Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 6,586 papers for “node coverage”

Dose coverage in each lymph node area.

Hyeon Seok Choi (18127202), Hyun-Cheol Kang (5047958), Eui Kyu Chie (7387454) et al. · Figshare · 2024

<p>Box and whisker plot representation of the Vx% for each lymph node area. Abbreviations—Vx%: volume receiving x% or more of the prescription dose.</p>

Cited by 0Open access

Model Coverage vs System-under-test Coverage in Model-based testing : Using Edge-pair coverage, Edge coverage, Node coverage and Mutation analysis

Rezkalla, George · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2021

Model-based testing (MBT) is a black-box software testing technique that focuses on specification of the system-under-test (SUT) and/or its environment. It uses models to automatically generate a large number of tests. To the best of our knowledge, no study has investigated the correlation of model coverage with SUT coverage using more advanced coverage criteria (such as edge-pair coverage) and the correlation of coverage (at model level and SUT level) with test suite effectiveness using non-adequate test suites in the context of MBT despite the prominence of non-adequate test suites in industry. To carry out the investigation, we extend an existing open-source MBT tool called Modbat to measure edge-pair coverage at model level, implement a new tool called PaCovForJbc to measure edge-pair coverage, edge coverage and node coverage at SUT level. Finally, we perform an experiment using these tools applied on three projects: “ArrayList”, and “LinkedList” of Java standard library, and “Apache ZooKeeper”. Overall, the results suggest the following: Edge and edge-pair coverage at model level often have a moderate to high correlation with the same type of coverage at SUT level, while that link between model and SUT for node coverage is weaker. Moreover, coverage criteria at SUT level often have a moderate to high correlation with test suite effectiveness, and a coverage criterion at SUT level has a slightly higher correlation with test suite effectiveness than the same type of coverage at model level. Regarding coverage at model level, edge and edge-pair coverage at model level have a slightly higher correlation with test suite effectiveness than node coverage at model level. Note that the mentioned suggestions need to be taken with discretion, because results vary depending on the project and/or coverage criterion under investigation.

Cited by 0Open access

Node Placement for Optimal Coverage in Sensor Networks

Yung-Tsung Hou, Tzu-Chen Lee, Chia-Mei Chen et al. · 2006

Wireless sensor networks provide an alternative way of improving our environments, such as environment surveillance, hazard monitoring, and other customized environmental applications. Good coverage of service in a sensor network is an essential issue to ensure the service of quality. This paper studies the deployment of new sensor nodes so that the improvement of coverage is optimized. We propose an optimal polynomial time algorithm for this problem. Based on computational geometry and graph theory, we show the properties of such a deployment and the correctness of its optimality.

Cited by 6

A Coverage Algorithm based on Key Node Scheduling

Li Zhu, Chunxiao Fan, Huarui Wu et al. · International Journal of Online and Biomedical Engineering (iJOE) · 2015

Network coverage rate is a key standard of measuring the quality of network coverage. This thesis aims at solving the differences between node energy and task, which causes coverage holes and blind spots of Wireless Sensor Network, and proposes a coverage algorithm, based on key node scheduling, divides node subsets, and adjusts the states of individual nodes according to node energy and coverage rate. This algorithm ensures network connectivity while reducing coverage holes and redundancy. Simulation results show that, the proposed algorithm can effectively reduce failure nodes, energy consumption, improve network coverage rate, and demonstrates network convergence and stability.

Cited by 0Open access

Node Coverage Algorithms in Wireless Sensor Networks Using

Raina Manik, Kumar Subhas, Patro Ranjeet · 2008

Abstract This paper proposes fault tolerant algorithms for routing mobile agents in a single channel wireless sensor network which cover every node in the network. These algorithms use local knowledge (assume no knowledge of global deployment or topology, etc). We propose the algorithms and show mathematical analysis to support our claims. The paper ends with simulation studies and discussion of results. Key words Wireless sensor networks, mobile agents 1

Cited by 0

Intermediate measurement node for extension of WSN coverage

Rabin Bilas Pant, Hans Petter Halvorsen, Frode Skulbru et al. · Journal of Cyber Security and Mobility · 2013

River Publishers - In the few years since our launch, River Publishers has managed to establish an excellent reputation amongst academics and professionals world-wide as a provider of high quality Scientific, Technical, Medical content! We are a global publisher of print and electronic products and we welcome all persons with publishing ideas which focus on scientific topics.

