Search

Internet of Medical Things (IoMT) platform serves as an interoperable medium for healthcare applications by connecting wearable sensors, end-users, and clinical diagnosis centers. This interoperable medium provides solutions for disease diagnosis; predicting and monitoring end-user health using physiological vital signs sensed wearable sensor data. The communicating and data exchanging internet of things (IoT) platform imposes latency and overloading uncertainties in the heterogeneous environment. This article introduces cognitive data processing for uncertainty analysis (CDP-UA) to improve WS data management's efficiency. CDP-UA addresses uncertainties in two levels namely aggregation and dissemination of WS data. The uncertainties in synchronizing aggregation and dissemination slot mapping are addressed using classification learning. In the dissemination process overloaded intervals are identified and segregated using regression learning and conditional sigmoid function analysis. The joint learning process helps to classify overloaded and latency-centric dissemination and aggregation instances to improve WS data delivery in the clinical/medical analysis center. The experimental analysis shows that the proposed method is reliable in achieving less uncertainty factor, latency, and overloaded intervals for varying disseminations and sensing intervals.

With the rise in technology especially the integration of internet of Things (IoT) with Wireless Sensor Networks (WSNs) an extensive network of interconnected devices and sensors is formed to frequently collect and transmit sensitive data. However, this data may get accessed by unauthorized persons during transmission leading to erroneous decisions and potentially disastrous consequences in domains like healthcare and industrial automation.Considering the importance of protecting data, a security-based route selection model for IoT-WSN is proposed. It involves pre-identification of malicious nodes and calculation of trust using the proposed ITrust mechanism. The IGWO (Improved Grey Wolf Optimization) model weighs nodes according to their trust, energy, and connection request factors. This judgment determines the node's next hop, guaranteeing secure communication. Using MATLAB software, the suggested trust-based approach's effectiveness is assessed through Energy consumption, Packet Delivery ratio (PDR) and delay factors. Experimental results show that our approach accurately identifies malicious nodes, ensuring integrity of data transmission. The efficient routing scheme significantly reduces delays and maximizes the PDR, resulting in improved communication efficiency and reliability. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT), an enlarged Internet-based network, is a key element of the next information technology revolution. With the evolution of numerous IoT based smart applications like smart city, healthcare, huge amount of heterogenous data called big data with varying volume, velocity and variety is getting space on various storage systems. Data redundancy is a serious problem that wastes a lot of storage capacity and network bandwidth with less data security in setups that blend cloud integrated IoT data storage. Data deduplication/redundancy elimination strategies can effectively decrease and control this issue by removing duplicate data in cloud-integrated IoT storage systems. Security and privacy of data is another major concern. To maximise the storage effectively and extremely securely, with maintained data integrity, confidentiality, minimal storage cost and increased storage use, data deduplication (DD) over encrypted data is also a key problem in cloud integrated IoT storage and computing environment. With the dynamic nature of big data, the majority of current data deduplication techniques which primarily revolve around the static scenes like the backup and archive systems, are inappropriate to be applicable for real time scenarios in IoT environment. To overcome the aforementioned issues this survey presents an analysis of literature on conventional deduplication techniques. It is highlighting the need of deduplication in IoT oriented big data, its parameters and properties of effectiveness, with taxonomy for secure and conventional deduplication and scope of implementing it with new technologies; Blockchain. Further, it elaborates on issues and challenges along with future scope of deduplication schemes in various application domains. [ABSTRACT FROM AUTHOR]

With the exponentially evolving trends in technology, IoT networks are vulnerable to serious security issues, allowing intruders to break into networks without authorization and manipulate the data. Their actions can be recognized and avoided by using a system that can detect intrusions. This paper presents a hybrid intelligent system and inverted hour-glass-based layered network classifier for feature selection and classification processes, respectively. To accomplish this task, three different datasets have been utilized in the proposed model for identifying old and new attacks. Moreover, a hybrid optimization feature selection technique has been implemented for selecting only those features that can enhance the accuracy of the detection rate. Finally, the classification is performed by using the inverted hour-glass-based layered network model in which data are up-sampled with the increase in the number of layers for effective training. Data up-sampling is performed when small subset of datapoints are observed for any class, which in turn helps in improving the accuracy of the proposed model. The proposed model demonstrated an accuracy of 99.967%, 99.567%, and 99.726% for NSL-KDD, KDD-CUP99, and UNSW NB15 datasets, respectively, which is significantly better than the traditional CNID model. These results demonstrate that our model can detect different attacks with high accuracy and is expected to show good results for new datasets as well. Additionally, to reduce the computational cost of the proposed model, we have implemented it on CPU-based core i3 processors, which are much cheaper than GPU processors. [ABSTRACT FROM AUTHOR]

