Advanced Context-Sensitive Access Management for Edge-Driven IoT Data Sharing as a Service

Multi-source Internet of Things (IoT) data, archived in institutions’ repositories, are becoming more and more widely open-sourced to make them publicly accessed by scientists, developers, and decision makers via web services to promote researches on geohazards prevention. In this paper, we design and implement a big data-turbocharged system for effective IoT data management following the data lake architecture. We first propose a multi-threading parallel data ingestion method to ingest IoT data from institutions’ data repositories in parallel. Next, we design storage strategies for both ingested IoT data and processed IoT data to store them in a scalable, reliable storage environment. We also build a distributed cache layer to enable fast access to IoT data. Then, we provide users with a unified, SQL-based interactive environment to enable IoT data exploration by leveraging the processing ability of Apache Spark. In addition, we design a standard-based metadata model to describe ingested IoT data and thus support IoT dataset discovery. Finally, we implement a prototype system and conduct experiments on real IoT data repositories to evaluate the efficiency of the proposed system.

With the popularisation and development of the internet of things, big data and cloud computing, the search of data in cloud-based internet of things becomes a hot research topic. However, the sensitive data, such as the medical data collected by wearable devices, is inevitable to be stored in the cloud server. Homomorphic encryption has the ability to calculate the ciphertext without decryption. We separate the calculating and the decryption into different security domains to preserve the privacy of sensitive data, so the original plaintext would not be exposed in the cloud. Hence, we can compare two ciphertexts to get the difference of them in a privacy preserving way. In order to accelerate the search process of range query, we build an encrypted self-balancing binary index tree. Based on oblivious RAM, the searching scheme can hide the access patterns of the node of tree. The actual nodes and logic relation of tree are stored on different servers. A sample implementation of the proposed scheme is given, and the experimental results and analysis are presented to illustrate the scheme's effectiveness and security.

This chapter describes how traditionally, Cloud Computing has been used for processing Internet of Things (IoT) data. This works fine for the analytical and batch processing jobs. But most of the IoT applications demand real-time response which cannot be achieved through Cloud Computing mainly because of inherent latency. Fog Computing solves this problem by offering cloud-like services at the edge of the network. The computationally powerful edge devices have enabled realising this idea. Witnessing the exponential rise of IoT applications, Fog Computing deserves an in-depth exploration. This chapter establishes the need for Fog Computing for processing IoT data. Readers will be able to gain a fair comprehension of the various aspects of Fog Computing. The benefits, challenges and applications of Fog Computing with respect to IoT have been mentioned elaboratively. An architecture for IoT data processing is presented. A thorough comparison between Cloud and Fog has been portrayed. Also, a detailed discussion has been depicted on how the IoT, Fog, and Cloud interact among them.

Privacy preserving Internet of Things: From privacy techniques to a blueprint architecture and efficient implementation

IoT (Internet of Things) has the capability of capturing dynamic context from the physical world into the digital world. Context-aware BPM (Business Process Management) should integrate IoT as a key perspective of dynamic context of a business process and to enhance the decision making in a business process. IoT is often used to automate the process execution or integrated in the process model as resources of smart devices and additional concepts. In this way, IoT data is directly used without processing or reasoning with other contextual data to obtain higher-order contextual knowledge, which impairs its potential capability. The context layer and the decision layer are still missing while integrating IoT in BPM to obtain context-awareness. Decisions are still considered within context-aware BPM in a traditional way. This paper provides a separate concern of decisions from the process flow. We propose that the context-aware BPM ecosystem consists of four components which are: context-aware process models, context models, decision models and context-aware process execution. A framework is proposed to connect the IoT infrastructure to the context-aware BPM ecosystem using IoT-integrated ontologies and IoT-enhanced decision models, which enables the capabilities of IoT to make business processes and the decision making involved aware of the dynamic context.

