A Compact Adaptive Particle Swarm Optimization Algorithm in the Application of the Mobile Sensor Localization

The mobile sensor network can sense and collect the data information of the monitored object in real time in the monitoring area. However, the collected information is meaningful only if the location of the node is known. This paper mainly optimizes the Monte Carlo Localization (MCL) in mobile sensor positioning technology. In recent years, the rapid development of heuristic algorithms has provided solutions to many complex problems. This paper combines the compact strategy into the adaptive particle swarm algorithm and proposes a compact adaptive particle swarm algorithm (cAPSO). The compact strategy replaces the specific position of each particle by the distribution probability of the particle swarm, which greatly reduces the memory usage. The performance of cAPSO is tested on 28 test functions of CEC2013, and compared with some existing heuristic algorithms, it proves that cAPSO has a better performance. At the same time, cAPSO is applied to MCL technology to improve the accuracy of node localization, and compared with other heuristic algorithms in the accuracy of MCL, the results show that cAPSO has a better performance.

Self-supervised learning for using overhead imagery as maps in outdoor range sensor localization

Traditional approaches to outdoor vehicle localization assume a reliable, prior map is available, typically built using the same sensor suite as the on-board sensors used during localization. This work makes a different assumption. It assumes that an overhead image of the workspace is available and utilizes that as a map for use for range-based sensor localization by a vehicle. Here, range-based sensors are radars and lidars. Our motivation is simple, off-the-shelf, publicly available overhead imagery such as Google satellite images can be a ubiquitous, cheap, and powerful tool for vehicle localization when a usable prior sensor map is unavailable, inconvenient, or expensive. The challenge to be addressed is that overhead images are clearly not directly comparable to data from ground range sensors because of their starkly different modalities. We present a learned metric localization method that not only handles the modality difference, but is also cheap to train, learning in a self-supervised fashion without requiring metrically accurate ground truth. By evaluating across multiple real-world datasets, we demonstrate the robustness and versatility of our method for various sensor configurations in cross-modality localization, achieving localization errors on-par with a prior supervised approach while requiring no pixel-wise aligned ground truth for supervision at training. We pay particular attention to the use of millimeter-wave radar, which, owing to its complex interaction with the scene and its immunity to weather and lighting conditions, makes for a compelling and valuable use case.

Sensor Localization Using Time of Arrival Measurements in a Multi-Media and Multi-Path Application of In-Situ Wireless Soil Sensing

The problem of localization of nodes of a wireless sensor network placed in different physical media (anchor nodes above ground and sensor nodes underground) is addressed in this article. We use time of arrival of signals transmitted between neighboring sensor nodes and between satellite nodes and sensor nodes as the ranging measurement. The localization problem is formulated as a parameter estimation of the joint distribution of the time of arrival values. The probability distribution of the time of arrival of a signal is derived based on rigorous statistical analysis and its parameters are expressed in terms of the location coordinates of the sensor nodes. Maximum likelihood estimates of the nodes’ location coordinates as parameters of the joint distribution of the various time of arrival variables in the network are computed. Sensitivity analysis to study the variation in the estimates with respect to error in measured soil complex permittivity and magnetic permeability is presented to validate the model and methodology.

Sensor Localization in Wireless Sensor Networks Using Cultural Algorithm

Evolutionary computing-based cultural algorithm (CA) has been developed for anchor-assisted, range-based, multi-stage localization of sensor nodes of wireless sensor networks (WSNs). The results of CA-based localization have been compared with those of swarm intelligence-based algorithms, namely the artificial bee colony algorithm and the particle swarm optimization algorithm. The algorithms have been compared in terms of mean localization error and computing time. The simulation results show that the CA performs the localization in a more accurate manner and at a higher speed than the other two algorithms.