IEEE 802.11k-Based Lightweight, Distributed, and Cooperative Access Point Coverage Estimation Scheme in IoT Networks

Abstract

In this work, we propose two lightweight access point (AP) localization and selection schemes, AP-Cov and improved AP-Cov (IAP-Cov), in IEEE 802.11-enabled IoT networks for reducing handover delay and maintaining uninterrupted connectivity during movement. The proposed scheme AP-Cov consists of two phases—the localization of APs and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> selection of APs in the direction of mobility. In the localization phase, an IoT device exploits the features of neighbor report request (NRR) and beacon report request (BRR) of IEEE 802.11k to get the nearby APs’ information along with their location and uses simple algebra to compute the location of next APs beforehand, instead of using heavyweight processing in resource-constrained IoT networks. In the AP selection phase, a linear optimization model is formulated to select the optimal AP for subsequent association. In IAP-Cov, a broadcast sequence of neighbor IoT is proposed for reducing the number of broadcasts. We implement the schemes in NS-3 and compare performance with the baseline standard, which shows that AP-Cov detects the availability of APs with 98.625% and 96.5% accuracy, improves accuracy over message complexity by 23% and 78% and reduces handover by 36.37% and 67.52% in constant-velocity and random-velocity mobility models, respectively. The evaluation of computational complexity shows that the proposed schemes are lightweight too. IAP-Cov reduces broadcasts and delay by 49%, 77.36% and 6%, 25% compared to AP-Cov using the constant-velocity and random-velocity mobility models, respectively.