Artificial Intelligence of Things (AIoT), as a fusion of artificial intelligence (AI) and Internet of Things (IoT), has become a new trend to realize the intelligentization of industry 4.0 and the data privacy and security is the key to its successful implementation. To enhance data privacy protection, the federated learning has been introduced in AIoT, which allows participants to jointly train AI models without sharing private data. However, in federated learning, malicious participants might provide malicious models by launching the poisoning attack, which will jeopardize the convergence and accuracy of the global model. To solve this problem, we propose a malicious model detection mechanism based on the isolation forest (iforest), named D2MIF, for the federated learning-empowered AIoT. In D2MIF, an iforest is constructed to compute the malicious score for each model uploaded by the corresponding participant, and then, the models will be filtered if their malicious scores are higher than the threshold, which is dynamically adjusted using reinforcement learning (RL). The validation experiment is conducted on two public data sets Mnist and Fashion_Mnist. The experimental results show that the proposed D2MIF can effectively detect malicious models and significantly improve the global model accuracy in federated learning-empowered AIoT.

The ability to detect which wireless devices are belonging to the same person from Wi-Fi access point (AP) enables many potential Internet-of-Things (IoT) applications, including continuous authentication and user-oriented devices isolation. The existing cryptographic-based solutions are not suitable for IoT devices with limited power and computing capabilities. The development of electronics and chip technology makes it possible to deploy machine learning (ML) algorithms on APs. In this article, we propose an on-body device clustering (OBDC) scheme. First, the OBDC extracts the trajectory and gait patterns from wireless signals when the user is moving. Second, it utilizes a hierarchical clustering algorithm to measure the similarity of wireless signal patterns between devices. Finally, if the devices are clustered into the same cluster, they are considered to be carried by the same person. Our real-world experimental results show that the devices from about 90% of users can be clustered correctly, while maintaining the devices from only 0.7% of users may be clustered into the same cluster with others’ devices incorrectly.

Abstract Family inheritance distribution is an interesting and challenging problem. When a person dies, all of their wealth is passed on to their heirs. Heirs are family members of the deceased with specific relationships. The distribution process of wealth in Islam has many rules that make it difficult to understand and require an expert to solve. Many interrelated rules make it difficult to teach and learn. Therefore, there have been multiple attempts to develop an expert system to help solve the family inheritance problem. The existing solutions have some limitations in terms of the completeness of rules, and user‐system interactions, which make these systems unreliable. In this study, we developed an expert system to calculate family inheritance. The two main contributions of this work are the development of a complete set of inheritance rules and the design of intelligent user‐system interaction. We innovate the design of the family tree, which plays the role of an interaction layer with the user. The results of our proposed system demonstrate an accuracy of approximately 93%.

Two-dimensional 1 arrays of bi-component structures made of cobalt and permalloy elliptical dots with thickness of 25 nm, length 1 mm and width of 225 nm, have been prepared by a self-aligned shadow deposition technique. Brillouin light scattering has been exploited to study the frequency dependence of thermally excited magnetic eigenmodes on the intensity of the external magnetic field, applied along the easy axis of the elements. This study aims to enhance the security for people's health, improve the medical level further, and increase the confidentiality of people's privacy information. Under the trend of wide application of deep learning algorithms, the convolutional neural network (CNN) is modified to build an interactive smart healthcare prediction and evaluation model (SHPE model) based on the deep learning model. The model is optimized and standardized for data processing. Then, the constructed model is simulated to analyze its performance. The results show that accuracy of the constructed system reaches 82.4%, which is at least 2.4% higher than other advanced CNN algorithms and 3.3% higher than other classical machine algorithms. It is proved based on comparison that the accuracy, precision, recall, and F1 of the constructed model are the highest. Further analysis on error shows that the constructed model shows the smallest error of 23.34 pixels. Therefore, it is proved that the built SHPE model shows higher prediction accuracy and smaller error while ensuring the safety performance, which provides an experimental reference for the prediction and evaluation of smart healthcare treatment in the later stage.

Technological development increases capacity of information systems, which with development of faster data transfer will be able to host variety of new devices. In this article we present electronic modules, infrastructure and fuzzy rules control model with implemented software for new generation home environment. The system is developed for the next IoT level based on 6G network communication standards. Proposed control model is efficient in water flow management, wind shield control, security aspects and carbon dioxide limitation via adaptive ventilation. Developed infrastructure is ready for new 6G communication standard, which will additionally improve efficiency and data flow at end-user devices and local area level.

