With the rapid growth of IoT devices and their diverse workloads, container-based microservices deployed at edge nodes have emerged as a lightweight, scalable solution. However, existing microservice scheduling algorithms often assume static resource availability, which is unrealistic when multiple containers are assigned to an edge node. Besides, containers suffer from cold-start inefficiencies during early-stage training in currently popular reinforcement learning (RL) algorithms. In this paper, we propose a hybrid learning framework that combines offline imitation learning (IL) with online Soft Actor-Critic (SAC) optimization to enable cold-start-aware microservice scheduling with dynamic resource allocation. We first formulate a delay-and-energy-aware scheduling problem and construct a rule-based expert to generate demonstration data for behavior cloning. Then, a GRU-enhanced policy network is designed within the policy network to extract correlations among multiple decisions by separately encoding slow-evolving node states and fast-changing microservice features, and an action selection mechanism is provided to speed up convergence. Extensive experiments show that our method significantly accelerates convergence and achieves superior final performance. Compared with baselines, our algorithm improves the total objective by 50% and convergence speed by 70%, and demonstrates the highest stability and robustness across various edge configurations.

Federated learning (FL) is a privacy-preserving distributed machine learning framework where multiple devices collaborate with the assistance of an aggregator. However, the limitations of aggregator communication result in only a portion of clients with high data quality being selected to participate in FL, but the quality of clients' data cannot be evaluated without access to the original data. Most existing methods for selecting clients employ a data quality metric with empirically defined scores, which may select clients with high non-ID degrees, thereby reducing the accuracy and freshness of the model. Furthermore, due to the unknown quality of clients' data, current incentive mechanisms lack FL convergence guarantees, which prevent the client behavior from improving the global model accuracy. To address these issues, in this paper, we propose using the gradient difference as a metric for the quality of clients' data, which can quantify the non-IID degree and contribution potential of each client. We formulate a client selection problem using the Combinatorial Multi-Armed Bandit (CMAB) model and design an effective selection strategy, improving the worst-case regret proof to provide a theoretical guarantee for it. Based on these results, we develop an incentive mechanism by the FL convergence analysis, quantifying the utility functions of the aggregator and clients, and modeling their interaction as a two-stage Stackelberg game. For the non-convex utility function, our method establishes the existence and uniqueness of the Stackelberg equilibrium, thereby enabling the determination of the optimal strategy for maximizing the utility of all participants. Finally, extensive simulation experiments on real-world datasets demonstrate the effectiveness of our proposed method compared to state-of-the-art approaches.

Edge intelligence is rapidly evolving to support computation-intensive applications, yet scheduling containerized microservices in GPU-CPU heterogeneous edge environments remains challenging due to highly dynamic resource availability. Two critical bottlenecks hinder performance: the distinct hardware preferences of microservices, particularly between CPU and GPU architectures, which lead to significant performance gaps when placements are mismatched, and the non-trivial latency of container image downloads. To address these coupled challenges, we propose CHES, a Container-aware Heterogeneous Edge Scheduling framework. Unlike conventional heuristics that evaluate node resources and task sequences independently, our method leverages spatiotemporal learning to capture their joint impact on system performance. Specifically, we design a lightweight CNN to extract spatial correlations from heterogeneous node features, including compute capacity and bandwidth, and employ a GRU to model temporal dependencies among sequential microservice arrivals, where earlier scheduling decisions directly affect resource availability and image cache states for subsequent tasks. These representations are integrated into a Soft Actor-Critic (SAC) agent, enabling an adaptive policy that balances immediate execution latency with long-term image availability costs. Extensive experiments demonstrate that CHES significantly outperforms baselines by effectively aligning hardware-sensitive microservices with optimal nodes and mitigating cold-start overheads in dynamic workloads.

