This review explores the evolution from IoT to the Internet of Everything (IoX) within the cyber-physical-social-thinking (CPST) hyperspace, centering on the emerging challenge of ”relationship explosion.” As interconnected systems grow in scale and complexity, the exponential proliferation of internal (e.g., device coordination, data aggregation) and cross-space (e.g., streaming, translating, adapting) relationships leading to scalability, security, and real-time processing challenges. Through a systematic literature review guided by five research questions, we analyze how this relational explosion intensifies across IoX domains—spanning IoT, IoP, and IoTk—and undermines the efficacy of Artificial Narrow Intelligence (ANI) in managing dynamic, heterogeneous environments. This review proposes that Artificial General Intelligence (AGI) offers a transformative solution, enabling adaptive reasoning, cognitive firewalls, and unified decision-making to navigate complex relationship networks. AGI-driven methodologies enhance system resilience, security, and efficiency in aggregating, moderating, and evolving relationships across CPST spaces. The paper outlines a classification of relationship types, evaluates AGI’s advantages over ANI, and proposes a future research roadmap emphasizing ethical governance, human-AGI collaboration, and sustainable architectures. By framing IoX development around the management of relationship explosion, we provide a roadmap for future research, emphasizing interdisciplinary efforts, ethical governance, and sustainable frameworks to foster intelligent, socially aware IoX ecosystems.

Cyberbullying on social media platforms remains a serious threat to digital well-being, requiring intelligent systems capable of detecting both explicit and subtle, emotionally charged abuse. Sentiment analysis (SA) plays a key role by interpreting emotional tone, polarity, and context, offering more nuanced and timely detection than keyword-based models. Emotions like anger, sarcasm, or veiled hostility often precede cyberbullying, especially during impulsive interactions. SA captures these affective cues, improving sensitivity to implicit abuse and coded language. This study presents the first systematic comparison of sentiment-enhanced transformer models such as ALBERT, DeBERTa, ELECTRA, HateBERT, and DeepSeek-coder-1.3b-base, fine-tuned for cyberbullying detection across Twitter (currently X), IMDB, and Amazon. Models were evaluated on predictive performance (Accuracy, Precision, Recall, F1-score), time and cost efficiency (inference time, memory, CPU/GPU use, and energy). ELECTRA + SA outperformed all models, achieving 91.85% accuracy, precision, and recall, and a 91.84% F1-score. It also excelled in efficiency, with 0.069 seconds inference time, 23.92 MB RAM use, 7.2% CPU/GPU usage, and 0.000075 kWh energy consumption, proving highly generalizable, sentiment-sensitive, and suitable for real-time, resource-aware deployment. These results highlight the importance of sentiment integration, dataset diversity, and computational efficiency in building scalable, real-world cyberbullying detection systems.

Cyberbullying significantly impacts mental health by adversely affecting victims’ psychological well-being. It is a prevalent issue on social media platforms, necessitating effective real-time detection systems to identify harmful content. However, current detection systems face challenges related to performance, dataset quality, time efficiency, and computational costs. This study compares existing text classification techniques for cyberbullying detection, evaluating their effectiveness on social media platforms. Large language models such as BERT, RoBERTa, XLNet, DistilBERT, and GPT-2.0 are assessed for their suitability. Results show that BERT achieves optimal performance, with 95% accuracy, precision, recall, and F1 score; a 5% error rate; 0.053 seconds inference time; 35.28 MB RAM usage; 0.4% CPU/GPU utilization; and 0.000263 kWh energy consumption. These findings highlight that while generative AI models are powerful, fine-tuned models often outperform them when adapted to specific datasets and tasks.

The autonomous connected ambulance (ACA) has been an unprecedented necessity in the demand–supply management sector of the healthcare sector. However, the traditional prototypes designed for such an unmanned vehicle do not match the demands of the advanced communication technologies incorporated in today’s sophisticated distributed networks. As a result, in the current era of edge computing strengthened by many AI-enabled algorithms, there is an urgent need to design a route resource recommendation (R3) protocol for ACA under Edge-AI. Designing such a protocol requires addressing the major challenges to optimize the routes for ACA and, thereby, enhance the services of emergency eHealth centers through a governing telehealth monitoring administrator. Therefore, in this article, a dedicated and novel ACA-R3 protocol is proposed to address the issues of connectivity and resource management in ACA to optimize the routes for ACA. The ACA-R3 protocol abides by the operational standards of both the eHealth protocol and governing protocol. The primary objective of the current research work was to reduce the handover time and simplify the patient-information exchange during the time of demanded emergencies. The proposed ACA-R3 protocol can enhance the collaborative distributive resource management for reliable decision making from the data generated by the GPS tracking unit in ACA and Edge-AI. The experimental results obtained from three different cases of traffic congestion are evaluated, validated, and reported in this article.

The future generation of the Internet of Things (IoT) systems is characterized by the fusion of technologies—from edge–fog–cloud computing to artificial intelligence (AI) and blockchain—closing the gap between the physical and digital worlds [A1]. Although these technologies have been developed separately over time, the synergy among them has taken a giant leap. We are witnessing a fast-paced convergence of these technologies resulting in a fundamental paradigm shift unlocking vast benefits and opportunities across vertical markets. However, there are still several barriers, such as a lack of consensus toward any reference models or best practices, hindering the full fusion of these technologies [A1]. To tackle these challenges and facilitate this promising transformation, this special issue was organized to provide a holistic multidisciplinary reference for solutions, architectures, protocols, services, and applications addressing all aspects of the future generation of IoT systems via the fusion of edge, cloud, AI, and blockchain, while considering the corresponding challenges. Thanks to the enormous support from the Editor-in-Chief, Prof. Honggang Wang, and the dedicated work of many reviewers, after a rigorous review process, 27 excellent articles out of 125 submissions were accepted for inclusion in this special issue of the IEEE Internet of Things Journal. We introduce these papers and highlight their key contributions below.

