Remote software update in trusted connection of long range IoT networking integrated with versatile edge cloud

  • Authors

    • R Caroline Kalaiselvi
    • S Mary Vennila
    2018-04-20
    https://doi.org/10.14419/ijet.v7i2.21.12170
  • Internet of Things, Low Power Wide Area Network, Reliable Connection, Mobile Computing.
  • The Internet of Things (IoT) prompts can administered by gathering data from little sensor gadgets. As of late, stockpiling less detecting gadgets have been utilized to actualize IoT administrations.  They rely upon conveyed programming from a system server to work benefit capacities and IoT administrations are in view of gathered client data. In this way, it is critical to keep up trusted associations aid programming conveyance or information transmission. In the event that a system association is deceitful, stable information transmission can't be accomplished. Dishonest information associations cause numerous issues in IoT administrations. In this manner, this paper proposes a product refresh strategy in trusted association of IoT organizing. The technique utilizes Low Power Wide Area Network (LPWAN) as long-go IoT organizing innovation and utilizations a portable edge cloud to enhance registering effectiveness in an entrance arrange that comprises of IoT gadgets with lacking assets. In the strategy, the versatile edge cloud is coordinated into a door, and forms detecting information and remote programming updates of LPWAN. IoT gadgets can get programming capacities from the versatile edge cloud. The proposed strategy investigates measurable data about associations in a get to arrange and decides the LPWAN put stock in associations. At that point, programming updates can be performed over the confided in association. Utilizing trusted associations prompts an expanded bundle conveyance rate and decreased transmission vitality utilization. The strategy is contrasted with at present accessible frameworks through PC recreation and through computer simulation and this method’s efficiency is validated.

     

  • References

    1. [1] Jung M, Kim DY & Kim S, “Efficient remote software management method based on dynamic address translation for IoT software execution platform in wireless sensor networkâ€, Indian Journal of Science and Technology, Vol.9, No.24, (2016).

      [2] Beck MT, Werner M, Feld S & Schimper T, “Mobile edge computing: A taxonomyâ€, In Proc. The 6th International Conference on Advances in Future Internet (AFIN), Lisbon, Portugal, (2014), pp.48-54.

      [3] Hu YC, Patel M, Sabella D, Sprecher N & Young V, “Mobile computing A key technology towards 5G,†ETSI White Paper, No.11, (2015).

      [4] Yaqoob I, Ahmed E, Gani A, Mokhtar S, Imran, M & Guizani S, “Mobile ad hoc cloud: A surveyâ€, Wireless Communications and Mobile Computing, Vol.16, No.16, (2016), pp.2572-2589.

      [5] Sornin N, Luis M, Eirich T, Kramp T & Hersent O, “LoRa Alliance LoRa WANTM Specification,†LoRaWAN Specification, (2016).

      [6] LoRa Alliance, https://www.lora-alliance.org, (2016).

      [7] Al-Kashoash HA & Kemp AH, A Comparison of 6LoWPAN and LPWAN for the Internet of Things. ICT Express, (2017).

      [8] Hui JW & Culler D, “The dynamic behavior of a data dissemination protocol for network programming at scaleâ€, In Proc. The ACM International Conference on Embedded Networked Sensor Systems (SenSys), (2014), pp. 81-94.

      1. Dunkels BG & Voigt T, “Contiki a lightweight and flexible operating system for tiny networked sensorsâ€, In Proc. IEEE International Conference on Local Computer Networks, (2004), pp. 455-462.

      [9] Han CC, Kumar R, Shea R, Kohler E & Srivastava M, “A dynamic operating system for sensor nodesâ€, in Proc. ACM International Conference on Mobile Systems, Applications, and Services (MobiSys), (2005), pp.163-176.

      [10] Mottola L., Picco GP & Amjad A, “FiGaRo: Fine-grained software reconfiguration for wireless sensor networksâ€, Lecture Notes in Computer Science, Vol.4913, (2008), pp.286-304.

      [11] Marsland S, Machine learning: An algorithmic perspective. Chapman & Hall, New York, NY, USA, (2009).

      [12] Alsheikh MA, Lin S., Niyato D & Tan HP, “Machine learning in wireless sensor networks: algorithms, strategies, and applicationsâ€, IEEE Commun. Surv. Tutorials, Vol.16, No.4, (2014), pp.1996-2018.

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  • How to Cite

    Caroline Kalaiselvi, R., & Mary Vennila, S. (2018). Remote software update in trusted connection of long range IoT networking integrated with versatile edge cloud. International Journal of Engineering & Technology, 7(2.21), 194-197. https://doi.org/10.14419/ijet.v7i2.21.12170