4.7 Article

A novel code data dissemination scheme for Internet of Things through mobile vehicle of smart cities

Publisher

ELSEVIER
DOI: 10.1016/j.future.2018.11.039

Keywords

Code data dissemination; Mobile vehicle; Coverage; Internet of Things; Low cost; Update duration

Funding

  1. National Natural Science Foundation of China [61772554]
  2. Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, China [NLBKF201804]

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More and more sensor-based devices are deployed for various applications to collect a huge amount of data for understanding the world, which constitute ubiquitous sensing Internet of Things (IoT) for smart cities. At the same time, Software-defined technology is also widely used in sensor-based devices. These sensor-based devices can get new functions and become smarter by updating their software. These sensor-based devices can be called smart sensors because they can generate smart data (which means filter out the noise and hold the valuable data). These smart sensors can adapt to the needs of new applications and form smart software-defined IoTs. However, it is a huge challenging issue that disseminating the update code to the smart sensors that are widespread and large quantity. We found that there are hundreds and thousands of mobile vehicles moving in the city, which can be used as the code mules to disseminate code with low cost. In this paper, a novel low-cost code dissemination model is proposed which disseminate the update code by using the mobile vehicles in the city with an opportunistic communication style. In order to improve the performance of such code dissemination model, a coverage based greedy deployment scheme for code stations and an optimized code selection algorithm are proposed to maximize coverage of code dissemination over the city with low cost and duration time. Extensive simulation experiments validate the proposed code dissemination model is feasible and indicate the effectiveness of the proposed schemes by using a real taxi trajectory dataset. (C) 2018 Elsevier B.V. All rights reserved.

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