Recent large wildfires in the United States and subsequent damage that they have caused have increased the importance of wildfire monitoring and tracking. However, human monitoring on the ground or in the air may be t...
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Recent large wildfires in the United States and subsequent damage that they have caused have increased the importance of wildfire monitoring and tracking. However, human monitoring on the ground or in the air may be too dangerous and therefore, there needs to be alternatives to monitoring wildfires. Unmanned Aerial Vehicles (UAVs) are currently being considered and used for applications such as reconnaissance, surveying, and monitoring in the spatial-time domain because they can be deployed in teams remotely to gather information and minimize the harm and risk to human operators. UAVs have been previously used in this problem domain to track and monitor wildfires with approaches such as potential fields and reinforcement learning. In this thesis, we aim to look at a team of UAVs, in a distributed approach, over an area to maximize the sensor coverage in dynamic wildfire environments and minimize the energy consumption of deployed UAVs in a network. The work implements and compares an implementation of Deb's NSGA-II to optimize potential fields, Experience Replay with Q-learning, a Deep Q-Network (DQN), and a Deep Q-Network with a state estimator (autoencoder) to track and cover wildfires. The application of this work is a not a final suggestion or an absolute solution for wildfire monitoring and tracking but instead compares the methods to declare the most promising method for future work and research.
We study fairness in arbitrary networks with multicast capabilities. Multicast traffic in Internet and ATM provides a motivation for studying these networks. A study of fairness in multicast networks poses several int...
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We study fairness in arbitrary networks with multicast capabilities. Multicast traffic in Internet and ATM provides a motivation for studying these networks. A study of fairness in multicast networks poses several interesting problems, e.g., the issue of inter-session fairness in addition to that of inter-session fairness in unicast networks. We develop a mathematical framework to model the fair allocation of bandwidth in multirate multicast networks with minimum and maximum rate constraints. We present distributed algorithms for computation of maxmin fair rates allocated to various source-destination pairs.
Summary form only given. Real-time systems are finding use in complex and dynamic environments such as cruise controllers, life support systems, nuclear reactors, etc., These systems have separate components that sens...
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Summary form only given. Real-time systems are finding use in complex and dynamic environments such as cruise controllers, life support systems, nuclear reactors, etc., These systems have separate components that sense, control and stabilize the environment towards achieving the mission or target. These consociate components synchronize, compute and control themselves locally or have a centralized component to do the above. distributed computing techniques improve the overall performance and reliability of large real-time systems with spread components. We propose and evaluate three distributed dispatching algorithms for partially clairvoyant schedules. For a job set of size n, the algorithms have dispatch times of O(1) per job. In the first algorithm, one processor executes all the jobs and other processors compute the dispatch functions. This scenario simplifies design and is better in situations where one processor controls all the devices. For the other algorithms, all the processors execute jobs assigned to them and compute the dispatch functions in a certain defined order; which is a plausible scenario in distributed controlling. We create various test-cases to test the algorithms due to the unavailability of benchmarks.
A class of novel interconnection topologies called the generalized Fibonacci cubes is presented. The generalized Fibonacci cubes include the hypercubes, the recently proposed Fibonacci cubes (W.-J. Hsu, Proc. Int. Con...
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A class of novel interconnection topologies called the generalized Fibonacci cubes is presented. The generalized Fibonacci cubes include the hypercubes, the recently proposed Fibonacci cubes (W.-J. Hsu, Proc. Int. Conf. on Parallel Processing, p.1722-3 (1991)), and some other asymmetric interconnection topologies bridging between the two mentioned above. The generalized Fibonacci cubes can serve as a framework for studying degraded hypercubes due to faulty nodes or links. Previously known algorithms for hypercubes do not generalize to this class of interconnection topologies. The authors present distributed routing and broadcasting algorithms that can be applied to all members of this class of interconnection topologies. It is shown that their distributed routing algorithm always finds a shortest and deadlock-free path. The broadcasting algorithms are designed and evaluated based on both the all-port and the 1-port communication models. The all-port broadcasting algorithm is provably optimal in terms of minimizing routing steps. An upper bound for the 1-port broadcasting algorithm is determined, which is shown to be optimal for certain cases.< >
distributed routing and broadcasting algorithms for the Fibonacci cube are presented. The routing algorithm is shown to find shortest and deadlock-free paths. Two communication models are considered for the broadcasti...
