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The Third Pole, i.e. the Hindu Kush Himalaya (HKH) and Tien Shan mountains, has been closely monitored for the past few decades because of its deteriorating environmental conditions.

Amount of ozone in the stratosphere is very small as compared to other trace gases in the atmosphere. Yet, significant changes in its concentrations have great consequences in the environment and ecosystems. However, such drastic changes in ozone happens only in the high and middle latitudes, particularly in the polar spring, where chemical ozone loss occurs due to halogen-catalysed chemistry on polar stratospheric clouds. In the tropics, the amount of column ozone is very small and the ozone loss is also very small over the years, as compared to high and mid-latitudes. The ozone analyses in the tropical latitudes show a consistent picture of ozone evolution in the past four decades, as there is no significant loss or increase, although small negative trends are found. Recent studies have demonstrated that these negative trends in the tropical upwelling region are due to the increase in the speed of Brewer-Dobson Circulation, which brings tropospheric ozone to the lower stratosphere. This is clearly pictured in the time series of tropical ozone in recent years. The long-term trend in tropical total column ozone also shows no noticeable difference in recent years from the past decades. No measurements and no analyses show any signature of ozone hole in the tropics and all claims so far made are scientifically not sound and unconvincing. It is very unlikely that we would get an ozone hole outside the polar region.

An outlier in the Internet of Things is an immediate change in data induced by a significant difference in the atmosphere (Event) or sensor malfunction (Error). Outliers in the data cause the decision-maker to make incorrect judgments about data analysis. Hence it is essential to detect outliers in any discipline. The detection of outliers becomes the most crucial task to improve sensor data quality and ensure accuracy, reliability and robustness. In this research, a novel outlier detection technique based on spatial, temporal correlations and attribute correlations is proposed to detect outliers (both Errors and Events). This research uses a correlation measure in the temporal correlation algorithm to determine outliers and the spatial correlation algorithm to classify the outliers, whether the outliers are events or errors. This research uses optimal clusters to improve network lifetime, and malicious nodes were also detected based on spatial–temporal correlations and attribute correlations in these clusters. The experimental results proved that the proposed method in this research outperforms some other models in terms of accuracy against the percentage of outliers infused and detection rate against the false alarm rate.

The Hindu Kush Himalaya and Tien Shan Mountain regions together are called the Third Pole (TP) of Earth, which encompasses ecologically fragile regions of 12 Asian countries. It is the highest mountain chain with the largest reserve of fresh ice mass on the planet outside the northern and southern polar regions. The TP region is experiencing high rate of glacier melting due to climate change for the past few decades, and is a great concern for water security of South Asia. Since changes in ozone concentrations in the atmosphere affect public health, ecosystem dynamics and climate, it is imperative to monitor its temporal evolution in an ecologically sensitive region such as TP. Here, the spatiotemporal characteristics of total column ozone (TCO) in TP and 20 selected cities in and around TP are investigated using a combined long-term data made from the satellite measurements of Ozone Monitoring Instrument (OMI) and Global Ozone Monitoring Experiment (GOME)-2B for the period 2005–2020. The spatial trends in TCO over TP are mostly negative in summer and autumn (from −0.2 DU/yr to −0.6 DU/yr), but positive in winter (up to +0.2 DU/yr). Among the selected 20 urban regions, the highest annual trend −0.42 ± 0.3 DU/yr and the lowest −0.01 ± 0.2 DU/yr are estimated in Xining and Chittagong, respectively. Analysis using a multiple regression model reveals that the ozone variability in TP is mostly driven by tropopause height with a contribution of 24.92%, Quasi-Biennial Oscillation (23.42%), aerosols (16.12%) and solar flux (15.34%). Our study suggests that the observed negative trend is mainly associated with human activities and climate change in TP, which would likely to enhance the surface temperature and thus, melting of glaciers in the region.

The large scale of the Internet of Things necessitates using long-lasting physical layer devices for data collection. Deploying large numbers of Wi-Fi-enabled devices is expensive, so the Internet of Everything (IoE) is equipped with multiple communication modules to collect data where Wi-Fi is unavailable. However, because of their extended communication capabilities, IoE devices face energy limitations. As a result, IoE devices must be provided with the necessary energy resources. This paper introduces a novel multi-hop cooperation communication mechanism for Wireless Energy Transfer (WET) in the Wireless Powered-Internet of Everything (WP-IoE). IoE devices are outfitted here with various communication devices such as RF, Bluetooth, and Wi-Fi. This research proposes a two-phase energy transmission schedule to address the energy requirements. For data collection, the first phase provides a distributed tree-based data communication plan. The proposed model’s second phase used the reverse data collection protocol to implement wireless energy transmission. By combining these two phases, an optimized WET framework was created without unmanned aerial vehicles or robots. The experimental findings show that the proposed method in this research increases the average lifetime of the network and has a more significant charge latency and average charge throughput than other models.

