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Enabling distributed intelligence for the Internet of Things with IOTA and mobile agents

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Abstract

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. This will challenge data processing capability, infrastructure scalability, and privacy. Several studies have demonstrated the benefits of using distributed intelligence (DI) to overcome these challenges. 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. MADIT enables distributed intelligence at two levels. First, multiple mobile agents are employed to cater for node level communications and collect transactions data at a low level. Second, high level intelligence uses a Tangle based architecture to handle transactions. The Proof-of-Work offloading computation mechanism improves efficiency and speed of processing, while reducing energy consumption. Extensive experiments show that transaction processing speed is improved by using mobile agents, thereby providing better scalability.

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Notes

  1. https://github.com/iotaledger/iri/releases/tag/v1.8.2-RELEASE.

  2. https://www.digitalocean.com.

References

  1. Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Computer Networks 54(15):2787–2805

    MATH  Google Scholar 

  2. Perera C, Qin Y, Estrella JC, Reiff-Marganiec S, Vasilakos AV (2017) Fog computing for sustainable smart cities: a survey. ACM Comput Surv 50(3):32:1–32:43

    Google Scholar 

  3. Doan TT, Safavi-Naini R, Li S, Avizheh S, Muni Venkateswarlu K, Fong PWL (2018) Towards a resilient smart home. In: Proceedings of the 2018 workshop on IoT security and privacy, IoT S&P@SIGCOMM 2018, Budapest, Hungary, Aug 20 2018, pp 15–21

  4. De Angelis E, Ciribini ALC, Tagliabue LC, Paneroni M (2015) The brescia smart campus demonstrator. renovation toward a zero energy classroom building. Proc Eng 118:735–743

    Google Scholar 

  5. Cisco. Internet of things at a glance. December 2016

  6. Gartner. Gartner says the internet of things installed base will grow to 26 billion units by 2020. December 2013

  7. API Research. More than 30 billion devices will wirelessly connect to the internet of everything in 2020. May 2013

  8. Al-Aqrabi H, Pulikkakudi JA, Hill R, Lane P, Liu L (2019) A multi-layer security model for 5g-enabled industrial internet of things. In: 7th International Conference on Smart City and Informatization (iSCI 2019), Guangzhou, China, Nov 12–15 2019, Lecture Notes in Computer Science, Switzerland, 8. Springer International Publishing AG

  9. Esposito C, Castiglione A, Pop F, Choo KR (2017) Challenges of connecting edge and cloud computing: a security and forensic perspective. IEEE Cloud Comput 4(2):13–17

    Google Scholar 

  10. Byers CC, Wetterwald P (2015) Fog computing distributing data and intelligence for resiliency and scale necessary for IoT: the internet of things (ubiquity symposium). Ubiquity 2015(November):41–412

    Google Scholar 

  11. Lynne Parker (2007) Distributed intelligence: Overview of the field and its application in multi-robot systems. In: The AAAI fall symposium series, AAAI digital library

  12. 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. Wirel Pers Commun 81(4):1415–1436

    Google Scholar 

  13. Popov Serguei. The tangle. (1), October 2017

  14. Alsboui T, Alrifaee M, Etaywi R, Jawad MA (2017) Mobile agent itinerary planning approaches in wireless sensor networks- state of the art and current challenges. In: Maglaras LA, Janicke H, Jones K (eds) Ind Netw Intell Syst. Springer, Cham, pp 143–153

    Google Scholar 

  15. Bondi Andre B (2000) Characteristics of scalability and their impact on performance. In: Workshop on software and performance, pp 195–203

  16. Valle SD (2018) Identity of thing based on iota tangle (visited on 10 Jan 2020)

  17. Min C, Taekyoung K, Yuan Y, Leung V (2006) Mobile agent based wireless sensor networks. J Comput 1:04

    Google Scholar 

  18. Massaguer D, Fok C-L, Venkatasubramanian N, Roman G-C, Lu C (2006) Exploring sensor networks using mobile agents. In: Proceedings of the 5th international joint conference on autonomous agents and multiagent systems, AAMAS ’06. ACM, New York, pp 323–325

  19. Lange DB, Oshima M (1999) Seven good reasons for mobile agents. Commun ACM 42(3):88–89

    Google Scholar 

  20. Venetis IE, Gavalas D, Pantziou GE, Konstantopoulos C (2018) Mobile agents-based data aggregation in wsns: benchmarking itinerary planning approaches. Wirel Netw 24(6):2111–2132

    Google Scholar 

  21. Aloui 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. In: Proceedings of the international conference on big data and advanced wireless technologies, BDAW ’16. ACM, New York, pp 36:1–36:9

  22. 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. Sensors 17:1280, 06

    Google Scholar 

  23. Chen M, Kwon T, Yuan Y, Choi Y, Leung VCM (2006) Mobile agent-based directed diffusion in wireless sensor networks. EURASIP J Adv Signal Process 2007(1):036871

    Google Scholar 

  24. Jiang F, Shi H, Xu Z, Dong X (2009) Improved directed diffusion-based mobile agent mechanism for wireless sensor networks. In: 4th International conference on communications and networking in China, pp 1–5

  25. Damianos G, Ioannis EV, Charalampos K, Grammati EP (2017) Mobile agent itinerary planning for WSN data fusion: considering multiple sinks and heterogeneous networks. Int J Commun Syst 30:1

    Google Scholar 

  26. El Fissaoui M, Beni-hssane A, Saadi M (2018) Multi-mobile agent itinerary planning-based energy and fault aware data aggregation in wireless sensor networks. EURASIP J Wirel Commun Netw 2018(1):92

