58 research outputs found

    Modeling and simulation of routing protocol for ad hoc networks combining queuing network analysis and ANT colony algorithms

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    The field of Mobile Ad hoc Networks (MANETs) has gained an important part of the interest of researchers and become very popular in last few years. MANETs can operate without fixed infrastructure and can survive rapid changes in the network topology. They can be studied formally as graphs in which the set of edges varies in time. The main method for evaluating the performance of MANETs is simulation. Our thesis presents a new adaptive and dynamic routing algorithm for MANETs inspired by the Ant Colony Optimization (ACO) algorithms in combination with network delay analysis. Ant colony optimization algorithms have all been inspired by a specific foraging behavior of ant colonies which are able to find, if not the shortest, at least a very good path connecting the colony’s nest with a source of food. Our evaluation of MANETs is based on the evaluation of the mean End-to-End delay to send a packet from source to destination node through a MANET. We evaluated the mean End-to-End delay as one of the most important performance evaluation metrics in computer networks. Finally, we evaluate our proposed ant algorithm by a comparative study with respect to one of the famous On-Demand (reactive) routing protocols called Ad hoc On-Demand Distance Vector (AODV) protocol. The evaluation shows that, the ant algorithm provides a better performance by reducing the mean End-to-End delay than the AODV algorithm. We investigated various simulation scenarios with different node density and pause times. Our new algorithm gives good results under certain conditions such as, increasing the pause time and decreasing node density. The scenarios that are applied for evaluating our routing algorithm have the following assumptions: 2-D rectangular area, no obstacles, bi-directional links, fixed number of nodes operate for the whole simulation time and nodes movements are performed according to the Random Waypoint Mobility (RWM) or the Boundless Simulation Area Mobility (BSAM) model. KEYWORDS: Ant Colony Optimization (ACO), Mobile Ad hoc Network (MANET), Queuing Network Analysis, Routing Algorithms, Mobility Models, Hybrid Simulation

    An architectural framework for self-configuration and self-improvement at runtime

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    [no abstract

    QoS Routing Solutions for Mobile Ad Hoc Network

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    A survey on engineering approaches for self-adaptive systems (extended version)

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    The complexity of information systems is increasing in recent years, leading to increased effort for maintenance and configuration. Self-adaptive systems (SASs) address this issue. Due to new computing trends, such as pervasive computing, miniaturization of IT leads to mobile devices with the emerging need for context adaptation. Therefore, it is beneficial that devices are able to adapt context. Hence, we propose to extend the definition of SASs and include context adaptation. This paper presents a taxonomy of self-adaptation and a survey on engineering SASs. Based on the taxonomy and the survey, we motivate a new perspective on SAS including context adaptation

    A comparative analysis of algorithms for satellite operations scheduling

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    Scheduling is employed in everyday life, ranging from meetings to manufacturing and operations among other activities. One instance of scheduling in a complex real-life setting is space mission operations scheduling, i.e. instructing a satellite to perform fitting tasks during predefined time periods with a varied frequency to achieve its mission goals. Mission operations scheduling is pivotal to the success of any space mission, choreographing every task carefully, accounting for technological and environmental limitations and constraints along with mission goals.;It remains standard practice to this day, to generate operations schedules manually ,i.e. to collect requirements from individual stakeholders, collate them into a timeline, compare against feasibility and available satellite resources, and find potential conflicts. Conflict resolution is done by hand, checked by a simulator and uplinked to the satellite weekly. This process is time consuming, bears risks and can be considered sub-optimal.;A pertinent question arises: can we automate the process of satellite mission operations scheduling? And if we can, what method should be used to generate the schedules? In an attempt to address this question, a comparison of algorithms was deemed suitable in order to explore their suitability for this particular application.;The problem of mission operations scheduling was initially studied through literature and numerous interviews with experts. A framework was developed to approximate a generic Low Earth Orbit satellite, its environment and its mission requirements. Optimisation algorithms were chosen from different categories such as single-point stochastic without memory (Simulated Annealing, Random Search), multi-point stochastic with memory (Genetic Algorithm, Ant Colony System, Differential Evolution) and were run both with and without Local Search.;The aforementioned algorithmic set was initially tuned using a single 89-minute Low Earth Orbit of a scientific mission to Mars. It was then applied to scheduling operations during one high altitude Low Earth Orbit (2.4hrs) of an experimental mission.;It was then applied to a realistic test-case inspired by the European Space Agency PROBA-2 mission, comprising a 1 day schedule and subsequently a 7 day schedule - equal to a Short Term Plan as defined by the European Space Agency.;The schedule fitness - corresponding to the Hamming distance between mission requirements and generated schedule - are presented along with the execution time of each run. Algorithmic performance is discussed and put at the disposal of mission operations experts for consideration.Scheduling is employed in everyday life, ranging from meetings to manufacturing and operations among other activities. One instance of scheduling in a complex real-life setting is space mission operations scheduling, i.e. instructing a satellite to perform fitting tasks during predefined time periods with a varied frequency to achieve its mission goals. Mission operations scheduling is pivotal to the success of any space mission, choreographing every task carefully, accounting for technological and environmental limitations and constraints along with mission goals.;It remains standard practice to this day, to generate operations schedules manually ,i.e. to collect requirements from individual stakeholders, collate them into a timeline, compare against feasibility and available satellite resources, and find potential conflicts. Conflict resolution is done by hand, checked by a simulator and uplinked to the satellite weekly. This process is time consuming, bears risks and can be considered sub-optimal.;A pertinent question arises: can we automate the process of satellite mission operations scheduling? And if we can, what method should be used to generate the schedules? In an attempt to address this question, a comparison of algorithms was deemed suitable in order to explore their suitability for this particular application.;The problem of mission operations scheduling was initially studied through literature and numerous interviews with experts. A framework was developed to approximate a generic Low Earth Orbit satellite, its environment and its mission requirements. Optimisation algorithms were chosen from different categories such as single-point stochastic without memory (Simulated Annealing, Random Search), multi-point stochastic with memory (Genetic Algorithm, Ant Colony System, Differential Evolution) and were run both with and without Local Search.;The aforementioned algorithmic set was initially tuned using a single 89-minute Low Earth Orbit of a scientific mission to Mars. It was then applied to scheduling operations during one high altitude Low Earth Orbit (2.4hrs) of an experimental mission.;It was then applied to a realistic test-case inspired by the European Space Agency PROBA-2 mission, comprising a 1 day schedule and subsequently a 7 day schedule - equal to a Short Term Plan as defined by the European Space Agency.;The schedule fitness - corresponding to the Hamming distance between mission requirements and generated schedule - are presented along with the execution time of each run. Algorithmic performance is discussed and put at the disposal of mission operations experts for consideration