Spatiotemporal Mobile CrowdSourcing (MCS) is a new intelligent sensing paradigm for large-scale data acquisition where requesters can recruit a crowd of workers to perform data collection tasks. How to recruit suitable workers in a dynamic environment to maximize platform utility is a key issue and has become a research hotspot. Many past studies have made great efforts in this regard, but most of them either assume that the worker quality is known in advance or ignore the limitations of workers’ short-term ability to provide resources. In this article, we consider a platform-centered online spatiotemporal MCS system where mobile workers have both long-term and short-term constraints for providing resources, and their quality is unknown to the platform, while the platform has a long-term budget constraint for recruiting workers. We aim to find an online worker scheduling scheme to maximize the platform’s long-term utility without violating the constraints of both workers and the platform. To address this problem, we first transform the long-term utility maximization problem into a real-time utility maximization problem by leveraging the Lyapunov optimization, then design algorithms based on the Upper Confidence Bound (UCB) and Markov approximation to solve each real-time utility maximization problem with unknown worker quality. We demonstrate that our UCB-based algorithm has a sublinear regret and prove that our proposed framework has a performance guarantee for the addressed problem. Finally, we evaluate our design through numerical simulation experiments, and the results demonstrate the effectiveness of our algorithm.

Multi-modal Federated Learning (FL) is a type of FL that considers utilizing multiple modalities of data to improve overall performance. While multi-modal data brings richer information, it also introduces more significant communication overhead. Reducing this overhead hinges on two key strategies: increasing the convergence speed of the training or reducing the communication overhead in each communication round. However, few studies have considered these two strategies simultaneously and formed a unified optimization framework. Thus, we propose a joint client and modality selection framework to reduce communication overhead. Modality selection executed on each client assigns weights to modalities based on their contribution to training potential, aiming at accelerating the convergence. Client selection executed on the server assigns weights to clients by considering different metrics, especially total training potential after the modality selection. We validate our proposed method on the five widely used open-source datasets, achieving satisfactory accuracy while reducing the total communication overhead to 2.43%–14.24% compared to without selection on different datasets, significantly outperforming existing state-of-the-art (SOTA) methods. Code is available at https://github.com/1643204431/OCETPMMFL .

The rapid growth in demand for large language models (LLMs) has strained cloud-edge infrastructure. While edges offer low latency and clouds provide vast resources, scheduling LLM requests efficiently remains a major challenge due to their unpredictable processing times, which leads to Headof-Line (HOL) blocking that degrades system throughput and responsiveness. To address this, we introduce the Online CloudEdge Collaborative Request Scheduling (OCE-CRS) framework. OCE-CRS models the proactive scheduling of LLM requests as a contextual combinatorial bandit problem. At its core is our novel Combinatorial Neural Delayed Upper Confidence Bound (CN DUCB) algorithm, which learns to predict request processing times from the semantic content of the request prompt alone. This enables an inspired policy based on Shortest Job First (SJF) that prioritizes shorter jobs for edge execution, simultaneously maximizing throughput and mitigating HOL blocking. To prevent time-consuming neural network training from blocking scheduling decisions, we employ an asynchronous mechanism. This decouples model updates from the real-time scheduling loop, effectively handling the resultant delayed feedback where observations from past rounds are used in later training steps. We provide a theoretical sublinear regret bound for our algorithm. Extensive experiments validate that OCE-CRS significantly improves throughput, Job Completion Time (JCT), and queueing delay, demonstrating superior performance and robustness in both static and continuous batching environments.

With the massive increase of Internet of Things devices and their data, executing applications by using micro-service architecture has emerged as the predominant trend. As the container technology emerges, microservices can be lightweightly deployed in resource-constrained edge nodes. However, existing container scheduling algorithms often overlook the allocation of computing resources on edge servers. When multiple containers are assigned to an edge node, it is usually assumed that they share a CPU frequency, which is obviously unrealistic. In this paper, we first formulate an online container-based microservice scheduling problem with dynamic computing power to minimize the total delay and energy consumption, where we need to determine the assignment between microservices and edge nodes and the allocation of computing power to each microservice in an edge node. Then, we propose a Soft Actor-Critic (SAC) based reinforcement learning algorithm to address this problem, where a GRU unit is designed in the policy network to extract the correlation among multiple decisions, and an action selection mechanism is given to speed up the convergence. Finally, a simulated scheduling system is implemented to validate our algorithm, which demonstrates that our algorithm outperforms the commonly used baselines by up to 65% in terms of the total objective on average.

