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

TCP Sintok: Transmission control protocol with delay-based loss detection and contention avoidance mechanisms for mobile ad hoc networks

Mobile Ad hoc Network (MANET) consists of mobile devices that are connected to each other using a wireless channel, forming a temporary network without the aid of fixed infrastructure; in which hosts are free to move randomly as well as free to join or leave. This decentralized nature of MANET comes with new challenges that violate the design concepts of Transmission Control Protocol (TCP); the current dominant protocol of the Internet. TCP always infers packet loss as an indicator of network congestion and causes it to perform a sharp reduction to its sending rate. MANET suffers from several types of packet losses due to its mobility feature and contention on wireless channel access and these would lead to poor TCP performance. This experimental study investigates mobility and contention issues by proposing a protocol named TCP Sintok. This protocol comprises two mechanisms: Delay-based Loss Detection Mechanism (LDM), and Contention Avoidance Mechanism (CAM). LDM was introduced to determine the cause of the packet loss by monitoring the trend of end-to-end delay samples. CAM was developed to adapt the sending rate (congestion window) according to the current network condition. A series of experimental studies were conducted to validate the effectiveness of TCP Sintok in identifying the cause of packet loss and adapting the sending rate appropriately. Two variants of TCP protocol known as TCP NewReno and ADTCP were chosen to evaluate the performance of TCP Sintok through simulation. The results demonstrate that TCP Sintok improves jitter, delay and throughput as compared to the two variants. The findings have significant implication in providing reliable data transfer within MANET and supporting its deployment on mobile device communication

A Survey on Issues and Challenges in Congestion Adaptive Routing in Mobile Ad hoc Network

Mobile ad hoc networks is the future wireless communication systems have recently emerged as an important trend. Mobile adhoc network is self-configurable and adaptive. Due to the mobility of nodes, the network congestion occurs and it is difficult to predict load on the network which leads to congestion. Mobile adhoc network suffers from a severe congestion controlling problem due to the nature of shared communication and mobility. Standard TCP controlling mechanism for congestion is not fit to the dynamic changing topology of MANETs. This provides a wide scope of research work in mobile ad hoc network. The purpose of this survey is to study and analyze various issues and challenges in congestion control mechanisms in adaptive routing protocols in Mobile Adhoc Network (MANET)

Predicting expected TCP throughput using genetic algorithm

Predicting the expected throughput of TCP is important for several aspects such as e.g. determining handover criteria for future multihomed mobile nodes or determining the expected throughput of a given MPTCP subflow for load-balancing reasons. However, this is challenging due to time varying behavior of the underlying network characteristics. In this paper, we present a genetic-algorithm-based prediction model for estimating TCP throughput values. Our approach tries to find the best matching combination of mathematical functions that approximate a given time series that accounts for the TCP throughput samples using genetic algorithm. Based on collected historical datapoints about measured TCP throughput samples, our algorithm estimates expected throughput over time. We evaluate the quality of the prediction using different selection and diversity strategies for creating new chromosomes. Also, we explore the use of different fitness functions in order to evaluate the goodness of a chromosome. The goal is to show how different tuning on the genetic algorithm may have an impact on the prediction. Using extensive simulations over several TCP throughput traces, we find that the genetic algorithm successfully finds reasonable matching mathematical functions that allow to describe the TCP sampled throughput values with good fidelity. We also explore the effectiveness of predicting time series throughput samples for a given prediction horizon and estimate the prediction error and confidence.Peer ReviewedPostprint (author's final draft