In recent years, the rapid development of Internet of Things (IoT) technology has led to a significant increase in the amount of data stored in the cloud. However, traditional IoT systems rely primarily on cloud data centers for information storage and user access control services . This practice creates the risk of privacy breaches on IoT data sharing platforms, including issues such as data tampering and data breaches. To address these concerns, blockchain technology, with its inherent properties such as tamper-proof and decentralization, has emerged as a promising solution that enables trusted sharing of IoT data. Still, there are challenges to implementing encrypted data search in this context. This paper proposes a novel searchable attribute cryptographic access control mechanism that facilitates trusted cloud data sharing. Users can use keywords To efficiently search for specific data and decrypt content keys when their properties are consistent with access policies. In this way, cloud service providers will not be able to access any data privacy-related information, ensuring the security and trustworthiness of data sharing, as well as the protection of user data privacy. Our simulation results show that our approach outperforms existing studies in terms of time overhead. Compared to traditional access control schemes ,our approach reduces data encryption time by 33%, decryption time by 5%, and search time by 75%.

Anomaly detection has proven effective in detecting cyber-attacks in industrial control systems (ICS). However, most existing anomaly detection methods suffer from low accuracy because they ignore the effects of packet loss and network delay on time features, the sequential nature of transition time, masquerade transitions, and system recovery. Meanwhile, current cyber-physical model (CPM) construction methods struggle to effectively address the state explosion problem and properly balance the removal and retention of low frequency states (LFS). In this article, we propose a novel baseline model for ICS to detect anomalies through learning device-level polling time patterns and system-level CPM. The polling time pattern learning method reduces the effects of packet loss and network delay on time features by extracting only matching packets and replacing outliers. The CPM construction method mitigates state explosion through mixed-event discretization, reduces the effects of network delay on transition/action times through outlier replacement, and captures the sequential nature of transition times with circular permutation sets (CPSets). CPM model optimization uses a post-pruning algorithm to balance the removal and retention of LFSs, and a CPM periodicity detection method that mitigates the effects of network delay to ensure that all industrial process periods are detected. A real-time anomaly detection method with a two-layer defence mechanism is proposed using the baseline model. Experimental results from two lab-scale ICSs with six process-related attacks confirm the effectiveness and superiority of the proposed method. It achieves average F1 scores of 98.81% and accuracy of 99.24%, outperforming the state-of-the-art work by 18.51% and 13.96%, respectively.

Satellite Edge Computing (SEC) enhances real-time data processing by deploying computational resources at the edge of satellite networks, reducing Task Completion Delay (TCD). To minimize TCD, the characteristics of satellites with large coverage, strong collaborative capabilities, and limited resources must be fully considered. This paper proposes a novel three-stage task offloading framework to optimize task execution in dynamic and resource-constrained satellite environments. First, to efficiently offload computational tasks among large-scale, heterogeneous users, we introduce a region division-based offloading strategy and develop the Adaptive Division Offloading Region (ADOR) algorithm, which dynamically partitions satellite coverage areas to improve offloading efficiency. Second, to enhance the collaborative computing capabilities of Low Earth Orbit (LEO) satellite constellations, we propose a Particle Swarm Optimization Genetic (PSOG) algorithm to optimize TS under dynamic conditions. Finally, to tackle the limited and interdependent computing resources of satellites, we design an Intelligent Parameter Adjustment (IPA) algorithm based on Q-learning, which dynamically adjusts computational parameters to maximize processing speed while ensuring system stability. Simulation results demonstrate that our proposed framework outperforms existing methods. Compared with the baseline algorithm, it achieves higher task offloading efficiency and better resource allocation. Additionally, it maintains stable satellite operations while reaching the highest processing speed.

