Existing zero-shot fault diagnosis methods are typically system-specific and numerically sensitive, which lack adaptive deployment capabilities across heterogeneous photovoltaic (PV) system scales and topologies. Multimodal large language models (MLLMs) emerge as a powerful solution in cross-system generalization, but their adoption in PV fault diagnosis has been limited by the lack of PV knowledge integration and challenges in processing diverse operating conditions. To bridge this gap, an MLLMs-empowered framework for zero-shot PV fault diagnosis is proposed for the first time, which jointly integrates data-driven and knowledge-driven schemes. First, a chain-of-thought-based data augmentation pipeline is constructed to achieve data-knowledge alignment and interpretable results. Second, a two-stage adaptation strategy is specifically designed for PV data to overcome system scales, diverse topologies, and numerical differences. It consists of a Kolmogorov–Arnold networks-based condition adaptive layer embedded in vision transformer and a low-rank adaptation-based PV domain fine-tuning. Third, we design a microservices-based architecture for PV-MLLM deployment that enables flexible component decoupling and adaptive inference, significantly reducing hardware requirements and resource consumption. The proposed method achieves 99.66% and 97.25% diagnostic accuracy on simulated and real-world datasets.

Data-driven fault diagnosis methods for photovoltaic modules often encounter issues of sample scarcity and insufficient generalization, whereas knowledge-driven multimodal large language models (MLLMs) can comprehend human-summarized prior knowledge for logical reasoning, significantly enhancing the model’s general applicability. This paper proposes a high-generalization diagnostic framework based on knowledge-driven approaches, featuring a state correction vision transformer on MLLMs to solve the problem of sample scarcity, thereby enabling fault diagnosis grounded in photovoltaic knowledge. To mitigate the high computational cost of the large model inference, the framework incorporates an edge-based small model using Support Vector Machines to filter faulty samples. Additionally, a carefully designed photovoltaic knowledge datasets, along with an adaptive fine-tuning method, facilitates efficient domain-specific refinement of the pre-trained model. To address the potential issue of frequent model updates, each framework component is encapsulated as a microservice for easy orchestration. The framework has been deployed and tested on real cloud-edge-end cluster, achieving a diagnostic accuracy of 97.25% and reducing the invocation of large models by 90%.

With the rapid development of smart manufacturing, existing microservice deployment strategies based on the TCP protocol tend to encounter problems such as network latency, excessive header length, and port exhaustion in high concurrency scenarios. MQTT Broker can relay data between microservices, effectively alleviating these issues. However, the communication overhead of the Broker is significantly related to its deployment and the dependency of microservices. An appropriate deployment solution can significantly improve service quality. To address this issue, this paper models the deployment of MQTT Broker and microservices as a graph with cycles. By introducing auxiliary variables, the NP-hard problem is transformed into a mixed-integer quadratic programming problem, improving problem-solving efficiency and optimizing the microservices deployment strategy to reduce communication overhead. Meanwhile, a real-world heterogeneous cluster was constructed to experiment on fault detection applications, elaborating on the superiority of the microservice deployment strategy designed in this paper. Experimental results demonstrate that our approach reduces communication overhead by 20% in comparison to both the greedy strategy and the K8s strategy. This suggests that achieving an optimal balance between the number of Brokers and microservices is vital for ensuring efficient resource utilization and maintaining low communication latency, particularly as the scale of services expands.

In this paper, we analytically study the performance of non-orthogonal multiple access (NOMA) transmissions in a random access network with truncated channel inversion power control. Specifically, in a slotted ALOHA network in support of NOMA transmissions, a wireless device randomly selects the transmission power with a certain probability, using channel inversion power control yet subject to the upper bound of the transmission power. Taking into consideration the stochastic nature of wireless fading channels, we first quantify two network areas such that devices in different areas have various choices of transmission powers for NOMA transmissions. An analytical model is developed to analyze the successful transmission probability and throughput of wireless devices located in different areas. Based on the analysis, two optimization problems are formulated to maximize the network throughput and the minimum throughput of wireless devices by tuning the transmission probabilities of each device. To solve the formulated combinatorial optimization problems, two heuristic algorithms are proposed. Extensive simulations are conducted to validate the analysis, and verify the efficiency of the proposed algorithm to attain the maximum network throughput and max-min fairness.

