Real-world IoT systems often require collaborative learning across multiple tasks performed by heterogeneous devices with varying computational resources. Conventional multi-task federated learning typically shares a pre-trained encoder across clients and trains task-specific classifiers locally. However, freezing the encoder during training leads to over-reliance on pre-training data and limits the model’s ability to capture inter-task relationships, reducing multi-task learning accuracy. Furthermore, significant variations in device capabilities highlight the need for heterogeneous model adaptation, making the unified model architecture assumption impractical for real-world IoT scenarios. To address these challenges, we propose MTFedSD, a novel multi-task federated learning across heterogeneous systems via self-distillation method. MTFedSD introduces multiple models of varying sizes and architectures to match clients’ resource capabilities, while enabling selective parameter sharing to extract consistent and transferable representations across tasks. A heterogeneous model aggregation strategy and self-distillation mechanism are integrated to reconcile differences across tasks and models, mitigating conflicts during joint training. Furthermore, the joint optimization strategy of shared encoder and pruning improves the communication efficiency in multi-task scenarios. Moreover, we provide a theoretical analysis that characterizes the convergence behavior of MTFedSD under heterogeneous and multi-task settings. Extensive experiments show that MTFedSD achieves robust and scalable performance across diverse tasks and device types, offering a practical solution for federated learning in complex IoT environments.

Large language models (LLMs) demonstrate remarkable capabilities in understanding complex tasks and have achieved commendable performance in graph-related tasks, such as node classification, link prediction, and subgraph classification.These tasks primarily depend on the local reasoning capabilities of the graph structure.However, research has yet to address the graph partitioning task that requires global perception abilities.Our preliminary findings reveal that vanilla LLMs can only handle graph partitioning on extremely small-scale graphs.To overcome this limitation, we propose a three-phase pipeline to empower LLMs for large-scale graph partitioning: coarsening, reasoning, and refining.The coarsening phase reduces graph complexity.The reasoning phase captures both global and local patterns to generate a coarse partition.The refining phase ensures topological consistency by projecting the coarse-grained partitioning results back to the original graph structure.Extensive experiments demonstrate that our framework enables LLMs to perform graph partitioning across varying graph scales, validating both the effectiveness of LLMs for partitioning tasks and the practical utility of our proposed methodology.

Air conditioning load (ACL) is an important flexibility resource in smart grids, and its analysis and evaluation have significant implications for demand response, which depends on the nonintrusive extraction of high-frequency ACL profiles. Existing methods based on thermodynamic models require high parameter accuracy, and high-frequency data-driven methods incur high costs for data collection and storage, which limit their widespread application. Considering that smart meters are widely deployed as low-frequency data sources, in this article, we propose a semi-supervised ACL monitoring method based on a small number of high-frequency ACL feature samples in low-frequency data scenarios. First, we introduce a low-frequency ACL state recognition model based on self-supervised contrastive representation learning, which enhances the smart meter data feature unsupervisedly. Then, by merging the identified ACL state with smart meter data, we present an ACL super-resolution generative adversarial network with a specific aggregated adversarial loss, for the super-resolution extraction of ACL curves. Validation on the Dataport dataset shows that the proposed method improves the accuracy of ACL state recognition under low-resolution smart meter data and can accurately extract and reconstruct high-resolution ACL curves.

Humanitarian relief operations are often accompanied by regional conflicts around the globe, at risk of deliberate, persistent and unpredictable attacks. However, the long-term channeling of aid resources into conflict areas may influence subsequent patterns of violence and expose local communities to new risks. In this paper, we quantitatively analyze the potential externalities associated with long-lasting humanitarian relief operations based on game-theoretical modeling and online planning approaches. Specifically, we first model the problem of long-lasting humanitarian relief operations in conflict areas as an online multi-stage rescuer-and-attacker interdiction game in which aid demands are revealed in an online fashion. Both models of single-source and multiple-source relief supply policy are established respectively, and two corresponding near-optimal online algorithms are proposed. In conjunction with a real case of anti-Ebola practice in conflict areas of DR Congo, we find that 1) long-lasting humanitarian relief operations aiming alleviation of crises in conflict areas can lead to indirect funding of local rebel groups; 2) the operations can activate the rebel groups to some extent, as evidenced by the scope expansion of their activities. Furthermore, the impacts of humanitarian aid intensity, frequency and supply policies on the above externalities are quantitatively analyzed, which will provide enlightening decision-making support for the implementation of related operations in the future.

