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Intelligent Wireless Technologies for Future Sensor Networks

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

Deadline for manuscript submissions: closed (31 January 2021) | Viewed by 32073

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Special Issue Editor

Prof. Hyung Seok Kim

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Department of Information and Communication Engineering, Sejong University, Seoul, Korea
Interests: wireless communication; sensor network; cognitive radio; tactile internet; machine learning and IoT
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Dynamically and efficiently allocating resources to meet the need for diversity in various services gives rise to intelligent wireless technologies. They enable the wireless systems to perceive and estimate the available resources and autonomously adapt to the wireless environment, and to reconfigure itself to maximize resource utilization.

As a promising machine learning, deep learning (DL) is becoming a powerful method to add intelligence to wireless networks. Cognitive technology, covering spectrum sensing, and access approaches can enhance spectrum utilization and reduce energy consumption. The perception capability and reconfigurability are essential elements for the cognitive technology and machine learning techniques provide effectiveness for adaptation in wireless communication.

This Special Issue anticipates state-of-the-art technologies for the cognitive technology and machine learning techniques for the future wireless sensor networks, covering new research results with a wide range of ingredients within the intelligent wireless technologies for future sensor networks.

Potential topics include but are not limited to the following:

Ultra-reliable and low-latency sensor networks
AI or machine learning-based intelligent sensor networks
Applications and protocols using optical wireless communication
Energy-efficient system and network design for various applications
Multiple access schemes considering energy consumption and delay constraints
Cooperative haptic sensor networks for the tactile Internet
Sensor network optimization using machine learning and game-theoretic approaches
Sensor Networks in 5G/B5G networks (URLLC and mMTC)
Cognitive Radio Sensor Networks
Security and privacy for sensor networks
Sensors and hardware for sensor networks
Resource management, edge computing, and network slicing for efficient sensor networks
Sensor networks for mission-critical applications
Other related issues.

Prof. Hyung Seok Kim
Guest Editor

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Keywords

Sensor networks
Artificial intelligence
Machine learning
Cognitive radio
Tactile internet
mMTC
URLLC

Published Papers (9 papers)

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Research

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29 pages, 10348 KiB

Open AccessArticle

A Spatiotemporal-Oriented Deep Ensemble Learning Model to Defend Link Flooding Attacks in IoT Network

by Yen-Hung Chen, Yuan-Cheng Lai, Pi-Tzong Jan and Ting-Yi Tsai

Sensors 2021, 21(4), 1027; https://doi.org/10.3390/s21041027 - 3 Feb 2021

Cited by 4 | Viewed by 2290

Abstract

(1) Background: Link flooding attacks (LFA) are a spatiotemporal attack pattern of distributed denial-of-service (DDoS) that arranges bots to send low-speed traffic to backbone links and paralyze servers in the target area. (2) Problem: The traditional methods to defend against LFA are heuristic and cannot reflect the changing characteristics of LFA over time; the AI-based methods only detect the presence of LFA without considering the spatiotemporal series attack pattern and defense suggestion. (3) Methods: This study designs a deep ensemble learning model (Stacking-based integrated Convolutional neural network–Long short term memory model, SCL) to defend against LFA: (a) combining continuous network status as an input to represent “continuous/combination attacking action” and to help CNN operation to extract features of spatiotemporal attack pattern; (b) applying LSTM to periodically review the current evolved LFA patterns and drop the obsolete ones to ensure decision accuracy and confidence; (c) stacking System Detector and LFA Mitigator module instead of only one module to couple with LFA detection and mediation at the same time. (4) Results: The simulation results show that the accuracy rate of SCL successfully blocking LFA is 92.95%, which is 60.81% higher than the traditional method. (5) Outcomes: This study demonstrates the potential and suggested development trait of deep ensemble learning on network security. Full article

(This article belongs to the Special Issue Intelligent Wireless Technologies for Future Sensor Networks)

