Security and Privacy for Green IoT-based Agriculture: Review, Blockchain solutions, and Challenges

open access articleThis paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture

Survey on IoT based Cyber Security Issues and Autonomous Solutions for Implantable Medical Devices

In today’s world the technology has got boomed up to the peak. So as a measure of this technology peak we could see that the enhancement of this has raised very large. This technology booming has also impacted health care sector. In our paper we are going to discuss much on implantable medical devices and its uses which plays a major role in patient’s life. This IMD’s are going to be the life changing aspect of each and every patient. These devices are highly controlled IoT devices (i.e.) those devices are connected through internet which will help doctors to track the details of the patients remotely. On the other hand since all these devices are connected to internet, these are easily hacked by the hackers. The factors of how those devices are much vulnerable and what are all the threats that will make these devices to malfunction and lead a problem to the patients is discussed. And also this will lead the health sector to fall in their reputation. IMD’s are of many types which are in existing in the Medical industry. But we are going to consider some IMD’s as example and we have planned to make a detailed study on the problems on those devices. All these devices are vulnerable since it is connected to internet. So our aim is to completely or partially reduce the risks on those devices via communication network. We have also showcased the possible threats and vulnerabilities chances on those devices. The main scenarios of device control issues and possible solutions have been discussed in this article

Smart Road Danger Detection and Warning

Road dangers have caused numerous accidents, thus detecting them and warning users are critical to improving traffic safety. However, it is challenging to recognize road dangers from numerous normal data and warn road users due to cluttered real-world backgrounds, ever-changing road danger appearances, high intra-class differences, limited data for one party, and high privacy leakage risk of sensitive information. To address these challenges, in this thesis, three novel road danger detection and warning frameworks are proposed to improve the performance of real-time road danger prediction and notification in challenging real-world environments in four main aspects, i.e., accuracy, latency, communication efficiency, and privacy. Firstly, many existing road danger detection systems mainly process data on clouds. However, they cannot warn users timely about road dangers due to long distances. Meanwhile, supervised machine learning algorithms are usually used in these systems requiring large and precisely labeled datasets to perform well. The EcRD is proposed to improve latency and reduce labeling cost, which is an Edge-cloud-based Road Damage detection and warning framework that leverages the fast-responding advantage of edges and the large storage and computation resources advantages of the cloud. In EcRD, a simple yet efficient road segmentation algorithm is introduced for fast and accurate road area detection by filtering out noisy backgrounds. Additionally, a light-weighted road damage detector is developed based on Gray Level Co-occurrence Matrix (GLCM) features on edges for rapid hazardous road damage detection and warning. Further, a multi-types road damage detection model is proposed for long-term road management on the cloud, embedded with a novel image-label generator based on Cycle-Consistent Adversarial Networks, which automatically generates images with corresponding labels to improve road damage detection accuracy further. EcRD achieves 91.96% accuracy with only 0.0043s latency, which is around 579 times faster than cloud-based approaches without affecting users' experience while requiring very low storage and labeling cost. Secondly, although EcRD relieves the problem of high latency by edge computing techniques, road users can only achieve warnings of hazardous road damages within a small area due to the limited communication range of edges. Besides, untrusted edges might misuse users' personal information. A novel FedRD named FedRD is developed to improve the coverage range of warning information and protect data privacy. In FedRD, a new hazardous road damage detection model is proposed leveraging the advantages of feature fusion. A novel adaptive federated learning strategy is designed for high-performance model learning from different edges. A new individualized differential privacy approach with pixelization is proposed to protect users' privacy before sharing data. Simulation results show that FedRD achieves similar high detection performance (i.e., 90.32% accuracy) but with more than 1000 times wider coverage than the state-of-the-art, and works well when some edges only have limited samples; besides, it largely preserves users' privacy. Finally, despite the success of EcRD and FedRD in improving latency and protecting privacy, they are only based on a single modality (i.e., image/video) while nowadays, different modalities data becomes ubiquitous. Also, the communication cost of EcRD and FedRD are very high due to undifferentiated data transmission (both normal and dangerous data) and frequent model exchanges in its federated learning setting, respectively. A novel edge-cloud-based privacy-preserving Federated Multimodal learning framework for Road Danger detection and warning named FedMRD is introduced to leverage the multi-modality data in the real-world and reduce communication costs. In FedMRD, a novel multimodal road danger detection model considering both inter-and intra-class relations is developed. A communication-efficient federated learning strategy is proposed for collaborative model learning from edges with non-iid and imbalanced data. Further, a new multimodal differential privacy technique for high dimensional multimodal data with multiple attributes is introduced to protect data privacy directly on users' devices before uploading to edges. Experimental results demonstrate that FedMRD achieves around 96.42% higher accuracy with only 0.0351s latency and up to 250 times less communication cost compared with the state-of-the-art, and enables collaborative learning from multiple edges with non-iid and imbalanced data in different modalities while preservers users' privacy.2021-11-2

Security Enhancement by Identifying Attacks Using Machine Learning for 5G Network

