Optimization of intersatellite routing for real-time data download

The objective of this study is to develop a strategy to maximise the available bandwidth to Earth of a satellite constellation through inter-satellite links. Optimal signal routing is achieved by mimicking the way in which ant colonies locate food sources, where the 'ants' are explorative data packets aiming to find a near-optimal route to Earth. Demonstrating the method on a case-study of a space weather monitoring constellation; we show the real-time downloadable rate to Earth

Nature Inspired Range Based Wireless Sensor Node Localization Algorithms

Localization is one of the most important factors highly desirable for the performance of Wireless Sensor Network (WSN). Localization can be stated as the estimation of the location of the sensor nodes in sensor network. In the applications of WSN, the data gathered at sink node will be meaningless without localization information of the nodes. Due to size and complexity factors of the localization problem, it can be formulated as an optimization problem and thus can be approached with optimization algorithms. In this paper, the nature inspired algorithms are used and analyzed for an optimal estimation of the location of sensor nodes. The performance of the nature inspired algorithms viz. Flower pollination algorithm (FPA), Firefly algorithm (FA), Grey Wolf Optimization (GWO) and Particle Swarm Optimization (PSO) for localization in WSN is analyzed in terms of localization accuracy, number of localized nodes and computing time. The comparative analysis has shown that FPA is more proficient in determining the coordinates of nodes by minimizing the localization error as compared to FA, PSO and GWO

Reactive approach for automating exploration and exploitation in ant colony optimization

Ant colony optimization (ACO) algorithms can be used to solve nondeterministic polynomial hard problems. Exploration and exploitation are the main mechanisms in controlling search within the ACO. Reactive search is an alternative technique to maintain the dynamism of the mechanics. However, ACO-based reactive search technique has three (3) problems. First, the memory model to record previous search regions did not completely transfer the neighborhood structures to the next iteration which leads to arbitrary restart and premature local search. Secondly, the exploration indicator is not robust due to the difference of magnitudes in distance matrices for the current population. Thirdly, the parameter control techniques that utilize exploration indicators in their feedback process do not consider the problem of indicator robustness. A reactive ant colony optimization (RACO) algorithm has been proposed to overcome the limitations of the reactive search. RACO consists of three main components. The first component is a reactive max-min ant system algorithm for recording the neighborhood structures. The second component is a statistical machine learning mechanism named ACOustic to produce a robust exploration indicator. The third component is the ACO-based adaptive parameter selection algorithm to solve the parameterization problem which relies on quality, exploration and unified criteria in assigning rewards to promising parameters. The performance of RACO is evaluated on traveling salesman and quadratic assignment problems and compared with eight metaheuristics techniques in terms of success rate, Wilcoxon signed-rank, Chi-square and relative percentage deviation. Experimental results showed that the performance of RACO is superior than the eight (8) metaheuristics techniques which confirmed that RACO can be used as a new direction for solving optimization problems. RACO can be used in providing a dynamic exploration and exploitation mechanism, setting a parameter value which allows an efficient search, describing the amount of exploration an ACO algorithm performs and detecting stagnation situations

