Node discovery and interpretation in unstructured resource-constrained environments

Internet of Things (IoT) is the network of the huge number of resource-personated nodes whose main feature is the reliable communication and fast data exchange among devices in any network topology. In the upcoming world of internet, proper selection of desire or appropriate destination node and finding the optimized path from the huge number of the activated nodes is a very important task. Here many nodes are available in one or more hop distance and each node has various network parameters in the IoT world, so the selection of best path and nodes is quite complex. For this reason, we need to develop an algorithm to find out the best path towards the destination node considering significant network parameters. This paper proposes a general algorithm considering any network topology for discovering the path towards the destination node from the source node and give a solution to the contradiction which may arise if there is more than one consonant path. The proposed algorithm considers the node property of all node for calculating the behavior value, path influence behavior value and finally discover the optimized path from source node to destination node. The proposed algorithm is also evaluated by simulation to determine the impact of parameters in path determination.

With the rapid growth of smart home IoT devices, users are increasingly exposed to various security risks, as evident from recent studies. While seeking answers to know more on those security concerns, users are mostly left with their own discretion while going through various sources, such as online blogs, and technical manuals; which may render higher complexity to the regular users to extract the necessary information from. This requirement does not go along with the common mindsets of smart home users and hence threatens the security of smart homes furthermore. In this paper, we aim to identify and address the major user-level security concerns in smart homes. Specifically, we develop a novel dataset of Q&A from public forums, capturing practical security challenges faced by smart home users. We extract major security concerns in smart homes from our dataset by leveraging the Latent Dirichlet Allocation (LDA). We fine-tune relatively “smaller” transformer models, such as T5 and Flan-T5, on this dataset to build a QA system tailored for smart home security. Unlike larger models like GPT and Gemini, which are powerful but often resource hungry, and requiring data sharing, smaller models are more feasible for deployment in resource-constrained or privacy-sensitive environments, like smart homes. The dataset is manually curated and supplemented with synthetic data to explore its potential impact on model performance. This approach significantly improves the system’s ability to deliver accurate and relevant answers, helping users address common security concerns with smart home IoT devices. Our experiments on real-world user concerns show that our work improves the performance of the base models.

The field of autonomous driving has seen continuous advances, yet achieving higher levels of automation in real-world applications remains challenging. A critical requirement for autonomous navigation is accurate map construction, particularly in novel and unstructured environments. In recent years, Simultaneous Localization and Mapping (SLAM) has evolved to support diverse sensor modalities, with some implementations incorporating machine learning to improve performance. However, these approaches often demand substantial computational resources. The key challenge lies in achieving efficiency within resource-constrained environments while minimizing errors that could degrade downstream tasks. This paper presents an enhanced ORB-SLAM2 (Oriented FAST and Rotated BRIEF Simultaneous Localization and Mapping, version 2) algorithm implemented on a Raspberry Pi 3 (ARM A53 CPU) to improve mapping performance under limited computational resources. ORB-SLAM2 comprises four main stages: Tracking, Local Mapping, Loop Closing, and Full Bundle Adjustment (BA). The proposed improvements include employing a more efficient feature descriptor to increase stereo feature-matching rates and optimizing loop-closing parameters to reduce accumulated errors. Experimental results demonstrate that the proposed system achieves notable improvements on the Raspberry Pi 3 platform. For monocular SLAM, RMSE is reduced by 18.11%, mean error by 22.97%, median error by 29.41%, and maximum error by 17.18%. For stereo SLAM, RMSE decreases by 0.30% and mean error by 0.38%. Furthermore, the ROS topic frequency stabilizes at 10 Hz, with quad-core CPU utilization averaging approximately 90%. These results indicate that the system satisfies real-time requirements while maintaining a balanced trade-off between accuracy and computational efficiency under resource constraints.

Due to the constantly moving of the communication nodes in the combat environment, the network topology is highly dynamic and uncertain. However, combat missions require the real-time and accurate information transmission through the military network. Therefore, it is of great practical interest to design a dynamic routing algorithm to accommodate to the dynamic and uncertain network topology. We consider this important problem in this paper and make the following contributions. First, a semi-Markov prediction model is proposed to predict the uncertainty of the network topology. Second, a dynamic resource table is updated by the formation transition probability which can describe the dynamic of the network topology. Third, the Q-learning method is applied to find an optimal routing in the uncertain and dynamic network topology. Numerical experiments demonstrate that the proposed method can predict the network topology more accurate and obtain an optimal routing strategy with improved transmission efficiency and reduced transmission delay.KeywordsMilitary networksSemi-MarkovTopology predictionQ-learningDynamic routing

