Optimisation of Mobile Communication Networks - OMCO NET

The mini conference “Optimisation of Mobile Communication Networks” focuses on advanced methods for search and optimisation applied to wireless communication networks. It is sponsored by Research & Enterprise Fund Southampton Solent University. The conference strives to widen knowledge on advanced search methods capable of optimisation of wireless communications networks. The aim is to provide a forum for exchange of recent knowledge, new ideas and trends in this progressive and challenging area. The conference will popularise new successful approaches on resolving hard tasks such as minimisation of transmit power, cooperative and optimal routing

Spatiotemporal Wireless Sensor Network Field Approximation with Multilayer Perceptron Artificial Neural Network Models

As sensors become increasingly compact and dependable in natural environments, spatially-distributed heterogeneous sensor network systems steadily become more pervasive. However, any environmental monitoring system must account for potential data loss due to a variety of natural and technological causes. Modeling a natural spatial region can be problematic due to spatial nonstationarities in environmental variables, and as particular regions may be subject to specific influences at different spatial scales. Relationships between processes within these regions are often ephemeral, so models designed to represent them cannot remain static. Integrating temporal factors into this model engenders further complexity. This dissertation evaluates the use of multilayer perceptron neural network models in the context of sensor networks as a possible solution to many of these problems given their data-driven nature, their representational flexibility and straightforward fitting process. The relative importance of parameters is determined via an adaptive backpropagation training process, which converges to a best-fit model for sensing platforms to validate collected data or approximate missing readings. As conditions evolve over time such that the model can no longer adapt to changes, new models are trained to replace the old. We demonstrate accuracy results for the MLP generally on par with those of spatial kriging, but able to integrate additional physical and temporal parameters, enabling its application to any region with a collection of available data streams. Potential uses of this model might be not only to approximate missing data in the sensor field, but also to flag potentially incorrect, unusual or atypical data returned by the sensor network. Given the potential for spatial heterogeneity in a monitored phenomenon, this dissertation further explores the benefits of partitioning a space and applying individual MLP models to these partitions. A system of neural models using both spatial and temporal parameters can be envisioned such that a spatiotemporal space partitioned by k-means is modeled by k neural models with internal weightings varying individually according to the dominant processes within the assigned region of each. Evaluated on simulated and real data on surface currents of theGulf ofMaine, partitioned models show significant improved results over single global models

Graph signal reconstruction techniques for IoT air pollution monitoring platforms

Air pollution monitoring platforms play a very important role in preventing and mitigating the effects of pollution. Recent advances in the field of graph signal processing have made it possible to describe and analyze air pollution monitoring networks using graphs. One of the main applications is the reconstruction of the measured signal in a graph using a subset of sensors. Reconstructing the signal using information from neighboring sensors is a key technique for maintaining network data quality, with examples including filling in missing data with correlated neighboring nodes, creating virtual sensors, or correcting a drifting sensor with neighboring sensors that are more accurate. This paper proposes a signal reconstruction framework for air pollution monitoring data where a graph signal reconstruction model is superimposed on a graph learned from the data. Different graph signal reconstruction methods are compared on actual air pollution data sets measuring O3, NO2, and PM10. The ability of the methods to reconstruct the signal of a pollutant is shown, as well as the computational cost of this reconstruction. The results indicate the superiority of methods based on kernel-based graph signal reconstruction, as well as the difficulties of the methods to scale in an air pollution monitoring network with a large number of low-cost sensors. However, we show that the scalability of the framework can be improved with simple methods, such as partitioning the network using a clustering algorithm.This work is supported by the National Spanish funding PID2019-107910RB-I00, by regional project 2017SGR-990, and with the support of Secretaria d’Universitats i Recerca de la Generalitat de Catalunya i del Fons Social Europeu.Peer ReviewedPostprint (author's final draft

Location prediction optimisation in WSNs using kriging interpolation

© The Institution of Engineering and Technology 2016. Many wireless sensor network (WSN) applications rely on precise location or distance information. Despite the potentials of WSNs, efficient location prediction is one of the subsisting challenges. This study presents novel prediction algorithms based on a Kriging interpolation technique. Given that each sensor is aware of its location only, the aims of this work are to accurately predict the temperature at uncovered areas and estimate positions of heat sources. By taking few measurements within the field of interest and by using Kriging interpolation to iteratively enhance predictions of temperature and location of heat sources in uncovered regions, the degree of accuracy is significantly improved. Following a range of independent Monte Carlo runs in different experiments, it is shown through a comparative analysis that the proposed algorithm delivers approximately 98% prediction accuracy