5G networks are expected to provide cost-efficient, reliable, and flexible services for industrial productions and applications potentially, by introducing emerging network technologies like blockchain and network functions virtualization (NFV), which virtualizes network functions and runs them on standard infrastructure rather than customized hardware. However, how to deal with the emerging security challenges and fulfil the requirement of ultra-reliable and low-latency communications (URLLC) has not been fully resolved. In this article, we present an NFV-enabled 5G paradigm for the industry with the guarantee of URLLC through service chain acceleration and dynamic blockchain-based spectrum resource sharing among a variety of industry applications running in NVF-based equipment. First, we elaborate the benefits and shortcomings of NFV for industry, by executing an industry application experiment in virtualized and nonvirtualized data center networks. Then, we illustrate an NFV-enabled 5G paradigm for URLLC in detail, with a special focus on the service chain acceleration and spectrum sharing built on NFV, blockchain, software-defined networking, and mobile edge computing. Finally, we establish a mathematical model to study the worst-cast transmission latency of NFV-enabled 5G with the input of the bursty traffic. The proposed model can be exploited to support the plan, management, and optimization of NFV-enabled 5G URLLC systems for industry.

A conventional power grid is criticized by its poor capability of power usage management, especially in handling dynamically varying power demands over time. The concept of smart grid has been introduced to mitigate this problem by satisfying not only real-time power demands, but also by restricting power usage within the capacity. Its consistent outperformance and new perspective in computer intelligence to control the grid for autonomous power consumption has been gradually replacing the conventional power grid. However, even in smart grid, providing high satisfaction to users often leads smart grid operator (SGO) to loss and vice versa. In this paper, we develop an optimal dynamic pricing mechanism for trading-off (ODPT), for SGOs that tradeoff between user utility and operator profit in smart grid systems. It allows the operator to purchase power from multiple energy producers and to set selling price to users dynamically following the demand-supply theory of economics. It also exploits an artificial neural network model to more accurately predict the power usage. The simulation results, carried out on a commercially available optimization modeling tool using practical power usage data, prove the effectiveness of the proposed ODPT in increasing the operator profit while satisfying user demands.

Summary Sybil attacks are increasingly prevalent in online social networks. A malicious user can generate a huge number of fake accounts to produce spam, impersonate other users, commit fraud, and reach many legitimate users. For security reasons, such fake accounts have to be detected and deactivated immediately. Various defense schemes have been proposed to deal with fake accounts. However, most identify fake accounts using only the structure of social graphs, leading to poor performance. In this paper, we propose a new and scalable defense scheme, SybilTrap. SybilTrap uses a semi‐supervised technique that automatically integrates the underlying features of user activities with the social structure into one system. Unlike other machine learning–based approaches, the proposed defense scheme works on unlabeled data, and it is effective in detecting targeted attacks, because it manipulates different levels of features of user profiles. We evaluate SybilTrap on a dataset collected from Twitter. We show that our proposed scheme is able to accurately detect Sybil nodes as well as huge conspiracies among them.

As for deployment of security strategy, corresponding forwarding rules for switches can be given in allusion to different traffic conditions. However, due to lack of global cognitive control for security strategy deployment in traditional Internet of Vehicles (IoV), it is quite difficult to realize global and optimized security strategy deployment scheme so as to meet security requirements in different traffic conditions. On basis of traditional IoV, cognitive engine is added in cognitive IoV (CIoV) to enhance the intelligence of traditional IoV. In allusion to CIoV, and in consideration of restrictions on transmission delay, the security strategy deployment for switches on core network is formulated in this paper, thus not only the safe transmission rules are met, but the transmission delay can also be the lowest. To be specific, the path selection of switches is modeled as 0-1 programming problem in this paper, and that optimization problem is proved to be a nonconvex optimization problem. Then we convert that problem into a convex optimization problem by log-det heuristic algorithm, thus to give path selection scheme to meet security requirements with the lowest delay on the whole. Experiment proves that cognitive engine-based security strategy deployment put forth in this paper is much better than other schemes.