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16 pages, 508 KiB

Open AccessArticle

On-Demand Scheduling of Command and Responses for Low-Power Multihop Wireless Networks

by Mingyu Park and Jeongyeup Paek

Sensors 2021, 21(3), 738; https://doi.org/10.3390/s21030738 - 22 Jan 2021

Cited by 6 | Viewed by 1740

Abstract

Many IoT applications require a mechanism to disseminate commands and collect responses over a wireless network in order to control and collect data from multiple embedded devices. However, severe collisions may occur if a large number of nodes attempt to respond simultaneously and promptly, not only among the responses, but also with the dissemination of commands. This is because low-power wireless network protocols for dissemination and collection have been designed separately. Tuning the parameters of one side of the protocol has clear trade-off between reliability and latency. To address this challenge, we propose SCoRe, an on-demand scheme for joint scheduling of command and responses on multihop low-power wireless networks to improve both reliability and latency simultaneously at runtime. SCoRe gathers the amount of time required by network nodes for dissemination and collection, and allocates relative timeslots to each node recursively over multihop on-demand when (and only when) disseminating a command. While doing so, information exchange occurs only between local neighbor nodes without a need for global routing table nor time synchronization. We implement SCoRe on a low-power embedded platform, and compare with well-known dissemination and collection schemes through both simulations and testbed experiments on 30 devices. Our evaluation results show that SCoRe can improve both latency and reliability without tuning the parameters for one metric, while the legacy schemes require careful parameter selection to match only one side of SCoRe, never both. Full article

(This article belongs to the Special Issue Intelligent Wireless Technologies for Future Sensor Networks)

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23 pages, 5031 KiB

Open AccessArticle

Optimizations for Energy Efficiency in Software-Defined Wireless Sensor Networks

by Sorin Buzura, Bogdan Iancu, Vasile Dadarlat, Adrian Peculea and Emil Cebuc

Sensors 2020, 20(17), 4779; https://doi.org/10.3390/s20174779 - 24 Aug 2020

Cited by 19 | Viewed by 3444

Abstract

Software-defined wireless sensor networking (SDWSN) is an emerging networking architecture which is envisioned to become the main enabler for the internet of things (IoT). In this architecture, the sensors plane is managed by a control plane. With this separation, the network management is facilitated, and performance is improved in dynamic environments. One of the main issues a sensor environment is facing is the limited lifetime of network devices influenced by high levels of energy consumption. The current work proposes a system design which aims to improve the energy efficiency in an SDWSN by combining the concepts of content awareness and adaptive data broadcast. The purpose is to increase the sensors’ lifespan by reducing the number of generated data packets in the resource-constrained sensors plane of the network. The system has a distributed management approach, with content awareness being implemented at the individual programmable sensor level and the adaptive data broadcast being performed in the control plane. Several simulations were run on historical weather and the results show a significant decrease in network traffic. Compared to similar work in this area which focuses on improving energy efficiency with complex algorithms for routing, clustering, or caching, the current proposal employs simple computing procedures on each network device with a high impact on the overall network performance. Full article

(This article belongs to the Special Issue Intelligent Wireless Technologies for Future Sensor Networks)

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25 pages, 6702 KiB

Open AccessArticle

Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns

by Arslan Musaddiq, Zulqar Nain, Yazdan Ahmad Qadri, Rashid Ali and Sung Won Kim

Sensors 2020, 20(15), 4158; https://doi.org/10.3390/s20154158 - 26 Jul 2020

Cited by 16 | Viewed by 3681

Abstract

The next generation of the Internet of Things (IoT) networks is expected to handle a massive scale of sensor deployment with radically heterogeneous traffic applications, which leads to a congested network, calling for new mechanisms to improve network efficiency. Existing protocols are based on simple heuristics mechanisms, whereas the probability of collision is still one of the significant challenges of future IoT networks. The medium access control layer of IEEE 802.15.4 uses a distributed coordination function to determine the efficiency of accessing wireless channels in IoT networks. Similarly, the network layer uses a ranking mechanism to route the packets. The objective of this study was to intelligently utilize the cooperation of multiple communication layers in an IoT network. Recently, Q-learning (QL), a machine learning algorithm, has emerged to solve learning problems in energy and computational-constrained sensor devices. Therefore, we present a QL-based intelligent collision probability inference algorithm to optimize the performance of sensor nodes by utilizing channel collision probability and network layer ranking states with the help of an accumulated reward function. The simulation results showed that the proposed scheme achieved a higher packet reception ratio, produces significantly lower control overheads, and consumed less energy compared to current state-of-the-art mechanisms. Full article

