Computational Intelligence Inspired Data Delivery for Vehicle-to-Roadside Communications

We propose a vehicle-to-roadside communication protocol based on distributed clustering where a coalitional game approach is used to stimulate the vehicles to join a cluster, and a fuzzy logic algorithm is employed to generate stable clusters by considering multiple metrics of vehicle velocity, moving pattern, and signal qualities between vehicles. A reinforcement learning algorithm with game theory based reward allocation is employed to guide each vehicle to select the route that can maximize the whole network performance. The protocol is integrated with a multi-hop data delivery virtualization scheme that works on the top of the transport layer and provides high performance for multi-hop end-to-end data transmissions. We conduct realistic computer simulations to show the performance advantage of the protocol over other approaches

A practical exploration of the convergence of case-based reasoning and explainable artificial intelligence.

As Artificial Intelligence (AI) systems become increasingly complex, ensuring their decisions are transparent and understandable to users has become paramount. This paper explores the integration of Case-Based Reasoning (CBR) with Explainable Artificial Intelligence (XAI) through a real-world example, which presents an innovative CBR-driven XAI platform. This study investigates how CBR, a method that solves new problems based on the solutions of similar past problems, can be harnessed to enhance the explainability of AI systems. Though the literature has few works on the synergy between CBR and XAI, exploring the principles for developing a CBR-driven XAI platform is necessary. This exploration outlines the key features and functionalities, examines the alignment of CBR principles with XAI goals to make AI reasoning more transparent to users, and discusses methodological strategies for integrating CBR into XAI frameworks. Through a case study of our CBR-driven XAI platform, iSee: Intelligent Sharing of Explanation Experience, we demonstrate the practical application of these principles, highlighting the enhancement of system transparency and user trust. The platform elucidates the decision-making processes of AI models and adapts to provide explanations tailored to diverse user needs. Our findings emphasize the importance of interdisciplinary approaches in AI research and the significant role CBR can play in advancing the goals of XAI

Dynamic neighbour aware power-controlled MAC for multi-hop ad hoc networks

In Ad Hoc networks, resources in terms of bandwidth and battery life are limited; so using a fixed high transmission power limits the durability of a battery life and causes unnecessary high interference while communicating with closer nodes leading to lower overall network throughput. Thus, this paper proposes a new cross layer MAC called Dynamic Neighbour Aware Power-controlled MAC (Dynamic NA -PMAC) for multi-hop Ad Hoc networks that adjust the transmission power by estimating the communication distance based on the overheard signal strength. By dynamically controlling the transmission power based on the receivable signal strength, the probability of concurrent transmission, durability of battery life and bandwidth utilization increases. Moreover, in presence of multiple overlapping signals with different strengths, an optimal transmission power is estimated dynamically to maintain fairness and avoid hidden node issues at the same time. In a given area, since power is controlled, the chances of overlapping the sensing ranges of sources and next hop relay nodes or destination node decreases, so it enhances the probability of concurrent transmission and hence an increased overall throughput. In addition, this paper uses a variable backoff algorithm based on the number of active neighbours, which saves energy and increases throughput when the density of active neighbours is less. The designed mechanism is tested with various random network scenarios using different traffic including CBR, Exponential and TCP in both scenarios (stationary and mobile with high speed) for single as well as multi-hop. Moreover, the proposed model is benchmarked against two variants of power-controlled mechanisms namely Min NA-PMAC and MaxRC-MinDA NA-PMAC to prove that using a fixed minimum transmission power may lead to unfair channel access and using different transmission power for RTS/CTS and Data/ACK leads to lower probability of concurrent transmission respectively

An Effective Wireless Sensor Network Routing Protocol Based on Particle Swarm Optimization Algorithm

Improving wireless communication and artificial intelligence technologies by using Internet of Things (Itoh) paradigm has been contributed in developing a wide range of different applications. However, the exponential growth of smart phones and Internet of Things (IoT) devices in wireless sensor networks (WSNs) is becoming an emerging challenge that adds some limitations on Quality of Service (QoS) requirements. End-to-end latency, energy consumption, and packet loss during transmission are the main QoS requirements that could be affected by increasing the number of IoT applications connected through WSNs. To address these limitations, an effective routing protocol needs to be designed for boosting the performance of WSNs and QoS metrics. In this paper, an optimization approach using Particle Swarm Optimization (PSO) algorithm is proposed to develop a multipath protocol, called a Particle Swarm Optimization Routing Protocol (MPSORP). The MPSORP is used for WSN-based IoT applications with a large volume of traffic loads and unfairness in network flow. For evaluating the developed protocol, an experiment is conducted using NS-2 simulator with different configurations and parameters. Furthermore, the performance of MPSORP is compared with AODV and DSDV routing protocols. The experimental results of this comparison demonstrated that the proposed approach achieves several advantages such as saving energy, low end-to-end delay, high packet delivery ratio, high throughput, and low normalization load.publishedVersio

