Multicasting Model for Efficient Data Transmission in VANET

VANETs (Vehicle Ad hoc Networks) are networks made up of a number of vehicular nodes that are free to enter and leave the network. The Location Aided Routing (LAR) protocol is the one that is most frequently utilized among them. Here, the route request packets are flooded across many pathways to the source node using the broadcasting strategy. The vehicles that have a direct path to the destination send the route reply packets back to the source. The least number of hops and the sequence number are used to determine the route from source to destination. This research study has used the multicasting approach to construct a path from the source node to the destination node. Within this multicasting strategy, the root nodes from the network are selected for data routing. The path between the source and the destination is chosen using a root node. The suggested approach is put into practice using the NS2, and some parametric values are computed to produce analytical findings

Cognitive Security Framework For Heterogeneous Sensor Network Using Swarm Intelligence

Rapid development of sensor technology has led to applications ranging from academic to military in a short time span. These tiny sensors are deployed in environments where security for data or hardware cannot be guaranteed. Due to resource constraints, traditional security schemes cannot be directly applied. Unfortunately, due to minimal or no communication security schemes, the data, link and the sensor node can be easily tampered by intruder attacks. This dissertation presents a security framework applied to a sensor network that can be managed by a cohesive sensor manager. A simple framework that can support security based on situation assessment is best suited for chaotic and harsh environments. The objective of this research is designing an evolutionary algorithm with controllable parameters to solve existing and new security threats in a heterogeneous communication network. An in-depth analysis of the different threats and the security measures applied considering the resource constrained network is explored. Any framework works best, if the correlated or orthogonal performance parameters are carefully considered based on system goals and functions. Hence, a trade-off between the different performance parameters based on weights from partially ordered sets is applied to satisfy application specific requirements and security measures. The proposed novel framework controls heterogeneous sensor network requirements,and balance the resources optimally and efficiently while communicating securely using a multi-objection function. In addition, the framework can measure the affect of single or combined denial of service attacks and also predict new attacks under both cooperative and non-cooperative sensor nodes. The cognitive intuition of the framework is evaluated under different simulated real time scenarios such as Health-care monitoring, Emergency Responder, VANET, Biometric security access system, and Battlefield monitoring. The proposed three-tiered Cognitive Security Framework is capable of performing situation assessment and performs the appropriate security measures to maintain reliability and security of the system. The first tier of the proposed framework, a crosslayer cognitive security protocol defends the communication link between nodes during denial-of-Service attacks by re-routing data through secure nodes. The cognitive nature of the protocol balances resources and security making optimal decisions to obtain reachable and reliable solutions. The versatility and robustness of the protocol is justified by the results obtained in simulating health-care and emergency responder applications under Sybil and Wormhole attacks. The protocol considers metrics from each layer of the network model to obtain an optimal and feasible resource efficient solution. In the second tier, the emergent behavior of the protocol is further extended to mine information from the nodes to defend the network against denial-of-service attack using Bayesian models. The jammer attack is considered the most vulnerable attack, and therefore simulated vehicular ad-hoc network is experimented with varied types of jammer. Classification of the jammer under various attack scenarios is formulated to predict the genuineness of the attacks on the sensor nodes using receiver operating characteristics. In addition to detecting the jammer attack, a simple technique of locating the jammer under cooperative nodes is implemented. This feature enables the network in isolating the jammer or the reputation of node is affected, thus removing the malicious node from participating in future routes. Finally, a intrusion detection system using `bait\u27 architecture is analyzed where resources is traded-off for the sake of security due to sensitivity of the application. The architecture strategically enables ant agents to detect and track the intruders threateningthe network. The proposed framework is evaluated based on accuracy and speed of intrusion detection before the network is compromised. This process of detecting the intrusion earlier helps learn future attacks, but also serves as a defense countermeasure. The simulated scenarios of this dissertation show that Cognitive Security Framework isbest suited for both homogeneous and heterogeneous sensor networks

