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Cities worldwide are facing the challenge of digital information governance: different and competing service providers operating Internet of Things (IoT) devices often produce and maintain large amounts of data related to the urban environment. As a consequence, the need for interoperability arises between heterogeneous and distributed information, to enable city councils to make data-driven decisions and to provide new and effective added value services to their citizens. In this paper, we present the Urban IoT suite of ontologies, a common conceptual model to harmonise the data exchanges between municipalities and service providers, with specific focus on the sharing mobility and electric mobility domains.

Bluetooth Low Energy (BLE) has become one of the enabling wireless technologies to facilitate the Internet of Things. Neighbor discovery is critical in BLE communications. BLE uses multiple (three) channels in neighbor discovery. It is challenging to achieve low-latency and low-energy-consumption BLE neighbor discovery due to the lack of analytical models for multichannel neighbor discovery. In this paper, we study BLE multichannel neighbor discovery for two advertising modes specified by BLE: periodic deterministic advertising (PDA) and pseudo-random delay advertising (RDA). We build two generic models, BLE 3-Circle model and BLE 1-Circle model, for characterizing BLE multichannel neighbor discovery. For PDA mode, we present a necessary and sufficient condition for BLE multichannel neighbor discovery, and provide a guideline for parameter setting. With the guideline, we derive the expected discovery latency in closed form, and demonstrate that the expected discovery latency is very close to a theoretical lower bound. For RDA mode, we build an analytical model based on Markov chain to accurately compute the expected discovery latency. Simulation and experimental results show accuracy of our analytical works. Interestingly, our parameter setting guideline works well for both PDA and RDA mode.

Internet of Things (IoT) has brought changes in many fields by joining physical space with the cyber space. The IoT devices are becoming increasingly complex. With the rapid deployment of cameras, tasks in IoT like visual information are more important, but IoT devices have limited computing resources, including power, computing ability, storage, etc. Some tasks that might be perfectly normal to perform on a computer would be rather challenging on an IoT device. Therefore, how to maintain acceptable performance while minimizing resources is becoming a more consequential part in IoT. In the paper, we aim to solve the problem of object detection and tracking in IoT while minimizing resources. The traditional algorithms need to use convolutional neural network (CNN) to identify different objects in each frame, and then determine the tracking target from identified objects, which typically requires a lot of computing resources. By incorporating spatial attention, and multi-domain network, we proposed a novel algorithm named as Spatial Attention Powered Multi-Domain Network (SA-MDNet). By adding spatial attention mechanism to the original MDNet model, and using multi class cross entropy loss, we are able to distinguish the background and the target in different video sequences effectively and efficiently. This novel algorithm achieves similar performance on the OTB 50/100/2013 datasets compared to several state-of-art models, while uses only a fraction of the memory compared to MDNet.

Recent advancements of Industrial IoT (IIoT) have revolutionized modern urbanization and smart cities. While IIoT data contains rich events and objects of interest, processing a massive amount of IIoT data and making predictions in real-time is challenging. Recent advancements in AI allow processing such a massive amount of IIoT data and generating insights for further decision-making processes. In this paper, we propose several key aspects of AI-enabled IIoT data for smart city monitoring. Firstly, we have combined a human-intelligence-enabled crowdsourcing application with that of an AI-enabled IIoT framework to capture events and objects from IIoT data in real-time. Secondly, we have combined multiple AI algorithms that can run on distributed edge and cloud nodes to automatically categorize the captured events and objects and generate analytics, reports, and alerts from the IIoT data in real-time. The results can be utilized in two scenarios. In the first scenario, the smart city authority can authenticate the AI-processed events and assign these events to the appropriate authority for managing the events. In the second scenario, the AI algorithms are allowed to interact with humans or IIoT for further processes. Finally, we will present the implementation details of the scenarios mentioned above and the test results. The test results show that the framework has the potential to be deployed within a smart city.