Images are essential for many Android applications or apps. Although images play a critical role in app functionalities and user experience, inefficient or improper image loading and displaying operations may severely impact the app performance and quality. Additionally, since these image loading defects may not be manifested by immediate failures, e.g., app crashes, existing GUI testing approaches cannot detect them effectively. In this paper, we identify five anti-patterns of such image loading defects, including image passing by intent, image decoding without resizing, local image loading without permission, repeated decoding without caching, and image decoding in UI thread. Based on these anti-patterns, we propose a static analysis technique, IMGDroid, to automatically and effectively detect such defects. We have applied IMGDroid to a benchmark of 21 open-source Android apps, and found that it not only successfully detects the 45 previously-known image loading defects but also finds 15 new such defects. Our empirical study on 1,000 commercial Android apps demonstrates that the image loading defects are prevalent.

Channel aggregation (CA) is proposed in IEEE 802.11ax to allow wireless users to aggregate multiple available channels, either contiguous or non-contiguous, to improve the network throughput. In this paper, the performance of CA is extensively investigated. It is shown that a simple CA that aggregates all available channels does not always promote but may degrade the network performance due to the increased inter-channel contentions in a random access wireless local area network (WLAN). Thus, it is of critical importance to select an appropriate set of channels for CA. To this end, we propose an efficient probabilistic channel aggregation scheme to maximize the network throughput under the quality of service constraints. That is, an ax user aggregates each secondary channel with a certain probability based on the traffic load of the secondary channel. A Proximal Policy Optimization (PPO) based approach is further applied to intelligently tune the aggregating probabilities of secondary channels to maximize the network throughput. Numerical results show that the proposed algorithm can greatly improve the network throughput compared with existing CA algorithms in the literature.

Energy harvesting is a promising solution to enable energy sustainable operation of IoT devices. Especially for under-water IoT network as it is difficult and costly for underwater IoT devices to replace the battery. Unlike traditional power supply, energy harvesting from green sources is a random process and is dependent on the charging environment, which poses new challenges for provisioning quality of services of IoT networks. Due to the high cost for low-powered IoT devices to update its energy status with the scheduler, distributed transmission protocol is more desirable for the IoT networks. In this work, we consider an IoT network where IoT devices use adaptive p-persistent ALOHA for data transmissions. Each IoT device can contend for channel access only when it is ready, i.e., it has a data for transmission and it harvests enough energy for communications. Due to stochastic energy harvesting and random access, the number of ready devices in the network may vary. As such, an analytical framework is first developed using a discrete Markov model to analyze the average number of ready devices. Next, an optimization problem is formulated to maximize the system throughput by tuning the transmission probability. Given that the wireless environment is unknown at different IoT devices, e.g., total number of contending devices, data arrival rates of other IoT devices, a multi-agent reinforcement learning algorithm is introduced for each device to autonomously tune the transmission probability in a distributed manner. In addition, game theory is applied to design the reward function to ensure an equilibrium and to closely approach the optimal parameter setting. Numerical results show that the proposed learning algorithm can greatly improve the throughput performance comparing with other algorithms.

In this study, the authors present a novel individual retrieval method based on oral cavity point cloud data and correntropy‐based registration algorithm. Since the three‐dimensional oral cavity data contains a large amount of noise and outliers, it may lead to a decrease in registration accuracy, which affects the accuracy of retrieval rate. Therefore, the authors introduce the correntropy into the rigid registration algorithm to solve this problem. Then, they filter the matched point cloud data and then use the mean squared error to judge the individual differences of the model data. Finally, the accurate retrieval of the oral cavity data is realised. Experimental results demonstrate the proposed retrieval three‐dimensional model algorithm can be successfully searched under different model data, which can help forensics use the characteristics of biological individuals to accurately search and identify, and improve recognition efficiency.

