A Secure and Privacy-Preserving Three-Factor Anonymous Authentication Scheme for Wireless Sensor Networks in Internet of Things

Wireless sensor networks are a promising application of the Internet of Things in the sustainable development of smart cities, and have been afforded significant attention since first being proposed. Authentication protocols aim to protect the security and confidentiality of legitimate users when accessing and transmitting data. However, existing protocols may suffer from one or more security flaws. Recently, Butt et al. proposed an energy-efficient three-factor authentication protocol for wireless sensor networks. However, their protocol is vulnerable to several attacks, and lacks certain security properties. In this paper, the causes of these design flaws are analyzed. Furthermore, we propose a novel three-factor authentication protocol (password, smart card, and biometric information) for wireless sensor networks in Internet of Things contexts. A dynamic anonymous strategy is designed to prevent privacy disclosure and to resist sensor node capture attacks, tracking attacks, and desynchronization attacks. The Find–Guess model and random oracle model are combined to prove the security of the proposed protocol. A comparative analysis with related schemes shows that the proposed protocol has higher security and is able to maintain a low computational overhead.

Improved sensor localization with intelligent trust model in heterogeneous wireless sensor network in Internet of Things (IoT) environment

The idea of Internet of Things as a platform in smart campus has become increasingly popular. It requires an infrastructure comprised of communication networks, sensor nodes, and gateways to connect to the Internet. Each sensor node is responsible to collect data from the surrounding environment. This paper designs a wireless sensor network in internet of thing for environmental monitoring application. There are two scenarios design for this project. One uses EPS8266 to send data directly to the internet, and the other is to use Arduino and XBee 802.15.4 in multi hop wireless network. In the latter, data from several nodes are collected by an aggregator and send them to the gateway. The wireless communication between the nodes and aggregator is based on XBee 802.15.4 Radio. The XBee radios that connected to the nodes will act as a router, while another one that connected to the gateway act as coordinator. Arduino Wi-Fi shield with 802.11 standard is used to send the information to a web server. A web server based on Webrick with Ruby on Rails platform is built to display the measurement results.

Wireless communication networks are highly prone to security threats. The major applications of wireless communication networks are in military, business, healthcare, retail, and transportations. These systems use wired, cellular, or adhoc networks. Wireless sensor networks, actuator networks, and vehicular networks have received a great attention in society and industry. In recent years, the Internet of Things (IoT) has received considerable research attention. The IoT is considered as future of the internet. In future, IoT will play a vital role and will change our living styles, standards, as well as business models. The usage of IoT in different applications is expected to rise rapidly in the coming years. The IoT allows billions of devices, peoples, and services to connect with others and exchange information. Due to the increased usage of IoT devices, the IoT networks are prone to various security attacks. The deployment of efficient security and privacy protocols in IoT networks is extremely needed to ensure confidentiality, authentication, access control, and integrity, among others. In this paper, an extensive comprehensive study on security and privacy issues in IoT networks is provided.

Source-location privacy (SLP) protection improves security in event monitoring wireless sensor networks (WSNs) for Internet of Things (IoT) applications. However, due to constrained energy resources, WSNs incur coverage holes that affect the level of SLP protection. Existing studies have ineffectively analysed the effects of coverage holes on SLP protection. Noting the limitation, this study investigated the coverage hole effects. Performance of various SLP routing protocols was evaluated based on the sensor node utilization ratio, attack success rate, end-to-end delay, and packet delivery ratio. Then, considering the challenge of limited battery power in IoT sensors, the performance gains and limitations of the protocols were identified. Simulation results demonstrate that the data dissemination SLP routing protocol (DIRP) outperforms other protocols. However, the performance of DIRP is significantly affected by coverage holes. The results suggest that for DIRP to be viable in IoT applications, the integration of distributed energy resources should be considered.

