With the development of smart agriculture, the Agricultural Artificial Intelligence Internet of Things (Agri-AIoT) has shown great potential in fields such as farmland monitoring and precision pesticide application. However, the computationally intensive tasks generated by massive heterogeneous agricultural sensing devices pose serious challenges to real-time and sustainability. To address this issue, this paper proposes a Cloud-Fog-Edge based collaborative computing framework, decoupling the problem into unmanned aerial vehicles (UAVs) deployment and task scheduling two sequential problems. Specifically, a hierarchical optimization framework is proposed with the goal of optimizing system latency and energy consumption. In network deployment stage, a semi-supervised K-Means based UAV deployment algorithm (SKm-UD) is designed, while an improved multi-agent deterministic policy gradient (MTD 3 PG) strategy is proposed in the distributed task offloading phase, which integrates dual delay network, a lightweight local policy update mechanism, and an adaptive learning rate adjustment strategy based on zebra optimization (ZOA-L) to support efficient computation offloading decisions in partially observable environments. Simulation results show that our proposed method can quickly converge to the optimal strategy, with network energy consumption decreases by at most 30.1%. In addition, the scalability of the mechanism in large-scale Agri-AIoT scenarios is also validated.

Modern cybersecurity infrastructure management is facing the challenge of an explosion in the number of machines and Key Performance Indicators(KPIs). As a fundamental data-mining technique, clustering can not only group data with similar characteristics but also provide informative insights that are critical for cybersecurity operations. In particular, the clustering of large-scale KPIs can significantly assist cybersecurity engineers in detecting system failures, reducing redundant alarms, and decreasing troubleshooting overhead-contributing to more effective threat detection and alert management. However, KPIs are reasonably long (that is, high dimensionality) because of the continuous collection of the monitoring system. For KPIs’ high dimensionality, existing KPIs clustering methods always have a trade-off between clustering quality and computational complexity. In this paper, we propose a novel method, SubCluster, which is a fast, accurate, and scalable approach for clustering large-scale KPIs with high quality. SubCluster aims to extract representative subsequences from numerous KPIs. In contrast to existing subsequence extraction-based clustering methods that compress the candidate set, SubCluster first conducts coarse-grained clustering with the nearest neighbor-based graph to obtain near-accurate groups from raw KPIs. SubCluster then attempts to extract representative subsequences for fine-grained clustering. Extensive experimental results on public time-series and large-scale KPIs datasets from the real world demonstrate the effectiveness and efficiency of SubCluster.

As the integration level of integrated circuits continues to increase and the feature size of nanoscale chips continues to shrink, the possibility of triple-node-upsets (TNUs) occurring in circuits increases significantly. This paper proposes a latch, namely CLTNUSL, which offers stable resilience against TNUs in radiative environments while achieving a good balance between reliability and overhead. Unlike the conventional latches consisting of multi-input C-elements, the proposed CLTNUSL latch mainly consists of 16 interlocked dual-input inverters. Due to the small number of transistors, CLTNUSL achieves low area overhead. Due to the use of high-speed paths and clock gating techniques, CLTNUSL achieves low latency and low power consumption. Simulation results demonstrate CLTNUSL’s full recovery from TNU in all scenarios. Compared with the conventional TNU-hardened latch, CLTNUSL achieves minimal latency, power consumption, area overhead, and the delay-power-area product (DPAP). CLTNUSL reduces delay by 13.22%, power by 52.74%, area by 40.90%, and DPAP by 71.44% on average, compared with state-of-the-art latches. Process-Voltage-Temperature (PVT) and Monte Carlo simulation results show that the CLTNUSL latch is less sensitive to temperature, voltage and process variations compared with conventional TNU self-recovery latches.

Including TOR, most of the anonymous communication systems adopt source routing, that the source has to share the globally consistent view of all relays and maintain the up-to-date information. To increase the scalability of TOR, researchers mainly utilize hop-by-hop routing during circuit extension. However, hop-by-hop routing has not been widely deployed since it suffers from route capture attacks, and most of the countermeasures require the source participate in the route extension indirectly, help verify the selection of next hop by intermediate nodes, thus introduces communication overhead. In this paper, we introduce a novel routing scheme called Jump Routing. In jump routing, the route extension follows the jumping way, that each relay chooses the successor of the next hop rather than the next hop itself. In particular, to the best of our knowledge, we are the first to route in the jumping way. In addition, to defend route capture attacks, enhance data privacy, and defend collusion attacks, we propose multiple schemes including jump verification, jump encryption, and corporative jump verification. Different from previous measures on route capture attacks, jump routingdoes not need the participation of the source, but deals with the attack by intermediate nodes only. We manage to realize the full jump routing prototype, and the evaluation results show that our jump routing is scalable, lightweight, and resilient.

