Mutual Authentication Scheme for IoT Application

Secured lightweight authentication for 6LoWPANs in machine-to-machine communications

Pattern transformation-invariant schemes for wireless sensor networks based on an edge detection gradient-based mechanism

Within the growing Internet of Things (IoT) paradigm, a Wireless Sensor Network (WSN) is a critical component. In a WSN, sensor node localization is typically utilized to identify the target node’s current location at the sink node (SN). This allows local data to be analysed, making it more meaningful. However, there exists an intrinsic problem with node localization and energy efficiency, as identified in the literature, which has led to poor performance, namely, poor estimation, transmission, and detection of the network. This intrinsic problem also directly affects energy efficiency in a WSN, resulting in energy loss and poor node distribution in the WSN. There seems to be no lasting and reliable solution to this intrinsic node localization problem in WSNs. Hence, this research study proposed hyper-heuristic optimization algorithms to improve node localization and energy efficiency in WSNs. This research adopts the Design Research (DR) methodology and the Theory of Modelling and Simulation as the theoretical frameworks of the study. The hyper-heuristic model designed, was considered the conceptual framework of the study. To validate the novel technique, different sizes of sensor networks, namely: - 100 sensor nodes; 100 to 1 500 nodes and 200 to 450 sensor nodes with 20 anchor nodes were simulated in an area measuring 100m x 100m. The novel hyper-heuristic model was implemented in a MATLAB R2020a environment. The hyper-heuristic optimization algorithm’s substantial simulated experiment results were benchmarked utilizing state-of-the-art (modern) techniques to solve challenges related to node localization error, total energy consumed, average consumed packet energy, network throughput, shortest path, dead nodes, packets dispatched to the base station (BS), and the probability of error within the entire network dependent on size. The Data Energy Efficiency Clustering-Gaussian (DEEC-GAUSS) method was used to provide solutions to challenges related to energy efficiency in WSNs. In addition, this research study explored the use of the novel DEEC-GAUSS Gradient Distance Elimination Algorithm (DGGDEA) as the hyper-heuristic optimisation model for localization of nodes in WSNs. DEEC-GAUSS and DGGDEA were valuable additions to the body of knowledge. The results showed that the novel DEEC-GAUSS was the most energy efficient algorithm for 100 sensor nodes and 1000 to 1500 sensor nodes when compared to other stateof-the-art algorithms. Furthermore, the results showed that the novel DGGDEA was able to drastically minimize the node estimation error for sensor nodes. Reliability, accuracy and convergence using hyper-heuristic models to enhance the communication within WSNs has been simulated with evidence that DEEC-GAUSS and DGGDEA has outperformed other stateof-the-art approaches

Three party secure data transmission in IoT networks through design of a lightweight authenticated key agreement scheme

The concept of the Internet of Things (IoT) has received much attention over the last five years. It is predicted that the IoT will influence every aspect of our lifestyles in the near future. Wireless Sensor Networks are one of the key enablers of the operation of IoTs, allowing data to be collected from the surrounding environment. However, due to limited resources, nature of deployment and unattended operation, a WSN is vulnerable to various types of attack. Security is paramount for reliable and safe communication between IoT embedded devices, but it does, however, come at a cost to resources. Nodes are usually equipped with small batteries, which makes energy conservation crucial to IoT devices. Nevertheless, security cost in terms of energy consumption has not been studied sufficiently. Previous research has used a security specification of 802.15.4 for IoT applications, but the energy cost of each security level and the impact on quality of services (QoS) parameters remain unknown. This research focuses on the cost of security at the IoT media access control (MAC) layer. It begins by studying the energy consumption of IEEE 802.15.4 security levels, which is followed by an evaluation for the impact of security on data latency and throughput, and then presents the impact of transmission power on security overhead, and finally shows the effects of security on memory footprint. The results show that security overhead in terms of energy consumption with a payload of 24 bytes fluctuates between 31.5% at minimum level over non-secure packets and 60.4% at the top security level of 802.15.4 security specification. Also, it shows that security cost has less impact at longer packet lengths, and more with smaller packet size. In addition, the results depicts a significant impact on data latency and throughput. Overall, maximum authentication length decreases throughput by almost 53%, and encryption and authentication together by almost 62%.