Mobile crowdsourcing (MCS) is a promising way to monitor urban-scale data by leveraging the crowds’ power and has attracted much attention recently. How to recruit suitable workers for requesters to perform the published sensing tasks is always a crucial problem and also a research hotspot. Many attempts have been made in past literature to maximize social welfare or to motivate workers to participate in the mobile crowdsourcing (MCS). However, most existing works do not consider the individual sensing quality requirements of tasks, which may not be suitable for some special scenarios, such as monitoring tasks of locations with different importance levels. In this work, we investigate the optimal worker selection problem for collaborative MCS, in which we study the recruitment cost minimization problem to meet individual sensing quality requirements of tasks for the requester-centric MCS, as well as the profit maximization problem for the platform-centric MCS. Both of the studied problems are proved to be NP-hard, and thus we design corresponding approximation algorithms for them. Specifically, to solve the recruitment cost minimization problem for requester-centric MCS, we design two different polynomial time algorithms, both of which have performance guarantees. For the profit maximization problem for platform-centric MCS, we introduce a double-greedy-based algorithm and then use the iterative pruning technique to ensure the performance guarantee of our algorithm with a much weaker condition. Finally, we evaluate our algorithms through numerical simulation experiments and validate the effectiveness of our designs by comparing them with baselines under different parameter settings.

Electric Vehicles (EVs) are becoming more and more popular in our daily life, which replaces traditional fuel vehicles to reduce carbon emissions and protect the environment. EVs need to be charged, but the number of charging piles in a Charging Station (CS) is limited, and charging is usually more time-consuming than fueling. According to this scenario, we propose a secure and efficient charging scheduling system based on a Directed Acyclic Graph (DAG)-blockchain and double-auction mechanism. In a smart area, it attempts to assign EVs to the available CSs in the light of their submitted charging requests and status information. First, we design a lightweight charging scheduling framework that integrates DAG-blockchain and modern cryptography technology to ensure security and scalability during performing scheduling and completing tradings. In this process, a constrained multi-item double-auction problem is formulated because of the limited charging resources in a CS, which motivates EVs and CSs in this area to participate in the market based on their preferences and statuses. Due to this constraint, our problem is more complicated and harder to achieve truthfulness as well as system efficiency compared to the existing double-auction model. To adapt to it, we propose two algorithms, namely, Truthful Mechanism for Charging (TMC) and Efficient Mechanism for Charging (EMC), to determine an assignment between EVs and CSs and pricing strategies. Then, both theoretical analysis and numerical simulations show the correctness and effectiveness of our proposed algorithms.

Identification of potent peptides through model prediction can reduce benchwork in wet experiments. However, the conventional process of model buildings can be complex and time consuming due to challenges such as peptide representation, feature selection, model selection and hyperparameter tuning. Recently, advanced pretrained deep learning-based language models (LMs) have been released for protein sequence embedding and applied to structure and function prediction. Based on these developments, we have developed UniDL4BioPep, a universal deep-learning model architecture for transfer learning in bioactive peptide binary classification modeling. It can directly assist users in training a high-performance deep-learning model with a fixed architecture and achieve cutting-edge performance to meet the demands in efficiently novel bioactive peptide discovery. To the best of our best knowledge, this is the first time that a pretrained biological language model is utilized for peptide embeddings and successfully predicts peptide bioactivities through large-scale evaluations of those peptide embeddings. The model was also validated through uniform manifold approximation and projection analysis. By combining the LM with a convolutional neural network, UniDL4BioPep achieved greater performances than the respective state-of-the-art models for 15 out of 20 different bioactivity dataset prediction tasks. The accuracy, Mathews correlation coefficient and area under the curve were 0.7-7, 1.23-26.7 and 0.3-25.6% higher, respectively. A user-friendly web server of UniDL4BioPep for the tested bioactivities is established and freely accessible at https://nepc2pvmzy.us-east-1.awsapprunner.com. The source codes, datasets and templates of UniDL4BioPep for other bioactivity fitting and prediction tasks are available at https://github.com/dzjxzyd/UniDL4BioPep.