The most cutting‐edge innovation in recent times, cloud technology, changed the whole computer paradigm from less capable, specific, user‐isolated offline systems to potent multi‐server interactive systems. Despite being a huge benefit, controlling the security and accessibility of data stored in the cloud is a difficult task. Hence, this article presents a four‐phased security paradigm for securing data generated by internet of things (IoT) devices and transmitted to fog servers. In the article, we outline a comprehensive, cutting‐edge architecture for safeguarding and protecting the information that IoT devices produce and transmit to the cloud. The suggested architecture blends the advantages of an innovative mutual authentication algorithm, a novel key distribution algorithm, and a novel encryption hybrid algorithm for maximum effectiveness and increased security. The findings demonstrate that the suggested protocol outperforms previous techniques in terms of time and resource consumption and throughput. In contrast to previous protocols, the suggested approach reduces encrypting time by 28% and decryption time by about 32%, while the volume of encrypted messages produced stays consistent. There has also been a 28% increase in encryption throughput. Proposed work demonstrates minimal resource utilization, good performance and a higher level of security robustness. [ABSTRACT FROM AUTHOR]

The number of affirmed COVID-19 cases showed an enormous increase in the recent past throughout the globe. Keeping in view the catastrophic destruction of this devastating virus, there is a must-need situation to maximize the use of existing healthcare technologies such as the healthcare Internet of Things (H-IoT). In healthcare, patient wearable devices are widely recognized as a dormant technology with enormous capabilities to assess and combat various diseases, e.g., cough, seizure, temperature, heartbeat, and so on. As we know, in the H-IoT, patient-wearable devices are dispersed in an infrastructure-free environment that exposes them to several private and public coercion while accumulating and transmitting high sensitive data over the wireless communication channel. Therefore, security is the main concern of these applications, and thus, the primary focus of this article to outline the limitations and challenges in the present literature from 2019 to 2021, to identify the requirements of H-IoT applications used in the context of COVID-19. Following this, we will move one step ahead to explore the current security techniques adopted in these applications. Consequently, we will identify the network architectural, cryptographic, protocols, and operational security challenges during our study to recommend viable research directions and opportunities, which could be helpful and capable to minimize the network architecture, deployment, and maintenance cost with more productive outcomes. [ABSTRACT FROM AUTHOR]

From the last few years, we have witnessed an exponential increase in the usage of delay-sensitive applications by the end-users because of the paradigm shift and revolution in different technologies starting from 1G to 5G most of which are having focus on QoS and QoE to the end-users. The existing standards on 5G mainly concentrate on the following issues: 10 Gigabyte data rate, 1-millisecond latency, high bandwidth per unit area, 99.999% availability, 100% coverage and 90% reduction in network energy usage. Hence, 5G has the capability to support various types of communications from the low power Local Area Network (LAN) to Wide Area Networks (WAN) with low-latency and high-speed. It allows human-to-human (H2H), human-to-machine (H2M) interactions for exchanging of data and signals. So, for better data transmission between different entities (for example, smart objects located across different geographic locations), efficient communication between billions of smart devices is required and the associated technology is called as "Internet of Things". However, issues such as- latency-tolerant, low-data rate, high complexity, privacy and security in existing solutions may deteriorate the performance of any implemented solution in this environment. To mitigate the above-mentioned problems, the literature suggests that fog computing can be one of the options as it provides ultra-low-latency for Tactile-based applications. Tactile Internet is an emerging technology used for H2M interactions to support high-reliability, ultra-responsive, and high fidelity. Keeping focus on all the aforementioned challenges and constraints, in this paper, we provide an analysis on the usage of the strong backbone infrastructure of fog computing for 5G-enabled Tactile Internet with a maximum bandwidth of 1 Gigabyte having the minimum latency of 1-millisecond. It supports low-latency and high-reliability in Tactile-based applications. Keeping focus on the issues such as- resource management, communication infrastructure, fog orchestration, fog networking, healthcare, security and privacy of fog system in Tactile-based applications, we have explored and compared the existing state-of-the-art proposals using various parameters such as- energy-efficient, QoS, scalability, mobility, and interoperability. In addition, a number of open research challenges of fog computing for 5G-enabled Tactile Internet are also explored to provide deep insights to the readers. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) is the new paradigm to perform different healthcare applications with different services in daily life. Healthcare applications based on IIoT paradigm are widely used to track patients health status using remote healthcare technologies. Complex biomedical sensors exploit wireless technologies, and remote services in terms of industrial workflow applications to perform different healthcare tasks, such as like heartbeat, blood pressure and others. However, existing industrial healthcare technoloiges still has to deal with many problems, such as security, task scheduling, and the cost of processing tasks in IIoT based healthcare paradigms. This paper proposes a new solution to the above-mentioned issues and presents the deep reinforcement learning-aware blockchain-based task scheduling (DRLBTS) algorithm framework with different goals. DRLBTS provides security and makespan efficient scheduling for the healthcare applications. Then, it shares secure and valid data between connected network nodes after the initial assignment and data validation. Statistical results show that DRLBTS is adaptive and meets the security, privacy, and makespan requirements of healthcare applications in the distributed network. [ABSTRACT FROM AUTHOR]

