Search results for: indoor network performance

Authors: Ranbir Singh, Deepinder Kaur

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Network security is an important aspect in every field like government offices, Educational Institute and any business organization. Network security consists of the policies adopted to prevent and monitor forbidden access, misuse, modification, or denial of a computer network. Network security is very complicated subject and deal by only well trained and experienced people. However, as more and more people become wired, an increasing number of people need to understand the basics of security in a networked world. The history of the network security included an introduction to the TCP/IP and interworking. Network security starts with authenticating, commonly with a username and a password. In this paper, we study about various types of attacks on network security and how to handle or prevent this attack.

Keywords: network security, attacks, denial, authenticating

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Authors: Diego A. Romero Espinosa

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Thermal simulations are used to evaluate comfort and energy consumption of buildings. However, the performance of different urban forms cannot be assessed precisely if an environmental control system and user schedules are considered. The outcome of such analysis would lead to conclusions that combine the building use, operation, services, envelope, orientation and density of the urban fabric. The influence of these factors varies during the life cycle of a building. The orientation, as well as the surroundings, can be considered a constant during the lifetime of a building. The structure impacts the thermal inertia and has the largest lifespan of all the building components. On the other hand, the building envelope is the most frequent renovated component of a building since it has a great impact on energy performance and comfort. Building services have a shorter lifespan and are replaced regularly. With the purpose of addressing the performance, an urban form, a specific orientation, and density, a thermal simulation method were developed. The solar irradiation is taken into consideration depending on the outdoor temperature. Incoming irradiation at low temperatures has a positive impact increasing the indoor temperature. Consequently, overheating would be the combination of high outdoor temperature and high irradiation at the façade. On this basis, the indoor temperature is simulated for a specific orientation of the evaluated urban form. Thermal inertia and building envelope performance are considered additionally as the materiality of the building. The results of different thermal zones are summarized using the 'Degree day method' for cooling and heating. During the early phase of a design process for a project, such as Masterplan, conclusions regarding urban form, density and materiality can be drawn by means of this analysis.

Keywords: building envelope, density, masterplanning, urban form

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Authors: Naveed Ghani

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With the arrival of higher speed communication links and mature application running over the internet, the requirement for reliable, efficient and robust network designs rising day by day. Multi-Protocol Label Switching technology (MPLS) Virtual Private Networks (VPNs) have committed to provide optimal network services. They are gaining popularity in industry day by day. Enterprise customers are moving to service providers that offer MPLS VPNs. The main reason for this shifting is the capability of MPLS VPN to provide built in security features and any-to-any connectivity. MPLS VPNs improved the network performance due to fast label switching as compare to traditional IP Forwarding but traffic classification and policing was still required on per hop basis to enhance the performance of real time traffic which is delay sensitive (particularly voice and video). QoS (Quality of service) is the most important factor to prioritize enterprise networks’ real time traffic such as voice and video. This thesis is focused on the study of QoS parameters (e.g. delay, jitter and MOS (Mean Opinion Score)) for the real time traffic over MPLS VPNs. DiffServ (Differentiated Services) QoS model will be used over MPLS VPN network to get end-to-end service quality.

Keywords: network, MPLS, VPN, DiffServ, MPLS VPN, DiffServ QoS, QoS Model, GNS2

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Authors: Gideon C. Joseph

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The ability to localize mobile devices is quite important, as some applications may require location information of these devices to operate or deliver better services to the users. Although there are several ways of acquiring location data of mobile devices, the WLAN fingerprinting approach has been considered in this work. This approach uses the Received Signal Strength Indicator (RSSI) measurement as a function of the position of the mobile device. RSSI is a quantitative technique of describing the radio frequency power carried by a signal. RSSI may be used to determine RF link quality and is very useful in dense traffic scenarios where interference is of major concern, for example, indoor environments. This research aims to design a system that can predict the location of a mobile device, when supplied with the mobile’s RSSIs. The developed system takes as input the RSSIs relating to the mobile device, and outputs parameters that describe the location of the device such as the longitude, latitude, floor, and building. The relationship between the Received Signal Strengths (RSSs) of mobile devices and their corresponding locations is meant to be modelled; hence, subsequent locations of mobile devices can be predicted using the developed model. It is obvious that describing mathematical relationships between the RSSIs measurements and localization parameters is one option to modelling the problem, but the complexity of such an approach is a serious turn-off. In contrast, we propose an intelligent system that can learn the mapping of such RSSIs measurements to the localization parameters to be predicted. The system is capable of upgrading its performance as more experiential knowledge is acquired. The most appealing consideration to using such a system for this task is that complicated mathematical analysis and theoretical frameworks are excluded or not needed; the intelligent system on its own learns the underlying relationship in the supplied data (RSSI levels) that corresponds to the localization parameters. These localization parameters to be predicted are of two different tasks: Longitude and latitude of mobile devices are real values (regression problem), while the floor and building of the mobile devices are of integer values or categorical (classification problem). This research work presents artificial neural network based intelligent systems to model the relationship between the RSSIs predictors and the mobile device localization parameters. The designed systems were trained and validated on the collected WLAN fingerprint database. The trained networks were then tested with another supplied database to obtain the performance of trained systems on achieved Mean Absolute Error (MAE) and error rates for the regression and classification tasks involved therein.

