scholarly journals Intelligent Rapid Adaptive Offloading Algorithm for Computational Services in Dynamic Internet of Things System

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3423 ◽  
Author(s):  
Li ◽  
Qin ◽  
Zhou ◽  
Cheng ◽  
Zhang ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Adaptive Learning ◽  
Optimization Problems ◽  
Estimation Algorithm ◽  
Superior Performance ◽  
Innovative Technology ◽  
Centralized Control ◽  
Asynchronous Sampling ◽  
Computational Performance ◽  
Experience Replay

As restricted resources have seriously limited the computational performance of massive Internet of things (IoT) devices, better processing capability is urgently required. As an innovative technology, multi-access edge computing can provide cloudlet capabilities by offloading computation-intensive services from devices to a nearby edge server. This paper proposes an intelligent rapid adaptive offloading (IRAO) algorithm for a dynamic IoT system to increase overall computational performance and simultaneously keep the fairness of multiple participants, which can achieve agile centralized control and solve the joint optimization problems related to offloading policy and resource allocation. For reducing algorithm execution time, we apply machine learning methods and construct an adaptive learning-based framework consisting of offloading decision-making, radio resource slicing and algorithm parameters updating. In particular, the offloading policy can be rapidly derived from an estimation algorithm based on a deep neural network, which uses an experience replay training method to improve model accuracy and adopts an asynchronous sampling trick to enhance training convergence performance. Extensive simulations with different parameters are conducted to maintain the trade-off between accuracy and efficiency of the IRAO algorithm. Compared with other candidates, the results illustrate that the IRAO algorithm can achieve superior performance in terms of scalability, effectiveness and efficiency.

scholarly journals AntNet: Distributed Stigmergetic Control for Communications Networks

1998 ◽  
Vol 9 ◽  
pp. 317-365 ◽  
Author(s):  
G. Di Caro ◽  
M. Dorigo
Keyword(s):  
Adaptive Learning ◽  
Mobile Agents ◽  
Optimization Problems ◽  
State Of The Art ◽  
Routing Algorithms ◽  
Superior Performance ◽  
Experimental Conditions ◽  
Communications Networks ◽  
Novel Approach ◽  
Routing Tables

This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.

scholarly journals An efficient pruning scheme of deep neural networks for Internet of Things applications

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chen Qi ◽  
Shibo Shen ◽  
Rongpeng Li ◽  
Zhifeng Zhao ◽  
Qing Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Internet Of Things ◽  
Deep Neural Networks ◽  
Computational Cost ◽  
Superior Performance ◽  
Compact Structure ◽  
Resource Limited ◽  
Benchmark Datasets ◽  
Iot Devices

AbstractNowadays, deep neural networks (DNNs) have been rapidly deployed to realize a number of functionalities like sensing, imaging, classification, recognition, etc. However, the computational-intensive requirement of DNNs makes it difficult to be applicable for resource-limited Internet of Things (IoT) devices. In this paper, we propose a novel pruning-based paradigm that aims to reduce the computational cost of DNNs, by uncovering a more compact structure and learning the effective weights therein, on the basis of not compromising the expressive capability of DNNs. In particular, our algorithm can achieve efficient end-to-end training that transfers a redundant neural network to a compact one with a specifically targeted compression rate directly. We comprehensively evaluate our approach on various representative benchmark datasets and compared with typical advanced convolutional neural network (CNN) architectures. The experimental results verify the superior performance and robust effectiveness of our scheme. For example, when pruning VGG on CIFAR-10, our proposed scheme is able to significantly reduce its FLOPs (floating-point operations) and number of parameters with a proportion of 76.2% and 94.1%, respectively, while still maintaining a satisfactory accuracy. To sum up, our scheme could facilitate the integration of DNNs into the common machine-learning-based IoT framework and establish distributed training of neural networks in both cloud and edge.

scholarly journals Elite Exploitation: A Combination of Mathematical Concept and EMO Approach for Multi-Objective Decision Making

Symmetry ◽  
2021 ◽  
Vol 13 (1) ◽  
pp. 136
Author(s):  
Wenxiao Li ◽  
Yushui Geng ◽  
Jing Zhao ◽  
Kang Zhang ◽  
Jianxin Liu
Keyword(s):  
Decision Making ◽  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Decision Makers ◽  
Superior Performance ◽  
Test Problems ◽  
Mathematical Function ◽  
Hyperbolic Tangent ◽  
Nsga Ii ◽  
Multi Objective

