Intrusion detection is an important and challenging problem that has a major impact on quality and reliability of smart city services. To this extent, replay attacks have been one of the most common threats on smart city infrastructure, which compromises authentication in a smart city network. For example, a replay attack may physically damage smart city infrastructure resulting in loss of sensitive data, incurring considerable financial damages. Therefore, towards securing smart cities from reply attacks, intrusion detection systems and frameworks based on deep learning have been proposed in the recent literature. However, the absence of the time dimension of these proposals is a major limitation. Therefore, we have developed a deep learning-based model for replay attack detection in smart cities. The novelty of the proposed methodology resides in the adoption of deep learning based models as an application for detecting replay attacks to improve detection accuracy. The performance of this model is evaluated by applying it to a real life smart city dataset, where replay attacks were simulated. Our results show that the proposed model is capable of distinguishing between normal and attack behaviours with relatively high accuracy. In addition, according to the results, our proposed model outperforms traditional classification and deep learning models. Last but not least, as an additional contribution, this paper presents a real life smart city data set with simulated replay attacks for future research.

The Ethereum blockchain generates a significant amount of data due to its intrinsic transparency and decentralized nature. It is also referred to as on-chain data and is openly accessible to the world. Moreover, the on-chain data is timestamped, integrated, and validated into an open ledger. This important blockchain feature enables us to assess the network’s health and usage. It serves as a massive data warehouse for complex prediction algorithms that can effectively detect systemic trends and forecast future behavior. We adopt a quantitative approach using a subset of these metrics to determine the network’s true monetary value by developing a Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) with the metrics most closely associated with the price as inputs. Since several hyperparameters regulate the learning process in an RNN, they are highly sensitive to their values. It is thus critical, to select optimal hyperparameters so that the training is quick and effective. Determining the optimal parameters of an RNN model is a tedious and complex process. Hence, previous studies have developed several self-adaptive approaches to determine the optimal values for various parameters effectively. However, none of the prior studies explore self-adaptive algorithms in deep learning models in conjunction with on-chain data to predict cryptocurrency prices. In this paper, we propose three self-adaptive techniques, each of which converges on a set of optimal parameters to predict the price of Ethereum accurately. We compare our results to a traditional LSTM model. Our approach exhibits 86.94% accuracy while maintaining a minimum error rate.

Despite increase in deployment of BS, MNOs are still faced with the daunting challenge of providing adequate coverage and capacity in indoor environments. Furthermore, the trust-less environment in which MNOs operate makes it further challenging to achieve interoperability across carriers. Recently the concept of mOs has emerged as a promising solution through deployment of small cells. However, their success has been severely hampered by the absence of a framework for creating and managing business agreements between key stakeholders, i.e., MNOs and mOs. This article proposes a blockchain-enabled SDN approach for managing radio spectrum access between MNOs using smart contracts over small cell networks. Specifically, our solution uses a smart contract to validate transactions between MNOs. Simulation results show that our solution guarantees seamless handoff and high availability between different operators in contrast to a break in connectivity in the absence of an agreement.

Bitcoin generates a massive amount of data every day due to its innate transparency and capacity of operating completely decentralised. In this paper, we introduce on-chain metrics derived from data on the bitcoin network that enable us to describe the state and usage of the underlying network. Based on their characteristics, we classify them into user, miner, exchange activities and run a correlation analysis with the price to understand the dynamics of bitcoin's price and its underlying mechanics. Using the correlated data, we develop a deep learning model. However, determining the best values of parameters in a deep learning model can be a very challenging and time-consuming task. Hence, we propose a self-adaptive technique using a jSO optimization algorithm to find the best values of these parameters to accurately predict the price of bitcoin. Compared to traditional LSTM model, our approach is highly accurate and optimised with a minimum error rate.

The advancement towards 6G networks necessitates innovative solutions for efficient deployment and management of heterogeneous networks. While the synergy of software-defined networks (SDN) and blockchain technology brings cost and spectrum efficiencies to future network deployments, their impact on network performance is yet to be investigated. This paper investigates the integration of software-defined networking (SDN) and blockchain technology to facilitate seamless roaming across multiple mobile network operators (MNOs). We develop analytical models based on queuing theory, specifically using M/M/1 models to characterize network performance and validate them through extensive NS3 simulations. Evaluating three consensus mechanisms—Practical Byzantine Fault Tolerance (PBFT), Raft, and Paxos—our findings demonstrate Raft’s superior performance, reducing end-to-end delays by 10.7% and 18.7% over PBFT and Paxos, respectively, with increasing miners. Additionally, Raft decreases handover delays by 12.32% and 25.73% compared to PBFT and Paxos, with increased femtocell spacing. Our research also identifies cell residence time as a critical factor impacting handover delays, influenced by user equipment speed and femtocell spacing. This study offers valuable insights for optimizing network deployments and enabling dynamic mobility solutions in future multi-operator networks, paving the way for enhanced user experiences and verticals. The selection of consensus mechanisms and understanding cell dynamics emerge as crucial considerations for network performance optimization in 6G and beyond.

Conventional chemical analysis relies on costly laboratory instrumentation, while current e-nose systems are expensive for widespread deployment. New opportunities for low-cost, accessible e-nose applications are emerging for diverse fields due to the rapid evolution of inexpensive sensor technologies. We developed a framework that enables rapid integration of newly available low-cost gas sensors into functional e-nose systems, continuously evaluating them as they become commercially available. By characterizing their performance in multi-sensor arrays that mimic biological olfaction, the framework demonstrates effective odor discrimination in a low-cost e-nose system through coordinated behavior of a heterogeneous sensor array. Our testing approach includes sensor sensitivity, selectivity, and stability, which are to be combined with appropriate pattern recognition and AI algorithms in the future for effective chemical discrimination. This work provides a pathway for continuously updating e-nose technology with the latest available sensors in a cost-effective manner, thereby making advanced chemical sensing accessible for resource-limited settings and enabling large-scale deployment in real-world applications with future potential applications such as food quality monitoring, environmental sensing, smart agriculture, etc.