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Showing papers by "Mohsen Guizani published in 2019"


Journal ArticleDOI
TL;DR: This paper designs secure building blocks, such as secure polynomial multiplication and secure comparison, by employing a homomorphic cryptosystem, Paillier, and constructs a secure SVM training algorithm, which requires only two interactions in a single iteration, with no need for a trusted third-party.
Abstract: Machine learning (ML) techniques have been widely used in many smart city sectors, where a huge amount of data is gathered from various (IoT) devices. As a typical ML model, support vector machine (SVM) enables efficient data classification and thereby finds its applications in real-world scenarios, such as disease diagnosis and anomaly detection. Training an SVM classifier usually requires a collection of labeled IoT data from multiple entities, raising great concerns about data privacy. Most of the existing solutions rely on an implicit assumption that the training data can be reliably collected from multiple data providers, which is often not the case in reality. To bridge the gap between ideal assumptions and realistic constraints, in this paper, we propose secureSVM , which is a privacy-preserving SVM training scheme over blockchain-based encrypted IoT data. We utilize the blockchain techniques to build a secure and reliable data sharing platform among multiple data providers, where IoT data is encrypted and then recorded on a distributed ledger. We design secure building blocks, such as secure polynomial multiplication and secure comparison, by employing a homomorphic cryptosystem, Paillier, and construct a secure SVM training algorithm, which requires only two interactions in a single iteration, with no need for a trusted third-party. Rigorous security analysis prove that the proposed scheme ensures the confidentiality of the sensitive data for each data provider as well as the SVM model parameters for data analysts. Extensive experiments demonstrates the efficiency of the proposed scheme.

299 citations


Journal ArticleDOI
TL;DR: A Blockchain-based infrastructure to support security- and privacy-oriented spatio-temporal smart contract services for the sustainable Internet of Things (IoT)-enabled sharing economy in mega smart cities.
Abstract: In this paper, we propose a Blockchain-based infrastructure to support security- and privacy-oriented spatio-temporal smart contract services for the sustainable Internet of Things (IoT)-enabled sharing economy in mega smart cities. The infrastructure leverages cognitive fog nodes at the edge to host and process offloaded geo-tagged multimedia payload and transactions from a mobile edge and IoT nodes, uses AI for processing and extracting significant event information, produces semantic digital analytics, and saves results in Blockchain and decentralized cloud repositories to facilitate sharing economy services. The framework offers a sustainable incentive mechanism, which can potentially support secure smart city services, such as sharing economy, smart contracts, and cyber-physical interaction with Blockchain and IoT. Our unique contribution is justified by detailed system design and implementation of the framework.

223 citations


Journal ArticleDOI
TL;DR: Wang et al. as mentioned in this paper investigated the underlying behavior of the timing channel from the perspective of the memory activity records and summarized the signature of timing channel in the underlying memory activities.
Abstract: Recently, the Infrastructure as a Service Cloud (IaaS) (e.g., Amazon EC2) has been widely used by many organizations. However, some IaaS security issues create serious threats to its users. A typical issue is the timing channel. This kind of channel can be a cross-VM information channel, as proven by many researchers. Owing to the fact that it is covert and traceless, the traditional identification methods cannot build an accurate analysis model and obtain a compromised result. We investigated the underlying behavior of the timing channel from the perspective of the memory activity records and summarized the signature of the timing channel in the underlying memory activities. An identification method based on the long-term behavior signatures was proposed. We proposed a complete set of forensics steps including evidence extraction, identification, record reserve, and evidence reports. We studied four typical timing channels, and the experiments showed that these channels can be detected and investigated, even with the disturbances from normal processes.

175 citations


Journal ArticleDOI
TL;DR: This survey aims to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications, highlighting key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications.
Abstract: Unmanned aerial vehicles (UAVs) have recently rapidly grown to facilitate a wide range of innovative applications that can fundamentally change the way cyber-physical systems (CPSs) are designed. CPSs are a modern generation of systems with synergic cooperation between computational and physical potentials that can interact with humans through several new mechanisms. The main advantages of using UAVs in CPS application is their exceptional features, including their mobility, dynamism, effortless deployment, adaptive altitude, agility, adjustability, and effective appraisal of real-world functions anytime and anywhere. Furthermore, from the technology perspective, UAVs are predicted to be a vital element of the development of advanced CPSs. Therefore, in this survey, we aim to pinpoint the most fundamental and important design challenges of multi-UAV systems for CPS applications. We highlight key and versatile aspects that span the coverage and tracking of targets and infrastructure objects, energy-efficient navigation, and image analysis using machine learning for fine-grained CPS applications. Key prototypes and testbeds are also investigated to show how these practical technologies can facilitate CPS applications. We present and propose state-of-the-art algorithms to address design challenges with both quantitative and qualitative methods and map these challenges with important CPS applications to draw insightful conclusions on the challenges of each application. Finally, we summarize potential new directions and ideas that could shape future research in these areas.