The digital transformation is characterized by the convergence of technologies—from the Internet of Things (IoT) to edge–fog–cloud computing, artificial intelligence (AI), and Blockchain—in multiple dimensions, blurring the lines between the physical and digital worlds. Although these innovations have evolved independently over time, they are increasingly becoming more intertwined, driving the development of new business models. With more adaptation, embracement, and development, we are witnessing a steady convergence and fusion of these technologies resulting in an unprecedented paradigm shift that is expected to disrupt and reshape the next-generation systems in vertical domains in a way that the capabilities of the technologies are aligned in the best possible way to complement each other. Despite the fact that the convergence of the four technologies can potentially tackle the main shortcomings of the existing systems, its adoption is still in its infancy phase, suffering from several issues, such as the absence of consensus toward any reference models or best practices. This article provides a comprehensive insight into the fusions of these paradigms by discussing a blend of topics addressing all the importation aspects from design to deployment. We will begin this article by providing an in-depth discussion on the main requirements, state-of-the-art reference architectures, applications, and challenges. Following this, we will present a reference architecture and a case study on privacy-preserving stress monitoring and management to better elaborate on the corresponding details and considerations.

The work consists of two subapproaches. In the first approach, an analytical model is developed using trapezium fuzzy numbers in decision-making problems for an Internet of Things-based water distribution network. The second phase explains the integration of the previous phase with the MCDM-based location routing protocol (M-LRP). The water distribution network has three components static water source, the utility center (UC) which can be located in the proper position, and the consumer. The objective of this work is to select an optimal route between the UC and the consumer by considering multiple criteria. The simulation result shows that the proposed multicriterion-based decision-making (MCDM)-based routing protocol outperforms both existing MCDM-based and non-MCDM-based routing schemes. The proposed model outperforms the existing models like non-MCDM-based and MCDM-based routing protocols by 51% and 11%, respectively.

This comprehensive abstract of the special issue presents an extensive array of collections of research studies that focus on the integration of state-of-the-art technologies and methodologies to advance healthcare services in smart cities through Internet of Things (IoT) applications. The studies explore innovative solutions across multiple aspects of healthcare, including privacy preservation, telemedicine, smart healthcare systems, security, abnormality detection, functional assessment, feature selection, health monitoring, diagnostics, medical vehicle routing, behavioral patterns discovery, federated learning, and personalized healthcare.

It is well known that the metaverse, also named general cyberspace (GC), is virtual-real fusion spaces, consisting of a virtual space, namely, cyberspace and virtual-real spaces, namely, cyber-enabled physical, social, and thinking (cyber-enabled) spaces. This article discusses the open issues of the metaverse in terms of science and technology and proposes a new discipline and interdiscipline hierarchy for the metaverse (GC), namely, cyberology first. Then, it explores various relevant standards of discipline classification and a discipline and interdiscipline hierarchy based on physical, social, and thinking spaces, and investigates the cyberspace and cyber-enabled spaces. On the basis of the above research, this article enriches the contents of cyberology in two terms: 1) the disciplines in cyberspace and 2) the interdisciplines in cyber-enabled spaces. Finally, this article gives predictions of cyberology on the future development of the metaverse from the aspects of cyber–physical space, cyber–social space and cyber-thinking space.

Internet of Things (IoT) is an expanding technology that facilitate physical devices to inter-connect each other over a public channel. Moreover, the security of the next-generation wireless mobile communication technology, namely 5G with IoT, has been a field of much interest among researchers in the last several years. Previously, Sharif et al. had suggested an IoTbased lightweight three-party authentication scheme proclaiming a secured scheme against different threats. However, it was found that the scheme could not achieve user anonymity and guarantee session key security. Additionally, the scheme fails to provide proper authentication in the login phase, and it s unable to update a new password in the password change phase. Thus, we propose an improved three-factor-based data transmission authentication scheme (TDTAS) to address the weaknesses. The formal security analysis has been proved using the Real-or-Random (RoR) model. The informal security analysis demonstrates that the scheme is secure against several known attacks and achieves more security features. In addition, the comparison of the work with other related schemes demonstrates the proposed scheme has less communicational and storage costs.

In recent years, the concept of the Metaverse has attracted considerable attention. This article provides a comprehensive overview of the Metaverse. First, the development status of the Metaverse is presented. We summarize the policies of various countries, companies, and organizations relevant to the Metaverse, as well as statistics on the number of Metaverse-related publications. Characteristics of the Metaverse are identified: 1) multitechnology convergence; 2) sociality; and 3) hyper-spatio-temporality. For the multitechnology convergence of the Metaverse, we divide the technological framework of the Metaverse into five dimensions. For the sociality of the Metaverse, we focus on the Metaverse as a virtual social world. Regarding the characteristic of hyper-spatio-temporality, we introduce the Metaverse as an open, immersive, and interactive 3-D virtual world which can break through the constraints of time and space in the real world. The challenges of the Metaverse are also discussed.

Human daily activity monitoring has its particular significance in smart health. Human activity recognition based on mmWave has drawn enormous research efforts and achieved significant progress. Most of these solutions, however, work on data that has been manually segmented for each piece to contain only a single activity, which is impractical in reality where the sensor continuously generates data containing a series of activities. To address this challenge, this paper proposes a multi-head attention model that can detect the transition from one activity to another in a stream of mmWave sensor data of various human activities by analyzing the inner correlation of mmWave radar data fragments with a sliding window mechanism. Furthermore, the model then recognizes the new activity type in the data once it detects an activity transition. The solution has been extensively evaluated with a sparse point cloud dataset generated by a mmWave radar, which contains five types of activities. The experiment results show that the solution can achieve an accuracy of 98% in detecting activity transition at its best.

The rapid growth of the Internet of Things (IoT) and the increasing number of connected devices have propelled the proliferation of offered applications, causing “applications exploding.” In the context of IoT, filtering and selecting the most relevant applications in a given situation is a challenging task. Thus, developing techniques that can alleviate applications exploding and meet users’ requirements is highly demanded for IoT development. This survey focuses on applications exploding in the IoT and reviews some of the existing techniques, such as intelligent sensing, content distribution network, microservices, and 5G, which help mitigate the effects of applications exploding. Furthermore, the survey discusses how to describe user requirements and offer application services to better match the two. In addition, this survey presents the smart home as an instance of typical IoT applications and explores how adaptive users’ requirements for food ordering can be better met when various food provider applications are available for choice. Finally, partially resolved and unresolved bottlenecks brought by applications exploding are put forward to be further researched by the technical and scientific community.

