Unmanned Transport System using SWARM Bots

Research Article| March 01, 2016 The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation Frank Sepulveda; Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Jay Pulliam Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Frank Sepulveda aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Jay Pulliam aDepartment of Geology, Baylor University, One Bear Place #97354, Waco, Texas 76798 U.S.A.frank_sepulveda@baylor.edu Publisher: Seismological Society of America First Online: 14 Jul 2017 Online Issn: 1938-2057 Print Issn: 0895-0695 © 2016 by the Seismological Society of America Seismological Research Letters (2016) 87 (2A): 345–357. https://doi.org/10.1785/0220150234 Article history First Online: 14 Jul 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Frank Sepulveda, Jay Pulliam; The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation. Seismological Research Letters 2016;; 87 (2A): 345–357. doi: https://doi.org/10.1785/0220150234 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search ABSTRACT The proliferation of commercial Internet of Things (IoT) devices is raising consumers’ awareness of the benefits of enhancing everyday objects with the ability to communicate, sense, and process information. Commercial‐off‐the‐shelf (COTS) versions of the embedded technologies responsible for the rise of the IoT are easy‐to‐use, inexpensive, and relatively powerful. IoT Makers, technological do‐it‐yourself enthusiasts, utilize these technologies and the IoT ecosystem to create IoT devices that range from casual hobbyist to entrepreneurial in nature.In an effort to develop a “no‐engineer‐needed” Internet of Geophysical Things (IoGT) device, we integrated a COTS embedded computer with a geoscience‐related COTS data acquisition system (DAS). Using skills common to geoscientists, we integrated the Raspberry Pi System‐On‐Chip with the REF TEK (a Trimble brand) 130‐01 DAS. This Raspberry Pi enhanced REF TEK (RaPiER) provides IoGT Makers a platform for the development of geoscientific sensor node or network enhancements. IoGT Makers can use the RaPiER to tinker with IoT capabilities while simultaneously acquiring research‐quality data. Geoscientific (i.e., seismic) application areas that may benefit from RaPiER, particularly RaPiER nodal processing, include earthquake and engineering seismology.In this article, we discuss the emerging technologies that allow IoGT Makers to build their own IoGT device. We review our selection of the Raspberry Pi and REF TEK 130‐01 for IoGT device integration. We provide a RaPiER system integration guide and REF TEK interface software compiled for the Raspberry Pi ( available in the electronic supplement to this article). We present methods to expand RaPiER capabilities using the Raspberry Pi’s ecosystem. Last, we discuss our evaluation of RaPiER’s performance and its suitability for realistic field deployments. You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

Since the Internet of Things (IoT) will be entwined with everything we use in our daily life, the consequence of security flaws escalates. Smart objects will govern most of the home appliances and car engines yielding potential disaster scenarios. In this context, successful attacks could lead to chaos and scary scenarios (www.darkreading.com). Unprotected personal information may expose sensitive and embarrassing data to the public and attacks may threaten not only our computers and smart devices, but our intimacy and perhaps our lives too. Because persons and objects will be bonded with each other, user consent becomes critical. Therefore, thing, object, and user Identity will be the focus of future IoT security solutions, yielding a Trust, Security, and Privacy (TSP) paradigm, which may constitute the Achilles' heel of IoT. While security issues are quite straightforward, mainly from background knowledge, privacy issues are far more complex. Privacy constitutes a rather challenging task, even for the m...

Internet of Things (IoT) is a successful area for many industries and academia domains, particularly healthcare is one of the application areas that uses IoT sensors and devices for monitoring. IoT transition replaces contemporary health services with scientific and socioeconomic viewpoints. Since the epidemic began, diverse scientific organizations have been making accelerated efforts to use a wide range of tools to tackle this global challenge and the founders of IoT analytics. Artificial intelligence (AI) plays a key role in measuring, assessing, and diagnosing the risk. It could be used to predict the number of alternate incidents, recovered instances, and casualties, also used for forecasting cases. Within the COVID-19 background, IoT technologies are used to minimize COVID-19 exposure to others by prenatal screening, patient monitoring, and postpatient incident response in specified procedures. In this study, the importance of IoT technology and artificial intelligence in COVID-19 is explored, and the 3 important steps discussed such as the evaluation of networks, implementations, and IoT industries to battle COVID-19, including early detection, quarantine times, and postrecovery activities, are reviewed. In this study, how IoT handles the COVID-19 pandemic at a new level of healthcare is investigated. In this research, the long short-term memory (LSTM) with recurrent neural network (RNN) is used for diagnosis purpose and in particular, its important architecture for the analysis of cough and breathing acoustic characteristics. In comparison with both coughing and respiratory samples, our findings indicate poor accuracy of the voice test.