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distributed routing and broadcasting algorithms for the Fibonacci cube are presented. The routing algorithm is shown to find shortest and deadlock-free paths. Two communication models are considered for the broadcasting. It is shown that the all-port broadcasting algorithm is optimal in terms of minimized routing steps. An upper bound is obtained for the one-port broadcasting algorithm, which is shown to be optimal for certain cases. The time complexities of the all- and one-port broadcasting algorithms are examined.< >
Summary form only given. The authors have developed algorithms which expand Maekawa's square root N algorithm to handle multiple mutual exclusion (AND-synchronization) problems, in which each node obtains mutually...
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Summary form only given. The authors have developed algorithms which expand Maekawa's square root N algorithm to handle multiple mutual exclusion (AND-synchronization) problems, in which each node obtains mutually exclusive access to a set of resources rather than to a single resource. It involves suspending a requesting process until it successfully locks several resources simultaneously. Unlike a sequence of requests for (separate) single mutual exclusion, this approach prevents deadlock. distributed multiple mutual exclusion algorithms can be used in many applications, such as updating a replicated database or as part of a deadlock-free two-phase lock protocol in a distributed environment. In a centralized system, two types of multiple mutual exclusion algorithms exist. One type of primitive (called a PBOTH primitive) prevents starvation, but resource utilization is low. Another type of primitive (called an SP/SV primitive) may cause starvation, but resource utilization is high. The authors have developed both types of distributed algorithms.< >
k-truss, a type of cohesive subgraphs of a network, is an important measure for a social network graph. However, with the emergence of large online social networks, the running time of the traditional batch algorithms...
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ISBN:
(纸本)9781479956678
k-truss, a type of cohesive subgraphs of a network, is an important measure for a social network graph. However, with the emergence of large online social networks, the running time of the traditional batch algorithms for k-truss decomposition is usually prohibitively long on such a graph with billions of edges and millions of vertices. Moreover, the size of a graph becomes too large to load into the main memory of a single machine. Currently, cloud computing has become an imperative way to process the big data. Thus, our aim is to design a scalable algorithm of k-truss decomposition in the scenario of cloud computing. In this paper, we first improve the existing distributed k-truss decomposition in the MapReduce framework. We then propose a theoretical basis for k-truss and use it to design an algorithm based on graph-parallel abstractions. Our experiment results show that our method in the graph-parallel abstraction significantly outperforms the methods based on MapReduce in terms of running time and disk usage.
To proactively defend against intruders from readily jeopardizing single-path data sessions, we propose a distributed secure multipath solution to route data across multiple paths so that intruders require much more r...
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To proactively defend against intruders from readily jeopardizing single-path data sessions, we propose a distributed secure multipath solution to route data across multiple paths so that intruders require much more resources to mount successful attacks. Our work exhibits several crucial properties that differentiate itself from previous approaches. They include (1) distributed routing decisions: routing decisions are made without the centralized information of the entire network topology, (2) bandwidth-constraint adaptation: the worst-case link attack is mitigated for any feasible session throughput subject to the link-bandwidth constraints, and (3) lexicographic protection: severe link attacks are suppressed based on lexicographic optimization. We devise two algorithms for the solution, termed the bound-control algorithm and the lex-control algorithm, and prove their convergence to the respective optimal solutions. Experiments show that the bound-control algorithm is more effective to prevent the worst-case single-link attack when compared to the single-path approach, and that the lex-control algorithm further enhances the bound-control algorithm by countering severe single-link attacks and various models of multi-link attacks. Moreover, the lex-control algorithm offers prominent protection after only a few execution rounds. Thus, system designers can sacrifice minimal routing security for significantly improved algorithm performance when deploying the distributed secure multipath solution.
This paper proposes two distributed algorithms for the heuristic solution of the Steiner tree problem in networks (SPN). The problem has a practical application in the construction of a minimum cost distribution tree ...
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This paper proposes two distributed algorithms for the heuristic solution of the Steiner tree problem in networks (SPN). The problem has a practical application in the construction of a minimum cost distribution tree for multicast transmission. Multicast transmission represents a necessary lower network service for the wide diffusion of new multimedia network applications. Currently, given the lack of efficient distributed methods, the existing protocols build the multicast distribution tree using some selected central node. The proposed distributed algorithms allow the construction of effective distribution trees using a coordination protocol among the network nodes. The algorithms have been implemented and tested both in simulation and on real networks, and their performance values are presented.
The authors present research on fault-tolerant distributed algorithms. The following problems in faulty networks are discussed: (1) spanning tree construction; (2) fault detection for a processor; and (3) fault detect...
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The authors present research on fault-tolerant distributed algorithms. The following problems in faulty networks are discussed: (1) spanning tree construction; (2) fault detection for a processor; and (3) fault detection for a link. These problems are not necessarily solvable in faulty networks. It is shown that the solvability is dependent on how much global information on the network each processor has a priori and whether the network is synchronous or not.< >
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