Showing papers by "Mohsen Guizani published in 2018"

Deep Learning for IoT Big Data and Streaming Analytics: A Survey

Abstract: In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.

Semisupervised Deep Reinforcement Learning in Support of IoT and Smart City Services

Abstract: Smart services are an important element of the smart cities and the Internet of Things (IoT) ecosystems where the intelligence behind the services is obtained and improved through the sensory data. Providing a large amount of training data is not always feasible; therefore, we need to consider alternative ways that incorporate unlabeled data as well. In recent years, deep reinforcement learning (DRL) has gained great success in several application domains. It is an applicable method for IoT and smart city scenarios where auto-generated data can be partially labeled by users’ feedback for training purposes. In this paper, we propose a semisupervised DRL model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent. The model utilizes variational autoencoders as the inference engine for generalizing optimal policies. To the best of our knowledge, the proposed model is the first investigation that extends DRL to the semisupervised paradigm. As a case study of smart city applications, we focus on smart buildings and apply the proposed model to the problem of indoor localization based on Bluetooth low energy signal strength. Indoor localization is the main component of smart city services since people spend significant time in indoor environments. Our model learns the best action policies that lead to a close estimation of the target locations with an improvement of 23% in terms of distance to the target and at least 67% more received rewards compared to the supervised DRL model.

Edge Computing in the Industrial Internet of Things Environment: Software-Defined-Networks-Based Edge-Cloud Interplay

Abstract: The emergence of the Industrial Internet of Things (IIoT) has paved the way to real-time big data storage, access, and processing in the cloud environment. In IIoT, the big data generated by various devices such as-smartphones, wireless body sensors, and smart meters will be on the order of zettabytes in the near future. Hence, relaying this huge amount of data to the remote cloud platform for further processing can lead to severe network congestion. This in turn will result in latency issues which affect the overall QoS for various applications in IIoT. To cope with these challenges, a recent paradigm shift in computing, popularly known as edge computing, has emerged. Edge computing can be viewed as a complement to cloud computing rather than as a competition. The cooperation and interplay among cloud and edge devices can help to reduce energy consumption in addition to maintaining the QoS for various applications in the IIoT environment. However, a large number of migrations among edge devices and cloud servers leads to congestion in the underlying networks. Hence, to handle this problem, SDN, a recent programmable and scalable network paradigm, has emerged as a viable solution. Keeping focus on all the aforementioned issues, in this article, an SDN-based edge-cloud interplay is presented to handle streaming big data in IIoT environment, wherein SDN provides an efficient middleware support. In the proposed solution, a multi-objective evolutionary algorithm using Tchebycheff decomposition for flow scheduling and routing in SDN is presented. The proposed scheme is evaluated with respect to two optimization objectives, that is, the trade-off between energy efficiency and latency, and the trade-off between energy efficiency and bandwidth. The results obtained prove the effectiveness of the proposed flow scheduling scheme in the IIoT environment.

5G D2D Networks: Techniques, Challenges, and Future Prospects

Abstract: The increasing number of mobile users has given impetus to the demand for high data rate proximity services. The fifth-generation (5G) wireless systems promise to improve the existing technology according to the future demands and provide a road map for reliable and resource-efficient solutions. Device-to-device (D2D) communication has been envisioned as an allied technology of 5G wireless systems for providing services that include live data and video sharing. A D2D communication technique opens new horizons of device-centric communications, i.e., exploiting direct D2D links instead of relying solely on cellular links. Offloading traffic from traditional network-centric entities to D2D network enables low computational complexity at the base station besides increasing the network capacity. However, there are several challenges associated with D2D communication. In this paper, we present a survey of the existing methodologies related to aspects such as interference management, network discovery, proximity services, and network security in D2D networks. We conclude by introducing new dimensions with regard to D2D communication and delineate aspects that require further research.