The phenomenal increase in the usage of Internet promotes the quality of trust in the scope of Internet of Things (IoT). Trust is beneficial in the provision of an effective, reliable, scalable, and trustworthy environment to users of the IoT network, where they can share their private information with each other on a secure communication platform. For successful communications among the Internet users, trust is an important factor to provide them with private infrastructures and secure environments, where exchanging data among devices becomes more easy and trustworthy. Therefore, trust management is a backbone for the successful and secure transmission of data among various nodes in a large-scale IoT network. To overcome the security issues, latency, and risk of malicious activities, a lightweight approach is proposed for those nodes in industrial IoT that cannot maintain security. LightTrust utilizes a centralized trust agent to generate and manage trust certificates that allow nodes to communicate for a specific time without performing trust computations. Trust agents also maintain a trust database to store the current trust degree for the aggregation/propagation purposes. Trust between two nodes is developed by direct observations in terms of compatibility, cooperativeness, and delivery ratio, whereas recommendations are used to develop trust in the context of indirect observations, i.e., experience or previous knowledge. The comparative simulations of the proposed and existing approaches are also performed whereby the results illustrate that the proposed approach efficiently maintains resilience and robust environments.

Federated Learning (FL) has attracted significant interest given its prominent advantages and applicability in many scenarios. However, it has been demonstrated that sharing updated gradients/weights during the training process can lead to privacy concerns. In the context of the Internet of Things (IoT), this can be exacerbated due to Intrusion Detection Systems (IDS), which are intended to detect security attacks by analyzing the devices' network traffic. Our work provides a comprehensive evaluation of Differential Privacy (DP) techniques, which are applied during the training of an FL-enabled IDS for Industrial IoT (IIoT). Unlike previous approaches, we deal with non-iid data over the recent ToN_IoT dataset and compare the accuracy obtained considering different privacy requirements and aggregation functions, namely FedAvg and the recently proposed Fed+. According to our evaluation, the use of Fed+ in our setting provides similar results even when noise is included in the federated training process.

The purpose of the present study is to comprehensively evaluate the present status of the Internet of Things (IoT) in fintech ecology and ahead. Study finds that the concept of communication between the devices and financial technology are not new yet only a considerable number of studies are done on IoT in fintech. Study highlights that both the IoT & fintech show an upward trend in the marketspace. Then study identifies that the increase in the demand for blockchain, internet, mobile network, cloud storage and IoT devices among the organization are the key drivers of IoT in fintech. Study also shows that the issues related to sustainable energy, digital payments and security are the current area of applications and challenges for IoT in Fintech. Thereafter study concludes that the growth of neurotech will influence the next generation IoT in fintech as the catalytic agent. Neurotech enabled IoT (NIoT) in Fintech will enhance the 3Ms (Man, Machine & Memory) relationship for business process innovation.

IoT enables the smart cities worldwide model. Smart houses, smart farming, smart surroundings, smart fitness, smart government, etc., are all kinds of intelligent communities. IoT is also used in the oil refining, gas mining and manufacturing sectors. IoT improving efficiency, optimizing prices, maximizes energy, maintaining predictions, and providing a great deal of convenience for people. Security risks are growing with increasingly heterogeneous systems and data processing. The main reasons for preventing IoT from flourishing are security and privacy issues. This paper contains an investigation of various security and privacy issues in Internet of Things.

The primary objective of university education is to provide students with the required information and skills to enable them to make a successful contribution to the national development effort. In order to evaluate the level of expertise and skill of students, this preparation includes frequent examination and appraisal in the form of tests. Manual exams are carried out by most universities; Post Graduate and Under Graduate in India. Due to multiple uses made of the outcomes of the students, these examinations have high stakes. Consequently, the experiments became subject to different types of malpractice. Test malpractice affects the success of students in exams and leaves the scores/grades earned inaccurate, hence a challenge to examination bodies and the nation's education system (India). These are all disturbing signs that pose a danger to the nation. Examination malpractice has eaten deep into the Indian culture of education. Therefore, the object of this paper is to explore the problem of examination malpractice in India. The paper covered relevant measures, procedure setting/implementation and suggestions for this action. And finally, a model is proposed to stop malpractices in the examination. This IoT based model is used for detecting any metallic object or object in a range more than normal IR proximity sensor range. The block diagram of proposed model and circuit diagram for that model discussed in detail. This proposed model may be used for any investigation in the universities examinations.