Information credibility on Twitter has been a topic of interest among researchers in the fields of both computer and social sciences, primarily because of the recent growth of this platform as a tool for information dissemination. Twitter has made it increasingly possible to offer near-real-time transfer of information in a very cost-effective manner. It is now being used as a source of news among a wide array of users around the globe. The beauty of this platform is that it delivers timely content in a tailored manner that makes it possible for users to obtain news regarding their topics of interest. Consequently, the development of techniques that can verify information obtained from Twitter has become a challenging and necessary task. In this paper, we propose a new credibility analysis system for assessing information credibility on Twitter to prevent the proliferation of fake or malicious information. The proposed system consists of four integrated components: a reputation-based component, a credibility classifier engine, a user experience component, and a feature-ranking algorithm. The components operate together in an algorithmic form to analyze and assess the credibility of Twitter tweets and users. We tested the performance of our system on two different datasets from 489,330 unique Twitter accounts. We applied 10-fold cross-validation over four machine learning algorithms. The results reveal that a significant balance between recall and precision was achieved for the tested dataset.

With the enormous growth and volume of online social networks and their features, along with the vast number of socially connected users, it has become difficult to explain the true semantic value of published content for the detection of user behaviors. Without understanding the contextual background, it is impractical to differentiate among various groups in terms of their relevance and mutual relations, or to identify the most significant representatives from the community at large. In this paper, we propose an integrated social media content analysis platform that leverages three levels of features, i.e., user-generated content, social graph connections, and user profile activities, to analyze and detect anomalous behaviors that deviate significantly from the norm in large-scale social networks. Several types of analyses have been conducted for a better understanding of the different user behaviors in the detection of highly adaptive malicious users. We attempted a novel approach regarding the process of data extraction and classification to contextualize large-scale networks in a proper manner. We also collected a significant number of user profiles from Twitter and YouTube, along with around 13 million channel activities. Extensive evaluations were conducted on real-world datasets of user activities for both social networks. The evaluation results show the effectiveness and utility of the proposed approach.

Summary This paper addresses the problem of finding credible sources among Twitter social network users to detect and prevent various malicious activities, such as spreading false information on a potentially inflammatory topic, forging accounts for false identities, etc. Existing research works related to source credibility are graph‐based, considering the relationships among users to predict the spread information; human‐based, using human perspectives to determine reliable sources; or machine learning‐based, relying on training classifiers to predict users' credibility. Very few of these approaches consider a user's sentimentality when analyzing his/her credibility as a source. In this paper, we propose a novel approach that combines analysis of the user's reputation on a given topic within the social network, as well as a measure of the user's sentiment to identify topically relevant and credible sources of information. In particular, we propose a new reputation metric that introduces several new features into the existing models. We evaluated the performance of the proposed metric in comparison with two machine learning techniques, determining that the accuracy of the proposed approach satisfies the stated purpose of identifying credible Twitter users. Copyright © 2016 John Wiley & Sons, Ltd.

Lately, Twitter has grown to be one of the most favored ways of disseminating information to people around the globe. However, the main challenge faced by the users is how to assess the credibility of information posted through this social network in real time. In this paper, we present a real-time content credibility assessment system named CredFinder, which is capable of measuring the trustworthiness of information through user analysis and content analysis. The proposed system is capable of providing a credibility score for each user's tweets. Hence, it provides users with the opportunity to judge the credibility of information faster. CredFinder consists of two parts: a frontend in the form of an extension to the Chrome browser that collects tweets in real time from a Twitter search or a user-timeline page and a backend that analyzes the collected tweets and assesses their credibility.

Summary This paper addresses the problem of efficient federation formation by the cloud providers (CPs) with an aim to fulfill the dynamic resource demands of users for supporting data‐intensive workloads. Existing works only focus on forming federations based on the highest profit gained by each of the CPs in a federation. Therefore, these approaches often suffer from the risk of selecting unreliable CPs in the federation resulting in additional penalty cost and loss of CPs's reputation due to service level agreement violation between the users and the federation. In contrast, we argue that a trust model is necessary to find the most promising cloud collaborators. Accordingly, we propose a novel cloud federation formation mechanism by utilizing a trust‐based cooperative game theory, which enables the CPs to dynamically form a federation based on profit maximization and penalty cost minimization as a result of selecting the trustworthy CPs. Simulation results show that the cloud federation formed by the proposed mechanism is stable, satisfies the fairness property, and yields higher profit for the participating CPs in the long run without incurring penalty cost as compared with the state‐of‐the‐art approaches. Copyright © 2015 John Wiley & Sons, Ltd.

