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تسجيل مستخدم جديدSummarization in Semantic Based Service Discovery in Dynamic IoT-Edge Networks
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In the last decade, many semantic-based routing protocols had been designed for peer-to-peer systems. However, they are not suitable for IoT systems, mainly due to their high demands in memory and computing power which are not available in many IoT devices. In this paper, we develop a semantic-based routing protocol for dynamic IoT systems to facilitate dynamic IoT capability discovery and composition. Our protocol is a fully decentralized routing protocol. To reduce the space requirement for routing, each node maintains a summarized routing table. We design an ontology-based summarization algorithm to smartly group similar capabilities in the routing tables and support adaptive routing table compression. We also design an ontology coding scheme to code keywords used in the routing tables and query messages. To complete the summarization scheme, we consider the metrics for choosing the summarization candidates in an overflowing routing table. Some of these metrics are novel and are difficult to measure, such as coverage and stability. Our solutions significantly reduce the routing table size, ensuring that the routing table size can be bounded by the available memory of the IoT devices, while supporting efficient IoT capability lookup. Experimental results show that our approach can yield significantly lower network traffic and memory requirement for IoT capability lookup when compared with existing semantic-based routing algorithms including a centralized solution, a DHT-based approach, a controlled flooding scheme, and a cache-based solution.
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In this paper, we consider the IoT data discovery problem in very large and growing scale networks. Specifically, we investigate in depth the routing table summarization techniques to support effective and space-efficient IoT data discovery routing.
Novel summarization algorithms, including alphabetical based, hash based, and meaning based summarization and their corresponding coding schemes are proposed. The issue of potentially misleading routing due to summarization is also investigated. Subsequently, we analyze the strategy of when to summarize in order to balance the tradeoff between the routing table compression rate and the chance of causing misleading routing. For experimental study, we have collected 100K IoT data streams from various IoT databases as the input dataset. Experimental results show that our summarization solution can reduce the routing table size by 20 to 30 folds with 2-5% increase in latency when compared with similar peer-to-peer discovery routing algorithms without summarization. Also, our approach outperforms DHT based approaches by 2 to 6 folds in terms of latency and traffic.
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