Moving is the key to modern life. Most things are in moving such as vehicles and user mobiles, so the need for high-speed wireless networks to serve the high demand of the wireless application becomes essential for an...
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This paper proposes a FOV-aware, area-based coordination framework for UAV-UGV collaborative surveillance under real-world constraints such as limited battery capacity and road-constrained UGV mobility. Unlike traditi...
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ISBN:
(数字)9798331513283
ISBN:
(纸本)9798331513290
This paper proposes a FOV-aware, area-based coordination framework for UAV-UGV collaborative surveillance under real-world constraints such as limited battery capacity and road-constrained UGV mobility. Unlike traditional point-based reconnaissance approaches, our method discretizes the surveillance region into realistic grid cells based on UAV camera footprints and guides UAVs to maximize coverage using heading-constrained field-of-view (FOV) planning. A single UGV navigates a predefined fixed route extracted from GeoJSON road data and serves as a mobile charging station with two wireless pads. We formulate the task as a Team Orienteering Problem (TOP) and address it using a structured meta-heuristic algorithm. Key innovations include heading-aware path construction, dynamic reward reinitialization to prevent local stagnation, and tight synchronization with an ILP-based UAV scheduling algorithm that considers operational flight time and charging constraints. UAVs autonomously select their next positions within $\mathrm{a}\pm 5^{\circ}$ heading cone to optimize new area coverage while minimizing redundant overlap. Simulation results conducted over the Texas A&M campus demonstrate that our method achieves up to 19% higher area coverage, reduces redundancy by 11.3%, and lowers UAV charging delays compared to point-based and naive area-based baselines. These findings validate the effectiveness of integrating FOV-driven spatial planning with temporal scheduling and adaptive reward modeling, offering a scalable and robust framework for autonomous persistent surveillance missions.
Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process that data. MapReduce is a parallel comput...
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Hadoop is a framework for storing and processing huge volumes of data on clusters. It uses Hadoop Distributed File System (HDFS) for storing data and uses MapReduce to process that data. MapReduce is a parallel computing framework for processing large amounts of data on clusters. scheduling is one of the most critical aspects of MapReduce. scheduling in MapReduce is critical because it can have a significant impact on the performance and efficiency of the overall system. The goal of scheduling is to improve performance, minimize response times, and utilize resources efficiently. A systematic study of the existing scheduling algorithms is provided in this paper. Also, we provide a new classification of such schedulers and a review of each category. In addition, scheduling algorithms have been examined in terms of their main ideas, main objectives, advantages, and disadvantages.
The paper gives an insight and overview of Cloud Computing, Fog computing and Task scheduling methods. When we require scalable along with on demand access for computing resources while using internet then Cloud Compu...
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ISBN:
(数字)9798331506452
ISBN:
(纸本)9798331506469
The paper gives an insight and overview of Cloud Computing, Fog computing and Task scheduling methods. When we require scalable along with on demand access for computing resources while using internet then Cloud Computing comes into the picture. It has the capability to deliver on request, Self-service, wider network access, resource pooling, swift elasticity and deliberate services. Fog computing can processes data at the edge of the network, because of which faster processing and the storage of data nearer to the data sources happens which in fact can enhance the capabilities. In cloud and fog computing environment task scheduling has an essential role as it has the capabilities to optimize the allocation of resources, enhancing the efficiency and it can guarantee the performance in latency-sensitive based applications. Here in this paper various challenges faced are discussed and it describes about variety of algorithms designed to enhance the scheduling efficiency.
In the era of real-time applications dominating mobile devices, balancing enhanced performance with prolonged battery life has become a significant challenge. This paper explores the complexities of real-time scheduli...
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ISBN:
(数字)9798331513894
ISBN:
(纸本)9798331513900
In the era of real-time applications dominating mobile devices, balancing enhanced performance with prolonged battery life has become a significant challenge. This paper explores the complexities of real-time scheduling algorithms, focusing on Earliest Deadline First (EDF) and Rate Monotonic (RM) algorithms, and their impact on energy consumption. Additionally, variations of these algorithms are introduced in the context of Dynamic Voltage Scaling (DVS), a technique crucial for achieving optimal performance with improved battery efficiency. Through an in-depth analysis of various conditions, including task numbers and worst-case processor utilization, the study demonstrates the effectiveness of these algorithms, making them indispensable in scenarios where energy efficiency is paramount.