167 citations


Journal ArticleDOI
TL;DR: An extensive analysis of trust management techniques along with their pros and cons is presented in a different context to help researchers understand that how various systems fit together to bring preferred functionalities without examining different standards.
Abstract: A vision of the future Internet is introduced in such a fashion that various computing devices are connected together to form a network called Internet of Things (IoT). This network will generate massive data that may be leveraged for entertainment, security, and most importantly user trust. Yet, trust is an imperative obstruction that may hinder the IoT growth and even delay the substantial squeeze of a number of applications. In this survey, an extensive analysis of trust management techniques along with their pros and cons is presented in a different context. In comparison with other surveys, the goal is to provide a systematic description of the most relevant trust management techniques to help researchers understand that how various systems fit together to bring preferred functionalities without examining different standards. Besides, the lessons learned are presented, and the views are argued regarding the primary goal trust which is likely to play in the future Internet.

166 citations


Journal ArticleDOI
TL;DR: This paper provides a summary of the existing IoT research that underlines enabling technologies, such as fog computing, wireless sensor networks, data mining, context awareness, real-time analytics, virtual reality, and cellular communications.
Abstract: The Internet of Things (IoT) is an emerging classical model, envisioned as a system of billions of small interconnected devices for posing the state-of-the-art findings to real-world glitches. Over the last decade, there has been an increasing research concentration in the IoT as an essential design of the constant convergence between human behaviors and their images on Information Technology. With the development of technologies, the IoT drives the deployment of across-the-board and self-organizing wireless networks. The IoT model is progressing toward the notion of a cyber-physical world, where things can be originated, driven, intermixed, and modernized to facilitate the emergence of any feasible association. This paper provides a summary of the existing IoT research that underlines enabling technologies, such as fog computing, wireless sensor networks, data mining, context awareness, real-time analytics, virtual reality, and cellular communications. Also, we present the lessons learned after acquiring a thorough representation of the subject. Thus, by identifying numerous open research challenges, it is presumed to drag more consideration into this novel paradigm.

145 citations


Journal ArticleDOI
TL;DR: This article considers two application cases of UAVs in conjunction with safeguarding the exchange of confidential messages, and demonstrates physical layer security mechanisms via two case studies to ensure security, and sheds light on new opportunities in the emerging network architecture.
Abstract: Wireless communications can leverage UAVs to provide ubiquitous connectivity to different device types. Recently, integrating UAVs into a macro cell network is drawing unprecedented interest for supplementing terrestrial cellular networks. Compared with communications with fixed infrastructure, a UAV has salient attributes, such as easy-to-deploy, higher capacity due to dominant LoS communication links, and additional design degree-of-freedom with the controlled mobility. While UAV communication offers numerous benefits, it also faces security challenges due to the broadcasting nature of the wireless medium. Thus, information security is one of the fundamental requirements. In this article, we first consider two application cases of UAVs (i.e., a UAV as a flying base station and a UAV as an aerial node) in conjunction with safeguarding the exchange of confidential messages. Then, we demonstrate physical layer security mechanisms via two case studies to ensure security, and numerically show superior performance gains. Finally, we shed light on new opportunities in the emerging network architecture that can serve as a guide for future research directions.