Smart cameras, as typical IoT devices, are widely adopted to provide surveillance on individuals, homes, and the environment. The unavoidably captured sensitive visuals via these cameras may raise significant security concerns, while the prevalent software defects and authentication misconfiguration issues aggravate the vulnerability of such devices. However, traditional cryptography techniques are inadequate to provide full protection of these devices due to the large computation overhead. In this context, realizing accountability for these surveillance systems shall be the last line of defense in the presence of fast-evolving and high-influential threats. We propose EviChain, a scalable blockchain-based solution to trace the operations on intelligent surveillance cameras and reserve the evidence for any misuse in tamper-proofing manipulation records. Building a blockchain over the distributed cameras is challenging due to the limited capacity of on-board memory. To tackle this challenge, we design a cooperative mechanism that enables cameras to adaptively join in groups and share storage for recording blocks. In addition, we present a computation efficiency and delay-aware block generation strategy to reduce the cost of the consensus process. We perform extensive simulations to validate the superior performance of EviChain over other baselines, for example, Practical Byzantine Fault Tolerance (PBFT).

Word-character lattice models have been proved to be effective for Chinese named entity recognition (NER), in which word boundary information is fused into character sequences for enhancing character representations. However, prior approaches have only used simple methods such as feature concatenation or position encoding to integrate word-character lattice information, but fail to capture fine-grained correlations in word-character spaces. In this paper, we propose DCSAN, a Dynamic Cross- and Self-lattice Attention Network that aims to model dense interactions over word-character lattice structure for Chinese NER. By carefully combining cross-lattice and self-lattice attention modules with gated word-character semantic fusion unit, the network can explicitly capture fine-grained correlations across different spaces (e.g., word-to-character and character-to-character), thus significantly improving model performance. Experiments on four Chinese NER datasets show that DCSAN obtains stateof-the-art results as well as efficiency compared to several competitive approaches.

With the development of edge computing, edge storage solutions are attracting widespread attention. When facing the requirements of lower latency and faster access speed from end devices, edge storage solutions are considered to be an alternative to the cloud. However, edges are usually owned by small organizations which have limited operations and maintenance capabilities. This makes these edge devices can be easily disabled by external attacks or internal hardware failures. Besides, the heterogeneity of the edge devices will also make it difficult to price the edge resources uniformly. To tackle these problems, we propose SmartStore: an auction mechanism based on blockchain to allocate edge resources. Considering centralized solutions have access bottlenecks and trust issues, we built SmartStore on the smart contract. With Bayesian game theory, SmartStore can analyze how data owners (DO) and edges price the resources can maximize their benefits. From an economic perspective, both DO and edges can make full use of edge heterogeneous resources with SmartStore. Besides, a two-stage submission strategy is proposed to complete the sealed auction. Furthermore, considering the reliability of edge storage, we propose a cluster-based block distribution algorithm for SmartStore's intelligent edge recommendation process. SmartStore ensures the reliability of edge storage while maximizing the benefits and resource utilization of both parties. Finally, we conduct specific experiments on the proposed auction smart contract through “Ethereum” and the experimental results of implementation show the effectiveness and efficiency of our SmartStore.

Edge computing emerges as an alternative to cloud computing in the scenarios where the end devices require lower latency and faster access speeds. Edge nodes are deployed at the proximity of the end devices to reduce response time. On the other hand, the edge nodes are usually owned by small organizations that have limited operations and maintenance capabilities. Data on the edge may be easily damaged, due to external attacks or internal hardware failures. Therefore, it is essential to verify data integrity in edge computing. However, edge environment requires a different trust model compared with other computing and storage paradigm. Besides, compared with cloud storage, edge storage is decentralized and storage service participants may pose greater internal and external threats. This paper proposes a blockchain-based intelligent crowdsourcing audit approach (Crowdauditing) to achieve on-chain and off-chain credibility of audit results. The model relies on an untrusted auditor committee from the crowd to audit data integrity and uses smart contracts as the core of the intelligent system to ensure the reliability of result submission, the accuracy of the result judgment, and reasonable punishments and rewards. Specifically, an unbiased selection algorithm is proposed to achieve fairness during the auditor committee construction. An innovative two-stage submission strategy is proposed to ensure that the auditor committee can reach a consensus on the off-chain audit results. An incentive mechanism is carefully designed to force auditors providing audit services honestly to maximize their own rewards. Moreover, we modeled that as a game of n players, which proves the reliability of the result. Finally, we implement a prototype of Crowdauditing based on smart contracts. The extensive experimental results demonstrate the effectiveness of Crowdauditing.