On Integrating Failure Localization with Survivable Design

In this thesis, I proposed a novel framework of all-optical failure restoration which jointly determines network monitoring plane and spare capacity allocation in the presence of either static or dynamic traffic. The proposed framework aims to enable a general shared protection scheme to achieve near optimal capacity efficiency as in Failure Dependent Protection(FDP) while subject to an ultra-fast, all-optical, and deterministic failure restoration process. Simply put, Local Unambiguous Failure Localization(L-UFL) and FDP are the two building blocks for the proposed restoration framework. Under L-UFL, by properly allocating a set of Monitoring Trails (m-trails), a set of nodes can unambiguously identify every possible Shared Risk Link Group (SRLG) failure merely based on its locally collected Loss of Light(LOL) signals. Two heuristics are proposed to solve L-UFL, one of which exclusively deploys Supervisory Lightpaths (S-LPs) while the other jointly considers S-LPs and Working Lightpaths (W-LPs) for suppressing monitoring resource consumption. Thanks to the ``Enhanced Min Wavelength Max Information principle'', an entropy based utility function, m-trail global-sharing and other techniques, the proposed heuristics exhibit satisfactory performance in minimizing the number of m-trails, Wavelength Channel(WL) consumption and the running time of the algorithm. Based on the heuristics for L-UFL, two algorithms, namely MPJD and DJH, are proposed for the novel signaling-free restoration framework to deal with static and dynamic traffic respectively. MPJD is developed to determine the Protection Lightpaths (P-LPs) and m-trails given the pre-computed W-LPs while DJH jointly implements a generic dynamic survivable routing scheme based on FDP with an m-trail deployment scheme. For both algorithms, m-trail deployment is guided by the Necessary Monitoring Requirement (NMR) defined at each node for achieving signaling-free restoration. Extensive simulation is conducted to verify the performance of the proposed heuristics in terms of WL consumption, number of m-trails, monitoring requirement, blocking probability and running time. In conclusion, the proposed restoration framework can achieve all-optical and signaling-free restoration with the help of L-UFL, while maintaining high capacity efficiency as in FDP based survivable routing. The proposed heuristics achieve satisfactory performance as verified by the simulation results

The distributed p-median problem in computer networks

The exponential growth of the Internet over the last decades has led to a significant evolution of the network services and applications. One of the challenges is to provide better services scalability by placing service replica in appropriate network locations. Finding the optimal solution to the facility location problem is particularly complex and is not feasible for large scale systems. Locating facilities in near-optimal locations have been extensively studied in many works and for different application domains. This work investigates one of the most notable problems in facility location, i.e. the p-median problem, which locates p facilities with a minimum overall communication cost. All previous studies on the p-median problem used a centralised approach to find the near-optimal solution. In this case the required information needs to be collected in order to apply a sequential algorithm to find a solution. The centralised approach is infeasible in large-scale networks due to the time and space complexity of the sequential algorithms as well as the large communication cost and latency to aggregate the global information. Therefore, this work investigates the p-median problem in a distributed environment. To the best of the author’s knowledge, this is the first work to study the distributed pmedian problem for large-scale computer networks. Solving the p-median problem in a fully distributed way is a challenging task due to the lack of global knowledge and of a centralised coordinator. Two new approaches for solving the p-median problem in a distributed environment are proposed in this thesis. Both are designed to be executed without any centralised collection of the data in a single node. These methods apply an iterative heuristic approach to improve a random initial solution and to converge to a final solution with a local minimum of the cost. The first approach builds a global view of the system and improves the current solution by replacing a single facility at each iteration. The second approach, is designed according to the well-known k-medoids clustering algorithm. At each iteration a local view of each cluster is generated and all facilities can be updated to optimise the solution. Both approaches were implemented within the Java-based PeerSim network simulator for investigating the performance in large-scale systems and tested against different parameters such as the size of networks, number of facilities to be placed and different initial solutions. The results have shown that the first protocol is better at addressing locations for facilities since it converges to a lower total cost of the solution than the second protocol. However, the second one is faster in optimising the solution

A PATH ENUMERATION REFORMULATION OF THE SCHEDULE MIXED INTEGER PROGRAM SUPPORTING EXPEDITIONARY ADVANCED BASE OPERATIONS.