Smartphone plays a key role in integrating the entire world into a small hand. This feature made these smartphones as another human organ of many people. One of the main feature in every smart phone is GPS which used to travel new places, to locate and find optimized way to reach their destination. As we aware GPS is an outdoor application, GPS location is not accurate in indoor and small scale areas. This leads to an advanced research to improve the accuracy in GPS positing for the benefit of indoor applications. This research proposes a new iBeacons based Improved Indoor Positioning System for indoor positing application using Bluetooth low energy (BLE) beacons. This model helps the mobile application to find the exact location at micro-level scale. The objective of this research work is to design a potable indoor positing system (IPS) for indoor applications with at least 100m accuracy with in the inbuilt energy resource limitations. The proposed model has been built and verified in all the aspects. The location accuracy and energy efficiency of the proposed model is compared and found better than the existing models.

Internet of things is the backbone of the smart applications, which attracts many types of research on the state-of-the-art network applications. Enormous research on sensor networks left more devices that are sensible in the day-to-day life. Hence, implementing new sensor networks for smart applications is not necessary. Many researchers have accepted and utilized existing networks for their request. In this case, techniques for identifying and registering existing sensible things are on demand. This paper proposed a hybrid framework for sensor identification and registration (HSIR) for new IoT applications. This research proposing HSIR as a framework aimed for user-friendliness in the IoT as well as addressed toward the scalability requirement of IoT applications. This model uses content- and context-based multicast communication instead of broadcast to reduce energy and time consumption in sensor identification. HSIR also proposed a public key to register the new network for application requirements. The behaviour of the proposed model has been assayed in realistic with simulations and proved by comparing other models.

The expensive manufacturing process and low efficiency at high working temperature of Solar cells necessitate the need for an alternate solar energy generation system. Solar concentrated thermal electric system has proved to be a promising candidate for a reliable energy conversion system. In this study, Fresnel lens had been chosen as the concentrating medium due to its low weight and easy installation. Optimum design of the Fresnel lens setup inclusive of the ability to track during an entire solar day has been devised. Standard TEG of material bismuth telluride was considered for energy harvesting from concentrated solar irradiance, and complete cost analysis was performed, and payback period of the proposed model was found out to be considerably less compared to conventional solar panel system.

Wireless sensor network (WSN) is an outdated technology that is used to monitor the physical changes in environment and take necessary actions. The advancement in WSN leads to automation in physical environment by uploading the sensed data to internet or cloud. The internet of things concept deals with the issues of making things connected to the internet as well as in a network of smart devices. IoT application development presents an enormous opportunity to reshape entire industries. According to McKinsey & Co, the merging of the physical and digital worlds via IoT could generate up to $11.1 trillion a year in economic value by 2025. Hence, the development of the web-based IoT applications will take automation research to the next level. Many authors have proposed many solutions to make internet of things possible in day-to-day life. This chapter gives an introduction about the web-based application development based on internet of things. The major objective of this chapter is to discuss and resolve the challenges in IoT to automate the real-time problems.

The idea of an intelligent, independent learning machine has fascinated humans for decades. The philosophy behind machine learning is to automate the creation of analytical models in order to enable algorithms to learn continuously with the help of available data. Since IoT will be among the major sources of new data, data science will make a great contribution to make IoT applications more intelligent. Machine learning can be applied in cases where the desired outcome is known (guided learning) or the data is not known beforehand (unguided learning) or the learning is the result of interaction between a model and the environment (reinforcement learning). This chapter answers the questions: How could machine learning algorithms be applied to IoT smart data? What is the taxonomy of machine learning algorithms that can be adopted in IoT? And what are IoT data characteristics in real-world which requires data analytics?

The distributed tree-based data communication protocol is a widely accepted efficient method in wireless sensor network-enabled Internet of Things (IoT). Due to increasing demand for IoT applications, cognitive radio sensors play a vital role in spectrum utilization. This short communication proposes a delay aware, energy-efficient data communication protocol (DEDC) for cognitive radio sensor networks (CRSN). This research proposes a first communication protocol that integrates the solution for delay and energy issues in CRSNs specific to IoT application. The respective simulation results and comparative studies with the existing models on other parameters prove the novel contribution of this research in terms of delay and energy utilization.

Vehicular ad hoc network (VANET) is a novice technique which has drawn the attention of several industries and academics. Security parameters in VANET are now receiving popularity in the research community. A defensive mechanism provides a solution to control the attacks across the VANET security. However, a single defence mechanism is unable to provide solution to the attack models as more sophisticated method is required for VANETs. This paper proposed a method termed heuristic approach for ant colony optimization (HAAC) for improved security in addition to better transportation, reliability and management. The heuristic based ant colony optimization is used to reduce the problem in finding known and unknown opponents in providing security to VANET. The characteristic of real ant colonies is used in VANET security in order to solve attack problems with shortest path. The Reinforcing VANET security using vehicle mode analysis is evaluated in an efficient manner using NS2 simulator. The excellent outcomes are obtained by an HAAC approach combined with a dynamic heuristic.