Nanotechnology has recently emerged as a pivotal field with wide-ranging implications. Its integration into the 6G-enabled Internet of Things (IoT) has given rise to the 6G-enabled IoNT (Internet of Nano Things) paradigm, impacting sectors such as healthcare, industries, smart homes, aerospace, and defense. This technology offers opportunities to revolutionize existing methodologies and enhance efficiency. Research efforts are now focusing on developing secure, scalable network infrastructures tailored for the healthcare sector at the nanoscale, leading to the concept of the Internet of Nano Medical Things (IoNMT). However, the unique characteristics of nanotechnology pose security challenges, particularly concerning privacy, confidentiality, dependability, latency, and the expensive consequences of blockchain-based storage. Authentication and transparency are vital for ensuring secure data handling in IoNMT networks, necessitating a secure access mechanism resistant to unauthorized interference. To tackle these challenges, this study proposes a smart contract-based authentication protocol developed specifically for 6G-IoNMT networks. The framework aims to manage real-time information with minimal latency through decentralized peer-to-peer cloud servers while addressing security and privacy concerns. Thorough security and privacy assessments, including ROR model evaluations, Scyther tool analysis, and informal security evaluations, validate the protocol's effectiveness. Moreover, the simulation highlights that this protocol offers superior security and efficiency as well as energy consumption compared to existing protocols. [ABSTRACT FROM AUTHOR]

Internet of Vehicles (IoV), as a special application of Internet of Things (IoT), has been widely used for Intelligent Transportation System (ITS), which leads to complex and heterogeneous IoV backbone networks. Network traffic prediction techniques are crucial for efficient and secure network management, such as routing algorithm, network planning, and anomaly and intrusion detection. This article studies the problem of end-to-end network traffic prediction in IoV backbone networks, and proposes a deep learning-based method. The constructed system considers the spatio-temporal feature of network traffic, and can capture the longrange dependence of network traffic. Furthermore, a threshold-based update mechanism is put forward to improve the real-time performance of the designed method by using Q-learning. The effectiveness of the proposed method is evaluated by a real network traffic dataset. [ABSTRACT FROM AUTHOR]

With an emerging development in transport technologies, many applications in vehicular environments access roadside unit (RSU) services for tasks offloading. These applications use different types of paid services (e.g., communication, WiFi, and computing nodes) for their execution. The vehicular fog cloud computing (VFCN) is a cooperative computing environment to handle vehicle applications' mobility and resource allocation. However, the aforementioned collaborative VFCN environment still suffers from mobility and security issues in the existing systems. To address the aforementioned issues, in this work we present a cost-efficient and secure VFCN which consists of a mobility-aware multi-scenario offloading phase (MAMSOP) to deal with the mobility and offloading costs. The objective of the work is to execute applications with minimum delays and costs in a secure way. For the security concern, we propose a security scheme based on fully-homomorphism encryption, which encrypts and decrypts data locally and involves computation on encrypted data rather than decrypting it into its original form. As most of the applications have deadline and mobility constraints, so we propose the fully polynomial-time approximation scheme (FPTAS) based search task scheduling (STS) to ensure the execution of all applications within their given constraints. The goal of STS-FPTAS is to determine the delay and cost-aware scheduling into knapsack-aware resources. The knapsack allows all tasks to be allocated to the available length of resources. The results show that the proposed work optimizes the costs by 40% compared to existing systems. [ABSTRACT FROM AUTHOR]

The emerging non-orthogonal multiple access (NOMA) technology can effectively improve the throughput performance of Internet of Things (IoT) devices. Besides throughput maximization, ensuring throughput fairness is a practical design issue when implementing NOMA in wireless powered communication networks (WPCN). To this end, we investigate the joint transmission time and power allocation problem for NOMA communication, aiming to improve the sum-throughput while guaranteeing different wireless devices’ (WDs’) throughput in multi-cell WPCN. In particular, we first analyze the feasibility of the problem by deriving the necessary and sufficient conditions for the existence of feasible solutions and propose an efficient algorithm to obtain the set of feasible values of transmission time allocation. We then propose an efficient algorithm for the transmission time allocation to improve the sum-throughput. During each search iteration, we adopt the successive convex approximation (SCA) approach to transform the non-convex power allocation problem into a sequence of convex problems and obtain the locally optimal transmit power under a fixed transmission time. Numerical simulations show that the proposed algorithm can improve the sum-throughput while guaranteeing each WD’s throughput. [ABSTRACT FROM AUTHOR]

With the increasing and rapid deployment of the Internet of Things (IoT), it has become necessary to design an efficient secure user authentication protocol to reduce security vulnerabilities and attacks that affect the performance of IoT applications. During the last decade, several authentication protocols have been proposed to provide secure communication between remote users and the IoT sensor nodes. Nevertheless, most of these contributions have serious security vulnerabilities and high computational overhead at the IoT sensor node side. In this article, we present a secure three-factor (i.e., password, biometrics, and smart device) user authentication and key agreement protocol (EPSAPI) based on the chaotic maps (CMs) and the fuzzy extractor to reduce the overhead on the IoT sensor node side. It satisfies the required security features and provides efficient communication and computational overheads for a restricted IoT environment. In addition, an informal and formal security analysis, including the Real-Or-Random (ROR) model, Burrows-Abadi-Needham (BAN) logic, and the popular simulation tool Automated Validation of Internet Security Protocols and Applications (AVISPA), concludes that the EPSAPI protocol is provably secure and can withstand all possible well-known attacks. Finally, the presented protocol is better than other recent protocols by performance comparison and it is practical by simulation study through the widely used tool NS-3. [ABSTRACT FROM AUTHOR]