The U.S. Marine Corps needs an accurate model for analyzing its logistical needs in support of Expeditionary Advanced Base Operations (EABO). EABO is a doctrinal method used by the U.S. Navy and Marine Corps for denying adversary forces access to the maritime global commons. Deployment and sustainment of forces engaged in EABO requires a distribution network supported by various surface and airborne connector platforms of differing capacity and speed. The Marine Corps currently has a model for analyzing its distribution networks in support of EABO, the Schedule Mixed Integer Program (S-MIP). However, the computational difficulty of S-MIP limits its usefulness in large-scale experiments. This thesis describes a path enumeration-based reformulation known as the Path Enumeration Mixed-Integer Program (PE-MIP). PE-MIP is designed to provide a less computationally difficult model than the antecedent model S-MIP. We compare the runtime of PE-MIP and the quality of its solutions with that of S-MIP model and find that PE-MIP provides faster and superior results to S-MIP. The application of PE-MIP by the research sponsor will further inform current Marine Corps and Navy operational plans, acquisition, and force structure decisions.Operational Analysis Directorate, USMC, QUANTICO, VA, 22134Major, United States Marine CorpsApproved for public release. Distribution is unlimited

Ant Colony Optimization

Ant Colony Optimization (ACO) is the best example of how studies aimed at understanding and modeling the behavior of ants and other social insects can provide inspiration for the development of computational algorithms for the solution of difficult mathematical problems. Introduced by Marco Dorigo in his PhD thesis (1992) and initially applied to the travelling salesman problem, the ACO field has experienced a tremendous growth, standing today as an important nature-inspired stochastic metaheuristic for hard optimization problems. This book presents state-of-the-art ACO methods and is divided into two parts: (I) Techniques, which includes parallel implementations, and (II) Applications, where recent contributions of ACO to diverse fields, such as traffic congestion and control, structural optimization, manufacturing, and genomics are presented

Reliable cost-optimal deployment of wireless sensor networks

Wireless Sensor Networks (WSNs) technology is currently considered one of the key technologies for realizing the Internet of Things (IoT). Many of the important WSNs applications are critical in nature such that the failure of the WSN to carry out its required tasks can have serious detrimental effects. Consequently, guaranteeing that the WSN functions satisfactorily during its intended mission time, i.e. the WSN is reliable, is one of the fundamental requirements of the network deployment strategy. Achieving this requirement at a minimum deployment cost is particularly important for critical applications in which deployed SNs are equipped with expensive hardware. However, WSN reliability, defined in the traditional sense, especially in conjunction with minimizing the deployment cost, has not been considered as a deployment requirement in existing WSN deployment algorithms to the best of our knowledge. Addressing this major limitation is the central focus of this dissertation. We define the reliable cost-optimal WSN deployment as the one that has minimum deployment cost with a reliability level that meets or exceeds a minimum level specified by the targeted application. We coin the problem of finding such deployments, for a given set of application-specific parameters, the Minimum-Cost Reliability-Constrained Sensor Node Deployment Problem (MCRC-SDP). To accomplish the aim of the dissertation, we propose a novel WSN reliability metric which adopts a more accurate SN model than the model used in the existing metrics. The proposed reliability metric is used to formulate the MCRC-SDP as a constrained combinatorial optimization problem which we prove to be NP-Complete. Two heuristic WSN deployment optimization algorithms are then developed to find high quality solutions for the MCRC-SDP. Finally, we investigate the practical realization of the techniques that we developed as solutions of the MCRC-SDP. For this purpose, we discuss why existing WSN Topology Control Protocols (TCPs) are not suitable for managing such reliable cost-optimal deployments. Accordingly, we propose a practical TCP that is suitable for managing the sleep/active cycles of the redundant SNs in such deployments. Experimental results suggest that the proposed TCP\u27s overhead and network Time To Repair (TTR) are relatively low which demonstrates the applicability of our proposed deployment solution in practice