In smart farming sector, Internet of Things (IoT) based smart sensing systems are vulnerable to failure, malfunction, and malicious attacks. Also, sensors are deployed often in an alien and harsh environment. Here, the conditions are not well supportive which either causes the sensor to fail prematurely or gives unusual and erroneous readings, known as outliers. This effects the smart networks performance and decision-making ability in many ways. Therefore, it is important to accurately detect the IoT sensor behaviour in legitimate, faulty, and compromised or attack scenarios. To distinguish the sensor behaviour in different scenarios we have proposed a feasible approach using spatial correlation theory which is validated using Morans I index tool. We have used Classification and Regression Trees (CART), Random Forest (RF), and Support Vector Machine (SVM) models to test our approach. For real-time anomaly detection we have used an edge computing technology. We have compared the proposed approach, using Forest Fire real dataset, with the three existing recent works. Our results are promising in terms of accurate detection of IoT sensor behaviours in real-time. This will assist the precision farming industry in making better decisions to securely manage IoT field network, increase productivity, and improves operational efficiency.

Organic fertilizers have been used since the dawn of mankind to improve soil fertility and promote crop growth. However, the process of preparing organic fertilizers for use in agricultural activities is a labour-intensive and time-consuming procedure. Thus, in order to reduce labour costs and improve the efficiency of organic fertilizer preparation, automated organic fertilizer mixers were developed. For this research work, an improved organic fertilizer mixer based on the Internet of Things (IoT) technology capable of monitoring the status of fertilizer production remotely and providing updates and alerts to workers was developed. The aim of this work was to further reduce labour workload and costs, thus increasing efficiency of the organic fertilizer mixing process. In order to accommodate the IoT monitoring system, several modifications and design changes were carried out on the prototype of an automated organic fertilizer mixer. Tests conducted on the prototype unit showed that it performs as required. The IoT monitoring system successfully captures and uploads information on the weight of agricultural waste mixture in the storage drum to the IoT platform. This information can then be used to alert workers on when to add more agricultural waste material to the mixture or remove the processed organic fertilizer. Furthermore, an overall operational cost comparison of different organic fertilizer production methods showed that the automated organic fertilizer prototype provides operational cost savings of over 5 times when compared to current automated systems. Future work includes further automation to the agricultural waste filling and organic fertilizer removal process.

With the development of mobile networks and intelligent equipment, as a new intelligent data sensing paradigm in large-scale sensor applications such as the industrial Internet of Things (IIoT), mobile crowd sensing (MCS) assigns industrial sensing tasks to workers for data collection and sharing, which has created a bright future for building a strong industrial system and improving industrial services. How to design an effective worker selection mechanism to maximize the utility of crowdsourcing is the research hotspot of mobile sensing technologies. This paper studies the problem of least workers selection to make large MCS system perform sensing tasks more effective and achieve certain coverage with certain constraints being meeting. A many-objective worker selection method (MaOWS) is proposed to achieve the desired tradeoff and an optimization mechanism is designed based on the enhanced differential evolution algorithm (EDEA) to ensure data integrity and search solution optimality. The effectiveness of the proposed method is verified through a large scale of experimental evaluation data sets collected from real world.

The idea of the Internet of Things that everything is linked to anything and everything interacts with the other is a great idea, and that the topic of integrating these devices will add a remarkable change in this world and increase its prosperity, and this interconnection occurs through the exchange of data and information and facilitating our communication with the things around us. But questions about security and privacy remain. When this massive amount of data is exchanged, it will certainly be an attractive environment for attackers and hackers. In this paper, we will present the issues of security and privacy, their main requirements, potential risks and tools to defend these risks, and a detailed review of security attacks in order to be able to deter these attacks achieve confidentiality, security and reliability for users.

Since we have required so much food to be a major economic country in the world, existing or traditional farming methods and storage methods are not adequate to increase production and maintenance for future purposes. The Internet of Things in Agriculture leads to creativity in areas such as Crop Maintenance and Tracking, Animal Farming, Fishing, Millet Crops, and so on. This paper investigates the different technologies, protocols, sensors used in recent research in sustainable farming.