Cited by 1Open access

Coordinated Truck-Drone Routing for Multi-Area Coverage with Flexible Coverage Node Sequencing

Haoling Shi, Hongtao Lei · 2025

This paper introduces a novel integrated mathematical model for multi-area coverage through the coordination of truck-drone operations. The proposed model concurrently addresses drone area coverage routing and truck-drone coordination routing, allowing for a flexible sequence of coverage node visits in a single model. In this model, a truck equipped with a drone performs area coverage tasks by launching and retrieving the drone at designated operational nodes. Meanwhile, the drone determines the optimal sequence of node visits based on launch and retrieval locations. The model effectively accommodates both convex and concave areas by employing the Boustrophedon route method and trapezoidal cell decomposition, generating a feasible and optimal coverage routing plan. Additionally, the model incorporates comprehensive timing constraints to ensure truck and drone schedules synchronization and optimality. Computational experiments performed with the Gurobi solver on both simplified and complex instances validate the proposed model and highlight improvements compared to the previous two-stage strategy.

Cited by 0

Research on node coverage in agricultural wireless sensor networks based on deterministic coverage

Zheng Wang · Applied and Computational Engineering · 2023

The application of wireless sensor network in agriculture has always been a concern. In the research of wireless sensor network extension, coverage problem is one of the hot issues in wireless sensor network. Research on the node coverage of agricultural wireless sensor networks based on deterministic deployment is of great significance to improve the coverage quality of agricultural wireless sensor networks and reduce energy consumption. According to the feature of deterministic coverage of wireless sensor network nodes in the agricultural field, this paper summarizes and analyzes the existing deterministic coverage schemes, and proposes to determine the minimum number of nodes according to the size of the repeated coverage area, which provides ideas for the problem of node coverage with deterministic area characteristics in the agricultural field.

Cited by 1Open access

Distributed node election for full coverage using connectivity information

Xiaoyun Li, Chris J. Bleakley · 2014

This paper proposes a distributed node election algorithm to efficiently select a subset of nodes which provide full coverage. In addition the boundary of un-triangulated holes can be accurately detected. This algorithm only uses binary connectivity information without any additional information such as sensing range or distance between nodes. Assumptions of a Unit Disk Graph (UDG) or d-quasi UDG network configuration are not required. The number of elected nodes is close to the minimum number of nodes for full coverage as shown in simulations with arbitrary topologies and various densities. The algorithm is scalable to any network size. Its time complexity is only sensitive to the density of the network (or average number of neighbors for each node) and is insensitive to the total number of nodes in the network.

Cited by 0

Maximizing Nano-Sensor Node Coverage using BWO in WNSNs

Ömer Güleç, Emre Şahin · 2023

Due to the energy constraints of nano-sensors, all network operations should be optimized in Wireless Nano-Sensor Networks (WNSNs). Nature-inspired metaheuristics have been widely used for solving the network problems such as routing, clustering, security and such areas in the literature. Therefore, in this paper, Black Widow optimization (BWO) based nano-node clustering algorithm is proposed in order to achieve maximum node coverage on WNSNs. The proposed algorithm is compared with 2 different Genetic Algorithm (GA) setups. According to the results, the proposed method successfully covers the nano-nodes by the chosen head nodes on the network up to 99.876% on average.

Cited by 1

Coverage-Based Lossy Node Localization for Wireless Sensor Networks

Forrest Sheng Bao, Yulei Pang, Wu-Jun Zhou et al. · IEEE Sensors Journal · 2016

To ensure the function of wireless sensor networks (WSNs), nodes that fail to forward packets must be localized efficiently and then fixed or replaced promptly. The state-of-the-art work frames lossy node localization in WSNs as an optimal sequential testing problem guided by end-to-end data. It combines both the active and passive measurements to minimize the testing cost and the number of iterations. However, this hybrid approach has many limitations. Inspired by the success of coverage-based software debugging, and the similarity between software debugging and lossy node localization, we propose a coverage-based lossy node detection for WSNs. Supported by established statistic theories, this approach greatly boosts the performance. Experiments on randomly generated networks and deployed networks show that the proposed algorithm can significantly reduce testing cost and number of iterations, which are the two optimization goals of previous work. We expect to use this approach for other diagnostic problems in WSNs.

Cited by 11