Managing Congestion in Vehicular Networks Using Tabu Search

© 2018, Springer Nature Switzerland AG. In this era of communication, exponentially growing networks bring a lot of challenges to address for smoother network functionalities. Among them is efficiency in handling packet traffic to avoid and control congestion. A particular case is applicable to Vehicular Ad-hoc Networks, which are known with unbalanced resource utilisation, communication overheads, high transmission delay and least transmission capacity. This paper aims to minimise the delay and jitter for enhancing the Quality of Service (QoS) in Vehicular Adhoc Networks (VANET) using tabu search algorithm with multi-channel allocation capability. We proposed a scheme that prioritises each message considering the basis of message type or its substances, such as crisis, reference point, and administration oriented etc., and uses tabu search for scheduling the transmission of queued messages in order to enhance the efficiency, security, and durability of VANET. A comprehensive simulation is conducted to validate the proposed scheme and to evaluate the performances in comparison with other state-of-the-art approaches

Intelligent mining of large-scale bio-data: bioinformatics applications

Today, there is a collection of a tremendous amount of bio-data because of the computerized applications worldwide. Therefore, scholars have been encouraged to develop effective methods to extract the hidden knowledge in these data. Consequently, a challenging and valuable area for research in artificial intelligence has been created. Bioinformatics creates heuristic approaches and complex algorithms using artificial intelligence and information technology in order to solve biological problems. Intelligent implication of the data can accelerate biological knowledge discovery. Data mining, as biology intelligence, attempts to find reliable, new, useful and meaningful patterns in huge amounts of data. Hence, there is a high potential to raise the interaction between artificial intelligence and bio-data mining. The present paper argues how artificial intelligence can assist bio-data analysis and gives an up-to-date review of different applications of bio-data mining. It also highlights some future perspectives of data mining in bioinformatics that can inspire further developments of data mining instruments. Important and new techniques are critically discussed for intelligent knowledge discovery of different types of row datasets with applicable examples in human, plant and animal sciences. Finally, a broad perception of this hot topic in data science is given

SOGI-FLL error-and-hold algorithm for improving the response in face of voltage sags and swells with a small computational burden

The Second Order Generalized Integrator–Frequency Locked Loop (SOGI-FLL) is a widely used and popular adaptive filter for estimating grid voltage parameters with minimal computational burden. However, it is vulnerable to voltage sag and swell faults, especially voltage sags that can significantly distort the estimated frequency. In this paper, we propose an error-and-hold algorithm for the SOGI-FLL that can quickly detect faults and hold the estimated frequency during these perturbations. The algorithm uses the absolute value of the SOGI's error, its average, and the average of the FLL's estimated frequency to operate. It reduces induced errors in the SOGI-FLL's quadrature outputs, improves the FLL's transient response, holds the estimated frequency, and restores the phase to its previous value before the fault. The proposed algorithm is a straightforward and low computational burden algorithm that can be executed on a low-cost processor. We validate the effectiveness of the proposed error-and-hold algorithm through simulations and experimental results.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantPostprint (published version