When Mobile Crowd Sensing Meets UAV: Energy-Efficient Task Assignment and Route Planning

Abstract: With the increasing popularity of unmanned aerial vehicles (UAVs), it is foreseen that they will play an important role in broadening the horizon of mobile crowd sensing (MCS). Specifically, UAV-aided MCS allows autonomous data collection anytime and anywhere due to the capability of fast deployment and controllable mobility. However, the on-board battery capacity of UAVs imposes a limitation on their endurance capability and performance. In this paper, we consider the fixed-wing UAV-aided MCS system and investigate the corresponding joint route planning and task assignment problem from an energy efficiency perspective. The formulated joint optimization problem is transformed into a two-sided two-stage matching problem, in which the route planning problem is solved in the first stage based on either dynamic programming or genetic algorithms, and the task assignment problem is addressed in the second stage by exploring the Gale–Shapley algorithm. We provide a comprehensive theoretical analysis, and elaborate the procedures of practical implementation. Numerical results demonstrate that significant performance improvement can be achieved by the proposed scheme.

Security in Mobile Edge Caching with Reinforcement Learning

Abstract: Mobile edge computing usually uses caching to support multimedia contents in 5G mobile Internet to reduce the computing overhead and latency. Mobile edge caching (MEC) systems are vulnerable to various attacks such as denial of service attacks and rogue edge attacks. This article investigates the attack models in MEC systems, focusing on both the mobile offloading and the caching procedures. In this article, we propose security solutions that apply reinforcement learning (RL) techniques to provide secure offloading to the edge nodes against jamming attacks. We also present lightweight authentication and secure collaborative caching schemes to protect data privacy. We evaluate the performance of the RL-based security solution for mobile edge caching and discuss the challenges that need to be addressed in the future.

A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security

Abstract: The Internet of Things (IoT) integrates billions of smart devices that can communicate with one another with minimal human intervention. It is one of the fastest developing fields in the history of computing, with an estimated 50 billion devices by the end of 2020. On the one hand, IoT play a crucial role in enhancing several real-life smart applications that can improve life quality. On the other hand, the crosscutting nature of IoT systems and the multidisciplinary components involved in the deployment of such systems introduced new security challenges. Implementing security measures, such as encryption, authentication, access control, network security and application security, for IoT devices and their inherent vulnerabilities is ineffective. Therefore, existing security methods should be enhanced to secure the IoT system effectively. Machine learning and deep learning (ML/DL) have advanced considerably over the last few years, and machine intelligence has transitioned from laboratory curiosity to practical machinery in several important applications. Consequently, ML/DL methods are important in transforming the security of IoT systems from merely facilitating secure communication between devices to security-based intelligence systems. The goal of this work is to provide a comprehensive survey of ML /DL methods that can be used to develop enhanced security methods for IoT systems. IoT security threats that are related to inherent or newly introduced threats are presented, and various potential IoT system attack surfaces and the possible threats related to each surface are discussed. We then thoroughly review ML/DL methods for IoT security and present the opportunities, advantages and shortcomings of each method. We discuss the opportunities and challenges involved in applying ML/DL to IoT security. These opportunities and challenges can serve as potential future research directions.

Multiple Moving Targets Surveillance Based on a Cooperative Network for Multi-UAV

Abstract: With the development of better links, enhanced coverage, comprehensive data resources, and network system stability, the cooperative network formed by wireless sensor networks and unmanned aerial vehicles is envisioned to provide immediate and long-term benefits in military and civilian fields. Previous works mainly focus on how to use UAVs to assist WSNs in sensing and data collection jobs, or target localization with a single data source in surveillance systems, while the potential of multi-UAV sensor networks has not been fully explored. To this end, we propose a new cooperative network platform and system architecture of multi-UAV surveillance. First we propose the design concepts of a multi- UAV cooperative resource scheduling and task assignment scheme based on the animal colony perception method. Second, we provide the moving small target recognition technique and localization and tracking model using the fusion of multiple data sources. In addition, this article discusses the establishment of suitable algorithms based on machine learning due to the complexity of the monitoring area. Finally, experiments of recognition and tracking of multiple moving targets are addressed, which are monitored by multi- UAV and sensors.

Towards Dynamic Coordination Among Home Appliances Using Multi-Objective Energy Optimization for Demand Side Management in Smart Buildings

Abstract: In this paper, we propose a home energy management system that employs load shifting strategy of demand side management to optimize the energy consumption patterns of a smart home. It aims to manage the load demand in an efficient way to minimize electricity cost and peak to average ratio while maintaining user comfort through coordination among home appliances. In order to meet the load demand of electricity consumers, we schedule the load in day-ahead and real-time basis. We propose a fitness criterion for proposed hybrid technique, which helps in balancing the load during ON-peak and OFF-peak hours. Moreover, for real-time rescheduling, we present the concept of coordination among home appliances. This helps the scheduler to optimally decide the ON/OFF status of appliances in order to reduce the waiting time of appliance. For this purpose, we formulate our real-time rescheduling problem as knapsack problem and solve it through dynamic programming. This paper also evaluates the behavior of the proposed technique for three pricing schemes including: time of use, real-time pricing, and critical peak pricing. Simulation results illustrate the significance of the proposed optimization technique with 95% confidence interval.

