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

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

Spatial Statistical Data Fusion on Java-enabled Machines in Ubiquitous Sensor Networks

Wireless Sensor Networks (WSN) consist of small, cheap devices that have a combination of sensing, computing and communication capabilities. They must be able to communicate and process data efficiently using minimum amount of energy and cover an area of interest with the minimum number of sensors. This thesis proposes the use of techniques that were designed for Geostatistics and applies them to WSN field. Kriging and Cokriging interpolation that can be considered as Information Fusion algorithms were tested to prove the feasibility of the methods to increase coverage. To reduce energy consumption, a compression method that models correlations based on variograms was developed. A second challenge is to establish the communication to the external networks and to react to unexpected events. A demonstrator that uses commercial Java-enabled devices was implemented. It is able to perform remote monitoring, send SMS alarms and deploy remote updates

Spectrum cartography techniques, challenges, opportunities, and applications: A survey

The spectrum cartography finds applications in several areas such as cognitive radios, spectrum aware communications, machine-type communications, Internet of Things, connected vehicles, wireless sensor networks, and radio frequency management systems, etc. This paper presents a survey on state-of-the-art of spectrum cartography techniques for the construction of various radio environment maps (REMs). Following a brief overview on spectrum cartography, various techniques considered to construct the REMs such as channel gain map, power spectral density map, power map, spectrum map, power propagation map, radio frequency map, and interference map are reviewed. In this paper, we compare the performance of the different spectrum cartography methods in terms of mean absolute error, mean square error, normalized mean square error, and root mean square error. The information presented in this paper aims to serve as a practical reference guide for various spectrum cartography methods for constructing different REMs. Finally, some of the open issues and challenges for future research and development are discussed.publishedVersio

Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey

Growing progress in sensor technology has constantly expanded the number and range of low-cost, small, and portable sensors on the market, increasing the number and type of physical phenomena that can be measured with wirelessly connected sensors. Large-scale deployments of wireless sensor networks (WSN) involving hundreds or thousands of devices and limited budgets often constrain the choice of sensing hardware, which generally has reduced accuracy, precision, and reliability. Therefore, it is challenging to achieve good data quality and maintain error-free measurements during the whole system lifetime. Self-calibration or recalibration in ad hoc sensor networks to preserve data quality is essential, yet challenging, for several reasons, such as the existence of random noise and the absence of suitable general models. Calibration performed in the field, without accurate and controlled instrumentation, is said to be in an uncontrolled environment. This paper provides current and fundamental self-calibration approaches and models for wireless sensor networks in uncontrolled environments

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

Feasibility of a low-cost weather sensor network for agricultural purposes : a preliminary assessment

