Building a P2P RDF Store for Edge Devices

The Semantic Web technologies have been used in the Internet of Things (IoT) to facilitate data interoperability and address data heterogeneity issues. The Resource Description Framework (RDF) model is employed in the integration of IoT data, with RDF engines serving as gateways for semantic integration. However, storing and querying RDF data obtained from distributed sources across a dynamic network of edge devices presents a challenging task. The distributed nature of the edge shares similarities with Peer-to-Peer (P2P) systems. These similarities include attributes like node heterogeneity, limited availability, and resources. The nodes primarily undertake tasks related to data storage and processing. Therefore, the P2P models appear to present an attractive approach for constructing distributed RDF stores. Based on P-Grid, a data indexing mechanism for load balancing and range query processing in P2P systems, this paper proposes a design for storing and sharing RDF data on P2P networks of low-cost edge devices. Our design aims to integrate both P-Grid and an edge-based RDF storage solution, RDF4Led for building an P2P RDF engine. This integration can maintain RDF data access and query processing while scaling with increasing data and network size. We demonstrated the scaling behavior of our implementation on a P2P network, involving up to 16 nodes of Raspberry Pi 4 devices.Comment: Accepted to IoT Conference 202

Data sharing in DHT based P2P systems

International audienceThe evolution of peer-to-peer (P2P) systems triggered the building of large scale distributed applications. The main application domain is data sharing across a very large number of highly autonomous participants. Building such data sharing systems is particularly challenging because of the "extreme" characteristics of P2P infrastructures: massive distribution, high churn rate, no global control, potentially untrusted participants... This article focuses on declarative querying support, query optimization and data privacy on a major class of P2P systems, that based on Distributed Hash Table (P2P DHT). The usual approaches and the algorithms used by classic distributed systems and databases forproviding data privacy and querying services are not well suited to P2P DHT systems. A considerable amount of work was required to adapt them for the new challenges such systems present. This paper describes the most important solutions found. It also identies important future research trends in data management in P2P DHT systems

A collaborative, semantic and context-aware search engine

Search engines help people to find information in the largest public knowledge system of the world: the Web. Unfortunately its size makes very complex to discover the right information. The users are faced lots of useless results forcing them to select one by one the most suitable. The new generation of search engines evolve from keyword-based indexing and classification to more sophisticated techniques considering the meaning, the context and the usage of information. We argue about the three key aspects: collaboration, geo-referencing and semantics. Collaboration distributes storage, processing and trust on a world-wide network of nodes running on users’ computers, getting rid of bottlenecks and central points of failures. The geo-referencing of catalogued resources allows contextualisation based on user position. Semantic analysis lets to increase the results relevance. In this paper, we expose the studies, the concepts and the solutions of a research project to introduce these three key features in a novel search engine architecture.213-21

Distributed, Secure Load Balancing with Skew, Heterogeneity, and Churn

Numerous proposals exist for load balancing in peer-to-peer (p2p) networks. Some focus on namespace balancing, making the distance between nodes as uniform as possible. This technique works well under ideal conditions, but not under those found empirically. Instead, researchers have found heavytailed query distributions (skew), high rates of node join and leave (churn), and wide variation in node network and storage capacity (heterogeneity). Other approaches tackle these less-thanideal conditions, but give up on important security properties. We propose an algorithm that both facilitates good performance and does not dilute security. Our algorithm, k-Choices, achieves load balance by greedily matching nodes’ target workloads with actual applied workloads through limited sampling, and limits any fundamental decrease in security by basing each nodes’ set of potential identifiers on a single certificate. Our algorithm compares favorably to four others in trace-driven simulations. We have implemented our algorithm and found that it improved aggregate throughput by 20% in a widely heterogeneous system in our experiments.Engineering and Applied Science