Future communication networksrequire higher bandwidth, greater coverage, and better throughput. The Space-Air-Ground Integrated Network (SAGIN) has the advantage of wide-area coverage and can cover global communications. It can meet the needs of network resources for maritime activities and remote mountainous areas, which is of great significance to the realization of a new generation of communications networks. With the development of the network and the emergence of delay-sensitive applications such as the Internet of Things, improving the delay performance of the system has received extensive attention. SAGIN involves multiple networks and is more complex than other networks. If there is no reasonable management between different networks, it is easy to lead to difficult link deployment and high time delay. On the basis of research and customization of SFC technology, it can provide a wide range of services and other advantages. It has significant performance in application scenarios involving multi network integration. In order to solve the above problems, this paper studies the SAGIN architecture of SFC based on business types. A service function chain mapping method based on delays prediction is proposed. Calculate the delay of the deployment path and select the path with the lowest delay as the SFC mapping path. The service model is constructed according to the mapping path, and the network slices are divided based on the service type. The simulation results show that the SFC mapping algorithm based on time delay prediction is compared with the traditional SFC mapping scheme. The algorithm does not affect other indicators, the CPU resource utilization rate is 27.8 $\%$ higher, and the link resource utilization rate is 22.7 $\%$ higher. The service acceptance rate increased by 21.5 $\%$ , the latency performance increased by 38.2 $\%$ , and the total resource consumption is reduced by 25.2 $\%$. [ABSTRACT FROM AUTHOR]

The rise in frequency and volume of Distributed Denial of Service (DDoS) attacks has made it a destructive weapon of attackers which can disrupt the services of any network including the Internet of Things. It is almost impossible to avoid DDoS attacks completely but still these attacks can be handled efficiently. It is a core security issue as these attacks can cause financial loss to organizations. So, it is necessary to know DoS/DDoS attack types and their solutions. This paper reviews major known DoS/DDoS attacks, attack tools, their prevention, detection, and traceback techniques. Solutions include techniques like filtering, honeypots, signature & anomaly-based detection, Link testing, Packet marking, and many more are reviewed in this paper. A proper analysis of these attacks and solutions is necessary for new strategy formation. This paper is beneficial for researchers and professionals working in this area to understand these attacks and see the latest scenarios. Areas that require more concentration can be identified and by working on them, a more secure working environment can be provided to end-users and organizations. [ABSTRACT FROM AUTHOR]

Authentication receives enormous consideration from the research community and is proven to be an interesting field in today's era. User authentication is the major concern because people have their private data on devices. To strengthen user authentication, passwords have been introduced. In the past, the text-based password was the traditional way of authentication, but this method has particular shortcomings. The graphical password has been introduced as an alternative, which uses a picture or a set of pictures to generate a password. In the future, it is a requirement of such approaches to maintain robustness and consume fewer energy resources to become suitable for the Green Internet of Things (IoT). Similarly, diverse graphical password authentication mechanisms have been used to provide users with better security and usability. In this article, we conduct an extensive survey on the existing approaches of graphical password authentication to highlight the challenges required to be addressed for Green IoT. In comparison to other existing surveys, the objective is to consolidate the graphical password technique and to identify the problem associated with it. Besides, this survey will also identify the vulnerabilities of the graphical password against several potential attacks. We have also examined the strengths and weaknesses of each technique along with the future research directions. This study also evaluates the usability of each approach by considering learnability, memorability, and so forth and also presents a comparative analysis with security. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) has the potential to leverage the immense amount of operational data of clouds, services, and social and communication networks. As a concrete example, AI techniques have been adopted by telcom operators to develop virtual assistants based on advances in natural language processing (NLP) for interaction with customers and machine learning (ML) to enhance the customer experience by improving customer flow. Machine learning has also been applied to finding fraud patterns which enables operators to focus on dealing with the activity as opposed to the previous focus on detecting fraud. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a robust authentication protocol for Vehicle-to-Grid (V2G) communication through lightweight and secure cryptographic primitives. In addition, we utilize signcrypt and unsigncrypt functions which facilitate the consumers to have secure access to the services. The security evaluation of the introduced framework shows its resilience against well-known security attacks. Moreover, the performance analysis shows that our protocol is efficient and reliable as compared to various related protocols but energy and computation cost is slightly higher than four other related protocols. [ABSTRACT FROM AUTHOR]