Cloud computing is one of the emerging techniques of the current era. It is the method of using different computing resources which can be used when needed in steed of purchasing the resources. Task scheduling is impo...
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ISBN:
(数字)9798331506452
ISBN:
(纸本)9798331506469
Cloud computing is one of the emerging techniques of the current era. It is the method of using different computing resources which can be used when needed in steed of purchasing the resources. Task scheduling is important aspect of cloud computing to manage the different task requests from the multiple users simultaneously. It takes a major role in distributing tasks to VMs based on different factors named makespan, efficiency, energy consumption, memory utilization, network connectivity etc. Different task scheduling algorithms such as nature-inspired, bio-inspired and Metaheuristic algorithms are available for making task scheduling more optimal and efficient. The proposed paper attempts a review existing scheduling mechanisms modeled by nature-inspired, bio-inspired, Metaheuristic approaches which addressed various scheduling parameters such as trust factor, fault tolerance etc.
This research explores the effectiveness of three electric vehicle (EV) charging models—Balance Load Sharing, First-Come, First-Served (FCFS), and Shortest Job First (SJF)—in managing electrical loads and preventing...
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ISBN:
(数字)9798350364316
ISBN:
(纸本)9798350364323
This research explores the effectiveness of three electric vehicle (EV) charging models—Balance Load Sharing, First-Come, First-Served (FCFS), and Shortest Job First (SJF)—in managing electrical loads and preventing transformer overloads within Thailand's distribution systems. This is achieved through collaboration between the Advanced Metering Infrastructure (AMI) smart meter and EV charger within the system. With the growing adoption of EVs, particularly during off-peak periods under Time-of-Use (TOU) tariffs, the study addresses the need for efficient load management to prevent system overloads. The Balance Load Sharing model, which dynamically adjusts charging power based on real-time load conditions, demonstrated superior efficiency by reducing overall charging times and eliminating queuing delays. In contrast, the FCFS and SJF models, while yielding similar overall charging times, showed significant differences in queue management. The FCFS model, which charges vehicles in their order of arrival, led to extended waiting times during peak periods. On the other hand, the SJF model, which prioritizes vehicles requiring shorter charging durations, effectively minimized queuing times, thereby enhancing overall system efficiency.
We propose new abstract problems that unify a collection of scheduling and graph coloring problems with general min-sum objectives. Specifically, we consider the weighted sum of completion times over groups of entitie...
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Server less computing solutions within the cloud presentsan exceptional opportunity for corporations to gain progressed scalability, availability, and fee efficiency. To reap such blessings, establishments need to uti...
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ISBN:
(数字)9798350394399
ISBN:
(纸本)9798350394405
Server less computing solutions within the cloud presentsan exceptional opportunity for corporations to gain progressed scalability, availability, and fee efficiency. To reap such blessings, establishments need to utilize the handiest dynamic scheduling algorithms that are tailor-made to their unique needs. These algorithms can include predictive scheduling, call for-driven scheduling, and application-conscious scheduling, among others. Predictive scheduling algorithms are looking for to expect capacity call for to prevent erratic performance. This study focuses on dynamic scheduling algorithms for serverless computing solutions in the cloud. The researchers explore the characteristics of serverless computing models and the challenges of dynamic scheduling. A comprehensive evaluation is conducted on various scheduling algorithms, taking into consideration performance metrics such as throughput, response time, and resource utilization. The results show that dynamic scheduling algorithms are effective in optimizing resource allocation and improving overall system performance. Specific values derived from the results include a significant reduction in resource wastage, improved scalability, and increased cost-effectiveness. These findings suggest that dynamic scheduling algorithms are crucial for efficient and scalable serverless computing solutions in the cloud. With the aid of applying the maximum suitable dynamic scheduling algorithms tailor-made to precise desires, corporations could be higher prepared to fulfill their formidable cloud-computing dreams.
Workloads in data processing clusters are often represented in the form of DAG (Directed Acyclic Graph) jobs. scheduling DAG jobs is challenging. Simple heuristic scheduling algorithms are often adopted in practice in...
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