BPDS: A Blockchain Based Privacy-Preserving Data Sharing for Electronic Medical Records

Abstract: Electronic medical record (EMR) is a crucial form of healthcare data, currently drawing a lot of attention. Sharing health data is considered to be a critical approach to improve the quality of healthcare service and reduce medical costs. However, EMRs are fragmented across decentralized hospitals, which hinders data sharing and puts patients' privacy at risks. To address these issues, we propose a blockchain based privacy-preserving data sharing for EMRs, called BPDS. In BPDS, the original EMRs are stored securely in the cloud and the indexes are reserved in a tamper-proof consortium blockchain. By this means, the risk of the medical data leakage could be greatly reduced, and at the same time, the indexes in blockchain ensure that the EMRs can not be modified arbitrarily. Secure data sharing can be accomplished automatically according to the predefined access permissions of patients through the smart contracts of blockchain. Besides, the joint-design of the CP-ABE-based access control mechanism and the content extraction signature scheme provides strong privacy preservation in data sharing. Security analysis shows that BPDS is a secure and effective way to realize data sharing for EMRs.

Caching in Information-Centric Networking: Strategies, Challenges, and Future Research Directions

Abstract: Information-Centric Networking (ICN) is an appealing architecture that has received a remarkable interest from the research community thanks to its friendly structure. Several projects have proposed innovative ICN models to cope with the Internet practice, which moves from host-centrism to receiver-driven communication. A worth mentioning component of these novel models is in-network caching, which provides flexibility and pervasiveness for the upturn of swiftness in data distribution. Because of the rapid Internet traffic growth, cache deployment and content caching have been unanimously accepted as conspicuous ICN issues to be resolved. In this article, a survey of cache management strategies in ICN is presented along with their contributions and limitations, and their performance is evaluated in a simulation network environment with respect to cache hit, stretch ratio, and eviction operations. Some unresolved ICN caching challenges and directions for future research in this networking area are also discussed.

Blockchain-Based Mobile Edge Computing Framework for Secure Therapy Applications

Abstract: Mobile edge computing (MEC) is being introduced and leveraged in many domains, but few studies have addressed MEC for secure in-home therapy management. To this end, this paper presents an in-home therapy management framework, which leverages the IoT nodes and the blockchain-based decentralized MEC paradigm to support low-latency, secure, anonymous, and always-available spatiotemporal multimedia therapeutic data communication within an on-demand data-sharing scenario. To the best of our knowledge, this non-invasive, MEC-based IoT therapy platform is first done by our group. This platform can provide a full-body joint range of motion data for physically challenged individuals in a decentralized manner. With MEC, the framework can provide therapy diagnostic and analytical data on demand to a large portion of humanity who are either born with disabilities or became disabled due to accidents, war-time injuries, or old age. For security, the framework uses blockchain–Tor-based distributed transactions to preserve the therapeutic data privacy, ownership, generation, storage, and sharing. Our initial test results from a complete implementation of the framework show that it can support a sufficiently large number of users without considerable increase in mean processing time.

Energy Efficient Integration of Renewable Energy Sources in the Smart Grid for Demand Side Management

Abstract: With the emergence of smart grid (SG), the consumers have the opportunity to integrate renewable energy sources (RESs) and take part in demand side management. In this paper, we introduce generic home energy management control system (HEMCS) to efficiently schedule the household load and integrate RESs. The HEMCS is based on the genetic algorithm, binary particle swarm optimization, wind-driven optimization (WDO), and our proposed genetic WDO algorithm to schedule appliances of single and multiple homes. For energy cost calculation, real-time pricing (RTP) and inclined block rate schemes are combined, because in case of only RTP, there is a possibility of building peaks during off-peak hours that may damage the entire power system. Moreover, to control the demand under the grid station capacity, the feasible region is defined and a problem is formulated using multiple knapsack. Energy efficient integration of RESs in SG is a challenging task due to time varying and their intermittent nature. The simulation results show that the proposed scheme avoids voltage rise problem in areas with high penetration of renewable energy. Moreover, the proposed scheme also reduces the electricity cost up to 48% and peak to average ratio of aggregated load up to 37.69%.

