FOMA: Flexible Overlay Multi-path Data Aggregation in Wireless Sensor Networks

Abstract-Data aggregation is an efficient method to conserve energy by reducing packet transmissions in WSNs. However, providing end-to-end data reliability is a major challenge when the network uses data aggregation. In this case, a packet loss can miss a complete subtree of values, thus greatly affecting the final results. Data transmission in multiple paths can tolerate this problem, but it may incur some computation errors for duplicatesensitive aggregates. In this paper, we propose a Flexible Overlay Multi-path data Aggregation protocol (FOMA) which uses the available path redundancy to deliver a correct aggregate result to the sink with high reliability in an energy-efficient manner. It aggregates data in two layers, routing layer and data aggregation layer, while eliminating the computation errors by using a signature-based method. We implement FOMA in TinyOs 2.x and test it by the TOSSIM simulator. The results reveal that the proposed algorithm outperforms other existing ones in terms of energy consumption and data accuracy

The Effect of Chemical and Non-Chemical Nutrition Systems on Some Growth Traits, Yield and Yield Components of Hashemi Variety Rice (Oryza sativa L.) - A Case Study in Lahijan City, Gilan Province

Introduction[1] Rice is a staple and valuable grain that is the main source of food for over 50 percent of the world's population after wheat (Lopez et al., 2019; Jabran and Chauhan, 2015). Rice production should increase by over 50 percent by 2050, which can be realized by improving its cultivars and applying sound agronomic management practices (Esfahani et al., 2005; Asadi et al., 2016). Nitrogen (N) is a key macroelement that is decisive for plants, but it is deficient in most farms. N fertilizer is applied chemically, organically, and biologically (Moslehi et al., 2015).  Materials and Methods This research was conducted as a factorial experiment based on a randomized complete block design with three replications at two sites at the experimental farm of Islami Azad University of Lahijan (the village of Tustan) and Kateshal farm in 2018-2019. The study site (Lat. 36°55' N., Long. 45°20' E. (first location) and Lat. 37°21' N., Long. 50°18' E. (second location)) has a temperate and humid climate with a 10-year mean annual precipitation of 1150 mm (Guilan Meteorological Quarterly, 2020). Table 1 presents the meteorological data of the region during the experiment. Before the experiment, the physical and chemical characteristics of the soil at the study site were measured in the laboratory of the Water and Soil Department of Rice Research Center. The experimental factors included organic, chemical fertilizer, and control as the three levels of the first factor and urban waste compost, biochar, and Azolla, and control as the four levels of the second factor. Statistical analysis of data, data conversion, and drawing of graphs and charts were done using SAS 9.2 and Excel 2010 software. The averages obtained were statistically compared with each other using Tukey's test and at the probability level of 5%. Results and Discussion The simple effects of the chemical, organic, and organic nutritional systems were found to be significant (P < 0.01) on grain yield. Based on the comparison of data means for both research farms, the highest grain yield of, on average, 3699 kg/ha was obtained from the treatment of chemical fertilizer and biochar, and the lowest one of 2209 kg.ha-1 (40% lower than its maximum counterpart) from the control (unfertilized) treatment. Among the subplots, the biochar treatment was the most effective, and the control (unfertilized) was the least effective in this trait. The treatments that were fertilized with chemical N fertilizer produced more panicles per plant than the treatments that weren’t. Among the sub-plots, the highest number of panicles per plant was related to the biochar treatments under no-fertilization, ecological, and chemical conditions, and the lowest number to the control (unfertilized treatment). The plants treated chemically and ecologically in the presence of biochar were the tallest, growing to a height of 127 and 124 cm, respectively, whereas the lowest plant height was 108 cm, related to the control (unfertilized plants).   Conclusion The use of organic fertilizers alone or in combination with chemical fertilizers, in addition to improving the quantitative and qualitative characteristics of rice, has a positive effect on the sustainability of production and preservation of the environment. The results of this research showed that the application of nitrogen fertilizer and biochar, in addition to optimizing the application of fertilizer, increased the yield of rice. It was also found that the consumption of biochar caused an increase in traits related to grain yield. The role of biochar was evident in the significant change of the studied traits of Hashemi rice in the main treatments (control, ecological, and chemical). Therefore, it is recommended to use biochar along with chemical fertilizer in order to maintain yield, prevent biological pollution and increase soil and rice fertility. Acknowledgments The assistance of the esteemed personnel of the Islamic Azad University, Lahijan branch, who helped us in the implementation of this research, is gratefully acknowledged

Towards Supporting IoT System Designers in Edge Computing Deployment Decisions

The rapidly evolving Internet of Things (IoT) systems demands addressing new requirements. This particularly needs efficient deployment of IoT systems to meet the quality requirements such as latency, energy consumption, privacy, and bandwidth utilization. The increasing availability of computational resources close to the edge has prompted the idea of using these for distributed computing and storage, known as edge computing. Edge computing may help and complement cloud computing to facilitate deployment of IoT systems and improve their quality. However, deciding where to deploy the various application components is not a straightforward task, and IoT system designer should be supported for the decision. To support the designers, in this thesis we focused on the system qualities, and aimed for three main contributions. First, by reviewing the literature, we identified the relevant and most used qualities and metrics. Moreover, to analyse how computer simulation can be used as a supporting tool, we investigated the edge computing simulators, and in particular the metrics they provide for modeling and analyzing IoT systems in edge computing. Finally, we introduced a method to represent how multiple qualities can be considered in the decision. In particular, we considered distributing Deep Neural Network layers as a use case and raked the deployment options by measuring the relevant metrics via simulation

Estimation of the land use changes using satellite data in an under management area of mountain forest

