Internet of Things (IoT) systems incorporate a multitude of resource-limited devices typically interconnected over Low Power and Lossy Networks (LLNs). Robust IP-based network routing among such constrained IoT devices can be effectively realized using the IPv6 Routing Protocol for LLN (RPL) which is an IETF-standardized protocol. The RPL design features a topology maintenance mechanism based on a version numbering system. However, such a design property makes it easy to initiate Version Number (VN) attacks targeting the stability, lifetime, and performance of RPL networks. Thus the wide deployment of RPL-based IoT networks would be hindered significantly unless internal routing attacks such as the VN attacks are efficiently addressed. In this research work, a lightweight and effective detection and mitigation solution against RPL VN attacks is introduced. With simple modifications to the RPL functionality, a collaborative and distributed security scheme is incorporated into the protocol design (referred to as CDRPL). As the experimental results indicated, it provides a secure and scalable solution enhancing the resilience of the protocol against simple and composite VN attacks in different experimental setups. CDRPL guaranteed fast and accurate attack detection as well as quick topology convergence upon any attack attempt. It also efficiently maintained network stability, control traffic overhead, QoS performance, and energy consumption during different scenarios of the VN attack. Compared to other similar approaches, CDRPL yields better performance results with lightweight node-local processing, no additional entities, and less communication overhead.

Effective security support still remains a challenge even for a standardized Internet of Things network protocol such as the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). It provides limited protection against external security attacks but stays highly vulnerable to internal routing attacks. The inherent RPL design of RPL, particularly its topology establishment and maintenance mechanism, makes it easy to initiate such kinds of attacks which target overall network performance and topology stability. Establishing a firm and practical understanding of the impacts of these attacks on RPL networks is still critically needed for further investigation. This is more demanding when considering varying-scale RPL deployments targeted by complex attack scenarios. In this research work, an extensive experimental study of these critical RPL routing attacks considering simple-to-complex attack scenarios in varying-scale RPL network setups is presented. It provides a practical contribution toward experimentally understanding the effectiveness of internal routing attacks when targeting RPL-based IoT networks. The main objective is to provide future research works with a practical reference to the effectiveness of these security attacks and the overall performance of RPL networks under routing attacks. The results indicate the adverse impacts of routing attacks on the overall performance of RPL networks. Even in simple attack scenarios, it was found that the attacked networks experienced noticeable degradation in QoS performance and topology stability. In addition, the attacks incurred considerable increases in energy consumption and control traffic overhead. These were more evident in large-scale experimental setups and also under composite and hybrid routing attacks. QoS performance and topology stability degraded by more than 90% whereas energy consumption and network overhead increased by more than 200%.

The increasing relevance and significant acceptance of Wireless Sensor Network (WSN) solutions have aided the creation of smart environments in a multitude of sectors, including the Internet of Things, and offer ubiquitous practical applications. We examine current research trends in WSN using Artificial Intelligence (AI) technologies and the potential application of these methods for WSN improvement in this study. We emphasize the security, fault detection and tolerance, and quality of service (QoS) concerns in WSN, and provide a detailed review of current research that used different AI technologies to satisfy particular WSN objectives from 2010 to 2022. Specifically, this study’s purpose is to give a current review that compares various AI methodologies in order to provide insights for tackling existing WSN difficulties. Furthermore, there has been minimal existing related work concentrating employing AI approaches to solve security, fault detection and tolerance, and quality of service (QoS) concerns associated to WSN, and our goal is to fill the gap in existing studies. The application of AI solutions for WSN is the goal of this work, and we explore all parts of it in order to meet different WSN challenges such as security, fault detection and tolerance, and QoS. This will lead to an increased understanding of current AI applications in the areas of security, fault detection and tolerance, and QoS. Secondly, we present a comprehensive study and analysis of various AI schemes utilized in WSNs, which will aid the researchers in recognizing the most widely used techniques and the merits of employing various AI solutions to tackle WSN-related challenges. Finally, a list of open research issues has been provided, together with considerable bibliographic information, which provides useful recent research trends on the topics and encourages new research directions and possibilities.

