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With the development of the Internet of Things, multi-access edge computing (MEC) is widely used in industrial IoT, intelligent transport systems, and IoT data management. As a decentralized, tamper-proof, multiparty trusted ledger technology, blockchain can enhance MEC security, privacy, and automatic use of resources. However, highly redundant storage and complex consensus prevent the blockchain from being directly deployed to edge computing nodes with limited resources. Unlike existing lightweight schemes, which do not consider liberating transaction data from blocks, this paper proposes a lightweight blockchain ledger protocol for MEC networks. The novelty of this work is that the transaction data in the block is compressed into a succinct proof through the zero-knowledge proof algorithm. To reduce the storage of block data, this paper proposes the proof-of-state-transfer model in which nodes verify the correctness of the global state transfer in the block without transactions. To reduce the communication complexity of consensus, this paper proposes a lightweight consensus protocol, which reduces the communication complexity of block voting and view switching to O (n). This paper develops the prototype system of TinyLedger to experiment and evaluate the performance of the system in terms of storage, verification, throughput, and communication. Experimental results show that compared to Ethereum, TinyLedger reduces storage capacity by 98.16%, communication volume by 74.53%, and block verification time by 87%. [ABSTRACT FROM AUTHOR]

• Proposing a novel reversible QCA-based half-adder structure based on the coplanar layout and simple quantum cells, focusing on low occupied area and energy consumption;. • Recommending a single-layer reversible QCA-based full adder design based on the coplanar layout and simple quantum cells with emphasis on the low-occupied area and low latency;. • Suggesting the reversible QCA-based multiplier design using the suggested structures for IoT devices such as printed devices and microcontroller units;. • Designing the proposed reversible QCA-based multiplier using a powerful quantum tool for evidence of its near zero-energy dissipation. An enormous variety of items and things are connected via wired or wireless connections and specific addressing schemes, which is known as the Internet of Things (IoT). However, IoT devices that adopt aggressive duty-cycling for high power efficiency and prolonged lifespan necessitate the incorporation of ultra-low power consumption always-on blocks. The multiplier plays a crucial role in enhancing the capabilities of low-power IoT devices, particularly those operating with energy-efficient batteries that offer extended battery life. The previous multipliers have a struggling speed, enormous occupied area, and high energy consumption; therefore, all prior flaws must be fixed by implementing it in a suitable technology, like the quantum computing. Therefore, this paper examines the ultra-low power circuit for nano-scale IoT platforms. It also suggests novel quantum-based adders for multiplier structure. The proposed designs are simulated using the QCADesignerE 2.2 tool by focusing on energy-efficient and occupied areas for miniaturizing IoT systems. [Display omitted] [ABSTRACT FROM AUTHOR]

The Internet of Nano Things (IoNT) is increasingly recognized as a superior alternative to the Internet of Things (IoT) due to its inherent benefits. However, the security of IoNT remains an open challenge that needs to be addressed at various levels. Given the highly sensitive data shared in IoNT, the development of security protocols becomes imperative. Despite the emergence of some methodologies, many security aspects have been overlooked, and no concrete cryptosystems have been developed. In this paper, we propose an Identity-Based Encryption (IBE) scheme built on the Learning With Errors (LWE) assumption. We design a lightweight protocol with the consideration of resource-constrained devices involved in the IoNT network. The proposed scheme is resistant to various attacks, including side-channel analysis, replay attacks, man-in-the-middle attacks, plaintext and ciphertext attacks, ensuring unforgeability. Additionally, performance evaluation indicates that the proposed scheme outperforms existing ones in terms of computational and communication costs. [ABSTRACT FROM AUTHOR]

Internet Protocol version 6 (IPv6) address configuration is needed to meet the needs of current portable devices and wearable applications, where each device needs its own unique identification number. High complexity in Wireless Sensor Network (WSN) and Internet of Things (IoT) environments has increased challenges, such as low energy consumption and high overhead in IPv6 address configuration. The proposed IPv6 generation is based on a hybrid mathematical approach that retains the IP address's confidentiality and prevents Denial of Service (DoS) attacks from new and existing nodes in the network. In this paper, the proposed hybrid protocol for DoS attacks from new and existing nodes is analyzed. It is evident from the results that the rate of successful configuration, overhead, and energy consumption is found to be better than the existing approach, namely, Programming Protocol-independent Packet Processors Duplicate Address Detection (P4DAD), match prevention technique, etc. • Focuses on IPv6 using a hybrid Duplicate Address Detection (DAD). • Reduces the energy consumption of the IPv6 as compared to P4DAD, etc. • Reduces the long wait time compared to EUI-64. • ASR is investigated for the Hybrid DAD under three different scenarios [ABSTRACT FROM AUTHOR]

• An unequal grouping algorithm is introduced in this work to address the energy-hole problem in Internet-of-things (IoT) and wireless sensor networks (WSNs). • The work introduced an energy level-based group head selection model that considers multi-objective parameters such as hop count, energy, and packet loss ratio. • An energy and delay aware routing optimization mechanism is introduced to reduce delay and minimize energy consumption. • Finally, a multipath-based route is established, and effective load balancing is done for transmitting packets with QoS (quality of service) and without QoS. • The existing methods' performance is generally studied considering total node death; however, the EDAR model is studied considering different scenarios such as first node death and total node death. • The simulation results show the EDAR is very efficient in reducing communication overhead, delay, enhanced lifetime, and improved throughput in comparison with the recent state-of-art routing mechanisms such as low energy adaptive clustering hierarchy (LEACH), unequal clustering algorithm (UCA), and reliable and energy efficient routing (REER). The adoption of modern technology into farming has seen significant expansion to enhance the quality and quantity of crop production. In this research work, wireless sensor networks (WSNs) and the Internet of Things (IoTs) are integrated to provide a real-time crop monitoring system to farmers. Usually, battery-powered IoT devices equipped with sensors like temperature, humidity, motion sensors, etc. are placed across agriculture areas for real-time monitoring of crops and cattle. In improving energy efficiency and reducing delay clustering multi-hop routing has been used with good effect; However, the existing methods failed to enhance network lifetime and target coverage due to failure in addressing load-balancing and hotspot problems. In addressing research problems this paper presents an Energy and Delay Aware Routing (EDAR) scheme for heterogenous WSN-IoTs leveraging unequal grouping of networks and adoption of multipath routing for provisioning IoT applications. The simulation outcome shows the EDAR model achieves very good performance in terms of throughput and lifetime performance with minimal delay and communication overhead. Graphical abstract of energy and delay aware routing design. [Display omitted] [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) consists of heterogeneous devices such as wearables and monitoring devices that collect data to provide autonomous decision-making and smart applications. IoT technologies, such as the Internet of Medical Things (IoMT), have become gradually popular for medical purposes, combining IoT and medical devices to achieve good health and well-being. However, IoMT devices are often tight and have resource constraints, which leads to limited local data processing in the device. Edge computing provides access to additional computation and storage resources for IoMT devices, bringing intelligent processing closer to the data sources. This technology opens up great possibilities for IoMT applications, especially when combined with Artificial Intelligence (AI). Edge AI runs AI computations close to the IoT devices and users instead of centralized services such as cloud servers. This paper investigates the potential of Edge AI and IoMT. In this sense, this survey is the first work to further detail Edge AI and Machine Learning Operations in IoMT domains and wearable technology, thus contributing to the literature by comprehensively exploring the potential of ML strategies and operations at the network's edge and intelligence distribution. This study also presents a case study on heart anomaly detection. [ABSTRACT FROM AUTHOR]