    Heterogeneous Robot Swarm – Hardware Design and Implementation

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    Swarm robotics is one the most fascinating, new research areas in the field of robotics, and one of it's grand challenge is the design of swarm robots that are both heterogeneous and self-sufficient. This can be crucial for robots exposed to environments that are unstructured or not easily accessible for a human operator, such as a collapsed building, the deep sea, or the surface of another planet. In Swarm robotics; self-assembly, self-reconfigurability and self-replication are among the most important characteristics as they can add extra capabilities and functionality to the robots besides the robustness, flexibility and scalability. Developing a swarm robot system with heterogeneity and larger behavioral repertoire is addressed in this work. This project is a comprehensive study of the hardware architecture of the homogeneous robot swarm and several problems related to the important aspects of robot's hardware, such as: sensory units, communication among the modules, and hardware components. Most of the hardware platforms used in the swarm robot system are homogeneous and use centralized control architecture for task completion. The hardware architecture is designed and implemented for UB heterogeneous robot swarm with both decentralized and centralized control, depending on the task requirement. Each robot in the UB heterogeneous swarm is equipped with different sensors, actuators, microcontroller and communication modules, which makes them distinct from each other from a hardware point of view. The methodology provides detailed guidelines in designing and implementing the hardware architecture of the heterogeneous UB robot swarm with plug and play approach. We divided the design module into three main categories - sensory modules, locomotion and manipulation, communication and control. We conjecture that the hardware architecture of heterogeneous swarm robots implemented in this work is the most sophisticated and modular design to date

    Controlled self-organisation using learning classifier systems

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    The complexity of technical systems increases, breakdowns occur quite often. The mission of organic computing is to tame these challenges by providing degrees of freedom for self-organised behaviour. To achieve these goals, new methods have to be developed. The proposed observer/controller architecture constitutes one way to achieve controlled self-organisation. To improve its design, multi-agent scenarios are investigated. Especially, learning using learning classifier systems is addressed

    Stigmergy for autonomous distributed coordination of satellite clusters

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    Design of a low-cost unmanned surface vehicle for swarm robotics research in laboratory environments

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    Swarm robotics is the study of groups of simple, typically inexpensive agents working collaboratively toward a common goal. Such systems offer several benefits over single-robot solutions: they are flexible, scalable, and robust to the failure of individual agents. The majority of existing work in this field has focused on robots operating in terrestrial environments but the benefits of swarm systems extend to applications in the marine domain as well. The current scarcity of marine robotics platforms suitable for swarm research is detrimental to progress in this field. Of the few that exist, no publicly available unmanned surface vehicles can operate in a laboratory environment; an indoor tank of water where the vessels, temperature, lighting, etc. can be observed and controlled at all times. Laboratory testing is a common intermediate step in the hardware validation of algorithms. This thesis details the design of the microUSV: a small, inexpensive, laboratory-based platform developed to fill this gap. The microUSV system was validated by performing laboratory testing of two algorithms: a waypoint-following controller and orbital retrieval. The waypoint-following controller was a simple PI controller implementation which corrects a vessel's speed and heading to seek predetermined goal positions. The orbital retrieval algorithm is a novel method for a swarm of unmanned surface vehicles to gather floating marine contaminants such as plastics. The vessels follow a circular path, orbiting around a central collection location and veer outwards to retrieve contaminants they detect outside the designated area. This method can potentially be used to cluster floating plastics together from a large region to facilitate cleanup

    Reactive approach for automating exploration and exploitation in ant colony optimization

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    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
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