(This article belongs to the Special Issue Intelligent Wireless Technologies for Future Sensor Networks)

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17 pages, 475 KiB

Open AccessArticle

An Efficient Superframe Structure with Optimal Bandwidth Utilization and Reduced Delay for Internet of Things Based Wireless Sensor Networks

by Sangrez Khan, Ahmad Naseem Alvi, Muhammad Awais Javed, Byeong-hee Roh and Jehad Ali

Sensors 2020, 20(7), 1971; https://doi.org/10.3390/s20071971 - 1 Apr 2020

Cited by 14 | Viewed by 3626

Abstract

Internet of Things (IoT) is a promising technology that uses wireless sensor networks to enable data collection, monitoring, and transmission from the physical devices to the Internet. Due to its potential large scale usage, efficient routing and Medium Access Control (MAC) techniques are vital to meet various application requirements. Most of the IoT applications need low data rate and low powered wireless transmissions and IEEE 802.15.4 standard is mostly used in this regard which offers superframe structure at the MAC layer. However, for IoT applications where nodes have adaptive data traffic, the standard has some limitations such as bandwidth wastage and latency. In this paper, a new superframe structure is proposed that is backward compatible with the existing parameters of the standard. The proposed superframe overcomes limitations of the standard by fine-tuning its superframe structure and squeezing the size of its contention-free slots. Thus, the proposed superframe adjusts its duty cycle according to the traffic requirements and accommodates more nodes in a superframe structure. The analytical results show that our proposed superframe structure has almost 50% less delay, accommodate more nodes and has better link utilization in a superframe as compared to the IEEE 802.15.4 standard. Full article

(This article belongs to the Special Issue Intelligent Wireless Technologies for Future Sensor Networks)

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18 pages, 5795 KiB

Open AccessArticle

Edge4TSC: Binary Distribution Tree-Enabled Time Series Classification in Edge Environment

by Chao Ma, Xiaochuan Shi, Wei Li and Weiping Zhu

Sensors 2020, 20(7), 1908; https://doi.org/10.3390/s20071908 - 30 Mar 2020

Cited by 11 | Viewed by 2516

Abstract

In the past decade, time series data have been generated from various fields at a rapid speed, which offers a huge opportunity for mining valuable knowledge. As a typical task of time series mining, Time Series Classification (TSC) has attracted lots of attention from both researchers and domain experts due to its broad applications ranging from human activity recognition to smart city governance. Specifically, there is an increasing requirement for performing classification tasks on diverse types of time series data in a timely manner without costly hand-crafting feature engineering. Therefore, in this paper, we propose a framework named Edge4TSC that allows time series to be processed in the edge environment, so that the classification results can be instantly returned to the end-users. Meanwhile, to get rid of the costly hand-crafting feature engineering process, deep learning techniques are applied for automatic feature extraction, which shows competitive or even superior performance compared to state-of-the-art TSC solutions. However, because time series presents complex patterns, even deep learning models are not capable of achieving satisfactory classification accuracy, which motivated us to explore new time series representation methods to help classifiers further improve the classification accuracy. In the proposed framework Edge4TSC, by building the binary distribution tree, a new time series representation method was designed for addressing the classification accuracy concern in TSC tasks. By conducting comprehensive experiments on six challenging time series datasets in the edge environment, the potential of the proposed framework for its generalization ability and classification accuracy improvement is firmly validated with a number of helpful insights. Full article

(This article belongs to the Special Issue Intelligent Wireless Technologies for Future Sensor Networks)

