A Socio-inspired CALM Approach to Channel Assignment Performance Prediction and WMN Capacity Estimation

A significant amount of research literature is dedicated to interference mitigation in Wireless Mesh Networks (WMNs), with a special emphasis on designing channel allocation (CA) schemes which alleviate the impact of interference on WMN performance. But having countless CA schemes at one's disposal makes the task of choosing a suitable CA for a given WMN extremely tedious and time consuming. In this work, we propose a new interference estimation and CA performance prediction algorithm called CALM, which is inspired by social theory. We borrow the sociological idea of a "sui generis" social reality, and apply it to WMNs with significant success. To achieve this, we devise a novel Sociological Idea Borrowing Mechanism that facilitates easy operationalization of sociological concepts in other domains. Further, we formulate a heuristic Mixed Integer Programming (MIP) model called NETCAP which makes use of link quality estimates generated by CALM to offer a reliable framework for network capacity prediction. We demonstrate the efficacy of CALM by evaluating its theoretical estimates against experimental data obtained through exhaustive simulations on ns-3 802.11g environment, for a comprehensive CA test-set of forty CA schemes. We compare CALM with three existing interference estimation metrics, and demonstrate that it is consistently more reliable. CALM boasts of accuracy of over 90% in performance testing, and in stress testing too it achieves an accuracy of 88%, while the accuracy of other metrics drops to under 75%. It reduces errors in CA performance prediction by as much as 75% when compared to other metrics. Finally, we validate the expected network capacity estimates generated by NETCAP, and show that they are quite accurate, deviating by as low as 6.4% on an average when compared to experimentally recorded results in performance testing

Statistical Relationship between Interference Estimates and Network Capacity

Interference is a major impediment to the performance of a wireless network as it has a significant adverse impact on Network Capacity. There has been a gradual and consistent densification of WiFi networks due to Overlapping Basic Service Set (OBSS) deployments. With the upcoming 802.11ax standards, dense and ultra-dense deployments will become the norm and the detrimental impact of Interference on Capacity will only exacerbate. However, the precise nature of the association between Interference and Network Capacity remains to be investigated, a gap we bridge in this work. We employ linear and polynomial regression to find answers to several unexplored questions concerning the Capacity Interference Relationship (CIR). We devise an algorithm to select regression models that best explain this relationship by considering a variety of factors including outlier threshold. We ascertain the statistical significance of their association, and also determine the explainability of variation in Network Capacity when Interference is varied, and vice versa. While the relationship is generally believed to be non-linear, we demonstrate that scenarios exist where a strong linear correlation exists between the two. We also investigate the impact of WMN topology on this relationship by considering four carefully designed Wireless Mesh Network (WMN) topologies in the experiments. To quantify endemic Interference, we consider four popular Theoretical Interference Estimation Metrics (TIEMs) viz., TID, CDALcost, CXLSwt, and CALM. To ensure a sound regression analysis, we consider a large set of 100 Channel Assignment (CA) schemes, a majority of which are generated through a Generic Interference aware CA Generator proposed in this work. Finally, we test the TIEMs in terms of their reliability and the ability to model Interference. We carry out the experiments on IEEE 802.11g/n WMNs simulated in ns-3

A Review of Thermal Comfort in Primary Schools and Future Challenges in Machine Learning Based Prediction for Children

Children differ from adults in their physiology and cognitive ability. Thus, they are extremely vulnerable to classroom thermal comfort. However, very few reviews on the thermal comfort of primary school students are available. Further, children-focused surveys have not reviewed the state-of-the-art in thermal comfort prediction using machine learning (AI/ML). Consequently, there is a need for discussion on children-specific challenges in AI/ML-based prediction. This article bridges these research gaps. It presents a comprehensive review of thermal comfort studies in primary school classrooms since 1962. It considers both conventional (non-ML) studies and the recent AI/ML studies performed for children, classrooms, and primary students. It also underscores the importance of AI/ML prediction by analyzing adaptive opportunities for children/students in classrooms. Thereafter, a review of AI/ML-based prediction studies is presented. Through an AI/ML case-study, it demonstrates that model performance for children and adults differs markedly. Performance of classification models trained on ASHRAE-II database and a recent primary students’ dataset shows a 29% difference in thermal sensation and 86% difference in thermal preference, between adults and children. It then highlights three major children-specific AI/ML challenges, viz., “illogical votes”, “multiple comfort metrics”, and “extreme class imbalance”. Finally, it offers several technical solutions and discusses open problems