This paper explores the combination of a classic mathematical function named “hyperbolic tangent” with a metaheuristic algorithm, and proposes a novel hybrid genetic algorithm called NSGA-II-BnF for multi-objective decision making. Recently, many metaheuristic evolutionary algorithms have been proposed for tackling multi-objective optimization problems (MOPs). These algorithms demonstrate excellent capabilities and offer available solutions to decision makers. However, their convergence performance may be challenged by some MOPs with elaborate Pareto fronts such as CFs, WFGs, and UFs, primarily due to the neglect of diversity. We solve this problem by proposing an algorithm with elite exploitation strategy, which contains two parts: first, we design a biased elite allocation strategy, which allocates computation resources appropriately to elites of the population by crowding distance-based roulette. Second, we propose a self-guided fast individual exploitation approach, which guides elites to generate neighbors by a symmetry exploitation operator, which is based on mathematical hyperbolic tangent function. Furthermore, we designed a mechanism to emphasize the algorithm’s applicability, which allows decision makers to adjust the exploitation intensity with their preferences. We compare our proposed NSGA-II-BnF with four other improved versions of NSGA-II (NSGA-IIconflict, rNSGA-II, RPDNSGA-II, and NSGA-II-SDR) and four competitive and widely-used algorithms (MOEA/D-DE, dMOPSO, SPEA-II, and SMPSO) on 36 test problems (DTLZ1–DTLZ7, WGF1–WFG9, UF1–UF10, and CF1–CF10), and measured using two widely used indicators—inverted generational distance (IGD) and hypervolume (HV). Experiment results demonstrate that NSGA-II-BnF exhibits superior performance to most of the algorithms on all test problems.

A weak derivative approach to optimization of threshold parameters in a multicomponent maintenance system

2001 ◽  
Vol 38 (02) ◽  
pp. 386-406 ◽  
Author(s):  
Bernd Heidergott
Keyword(s):  
Optimization Problems ◽  
Estimation Algorithm ◽  
Replacement Policy ◽  
Cost Performance ◽  
Weak Derivative ◽  
Preventive Replacement ◽  
Maintenance System ◽  
Threshold Optimization ◽  
Age Replacement ◽  
The Cost

We consider a multicomponent maintenance system controlled by an age replacement policy: when one of the components fails, it is immediately replaced; all components older than a threshold age θ are preventively replaced. Costs are associated with each maintenance action, such as replacement after failure or preventive replacement. We derive a weak derivative estimator for the derivative of the cost performance with respect to θ. The technique is quite general and can be applied to many other threshold optimization problems in maintenance. The estimator is easy to implement and considerably increases the efficiency of a Robbins-Monro type of stochastic approximation algorithm. The paper is self-contained in the sense that it includes a proof of the correctness of the weak derivative estimation algorithm.

Data fusion and simulation-based planning and control in cyber physical system for digital assembly of aeroplane

2015 ◽  
Vol 06 (03) ◽  
pp. 1550027 ◽  
Author(s):  
Hui Li ◽  
Linxuan Zhang ◽  
Tianyuan Xiao ◽  
Jietao Dong
Keyword(s):  
Data Fusion ◽  
Hybrid Control ◽  
Sliding Mode ◽  
Estimation Algorithm ◽  
Asynchronous Sampling ◽  
Planning And Control ◽  
Adaptive Sliding Mode Controller ◽  
Simulation Based ◽  
And Control ◽  
Distributed Fusion

This paper introduces a CPS application for intelligent aeroplane assembly. At first, the CPS structure is presented, which acquires the characteristics of general CPS and enables "simulation-based planning and control" to achieve high level intelligent assembly. Then the paper puts forward data fusion estimation algorithm under synchronous and asynchronous sampling, respectively. The experiment shows that global optimal distributed fusion estimation under synchronized sampling proves to be closer to the actual value compared with ordinary weighted estimation, and multi-scale distributed fusion estimation algorithm of wavelet under asynchronous sampling does not need time registration, it can also directly link to data, and the error is smaller. This paper presents hybrid control strategy under the circumstance of joint action of the inner and outer loop to address the problems caused by the less controllable feature of the parallel mechanism when undertaking online process simulation and control. A robust adaptive sliding mode controller is designed based on disturbance observer to restrain inner interference and maintain robustness. At the same time, an outer collaborative trajectory planning is also designed. All the experiment results show the feasibility of above proposed methods.

scholarly journals Ls-II: An Improved Locust Search Algorithm for Solving Optimization Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Octavio Camarena ◽  
Erik Cuevas ◽  
Marco Pérez-Cisneros ◽  
Fernando Fausto ◽  
Adrián González ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Optimization Method ◽  
Superior Performance ◽  
Solution Quality ◽  
Original Algorithm ◽  
Motion Models ◽  
Search Mechanism ◽  
Solitary Phase

The Locust Search (LS) algorithm is a swarm-based optimization method inspired in the natural behavior of the desert locust. LS considers the inclusion of two distinctive nature-inspired search mechanism, namely, their solitary phase and social phase operators. These interesting search schemes allow LS to overcome some of the difficulties that commonly affect other similar methods, such as premature convergence and the lack of diversity on solutions. Recently, computer vision experiments in insect tracking methods have conducted to the development of more accurate locust motion models than those produced by simple behavior observations. The most distinctive characteristic of such new models is the use of probabilities to emulate the locust decision process. In this paper, a modification to the original LS algorithm, referred to as LS-II, is proposed to better handle global optimization problems. In LS-II, the locust motion model of the original algorithm is modified incorporating the main characteristics of the new biological formulations. As a result, LS-II improves its original capacities of exploration and exploitation of the search space. In order to test its performance, the proposed LS-II method is compared against several the state-of-the-art evolutionary methods considering a set of benchmark functions and engineering problems. Experimental results demonstrate the superior performance of the proposed approach in terms of solution quality and robustness.

