Traditional PID control algorithms are limited in dynamic response and disturbance rejection, making them inadequate for handling the complex variations across different operating conditions in fuel cells. To tackle this challenge, this paper introduces a particle swarm optimization (PSO)-tuned active disturbance rejection control (ADRC) strategy aimed at improving efficiency and robustness in PEMFC thermal management. By integrating PSO with ADRC, the controller parameters are adaptively adjusted to adapt to system dynamics and external disturbances. Simulation results show that the PSO-optimized ADRC scheme achieves superior temperature stability, faster dynamic response and stronger disturbance rejection compared to a traditional ADRC. This improvement contributes to the long-term reliability and efficiency of PEMFC operation.

With the exponential growth of Internet of Things (IoT) devices, deploying computationally-demanding applications at the edge has become a prevailing trend. Current mainstream research primarily focuses on the balance between system stability and energy optimization, while largely ignoring the differential latency requirements of tasks. This study proposes a multi-objective optimization framework that not only ensures system stability but also minimizes energy consumption while meeting differentiated latency requirements. This study implements a dual-priority queuing architecture where each terminal device maintains distinct high-priority and low-priority queues to isolate high-latency and low-latency tasks. By establishing corresponding virtual queues for each queue, the original queue threshold constraints are transformed into virtual queue stability constraints, enabling the construction of a quantifiable mathematical model. Leveraging Lyapunov optimization theory, the original optimal control problem is reformulated as a stochastic optimization problem requiring only current system information. A novel adaptive offloading strategy is designed to minimize long-term energy consumption while preserving queue length constraints. To empirically demonstrate the superiority of our method, comprehensive simulation experiments are conducted across diverse scenarios. Compared with the baseline algorithm, the proposed algorithm achieves significant energy efficiency improvements, with a reduction in power consumption ranging from 20% to 30%. Concurrently, it demonstrates approximately 25% enhancement in system load processing capacity, indicating superior resource utilization efficiency.

The rapid growth of Internet of Things (IoT) devices has imposed higher demands on computational capabilities, which traditional cloud computing struggles to meet in real-time scenarios due to latency issues. Mobile edge computing (MEC) addresses these challenges by processing data at the network edge, thereby reducing latency and enhancing computational efficiency. However, MEC alone is insufficient for handling complex tasks, requiring more robust solutions. This paper proposes a hybrid cloud-edge computing framework that enhances system performance by integrating cloud and edge computing. A game-theoretic model is used to optimize wireless bandwidth allocation, and a Stackelberg game mechanism is introduced to incentivize task offloading. This approach orchestrates resource allocation and task offloading dynamics through game theory, ensuring cost minimization and delay requirements are met while fostering cloud-edge collaboration. Theoretical analysis demonstrates the existence of Nash equilibria in both layers of the game, ensuring the system’s stability and effectiveness in complex environments. Based on this, the GA-based resource allocation and offloading (GRAO) algorithm, and the iterative game-theoretic offloading (IGTO) algorithm are proposed. Experimental results validate the proposed algorithms, showing that the IGTO algorithm reduces the average cost for mobile devices (MDs) by 49.8% compared to the best baseline, while enhancing overall performance for both MEC servers and the cloud.

Vehicular Edge Computing (VEC) is an emerging paradigm that offloads computationally intensive tasks to nearby VEC servers instead of cloud servers, effectively reducing data transmission latency and enhancing system real-time performance and reliability. However, heterogeneous communication technologies and task diversity introduce significant challenges in resource allocation, rendering traditional optimization methods inadequate for dynamic network environments and long-term queue stability requirements. This paper proposed a Lyapunov Adaptive Deterministic Policy Gradient (LADPG) algorithm to address the dynamic multi-objective optimization problem of online task offloading and resource allocation in a single VEC server scenario. LADPG integrates Lyapunov optimization with the Deep Deterministic Policy Gradient (DDPG) framework to transform long-term queue stability constraints into short-term reward functions. It dynamically adjusts communication and VEC server computation resource allocations to jointly optimize energy consumption and utility costs under latency constraints. Additionally, a dynamic channel selection mechanism is designed to mitigate congestion and enhance transmission efficiency. Extensive simulations demonstrate that LADPG significantly improves system performance, particularly in high-load and dynamic scenarios.

