Name
Abstract | ||
|---|---|---|
IoT applications are built on top of M2M platforms which facilitate the communication infrastructure among devices and to the clouds. Because of increasing M2M communication traffic and limited edge network bandwidth, it has become a crucial problem of M2M platform to prevent network congestion and service delay. A general approach is to deploy IoT service modules in M2M platform, so that data can be pre-processed and reduced before transmitting over the networks. Moreover, the service modules often need to be deployed dynamically at various locations of M2M platform to accommodate the mobility of devices moving across access networks, and the on-demand service requirement from users. However, existing M2M platforms have limited support to deployment dynamically and automatically. Therefore, the objective of our work is to build a dynamic module deployment framework in M2M platform to manage and optimize module deployment automatically according to user service requirements. We achieved the goal by implementing a solution that integrates a OSGi-based Application Framework(Kura), with a M2M platform(OM2M). By exploiting the resource reuse method in OSGi specification, we were able to reduce the module deployment time by 50~52%. Finally, a computation efficient and near-optimal algorithm was proposed to optimize the the module placement decision in our framework. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/SC2.2017.37 | 2017 IEEE 7th International Symposium on Cloud and Service Computing (SC2) |
Keywords | Field | DocType |
Internet-of-Things,Machine-to-Machine,Module Deployment,Placement Optimization | Software deployment,Reuse,Computer science,Server,Enhanced Data Rates for GSM Evolution,Computer network,Bandwidth (signal processing),Network congestion,Access network,Cloud computing | Conference |
ISBN | Citations | PageRank |
978-1-5386-5863-5 | 0 | 0.34 |
References | Authors | |
6 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bing-Liang Chen | 1 | 0 | 1.35 |
| Shih-Chun Huang | 2 | 0 | 1.35 |
| Yu-Cing Luo | 3 | 0 | 1.35 |
| Yeh-Ching Chung | 4 | 983 | 97.16 |
| Jerry Chou | 5 | 23 | 8.25 |