The Industrial Internet of Things (IIoT) offers the manufacturing sector opportunities for transformation and upgrade but also carries significant security risks. Traditional federated learning (FL) as a potential security solution is challenging in complicated application environments with heterogeneous data, imbalanced data, and poisoning attacks. To address these challenges, we construct a clustered FL Framework for IIoT intrusion detection (CFL-IDS) based on local models’ evaluation metrics (EMs). First, we designed an intrusion detection model with a dynamic focal loss (DFL) for all edge nodes (ENs). This model’s performance is enhanced under various imbalanced data partitions by dynamically altering the focus on samples during the loss minimization training process. Second, the time series of EMs of local models to reflect the data distribution of ENs implicitly, and use clustering algorithms to facilitate knowledge sharing among those ENs with similar data distribution to co-optimize a common model for them. Finally, an intelligent cooperative model aggregation mechanism (ICMAM) adaptively adjusts each local model’s weight distribution, which substantially improves the benefits of FL and alleviates subpar models’ alleviates interference from subpar models to FL. Experiments demonstrate that CFL-IDS has stronger robustness and displays superior performance under data imbalance and non-independent and identically distributed (non-IID) situations while being effective against poisoning attacks.

ICN treats contents as the first citizens and faces serious scalability challenges. Especially for the content provider mobility scenario, the routing updates and routing efficiency should be cost-effective. This paper proposes a hierarchical name-based routing (HNR) for provider mobility. HNR focuses on how to find the mobile provider when given an immutable provider name. Based on the idea that the content provider generally moves within a certain topology area over a period of time, HNR first divides the topology into multiple partition topologies, then it adopts a hierarchical routing scheme that combines two suitable routing schemes for local mobility and global mobility. The experiments by simulation demonstrate that HNR achieves a good tradeoff between efficiency and scalability.

It is important to locate the fault distance and identify the fault types quickly, take effective measures to maintain line stability, and minimize the losses timely when there are short-circuit faults in transmission lines. For this purpose, a method based on deep learning is proposed for short-circuit faults identification in the transmission line. According to the similarity of samples in the reconstruction phase, a minimum neighborhood sample set is selected from the massive samples firstly, and then, the samples are trained using the back propagation algorithm along time in a recurrent neural network (RNN) with long-short term memory (LSTM) units. Compared with existing algorithms, the experimental results show that this algorithm meets the requirements of rapid fault diagnosis in the case of variable parameters, and higher fault type recognition accuracy and lower fault distance error can be obtained.

Transfer learning techniques have been broadly applied in applications where labeled data in a target domain are difficult to obtain while a lot of labeled data are available in related source domains. In practice, there can be multiple source domains that are related to the target domain, and how to combine them is still an open problem. In this paper, we seek to leverage labeled data from multiple source domains to enhance classification performance in a target domain where the target data are received in an online fashion. This problem is known as the online transfer learning problem. To achieve this, we propose novel online transfer learning paradigms in which the source and target domains are leveraged adaptively. We consider two different problem settings: homogeneous transfer learning and heterogeneous transfer learning. The proposed methods work in an online manner, where the weights of the source domains are adjusted dynamically. We provide the mistake bounds of the proposed methods and perform comprehensive experiments on real-world data sets to demonstrate the effectiveness of the proposed algorithms.

This paper proposes a semantic pattern-based context-aware (SPBCA) middleware for ubiquitous computing environments. It introduces two concepts, the ontology and the proactive conversion, to solve the heterogeneity issues existing in the interaction process in the ubiquitous computing environments. Furthermore, it can support user-centric context awareness not only by automatic reactive information processing, but also by service customization at run-time. To enable semantic pattern adaptation, we employ ontologies to define semantics of various concepts. The functions of proactive conversion and run-time service customization are implemented with a reactive event mechanism. User defined services are represented in SWRL language which is triggered by Pellet engine. The SPBCA middleware is applicable to an internal, local network environment. Hence, we exploit a tuple space based decentralized architecture as our middleware basis which supports asynchronous, context-sensitive communication, and privacy sensitivity.

