This article investigates the dynamic multiobjective co-optimization problem in unmanned aerial vehicle (UAV)-assisted remote sensing systems, aiming to jointly optimize UAV placement, task scheduling strategies, and computing/communication resource allocation to minimize the system’s average processing latency and the...
Bo Wang, Xiaoyun Qin, Zhifeng Zhang et al.· IEEE Internet of Things Jour...· 0 citations
Vehicular edge computing (VEC), a key enabler for the Internet of Things (IoT) in intelligent transportation, addresses onboard processing constraints through collaborative task offloading among vehicles, facilitating latency-sensitive applications such as autonomous driving. However, developing efficient offloading st...
Momiao Zhou, Yimin Zhou, Yanshi Sun et al.· IEEE Internet of Things Jour...· 0 citations
Edge devices capable of running artificial intelligence (AI) applications have seen a surge in demand for energy-efficient and high-throughput computation. In this study, a 22-nm edge–AI processor, incorporating an accelerator with error-free Booth-value-confined (BVC) multiprecision (MP) multiplier and near-memory com...
Quan Cheng, Longyang Lin, Mingqiang Huang et al.· IEEE Transactions on Very La...· 0 citations
Graph convolutional networks (GCNs) have demonstrated strong capability in modeling graph-structured data and have been widely adopted in various industrial applications. With the rapid growth of edge computing, designing GCN accelerators that maintain high performance under strict resource constraints has become incre...
Yi-Zhang Wu, Xiang-Yin Chen, Chen Yang· IEEE Transactions on Very La...· 0 citations
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Mobile edge computing (MEC) has emerged as a promising paradigm to support latency-sensitive applications by deploying computing and storage resources closer to end users. Service caching at the network edge is an effective approach to further reduce response latency and improve quality of service (QoS). However, the i...
Tianyang Zheng, Pengfei Yang, Kang Shen et al.· IEEE Internet of Things Jour...· 0 citations
Autonomous driving vehicles (ADVs) are transforming urban mobility with advanced sensors for real-time decision-making, promising safer and more efficient transportation. Despite recent advancements targeting accident reduction and efficiency improvement, challenges like sensor range limitations persist. Edge-assisted...
Zhe Jiang, Xiaoxuan Wang, Zhenghui Guo et al.· IEEE Transactions on Compute...· 0 citations
Multiaccess edge computing (MEC) has emerged as a key paradigm to empower resource-constrained edge devices through task offloading. However, dynamically fluctuating user preferences regarding latency and energy tradeoffs severely complicate the task scheduling and resource allocation, while existing methods struggle t...
Honggang Yuan, Yuxiang Deng, Xiao Du et al.· IEEE Internet of Things Jour...· 0 citations
Vehicular edge computing (VEC) is viewed as an important component for vehicle networks due to the prevalence of smart vehicles. However, stringent requirements such as ultralow latency and minimal energy consumption should be achieved for the emerging transmission-and-computation-intensive applications due to the limi...
Zhaobin Li, Qiqi Zhang, Ziqiang Zhang et al.· IEEE Internet of Things Jour...· 0 citations
Passenger demand prediction is essential for intelligent transportation systems (ITSs) and the Internet of Vehicles (IoV), supporting efficient transit management, network bandwidth allocation, onboard content distribution, and edge computing scheduling. With buses increasingly equipped with wireless connectivity, pass...
Xiangjie Kong, Hao Tan, Zhehui Shen et al.· IEEE Internet of Things Jour...· 0 citations
The Intelligent Internet of Things (IIoT) is transitioning from a data-centric to a human-centric paradigm, creating an urgent demand for reliable human–machine interaction. While transformer-based brain foundation models have emerged to decode human intentions, most existing studies focus on improving performance for...
Ang Li, Zhenyu Wang, Tianheng Xu et al.· IEEE Internet of Things Jour...· 0 citations
The consumer Internet of Things (CIoT) enables large-scale sensing and data-driven services by connecting billions of devices. However, the privacy-preserving data aggregation in mobile edge computing (MEC)-enabled CIoT remains challenging when the edge aggregator is malicious, rather than simply honest-but-curious. Sp...
Jianhong Zhang, Miao Xue, Xinyu Bai· IEEE Internet of Things Jour...· 0 citations
The growing demand for efficient deep-learning inference on edge platforms requires hardware that is both energy-efficient and practically implementable. This work presents a 16-Kb all-digital static random-access memory (SRAM)-based compute-in-memory (CIM) macro for low-bit CNN inference, featuring a hierarchical adde...
Vikash Vishwakarma, Gopal R. Raut, Amit Mittal et al.· IEEE Transactions on Very La...· 0 citations
A new computational approach reveals how cells communicate to assemble into tissues. This could accelerate stem cell engineering, improve disease models, and inform therapies.
Microsoft Research Blog· microsoft.comOct 15, 2026
Zero-knowledge proofs can verify computing results without rerunning workloads or exposing sensitive data. Magnetar helps bring them into the mainstream with support for standard programming languages, major speed gains, and quantum-resistant security. The post Proof-based general-purpose computing with Magnetar appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 8, 2026
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.
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