Findings from a literature review

Mentzingen, H., António, N., & Bação, F. (2023). Automation of legal precedents retrieval: Findings from a literature review. International Journal of Intelligent Systems, 2023, 1-22. [6660983]. https://doi.org/10.21203/rs.3.rs-2292464/v1, https://doi.org/10.21203/rs.3.rs-2292464/v2, https://doi.org/10.1155/2023/6660983---This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project-UIDB/04152/2020-Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS.Judges frequently rely their reasoning on precedents. Courts must preserve uniformity in decisions while, depending on the legal system, previous cases compel rulings. The search for methods to accurately identify similar previous cases is not new and has been a vital input, for example, to case-based reasoning (CBR) methodologies. This literature review offers a comprehensive analysis of the advancements in automating the identification of legal precedents, primarily focusing on the paradigm shift from manual knowledge engineering to the incorporation of Artificial Intelligence (AI) technologies such as natural language processing (NLP) and machine learning (ML). While multiple approaches harnessing NLP and ML show promise, none has emerged as definitively superior, and further validation through statistically significant samples and expert-provided ground truth is imperative. Additionally, this review employs text-mining techniques to streamline the survey process, providing an accurate and holistic view of the current research landscape. By delineating extant research gaps and suggesting avenues for future exploration, this review serves as both a summation and a call for more targeted, empirical investigations.publishersversionpublishe

Resource management research in ethernet passive optical networks

The last decades, we have witnessed different phenomenology in the telecommunications sector. One of them is the widespread use of the Internet, which has brought a sharp increase in traffic, forcing suppliers to continuously expand the capacity of networks. In the near future, Internet will be composed of long-range highspeed optical networks; a number of wireless networks at the edge; and, in between, several access technologies. Today one of the main problems of the Internet is the bottleneck in the access segment. To address this issue the Passive Optical Networks (PONs) are very likely to succeed, due to their simplicity, low-cost, and increased bandwidth. A PON is made up of fiber optic cabling and passive splitters and couplers that distribute an optical signal to connectors that terminate each fiber segment. Among the different PON technologies, the Ethernet-PON (EPON) is a great alternative to satisfy operator and user needs, due to its cost, flexibility and interoperability with other technologies. One of the most interesting challenges in such technologies relates to the scheduling and allocation of resources in the upstream (shared) channel, i.e., the resource management. The aim of this thesis is to study and evaluate current contributions and propose new efficient solutions to address the resource management issues mainly in EPON. Key issues in this context are future end-user needs, quality of service (QoS) support, energy-saving and optimized service provisioning for real-time and elastic flows. This thesis also identifies research opportunities, issue recommendations and proposes novel mechanisms associated with access networks based on optical fiber technologies.Postprint (published version

Mine Action 2020: Book of Papers

Book of papers that was to be presented at the 2020 Mine Action Symposium in Croatia. The Symposium canceled due to COVID-19

Improving the Performance of Wireless LANs

This book quantifies the key factors of WLAN performance and describes methods for improvement. It provides theoretical background and empirical results for the optimum planning and deployment of indoor WLAN systems, explaining the fundamentals while supplying guidelines for design, modeling, and performance evaluation. It discusses environmental effects on WLAN systems, protocol redesign for routing and MAC, and traffic distribution; examines emerging and future network technologies; and includes radio propagation and site measurements, simulations for various network design scenarios, numerous illustrations, practical examples, and learning aids

Performance Comparison of Dual Connectivity and Hard Handover for LTE-5G Tight Integration in mmWave Cellular Networks

MmWave communications are expected to play a major role in the Fifth generation of mobile networks. They offer a potential multi-gigabit throughput and an ultra-low radio latency, but at the same time suffer from high isotropic pathloss, and a coverage area much smaller than the one of LTE macrocells. In order to address these issues, highly directional beamforming and a very high-density deployment of mmWave base stations were proposed. This Thesis aims to improve the reliability and performance of the 5G network by studying its tight and seamless integration with the current LTE cellular network. In particular, the LTE base stations can provide a coverage layer for 5G mobile terminals, because they operate on microWave frequencies, which are less sensitive to blockage and have a lower pathloss. This document is a copy of the Master's Thesis carried out by Mr. Michele Polese under the supervision of Dr. Marco Mezzavilla and Prof. Michele Zorzi. It will propose an LTE-5G tight integration architecture, based on mobile terminals' dual connectivity to LTE and 5G radio access networks, and will evaluate which are the new network procedures that will be needed to support it. Moreover, this new architecture will be implemented in the ns-3 simulator, and a thorough simulation campaign will be conducted in order to evaluate its performance, with respect to the baseline of handover between LTE and 5G.Comment: Master's Thesis carried out by Mr. Michele Polese under the supervision of Dr. Marco Mezzavilla and Prof. Michele Zorz