The integrity of Internet of Things (IoT) devices has caused a fast spread in an era of data-driven decision-making across businesses. This tutorial survey provides a comprehensive review of current IoT data handling advances, focusing on data quality management (DQM). The article starts with the key aspects of IoT data management. In this regard, we shed light on the data source, volume and velocity, variety, lifecycle, security and privacy, scalability and distribution processing, anomaly detection, and energy efficiency. Then, We present a comprehensive taxonomy of IoT DQM based on the application type, such as smart cities, healthcare, agriculture, environmental monitoring, retail and supply chain, and smart grids (SGs). As IoT data processing, analysis, and security play a significant role in DQM; this tutorial survey carefully addresses how modern technologies maintain this role. More particularly, this work investigates the use of edge computing for real-time data processing and the incorporation of synthetic data to supplement restricted resources incorporating the issues of managing the massive datasets created by IoT implementations. In addition, the paper addresses the use of machine learning (ML) algorithms for in-depth analysis of IoT data streams, DQ evaluation protocols, and detection tactics during data transfer. Moreover, the article investigates complete security measures for protecting sensitive data, such as access control regulations and several security techniques, including authentication, encryption, and secure communication protocols that enable IoT data management. Besides, blockchain technology’s significant roles in this regard have been comprehensively addressed. Along with summarizing and reviewing the latest efforts in DQM in IoT-based systems, we shed light on their strong and weak points and discuss upcoming trends and potential difficulties in IoT data management. Last but not least, we continue by emphasizing the cumulative impact of these advances and shedding light on the open issues and challenges. Finally, this in-depth tutorial survey aims to be a significant resource for academics, practitioners, and stakeholders interested in the changing environment of IoT data management, with a particular emphasis on DQ.

IoT-based anomaly detection with the hybrid activation and the attention mechanism enabled LSTM-GRU

As the number of Internet of Things (IoT) devices and systems have surged, IoT data analytics techniques have been developed to detect malicious cyber-attacks and secure IoT systems; however, concept drift issues often occur in IoT data analytics, as IoT data is often dynamic data streams that change over time, causing model degradation and attack detection failure. This is because traditional data analytics models are static models that cannot adapt to data distribution changes. In this paper, we propose a Performance Weighted Probability Averaging Ensemble (PWPAE) framework for drift adaptive IoT anomaly detection through IoT data stream analytics. Experiments on two public datasets show the effectiveness of our proposed PWPAE method compared against state-of-the-art methods.

ABSTRACTContextSmart home devices have become increasingly popular in modern households, powered by the Internet of Things (IoT) advances. The data generated by smart devices can provide valuable insights into users' behavior and preferences. By analyzing the data, one can understand how people interact with their homes, thus creating a “smart home profile”. To comprehend the complete IoT ecosystem dynamics of an intelligent environment, it is necessary to learn from each IoT device to predict its status in the future time. Nevertheless, dealing with real‐world IoT data structure requires considerable preprocessing tasks and the employment of classifiers that can learn multiple IoT inputs from a single IoT message.ObjectiveAware of these challenges, this paper proposes a novel methodology to process multi‐label IoT data and provide a comprehensive comparison of multi‐label classifiers for forecasting the status of smart devices, considering their efficiency and accuracy.MethodWe propose a data transformation method to preprocess the IoT data to be used by multi‐label classifiers. This method is based on real data structure.ResultsWe evaluate our proposal in two real‐world scenarios and various multi‐label classifiers. The promising findings indicate that efficient classifiers can generate many correct predictions for a comprehensive IoT ecosystem in a small fraction of a second.ConclusionsOur proposed data transformation can fit the context of prediction to smart homes and work with multi‐label classifiers to understand user behavior.

In cybersecurity, the constant evolution of threats demands the development of next-generation Intrusion Detection and Prevention Systems (IDPS) to safeguard IT infrastructure and networks effectively. This research embarks on the journey of designing an innovative IDPS using a Dense VGG classifier, fueled by IoT data as its primary input source. Our approach combines the robustness of the Dense VGG architecture with the rich information generated by Internet of Things (IoT) devices, enhancing the system ability to detect and prevent intrusions. We gather diverse IoT data from sensors and devices within the IT infrastructure, ensuring the availability of labeled data that signifies known intrusion events. After meticulous preprocessing and feature engineering, we adapt the Dense VGG model, originally designed for image classification, to work with tabular IoT data. Transfer learning techniques are applied, leveraging pre-trained VGG models to expedite convergence and enhance performance. Real-time data streaming mechanisms are established to seamlessly integrate IoT data, making the system proactive in identifying threats. Upon detection, the system can respond by isolating affected devices, blocking suspicious network traffic, or initiating incident response protocols. Continuous monitoring and evaluation ensure the system reliability, with key metrics serving as indicators of its efficacy. Deployment considerations, such as scalability and redundancy, guarantee the system readiness to handle the influx of IoT data. Furthermore, integration with other security tools and compliance with regulatory standards strengthen the system overall cybersecurity posture. The core of our system lies in its intrusion detection logic, a set of rules and thresholds that trigger alerts or preventive measures based on model predictions. In testing, our system demonstrated an impressive intrusion detection accuracy of over 95%, significantly reducing false positives.