Due to the limited computing resources of both mobile devices (MDs) and the mobile edge computing (MEC) server, devising reasonable strategies for MD task offloading, MEC server resource pricing, and resource allocation is crucial. In this paper, a scenario is considered, comprising multiple MDs and a single MEC server. Each MD has a divisible task in each time slot, allowing for partial offloading and the option to discard parts of the task. The MEC server contains multiple computing units with the same computing power, and its computing resources can be dynamically adjusted through dynamic voltage and frequency scaling (DVFS) according to the size of tasks offloaded by MDs. At any given time slice, a Stackelberg game is formulated based on the strategies of the MDs and the strategy of the MEC server. An iterative evolution algorithm is employed to explore the optimal strategies for MDs and the MEC server. Simulation results demonstrate that both parties can reach an equilibrium state through the game, and these experiments confirm that the algorithm effectively enhances system efficiency.

With the rapid development of artificial intelligence, a substantial number of computing-intensive applications have emerged in Internet of Things (IoT) devices. The mobile edge computing (MEC) architecture enables the provision of abundant computing and storage resources in close proximity to end users (EUs), thereby effectively enhancing their quality of experience (QoE). Nonetheless, both the MEC server and EUs are self-interests, it is crucial to establish suitable incentive mechanism to promote active engagement from both parties in the offloading process. Therefore, we employ the Stackelberg game to describe the interaction process between EUs and the MEC server, and an optimal relationship between bandwidth and offloading task size is established to simplify the decision problem for EUs. Then, the optimal strategies for the MEC server and EUs are solved using reverse induction. Given the limited resources of the MEC server, we propose a dynamic programming-based resource allocation (DPRA) algorithm to maximize the revenue of the MEC server while ensuring the cost of each EU. The simulation results demonstrate that the DPRA algorithm can reduce latency and energy consumption costs, significantly outperforming other comparative strategies in terms of performance at both EUs and the MEC server.

The widespread adoption of the Internet of Things (IoT) has increased demand for task processing via mobile edge computing (MEC). In this study, we designed a directed acyclic graph (DAG) task offloading workflow in MEC. Traditional task offloading often does not simultaneously take into account task upload delay and task communication delay, failing to accurately reflect real-world issues. The constraints between task execution delay, upload delay and communication delay were introduced to model system response time and energy consumption for optimization. To satisfy task dependencies, the edge rank_u sorting (ERS) algorithm is used to generate specific offloading queues. A federated deep q-network (FDQN) algorithm addresses the offloading issue. It is different from the traditional approach of uploading task information data to the edge and facing data privacy risks. FDQN deploies the model locally and only collects model parameters for aggregation to update the local model. The algorithm improves the performance and stability of the model while protecting user privacy. To automatically tune hyperparameters for multiple devices, we used the tree of parzen estimators (TPE) algorithm, and named the whole process federated DQN with automated hyperparameter optimization (FDAHO). Experimental results show that FDAHO outperforms other algorithms in scenarios of different task number, task types, and user numbers, with consideration of benchmarks.

The efficiency of production and equipment maintenance costs in the Industrial Internet of Things (IIoT) are directly impacted by equipment lifetime, making it an important concern. Mobile edge computing (MEC) can enhance network performance, extend device lifetime, and effectively reduce carbon emissions by integrating energy harvesting (EH) technology. However, when the two are combined, the coupling effect of energy and the system's communication resource management pose a great challenge to the development of computational offloading strategies. This paper investigates the problem of maximizing the energy efficiency of computation offloading in a two-tier MEC network powered by wireless power transfer (WPT). First, the corresponding mathematical models are developed for local computing, edge server processing, communication, and EH. The proposed fractional problem is transformed into a stochastic optimization problem by Dinkelbach method. In addition, virtual power queues are introduced to eliminate energy coupling effects by maintaining the stability of the battery power queues. Next, the problem is then resolved through the utilization of both Lyapunov optimization and convex optimization method. Consequently, a wireless energy transmission-based algorithm for maximizing energy efficiency is proposed. Finally, energy efficiency, an important parameter of network performance, is used as an indicator. The excellent performance of the EEMA-WET algorithm is verified through extensive extension and comparison experiments.