Keywords: indoor localization, WLAN fingerprinting, neural networks, classification, regression

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Authors: Chung-Yuan Huang, Yu-Hsiang Fu, Chuen-Tsai Sun

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Community detection represents an important data-mining tool for analyzing and understanding real-world complex network structures and functions. We believe that at least four criteria determine the appropriateness of a community detection algorithm: (a) it produces useable normalized mutual information (NMI) and modularity results for social networks, (b) it overcomes resolution limitation problems associated with synthetic networks, (c) it produces good NMI results and performance efficiency for Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks, and (d) it produces good modularity and performance efficiency for large-scale real-world complex networks. To our knowledge, no existing community detection algorithm meets all four criteria. In this paper, we describe a simple hierarchical arc-merging (HAM) algorithm that uses network topologies and rule-based arc-merging strategies to identify community structures that satisfy the criteria. We used five well-studied social network datasets and eight sets of LFR benchmark networks to validate the ground-truth community correctness of HAM, eight large-scale real-world complex networks to measure its performance efficiency, and two synthetic networks to determine its susceptibility to resolution limitation problems. Our results indicate that the proposed HAM algorithm is capable of providing satisfactory performance efficiency and that HAM-identified communities were close to ground-truth communities in social and LFR benchmark networks while overcoming resolution limitation problems.

Keywords: complex network, social network, community detection, network hierarchy

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Authors: Asmir Gogic, Aljo Mujcic, Sandra Ibric, Nermin Suljanovic

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Ubiquity of natural disasters during last few decades have risen serious questions towards the prediction of such events and human safety. Every disaster regardless its proportion has a precursor which is manifested as a disruption of some environmental parameter such as temperature, humidity, pressure, vibrations and etc. In order to anticipate and monitor those changes, in this paper we propose an overall system for disaster prediction and monitoring, based on wireless sensor network (WSN). Furthermore, we introduce a modified and simplified WSN routing protocol built on the top of the trickle routing algorithm. Routing algorithm was deployed using the bluetooth low energy protocol in order to achieve low power consumption. Performance of the WSN network was analyzed using a real life system implementation. Estimates of the WSN parameters such as battery life time, network size and packet delay are determined. Based on the performance of the WSN network, proposed system can be utilized for disaster monitoring and prediction due to its low power profile and mesh routing feature.

Keywords: bluetooth low energy, disaster prediction, mesh routing protocols, wireless sensor networks

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Authors: Riznaldi Akbar

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In this study, we compared the performance of the Artificial Neural Network (ANN) model with back-propagation algorithm in correctly predicting in-sample and out-of-sample external debt crisis in Indonesia. We found that exchange rate, foreign reserves, and exports are the major determinants to experiencing external debt crisis. The ANN in-sample performance provides relatively superior results. The ANN model is able to classify correctly crisis of 89.12 per cent with reasonably low false alarms of 7.01 per cent. In out-of-sample, the prediction performance fairly deteriorates compared to their in-sample performances. It could be explained as the ANN model tends to over-fit the data in the in-sample, but it could not fit the out-of-sample very well. The 10-fold cross-validation has been used to improve the out-of-sample prediction accuracy. The results also offer policy implications. The out-of-sample performance could be very sensitive to the size of the samples, as it could yield a higher total misclassification error and lower prediction accuracy. The ANN model could be used to identify past crisis episodes with some accuracy, but predicting crisis outside the estimation sample is much more challenging because of the presence of uncertainty.

