Challenges, Applications, and Future of Wireless Sensors in Internet of Things: A Review

Jamshed, Muhammad Ali; Ali, Kamran; Abbasi, Qammer H.; Imran, Muhammad Ali; Ur-Rehman, Masood

doi:10.1109/jsen.2022.3148128

Cited by 118 publications

(19 citation statements)

References 117 publications

(91 reference statements)

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“…2 shows the important features that have the highest scores using RuleFit method on UNSW-NB15 and NSL-KDD datasets, respectively. For the UNSW-NB15 dataset, the highest scoring features corresponds to the following features: (1) sttl: which is the Source to destination time to live; (2) This article has been accepted for publication in IEEE Open Journal of the Communications Society. This is the author's version which has not been fully edited and ct state ttl: which is the Number of each state according to a range of values for source/destination time to live (ttl); service: which is the protocol used e.g., http, dns, ssh; and (4) dsport: which is the destination port number.…”

Section: Methodsmentioning

confidence: 99%

“…I NTERNET of Things (IoT) is an emerging technology that is becoming an integral part of our everyday life [1] [2]. IoT is shaping our future by revolutionizing worldwide cities into smart cities [3].…”

Section: Introductionmentioning

confidence: 99%

“…IoT is shaping our future by revolutionizing worldwide cities into smart cities [3]. IoT consists to connect and deploy billions of devices, estimated at 75 billion IoT devices by 2025 [3], through emergent communication technologies, to realize various applications related to multiple industries, including agriculture, Healthcare, factories, and transportation [2] [3]. However, with this rapid revolution, various cybersecurity attacks are also increasing, which is mainly due to data sharing and constant connectivity, in addition to resourcelimited nature of IoT networks [4] [5].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

“Why Should I Trust Your IDS?”: An Explainable Deep Learning Framework for Intrusion Detection Systems in Internet of Things Networks

Houda

Brik

Khoukhi

2022

IEEE Open J. Commun. Soc.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

“Why Should I Trust Your IDS?”: An Explainable Deep Learning Framework for Intrusion Detection Systems in Internet of Things Networks

Houda

Brik

Khoukhi

2022

IEEE Open J. Commun. Soc.

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“…One additional burgeoning expansion of sensors with rapidly growing popularity that should be mentioned is the Internet of Things (IoT). Any IoT device relies heaving on sensors or sensor networks which play a critical role in this new paradigm where it is possible to exchange data over a network of interconnected elements or nodes [ 349 , 350 ].…”

Section: Further Characteristics and Areas Of Applicationsmentioning

confidence: 99%

Coupled Multiphysics Modelling of Sensors for Chemical, Biomedical, and Environmental Applications with Focus on Smart Materials and Low-Dimensional Nanostructures

Singh

Melnik

2022

Chemosensors

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Low-dimensional nanostructures have many advantages when used in sensors compared to the traditional bulk materials, in particular in their sensitivity and specificity. In such nanostructures, the motion of carriers can be confined from one, two, or all three spatial dimensions, leading to their unique properties. New advancements in nanosensors, based on low-dimensional nanostructures, permit their functioning at scales comparable with biological processes and natural systems, allowing their efficient functionalization with chemical and biological molecules. In this article, we provide details of such sensors, focusing on their several important classes, as well as the issues of their designs based on mathematical and computational models covering a range of scales. Such multiscale models require state-of-the-art techniques for their solutions, and we provide an overview of the associated numerical methodologies and approaches in this context. We emphasize the importance of accounting for coupling between different physical fields such as thermal, electromechanical, and magnetic, as well as of additional nonlinear and nonlocal effects which can be salient features of new applications and sensor designs. Our special attention is given to nanowires and nanotubes which are well suited for nanosensor designs and applications, being able to carry a double functionality, as transducers and the media to transmit the signal. One of the key properties of these nanostructures is an enhancement in sensitivity resulting from their high surface-to-volume ratio, which leads to their geometry-dependant properties. This dependency requires careful consideration at the modelling stage, and we provide further details on this issue. Another important class of sensors analyzed here is pertinent to sensor and actuator technologies based on smart materials. The modelling of such materials in their dynamics-enabled applications represents a significant challenge as we have to deal with strongly nonlinear coupled problems, accounting for dynamic interactions between different physical fields and microstructure evolution. Among other classes, important in novel sensor applications, we have given our special attention to heterostructures and nucleic acid based nanostructures. In terms of the application areas, we have focused on chemical and biomedical fields, as well as on green energy and environmentally-friendly technologies where the efficient designs and opportune deployments of sensors are both urgent and compelling.

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“…Recently, the increased performance of radio and embedded systems and their decreased price have led to the widespread use of Wireless Sensor Networks (WSNs) [ 1 , 2 , 3 ]. WSNs commonly consist of numerous sensors in the surveillance area, and the sensing unit of a sensor can sense environmental information (e.g., wind speed, temperature, humidity, solar radiation, etc.)…”

Section: Introductionmentioning

confidence: 99%

An Energy-Efficient Clustering Method for Target Tracking Based on Tracking Anchors in Wireless Sensor Networks

2022

Sensors

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As a key technology in wireless sensor networks (WSNs), target tracking plays an essential role in many applications. To improve energy efficiency, clustering is widely used in tracking to organize the network to achieve data fusion and reduce communication costs. Many existing studies make dynamic adjustments based on static clusters to track moving targets. However, the additional overhead caused by frequent cluster reconstruction and redundant data transmission is rarely considered. To address this issue, we propose a tracking-anchor-based clustering method (TACM) in this paper, in which tracking anchors are introduced to provide activation indications for sensors according to the target position. We use the rough fuzzy C-means (RFCM) algorithm to locate the anchors and use the membership table to activate sensors to form a cluster. Since there are no sending, receiving, and fusing data tasks for anchors, they are lightly burdened and can significantly reduce the frequency of being rotated. Moreover, the state of cluster members (CMs) is scheduled using the linear 0–1 programming to reduce redundant transmissions. The simulation results demonstrate that, compared with some existing clustering methods, the proposed TACM effectively reduces the energy consumption when tracking a moving target, thus prolonging the network lifetime.

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Challenges, Applications, and Future of Wireless Sensors in Internet of Things: A Review

Cited by 118 publications

References 117 publications

“Why Should I Trust Your IDS?”: An Explainable Deep Learning Framework for Intrusion Detection Systems in Internet of Things Networks

“Why Should I Trust Your IDS?”: An Explainable Deep Learning Framework for Intrusion Detection Systems in Internet of Things Networks

Coupled Multiphysics Modelling of Sensors for Chemical, Biomedical, and Environmental Applications with Focus on Smart Materials and Low-Dimensional Nanostructures

An Energy-Efficient Clustering Method for Target Tracking Based on Tracking Anchors in Wireless Sensor Networks

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