The Internet of Things (IoT) enables all objects to connect to the Internet and exchange data via different emerging technologies, which makes the intelligent identification and management a reality. Wireless sensor networks (WSNs), as a crucial basis of IoT, have been applied in many fields like smart health care and smart transportation. With the development of WSNs, data security has attracted more and more attention, and user authentication is a popular mechanism to ensure the information security of WSNs. Recently, many authentication mechanisms for wireless medical sensor networks (WMSNs) have been proposed, but most of the protocols cannot achieve the features of local password change and forward secrecy while resisting stolen smart card attack. To enhance the security based on previous work, an ECC-based secure three-factor authentication protocol with forward secrecy for WMSN is proposed in this paper. It utilizes a fuzzy commitment scheme to handle the biometric information. Meanwhile, fuzzy verifier and honey_list techniques are used to solve the contradiction of local password verification and mobile device lost attack. The security of our protocol is evaluated by provable security, Proverif tool, and information analysis. Besides, the comparisons with the relevant protocols are given, and the results indicate that our protocol is robust and secure for WMSN systems.

Internet of Things (IoT) networks are being designed to connect billions of sensors/devices for an application-specific environment to provide services and carry out special tasks. It enables the virtual connection of the real-world physical devices with Internet cyberspace. While, there is an exponentially growing trend in the use of IoT technology for diverse applications such as smart agriculture, smart industries, smart hospitals, smart homes, etc., there are pressing design issues which affect the IoT network performance in the long run, for example, replacement of batteries for increasing the lifetime of devices to maintain the network longevity. To tackle this challenge, various wireless powered-based energy transfer paradigms were proposed over time. Backscatter communication (BackCom) is likewise one of the energy-efficient solutions proposed, which can satisfy the stringent green communication aspects of IoT networks. To achieve massive connectivity in BackCom-enabled IoT network, selecting a suitable multiple access scheme is also a crucial part. Most multiple access schemes proposed for BackCom systems are based on frequency or timeslots sharing techniques. Multiple access techniques proposed for BackCom IoT system can reap the gain of non-orthogonal multiple access (NOMA) principle. In NOMA, multiple end nodes (ENs) of sensors network is served in the same resource block, thereby, increasing the spectrum efficiency of BackCom IoT system. In this chapter, we discuss the prospects of backscatter technique with application of NOMA for green communication in IoT network.

<p>This paper investigates the reliability assessment and availability prediction techniques used in modeling of advanced (next generation) wireless communication networks. The 5G, 5G+, beyond 5G (B5G) and 6G communication technologies are leading to emerging applications of wireless communication that use cloud computing, edge computing, and fog computing. In the last decade, various user-centric and service-oriented networks such as internet of things (IoT), smart cities, smart homes, smart grids, drones, and unmanned aerial vehicles (UAV) have been deployed that use technologies such as network function virtualization (NVF), software defined networking (SDN), and 5G. This has led to proliferation of IoT devices and IoT applications in various critical usage systems such as intelligent transportation systems, smart healthcare, and e-commerce. The availability and reliability of wireless connectivity in IoT devices and nodes is of significant importance as unavailability of nodes or end-user devices even for a millisecond could cause failure of healthcare systems or lead to malfunction of connected and autonomous vehicles, or compromise the smart grids power generation and distribution, leading to fatal outcomes. </p>

Wireless networks are prone to many types of attacks. The open access of wireless technology puts the whole wireless environment into a threat. The new wireless technologies like wireless sensor networks (WSN) and the Internet of Things (IoT) face the challenges of security. The Internet of Things is yet to be standardized because of the unavailability of security solutions to certain wireless threats. Internet of Things uses different technologies for communication such as Bluetooth, Wi-Fi, Li-Fi, and 6LoPAN. Of all, Wi-Fi is mainly preferred. But Wi-Fi itself possesses certain security issues. One of the issues of Wi-Fi is the nature of Management Frames in its 802.11 standard. The Management Frames in 802.11 standard are unencrypted, hence easily interpretable. This very nature of Wi-Fi leads to many attacks like De-authentication attacks and Dis-association attacks. This paper provides an approach to detect De-authentication DoS attacks in Wi-Fi-based IoT networks, using the sniffing concept. For network sniffing, a Python-based tool known as Scapy is used. Attacks and Detection are done in a real-time environment. We used Kali Linux tools and Scapy for real-time attacks. The De-authentication DoS attacks are performed on a Wi-Fi-based IoT network, and the detections are triggered on a Scapy-based Sniffer program. Our algorithm also detects spoofed De-authentication DoS attacks.