The rapid advancement in information and communication technology has revolutionized military departments and their operations. This advancement also gave birth to the idea of the Internet of Battlefield Things (IoBT). The IoBT refers to the fusion of the Internet of Things (IoT) with military operations on the battlefield. Various IoBT-based frameworks have been developed for the military. Nonetheless, many of these frameworks fail to maintain a high Quality of Service (QoS) due to the demanding and critical nature of IoBT. This study makes the use of mist computing while leveraging machine learning. Mist computing places computational capabilities on the edge itself (mist nodes), e.g., on end devices,wearables, sensors, andmicro-controllers. This way, mist computing not only decreases latency but also saves power consumption and bandwidth as well by eliminating the need to communicate all data acquired, produced, or sensed. A mist-based version of the IoTNetWar framework is also proposed in this study. The mist-based IoTNetWar framework is a four-layer structure that aims at decreasing latency while maintaining QoS. Additionally, to further minimize delays, mist nodes utilize machine learning. Specifically, they use the delay-based K nearest neighbour algorithm for device-to-device communication purposes. The primary research objective of this work is to develop a system that is not only energy, time, and bandwidth-efficient, but it also helps military organizations with time-critical and resources-critical scenarios to monitor troops. By doing so, the system improves the overall decision-making process in a military campaign or battle. The proposed work is evaluated with the help of simulations in the EdgeCloudSim. The obtained results indicate that the proposed framework can achieve decreased network latency of 0.01 s and failure rate of 0.25% on average while maintaining high QoS in comparison to existing solutions. [ABSTRACT FROM AUTHOR]

The continuous innovation and progression in hardware, software and communication technologies helped the expansion and accelerated growth in Internet of Things based drone networks (IoD), for the devices, applications and people to communicate and share data. IoD can enhance comfort in many applications including, daily life, commercial, and military/rescue operations in smart cities. However, this growth in infrastructure smartness is also subject to new security threats and the countermeasures require new customized solutions for IoD. Many schemes to secure IoD environments are proposed recently; however, some of those were proved as insecure and some degrades the efficiency. In this article, using elliptic curve cryptography, we proposed a new authentication scheme to secure the communication between a user and a drone flying in some specific flying zone. The security of the proposed scheme is solicited using formal Random oracle method along with a brief discussion on security aspects provided by proposed scheme. Finally, the comparisons with some related and latest schemes is illustrated. [ABSTRACT FROM AUTHOR]

Agriculture is a vital area for the sustenance of mankind engulfing manufacturing, security, traceability, and sustainable resource management. With the resources receding expeditiously, it is of utmost significance to innovate techniques that help in the subsistence of agriculture. The growth of Internet of Things (IoT) and Blockchain technology as two rapidly emerging fields can ameliorate the state of food chain today. This paper provides a rigorous literature review to inspect the state-of-the-art development of the schemes that provide information security using blockchain technology. After identifying the core requirements in smart agriculture, a generalized blockchain-based security architecture has been proposed. A detailed cost analysis has been conducted on the studied schemes. A meticulous comparative analysis uncovered the drawbacks in existing research. Furthermore, detailed analysis of the literature has also revealed the security goals towards which the research has been directed and helped to identify new avenues for future research using artificial intelligence. [ABSTRACT FROM AUTHOR]

Internet usage became increasingly ubiquitous. The concern regarding security and privacy has become essential for Internet users. As the usage of the Internet increases the number of cyber‐attacks also increases substantially. Intrusion detection is one of the challenging aspects of network security. Efficient intrusion detection is crucial for every organization to mitigate the vulnerability. This paper presents a novel intrusion detection system to detect malicious attacks targeted at a smart environment. The proposed Intrusion detection method uses a correlation tool and a random forest method to detect the predominant independent variables for improvising neural‐based attack classifier. To detect a malicious attack, a shallow neural network and an optimized neural‐based classifier are presented. The designed intrusion detection system has experimented on the KDDCUP99 dataset. The experimental results reveal that the performance of the proposed intrusion detection system is superior in terms of quantitative metrics. Thus, the proposed system can be deployed in the IoT and wireless networks to detect cyber‐attacks. [ABSTRACT FROM AUTHOR]

Recent advancement of Bitcoin revolution has propelled unlimited opportunities for futuristic digital ecosystem design. Transactions belonging to Bitcoin are being fully supported, realized, and disseminated by a set of innovative hot and cold wallet technologies within the blockchain framework. While deploying smart applications under the niche of Internet of Things (IoT) plethora, such Bitcoin transactions aroused the issue of unsupported resource-constrained tool sets and demanded for a novel architecture. To cater this challenge, this article proposes a novel Bitcoin lightweight IoT node-based system model while incorporating the improved simplified payment verification (SPV) process for e-healthcare application. This article, first formulates necessary models of background arts utilized in the work that include inhomogeneous Poisson point process, block header structure formulation, depth and height relationship, bloom filter mechanism, pay-to-public key hash (P2PKH) base stack process, and transaction management. Second, developed models are deployed through several key algorithms and associated procedures. Finally, analysis and discussions are made that encompass the block confirmation time, appropriateness of the byzantine fault tolerance, smart contract policy, and SPV response in the proposed scenario. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) and blockchain technologies are being heavily exploited and used in may domains, especially for e-healthcare. In healthcare, IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed. Collected IoT data are subjected to centralized computation, processing, and storage. Such centralization can be problematic, as it can be a single point of failure, mistrust, data manipulation and tampering, and privacy evasion. Blockchain can solve such serious problems by providing decentralized computation and storage for IoT data. Therefore, the integration IoT and blockchain technologies can become a reasonable choice for the design of a decentralized IoT-based e-healthcare systems. In this article, first, we give a brief background on blockchain. Second, popular consensus algorithms used in blockchain are discussed in the context of e-health. Third, blockchain platforms are reviewed for their appropriateness in IoT-based e-healthcare. Finally, few use cases are methodologically given to show how key features of the IoT and blockchain can be leveraged to support healthcare services and ecosystems. We also propose a data-flow architecture that combines the IoT with blockchain, called IoBHealth, that can be used for storing, accessing, and managing of e-healthcare data. [ABSTRACT FROM AUTHOR]