Auction Design and Analysis for SDN-Based Traffic Offloading in Hybrid Satellite-Terrestrial Networks

Abstract: Recently, hybrid satellite-terrestrial networks (H-STNs) are expected to support extremely high data rates and exponentially increasing demands of data, which require new spectrum sharing and interference control technology paradigms By achieving an efficient spectrum sharing among H-STN, traffic offloading is a promising solution for boosting the capacity of traditional cellular networks In this paper, a software-defined network-based spectrum sharing, and traffic offloading mechanism is proposed to realize the cooperation and competition between the ground base stations (BSs) of the cellular network and beam groups of the satellite-terrestrial communication (STCom) system Assume that all BSs are operated by the same mobile network operator (MNO) Under the cooperation mode, all the BSs stop occupying a corresponding channel, and a selected beam group of the satellite helps offload the traffic from the BSs by exclusively using this channel To facilitate the offloading negotiation between the MNO and satellite, we design a second-price auction mechanism which presents positive allocative externalities, ie, other uncooperative beam groups of the satellite can benefit from the cooperation between BSs and the beam group performing offloading Meanwhile, the unique optimal biding strategies for different beam groups of the satellite to achieve the symmetric Bayesian equilibrium as well as the expected utility of the MNO are derived and obtained in this paper The performance of the proposed traffic offloading mechanism is validated in the simulations, which also reveal that there exists the unique optimal offloading threshold for the MNO to achieve the maximum expected utility

Privacy in the Internet of Things for Smart Healthcare

Abstract: With the rapid development of wearable biosensors and wireless communication technologies, various smart healthcare systems are proposed to monitor the health of patients in real time. However, many security problems exist in these systems. For example, a password guessing attack can compromise IoT devices, leading to invasion of health data privacy. After giving an overview of security threats of healthcare IoT, this article studies security vulnerabilities of password building and presents a password strength evaluation method that takes into account users' personal information.

Privacy-Preserving DDoS Attack Detection Using Cross-Domain Traffic in Software Defined Networks

Abstract: Existing distributed denial-of-service attack detection in software defined networks (SDNs) typically perform detection in a single domain. In reality, abnormal traffic usually affects multiple network domains. Thus, a cross-domain attack detection has been proposed to improve detection performance. However, when participating in detection, the domain of each SDN needs to provide a large amount of real traffic data, from which private information may be leaked. Existing multiparty privacy protection schemes often achieve privacy guarantees by sacrificing accuracy or increasing the time cost. Achieving both high accuracy and reasonable time consumption is a challenging task. In this paper, we propose Predis , which is a privacy-preserving cross-domain attack detection scheme for SDNs. Predis combines perturbation encryption and data encryption to protect privacy and employs a computationally simple and efficient algorithm k-Nearest Neighbors (kNN) as its detection algorithm. We also improve kNN to achieve better efficiency. Via theoretical analysis and extensive simulations, we demonstrate that Predis is capable of achieving efficient and accurate attack detection while securing sensitive information of each domain.

Security in Mobile Edge Caching with Reinforcement Learning

Abstract: Mobile edge computing usually uses cache to support multimedia contents in 5G mobile Internet to reduce the computing overhead and latency. Mobile edge caching (MEC) systems are vulnerable to various attacks such as denial of service attacks and rogue edge attacks. This article investigates the attack models in MEC systems, focusing on both the mobile offloading and the caching procedures. In this paper, we propose security solutions that apply reinforcement learning (RL) techniques to provide secure offloading to the edge nodes against jamming attacks. We also present light-weight authentication and secure collaborative caching schemes to protect data privacy. We evaluate the performance of the RL-based security solution for mobile edge caching and discuss the challenges that need to be addressed in the future.

