Search Results (62)

The use of resource-constrained Low-Power and Lossy Networks (LLNs), where the IPv6 Routing Protocol for LLNs (RPL) is the de facto routing standard, has increased due to the Internet of Things’ (IoT) explosive growth. Because of the dynamic nature of IoT deployments and the lack of in-protocol security, RPL is still quite susceptible to routing-layer attacks like Blackhole, Lowered Rank, version number manipulation, and Flooding despite its lightweight architecture. Lightweight, data-driven intrusion detection methods are necessary since traditional cryptographic countermeasures are frequently unfeasible for LLNs. However, the lack of RPL-specific control-plane semantics in current cybersecurity datasets restricts the use of machine learning (ML) for practical anomaly identification. In order to close this gap, this work models both static and mobile networks under benign and adversarial settings by creating a novel, large-scale multiclass RPL attack dataset using Contiki-NG’s Cooja simulator. To record detailed packet-level and control-plane activity including DODAG Information Object (DIO), DODAG Information Solicitation (DIS), and Destination Advertisement Object (DAO) message statistics along with forwarding and dropping patterns and objective-function fluctuations, a protocol-aware feature extraction pipeline is developed. This dataset is used to evaluate fifteen classifiers, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbors (KNN), Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), AdaBoost (AB), and XGBoost (XGB) and several ensemble strategies like soft/hard voting, stacking, and bagging, as part of a comprehensive ML-based detection system. Numerous tests show that ensemble approaches offer better generalization and prediction performance. With overfitting gaps less than 0.006 and low cross-validation variance, the Soft Voting Classifier obtains the greatest accuracy of 99.47%, closely followed by XGBoost with 99.45% and Random Forest with 99.44%. Full article

With the increasing adoption of advanced drone technologies across diverse fields, the Internet of Drones (IoD) has emerged as a novel mobility paradigm, particularly enhancing Intelligent Transportation Systems (ITS) in urban environments. Despite its significant potential, the IoD faces substantial challenges due to inherent resource constraints such as limited computational power and energy capacity, which hinder the implementation of robust cybersecurity solutions. These limitations expose IoD networks to various security vulnerabilities and privacy threats, necessitating an exhaustive analysis and understanding of these risks. In this paper we introduce SafeIoD, a comprehensive security framework designed to establish standardized procedures for proactive risk identification in Internet of Drones (IoD) devices. It involves sequential steps to determine the trustworthiness of devices subjected to these certification. Therefore, SafeIoD seeks to ensure a basic security level before implementation in a real scenario, where the network devices are evaluated in regards to the specific security requirements. Validation through experimental testing with 15 participants across four IoD deployment scenarios and one military certification case demonstrated the framework’s effectiveness: the tool achieved 73% user satisfaction rating, successfully identified an average of 3.0 security requirements per device, and provided specific lightweight cryptographic algorithm recommendations for 62.2% of elicited requirements. In a tactical military scenario simulation, the framework accurately predicted risk propagation patterns, with COOJA network simulations confirming that implementation of framework-recommended protocols reduced successful attack propagation from 60% to below 5% of the network. Full article

The rapid expansion of Internet of Things (IoT) systems in critical infrastructures has raised significant concerns regarding network security and reliability. In particular, RPL (Routing Protocol for Low-Power and Lossy Networks), widely adopted in IoT communications, remains vulnerable to topological manipulation attacks such as Decreased Rank, Increased Rank, and the less-explored Worst Parent Selection (WPS). While several RPL security approaches address rank manipulation attacks, most assume static topologies and offer limited support for mobility. Moreover, trust-based routing and hybrid IDS (Intrusion Detection System) approaches are seldom integrated, which limits detection reliability under mobility. This study introduces a unified IDS framework that combines mobility awareness with trust-based decision-making to detect multiple rank-based attacks. We evaluate two lightweight, rule-based IDS architectures: a fully distributed model and a hybrid model supported by designated monitoring nodes. A trust-based mechanism is incorporated into both architectures, and their performance is assessed under static and mobile scenarios. Results show that while the distributed IDS provides rapid local responsiveness, the hybrid IDS maintains more stable latency and packet delivery under mobility. Additionally, incorporating trust metrics reduces false alerts and improves detection reliability while preserving low latency and energy usage, supporting time-sensitive applications such as healthcare monitoring. Full article

