An efficient wireless access point selection algorithm for location determination based on RSSI interval overlap degree determination

Abstract

Optimally choosing wireless Access Points (APs) as urban areas become more densely packed with them becomes increasingly challenging. In WiFi-based Indoor Positioning Systems (IPS), Selecting Wireless Access Point (AP), namely, WiFi routers, is significant as the more APs that are selected, the higher the computation, energy and time cost. This is unsuitable for networking low-resource devices as part of an Internet of Things. In addition, selecting the optimum number of APs not only reduces redundant information but also improves the positioning accuracy. In this paper, we present a novel AP selection method that uses the RSSI Interval Overlap Degree (IOD) to discriminate between known location Reference Points. We validated our algorithm in an office-like indoor space at a Queen Mary computer science lab. The results show that our algorithm has an improved performance, which is 13.6%, 18.2%, and 7.6% better than IG (information gain), MI (mutual information), SD (standard deviation) used as baseline algorithms, respectively.

Device-Free, Activity During Daily Life, Recognition Using a Low-Cost Lidar

“…Third, we aim to investigate the application of other state-of-art zero-shot/one-shot learning to use less samples to train the system. Fourth, we will combine our previous research using RSSI localization methods [6], [20] to raise the recognition accuracy. Fifth, we will look at more distributed AI algorithms to analyse ADL data [21], [22].…”

A WiFi RSSI ranking fingerprint positioning system and its application to indoor activities of daily living recognition

“…In our IPS, we also have such two phases, but after we collected the fingerprints, we select APs to improve location accuracy as some are noisey. In our previous work, 7 we proposed an interval overlap degree (IOD) determination method to do the AP selection. However, this method is based on the raw RSSI measurements, which is not suitable for ranking-based AP selection.…”

“…Here, we use the performance of our validation dataset to measure the fitness; it can also be combined with information gain (IG) and mutual information (MI), which is mentioned in our previous paper. 7 Crossover. This procedure combines two parents to form children for the next generation.…”

“…In addition, an AP selection algorithm to select a subset of all available APs not only reduces the computational cost for online positioning but also increases the positioning accuracy, for example, because some AP RSSIs are noisy or weak. 7 An IPS can be used to track the trajectories, paths or tracks of sequential locations of users, enabling indoor navigation services. Since the trajectory is based on sequential movement, motion models can be applied to help increase the tracking accuracy, for example, through using a particle filter or Kalman filter (KF).…”

Client-Centric Access Device Selection for Heterogeneous QoS Requirements in Beyond 5G IoT Networks

“…For example, the decision to select between multiple WiFi APs in today's end-devices is fully distributed. A client usually selects among available WiFi APs based on a single Received Signal Strength Indicator (RSSI) metric [35]. A multi-RAT selection algorithm to select between cellular networks and WiFi APs also exists in today's end-devices.…”