With the explosive emergence of computation-intensive and latency-sensitive applications, data processing could be envisioned to perform closer to the data source. Similar to edge and fog computing, dispersed computing is considered as a complementary computing paradigm, which can excavate potential computation resources in the network to users, and serve as a supplement for sharing the computational burden when the edge is overloaded. In this article, we first make full use of idle and geographically dispersed computation resources via task offloading, contributing to conserve energy for mobile devices. Especially, a dispersed computing offloading framework concerning the interests of users and networked computation points is proposed. We further transform the initial problem into a multiobjective optimization problem subject to latency and resource constraints. To tackle such a complex problem, an energy-saving bilateral matching algorithm is designed to obtain the optimal task offloading strategy. The simulation results demonstrate that our proposed algorithm can outperform the benchmark schemes in terms of user fairness and can achieve a relatively balanced energy cost ratio. Furthermore, comparative experiments with edge computing are implemented in Amber Response and Disaster Relief scenarios, respectively, to reveal the advantages of the proposed framework.

Surveillance cameras have been extensively used in smart cities and high security zones. However, with the exploding deployment of smart cameras, the rapid growth of cloud workloads from vision-based IoT applications are becoming a huge burden for all cloud service providers. Some researchers have proposed mechanisms, such as compression and deduplication to reduce the video traffic size, but these methods cannot offset the enormous growth of data volume. Most of the surveillance video data do not need to be proceeded in real time. By making use of the IoT camera’s onboard resources to store the data, the cloud workloads can be fundamentally reduced. However, recent incidents have posed a new, powerful geo-range attack, where the attacker may compromise a group of surveillance cameras located within an area. Existing simple onboard solutions cannot offer secure defense against such geo-range attacks. To tackle the problem, we developCentipede, a cooperative video data storage system that distributes video content across geographically dispersed surveillance cameras. It generates secure copies for the video content and enhances data security by judiciously distributing erasure-coded video blocks across optimally-chosen surveillance cameras. In this article, we implementCentipedeand evaluate its performance.Centipedeis the first solution that can fundamentally reduce the cloud workload and defend against geo-range attacks.

Surveillance cameras have been extensively used in smart cities and high security zones. Recent incidents have posed a new, powerful geo-range attack, where the attacker may compromise a group of surveillance cameras located within an area. To tackle the problem, we develop a distributed camera storage system that distributes video content across geographically dispersed surveillance cameras. It generates secure copies for the video content and enhances robustness by judiciously distributing erasure coded video blocks across optimally-chosen surveillance cameras. We implement the distributed storage system for surveillance cameras and evaluate its performance via real-world field test. Our system is the first solution that can defend against geo-range attacks in a robust and privacy-preserving manner.

IT companies need to monitor various Key Performance Indicators (KPIs) and detect anomalies in real time to ensure the quality and reliability of Internet-based services. However, due to the diversity of KPIs, the ambiguity and scarcity of anomalies and the lack of labels, anomaly detection for various KPIs has been a great challenge. Existing KPI anomaly detection methods have not explored the properties of anomalies in KPIs in detail to our best knowledge. Therefore, we explore anomalies in KPIs and recognize a common and important form of anomalies namedabrupt changes, which often indicate potential failures in the relevant services. Forabrupt changesin various KPIs, we proposeDDCOL, an unsupervised online anomaly detection algorithm with parameter adaptation from the perspective of anomalies for the first time. We propose three techniques: high order${D}$ifference extraction and combination,${D}$ensity-based${C}$lustering with parameter adaptation and${O}\text{n}{L}$ine detection with subsampling (DDCOL). Compared with traditional statistical methods and unsupervised learning methods, extensive experimental results and analysis on a large number of public KPIs show the competitive performance ofDDCOLand the significance ofabrupt changes. Furthermore, we provide an interpretation for the promising results, which shows thatDDCOLcan be robust to KPI expected concept drifts, and obtain a good feature distribution of normal data in KPIs.