140 citations


Journal ArticleDOI
TL;DR: The challenges in blockchain-based energy trading are identified, and the existing energy trading schemes are studied and classified into three categories based on their main focus: energy transaction, consensus mechanism, and system optimization.
Abstract: With the rapid growth of renewable energy resources, energy trading has been shifting from the centralized manner to distributed manner. Blockchain, as a distributed public ledger technology, has been widely adopted in the design of new energy trading schemes. However, there are many challenging issues in blockchain-based energy trading, e.g., low efficiency, high transaction cost, and security and privacy issues. To tackle these challenges, many solutions have been proposed. In this survey, the blockchain-based energy trading in the electrical power system is thoroughly investigated. Firstly, the challenges in blockchain-based energy trading are identified and summarized. Then, the existing energy trading schemes are studied and classified into three categories based on their main focuses: energy transaction, consensus mechanism, and system optimization. Blockchain-based energy trading has been a popular research topic, new blockchain architectures, models and products are continually emerging to overcome the limitations of existing solutions, forming a virtuous circle. The internal combination of different blockchain types and the combination of blockchain with other technologies improve the blockchain-based energy trading system to better satisfy the practical requirements of modern power systems. However, there are still some problems to be solved, for example, the lack of regulatory system, environmental challenges and so on. In the future, we will strive for a better optimized structure and establish a comprehensive security assessment model for blockchain-based energy trading system.

134 citations


Journal ArticleDOI
TL;DR: Different IoT-based machine learning mechanisms that are used in the mentioned fields among others are studied and the lessons learned are reported and the assessments are explored viewing the basic aim machine learning techniques are expected to play in IoT networks.

129 citations


Journal ArticleDOI
TL;DR: The results indicate that the evaluation method is able to depict the evolving process of the dynamic attacking strategies in a vehicular network, and the final state of the simulation could be used to quantify the protection effectiveness of the reputation management scheme.
Abstract: Conducting reputation management is very important for Internet of vehicles. However, most of the existing work evaluate the effectiveness of their schemes with settled attacking behaviors in their simulation, which cannot represent the real scenarios. In this paper, we propose to consider dynamical and diversity attacking strategies in the simulation of reputation management scheme evaluation. To that end, we apply evolutionary game theory to model the evolution process of malicious users’ attacking strategies, and discuss the methodology of the evaluation simulations. We further apply our evaluation method to a reputation management scheme with multiple utility functions, and discuss the evaluation results. The results indicate that our evaluation method is able to depict the evolving process of the dynamic attacking strategies in a vehicular network, and the final state of the simulation could be used to quantify the protection effectiveness of the reputation management scheme.

128 citations


Journal ArticleDOI
TL;DR: Deep learning methods are applied to build a high-precision BGP route decision process model that handles as much available routing data as possible to promote the prediction accuracy and could help in detecting routing dynamics and route anomalies for routing behavior analysis.

Journal ArticleDOI
TL;DR: A convex–concave-procedure-based contract optimization algorithm for server recruitment and a matching-learning-based task offloading mechanism, which takes both occurrence awareness and conflict awareness into consideration, are proposed.
Abstract: Vehicular fog computing has emerged as a cost-efficient solution for task processing in vehicular networks. However, how to realize effective server recruitment and reliable task offloading under information asymmetry and uncertainty remains a critical challenge. In this paper, we adopt a two-stage task offloading framework to address this challenge. First, we propose a convex–concave-procedure-based contract optimization algorithm for server recruitment, which aims to maximize the expected utility of the operator with asymmetric information. Then, a low-complexity and stable task offloading mechanism is proposed to minimize the total network delay based on the pricing-based matching. Furthermore, we extend the work to the scenario of information uncertainty and develop a matching-learning-based task offloading mechanism, which takes both occurrence awareness and conflict awareness into consideration. Simulation results demonstrate that the proposed algorithm can effectively motivate resource sharing and guarantee bounded deviation from the optimal performance without the global information.

Journal ArticleDOI
TL;DR: This paper investigates ICN as communication enabler for IoT domain specific use cases, and the use of ICN features for the benefit of IoT networks, including IoT device & content naming, discovery, and caching.

Journal ArticleDOI
TL;DR: This letter study and discuss the applicability of merging deep learning (DL) models, i.e., convolutional neural network (CNN), recurrent neural network and reinforcement learning (RL), with IoT and information-centric networking which is a promising future Internet architecture, combined all together with the EC concept.
Abstract: Various Internet solutions take their power processing and analysis from cloud computing services. Internet of Things (IoT) applications started discovering the benefits of computing, processing, and analysis on the device itself aiming to reduce latency for time-critical applications. However, on-device processing is not suitable for resource-constraints IoT devices. Edge computing (EC) came as an alternative solution that tends to move services and computation more closer to consumers, at the edge. In this letter, we study and discuss the applicability of merging deep learning (DL) models, i.e., convolutional neural network (CNN), recurrent neural network (RNN), and reinforcement learning (RL), with IoT and information-centric networking which is a promising future Internet architecture, combined all together with the EC concept. Therefore, a CNN model can be used in the IoT area to exploit reliably data from a complex environment. Moreover, RL and RNN have been recently integrated into IoT, which can be used to take the multi-modality of data in real-time applications into account.