Energy-efficient routing protocols in heterogeneous wireless sensor networks

Sensor networks feature low-cost sensor devices with wireless network capability, limited transmit power, resource constraints and limited battery energy. The usage of cheap and tiny wireless sensors will allow very large networks to be deployed at a feasible cost to provide a bridge between information systems and the physical world. Such large-scale deployments will require routing protocols that scale to large network sizes in an energy-efficient way. This thesis addresses the design of such network routing methods. A classification of existing routing protocols and the key factors in their design (i.e., hardware, topology, applications) provides the motivation for the new three-tier architecture for heterogeneous networks built upon a generic software framework (GSF). A range of new routing algorithms have hence been developed with the design goals of scalability and energy-efficient performance of network protocols. They are respectively TinyReg - a routing algorithm based on regular-graph theory, TSEP - topological stable election protocol, and GAAC - an evolutionary algorithm based on genetic algorithms and ant colony algorithms. The design principle of our routing algorithms is that shortening the distance between the cluster-heads and the sink in the network, will minimise energy consumption in order to extend the network lifetime, will achieve energy efficiency. Their performance has been evaluated by simulation in an extensive range of scenarios, and compared to existing algorithms. It is shown that the newly proposed algorithms allow long-term continuous data collection in large networks, offering greater network longevity than existing solutions. These results confirm the validity of the GSF as an architectural approach to the deployment of large wireless sensor networks

Improving relay based cellular networks performance in highly user congested and emergency situations

PhDRelay based cellular networks (RBCNs) are the technologies that incorporate multi-hop communication into traditional cellular networks. A RBCN can potentially support higher data rates, more stable radio coverage and more dynamic services. In reality, RBCNs still suffer from performance degradation in terms of high user congestion, base station failure and overloading in emergency situations. The focus of this thesis is to explore the potential to improve IEEE802.16j supported RBCN performance in user congestion and emergency situations using adjustments to the RF layer (by antenna adjustments or extensions using multi-hop) and cooperative adjustment algorithms, e.g. based on controlling frequency allocation centrally and using distributed approaches. The first part of this thesis designs and validates network reconfiguration algorithms for RBCN, including a cooperative antenna power control algorithm and a heuristic antenna tilting algorithm. The second part of this thesis investigates centralized and distributed dynamic frequency allocation for higher RBCN frequency efficiency, network resilience, and computation simplicity. It is demonstrated that these benefits mitigate user congestion and base station failure problems significantly. Additionally, interweaving coordinated dynamic frequency allocation and antenna tilting is investigated in order to obtain the benefits of both actions. The third part of this thesis incorporates Delay Tolerate Networking (DTN) technology into RBCN to let users self-organize to connect to functional base station through multi-hops supported by other users. Through the use of DTN, RBCN coverage and performance are improved. This thesis explores the augmentation of DTN routing protocols to let more un-covered users connect to base stations and improve network load balancin

Design and Analysis of An Improved AODV Protocol Based on Clustering Approach for Internet of Vehicles (AODV-CD)

The Internet of Vehicles (IoVs) has become a vital research area in order to enhance passenger and road safety, increasing traffic efficiency and enhanced reliable connectivity. In this regard, for monitoring and controlling the communication between IoVs, routing protocols are deployed. Frequent changes that occur in the topology often leads to major challenges in IoVs, such as dynamic topology changes, shortest routing paths and also scalability. One of the best solutions for such challenges is “clustering”. This study focuses on IoVs’ stability and to create an efficient routing protocol in dynamic environment. In this context, we proposed a novel algorithm called Cluster-based enhanced AODV for IoVs (AODV-CD) to achieve stable and efficient clustering for simplifying routing and ensuring quality of service (QoS). Our proposed protocol enhances the overall network throughput and delivery ratio, with less routing load and less delay compared to AODV. Thus, extensive simulations are carried out in SUMO and NS2 for evaluating the efficiency of the AODV-CD that is superior to the classic AODV and other recent modified AODV algorithms.