The DLMS (Device Language Message Specification) standard is widely adopted in smart metering systems for utility services, as it defines the mechanisms that enable the standardized and interoperable exchange of data between metering devices. Wi-SUN FAN (Wireless Smart Ubiquitous Network Field Area Network) is a wireless communication standard that has gained increasing attention in the field of smart metering networks, due to its capability to operate in mesh topologies and support multi-hop communications in an efficient and scalable manner. To date, no studies have been reported that evaluate the combined performance of the DLMS and Wi-SUN FAN standards. In this context, this work evaluates the performance of the DLMS standard over a Wi-SUN FAN network through detailed simulations conducted using the Contiki-NG/Cooja platform. The study implements the DLMS data transmission process and evaluates key performance metrics such as Packet Delivery Rate (PDR) and latency, considering both linear and mesh network topologies. The results demonstrate that a Wi-SUN FAN network can provide efficient and reliable communication services, achieving PDR values above 86.02% in both topologies, thereby confirming its feasibility for DLMS-based smart metering applications. Full article

The Internet of Things (IoT) has rapidly evolved into a central component of today’s technological landscape, enabling seamless connectivity and communication among a vast array of devices. It underpins automation, real-time monitoring, and smart infrastructure, serving as a foundation for Industry 4.0 and paving the way toward Industry 5.0. Despite the potential of IoT systems to transform industries, these systems face a number of challenges, most notably the lack of processing power, storage space, and battery life. Whereas cloud and fog computing help to relieve computational and storage constraints, energy limitations remain a severe impediment to long-term autonomous operation. Among the threats that exploit this weakness, the Denial-of-Sleep (DoSl) attack is particularly problematic because it prevents nodes from entering low-power states, leading to battery depletion and degraded network performance. This research investigates machine-learning (ML) and deep-learning (DL) methods for identifying such energy-wasting behaviors to protect IoT energy resources. A dataset was generated in a simulated IoT environment under multiple DoSl attack conditions to validate the proposed approach. Several ML and DL models were trained and tested on this data to discover distinctive power-consumption patterns related to the attacks. The experimental results confirm that the proposed models can effectively detect anomalous behaviors associated with DoSl activity, demonstrating their potential for energy-aware threat detection in IoT networks. Specifically, the Random Forest and Decision Tree classifiers achieved accuracies of 98.57% and 97.86%, respectively, on the held-out 25% test set, while the Long Short-Term Memory (LSTM) model reached 97.92% accuracy under a chronological split, confirming effective temporal generalization. All evaluations were conducted in a simulated environment, and the paper also outlines potential pathways for future physical testbed deployment. Full article

Predicting the behavior of Internet of Things (IoT) networks under irregular topologies and heterogeneous battery conditions remains a significant challenge. Simulation tools can capture these effects but can require high manual effort and computational capacity, motivating the use of machine learning surrogates. This work introduces an automated pipeline for generating large-scale IoT network datasets by bringing together the Contiki-NG firmware, parameterized topology generation, and Slurm-based orchestration of Cooja simulations. The system supports a variety of network structures, scalable node counts, randomized battery allocations, and routing protocols to reproduce diverse failure modes. As a case study, we conduct over 10,000 Cooja simulations with 15–75 battery-powered motes arranged in sparse grid topologies and operating the RPL routing protocol, consuming 1300 CPU-hours in total. The simulations capture realistic failure modes, including unjoined nodes despite physical connectivity and cascading disconnects caused by battery depletion. The resulting graph-structured datasets are used for two prediction tasks: (1) estimating the last successful message delivery time for each node and (2) predicting network-wide spatial coverage. Graph neural network models trained on these datasets outperform baseline regression models and topology-aware heuristics while evaluating substantially faster than full simulations. The proposed framework provides a reproducible foundation for data-driven analysis of energy-limited IoT networks. Full article

The Internet of Things (IoT) continues to expand rapidly, with the Routing Protocol for Low-Power and Lossy Networks (RPL) serving as its core communication backbone. However, RPL remains vulnerable to a range of insider routing attacks such as the Version Number Attack (VNA) and Hello Flooding Attack (HFA), particularly in constrained IoT environments. In our previous work, IbiboRPLChain, we proposed a blockchain-based authentication mechanism to secure communication between routing and sensor nodes. This paper presents an evolved framework, IbiboRPLChain II, which integrates smart contracts, decentralised authentication nodes, and composite blockchain mechanisms to improve network resilience, scalability, and security. Our experiments, conducted using Cooja and Contiki OS, evaluate the system across multiple simulation seeds, demonstrating significant gains in Packet Delivery Ratio (PDR), energy efficiency, and delay mitigation. IbiboRPLChain II proves to be a robust solution for secure, lightweight, and scalable RPL-based IoT environments. Full article