Journal ArticleDOI
TL;DR: This paper forms the problem of an energy-efficient online SFC request that is orchestrated across multiple clouds as an integer linear programming (ILP) model to find an optimal solution and proposes a low-complexity heuristic algorithm named EE-SFCO-MD for near-optimally solving the mentioned problem.

Journal ArticleDOI
TL;DR: This work proposes an activity monitoring and recognition framework, which is based on multi-class cooperative categorization procedure to improve the activity classification accuracy in videos supporting the fog or cloud computing-based blockchain architecture.

Journal ArticleDOI
TL;DR: A taxonomy of the Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users.
Abstract: Social networking sites engage millions of users around the world. The users' interactions with these social sites, such as Twitter and Facebook have a tremendous impact and occasionally undesirable repercussions for daily life. The prominent social networking sites have turned into a target platform for the spammers to disperse a huge amount of irrelevant and deleterious information. Twitter, for example, has become one of the most extravagantly used platforms of all times and therefore allows an unreasonable amount of spam. Fake users send undesired tweets to users to promote services or websites that not only affect legitimate users but also disrupt resource consumption. Moreover, the possibility of expanding invalid information to users through fake identities has increased that results in the unrolling of harmful content. Recently, the detection of spammers and identification of fake users on Twitter has become a common area of research in contemporary online social Networks (OSNs). In this paper, we perform a review of techniques used for detecting spammers on Twitter. Moreover, a taxonomy of the Twitter spam detection approaches is presented that classifies the techniques based on their ability to detect: (i) fake content, (ii) spam based on URL, (iii) spam in trending topics, and (iv) fake users. The presented techniques are also compared based on various features, such as user features, content features, graph features, structure features, and time features. We are hopeful that the presented study will be a useful resource for researchers to find the highlights of recent developments in Twitter spam detection on a single platform.

Journal ArticleDOI
TL;DR: A novel EoT computing framework for secure and smart healthcare surveillance services that rapidly accelerates the analysis response time and performance of the encrypted data processing while preserving a high level of analysis accuracy and data privacy.
Abstract: The vast development of the Internet of Things (IoT) and cloud-enabled data processing solutions provide the opportunity to build novel and fascinating smart, connected healthcare systems. Smart healthcare systems analyze the IoT-generated patient data to both enhance the quality of patient care and reduce healthcare costs. A major challenge for these systems is how the Cloud of Things can handle the data generated from billions of connected IoT devices. Edge computing infrastructure offers a promising solution by operating as a middle layer between the IoT devices and cloud computing. The Edge of Things (EoT) can offer small-scale real-time computing and storage capabilities that ensures low latency and optimal utilization of the IoT resources. However, the EoT has privacy-preservation issues, which is a significant concern for the healthcare systems that contain sensitive patient data. This paper introduces a novel EoT computing framework for secure and smart healthcare surveillance services. Fully homomorphic encryption preserves data privacy and is stored and processed within an EoT framework. A distributed approach for clustering-based techniques is developed for the proposed EoT framework with the scalability to aggregate and analyze the large-scale and heterogeneous data in the distributed EoT devices independently before it is sent to the cloud. We demonstrate the proposed framework by evaluating a case study for the patient biosignal data. Our framework rapidly accelerates the analysis response time and performance of the encrypted data processing while preserving a high level of analysis accuracy and data privacy.

