An Intelligent Task and Data Deployment Method for SIoT Systems

The Social Internet of Things (SIoT) system enables connectivity among smart devices by integrating social networks with the Internet of Things. This integration is essential for advancing intelligent services and applications, as well as enhancing the commercial value of data. Rational task and data deployment strategies allow different types of devices to perform optimally in their areas of expertise, reducing network load, improving the timeliness of data processing, and ensuring efficient collaboration across the entire system. However, the limited computational capacity and network bandwidth of SIoT devices result in communication delays that significantly impact job responsiveness and energy consumption. The heterogeneity in computational power and bandwidth across different devices leads to resource overload or underutilization with traditional data partitioning methods, further impacting the performance of SIoT systems. This article proposes an intelligent task and data deployment method to address these issues. The proposed method abstracts the job execution process as JobGraph instance model and optimizes the mapping relationship of operators within TaskProcess. Additionally, an improved linear programming model is used to optimize the data distribution ratio among operators in heterogeneous computing environments. The proposed task deployment method achieves an average improvement of 19.6%-30.2% in job efficiency while reducing interdevice data transmission by 34.2%. In heterogeneous computing environments, the combination of the two deployment optimization methods further reduces job execution time, achieving efficiency improvements of over two times in optimal scenarios.