Human activity recognition (HAR) using Wi-Fi-based systems is essential for applications such as smart homes, healthcare, and security. Traditional HAR methods, which rely on channel state information (CSI) to detect human movements, often struggle to balance recognition accuracy with computational efficiency. In this article, we introduce time-frequency Mamba (TF-Mamba), a lightweight state-space model (SSM) optimized for HAR through time-frequency analysis of CSI data. TF-Mamba features a dual-stream architecture comprising Time-Mamba and Frequency-Mamba blocks, efficiently capturing both the temporal (time-domain) and spectral (frequency-domain) features. The incorporation of a 2-D discrete wavelet transform (DWT) enhances the model’s capability to extract comprehensive features from CSI data. Extensive experiments on benchmark datasets, including UT-HAR, NTU-Fi, and HUST-HAR, confirm TF-Mamba’s superiority over existing methods. Notably, TF-Mamba achieves an accuracy of 99.72% on the HUST-HAR dataset while reducing computational complexity by up to 98.9% in multiply-accumulate operations (MACs) compared with previous state-of-the-art (SOTA) models. These results highlight TF-Mamba’s potential as an efficient and highly accurate solution for HAR, especially in resource-constrained environments. Source code and datasets are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Junshuo-Lau/HUST_HAR</uri>.

The rapid growth of Internet of Things (IoT)-enabled smart home environments has amplified the need for robust, scalable, and context-aware access control mechanisms. Traditional Role-Based Access Control (RBAC) and Attribute-Based Access Control (ABAC) models offer distinct advantages such as hierarchical simplicity and contextual granularity, respectively, yet exhibit critical limitations when deployed in isolation. To address these constraints, this study proposes and implements a discrete, hybrid RBAC–ABAC access control framework, engineered as a smart contract on the Binance Smart Chain (BSC), to ensure a secure, decentralized model for managing access to IoT resources. The framework leverages blockchain’s inherent strengths cryptographic integrity, immutable message digests, and tamper-resistant audit trails to reinforce data confidentiality and policy enforcement. Validated on a real-world testbed comprising ESP32-based IoT devices, a Raspberry Pi gateway, and a blockchain backend, the proposed system demonstrates enhanced resilience and trust. Quantitative evaluations benchmark the hybrid model against standalone RBAC and ABAC mechanisms across key metrics, including gas consumption, access latency, access decision accuracy, and scalability. The results indicate the hybrid model achieves superior accuracy (97.2%) and scalability (supporting 50 concurrent users and 30 devices), with moderate gas usage (15,000 gwei) and acceptable latency (1.8 seconds). Furthermore, the use of laser-secure transaction protocols and cryptographic primitives within the smart contract enhances the protection of access rights and resource interactions. This hybrid smart contract architecture not only bridges the gap between theoretical access control models and practical blockchain deployments but also sets a foundation for adaptive, secure, and decentralized policy enforcement in next-generation smart homes, particularly in resource-constrained environments.

This paper presents a compact, low-power UART architecture integrated with Single Error Correction and Double Error Detection (SECDED) for reliable serial communication in noisy and resource-constrained environments. The design incorporates an extended Hamming encoder—decoder within an FSM-controlled pipelined framework, enabling real-time error correction without violating standard UART timing conventions. Operating at only six times the baud rate, the proposed approach reduces hardware complexity and power consumption compared to traditional oversampling schemes. Implemented in Verilog and synthesized on 150 nm ASIC and FPGA platforms, the architecture achieves error-free data transfer while consuming just 0.099 W, with dynamic power limited to 2% through optimized clock gating. FPGA resource utilization remains below 1%. Extensive verification—including unit tests, errorinjection, and timing analysis—confirms protocol compliance and robust SECDED operation. With minimal area overhead and enhanced reliability, the proposed UART is well suited for IoT, smart home, and other safety-critical applications requiring energy-efficient and fault-tolerant serial links.