Recently, extensive research into healthcare services and their technological advancements is being carried out globally. The Internet of Things (IoT) has proven to be a predominantly potential candidate in these advancements. IoT supports infrastructure development in connecting a wide range of medical devices, sensors, and health practitioners in order to ensure the best medical treatment for patients in both cities and remote places. This has enabled the IoT to provide major support in medical application, giving rise to assisted healthcare facilities. The IoT ecosystem has a complicated architecture, with several components interacting and an evolving variety of applications for users. This is a comprehensive and interdependent system that involves real-time data analysis, interconnections of device, data transfers, and analysis. The IoT creates a connected ecosystem developed from several intelligent systems that integrates respondents with machines and allows for statistics decision making. A network of interconnected devices that perceive vital data related to medical conditions of patients in real time is referred to as the Internet of Medical Things (IoMT), a healthcare application of IoT technology. Sensors used for sensing real-time data related to monitoring temperature, blood pressure levels, heartbeat levels, specialized signal converters, and communication interfaces can be added to existing medical facilities to turn them into IoMT devices. IoMT can be used to create wearable devices, home-care and personal-care medical devices, and mobile healthcare apps, along with creating communication with medical specialists in a given area. The IoMT system’s challenges and ecosystem interface requirements are also a matter of concern considering the complexity of the systems. This study offers a comprehensive evaluation of the various domains of use of IoMT, as well as its future scope and challenges. Recent advances in sensor technology, networks, cloud storage and processing, mobility, and big data analytics have made it possible to create affordable smart medical devices and a connected healthcare environment.

As an emerging technology, Integrated Sensing and Communications (ISAC) is expected to be applied to uncrewed aerial vehicle (UAV)-assisted Internet of Things (IoT) systems to sense and acquire information from IoT devices in complex environments. In this paper, we design a UAV-ISAC assisted IoT system, where the UAV senses the IoT nodes in unknown locations and simultaneously forwards the sensing information to the ground base station (BS). The UAV is required to complete the sensing of all IoT nodes before returning to the starting position. However, several challenges arise in designing this system, including unknown IoT node positions, a non-convex optimization problem, constrained resources, a dynamic environment, and complex resource allocation. We propose using radar estimation rate as the measure for evaluating the UAV sensing performance. We formulate a UAV trajectory optimization problem that aims to maximize the radar estimation rate and minimize the UAV flight time, while adhering to constraints on radar sensing performance, information rate, and sensing all the nodes. However, solving this problem using standard optimization techniques is challenging due to the lack of information about the locations of IoT nodes and the non-convex nature of the problem. To address this challenge, we propose an algorithm by adopting multi-step learning Dueling Double Deep Q-learning network (DDQN) based on deep reinforcement learning (DRL) to solve this intractable optimization problem. The simulation results demonstrate that our proposed algorithm achieves stable convergence under different constraint thresholds. Furthermore, we verify the superiority of our algorithm by comparing it with other benchmark algorithms.

The trustworthiness of a system is not just about proving the identity or integrity of the hardware but also extends to the data, control, and management planes of communication between devices and the software they are running. This trust in data and device integrity is desirable for Internet of Things (IoT) systems, especially in critical environments. In this study, we developed a security framework, IoTAttest, for building IoT systems that leverage the Trusted Platform Module 2.0 and remote attestation technologies to enable the establishment of IoT devices’ collected data and control plan traffic integrity. After presenting the features and reference architecture of IoTAttest, we evaluated the privacy preservation and validity through the implementation of two proof-of-concept IoT applications that were designed by two teams of university students based on the reference architecture. After the development, the developers answered open questions regarding their experience and perceptions of the framework’s usability, limitations, scalability, extensibility, potential, and security. The results indicate that IoTAttest can be used to develop IoT systems with effective attestation to achieve device and data integrity. The proof-of-concept solutions’ outcomes illustrate the functionalities and performance of the IoT framework. The feedback from the proof-of-concept developers affirms that they perceived the framework as usable, scalable, extensible, and secure.

