Two-tier Network Architecture Research Articles

Scalable and Low-Latency Federated Learning With Cooperative Mobile Edge Networking

Federated learning (FL) enables collaborative model training without centralizing data. However, the traditional FL framework is cloud-based and suffers from high communication latency. On the other hand, the edge-based FL framework that relies on an edge server co-located with mobile base station for model aggregation has low communication latency but suffers from degraded model accuracy due to the limited coverage of edge server. In light of high-accuracy but high-latency cloud-based FL and low-latency but low-accuracy edge-based FL, this paper proposes a new FL framework based on cooperative mobile edge networking called cooperative federated edge learning (CFEL) to enable both high-accuracy and low-latency distributed intelligence at mobile edge networks. Considering the unique two-tier network architecture of CFEL, a novel federated optimization method dubbed cooperative edge-based federated averaging (CE-FedAvg) is further developed, wherein each edge server both coordinates collaborative model training among the devices within its own coverage and cooperates with other edge servers to learn a shared global model through decentralized consensus. Experimental results based on benchmark datasets show that CFEL can largely reduce the training time to achieve a target model accuracy compared with prior FL frameworks.

Design of Teaching System of Track and Field Sports Training Course for College Students Majoring in Electronic Engineering Education

In order to improve the unstable operation of teaching system caused by data security, this paper puts forward the design of teaching system of track and field sports training course for college students majoring in electronic engineering education. This paper analyzes the demand of track and field sports training course for teaching system, and on this basis, adopts B / S architecture designs the physical architecture of the system, and designs a two-tier network architecture. According to the application requirements of the system architecture, the hardware environment with high memory is designed. The privacy homomorphism technology is used to encrypt the system data, and the blockchain technology is used to transmit the system data, so as to improve the security of the system data. The experimental results show that the design of the system in the process of operation.

Energy-Efficiency-Oriented Charge Scheduling and Beamforming for a Two-Tier Wireless Powered Network

Long-range radio-frequency wireless charging has received more and more attention in recent years. Unlike the literature that focuses on the mobile charging scenario, which is formulated as a path-planning problem, we consider a static network that consists of a power beacon (PB) and multiple wireless powered user nodes. Each user node is equipped with a rechargeable battery and harvests energy only from the PB. We assume that all user nodes are either in charge or work mode. Under this assumption, we first propose a charge scheduling scheme that achieves the system’s maximal energy efficiency. We then further investigate the system’s improvement with a multiantenna PB using the energy beamforming technique. Next, we extend our scheduling scheme to a two-tier network architecture, where a first-tier PB first transfers energy to the second-tier sub-PBs, and then those sub-PBs deliver energy to the user nodes which belong to their clusters. It is shown that energy beamforming with multiple antennas brings a significant improvement to the system’s performance. Moreover, the two-tier architecture is shown to be superior to the one-tier architecture in terms of the energy transfer efficiency and the system’s realization complexity. Finally, the simulation results demonstrate our theoretical findings.

Hierarchical Capacity Provisioning for Fog Computing

The concept of fog computing is centered around providing computation resources at the edge of the network, thereby reducing the latency and improving the quality of service. However, it is still desirable to investigate how and where at the edge of the network the computation capacity should be provisioned. To this end, we propose a hierarchical capacity provisioning scheme. In particular, we consider a two-tier network architecture consisting of shallow and deep cloudlets and explore the benefits of hierarchical capacity provisioning based on queuing analysis. Moreover, we explore two different network scenarios in which the network delay between the two tiers is negligible and the case that the deep cloudlet is located somewhere deeper in the network and thus the delay is significant. More importantly, we model the first network delay scenario with bufferless shallow cloudlets and the second scenario with finite-size buffer shallow cloudlets, and formulate an optimization problem for each model. We also use stochastic ordering to solve the optimization problem formulated for the first model and an upper bound-based technique is proposed for the second model. The performance of the proposed scheme is evaluated via simulations in which we show the accuracy of the proposed upper bound technique and the queue length estimation approach for both randomly generated input and real trace data.

Building Transmission Backbone for Highway Vehicular Networks: Framework and Analysis

The highway vehicular ad hoc networks, where vehicles are wirelessly inter-connected, rely on the multi-hop transmissions for end-to-end communications. This, however, is severely challenged by the unreliable wireless connections, signal attenuation and channel contentions in the dynamic vehicular environment. To overcome the network dynamics, selecting appropriate relays for end-to-end connections is important. Different from the previous efforts (\emph{e.g.}, clustering and cooperative downloading), this paper explores the existence of stable vehicles and propose building a stable multi-hop transmission backbone network in the highway vehicular ad hoc network. Our work is composed of three parts. Firstly, by analyzing the real-world vehicle traffic traces, we observe that the large-size vehicles, \emph{e.g.}, trucks, are typically stable with low variations of mobility and stable channel condition of low signal attenuation; this makes their inter-connections stable in both connection time and transmission rate. Secondly, by exploring the stable vehicles, we propose a distributed protocol to build a multi-hop backbone link for end-to-end transmissions, accordingly forming a two-tier network architecture in highway vehicular ad hoc networks. Lastly, to show the resulting data performance, we develop a queueing analysis model to evaluate the end-to-end transmission delay and throughput. Using extensive simulations, we show that the proposed transmission backbone can significantly improve the reliability of multi-hop data transmissions with higher throughput, less transmission interruptions and end-to-end delay.