Need of security enhancement for 5G network has been increased in last decade. Data transmitted over network need to be secure from external attacks. Thus there is need to enhance the security during data transmission over 5G network. There remains different security system that focus on identification of attacks. In order to identify attack different machine learning mechanism are considered. But the issue with existing research work is limited security and performance issue. There remains need to enhance security of 5G network. To achieve this objective hybrid mechanism are introduced. Different treats such as Denial-of-Service, Denial-of-Detection, Unfair use or resources are classified using enhanced machine learning approach. Proposed work has make use of LSTM model to improve accuracy during decision making and classification of attack of 5G network. Research work is considering accuracy parameters such as Recall, precision and F-Score to assure the reliability of proposed model. Simulation results conclude that proposed model is providing better accuracy as compared to conventional model

UAV Based 5G Network: A Practical Survey Study

Unmanned aerial vehicles (UAVs) are anticipated to significantly contribute to the development of new wireless networks that could handle high-speed transmissions and enable wireless broadcasts. When compared to communications that rely on permanent infrastructure, UAVs offer a number of advantages, including flexible deployment, dependable line-of-sight (LoS) connection links, and more design degrees of freedom because of controlled mobility. Unmanned aerial vehicles (UAVs) combined with 5G networks and Internet of Things (IoT) components have the potential to completely transform a variety of industries. UAVs may transfer massive volumes of data in real-time by utilizing the low latency and high-speed abilities of 5G networks, opening up a variety of applications like remote sensing, precision farming, and disaster response. This study of UAV communication with regard to 5G/B5G WLANs is presented in this research. The three UAV-assisted MEC network scenarios also include the specifics for the allocation of resources and optimization. We also concentrate on the case where a UAV does task computation in addition to serving as a MEC server to examine wind farm turbines. This paper covers the key implementation difficulties of UAV-assisted MEC, such as optimum UAV deployment, wind models, and coupled trajectory-computation performance optimization, in order to promote widespread implementations of UAV-assisted MEC in practice. The primary problem for 5G and beyond 5G (B5G) is delivering broadband access to various device kinds. Prior to discussing associated research issues faced by the developing integrated network design, we first provide a brief overview of the background information as well as the networks that integrate space, aviation, and land

IoT Health Devices: Exploring Security Risks in the Connected Landscape

The concept of the Internet of Things (IoT) spans decades, and the same can be said for its inclusion in healthcare. The IoT is an attractive target in medicine; it offers considerable potential in expanding care. However, the application of the IoT in healthcare is fraught with an array of challenges, and also, through it, numerous vulnerabilities that translate to wider attack surfaces and deeper degrees of damage possible to both consumers and their confidence within health systems, as a result of patient-specific data being available to access. Further, when IoT health devices (IoTHDs) are developed, a diverse range of attacks are possible. To understand the risks in this new landscape, it is important to understand the architecture of IoTHDs, operations, and the social dynamics that may govern their interactions. This paper aims to document and create a map regarding IoTHDs, lay the groundwork for better understanding security risks in emerging IoTHD modalities through a multi-layer approach, and suggest means for improved governance and interaction. We also discuss technological innovations expected to set the stage for novel exploits leading into the middle and latter parts of the 21st century

Health Care Equity Through Intelligent Edge Computing and Augmented Reality/Virtual Reality: A Systematic Review

Intellectual capital is a scarce resource in the healthcare industry. Making the most of this resource is the first step toward achieving a completely intelligent healthcare system. However, most existing centralized and deep learning-based systems are unable to adapt to the growing volume of global health records and face application issues. To balance the scarcity of healthcare resources, the emerging trend of IoMT (Internet of Medical Things) and edge computing will be very practical and cost-effective. A full examination of the transformational role of intelligent edge computing in the IoMT era to attain health care equity is offered in this research. Intelligent edge computing-aided distribution and collaborative information management is a possible approach for a long-term digital healthcare system. Furthermore, IEC (Intelligent Edge Computing) encourages digital health data to be processed only at the edge, minimizing the amount of information exchanged with central servers/the internet. This significantly increases the privacy of digital health data. Another critical component of a sustainable healthcare system is affordability in digital healthcare. Affordability in digital healthcare is another key component of a sustainable healthcare system. Despite its importance, it has received little attention due to its complexity. In isolated and rural areas where expensive equipment is unavailable, IEC with AR / VR, also known as edge device shadow, can play a significant role in the inexpensive data collection process. Healthcare equity becomes a reality by combining intelligent edge device shadows and edge computing

Internet of Things: A Model for Cybersecurity Standards and the Categorisation of Devices

The networking of physical devices, including their infrastructure and data, is known as the Internet of Things. The number of networked devices is con- stantly increasing over the last years and is expected to continue to rise in the future. This also results in an increasing number of attacks on these devices which are considered potentially insecure. The reasons for the lack of cyber- security are diverse and lead, for example, to botnets and similar problems. Mandatory standards and guidelines can help to ensure cybersecurity re- gardless of a fast pace of development and a low price of the devices. In some areas, the development of these guidelines is already well advanced, ideally across countries as a European standard. However, problems with standardiza- tion are the different definitions of device categories and thus, the assignment of a device to a standard. Even in academia, definitions and categories for Internet of Things devices are ambiguous or completely lacking. This makes it difficult to find relevant publications. Therefore, a model of the Internet of Things was researched to solve these problems and define clear categories. The model divides the Internet of Things into categories, supplements the definitions with characteristics and distinguishes the different device types. The architectures and associated components are also considered. The model can be applied to all devices and available cybersecurity standards which is shown by mapping them to the model. The real-world applications are diverse and illustrated as different use cases. As digitalization evolves rapidly, the researched model is designed to adapt flexibly to new developments