The internet of things (IoT) can effectively manage remote patient healthcare monitoring systems, particularly in predicting chronic kidney disease levels. When IoT devices collect patient data, they transmit this information to a software platform that can be accessed by healthcare professionals or patients themselves. The healthcare industry, one of the largest globally, is experiencing significant changes due to the introduction of IoT. Many healthcare organizations are making substantial investments to transform their services and leverage the advantages of IoT, which has led to the development of the internet of medical things (IoMT), a network of medical sensors and supporting infrastructure. IoMT offers numerous benefits, such as enabling remote healthcare by monitoring patients' health from a distance, providing medical care to elderly individuals, and tracking the health status of large populations to detect and prevent epidemics.

Healthcare is a rapidly growing industry worldwide. This sector consists of small to multispecialty hospitals, nursing homes and clinics, research centers, etc. Since 2014, it reached a value of nearly $8,452 billion in 2018 with a compound annual growth rate (CAGR) of 7.3%. It is expected to grow up to $11,909 billion by 2022. Moreover, the healthcare industry in India is growing at a rate of 17% annually and is expected to reach revenues of $300 billion by 2022. The healthcare sector generates a significant amount of infectious and/or toxic waste which is classified as hazardous waste. Typical hospital waste could be categorized into municipal waste, human blood and blood products, microbiological and pathological waste, contaminated/uncontaminated sharp waste, and pharmaceutical waste. It also contains a high concentration of heavy metals, low-level radioactive wastes, chemical waste, etc. In some cases, direct or indirect exposure to hospital workers is being observed of such waste. Thus, it is imperative to handle biomedical waste safely in order to avoid health and environmental risks for all healthcare units and at all stages of management, including segregation, collection, packaging, storage, transport, treatment, and disposal. This chapter reviews the current and previous applications of IoT and technology for addressing issues regarding hospital waste management sustainably in the long term. In order to manage hospital waste effectively, there is the need for automation in the biomedical waste management process as per the new biomedical waste management rule, 2016. Therefore, the Internet of Things (IoT) plays a vital role in appropriately handling this waste, which is with the use of connected sensor devices and a planned IoT-based environment. It helps in identification based on images and gathering data on their own, and also removes possible human errors. IoT automation helps to reduce the risk of error, and increases efficiency, lowers costs, and improves quality as well.

The concept of the Internet of Things (IoT) has received much attention over the last five years. It is predicted that the IoT will influence every aspect of our lifestyles in the near future. Wireless Sensor Networks are one of the key enablers of the operation of IoTs, allowing data to be collected from the surrounding environment. However, due to limited resources, nature of deployment and unattended operation, a WSN is vulnerable to various types of attack. Security is paramount for reliable and safe communication between IoT embedded devices, but it does, however, come at a cost to resources. Nodes are usually equipped with small batteries, which makes energy conservation crucial to IoT devices. Nevertheless, security cost in terms of energy consumption has not been studied sufficiently. Previous research has used a security specification of 802.15.4 for IoT applications, but the energy cost of each security level and the impact on quality of services (QoS) parameters remain unknown. This research focuses on the cost of security at the IoT media access control (MAC) layer. It begins by studying the energy consumption of IEEE 802.15.4 security levels, which is followed by an evaluation for the impact of security on data latency and throughput, and then presents the impact of transmission power on security overhead, and finally shows the effects of security on memory footprint. The results show that security overhead in terms of energy consumption with a payload of 24 bytes fluctuates between 31.5% at minimum level over non-secure packets and 60.4% at the top security level of 802.15.4 security specification. Also, it shows that security cost has less impact at longer packet lengths, and more with smaller packet size. In addition, the results depicts a significant impact on data latency and throughput. Overall, maximum authentication length decreases throughput by almost 53%, and encryption and authentication together by almost 62%.

