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Adaptive Resource Allocation for Internet of Things and Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (27 March 2024) | Viewed by 11732

Special Issue Editors


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Guest Editor
School of Computer Science and Technology, Xidian University, Xi'an 710071, China
Interests: network modelling; task scheduling and resource allocation; artificial intelligence; machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Faculty of Natural Sciences and Forestry, Department of Computer Science, University of Eastern Finland, 70211 Kuopio, Finland
Interests: nature-inspired computing methods
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou 350118, China
Interests: semantic sensor network; artificial Internet of Things; intelligent computation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Internet of Things (IoT) technologies are a remarkable technical revolution, promising the future of communications and control at a higher level. An IoT ecosystem has millions of heterogeneous devices which are connected through a network. Efficient resource management is required to raise the quality of services. Resource allocation for traditional multiple-access communication systems has been widely studied, including time division multiple access (TDMA), orthogonal frequency-division multiple access (OFDMA), and code-division multiple access (CDMA). However, the network heterogeneity and diversity of IoT devices makes resource allocation a challenging task.

This Special Issue aims to provide a platform for researchers and practitioners to exchange and publish the latest challenges, research trends, and results that can effectively address resource allocation problems in IoT devices. The topics of interest include, but are not limited to:

  • IoT resource allocation;
  • Energy-efficient resource management;
  • Quality of services;
  • Cloud, fog, or edge nodes for computing and storage;
  • Machine learning;
  • Artificial intelligence;
  • Network modelling;
  • Task scheduling and resource allocation in networks.

Prof. Dr. Yuping Wang
Prof. Dr. Xiao-Zhi Gao
Prof. Dr. Xingsi Xue
Guest Editors

Manuscript Submission Information

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Published Papers (8 papers)

