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

IEEE Access special section editorial: Artificial intelligence enabled networking

With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS)

Intelligent XML Tag Classification Techniques for XML Encryption Improvement

Flexibility, friendliness, and adaptability have been key components to use XML to exchange information across different networks providing the needed common syntax for various messaging systems. However excess usage of XML as a communication medium shed the light on security standards used to protect exchanged messages achieving data confidentiality and privacy. This research presents a novel approach to secure XML messages being used in various systems with efficiency providing high security measures and high performance. system model is based on two major modules, the first to classify XML messages and define which parts of the messages to be secured assigning an importance level for each tag presented in XML message and then using XML encryption standard proposed earlier by W3C [3] to perform a partial encryption on selected parts defined in classification stage. As a result, study aims to improve both the performance of XML encryption process and bulk message handling to achieve data cleansing efficiently