With the evolvement of the Internet of things (IoT), privacy and security have become the primary indicators for users to deploy IoT applications. In the gateway-based IoT architecture, gateways aggregate data collected by perception-layer devices and upload message packets to platforms, while platforms automatically push different categories of data to different applications. However, security in processes of data transmission via gateways, storage in platforms, access by applications is the major challenge for user privacy protection. To tackle this challenge, this article presents a secure IoT scheme based on a fine-grained multi-receive sign-cryption scheme to realize end-to-end secure transmission and data access control. To enhance the security of online application decryption keys, we design a distributed threshold decryption scheme based on secret-sharing. Moreover, from the provable security perspective, we demonstrate that the scheme can achieve the expected IND-CCA security and EUF-CMA security. After the performance analysis, evaluation results show that the computational performance is efficient and linearly subject to the number of messages and the number of receivers. [ABSTRACT FROM AUTHOR]

One of the major bottlenecks toward realizing IoT systems is the energy constraint of sensors. Prolonging network lifetime is a fundamental issue for implementing IoT systems. The energy optimization problem, being NP-hard in nature for scalable networks, has been addressed in the literature using traditional metaheuristic techniques. Quantum inspired metaheuristics have shown better performance than their traditional counterparts in solving such optimization problems in different domains. Toward this end, this article proposes a quantum inspired green communication framework for Energy Balancing in sensor enabled IoT systems (Q-EBIoT). First, an energy optimization model for sensor enabled IoT environments is presented, where energy consumption is derived as cost of the energy-oriented paths. Second, a quantum computing oriented solution is developed for the optimization problem focusing on energy centric solution representation, measurement, and rotation angle. The proposed solution is implemented to evaluate the comparative performance with the state-of-the-art techniques. The evaluation demonstrates the benefit of the proposed framework in terms of various energy-related metrics for sensor enabled IoT environments. [ABSTRACT FROM AUTHOR]

With the growth in the Internet of things (IoT) paradigm, there has been a tremendous makeshift in how the distributed devices work to achieve a common goal. However, the use of IoT edge devices in a traditional IoT paradigm results in better utilization of the available resources as well as improving the overall quality of service. These technologies bring the computation and communication capabilities closer to the IoT devices where some of the services can be offloaded to the edge devices. [Extracted from the article]

With the development of communication and information technologies, smart tourism is gradually changing the tourism industry. internet of Things (ioT) plays an important role in smart tourism. However, it is a challenge to apply ioT for smart tourism because of the need for dealing with a vast amount of data and low-latency communication. To this end, in this article, we outline 5G and Ai-empowered ioT systems for smart tourism. Efficient data transmission based on 5G technology and smart data processing based on Ai technology are significant to unlocking ioT based smart tourism applications. To demonstrate the superior performance of our proposed method, we perform a case study on POi recommendation. The experiment results demonstrate the efficiency and effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Recent developments in technologies like the internet of Things (ioT) and massive machine-type communications (mMTC), the number of wireless communication devices, the demand for spectrum resources, the power consumed in transmission, processing, and reception will proliferate. Non-orthogonal multiple access (NOMA) and cooperative schemes are attracting researchers across the globe to use NOMA to solve the problem of spectrum scarcity by serving multiple users simultaneously, whereas cooperative schemes can help in reducing transmission power consumption by employing relay nodes in between. Therefore, to condense the system's power consumption, this article integrates NOMA and cooperative technology. This article proposes two schemes: a battery efficient cooperative NOMA scheme and a customized sector model. The introduced battery efficient cooperative NOMA takes advantage of relay and device-to-device (D2D) cooperation for introducing different modes of operation in the NOMA scheme, aiming to reduce the battery power consumption at the transmitter and the receiver side, whereas the customized sector model intends to optimize the energy of the system through efficient deployment of the NOMA cell-sector model. Finally, the discussion of various challenges and future opportunities for unveiling the NOMA technique is presented. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) involves all the surrounding with smart devices to Internet and has been gaining more recognition because it provides substantial benefits in comparison to other systems. Further, due to the decentralized nature of IoT network many types of active and passive attacks are possible. The distributed denial of service is the active type of attack which reduces network performance. In this research work, technique of mutual authentication is designed which detects and isolates malicious nodes from the network and thus improves session resumption technique for minimizing execution time. The proposed technique has been implemented and also compared with existing technique in terms of certain parameters. These parameters are bandwidth consumption, routing overhead, and packet delivery ratio. The performance of proposed technique is evaluated in NS2 and results shows that proposed algorithm performs well in comparison to existing results. [ABSTRACT FROM AUTHOR]