• Collaborating on data collection through multi-mobile sinks reduces the average delay. • Multiple agents acquire optimal strategies through interactions with environment. • Improved experience replay strategy solves the problem of inefficient sampling. • The path planning algorithm increases the task completion rate in complex environment. As one of the most promising research areas in the field of reinforcement learning, multi-agent collaborative decision-making faces the challenge of real-time control in dynamic environments. This paper focuses on the path planning problem of multi-mobile sink collaborative data collection in the Internet of Things. Based on the excellent collaborative decision-making performance of multi-agent deep deterministic policy gradients (MADDPG), a novel path planning algorithm is proposed in the constructed wireless sensor network model. Furthermore, a combined prioritized experience replay strategy is designed to increase the utilization of important experiences in MADDPG. Extensive experiments conducted in various conditions show that compared with traditional MADDPG and MADDPG with prioritized experience replay, the proposed algorithm demonstrates the best performance. The accelerated convergence speed, enhanced training effectiveness and shortened paths indicate the capability of the proposed algorithm in multi-agent collaborative path planning problem. [Display omitted] [ABSTRACT FROM AUTHOR]

The rapid growth of the Internet of Things (IoT) highlights the importance of lightweight cryptography in maintaining security. However, enhancing performance while ensuring the same level of security remains a significant challenge. This paper presents two innovative architectures for the CRAFT lightweight block cipher, aiming to enhance performance without compromising security. The novel Serial and Unrolled architectures are introduced to achieve low area usage and high throughput, respectively. Specifically, the Serial architecture reduces the datapath from 64-bit to 4-bit, significantly decreasing the area. The Unrolled architecture, on the other hand, minimizes latency from 32 to 16. Additionally, Boolean satisfiability (SAT) solvers are employed to identify a lower-cost area implementation of the S-Box. The proposed designs underwent evaluation on three distinct FPGA platforms: Artix-7, Kintex-7, and Spartan-7. The results show that the low area design reduces area usage by 10.16% compared to the previous design. Additionally, the S-Box implementation achieves a significant area reduction of 28.9%. On the other hand, the unrolled design enhances the maximum throughput by 40.53% compared to the previous design. Therefore, the proposed designs could offer enhanced performance while maintaining security for IoT devices. [ABSTRACT FROM AUTHOR]

Digital Twin (DT) technology has emerged as a robust mechanism for overseeing and improving the lifecycle processes of the Industrial Internet of Things (IIoT) by creating their virtual counterparts. The widespread employment of cloud computing is apparent in delivering computational and storage services for the DT within the IIoT environment. Ensuring the confidentiality of DT data is crucial since data owners, cloud service providers, and users operate across separate trust domains. Accordingly, public key encryption is a viable solution for managing access. However, traditional public key encryption encounters challenges in achieving effective search and secure sharing of encrypted DT data in the cloud. This paper introduces a proxy re-encryption scheme with plaintext checkable encryption for data access control (PRE-IBSC-PCE), which addresses the challenges associated with searching for and sharing encrypted digital twin data in the cloud. The PRE-IBSC-PCE achieves efficient data search and sharing while ensuring privacy, integrity, non-repudiation, and authenticity. Meanwhile, we prove that PRE-IBSC-PCE is secure under the decisional bilinear Diffie–Hellman assumption in the random oracle model. Lastly, when compared to similar schemes, the performance analysis of the PRE-IBSC-PCE is more efficient and suitable for DT applications in IIoT. [ABSTRACT FROM AUTHOR]

Many IoT devices are presently in use without sufficient security measures. The vulnerability of these devices to malware highlights the necessity for effective methods to identify malicious activity while protecting data privacy. Federated Learning is a collaborative learning technique that can create a fair model while keeping the local data private. To this end, this paper presents the SIM-FED, a novel model for detecting malware in IoT devices. This model utilizes both deep learning and federated learning algorithms. The SIM-FED offers a privacy-preserving solution for IoT malware detection, without sharing data, enhancing security in distributed environments. The model utilizes a lightweight one-dimensional CNN with optimized hyperparameters, reducing preprocessing, and computational overhead. Various federated aggregation strategies are evaluated, and the FedAvg strategy is selected to integrate the results of local models in the proposed model. Moreover, the model's resilience against white-box and black box cyber-attacks is assessed, revealing minimal performance degradation and highlighting its robustness, which is often overlooked in previous studies. The performance of the SIM-FED is assessed by conducting a series of experiments using the IoT-23 dataset. The results show that the SIM-FED outperforms other deep learning and federated learning models in terms of all evaluation metrics, achieving a remarkable 99.52 % accuracy. [ABSTRACT FROM AUTHOR]

• The authors proposed a novel KNN classifier-based H.265 protocol with a single buffer model in this paper. Multiple sensors are placed at the transmitter and receiver base stations to exchange data efficiently and accurately. • The data transmission performance is measured using collision error, propagation error, sensing error, and visual security with encryption. • The proposed novel MS-KNN classifier-based single buffer technique with H.265 protocol performs better than the existing LE-based approach. The Hidden Markov Model-based queuing technique with CD, VS, and SP estimates the sensor performance. • There is a significant improvement for the proposed MS-KNN-based single buffer method in the parameters of sensing error, propagation error, Busy receiver error, and collision error are 2%, 3%, 3%, and 5%, respectively, at 200 meters distance when compared to LE-single buffer technique. • Similarly, significant improvement in received packet ratio at 10 dBm, 20 dBm and 30 dBm is 4%, 2% and 2%, respectively. Hence, the proposed single buffer method performs better than the existing double buffer method in the case of sensory errors and received packet ratio at various levels. Fifth Generation (5G) communication access technology has been implemented to provide highly reliable and efficient video data streaming in telemedicine applications. The Internet of Things (IoT) advancement enhances the 5G network for smart healthcare services and applications. The existing research work focused only on Lagrangian Encoder (LE) based video compression technique with H.265 Protocol for video data transmission in 5G networks for telemedicine applications. This paper proposes a novel KNN classifier-based H.265 protocol with a single buffer model incorporated with multiple sensors for telemedicine applications. The proposed multiple sensors are placed at the transmitter and receiver base stations to exchange data efficiently and accurately between transmitter and receiver devices. The data transmission performance is measured using collision error, propagation error, sensing error, and visual security with encryption for the proposed and existing methods. The performance of the proposed model is compared with the existing LE-based single buffer and identifies the proposed KNN classifier-based single buffer with a multi-sensor technique that performs better. [Display omitted] A buffer with the sensor is placed at the receiver device to sense the data availability in the buffer using energy consumption level. If the energy consumption falls below the threshold level or half, the sensor sends the request notification to the Transmitter sensor. [ABSTRACT FROM AUTHOR]

