![]() Qadori H, Zukarnain Z, Zurina MH, Subramaniam S (2017) A spawn mobile agent itinerary planning approach for energy-efficient data gathering in wireless sensor networks. In: Proceedings of the international conference on big data and advanced wireless technologies, BDAW ’16. Wirel Netw 24(6):2111–2132Īloui I, Kazar O, Kahloul L, Aissaoui A, Sylvie S (2016) A new “data size” based algorithm for itinerary planning among mobile agents in wireless sensor networks. Venetis IE, Gavalas D, Pantziou GE, Konstantopoulos C (2018) Mobile agents-based data aggregation in wsns: benchmarking itinerary planning approaches. Lange DB, Oshima M (1999) Seven good reasons for mobile agents. In: Proceedings of the 5th international joint conference on autonomous agents and multiagent systems, AAMAS ’06. Massaguer D, Fok C-L, Venkatasubramanian N, Roman G-C, Lu C (2006) Exploring sensor networks using mobile agents. Min C, Taekyoung K, Yuan Y, Leung V (2006) Mobile agent based wireless sensor networks. Valle SD (2018) Identity of thing based on iota tangle (visited on ) ![]() In: Workshop on software and performance, pp 195–203 Springer, Cham, pp 143–153īondi Andre B (2000) Characteristics of scalability and their impact on performance. In: Maglaras LA, Janicke H, Jones K (eds) Ind Netw Intell Syst. (1), October 2017Īlsboui T, Alrifaee M, Etaywi R, Jawad MA (2017) Mobile agent itinerary planning approaches in wireless sensor networks- state of the art and current challenges. Van den Abeele F, Hoebeke J, Teklemariam GK, Moerman I, Demeester P (2015) Sensor function virtualization to support distributed intelligence in the internet of things. In: The AAAI fall symposium series, AAAI digital library Lynne Parker (2007) Distributed intelligence: Overview of the field and its application in multi-robot systems. IEEE Cloud Comput 4(2):13–17īyers CC, Wetterwald P (2015) Fog computing distributing data and intelligence for resiliency and scale necessary for IoT: the internet of things (ubiquity symposium). Springer International Publishing AGĮsposito C, Castiglione A, Pop F, Choo KR (2017) Challenges of connecting edge and cloud computing: a security and forensic perspective. In: 7th International Conference on Smart City and Informatization (iSCI 2019), Guangzhou, China, Nov 12–15 2019, Lecture Notes in Computer Science, Switzerland, 8. May 2013Īl-Aqrabi H, Pulikkakudi JA, Hill R, Lane P, Liu L (2019) A multi-layer security model for 5g-enabled industrial internet of things. More than 30 billion devices will wirelessly connect to the internet of everything in 2020. Gartner says the internet of things installed base will grow to 26 billion units by 2020. renovation toward a zero energy classroom building. In: Proceedings of the 2018 workshop on IoT security and privacy, IoT 2018, Budapest, Hungary, Aug 20 2018, pp 15–21ĭe Angelis E, Ciribini ALC, Tagliabue LC, Paneroni M (2015) The brescia smart campus demonstrator. ACM Comput Surv 50(3):32:1–32:43ĭoan TT, Safavi-Naini R, Li S, Avizheh S, Muni Venkateswarlu K, Fong PWL (2018) Towards a resilient smart home. Perera C, Qin Y, Estrella JC, Reiff-Marganiec S, Vasilakos AV (2017) Fog computing for sustainable smart cities: a survey. Extensive experiments show that transaction processing speed is improved by using mobile agents, thereby providing better scalability.Ītzori L, Iera A, Morabito G (2010) The internet of things: a survey. The Proof-of-Work offloading computation mechanism improves efficiency and speed of processing, while reducing energy consumption. ![]() Second, high level intelligence uses a Tangle based architecture to handle transactions. First, multiple mobile agents are employed to cater for node level communications and collect transactions data at a low level. MADIT enables distributed intelligence at two levels. ![]() We propose a Mobile-Agent Distributed Intelligence Tangle-Based approach (MADIT) as a potential solution based on IOTA (Tangle), where Tangle is a distributed ledger platform that enables scalable, transaction-based data exchange in large P2P networks. Several studies have demonstrated the benefits of using distributed intelligence (DI) to overcome these challenges. This will challenge data processing capability, infrastructure scalability, and privacy. It is estimated that there will be approximately 125 billion Internet of Things (IoT) devices connected to the Internet by 2030, which are expected to generate large amounts of data. ![]()
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