The Internet of Geophysical Things: Raspberry Pi Enhanced REF TEK (RaPiER) System Integration and Evaluation

The number of Internet of Things (IoT) devices has increased recently and will increase further in the future. IoT devices are vulnerable, so vendors update and distribute firmware. At the time of firmware distribution, a huge number of IoT devices may connect to the firmware server and excessive network traffic may occur. Consequently, a method using a peer-to-peer file sharing system and a blockchain has been proposed. One study proposed an update scheme considering incentives for distributors to help with updating. However, this method requires the IoT devices, which have limited processing power, to decrypt encrypted files, and the distribution nodes to pay the transaction cost. This paper proposes a firmware distribution method that provides incentives for distributors to help with distribution to reduce the gas costs, using a smart contract and access control based on update records. The additional computations performed by IoT devices and distributors key management were reduced compared to those in previous studies by using access control instead of encryption. Further, the proposed approach was implemented and evaluated using a Raspberry Pi and laptop. The evaluation demonstrated that the gas cost per update was successfully lowered compared to the costs in previous studies.

Extensive internet of things (IoT) devices being in-fected by malware are an increasingly important viable objective in IoT cyberattacks e.g., botnet, virus, trojan, etc. The botnets got leverage from unsecured IoT devices (e.g., CCTV, Raspberry Pi, Arduino Uno, ESP8266, etc) that operates by using the Internet traffic. In recent year, the high-profile IoT device’s vendor and the researcher from all over universities are exploring the robustness of IoT devices against botnet attacks. This research uses a deep learning approach to prevent botnet attacks on IoT networks. The deep residual one-dimensional CNN (1DCNN) model as the proposed method is used for botnet traffic detection. Two algorithms are provided: data processing for the N-BaIoT dataset and IoT botnet detection training and testing. For data processing, training, and testing, the datasets were evaluated, and the model was optimized with different optimizers. This research used RMS Prop, ADaDelta, AdaGrad, AdaMax, and Adam as optimizers and the CNN was compared with LSTM, CNN with RNN, and Deep residual 1DCNN, respectively. The results showed that Deep Residual 1DCNN with Adam has the highest training accuracy of 88.67%, 88.67% for validation accuracy, and 88.53%for test accuracy.

Usually, IoT devices are designed to perform specific tasks, while robots have to adapt to unpredictable situations. Artificial intelligence and machine learning help these robots cope with the emerging unexpected conditions. The Internet of Robotic Things is an evolving concept that brings together all-encompassing sensors and devices with robotic and autonomous systems. Both IoT devices and robots rely on sensors to understand the surrounding environment, to process data quickly, and to decide how to respond. Nevertheless, while most IoT systems can handle only well-defined tasks, robots can handle expected situations as well. The Internet of Robotic Things is a better Internet of Things (IoT) solution due to its ability to bridge the gap between IT and real operations. The IoRT may face several challenges in power consumption and network bandwidth limitation due to the growth in the information dissemination over the Robotic network. The main objective of this paper is to introduce an overview of the concepts and challenges in the Internet of Robotic Things based on Fog Computing technique. Further, a framework of Fog based IoRT is introduced.

The Internet of Things (IoT) devices is able to communicate with another IoT devices in heterogeneous environments. They are also able to create a network by their own using wireless capabilities without a centralized approach. This network is called wireless mobile ad hoc network (MANET). The MANET facilitate IoT devices to communicate with each other in an infrastructure less network of the Internet of Things environment. The IoT devices can dynamically join, create connection and exchange information among them. The role of cloud in this framework is to store and process the data by IoT devices. The communication Security has been raised as one of the most critical issues of cloud computing where resolving such an issue would result in a constant growth in the use and popularity of cloud. Our purpose of this study is to create a framework for providing the communication in an integrated framework of Cloud and MANET among IoT devices. Our main contribution links a new study that integrates cloud computing to the MANET and provides communication to the internet of things. This study can be used in the framework of the Internet of Things in future.

Internet of Things (IoT) is an emerging technology that consists of tiny sensors embedded with IoT devices used in e-Health, smart cities and assisted living. IoT devices are equipped with constrained power, memory, and computing capabilities, which creates new security challenges. By integrating sensors and wireless technologies, this cutting-edge technology automates numerous routine tasks in different domains. The main goal of advancing IoT in the existing healthcare system is to automate the monitoring of hospitals and patients’ health by spreading goodwill toward the IoT vision. IoT has gained popularity on practically all platforms that are available. This paper presents a remote patient health monitoring and diagnosis system that uses Message Queuing Telemetry Transport protocol (MQTT) and Internet of Things (IoT) devices with one time pad cryptography. The proposed plan offers end-to-end data secrecy for an IoT system using MQTT that enables e-health and mobility. Patients’ movements inside nursing homes and healthcare facilities are also managed safely without the need for periodic reconfigurations. Prior to communicating through the MQTT protocol, this secure crypto scheme encrypts the message on both the publisher and subscriber ends. According to the results, the proposed scheme is more efficient than delegation-based architecture in terms of security and lightweight communication. The proposed system established communication between patients and medical experts using IoT technologies to alleviate the hospital strain caused by congestion in medical treatment and enable an expressway of medical responses. According to our results the proposed scheme has low computational overhead compared to other solutions that use conventional encryption to guarantee robust protection against most of the IoT security attacks.

