Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of-the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: quality-of-service and video communication, routing protocol and cross-layer design. A few interesting problems about security and delay-tolerant networks are also discussed. This book is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks

Particle swarm optimization for routing and wavelength assignment in next generation WDM networks.

PhDAll-optical Wave Division Multiplexed (WDM) networking is a promising technology for long-haul backbone and large metropolitan optical networks in order to meet the non-diminishing bandwidth demands of future applications and services. Examples could include archival and recovery of data to/from Storage Area Networks (i.e. for banks), High bandwidth medical imaging (for remote operations), High Definition (HD) digital broadcast and streaming over the Internet, distributed orchestrated computing, and peak-demand short-term connectivity for Access Network providers and wireless network operators for backhaul surges. One desirable feature is fast and automatic provisioning. Connection (lightpath) provisioning in optically switched networks requires both route computation and a single wavelength to be assigned for the lightpath. This is called Routing and Wavelength Assignment (RWA). RWA can be classified as static RWA and dynamic RWA. Static RWA is an NP-hard (non-polynomial time hard) optimisation task. Dynamic RWA is even more challenging as connection requests arrive dynamically, on-the-fly and have random connection holding times. Traditionally, global-optimum mathematical search schemes like integer linear programming and graph colouring are used to find an optimal solution for NP-hard problems. However such schemes become unusable for connection provisioning in a dynamic environment, due to the computational complexity and time required to undertake the search. To perform dynamic provisioning, different heuristic and stochastic techniques are used. Particle Swarm Optimisation (PSO) is a population-based global optimisation scheme that belongs to the class of evolutionary search algorithms and has successfully been used to solve many NP-hard optimisation problems in both static and dynamic environments. In this thesis, a novel PSO based scheme is proposed to solve the static RWA case, which can achieve optimal/near-optimal solution. In order to reduce the risk of premature convergence of the swarm and to avoid selecting local optima, a search scheme is proposed to solve the static RWA, based on the position of swarm‘s global best particle and personal best position of each particle. To solve dynamic RWA problem, a PSO based scheme is proposed which can provision a connection within a fraction of a second. This feature is crucial to provisioning services like bandwidth on demand connectivity. To improve the convergence speed of the swarm towards an optimal/near-optimal solution, a novel chaotic factor is introduced into the PSO algorithm, i.e. CPSO, which helps the swarm reach a relatively good solution in fewer iterations. Experimental results for PSO/CPSO based dynamic RWA algorithms show that the proposed schemes perform better compared to other evolutionary techniques like genetic algorithms, ant colony optimization. This is both in terms of quality of solution and computation time. The proposed schemes also show significant improvements in blocking probability performance compared to traditional dynamic RWA schemes like SP-FF and SP-MU algorithms

SDSF : social-networking trust based distributed data storage and co-operative information fusion.

As of 2014, about 2.5 quintillion bytes of data are created each day, and 90% of the data in the world was created in the last two years alone. The storage of this data can be on external hard drives, on unused space in peer-to-peer (P2P) networks or using the more currently popular approach of storing in the Cloud. When the users store their data in the Cloud, the entire data is exposed to the administrators of the services who can view and possibly misuse the data. With the growing popularity and usage of Cloud storage services like Google Drive, Dropbox etc., the concerns of privacy and security are increasing. Searching for content or documents, from this distributed stored data, given the rate of data generation, is a big challenge. Information fusion is used to extract information based on the query of the user, and combine the data and learn useful information. This problem is challenging if the data sources are distributed and heterogeneous in nature where the trustworthiness of the documents may be varied. This thesis proposes two innovative solutions to resolve both of these problems. Firstly, to remedy the situation of security and privacy of stored data, we propose an innovative Social-based Distributed Data Storage and Trust based co-operative Information Fusion Framework (SDSF). The main objective is to create a framework that assists in providing a secure storage system while not overloading a single system using a P2P like approach. This framework allows the users to share storage resources among friends and acquaintances without compromising the security or privacy and enjoying all the benefits that the Cloud storage offers. The system fragments the data and encodes it to securely store it on the unused storage capacity of the data owner\u27s friends\u27 resources. The system thus gives a centralized control to the user over the selection of peers to store the data. Secondly, to retrieve the stored distributed data, the proposed system performs the fusion also from distributed sources. The technique uses several algorithms to ensure the correctness of the query that is used to retrieve and combine the data to improve the information fusion accuracy and efficiency for combining the heterogeneous, distributed and massive data on the Cloud for time critical operations. We demonstrate that the retrieved documents are genuine when the trust scores are also used while retrieving the data sources. The thesis makes several research contributions. First, we implement Social Storage using erasure coding. Erasure coding fragments the data, encodes it, and through introduction of redundancy resolves issues resulting from devices failures. Second, we exploit the inherent concept of trust that is embedded in social networks to determine the nodes and build a secure net-work where the fragmented data should be stored since the social network consists of a network of friends, family and acquaintances. The trust between the friends, and availability of the devices allows the user to make an informed choice about where the information should be stored using `k\u27 optimal paths. Thirdly, for the purpose of retrieval of this distributed stored data, we propose information fusion on distributed data using a combination of Enhanced N-grams (to ensure correctness of the query), Semantic Machine Learning (to extract the documents based on the context and not just bag of words and also considering the trust score) and Map Reduce (NSM) Algorithms. Lastly we evaluate the performance of distributed storage of SDSF using era- sure coding and identify the social storage providers based on trust and evaluate their trustworthiness. We also evaluate the performance of our information fusion algorithms in distributed storage systems. Thus, the system using SDSF framework, implements the beneficial features of P2P networks and Cloud storage while avoiding the pitfalls of these systems. The multi-layered encrypting ensures that all other users, including the system administrators cannot decode the stored data. The application of NSM algorithm improves the effectiveness of fusion since large number of genuine documents are retrieved for fusion

Sensor Network Based Collision-Free Navigation and Map Building for Mobile Robots

Safe robot navigation is a fundamental research field for autonomous robots including ground mobile robots and flying robots. The primary objective of a safe robot navigation algorithm is to guide an autonomous robot from its initial position to a target or along a desired path with obstacle avoidance. With the development of information technology and sensor technology, the implementations combining robotics with sensor network are focused on in the recent researches. One of the relevant implementations is the sensor network based robot navigation. Moreover, another important navigation problem of robotics is safe area search and map building. In this report, a global collision-free path planning algorithm for ground mobile robots in dynamic environments is presented firstly. Considering the advantages of sensor network, the presented path planning algorithm is developed to a sensor network based navigation algorithm for ground mobile robots. The 2D range finder sensor network is used in the presented method to detect static and dynamic obstacles. The sensor network can guide each ground mobile robot in the detected safe area to the target. Furthermore, the presented navigation algorithm is extended into 3D environments. With the measurements of the sensor network, any flying robot in the workspace is navigated by the presented algorithm from the initial position to the target. Moreover, in this report, another navigation problem, safe area search and map building for ground mobile robot, is studied and two algorithms are presented. In the first presented method, we consider a ground mobile robot equipped with a 2D range finder sensor searching a bounded 2D area without any collision and building a complete 2D map of the area. Furthermore, the first presented map building algorithm is extended to another algorithm for 3D map building