In the era of rapid artificial intelligence development, human–agent collaboration holds the potential to significantly enhance work efficiency. While existing studies have explored various collaboration strategies and resource methods, there remains a notable lack of in-depth research on how to economically allocate a limited budget to acquire both human and agent computing capacities. To address this gap, we first construct a developer model based on theories from psychology and economics, providing a quantitative description of human working time and efficiency. Building upon this, we further investigate the impact of dynamic budget allocation strategies on consumer decision-making. Specifically, a novel budget recycling mechanism is introduced to redistribute unused resources, thereby enhancing system responsiveness. Experimental results demonstrate a 56% improvement in resource utilization and a 32% increase in task completion. This confirms the effectiveness of our proposed method in optimizing collaboration and supporting sustainable project execution.

The rapid advancement of Internet of Things (IoT) and multi-agent systems has transformed how complex IoT tasks are managed across domains. While individual edge agents demonstrate proficiency in specialized tasks such as data collection and edge learning, they encounter substantial challenges when confronting complex IoT scenarios that demand diverse skill sets. This paper introduces a novel group formation framework facilitating effective IoT agent collaboration in complex task environments, including smart manufacturing, intelligent transportation, and smart cities. We propose a hybrid competition mechanism that optimizes initial multi-agent cooperation strategies by integrating task requirements, agent capabilities, and system-wide performance metrics. Our approach combines intra-task and inter-task competition to achieve optimal agent-task matching and resource allocation in large-scale IoT networks. Through comprehensive simulations across various IoT scenarios, we demonstrate that our framework substantially enhances task completion efficiency and system performance compared to existing methods. The results confirm our approach’s effectiveness in resource-constrained environments, achieving minimal agent grouping time costs while increasing total task revenue by 30%-42% and resource utilization by 38% compared to baseline heuristic methods.

Deduplication (redundancy elimination) and cooperative video delivery are two effective ways to save the bandwidth and energy consumption and ensure video collection in damaged networks. However, deduplication in mobile crowd sensing (MSC) is primarily performed on texts and images. Furthermore, most of deduplication technologies require global information and are separated from video routing. To solve such problems, this paper propose a cooperative upload method for sensing videos, which performs the local video deduplication without excessive comparison and feature exchange. Also, we combine the content-aware deduplication with the dynamic relay selection to avoid the propagation of redundant items caused by the content-free video routing. Besides, we integrate a novel mutual-assisted mechanism into our method to motivate relay cooperation and load balance. We formulate the deduplication-supported cooperative video upload as a multi-stage decision problem. To solve the uncertainty of destinations in the decision problem, we develop a stepwise Mutual-Assisted Video Upload Algorithm (MAVU) to schedule video chunks and remove duplicates. Extensive experiments are conducted to compare MAVU with the existing algorithms. The numerical results validate that our MAVU has advantages over the other algorithms in collected video size and upload latency

With the rapid development of multimedia services in wireless mobile networks, the video data traffic has increased exponentially. Traditional video traffic service based on cloud computing caused a large amount of traffic load and longer access delay, which severely reduces the quality of service (QoS) of users. Mobile edge caching as one of the use cases of mobile edge computing (MEC), can directly serve user requests so as to greatly reduce the traffic load and shorten access delay. At present, distributed caching is widely used in the caching deployment of base stations (BSs). However, the caching capacity of a single BS is generally particularly limited, which will degrade the performance of wireless mobile network. In this paper, a collaborative caching strategy in the heterogeneous MEC networks is designed, and the edge caching of macro base station (MBS) and small base stations (SBSs) is utilized to bring storage resources closer to users. In addition, an optimization problem of content caching is formulated to minimize the total delay cost of all users requesting content in the MEC networks. In order to solve this problem, cache management strategy (GCS) is proposed, which consists of greedy cache placement strategy and greedy cache update strategy. Finally, numerical simulations demonstrate that the GCS scheme effectively improves the cache hit rate, and significantly reduces the average delay and backhaul traffic load.

