Applications of quantum computing are growing at a very fast pace, for example, from quantum computers to quantum algorithms and even to the development of the quantum Internet. However, the use of quantum technology in wireless sensor networks has not been thoroughly investigated just yet. This is in part due to the complexity of using big, costly, and highly energy-consuming machines that are quantum computers to this date, compared to the nodes used in wireless sensor networks which are small, inexpensive, and operate with very low energy consumption requirements. However, we can expect that in the future (possibly in the next decade) quantum computers will be commercial and reduced in size, and hence, they can be used for sensor network applications which are the basis of the Internet of Things. In this review, we study the road from quantum computing to quantum wireless sensor networks and how the analysis and design of these systems have to change to accommodate quantum capabilities in sensors, processors, communication links, and overall performance of these monitoring networks.
Applications of quantum computing are growing at a very fast pace, for example, from quantum computers to quantum algorithms and even to the development of the quantum Internet. However, the use of quantum technology in wireless sensor networks has not been thoroughly investigated just yet. This is in part due to the complexity of using big, costly, and highly energy-consuming machines that are quantum computers to this date, compared to the nodes used in wireless sensor networks which are small, inexpensive, and operate with very low energy consumption requirements. However, we can expect that in the future (possibly in the next decade) quantum computers will be commercial and reduced in size, and hence, they can be used for sensor network applications which are the basis of the Internet of Things. In this review, we study the road from quantum computing to quantum wireless sensor networks and how the analysis and design of these systems have to change to accommodate quantum capabilities in sensors, processors, communication links, and overall performance of these monitoring networks.
Faster, ultra-reliable, low-power, and secure communications has always been high on the wireless evolutionary agenda. However, the appetite for faster, more reliable, greener, and more secure communications continues to grow. The state-of-the-art methods conceived for achieving the performance targets of the associated processes may be accompanied by an increase in computational complexity. Alternatively, a degraded performance may have to be accepted due to the lack of jointly optimized system components. In this survey we investigate the employment of quantum computing for solving problems in wireless communication systems. By exploiting the inherent parallelism of quantum computing, quantum algorithms may be invoked for approaching the optimal performance of classical wireless processes, despite their reduced number of cost-function evaluations. In this contribution we discuss the basics of quantum computing using linear algebra, before presenting the operation of the major quantum algorithms, which have been proposed in the literature for improving wireless communications systems. Furthermore, we investigate a number of optimization problems encountered both in the physical and network layer of wireless communications, while comparing their classical and quantum-assisted solutions. Finally, we state a number of open problems in wireless communications that may benefit from quantum computing.
Quantum computing has emerged as a promising frontier in computational technology, offering the potential to revolutionize multiple fields, including wireless sensor networks (WSNs). By leveraging quantum bits (qubits), quantum computing introduces the possibility of enhanced computational power and efficiency for processing vast amounts of data in real-time. This article explores the intersection of quantum computing and WSNs, focusing on the ways quantum computing can address current limitations in data processing, security, and optimization. The article also examines future applications and challenges of integrating quantum algorithms into sensor networks.
The rapid advancement of Wireless Sensor Networks (WSNs) and Internet of Things (IoT) technologies has transformed modern healthcare by enabling continuous acquisition, transmission, and intelligent analysis of medical data. In medical image diagnostics, these technologies support remote monitoring, early disease identification, and timely clinical decision-making. However, transmitting and processing high-dimensional medical images through resource-constrained WSN-assisted IoT environments introduces challenges related to energy consumption, bandwidth limitations, computational complexity, and image-quality preservation. Conventional Convolutional Neural Networks (CNNs) provide strong diagnostic performance but often demand substantial computational resources, limiting their suitability for energy-constrained healthcare devices. Quantum Convolutional Neural Networks (QCNNs) have consequently emerged as a promising computational paradigm for efficient medical image analysis. By exploiting quantum principles such as superposition and entanglement, QCNNs offer opportunities for efficient feature representation and reduced computational overhead. Integrating attention mechanisms can further strengthen diagnostic performance by emphasizing clinically significant image regions while suppressing irrelevant information. This review examines energy-efficient QCNNs integrated with attention-based models for quality-preserving medical image diagnostics in WSN-assisted IoT environments. It critically evaluates recent architectures, energy-efficiency strategies, diagnostic performance, and quality-preservation methods while identifying existing limitations and research gaps. The review further highlights future directions toward scalable, intelligent, and resource-efficient IoT healthcare diagnostic frameworks.