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Research

33 pages, 13963 KiB  
Article
A Multichannel Conflict-Free Mac Protocol for Enhancing RPMA Scalability
by Enas Ali Alsaeedi and Fatma Bouabdallah
Sensors 2023, 23(23), 9363; https://doi.org/10.3390/s23239363 - 23 Nov 2023
Viewed by 576
Abstract
The internet of things (IoT) revolutionized human life, whereby a large number of interrelated devices are connected to exchange data in order to accomplish many tasks, leading to the rapid growth of connected devices, reaching the tens of billions. The Low Power Wide [...] Read more.
The internet of things (IoT) revolutionized human life, whereby a large number of interrelated devices are connected to exchange data in order to accomplish many tasks, leading to the rapid growth of connected devices, reaching the tens of billions. The Low Power Wide Area (LPWA) protocols paradigm has emerged to satisfy the IoT application requirements, especially in terms of long-range communication and low power consumption. However, LPWA technologies still do not completely meet the scalability requirement of IoT applications. The main critical issues are the restrictive duty cycle regulations of the sub-GHz band in which most LPWA technologies operate, as well as the random access to the medium. Ingenu Random Phase Multiple Access (RPMA) is an LPWA technology that uses the 2.4 GHz band that is not subject to the duty cycle constraint. Furthermore, RPMA uses Direct-Sequence Spread Spectrum (DSSS) as a modulation technique; hence, it is an excellent candidate technology for handling scalable LPWA networks. In this paper, we perform mathematical and simulation analysis to assess RPMA scalability and the factors that affect it, especially when all the available channels are used. The results indicate that RPMA has impressive scalability. Indeed, by taking advantage of the multichannel feature in RPMA, the network capacity can be increased by up to 38 times. Aditionally, randomly selecting the Spreading Factors (SF) degrades the network scalability, as working on higher SFs will increase the probability of collision. Thus, we proposed an SF distribution algorithm that ensures effective packet delivery with minimum collision. Full article
(This article belongs to the Special Issue Adaptive Resource Allocation for Internet of Things and Networks)
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29 pages, 2493 KiB  
Article
A Quality-of-Service-Aware Service Composition Method in the Internet of Things Using a Multi-Objective Fuzzy-Based Hybrid Algorithm
by Marzieh Hamzei, Saeed Khandagh and Nima Jafari Navimipour
Sensors 2023, 23(16), 7233; https://doi.org/10.3390/s23167233 - 17 Aug 2023
Cited by 6 | Viewed by 1315
Abstract
The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality [...] Read more.
The Internet of Things (IoT) represents a cutting-edge technical domain, encompassing billions of intelligent objects capable of bridging the physical and virtual worlds across various locations. IoT services are responsible for delivering essential functionalities. In this dynamic and interconnected IoT landscape, providing high-quality services is paramount to enhancing user experiences and optimizing system efficiency. Service composition techniques come into play to address user requests in IoT applications, allowing various IoT services to collaborate seamlessly. Considering the resource limitations of IoT devices, they often leverage cloud infrastructures to overcome technological constraints, benefiting from unlimited resources and capabilities. Moreover, the emergence of fog computing has gained prominence, facilitating IoT application processing in edge networks closer to IoT sensors and effectively reducing delays inherent in cloud data centers. In this context, our study proposes a cloud-/fog-based service composition for IoT, introducing a novel fuzzy-based hybrid algorithm. This algorithm ingeniously combines Ant Colony Optimization (ACO) and Artificial Bee Colony (ABC) optimization algorithms, taking into account energy consumption and Quality of Service (QoS) factors during the service selection process. By leveraging this fuzzy-based hybrid algorithm, our approach aims to revolutionize service composition in IoT environments by empowering intelligent decision-making capabilities and ensuring optimal user satisfaction. Our experimental results demonstrate the effectiveness of the proposed strategy in successfully fulfilling service composition requests by identifying suitable services. When compared to recently introduced methods, our hybrid approach yields significant benefits. On average, it reduces energy consumption by 17.11%, enhances availability and reliability by 8.27% and 4.52%, respectively, and improves the average cost by 21.56%. Full article
(This article belongs to the Special Issue Adaptive Resource Allocation for Internet of Things and Networks)
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23 pages, 3719 KiB  
Article
SCAP SigFox: A Scalable Communication Protocol for Low-Power Wide-Area IoT Networks
by Halah Alqurashi, Fatma Bouabdallah and Enas Khairullah
Sensors 2023, 23(7), 3732; https://doi.org/10.3390/s23073732 - 04 Apr 2023
Cited by 1 | Viewed by 2055
Abstract
The Internet of Things (IoT) is a new future technology that is aimed at connecting billions of physical-world objects to the IT infrastructure via a wireless medium. Many radio access technologies exist, but few address the requirements of IoT applications such as low [...] Read more.