Ontology technology has been investigated in a wide range of areas and is currently being utilized in many fields. In the e-learning context, many studies have used ontology to address problems such as the interoperability in learning objects, modeling and enriching learning resources, and personalizing educational content recommendations. We systematically reviewed research on ontology for e-learning from 2008 to 2020. The review was guided by 3 research questions: “How is ontology used for knowledge modeling in the context of e-learning?”, “What are the design principles, building methods, scale, level of semantic richness, and evaluation of current educational ontologies?”, and “What are the various ontology-based applications for e-learning?” We classified current educational ontologies into 6 types and analyzed them by 5 measures: design methodology, building routine, scale of ontology, level of semantic richness, and ontology evaluation. Furthermore, we reviewed 4 types of ontology-based e-learning applications and systems. The observations obtained from this survey can benefit researchers in this area and help to guide future research.

With the technological advances in wireless multimedia domain, these videos made by mobile edge devices dominate network traffics. The video summarization technology enables users to understand the storyline of a video before a client requests the complete video content. Summarizing a video on edge devices and transmitting the summary between them requires a user-oriented and adaptive solution due to the limited capability and the dynamic wireless links of edge devices. Therefore, it is beneficial to improve the user's viewing experience and the bandwidth utilization ratio if we generate and transmit a video summary based on network connections and the user's tolerant latency. Unfortunately, previous summarization approaches are incapable of adjusting the summary size adapted to the varying network bandwidth and the user's attitude towards latency. To timely and flexibly deal with mobile videos, we first formulate the video summarization optimization problem with the elastic number of selected representative segments and the outlier detection within a bounded time budget. Furthermore, we develop an online greedy algorithm called the Elastic Video Summarization Algorithm (EVS) to solve the NP hard problem. We analyze the properties associated with EVS and further design an improved EVS-II to reduce computation complexity. Finally, the experimental results demonstrate that our proposed algorithms outperform other existing researches in fitting network bandwidth and detecting outliers.

Mobile edge computing (MEC) has become a promising technique to accommodate demands of resource-constrained mobile devices by offloading the task onto edge clouds nearby. However, most existing works only focus on whether or where a task is offloaded but ignore the motivation of the edge cloud to offer service. To stimulate service provisioning by edge clouds, it is essential to design an incentive mechanism that charges mobile devices and rewards edge clouds. In this paper, we utilize market-based pricing model to establish a relationship between the resources provided by edge clouds and the price paid by the mobile devices in a non-competitive environment. Furthermore, we design a profit maximization multi-round auction (PMMRA) mechanism for the resource trading between edge clouds as sellers and mobile devices as buyers in a competitive environment. The mechanism can effectively determine the price paid by the buyers to use the resources provided by the sellers and make the corresponding match between edge clouds and mobile devices. Finally, numerical results show that proposed mechanism outperforms other existing algorithms in maximizing the profits of resource providers.

With the popularity of mobile multimedia services, the explosive increase in video traffic not only causes huge load and energy consumption of base station (BS), but also affects the QoS of users. The device to device (D2D) multicast technology can effectively reduce the number of redundant video transmissions and improve energy efficiency of the BS. However, the existing researches about D2D technology assume that there is not unconstrained communication between local users, which is not in line with the actual situation. Moreover they do not take full advantage of attributes of users in mobile social networks. In this paper, we first propose a clustering method and select the cluster head users (CHUs) as relay nodes to distribute videos by considering the user's physical conditions and user's social attributes based on Chinese Restaurant Process (CRP). Then we put forward with a video distribution and sharing mechanism based on scalable video coding (SVC). In this mechanism, edge users collaboratively share videos on the basis of user's mobility, where Zipf distribution combines with SVC to effectively reduce the energy consumption of the BS. Finally, the experimental results show that the proposed strategy can not only reduce the energy consumption of the BS, but also improve the throughput and the stability and flexibility of video distribution.

The redundancy of sensing data in wireless sensor networks (WSNs) gives rise to longer transmission delays and more energy consumption. In this paper, we focus on the energy-efficient data redundancy elimination and compression with the objective of recovering the original data. To balance aggregation load of a large-scale WSN, we propose a novel energy-efficient dynamic clustering algorithm by utilizing spatial correlation, which can achieve a distributed compressive data aggregation in each cluster head. Furthermore, we propose a distributed fast data compression approach based on eliminable lifting wavelet to reduce the amount of raw data. Also, it offers high fidelity recovery for the raw data. Extensive experimental results demonstrate that our clustering method based on data correlation clustering (CDSC) for data aggregation outperforms other methods on prolonging network lifetime and reducing the amount of data transmitted. In particular, our data compression aggregation algorithm can achieve 98.4% recovery accuracy when the compression ratio equals 1.3333.