Researches confirm the existence of widespread pollution in Iraq, where a large number of citizens suffer from symptoms related to environmental pollutants. This is accompanied by the lack of an integrated monitoring system that provides wide-scale, real-time monitoring of air pollution for pollutants (Particulate Matter (PM), Sulfur Dioxide (SO2), Ground-Level Ozone (O3), Carbon Monoxide (CO), and Nitrogen Dioxide (NO2), ionizing radiation). In this paper, an integrated system was designed and implemented to meet the requirements of air pollution monitoring in Iraq on a wide-scale and provide its results in real-time to the end-users in community service as a significant step towards smart cities. Distributed computing concepts and design patterns were used to realize the system in the form of web services along with the embedded sensing units. Where the readings from the former are sent in real-time to the later to be aggregated and analysed in database systems. Reporting services are run on the data in web-services to inform the citizens with the air quality, radioactively polluted areas, and provide real-time alerts and recommendations to those with respiratory diseases.
Long Range (LoRa) Technology offers robust wireless solutions for data collection and communication in smart buildings and smart cities by providing superior performance as a solution for the Internet of Things infrastructure. Its four powerful parameters usually govern the communication performance of LoRa; the transmit power, Spreading Factor (SF), Code Rate (CR), and the Bandwidth (BW). This work introduces a comprehensive study of LoRa’s behavior and the role of the abovementioned parameters based on experimental validation of the various performance metrics of LoRa. LoRa is tested to evaluate its reliability and stability in reality, where the different environment is adopted in Electrical Engineering Technical College in Baghdad, Iraq. In terms of LoRa reliability, the results show an acceptable Mean Absolute Error (MAE) equals to 11.33dB when comparing reference and experimental LoRa sensitivity for all SF values. Moreover, 18% of packet loss has resulted from a heavy fading environment with minimal power and SF. On the other hand, LoRa sensitivity test shows that the variance calculated for 200 RSS samples in a heavy fading condition equals 29.9064. Both tests quantify the efficiency of LoRa in such a harsh environment and pave the way for future LoRa applications implementation.
Path Loss (PL) models are an essential factor affecting the network design and its operation. With different environmental conditions, interpreting the PL characteristics in an open environment is a complex problem. In this work, the propagation of LoRa technology in a campus is investigated in order to propose an accurate PL model. The measurements are taken place in two outdoor regions of the Electrical Engineering Technical College in Baghdad, Iraq. Measured field data correlates with global propagation models, demonstrating that ERICSON model results after an evaluation are likely to produce positive results. Different environment conditions make the global PL models difficult to generalize, yield some errors between the measured and estimated PL. For addressing this downside, Particle Swarm Optimization (PSO) has been based to develop the model parameters, hence matching the model to reality. The ERICSON model’s parameters have been improved to the best fit with measured data, and the lowest Root Mean Square Error (RMSE) is gained equals to 3.7168dB and 5.4030dB for the two adopted regions.
Network connectivity in dynamic spectrum access (DSA) networks has been well studied–most of which are under the unit disk model. However, the disk model does not capture the primary-secondary and secondary-secondary interference; hence signal to interference and noise ratio (SINR) based models are more appropriate. Moreover, in the SINR regime, there is no unique way to characterize connectivity and hence its maximization becomes even more challenging. In this paper, we develop the long eluding network connectivity objective function which we use to build three connectivity optimization techniques each of which targets a particular network setup. The proposed techniques are: i) Fittest deployment density, ii) Fittest receive-only ratio, and iii) Fittest TDMA slotting. To develop the aforementioned objective function, we start by addressing the lack of any relation between deployment density and network connectivity in interference-limited DSA networks. Next, percolation theory in conjunction with the Boolean model are utilized to develop such a relationship between the density of the percolation visible nodes and the network connectivity in interference limited environments. Finally, we use that relation to build the objective function for connectivity maximization along three optimization techniques. Theoretical findings are validated by simulating networks under various scenarios. Results provide a blueprint to establish and maximize connectivity using physical layer parameters (density, coverage radius, etc.) which can be used in conjunction with higher layer techniques. Also, tackling the connectivity problem at the physical layer relieves the other higher layers like the MAC layer from excess signaling and complex protocol designs.
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