Keywords: debt crisis, external debt, artificial neural network, ANN

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Authors: D. G. Badagha, C. D. Modhera, S. A. Vasanwala

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There is a great challenge for civil engineering field to contribute in environment prevention by finding out alternatives of cement and natural aggregates. There is a problem of global warming due to cement utilization in concrete, so it is necessary to give sustainable solution to produce concrete containing waste. It is very difficult to produce designated grade of concrete containing different ingredient and water cement ratio including waste to achieve desired fresh and harden properties of concrete as per requirement and specifications. To achieve the desired grade of concrete, a number of trials have to be taken, and then after evaluating the different parameters at long time performance, the concrete can be finalized to use for different purposes. This research work is carried out to solve the problem of time, cost and serviceability in the field of construction. In this research work, artificial neural network introduced to fix proportion of concrete ingredient with 50% waste replacement for M20, M25, M30, M35, M40, M45, M50, M55 and M60 grades of concrete. By using the neural network, mix design of high performance concrete was finalized, and the main basic mechanical properties were predicted at 3 days, 7 days and 28 days. The predicted strength was compared with the actual experimental mix design and concrete cube strength after 3 days, 7 days and 28 days. This experimentally and neural network based mix design can be used practically in field to give cost effective, time saving, feasible and sustainable high performance concrete for different types of structures.

Keywords: artificial neural network, high performance concrete, rebound hammer, strength prediction

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Authors: Fengxia Li, Lufeng Zhang, Haibo Wang

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The tight sandstone gas reservoir in the Xujiahe Formation of the Sichuan Basin has huge reserves, but its utilization rate is low. Fracturing and stimulation are indispensable technologies to unlock their potential and achieve commercial exploitation. Slickwater is the most widely used fracturing fluid system in the fracturing and renovation of tight reservoirs. However, its viscosity is low, its sand-carrying performance is poor, and the risk of sand blockage is high. Increasing the sand carrying capacity by increasing the displacement will increase the frictional resistance of the pipe string, affecting the resistance reduction performance. The variable viscosity slickwater can flexibly switch between different viscosities in real-time online, effectively overcoming problems such as sand carrying and resistance reduction. Based on a self-developed indoor loop friction testing system, a visualization device for proppant transport, and a HAAKE MARS III rheometer, a comprehensive evaluation was conducted on the performance of variable viscosity slickwater, including resistance reduction, rheology, and sand carrying. The indoor experimental results show that: 1. by changing the concentration of drag-reducing agents, the viscosity of the slippery water can be changed between 2~30mPa. s; 2. the drag reduction rate of the variable viscosity slickwater is above 80%, and the shear rate will not reduce the drag reduction rate of the liquid; under indoor experimental conditions, 15mPa. s of variable viscosity and slickwater can basically achieve effective carrying and uniform placement of proppant. The layered fracturing effect of the JiangX well in the dense sandstone of the Xujiahe Formation shows that the drag reduction rate of the variable viscosity slickwater is 80.42%, and the daily production of the single layer after fracturing is over 50000 cubic meters. This study provides theoretical support and on-site experience for promoting the application of variable viscosity slickwater in tight sandstone gas reservoirs.

Keywords: slickwater, hydraulic fracturing, dynamic sand laying, drag reduction rate, rheological properties

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Authors: Chun Yu, Tahlia M. Farrant, Max G. Marschall

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Most of Australia’s school classrooms are equipped with operable windows and occupant-controlled air-conditioners that do not provide fresh air. This can result in insufficient ventilation and high indoor CO₂ levels, which comes at a detriment to occupant productivity and health. This paper reports on the results of an in-situ study capturing indoor CO₂ levels in classrooms at a school in Victoria, Australia. The study consisted of 3 measurement periods: First, CO₂ levels pre-pandemic were measured, finding that the readings exceeded the recommended ASHRAE threshold of 1000 ppm more than 50% of the time, with levels often rising as high as 5000 ppm. Then, after the staff had been informed of the poor indoor air quality and the Victorian government had put COVIDSAFE measures in place, a second data set was captured; the impact was significant, with now only about 30% of readings above the ASHRAE threshold, and values rarely exceeding 2500 ppm. Finally, devices were installed that gave the occupants visual feedback when CO₂ levels were high, thus prompting them to open the windows; this further improved the air quality, with now less than 20% of readings above the threshold and values rarely exceeding 1500 ppm. The study suggests that, while relying on occupants to operate windows can lead to poor indoor air quality due to insufficient ventilation, it is possible to considerably influence occupant behavior through education and feedback devices. While these interventions alone did not mitigate the problem of inadequate ventilation entirely, they were sufficient to keep CO₂ levels within a generally healthy range. Considering the large energy savings that are possible by foregoing mechanical ventilation, it is evident that natural ventilation is a feasible operation method for school buildings in temperate climates, as long as classrooms are equipped with CO₂ feedback devices.

