Flexible cross layer design for improved quality of service in MANETs

This thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityMobile Ad hoc Networks (MANETs) are becoming increasingly important because of their unique characteristics of connectivity. Several delay sensitive applications are starting to appear in these kinds of networks. Therefore, an issue in concern is to guarantee Quality of Service (QoS) in such constantly changing communication environment. The classical QoS aware solutions that have been used till now in the wired and infrastructure wireless networks are unable to achieve the necessary performance in the MANETs. The specialized protocols designed for multihop ad hoc networks offer basic connectivity with limited delay awareness and the mobility factor in the MANETs makes them even more unsuitable for use. Several protocols and solutions have been emerging in almost every layer in the protocol stack. The majority of the research efforts agree on the fact that in such dynamic environment in order to optimize the performance of the protocols, there is the need for additional information about the status of the network to be available. Hence, many cross layer design approaches appeared in the scene. Cross layer design has major advantages and the necessity to utilize such a design is definite. However, cross layer design conceals risks like architecture instability and design inflexibility. The aggressive use of cross layer design results in excessive increase of the cost of deployment and complicates both maintenance and upgrade of the network. The use of autonomous protocols like bio-inspired mechanisms and algorithms that are resilient on cross layer information unavailability, are able to reduce the dependence on cross layer design. In addition, properties like the prediction of the dynamic conditions and the adaptation to them are quite important characteristics. The design of a routing decision algorithm based on Bayesian Inference for the prediction of the path quality is proposed here. The accurate prediction capabilities and the efficient use of the plethora of cross layer information are presented. Furthermore, an adaptive mechanism based on the Genetic Algorithm (GA) is used to control the flow of the data in the transport layer. The aforementioned flow control mechanism inherits GA’s optimization capabilities without the need of knowing any details about the network conditions, thus, reducing the cross layer information dependence. Finally, is illustrated how Bayesian Inference can be used to suggest configuration parameter values to the other protocols in different layers in order to improve their performance.National Foundation of Scholarships of Greece(I.K.Y.

Mobile Ad Hoc Networks

Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms

Mobile Ad Hoc Networks

Guiding readers through the basics of these rapidly emerging networks to more advanced concepts and future expectations, Mobile Ad hoc Networks: Current Status and Future Trends identifies and examines the most pressing research issues in Mobile Ad hoc Networks (MANETs). Containing the contributions of leading researchers, industry professionals, and academics, this forward-looking reference provides an authoritative perspective of the state of the art in MANETs. The book includes surveys of recent publications that investigate key areas of interest such as limited resources and the mobility of mobile nodes. It considers routing, multicast, energy, security, channel assignment, and ensuring quality of service. Also suitable as a text for graduate students, the book is organized into three sections: Fundamentals of MANET Modeling and Simulation—Describes how MANETs operate and perform through simulations and models Communication Protocols of MANETs—Presents cutting-edge research on key issues, including MAC layer issues and routing in high mobility Future Networks Inspired By MANETs—Tackles open research issues and emerging trends Illustrating the role MANETs are likely to play in future networks, this book supplies the foundation and insight you will need to make your own contributions to the field. It includes coverage of routing protocols, modeling and simulations tools, intelligent optimization techniques to multicriteria routing, security issues in FHAMIPv6, connecting moving smart objects to the Internet, underwater sensor networks, wireless mesh network architecture and protocols, adaptive routing provision using Bayesian inference, and adaptive flow control in transport layer using genetic algorithms

A Novel Energy Aware Clustering Mechanism with Fuzzy Logic in MANET Environment

A Mobile Ad Hoc Networks (MANETs) comprises of the vast range of devices such as sensors, smart phones, laptops and other mobile devices that connect with each other across wireless networks and collaborate in a dispersed fashion to offer network functions in the absence of a permanent infrastructure. The Cluster Head (CH) selection in a clustered MANET is still crucial for lowering each node's energy consumption and increasing the network's lifetime. However, in existing clustering mechanism trust of the all nodes are presumed those causes increased challenge in the MANET environment. Security is a crucial factor when constructing ad-hoc networks. In a MANET, energy consumption in route optimization is dependent on network resilience and connectivity. The primary objective of this study is to design a reliable clustering mechanism for MANETs that takes energy efficiency into account. For trusted energy-efficient CH in the nodes, a safe clustering strategy integrating energy-efficient and fuzzy logic based energy clustering is proposed to address security problems brought about by malicious nodes and to pick a trustworthy node as CH. To improve the problem findings Bat algorithm (BAT) is integrated with Particle Swarm Optimization (PSO). The PSO technique is inspired because it imitates the sociological characteristics of the flock of the birds through random population. The BAT is a metaheuristic algorithm inspired by microbat echolocation behavior that uses pulse average with global optimization of the average path in the network. Hybrid Particle Swarm Optimization (HPSO) and BAT techniques are applied to identify the best route between the source and destination. According to the simulation results, the suggested Fuzzy logic Particle Swarm Optimization BAT (FLPSO-BAT) technique has a minimum latency of 0.0019 milliseconds, with energy consumption value of 0.09 millijoules, maximal throughput of 0.76 bits per sec and detection rate of 90.5% without packet dropping attack

