Improving Age of Information with Interference Problem in Long-Range Wide Area Networks

Low Power Wide Area Networks (LPWAN) offer a promising wireless communications technology for Internet of Things (IoT) applications. Among various existing LPWAN technologies, Long-Range WAN (LoRaWAN) consumes minimal power and provides virtual channels for communication through spreading factors. However, LoRaWAN suffers from the interference problem among nodes connected to a gateway that uses the same spreading factor. Such interference increases data communication time, thus reducing data freshness and suitability of LoRaWAN for delay-sensitive applications. To minimize the interference problem, an optimal allocation of the spreading factor is requisite for determining the time duration of data transmission. This paper proposes a game-theoretic approach to estimate the time duration of using a spreading factor that ensures on-time data delivery with maximum network utilization. We incorporate the Age of Information (AoI) metric to capture the freshness of information as demanded by the applications. Our proposed approach is validated through simulation experiments, and its applicability is demonstrated for a crop protection system that ensures real-time monitoring and intrusion control of animals in an agricultural field. The simulation and prototype results demonstrate the impact of the number of nodes, AoI metric, and game-theoretic parameters on the performance of the IoT network

Rate-Monotonic Scheduler For LoRa-Based Smart Space Monitoring System

Smart spaces system equipped with sensors to collect data that can be used to generate insights about its environmental conditions. Those collected data is then transmitted to the applications to enhance the comfort, quality of life, and security of the space. Long Range (LoRa) technology provides long distance coverage and consumes low energy which makes it suitable for smart space application. There are six virtual channels to transmit data in LoRa, however network faces the interference problem when nodes transmitted data at the same time. The interference problem makes LoRa less suitable for time-critical applications. To mitigate the interference problem, a spreading factor should be allocated in an optimal way. This paper assigns the spreading factor to the LN using Rate-Monotonic scheduler to ensures data transmission within deadline with minimum energy consumption. To quantify delay in receiving the information, we use the \u27Age of Information\u27 metric. The proposed approach is validated using Network Simulator-3 and results show that it effectively reduces delay and energy and prolongs the network utility

An Energy Efficient Smart Metering System using Edge Computing in LoRa Network

An important research issue in smart metering is to correctly transfer the smart meter readings from consumers to the operator within the given time period by consuming minimum energy. In this paper, we propose an energy efficient smart metering system using Edge computing in Long Range (LoRa). We assume that all appliances in a house are connected to a smart meter that is affixed with Edge device and LoRa node for processing and transferring the processed smart meter readings, respectively. The energy consumption of the appliances can be represented as an energy multivariate time series. The system first proposes a deep learning-based compression-decompression model for reducing the size of the energy time series at the Edge devices. Next, it formulates an optimization problem for finding the suitable compressed energy time series to reduce the energy consumption and delay of the system. Finally, the system presents an algorithm for selecting the suitable spreading factors to transfer the compressed time series to the operator in the given time. Our simulation and prototype results demonstrate the impact of the parameters of the compression model, network, and the number of smart meters and appliances on delay, energy consumption, and accuracy of the system

An Energy Efficient Smart Metering System using Edge Computing in LoRa Network

An important research issue in smart metering is to correctly transfer the smart meter readings from consumers to the operator within the given time period by consuming minimum energy. In this paper, we propose an energy efficient smart metering system using Edge computing in Long Range (LoRa). We assume that all appliances in a house are connected to a smart meter that is affixed with Edge device and LoRa node for processing and transferring the processed smart meter readings, respectively. The energy consumption of the appliances can be represented as an energy multivariate time series. The system first proposes a deep learning based compression-decompression model for reducing the size of the energy time series at the Edge devices. Next, it formulates an optimization problem for finding the suitable compressed energy time series to reduce the energy consumption and delay of the system. Finally, the system presents an algorithm for selecting the suitable spreading factors to transfer the compressed time series to the operator in the given time. Our simulation and prototype results demonstrate the impact of the parameters of the compression model, network, and the number of smart meters and appliances on delay, energy consumption, and accuracy of the system