Internet of Things (IoT) has the potential to become a technology revolution with a vision of creating very large scale network, comprising of unprecedented number of connected devices. These devices, often referred to as smart items or intelligent things can be home appliances, healthcare devices, vehicles, buildings, factories and almost anything networked and fitted with sensors, actuators, embedded computers. There has been sustained research work and standardization effort from different IoT perspectives like integration of sensor and RFID devices to the Internet. With the increasing trend of gathering business insights from unstructured data, the high volume of data generated by such devices is also of interest. Cloud based data mining platforms are suitable for analyses of such data and researchers have proposed architectures where personal mobile phones can act as Edge Gateway between the sensor network and cloud analytics platform. It seems that the surge in the volume of data generated by huge number of Smart Items can only be matched if a large percentage of mobile users start sharing the computation capability of their personal devices and work together towards true Participatory Computing in the IoT systems. In this work we try to understand the challenges associated with running computation jobs on the mobile devices using different types of workload often observed in IoT applications. Based on the insights gained from experiments performed by us, we propose a scheme where mobile phones, residential gateways and other edge devices offer free slots to servers in a cloud based data analytics system. Based on the free time slots offered by the mobile phones, if commensurately sized computational jobs can be scheduled, the unpredictability associated with using mobile phones as grid resources can be solved.

By combining the different monitoring and automation techniques available today, we can develop cutting-edge internet of things (IoT) systems that can support sustainable development through smart agriculture. Systems are able to monitor the farming areas and react to the parameters being monitored on their own without the presence of human beings. This automation can result in a more precise way of maintaining the aspects that affect the growth of plants, leading to an increase in the food production on farmlands. This chapter focuses on IOT for automation in smart agriculture and provides a pathway to develop automation system in the smart environment.

The Internet of things (IoT) facilitated various aspects of people’s daily lives. In simple terms, the IoT refers to the connection of sensors, actuators, and devices to a network by which they can interact with each other and their users. It is a practical model that works on connecting a group of sensors and actuators that form a network that works to carry out several tasks, such as collecting information from the surrounding environment and processing it and providing the beneficiary of the network with this critical information is used in many cases in making important decisions and missions. Over time, the importance of IoT will increase due to its use in many fields. This technology is expected to become an essential milestone in developing many sectors and sciences. Therefore, to benefit from IoT technology, security in the IoT must focus on raising its efficiency. The security of IoT is a big challenge because of its complexity, heterogeneity, and a large number of related resources. It is necessary to improve security protocols to guarantee IoT connection and ensure all devices in this network are trusted and prevent an attacker from accessing any device in the network because these attacks penetrate privacy and expose the personal property to damage. This research introduces a new framework to improve the confidentiality, integrity, authenticity, and availability of IoT devices in indoor environments, by forcing new devices (IoT-MD), middleware (Security Centre SC), and protocols (add a new authorized device and IoT Traffic Control Protocol IoT-TCP). Security in IoT will be guaranteed by using this framework which enforces isolation between the IoT devices and networks to prevent unauthorized IoT devices. Moreover, prevent attacks in the IoT indoor environment.

The Internet of Things (IoT) connects a plethora of smart devices globally across various applications like smart cities, autonomous vehicles, and health monitoring. Simulation plays a key role in the testing of IoT systems, noting that field testing of a complete IoT product may be infeasible or prohibitively expensive. This paper addresses a specific yet important need in simulation-based testing for IoT: Stress testing of cloud systems that are increasingly employed in IoT applications. Existing stress testing solutions for IoT demand significant computational resources, making them ill-suited and costly. We propose a lean simulation framework designed for IoT cloud stress testing. The framework enables efficient simulation of a large array of IoT and edge devices that communicate with the cloud. To facilitate simulation construction for practitioners, we develop a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">domain-specific language (DSL)</i> , named IoTECS, for generating simulators from model-based specifications. We provide the syntax and semantics of IoTECS and implement IoTECS using Xtext and Xtend. We assess simulators generated from IoTECS specifications for stress testing two real-world systems: a cloud-based IoT monitoring system developed by our industry partner and an IoT-connected vehicle system. Our empirical results indicate that simulators created using IoTECS: (1) achieve best performance when configured with Docker containerization; (2) effectively assess the service capacity of our case-study systems, and (3) outperform industrial stress-testing baseline tools, JMeter and Locust, by a factor of 3.5 in terms of the number of IoT and edge devices they can simulate using identical hardware resources. To gain initial insights about the usefulness of IoTECS in practice, we interviewed two engineers from our industry partner who have firsthand experience with IoTECS. Feedback from these interviews suggests that IoTECS is effective in stress testing IoT cloud systems, saving significant time and effort.