Control plane routing in photonic networks

The work described in the thesis investigates the features of control plane functionality for routing wavelength paths to serve a set of sub-wavelength demands. The work takes account of routing problems only found in physical network layers, notably analogue transmission impairments. Much work exists on routing connections for dynamic Wavelength-Routed Optical Networks (WRON) and to demonstrate their advantages over static photonic networks. However, the question of how agile the WRON should be has not been addressed quantitatively. A categorization of switching speeds is extended, and compared with the reasons for requiring network agility. The increase of effective network capacity achieved with increased agility is quantified through new simulations. It is demonstrated that this benefit only occurs within a certain window of network fill; achievement of significant gain from a more-agile network may be prevented by the operator’s chosen tolerable blocking probability. The Wavelength Path Sharing (WPS) scheme uses semi-static wavelengths to form unidirectional photonic shared buses, reducing the need for photonic agility. Making WPS more practical, novel improved routing algorithms are proposed and evaluated for both execution time and performance, offering significant benefit in speed at modest cost in efficiency. Photonic viability is the question of whether a path that the control plane can configure will work with an acceptable bit error rate (BER) despite the physical transmission impairments encountered. It is shown that, although there is no single approach that is simple, quick to execute and generally applicable at this time, under stated conditions approximations may be made to achieve a general solution that will be fast enough to enable some applications of agility. The presented algorithms, analysis of optimal network agility and viability assessment approaches can be applied in the analysis and design of future photonic control planes and network architectures

Optimum Allocation of Inspection Stations in Multistage Manufacturing Processes by Using Max-Min Ant System

In multistage manufacturing processes it is common to locate inspection stations after some or all of the processing workstations. The purpose of the inspection is to reduce the total manufacturing cost, resulted from unidentified defective items being processed unnecessarily through subsequent manufacturing operations. This total cost is the sum of the costs of production, inspection and failures (during production and after shipment). Introducing inspection stations into a serial multistage manufacturing process, although constituting an additional cost, is expected to be a profitable course of action. Specifically, at some positions the associated inspection costs will be recovered from the benefits realised through the detection of defective items, before wasting additional cost by continuing to process them. In this research, a novel general cost modelling for allocating a limited number of inspection stations in serial multistage manufacturing processes is formulated. In allocation of inspection station (AOIS) problem, as the number of workstations increases, the number of inspection station allocation possibilities increases exponentially. To identify the appropriate approach for the AOIS problem, different optimisation methods are investigated. The MAX-MIN Ant System (MMAS) algorithm is proposed as a novel approach to explore AOIS in serial multistage manufacturing processes. MMAS is an ant colony optimisation algorithm that was designed originally to begin an explorative search phase and, subsequently, to make a slow transition to the intensive exploitation of the best solutions found during the search, by allowing only one ant to update the pheromone trails. Two novel heuristics information for the MMAS algorithm are created. The heuristic information for the MMAS algorithm is exploited as a novel means to guide ants to build reasonably good solutions from the very beginning of the search. To improve the performance of the MMAS algorithm, six local search methods which are well-known and suitable for the AOIS problem are used. Selecting relevant parameter values for the MMAS algorithm can have a great impact on the algorithm’s performance. As a result, a method for tuning the most influential parameter values for the MMAS algorithm is developed. The contribution of this research is, for the first time, a methodology using MMAS to solve the AOIS problem in serial multistage manufacturing processes has been developed. The methodology takes into account the constraints on inspection resources, in terms of a limited number of inspection stations. As a result, the total manufacturing cost of a product can be reduced, while maintaining the quality of the product. Four numerical experiments are conducted to assess the MMAS algorithm for the AOIS problem. The performance of the MMAS algorithm is compared with a number of other methods this includes the complete enumeration method (CEM), rule of thumb, a pure random search algorithm, particle swarm optimisation, simulated annealing and genetic algorithm. The experimental results show that the effectiveness of the MMAS algorithm lies in its considerably shorter execution time and robustness. Further, in certain conditions results obtained by the MMAS algorithm are identical to the CEM. In addition, the results show that applying local search to the MMAS algorithm has significantly improved the performance of the algorithm. Also the results demonstrate that it is essential to use heuristic information with the MMAS algorithm for the AOIS problem, in order to obtain a high quality solution. It was found that the main parameters of MMAS include the pheromone trail intensity, heuristic information and evaporation of pheromone are less sensitive within the specified range as the number of workstations is significantly increased