The rapid development of wireless sensor networks motivates the researchers and industries to implement large scale wireless sensor network in highly sensitive applications. Since the data aggregation is the major functionality of the wireless sensor network, the network implementation should avoid data aggregation issues like energy, collision, delay and security. As sensor nodes are deployed in hostile environments, the security of the sensitive information such as authenticity, confidentiality and integrity should be considered. To provide a unique solution that resolve the security and energy issues, this paper proposes a hybrid secure data aggregation (HSDA) to provide high secure data aggregation in an energy efficient way. HSDA implements an end to end symmetric key cryptography for secure authentication using shared public key and it uses hop by hop asymmetric key cryptography with the private keys of each node for data integrity and confidentiality. The proposed model performs the private key generation and encryption at the leaf node to reduce the communication and computation overhead of the sensor nodes. The proposed energy efficient way for achieving the secure data aggregation is proved through simulation results. Compared with existing models, the proposed model provides a new solution that resolves energy as well as security issues in data aggregation.

Localization is considered as an important parameter in wireless sensor network where it support the node to identify its location so that routing can take place in power efficient way in a dynamic network topology. The issue of localization has been addressed in many research areas such as vehicle navigation systems, virtual reality systems, user localization in wireless sensor networks (WSNs). The proposed model presents a unique localization framework considering the presence of obstruction in the environment of wireless sensor network with random node placement and irregular radio patterns for minimizing the estimates of the range free localization errors. The mobile anchor nodes frequently broadcast beacon message, including their current location approach. The range-free localization algorithms evaluate the node ranging error accuracy in the anisotropic networks to address the localization inequality issues for the empty feasible set. Here, we applied the approach of convex optimization for localization since the computation is faster. Depending upon the node density the regular node need to communicate with the mobile anchor for the localization process by participating the regular nodes in the cooperative localization process.

Cooling towers are the heat transfer devices commonly found in industries which are used to extract the high temperature from the coolants and make it reusable in various plants. Basically, the cooling towers has Fills made of PVC sheets stacked together to increase the surface area exposure of the cooling liquid flowing through it. This paper focuses on the study in such a manufacturing plant where fills are being manufactured. The productivity using the current manufacturing method was only 6 to 8 fills per day, where the ideal capacity was of 14 fills per day. In this plant manual labor was employed in the manufacturing process. A change in the process modification designed and implemented will help the industry to increase the productivity to 14. In this paper, initially the simulation study was done using ARENA the simulation package and later the new design was done using CAD Package and validated using Ansys Mechanical APDL. It's found that, by the implementation of the safe design the productivity can be increased to 196 Units.

The feedback obtained from users/occupants would become the primary data set to evaluate performance of any built facility. Meeting user needs, expectations and aspirations are the prime objectives of a facility provider. It becomes necessary to evaluate the built facility with respect to meeting user needs/expectations, in order to obtain right feedback during building performance evaluation. Among various methods being used to obtain user feedback, questionnaire remains the foremost and most commonly used tool. The quality of feedback and its subsequent analysis entirely depend on the robustness of the questionnaire which in turn depends on its content. Such survey instrument comprises of questions framed on various attributes of a built facility. The purpose of survey dictates the nature of questions and the attributes about which the data are collected. These attributes can be categorized into functional attributes, maintenance attributes, and societal attributes. In order to obtain the right feedback in levels of satisfaction with respect to these attributes, there is a need to have appropriate descriptors for incorporation in a survey instrument. This paper identifies attributes that indicate building performance and provides simple description of these attributes based on which items can be generated for a questionnaire. Such items can enable any user/occupant to easily understand the characteristics of these attributes and offer an objective feedback during questionnaire survey.

The rapid development of wireless sensor networks motivates the researchers and industries to implement large scale wireless sensor network in highly sensitive applications. Since the data aggregation is the major functionality of the wireless sensor network, the network implementation should avoid data aggregation issues like energy, collision, delay and security. As sensor nodes are deployed in hostile environments, the security of the sensitive information such as authenticity, confidentiality and integrity should be considered. To provide a unique solution that resolve the security and energy issues, this paper proposes a hybrid secure data aggregation (HSDA) to provide high secure data aggregation in an energy efficient way. HSDA implements an end to end symmetric key cryptography for secure authentication using shared public key and it uses hop by hop asymmetric key cryptography with the private keys of each node for data integrity and confidentiality. The proposed model performs the private key generation and encryption at the leaf node to reduce the communication and computation overhead of the sensor nodes. The proposed energy efficient way for achieving the secure data aggregation is proved through simulation results. Compared with existing models, the proposed model provides a new solution that resolves energy as well as security issues in data aggregation.