Wireless Sensor Networks (WSNs) have become increasingly prominent, especially as they are integral to the expanding realm of Internet of Things (IoT). The IoT ecosystem is composed of many small Sensor Nodes (SNs) that operate on finite battery resources. These SNs are scattered across an area where they perform the function of detecting and gathering environmental data. The information they amass is relayed to a main Base Station (BS) for further examination and action. One of the paramount issues in the IoT space is the need for effective routing strategies that can navigate the challenges posed by the limited power reserves of the SNs. This chapter provides an in-depth examination of various routing algorithms within the IoT framework that utilize Machine Learning (ML) methods. These protocols incorporate complex techniques, such as Distributed Regression (DR), Self-Organizing Map (SOM), and Reinforcement Learning (RL) to enhance their efficiency. The objective of this research is to highlight how ML-infused routing approaches can successfully adapt to changing network dynamics and continuously evolving demands. The pivotal contribution of WSNs to IoT applications is crucial. With the escalating need for instantaneous data capture and analysis, it is essential to craft routing mechanisms that not only extend the functional life of SNs but also ensure efficient data transmission to the base station. This chapter scrutinizes machine learning-aware routing protocols, evaluating their flexibility and growth potential. It offers a perspective on how these protocols are evolving to keep pace with the dynamic requirements of contemporary WSNs.

The miniaturization of sensors, the increasingly low cost, the wide range of types of sensors available as well as the wireless communication medium used, allow sensor networks to develop in several fields of application. Sensor networks can be very useful in many applications when it comes to collecting and processing information from the environment. In addition, today these micro devices are among the main components of the Internet of Things (IoT) paradigm. Several constraints block the proper functioning of the wireless sensor network. These limitations motivate a large part of research problems in the field of wireless sensor networks (WSNs), in particular the energy and security constraints that are a fundamental problem. In sensor networks, it is essential that each sensor node and base station can verify that the data received was sent by a trusted sender and not by an adversary who tricked legitimate nodes into accepting fake data. False data can change the way a network might be predicted. Data integrity should be maintained. The data must not change and the precise data must reach the end of the user. In this paper, we tackle security problems in WSN. And we focus on protection against so-called “denial of service” attacks. So the aim of the work presented is therefore to propose a set of effective detection and reaction methods to denial of service attacks, while saving or distributing the energy consumption of the sensors as well as possible to extend the network's operating time as much as possible.

Wireless Sensor Network (WSN) is a spatially distributed network. It contains many numbers of distributed, self-directed, small, battery powered devices called sensor nodes or motes. In recent years the deployment of WSN in various application domains are growing in a rapid pace as with the upcoming boom of Internet of Things (IoT) and Internet of Everything (IoE). However, the effectiveness of the WSN deployment is restricted due to the constrained computation and power source. Hence, many researchers have been proposing new approaches and models to improve the efficiency of the domain specific WSN deployment procedures. Though, many research communities addressing various issues in WSN deployment, still the privacy and security of such networks are susceptible to various network attacks. Thus, it is necessary to practice different models for authentication and privacy preservation in a highly dynamic resource constrained WSN environment to realize the effectiveness and efficiency of the deployment. Hence, this paper addressing an authentication scheme that can reduce energy consumption without compromising on security and privacy. In order to provide a light weight authentication mechanism, this paper proposing an authentication mechanism for WSN deployment by combining the features of Elliptic Curve Cryptography (ECC) and Hexagonal numbers. The feature of ECC is used to reduce the key size and the effectiveness of generating hexagonal numbers is used for minimizing the energy consumption in a resource constrained WSN environment. The results of the proposed approach are evaluated with the different authentication models and the results were indicating that the proposed approach can perform better than the other approaches.