The key goal of Internet of Things (IoT) has been the provision of value-added services based on the ubiquitously available smart devices that can offer diverse services by interacting with each other. However, the paradigm has evolved to its next phase, Social Internet of Things (SIoT), with the inception of an idea to empower these devices with consciousness. This cognizance enables these smart devices to socialize with each other based on shared context and mutual interests. The Social Internet of Vehicles (SIoV) applies SIoT concepts in the vehicular domain to revolutionize the existing ITS (Intelligent Transport System) by adding value to existing VANET (Vehicular Ad-hoc Network) technology. This paper presents a scalable SIoV architecture based on Restful web technology. Furthermore, this paper emphasizes the importance of web technology to meet the required interoperability to support the composition of numerous services. The paper also discusses the enabling technologies and protocols. [ABSTRACT FROM AUTHOR]

The concept of an Internet of Things is already mature, enough to start evolving towards an Internet of Everything, over which humans, objects and virtual items can be interconnected. For this to be feasible though, there is the need for a framework which not only supports the interconnection between the entities of this Internet, but also allows the meaningful interaction between them. In this paper, we present an idea on how this can be materialized, based on the use of semantic web technologies and smart software agents. These middleware technologies, combined with machine learning techniques, can lead to cognitive friendship and goal management. The paper includes a presentation of the theoretical framework, a use case scenario, and results over the use of a simulation tool. [ABSTRACT FROM AUTHOR]

With the emergence of Internet of Things (IoT) and allied applications for smart cities, sustainability goals have seen a prominent emphasis. This paper focuses on the energy efficiency aspect of such sustainable smart city goals. Although energy efficiency has been studied at different levels of a smart city's Information and Communication Technology (ICT) infrastructure, this paper specially focuses on device level energy minimization strategy by means of modelling the energy consumption while accounting for the Clusterheads (CluH) and duty cycling and thereby using evolutionary algorithms. In this paper, a Genetic Algorithm (GA) and a hybrid Artificial Neural Network based Particle Swarm Optimization (PSO), namely Feed Forward Neural Network based PSO(FFNN-PSO) has been used to solve the energy minimization problem. Simulation experiments carried out for different scenarios with varying configuration demonstrate the efficacy of the hybrid neuro evolutionary scheme. [Display omitted] • IoT-based energy optimization model for smart city scenarios. • Genetic Algorithm (GA) and a hybrid Artificial Neural Network based Particle Swarm Optimization (PSO), namely Feed Forward Neural Network based PSO(FFNN-PSO). • Several output metrics, such as the number of alive nodes, load, residual energy, and cost function, were used to pick the best cluster head nodes in IoT network clusters. • The proposed method enacts an intelligent duty cycling by predicting sleep–wake cycles. [ABSTRACT FROM AUTHOR]

Data collected from densely deployed IoT sensor nodes via UAVs may incur remarkable redundancy, unnecessarily wasting the UAV power. This paper introduces a framework of UAV trajectory planning for optimizing energy efficiency via data-similarity-based node selection while maintaining sufficient information integrity out of the refined data. The proposed framework consists of three phases, namely data similarity determination, redundant nodes removal , and UAV trajectory planning. In particular, we propose a sliding-window dynamic time warping (SDTW) algorithm to quantify the data similarity between nodes. Then a hybrid genetic ant colony algorithm (HGACA) is introduced for the redundant node removal, where data similarity and UAV energy consumption are jointly considered. Finally, we formulate the trajectory planning problem as a three-stage integer linear programming (ILP) model, which clusters the nodes with minimal overlap and finds the shortest UAV cruising path that traverses each cluster head once and only once. The simulation result demonstrates that the proposed framework outperforms all the considered counterparts under various threshold values of data similarity in terms of execution time and power consumption while maintaining information integrity. [ABSTRACT FROM AUTHOR]

• Scheduling IoT service by hybrid optimization of Particle Swarm and Chemical Reaction. • Offline resource provisioning in an environment comprising both fog and cloud servers. • Minimizing service delay and delay violation by provisioning on faster nodes. • Minimizing communication traffic load between servers due to offloading tasks. • Requiring minimum information about services by aggregating incoming traffic to nodes. This paper addresses the escalating challenges posed by the ever-increasing data volume, velocity, and the demand for low-latency applications, driven by the proliferation of smart devices and Internet of Things (IoT) applications. To mitigate service delay and enhance Quality of Service (QoS), we introduce a hybrid optimization of Particle Swarm (PSO) and Chemical Reaction (CRO) to improve service delay in FogPlan, an offline framework that prioritizes QoS and enables dynamic fog service deployment. The method optimizes fog service allocation based on incoming traffic to each fog node, formulating it as an Integer Non-Linear Programming (INLP) problem, considering various service attributes and costs. Our proposed algorithm aims to minimize service delay and QoS degradation. The evaluation using real MAWI Working Group traffic data demonstrates a substantial 29.34% reduction in service delay, a 66.02% decrease in service costs, and a noteworthy 50.15% reduction in delay violations compared to the FogPlan framework. [Display omitted] [ABSTRACT FROM AUTHOR]

The usage of connected Internet of Things (IoT) devices has increased exponentially. The data generated from these devices is used for real-time analysis to address real-world problems after processing with the help of fog servers. Various metrics are proposed in the literature to access fog servers' performance. This paper presents a detailed review of papers focusing on fog computing metrics like offloading, resource management, QoS, and privacy & security with their parameters such as latency, execution time, makespan, etc. Fog metrics are classified as general metrics and green IoT metrics, which affect the performance of fog environments in multiple ways. The critical analysis highlights the dependency of fog environments on multiple parameters and a trade-off between these parameters is necessary while creating an optimized environment. This paper explores the tools used and optimized parameters and limitations for deeper insight. The analysis is extended for the applicability of fog environments in different application areas and future directions. [ABSTRACT FROM AUTHOR]

The sudden outbreak of the novel coronavirus disease in 2019, known as COVID-19 has impacted the entire globe and has forced governments of various countries to a partial or full lockdown in the fear of the rapid spread of this disease. The major lesson learned from this pandemic is that there is a need to implement a robust system by using non-pharmaceutical interventions for the prevention and control of new contagious viruses. This goal can be achieved using the platform of the Internet of Things (IoT) because of its seamless connectivity and ubiquitous sensing ability. This technology-enabled healthcare sector is helpful to monitor COVID-19 patients properly by adopting an interconnected network. IoT is useful for improving patient satisfaction by reducing the rate of readmission in the hospital. The presented work discusses the applications and technologies of IoT like smart and wearable devices, drones, and robots which are used in healthcare systems to tackle the Coronavirus pandemic This paper focuses on applications of cognitive radio-based IoT for medical applications, which is referred to as "Cognitive Internet of Medical Things" (CIoMT). CIoMT is a disruptive and promising technology for dynamic monitoring, tracking, rapid diagnosis, and control of pandemics and to stop the spread of the virus. This paper explores the role of the CIoMT in the health domain, especially during pandemics, and also discusses the associated challenges and research directions. [ABSTRACT FROM AUTHOR]