Nowadays, IoT technology has become an essential part of many aspects of life and business. Nevertheless, such widespread application has come at the cost of many security concerns that threaten data privacy and diminish IoT utilization momentum in critical applications such as the smart grid and intelligent transportation systems. To address this challenge, several approaches have been proposed to detect and prevent IoT cyberthreats from materializing. Anomaly detection is one of these approaches that defines the boundaries of legitimate (normal) behavior. Any behavior that falls outside these boundaries is considered anomalous. However, these solutions should have the capability to adapt and adjust to environmental changes that prompt IoT nodal behavioral aberrations, except they only assume that these nodes show the same behavior. This assumption does not hold due to the heterogeneity of IoT nodes and the dynamic nature of an IoT network topology. Furthermore, existing adaptive solutions rely on static (pre-defined) thresholds to control the moment for retraining updates. The cost is heavy for highly dynamic environments like IoT as it leads to an unnecessary higher frequency of retraining. Consequently, the model becomes unstable and adversely affects its accuracy and robustness. This paper addresses these problems by offering an improved Adaptive Anomaly Detection (AAD) methodology that resolves the heterogeneity issues by building local profiles that define normal behavior at each IoT node. The One Class Support Vector Machines (OC-SVM) was used to build these profiles. Then, K-Means clustering was used to build a global profile that represents all network nodes. A Local-Global Ratio-Based (LGR) Anomaly Detection scheme is advanced and was enlisted to control the adaptation process by adjusting the threshold of adaptive functionality dynamically based on the “current” situation to prevent unnecessary retraining. An Ensemble of Deep Belief Networks (EDBN) is developed and used to train the anomaly detection model. Additionally, this study's proposes a new Minimized Redundancy Discriminative Feature Selection (MRD-FS) technique to resolve the issue of redundant features. The MRD-FS experimental evaluation shows detection accuracy higher than those of the related solutions including lower false alarm rates. This validates the efficacy of the proposed model for various IoT applications such as smart grids, smart homes, smart cities and intelligent transportation systems.

Smart environments change our daily life and our daily mobility. Especially in growing smart cities we need innovative solutions of smart multi-modal transport services for travelers. In the near future, most travelers will start their multi-modal journey through a seamlessly connected smart city with intelligent mobility services at home. Nevertheless, there is a lack of well-founded requirements for smart in-house mobility services. Therefore, this paper presents a first step towards a better understanding of the situation in which travelers use digital services at home in order to inform themselves about their mobility options. As an initial step, we used an online survey to question participants \((\text{n} = 158)\) about their current mobility-centered needs at home. This includes five main aspects, namely (1) the pain points and the stress level regarding mobility-centered information search at home, (2) the time and type of mobility-centered information search at home, (3) the interest in vehicle-centered information at home, (4) the current and future use of different smart home features and (5) additional non-mobility needs that are also relevant in this situation for the user. Interestingly, a lack of availability of mobility-centered information was identified as the most pressing pain point regarding mobility-centered information at home. Additionally, most participants reported a growing need to access vehicle-centered information at home and a growing interest in using a variety of smart home features. Since these insights can help practitioners to develop user-centered mobility services for smart homes, our results will help to maximize customer value.

The rapid expansion of IoT in domains like smart homes and wearables has intensified the need for lightweight and secure encryption . In this work, we propose a novel Corona Graph-Based Encryption scheme tailored for resource-constrained IoT environments. Our method constructs a corona graph, labels each node with pseudorandom values (from a PRNG seeded by a shared key), and performs encryption by XOR-ing plaintext blocks with the sequence of node labels obtained along a predefined traversal path. Decryption inverts this process using the same PRNG and graph path. We benchmark the scheme against AES-128 and ChaCha20, evaluating CPU usage, memory footprint, throughput, and output entropy. Simulated results indicate that while Corona Graph encryption incurs higher CPU overhead (due to graph traversal), it achieves comparable ciphertext entropy, suggesting strong security properties. We discuss use cases in smart homes, drones, and wearables, and argue that Corona-Graph encryption offers a flexible, structurally-rich alternative for IoT data protection.

The rapid development of Internet of Vehicles (IoV) presents critical challenges in secure storage and sharing of massive vehicular data. Traditional blockchain's full-node storage paradigm incurs prohibitive overhead in resource-constrained vehicular environments, while existing coded blockchain schemes struggle with IoV's dynamic network topology and fluctuating node population. This paper proposes an adaptive rateless coded blockchain architecture tailored for dynamic IoV environments, employing systematic Raptor codes for block compression, VRF-based lightweight consensus, and dynamic parameter adjustment that adapts redundancy in real-time without re-encoding existing blocks. The scheme achieves an average compression ratio of 97.88% while ensuring decoding failure probability below 10^-12, significantly reducing storage overhead and demonstrating robustness in dynamic vehicular networks.