In times of the Internet of Everything (IoE), the power of the Internet is growing exponentially, followed by a surge in the number of network requests. The conflict between people’s high requirements for the Quality of Experience (QoE) and limited computing resources are becoming increasingly prominent. Therefore, an appropriate offloading method is required to better ease this conflict. In this article, a highly efficient scheduling architecture of information processing under the big data flow of the IoE is proposed to enhance the scheduling performance. First, we construct a dual-channel processing model to describe the entire data flow and node devices. Second, we carefully consider the choice of the weighting method to better find a balance between dual objectives. Third, a dual-objective deep$Q$-network (DQN)-based offloading algorithm with principal component analysis weighting method (D2OP) is proposed to collaboratively minimize task response time and machine load in a more reasonable allocation. To verify the performance of the D2OP, a series of experiments are conducted from multiple angles. The experimental results demonstrate its better performance than the three comparison algorithms in reducing response time, load balance, and increasing task success ratio.

With the explosive increase of Internet of Things (IoT) devices, an increasing number of computation-intensive applications are emerging in IoT system. However, most IoT devices are limited by size and location, equipped with low-performance CPUs and low-capacity batteries, which cannot go well with computation-intensive applications. Mobile edge computing (MEC) is considered as a promising solution to provide computation-intensive and latency-sensitive services in IoT system, but it is still challenging to improve the throughput and extend the battery life of IoT devices under communication constraints. This paper focuses on the task offloading problem for an MEC system with multiple energy harvesting (EH) devices. To accommodate the system dynamics and ensure the system stability in terms of task queue and battery level, we apply Lyapunov optimization theory, and design a computation tasks maximum offloading algorithm to maximize the system throughput. The algorithm can determine the offloading decision in real-time without knowing any statistical information about the system. We first give a series of mathematical analysis to verify the system stability and discuss the performance of the algorithm. In addition, a number of simulation experiments are conducted to present the efficiency of the algorithm.

By offloading computation-intensive tasks to the edge cloud, mobile edge computing (MEC) has been regarded as an effective technology for enhancing computational capacity and extending the battery lifetime of mobile devices (MDs). However, due to the limitation of bandwidth and computing resources in MEC, unreasonable task offloading might lead to intensive resource competition, which recedes the performance gains benefit from offloading. When the tasks are latency-sensitive, a proper task offloading strategy is more important. Considering the heterogeneous delay constraints and resource competition comprehensively, we aim at minimizing the energy consumption of MDs subject to the individual delay constraints of tasks by jointly optimizing the task offloading and resource allocation in terms of wireless channel and remote computation capacity in a multi-MD MEC system in this paper. Due to the complexity of the primal optimization problem, a heuristic algorithm is devised. In the algorithm, a subset of tasks to be offloaded is incrementally constructed, and the corresponding offloading sub-problem is then repeatedly solved for this task subset using a two-stage algorithm until the total energy consumption can no longer be further reduced. The first stage of solving the sub-problem is to find the optimal full offloading scheme for the to-offload tasks, which is proved to be a convex optimization problem. For the task subset without a full offloading solution, an effective iterative algorithm is employed in the second stage where the channel allocation and computing resource allocation are optimized alternately. A great number of experiments are given to verify the performance of the proposed algorithm. We observe that the heuristic algorithm shows different performance when adopting different task ordering schemes. The proposed heuristic algorithm is evaluated against three reference schemes, and the results show that it can save up to 14.20% of energy consumption while guaranteeing the delay requirements of all tasks.

In the Internet of Everything era, various Internet of Things (IoT) devices have become popular, and the number of computing-intensive applications has increased substantially. As an emerging technology, mobile-edge computing (MEC) gives network edge nodes stronger computing and storage capabilities, bringing users a good Quality of Experience (QoE). By offloading some computing tasks to the edge for processing, the burden on IoT devices can be effectively reduced. However, this approach exacerbates the computing and storage resource depletion of the MEC server and the bandwidth and transmission cost of the wireless link used to offload computing tasks. Additionally, making an offloading decision online without future system status information is a considerable challenge. Therefore, we should study and design a reasonable offloading strategy to reduce the additional overhead, which is of significance. We establish a virtual queue model to describe the workload offloading problem of IoT devices in a two-layer MEC network. This is a stochastic optimization problem. Based on Lyapunov optimization, we transform the research problem into a deterministic optimization problem. A Lyapunov online energy consumption optimization algorithm (LOECOA) is proposed to effectively balance the system’s queue backlog and energy consumption. Based on theoretical analysis and a large number of experimental and numerical results, our algorithm performs better on energy consumption while satisfying the system constraints under a dynamic task arrival rate.