Excessive energy consumption of communication protocols in IoT/IIoT systems constitutes one of the key constraints for the operational longevity of remote sensor nodes, where radio transmission often incurs higher energy costs than data acquisition or local computation. Previous studies have remained fragmented, typically focusing on selected technologies or specific layers of the communication stack, which has hindered the development of comparable quantitative metrics across protocols. The aim of this study is to design and validate a unified evaluation framework enabling consistent assessment of both wired and wireless protocols in terms of energy efficiency, reliability, and maintenance costs. The proposed approach employs three complementary research methods: laboratory measurements on physical hardware, profiling of SBC devices, and simulations conducted in the COOJA/Powertrace environment. A Unified Comparative Method was developed, incorporating bilinear interpolation and weighted normalization, with its robustness confirmed by a Spearman rank correlation coefficient exceeding 0.9. The analysis demonstrates that MQTT-SN and CoAP (non-confirmable mode) exhibit the highest energy efficiency, whereas HTTP/3 and AMQP incur the greatest energy overhead. Results are consolidated in the ICoPEP matrix, which links protocol characteristics to four representative RS-IoT scenarios: unmanned aerial vehicles (UAVs), ocean buoys, meteorological stations, and urban sensor networks. The framework provides well-grounded engineering guidelines that may extend node lifetime by up to 35% through the adoption of lightweight protocol stacks and optimized sampling intervals. The principal contribution of this work is the development of a reproducible, technology-agnostic tool for comparative assessment of IoT/IIoT communication protocols. The proposed framework addresses a significant research gap in the literature and establishes a foundation for further research into the design of highly energy-efficient and reliable IoT/IIoT infrastructures, supporting scalable and long-term deployments in diverse application environments. Full article

The rapid expansion of Internet of Things (IoT) devices has heightened the demand for lightweight and secure cryptographic mechanisms suitable for resource-constrained environments. While SHA-256 remains a widely used standard, the emergence of SHA-3 particularly the KangarooTwelve variant offers potential benefits in flexibility and post-quantum resilience for lightweight resource-constrained devices. This paper presents a comparative evaluation of the energy costs associated with SHA-256 and SHA-3 hashing in Contiki 3.0, using three generationally distinct IoT platforms: Sky Mote, Z1 Mote, and Wismote. Unlike previous studies that rely on hardware acceleration or limited scope, our work conducts a uniform, software-only analysis across all motes, employing consistent radio duty cycling, ContikiMAC (a low-power Medium Access Control protocol) and isolating the cryptographic workload from network overhead. The empirical results from the Cooja simulator reveal that while SHA-3 provides advanced security features, it incurs significantly higher CPU and, in some cases, radio energy costs particularly on legacy hardware. However, modern platforms like Wismote demonstrate a more balanced trade-off, making SHA-3 viable in higher-capability deployments. These findings offer actionable guidance for designers of secure IoT systems, highlighting the practical implications of cryptographic selection in energy-sensitive environments. Full article

The Internet of Drones (IoD) is rapidly expanding into sensitive applications, necessitating robust and efficient authentication. Traditional methods struggle against prevalent attacks, especially considering the unique vulnerabilities of the IoD, such as drone physical capture. This paper proposes Bio-2FA-IoD, a novel biometric-enhanced two-factor authentication protocol designed for secure IoD operations. Drawing on established 2FA principles and fuzzy extractor technology, Bio-2FA-IoD achieves strong mutual authentication between an operator (via an operator device), a drone (as a relay), and a ground control station (GCS), supported by a trusted authority. We detail the protocol’s registration and authentication phases, emphasizing reliable biometric key generation. A formal security analysis using BAN logic demonstrates secure belief establishment and key agreement, while a proof sketch under the Bellare–Pointcheval–Rogaway (BPR) model confirms its security against active adversaries in Authenticated Key Exchange (AKE) contexts. Furthermore, a comprehensive performance evaluation conducted using the Contiki OS and Cooja simulator illustrates Bio-2FA-IoD’s superior efficiency in computational and communication costs, alongside very low latency, high packet delivery rate, and minimal energy consumption. This positions it as a highly viable and lightweight solution for resource-constrained IoD environments. Additionally, this paper conceptually explores potential extensions to Bio-2FA-IoD, including the integration of Diffie–Hellman for enhanced perfect forward secrecy and a Sybil-free pseudonym management scheme for improved user anonymity and unlinkability. Full article