As the trajectory of the Internet of Things (IoT) moving at a rapid pace and with the rapid worldwide development and public embracement of wearable sensors, these days, most companies and organizations are awash in massive amounts of data. Determining how to profit from data deluge can give companies an edge in the market because data have the potential to add tremendous value to many aspects of a business. The market has already seen a level of monetization across vertical domains in the form of layering connected devices with a variety of Software-as-a-Service (SaaS) choices, such as subscription plans or smart device insights. Out of this arena is evolving a “machine economy” in which the ability to correctly monetize data rather than simply hoard it, will provide a significant advantage in a competitive digital environment. The recent advent of the technological advances in the fields of big data, analytics, and artificial intelligence (AI) has opened new avenues of competition, where data are utilized strategically and treated as a continuously changing asset able to unleash new revenue opportunities for monetization. Such growth has made room for an onslaught of new tools, architectures, business models, platforms, and marketplaces that enable organizations to successfully monetize data. In fact, emerging business models are striving to alter the power balance between users and companies that harvest information. Start-ups and organizations are offering to sell user data to data analytics companies and other businesses. Monetizing data goes beyond just selling data. It is also possible to include steps that add value to data. Generally, organizations can monetize data by: 1) utilizing it to make better business decisions or improve processes; 2) surrounding flagship services or products with data; or 3) selling information to current or new markets. This article will address all important aspects of IoT data monetization with more focus on the healthcare industry and discuss the corresponding challenges, such as data management, scalability, regulations, interoperability, security, and privacy. In addition, it presents a holistic reference architecture for the healthcare data economy with an in-depth case study on the detection and prediction of cardiac anomalies using multiparty computation (MPC) and privacy-preserving machine learning (PPML) techniques.

As The trajectory of the Internet of Things (IoT) is moving at a rapid pace, most companies and organizations are awash and drowning in massive amounts of data. Determining how to profit from data deluge and unlock its value can give companies an edge in the market because data have the potential to add tremendous value to many aspects of a business [A1]. The market has already seen a level of monetization across vertical domains e.g., in the form of layering connected devices with a variety of Insights-as-a- Service options. Out of this arena, the data economy concept has been evolving, characterized by correctly monetizing data rather than simply hoarding it, which will provide a significant advantage in a competitive digital environment [A1]. The recent advent of technological advances in the fields of Big Data, Analytics, and Artificial Intelligence (AI) has opened new avenues of competition, where IoT data is considered a living and evolving entity that can unlock enormous opportunities for monetization. Such growth brought forth a slew of new tools, architectures, business models, platforms, and marketplaces, enabling organizations to monetize data successfully. In this context, emerging business models also strive to alter the power balance between users and companies that harvest information by utilizing usage policy enforcement and privacypreserving machine learning techniques. Monetizing data goes beyond just selling data. It is also possible to include steps that add value to data. Generally, organizations can monetize data by 1) utilizing it to make better business decisions or improve processes; 2) surrounding flagship services or products with data; or 3) selling information to current or new markets [A1].

Recent years have witnessed considerable progress in the application of Internet of Things (IoT) technology in smart transportation systems. The wider presence of Wi-Fi networks in subway gates allows passengers to use the quick response (QR) code of mobile phone applications for entrance. The network established by gates has become a medium which connects stations and passengers. However, in addition to directly monitoring the passenger flow, the potential application of the smart card usage data collected by the gates remains an open topic. Although there are several clustering-based works devoted to revealing passengers’ travel behavior patterns, research on the social attributes of subway passengers is very limited. To fill the gap, this article proposes a novel method to mine similarity information of passengers by leveraging passengers’ communication behaviors hidden in subway card usage data. Passengers are first organized as a graph, which not only reflects the interactions between them but also incorporates the context information of subway stations. Then, the node embedding is used to encode the information contained in the graph and with the use of cosine similarity, the similarity between two passengers is measured. Extensive experiments on two real-world location-based social network data sets and extended experiments on a Shanghai subway data set are conducted. The results show that the proposed method can effectively improve the accuracy of similarity measurement and provide social features that are distinguishable from travel behavior patterns.

Internet of Things (IoT) data is one of the most important assets in business models for offering various ubiquitous and brilliant services. The IoT is provided with the advantage of susceptibility that cybercriminals and other malicious users. Even though smart cities are intended to extend productivity and efficiency, residents and authorities face risks when they avoid cybersecurity. The conventional blockchain methods were introduced to ensure the secure management and examination of the smart city big data. But, the blockchains are found to have computationally high costs, and failed to improve the security, not adequate resource-constrained IoT devices have been designated for smart cities. In order to address these issues, the proposed novel blockchain model called blockchain secured Merkle hash zero correlation distinguisher (BSMH-ZCD) is suitable for IoT devices within the cloud infrastructure. The objective of the BSMH-ZCD method is to enhance security and reduce the run time and computational overhead. Initially, the Merkle hash tree is used to create the hash value with every transaction. Next, the zero correlation distinguisher is applied to perform the data encryption and decryption operation for the ARX block for obtaining proficient secure data access in the IoT devices. Experimental assessment of the proposed BSMH-ZCD method and existing methods are carried out by using the taxi driver data set and Novel Corona Virus 2019 data set with different factors, such as running time, computational complexity, and security with respect to a number of blocks and executions. By using the taxi driver data set, the experimental results reveal that the BSMH-ZCD method performs better with a 19% improvement in security, 20% reduction of computational complexity, and 29% faster running time for IoT compared to existing works.

The emergence of software-defined networking (SDN) has revolutionized the management of an enterprise network. The SDN-based design provides flexibility in network management, which spans over multiple applications, e.g., routing, switching, forwarding, and controlling. It reduces the reliance on vendor-specific devices and middlebox solutions, such as firewalls, intrusion detection systems (IDSs), intrusion prevention systems (IPSs), etc. Furthermore, due to the integration of different technologies, privacy is one of the core issues faced by the enterprise. Host anonymity is one of the techniques to safeguard against privacy attacks; however, the existing anonymization solutions provide better anonymity, but at the cost of higher latency and are most suited for Internet traffic. To tackle this issue in an enterprise network, we propose an SDN-based communication framework using enterprise integration patterns (EIPs) that offers anonymous communication in an enterprise environment. Host anonymity is achieved by replacing the real IP address with the spoofed IP address during the transmission of data packets inside the network. Unlike the traditional networks, SDN can modify the header fields of packets as they traverse in the network from the source to the destination. In addition to the host anonymity, this framework also provides context-aware communication by leveraging the SDN global visibility characteristic, where application services are discoverable on the network without disclosing the addresses of the application servers. Moreover, context-aware services enable network traffic to be routed based on the application-layer services rather than the network-layer information. In the end, evaluation of the proposed framework is carried out with respect to the performance of anonymous communication, computational complexity, and security of the complete proposed framework. In addition, we also highlighted that the proposed framework is more suitable for heterogeneous network environments such as Internet of Things-based solutions.