The Internet of Drones (IoD) is widely used in a wide range of applications from military to civilian applications from the past years. However, during communication either with the control room/ground station server(s) or moving access points in the sky, security and privacy is one the crucial issues which needs to be tackled efficiently. In this direction, blokchain technology can be one of the viable solutions due to the immutability and traceability of various transactions and decentralized nature. In this paper, we provide in-depth challenges and issues of applicability of blokchain in 5G-based Internet of Things (IoT)-enabled IoD environment. We propose and analyze a new blokchain based secure framework for data management among IoD communication entities. The proposed scheme has ability to resist several potential attacks that are essential in IoT-enabled IoD environment. A detailed comparative analysis exhibits that the proposed scheme offers better security and functionality requirements, and also provides less communication and computation overheads as compared to other related schemes. [ABSTRACT FROM AUTHOR]

The increasingly sophisticated cyber attacks have become a serious challenge for Industrial Internet of Things (IIoT), which presents two new characteristics: low frequency and multi-stage. That is, hackers could gain authority to attack industrial equipment/infrastructure gradually in a long interval through lurking, lateral intrusion and privilege escalation. While, the existing Machine Learning (ML) based intrusion detection schemes all require the participation of expert knowledge, so it is difficult to adaptively select an attack interval and a retraining period of the detection model in IIoT, resulting in poor detection performance. To solve above problems, a bidirectional long and short-term memory network with multi-feature layer (B-MLSTM) is designed. Firstly, sequence and stage feature layers are introduced in the model training phase model which can learn the corresponding attack interval from historical data, so that the model can effectively detect attacks with different intervals. Then, a double-layer reverse unit is introduced to update the detection model. By collecting information from test data and making association analysis with historical data, the retraining period is adaptively selected to match the new attack interval. Compared with the previous works, our proposed scheme has a lower false positive rate than existing schemes by at least 46.79%, and the false negative rate is reduced by at least 79.85%, which are carried out on three classic IIoT datasets. [ABSTRACT FROM AUTHOR]

In recent decades, the dramatic increase in global industrial activity has resulted in a significant rise in the use of fossil fuel energy resources, while technological progress has increased the carbon footprint and hence global warming. The significant increase in energy consumption caused by the Internet of Things (IoT) technology has created a new challenge and diverted our focus toward creating a more eco-friendly IoT ecosystem. The green IoT is an emerging field that has attracted a great deal of attention from researchers and industries, as it provides energy efficient services and enables the production and use of renewable energy. Meanwhile, blockchain technology has become a highly adoptable technology in the IoT and is attracting considerable interest from energy supply companies, business start-ups, financial institutions, governments and researchers. This study focuses on the role of the emerging blockchain technology in the green IoT ecosystem, presents the crucial factors that need to be taken into consideration in order to build a green IoT ecosystem, and discusses how blockchain technology contributes to make the IoT ecosystem greener. It also presents the open issues and future research directions to be considered when creating a sustainable green IoT ecosystem using blockchain technology. [ABSTRACT FROM AUTHOR]

The convergence of cloud computing and Internet of Things (IoT) is partially due to the pragmatic need for delivering extended services to a broader user base in diverse situations. However, cloud computing has its limitation for applications requiring low-latency and high mobility, particularly in adversarial settings (e.g. battlefields). To some extent, such limitations can be mitigated in a fog computing paradigm since the latter bridges the gap between remote cloud data center and the end devices (via some fog nodes). However, fog nodes are often deployed in remote and unprotected places. This necessitates the design of security solutions for a fog-based environment. In this paper, we investigate the fog-driven IoT healthcare system, focusing only on authentication and key agreement. Specifically, we propose a three-party authenticated key agreement protocol from bilinear pairings. We introduce the security model and present the formal security proof, as well as security analysis against common attacks. We then evaluate its performance, in terms of communication and computation costs. [ABSTRACT FROM AUTHOR]

With an exponential growth of raw data generated from different sensors, actuators, and mobile devices, data analytics is becoming a challenging issue keeping in view of the heterogeneity of the data generated. The traditional database systems are not able to handle this huge amount of data. Current research and development are mainly focused on the analytics of this big data generated from different devices and overlook the difficulty of its secure streaming. In this study, the authors analyse and provide an overview on how to handle secure streaming data generated from different devices. It includes the major threats and risks, introduced during big data processing in Internet of things environment. Moreover, they analyse the existing security approaches and highlight emerging challenges required to process the heterogeneous big data in a secure manner for IoT applications, such as denial of service, malware, and phishing. The architectural details and security approaches required in each phase of big data processing life‐cycle are explored in detail. Finally, various research challenges along with a case study are discussed and analysed. [ABSTRACT FROM AUTHOR]