In recent years, more and more large-scale data processing and computing workflow applications run on heterogeneous clouds. Such cloud applications with precedence-constrained tasks are usually deadline-constrained and their scheduling is an essential problem faced by cloud providers. Moreover, minimizing the workflow execution cost based on cloud billing periods is also a complex and challenging problem for clouds. In realizing this, we first model the workflow applications as I/O Data-aware Directed Acyclic Graph (DDAG), according to clouds with global storage systems. Then, we mathematically state this deadline-constrained workflow scheduling problem with the goal of minimum execution financial cost. We also prove that the time complexity of this problem is NP-hard by deducing from a multidimensional multiple-choice knapsack problem. Third, we propose a heuristic cost-efficient task scheduling strategy called CETSS, which includes workflow DDAG model building, task subdeadline initialization, greedy workflow scheduling algorithm, and task adjusting method. The greedy workflow scheduling algorithm mainly consists of dynamical task renting billing period sharing method and unscheduled task subdeadline relax technique. We perform rigorous simulations on some synthetic randomly generated applications and real-world applications, such as Epigenomics, CyberShake, and LIGO. The experimental results clearly demonstrate that our proposed heuristic CETSS outperforms the existing algorithms and can effective save the total workflow execution cost. In particular, CETSS is very suitable for large workflow applications.

The use of social media runs through our lives, and users' emotions are also affected by it. Previous studies have reported social organizations and psychologists using social media to find depressed patients. However, due to the variety of content published by users, it isn't effortless for the system to consider the text, image, and even the hidden information behind the image. To address this problem, we proposed a new system for social media screening of depressed patients named BlueMemo. We collected real-time posts from Twitter. Based on the posts, learned text features, image features, and visual attributes were extracted as three modalities and were fed into a multi-modal fusion and classification model to implement our system. The proposed BlueMemo has the power to help physicians and clinicians quickly and accurately identify users at potential risk for depression.

More than 300 million people have been affected by depression all over the world. Due to the medical equipment and knowledge limitations, most of them are not diagnosed at the early stages. Recent work attempts to use social media to detect depression since the patterns of opinions and thoughts expression of the posted text and images, can reflect users' mental state to some extent. In this work, we design a system dubbed SenseMood to demonstrate that the users with depression can be efficiently detected and analyzed by using proposed system. A deep visual-textual multimodal learning approach has been proposed to reveal the psychological state of the users on social networks. The posted images and tweets data from users with/without depression on Twitter have been collected and used for depression detection. CNN-based classifier and Bert are applied to extract the deep features from the pictures and text posted by users respectively. Then visual and textual features are combined to reflect the emotional expression of users. Finally our system classifies the users with depression and normal users through a neural network and the analysis report is generated automatically.

Evidence-Based Medicine (EBM) aims to apply the best available evidence gained from scientific methods to clinical decision making. A generally accepted criterion to formulate evidence is to use the PICO framework, where PICO stands for Problem/Population, Intervention, Comparison, and Outcome. Automatic extraction of PICO-related sentences from medical literature is crucial to the success of many EBM applications. In this work, we present our Aceso1system, which automatically generates PICO-based evidence summaries from medical literature. In Aceso, we adopt an active learning paradigm, which helps to minimize the cost of manual labeling and to optimize the quality of summarization with limited labeled data. An UMLS2Vec model is proposed to learn a vector representation of medical concepts in UMLS,2and we fuse the embedding of medical knowledge with textual features in summarization. The evaluation shows that our approach is better on identifying PICO sentences against state-of-the-art studies and outperforms baseline methods on producing high-quality evidence summaries.

