Distributed spatial-temporal compressive data gathering for large-scale WSNs

2013 ◽  
Author(s):  
Xuangou Wu ◽  
Yan Xiong ◽  
Mingxi Li ◽  
Wenchao Huang
Keyword(s):  
Large Scale ◽  
Data Gathering

Survey of graduate supply chain courses: content, coverage and gaps

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Heather Lutz ◽  
Laura Birou ◽  
Joe Walden
Keyword(s):  
Higher Education ◽  
Supply Chain ◽  
Supply Chain Management ◽  
Large Scale ◽  
Gap Analysis ◽  
Data Gathering ◽  
Graduate Courses ◽  
Chain Management ◽  
Content Type ◽  
Course Content

PurposeThis paper aims to provide the results of a survey of courses dedicated to the field of supply chain management in higher education. This research is unique because it represents the first large-scale study of graduate supply chain management courses taught at universities globally. Design/methodology/approachContent analysis was performed on each syllabus to identify the actual course content: requirements, pedagogy and content emphasis. This aggregated information was used to compare historical research findings in this area, with the current skills identified as important for career success. This data provides input for a gap analysis between offerings in higher education and those needs identified by practitioners. FindingsData gathering efforts yielded a sample of 112 graduate courses representing 61 schools across the world. The aggregate number of topics covered in graduate courses totaled 114. The primary evaluation techniques include exams, projects and homework. Details regarding content and assessment techniques are provided along with a gap analysis between the supply chain management course content and the needs identified by APICS Supply Chain Manager Competency Model (2014). Originality/valueThe goal is to use this data as a means of continuous improvement in the quality and value of the educational experience on a longitudinal basis. The findings are designed to foster information sharing and provide data for benchmarking efforts in the development of supply chain management courses and curricula in academia, as well as training, development and recruitment efforts by professionals in the field of supply chain management.

ON THE CHARACTERISTICS OF INNOVATIVE FIRMS IN KOREA: THE ROLE OF R&D AND INNOVATION TYPE

1999 ◽  
Vol 03 (01) ◽  
pp. 111-131 ◽  
Author(s):  
YONG-TAE PARK ◽  
CHUL-HYUN KIM ◽  
JI-HYO LEE
Keyword(s):  
Large Scale ◽  
Data Gathering ◽  
Policy Implications ◽  
Future Research ◽  
Different Types ◽  
Innovative Firms ◽  
Unexpected Findings ◽  
Innovation Type ◽  
Recent Extension

In spite of the recent extension of our knowledge on technological innovation, little inquiry has been made of the distinctive characteristics between R&D firms and non-R&D firms, as well as between product-innovative firms and process-innovative firms. To this end, the main objective of this empirical study, grounded on a large-scale innovation survey of Korean manufacturing firms, is to contrast these two types of firms. The results were mixed. Some hypotheses were confirmed while others were discordant with expectation. By and large, R&D firms and product-innovative firms seem to share a similar propensity, whereas non-R&D firms and process-innovative firms are alike in character. However, there were some unexpected findings which merit attention and are worthy of in-depth examination. Although the study is subject to limitations in terms of its research design and data gathering, the results render some important policy implications. Furthermore, comparative analyses between different types of innovations need to be addressed more extensively in future research.

scholarly journals ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 930 ◽  
Author(s):  
Rosana Lachowski ◽  
Marcelo Pellenz ◽  
Edgard Jamhour ◽  
Manoel Penna ◽  
Glauber Brante ◽  
...  
Keyword(s):  
Big Data ◽  
Sensor Networks ◽  
Large Scale ◽  
Data Gathering ◽  
Essential Element ◽  
State Recovery ◽  
Periodic Data ◽  
Soft State ◽  
Lossy Links ◽  
Large Scale Networks

Wireless Sensors Networks (WSNs) are an essential element of the Internet of Things (IoT), and are the main producers of big data. Collecting a huge amount of data produced by a resource-constrained network is a very difficult task, presenting several challenges. Big data gathering involves not only periodic data sensing, but also the forwarding of queries and commands to the network. Conventional network protocols present unfeasible strategies for large-scale networks and may not be directly applicable to IoT environments. Information-Centric Networking is a revolutionary paradigm that can overcome such big data gathering challenges. In this work, we propose a soft-state information-centric protocol, ICENET (Information Centric protocol for sEnsor NETworks), for big data gathering in large-scale WSNs. ICENET can efficiently propagate user queries in a wireless network by using a soft-state recovery mechanism for lossy links. The scalability of our solution is evaluated in different network scenarios. Results show that the proposed protocol presents approximately 84% less overhead and a higher data delivery rate than the CoAP (Constrained Application Protocol), which is a popular protocol for IoT environments.

scholarly journals A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 575 ◽  
Author(s):  
Yu Gao ◽  
Jin Wang ◽  
Wenbing Wu ◽  
Arun Sangaiah ◽  
Se-Jung Lim
Keyword(s):  
Energy Consumption ◽  
Mobile Agent ◽  
Hybrid Method ◽  
Large Scale ◽  
Data Gathering ◽  
Sensor Field ◽  
Efficient Data ◽  
Anchor Nodes ◽  
Large Scale Networks ◽  
Moving Path

Wireless Sensor Networks (WSNs) are usually troubled with constrained energy and complicated network topology which can be mitigated by introducing a mobile agent node. Due to the numerous nodes present especially in large scale networks, it is time-consuming for the collector to traverse all nodes, and significant latency exists within the network. Therefore, the moving path of the collector should be well scheduled to achieve a shorter length for efficient data gathering. Much attention has been paid to mobile agent moving trajectory panning, but the result has limitations in terms of energy consumption and network latency. In this paper, we adopt a hybrid method called HM-ACOPSO which combines ant colony optimization (ACO) and particle swarm optimization (PSO) to schedule an efficient moving path for the mobile agent. In HM-ACOPSO, the sensor field is divided into clusters, and the mobile agent traverses the cluster heads (CHs) in a sequence ordered by ACO. The anchor node of each CHs is selected in the range of communication by the mobile agent using PSO based on the traverse sequence. The communication range adjusts dynamically, and the anchor nodes merge in a duplicated covering area for further performance improvement. Numerous simulation results prove that the presented method outperforms some similar works in terms of energy consumption and data gathering efficiency.

Sign in / Sign up

Export Citation Format

Share Document