In the present world, women are the primary caretakers of everyone but the major problem that runs in every woman’s mind is about her safety & free from harassments and what every girl need is a proper freedom to go wherever she wants even in odd hours without worrying about her safety. Now-a-days women are being molested, harassed and even kidnapped in public places and public transport as well. So, the main aim of this project is to provide safety to every woman by using latest technology. This project mainly focuses on security of women so that they will never feel helpless in any situation. This project mainly consists of various modules such as Arduino, Raspberry Pi, 4G LTE + GPS module, camera and high voltage DC shock circuit. Now-a-days women are going through many issues & its high time where everyone should think about women safety. This project main intension is to develop a women safety jacket. The entire equipment is connected in the jacket, so that every woman can wear it everywhere.

In recent era, IoT-assisted robotics (IoRT) plays a major part in collaborating IoT sensor nodes, robots and human beings to establish a collaborative communication. In this paper, we develop an IoRT system with a modern architecture that encompasses the integration of IoT sensor nodes and robots in smart cities. The study develops a cloud platform to access smart vehicle mobility data in smart cities without collision. A framework is then developed to assist the robots and IoT sensor node in managing and making quick decisions on traffic collision. Hence, it is developed to connect, share, and exchange the information between the cloud and the IoT nodes/robots. The experimental validation shows improved traffic control and collision free traffic in smart cities than other smart city traffic management mechanism.

UVM is the most important mechanism in the field of SoC Verification. But complex characteristics, implementation difficulties in UVM still persist which leads to at most challenging part for the improvisation. To solve this issue, a new verification methodology called VEDA (Verification Environment for IP Architectures) which integrates python as the verification language is proposed. The powerful features of Python are used to bring the most versatile environment for verification and to make the UVM as the user friendly mechanism. The paper proposes the verification mechanism for the different IP’s which plays asignificant role in all IoT applications. Further the proposed method is being compared to existing UVM and also other existing python HDL tools and proved to be more advantages when compared to the existing methodologies.

The problems linked to overheads and resource usage have long been investigated with the increased use of virtualization technologies. Virtualization includes first instance expenses, security, software licensing and complexity. The whole virtualisation environment is directly affected by these characteristics. The virtual machines used in typical cloud implementations employ a whole operating system together with the separate memory that is not working in the high-performance distributed computing environment.

Many cities now have public trash cans that are overflowing due to the continual growth of rubbish. The dustbins are not cleaned on time by the bin collector. As a result, the city becomes unclean, the environment becomes contaminated, and the atmosphere becomes unsanitary. It distributes a foul odor in the waste, which may be harmful to our human health. To avoid this problem, keep the environment clean and create a healthy environment. The ultrasonic sensor detects the amount of trash in the trash can. We created a low-cost embedded device for city sanitation that allows the web server to track the location of each street. It’s simple to tell whether a dustbin is full or not. As soon as the level hits a certain maximum pre-setted limit, automatically the information is sent to the bin collector. The bin collector will use the internet to access their mobile phone and will take immediate action to clean up the city.

Federated Learning (FL) is an emerging paradigm through which decentralized devices can collaboratively train a common model. However, a serious concern is the leakage of privacy from exchanged gradient information between clients and the parameter server (PS) in FL. To protect gradient information, clients can adopt differential privacy (DP) to add additional noises and distort original gradients before they are uploaded to the PS. Nevertheless, the model accuracy will be significantly impaired by DP noises, making DP impracticable in real systems. In this work, we propose a novel Noise Information Secretly Sharing (NISS) algorithm to alleviate the disturbance of DP noises by sharing negated noises among clients. We theoretically prove that: 1) If clients are trustworthy, DP noises can be perfectly offset on the PS; 2) Clients can easily distort negated DP noises to protect themselves in case that other clients are not totally trustworthy, though the cost lowers model accuracy. NISS is particularly applicable for FL across multiple IoT (Internet of Things) systems, in which all IoT devices need to collaboratively train a model. To verify the effectiveness and the superiority of the NISS algorithm, we conduct experiments with the MNIST and CIFAR-10 datasets. The experiment results verify our analysis and demonstrate that NISS can improve model accuracy by 19% on average and obtain better privacy protection if clients are trustworthy.