scholarly journals Assessing Impacts of 5th Generation (5G) Cellular Telecommunications on Internet of Things (IoT) Deployments

2020 ◽  
Vol 4 (2) ◽  
Author(s):  
Michael Love ◽  
Marwan Jamal
Keyword(s):  
Internet Of Things ◽  
Denial Of Service ◽  
Holistic Approach ◽  
Chief Information Officer ◽  
Superior Performance ◽  
Cellular Transport ◽  
Government Accountability ◽  
Business Services ◽  
High Bandwidth ◽  
The Internet Of Things

The Internet of Things (IoT) is emerging as a primary enabler of the transformation to digital business services in today’s economy. At the same time, 5th Generation (5G) cellular transport technology is emerging as a viable means to support IoT data requirements. Although 5G is still in its infancy, DoD leaders need to understand the impacts that 5G will have on IoT projects as there are several challenges involved with integrating 5G into IoT. In order to achieve the maximum benefits of high bandwidth, low latency and superior performance, an entirely new grid of cell sites and access units must be installed. A second challenge is with malware and intrusion attacks. 5G is an IP-based network technology that will expose 5G connected devices to common IP-based malware and intrusion risks such as denial-of-service (DOS and DDOS) and intrusion attacks. A third challenge is with standardized security and encryption of data from end-devices. The larger challenge with standardization involves interoperability. In order for 5G to work seamlessly across carriers with device agnostic hardware and compatible software, a holistic approach should be considered. Hardware compatibility will determine interoperability between sensors, platforms and gateways. In addition, software compatibility will be governed by 5G standards as well as industry partnerships. 5G standards are currently still being developed. Organizations, including the DoD Chief Information Officer and Government Accountability Office should specifically address implications of 5G cellular technology for IoT-related studies and use cases in the DoD.

scholarly journals A Survey on Dragonfly Algorithm and its Applications in Engineering

2020 ◽  
Author(s):  
Chnoor M. Rahman ◽  
Tarik A. Rashid
Keyword(s):  
Review Paper ◽  
Optimization Problems ◽  
Optimization Techniques ◽  
Superior Performance ◽  
Test Functions ◽  
Dragonfly Algorithm ◽  
Engineering Problems ◽  
Complex Optimization ◽  
Metaheuristic Techniques ◽  
Benchmark Test Functions

<p></p><p></p><p>Dragonfly algorithm developed in 2016. It is one of the algorithms used by the researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examine the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with some techniques, the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.</p><br><p></p><p></p>

scholarly journals Internet of Things Energy Efficient Cluster-Based Routing Using Hybrid Particle Swarm Optimization for Wireless Sensor Network

2021 ◽  
Author(s):  
Senthil G A ◽  
Arun Raaza ◽  
N Kumar
Keyword(s):  
Particle Swarm Optimization ◽  
Internet Of Things ◽  
Optimal Path ◽  
Particle Swarm ◽  
Base Station ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Energy Usage ◽  
Swarm Optimization ◽  
Hybrid Particle Swarm Optimization

Abstract Specialized transducers in Wireless Sensor Networks (WSNs) that offer sensing services to the Internet of Things (IoT) devices have limited storage and energy resources. One of the most vital issues in WSN design is power usage, as it is nearly impossible to recharge or replace sensor nodes’ batteries. A prominent role in conserving power for energy-constrained networks is served by the clustering algorithm. It is possible to reduce network energy usage and network lifespan prolongation by proper balancing of the network load with Cluster Head (CH) election. The single-hop inter-cluster routing technique, in which there is a direct transfer from CHs to the Base Station (BS), is done by the Low Energy Adaptive Clustering Hierarchy (LEACH). However, for networks with large-regions, this technique is not viable. An optimized Orphan-LEACH (O-LEACH) has been proposed in this work to facilitate the formation of a novel process of clustering, which can result in minimized usage of energy as well as enhanced network longevity. Sufficient energy is possessed by the orphan node, which will attempt to be cover the network. The proposed work’s primary novel contribution is the O-LEACH protocol that supplies the entire network’s coverage with the least number of orphaned nodes and has extremely high connectivity rates. A hybrid optimization utilizing Simulated Annealing (SA) with Lightning Search Algorithm (LSA) (SA-LSA), and Particle Swarm Optimization (PSO) with LSA (PSO-LSA) Algorithm is proposed. These proposed techniques effectivelymanage the CH election achieving optimal path routing and minimization in energy usage, resulting in the enhanced lifespan of the WSN. The proposed technique’s superior performance, when compared with other techniques, is confirmed from the outcomes of the experimentations.

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