Emmanuel Adewale Adedokun

In conserving energy during routing in Wireless Sensor networks (WSN), Software Define Networking (SDN) was integrated into WSN and referred to as Software Defined Wireless Sensor Network (SDWSN). This is to exclude sensor nodes from routing decisions. Thus, enabling the SDN controller to handle hierarchical routing decisions. The existing WSN hierarchical routing protocols are not adequate for SDWSN due to their unbalanced characteristics in clustering and cluster head selection. In this regard, a Balanced Machine Learning-based Clustering (B-MLC) algorithm is proposed and compared with two closely related hierarchical algorithms (LEACH and FCM) for routing. The outcome indicated that, the B-MLC algorithm maintained a low average packet loss and is efficient in network lifetime elongation, with an average improvement of 60.4% and 89.8% respectively, over LEACH and FCM. Hence, the B-MLC can be adopted in SDWSN for complex monitoring applications.

Machine learning (ML) classifiers have lately gained traction in the realm of intelligent transportation systems as a means of enhancing road navigation while also assisting and increasing automotive user safety and comfort. The feature extraction stage, which defines the performance accuracy of the ML classifier, is critical to the success of any ML classifiers used. Nonetheless, the efficacy of various ML feature extractor filters on image data of road surface conditions obtained in a variety of illumination settings is uncertain. Thus, an examination of eight different feature extractor filters, namely Auto colour, Binary filter, Edge Detection, Fuzzy Color Texture Histogram Filter (FCTH), J-PEG Color, Gabor filter, Pyramid of Gradients (PHOG), and Simple Color, for extracting pothole anomalies feature from road surface conditions image data acquired under three environmental scenarios, namely bright, hazy, and dim conditions, prior classification using J48, JRip, and Random Forest ML models. According to the results of the experiments, the auto colour image filter is better suitable for extracting features for categorizing road surface conditions image data in bright light circumstances, with an average classification accuracy of roughly 96%. However, with a classification accuracy of around 74%, the edge detection filter is best suited for extracting features for the classification of road surface conditions image data captured in hazy light circumstances. The autocolor filter, on the

The particle swarm optimization (PSO) algorithm is widely used for optimization purposes across various domains, such as in precision agriculture, vehicular ad hoc networks, path planning, and for the assessment of mathematical test functions towards benchmarking different optimization algorithms. However, because of the inherent limitations in the velocity update mechanism of the algorithm, PSO often converges to suboptimal solutions. Thus, this paper aims to enhance the convergence rate and accuracy of the PSO algorithm by introducing a modified variant, which is based on a hybrid of the PSO and the smell agent optimization (SAO), termed the PSO-SAO algorithm. Our specific objective involves the incorporation of the trailing mode of the SAO algorithm into the PSO framework, with the goal of effectively regulating the velocity updates of the original PSO, thus improving its overall performance. By using the trailing mode, agents are continuously introduced to track molecules with higher concentrations, thus guiding the PSO’s particles towards optimal fitness locations. We evaluated the performance of the PSO-SAO, PSO, and SAO algorithms using a set of 37 benchmark functions categorized into unimodal and non-separable (UN), multimodal and non-separable (MS), and unimodal and separable (US) classes. The PSO-SAO achieved better convergence towards global solutions, performing better than the original PSO in 76% of the assessed functions. Specifically, it achieved a faster convergence rate and achieved a maximum fitness value of −2.02180678324 when tested on the Adjiman test function at a hopping frequency of 9. Consequently, these results underscore the potential of PSO-SAO for solving engineering problems effectively, such as in vehicle routing, network design, and energy system optimization. These findings serve as an initial stride towards the formulation of a robust hyperparameter tuning strategy applicable to supervised machine learning and deep learning models, particularly in the domains of natural language processing and path-loss modeling.

Authentication of user on a computer or network enable privacy protection and directing information to appropriate audience. Present authentication mechanisms only authenticate user once at the beginning of a communication session. The new wave of attacks that are used to steal information has made one-time authentication of users inadequate because the authenticated session can now be hijacked. Thus, it has become necessary for the communicating parties in a computer transaction session to reconfirm the other party on the other end periodically. Researchers have proposed different solutions to prevent or detect malicious taking over of a computer session. The solutions either work for particular types of attacks or only suitable for specific applications used to develop it. Others would fail in the face of spoofing attacks. This research proposed PERT for mitigating session hijacking and Cross-Site Request Forgery attacks. PERT ensure that a node is communicating only with a known system that it had previously successfully transacted with. The prototype was tested in NS-3 testbed. The tests were carried out to observe the proposed solution's performance against spoofing attacks and identity theft attacks. PERT performed satisfactorily better than two other solutions used to benchmark it as presented in the results section. It recorded 35% longer average execution time than the faster benchmark but 20.06% shorter average execution time than the slower benchmark. It prevented 97% and 95% of requests from an identity thief and a spoofing attacker respectively. The benchmark solutions recorded lower prevention efficiency.

This research work proposes a method for the securing and monitoring of petroleum product distribution records in a decentralized ledger database using blockchain technology. The aim of using this technique is to secure the transaction of distributed ledgers in a database and to protect records from tampering, fraudulent activity, and corruption by the chain participants. The blockchain technology approach offers an efficient security measure and novel advantages, such as in the transaction existence and distribution ledger management between the depot, transporter, and retailing filling station. Others advantages are transparency, immunity to fraud, insusceptibility to tampering, and maintaining record order. The technique adopted for this secure distributed ledger database is crypto hash algorithm-1 (SHA-1)-based public permissioned blockchain and telematics, while this telematics approach is an embedded system integrated into an in-vehicle model for remote tracking of geolocation (using Global Positioning System (GPS)), monitoring, and far-off data acquisition in a real-time. The scope of the data in the secure distributed ledger database (using blockchain) developed are identification (ID) of the tanker operator, Depot name, Source station ID, Destination station ID, Petroleum product volume, Transporter ID, and Geographic automobiles location. This system proved to be efficient, secure, and easy to maintain as it does not permit any individual for records tampering, but supports agreement of ~75% of participants in the chain to make changes.