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
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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
Edge computing has gradually become a critical means of addressing the latency and bandwidth bottlenecks inherent in cloud computing. By deploying computing and storage resources at network edge nodes, edge computing is capable of providing low-latency and high-bandwidth services to Internet of Things (IoT) devices. To...
B. Su, Ren-Cong Lin, Zhenxing Li et al.· IEEE Internet of Things Jour...· 0 citations
As computing demands continue to grow, a single server is no longer sufficient to meet user requirements, leading to increasing interest in multiserver collaborative edge computing. However, load imbalance is a prevalent issue in multiserver edge computing systems, resulting in inefficient resource utilization and degr...
Jingzhe Wang, Si-yu Lin, Qingqing Pan et al.· IEEE Internet of Things Jour...· 0 citations
The rapid growth of cloud-edge collaborative computing and its increasing electricity demand are forging strong interdependencies between computing and power markets. This paper proposes a market-driven joint trading strategy that coordinates computing services and electricity procurement within a cloud-edge collaborat...
Lirong Deng, Yi Sun, Yang Fu· IEEE Transactions on Smart G...· 1 citation
Federated learning (FL) is a distributed machine learning (ML) paradigm that has been widely used to train ML models on massive amounts of data in edge computing (EC) environments. However, FL faces significant challenges from device heterogeneity, edge dynamics, and limited communication resources. To address these ch...
Junyi Deng, Jiahua Liu, Yanheng Liu et al.· IEEE Internet of Things Jour...· 0 citations
A novel AI-driven distributed NIDS that considers the computing capabilities of resource-constrained nodes while enabling efficient learning in distributed environments is proposed and can achieve accuracy comparable to a centralized model while reducing local computational overhead and maintaining stable convergence u...
Cheolhee Park, Kyungmin Park, Jihyeon Song et al.· IEEE Internet of Things Jour...· 0 citations
This article introduces KirchhoffNet, a novel class of neural network models inspired by the principles of analog electronic circuitry, specifically Kirchhoff’s laws. KirchhoffNet operates as an analog circuit, where the network input is represented by initial node voltages, and the output corresponds to the node volta...
Su Zheng, Zhengqi Gao, Fan-Keng Sun et al.· IEEE Transactions on Compute...· 0 citations
The analysis shows that services with stringent latency deadlines and larger computing demands are more sensitive to temporal variabilities, making local execution the preferred option, and that effective service offloading must jointly consider service requirements and sources of temporal variability to guarantee dete...
K. Aghababaiyan, Javier Gozálvez, B. Coll-Perales· 0 citations
Host Sowbhagya Suresh shares some of the latest news and cutting-edge research from across Microsoft—from understanding the challenges facing coding agents to uncovering how people use AI at work and improving extended-range weather forecasting. The post Microsoft Research Focus – Edition #97 appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduOct 28, 2026
Professor Paris Smaragdis develops computers with superhuman hearing for applications like speech recognition, music understanding, and environmental monitoring.
For decades, a fundamental question about the difficulty of computing in high-dimensional spaces has resisted proof. New research resolves this question, removing a major theoretical obstacle to understanding the efficiency of randomized algorithms used across science and computing. The Kannan–Lovász–Simonovits (KLS) conjecture was proposed in 1995. It asks whether, for a broad class of high-dimensional […] The post Solving a 31-year-old grand challenge in mathematics – the KLS conjecture appear…
MIT News · Artificial Intelligence· news.mit.eduOct 15, 2026
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.
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