To improve the effect of smart transportation planning in the decision-making process of urban planning space, this paper analyzes the coupling of the smart transportation process. Moreover, this paper analyzes the signal influencing factors of the communication process of smart traffic information based on the Internet of Things, and combines sensor analysis technology to improve the transmission effect of smart traffic signals based on the Internet of Things. Furthermore, this paper proposes a spatial decision-making model of smart transportation and urban planning based on the coupling principle and proposes the establishment and update rules of the road network congestion model, as well as the cluster queuing communication between vehicles. Finally, this paper presents a congestion notification method and a road diversion scheme based on vehicle queues. The experimental study shows that the smart transportation and urban planning spatial decision model based on the coupling principle proposed in this paper has a good effect. [ABSTRACT FROM AUTHOR]

The field of Internet of Things (IoT) is evolving at an exponential rate where megabytes of data is being processed each passing minute. Fog computing is an emerging field, regarded as the new way forward to prevent the suspected data outburst of the exhausted IoT devices. Fogging is believed to reduce latency, and enhance efficiency, ease of deployment and flexibility, however, various security and privacy concerns hinder its deployment by major platforms. Among all the security concerns, lack of a proper trust management system is of primary importance. This paper explores the Service-oriented Internet of Things (SIoT) domain to study its similarity with Fogging and creates a trust management scheme (TMS). The paper further explores the necessary trust requirements for Fogging and incorporates them into a two-way trust management scheme based on Bayes model, the model allows both the service requestor and the service provider to validate each other before connecting. The model calculates the value of trust using these metrics and combines them in a unique way with Bayes trust to have an accurate trust value. The proposed scheme is simulated in Netlogo, an agent-based network simulator. The subject scheme is capable of effectively preventing a legitimate node from connecting with a malicious node. The results illustrates high accuracy and faster convergence and also shows resilience against trust-based network attacks. The system is compared against SIoT trust management models due to lack of similar trust management models in Fog Computing. [ABSTRACT FROM AUTHOR]

One of the most dynamic industries nowadays is the automotive industry. It has dramatically changed from safe, comfortable, and equipped with many entertainment systems to connected autonomous vehicles during the last decade. Although the "Connected Autonomous vehicles" trend dramatically influences the economy, it increases automation and connectivity. Consequently, vehicles can be considered an Internet of Things (IoT) network. Accordingly, the cyberattack surface is more significant, with many vulnerabilities. Considering the fundamental security weakness of automotive onboard networks, besides their superimposing, we firmly believe that a unified security solution is required to resolve the current automotive security problem. This paper proposes an automotive security solution called Generic Automotive Security Suit (GASS). (GASS) is designed to be a practical security solution deployed in the complicated automotive environment. In addition, it defines a standard secure inter-communication between onboard networks. We also introduce the Lightweight Generic Authentication Protocol (LXAP) as the building block of the GASS. We address different security scenarios which face the industry nowadays. The new proposed system does not affect the complexity level of the automotive network. In addition, our simulation results have shown that the LXAP enables security preservation over vehicles' complex networks. [Display omitted] [ABSTRACT FROM AUTHOR]

• IIoT-enabled technologies, challenges, and future directions are explored. • A blockchain-based cement industry security framework can overcome 51% of security issues. • The performance of major companies depends on well-designed IIoT infrastructure. • Industrial technologies will improve as research and experimentation advance IIoT infrastructure. The Industrial Internet of Things (IIoT) is recognized as the fourth industrial revolution as it enhances productivity, dependability, and competitive performance by concentrating on profitability. IIoT-enabled technologies have been reviewed and implemented in several research, but more research into the opportunities and challenges they present is necessary. This paper explores IIoT-enabled technologies and infrastructure, their role in global industrial growth, applications, challenges, and future directions. IIoT applications use the intelligence of things to solve industrial problems like supply chain mismanagement, data privacy risks, a weak cloud strategy, cost containment, and others. For instance, fog computing reduces parking, platform, fuel, and CO 2 emissions. A blockchain-based security framework for the cement sector can resolve 51% of security issues and Sybil attacks caused by consensus algorithms like Proof of Work (PoW). Major companies' performance depends on well-designed IIoT infrastructure, despite significant challenges. Industrial technologies will improve as research and experimentation advance IIoT infrastructure. [Display omitted] [ABSTRACT FROM AUTHOR]

One of the integral components in the architectural design and development of Internet of Things (IoT) is Microservice. Microservices are basically an architectural and organizational approach in the process of software development where the software is composed of small but independent services that would communicate over well-defined APIs (Application Programming Interfaces). It is quite challenging to ensure data integrity and data availability in the architecture design of microservices. As Blockchain technology has emerged as a panacea to many of the other domains, the distributed microservice architecture can also utilize it. We know that the consensus algorithms are used in the Blockchain technology to validate the transactions alongside providing extra level of security. Taking the advantage of consensus algorithms in blockchain-based architecture models, in this paper, we propose TCA (Trustworthy Consensus Algorithm), which is designed to solve the data integrity and availability challenges in microservice architectures. We have evaluated the proposed algorithm against the known alternatives and it shows good level of efficiency. [Display omitted] • One of the integral components of Internet of Things (IoT) is Microservice. • It is a challenge to ensure data integrity and data availability. • The consensus algorithms in blockchain provide extra level of security. • Trustworthy Consensus Algorithm enhances the security of microservice. [ABSTRACT FROM AUTHOR]

There are currently numerous automated fall detection devices available, but most require the deployment of additional specialized equipment, which can create inconvenience for elderly individuals in their daily lives. This paper proposes a solution that reuses existing smartphone and WiFi infrastructure, eliminating the need for extra equipment and greatly simplifying the implementation of fall event detection. Due to the lower quality of data collected by smartphones, we have developed an adaptive DBSCAN clustering algorithm to process and extract useful data. We then train a deep convolutional LSTM model to classify the collected data in real-time. When a fall is detected, the smartphone can transmit an alert via the internet to notify relatives or medical staff of the incident. Our experimental results demonstrate that utilizing smartphones for fall detection is both feasible and accurate. [Display omitted] • Reduce costs by reusing existing infrastructure without deploying additional equipment. • Improve signal quality by using clustering methods. • Selecting appropriate parameters for the clustering algorithm to improve the clustering effect. • Experimental results show the feasibility and accuracy of the scheme. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is a fast-growing trend in wireless communication where various connected objects become an everyday part of our lives. The richness of the network is leading to open challenges and exposure to various attacks. Thus, security remains a major concern. Our purpose for this paper is to propose a smart card-based protocol that enables mutual authentication between a user and a server and allows for the establishment of a secure session key. In this context, we use Elliptic curve cryptography (ECC) and other lightweight operations as hash function and exclusive OR (XOR). We perform an evaluation of our protocol using Burrows–Abadi–Needham (BAN) logic, Scyther verification tool and Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. The results show that our solution is robust to most known attacks, suitable to IoT devices and more efficient compared to recent related protocols. [Display omitted] • Development of a novel security protocol based on smart cards for IoT networks. • ScPUAK is based on Elliptic curve cryptography, user's biometry and hash functions. • The formal security analysis shows its efficiency and safety against known attacks. • The protocol exhibits security properties while maintaining reasonable lightness. [ABSTRACT FROM AUTHOR]