The environmental impacts that are associated with the interconnected sensor devices in the Internet of Things (IoT) network are minimized with the utilization of green communication. This is because, when the IoT devices tend to grow, the IoT system becomes more prone to issues regarding the utilization of resources, sustainability, and energy consumption within the communication system. Tuning the communication protocols, lowering the IoT device’s energy usage rate, and establishing an effective technique for the transmission of data are performed. This is achieved by the green communication technique, which aids in the establishment of significant energy savings in the IoT network. For devices with limited power sources, the green communication technology offers an extended battery life. Despite the enormous potential of IoT technology, a number of obstacles need to be overcome, including those related to load balancing, security, storage, privacy, energy management, and device heterogeneity. In response to the challenges posed by conventional models, an innovative solution for the selection of Cluster Head (CH) using a hybrid approach is devised here. The energy consumption of sensor nodes is influenced by various factors, such asCH load, temperature, distance, residual energy, the number of alive nodes, delay, and so on. To address these challenges in IoT networks, a hybrid approach that combines the Squid Game Optimizer (SGO) and the Artificial Gorilla Troops Optimizer (AGTO) for the optimal selection of CH is suggested. The implemented approach is named Hybrid Squid Game with Artificial Gorilla Troops Optimizer (HSG-AGTO). This approach aims to optimize the above-mentioned factors and overcome energy consumption challenges in IoT networks. The effectiveness of the model is validated, and the results demonstrate the superior performance of the suggested model.

When the Wireless Sensor Network (WSN) is combined with the Internet of Things (IoT), it can be employed in a wide range of applications, such as agriculture, industry 4.0, health care, smart homes, among others. Accessing the big data generated by these applications in Cloud Servers (CSs), requires higher levels of authenticity and confidentiality during communication conducted through the Internet. Signcryption is one of the most promising approaches nowadays for overcoming such obstacles, due to its combined nature, i.e., signature and encryption. A number of researchers have developed schemes to address issues related to access control in the IoT literature, however, the majority of these schemes are based on homogeneous nature. This will be neither adequate nor practical for heterogeneous IoT environments. In addition, these schemes are based on bilinear pairing and elliptic curve cryptography, which further requires additional processing time and more communication overheads that is inappropriate for real-time communication. Consequently, this paper aims to solve the above-discussed issues, we proposed an access control scheme for IoT environments using heterogeneous signcryption scheme with the efficiency and security hardiness of hyperelliptic curve. Besides the security services such as replay attack prevention, confidentiality, integrity, unforgeability, non-repudiations, and forward secrecy, the proposed scheme has very low computational and communication costs, when it is compared to existing schemes. This is primarily because of hyperelliptic curve lighter nature of key and other parameters. The AVISPA tool is used to simulate the security requirements of our proposed scheme and the results were under two backbends (Constraint Logic-based Attack Searcher (CL-b-AtSER) and On-the-Fly Model Checker (ON-t-FL-MCR)) proved to be SAFE when the presented scheme is coded in HLPSL language. This scheme was proven to be capable of preventing a variety of attacks, including confidentiality, integrity, unforgeability, non-repudiation, forward secrecy, and replay attacks.

Wireless Sensor Networks (WSNs) are a major element of Internet of Things (IoT) networks which offer seamless sensing and wireless connectivity. Disaster management in smart cities can be considered as a safety critical application. Therefore, it becomes essential in ensuring network accessibility by improving the lifetime of IoT assisted WSN. Clustering and multihop routing are considered beneficial solutions to accomplish energy efficiency in IoT networks. This article designs an IoT enabled energy aware metaheuristic clustering with routing protocol for real time disaster management (EAMCR-RTDM). The proposed EAMCR-RTDM technique mainly intends to manage the energy utilization of nodes with the consideration of the features of the disaster region. To achieve this, EAMCR-RTDM technique primarily designs a yellow saddle goatfish based clustering (YSGF-C) technique to elect cluster heads (CHs) and organize clusters. In addition, enhanced cockroach swarm optimization (ECSO) based multihop routing (ECSO-MHR) approach was derived for optimal route selection. The YSGF-C and ECSO-MHR techniques compute fitness functions using different input variables for achieving improved energy efficiency and network lifetime. The design of YSGF-C and ECSO-MHR techniques for disaster management in IoT networks shows the novelty of the work. For examining the improved outcomes of the EAMCR-RTDM system, a wide range of simulations were performed and the extensive results are assessed in terms of different measures. The comparative outcomes highlighted the enhanced outcomes of the EAMCR-RTDM algorithm over the existing approaches.