Keywords: COVID, CO₂, education, feedback devices, health, indoor air quality, natural ventilation, occupant behaviour

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Authors: Sara ElElimy, Samir Moustafa

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Mobile network operators start to face many challenges in the digital era, especially with high demands from customers. Since mobile network operators are considered a source of big data, traditional techniques are not effective with new era of big data, Internet of things (IoT) and 5G; as a result, handling effectively different big datasets becomes a vital task for operators with the continuous growth of data and moving from long term evolution (LTE) to 5G. So, there is an urgent need for effective Big data analytics to predict future demands, traffic, and network performance to full fill the requirements of the fifth generation of mobile network technology. In this paper, we introduce data science techniques using machine learning and deep learning algorithms: the autoregressive integrated moving average (ARIMA), Bayesian-based curve fitting, and recurrent neural network (RNN) are employed for a data-driven application to mobile network operators. The main framework included in models are identification parameters of each model, estimation, prediction, and final data-driven application of this prediction from business and network performance applications. These models are applied to Telecom Italia Big Data challenge call detail records (CDRs) datasets. The performance of these models is found out using a specific well-known evaluation criteria shows that ARIMA (machine learning-based model) is more accurate as a predictive model in such a dataset than the RNN (deep learning model).

Keywords: big data analytics, machine learning, CDRs, 5G

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Authors: Jiahao Cui

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In the past centuries, due to climate change and intense whaling, the global whale population has dramatically declined. Among the various whale species, the fin whale experienced the most drastic drop in number due to its popularity in whaling. Under this background, identifying fin whale calls could be immensely beneficial to the preservation of the species. This paper uses feature extraction to process the input audio signal, then a network based on AlexNet and three networks based on the ResNet model was constructed to classify fin whale calls. A mixture of the DOSITS database and the Watkins database was used during training. The results demonstrate that a modified ResNet network has the best performance considering precision and network complexity.

Keywords: convolutional neural network, ResNet, AlexNet, fin whale preservation, feature extraction

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Authors: Emad Alenany, M. Adel El-Baz

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In this paper, the flow of different classes of patients into a hospital is modelled and analyzed by using the queueing network analyzer (QNA) algorithm and discrete event simulation. Input data for QNA are the rate and variability parameters of the arrival and service times in addition to the number of servers in each facility. Patient flows mostly match real flow for a hospital in Egypt. Based on the analysis of the waiting times, two approaches are suggested for improving performance: Separating patients into service groups, and adopting different service policies for sequencing patients through hospital units. The separation of a specific group of patients, with higher performance target, to be served separately from the rest of patients requiring lower performance target, requires the same capacity while improves performance for the selected group of patients with higher target. Besides, it is shown that adopting the shortest processing time and shortest remaining processing time service policies among other tested policies would results in, respectively, 11.47% and 13.75% reduction in average waiting time relative to first come first served policy.

Keywords: queueing network, discrete-event simulation, health applications, SPT

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Authors: G. R. Kavitha, T. S. Indumathi

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With the constantly rising demands of the multimedia services, the requirements of long haul transport network are constantly changing in the area of optical network. Maximum data transmission using optimization of the communication channel poses the biggest challenge. Although there has been a constant focus on this area from the past decade, there was no evidence of a significant result that has been accomplished. Hence, after reviewing some potential design of optical network from literatures, it was understood that optical signal to noise ratio was one of the elementary attributes that can define the performance of the optical network. In this paper, we propose a framework termed as ECON (Enhanced Constraint-based Optical Network) that primarily optimize the optical signal to noise ratio using ROADM. The simulation is performed in Matlab and optical signal to noise ratio is extracted considering the system matrix. The outcome of the proposed study shows that optimized OSNR as compared to the existing studies.

Keywords: component, optical network, reconfigurable optical add-drop multiplexer, optical signal-to-noise ratio

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Authors: Zoliana Bawitlung, P. C. Rohmingliana, L. Z. Chhangte, Remlal Siama, Hming Chungnunga, Vanram Lawma, L. Hnamte, B. K. Sahoo, B. K. Sapra, J. Malsawma

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Mizoram state has the highest lung cancer incidence rate in India due to its high-level consumption of tobacco and its products which is supplemented by the food habits. While smoking is mainly responsible for this incidence, the effect of inhalation of indoor radon gas cannot be discarded as the hazardous nature of this radioactive gas and its progenies on human population have been well-established worldwide where the radiation damage to bronchial cells eventually can be the second leading cause of lung cancer next to smoking. It is also known that the effect of radiation, however, small may be the concentration, cannot be neglected as they can bring about the risk of cancer incidence. Hence, estimation of indoor radon concentration is important to give a useful reference against radiation effects as well as establishing its safety measures and to create a baseline for further case-control studies. The indoor radon/thoron concentrations in Mizoram had been measured in 41 dwellings selected on the basis of spot gamma background radiation and construction type of the houses during 2015-2016. The dwellings were monitored for one year, in 4 months cycles to indicate seasonal variations, for the indoor concentration of radon gas and its progenies, outdoor gamma dose, and indoor gamma dose respectively. A time-integrated method using Solid State Nuclear Track Detector (SSNTD) based single entry pin-hole dosimeters were used for measurement of indoor Radon/Thoron concentration. Gamma dose measurements for indoor as well as outdoor were carried out using Geiger Muller survey meters. Seasonal variation of indoor radon/ thoron concentration was monitored. The results show that the annual average radon concentrations varied from 54.07 – 144.72 Bq/m³ with an average of 90.20 Bq/m³ and the annual average thoron concentration varied from 17.39 – 54.19 Bq/m³ with an average of 35.91 Bq/m³ which are below the permissible limit. The spot survey of gamma background radiation level varies between 9 to 24 µR/h inside and outside the dwellings throughout Mizoram which are all within acceptable limits. From the above results, there is no direct indication that radon/thoron is responsible for the high lung cancer incidence in the area. In order to find epidemiological evidence of natural radiations to high cancer incidence in the area, one may need to conduct a case-control study which is beyond this scope. However, the derived data of measurement will provide baseline data for further studies.