This research article reports a compact fractal 4 × 4 UWB extended bandwidth MIMO antenna with physical dimensions of 44 × 44 mm² for high-speed wireless applications. The reported antenna comprises four fractal radiating elements that are symmetrical and placed orthogonal to each other with a respective rectangular ground printed on the opposite plane. A higher isolation is achieved between the radiating elements by the placement of a fractal patch orthogonally and no separate decoupling structure is required. The antenna offers a −10 dB transmission capacity of 2.84–15.88 GHz. The fractal radiating element, which is embedded by an inverted T-type stub placed within a rectangular slot and an etched rotated C-type slot, provides band-stop filters for WiMAX (Worldwide inter-operability for Microwave Access) and WLAN (wireless local area network)-interfering bands. The key parameters of diversity performance are compared by simulation and measurement (fabricated prototype) of ECC (envelope correlation coefficient), DG (directive gain), TARC (total active reflection coefficient) and CCL (channel capacity loss). The antenna offers an omnidirectional radiation pattern with an average gain of 3.52 dBi.

The exponential growth of the Internet of Things (IoT) is being witnessed nowadays in different sectors. This makes IoT data communications more complex and harder to manage. Addressing such a challenge using a centralized model is an ineffective approach and would result in security and privacy difficulties. Technologies such as blockchain provide a potential solution to enable secure and effective management of IoT data communication in a distributed and trustless manner. In this paper, a novel lightweight blockchain-centric IoT architecture is proposed to address effective IoT data communication management. It is based on an event-driven smart contract that enables manageable and trustless IoT data exchange using a simple publish/subscribe model. To maintain system complexity and overhead at a minimum, the design of the proposed system relies on a single smart contract. All the system operations that enable effective IoT data communication among the different parties of the system are defined in the smart contract. There is no direct blockchain–IoT-device interaction, making the system more useable in wide IoT deployments incorporating IoT devices with limited computing and energy resources. A practical Ethereum-based implementation of the system was developed with the ability to simulate different IoT setups. The evaluation results demonstrated the feasibility and effectiveness of the proposed architecture. Considering varying-scale and varying-density experimental setups, reliable and secure data communications were achieved with little latency and resource consumption.

Internet of Things (IoT) networks are being widely deployed for a broad range of critical applications. Without effective security support, such a trend would open the doors to notable security challenges. Due to their inherent constrained characteristics, IoT networks are highly vulnerable to the adverse impacts of a wide scope of IoT attacks. Among these, flooding attacks would cause great damage given the limited computational and energy capacity of IoT devices. However, IETF-standardized IoT routing protocols, such as the IPv6 Routing Protocol for Low Power and Lossy Networks (RPL), have no relevant security-provision mechanism. Different variants of the flooding attack can be easily initiated in RPL networks to exhaust network resources and degrade overall network performance. In this paper, a novel variant referred to as the Destination Information Object Flooding (DIOF) attack is introduced. The DIOF attack involves an internal malicious node disseminating falsified information to instigate excessive transmissions of DIO control messages. The results of the experimental evaluation demonstrated the significant adverse impact of DIOF attacks on control overhead and energy consumption, which increased by more than 500% and 210%, respectively. A reduction of more than 32% in Packet Delivery Ratio (PDR) and an increase of more than 192% in latency were also experienced. These were more evident in cases in which the malicious node was in close proximity to the sink node. To effectively address the DIOF attack, we propose a new lightweight approach based on a collaborative and distributed security scheme referred to as DIOF-Secure RPL (DSRPL). It provides an effective solution, enhancing RPL network resilience against DIOF attacks with only simple in-protocol modifications. As the experimental results indicated, DSRPL guaranteed responsive detection and mitigation of the DIOF attacks in a matter of a few seconds. Compared to RPL attack scenarios, it also succeeded in reducing network overhead and energy consumption by more than 80% while maintaining QoS performance at satisfactory levels.