Attracted by the inherent security and privacy protection of the blockchain, incorporating blockchain into Internet of Things (IoT) has been widely studied in these years. However, the mining process requires high computational power, which prevents IoT devices from directly participating in blockchain construction. For this reason, edge computing service is introduced to help build the IoT blockchain, where IoT devices could purchase computational resources from the edge servers. In this paper, we consider the case that IoT devices also have other tasks that need the help of edge servers, such as data analysis and data storage. The profits they can get from these tasks is closely related to the amounts of resources they purchased from the edge servers. In this scenario, IoT devices will allocate their limited budgets to purchase different resources from different edge servers, such that their profits can be maximized. Moreover, edge servers will set “best” prices such that they can get the biggest benefits. Accordingly, there raise a pricing and budget allocation problem between edge servers and IoT devices. We model the interaction between edge servers and IoT devices as a multi-leader multi-follower Stackelberg game, whose objective is to reach the Stackelberg Equilibrium (SE). We prove the existence and uniqueness of the SE point, and design efficient algorithms to reach the SE point. In the end, we verify our model and algorithms by performing extensive simulations, and the results show the correctness and effectiveness of our designs.

Real-time traffic monitoring is a fundamental mission in a smart city to understand traffic conditions and avoid dangerous accidents. In this article, we propose a reliable and efficient traffic monitoring system that integrates blockchain and the Internet of Vehicles technologies effectively. It can crowdsource its tasks of traffic information collection to vehicles that run on the road instead of installing cameras in every corner. First, we design a lightweight blockchain-based information trading framework to model the interactions between traffic administration and vehicles. It guarantees reliability, efficiency, and security during executing trading. Second, we define the utility functions for the entities in this system and come up with a budgeted auction mechanism that motivates vehicles to undertake the collection tasks actively. In our algorithm, it not only ensures that the total payment to the selected vehicles does not exceed a given budget but also maintains the truthfulness of the auction process that prevents some vehicles from offering unreal bids for getting greater utilities. Finally, we conduct a group of numerical simulations to evaluate the reliability of our trading framework and performance of our algorithms, whose results demonstrate their correctness and efficiency perfectly.

Along with the development in the Internet of Things technology and smart city, a distributed network has been formed among cities. This makes it easy to integrate distributed electric energy into the power grid, thus become an efficient way to use energy. However, how to guarantee the security and privacy protection of distributed electricity trading has not been solved effectively. In this article, we propose a blockchain-based electricity trading (B-ET) ecosystem and design a smart contract to ensure transactions are conducted in a safe and reliable manner. To overcome the shortcomings of high latency in traditional Proof-of-Work (PoW) consensus, we proposed a credit-based PoW consensus mechanism by integrating the concept of “stake” to improve the consortium blockchain under the B-ET ecosystem. Then, we take combined cooling, heating, and power (CCHP) system as an example that supplies distributed energy, and model its interactions with the agent of power grid by a novel Stackelberg game. We show that the optimal utilities of entities in a city can be obtained at the Stackelberg equilibrium by a distributed algorithm, which is guaranteed to exist and be unique. In the end, we conduct a number of numerical simulations to evaluate our proposed model and verify our algorithms, which demonstrate their correctness and efficiency completely.