Mixed RF/FSO Cooperative Relaying Systems With Co-Channel Interference

Abstract: In this paper, we provide a global framework analysis of a dual-hop mixed radio frequency (RF)/free space optical (FSO) system with multiple branches/relays wherein the first and second hops, respectively, consist of RF and FSO channels. To cover various cases of fading, we propose generalized channels’ models for RF and FSO links that follow the Nakagami-m and the double generalized gamma distributions, respectively. Moreover, we suggest channel state information (CSI)-assisted relaying or variable relaying gain based amplifiy-and-forward amplification. Partial relay selection with outdated CSI is assumed as a relay selection protocol based on the knowledge of the RF CSI. In order to derive the end-to-end signal-to-interference-plus-noise ratio statistics, such as the cumulative distribution function, the probability density function, the higher order moments, the amount of fading and the moment generating function, the numerical values of the fading severity parameters are only valid for integer values. Based on these statistics, we derive closed-forms of the outage probability, the bit error probability, the ergodic capacity, and the outage capacity in terms of Meijer-G, univariate, bivariate, and trivariate Fox-H functions. Capitalizing on these expressions, we derive the asymptotic high SNR to unpack valuable engineering insights of the system performance. Monte Carlo simulation is used to confirm the analytical expressions.

Deep Multi-Layer Perceptron Classifier for Behavior Analysis to Estimate Parkinson’s Disease Severity Using Smartphones

Abstract: Although the preclinical detection of Parkinson’s disease (PD) has been explored, a practical, inexpensive, and overall screening diagnosis has yet to be made available. However, due to the large variability and complexity in progress of PD and the difficulties in gathering a single time-point measurement of a single sign, the goal of precision treatment and assessment severity would be impossible to achieve. Hence, the repeated monitoring and tracking of individuals during their daily living activities at different times would also be of great importance for treating this chronic disease. We propose a deep multi-layer perceptron (DMLP) classifier for behavior analysis to estimate the progression of PD using smartphones. This paper aims to identify severity in PD patients’ actions by analyzing their speech and movement patterns, as measured with a smartphone accelerometer in their pocket at different times of the day. Popular machine learning classification algorithms, such as logistic regression, random forests, k-nearest neighbors, M5P, and DMLP, are applied on one dataset from the University of California Irvine and another dataset collected by the authors to classify each patient as being Parkinson positive or negative. We further measure the success of each method for their ability to correctly classify the patients into one of these categories. Of the experimental models, it is demonstrated that DMLP performs the best in both datasets.

File-Centric Multi-Key Aggregate Keyword Searchable Encryption for Industrial Internet of Things

Abstract: Cloud storage has been used to reduce the cost and support convenient collaborations for industrial Internet of things (IIoT) data management. When data owners share IIoT data with authorized parties for data interaction, secure cloud data searching and file access control are fundamental security requirements. In this paper, first we discuss a new insider attack to the Cui's multi-key aggregate searchable encryption scheme, where the unauthorized inside users can guess the other users private keys. Then, we propose a novel file-centric multi-key aggregate keyword searchable encryption (Fc-MKA-KSE) system for the IIoT data in the file-centric framework. Specifically, we present two formal security models, namely, the security models of the indistinguishable selective-file chosen keyword attack and the indistinguishable selective-file keyword guessing attack, which can satisfy the security requirements. Our experimental results show that the proposed scheme achieves computational efficiency.

Security in the Internet of Things Supported by Mobile Edge Computing

Abstract: While applications of the heterogeneous Internet of Things (IoT) proliferate, their performance requirements in terms of latency and jitter become more stringent and difficult to be met by the traditional cloud computing paradigm. As a result, a new paradigm called multi-access edge computing has emerged in recent years. It complements the centralized cloud platform by providing additional resources at the edge of radio access networks, and provides better support of IoT applications. One of the challenges of supporting IoT applications in mobile edge computing (MEC) is security. In this article, we will scrutinize the security issues of two IoT applications that can exploit the benefits of MEC. The first one is an environment perception system based on an industrial IoT network. The second one, which is gaining momentum fast, is mobile IoT based on a network of unmanned aerial vehicles.