Vertical Handoff Decision Using Fuzzification and Combinatorial Fusion

This letter proposes a new approach to making network selection simple yet effective. Under consideration is a heterogeneous environment where a mobile user roams within a two-tier network architecture over different radio access technologies. Allowing for multiple determinants, we design a scheme for a user equipment to deduce whether handoff is imminent by fusing several metrics-received signal strength, data rate, and network latency. Our method harnesses strengths of fuzzy logic while maintaining lower computational complexity by means of combinatorial fusion. Our results demonstrate that the new approach outperforms other previous proposed fuzzy logic-based strategies in terms of time efficiency and operational effectiveness with the same level of vertical handoffs.

Statistical Dissemination Control in Large Machine-to-Machine Communication Networks

Cloud based machine-to-machine (M2M) communications have emerged to achieve ubiquitous and autonomous data transportation for future daily life in the cyber-physical world. In light of the need of network characterizations, we analyze the connected M2M network in the machine swarm of geometric random graph topology, including degree distribution, network diameter, and average distance (i.e., hops). Without the need of end-to-end information to escape catastrophic complexity, information dissemination appears an effective way in machine swarm. To fully understand practical data transportation, G/G/1 queuing network model is exploited to obtain average end-to-end delay and maximum achievable system throughput. Furthermore, as real applications may require dependable networking performance across the swarm, quality of service (QoS) along with large network diameter creates a new intellectual challenge. We extend the concept of small-world network to form shortcuts among data aggregators as infrastructure-swarm two-tier heterogeneous network architecture, then leverage the statistical concept of network control instead of precise network optimization, to innovatively achieve QoS guarantees. Simulation results further confirm the proposed heterogeneous network architecture to effectively control delay guarantees in a statistical way and to facilitate a new design paradigm in reliable M2M communications.

Multi-Layer Simulation Design and Validation for a Two-Tier Fault-Tolerant WDM LAN

In this paper, we report our progress on the development of a simulation framework for optical local area networks, which enables a multi-layer network simulation and hence bridges the gap between the traditional signal-level and traffic-level simulators. The framework, called DRAGON (discrete-model register for Artifex-based general-purpose optical local area network simulation), is built upon the Artifex modeling platform, and it models a wide register of fiber-optic components, emulating features at multiple network layers, spanning over signal and noise power monitoring, bit error rate (BER) estimation, packet collision detection, and propagation/transmission delay emulation. We demonstrate the effectiveness of DRAGON by modeling a novel two-tier fault-tolerant wavelength division multiplexing local area network (WDM LAN) architecture, and we show the impact of the proposed WDM LAN architecture on the quality of service requirements of WDM traffic.

Real-Time Communications for Wireless Sensor Networks: A Two-Tiered Architecture

While many approaches have been proposed to deal with energy/latency trade-offs, they are likely to be insufficient for the applications where reduced delay guarantee is the main concern. In this article, we investigated the potential application of a decentralized two-tiered network architecture, in large-scale wireless sensor networks, where an upper layer Wireless Local Area Network (WLAN), offering more powerful capabilities, serves as a backbone to an adaptively-clustered Low-Energy Adaptive Clustering Hierarch (LEACH)-based wireless sensor network. The WLAN layer will be involved in the communication between the sensor network and the control station, mitigating the impact of the limited capacities of the sensor nodes. With this two-tiered architecture we target to provide more reliable data delivery with reduced delay bounds, and lower energy consumption in the underlying sensor network, thereby increasing its lifetime. Simulation results show that the two-tiered network architecture achieved a relatively long lifetime, while preserving remarkably low latencies, compared to a single-tiered LEACH and a super-clustered LEACH-based network architectures.

SeRLoc

Many distributed monitoring applications of Wireless Sensor Networks (WSNs) require the location information of a sensor node. In this article, we address the problem of enabling nodes of Wireless Sensor Networks to determine their location in an untrusted environment, known as the secure localization problem. We propose a novel range-independent localization algorithm called SeRLoc that is well suited to a resource constrained environment such as a WSN. SeRLoc is a distributed algorithm based on a two-tier network architecture that allows sensors to passively determine their location without interacting with other sensors. We show that SeRLoc is robust against known attacks on a WSNs such as the wormhole attack , the Sybil attack , and compromise of network entities and analytically compute the probability of success for each attack. We also compare the performance of SeRLoc with state-of-the-art range-independent localization schemes and show that SeRLoc has better performance.