prevailing technologies that enhance the interplay between the cyber space and the physical world. In this paper, a spectral graph based manifold learning method is proposed to alleviate the impact of noisy, sparse and high-dimensional dataset. Correlation analysis of two physical processes is enhanced by semi-supervised machine learning. Preliminary evaluations on the correlation of traffic density and air quality reveal great potential of our method in future intelligent evironment study.

Distributed faulty node detection and isolation in metropolitan-area delay-tolerant vehicular sensor networks are investigated in this paper. For the intermittently connected large-scale dynamic networks, we design a practical faulty node detection algorithm that vehicles can perform on board to detect faulty sensors by utilizing the local data's spatial correlation. Based on the distributed detection algorithm, a faulty node isolation scheme is proposed to control the diffusion of the faulty data. The parameters of the detection and isolation algorithms to achieve the best performance are investigated based on simulations over real-world data sets. The results show that our detection algorithm performs almost as well as other relative algorithms but demands much less on sensors' communication ability. More than 70 percent of faulty nodes can be identified, and more than half of the faulty data can be reduced in the network by the means of isolation while the total overhead decreases by 35%.

With the advancement of the sensor network technology and cyber physical systems [2], the merging between the virtual cyber space and the real physical world is bound to happen, which will impact the lifestyle of the human being. The metropolitan area sensor networks, composed of millions of heterogeneous sensors and penetrating every aspect of the city life, provide gigantic amount of real-time information of the city, which makes it possible to reconstruct the real world in an on-line cyber space.

Environmental surveillance applications usually require a long living cycle of sensor nodes, especially when they are powered by non-charged batteries. Inspired by cellular automata, we propose a non-cluster based topology control method to prolong the lifetime of wireless sensor networks. Differing from the traditional cluster-based methods, our approaches lead a special way to maintain longer system lifetime by sacrificing a small proportion of network coverage and connectivity degrees. The demonstration contains both theoretical simulation and system-level experiment, which will be instructive for future study.

Monitoring traffic pattern is always an important approach to improve the performance of computer networks. In recent years, peer-to-peer (P2P) service gradually dominates the Internet traffic against traditional client-server (C-S) service such as Web. The changed situation of networks is mainly in two aspects. One is that popular P2P file-sharing applications require both board downstream and upstream bandwidth, which conflicts to asymmetry structure of the C-S service oriented networks, and another is that the topology of P2P overlay networks is varying with peers joining and leaving. The dramatic shift of Internet traffic may cause different patterns from that observed in early years. This leads a new round of Internet measurement, which requires effective monitoring of network-wide traffic within the region of ISP or autonomous system. Unfortunately, few of technologies can capture well the dynamics of C-S and P2P traffic in a macroscopic level. In this paper, we propose a method based on random matrix theory (RMT) for the detection of networkwide traffic pattern. Using only a few observation points, our method can monitor the macroscopic effect of the Internet traffic. We show that such macroscopic-level monitoring can be used to capture shifts in spatial-temporal patterns caused by P2P and C-S traffic, and inform where and when P2P and C-S traffic possibly arise in transit networks.

A novel CEQRP (cost-efficient QoS routing protocol) for mobile ad hoc networks (MANETs) is proposed. CEQRP can provide QoS guarantee and is aimed at extending the battery's lifetime at each host by selecting the hosts with a long battery's remaining lifetime to forward packets. Simulation results show that CEQRP has good network performance. Furthermore, CEQRP shows distinct performance advantages in using a host's electric energy efficiently.

Based on the idea of contention reservation and polling, a user-dependent perfect-scheduling multiple access (UPMA) protocol for supporting node mobility and multihop architecture is described. In the protocol, only the nodes have packets to send contend to use the channel and the nodes without traffic transmission are immediately deleted from the polling list, thus it improves channel utilization greatly. Moreover, a collision avoidance and resolution protocol is proposed to guarantee a node to access the channel rapidly. Finally, taking a wireless network with star topology as an example, the network performance of Internet data traffic is simulated and related conclusions are given.