Search engine optimization (SEO) is a significant problem for enhancing a website's visibility with search engine results. SEO issues, such as Site Popularity, Content Quality, Keyword Density, and Publicity, were not considered during the search engine optimization process. Therefore, the retrieval rate of the existing techniques is inadequate. In this study, Triangular Fuzzy Deep Structured Learning-Based Predictive Page Ranking (TFDSL-PPR) technique is proposed to solve these limitations. First, the TFDSL-PPR technique takes a number of user queries as input in the input layer, and then it employs four hidden layers in order to deeply analyze the web pages based on an input query. The first hidden layer determines the keywords from the user query. The second hidden layer measures the site popularity, content quality, keyword density and publicity of all web pages in the search engine. It then accomplishes Goodman and Kruskal's Gamma Predictive Ranking process in the third hidden layer, where it ranks the web pages by considering their similarities. The proposed TFDSL-PPR technique is applied to the ClueWeb09 Dataset with respect to a variety of user queries. The results are benchmarked by existing methods based on several metrics such as retrieval rate, time, and false-positive rate.

Internet of Things (IoT) has become an integral part of a smart community that connects computing devices, smart objects, and mechanical and digital machines, having unique identifiers, to communicate with each, without human intervention. This smart connectivity within a building assists the users in real time to provide an enormous range of connected applications and facilitate the optimized use of multiple resources. These smart building applications can make our lives more comfortable and provide a more sustainable, healthy, and safe workplace. A key challenge for IoT toward smart buildings is to ensure that the service has been taken from a trustworthy service provider. It requires a reliable, robust, and adaptive context-oriented trust mechanism that conforms to the specific requirements of the end users. To enhance the security of smart buildings in IoT, this research proposes an adaptive context-based trust evaluation system for smart building (CTES-SB) applications. The trust score for service is calculated based on the client's previous interaction and recommendation from context-similar clients. Using CTES-SB, the client selects the best service provider based on the previous and current trust scores for the next interaction. The model also helps to filter out malicious nodes through an indirect trust calculation process. This process dynamically assigns weights based on direct interactions and trustworthy recommendations for detecting and avoiding malicious interactions. We have demonstrated the effectiveness of CTES-SB by simulating multiple smart building scenarios under malicious attacks. The proposed architecture of CTES-SB has been experimentally evaluated to benchmark its performance for best service selection and resiliency against malicious nodes. The CTES-SB is proved to be efficient by having a comparison with the state-of-the-art algorithms. The comparison is in terms of filtering the malicious nodes from the network and the result shows that the trust converges quickly toward the ground-truth value.

With the recent advances of the Internet of Things (IoT), and the increasing accessibility to ubiquitous computing resources and mobile devices, the prevalence of rich media contents, and the ensuing social, economic, and cultural changes, computing technology and applications have evolved quickly over the past decade. They now go beyond personal computing, facilitating collaboration and social interactions in general, causing a quick proliferation of social relationships among IoT entities. The increasing number of these relationships and their heterogeneous social features have led to computing and communication bottlenecks that prevent the IoT network from taking advantage of these relationships to improve the offered services and customize the delivered content, known as social relationships explosion. On the other hand, the quick advances in artificial intelligence applications in social computing have led to the emerging of a promising research field known as artificial social intelligence (ASI) that has the potential to tackle the social relationships explosion problem. This article discusses the role of IoT in social relationships management, the problem of social relationships explosion in IoT, and reviews the proposed solutions using ASI, including social-oriented machine-learning and deep-learning techniques.

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This article provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas, where IoT can contribute are discussed, namely: 1) tracking and tracing; 2) remote patient monitoring (RPM) by wearable IoT (WIoT); 3) personal digital twins (PDTs); and 4) real-life use case: ICT/IoT solution in South Korea. Second, the role and novel applications of AI are explained, namely: 1) diagnosis and prognosis; 2) risk prediction; 3) vaccine and drug development; 4) research data set; 5) early warnings and alerts; 6) social control and fake news detection; and 7) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including: 1) crowd surveillance; 2) public announcements; 3) screening and diagnosis; and 4) essential supply delivery. Finally, we discuss how distributed ledger technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.

Internet of Things (IoT) has been the most emerging technology in the last decade because the number of smart devices and its associated technologies has rapidly grown in both industrial and research prospectives. The applications are developed using IoT techniques for real-time monitoring. Due to low processing power and storage capacity, smart things are vulnerable to the attacks as existing security or cryptography techniques are not suitable. In this study, we initially review and identify the security and privacy issues that exist in the IoT system. Second, as per blockchain technology, we provide some security solutions. The detailed analysis, including enabling technology and integration of IoT technologies, is explained. Finally, a case study is implemented using the Ethererum-based blockchain system in a smart IoT system and the results are discussed.

Internet of Things (IoT) in the agriculture field provides crops-oriented data sharing and automatic farming solutions under single network coverage. The components of IoT collect the observable data from different plants at different points. The data gathered through IoT components, such as sensors and cameras, can be used to be manipulated for a better farming-oriented decision-making process. This work proposes a system that observes the crops' growth and leaf diseases continuously for advising farmers in need. To provide analytical statistics on plant growth and disease patterns, the proposed framework uses machine learning (ML) techniques, such as support vector machine (SVM) and convolutional neural network (CNN). This framework produces efficient crop condition notifications to terminal IoT components which are assisting in irrigation, nutrition planning, and environmental compliance related to the farming lands. In this regard, this work proposes ensemble classification and pattern recognition for crop monitoring system (ECPRC) to identify plant diseases at the early stages. The proposed ECPRC uses ensemble nonlinear SVM (ENSVM) for detecting leaf and crop diseases. In addition, this work performs comparative analysis between various ML techniques, such as SVM, CNN, naïve Bayes, and K-nearest neighbors. In this experimental section, the results show that the proposed ECPRC system works optimally compared to the other systems.