    Google Scholar 

  27. Tseng Y-C, Kuo S-P, Lee H-W, Huang C-F (2003) Location tracking in a wireless sensor network by mobile agents and its data fusion strategies. In: Zhao F, Guibas L (eds) Inf Process Sens Netw. Springer, Berlin, pp 625–641

    Google Scholar 

  28. Pottie GJ, Kaiser WJ (2000) Wireless integrated network sensors. Commun ACM 43(5):51–58

    Google Scholar 

  29. Peng K, Leung V, Xiaolong X, Zheng L, Wang J, Huang Q (2018) A survey on mobile edge computing: focusing on service adoption and provision. Wirel Commun Mob Comput 2018:10

    Google Scholar 

  30. IOTA Foundation (2017) Minimum weight magnitude (visited on 10 Jan 2020)

  31. Elsts A, Mitskas E, Oikonomou G (2018) Distributed ledger technology and the internet of things: a feasibility study. pp 7–12

  32. IOTA Foundation (2018) Pyota: the iota python API library (visited on 8 Aug 2019)

  33. Alsboui T, Abuarqoub A, Hammoudeh M, Bandar Z, Nisbet A (2012) Information extraction from wireless sensor networks: system and approaches. Sens Transduc 14(2):1

    Google Scholar 

  34. IOTA Foundation (2017) IRI configuration options. 3:15–34

  35. Sahni Y, Cao J, Zhang S, Yang L (2017) Edge mesh: a new paradigm to enable distributed intelligence in internet of things. IEEE Access 5:16441–16458

    Google Scholar 

  36. Rahman H, Rahmani R (2018) Enabling distributed intelligence assisted future internet of things controller (FITC). Appl Comput Inf 14(1):73–87

    Google Scholar 

  37. Vazquez JI, Almeida A, Doamo I, Laiseca X, Orduña P (2009) Flexeo: An architecture for integrating wireless sensor networks into the internet of things. In: Corchado JM, Tapia DI, Bravo J (eds) 3rd Symposium of ubiquitous computing and ambient intelligence 2008. Springer, Berlin, pp 219–228

  38. Uckelmann D, Harrison M, Michahelles F (2011) An architectural approach towards the future Internet of Things. Springer, Berlin, pp 1–24

  39. Al-Aqrabi H, Hill R (2018) A secure connectivity model for internet of things analytics service delivery. In: 2018 IEEE SmartWorld, ubiquitous intelligence and computing, advanced and trusted computing, scalable computing and communications, cloud and big data computing, internet of people and smart city innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI). IEEE, pp 9–16

  40. Yi S, Li C, Li Q (2015) A survey of fog computing: concepts, applications and issues. In: Proceedings of the 2015 workshop on mobile big data, Mobidata 15. ACM, New York, pp 37–42

  41. Gillam L, Katsaros K, Dianati M, Mouzakitis A (2018) Exploring edges for connected and autonomous driving. In: IEEE INFOCOM 2018—IEEE conference on computer communications workshops (INFOCOM WKSHPS), pp 148–153

  42. Rahman H, Rahmani R, Kanter T (2019) The role of mobile edge computing towards assisting IoT with distributed intelligence: a smartliving perspective. Springer International Publishing, Cham, pp 33–45

    Google Scholar 

  43. Pacheco LAB, Pelinson EA, Barreto M, Solís PA (2018) Device-based security to improve user privacy in the internet of things. In: Sensors

  44. Mora H, Pont MT, Gil D, Johnsson M (2018) Collaborative working architecture for IoT-based applications. Sensors 18:1676

    Google Scholar 

  45. Perera C, Zaslavsky A, Christen P, Georgakopoulos D (2014) Context aware computing for the internet of things: a survey. IEEE Commun Surv Tutor 16(1):414–454

    Google Scholar 

  46. Al-Aqrabi H, Hill R (2019) Dynamic multiparty authentication of data analytics services within cloud environments. In: Proceedings of the 20th international conference on high performance computing and communications, 16th international conference on smart city and 4th international conference on data science and systems, HPCC/SmartCity/DSS 2018. IEEE Computer Society, pp 742–749

  47. Sun G, Chang V, Ramachandran M, Sun Z, Li G, Hongfang Y, Liao D (2017) Efficient location privacy algorithm for internet of things (iot) services and applications. J Netw Comput Appl 89:3–13

    Google Scholar 

  48. Sohal AS, Sandhu R, Sood SK, Chang V (2018) A cybersecurity framework to identify malicious edge device in fog computing and cloud-of-things environments. Comput Secur 74:340–354

    Google Scholar 

  49. Mottola L, Picco GP (2011) Programming wireless sensor networks: fundamental concepts and state of the art. ACM Comput Surv 43(3):19:1–19:51

    Google Scholar 

  50. Zhao D, Ren J, Lin R, Xu S, Chang V (2019) On orchestrating service function chains in 5g mobile network. IEEE Access 7:39402–39416

    Google Scholar 

  51. Papadopoulos GA, Arbab F (1998) Coordination models and languages. Volume 46 of advances in computers. Elsevier, pp 329 – 400

  52. Klimos P (2018) The distributed ledger technology: a potential revamp for financial markets? Cap Mark Law J 13(2):194–222

    Google Scholar 

  53. Shadija D, Rezai M, Hill R (2017) Microservices: granularity vs. performance. In: UCC 2017 Companion—companion proceedings of the 10th international conference on utility and cloud computing. Association for Computing Machinery, Inc., pp 215–220

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Correspondence to Tariq Alsboui.

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Alsboui, T., Qin, Y., Hill, R. et al. Enabling distributed intelligence for the Internet of Things with IOTA and mobile agents. Computing 102, 1345–1363 (2020). https://doi.org/10.1007/s00607-020-00806-9

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