Trust-based Selfish Node Detection Mechanism using Beta Distribution in Wireless Sensor Network

Wireless sensor networks (WSNs) are placed in open environments for the collection of data and are vulnerable to external and internal attacks. The cryptographic mechanisms implemented so far, such as authorization and authentication, are used to restrict external sensor node attacks but cannot prevent internal node attacks. In order to evade internal attacks trust mechanisms are used. In trust mechanisms, firstly, the sensor nodes are monitored using the popular Watchdog mechanism. However, traditional trust models do not pay much attention to selective forwarding and consecutive packet dropping. Sometimes, sensitive data are dropped by internal attackers. This problem is addressed in our proposed model by detecting selective forwarding and consecutive failure of sending packets using the Beta probability density function model

Outlier Detection Mechanism for Ensuring Availability in Wireless Mobile Networks Anomaly Detection

Finding things that are significantly different from, incomparable with, and inconsistent with the majority of data in many domains is the focus of the important research problem of anomaly detection. A noteworthy research problem has recently been illuminated by the explosion of data that has been gathered. This offers brand-new opportunities as well as difficulties for anomaly detection research. The analysis and monitoring of data connected to network traffic, weblogs, medical domains, financial transactions, transportation domains, and many more are just a few of the areas in which anomaly detection is useful. An important part of assessing the effectiveness of mobile ad hoc networks (MANET) is anomaly detection. Due to difficulties in the associated protocols, MANET has become a popular study topic in recent years. No matter where they are geographically located, users can connect to a dynamic infrastructure using MANETs. Small, powerful, and affordable devices enable MANETs to self-organize and expand quickly. By an outlier detection approach, the proposed work provides cryptographic property and availability for an RFID-WSN integrated network with node counts ranging from 500 to 5000. The detection ratio and anomaly scores are used to measure the system's resistance to outliers. The suggested method uses anomaly scores to identify outliers and provide defence against DoS attacks. The suggested method uses anomaly scores to identify outliers and provide protection from DoS attacks. The proposed method has been shown to detect intruders in a matter of milliseconds without interfering with authorised users' privileges. Throughput is improved by at least 6.8% using the suggested protocol, while Packet Delivery Ratio (PDR) is improved by at least 9.2% and by as much as 21.5%

Node Activities Learning(NAL)Approach to Build Secure and Privacy-Preserving Routing in Wireless Sensor Networks

Wireless networks are becoming the most popular in today communication systems, where users prefer to have wireless connectivity regardless of its geographic location. But the open environment of wireless communication increasing threat on the wireless networks under diverse network circumstances. The random and dynamic activity increases the  vulnerability due to the complete dependency on the intermediate nodes which frequently join and leave the network. It is extremely significant to have a secure routing in such a dynamic network to preserve the data privacy. In this paper, we propose a secure and privacy routing based on Node Activities Learning (NAL) approach. This approach knows the runtime activities of the node to predict the probability of activity transformation for the intentional and unintentional activities which interrupt the data communication and affects the privacy. The mean of privacy is decided based on the node individual trust factor. It also suggests a method for the node which loses their trust due to the unintentional activities. A simulation-based evaluation study shows positive improvisation in secure routing in different malicious node environment

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Adaptive network protocols to support queries in dynamic networks

Recent technological advancements have led to the popularity of mobile devices, which can dynamically form wireless networks. In order to discover and obtain distributed information, queries are widely used by applications in opportunistically formed mobile networks. Given the popularity of this approach, application developers can choose from a number of implementations of query processing protocols to support the distributed execution of a query over the network. However, different inquiry strategies (i.e., the query processing protocol and associated parameters used to execute a query) have different tradeoffs between the quality of the query's result and the cost required for execution under different operating conditions. The application developer's choice of inquiry strategy is important to meet the application's needs while considering the limited resources of the mobile devices that form the network. We propose adaptive approaches to choose the most appropriate inquiry strategy in dynamic mobile environments. We introduce an architecture for adaptive queries which employs knowledge about the current state of the dynamic mobile network and the history of previous query results to learn the most appropriate inquiry strategy to balance quality and cost tradeoffs in a given setting, and use this information to dynamically adapt the continuous query's execution