Wireless sensor networks are now in widespread use to monitor regions, detect events and acquire information. To reduce the amount of sending data, an aggregation approach can be applied along the path from sensors to the sink. However, usually the carried information contains confidential data. Therefore, an end-to-end secure aggregation approach is required to ensure a healthy data reception. End-to-end encryption schemes that support operations over cypher-text have been proved important for private party sensor network implementations. Unfortunately, nowadays these methods are very complex and not suitable for sensor nodes having limited resources. In this paper, we propose a secure end-to-end encrypted-data aggregation scheme. It is based on elliptic curve cryptography that exploits a smaller key size. Additionally, it allows the use of higher number of operations on cypher-texts and prevents the distinction between two identical texts from their cryptograms. These properties permit to our approach to achieve higher security levels than existing cryptosystems in sensor networks. Our experiments show that our proposed secure aggregation method significantly reduces computation and communication overhead and can be practically implemented in on-the-shelf sensor platforms. By using homomorphic encryption on elliptic curves, we thus have realized an efficient and secure data aggregation in sensor networks.

Being one of the main technologies for Internet of Things (IoT), wireless sensor network is absolutely critical and plays an increasingly important role in the development of Internet of Things. Wireless sensor network is mobile network composed of sensor nodes with limited resources, like computation capability, energy, memory storage, and communication capability. Its characteristic results in that the security of wireless sensor network faces a great challenge. Hence, the security of wireless sensor network is an important issue for the development of IoT and shall be focused on. In this paper, a novel authentication scheme is proposed for wireless sensor networks based on theory of quadratic residues. It is making the use of master key to achieve simple symmetric cryptographic primitives and authentication operation and reach the aims of great resistance against the attacks and low energy consumption. Then the proposed scheme is compared with other network-wide key management schemes, obtaining better results in the aspects of security, efficiency, and scalability.

With the deepening of the research and development in the field of embedded devices, the paradigm of the Internet of things (IoT) is gaining momentum. Its technology’s widespread applications increasing the number of connected devices constantly. IoT is built on sensor networks, which are enabling a new variety of solutions for applications in several fields (health, industry, defense, agrifood and agro sectors, etc.). Wireless communications are indispensable for taking full advantage of sensor networks but implies new requirements in the security and privacy of communications. Security in wireless sensor networks (WSNs) is a major challenge for extending IoT applications, in particular those related to the smart-agro. Moreover, limitations on processing capabilities of sensor nodes, and power consumption have made the encryption techniques devised for conventional networks not feasible. In such scenario, symmetric-key ciphers are preferred for key management in WSN; key distribution is therefore an issue. In this work, we provide a concrete implementation of a novel scalable group distributed key management method and a protocol for securing communications in IoT systems used in the smart agro sector, based on elliptic curve cryptography, to ensure that information exchange between layers of the IoT framework is not affected by sensor faults or intentional attacks. In this sense, each sensor node executes an initial key agreement, which is done through every member’s public information in just two rounds and uses some authenticating information that avoids external intrusions. Further rekeying operations require just a single message and provide backward and forward security.

The appearance of wireless sensor networks has attracted worldwide attention. Wireless Sensor Networks (WSNs) and Wireless Multimedia Sensor Networks (WMSNs) consist of wirelessly interconnected sensor nodes organised arbitrarily or deterministically in an environmental area which can gather, distribute and route information in different application areas. Power consumption in these networks is the foremost problem. WSNs are a recent generation of sensor networks, have an ample range of applications, their expansion and application will convey about extensive impact in human life and construction of all areas. Some of the applications include disaster management, landslide detection, glacial monitoring, wildlife tracking, healthcare, military applications, environmental monitoring, security surveillance, industrial process control and a large number of applications to robotics and Internet of Things (IOT) projects. This paper will demonstrate the elementary description of WSN followed by different energy-efficient power consumption protocols. Here we have performed the comparative performance analysis of different energy-efficient protocols. In our work we have compared MODLEACH with TSEP followed by TSEP with RELSEP.