The Internet of Things (IoT) is a new future technology that is aimed at connecting billions of physical-world objects to the IT infrastructure via a wireless medium. Many radio access technologies exist, but few address the requirements of IoT applications such as low cost, low energy consumption, and long range. Low-Power wide-area network (LPWAN) technologies, especially SigFox, have a low data rate that makes them suitable for IoT applications, especially since the lower the data rate, the longer the usable distance for the radio link. SigFox technology achieves as a main objective network reliability by striving for the successful delivery of data messages through redundancy. Doing so results in one of the SigFox weaknesses, namely the high collision rate, which questions SigFox scalability. In this work, we aimed at avoiding collisions by changing SigFox’s Aloha-based medium access protocol to TDMA and by using only orthogonal channels while removing redundancy. Consequently, every node sends a single copy of the data message on a given orthogonal channel in a specific time slot. To achieve this, we implemented a slot- and channel-allocation protocol (SCAP) on top of SigFox. In other words, our goal was to improve SigFox’s scalability by implementing two mechanisms: time slot allocation and channel allocation. Performance analysis was conducted on large networks with sizes ranging from 1000 to 10,000 nodes to evaluate both technologies: the original SigFox and SCAP SigFox. The simulation results showed that SCAP SigFox highly reduced the probability of collision and energy consumption when compared to the original SigFox. Additionally, SCAP SigFox had a greater throughput and packet delivery ratio (PDR). Full article
(This article belongs to the Special Issue Adaptive Resource Allocation for Internet of Things and Networks)
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17 pages, 3966 KiB  
Article
Maximizing Heterogeneous Server Utilization with Limited Availability Times for Divisible Loads Scheduling on Networked Systems
by Xiaoli Wang, Bharadwaj Veeravalli, Xiaobo Song and Kaiqi Zhang
Sensors 2023, 23(7), 3550; https://doi.org/10.3390/s23073550 - 28 Mar 2023
Viewed by 887
Abstract
Most of the available divisible-load scheduling models assume that all servers in networked systems are idle before workloads arrive and that they can remain available online during workload computation. In fact, this assumption is not always valid. Different servers on networked systems may [...] Read more.
Most of the available divisible-load scheduling models assume that all servers in networked systems are idle before workloads arrive and that they can remain available online during workload computation. In fact, this assumption is not always valid. Different servers on networked systems may have heterogenous available times. If we ignore the availability constraints when dividing and distributing workloads among servers, some servers may not be able to start processing their assigned load fractions or deliver them on time. In view of this, we propose a new multi-installment scheduling model based on server availability time constraints. To solve this problem, we design an efficient heuristic algorithm consisting of a repair strategy and a local search strategy, by which an optimal load partitioning scheme is derived. The repair strategy guarantees time constraints, while the local search strategy achieves optimality. We evaluate the performance via rigorous simulation experiments and our results show that the proposed algorithm is suitable for solving large-scale scheduling problems employing heterogeneous servers with arbitrary available times. The proposed algorithm is shown to be superior to the existing algorithm in terms of achieving a shorter makespan of workloads. Full article
(This article belongs to the Special Issue Adaptive Resource Allocation for Internet of Things and Networks)
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16 pages, 4729 KiB  
Article
Linear Interval Approximation of Sensor Characteristics with Inflection Points
by Marin B. Marinov, Nikolay Nikolov, Slav Dimitrov, Borislav Ganev, Georgi T. Nikolov, Yana Stoyanova, Todor Todorov and Lachezar Kochev
Sensors 2023, 23(6), 2933; https://doi.org/10.3390/s23062933 - 08 Mar 2023
Cited by 3 | Viewed by 1275
Abstract
The popularity of smart sensors and the Internet of Things (IoT) is growing in various fields and applications. Both collect and transfer data to networks. However, due to limited resources, deploying IoT in real-world applications can be challenging. Most of the algorithmic solutions [...] Read more.
The popularity of smart sensors and the Internet of Things (IoT) is growing in various fields and applications. Both collect and transfer data to networks. However, due to limited resources, deploying IoT in real-world applications can be challenging. Most of the algorithmic solutions proposed so far to address these challenges were based on linear interval approximations and were developed for resource-constrained microcontroller architectures, i.e., they need buffering of the sensor data and either have a runtime dependency on the segment length or require the sensor inverse response to be analytically known in advance. Our present work proposed a new algorithm for the piecewise-linear approximation of differentiable sensor characteristics with varying algebraic curvature, maintaining the low fixed computational complexity as well as reduced memory requirements, as demonstrated in a test concerning the linearization of the inverse sensor characteristic of type K thermocouple. As before, our error-minimization approach solved the two problems of finding the inverse sensor characteristic and its linearization simultaneously while minimizing the number of points needed to support the characteristic. Full article
(This article belongs to the Special Issue Adaptive Resource Allocation for Internet of Things and Networks)
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17 pages, 2664 KiB  
Article
AQMDRL: Automatic Quality of Service Architecture Based on Multistep Deep Reinforcement Learning in Software-Defined Networking