Keywords: background gamma radiation, indoor radon/thoron, lung cancer, seasonal variation

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Authors: G. Baraneedaran, Deepak Singh, Kollipara Tejesh

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The wireless sensors network has been an active research area over the y-ear passed. Due to the limited energy and communication ability of sensor nodes, it seems especially important to design a routing protocol for WSNs so that sensing data can be transmitted to the receiver effectively, an energy-balanced routing method based on forward-aware factor (FAF-EBRM) is proposed in this paper. In FAF-EBRM, the next-hop node is selected according to the awareness of link weight and forward energy density. A spontaneous reconstruction mechanism for Local topology is designed additionally. In this experiment, FAF-EBRM is compared with LEACH and EECU, experimental results show that FAF-EBRM outperforms LEACH and EECU, which balances the energy consumption, prolongs the function lifetime and guarantees high Qos of WSN.

Keywords: energy balance, forward-aware factor (FAF), forward energy density, link weight, network performance

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Authors: F. Amin, S. Feizi

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Big data has the potential to improve the quality of services; enable infrastructure that businesses depend on to adapt continually and efficiently; improve the performance of employees; help organizations better understand customers; and reduce liability risks. Analytics and marketing models of fixed and mobile operators are falling short in combating churn and declining revenue per user. Big Data presents new method to reverse the way and improve profitability. The benefits of Big Data and next-generation network, however, are more exorbitant than improved customer relationship management. Next generation of networks are in a prime position to monetize rich supplies of customer information—while being mindful of legal and privacy issues. As data assets are transformed into new revenue streams will become integral to high performance.

Keywords: big data, next generation networks, network transformation, strategy

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Authors: Danladi Ali, Abdullahi Mukaila

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In this work, GSM signal strength (power) was monitored in an indoor environment. Samples of the GSM signal strength was measured on mobile equipment (ME). One-dimensional multilevel wavelet is used to predict the fading phenomenon of the GSM signal measured and neural network clustering to determine the average power received in the study area. The wavelet prediction revealed that the GSM signal is attenuated due to the fast fading phenomenon which fades about 7 times faster than the radio wavelength while the neural network clustering determined that -75dBm appeared more frequently followed by -85dBm. The work revealed that significant part of the signal measured is dominated by weak signal and the signal followed more of Rayleigh than Gaussian distribution. This confirmed the wavelet prediction.

Keywords: decomposition, clustering, propagation, model, wavelet, signal strength and spectral efficiency

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Authors: Kwok W. Mui, Ling T. Wong, F. Xiao, Chin T. Cheung, Ho C. Yu

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This study addresses a concept of the Sustainable Building Environmental Model (SBEM) developed to optimize energy consumption in air conditioning and ventilation (ACV) systems without any deterioration of indoor environmental quality (IEQ). The SBEM incorporates two main components: an adaptive comfort temperature control module (ACT) and a new carbon dioxide demand control module (nDCV). These two modules take an innovative approach to maintain satisfaction of the Indoor Environmental Quality (IEQ) with optimum energy consumption, they provide a rational basis of effective control. A total of 2133 sets of measurement data of indoor air temperature (Ta), relative humidity (Rh) and carbon dioxide concentration (CO2) were conducted in some Hong Kong offices to investigate the potential of integrating the SBEM. A simulation was used to evaluate the dynamic performance of the energy and air conditioning system with the integration of the SBEM in an air-conditioned building. It allows us make a clear picture of the control strategies and performed any pre-tuned of controllers before utilized in real systems. With the integration of SBEM, it was able to save up to 12.3% in simulation and 15% in field measurement of overall electricity consumption, and maintain the average carbon dioxide concentration within 1000ppm and occupant dissatisfaction in 20%.