It is crucial to use an efficient network-aware overlay network to enable Internet-scale multicast streaming service to adjust under the dynamic underlying network conditions and overlay node churn without extensive network measurements. We utilize our previously proposed network-aware SuperPeers-Peers geometric overlay network hierarchy which exploits the underlying network locality and proximity of the nodes, to build massive scale overlay multicast streaming network (Bos). We create source routed overlay multicast tree from the resulting optimal geometric routing paths based on the shortest geometric distances between SuperPeers at the SuperPeers layer. The overlay multicast tree is self-healed locally in a distributed manner using an ancestor-recovery protocol. The high bandwidth multicast backbone infrastructure at the SuperPeers layer will serve the Peers in the lower layer. We evaluate Bos on ten massive scale networks each consisting of 100,000 nodes and in the PlanetLab. Our massive scale overlay multicast performance results show high efficiency and good scalability.

Peer-to-Peer (P2P) overlay networks can be utilized to deploy massive Internet overlay services such as multicast, content distribution, file sharing, etc. efficiently without any underlying network support. The crucial step to meet this objective is to design network-aware overlay network topologies connecting all nodes that offer promising properties in terms of excellent communication quality. We exploit the underlying network locality and proximity of the nodes for overlay routing and node placement strategy. In this paper, we describe in greater specific details our network-aware SuperPeers-Peers geometric overlay network hierarchy and study its communication quality in a massive scale network environment. We evaluate our proposal using ten massive scale networks each consisting of 100,000 nodes. Our experimental results show high communication efficiency, quality and performance.

The use of superpeers has been proposed to improve the performance of both structured and unstructured peer-to-peer (P2P) networks. In this paper, we study the performance of Yao-graph based superpeer topologies for hierarchical P2P networks. Since a Yao-graph is defined as a geometric structure, we are using the "highways" proximity clustering and placement scheme to assign geometric co-ordinates to superpeers and peers with respect to the underlying network conditions. Because of the lightweight structure of Yao-graphs, the resulting hierarchical P2P networks have promising properties with regard to scalability and performance, while still offering the benefits of the P2P approach with regard to resiliency.

Application Layer (AL) multicast emerged as a response to a slow deployment of IP multicast. However, the gain of AL multicast over unicast is questionable. Here, we investigate the efficiency of the two prominent protocols, MCAN and Scribe, in terms of the number of hops. We compare the efficiency of these algorithms to the efficiency of unicast and IP multicast via extensive simulations, as well as via measurements on the PlanetLab network. We introduce modifications to the MCAN algorithm that lead to a reduction in the hopcount. Finally, we demonstrate that the topology unawareness under certain conditions can make these schemes less efficient than unicast.

We evaluate Web browsing performance over a multiple access satellite channel for three different MAC layer protocols. A Web user behavior model is used to generate realistic source traffic. A new transport layer protocol called RWBP is proposed to solve the TCP problems inside satellite networks. RWBP uses per-flow queuing, round robin scheduling and receiver window backpressure for congestion control. We compare its performance with TCP over the three multiple access protocols. Our simulation results show that RWBP can reduce the multiple access channel load and at the same time improve the Web page response time when driven by realistic Web traffic. For the MAC layer protocols, combined free demand assignment multiple access (CFDAMA) always performs better than the other two protocols.

Crucial for the performance of peer-to-peer networks based on geometric topologies is the measurement complexity and quality of the mapping function used to map a node in the network to a point in the geometric target space. In this paper we study how results from mathematics as well as data mining can be applied to this mapping problem. Using a metric space model for networks and results from mathematics a relation between the number of nodes to be mapped, the worst case error of the mapping and the dimension of the geometric target space is formulated. As a main result geometric cluster placement (GCP) is presented, an improved and resilient placement algorithm based on GNP. An evaluation of GCP presented is based on measurement data from the RIPE NCC test traffic measurement (TTM) project.