A Review of Thermal Comfort in Primary Schools and Future Challenges in Machine Learning Based Prediction for Children

Children differ from adults in their physiology and cognitive ability. Thus, they are extremely vulnerable to classroom thermal comfort. However, very few reviews on the thermal comfort of primary school students are available. Further, children-focused surveys have not reviewed the state-of-the-art in thermal comfort prediction using machine learning (AI/ML). Consequently, there is a need for discussion on children-specific challenges in AI/ML-based prediction. This article bridges these research gaps. It presents a comprehensive review of thermal comfort studies in primary school classrooms since 1962. It considers both conventional (non-ML) studies and the recent AI/ML studies performed for children, classrooms, and primary students. It also underscores the importance of AI/ML prediction by analyzing adaptive opportunities for children/students in classrooms. Thereafter, a review of AI/ML-based prediction studies is presented. Through an AI/ML case-study, it demonstrates that model performance for children and adults differs markedly. Performance of classification models trained on ASHRAE-II database and a recent primary students’ dataset shows a 29% difference in thermal sensation and 86% difference in thermal preference, between adults and children. It then highlights three major children-specific AI/ML challenges, viz., “illogical votes”, “multiple comfort metrics”, and “extreme class imbalance”. Finally, it offers several technical solutions and discusses open problems

Indoor Thermal Comfort and Adaptive Thermal Behaviors of Students in Primary Schools Located in the Humid Subtropical Climate of India

This study investigated children’s perceptions and adaptive behaviors related to indoor thermal conditions of classrooms in primary schools with no air-conditioning systems during both summer and winter in Dehradun City, Uttarakhand, India. Responses were collected from 5297 school children aged 6–13 years. During the measurement periods, 100% and 94% of the samples were obtained under conditions outside an 80% thermally acceptable comfort range in winter and summer, respectively. The analysis using receiver operating characteristics suggested that the students had the least sensitivity to the temperature variation for all scales of the thermal sensation vote (TSV). Approximately 95.1% of students were “very satisfied”, “satisfied”, or “slightly satisfied” with the thermal conditions under the condition of “extreme caution” or “danger” of heat risk. In contrast, adaptive thermal behaviors, such as adjusting clothing insulation ensembles, opening or closing classroom windows and doors, and utilizing ceiling fans, were found to be the most affordable options for optimizing indoor thermal comfort. Children’s reports of thermal sensations and thermal satisfaction did not correspond to the actual physical environment. This draws attention to the adequacy of applying widely used methods of TSV-based identification of the thermal comfort range in classrooms for children, especially in hot environments. The findings of this study are expected to serve as an evidence-based reference for local governments and authorities to take appropriate measures to mitigate heat risks for schoolchildren in the future

Multi-Task Learning for Concurrent Prediction of Thermal Comfort, Sensation and Preference in Winters

Indoor thermal comfort immensely impacts the health and performance of occupants. Therefore, researchers and engineers have proposed numerous computational models to estimate thermal comfort (TC). Given the impetus toward energy efficiency, the current focus is on data-driven TC prediction solutions that leverage state-of-the-art machine learning (ML) algorithms. However, an occupant’s perception of indoor thermal comfort (TC) is subjective and multi-dimensional. Different aspects of TC are represented by various standard metrics/scales viz., thermal sensation (TSV), thermal comfort (TCV), and thermal preference (TPV). The current ML-based TC prediction solutions adopt the Single-task Learning approach, i.e., one prediction model per metric. Consequently, solutions often focus on only one TC metric. Moreover, when several metrics are considered, multiple ML models for a single indoor space lead to conflicting predictions, rendering real-world deployment infeasible. This work addresses these problems by leveraging Multi-task Learning for TC prediction in naturally ventilated buildings. First, a survey-and-measurement study is conducted in the composite climatic region of north India, in 14 naturally ventilated classrooms of 5 schools, involving 512 primary school students. Next, the dataset is analyzed for important environmental, physiological, and psycho-social factors that influence thermal comfort of children. Further, “DeepComfort”, a deep neural network based Multi-task Learning model is proposed. DeepComfort predicts multiple TC output metrics viz., TSV, TPV, and TCV, simultaneously through a single model. It is validated on ASHRAE-II database and the primary student dataset created in this study. It demonstrates high F1-scores, Accuracy (≈90%), and generalization capability, despite the challenges of illogical responses and data imbalance. DeepComfort is also shown to outperform 6 popular metric-specific single-task machine learning algorithms