As Internet of Things (IoT) is a fascinating paradigm in which all things and objects are connected together, it holds a significant position in fostering intelligent high-level services. However, the future IoT architecture is still under evolution profiting from the overwhelming development of cyberspace and cyber technologies. Based on the traditional physical-based IoT, social-inspired Internet of People (IoP) and brain-abstracted Internet of Thinking (IoTk), an intelligent embryo of cyber-enabled Internet of X (IoX) is being established where all things, entities, people and thinking are interacted seamlessly. In this article, we clearly introduce the cyber-enabled IoX from perspective of both ubiquitous connections and space convergence, and design an architecture with four pillars, namely, things, people, thinking and cyberentities in respective spaces. In addition, we analyze the fundamental issues in IoX development, such as information exploding, link exploding and application exploding from the view of ubiquitous connections, entity exploding and relationship exploding on the basis of space convergence, and service exploding from overall aspects, where potential solutions are discussed at the same time. The intelligent cyber-enabled IoX will be the cornerstone for future techniques and applications, and proves to be the solid foundation for upcoming intelligent and proactive era.

Physical space appeared with the birth of the earth. With the emergence of social space, thinking space, and cyberspace (STCs), things in physical space are continuously enriched. Consequently, the PhiNet, an abstracted network concept such as relationships between things, is becoming more complex. Physical space plays a fundamental role in promoting the connection and development with the other three spaces. We think that PhiNet of Things (PoT) is a unified description of pure physical space and the evolving physical space affected by STCs. While the Internet of Things (IoT) is described from a cyber view, in this article, the definition of PoT is put forward from a natural view. Besides, the evolution of PoT is identified by the time sequence in which the four spaces appeared. At each stage, research is carried out from two perspectives of things and PhiNet, and two specific examples are presented to illustrate the change process of things and the related PhiNet. In addition, two applications are listed to explain the usability of PoT in the current development stage. Finally, the article gives the possible future development direction of PoT. The purpose of this article is to illustrate the fundamental role of physical space in the continuous development through time sequence.

The exponential growth in the number of connected devices as well as the data produced from these devices call for a secure and efficient access control mechanism that can ensure the privacy of both users and data. Most of the conventional key management mechanisms depend upon a trusted third party like a registration center or key generation center for the generation and management of keys. Trusting a third party has its own ramifications and results in a centralized architecture; therefore, this article addresses these issues by designing a Blockchain-based distributed IoT architecture that uses hash chains for secure key management. The proposed architecture exploits the key characteristics of the Blockchain technology, such as openness, immutability, traceability, and fault tolerance, to ensure data privacy in IoT scenarios and, thus, provides a secure environment for communication. This article also proposes a scheme for secure and efficient key generation and management for mutual authentication between communication entities. The proposed scheme uses a one-way hash chain technique to provide a set of public and private key pairs to the IoT devices that allow the key pairs to verify themselves at any time. Experimental analysis confirms the superior performance of the proposed scheme to the conventional mechanisms.

Today, the Internet of Things (IoT) is increasingly flourishing with establishing ubiquitous connections between smart devices and objects, and by 2020, there will be a total of 30 billion connected things reported by IDC. The unprecedented data explosion provides immense opportunities for valuable information mining. At the same time, it also floods the infrastructure with tremendous values it necessarily handles and proposes high challenges to traditional data storing or processing techniques. On the other hand, artificial intelligence (AI) has become a key component for many applications that profoundly change our lives. Machine learning, especially deep learning (DL) technologies, vastly improves traditional computer science and networking technologies. The convergence of AI and IoT enables data to be quickly explored and turned into significant decisions. For companies and enterprises, AI enhances the speed and accuracy of data processing for instant market strategies.

In the era of rapid development of artificial intelligence, the integration of multimedia and human-artificial intelligence has become an important research hotspot. Especially in the multimedia environment, effective remote visual monitoring has become the exploration direction of many scholars. The use of traditional correlation filtering (CF) algorithm for real-time monitoring in the context of multimedia is a practical strategy. However, most existing filtering-based visual monitoring algorithms still have the problem of insufficient robustness and effectiveness. Therefore, by considering the strategy of updating human memory, this paper proposes a multi-layer template update mechanism to achieve effective monitoring in a multimedia environment. In this strategy, the weighted template of the high-confidence matching memory is used as the confidence memory, and the unweighted template of the low-confidence matching memory is used as the cognitive memory. Through the alternate use of confidence memory, matching memory, and cognitive memory, it is ensured that the target will not be lost during the monitoring process. Experimental results show that this strategy does not affect the speed (still real-time) and improves the robustness in the multimedia background.

The Internet of Things (IoT) has been extensively deployed in smart cities. However, with the expanding scale of networking, the failure of some nodes in the network severely affects the communication capacity of IoT applications. Therefore, researchers pay attention to improving communication capacity caused by network failures for applications that require high quality of services (QoS). Furthermore, the robustness of network topology is an important metric to measure the network communication capacity and the ability to resist the cyber-attacks induced by some failed nodes. While some algorithms have been proposed to enhance the robustness of IoT topologies, they are characterized by large computation overhead, and lacking a lightweight topology optimization model. To address this problem, we first propose a novel robustness optimization using evolution learning (ROEL) with a neural network. ROEL dynamically optimizes the IoT topology and intelligently prospects the robust degree in the process of evolutionary optimization. The experimental results demonstrate that ROEL can represent the evolutionary process of IoT topologies, and the prediction accuracy of network robustness is satisfactory with a small error ratio. Our algorithm has a better tolerance capacity in terms of resistance to random attacks and malicious attacks compared with other algorithms.

Keeping patients' sensitive information secured and untampered in the e-Health system is of paramount importance. Emerging as a promising technology to build a secure and reliable distributed ledger, blockchain can protect data from being falsified, which has attracted much attention from both academia and industry. However, with limited computational resources, medical IoT devices do not have efficient ability to fulfill the functionalities as a full node in wireless blockchain network (WBN). Facing this dilemma, Mobile Edge Computing (MEC) brings us dawn and hope through offloading the high resource demanding blockchain functionalities at the IoT devices to the MEC. However, aiming to maximize the mining profit, most of existing offloading strategies have ignored the other needs of wireless devices, e.g., faster transaction writing. In this paper, according to different needs, blockchain nodes are firstly divided into two categories. One is blockchain users whose needs are faster transaction uploading, the other is blockchain miners whose goals are maximum revenue. Then, to maximize both the utilities of blockchain users and blockchain miners, a Stackelberg game is introduced to formulate the interaction between them. From the simulation results, this game is proved to converge to a unique optimal equilibrium.