Smart Grid (SG) is a relatively new subject in which intelligence comes from computer systems. Edge Computing (EC) processes data near the SG information source — potentially attenuating latency problems. While Internet of Things (IoT) collects, transmits and stores data, EC allows the employment of data analysis with Machine Learning (ML) at its edge. This work uses a systematic mapping methodology to encompass 15081 works related to four SGs technologies: data analysis, EC, IoT, and context awareness. With 37 works after the filtering process, this review revealed that most papers use one to three approaches, while only two use all four technologies. The results also indicate that EC has been extensively used in SG solutions, with 22 selected studies. Distinctively, only 9 works use context awareness, which may indicate a path for future developments in SG. The study also allowed the learning of 7 lessons that are presented in this paper. [Display omitted] • We review the state of the art related to smart grid computational techniques. • The paper covers internet of things, edge computing, and context-aware computing. • The work considers literature of the last decade through a well-defined methodology. • The study covers 15081 papers, answering one general and eight focused issues. • The paper proposes a taxonomy for smart grid Technologies. [ABSTRACT FROM AUTHOR]

• This paper proposes a solution to obtain the security and privacy levels that can be granted by Cloud-based IoT critical infrastructures, such as healthcare systems composed of multiple components in Cloud and IoT devices. • Particularly, this paper presents a methodology to compose Security Service Level Agreements (SecSLAs) and Privacy SLAs (PLAs) of Cloud-based IoT applications on top of standard controls, which aid in the formalization and assessment of the security and privacy levels of these composite applications. • The methodology includes a technique to quantitatively compute the Service Level Objectives (SLO) of the controls declared in the Composed SLA, based on the SLOs granted by individual components. • The method relies on the analysis of the relationships between the application components and the security controls implementation. For each component a preliminary SLA template is built, based on security self-assessment techniques. • Finally, the paper presents the validation of the methodology showing the creation of the SecSLAs and PLAs of a real multiCloud-based IoT application in the eHealth domain. The growing markets of Cloud services and IoT platforms have dramatically raised system flexibility and deployment options. However, increasing complexity and dependency on third-party providers make it difficult to assess the security and privacy levels that distributed systems can offer to their users. In the last years, machine-readable Service Level Agreements (SLAs) have been studied as an optimal method for copying with security and privacy policies. Still, the computation of the SLAs of applications distributed in diverse infrastructures remains a challenging task. This paper presents a methodology to compose security SLAs (SecSLAs) and privacy SLAs (PLAs) of Cloud-based IoT applications on top of standard controls. The composition considers individual components' SLAs and the control delegation relationships between the components with respect to different types of controls (common, system-specific or hybrid controls). Furthermore, we propose a technique to calculate the Service Level Objectives (SLO) of the controls declared in the composite SLA based on the SLOs granted by individual components. Finally, the paper presents the validation of the methodology carried out to create the SecSLAs and PLAs of a real multiCloud-based IoT application in the eHealth domain. [Display omitted] [ABSTRACT FROM AUTHOR]

• Review of edge network architectures, applications, and trends in IoT-based healthcare solutions. • Study of algorithms based on machine learning (ML) techniques and their use in conjunction with edge devices for high-risk pregnancy monitoring. • Classification mechanism to predict childbirth outcome in pregnancy: Averaged one-dependence estimators. • Performance evaluation using a real dataset from the Maternity School Assis Chateaubriand through a 10-fold cross-validation method. The development of edge computing has allowed the Internet of Things (IoT) to reach higher levels of operational efficiency, knowledge generation, and decision-making through a better relationship between applications and their users. The principal idea is the processing of large amounts of data close to the network edge, where these data are generated. Hence, in this paper, the use of a machine learning technique, known as averaged one-dependence estimators, is proposed for real-time pregnancy data analysis from IoT devices and gateways. This statistical technique is useful for decentralized pre-processing of data and its intermediate storage, reducing the amount of data to be transferred to the cloud and ensuring operability, even in an event of network failure. The results show that this technique presents accurate results with a low computational cost and could be a useful tool to better take advantage of the potential of IoT solutions in healthcare. [ABSTRACT FROM AUTHOR]

Abstract The increasing attacks against Internet of Things (IoT) systems are a determining factor to focus attention on IoT security. The use of Smart Cards (SCs) to protect these systems is pertinent, as they are resistant to physical attacks, portable, and have low cost. Nonetheless, SC processing and memory capacity are not sufficient to meet IoT demands. Therefore, the use of multiple clustered SCs seems interesting. The Field Programmable Gate Array can manage multiple SCs since it has the ability to parallelize tasks. In this work, Smart Card Cluster (SCC) is applied to ensure message authenticity and integrity through hardware signing. For instance, with only three SCs, a SCC supports signing about 26 messages per minute. In this paper, we propose a SCC that is modular, portable, flexible and cost-effective. Through experiments, we demonstrate that our SCC outperforms existing solutions by as much as 69%, while the cost is lower. [ABSTRACT FROM AUTHOR]

Abstract Complex Event Processing (CEP) is a promising approach for real-time processing of big-data streams originating from Internet of Things (IoT) devices. Even though scalability and flexibility are key issues for IoT applications, current studies are mostly based on centralized solutions and restrictive query languages. Moreover, development, deployment and operation of big-data applications require significant amount of technical expertise. Hence, a framework that provides a higher abstraction level programming model and a streamlined execution environment is essential to facilitate widespread user adoption. In this paper, we propose a data-flow based visual programming model that enables development of big-data applications in IoT domain even by users who have limited or no experience in big-data technologies. A scalable CEP engine is also devised to execute the applications developed using this language. A prototype is implemented by using the state-of-the-art big-data and real-time stream processing technologies to evaluate the applicability and usability of the framework. [ABSTRACT FROM AUTHOR]