Along with the development of cloud computing, more and more applications are migrated into the cloud. An important feature of cloud computing is pay-as-you-go. However, most users always should pay more than their actual usage due to the one-hour billing cycle. In addition, most cloud service providers provide a certain discount for long-term users, but short-term users with small computing demands cannot enjoy this discount. To reduce the cost of cloud users, we introduce a new role, which is cloud broker. A cloud broker is an intermediary agent between cloud providers and cloud users. It rents a number of reserved VMs from cloud providers with a good price and offers them to users on an on-demand basis at a cheaper price than that provided by cloud providers. Besides, the cloud broker adopts a shorter billing cycle compared with cloud providers. By doing this, the cloud broker can reduce a great amount of cost for user. In addition to reduce the user cost, the cloud broker also could earn the difference in prices between on-demand and reserved VMs. In this paper, we focus on how to configure a cloud broker and how to price its VMs such that its profit can be maximized on the premise of saving costs for users. Profit of a cloud broker is affected by many factors such as the user demands, the purchase price and the sales price of VMs, the scale of the cloud broker, etc. Moreover, these factors are affected mutually, which makes the analysis on profit more complicated. In this paper, we first give a synthetically analysis on all the affecting factors, and define an optimal multiserver configuration and VM pricing problem which is modeled as a profit maximization problem. Second, combining the partial derivative and bisection search method, we propose a heuristic method to solve the optimization problem. The near-optimal solutions can be used to guide the configuration and VM pricing of the cloud broker. Moreover, a series of comparisons are given which show that a cloud broker can save a considerable cost for users.

Cloud computing is becoming more and more popular and has received considerable attention recently. As a new kind of Information Technology (IT) commercial model, understanding the economics of cloud computing becomes critically important. From the cloud service providers' perspective, profit maximization is the top issue for them. Because a multi-server system is devoted to serving one type of service requests and application, service providers should build multiple multi-server systems to satisfy the market requirements of different application domains. Because available funding for a service provider is generally limited, it cannot afford to invest in all application domains. Hence, how to select appropriate application domains for investment and allocate funding such that the total profit is maximized are important issues for service providers. To address this problem, a fund-constrained profit maximization model is proposed. However, the exact solution of this optimization model is very difficult to formulate due to its complexity. Hence, this paper presents a heuristic strategy to search for a high quality solution. In our strategy, the optimization problem is solved in four stages, and the solution is optimized gradually. Through the proposed heuristic investment strategy, an appropriate investment scheme can be developed that synthesizes the market requirement, the fund constraint, the service level agreement, and so forth. A series of numerical calculations is executed to assess the performance of the proposed strategy. Then, six other investment strategies are compared to our strategy. Our results show that the investment scheme designed using our strategy can produce much more profit than these six other strategies.

With the better availability of healthcare data, such as Electronic Health Records (EHR), more and more data analytics methodologies are developed aiming at digging insights from them to improve the quality of care delivery. There are many challenges on analyzing EHR, such as high dimensionality and event sparsity. Moreover, different from other application domains, the EHR analysis algorithms need to be highly interpretable to make them clinically useful. This makes representation learning from EHRs of key importance. In this paper, we propose an algorithm called Predictive Task Guided Tensor Decomposition (TaGiTeD), to analyze EHRs. Specifically, TaGiTeD learns event interaction patterns that are highly predictive for certain tasks from EHRs with supervised tensor decomposition. Compared with unsupervised methods, TaGiTeD can learn effective EHR representations in a more focused way. This is crucial because most of the medical problems have very limited patient samples, which are not enough for unsupervised algorithms to learn meaningful representations form. We apply TaGiTeD on real world EHR data warehouse and demonstrate that TaGiTeD can learn representations that are both interpretable and predictive.