Wireless sensor networks (WSNs) is a virtual layer in the paradigm of the Internet of Things (IoT). It inter-relates information associated with the physical domain to the IoT drove computational systems. WSN provides an ubiquitous access to location, the status of different entities of the environment, and data acquisition for long-term IoT monitoring. Since energy is a major constraint in the design process of a WSN, recent advances have led to project various energy-efficient protocols. Routing of data involves energy expenditure in considerable amount. In recent times, various heuristic clustering protocols have been discussed to solve the purpose. This article is an improvement of the existing stable election protocol (SEP) that implements a threshold-based cluster head (CH) selection for a heterogeneous network. The threshold maintains uniform energy distribution between member and CH nodes. The sensor nodes are also categorized into three different types called normal, intermediate, and advanced depending on the initial energy supply to distribute the network load evenly. The simulation result shows that the proposed scheme outperforms SEP and DEEC protocols with an improvement of 300% in network lifetime and 56% in throughput.

Internet of Things (IoT) is transforming everyone's life by providing features, such as controlling and monitoring of the connected smart objects. IoT applications range over a broad spectrum of services including smart cities, homes, cars, manufacturing, e-healthcare, smart control system, transportation, wearables, farming, and much more. The adoption of these devices is growing exponentially, that has resulted in generation of a substantial amount of data for processing and analyzing. Thus, besides bringing ease to the human lives, these devices are susceptible to different threats and security challenges, which do not only worry the users for adopting it in sensitive environments, such as e-health, smart home, etc., but also pose hazards for the advancement of IoT in coming days. This article thoroughly reviews the threats, security requirements, challenges, and the attack vectors pertinent to IoT networks. Based on the gap analysis, a novel paradigm that combines a network-based deployment of IoT architecture through software-defined networking (SDN) is proposed. This article presents an overview of the SDN along with a thorough discussion on SDN-based IoT deployment models, i.e., centralized and decentralized. We further elaborated SDN-based IoT security solutions to present a comprehensive overview of the software-defined security (SDSec) technology. Furthermore, based on the literature, core issues are highlighted that are the main hurdles in unifying all IoT stakeholders on one platform and few findings that emphases on a network-based security solution for IoT paradigm. Finally, some future research directions of SDN-based IoT security technologies are discussed.

Internet-of-Things (IoT)-enabled sensors and services have increased exponentially recently. Transmitting the massive generated data and control messages becomes an overhead on the communication system infrastructure. Many architectures and paradigms have been introduced to address the connection exploding, such as cloudlets, fog, and mist computing. Besides, software-related solutions, such as mobile Internet technologies and software-defined network also take part in mitigating the communication overhead. All of those new techniques have the same purposes summarized in achieving low latency, high throughput, and less storage and computing at the cloud level in addition to other objectives discussed through this survey. We list the proposed solutions, show their advantages and schemes, highlight some of the newest IoT-enabled applications, and show how they benefit from applying the new paradigms.

With the development of the Internet of Things (IoT), the physical space we are living in is experiencing unprecedented digitalization and virtualization. It is an overwhelming trend to achieve the convergence between the physical space and cyberspace, where the fundamental problem is to realize the accurate mapping between the two spaces. Therefore, identity modeling and identity addressing, which serve as the main bridge between the physical space and cyberspace, are regarded as important research areas. This article summarizes the related works regarding identity modeling and identity addressing in IoT, and makes a general comparison and analysis based on their respective features. Following that a flexible and low coupling framework, with strong independence between different modules is proposed, where both identity modeling and identity addressing are integrated. Meanwhile, we discuss and analyze the future development and challenges of identity modeling and addressing. It is proved that identity modeling and identity addressing are extremely significant topics in the era of IoT.

Molecular communication (MC), in which molecules serve as the carrier for data transmission, plays an essential role in nanonetworks. In this article, a mobile diffusive-drift MC model is investigated, which consists of a mobile transmit nanomachine (TN) and a mobile receive nanomachine (RN). The depleted molecule shift keying (D-MoSK) modulation is utilized in this model to perform end-to-end communication. To explore the performance of D-MoSK, we derive the closed-form expressions of symbol error rate (SER) as well as the channel capacity, and then we give out the numerical results. It is observed from the numerical results that, if compared with the molecule shift keying modulation, the D-MoSK modulation can exhibit better performances in terms of SER, channel capacity, and complexity under the employed model. Also, the impacts of several crucial parameters on the performance are evaluated and discussed comprehensively. The obtained results are expected to provide guidance significance for the design of a practical mobile diffusive-drift MC system.

Molecular communication (MC) is a promising technique of using molecules to realize communication between nanomachines for Internet of Bio-Nano Things in the body area nanonetwork. Due to the properties of diffusion and the attenuation of molecular transmission, the diffusion-based MC confronts with challenges in terms of the communication range and the signal detection accuracy. To extend the coverage, the intermediate nanomachine is deployed as relay between transmitter and its intended receiver. In this article, amplify-and-forward (AF) relaying is researched, and the performance under diverse signal detection schemes is analyzed, including mean square error (MSE) detection, maximum a posteriori probability detection, minimum error probability (MEP) detection under stationary fluid environment, and the MEP detection with a drift velocity simulation. The key parameters, such as the number of released molecules, receiving radius, and the relay position, influencing on the AF relaying performance under different detection methods are explored. The simulation results show that the MEP detection can achieve the best performance gain for the AF relay with a drift velocity channel. In particular, when the number of released molecules is 500, the gain is up to 35 dB.

The joint user association and cache placement problem is challenging in fog radio access networks (F-RANs) due to its difficulty to present the optimal solution with low complexity. Motivated by the recent development of artificial intelligence, we divide the original optimization problem into two subproblems. In particular, the user association problem is solved by a reinforcement-learning-based algorithm in which the enhanced fog access point content placement profiles and the fronthaul constraint are considered. On the other hand, since the popularity profile of the contents is hard to acquire in practice, a stacked autoencoder-based scheme is presented to predict the content popularity, which considers both the local and global user request status within a specified time interval. Based on the popularity prediction, the edge content placement problem is solved by a deep-reinforcement-learning-based algorithm, aiming at maximizing the F-RAN network payoff. Moreover, the complicated interactions and the cyclic dependency among the short time-scale user association and the long time-scale content popularity prediction and placement problems are studied by applying the Stackelberg game theory. The simulation validates the accuracy of the analytical results and proves that the proposal can further improve the performance of F-RANs.