Purpose– The purpose of this paper is to examine the role of capability and alliance arising from the internet of things (IoT), specifically in the relationships between strategic orientations (entrepreneurial and market foci) with product and process innovations. In addition, it investigates the direct relationship between IoT capability and alliance. Improving these relationships assist in ensuring that new knowledge from the IoT can be translated into tangible business innovations that contribute to economic development.Design/methodology/approach– Data from 207 new high-technology IoT ventures in China were obtained after three-wave mailing (i.e. two reminders). Following a rigorous process to purify and validate the measurement scale items, the study used structural equation modeling to test the conceptual model.Findings– Findings demonstrate that an IoT capability only enhances product innovation, however, with the addition and support from IoT alliance, both product and process innovation can be achieved in new high-tech IoT ventures. This nuanced insight suggests that new high-tech IoT ventures should focus on building their IoT capability, and at the same time, develop IoT alliances with value chain partners in order to fully take advantage of IoT and gain a better position to formulate more novel offerings.Originality/value– The study is first to contribute with a much needed framework of IoT and entrepreneurship by examining the role of IoT capability further in the relationships between: entrepreneurial orientation and market orientation with product and process innovations arising from IoT; and the role of IoT alliance (interfirm relations, partnerships, etc.) on the relationship above.

Recently, Internet of Things (IoT) has drawn a great deal of attention and is envisioned in various sectors in the near future due to its promising benefits. However, the constant and rapid growth in IoT devices also brings new challenges due to constrained power and resources associated to them. One of the challenges is to provide seamless connectivity in mobile IoT. Secondly, IoT devices may stream enormous amount of data; hence, providing a solution that can effectively reduce service cost of data transfer. Finally, there are challenges in management and deployment of services running at mobile IoT Edge Gateway. In this context, containerized virtualization solution could play a key role in support of efficient management and deployment of microservices to provide seamless connectivity. This paper proposes a lightweight container-based virtualization technology for IoT, which employs Docker container-based microservices architecture for effectively deploying applications in a virtualized ecosystem. We evaluated the performance of the proposed solution on real IoT testbed using Raspberry Pi 3 as a mobile IoT Edge Gateway for network handover decision making among various alternatives, such as Wi-Fi, Radio, and Satellite. The results demonstrated better performance compared with the native environment, i.e., the one without introduction of a virtualization layer. The results also showed that the Docker container produces negligible resource overhead and can be used on resource constrained mobile IoT Edge Gateway devices like Raspberry Pi 3 for efficiently managing IoT application and services.

Recent growth in the Internet of Things (IoT) looks promising for realizing a smart environment of the future. However, concerns about the security of IoT devices are escalating as they are inherently constrained by limited resources, heterogeneity, and lack of standard security controls or protocols. Due to their inability to support state-of-the-art secure network protocols and defense mechanisms, standard security solutions are unsuitable for dynamic IoT environments that require large and smart IoT infrastructure deployments. At present, the IoT based smart environment deployments predominantly use cloud-centric approaches to enable continuous and on-demand data exchange that leads to further security and privacy risks. While standard security protocols, such as Virtual Private Networks (VPNs), have been explored for certain IoT environments recently, the implementation models reported have several variations and are not practically scalable for any dynamically scalable IoT deployment. This paper addresses current drawbacks in providing the required flexibility, interoperability, scalability, and low-cost practical viability of a secure IoT infrastructure. We propose an adaptive end-to-end security model that supports the defense requirements for a scalable IoT infrastructure. With low-cost embedded controllers, such as the Raspberry Pi, allowing for the convergence of more sophisticated networking protocols to be embedded at the IoT monitoring interface, we propose a scalable IoT security model integrating both the IoT devices and the controller as one embedded device. Our approach is unique, with a focus on the integration of a security protocol at the embedded interface. In addition, we demonstrate a prototype implementation of our IoT security model for a smart water monitoring system. We believe that our modest first step would instill future research interests in this direction.