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23 pages, 5168 KiB

Open AccessArticle

Traffic Offloading in Multicast Device-to-Device Cellular Networks: A Combinatorial Auction-Based Matching Algorithm

by Devarani Devi Ningombam and Seokjoo Shin

Sensors 2020, 20(4), 1128; https://doi.org/10.3390/s20041128 - 19 Feb 2020

Cited by 4 | Viewed by 2352

Abstract

In the last few years, multicast device-to-device (D2D) cellular networks has become a highly attractive area of research. However, a particularly challenging class of issues in this area is data traffic, which increases due to increase in video and audio streaming applications. Therefore, there is need for smart spectrum management policies. In this paper, we consider a fractional frequency reuse (FFR) technique which divides the whole spectrum into multiple sections and allows reusing of spectrum resources between the conventional cellular users and multicast D2D users in a non-orthogonal scenario. Since conventional cellular users and multicast D2D users shared same resources simultaneously, they generate severe data traffic and high communication overhead. To overcome these issues, in this paper we propose Lagrange relaxation technique to solve the non-convex problem and combinatorial auction-based matching algorithm to select the most desirable resource reuse partners by fulfilling the quality of service (QoS) requirements for both the conventional cellular users and multicast D2D users. Then, we formulate an optimization problem to maximize the overall system performance with least computational complexity. We demonstrate that our method can exploit a higher data rate, spectrum efficiency, traffic offload rate, coverage probability, and lower computational complexity. Full article

(This article belongs to the Special Issue Intelligent Wireless Technologies for Future Sensor Networks)

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Review

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32 pages, 1476 KiB

Open AccessReview

Resource Management in Cloud Radio Access Network: Conventional and New Approaches

by Rehenuma Tasnim Rodoshi, Taewoon Kim and Wooyeol Choi

Sensors 2020, 20(9), 2708; https://doi.org/10.3390/s20092708 - 9 May 2020

Cited by 39 | Viewed by 5332

Abstract

Cloud radio access network (C-RAN) is a promising mobile wireless sensor network architecture to address the challenges of ever-increasing mobile data traffic and network costs. C-RAN is a practical solution to the strict energy-constrained wireless sensor nodes, often found in Internet of Things (IoT) applications. Although this architecture can provide energy efficiency and reduce cost, it is a challenging task in C-RAN to utilize the resources efficiently, considering the dynamic real-time environment. Several research works have proposed different methodologies for effective resource management in C-RAN. This study performs a comprehensive survey on the state-of-the-art resource management techniques that have been proposed recently for this architecture. The resource management techniques are categorized into computational resource management (CRM) and radio resource management (RRM) techniques. Then both of the techniques are further classified and analyzed based on the strategies used in the studies. Remote radio head (RRH) clustering schemes used in CRM techniques are discussed extensively. In this research work, the investigated performance metrics and their validation techniques are critically analyzed. Moreover, other important challenges and open research issues for efficient resource management in C-RAN are highlighted to provide future research direction. Full article

(This article belongs to the Special Issue Intelligent Wireless Technologies for Future Sensor Networks)

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26 pages, 1349 KiB

Open AccessReview

Advancing the State of the Fog Computing to Enable 5G Network Technologies

by Yahui Meng, Muhammad Ali Naeem, Alaa Omran Almagrabi, Rashid Ali and Hyung Seok Kim

Sensors 2020, 20(6), 1754; https://doi.org/10.3390/s20061754 - 21 Mar 2020

Cited by 28 | Viewed by 5679

Abstract

Fog Computing (FC) is promising to Internet architecture for the emerging of modern technological approaches such as Fifth Generation (5G) networks and the Internet of Things (IoT). These are the advanced technologies that enable Internet architecture to enhance the data dissemination services based on numerous sensors generating continuous sensory information. It is tough for the current Internet architecture to meet up with the growing demands of the users for such a massive amount of information. Therefore, it needs to adopt modern technologies for efficient data dissemination services across the Internet. Thus, the FC and 5G are updating the data transmission using new technological approaches that are intelligently processing data to provide enhanced communications. This study proposes necessary measures to boost the growth of FC to 5G network usage. It is done by taking an extensive review of how 5G operates as well as studying its taxonomy, the idea of IoT, reviewed projects on IoT applicability, comparison of computing technologies, and the importance of FC. Moreover, it elaborates dynamic issues of computing network technologies, and information is provided on how to remedy these for future recommendations in the field of research and computing network technologies. This paper heavily focuses on the applications of FC as an enabler to the 5G network by identifying the necessary services and network-oriented features that are needed to be used in the place for an improved future enterprise network technology. Full article

(This article belongs to the Special Issue Intelligent Wireless Technologies for Future Sensor Networks)

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