With the ongoing trend of smart and Internet-connected objects being deployed across a broad range of applications, there is also a corresponding increase in the amount of data movement across different geographical regions. This, in turn, poses a number of challenges with respect to big data storage across multiple locations, including cloud computing platform. For example, the underlying distributed file system has a large number of directories and files in the form of gigantic trees, which are difficult to parse in polynomial time. Moreover, with the exponential increase of big data streams (i.e., unbounded sets of continuous data flows), challenges associated with indexing and membership queries are compounded. The capability to process such significant amount of data with high accuracy can have significant impact on decision-making and formulation of business and risk-related strategies, particularly in our current Industrial Internet of Things environment (IIoT). However, existing storage solutions are deterministic in nature. In other words, they tend to consume considerable memory and CPU time to yield accurate results. This necessitates the design of efficient quality of service-aware IIoT applications that are able to deal with the challenges of data storage and retrieval in the cloud computing environment. In this paper, we present an effective space-effective strategy for massive data storage using bloom filter (BF). Specifically, in the proposed scheme, the standard BF is extended to incorporate fuzzy-enabled folding approach, hereafter referred to as fuzzy folded BF (FFBF). In FFBF, fuzzy operations are used to accommodate the hashed data of one BF into another to reduce storage requirements. Evaluations on UCI ML AReM and Facebook datasets demonstrate the efficacy of FFBF, in terms of dealing with approximately 1.9 times more data as compared to using the standard BF. This is also achieved without affecting the false positive rate and query time. [ABSTRACT FROM AUTHOR]

With the exponential growth of technologies such as IoT, edge computing, and 5G, a tremendous amount of structured and unstructured data is being generated from different applications in the smart citiy environment in recent years. Thus, there is a need to develop sophisticated techniques that can efficiently process such huge volumes of data. One of the important components of smart cities, ITS, has led to many applications, including surveillance, infotainment, real-time traffic monitoring, and so on. However, its security, performance, and availability are major concerns facing the research community. The existing solutions, such as cellular networks, RSUs, and mobile cloud computing, are far from perfect because these are highly dependent on centralized architecture and bear the cost of additional infrastructure deployment. Also, the conventional methods of data processing are not capable of handling dynamic and scalable data efficiently. To mitigate these issues, this article proposes an advanced vehicular communication technique where RSUs are proposed to be replaced by edge computing platforms. Then secure V2V and V2E communication is designed using the Quotient filter, a probabilistic data structure. In summary, a smart security framework for VANETs equipped with edge computing nodes and 5G technology has been designed to enhance the capabilities of communication and computation in the modern smart city environment. It has been experimentally demonstrated that use of edge nodes as an intermediate interface between vehicle and cloud reduces access latency and avoids congestion in the backbone network, which allows quick decisions to be made based on the traffic scenario in the geographical location of the vehicles. The proposed scheme outperforms the conventional vehicular models by providing an energy-efficient secure system with minimum delay. [ABSTRACT FROM AUTHOR]

The rapid growth in the transportation sector has led to the emergence of smart vehicles that are equipped with ICT. These modern smart vehicles are connected to the Internet to access various services such as road condition information, infotainment, and energy management. This kind of scenario can be viewed as a vehicular cyber-physical system (VCPS) where the vehicles are at the physical layer and services are at the cyber layer. However, network traffic management is the biggest issue in the modern VCPS scenario as the mismanagement of network resources can degrade the quality of service (QoS) for end users. To deal with this issue, we propose a software defined networking (SDN)-enabled approach, named SeDaTiVe, which uses deep learning architecture to control the incoming traffic in the network in the VCPS environment. The advantage of using deep learning in network traffic control is that it learns the hidden patterns in data packets and creates an optimal route based on the learned features. Moreover, a virtual-controller-based scheme for flow management using SDN in VCPS is designed for effective resource utilization. The simulation scenario comprising 1000 vehicles seeking various services in the network is considered to generate the dataset using SUMO. The data obtained from the simulation study is evaluated using NS-2, and proves that the proposed scheme effectively handles real-time incoming requests in VCPS. The results also depict the improvement in performance on various evaluation metrics like delay, throughput, packet delivery ratio, and network load by using the proposed scheme over the traditional SDN and TCP/IP protocol suite. [ABSTRACT FROM AUTHOR]

With the evolution of the Internet of Things, mutual sharing of information becomes easy among various smart devices across the globe. In such an environment, smart healthcare also needs to be expedited with an aim to provide remote diagnosis to patients located across the globe. However, as the sensitive information about a patient travels using an open channel (i.e., the Internet), there are chances of misusing this information. Hence, there is a requirement for a strong crypto mechanism for securely processing smart healthcare data. Most of the solutions reported in the literature use heavy computation-based solutions for providing security to smart healthcare data, which may not be suitable in view of less resources for modern smart devices. In this article, we propose a novel lattice-based secure cryptosystem for smart healthcare (LSCSH) in future smart cities. LSCSH uses a lightweight key exchange and an authentication mechanism at several stages. The proposed scheme has been evaluated with respect to the communication and computation costs in comparison to the other competitive schemes. The results obtained prove the efficacy of the proposed scheme in comparison to the existing schemes. [ABSTRACT FROM AUTHOR]