Abstract Internet of things provides interaction with billions of objects across the world using the Internet. In Internet of thing era, Healthcare Industry has grown-up from 1.0 to 4.0 generation. Healthcare 3.0 was hospital centric, where patients of long-lasting sickness suffered a lot due to multiple hospital visits for their routine checkups. This in turn, prolonged the treatment of such patients along with an increase in the overall expenditure on treatment of patients. However, with recent technological advancements such as fog and cloud computing, these problems are mitigated with a minimum capital investment on computing and storage facilities related to the data of the patients. Motivated from these facts, this paper provide an analysis of the role of fog computing, cloud computing, and Internet of things to provide uninterrupted context-aware services to the end users as and when required. We propose a three layer patient-driven Healthcare architecture for real-time data collection, processing and transmission. It gives insights to the end users for the applicability of fog devices and gateways in Healthcare 4.0 environment for current and future applications. [ABSTRACT FROM AUTHOR]

Abstract The main issue to be addressed in Wireless Sensor networks (WSN) applications is the limited life time of sensors and short communication range. Professional data aggregation techniques are, therefore, needed. In this paper, we consider the problem of increasing the WSN lifetime using a cluster-based data aggregation algorithm. We propose a novel method in tackling the problem. We use Mobile Elements (ME) in Internet of Things (IoT) environment to act as Cluster Heads in a cluster-based aggregation algorithm. We believe that utilizing the IoT technology by mixing it with the WSN technology leads to good results. Our experiments show an impressive extend the network lifetime, while not effecting the quality of data gathering. [ABSTRACT FROM AUTHOR]

Abstract Next-generation smart cities and Internet of Things (IoT) are getting more mature in terms of services and infrastructure requirements. Multiple smart vehicle applications are being conceived these days, including road traffic, road safety and infotainment, all of which are suffering from the WAN-latency problem. In this paper, we propose a Vehicular Adhoc Network (VANET)-based Software-Defined Edge Computing infrastructure supporting content delivery services among connected vehicles. The proposed approach leverages network base stations to embed mobile edge computing (MEC) services closer to the vehicles. Our approach can enable the delivery of more competitive services with reduced-latency by utilizing cooperative MEC search strategy for vehicle to infrastructure (V2I) communications as well as utilizing vehicle-level caching for vehicle to vehicle (V2V) communications between peers. The framework prototype has been implemented as a clean extension of the Mininet-WiFi emulator. Preliminary results serve as validation of the proposed framework and point out the potential benefits of the approach in mitigating WAN-latency in VANET. [ABSTRACT FROM AUTHOR]

In this paper, CMOS technology-based neural session key generation is proposed for integration with the Internet-of-Things (IoT) to enhancing security. Recent technological developments in the IoT era enable improved strategies to exacerbate the maintenance of energy efficiency and stability issues. The existing security solutions do not properly address IoT's security. A small logic area ASIC implementation of a re-keying enabled Triple Layer Vector-Valued Neural Network (TLVVNN) using CMOS architectures with measurements of 65 and 130 nanometers are proposed for integration with IoT. The paper aims to defend IoT devices using TLVVNN synchronization to enhance security. For a 20% weight misalignment in the re-keying phase, the synchronization period may be decreased from 1.25 ms to less than 0.7 ms, according to behavioral simulations. Experiments to verify the proposed technique's performance are conducted, and the findings demonstrate that the proposed method has greater performance benefits than the existing related techniques. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) paved the way to realize ubiquitous access towards manifold services and applications by enabling connectivity between different physical devices. The devices, having heterogeneous computation power, protocol stack, or storage power, can send or receive various services by communicating through the internet. These services inevitably demand a secure and reliable network system to interchange the data or information for their impressive utilization. One proven solution to attain this objective is embedding Trust Management System (TMS) that aims to reduce risk or uncertainty associated with interacting adversary devices by capturing their dynamic behavior in the system. With TMS, devices compute and maintain trust value on the interacting devices by observing their behavior with different interaction instances. This trust information is exchanged between the devices to assess the global reputation of devices and facilitate devices' decisions when there is a lack of direct interactions. For systems having enormous low-power devices, one crucial aspect of TMS is secure and timely dissemination, management, and storage of trust information to accurately assess devices' dynamic behavior. Addressing this issue, we present a blockchain-assisted scalable trust model, BLAST-IoT, for IoT systems of heterogeneous devices in this paper. BLAST-IoT intends to ensure secure dissemination and storage of trust information leveraging on the functionalities provided by blockchain technology. It adopts a semi-distributed approach where direct trust information, computed by IoT end devices, is processed and stored by some high-power edge devices running public blockchain networks to handle impediments caused by power-deficient IoT devices. The effectiveness of BLAST-IoT has been evaluated through implementation and simulation-based results for trust convergence and assessment accuracy in the dynamic IoT environment. Further, the throughput performance of BLAST-IoT has been examined with an increase in network load. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) platform allows physical devices to connect directly to the internet and upload data continuously. Insecure access makes IoT platforms vulnerable to different network intrusion attacks. As a result, the Intrusion Detection System (IDS) is a core component of a modern IoT platform. However, traditional IDS often follows rule-based detection where the rules can be changed and exposed to the attacker and becomes weak over time. An efficient IDS also needs to be dynamic and effective in real time. This paper proposes a deep learning-based algorithm to protect the network against Distributed Denial-of-Service (DDoS) attacks, insecure data flow, and similar network intrusions. A system architecture is designed for a cloud-based IoT framework to implement the proposed algorithm efficiently. The performance evaluation using standard datasets demonstrates that the proposed model provides an accuracy of up to 99.99%. [ABSTRACT FROM AUTHOR]

• AI-enabled Malware Detection System for Industrial Internet of Things is presented. • Double Density Discrete Wavelet Transform method is used for learning the features. • Hybrid CNN-LSTM model is used for the identification of the malware. • Higher classification accuracy is achieved using the proposed method. With the significant growth in Industrial Internet of Things (IIoT) technologies, various IIoT -based applications have emerged in the last decade. In recent years, various malware-based cyber-attacks have been reported on IIoT -based systems. Thus, this research work outlines the design of an efficient Artificial Intelligence (AI) -empowered zero-day malware detection system for IIoT. In this paper, a hybrid deep learning-based malware detection framework is proposed in which a Double-Density Discrete Wavelet Transform (D3WT) is used for feature extraction and a hybrid of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) model is used for identification and classification of malware. The assessment of the proposed framework is evaluated using three datasets such as IoT malware, Microsoft BIG-2015 and Malimg dataset. Experimental results show that the proposed model achieved 99.98% accuracy on the IoT malware, 96.97% accuracy on the Microsoft BIG-2015 and 99.96% accuracy with the Malimg dataset. [Display omitted] [ABSTRACT FROM AUTHOR]