In this article, we study an energy sustainable Internet-of-Underwater Things (IoUT) network with tidal energy harvesting. Specifically, an analytical model is first developed to analyze the performance of the IoUT network, characterizing the stochastic nature of energy harvesting and traffic demands of IoUT nodes, and the salient features of acoustic communication channels. It is found that the spatial uncertainty resulting from underwater acoustic communication may cause a severe fairness issue. As such, an optimization problem is formulated to maximize the network throughput under fairness constraints, by tuning the random access parameters of each node. Given the global network information, including the number of nodes, energy harvesting rates, communication distances, etc., the optimization problem can be efficiently solved with the Branch and Bound (BnB) method. Considering a realistic network where the network information may not be available at the IoUT nodes, we further propose a multiagent reinforcement learning approach for each node to autonomously adapt the random access parameter based on the interactions with the dynamic network environment. The numerical results show that the proposed learning algorithm greatly improves the throughput performance compared with the existing solutions, and approaches the derived theoretical bound.

In this paper, we analytically study the performance of opportunistic multi-channel bonding protocol supporting delay-sensitive multimedia services. We consider a multi-channel system shared by IEEE 802.11ac users who can transmit over multiple channels and legacy users who can only transmit over one single channel. By analyzing the channel bonding behavior of IEEE 802.11ac users and the random access of legacy users, bonding probability and successful bonding probability of IEEE 802.11ac users can be derived. Furthermore, the access delays of both legacy and 802.11ac users are analyzed. According to the analytical results, the network capacity which quantifies the maximum number of multimedia flows that can be supported with guaranteed delay is then presented. Additionally, the impacts of different parameters such as traffic data rate on the network capacity are investigated. Our analytical results show that channel bonding is favorable when the secondary channels are underutilized. But channel bonding should be disabled when there are already intense contentions from legacy users. Based on the analytical results, we propose a heuristic bonding policy which can provide important guidelines to control the number of flows to satisfy the QoS requirement and achieve the maximum network capacity. Extensive simulations have been conducted to validate the analytical results.

This paper proposes a transmission protocol for massive Internet of Things (IoTs) networks with energy harvesting (EH). Specifically, the IoT devices harvest energy from the renewable natural sources, such as solar and wind, and use the harvested energy to transmit data to a base station (BS). Due to the massive number of IoT devices in the network, it is very challenging, if not impossible, to schedule data transmissions of IoT devices with variable energy supplies. To this end, a hierarchical chain based transmission model is proposed to attain high transmission efficiency of massive IoT devices, considering the stochastic nature of EH and large number of IoT devices. Specially, massive IoT devices are grouped based on their geographic locations; and IoT in one geographic area form a transmission chain to relay the data to the BS. Based on the proposed model, we propose a random chain based transmission protocol, where IoT devices randomly select next hop receiver to relay the data to the BS. The probability density function (pdf) of the size of the random chain is derived, based on which the sustainable energy throughput can be obtained. Finally, extensive simulations validate the analysis and demonstrate that chain based transmission protocol significantly outperform the hierarchal cluster-based transmission protocols.

Radio frequency (RF) energy harvesting is a promising technology that enables self-sustainable wireless Internet of Things (IoT) networks. In this article, we analyze the energy harvesting performance of a Wi-Fi-based IoT network, where a large number of IoT devices are connected via Wi-Fi for both data communication and energy transfer. Applying probability theory and statistical geometry, we first develop an analytical model to study the energy sustainability of wireless IoT devices with Wi-Fi charging, which operate in an active/charging mode and access the channel using carrier sensing multiple access with collision avoidance (CSMA/CA) protocol. Based on the analysis, we derive the necessary and sufficient conditions for the AP beaconing frequency and the charging period of IoT devices to ensure that a network with a general random topology is long-term energy sustainable. It is shown that transmission collisions due to random access result in too much energy consumption that makes it difficult to achieve energy sustainability of IoT devices. To maximize the total network throughput while ensuring long-term energy sustainability of Wi-Fi IoT devices, a distributed energy-sustainable throughput-optimal algorithm is proposed for user charging period selection. Extensive simulations using NS-3 validate the analysis and demonstrate the efficiency of the proposed algorithm.

To support bandwidth-intensive services such as virtual reality video applications, next generation WLANs will allow users to transmit over multiple channels for high data rate transmissions. In this paper, an analytical model is developed to study the performance of the dynamic channel bonding in IEEE 802.11ac, and non-contiguous channel aggregation in IEEE 802.11ax, with coexisting legacy single channel users. By modeling the transmissions of single channel and multi-channel users with and without channel bonding as a two-level renewal process, the bonding probability of multi-channel users, along with the throughput of different users in each channel, are derived. Our analysis shows that multi-channel users can boost their throughput at the cost of degraded throughput of legacy users. Furthermore, it is shown that 802.11ax provides higher spectrum utilization compared to 802.11ac, while 802.11ac provides a friendlier coexistence with single channel users. Based on the analysis, a heuristic algorithm for primary channel selection is further proposed to maximize the throughput of multi-channel users. Extensive simulations using NS-3 validate the analysis and demonstrate the efficiency of the proposed channel selection algorithm.