Internet of Things (IoT) is a technology that allows various physical devices to connect to each other and exchange data via the internet network. The application of IoT is increasingly widespread in various sectors such as smart homes, manufacturing industries, smart agriculture, and healthcare. However, along with the increasing number of devices and the volume of data traffic sent, the potential risk of cybersecurity threats is also increasing. The large number of IoT devices that have limited computing capabilities makes the system more vulnerable to various attacks, including intrusion, exploitation of system weaknesses, and Distributed Denial of Service (DDoS) attacks. Therefore, early detection of anomalies in network traffic is a crucial aspect to maintain the security and stability of IoT systems. This study aims to develop and implement a Support Vector Machine (SVM)-based architecture as a classification method in an anomaly detection system on an IoT network. SVM was chosen because of its ability to handle high-dimensional data and non-linear classification effectively. The methodology used includes the process of extracting features from IoT network traffic datasets, data normalization, model training using the SVM algorithm, and evaluating model performance in distinguishing between normal and anomalous traffic. Thus, the implementation of SVM architecture can be an effective and efficient solution in intrusion detection systems for IoT networks. This research also opens up opportunities for the development of more adaptive security systems by integrating machine learning-based detection models into large-scale IoT infrastructures.

The rapid expansion of the Internet of Things (IoT) has introduced unprecedented cybersecurity challenges, as traditional cryptographic methods struggle to provide both efficiency and quantum resistance in resource-constrained environments. This research introduces Adaptive Quantum Cryptography (AQC), a novel security framework that integrates Quantum Key Distribution (QKD), Post-Quantum Cryptography (PQC), Quantum Random Number Generators (QRNGs), and AI-driven dynamic adaptation to safeguard IoT networks against evolving cyber threats. Unlike classical encryption techniques such as RSA and ECC, which are vulnerable to quantum attacks, AQC leverages quantum mechanical principles—such as superposition, entanglement, and the no-cloning theorem—to ensure provably secure key exchange and data encryption. The proposed AQC system dynamically adjusts cryptographic protocols based on real-time network conditions, computational resources, and threat intelligence, optimizing security without imposing excessive overhead. Experimental evaluations demonstrate that AQC achieves a 70% improvement in key exchange efficiency, a 50% reduction in computational load, and a 95% resilience rate against quantum attacks compared to conventional encryption methods. Additionally, the integration of QRNGs enhances cryptographic key entropy, while AI-driven anomaly detection enables proactive threat mitigation. This framework is particularly suited for next-generation IoT applications, including smart grids, autonomous vehicles, and industrial automation, where security, scalability, and energy efficiency are critical. By combining quantum-resistant algorithms with adaptive security mechanisms, AQC provides forward secrecy, resistance to quantum adversaries, and robust authentication, ensuring long-term protection for IoT ecosystems in the post-quantum era. The findings underscore AQC’s potential as a foundational security architecture for future quantum-safe IoT networks.

Internet of Things (IoT) has created huge vibes in the technology arena. It has been pivotal in providing cutting-edge solutions in diversified fields. Few applications include smart homes, smart cities, medical, transport and logistics. IoT provides real-time data exchange through wired or wireless networks with no standards being developed to regulate the flow of information within system. As such, the security and access control in the IoT has become an issue of prominence. Now days, researchers understood the potentiality of the Blockchain (BC) in context with the security and authentication. More specifically the decentralized nature of BC makes it apt for integration with the IoT networks. In this paper, we propose a model that makes use of the blockchain concept to bridle the security and authentication problems in the IoT network. We consider various factors that effect this integration of blockchain and IoT. Our investigations conclude that this hybrid model is powerful and can provide significant transformations in building novel applications across distributed platforms.