897-902Total RMSE (Root-Mean-Square Error) in geometric correction was obtained as 0.42 and 0.36 for images of 1999 and 2013, respectively. Then , the best band combination was specified in the images of 1999 and 2013 which was the combination of 2, 3, and 5 bands in the image of 1999 and the combination of 3, 6, and 7 bands in the image of 2013). The classification was performed and results showed a reduction in the area of residential use classes from 372.87 to 329.31, a reduction in dense forest from 1126.44 to 844.11, an increase in tea gardens from 1225.17 to 1519.92, and an increase in semi-dense forest from 121.59 to 152.73 hectares. Investigating the maximum likelihood classification also showed that the images of 1999 with kappa coefficient of 95.51 and the overall accuracy of 96. 80 % have higher accuracy than the images of 2013 with kappa coefficient of 89.30 and overall accuracy of 92.80%.During the recent decades, deciduous forests have been molested by human intervention. Easy access, abundance and diversity of valuable forest products have led to increased population, creating new residential areas and deforestation activities and use satellite data suggest to management natural resources

The Trend of Injuries in Building Fire in Tehran from 2002 to 2012

Analysis of fire data is a way for the implementation of any plan to improve the level of safety in cities. Such an analysis is able to reveal signs of changes in a given period and can be used as a measure of safety. The information of about 66,341 fires (from 2002 to 2012) released by Tehran Safety Services and Fire-Fighting Organization and data on the population and the number of households provided by Tehran Municipality and the Statistical Yearbook of Iran were extracted. Using the data, the fire changes, the rate of injuries, and mortality rate were determined and analyzed. The rate of injuries and mortality rate of fires per one million population of Tehran were 59.58% and 86.12%, respectively. During the study period, the number of fires and fire stations increased by 104.38% and 102.63%, respectively. Most fires (9.21%) happened in the 4th District of Tehran. The results showed that the recorded fire data have not been systematically planned for fire prevention since one of the ways to reduce injuries caused by fires is to develop a systematic plan for necessary actions in emergency situations. To determine a reliable source for fire prevention, the stages, definitions of working processes and the cause and effect chains should be considered. Therefore, a comprehensive statistical system should be developed for reported and recorded fire data

Cloud, Edge, or Both? Towards Decision Support for Designing IoT Applications

The rapidly evolving Internet of Things (IoT) includes applications which might generate a huge amount of data, this requires appropriate platforms and support methods. Cloud computing offers attractive computational and storage solutions to cope with these issues. However, sending to centralized servers all the data generated at the edge of the network causes latency, energy consumption, and high bandwidth demand. Performing some computations at the edge of the network, known as Edge computing, and using a hybrid Edge-Cloud architecture can help addressing these challenges. While such architecture may provide new opportunities to distribute IoT applications, making optimal decisions regarding where to deploy the different application components is not an easy and straightforward task for designers. Supporting designers’ decisions by considering key quality attributes impacting them in an Edge-Cloud architecture has not been investigated yet. In this paper, we: explore the importance of decision support for the designers, discuss how different attributes impact the decisions, and describe the required steps toward a decision support framework for IoT application designers

Quality attributes in edge computing for the Internet of Things : A systematic mapping study

Many Internet of Things (IoT) systems generate a massive amount of data needing to be processed and stored efficiently. Cloud computing solutions are often used to handle these tasks. However, the increasing availability of computational resources close to the edge has prompted the idea of using these for distributed computing and storage. Edge computing may help to improve IoT systems regarding important quality attributes like latency, energy consumption, privacy, and bandwidth utilization. However, deciding where to deploy the various application components is not a straightforward task. This is largely due to the trade-offs between the quality attributes relevant for the application. We have performed a systematic mapping study of 98 articles to investigate which quality attributes have been used in the literature for assessing IoT systems using edge computing. The analysis shows that time behavior and resource utilization are the most frequently used quality attributes; further, response time, turnaround time, and energy consumption are the most used metrics for quantifying these quality attributes. Moreover, simulation is the main tool used for the assessments, and the studied trade-offs are mainly between only two qualities. Finally, we identified a number of research gaps that need further study

Cloud, Edge, or Both? Towards Decision Support for Designing IoT Applications

The rapidly evolving Internet of Things (IoT) includes applications which might generate a huge amount of data, this requires appropriate platforms and support methods. Cloud computing offers attractive computational and storage solutions to cope with these issues. However, sending to centralized servers all the data generated at the edge of the network causes latency, energy consumption, and high bandwidth demand. Performing some computations at the edge of the network, known as Edge computing, and using a hybrid Edge-Cloud architecture can help addressing these challenges. While such architecture may provide new opportunities to distribute IoT applications, making optimal decisions regarding where to deploy the different application components is not an easy and straightforward task for designers. Supporting designers’ decisions by considering key quality attributes impacting them in an Edge-Cloud architecture has not been investigated yet. In this paper, we: explore the importance of decision support for the designers, discuss how different attributes impact the decisions, and describe the required steps toward a decision support framework for IoT application designers

Edge Computing Simulators for IoT System Design: An Analysis of Qualities and Metrics

The deployment of Internet of Things (IoT) applications is complex since many quality characteristics should be taken into account, for example, performance, reliability, and security. In this study, we investigate to what extent the current edge computing simulators support the analysis of qualities that are relevant to IoT architects who are designing an IoT system. We first identify the quality characteristics and metrics that can be evaluated through simulation. Then, we study the available simulators in order to assess which of the identified qualities they support. The results show that while several simulation tools for edge computing have been proposed, they focus on a few qualities, such as time behavior and resource utilization. Most of the identified qualities are not considered and we suggest future directions for further investigation to provide appropriate support for IoT architects