With the steady growth in the adoption of IoT in an industrial context, security challenges intensify. Security threats to IoT devices in a nuclear plant or an oil refinery are much higher risk compared to home appliances. In this paper, we present an explainable, efficient, and highly accurate machine-learning based intrusion detection system for industrial IoT, referred to as E2I3DS. This work successfully reduces the needed features within the WUSTL-IIOT-2021 dataset from 48 features to 11 only, while maintaining very high accuracy. Experiments utilizing the 11-feature dataset showed that proposed system has a higher accuracy of 99.97%, with a false positive rate of 0.01% and a false negative rate of 0.04%. The proposed system also proved to be very efficient with an excellent detection time of 0.1517 μ s. In addition, the model was explained using Shapley additive explanations. [Display omitted] • Introducing a high-efficiency, and high-accuracy intrusion detection system for Industrial IoT. • Selecting 11 network-flow features as the most effective ones in intrusion detection. • Explaining the impact of the features on the prediction decisions using Shapley Additive Explanation. This ensures that the detection decisions originate from an explainable system rather than a black-box. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a collection of heterogeneous, interconnected mobile ad-hoc networks (MANETs) devices. High-level security is always required to protect sensitive information travelling over MANET-IoT networks. IoT security ensures the privacy, confidentiality, and security of the users, infrastructure, and data. The Enhanced Multi-Level Intelligent Trust Scheme (EMITS) initially designs to authenticate and validate the data for the Mobile IoT (MIoT) ecosystem. The proposed model used the AODV (Adhoc On-Demand Distance Vector Routing) routing protocol with an intelligent trust scheme. This paper aims to present a MANET-IoT-based model combined with a hybrid cryptographic scheme that ensures secure communication between mobile IoT ecosystems against the black hole attack. The performance of the proposed EMITS-MIoT model is verified based on the packet delivery ratio (PDR), packet loss ratio (PLR), false positive ratio (FPR), false negative ratio (FNR), and attack detection rate (DR). The EMTS-IoT technique shows a 3–5% success in data transmission over existing techniques. [ABSTRACT FROM AUTHOR]

Blockchain (BC) technology has overtaken Industrial Internet of Things (IIoT) platforms. It is necessary to explore efficient implementation. Fault tolerance, decentralised control, authentication, cryptographic security, immutability, data integrity, and BC smart contracts are recommended IIoT features. If entities are authenticated and trusted, the internet can be used for industrial activities. Despite several methods, communication is insecure due to scalability, dependability, latency, insufficient transmission security, and uneven data loads. The paper created safe User authentication and optimal BC node selection using AFHENN (Fully Homomorphic encryption neural network) for IIoT to solve the problem. Mutual authentication, secrecy, and integrity protect user data. A registration process secures new User authentication. To protect registered data, it uses cryptographic methods like Transient key congruential generator based Elliptic curve cryptography (TKCG-ECC) and Dual keyed Cipolla's Extended Euclidean Algorithm based lattice cryptosystem (DKCEED-LC). To access BCN, the gateway verifies registered users utilising keyed-based Zero Knowledge of Proof (k-ZKP) and Approximation Fully Homomorphic encryption neural network-based Blockchain. Finally, Approximation Fully Homomorphic encryption neural network-based Blockchain networking authenticates data (AFHENN-BCN). The BCN avoids legal selection of miner nodes and harmful activities. Compared to top techniques, the proposed work achieves improved throughput and PDR (Packet Delivery Ratio) values with minimal computing time and strong security. [ABSTRACT FROM AUTHOR]

• The rule conflicts are subdivided into seven categories. • Different rule conflicts are formally expressed so that the detection of rows can be automated to improve the accuracy. • Designs and implementation of prototype system for rule conflict detection. • F1 value of proposed rule conflict detection in the IIOT system is better. Improving Industrial Internet of Things (IIOT), device flexibility and lowering maintenance costs are significant problems. However, as the scale of the Intelligent Industrial Internet of Things (IIIOTs) system expands, the interactions between the rules get more sophisticated, potentially leading to rule discrepancies. The algorithm for Formal Rule Conflict Detection (FRCD) is created, and a thorough explanation of the procedure is given in this paper. Two IIIOT systems were used in experiments and the results were compared with three existing standard IIIOT rule conflict detection techniques. They include policy conflict detection systems based on Web semantics (Semantic Web-Based Policy Interaction Detection with Rules, (SPIDER)), conflict detection methods based on Users, Triggers, Environment entities, and Actuators (UTEA), and semiformal conflict detection methods (Identifying Requirements Interactions using Semiformal (IRIS)). The experimental results show that the FRCD rule conflict detection method is superior. [Display omitted] [ABSTRACT FROM AUTHOR]

• Network integration in cyber-physical systems (CPSs) leads to cyber attacks due to the integration of insecure networking devices. • A new swarm-based feature selection algorithm is proposed to improve attack detection in an IoT-based CPS environment. • The proposed algorithm uses enhanced chicken swarm optimization (ECSO) with self-learning ability-based feature selection to select relevant features from preprocessed data. • The proposed ECSO-based ensemble classifier is tested on the NSL-KDD dataset and shows effective performance using various statistical measures. To provide remote access, surveillance, and analysis, network integration is common in Cyber-Physical Systems (CPSs). This leads to cyber attacks due to the integration of insecure networking devices. In violating internet security, the attackers interfere with system function, which leads to shattering consequences. With the incorporation of Fog with IoT, the attacks in the CPS can be detected in less time than in cloud-based CPS. The detection of attacks on CPS is targeted by cybercriminals which increases the identification difficulty. This paper proposes a new swarm-based feature selection algorithm to improve attack detection in an IoT-based CPS environment. An Enhanced Chicken swarm optimization (ECSO) with self-learning ability-based feature selection is used to select the relevant features from the preprocessed data. Next, the ensemble classifiers are executed with the desired features on the cloud. The proposed ECSO-based ensemble classifier has experimented against the NSL-KDD dataset. The evaluated results show the adequate performance of the proposed system using various statistical measures. The major aim of this system is to select the best ensemble method with the proposed feature selection model and use that approach for real-time attack detection in the CPS environment. The overview consists of four phases such as (i) data collection and preprocessing (ii) feature selection using proposed ECSO (iii) attack detection using ensemble model and (iv) running the best model in the fog for real-time attack detection. Overview of proposed IoT attack detection in Fog-enabled CPS. [Display omitted] [ABSTRACT FROM AUTHOR]

• Disease prediction based on different user symptoms is done. • Different machine learning-based approaches are designed and employed for different tasks. • Utilizes sensors to measure corresponding symptoms, the study combines the IoT and cutting-edge deep learning models to yield an optimal result. • Microservice-based user interface is designed. COVID-19 disrupted lives and livelihoods and affected various sectors of the economy. One such domain was the already overburdened healthcare sector, which faced fresh challenges as the number of patients rose exponentially and became difficult to deal with. In such a scenario, telemedicine, teleconsultation, and virtual consultation became increasingly common to comply with social distancing norms. To overcome this pressing need of increasing 'remote' consultations in the 'post-COVID' era, the Internet of Things (IoT) has the potential to play a pivotal role, and this present paper attempts to develop a novel system that implements the most efficient machine learning (ML) algorithm and takes input from the patients such as symptoms, audio recordings, available medical reports, and other histories of illnesses to accurately and holistically predict the disease that the patients are suffering from. A few of the symptoms, such as fever and low blood oxygen, can also be measured via sensors using Arduino and ESP8266. It then provides for the appropriate diagnosis and treatment of the disease based on its constantly updated database, which can be developed as an application-based or website-based platform. [Display omitted] [ABSTRACT FROM AUTHOR]