Aggregate Hardware Impairments Over Mixed RF/FSO Relaying Systems With Outdated CSI

Abstract: In this paper, we propose a dual-hop radio-frequency (RF)/free-space optical system with multiple relays employing the decode-and-forward and amplify-and-forward with a fixed gain relaying scheme. The RF channels are subject to a Rayleigh distribution while the optical links experience a unified fading model emcopassing the atmospheric turbulence that follows the Malaga distribution (or also called the $\mathcal {M}$ -distribution), the atmospheric path loss, and the pointing error. Partial relay selection with outdated channel state information is proposed to select the candidate relay to forward the signal to the destination. At the reception, the detection of the signal can be achieved following either heterodyne or intensity modulation and direct detection. Many previous attempts neglected the impact of the hardware impairments and assumed ideal hardware. This assumption makes sense for low data rate systems but it would no longer be valid for high data rate systems. In this paper, we propose a general model of hardware impairment to get insight into quantifying its effects on the system performance. We will demonstrate that the hardware impairments have small impact on the system performance for low signal-to-noise ratio (SNR), but it can be destructive at high SNR values. Furthermore, analytical expressions and upper bounds are derived for the outage probability and ergodic capacity while the symbol error probability is obtained through the numerical integration method. Capitalizing on these metrics, we also derive the high SNR asymptotes to get valuable insight into the system gains, such as the diversity and the coding gains. Finally, analytical and numerical results are presented and validated by the Monte Carlo simulation.

Semi-supervised Deep Reinforcement Learning in Support of IoT and Smart City Services

Abstract: Smart services are an important element of the smart cities and the Internet of Things (IoT) ecosystems where the intelligence behind the services is obtained and improved through the sensory data. Providing a large amount of training data is not always feasible; therefore, we need to consider alternative ways that incorporate unlabeled data as well. In recent years, Deep reinforcement learning (DRL) has gained great success in several application domains. It is an applicable method for IoT and smart city scenarios where auto-generated data can be partially labeled by users' feedback for training purposes. In this paper, we propose a semi-supervised deep reinforcement learning model that fits smart city applications as it consumes both labeled and unlabeled data to improve the performance and accuracy of the learning agent. The model utilizes Variational Autoencoders (VAE) as the inference engine for generalizing optimal policies. To the best of our knowledge, the proposed model is the first investigation that extends deep reinforcement learning to the semi-supervised paradigm. As a case study of smart city applications, we focus on smart buildings and apply the proposed model to the problem of indoor localization based on BLE signal strength. Indoor localization is the main component of smart city services since people spend significant time in indoor environments. Our model learns the best action policies that lead to a close estimation of the target locations with an improvement of 23% in terms of distance to the target and at least 67% more received rewards compared to the supervised DRL model.

An Energy-Efficient VM Prediction and Migration Framework for Overcommitted Clouds

Abstract: We propose an integrated, energy-efficient, resource allocation framework for overcommitted clouds. The framework makes great energy savings by 1) minimizing Physical Machine (PM) overload occurrences via VM resource usage monitoring and prediction, and 2) reducing the number of active PMs via efficient VM migration and placement. Using real Google data consisting of a 29-day traces collected from a cluster containing more than 12K PMs, we show that our proposed framework outperforms existing overload avoidance techniques and prior VM migration strategies by reducing the number of unpredicted overloads, minimizing migration overhead, increasing resource utilization, and reducing cloud energy consumption.

Emerging Trends, Issues, and Challenges in Big Data and Its Implementation toward Future Smart Cities: Part 2

Abstract: The articles in this special section focus on Big Data as it impacts future smart cities. The world is experiencing a period of extreme urbanization. Moreover, this process will continue, and the global urban population is expected to double by 2050. Smart city has been proposed to improve the efficiency of services and meet residents’ needs for better quality of life. Essentially, smart city integrates the Internet of Things and emerging communication technologies such as fifth generation (5G) solutions to manage the citys’ assets, including transportation systems, hospitals, water supply networks, waste management, and so on. Therefore, smart city is driving innovation and new technologies, especially big data technologies for the big data era. In the future smart city, there is an urgent need to address the following issues: how to design algorithms to process mass data and how to utilize big data to improve the quality of service (QoS) for future smart cities.

