Mobile Ad hoc Networks (MANETs) are of great interest to researchers and have become very popular in the last few years. One of the great challenges is to provide a routing protocol that is capable of offering the shortest and most reliable path in a MANET in which users are moving continuously and have no base station to be used as a reference for their position. This thesis proposes some new routing protocols based on the angles (directions) of the adjacent mobile nodes and also the node density. In choosing the next node in forming a route, the neighbour node with the closest heading angle to that of the node of interest is selected, so the connection between the source and the destination consists of a series of nodes that are moving in approximately the same direction. The rationale behind this concept is to maintain the connection between the nodes as long as possible. This is in contrast to the well known hop count method, which does not consider the connection lifetime. We propose three enhancements and modifications of the Ad-hoc on demand distance vector (AODV) protocol that can find a suitable path between source and destination using combinations and prioritization of angle direction and hop count. Firstly, we consider that if there are multiple routing paths available, the path with the minimum hop count is selected and when the hop counts are the same the path with the best angle direction is selected. Secondly, if multiple routing paths are available the paths with the best angle direction are chosen but if the angles are the same (fall within the same specified segment), the path with minimum hop count is chosen. Thirdly, if there is more than one path available, we calculate the average of all the heading angles in every path and find the best one (lowest average) from the source to the destination. In MANETs, flooding is a popular message broadcasting technique so we also propose a new scheme for MANETS where the value of the rebroadcast packets for every host node is dynamically adjusted according to the number of its neighbouring nodes. A fixed probabilistic scheme algorithm that can dynamically adjust the rebroadcasting probability at a given node according to its ID is also proposed; Fixed probabilistic schemes are one of the solutions to reduce rebroadcasts and so alleviate the broadcast storm problem. Performance evaluation of the proposed schemes is conducted using the Global Mobile Information System (GloMoSim) network simulator and varying a number of important MANET parameters, including node speed, node density, number of nodes and number of packets, all using a Random Waypoint (RWP) mobility model. Finally, we measure and compare the performance of all the proposed approaches by evaluating them against the standard AODV routing protocol. The simulation results reveal that the proposed approaches give relatively comparable overall performance but which is better than AODV for almost all performance measures and scenarios examined.

In this thesis, we tackle a significant optimization challenge within Vehicular Ad Hoc Networks (VANETs) by employing a simulated annealing approach. We focus on developing an efficient Vehicle Routing Problem (VRP) algorithm to sift through numerous potential solutions and identify the best one. Our VANET scenario revolves around four distinct vehicles traversing four unique routes. The primary objective is to minimize the total distance covered by these vehicles while ensuring that they visit all designated waypoints. We implement this problem using MATLAB to establish initial routes for each simulation uniquely. Simulated annealing proves to be a valuable tool in optimizing VANETs. The gradual cooling process reduces the likelihood of accepting suboptimal solutions over time, allowing the algorithm to escape local optima and converge towards nearly optimal solutions. Regarding routing protocol parameter configuration, simulated annealing is the technique of choice for identifying the most influential parameters. It evaluates the cost and creates new routes based on neighboring nodes, calculating the cost function for these new routes. Starting from an initial configuration, the algorithm iteratively refines it by introducing random changes, retaining only those that enhance the objective function. Our objective function defines the Quality of Service (QoS) and communication efficiency of the routing protocol. The gradual reduction in the acceptance of less favorable configurations over time is called the annealing schedule, enabling the algorithm to escape local optima and approach nearly optimal designs.

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