Statistical Relationship between Interference Estimates and Network Capacity

Interference is a major impediment to the performance of a wireless network as it has a significant adverse impact on Network Capacity. There has been a gradual and consistent densification of WiFi networks due to Overlapping Basic Service Set (OBSS) deployments. With the upcoming 802.11ax standards, dense and ultra-dense deployments will become the norm and the detrimental impact of Interference on Capacity will only exacerbate. However, the precise nature of the association between Interference and Network Capacity remains to be investigated, a gap we bridge in this work. We employ linear and polynomial regression to find answers to several unexplored questions concerning the Capacity Interference Relationship (CIR). We devise an algorithm to select regression models that best explain this relationship by considering a variety of factors including outlier threshold. We ascertain the statistical significance of their association, and also determine the explainability of variation in Network Capacity when Interference is varied, and vice versa. While the relationship is generally believed to be non-linear, we demonstrate that scenarios exist where a strong linear correlation exists between the two. We also investigate the impact of WMN topology on this relationship by considering four carefully designed Wireless Mesh Network (WMN) topologies in the experiments. To quantify endemic Interference, we consider four popular Theoretical Interference Estimation Metrics (TIEMs) viz., TID, CDALcost, CXLSwt, and CALM. To ensure a sound regression analysis, we consider a large set of 100 Channel Assignment (CA) schemes, a majority of which are generated through a Generic Interference aware CA Generator proposed in this work. Finally, we test the TIEMs in terms of their reliability and the ability to model Interference. We carry out the experiments on IEEE 802.11g/n WMNs simulated in ns-3

The Challenge of Multiple Thermal Comfort Prediction Models: Is TSV Enough?

Classroom thermal comfort has a direct effect on student health and educational outcomes. However, measuring thermal comfort (TC) is a non-trivial task. It is represented by several subjective metrics e.g., Thermal Sensation Vote, Thermal Comfort Vote, Thermal Preference Vote, etc. Since machine learning (ML) is being increasingly used to predict occupant comfort, multiple TC metrics for the same indoor space may yield contradictory results. This poses the challenge of selecting the most suitable single TC metric or the minimal TC metric combination for a given indoor space. Ideally, it will be a metric that can be used to predict all other TC metrics and occupant behavior with high accuracy. This work addresses this problem by using a primary student thermal comfort dataset gathered from 11 schools and over 500 unique students. A comprehensive evaluation is carried out through hundreds of TC prediction models using several ML algorithms. It evaluates the ability of TC metrics to predict (a) other TC metrics, and (b) the adaptive behavior of primary students. An algorithm is proposed to select the most suitable single TC metric or the minimal TC metric input combination. Results show that ML models can accurately predict all TC metrics and occupant-adaptive behavior using a small subset of TC metrics with an average accuracy as high as 79%. This work also found Thermal Sensation Vote to be the most significant single TC predictor, followed by Thermal Satisfaction Level. Interestingly, satisfaction with clothing was found to be as equally relevant as thermal preference. Furthermore, the impact of seasons and choice of ML algorithms on TC metric and occupant behavior prediction is shown

Transversal Pertussis Sero-Epidemiological Study in Fully Vaccinated Children and Adolescents in Antananarivo, Madagascar, and in Dapaong, Togo

International audienceIntroduction: African region is thought to contribute to >50% of deadly cases of pertussis worldwide, however surveillance and available data on Bordetella pertussis circulation are limited. Currently, pertussis vaccination schedule in most African countries is restricted to a primary immunization consisting of three doses during the first year of life using whole pertussis vaccines for which effectiveness is not known. Methods: Primary vaccinated children aged 3-15 years were recruited in Antananarivo city and suburban areas, Madagascar, and in Dapaong, Togo. Details on vaccine injections and a capillary serum sample were collected, and anti-pertussis toxin immunoglobulin G (anti-PT IgG) were quantified by ELISA. Results: Seropositivity rate was 56.9% (588/1033) and 62.2% (565/908) in Antananarivo and Dapaong, respectively. Significantly less children with anti-PT IgG were observed among those aged 3-5 years in Madagascar, seropositivity rate increased thereafter in children older ≥ 6 years old, with substantial sign of recent infection in particular in 6-8 years old children (18.9%; 39/206). In Dapaong, 65.3% (111/170) of the 3-5 years old children were seropositive and frequency did not significantly vary among age groups. Compliance was 89.0% (886/996) and 77.2% (596/772), and children vaccinated at age > 8 weeks represented 33.4% (337/996) and 39.4% (304/772) in Antananarivo and Dapaong, respectively. Compliance was higher among the 3-5 years children in both countries