Edge-enabled two-stage scheduling based on deep reinforcement learning for internet of everything
Nowadays, the concept of Internet of Everything (IoE) is becoming a hotly discussed topic,
which is playing an increasingly indispensable role in modern intelligent applications. These …
which is playing an increasingly indispensable role in modern intelligent applications. These …
Energy-aware task scheduling and offloading using deep reinforcement learning in SDN-enabled IoT network
Abstract The fifth-generation (5G) mobile network services have made tremendous growth in
the Internet of Things (IoT) network. A counters number of battery-powered IoT devices are …
the Internet of Things (IoT) network. A counters number of battery-powered IoT devices are …
Smart architectural framework for symmetrical data offloading in IoT
With new technologies coming to the market, the Internet of Things (IoT) is one of the
technologies that has gained exponential rise by facilitating Machine to Machine (M2M) …
technologies that has gained exponential rise by facilitating Machine to Machine (M2M) …
Computation offloading for distributed mobile edge computing network: A multiobjective approach
Mobile edge computing (MEC) is emerging as a cornerstone technology to address the
conflict between resource-constrained smart devices (SDs) and the ever-increasing …
conflict between resource-constrained smart devices (SDs) and the ever-increasing …
Meeting the requirements of internet of things: The promise of edge computing
Over the last few decades, Internet of Things (IoT) has become the spotlight area of research
within the Industries and Academics. Primarily, IoT devices are characterized by small and …
within the Industries and Academics. Primarily, IoT devices are characterized by small and …
Adjusting forwarder nodes and duty cycle using packet aggregation routing for body sensor networks
In the body sensor networks (BSNs), the data redundancy and transmission delay are two
problems for improving network performance. In the previous scheme, multi-sensor fusion is …
problems for improving network performance. In the previous scheme, multi-sensor fusion is …
Task allocation with unmanned surface vehicles in smart ocean IoT
J Zhang, M Dai, Z Su - IEEE Internet of Things Journal, 2020 - ieeexplore.ieee.org
The unmanned surface vehicles (USVs) have been regarded as a promising paradigm to
automatically perform emergency tasks in a dynamic maritime traffic environment. However …
automatically perform emergency tasks in a dynamic maritime traffic environment. However …
Augmented grasshopper optimization algorithm by differential evolution: A power scheduling application in smart homes
With the increasing number of electricity consumers, production, distribution, and
consumption problems of produced energy have appeared. This paper proposed an …
consumption problems of produced energy have appeared. This paper proposed an …
[HTML][HTML] EneA-FL: Energy-aware orchestration for serverless federated learning
Federated Learning (FL) represents the de-facto standard paradigm for enabling distributed
learning over multiple clients in real-world scenarios. Despite the great strides reached in …
learning over multiple clients in real-world scenarios. Despite the great strides reached in …
Deep reinforcement learning for energy-efficient task scheduling in SDN-based IoT network
The growing demand and the diverse traffic patterns coming from various heterogeneous
Internet of Things (IoT) systems place an increasing strain on the IoT infrastructure at the …
Internet of Things (IoT) systems place an increasing strain on the IoT infrastructure at the …