In-band full duplex (IBFD) wireless is of utmost interest to future wireless communication and networking due to great potentials of spectrum efficiency. IBFD wireless, how- ever, is throttled by its key challenge, namely self-interference. Therefore, effective self- interference cancellation is the key to enable IBFD wireless. This paper proposes a real-time non- linear self-interference cancellation solution: Deep learning based Self-Interference Cancellation (DSIC) to enable IBFD wireless. In this solution, a self-interference channel is modeled by a deep neural network (DNN). Synchronized self- interference channel data is first collected to train the DNN of the self-interference channel. Afterwards, the trained DNN is used to cancel the self-interference at a wireless node. This solution has been implemented on a USRP SDR testbed and evaluated in real world in multiple scenarios with various modulations in transmitting information including numbers, texts as well as images. It results in the performance of 17dB in digital cancellation, which is very close to the self-interference power and nearly cancels the self- interference at a SDR node in the testbed. The solution yields an average of 8.5% bit error rate (BER) over many scenarios and different modulation schemes.

Online product review is becoming one of important reference indicators for people shopping, but the current product review site contains a lot of fraudulent reviews. Group review spamming, which involves a group of fraudulent reviewers writing a lot of fraudulent reviews for one or more target products, becomes the main form of review spamming. However, solutions for group spammer detection are very limited, and due to lack of ground-truth review data, this problem has never been completely solved. In this paper, we propose a novel three-step method to detect group spammers based on Clique Percolation Method (CPM) in a completely unsupervised way, called GSCPM. First, it utilizes clues from behavioral data (timestamp, rating) and relational data (network) to construct a suspicious reviewer graph. Then, it breaks the whole suspicious reviewer graph into k-clique clusters based on CPM, and we consider such k-clique clusters as highly suspicious candidate group spammers. Finally, it ranks candidate groups by group-based and individual-based spam indicators. We use three real-world review datasets from Yelp.com to verify the performance of our proposed method. Experimental results show that our proposed method outperforms four compared methods in terms of prediction precision.

Wireless sensor networks (WSNs) groups specialized transducers that provide sensing services to Internet of Things (IoT) devices with limited energy and storage resources. Since replacement or recharging of batteries in sensor nodes is almost impossible, power consumption becomes one of the crucial design issues in WSN. Clustering algorithm plays an important role in power conservation for the energy constrained network. Choosing a cluster head (CH) can appropriately balance the load in the network thereby reducing energy consumption and enhancing lifetime. This paper focuses on an efficient CH election scheme that rotates the CH position among the nodes with higher energy level as compared to other. The algorithm considers initial energy, residual energy, and an optimum value of CHs to elect the next group of CHs for the network that suits for IoT applications, such as environmental monitoring, smart cities, and systems. Simulation analysis shows the modified version performs better than the low energy adaptive clustering hierarchy protocol by enhancing the throughput by 60%, lifetime by 66%, and residual energy by 64%.

Internet of Things (IoT) is a worldwide system of “smart devices” that can sense and connect with their surroundings and interact with users and other systems. Global air pollution is one of the major concerns of our era. Existing monitoring systems have inferior precision, low sensitivity, and require laboratory analysis. Therefore, improved monitoring systems are needed. To overcome the problems of existing systems, we propose a three-phase air pollution monitoring system. An IoT kit was prepared using gas sensors, Arduino integrated development environment (IDE), and a Wi-Fi module. This kit can be physically placed in various cities to monitoring air pollution. The gas sensors gather data from air and forward the data to the Arduino IDE. The Arduino IDE transmits the data to the cloud via the Wi-Fi module. We also developed an Android application termed IoT-Mobair, so that users can access relevant air quality data from the cloud. If a user is traveling to a destination, the pollution level of the entire route is predicted, and a warning is displayed if the pollution level is too high. The proposed system is analogous to Google traffic or the navigation application of Google Maps. Furthermore, air quality data can be used to predict future air quality index (AQI) levels.

Nature-inspired approaches have been widely used for different purposes over the last two decades and are still extensively researched, especially for complex real-world problems. Biological systems, or nature in general, serve as the source of the intelligence of nature-inspired approaches. The efficiency of nature-inspired approaches is due to their significant ability to imitate the best features of nature that evolved by natural selection over millions of years. These approaches have been successfully used for Internet of Things (IoT) and big data handling and relevant examples of these topics may be artificial neural networks (ANNs) and deep learning applications. On this basis, the main theme of this special issue (SI) addresses recent advances in the use of the nature-inspired approaches for IoT and big data problems.

The Internet of Things (IoT) connects more and more devices and supports an ever-growing diversity of applications. The heterogeneity of the cross-industry and cross-platform device resources is one of the main challenges to realize the unified management and information sharing, ultimately the large-scale uptake of the IoT. Inspired by software-defined networking, we propose the concept of software-defined device (SDD) and further elaborate its definition and operational mechanism from the perspective of cyber-physical mapping. Based on the device-as-a-software concept, we develop an open IoT system architecture which decouples upper-level applications from the underlying physical devices (Physical-D) through the SDD mechanism. A logically centralized controller is designed to conveniently manage Physical-D and flexibly provide the device discovery service and the device control interfaces for various application requests. We also describe an application use scenario which illustrates that the SDD-based system architecture can implement the unified management, sharing, reusing, recombining, and modular customization of device resources in multiple applications, and the ubiquitous IoT applications can be interconnected and intercommunicated on the shared Physical-D.

The ubiquitous connections of physical objects in Internet of Things (IoT) is undoubtedly challenging the consistency mapping between physical space and cyberspace. As the key techniques for establishing the correspondences between physical objects and cyber entities, the objects identification and resolution (IR) attracted extensive attention. Conventional IR schemes in IoT generally rely on a single-mode of identification (ID) or nonidentification (nID) IR, which has big limitations in adaptability and reliability. In this case, a combined IR scheme based on ID and nID is proposed in this paper. In our proposed scheme, the ID code and nID features complement each other so as to break the restrictions of application domain and to provide better and humanized services. In order to improve the efficiency of the scheme, edge computing is introduced to reduce network transmission load and the computing burden of cloud, especially when the input data requires large amount of computing and storage resources. Furthermore, we design and implement a prototype of electronic product code (EPC) (ID) and fingerprint (nID) combined IR. Simulation results show that the edge computing-based ID and nID combined IR scheme has advantages in resolution accuracy and efficiency.