In recent years, wireless sensing has garnered increasing attention from both industry and academia. Despite its promise, it still faces several fundamental challenges that are difficult to overcome, including hardware thermal noise limitation, antenna size constraint, and limited frequency band resources. At the same time, rapid advancements in quantum technology are driving continuous innovations. By thoroughly understanding the principles of both wireless sensing and quantum physics, we propose a novel paradigm: quantum wireless sensing. Although quantum wireless sensing originates from quantum measurement, its focus has shifted toward practical wireless sensing tasks. This evolution calls for a reassessment of parameter estimation, grounded in the fundamental principles of quantum physics. We believe the unique advantages of quantum receivers - specifically, their high sensitivity and wide frequency range - can unlock powerful quantum sensing capabilities across various domains, including terrestrial, aerospace, and maritime applications. This article articulates our vision for this emerging field, highlighting recent advancements in quantum wireless sensing and exploring promising directions for future research.
In the Internet of Things (IoT), numerous devices can interact with each other over the Internet. A wide range of IoT applications have already been deployed, such as transportation systems, healthcare systems, smart buildings, smart factories, and smart cities. Wireless sensor networks (WSNs) play crucial roles in these IoT applications. Researchers have published effective (but not entirely secure) approaches for merging WSNs into IoT environments. In IoT environments, the security effectiveness of remote user authentication is crucial for information transmission. Computational efficiency and energy consumption are crucial because the energy available to any WSN is limited. This paper proposes a notably efficient and secure authentication scheme based on temporal credential and dynamic ID for WSNs in IoT environments. The Burrows-Abadi-Needham (BAN) logic method was used to validate our scheme. Cryptanalysis revealed that our scheme can overcome the security weaknesses of previously published schemes. The security functionalities and performance efficiency of our scheme are compared with those of previous related schemes. The result demonstrates that our scheme's security functionalities are quantitatively and qualitatively superior to those of comparable schemes. Our scheme can improve the effectiveness of authentication in IoT environments. Notably, our scheme has superior performance efficiency, low computational cost, frugal energy consumption, and low communication cost.
Enhancement in Wireless sensing technology had widened its application scope and deployment possibilities. Since 2004, Wireless Sensor Network(WSN) became one of the top interesting topics for the researchers due to its advantages and futuristic aspects. After the emergence of the Internet of Things(IoT), WSN became an integral part of IoT. IoT is the technology which aims to provide any service to anyone at any time with the improved quality of service. IoT invites many entities to communicate in its large scale application and allow entities to share collected information in either wired or wireless manner. After studying most of the literature, we found out that most communication in IoT scenario occurred over public channel rather than secured channels and this aspect approached many researchers to focus on the security part of IoT communication. Billions of devices/humans do the communication with each other as well as provide the critical data to each other on public channels so it becomes important for the communicating parties to validate each other as well as generate a communication secret for sharing a secret or critical information. Since 2009, many authors had focused on Remote User Authentication(RUA) for Wireless Sensor Network. In this paper, we have reviewed the RUA scheme provided by Chen et al. [1] and listed out some shortcoming of their scheme. Authors have made use of famous light weight and secured cryptographic approach called as Elliptic Curve Cryptography(ECC) for their protocol design but due to adversary capabilities, we have found their there scheme is not secured against node capturing attack and gateway node bypassing attack. We have also discussed the network model followed by authors in their paper which may help other researchers to study and apply in their RUA protocol design. This activity would help other protocol designers to improve their approaches in the field of RUA protocol designing for IoT environment.