The proliferation of mobile computing and smartphone technologies has resulted in an increasing number and range of services from myriad service providers. These mobile service providers support numerous emerging services with differing quality metrics but similar functionality. Facilitating an automated service workflow requires fast selection and composition of services from the services pool. The mobile environment is ambient and dynamic in nature, requiring more efficient techniques to deliver the required service composition promptly to users. Selecting the optimum required services in a minimal time from the numerous sets of dynamic services is a challenge. This work addresses the challenge as an optimization problem. An algorithm is developed by combining particle swarm optimization and k-means clustering. It runs in parallel using MapReduce in the Hadoop platform. By using parallel processing, the optimum service composition is obtained in significantly less time than alternative algorithms. This is essential for handling large amounts of heterogeneous data and services from various sources in the mobile environment. The suitability of this proposed approach for big data-driven service composition is validated through modeling and simulation.

Currently, microblogs such as the well-known social network Twitter are one of the most important sources of information in an era of information overload, restiveness and uncertainty. Consequently, developing models to verify information from Twitter has become both a challenging and necessary task. In this paper, we propose a novel multi-stage credibility analysis framework to identify implausible content in Twitter in order to prevent the proliferation of fake or malicious information. We used Naïve Bayes classifier and it is enhanced by considering the relative importance of the used features to improve the classification accuracy. We examine the classifier with 1000 unique tweets along with 700 account. The result quite motivating with accuracy 90.3%, 86.24% Precision and 98.8% recall.

In this paper, we propose a new notion of public key encryption scheme with authorized equality test (PKE-AET), which allows authorized users those who have warrants to test the equivalence between two messages, where the messages are encrypted using different public keys. Comparing with the existing researches, our PKE-AET provides two kinds of warrants that are referred to as receiver's warrants and cipher-warrants. The proposed PKE-AET is able to deal with the following complicated scenario: Assume that a receiver authorizes a receiver's warrant to a tester, which makes the tester be able to perform equality test on all of receivers’ ciphertext; on the other hand, if receiver authorizes a cipher-warrant corresponding to a specific ciphertext to the tester, then the tester only acquires the equality test on that particular ciphertext. The equality between two ciphertexts can be verified by the tester without decryption after he or she receives two warrants and varies their validations. Moreover, for security analysis, we define two types of adversaries and security notions for PKE-AET in the multi-user setting. Furthermore, we prove that our PKE-AET is one-way CCA secure against type-I adversaries and IND-CCA secure against type-II adversaries. Finally, the proposed scheme leads better efficiency than most of previous equality test schemes.

Summary In a mobilead hocnetwork, the data packet may fail to be delivered for various reasons mostly for route failure, congestion, and battery energy drain. Hence, providing reliable and timely data delivery in this network in an energy‐efficient way is challenging. Although there exist several solutions to solve these problems, they can handle either route failure or congestion or energy‐efficient routing. Hence, to cope up with all the problems simultaneously, we propose a route failure and congestion‐aware energy‐efficient cross‐layer design that spans the transport and network layer. In the transport layer, we introduce the concept of local packet buffering during link failure and congestion. As a result, the packet dropping rate of the network and energy consumption decreases. In the network layer, a routing protocol is proposed for selecting the energy‐efficient path for data transmission. It uses the buffering mechanism in case of route maintenance. In addition, we employ a multilevel congestion detection and control mechanism at the source and intermediate nodes that can judiciously take the most appropriate decision for congestion control in the network proactively. The simulation results showed that the proposed cross‐layer design provided better performance as compared with the state‐of‐the‐art protocols. Copyright © 2014 John Wiley & Sons, Ltd.

Summary Network reprogramming faces lots of threats from both external attackers and potentially compromised nodes. Security thus becomes a critical requirement for network reprogramming protocols. This paper describes a secure network reprogramming system called SecNRCC for dynamically reprogramable wireless sensor network. In SecNRCC, a light weight authentication method is firstly introduced for the reboot control command. Secondly, a program image preprocess method with security and loss‐tolerance consideration is proposed. Furthermore, a novel immediate packet authentication algorithm with confidentiality consideration is also presented to resist the denial of service attacks exploiting the authentication delay, and finally, a weak authentication operation is performed before the digital signature verification to mitigate denial of service attacks against signature packets. The experimental results show that SecNRCC can securely disseminate the program image to all of node in the wireless sensor networks with acceptable latency and message cost. Copyright © 2014 John Wiley & Sons, Ltd.