This study has focused on challenges encountered when setting up a weather-network for agricultural purposes (e.g. linking temperature to the suitability of crops and pest incidence) with only low-cost sensors and materials. The study included a set of experiments in a meteorological station and a one-month period of observations in a large coffee plantation with a complex terrain in Costa Rica. The created network is intended to be linked to agronomic trials, and are part of a package that will make farmers the scientists in a range of extension projects. Ongoing projects provide farmers with a range of seeds that are tested, while feedback is provided by a crowd-sourcing approach. Farmers send text messages back to the project managers. The coordinates of the farmers' field can be extracted from a stack of rasters with climate information, resulting in a clear understanding of the conditions during the agronomic trials. Experiments with different sensor (iButton DS1923) resolution showed that losses in precision when using a low temperature resolution are small compared to other losses (e.g. interpolation in time and space). Using a high-resolution for humidity observations provides very small improvements over low-resolution, as it provides data with the same accuracy. Experiments also focused on adjustment to PVC tubes, which functioned as sensor-shielding. All adjustments provided large differences with a certified shielding for the maximum temperature on sunny days. The best coating to limit the impact of radiation was insulating foil, and it is recommended that future experiments focus more on aeration, as this has not yet provided the expected benefits. As no (combination of) adjustments provided data in line with the reference station, different types of data calibration were tested. While direct correction of data by a polynomial regression model provided reasonable results, the main difference between PVC shields and the certified sensor was caused by the faster heating/cooling over time (thermal-inertia properties). By creating a linear model between change in time in the PVC and certified shield, a calibration model was developed that has been used to correct data. This has been done by setting an anchor point on each day, to which the corrected change was added/subtracted. With some minor additional calibration, this model provided data that was very similar to data in the certified shield. After the initial experiments were analysed, one hundred sensors were placed in a large coffee plantation with a 500 meter elevation gradient; fourteen sensors were lost and six provided incorrect data. The correlation between temperature and a range of variables was assessed. This included static (elevation, slope, aspect, canopy height, leaf-area-index and daily radiation) and dynamic (hourly radiation) covariates. On average, 52% of variance in temperature could be explained by static covariates. Including hourly radiation as covariate instead of daily radiation improved this model by 1%. Elevation is by far the most important independent variable (± 67%), although this influence is lower during periods with high temperature. A higher daily maximum temperature reduces the strength of elevation/temperature correlation. These are periods during which temperature is harder to predict based on interpolation in the complex terrain. A lower correlation between elevation and temperature can partly be compensated by a stronger correlation with other covariates; hourly radiation contributes on average 20% to the temperature-predicting models during hours with sun (although the models can only predict 54% of variance during these hours). Geostatistical interpolation has been tested for 80, 40, 20 and 8 sensors, with different kriging approaches and sets of covariates. Cross-validation provided the best results for universal kriging with elevation. Dynamic kriging provided smaller errors only with the full 80-sensor network. Co- and Spatio-Temporal kriging provided larger errors in predicting a left-out sensor, while data of the sensors included in the kriging showed least modification. The preferred approach depends on the network objective and reliability of data. While the network in this study cost ±US$8,700, a sufficiently accurate network of 25 sensors, can be created with a smaller budget: 20 low-res (temperature) iButton sensors (DS1922L-F5), 5 high-res (temperature and humidity) iButton sensors (DS1923-F5), 50m thin white PVC, 50 PVC elbows, 1m2 insulating foil, a small amount of fibre-glass mesh, and labor for construction (drilling holes and assembling). The cost for this weather network - which can store 341 days of 1-hour resolution data - will be approximately US$ 1,450. A 50-sensor network would still cost 500m) in the area. The other factors that were included showed smaller links to temperature, although at certain moments this could become more important. This link of temperature with different factors that can be created from existing maps and algorithms can be used to reduce the number of sensors that would be required in a certain network. However, hours during which it was warm and sunny showed very little linkages to the commonly available factors (variables). Including hourly sunshine could partly compensate this, by having a relatively strong link to temperature, but this is still smaller than during nighttime/cloudy periods. The percentage of variation in temperature that can be explained at different hours (dotted line) and the relative contribution of the studied factors is shown in the figure below. Based on this knowledge, different approaches of data interpolation (creating a network from point-based observations) have been tested, while reducing the number of sensors to 40, 20 and 8. The most useful results were found for an approach that only included elevation, as this is the most stable variable. More advanced (geo-statistical) approached require a larger number of sensors (> 50), which places them outside the objective of these networks: to be available at low costs. The first set of experiments, aiming to create a shield that would perform similar to a WMO certified shield, did not provide very good results. The influence of radiation could not be avoided by improving air flow (drilling holes) and adding different types of foil and tape. While correction would thus still be required, the selected correction approach did perform very well at different locations. It would still be important to better study the actual shield, by experimenting by even more combinations of materials. The data that was derived from the network in a large coffee plantation in Costa Rica, showed that variation in temperature is especially linked to differences in elevation. Factors such as density and height of the canopy played a smaller role. During hours with sun, elevation could explain less of the variation, but the hourly differences in sun/shadow could partly compensate this. The different factors can be used to create a relatively accurate networks with around 25 sensors, which would cost less than US$ 1,500 and can measure every hour for a period of around one year. This would be ideal for most annual crops