by Junyan Chen, Cenhuishan Liao, Yong Wang, Lei Jin, Xiaoye Lu, Xiaolan Xie and Rui Yao
Sensors 2023, 23(1), 429; https://doi.org/10.3390/s23010429 - 30 Dec 2022
Cited by 2 | Viewed by 2342
Abstract
Software-defined networking (SDN) has become one of the critical technologies for data center networks, as it can improve network performance from a global perspective using artificial intelligence algorithms. Due to the strong decision-making and generalization ability, deep reinforcement learning (DRL) has been used [...] Read more.
Software-defined networking (SDN) has become one of the critical technologies for data center networks, as it can improve network performance from a global perspective using artificial intelligence algorithms. Due to the strong decision-making and generalization ability, deep reinforcement learning (DRL) has been used in SDN intelligent routing and scheduling mechanisms. However, traditional deep reinforcement learning algorithms present the problems of slow convergence rate and instability, resulting in poor network quality of service (QoS) for an extended period before convergence. Aiming at the above problems, we propose an automatic QoS architecture based on multistep DRL (AQMDRL) to optimize the QoS performance of SDN. AQMDRL uses a multistep approach to solve the overestimation and underestimation problems of the deep deterministic policy gradient (DDPG) algorithm. The multistep approach uses the maximum value of the n-step action currently estimated by the neural network instead of the one-step Q-value function, as it reduces the possibility of positive error generated by the Q-value function and can effectively improve convergence stability. In addition, we adapt a prioritized experience sampling based on SumTree binary trees to improve the convergence rate of the multistep DDPG algorithm. Our experiments show that the AQMDRL we proposed significantly improves the convergence performance and effectively reduces the network transmission delay of SDN over existing DRL algorithms. Full article
(This article belongs to the Special Issue Adaptive Resource Allocation for Internet of Things and Networks)
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22 pages, 3391 KiB  
Article
Solving the Static Resource-Allocation Problem in SDM-EONs via a Node-Type ILP Model
by Jiading Wang, Sibo Chen, Qian Wu, Yiliu Tan and Maiko Shigeno
Sensors 2022, 22(24), 9710; https://doi.org/10.3390/s22249710 - 11 Dec 2022
Cited by 1 | Viewed by 1120
Abstract
Space division multiplexing elastic optical networks (SDM-EONs) are one of the most promising network architectures that satisfy the rapidly growing traffic of the internet. However, different from traditional wavelength division multiplexing (WDM)-based networks, the problems of resource allocation become more complicated because SDM-EONs [...] Read more.
Space division multiplexing elastic optical networks (SDM-EONs) are one of the most promising network architectures that satisfy the rapidly growing traffic of the internet. However, different from traditional wavelength division multiplexing (WDM)-based networks, the problems of resource allocation become more complicated because SDM-EONs have smaller spectrum granularity and have to consider several novel network resources, such as modulation formats and spatial dimensions. In this work, we propose an integer linear programming (ILP) model without space lane change (SLC) that provides theoretically exact solutions for the problem of routing, modulation format, space, and spectrum assignment (RMSSA). Moreover, to more efficiently solve our model which is difficult to solve directly, we propose three exact algorithms based on model decomposition and evaluate their performance via simulation experiments, and we find that two of our exact algorithms can solve the model effectively in small-scale instances. Full article
(This article belongs to the Special Issue Adaptive Resource Allocation for Internet of Things and Networks)
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15 pages, 313 KiB  
Article
A Resource and Task Scheduling Based Multi-Objective Optimization Model and Algorithms in Elastic Optical Networks
by Yuping Wang, Qingdong Yang and Xiaofang Guo
Sensors 2022, 22(24), 9579; https://doi.org/10.3390/s22249579 - 07 Dec 2022
Cited by 1 | Viewed by 985
Abstract
The elastic optical network (EON) adopting virtual network function (VNF) is a new type of network, in which the routing, spectrum, and data center allocation are key and challenging problems, and solving these three problems simultaneously can not only improve the network efficiency [...] Read more.
The elastic optical network (EON) adopting virtual network function (VNF) is a new type of network, in which the routing, spectrum, and data center allocation are key and challenging problems, and solving these three problems simultaneously can not only improve the network efficiency for network providers, but also let users obtain better service. However, few existing works handle these three problems simultaneously. To tackle the three problems simultaneously, given a set of network function chains (i.e., a set of tasks), we set up a new multi-objective optimization model in which the total length of paths for all tasks is minimized, the totally occupied spectrums are minimized, and the loads on all data centers are most balanced, simultaneously. To solve the model, we design two new evolutionary algorithms. The experiments are conducted on 16 cases of 4 widely used types of networks, and the results indicate that the proposed model and algorithms are effective. Full article
(This article belongs to the Special Issue Adaptive Resource Allocation for Internet of Things and Networks)
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