Background: Internet of Things (IoT) is the next emerging technique in which each and every thing in the world is getting connected to the internet by means of various communication techniques. By using the technique of multicasting, these things communicate with each other to transfer the data. So there is a need of an authentication scheme in order to prevent the data form getting corrupted while transmitting. As we know trillions of devices are connected in IoT, a secure communication should be implemented for the same. So a light weight authentication should be provided for IoT. Methods: The IoT consist of many objects, the objects may me mobile phones, refrigerator, air cooler etc. along with sensors, actuators, base station. The objects are mainly connected with sensors in order to retrieve data from each of them. Since sensor network is adhoc networks and the computational power consumed of sensor networks are also very low. Hence in the Wireless Sensor Network (WSN) many sensors are connected with one or more base station. The base station in wireless sensor network manages all the sensor nodes with the help of processor and memory. Since we are transforming from wireless sensor networks to IoT, the objects are connected to internet by providing address to individual object. So, the base station not having the capable to provide a secure communication between the objects along with sensors and internet.In this paper, we propose a new authentication scheme by means of two different approaches. Since IoT contains many numbers of objects, we connect certain objects which are in same area and provide a database for that object. The data related to particular node is stored and updated frequently in the database and maintained by Data Base Management System (DBMS) and it is connected to internet. Results: Whenever a user wants to access the data, the authentication is provided by means of login id with hashing password or with the help of MAC passwords. These two authentication scheme provide better when compared to existing method which are shown in results. The metrics that measures the performance of the proposed approach are the resistance against node compromise,computation overhead,communication overhead, robustness to packet loss and message entropy. Applications: This main application of mutual authentication scheme for IoT is to provide a authentication between the end user and the sensor network data. This method is also suitable for smart house application where the user can securely access the data from anywhere in the world.

The Internet of Things (IoT) is a network of sensors that helps collect data 24/7 without human intervention. However, the network may suffer from problems such as the low battery, heterogeneity, and connectivity issues due to the lack of standards. Even though these problems can cause several performance hiccups, security issues need immediate attention because hackers access vital personal and financial information and then misuse it. These security issues can allow hackers to hijack IoT devices and then use them to establish a Botnet to launch a Distributed Denial of Service (DDoS) attack. Blockchain technology can provide security to IoT devices by providing secure authentication using public keys. Similarly, Smart Contracts (SCs) can improve the performance of the IoT–blockchain network through automation. However, surveyed work shows that the blockchain and SCs do not provide foolproof security; sometimes, attackers defeat these security mechanisms and initiate DDoS attacks. Thus, developers and security software engineers must be aware of different techniques to detect DDoS attacks. In this survey paper, we highlight different techniques to detect DDoS attacks. The novelty of our work is to classify the DDoS detection techniques according to blockchain technology. As a result, researchers can enhance their systems by using blockchain-based support for detecting threats. In addition, we provide general information about the studied systems and their workings. However, we cannot neglect the recent surveys. To that end, we compare the state-of-the-art DDoS surveys based on their data collection techniques and the discussed DDoS attacks on the IoT subsystems. The study of different IoT subsystems tells us that DDoS attacks also impact other computing systems, such as SCs, networking devices, and power grids. Hence, our work briefly describes DDoS attacks and their impacts on the above subsystems and IoT. For instance, due to DDoS attacks, the targeted computing systems suffer delays which cause tremendous financial and utility losses to the subscribers. Hence, we discuss the impacts of DDoS attacks in the context of associated systems. Finally, we discuss Machine-Learning algorithms, performance metrics, and the underlying technology of IoT systems so that the readers can grasp the detection techniques and the attack vectors. Moreover, associated systems such as Software-Defined Networking (SDN) and Field-Programmable Gate Arrays (FPGA) are a source of good security enhancement for IoT Networks. Thus, we include a detailed discussion of future development encompassing all major IoT subsystems.