Journal ArticleDOI
22 Feb 2019
TL;DR: A redactable consortium blockchain which is efficient for IIoT devices to operate and allows a group of authorized sensors to write and rewrite blockchain without causing any hard forks is built.
Abstract: Applying consortium blockchain as a trust layer for heterogeneous industrial Internet-of-Things devices is cost-effective. However, with an increase in computing power, some powerful attacks (e.g., the 51% attack) are inevitable and will cause severe consequences. Recent studies also confirm that anonymity and immutability of blockchain have been abused to facilitate black market trades, etc. To operate controllable blockchain for IIoT devices, it is necessary to rewrite blockchain history back to a normal state once the chain is breached. Ateniese et al. proposed redactable blockchain by using chameleon hash (CH) to replace traditional hash function, it allows blockchain history to be written when needed (EuroSP (2) update the signatures accordingly to authenticate the redacted contents; (3) satisfy the low-computing need of the individual IIoT device. In this paper, we overcome the above issues by proposing the first threshold chameleon hash (TCH) and accountable-and-sanitizable chameleon signature (ASCS) schemes. Based on them, we build a redactable consortium blockchain which is efficient for IIoT devices to operate. It allows a group of authorized sensors to write and rewrite blockchain without causing any hard forks. Basically, TCH is the first TCH and ASCS is a public-key signature supporting file-level and block-level modifications of signatures without impairing authentications. Additionally, ASCS achieves accountability to avoid abuse of redaction. While security analysis validates our proposals, the simulation results show that redaction is acceptably efficient if it is executed at a small scale or if we adopt a coarse-grained redaction while sacrificing some securities.

Journal ArticleDOI
TL;DR: A Holistic Cross-domain trust management model (HoliTrust) that is based on multilevel central authorities and focuses on several holistic trust objectives, such as trust relation and decision, data perception trust, and privacy preservation is proposed.
Abstract: Internet of Things (IoT) is proposed and used in diverse application domains. In IoT, nodes commonly have a low capacity to maintain security on their own expenses, which increases the vulnerability for several attacks. Many approaches have been proposed that are based on privacy and trust management to reduce these vulnerabilities. Existing approaches neglect the aspects of cross-domain node communications and the significance of cross-domain trust management. In this paper, we propose a Holistic Cross-domain trust management model (HoliTrust) that is based on multilevel central authorities. To provide multilevel security, the HoliTrust divides domains into communities on the basis of similarities and interests. Every community has its dedicated server to calculate and manage the degree of trust. In addition, these domains also have their dedicated servers to manage their specific domains, to communicate with the trust server, and to sustain trust among other domain servers. The trust sever is introduced in the HoliTrust that controls the domains, calculates the domain trust, manages the trust values, and distributes standard trust certificates to domains based on a degree of trust. Trust computation is performed on the basis of direct and indirect trust parameters. Furthermore, if a trustor communicates through the community, then the community server includes community trust of the trustee during the trust evaluation. If the communication of the trustor is across the domain, then the community server includes the domain trust along with the community trust of the trustee comprising direct and indirect observations. The overall trust evaluation of communities and domains is time-driven and the responsible authority computes trust after a specific interval of time. We have also compared the HoliTrust with the existing trust mechanisms by focusing on several holistic trust objectives, such as trust relation and decision, data perception trust, and privacy preservation.

Journal ArticleDOI
TL;DR: The analyses and simulation results validate that the proposed ALP prolongs the network lifetime and has a higher packet delivery ratio than the existing protocols.
Abstract: In this paper, an autonomous underwater vehicle (AUV) location prediction (ALP)-based data collection scheme (ALP) has been proposed to overcome high and unbalanced energy consumption for underwater wireless sensor networks. In our scheme, an AUV travels around the network, follows a predefined trajectory, and collects data from the sensor nodes. The nodes near the trajectory send their data to the AUV directly, while the others send data to their neighbors that are closer to the trajectory. To overcome the “hot region” problem, which means the nodes near the trajectory of the AUV consume energy faster and die early, a trajectory adjustment mechanism is applied. A mathematical model is proposed to adjust the trajectory periodically. To guarantee an efficient communication between the nodes and the AUV, a reliable time mechanism is proposed. In this mechanism, only the nodes with a sufficient amount of time are capable to communicate and send their data to the AUV directly. The analyses and simulation results validate that our proposed ALP prolongs the network lifetime and has a higher packet delivery ratio than the existing protocols.