The Internet of things (IoT) and wireless sensor networks (WSNs) have been rapidly and tremendously developing recently as computing technologies have brought about a significant revolution. Their applications and implementations can be found all around us, either individually or collaboratively. WSN plays a leading role in developing the general flexibility of industrial resources in terms of increasing productivity in the IoT. The critical principle of the IoT is to make existing businesses sufficiently intelligent to recognize the need for significant fault mitigation and short-cycle adaptation to improve effectiveness and financial profits. This article presents efficiently applied security protocols at the data link layer for WSN and IoT-based frameworks. It outlines the importance of WSN–IoT applications as well as the architecture of WSN in the IoT. Our primary aim is to highlight the research issues and limitations of WSNs related to the IoT. The fundamental goal of this work is to emphasize a suggested architecture linked to WSN–IoT to enhance energy and power consumption, mobility, information transmission, QoS, and security, as well as to present practical solutions to data link layer difficulties for the future using machine learning. Moreover, we present data link layer protocol issues, attacks, limitations, and research gaps for WSN frameworks based on the recent work conducted on the data link layer concerning WSN applications. Current significant issues and challenges pertain to flow control, quality of service (QoS), security, and performance. In the context of the literature, less work has been undertaken concerning the data link layer in WSN and its relation to improved network performance.

Research Article| March 01, 2016 The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation Frank Sepulveda; Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Jay Pulliam Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Publisher: Seismological Society of America First Online: 14 Jul 2017 Online Issn: 1938-2057 Print Issn: 0895-0695 © 2016 by the Seismological Society of America Seismological Research Letters (2016) 87 (2A): 345–357. https://doi.org/10.1785/0220150234 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Frank Sepulveda, Jay Pulliam; The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation. Seismological Research Letters 2016;; 87 (2A): 345–357. doi: https://doi.org/10.1785/0220150234 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The proliferation of commercial Internet of Things (IoT) devices is raising consumers’ awareness of the benefits of enhancing everyday objects with the ability to communicate, sense, and process information. Commercial‐off‐the‐shelf (COTS) versions of the embedded technologies responsible for the rise of the IoT are easy‐to‐use, inexpensive, and relatively powerful. IoT Makers, technological do‐it‐yourself enthusiasts, utilize these technologies and the IoT ecosystem to create IoT devices that range from casual hobbyist to entrepreneurial in nature.In an effort to develop a “no‐engineer‐needed” Internet of Geophysical Things (IoGT) device, we integrated a COTS embedded computer with a geoscience‐related COTS data acquisition system (DAS). Using skills common to geoscientists, we integrated the Raspberry Pi System‐On‐Chip with the REF TEK (a Trimble brand) 130‐01 DAS. This Raspberry Pi enhanced REF TEK (RaPiER) provides IoGT Makers a platform for the development of geoscientific sensor node or network enhancements. IoGT Makers can use the RaPiER to tinker with IoT capabilities while simultaneously acquiring research‐quality data. Geoscientific (i.e., seismic) application areas that may benefit from RaPiER, particularly RaPiER nodal processing, include earthquake and engineering seismology.In this article, we discuss the emerging technologies that allow IoGT Makers to build their own IoGT device. We review our selection of the Raspberry Pi and REF TEK 130‐01 for IoGT device integration. We provide a RaPiER system integration guide and REF TEK interface software compiled for the Raspberry Pi ( available in the electronic supplement to this article). We present methods to expand RaPiER capabilities using the Raspberry Pi’s ecosystem. Last, we discuss our evaluation of RaPiER’s performance and its suitability for realistic field deployments. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

A secure and efficient AKE scheme for IoT devices using PUF and cancellable biometrics