Along with the development of cloud computing, an increasing number of enterprises start to adopt cloud service, which promotes the emergence of many cloud service providers. For cloud service providers, how to configure their cloud service platforms to obtain the maximum profit becomes increasingly the focus that they pay attention to. In this paper, we take customer satisfaction into consideration to address this problem. Customer satisfaction affects the profit of cloud service providers in two ways. On one hand, the cloud configuration affects the quality of service which is an important factor affecting customer satisfaction. On the other hand, the customer satisfaction affects the request arrival rate of a cloud service provider. However, few existing works take customer satisfaction into consideration in solving profit maximization problem, or the existing works considering customer satisfaction do not give a proper formalized definition for it. Hence, we first refer to the definition of customer satisfaction in economics and develop a formula for measuring customer satisfaction in cloud computing. And then, an analysis is given in detail on how the customer satisfaction affects the profit. Lastly, taking into consideration customer satisfaction, service-level agreement, renting price, energy consumption, and so forth, a profit maximization problem is formulated and solved to get the optimal configuration such that the profit is maximized.

Anomaly detection in healthcare data like patient records is no trivial task. The anomalies in these datasets are often caused by mismatches between different types of feature, e.g., medications that do not match with the diagnoses. Existing anomaly detection methods do not perform well when detecting "mismatches" between multiple types of feature, especially when the feature space is high-dimensional and sparse. This paper introduces a novel anomaly detection paradigm: Probabilistic-Mismatch Anomaly Detection (PMAD), which detects mismatches between features by modeling a normal instance with a common latent probability distribution that governs the generation of all types of feature. Under this paradigm, the target of anomaly detection is to find instances with dissimilar latent distributions. We further propose Topical PMAD based on an extended Latent Dirichlet Allocation (LDA) model, which is able to capture the latent relationship between features in a high-dimensional space. Experiments on both synthetic data and real-world patient records show that Topical PMAD can effectively detect anomalies with mismatched features, and is highly robust against high-dimensional data as well as inaccurate model selection. The real-world anomalies detected on a patient record dataset show a promising application prospect.

As an effective and efficient way to provide computing resources and services to customers on demand, cloud computing has become more and more popular. From cloud service providers' perspective, profit is one of the most important considerations, and it is mainly determined by the configuration of a cloud service platform under given market demand. However, a single long-term renting scheme is usually adopted to configure a cloud platform, which cannot guarantee the service quality but leads to serious resource waste. In this paper, a double resource renting scheme is designed firstly in which short-term renting and long-term renting are combined aiming at the existing issues. This double renting scheme can effectively guarantee the quality of service of all requests and reduce the resource waste greatly. Secondly, a service system is considered as an M/M/m+D queuing model and the performance indicators that affect the profit of our double renting scheme are analyzed, e.g., the average charge, the ratio of requests that need temporary servers, and so forth. Thirdly, a profit maximization problem is formulated for the double renting scheme and the optimized configuration of a cloud platform is obtained by solving the profit maximization problem. Finally, a series of calculations are conducted to compare the profit of our proposed scheme with that of the single renting scheme. The results show that our scheme can not only guarantee the service quality of all requests, but also obtain more profit than the latter.

Since Representational State Transfer (REST) architecture was proposed by Fielding in early 1990s for distributed hypermedia systems, it has become a popular architectural style of choice in various computing environments. However, REST was not originally designed to support enterprise requirements, in particular the accountability requirements that are crucial for the business services offered through the Software as a Service (SaaS) and Cloud Computing environments. In this paper, we propose an Accountable State Transfer (AST) architecture to bridge the accountability gap in REST. With AST, service participants can be held accountable for each representational state transfer during service consumption. A formal service contract model with a hybrid reasoning mechanism and a novel accountable state transfer protocol are designed as the mechanisms underpinning the AST architecture. Moreover, we implement a Credit Check service prototype based on AST, demonstrating the practicality of such architecture. Inheriting REST's scalability, AST architecture provides the much needed accountability capabilities for the virtual service delivery environment.

Uniting ontologies and rules has become a central topic in the Semantic Web. Bridging the discrepancy between these two knowledge representations, this paper introduces DatalogDL as a family of hybrid languages, where Datalog rules are parameterized by various DL (description logic) languages ranging from to . Making DatalogDL a decidable system with complexity of EXPTIME, we propose independent properties in the DL body as the restriction to hybrid rules, and weaken the safeness condition to balance the trade‐off between expressivity and reasoning power. Building on existing well‐developed techniques, we present a principled approach to enrich (RuleML) rules with information from (OWL) ontologies, and develop a prototype system combining a rule engine (OO jDREW) with a DL reasoner (RACER).