Small-World and Scale-Free Network Models for IoT Systems

Sohn, Insoo

doi:10.1155/2017/6752048

Cited by 34 publications

(29 citation statements)

References 31 publications

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“…Barabasi-Albert is a model for scale-free 1 networks such as the World Wide Web (w3), characterized by a highly heterogeneous degree distribution and high modularity (groups of the nodes that are more densely connected together than to the rest of the network). Erdos-Renyi model, known as a random network, has low heterogeneity, short average paths, and low clustering [70], [71]. Watts-Strogatz is a model for small-world 2 networks which are very close structurally to social networks.…”

Section: Discussionmentioning

confidence: 99%

Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things

et al. 2021

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The Internet of Things (IoT) requires a new processing paradigm that inherits the scalability of the cloud while minimizing network latency using resources closer to the network edge. On the one hand, building up such flexibility within the edge-to-cloud continuum consisting of a distributed networked ecosystem of heterogeneous computing resources is challenging. On the other hand, IoT traffic dynamics and the rising demand for low-latency services foster the need for minimizing the response time and a balanced service placement. Load-balancing for fog computing becomes a cornerstone for cost-effective system management and operations. This paper studies two optimization objectives and formulates a decentralized load-balancing problem for IoT service placement: (global) IoT workload balance and (local) quality of service (QoS), in terms of minimizing the cost of deadline violation, service deployment, and unhosted services. The proposed solution, EPOS Fog, introduces a decentralized multi-agent system for collective learning that utilizes edge-to-cloud nodes to jointly balance the input workload across the network and minimize the costs involved in service execution. The agents locally generate possible assignments of requests to resources and then cooperatively select an assignment such that their combination maximizes edge utilization while minimizes service execution cost. Extensive experimental evaluation with realistic Google cluster workloads on various networks demonstrates the superior performance of EPOS Fog in terms of workload balance and QoS, compared to approaches such as First Fit and exclusively Cloud-based. The results confirm that EPOS Fog reduces service execution delay up to 25% and the load-balance of network nodes up to 90%. The findings also demonstrate how distributed computational resources on the edge can be utilized more cost-effectively by harvesting collective intelligence.INDEX TERMS Agent, cloud computing, collective learning, distributed optimization, edge computing, fog computing, internet of things (IoT), load-balancing, service placement.

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Section: Discussionmentioning

confidence: 99%

Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things

et al. 2021

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“…Here, E i is the real edges of the neighbor node i, K i is the total neighbors connected to the neuron i, and K i (K i − 1)/2 is the maximum number of possible connectivities between the neighbor neurons. Hence, C i denotes a ratio of real neighbor neuron connectivity to the maximum possible connectivities [42]. The clustering coefficient for HCESN-KM, HCESN-GA, HCESN-DE, and HCESN-PSO reservoirs was computed as C HCESN-KM = 0.4727, C HCESN-GA = 0.4811, C HCESN-DE = 0.4827, and C HCESN-PSO = 0.4916.…”

Section: Small-world Propertymentioning

confidence: 99%

Design of a reservoir for cloud-enabled echo state network with high clustering coefficient

Akrami

Rostami

Khosravi

2020

J Wireless Com Network

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Reservoir computing (RC) is considered as a suitable alternative for descending gradient methods in recursive neural networks (RNNs) training. The echo state network (ESN) is a platform for RC and nonlinear system simulation in the cloud environment with many external users. In the past researches, the highest eigenvalue of reservoir connection weight (spectral radius) was used to predict reservoir dynamics. Some researchers have illustrated; the characteristics of scale-free and small-world can improve the approximation capability in echo state networks; however, recent studies have shown importance of the infrastructures such as clusters and the stability criteria of these reservoirs as altered. In this research, we suggest a high clustered ESN called HCESN that its internal neurons are interconnected in form of clusters. Each of the clusters contains one backbone and a number of local nodes. We implemented a classical clustering algorithm, called K-means, and three optimization algorithms including genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO) to improve the clustering efficiency of the new reservoir and compared them with each other. For investigating the spectral radius and predictive power of the resulting reservoirs, we also applied them to the laser time series and the Mackey-Glass dynamical system. It is demonstrated that new clustered reservoirs have some specifications of biologic neural systems and complex networks like average short path length, high clustering coefficient, and power-law distribution. The empirical results illustrated that the ESN based on PSO could strikingly enhance echo state property (ESP) and obtains less chaotic time series prediction error compared with other works and the original version of ESN. Therefore, it can approximate nonlinear dynamical systems and predict the chaotic time series.

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“…However, the scale-free networks exhibit a power-law degree distribution, which predicts the existence of a small number of well-connected nodes. 20 For the optical network simulation, both the models mentioned above have been widely used, 21,22 and however the accurate model for the real optical network is still under investigation. 23,24 In this article, the ONUs and OLTEs are regarded as the nodes in the network, and we only focus on the logical links between these nodes.…”

Section: Random and Scale-free Networkmentioning

confidence: 99%

A novel optical network model for network burden release and congestion reduction based on passive optical network

Yin

Lin

2018

International Journal of Distributed Sensor Networks

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In this article, a new optical network structure coping with network congestion is proposed, which is based on passive optical network, and adopts data storage devices coupled with optical line terminal to release network burden. It is shown by our network performance simulation that this network has many merits such as free-scale, fewer connections, larger clustering coefficient, and smaller average shortest path length in comparison to the passive optical network. The novel network structure can replace the far-end service connections with the near-end ones, reduce congestions in an optical network, and, furthermore, relieve traffic burden in optical backbone and metropolitan area networks.

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Small-World and Scale-Free Network Models for IoT Systems

Cited by 34 publications

References 31 publications

Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things

Decentralized Edge-to-Cloud Load Balancing: Service Placement for the Internet of Things

Design of a reservoir for cloud-enabled echo state network with high clustering coefficient

A novel optical network model for network burden release and congestion reduction based on passive optical network

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