Application of Radio environment map reconstruction techniques to platoon-based cellular V2X communications

Vehicle platoons involve groups of vehicles travelling together at a constant inter-vehicle distance, with different common benefits such as increasing road efficiency and fuel saving. Vehicle platooning requires highly reliable wireless communications to keep the group structure and carry out coordinated maneuvers in a safe manner. Focusing on infrastructure-assisted cellular vehicle to anything (V2X) communications, the amount of control information to be exchanged between each platoon vehicle and the base station is a critical factor affecting the communication latency. This paper exploits the particular structure and characteristics of platooning to decrease the control information exchange necessary for the channel acquisition stage. More precisely, a scheme based on radio environment map (REM) reconstruction is proposed, where geo-localized received power values are available at only a subset of platoon vehicles, while large-scale channel parameters estimates for the rest of platoon members are provided through the application of spatial Ordinary Kriging (OK) interpolation. Distinctive features of the vehicle platooning use case are explored, such as the optimal patterns of vehicles within the platoon with available REM values for improving the quality of the reconstruction, the need for an accurate semivariogram modeling in OK, or the communication cost when establishing a centralized or a distributed architecture for achieving REM reconstruction. The evaluation results show that OK is able to reconstruct the REM in the platoon with acceptable mean squared estimation error, while reducing the control information for REM acquisition in up to 64% in the best-case scenario

Spatial snow water equivalent estimation for mountainous areas using wireless-sensor networks and remote-sensing products

We developed an approach to estimate snow water equivalent (SWE) through interpolation of spatially representative point measurements using a k-nearest neighbors (k-NN) algorithm and historical spatial SWE data. It accurately reproduced measured SWE, using different data sources for training and evaluation. In the central-Sierra American River basin, we used a k-NN algorithm to interpolate data from continuous snow-depth measurements in 10 sensor clusters by fusing them with 14 years of daily 500-m resolution SWE-reconstruction maps. Accurate SWE estimation over the melt season shows the potential for providing daily, near real-time distributed snowmelt estimates. Further south, in the Merced-Tuolumne basins, we evaluated the potential of k-NN approach to improve real-time SWE estimates. Lacking dense ground-measurement networks, we simulated k-NN interpolation of sensor data using selected pixels of a bi-weekly Lidar-derived snow water equivalent product. k-NN extrapolations underestimate the Lidar-derived SWE, with a maximum bias of −10 cm at elevations below 3000 m and +15 cm above 3000 m. This bias was reduced by using a Gaussian-process regression model to spatially distribute residuals. Using as few as 10 scenes of Lidar-derived SWE from 2014 as training data in the k-NN to estimate the 2016 spatial SWE, both RMSEs and MAEs were reduced from around 20–25 cm to 10–15 cm comparing to using SWE reconstructions as training data. We found that the spatial accuracy of the historical data is more important for learning the spatial distribution of SWE than the number of historical scenes available. Blending continuous spatially representative ground-based sensors with a historical library of SWE reconstructions over the same basin can provide real-time spatial SWE maps that accurately represents Lidar-measured snow depth; and the estimates can be improved by using historical Lidar scans instead of SWE reconstructions

Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites

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Radio frequency channel characterization for energy harvesting in factory environments

This thesis presents ambient energy data obtained from a measurement campaign carried out at an automobile plant. At the automobile plant, ambient light, ambient temperature and ambient radio frequency were measured during the day time over two days. The measurement results showed that ambient light generated the highest DC power. For plant and operation managers at the automobile plant, the measurement data can be used in system design considerations for future energy harvesting wireless sensor nodes at the plant. In addition, wideband measurements obtained from a machine workshop are presented in this thesis. The power delay profile of the wireless channel was obtained by using a frequency domain channel sounding technique. The measurements were compared with an equivalent ray tracing model in order to validate the suitability of the commercial propagation software used in this work. Furthermore, a novel technique for mathematically recreating the time dispersion created by factory inventory in a radio frequency channel is discussed. As a wireless receiver design parameter, delay spread characterizes the amplitude and phase response of the radio channel. In wireless sensor devices, this becomes paramount, as it determines the complexity of the receiver. In reality, it is sometimes difficult to obtain full detail floor plans of factories for deterministic modelling or carry out spot measurements during building construction. As a result, radio provision may be suboptimal. The method presented in this thesis is based on 3-D fractal geometry. By employing the fractal overlaying algorithm presented, metallic objects can be placed on a floor plan so as to obtain similar radio frequency channel effects. The environment created using the fractal approach was used to estimate the amount of energy a harvesting device can accumulate in a University machine workshop space