The Internet of Things (IoT) pervades our lives every day and has given end users the opportunity of accessing personalized and advanced services based on the analysis of the sensed data. However, IoT services are also characterized by new challenges related to security and privacy because end users often share sensitive data with different consumers without precise knowledge of how they will be managed and used. To cope with these issues, we propose a blockchain-based privacy enforcement framework where users can define how their data can be used and check if their will is respected without relying on a centralized manager. The preliminary tests we performed, simulating different scenarios, show the feasibility of our approach.

Internet of Things (IoT) refers to a technology where computing devices are connected and form a network. IoT faces many security and privacy issues due to less computation power, heterogeneity, and limited resources available with its devices. Data is transferred among these devices with little or no human interaction. Data Confidentiality and Integrity are very critical parameters and can be achieved by securely sharing information in IoT scenarios. Managing and maintaining trust in exchanging information over IoT becomes very significant. Recent researches have focused on the applications of Blockchain technology for assuring trust management in IoT networks. Blockchain provides completely distinct and more secure approaches. This survey paper aims to illustrate the significance of integrating Blockchain technology in the IoT environment to ensure trust among IoT devices. Particularly, first we give an overview and security aspects of IoT and Blockchain technologies. Then we trace out some important challenges and issues of trusted IoT with potential solutions by Blockchain. Following this, we highlight some complications in the integration of Blockchain with IoT. Finally, we present a comparative analysis between traditional and Blockchain-based trust management techniques as proof of work to represent the significance of Blockchain in ensuring trust.

With the significant impact of Internet services and the evolution in the hardware and software fields, the Internet size and usage scope are continuously covering smaller and resource-constrained devices. Such devices, commonly called IoT devices, with sensing/actuation capabilities, are now capable of managing the complexity of communications over the Internet. However, considering the current IoT ecosystem, this field is still fragmented with respect to the communication technologies, protocols, and data formats used by the different providers. This heterogeneity makes IoT devices/systems unable to communicate seamlessly, leading to limited cooperation and tightly coupled deployments. To overwhelm the interoperability barriers in IoT, we propose, in this paper, a Cloud-based approach with a Dynamic Domain Name System (DDNS) mechanism enabling the IoT devices to communicate using the REST model. An approach that follows the Web of things (WoT) paradigm. In particular, the system enables the IoT devices' hosted resources (e.g., sensors and actuators) to be steered using globally resolvable (over the Internet) Uniform Resource Locators (URLs) even when deployed behind Network Address Translators (i.e., NATs). Besides, the solution considers security aspects related to data transmission by leveraging the use of HTTPS with a Dynamic machnism managing Domain Validation (DV) certificates.

Gateways in the Internet of Things (IoT) play a decisive role in routing the preprocessed filtered data to cloud platforms. While investigating gateways, the need for a comprehensive systematic literature review that can elaborate the working and functionalities of IoT gateways was felt. This paper presents a systematic literature review of IoT gateways in general and smart gateways in particular. It considered papers published over the last 10 years, i.e., from 2011 till July 2021. A methodical literature analysis technique is used in this review; out of 2347 papers, 67 articles are selected for complete analysis based on well-defined criteria. The survey starts with the broad categorization of gateways in IoT as basic and smart gateways. Further, smart gateways are sub-divided into three categories as passive gateways, semi-automated and fully-automated gateways. The survey is performed based on well-defined criteria, which include: the type of gateways, their requirements, tools/platforms, the approach adopted, evaluation and their application domain. This paper demonstrates further bifurcation of functional requirements of IoT gateways. Research gaps and open issues have been identified in the area. Future prospects have accordingly being suggested to help academicians embark on advanced research.

Urban crowd flow prediction is very challenging for public management and planning in smart city applications. Existing work mostly focuses on spatial and temporal dependence based flow prediction that are not well suited for predictions of instantaneous flow change usually due to social emergency incidents and accidents. In this paper, we propose an Online to Offline Interaction based Dilated Casual Convolutional Neural Network framework (O2O-DCNN) for crowd flow prediction. Both online attention behavior and offline crowd shift factors are considered, in case to capture the dependence between them and make more accurate predictions especially for instantaneous flow variations. The online and offline features are processed by dilated casual convolutions and then put into CBOW model based full connected network to make interactions. The performance evaluations are based on realistic User Detail Record (UDR) dataset of a southern city in China provided by China Unicom. O2O-DCNN is compared with the other related baselines in terms of MASE and MAE. The results show that our framework is with much better accuracy, especially for instantaneous flow variation scenario.