With the advancement of semiconductor technology, circuits have become increasingly susceptible to errors induced by radiation. Traditional approaches to enhancing the resilience of circuits against single-node upsets (SNUs) are insufficient to meet the robustness standards of modern designs. This article proposes a high-performance, low-power latch, named high-performance low-power double-node upset resilient latch (HLDRL), which is designed to exhibit exceptional resilience against double-node upsets (DNUs). Its design has six intricately interconnected C-elements (CEs) and two three-input CEs, for error interception, ensuring robust performance even in the case of DNUs. The Technology Computer Aided Design (TCAD) tool is used to validate the effectiveness of the HLDRL. Besides, comprehensive simulations are conducted utilizing the advanced SMIC 55-nm process technology. These simulation results show that our proposed HLDRL latch can autonomously recover from any DNU and thereby ensure the integrity of the system. Moreover, compared with existing DNU-resilient latches, the proposed HLDRL latch exhibits substantial improvements in terms of multiple metrics. On average, the proposed latch achieves an impressive 29.39% dynamic power saving, a remarkable 40.04% increase in speed, a notable 4.81% reduction in area, and a substantial 53.96% decrease in the power-delay–area product (PDAP). In the post-layout simulation, the proposed latch achieves a 30.39% dynamic power saving, a 36.47% increase in speed, and an impressive 52.38% decrease in PDAP. Furthermore, the proposed latch demonstrates enhanced resilience against variations in process, supply voltage, and temperature (PVT).

Network technology is the basis for large-scale high-efficiency network computing, such as supercomputing, cloud computing, big data processing, and artificial intelligence computing. The network technologies of network computing systems in different fields not only learn from each other but also have targeted design and optimization. Considering it comprehensively, three development trends, i.e., integration, differentiation, and optimization, are summarized in this paper for network technologies in different fields. Integration reflects that there are no clear boundaries for network technologies in different fields, differentiation reflects that there are some unique solutions in different application fields or innovative solutions under new application requirements, and optimization reflects that there are some optimizations for specific scenarios. This paper can help academic researchers consider what should be done in the future and industry personnel consider how to build efficient practical network systems.

Due to the revelations of global-scale pervasive surveillance programs, Internet users have an increasing demand for privacy. However, this is usually undesirable for network service providers because attackers would be able to anonymize themselves and avoid regulation while conducting network attacks. Therefore, network service providers want to hold users accountable and it has been widely considered as a tussle to find a good balance point between the accountability and privacy for the Internet. In this work, we first show that existing representative approaches mainly suffer from narrow-range accountability, low efficiency or risky key management. Motivated by these observations, we propose an efficient network layer protocol called APGS to balance the accountability and privacy. At the core of our APGS is the group signature which, however, is not trivial to apply for the network layer mainly due to the efficiency, revocation, and privacy issues. We manage to overcome these challenges via proposing some novel approaches, including challenge-based cache strategy, scalable verifier-local revocation strategy, and Onion-then-Case strategy. We then evaluate the efficiency of APGS and conclude that in our environment, APGS can generate packets up to 20k pkts/s on a desktop and achieve approximately 80% of IP's goodput at most on a software router.

Anonymous communication networks (ACNs) are intended to protect the metadata during communication. As classic ACNs, onion mix-nets are famous for strong anonymity, in which the source defines a static path and wraps the message multi-times with the public keys of nodes on the path, through which the message is relayed to the destination. However, onion mix-nets lacks in resilience when the static on-path mixes fail. Mix failure easily results in message loss, communication failure, and even specific attacks. Therefore, it is desirable to achieve resilient routing in onion mix-nets, providing persistent routing capability even though node failure. The state-of-theart solutions mainly adopt mix groups and thus need to share secret keys among all the group members which may cause single point of failure. To address this problem, in this work we propose a hybrid routing approach, which embeds the onion mix-net with hop-by-hop routing to increase routing resilience. Furthermore, we propose the threshold hybrid routing to achieve better key management and avoid single point of failure. As for experimental evaluations, we conduct quantitative analysis of the resilience and realize a local T-hybrid routing prototype to test performance. The experimental results show that our proposed routing strategy increases routing resilience effectively, at the expense of acceptable latency.

This work aims at proposing a transfer learning (TL)-based framework to enhance system scalability of fingerprint-based indoor localization by reducing offline training overhead without jeopardizing the localization accuracy. The basic principle is to reshape data distributions in the target domain based on the transferred knowledge from the source domains, so that those data belonging to the same cluster will be logically closer to each other, whereas others will be further apart from each other. Specifically, the TL-based framework consists of two parts, metric learning and metric transfer, which are used to learn the distance metrics from source domains and identify the most suitable metric for the target domain, respectively. Furthermore, this work implements a prototype of the fingerprint-based indoor localization system with the proposed TL-based framework embedded. Finally, extensive real-world experiments are conducted to demonstrate the effectiveness and the generality of the TL-based framework.