Enhancing Performance in Worst-Case Scenarios of BLE Periodic Advertising for Dense IoT Networks

In dense Internet of Things (IoT) networks, where numerous devices transmit simultaneously, the conventional periodic advertising scheme, with its fixed advertising period, results in persistent collisions, leading to significant delays and higher energy consumption. These issues become more pronounced as the network grows. To overcome these limitations, this work proposes a novel adaptive periodic advertising scheme that dynamically adjusts the advertising period for each device for each transmission, effectively reducing collisions, improving delay performance, and enhancing energy efficiency. Based on the proposed scheme (PS), an analytical framework is developed to analyze delay and energy consumption. In addition, an analytical expression determining the schedule of each advertiser, perfectly syncing the scanner for efficient reception, is derived. As a result, it significantly improves the reliability of the PS. Furthermore, based on analytical framework, simplified closed-form expressions are derived to choose the optimal advertising period. Moreover, based on these closed-form analytical expressions, algorithms are proposed that autonomously optimize the delay in such applications. The performance of the PS has also been compared with conventional periodic advertising scheme, and it is found that the PS outperforms conventional scheme in terms of delay and energy. Lastly, analytical results were validated using a custom-built simulation framework based on the widely recognized Riverbed Modeler (formerly OPNET).