With the rapid advancement of the manufacturing industry from digital stage to network stage, the massive data generated by the production, consumption and other links in power IoT (PIoT) needs to be analyzed and processed urgently. However, due to the limited storage and computing capacity of PIoT devices, the collected industrial data are usually encrypted and stored in the cloud. Data security will be highly dependent on key security, and it is crucial for data privacy protection in PIoT to securely manage keys. Therefore, this paper proposes a Blockchain-Assisted Threshold Cryptography for Key Security Management in Power IoT Data Sharing (BCTC-KSM). We use the Pedersen threshold secret sharing algorithm off-chain to split the symmetric keys used to encrypt cloud-outsourcing data in PIoT into multiple key fragments and publish them on-chain, and use the ledger technology of blockchain to record the rotation history of each key fragment and its access subjects, and then restrict the user access to the key fragments through attribute access control policies on-chain. Security analysis and experimental results show that the BCTC-KSM can significantly improve the security and availability in PIoT data sharing at a slightly higher time cost than the state-of-the-art cryptography scheme. [ABSTRACT FROM AUTHOR]

The education system is one of the mechanisms and aspiration to build the society and contribute to human capital, well-being, and wealth. Security and privacy concerns in the educational organization is always significant due to various violent and terrorist activities. Technologies have been adopted for smart learning systems to improve the learning experience whereas security has been neglected inside or outside the institutions. The new integrated technologies have been adopted by using smart monitoring and sensing devices. The main objective of this paper is to analyze the Internet of Things (IoT) solutions specially designed for schools to provide smart and secure systems for educational settings. This paper also proposes a Secure system for the Internet of Schools Things (S-IoST) for smart schools based on a new advanced communication system integrated with 5 G cellular systems, sensing technologies, intelligent transportation systems, and IoT networks. The proposed system provides a more secure alert mechanism and facilitates the users at school and during mobility to the school or home. The proposed system evaluates in terms of data delivery, time, and response alert parameters. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) has been a major influence in Agriculture since its application to the sector. This paper provides an extensive review of the use of smart technologies in agriculture and elaborates the state-of-the-art technologies for smart agriculture including, Internet of Things, cloud computing, machine learning, and artificial intelligence. The application of smart farming to crop and animal production and post-harvesting is discussed. The impact of climate change on agriculture is also considered. This paper contributes to knowledge by iterating the challenges of smart technology to agriculture while highlighting the issues identified from existing framework of smart agriculture. The authors identify many gaps in existing research affecting the application of IoT in smart farming, and suggest further research to improve the current food production globally, to provide better food management and sustainability measures across the globe. [ABSTRACT FROM AUTHOR]

• Model for the identification of trusted IoT devices for successful delivery of services, while protecting sensitive data. • An ontology to select appropriate IoT services according to device capabilities and data confidentiality level. • Addresses the issue of service matching while performing a task owned by a resource-constrained device within a trusted boundary. • Explores delegation in IoT with respect to authentication and secure delegation. In the era of ubiquitous computing, Internet of Things (IoT) devices have a remarkable impact on human lives. In addition to numerous services provided by IoT devices, there are some constraints and limitations in their service provision. For instance, due to their resource constraint nature, sometimes these devices have insufficient resources to perform assigned tasks. Therefore, a delegation mechanism is usually required to handle task requests assigned to devices with limited resources. However, at the same time, the data possessed by these devices can be sensitive, so a trust mechanism is needed to identify trusted devices for delegating data sensitive services. Ontologies provide a robust method to integrate models with their corresponding real-world scenarios. This paper proposes a model for the identification of trusted IoT devices for delegation, designed through ontologies to resolve the problems of incomplete task requests owned by resource-constrained IoT devices. The proposed ontology provides a unique view of domain knowledge from the operational knowledge of pervasive and ubiquitous systems, like IoT devices. The paper also proposes a semantic matching algorithm to rank highly favorable and trusted devices to handle resource and trust priorities. The proposed ontology is developed in protégé editor and evaluated through a web query language. The experimental results based on simulation and parametric evaluations reflect that the proposed system performs better than existing approaches. [Display omitted] [ABSTRACT FROM AUTHOR]

The object tracking in video surveillance for intelligent traffic handling in smart cities requires an enormous amount of data called big data to be transmitted over the network using the Internet of Things. Manual monitoring and surveillance are impossible because traditional computer vision technologies are no more useful for massive processing and intelligent decision making. In this paper, a framework is proposed which enables both on spot data processing and intelligent decision making by using cloud computing. The developed application is a trained on Artificial Neural Network, which can handle different traffic techniques with congested traffic scenario and priorities traffic such as ambulance handling. The Message Queue Telemetry Transport protocol is used for green transmission with mobile access to traffic data. The results analyzed with thirty videos processed data which handle real-time data prioritization for the people for smart surveillance to fastest route and enhance the intelligent data transmission. [ABSTRACT FROM AUTHOR]

The era of the Internet of Things (IoT) has arrived and much information is transmitted through various small IoT devices. Public key cryptography can be used in the present internet environment to avoid eavesdropping. The well-known public key cryptography, Rivest–Shamir–Adleman cryptography and Elliptic Curve Cryptography are apt to be broken when quantum computing is introduced. Therefore, lattice-based cryptography has been proposed as a new public key cryptography to replace them. The Ring-LWE scheme has been proposed to implement lattice-based cryptography. To apply the scheme to IoT devices using 8-bit, 32-bit, or 64-bit microcontrollers, optimization is inevitable. Further, the 8-bit environment is more important for small IoT devices. However, Ring-LWE may be vulnerable to side-channel attacks. This paper analyzes the attack scenario and tenders a countermeasure through bit checking for IoT applications using 8-bit microcontrollers. [ABSTRACT FROM AUTHOR]

The recent trend towards IPv6 (Internet protocol version 6) has gained a great deal of interest with regard to smart cities due to its increased address space. A vital issue in designing a router is to establish the output port of an incoming IP packet along with its destination. Thus, the IP lookup architecture must achieve high speeds, supporting IPv6 with quick real-time updates to serve perpetually developing routing tables. The problem with IP lookup is that of achieving a throughput of more than 100 gigabits per second. In addition, it is difficult to store all the IP lookups in the on-chip memory devices. This paper proposes a new pipeline design called the linear pipelined IPv6 lookup architecture that overcomes the shortcomings of the POLP (parallel optimized linear pipeline) and BPFL (balanced parallelized frugal lookup) algorithms. The new B-tree algorithm is based on the idea of specific prefixes along with robust routing tables. The linear pipelined IP lookup design has been implemented in Verilog HDL using an Altera Quartus Stratix II device. Three different sizes of lookup tables are utilized to assess the proposed method. The logic elements, SRAM, memory and maximum frequency results are better for the proposed method compared with the POLP and BPFL methods. [ABSTRACT FROM AUTHOR]