Optimal packet routing for wireless body area network using software defined network to handle medical emergency

The packet forwarding node selection is one of the main constraints in the Software Defined Network (SDN). To improve the network performance, the SDN controller has to choose the shortest and optimised path between source and destination in routine and emergency packet transmission. In e-health service, information of the emergency patient has to be transferred immediately to remote hospitals or doctors by using efficient packet routing approach in Wireless Body Area Network (WBAN). In WBAN, to improve the packet transmission, the optimal packet routing policy developed based on packets priority with the support of a greedy algorithm for SDN. The SDN Controller selects the forwarding node based on node propagation delay and available bandwidth between two forwarding nodes. The mesh network topology network created for implementation, implementation results are compared with existing research works. Finally, this algorithm implemented in our institution, Software defined communication testbed laboratory (SDCTestbed Lab) with the support of 13 Zodiac-Fx (Forwarding device), 2 Raspberry-Pi3 B+ Model (host) and Arduino kit (sensor node)

An Intelligent Mobility Prediction Scheme for Location-Based Service over Cellular Communications Network

One of the trickiest challenges introduced by cellular communications networks is mobility prediction for Location Based-Services (LBSs). Hence, an accurate and efficient mobility prediction technique is particularly needed for these networks. The mobility prediction technique incurs overheads on the transmission process. These overheads affect properties of the cellular communications network such as delay, denial of services, manual filtering and bandwidth. The main goal of this research is to enhance a mobility prediction scheme in cellular communications networks through three phases. Firstly, current mobility prediction techniques will be investigated. Secondly, innovation and examination of new mobility prediction techniques will be based on three hypothesises that are suitable for cellular communications network and mobile user (MU) resources with low computation cost and high prediction success rate without using MU resources in the prediction process. Thirdly, a new mobility prediction scheme will be generated that is based on different levels of mobility prediction. In this thesis, a new mobility prediction scheme for LBSs is proposed. It could be considered as a combination of the cell and routing area (RA) prediction levels. For cell level prediction, most of the current location prediction research is focused on generalized location models, where the geographic extent is divided into regular-shape cells. These models are not suitable for certain LBSs where the objectives are to compute and present on-road services. Such techniques are the New Markov-Based Mobility Prediction (NMMP) and Prediction Location Model (PLM) that deal with inner cell structure and different levels of prediction, respectively. The NMMP and PLM techniques suffer from complex computation, accuracy rate regression and insufficient accuracy. In this thesis, Location Prediction based on a Sector Snapshot (LPSS) is introduced, which is based on a Novel Cell Splitting Algorithm (NCPA). This algorithm is implemented in a micro cell in parallel with the new prediction technique. The LPSS technique, compared with two classic prediction techniques and the experimental results, shows the effectiveness and robustness of the new splitting algorithm and prediction technique. In the cell side, the proposed approach reduces the complexity cost and prevents the cell level prediction technique from performing in time slots that are too close. For these reasons, the RA avoids cell-side problems. This research discusses a New Routing Area Displacement Prediction for Location-Based Services (NRADP) which is based on developed Ant Colony Optimization (ACO). The NRADP, compared with Mobility Prediction based on an Ant System (MPAS) and the experimental results, shows the effectiveness, higher prediction rate, reduced search stagnation ratio, and reduced computation cost of the new prediction technique

The Application of Ant Colony Optimization

The application of advanced analytics in science and technology is rapidly expanding, and developing optimization technics is critical to this expansion. Instead of relying on dated procedures, researchers can reap greater rewards by utilizing cutting-edge optimization techniques like population-based metaheuristic models, which can quickly generate a solution with acceptable quality. Ant Colony Optimization (ACO) is one the most critical and widely used models among heuristics and meta-heuristics. This book discusses ACO applications in Hybrid Electric Vehicles (HEVs), multi-robot systems, wireless multi-hop networks, and preventive, predictive maintenance