As devices for the Internet of Things (IoT) are typically battery-powered, energy efficiency is a major challenge for IoT networks. In this paper, we leverage the in-network caching of Information-Centric Networking (ICN) to propose a novel cooperative caching scheme, based on the IoT data lifetime and user request rate, to improve the energy efficiency of IoT networks. By caching IoT data at different nodes (such as content routers, base stations, etc.), IoT devices can stay in sleep mode for a larger portion of time and therefore reduce the overall energy consumption. With the help of an auto- configuration mechanism, the proposed IoT data Lifetime-based Cooperative Caching (LCC) scheme can dynamically adapt to the change of request rate. Extensive evaluations were performed and the simulation results show that LCC outperforms existing schemes in terms of total energy consumption reduction (up to 40%) and the reduction in the average number of hops traversed along the path (up to 20%), which is also directly related to the response time. Keywords- Internet of Things (IoT), Cooperative Caching, Information-Centric Network (ICN).

Internet of Things (IoT) system provides personalized services to users by analyzing the amount of data collected from billions of sensors. It is important to ensure the trustworthiness of the heterogeneous IoT data. To address this requirement, the data provenance needs to be used in the IoT system. In this article, we integrate the PROV-DM model into the IoT architecture and propose an IoT data provenance model. To avoid corrupting and forging the provenance information, we record the provenance data onto the blockchain. In addition, we use the IoT secure and computable data sharing system to illustrate that the proposed model can construct a provenance graph to detect which agent operates the IoT data abnormally. Finally, we implement and evaluate the proposed model on a public IoT database.

With one-tap check-ins, digital concierge services, voice-activated gadgets, chatbots, smart in-room technology, and advanced analytics, the hotel sector has been quickly embracing new technologies to meet and exceed consumer expectations and digitize the customer experiences. The traditional hotel management with internet-based customer service could not handle dynamic real-time data efficiently due to increased data volume. Thus, this work analyzes hotel management practices with the internet of things (IoT) and big data. The IoT and big data significantly impact the guest experience since businesses can provide consumers with unique services to their needs. Automated check-in and checkout, pre-booking, registration, and user-chosen payment methods are just a few self-services that improve the visitor experience. For enhancing visitor satisfaction and offering tailored services, this paper looks at how IoT and big data analytics can help the hotel sector. It moreover examines how IoT can be used within the business. This extended research finds excellent results in hotel management through IoT and big data. A client occupancy detection model (CODM) simulation scenario finds the best detection accuracy of 97.51%.

In order to improve the classification accuracy and shorten the classification time of mass data, a fast classification method of mass data in the Internet of things based on fuzzy clustering maximum tree algorithm is proposed. Reduce the dimension to process the mass data of the Internet of things, establish the time series of the mass data of the Internet of things, and complete the preprocessing of the mass data of the Internet of things. Extract the feature vector of the Internet of things mass data, and use the fuzzy clustering maximum tree algorithm to perform fuzzy clustering analysis on the Internet of things mass data, so as to realize the classification of the Internet of things mass data. The results show that the recall rate of the proposed method is as high as 97.5%, the root mean square error is only 0.030, and the classification time is only 12.3 ms.

The Internet of Things (IoT) is a emerging revolution technology that mankind has ever seen. IoT represents the future vision of computing and communication. IoT introduce the world of information and communication technology (ICT) for connectivity of anything at anytime from anywhere. Current Internet is considered as “Internet of People”, connecting the users of all over the world with various ways. IoT semantically means a network of interconnected things (Objects) with unique addressability and connected using a standard communication protocol technology. IoT can connect physical world to the virtual world. Data in IoT should be gathered by radio frequency identification (RFID), wireless sensor network (WSN) or electronic product code (EPC), etc. Recently scientists, academies and industries have put efforts to find a way for standard communication between physical object and real world, but there are many challenges in this area. This paper will represent the future of ubiquitous computing and showing the specific definition for standard architecture. Also we introduced two models architecture (3layer and 5layer architecture) in IoT. We will discuss the several challenges in IoT which recently researcher and architecture trying to solve these challenges. Finally we will be showing the Database scenario in IoT.