Journal ArticleDOI
TL;DR: This paper modeled the network content as a heterogeneous information network (HIN) to achieve the automatic selection, storage and delivery of popular content in the IoV and found that this popular content caching method is more secure and effective, which can reduce the network load, improve user satisfaction and bring a higher quality of experience (QoE) and a better quality of service (QoS) to users.
Abstract: With the rapid development of the Internet of Vehicles (IoV), the data in the network have become more complicated, and user demand for popular content has been growing. The focus of this paper is how to address the ever-changing mobile network environment of the IoV with the popular content caching strategy. To achieve the automatic selection, storage and delivery of popular content in the IoV, this paper modeled the network content as a heterogeneous information network (HIN). In this way, we can greatly reduce the load of the limited network with a small computational cost and give the user in the car a better experience with the automatic caching mode, in which the popular content can be cached in real time. For high-risk driving behavior, this popular content caching method is more secure and effective, which can reduce the network load, improve user satisfaction and bring a higher quality of experience (QoE) and a better quality of service (QoS) to users, as confirmed by the experimental results.

Journal ArticleDOI
TL;DR: A cross-domain robust distributed trust management (RobustTrust) system is proposed, which makes a device fit for assessing trust towards different devices locally and is event-driven that helps nodes to evaluate trust more effectively as well as enhance the system efficiency.
Abstract: In the promising time of the Internet, connected things have the ability to communicate and share information. The Internet of Things (IoT) cannot be implemented unless the security-related concerns have been resolved. Sharing information among different devices can compromise the private information of users. Thus, a suitable mechanism is needed to exclude the risk of malicious and compromised nodes. As follows, trust has been proposed in the literature as a useful technology to maintain users' security. Prior studies have proposed diverse trust management mechanisms to achieve adequate trust. The approach of cross-domain trust management is neglected that requires enormous considerations to address the difficulties related to cross-domain communication. In this paper, a cross-domain robust distributed trust management (RobustTrust) system is proposed, which makes a device fit for assessing trust towards different devices locally. In this system, the trust is divided into three components of security that help IoT nodes to become robust against compromised and malicious devices/nodes. The novelty of the proposed mechanism can be summarized in these aspects: A highly scalable trust mechanism, multiple components of evaluation to enhance robustness against attacks, and use of recommendations along with the feedback to build knowledge. Furthermore, the proposed mechanism is event-driven that helps nodes to evaluate trust more effectively as well as enhance the system efficiency. The proposed work is compared with the available trust evaluation schemes by concentrating on various attributes, such as trustworthiness, usability, and accuracy among others. The RobustTrust is validated by the extensive simulations considering absolute trust value's performance, the accuracy of trust estimation, and several potential attacks.

Proceedings ArticleDOI
01 Dec 2019
TL;DR: This paper addressed the computational issues of existing ethereum blockchain by proposing a proof of authority consensus protocol through the Pagerank mechanism in order to derive the reputation scores and shows the efficiency of the proposed model to minimize privacy risk, and maximize aggregator's profit.
Abstract: The emergence of smart home appliances has generated a high volume of data on smart meters belonging to different customers. However, customers can not share their data in deregulated smart grids due to privacy concern. Although, these data are important for the service provider in order to provide an efficient service. To encourage the customers' participation, this paper proposes an access control mechanism by fairly compensating customers for their participation in data sharing via blockchain using the concept of differential privacy. We addressed the computational issues of existing ethereum blockchain by proposing a proof of authority consensus protocol through the Pagerank mechanism in order to derive the reputation scores. Experimental results show the efficiency of the proposed model to minimize privacy risk, and maximize aggregator's profit. In addition, gas consumption, as well as the cost of the computational resources, is reduced.

Journal ArticleDOI
TL;DR: A comprehensive view of the IoMT and its related Machine Learning (ML)-based developed frameworks designed, or being utilized, in the last decade, i.e., from 2010 to 2019 is presented.
Abstract: Impressive growth in the number of wearable health monitoring devices has affected global health industry as they provide rapid and intricate details related to physical examinations, such as discomfort, heart rate, and blood glucose level, which enable doctors to efficiently diagnose sensitive heart troubles. The Internet of Medical Things (IoMT) is a phenomenon wherein computer networks and medical equipment are connected through the Internet to provide real-time interaction between physicians and patients. In this article, we present a comprehensive view of the IoMT and its related Machine Learning (ML)-based developed frameworks designed, or being utilized, in the last decade, i.e., from 2010 to 2019. The presented techniques are designed for monitoring limbs, controlling rural healthcare, identifying e-health applications, monitoring health through mobile apps, classifying heart sounds, detecting stress in drivers, monitoring cardiac diseases, making the decision to predict heart attacks, recognizing human activities, and classifying breast cancer. The aim is to provide a clear picture of the existing IoMT environment so that the analysis may pave the way for the diagnosis of critical disorders such as cancer, heart attack, and blood pressure among others. In the end, we also provide some unresolved challenges that are confronted in the deployment of the secure IoMT-based healthcare systems.