A secure and energy-efficient network architecture is essential due to the rapid proliferation of Internet of Things (IoT) devices in critical sectors such as healthcare, smart cities, and industrial automation. In this paper, we propose a secure and energy-efficient cross-layer IoT architecture. The security features and energy-saving techniques across various open-system interconnected protocol layers are incorporated in the proposed architecture. The improved security and energy efficiency are achieved using the lightweight Speck and Present ciphers, as well as adaptive communication strategies. The system performance is evaluated by testbeds and extensive simulation experiments using Cooja (Contiki operating systems) and NS-3. The simulation results obtained show a 95% attack mitigation effectiveness, 30% reduction in energy usage, and 95% packet delivery ratio. In a 20-node network scenario, Speck uses 5.2% less radio power than the Advanced Encryption Standard (AES), making it the best tradeoff among the investigated encryption techniques. The NS-3 simulation results confirm that lightweight encryption, such as Present and Speck, uses much less power than the traditional AES, which makes them more appropriate for IoT contexts with limited energy. The scalability across various IoT contexts is ensured through a hybrid assessment approach that combines hardware testbeds and simulation for system validation. Our research findings highlight opportunities for advancing IoT systems toward secure and energy-efficient smart ecosystems. Full article

Network diffusion algorithms and simulators play a critical role in understanding how information, data, and malware propagate across various network topologies in Internet of Things and Space IoT configurations. This paper conducts a systematic literature review (SLR) of the key diffusion algorithms and network simulators utilized in studies over the past decade. The review focuses on identifying the algorithms and simulators employed, their strengths and limitations, and how their performance is evaluated under different IoT network topologies. Common network simulators, such as NS-3, Cooja, and OMNeT++ are explored, highlighting their features, scalability, and suitability for different IoT network scenarios. Additionally, network diffusion algorithms, including epidemic, cascading, and threshold models, are analyzed in terms of their effectiveness, complexity, and applicability in IoT environments with diverse network topologies. This SLR aims to provide a comprehensive reference for researchers and practitioners when selecting appropriate tools and methods for simulating and analyzing network diffusion across IoT and Space IoT configurations. Full article

IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) are specifically designed for applications that require lower data rates and reduced power consumption in wireless internet connectivity. In the context of 6LoWPAN, Internet of Things (IoT) devices with limited resources can now seamlessly connect to the network using IPv6. This study focuses on examining the performance and power consumption of routing protocols in the context of 6LoWPAN, drawing insights from prior research and utilizing simulation techniques. The simulation involves the application of routing protocols, namely Routing Protocol for Low-power and Lossy (RPL) Networks, Ad hoc On-demand Distance Vector (AODV), Lightweight On-demand Ad hoc Distance-vector Next Generation (LOADng), implemented through the Cooja simulator. The simulation also runs in different network topologies to gain an insight into the performance of the protocols in the specific topology including random, linear, and eclipse topology. The raw data gathered from the tools including Powertrace and Collect-View were then analyzed with Python code to transfer into useful information and visualize the graph. The results demonstrate that the power consumption, specifically CPU power, Listen Power, and Total Consumption Power, will increase with the incremental of motes. The result also shows that RPL is the most power-efficient protocol among the scenarios compared to LOADng and AODV. The result is helpful because it brings insights into the performance, specifically power consumption in the 6LoWPAN network. This result is valuable to further implement these protocols in the testbed as well as provide an idea of the algorithmic enhancements. Full article

The rapid growth of the Internet of Things (IoT) has contributed to significant challenges in dealing with congestion within IoT communications due to high packet error rates, latency, and interference in networks. With an emphasis on the Constrained Application Protocol (CoAP), the present study aims to propose the design and development of a novel congestion control mechanism, namely, Fast Response Congestion Control—Random Early Detection, abbreviated as FaCoCo-RED, along with performance analysis and comparison of congestion management efficacy between FaCoCo-RED and Default CoAP Congestion Control (Default CoAP CC) under a Cooja simulator on the Contiki OS platform. The findings from both experiment and performance analysis, which were based on statistical testing, showed that, under medium-scale to large-scale node networks across all traffic scenarios in this study, FaCoCo-RED significantly outperformed Default CoAP CC. The improvement can be seen in such metrics as average throughput, packet loss, response time, settling time, and retransmission timeout values (RTOs). The experimental findings also showed that FaCoCo-RED can perform effectively within the IoT networks, thus potentially enhancing the reliability and scalability of CoAP for large-scale and more complex IoT applications in the future. Full article

In recent years, the cost-effectiveness and versatility of Unmanned Aerial Vehicles (UAVs) have led to their widespread adoption in both military and civilian applications, particularly for operations in remote or hazardous environments where human intervention is impractical. The use of multi-agent UAV systems has notably increased for complex tasks such as surveying and monitoring, driving extensive research and development in control, communication, and coordination technologies. Evaluating and analysing these systems under dynamic flight conditions present significant challenges. This paper introduces a mathematical model for leader–follower structured Quadrotor UAVs that encapsulates their dynamic behaviour, incorporating a novel multi-agent ad hoc coordination network simulated via COOJA. Simulation results with a pipeline surveillance case study demonstrate the efficacy of the coordination network and show that the system offers various improvements over contemporary pipeline surveillance approaches. Full article