ABSTRACTIn recent years, the emerging development of Internet of things (IoT) has created new opportunities for IoT service provider, device manufacturer and customer (end user). In agriculture, employing IoT can help reducing manpower as well as standardizing agricultural processes, thus increasing the benefit for customer who does agricultural business. Beside the opportunities, there are also issues while doing maintenance for an agricultural IoT system. One of them is how to automate the process to detect faults of IoT devices in a large deployment area. While previous studies focus on investigating algorithms as well as theoretical model to detect faults for specific IoT devices, automatically expanding of IoT management process for detecting faults of sensor/actuator has not been investigated yet. To achieve this, we should be able to deal with the relationship between IoT device manufacturer, service provider and customer. In this study, we conceptualize the knowledge of IoT devices, management process and fault detection requirement. To realize this model, we introduce a runtime system to expand the IoT management process for detecting sensor/actuator faults which is able to provide the above features. We do several experiments to show the effectiveness and usefulness of the proposed solution.

The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.

Internet of Things (IoT) have opened the door to a world of unlimited possibilities for implementations in varied sectors in society, but it also has many challenges. One of those challenges is security and privacy. IoT devices are more susceptible to security threats and attacks. Due to constraints of the IoT devices such as area, power, memory, etc., there is a lack of security solutions that are compatible with IoT devices and applications, which is leading this world of securely connected things to the “internet of insecure things.” A promising solution to this problem is going beyond the standard or classical techniques to implementing the security solutions in the hardware of the IoT device. The integration of emerging technologies in IoT networks, such as machine learning, blockchain, fog/edge/cloud computing, and quantum computing have added more vulnerable points in the network. This paper introduces a comprehensive study on IoT security threats and solutions. Additionally, this survey outlines how emerging technologies such as machine learning and blockchain are integrated in IoT, challenges resulted from this integration, and potential solutions to these challenges. The paper utilizes the 4-layer IoT architecture as a reference to identify security issues with corresponding solutions.

Scheduling is one of the most important factors used in scheduling processes insideCPU. CPU scheduling is a concept of multiprogramming, where the CPU is used to schedule the incoming processes alternately. Many algorithms can be used to schedule processesinside CPU, but not all can be real-time. Long waiting times and response times often be problems in scheduling processes in realtime. FIFO and Round Robin algorithms can be implemented to schedule the processes in realtime. In this paper, the authors schedule the process of several sensors that are used to collect landslide data. The data processing is sent by FIFO scheduling and Round Robin scheduling separately on the Internet of Things (IoT) device. The author only analyzes the performance of FIFO and Round Robin algorithms in scheduling the incoming processes in real-time on the IoT operating system by considering the waiting time and response time. The analysis is expected to create a quick response time and waiting time so that proper algorithm is decided to complement the IoT architecture in landslide detection. The FIFO and Round Robin algorithms are implementedat the Raspbian and Arch Linux operating systems in the IoT device, the Raspberry Pi 3 Model B, which uses a 64-bit 64-bit ARM Cortex-AS3 64-bit process at 1.2GHz.

Seeing is not always believing: Insights on IoT manufacturing from firmware composition analysis and vendor survey

With the advancement in science and technology, the demand for electronic Internet of Things (IoT) devices has increased to store and process digital data. With the advancement in IoT technology, we are more reliant on IoT devices to perform our regular tasks. IoT Forensics deals with the investigation of IoT devices such as smartphones, tablets, smart watches, or any device which is connected to the Internet. The data on these devices can still be retrieved via IoT forensics even if they are destroyed, resold, or misplaced. Before discarding any IoT device, we must consider how to secure the data stored on them. We are then going to discuss the several types of personal data that could be retrieved from discarded IoT devices. Finally, we are going to discuss the protection measures for the safe disposal of IoT devices.

In opportunistic networks the communication opportunities (contacts) are intermittent and there is no need to establish an end-to-end link between the communication nodes. The enormous growth of devices having access to the Internet, along the vast evolution of the Internet and the connectivity of objects and devices, has evolved as Internet of Things (IoT). There are different issues for these networks. One of them is the selection of IoT devices in order to carry out a task in opportunistic networks. In this work, we implement a Fuzzy-Based System for IoT device selection in opportunistic networks. For our system, we use four input parameters: IoT Device’s Number of Past Encounters (IDNPE), IoT Contact Duration (IDCD), IoT Device Storage (IDST) and IoT Device Remaining Energy (IDRE). The output parameter is IoT Device Selection Decision (IDSD). The simulation results show that the proposed system makes a proper selection decision of IoT devices in opportunistic networks. The IoT device selection is increased up to 15% and 27% by increasing IDNPE and IDRE, respectively.