Keywords: sustainable building environmental model (SBEM), adaptive comfort temperature (ACT), new demand control ventilation (nDCV), energy saving

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Authors: Yassine Meraihi, Dalila Acheli, Rabah Meraihi

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Vehicular Ad hoc Network (VANET) is a special case of Mobile Ad hoc Network (MANET) used to establish communications and exchange information among nearby vehicles and between vehicles and nearby fixed infrastructure. VANET is seen as a promising technology used to provide safety, efficiency, assistance and comfort to the road users. Routing is an important issue in Vehicular Ad Hoc Network to find and maintain communication between vehicles due to the highly dynamic topology, frequently disconnected network and mobility constraints. This paper evaluates the performance of two most popular proactive routing protocols OLSR and DSDV in real city traffic scenario on the basis of three metrics namely Packet delivery ratio, throughput and average end to end delay by varying vehicles density and transmission range.

Keywords: DSDV, OLSR, quality of service, routing protocols, VANET

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Authors: Abderrahmane Soufi

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The building sector is the largest consumer of energy in France, accounting for 44% of French consumption. To reduce energy consumption and improve energy efficiency, France implemented an energy transition law targeting 40% energy savings by 2030 in the tertiary building sector. Building simulation tools are used to predict the energy performance of buildings but the reliability of these tools is hampered by discrepancies between the real and simulated energy performance of a building. This performance gap lies in the simplified assumptions of certain factors, such as the behavior of occupants on air conditioning and heating, which is considered deterministic when setting a fixed operating schedule and a fixed interior comfort temperature. However, the behavior of occupants on air conditioning and heating is stochastic, diverse, and complex because it can be affected by many factors. Probabilistic models are an alternative to deterministic models. These models are usually derived from statistical data and express occupant behavior by assuming a probabilistic relationship to one or more variables. In the literature, logistic regression has been used to model the behavior of occupants with regard to heating and air conditioning systems by considering univariate logistic models in residential buildings; however, few studies have developed multivariate models for higher education and vocational training buildings in a Mediterranean climate. Therefore, in this study, occupant behavior on heating and air conditioning systems was modeled using logistic regression. Occupant behavior related to the turn-on heating and air conditioning systems was studied through experimental measurements collected over a period of one year (June 2023–June 2024) in three classrooms occupied by several groups of students in engineering schools and professional training. Instrumentation was provided to collect indoor temperature and indoor relative humidity in 10-min intervals. Furthermore, the state of the heating/air conditioning system (off or on) and the set point were determined. The outdoor air temperature, relative humidity, and wind speed were collected as weather data. The number of occupants, age, and sex were also considered. Logistic regression was used for modeling an occupant turning on the heating and air conditioning systems. The results yielded a proposed model that can be used in building simulation tools to predict the energy performance of teaching buildings. Based on the first months (summer and early autumn) of the investigations, the results illustrate that the occupant behavior of the air conditioning systems is affected by the indoor relative humidity and temperature in June, July, and August and by the indoor relative humidity, temperature, and number of occupants in September and October. Occupant behavior was analyzed monthly, and univariate and multivariate models were developed.

Keywords: occupant behavior, logistic regression, behavior model, mediterranean climate, air conditioning, heating

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Authors: Dawei Cai, Yuya Tokuda

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The market of localization based service (LBS) is expanding. The acquisition of physical location is the fundamental basis for LBS. GPS, the de facto standard for outdoor localization, does not work well in indoor environment due to the blocking of signals by walls and ceiling. To acquire high accurate localization in an indoor environment, many techniques have been developed. Triangulation approach is often used for identifying the location, but a heavy and complex computation is necessary to calculate the location of the distances between the object and several source points. This computation is also time and power consumption, and not favorable to a mobile device that needs a long action life with battery. To provide a low power consumption approach for a mobile device, this paper presents a fast calculation approach to identify the location of the object without online solving solutions to simultaneous quadratic equations. In our approach, we divide the location identification into two parts, one is offline, and other is online. In offline mode, we make a mapping process that maps the location area to distance space and find a simple formula that can be used to identify the location of the object online with very light computation. The characteristic of the approach is a good tradeoff between the accuracy and computational amount. Therefore, this approach can be used in smartphone and other mobile devices that need a long work time. To show the performance, some simulation experimental results are provided also in the paper.