Background: Localization is an important area of implementation of the internet of things based on Wireless Sensor Networks. Outdoor user tracking is possible using the global positioning system; however, the global positioning system accuracy decreases in indoor environments. To overcome this problem, the wireless sensor network is used in internet of things-based technology for localization. Objective: The wireless sensor network-based indoor localization is categorized into two categories; rangebased and range-free localization. In range-based localization, it first computes the relative distance and then calculates relative coordinates. In range-based techniques, distance and position are calculated. The internet of things-based localization uses the range-based and range-free techniques of a wireless sensor network to localize any object. The Light Dependent Resistor-based localization work has been proposed in previous research. In this research, the light-dependent resistor traces a person’s entry /exit event as the person switches ON/OFF lights of the building. However, it was not a sufficient effort to localize a person using light dependent resistor. To overcome the problem of light dependent resistor, the two PIR sensors have been used in each room along with one RFID based approach in this study. Methods: An indoor scenario has been considered in this study. The hardware setup has been configured to trace the user. When a user enters inside a building, he will switch on the lights, and the light sensor records the light intensity and gives some reading. The difference in the reading of the light sensor (before switching ON the light and after switching OFF the light) gives some clue about a user in an indoor scenario. Nevertheless, if the lights of many rooms remain switched ON, then the user cannot be localized using the above method. In order to sort out this ambiguity of light sensors, two passive infrared sensors in each room along with one radio frequency identification-based model have been proposed in the present study. Implementing the single-user localization using a light-dependent resistor sensor becomes erroneous if a person moves from one room to another and the lights get turned ON/OFF. Moreover, the LDR-based model is affected by sunlight during the daytime. Results: As the implementation of passive infrared sensors along with one RFID-based localization technique gives 93% efficiency, the light-dependent resistor-based localization system gives an efficiency of 35%. Conclusion: The light-dependent resistor-based approach is prone to more errors because the user may enter multiple rooms while the lights of each room remain ON. To overcome this problem, a passive infrared sensor and radio frequency identification-based approach for a single user indoor localization has been proposed. The proposed techniques are easy and cost-effective for implementation. The results show that the proposed technique provides better localization accuracy than a light-dependent sensor-based technique for single-user indoor localization.

With ever-expanding computation and communication capabilities of modern embedded platforms, Internet of Things (IoT) technologies enable development of Reconfigurable Manufacturing Systems---a new generation of highly modularized industrial equipment suitable for highly-customized manufacturing. Sequential control in these systems is largely based on discrete events, while their formal execution semantics is specified as Control Interpreted Petri Nets (CIPN). Despite industry-wide use of programming languages based on the CIPN formalism, formal verification of such control applications in the presence of adversarial activity is not supported. Consequently, in this paper we focus on security-aware modeling and verification challenges for CIPN-based sequential control applications. Specifically, we show how CIPN models of networked industrial IoT controllers can be transformed into Time Petri Net (TPN)-based models, and composed with plant and security-aware channel models in order to enable system-level verification of safety properties in the presence of network-based attacks. Additionally, we introduce realistic channel-specific attack models that capture adversarial behavior using nondeterminism. Moreover, we show how verification results can be utilized to introduce security patches and motivate design of attack detectors that improve overall system resiliency, and allow satisfaction of critical safety properties. Finally, we evaluate our framework on an industrial case study.