Significant efforts are being made at present to define, evaluate, and ultimately deploy, various forms of intelligent transportation systems (ITS). These systems permit the implementation of advanced traffic control strategies through the application of advanced surveillance, control and communication systems. Traditionally, the evaluation of the performance of these communication systems and traffic networks has been performed predominantly independent of each other. This paper describes the development and application of an extension to an ITS benefits evaluation model to permit a partially coupled evaluation of the communication system loading and the ATMS/ATIS functions. This extended model is capable of estimating dynamic local and network wide communication loadings that depend on the spatial and temporal traffic demands, the network topology, the characteristics of the communication hardware and the communication system operating rules. The model is applied in this paper to a simple hypothetical network to demonstrate the potential benefits of carrying out a partially coupled evaluation of communication system loading and traffic network efficiency. A sensitivity analysis was carried out to determine the impact of the fraction of ITS equipped vehicles and the level of congestion on the level of communication loadings. It was demonstrated that, as a result of congestion and traffic diversion, antenna communication loads did not uniformly increase in direct proportion to the average number of equipped vehicles entering the network.

This paper contributes to building a systematic view of the mobility characteristics of smart cities by reviewing the lessons learned from the best practices implemented around the world. The main features of smart cities, such as smart homes, smart infrastructure, smart operations, smart services, smart utilities, smart energy, smart governance, smart lifestyle, smart business, and smart mobility in North America, Asia, and Europe are briefly reviewed. The study predominantly focuses on smart mobility features and their implications in newly built smart cities. As a case study, the modern city of Lusail located in the north of Doha, Qatar is considered. The provision of car park management and guidance, real-time traffic signal control, traffic information system, active-modes arrangement in promenade and busy urban avenues, LRT, buses, taxis, and water taxis information system, and multimodal journey planning facilities in the Lusail smart city is discussed in this study. Consequently, the implications of smart mobility features on adopting Intelligent Transportation Systems (ITS) will be studied. The study demonstrates that the implementation of Information and Communication Technologies (ICT) when supported by Intelligent Transportation Systems (ITS), could result in making the most efficient use of existing transportation infrastructure and consequently improve the safety and security, mobility, and the environment in urban areas. The findings of this study could be considered an initial step in the implementation of Mobility-as-a-Service (MaaS) in cities with advanced public transportation such as Doha, the capital of Qatar. Doi: 10.28991/CEJ-2022-08-10-09 Full Text: PDF

In this paper, we investigate the problem of securing exponentially fast consensus (exponential consensus for short) for identical agents with finite-dimensional linear system dynamics over dynamic network topology. Our aim is to find the weakest possible conditions that guarantee exponentially fast consensus using a Lyapunov function consisting of a sum of terms of the same functional form. We first investigate necessary conditions, starting by examining the system (both agent and network) parameters. It is found that controllability of the linear agents is necessary for reaching consensus. Then, to work out necessary conditions incorporating the network topology, we construct a set of Laplacian matrix-valued functions. The precompactness of this set of functions is shown to be a significant generalization of existing assumptions on network topology, including the common assumption that the edge weights are bounded piecewise constant functions or continuous functions. With the aid of such a precompactness assumption and restricting the Lyapunov function to one consisting of a sum of terms of the same functional form, we prove that a joint <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\delta, T)$</tex-math></inline-formula> -connectivity condition on the network topology is necessary for exponential consensus. Finally, we investigate how the above two “necessities” work together to guarantee exponential consensus. To partially address this problem, we define a synchronization index to characterize the interplay between agent parameters and network topology. Based on this notion, it is shown that by designing a proper feedback matrix and under the precompactness assumption, exponential consensus can be reached globally and uniformly if the joint <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$(\delta,T)$</tex-math></inline-formula> -connectivity and controllability conditions are satisfied, and the synchronization index is not less than one.

The intelligent transportation system (ITS) was supported by the sixth generation (6G) wireless networks, since it has great potential to realize intelligent transportation with the benefit for the society and economy. In order to overcome the practical problem of spectrum scarcity, ultra-low latency, large-scale connectivity in ITS, we propose a cooperative multiple-input single output non-orthogonal multiple access (MISO-NOMA) for ITS with intelligent reflecting surface (IRS) and simultaneous wireless information and power transfer (SWIPT). An user fairness optimization problem is formulated to maximize the fairness rate of the vehicles, subject to the quality of service requirements of the vehicles and the successive interference cancellation. The optimization problem involves the transmit beamformers design, the IRS reflection matrix design, and the power splitting ratio of the SWIPT, which lead to the problem is difficult to solve. For solving the challenging problem, an iterative successive convex approximation and semi-definite relaxation based algorithm is proposed. Explicitly, we firstly adopt the method of reconstructing epigraph for simplification due to the objective function is non-convex, and then the original problem is decomposed into two sub-problems that are easy to solve. Finally, Experimental results illustrate that the user fairness of the proposed cooperative MISO-NOMA for ITS with IRS and SWIPT is better than that of both the IRS-NOMA for ITS without SWIPT and the IRS-OMA for ITS.