As one of the basic protocols of the wireless sensor network (WSN), neighbor discovery aims at initializing the network topology and maintaining the runtime connectivity. It is a quite big challenge to reduce the neighbor discovery latency in low-duty-cycle WSN, because the long dormancy of nodes handicaps the quick neighborhood establishment in their vicinity. Recently preliminary efforts have been directed toward the referring-based neighbor discovery, which proactively refers or recommends potential neighbors to nearby nodes in order to reduce the total discovery latency. Without delicate considerations, however, such proactive references will incur a great waste of node energy. In response to this limitation, we design and implement xBOND, a novel referring-based neighbor discovery protocol for the WSN with low-duty cycles. The xBOND protocol involves four perspectives of featuring the physical proximity of nodes. These features can be easily evaluated, and thus the node willing to do neighbor recommendation does not need to mull over them. With the features evaluated, xBOND leverages the XGBoost (a powerful classifier in machine learning) to achieve precise neighbor recommendations during neighbor discovery and then a better tradeoff between energy overhead and discovery latency. We also concretize the xBOND so that it can complete the neighbor discovery in a distributed way. Finally, we validate xBOND through extensive simulation experiments that show significant efficiency gains in discovery latency and energy consumption over the state-of-the-art referring-based approach.

Mobile crowd sensing (MCS) is a new paradigm for urban-scale monitoring. This article concentrates on the Cost-Fair Task Allocation (CFA) problem for the MCS scenario where the collaboration of multiple probabilistic mobilephone users is needed to yield more reliable observation. CFA aims to allocate sensing tasks to users so that the sensing costs undertaken by all users are as balancing as possible, while the requirement of the requester for data reliability can be satisfied. CFA is greatly important to MCS campaigns in terms of reliability and sustainability. We design two algorithms to solve the CFA problem in the offline and online cases, respectively. Specifically, we propose a novel penalty-based model to reformulate the offline CFA problem, and based on this model, we design an offline algorithm, which can yield a computation-efficient ε-solution with any small ε > 0. For the online case, we design a polynomial-time approximation algorithm, which struggles to allocate each of the sequentially arriving tasks to users as fairly as possible, and can achieve an upper-bounded competitiveness relative to the optimal CFA solution. Finally, we conduct extensive numeric analyses to validate the performance of our algorithms under diverse experimental setups.

As a promising technology to achieve perpetual operation of battery-powered wireless sensor devices, wireless power transfer has attracted much attention recently. In wireless power transfer, the charger enables the energy to be wirelessly transmitted to the rechargeable sensor devices that are hungry for energy. Previous works mainly focus on maximizing the charging utility or minimizing the charging delay. This paper concerns a more practical issue of placing wireless chargers, which aims at minimizing the deployment cost of chargers while satisfying the overall requirement for charging utility. We investigate the above cost-minimum charger placement problem under two typical scenarios in which omni chargers and directional chargers are used, respectively. To resolve this problem under the two charging models, we first prove its NP-hardness and then propose two approximation algorithms with proven performance guarantees. Finally, we conduct extensive simulation experiments to validate our designs, and the experimental results demonstrate that the proposed algorithms significantly outperform the baselines.

Mobile crowdsensing (MCS) is a new and promising tool in urban sensing. It exploits a crowd of smartphone-carried mobile users and transfers their sensory data to requesters who usually publish spatio-temporal tasks of sensing city area. In reality, mobile users can probabilistically move in the sensing region in their daily mobility and stay there for a period of time; and then these probabilistic users can be recruited to collaboratively perform MCS sensing tasks. Such an MCS depending on the probabilistic collaboration of mobile users is usually called nondeterministic MCS. In this paper, we focus on the budget-feasible user recruitment (BFUR) problem in non-deterministic MCS, which is the first work to maximize the requester's utility under a given budget constraint. Because of the NP-hardness of BFUR, we reformulate it as a monotone submodular maximization problem and propose a greedy algorithm (called uMax) with provable constant-factor competitiveness. Unlike previous works for nondeterministic MCS, however, this paper specially puts effort on predicting the mobility patterns of users, especially their stay time in requester's sensing region, and then designs an effective predictor based on bi-directional long short-term memory neural network. Such a prediction of user's stay time not only connects the BFUR problem modeling defined in this paper and the actual mobility uncertainty of users, but also can apply to any nondeterministic MCS campaign that depends on the knowledge of user's stay patterns. We finally validate the performance of the proposed predictor under a real-world dataset of wireless mobile networks, and evaluate algorithm uMax by comparing it with two other baseline algorithms.