The Internet of Things (IoT) is an emerging paradigm and has penetrated deeply into our daily life. Due to the seamless connections of the IoT devices with the physical world through the Internet, the IoT applications use the cloud to store and provide ubiquitous access to collected data. Sharing of data with third party services and other users incurs potential risks and leads to unique security and privacy concerns, e.g., data breaches. Existing cryptographic solutions are inapt for resource-constrained IoT devices, because of their significant computational overhead. To address these concerns, we propose a data protection scheme to store the encrypted IoT data in a cloud, while still allowing query processing over the encrypted data. Our proposed scheme features a novel encrypted data sharing scheme based on Boneh-Goh-Nissim (BGN) cryptosystem, with revocation capabilities and in-situ key updates. We perform exhaustive experiments on real datasets, to assess the feasibility of the proposed scheme on the resource constrained IoT devices. The results show the feasibility of our scheme, together with the ability to provide a high level of security. The results also show that our scheme significantly reduces the computation, storage and energy overheads than the best performed scheme in the state-of-the-art.

Nowadays, with the advancement of smart technologies, the Internet of Things (IoT) emerged as a booming technology that can provide better quality and facilities for the residents of smart cities. Smart cities can offer several services and have several applications in healthcare, transportation, education etc. Despite such a potential vision, the privacy of users on these IoT devices is a major concern. Most authentication schemes do not provide privacy and anonymity to legitimate users. to tackle this problem, we propose an efficient Zero Knowledge-based authentication scheme in the paper that authenticates devices on the network without knowing the information about user identity or revealing any other data entered by users. To explain our system framework at the micro-level, we apply our privacy-preserving scheme to IoT based healthcare applications, but it can easily be extended to the more general use cases where privacy-preserving authentication is required. This paper’s second major contribution is designing the data encryption algorithm ZKNimble that is mainly suitable for lightweight devices. Once the user is authenticated using Zero Knowledge Proof, the ZKNimble cipher can be used for legitimate users’ encryption and decryption processes.

Water conservation is critical because it is one of the most significant factors for all species' survival. According to the Central Ground Water Board's estimates, underground water will be scarce in 15 Indian states by 2025 [1]. However, as per the report from Ministry of Environment and Forest, only 10 to 20% rainwater is harvested. In urban areas, it is seen that nowadays concrete structures are constructed everywhere, so it is not possible to recharge the water table by natural process of raining. All the rainwater which is one of the sources for fresh water goes to drain. This causes the depletion of groundwater level which may be fatal in the future. Therefore, a rainwater harvesting system is required in Indian household to conserve water. On the other hand, the urban cities are having high pollutant levels which in turn affecting the quality of water. The government has defined certain parameters for quality checking of water. With the aid of the suitable sensors and IoT boards, quality checking, monitoring and water segregation can be easily achieved. In this work, an efficient and cost effective technique of rainwater harvesting has been presented. Some real case studies have been done at Indian houses in order to validate the effectiveness of system in terms of its implementation and cost analysis. As a result, the proposed system becomes one of the smart solutions to address the modern problem of rainwater harvesting with water quality management in low cost.

Traditional malware propagation control schemes do not prevent device-to-device (D2D) malware spread, have high time cost, and may result in low probability of detecting compromised devices. Moreover, the unprecedented scale and heterogeneity of Internet of Things (IoT) devices make these schemes inapplicable to IoT networks. Therefore, to rectify these issues, this article presents a secure patching framework for IoT with different network isolation levels to efficiently mitigate and control malware propagation. It uses remote attestation to detect compromised devices with a high probability and identify the origin of malicious activities. It also proposes virtual patching of devices via physical unclonable functions (PUFs) to contain the malware spread. The isolation levels are based on the susceptible, exposed, infected, and resistant (SEIR) model that act as an access control list to quantify device operation and mitigate D2D malware spread. We present a security analysis based on the access control logic model. A performance evaluation with a comparative analysis is also discussed using the SEIR model. These analyses confirm the reduction in patching time and superior performance of our framework, i.e., with 10% of initially infected devices, IoT-Proctor had a reduction rate of malware five times faster than the existing techniques.

