相关论文: ICNLoWPAN -- Named-Data Networking for Low Power I…

Federated Learning (FL) is a promising distributed method for edge-level machine learning, particularly for privacysensitive applications such as those in military and medical domains, where client data cannot be shared or transferred to a…

A novel semantic approach to data selection and compression is presented for the dynamic adaptation of IoT data processing and transmission within "wireless islands", where a set of sensing devices (sensors) are interconnected through…

In the past decade, we have witnessed explosive growth in the number of low-power embedded and Internet-connected devices, reinforcing the new paradigm, Internet of Things (IoT). The low power wide area network (LPWAN), due to its…

Non-orthogonal multiple access (NOMA) and beamforming are well-established techniques for enabling massive connectivity in future wireless networks. However, many optimal beamforming solutions rely on highly complex iterative algorithms and…

In order to scale web or other services, the load on single instances of the respective service has to be balanced. Many services are stateful such that packets belonging to the same connection must be delivered to the same instance. This…

The concept of compressing deep Convolutional Neural Networks (CNNs) is essential to use limited computation, power, and memory resources on embedded devices. However, existing methods achieve this objective at the cost of a drop in…

The traditional industrial communication networks may not meet the requirements of the main smart factory applications together, such as Remote control and safety applications (which considered as strict real time applications) and…

Collaboration among industrial Internet of Things (IoT) devices and edge networks is essential to support computation-intensive deep neural network (DNN) inference services which require low delay and high accuracy. Sampling rate adaption…

This paper presents a hardware-efficient deep neural network (DNN), optimized through hardware-aware neural architecture search (HW-NAS); the DNN supports the classification of session-level encrypted traffic on resource-constrained…

In this paper, we study the data gathering problem in the context of power grids by using a network of sensors, where the sensed data have inter-node redundancy. Specifically, we propose a new transmission method, calledquantized network…

In-network caching is a central aspect of Information-Centric Networking (ICN). It enables the rapid distribution of content across the network, alleviating strain on content producers and reducing content delivery latencies. ICN has…

Low-power wide-area (LPWA) networks are attracting extensive attention because of their abilities to offer low-cost and massive connectivity to Internet of Things (IoT) devices distributed over wide geographical areas. This article provides…

Environmental concerns have motivated network designers to further reduce energy consumption of access networks. This paper focuses on reducing energy consumption of Ethernet passive optical network (EPON) as one of the most efficient…

Even though the TCP/IP architecture has served the Internet quite satisfactorily during its more than forty years of lifespan, there are doubts about whether this host-centric paradigm is well suited for the communication patterns of modern…

The Narrowband Internet of Things (NB-IoT) is a cellular technology introduced by the Third Generation Partnership Project (3GPP) to provide connectivity to a large number of low-cost IoT devices with strict energy consumption limitations.…

Low Power Wide Area (LPWA) networks are attracting a lot of attention primarily because of their ability to offer affordable connectivity to the low-power devices distributed over very large geographical areas. In realizing the vision of…

As state of the art neural networks (NNs) continue to grow in size, their resource-efficient implementation becomes ever more important. In this paper, we introduce a compression scheme that reduces the number of computations required for…

This paper considers an intelligent reflecting surface(IRS)-aided wireless powered communication network (WPCN), where devices first harvest energy from a power station (PS) in the downlink (DL) and then transmit information using…

Recent increases in the computational demands of deep neural networks (DNNs), combined with the observation that most input samples require only simple models, have sparked interest in $input$-$adaptive$ multi-exit architectures, such as…

Recent advances in deep learning motivate the use of deep neutral networks in sensing applications, but their excessive resource needs on constrained embedded devices remain an important impediment. A recently explored solution space lies…