Keywords: indoor localization, location based service, triangulation, fast calculation, mobile device

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Authors: Avatharam Ganivada, Krishna Shah

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The Mask R-CNN is recently introduced by the team of Facebook AI Research (FAIR), which is mainly concerned with instance segmentation in images. Here, the Mask R-CNN is based on ResNet and feature pyramid network (FPN), where a single dropout method is employed. This paper provides a modified Mask R-CNN by adding multiple dropout methods into the Mask R-CNN. The proposed model has also utilized the concepts of Resnet and FPN to extract stage-wise network feature maps, wherein a top-down network path having lateral connections is used to obtain semantically strong features. The proposed model produces three outputs for each object in the image: class label, bounding box coordinates, and object mask. The performance of the proposed network is evaluated in the segmentation of every instance in images using COCO and cityscape datasets. The proposed model achieves better performance than the state-of-the-networks for the datasets.

Keywords: instance segmentation, object detection, convolutional neural networks, deep learning, computer vision

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Authors: Sinem Yıldırım, Çimen Özburak, Özge Özden

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Cities are facing a growing environmental issue as a result of the combined effect of urbanization and climate change. Climate change is the most conspicuousimpact on environmental issues. Nowadays, energy conservation is a very important subject for planners. It is known that green roofs can provide environmental benefits, which include building insulation and mitigating urban heat island effect within the cities. Some of the studies shown that green roofs regulate roof temperature and they have an effect on indoor temperatures of buildings. This research looks at the experimental investigation of different type green roof vegetation with control of no vegetation and their effect on indoor temperatures. The research has been carried out at Near East University Campus with the duration of four months in Nicosia, Cyprus. The experiment was consisting of four green roof types; three of them covered with vegetation, and one of them was not vegetated for control of the experiment. Each hut had 2.7 m2 roof areas, and the soil depth was 8 cm. Mediterranean climate drought resistant ground covers and shrubs were planted on the roof of the three huts. Three different vegetation type was used: 1-Low growing ground cover succulents 2-Mixture of low growing succulents and low shrubs 3-Mixture of low growing succulents, low shrubs, and high growing foliage plantsElitech RC-5 temperature data loggers were used in order to measure indoor temperatures of the huts. Research results were shown that the hut with a highly vegetated roof had the lowest temperatures during hot summer period in Cyprus.

Keywords: green roofs, indoor temperature, vegetation, mediterranean, cyprus

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Authors: Jameson McFarlane, Thorne J. McFarlane, Leon Bernard

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Social network sites (SNS) is an emerging phenomenon that is here to stay. The popularity and the ubiquity of the SNS technology are undeniable. Because most SNS are free and easy to use people from all walks of life and from almost any age are attracted to that technology. College age students are by far the largest segment of the population using SNS. Since most SNS have been adapted for mobile devices, not only do you find students using this technology in their study, while working on labs or on projects, a substantial number of students have been found to use SNS even while listening to lectures. This study found that SNS use has a significant negative impact on the grade point average of college students particularly in the first semester. However, this negative impact is greatly diminished by the end of the third semester partly because the students have adjusted satisfactorily to the challenges of college or because they have learned how to adequately manage their time. It was established that the kinds of activities the students are engaged in during the SNS use are the leading factor affecting academic performance. Of those activities, using SNS during a lecture or while studying is the foremost contributing factor to lower academic performance. This is due to “cognitive” or “information” bottleneck, a condition in which the students find it very difficult to multitask or to switch between resources leading to inefficiency in information retention and thus, educational performance.

Keywords: social network sites, social network analysis, regression coefficient, psychological engagement

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Authors: Fatema Tasmia, Brishti Majumder, Atiqur Rahman

Abstract:

Attaining appropriate comfort conditions in a place where the climate is hot and humid can be perplexing. Especially, when it is resided at a congested place like old Dhaka Bangladesh, the provision of giving cross ventilation and building with proper orientation is quite difficult. Courtyards are the part of buildings which are used as space for outdoor household activities, social gathering and it is also proved to have indoor thermal comfort as an effect of courtyard. This paper aims to investigate the effect of courtyard in indoor thermal environment of a room adjacent to the courtyard and a room next to transitional space after a courtyard through field measurements of a case study house. The field measurement was conducted in a two-storey house. Among different aspects of thermal environment, the study of this paper is based on the analysis of temperature in both situations. Ventilation or air movement was considered to have no impact because of the rooms’ layout and location. Other aspects and their variables were considered as constant (especially material) for accuracy and avoidance of confusion. This study focuses on the outcome that can ultimately contribute to the configuration of courtyards and in its relation to indoor space while achieving thermal comfort.