The ever growing Internet of Things (IoT) paradigm aims to put people’s incorporation into the new phase of connectivity and sensor technology. While, IoT has the potential to deliver new value-added services in order to make life easier and healthier for people, there are still several issues to be addressed in order to harness the widespread dissemination and adoption of the IoT paradigm, considering its potential benefits. In this context, considering an IoT ecosystem holistically i.e. end-to-end which includes the proprietary Operational Technology (OT) comprising of software and hardware to monitor, detect and control the equipment through sensors and actuators and the general Information Technology (IT) which comprises of the data center infrastructure catering to the backend needs of IoT such as compute, storage and network, one major challenge is converging OT with IT. Such an OT/IT convergence has the potential to explore many problems that exist in the end-to-end IoT ecosystem. One such problem is related to drawing actionable insights through operational analytics. The data center paradigm which concerns this research is the Software-Defined Data Center (SDDC) which is touted as the preferred data center paradigm for IoT. While most of the research happening on IoT today deals with the data from the IoT applications and sensors, this research focuses on the plethora of the metadata contained in the end-to-end IoT ecosystem. This research demonstrates the modelling of the end-to-end IoT ecosystem using semantic-based approach such as ontology. We arrive at a cohesive unified ontology unifying the OT and the IT constituents of the IoT fabric with SDDC together with rich context aware attributes embedded in the unified ontology, thereby addressing the problem of OT/IT convergence as well as laying the foundation to explore many solutions to problems pertinent in such a converged OT/IT ecosystem.

Edge computing is a paradigm that extends cloud computing with storage and processing capacity close to the edge of the network that can be materialized by using many fog nodes placed in multiple geographic locations. Fog nodes are likely to be vulnerable to tampering, so it is important to secure the functions they provide. A key building block of many distributed applications is an ordering service that keeps track of cause-effect dependencies among events and that allows events to be processed in an order that respects causality. In this paper we present the design and implementation of a secure event ordering service for fog nodes. Our service, named Omegae, leverages the availability of a Trusted Execution Environment (TEE) based on Intel SGX technology to offer fog clients guarantees regarding the order in which events are applied and served, even when fog nodes are compromised. We have also built OmegaKV, a key-value store that uses Omega e to offer causal consistency. Experimental results show that the ordering service can be secured without violating the latency constraints of time-sensitive edge applications, despite the overhead associated with using a TEE.

The world is entering a new era with ubiquitous connectivity among billions of humans and machines, i.e., the 6G massive Internet of Things (IoT). Radar and communication need to be integrated into this system to achieve target detection and massive connectivity. In this work, we first develop a shape-adaptive antenna array composed of multiple sub-arrays spaced at regular intervals. The shape-adaptive antenna array uses flexible material that enables changes in the physical structure to achieve adaptive gain. Subsequently, we investigate the micro-movement of IoT targets, such as the micro-motion characteristics and micro-Doppler effects of rotary-wing drones. The simulation results demonstrate the effectiveness and efficiency of the proposed schemes for detecting UAVs in an IoT scenario.

Deteriorating water quality leads to the freshwater biodiversity crisis. The interrelationships among water quality parameters and the relationships between these parameters and taxa groups are complicated in affecting biodiversity. Never-theless, due to the limited types of Internet of Things (IoT) sensors available on the market, a large number of chemical and biological parameters still rely on laboratory tests. With the latest advancement in artificial intelligence and the IoT (AIoT), this technique can be applied to real-time monitoring of water quality, and further conserving biodiversity. In this paper, we conducted a comprehensive literature review on water quality parameters that impact the biodiversity of freshwater and identified the top-10 crucial water quality parameters. Among these parameters, the interrelationships between the IoT measurable parameters and IoT unmeasurable parameters are estimated using a general regression neural network model and a multivariate polynomial regression model based on historical water quality monitoring data. Conventional field water sampling and in-lab experiments, together with the developed IoT-based water quality monitoring system were jointly used to validate the estimation results along an urban river in Hong Kong. The general regression neural network model can successfully distinguish the abnormal increase of parameters against normal situations. For the multivariate polynomial regression model of degree eight, the coefficients of determination results are 0.89, 0.78, 0.87, and 0.81 for NO3-N, BOD5, PO4, and NH3-N, respectively. The effectiveness and efficiency of the proposed systems and models were validated against laboratory results and the overall performance is acceptable with most of the prediction errors smaller than 0.2mg/L, which provides insights into how AIoT techniques can be applied to pollutant discharge monitoring and other water quality regulatory applications for freshwater biodiversity conservation.