The rapid growth of the demand for mobile sensing makes it difficult to process all sensing tasks on a single mobile device. Therefore, the concept of distributed computing was proposed, in which the computation tasks are distributed to all available devices in the same edge network to achieve faster data processing. However, in some critical scenarios, in which the network condition of the edge is poor, the bandwidth of the edge network is limited, and the connection is unstable, which can significantly affect the performance of distributed computing. To overcome such issues, we propose a resilient mobile distributed computing framework adopting an integrated solution combining Coded Computing (CC) and Named Data Networking (NDN). With NDN, the network traffic and information sharing within the edge network is optimized dynamically to adapt to the timevarying network condition. The CC technique can recover some of the missing computation results when an edge node is failed or disconne
In contemporary defense training and operations, users regularly encounter complicated and dynamic environments that generate large amounts of knowledge derived from locally acquired data. In order to facilitate collaborative decision making, users need to effectively share and distribute locally learned knowledge in a timely manner. This paper presents a semantic-based knowledge and information sharing system (S-KISS): a forum application for efficient peer-to-peer knowledge sharing. S-KISS enables simple and casual peer-to-peer information exchange, while retaining the quality of widely disseminated content for judicious knowledge consumption. Based on advanced semantic analysis technologies, S-KISS also supports effective semantic-based knowledge searching and semi-automated knowledge management with two knowledge management methods: (1) knowledge similarity searching based on WordNet and BERTScore, and (2) semantic similarity-based knowledge graph construction and knowledge grouping. The searching method focused on the semantics of text instead of word spans. Meanwhile, the grouping method constructs a knowledge graph where each node represents a posting and the links between nodes along with their semantic similarities. Postings can be grouped into multiple clusters of similar topics using Markov clustering algorithm, which allows users to look up related content quickly and effectively. The feasibility and effectiveness of S-KISS is demonstrated via a web-based prototype using practical scenarios and a real-world benchmark dataset curated from the sub-Reddit online forum ‘r/newtothenavy’. With broad and generic language models, the capabilities developed in S-KISS are applicable for knowledge information management in any space, air, sea, marine, and cyber domains. S-KISS can be utilized in other relevant software applications such as collaborative communication platforms and e-training discussion forums.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.