The promising potential and emerging applications of cloud gaming have drawn increasing interest from academia, industry, and the general public. However, providing a high-quality gaming experience in the cloud gaming framework is a challenging task because of the tradeoff between resource consumption and player emotion, which is affected by the game screen. We tackle this problem by leveraging emotion-aware screen effects in the cloud gaming framework and combining them with remote display technology. The first stage in the framework is the learning or training stage, which establishes a relationship between screen features and emotions using Gaussian mixture model-based classifiers. In the operating stage, a linear programming model provides appropriate screen changes based on the real-time user emotion obtained in the first stage. Our experiments demonstrate the effectiveness of the proposed framework. The results show that our proposed framework can provide a high quality gaming experience while generating an acceptable amount of workload for the cloud server in terms of resource consumption.

Wireless sensor network (WSN) consists of resource constraint sensor nodes where nodes (sensors) send data to the base station/sink node and communicate with each other by either forming a cluster or without forming a cluster. Data aggregation in WSN takes place at the responsible nodes (aggregators) in a cluster before sending data to the base station, based on the query that is received from the base station. Thus data aggregation reduces energy consumption of the nodes due to minimized communication. As a result, the life time of the individual sensors prolong in the case of aggregation compared to the data transmission that occurs without performing aggregation. One of the major security challenges for data aggregation in WSN is that the aggregators expose clear data at the aggregation level. Therefore, this aggregation level is vulnerable to attacks by intruders. Existing research has addressed this problem and proposed solutions by considering static node topology of WSN. However, in WSN the nodes can either be static or dynamic. Therefore, the existing approaches do not tackle the security issues that arise in dynamic node WSN. The proposed research aims to explore this problem and propose solutions based on a cryptographic approach.

Cognitive radio (CR) networks are becoming an increasingly important part of the wireless networking landscape due to the ever-increasing scarcity of spectrum resources throughout the world. Nowadays CR media is becoming popular wireless communication media for disaster recovery communication network. Although the operational aspects of CR are being explored vigorously, its security aspects have gained less attention to the research community. The existing research on CR network mainly focuses on the spectrum sensing and allocation, energy efficiency, high throughput, end-to-end delay and other aspect of the network technology. But, very few focuses on the security aspect and almost none focus on the secure anonymous communication in CR networks (CRNs). In this research article we would focus on secure anonymous communication in CR ad hoc networks (CRANs). We would propose a secure anonymous routing for CRANs based on pairing based cryptography which would provide source node, destination node and the location anonymity. Furthermore, the proposed research would protect different attacks those are feasible on CRANs.

E-Health systems provide a collaborative platform for sharing patients medical data typically stored in distributed autonomous healthcare data sources. Each autonomous source stores its medical and multimedia data without following any global structure. This causes heterogeneity in the underlying sources with respect to the data and storage structure. Therefore, a data interoperability mechanism is required for sharing the data among the heterogeneous sources. A proper metadata structure is also necessary to represent multimedia content in the sources to enable efficient query processing. Considering these needs, we present an interoperability solution for sharing data among heterogeneous data sources. We also propose a metadata management framework for medical multimedia con-tent including X-ray, ECG, MRI, and ultrasound images. The framework identifies features, generates and represents metadata, and produces identifiers for the medical multimedia content to facilitate efficient query processing. The framework has been tested with various user queries and the accuracy of the query results evaluated by means of precision, recall, and user feedback methods. The results confirm the effectiveness of the proposed approach.

Abstract This paper addresses several key issues in distributed multimedia services management and composition such as scalability, heterogeneity, and quality of service (QoS). The proposed framework introduces biologically inspired multimedia service management through the composition of basic multimedia services such as streaming services and different transcoding services. The biologically inspired approach is used for collecting the QoS requirements from individual transcoding services in order to select the most suitable services for the desired composition process. A prototype of the proposed framework is designed, implemented, and evaluated in terms of scalability and load balancing. Copyright © 2009 John Wiley & Sons, Ltd.

This paper describes a haptic enabled UML CASE tool that enables software engineering developers to physically manipulate and touch the UML modeling elements and feel the force feedback. We propose an architecture and a software design for the tool. The current implementation of the tool uses the Omni Phantom device, a quite common haptic interface among the haptic research community. The CASE tool, from a user perspective, consists of three parts: a drawing area, a palette, and a tool bar. Our preliminary usability study demonstrated the potential of adding the haptic modality to UML development tools.