Journal ArticleDOI
TL;DR: This paper proposes a prediction-based delay optimization data collection algorithm (PDO-DC), which uses Kernel Ridge Regression (KRR) via cluster member nodes to obtain the corresponding prediction models and can effectively reduce the collection delay of the AUV.
Abstract: The past years have seen a rapid development of autonomous underwater vehicle-aided (AUV-aided) data-gathering schemes in underwater acoustic sensor networks (UASNs). The use of AUVs efficiently reduces energy consumption of sensor nodes. However, all AUV-aided solutions face severe problems in data collection delay, especially in a large-scale network. In this paper, to reduce data collection delay, we propose a prediction-based delay optimization data collection algorithm (PDO-DC). On the contrary to the traditional delay optimization algorithms, Kernel Ridge Regression (KRR) is utilized via cluster member nodes to obtain the corresponding prediction models. Then, the AUV can obtain all cluster data by traversing less cluster head nodes, which can effectively reduce the collection delay of the AUV. The experimental results demonstrate that the proposed method is both feasible and effective.

Journal ArticleDOI
TL;DR: A detail survey of existing Location Privacy Protection techniques in WSNs is presented, which provides a review of the literature about location privacy protection, which is classified into the following three categories: 1) source nodes' location privacy preserving, 2) sink nodes' Location Privacy preserving, and 3) location Privacy preserving for both source and sink nodes.

Journal ArticleDOI
TL;DR: The aim of this paper is to identify and explore the new paradigm of MCS that is using smartphone for capturing and sharing the sensed data between many nodes and discusses the current challenges facing the collection methodologies of the participants’ data in task management.
Abstract: Mobile crowd-sensing (MCS) is a new sensing paradigm that takes advantage of the extensive use of mobile phones that collect data efficiently and enable several significant applications. MCS paves the way to explore new monitoring applications in different fields such as social networks, lifestyle, healthcare, green applications, and intelligent transportation systems. Hence, MCS applications make use of sensing and wireless communication capabilities provided by billions of smart mobile devices, e.g., Android and iOS-based mobile devices. The aim of this paper is to identify and explore the new paradigm of MCS that is using smartphone for capturing and sharing the sensed data between many nodes. We discuss the main components of the infrastructure required to support the proposed framework. The existing and potential applications leveraging MCS are laid out. Furthermore, this paper discusses the current challenges facing the collection methodologies of the participants’ data in task management. The recent issues in the MCS findings are reviewed as well as the opportunities and challenges in sensing methods are analyzed. Finally, open research issues and future challenges facing MCS are highlighted.

Journal ArticleDOI
TL;DR: A new scheme for trust authority (TA) security queries in fog computing to obtain outsourced encrypted map lists (MPLs) of the participants to achieve online traceability and identity retrieval for malicious participants is proposed in this study, which can reduce the storage burden of TA.

Journal ArticleDOI
24 Jul 2019
TL;DR: This article presents fog-computing-based VANet architecture along with an infotainment application scenario and presents various benefits of a fog-enabled VANET while investigating future challenges and their potential solutions.
Abstract: Emerging vehicular ad hoc network (VANET) applications are requiring a lot more communication and computation capabilities. Today's cloud infrastructures are not specifically designed to cope with the requirements presented by promising VANET applications. In this regard, fog computing focuses on moving computational resources toward the edge of the network to cater for these increasing processing and storage requirements. Thus, vehicular fog extends the fog computing paradigm to traditional vehicular networks that consist of several IoT devices. This overcomes the shortcomings of vehicular clouds by enabling ubiquitous vehicles, efficient communication, location-aware service provision, improved response time, and lower latency. This article presents fog-computing-based VANET architecture along with an infotainment application scenario. In the experiments, the cache size of fog nodes has been used as a parameter to evaluate its impact on different performance metrics in the fog-enabled VANET environment. We conclude the article by presenting various benefits of a fog-enabled VANET while investigating future challenges and their potential solutions.