Sleep Disturbances and Binge Eating Disorder Symptoms during and after Pregnancy

STUDY OBJECTIVE: We compared sleep problems during pregnancy and sleep dissatisfaction 18 months after pregnancy in pregnant women with binge eating disorder (BED) symptoms and pregnant women without an eating disorder. DESIGN: Norwegian Mother and Child Cohort Study (MoBa). PATIENTS OR PARTICIPANTS: Data were gathered from 72,435 women. A total of 1,495 (2.1%) women reported having BED symptoms both before and during pregnancy; 921 (1.3%) reported pre-pregnancy BED symptoms that remitted during pregnancy; 1,235 (1.7%) reported incident BED symptoms during pregnancy; and 68,784 (95.0%) reported no eating disorder symptoms before or during pregnancy (referent). MEASUREMENTS AND RESULTS: Questionnaires were collected at 3 time points, with a median completion time of 17.1 weeks gestation, 30.1 weeks gestation, and 18.7 months after childbirth. We collected information on demographics, eating disorder status before and during pregnancy, sleep problems during the first 18 weeks of pregnancy, hours of sleep during the third trimester, and sleep satisfaction 18 months after childbirth. All BED symptom groups were significantly more likely to report sleep problems during the first 18 weeks of pregnancy than the referent (adjusted odds ratio [OR] = 1.26-1.42, false discovery rate [FDR] P < 0.05). In the third trimester, women with incident BED symptoms during pregnancy were more likely to report more hours of sleep than the referent (adjusted OR = 1.49, FDR P < 0.01). All BED symptom groups had higher odds of reporting more dissatisfaction with sleep 18 months after childbirth (adjusted ORs = 1.28-1.47, FDR P < 0.01). CONCLUSIONS: BED before or during pregnancy is associated with sleeping problems during pregnancy and dissatisfaction with sleep 18 months after childbirth. Health care professionals should inquire about BED during pregnancy as it may be associated with sleep disturbances, in addition to the hallmark eating concerns. CITATION: Ulman TF; Von Holle A; Torgersen L; Stoltenberg C; Reichborn-Kjennerud T; Bulik CM. Sleep disturbances and binge eating disorder symptoms during and after pregnancy. SLEEP 2012;35(10):1403-1411

The Neurodevelopmental Hypothesis of Schizophrenia, Revisited

While multiple theories have been put forth regarding the origin of schizophrenia, by far the vast majority of evidence points to the neurodevelopmental model in which developmental insults as early as late first or early second trimester lead to the activation of pathologic neural circuits during adolescence or young adulthood leading to the emergence of positive or negative symptoms. In this report, we examine the evidence from brain pathology (enlargement of the cerebroventricular system, changes in gray and white matters, and abnormal laminar organization), genetics (changes in the normal expression of proteins that are involved in early migration of neurons and glia, cell proliferation, axonal outgrowth, synaptogenesis, and apoptosis), environmental factors (increased frequency of obstetric complications and increased rates of schizophrenic births due to prenatal viral or bacterial infections), and gene-environmental interactions (a disproportionate number of schizophrenia candidate genes are regulated by hypoxia, microdeletions and microduplications, the overrepresentation of pathogen-related genes among schizophrenia candidate genes) in support of the neurodevelopmental model. We relate the neurodevelopmental model to a number of findings about schizophrenia. Finally, we also examine alternate explanations of the origin of schizophrenia including the neurodegenerative model

A 2a adenosine receptor: Structures, modeling, and medicinal chemistry

Many selective agonists and antagonists of the A 2A adenosine receptor (AR) have been reported, while allosteric modulators specific for this receptor are still needed. Many heterocyclic chemotypes have been discovered as A 2A AR antagonists, while most of the known AR agonists are nucleosides or 3,5-dicyanopyridine derivatives. A few A 2A AR ligands have been in clinical trials as antihypertensives, anti-inflammatory or diagnostic compounds (agonists), and as drugs for treating Parkinson’s disease and cancer (antagonists). The A 2A AR has become one of the most widely investigated G protein-coupled receptor (GPCR) structures using X-ray crystallography and also biophysical techniques such as NMR. Thus, the design of agonists, antagonists, and allosteric modulators has become structure-based, with numerous examples of in silico approaches, including virtual ligand screening (VLS), leading to the discovery of both novel agonists and antagonists