Keywords: courtyard, old Dhaka, temperature, thermal comfort, transitional space

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Authors: Jingjing Yan, Gengen He, Anahid Basiri

Abstract:

The design of an indoor navigation system for fire evacuation support requires not only physical feasibility but also a relatively thorough consideration of the human factors. This study has taken a survey to investigate the fire response performances (FRP) of the indoor occupants in age of 20s, virtually in an environment for their routine life, focusing on the aspects of indoor familiarity (spatial cognition), psychological stress and decision makings. For indoor familiarity, it is interested in three factors, i.e., the familiarity to exits and risky places as well as the satisfaction degree of the current indoor sign installation. According to the results, males have a higher average familiarity with the indoor exits while both genders have a relatively low level of risky place awareness. These two factors are positively correlated with the satisfaction degree of the current installation of the indoor signs, and this correlation is more evident for the exit familiarity. The integration of the height factor with the other two indoor familiarity factors can improve the degree of indoor sign satisfaction. For psychological stress, this study concentrates on the situated cognition of moving difficulty, nervousness, and speed reduction when using a bending posture during the fire evacuation to avoid smoke inhalation. The results have shown that both genders have a similar mid-level of hardness sensation. The females have a higher average level of nervousness, while males have a higher average level of speed reduction sensation. This study has assumed that the growing indoor spatial cognition can help ease the psychological hardness and nervousness. However, it only seems to be true after reaching a certain level. When integrating the effects from indoor familiarity and the other two psychological factors, the correlation to the sensation of speed change can be strengthened, based on a stronger positive correlation with the integrated factors. This study has also investigated the participants’ attitude to the navigation support during evacuation, and the majority of the participants have shown positive attitudes. For following the guidance under some extreme cases, i.e., changing to a longer path and to an alternative exit, the majority of the participants has shown the confidence of keeping trusting the guidance service. These decisions are affected by the combined influences from indoor familiarity, psychological stress, and attitude of using navigation service. For the decision time of the selected extreme cases, it costs more time in average for deciding to use a longer route than to use an alternative exit, and this situation is more evident for the female participants. This requires further considerations when designing a personalized smartphone-based navigation app. This study has also investigated the calming factors for people being trapped during evacuation. The top consideration is the distance to the nearest firefighters, and the following considerations are the current fire conditions in the surrounding environment and the locations of all firefighters. The ranking of the latter two considerations is very gender-dependent according to the results.

Keywords: fire response performances, indoor spatial cognition, situated cognition, survey analysis

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Authors: Jai Prakash Prasad, Suresh Chandra Mohan

Abstract:

Modern wireless sensor networks (WSN) consist of small size, low cost devices which are networked through tight wireless communications. WSN fundamentally offers cooperation, coordination among sensor networks. Potential applications of wireless sensor networks are in healthcare, natural disaster prediction, data security, environmental monitoring, home appliances, entertainment etc. The design, development and deployment of WSN based on application requirements. The WSN design performance is optimized to improve network lifetime. The sensor node resources constrain such as energy and bandwidth imposes the limitation on efficient resource utilization and sensor node management. The proposed N-Tier GRID routing protocol focuses on the design of energy efficient large scale wireless sensor network for improved performance than the existing protocol.

Keywords: energy efficient, network lifetime, sensor networks, wireless communication

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Authors: Xinyan Zhang, Zhipeng Tan, Shan Fan

Abstract:

The first layer of defense against data loss is the backup data. This paper implements an agent-based network backup system used the backup, server-storage and server-backup agent these tripartite construction, and we realize the snapshot and hierarchical index in the NSBS. It realizes the control command and data flow separation, balances the system load, thereby improving the efficiency of the system backup and recovery. The test results show the agent-based network backup system can effectively improve the task-based concurrency, reasonably allocate network bandwidth, the system backup performance loss costs smaller and improves data recovery efficiency by 20%.

Keywords: agent, network backup system, three architecture model, NSBS

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Authors: A. Fayad, Q. Alqhazaly, T. Cinkler

Abstract:

In light of the substantial increase in end-user requirements and the incessant need of network operators to upgrade the capabilities of access networks, in this paper, the performance of the different modulation formats on eight-channels Time and Wavelength Division Multiplexing Passive Optical Network (TWDM-PON) transmission system has been examined and compared. Limitations and features of modulation formats have been determined to outline the most suitable design to enhance the data rate and transmission reach to obtain the best performance of the network. The considered modulation formats are On-Off Keying Non-Return-to-Zero (NRZ-OOK), Carrier Suppressed Return to Zero (CSRZ), Duo Binary (DB), Modified Duo Binary (MODB), Quadrature Phase Shift Keying (QPSK), and Differential Quadrature Phase Shift Keying (DQPSK). The performance has been analyzed by varying transmission distances and bit rates under different channel spacing. Furthermore, the system is evaluated in terms of minimum Bit Error Rate (BER) and Quality factor (Qf) without applying any dispersion compensation technique, or any optical amplifier. Optisystem software was used for simulation purposes.

Keywords: BER, DuoBinary, NRZ-OOK, TWDM-PON

Procedia PDF Downloads 138