Unstable transmission conditions degrade the offloading performance of Mobile Edge Computing (MEC) servers in vehicular communication environments. Cache-enabled uncrewed aerial vehicles (UAVs) with computation and caching capabilities offer a potential solution by acting as mobile edge servers in the air. Due to super...
Integrated sensing, communication, and computation (ISCC) systems in space-air-ground integrated networks (SAGINs) offer a promising foundation for next-generation intelligent infrastructure. However, their highly dynamic links, heterogeneous computing resources, and stringent real-time requirements pose critical chall...
Bo Yin, Dong-Bo Li, Hong-Kai Chen et al.· IEEE Transactions on Mobile...· 0 citations
The execution of DAG-based applications in Mobile Edge Computing (MEC) systems faces significant challenges due to dynamic resource availability, heterogeneous container image layers, and their tight coupling with task scheduling decisions. Existing DAG- or priority-based schedulers typically rely on fixed dependencies...
Jing-Jie Zhang, Tony Q. S. Quek, Ming-Xiong Zhao· IEEE Transactions on Mobile...· 0 citations
Multi-UAV networks are promising for supporting time-sensitive IoT applications, yet most existing studies focus on a single service type and fail to address the coexistence of heterogeneous tasks with fundamentally different timeliness and resource characteristics. In hybrid MEC–DC systems, data collection (DC) and mo...
Kang Fu, Qing-Jie Zhao· IEEE Transactions on Mobile...· 0 citations
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The rapid proliferation of data-intensive and delay-sensitive applications has accelerated the evolution from centralized cloud computing to mobile edge-cloud systems. However, the decentralized and heterogeneous characteristics introduce instability and complexity, making efficient scheduling increasingly challenging....
Yun-Feng Zhao, Chao Qiu, Xiao-Fei Wang et al.· IEEE Transactions on Mobile...· 0 citations
Video analytics services utilizing multiple deep neural network models (DNNs) are increasingly being adopted in various edge intelligence applications. To avoid the accuracy reduction caused by data drift, existing works leverage continual learning that directly retrains DNN models in multi-model applications. Accordin...
Tian-En Liu, Bo-Rui Li, Wei-Long Wang et al.· IEEE Transactions on Mobile...· 1 citation
In edge intelligence networks, AI model inference and training tasks coexist and complement each other. Inference tasks demand low latency and diversity to support real-time services, whereas training tasks are computationally intensive and rely on distributed user devices (UDs) data for continuous model enhancement.Ho...
Xiongfei Chun, Wen-Hao Fan, Ruimin Zhang et al.· IEEE Transactions on Mobile...· 0 citations
As one of the most representative applications of edge computing, video analytics typically involves multiple vision components in the pipeline, which together determine the quality of service (QoS) for users. By exploiting diverse resource demands of different components, a fine-grained hierarchical orchestration with...
Kongyange Zhao, Tao Ouyang, Zhi-Xuan Liao et al.· IEEE Transactions on Mobile...· 0 citations
With the development of social media, mobile computing, and edge intelligence, the communication topology frequently changes over time, making it difficult to maintain connectivity. Traditional average consensus relies on continuous state exchange, which can easily lead to state-evolution trajectory leakage. Most exist...
Guang-Qiang Xie, Si-Cheng Zhao, Rui-Kai Chen et al.· IEEE Transactions on Mobile...· 0 citations
Serving text-to-image (T2I) generation on edge servers can reduce the serving latency and enhance the preservation of user privacy. Currently, mainstream T2I models generate images iteratively, and the more iterations, usually the higher the generation quality, but the longer the generation latency. Existing T2I servin...
Sen Dong, Ke Cheng, Ling-Kun Meng et al.· IEEE Transactions on Mobile...· 0 citations
In vehicular edge computing (VEC), existing task offloading approaches enable mobile vehicles (MVs) to offer ultra-low latency services for computation-intensive tasks. However, centralized offloading strategies are impractical due to MVs’ limited communication range and high mobility, requiring autonomous cooperation...
Zheng-Wei Gao, Chang-Mao Wu, Lei Yang et al.· IEEE Transactions on Mobile...· 0 citations
The integrated space-terrestrial network is playing an increasingly crucial role and is expected to become an indispensable component of future communication systems. As the demand for user terminals surges, task congestion issues are becoming more prominent, gradually emerging as a key factor affecting service quality...
Li-Jun Huo, Yi-Bing Liu, Xiao-Zhou Zhu et al.· IEEE Transactions on Mobile...· 0 citations
Jennifer Neville did not want to go into computer science—but that’s exactly where she landed. Neville discusses the starts and stops that led to her professional sweet spot and her work identifying “surprising failures” making it hard for AI to handle complexity. The post What AI gets wrong and what failure teaches us appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 2, 2026