Assured Data Deletion With Fine-Grained Access Control for Fog-Based Industrial Applications

Abstract: The advances of cloud computing, fog computing, and Internet of things (IoT) make industries more prosperous than ever. A wide range of industrial systems such as transportation and manufacturing systems have been developed by integrating cloud computing, fog computing, and IoT infrastructure successfully. However, in this sophisticated system, security and privacy issues are major concerns that hinder the widespread adoptions of these novel techniques. In this paper, we focus on assured data deletion, an issue that is important but received less attention in academia and industry. We first propose a framework to integrate the cloud, the fog, and the things together to manage stored data from industries or individuals. We then focus on secure data deletion in this framework by proposing an assured data deletion scheme that fulfills verifiable data deletion as well as flexible access control over sensitive data. Only data owners and fog devices are involved when deleting cloud data and validating the deletion of these data, which makes the protocol practical due to the features of low latency as well as real-time interaction with fog. The proposed protocol takes advantage of the attribute-based encryption, whose security can be proved under the standard model. The theoretical analysis shows good performance and functionality requirements while the implementation results demonstrate the feasibility of our proposal.

KCLP: A k-Means Cluster-Based Location Privacy Protection Scheme in WSNs for IoT

Abstract: While enjoying the convenience brought by the Internet of Things (IoT), people also encounter many problems with wireless sensor networks (WSNs), the foundation of IoT. Security problems are especially of concern. In this article, we focus on location privacy, which is a major security issue in WSNs, and propose a k-means cluster-based location privacy (KCLP) protection scheme for IoT. To protect the source location, fake source nodes are used to simulate the function of the real sources. Then, to protect the sink location privacy, fake sink nodes and a specific transmission pattern are utilized. In order to improve safety time, a k-means cluster is applied to create clusters and fake packets that must pass through the area. Compared to contrasting algorithms, the KCLP scheme can increase the safety time and reduce delay at minor expense in energy consumption.

BPDS: A Blockchain based Privacy-Preserving Data Sharing for Electronic Medical Records

Abstract: Electronic medical record (EMR) is a crucial form of healthcare data, currently drawing a lot of attention. Sharing health data is considered to be a critical approach to improve the quality of healthcare service and reduce medical costs. However, EMRs are fragmented across decentralized hospitals, which hinders data sharing and puts patients' privacy at risks. To address these issues, we propose a blockchain based privacy-preserving data sharing for EMRs, called BPDS. In BPDS, the original EMRs are stored securely in the cloud and the indexes are reserved in a tamper-proof consortium blockchain. By this means, the risk of the medical data leakage could be greatly reduced, and at the same time, the indexes in blockchain ensure that the EMRs can not be modified arbitrarily. Secure data sharing can be accomplished automatically according to the predefined access permissions of patients through the smart contracts of blockchain. Besides, the joint-design of the CP-ABE-based access control mechanism and the content extraction signature scheme provides strong privacy preservation in data sharing. Security analysis shows that BPDS is a secure and effective way to realize data sharing for EMRs.

Bus-Trajectory-Based Street-Centric Routing for Message Delivery in Urban Vehicular Ad Hoc Networks

Abstract: This paper focuses on the routing algorithm for the communications between vehicles and places in urban vehicular ad hoc networks. As one of the basic transportation facilities in an urban setting, buses periodically run along their fixed routes and cover many city streets. The trajectory of bus lines can be seen as a submap of a city. Based on the characters of bus networks, we propose a bus-trajectory-based street-centric (BTSC) routing algorithm, which uses buses as the main relay to deliver messages. In BTSC, we build a routing graph based on the trajectories of bus lines by analyzing the probability of bus appearing on every street. We propose two novel concepts, i.e., the probability of street consistency and the probability of path consistency, which are used as metrics to determine routing paths for message delivery. This aims to choose the best path with higher density of busses and lower probability of transmission direction deviating from the routing path. In order to improve the bus forwarding opportunity, we design a bus-based forwarding strategy with ant colony optimization to find a reliable and a steady multihop link between two relay buses in order to decrease the end-to-end delay. The BTSC makes improvements in the selection of routing paths and a strategy of message forwarding. Simulation results show that our proposed routing algorithm has a better performance in terms of the transmission ratio, transmission delay, and adaptability to different networks.

Privacy and Security Issues in Online Social Networks

Abstract: The advent of online social networks (OSN) has transformed a common passive reader into a content contributor. It has allowed users to share information and exchange opinions, and also express themselves in online virtual communities to interact with other users of similar interests. However, OSN have turned the social sphere of users into the commercial sphere. This should create a privacy and security issue for OSN users. OSN service providers collect the private and sensitive data of their customers that can be misused by data collectors, third parties, or by unauthorized users. In this paper, common security and privacy issues are explained along with recommendations to OSN users to protect themselves from these issues whenever they use social media.