Federated learning (FL) has been proposed to efficiently and privacy-preserving distributed machine learning architecture for the Internet of Things (IoT). In a wireless FL system, clients in IoT devices train their local models over the local data sets. The derived local models are uploaded to an FL server to generate a global model, broadcasted to the clients in the next global iteration for further training. Owing to the heterogeneous feature of the clients, client selection is critical to determine the overall training time. Traditionally, the objective of client selection is to select the maximum number of clients who can derive and upload their local models before the deadline in each global iteration. However, selecting more clients increases the energy consumption of the clients. Moreover, selecting the maximum number of clients is unnecessary as having fewer clients in early global iterations and more clients in later global iterations have been proved to achieve higher model accuracy. Hence, this article proposes to dynamically adjust and optimize the tradeoff between maximizing the number of selected clients and minimizing the total energy consumption of the clients by selecting suitable clients and allocating appropriate resources in terms of CPU frequency and transmission power. We formulate the joint client selection and resource management problem and design the energy and latency-aware resource management and client selection (ELASTIC) algorithm to efficiently solve the problem. Extensive simulations are conducted to demonstrate the performance of ELASTIC.

We propose a novel network architecture by leveraging the cloudlet concept, the software defined networking technology, and the cellular network infrastructure to bring the computing resource to the mobile edge. In order to minimize the average response time for mobile users (MUs) in offloading their application workloads to the geographically distributed cloudlets, we propose the latency-aware workload offloading (LEAD) strategy to allocate MUs' application workloads into suitable cloudlets. Simulation results demonstrate that LEAD incurs the lowest average response time as compared with two other existing strategies.

In this paper, we propose a Mobile Edge Internet of Things (MEIoT) architecture by leveraging the fiber-wireless access technology, the cloudlet concept, and the software defined networking framework. The MEIoT architecture brings computing and storage resources close to Internet of Things (IoT) devices in order to speed up IoT data sharing and analytics. Specifically, the IoT devices (belonging to the same user) are associated to a specific proxy Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes the IoT data (generated by its IoT devices) in realtime. Moreover, we introduce the semantic and social IoT technology in the context of MEIoT to solve the interoperability and inefficient access control problem in the IoT system. In addition, we propose two dynamic proxy VM migration methods to minimize the end-to-end delay between proxy VMs and their IoT devices and to minimize the total on-grid energy consumption of the cloudlets, respectively. Performance of the proposed methods is validated via extensive simulations.

To provision IT solutions with reduced operating expenses, many businesses are moving their IT infrastructures into public data centers or are starting to build their own private data centers. Data centers can provide flexible resource provisioning in order to accommodate the workload demand. In this paper, we present a comprehensive survey of most relevant research activities on resource management of data centers that aim to optimize the resource utilization. We first describe the resource overprovisioning problem in current data centers. Then, we summarize two important components in the resource management platform and present the benefit of accurately predicting the workload in resource management. Afterwards, we classify existing resource management in a data center into three categories: 1) virtual machine-based, 2) physical machine-based, and 3) application-based resource management mechanisms. We discuss the performance degradation for implementing these three kinds of resource management in a heterogeneous data center. Finally, we present three important issues arose in the data center resource management and some potential approaches to address the issues. This paper presents a timely survey on resource management in a data center, and provides a comprehensive reference for further research in this field.

We propose to apply CoAP Publish/Subscribe to cache popular IoT resources in a broker to reduce the energy consumption of servers (which host these popular resources) and the average delay for delivering the IoT resources’ contents to the clients. We provide the smart parking application in smart cities as an example to demonstrate the benefit for conducting popular IoT resource caching. However, caching popular IoT resources in the broker may not always be the optimal choice, i.e., the broker may be congested by caching too many IoT resources, and so the average delay for enabling the broker to deliver contents of the IoT resources may be unbearable. Thus, we propose a novel Energy Aware and latency guaranteed dynamic reSourcE caching (EASE) strategy to enable the broker to cache suitable popular resources such that the energy savings from the servers are maximized, while the average delay for publishing the contents of the resources to the corresponding clients is minimized. We demonstrate the performances of EASE via simulations as compared to other two baseline IoT resource caching strategies.

Deploying drone-mounted base stations (DBSs) can quickly recover the communications of the mobile users (MUs) in a disaster struck area. That is, the DBSs can act as relay nodes to transmit data from remote working base stations to the MUs. Since the DBSs could be deployed very close to the MUs, the access link data rates between the DBSs and the MUs are well provisioned. However, the DBSs may be far away from the remote working base stations, and thus the backhaul link data rate between a DBS and the remote working base station could be throttled. Free Space Optics (FSO), which has been demonstrated to provision high speed point-to-point wireless communications, can be leveraged to improve the capacity of the backhaul link. Since FSO requires line-of-sight between a DBS and a remote working macro base station, DBSs have to carefully deployed. In this paper, we design a QoS aware drone base station placement and mobile user association strategy (RESCUE) to jointly optimize the DBS deployment, MU association, and bandwidth allocation such that the number of the served MUs in the disaster struck area could be maximized. The performance of RESCUE is validated via extensive simulations.