Long Term Evolution (LTE) deployment in the unlicensed spectrum is considered a promising solution to overcome spectrum shortage. To ensure fair coexistence among unlicensed users, two Load Based Equipment (LBE) access technologies are introduced in European Telecommunications Standards Institute (ETSI) standard. However, it is not clear whether the two LBE protocols can ensure fair channel access between unlicensed Licensed Assisted Access (LAA) and Wi-Fi users. To this end, renewal theory based analytical models are developed to study the performance of these two LBE random access protocols. Specifically, the throughput performance of Wi-Fi users and LAA users is first derived and compared. Our results show that both options may not achieve throughput fairness among Wi-Fi and LAA users if the key protocol parameters are not fine tuned. Generally option A favors Wi-Fi users, while option B favors LAA users. To improve the fairness performance, channel access parameters in both protocols should be adapted to network conditions in order to achieve the best coexisting performance in terms of both fairness and network throughput. The analysis provides important guidance for the implementation of Listen-before-Talk based access mechanisms in LAA. Extensive simulations using NS-3 are conducted to validate the accuracy of the models.

In this paper, an analytical model is developed to study the performance of distributed and opportunistic multichannel bonding in IEEE 802.11ac WLANs, with co-existing legacy IEEE 802.11a/b/g users. By modeling the transmissions of legacy users and ac users with and without channel bonding in each channel as a two-level renewal process, the channel bonding probability of ac users in each secondary channel is derived. Based on the bonding probability, the throughput of legacy users and ac users can be analyzed respectively. Our analysis shows that ac users with bonding capabilities achieve higher throughput at the cost of degraded throughput of legacy users. The overall network throughput also decreases due to the increased contention level imposed by ac users in secondary channels. Based on the analysis, we further propose a channel selection scheme for ac users to select the best primary channel, in order to mitigate the contentions in the network and attain the maximal network throughput. Extensive simulations using NS-3 validate the analysis and demonstrate the efficiency of the proposed channel selection scheme.

An analytical model is developed to evaluate the network performance of an IEEE 802.11ac Wireless Local Area Network (WLAN) in support of delay sensitive video services over multiple channels. Specifically, the channel bonding probability and the channel access delay of wireless users are analyzed, considering the contentions among legacy and ac users in the same channel and across multiple channels. Based on the analysis, the network capacity region, i.e., the maximum number of traffic flows can be supported with the bounded delay performance in a multi-channel WLAN with and without channel bonding, is then derived. Our analysis shows that channel bonding can greatly improve the network capacity when the channel is under-utilized with a small number of legacy users co-existing with the ac users; yet channel bonding is not always favorable and it may degrade the network capacity when the number of legacy users increases due to the increased contentions in the network. The analysis provides important guidance for effective admission control and channel bonding strategies to guarantee the bonded service delay of realtime applications. Extensive simulations validate the analysis.

In this paper, an analytical framework is developed to study the performance of opportunistic channel bonding in IEEE 802.11 WLANs. Specifically, we consider a WLAN operating on multiple channels shared by both legacy users and IEEE 802.11ac users with channel bonding capability. By capturing the opportunistic channel bonding from the IEEE 802.11ac users in the primary channel and the random access of legacy users in the secondary channels, we derive the successful channel bonding probability, and the throughput of both legacy and IEEE 802.11ac users. Our analysis shows that with multi-channel bonding, ac users achieves a higher throughput at the cost of reduced throughput of legacy users in the secondary channels. The channel bonding achieves a higher total network throughput only when there is no legacy user in the secondary channels, and the total throughput decreases when legacy users exist due to the increased contentions in the secondary channels. The analysis provides important guidance for the deployment of multi-channel WLANs where ac users should select a proper primary channel to maximize its bonding opportunity and to attain the maximum throughput. Extensive simulations are conducted to validate the analysis.