The increasing growth of big data is certainly challenging ubiquitous sensing in the Internet of Things (IoT) paradigm because of the limitations in sensing resources. Processing huge amounts of sensed data requires an enormous and unnecessary pool of resources. Both reasons strongly support the idea of adopting a selective sensing solution to handle the mapping between physical space and cyberspace and to lighten the load of data processing in IoT applications. Inspired by the ability of creatures that fleetly select the information of interest from a noisy environment and process them with limited attention resources, in this paper the biological attention mechanism is introduced to design a novel selective sensing framework called attention mechanism inspired selective sensing (AMiSS). In order to illustrate the functionality of the AMiSS platform, a use case scenario in reference to the security system of a modern transport station is presented. Further, we implement a proof-of-concept simulation using video-based object tracking to verify the feasibility and effectiveness of the AMiSS framework in IoT applications. Although it is just a narrow demonstration, the simulation still shows the effect of the AMiSS platform in reducing the amount of data processed by the higher layers.

The edge cache is an effective way to reduce the heavy traffic load and the end-to-end latency in radio access networks (RANs) for supporting a number of critical Internet of Things (IoT) services and applications. It has been verified to provide high spectral efficiency (SE), high energy efficiency (EE), and low latency. Along with several key techniques that have been applied, such as device-to-device communication and predictive caching, the edge cache techniques in RANs for IoT are becoming diversified. This paper comprehensively surveys the recent advances of the edge cache in RANs, including the key techniques and the corresponding performances. In particular, the key techniques are presented from the viewpoints of the deployment location of edge caches, content placement strategy, and coded caching. An advanced hierarchical edge cache structure is presented, and the main impacts on SE, EE, and latency of the key techniques are mainly summarized. Several open issues and challenges are identified as well to spur future investigations, in which the joint optimization of radio and cache resources, the edge cache with mobile edge computing and network intelligence, privacy, and security are discussed.

Cyberspace is the digital world created based on traditional physical, social, and thinking spaces (PST) but in turn makes a great difference on PST. The cyberization and the emergence of cyber-enabled spaces can be viewed as the bridge between cyberspace and PST, which reshaped the current definition of cyberspace and contributed to a novel concept general cyberspace (GC). Generally, GC is a unified description of conventional cyberspace (also shortly cyberspace in this paper) and cyber-enabled PST. It essentially emerges from cyberspace based on ubiquitous connections between things and the deep convergence of spaces. This paper proposes the definition of GC and investigates it from its three main aspects: 1) existence; 2) interactions; and 3) applications/services, respectively, in terms of philosophy, science, and technology outlook.

Taking full advantage of centralized cooperation and edge caching, fog radio access network has been considered as a promising paradigm to provide high spectral efficiency and energy efficiency. However, the overheads on fronthaul transmission and content caching also significantly affect the affordability, which makes it an increasingly urgent problem to achieve the performance improvement with reasonable overheads. In this paper, the economical energy efficiency (E3) metric is adopted to comprehensively consider the impacts on different aspects. Under the constraints of maximum transmitting power, cache status, and fronthaul capacity, a resource allocation problem is formulated, in which throughput, energy consumption, as well as cost on fronthaul transmission and content caching are jointly considered. The problem is solved using fractional programming, weighted minimum mean square error approach, and greedy algorithm, and an adaptive transmitting method selection algorithm is proposed. The simulation results demonstrate the effectiveness and gains of the proposed algorithm, and the corresponding key factors impacting on E3are accordingly analyzed and evaluated.

Wireless Body Area Network (WBAN) applications are becoming popular today. To protect patients' private data during transportation, secure wireless communications are mandatory in WBANs. Encryptions and secret keys are the base of secure communications over insecure wireless environments. Given most wireless nodes in WBANs are resource-constrained, efficiency is an implicit requirement of the key generation methods for WBAN wireless communications. Finding secure and efficient key generation method for WBANs is the goal of this article. We propose a practical, pure software method in this article. The new method has been proved to be highly secure and efficient.

This paper develops the traffic models of smart grid electronic data (E data) and multimedia video over cognitive radio (CR). Unlike the traditional “Poisson” arrival model, each arrival monitoring stream follows fixed time triggered Gaussian distribution which approximates to the reality, and the video data is classified as key frame data with higher priority than nonkey frame data to reduce communication burden. To enhance the delivery probability of E data and multimedia data, we adopt a buffer mechanism to store the “sending fail” data and try to resend them together with new coming data by using the new data's sending opportunity. To avoid buffer overflow, the unsent data should be compressed and some should be removed, and the new coming data rate should be reduced to alleviate the congestion of the CR communication network. In this paper, we propose a new binary exponential backoff (NBEB) algorithm to “compress” the unsent data which can keep key information but recover the electronic tendency as much as possible. With NBEB, the new coming data can be temporally selected and thrown into the buffer and more new data can be put in the buffer. The algorithm can reduce the arrival traffic rate exponentially related with the sending failure times. The results show that NBEB can significantly decrease the blocking/dropping probability, increase the communication success probability, and improve the communication performance.

The prosperity of social networks provides users with convenient communication but also attracts a large number of spammers. To solve this problem, this paper combines supervised learning and unsupervised learning algorithms, and proposes a novel hybrid model based on OPTICS and SVM. First, we collected a dataset from Sina Weibo including 10,000 users and 134,188 messages; then extracted the content based features and user behavior based features from the dataset; afterwards, we applied the features into the hybrid model to establish the classification model. The experiment shows that the proposed approach is capable of detecting spammers effectively with 87.6% spammers and 94.7% legitimate users correctly classified.

Coordinated Multi-Point (CoMP) is an effective way to improve user performance in next-generation wireless cellular networks, such as 3GPP LTE-Advanced(LTE-A). The base station cooperation can reduce interference, and increase the signal to interference and noise ratio (SINR) of cell-edge users and improve the system capacity. However, the base station cooperation also adds additional power consumption for signal processing and sharing information through back-haul links between cooperative base stations. As such, CoMP may potentially consume more energy. This paper studies such energy consumption issue in CoMP, presents a semi-dynamic CoMP cluster division algorithm based on energy efficiency (SCCD-EE) that can effectively adapt to users' real-time interference, and employs the idea of Maximal Independent Set (MIS) to solve the problem of cluster overlapping. To verify the feasibility of the proposed algorithm, this paper performs comprehensive evaluations in terms of energy efficiency and system capacity. The simulation results show that the proposed semi-dynamic cluster division algorithm can not only improve the system capacity and the quality of service (QoS) of cell-edge users, but also achieve higher network energy efficiency compared with static cluster methods and Non-CoMP approaches. Copyright © 2016 John Wiley & Sons, Ltd.

As an important branch of wireless sensor networks, wireless body area networks (WBAN) has attracted widespread attention in various fields because of its portability and mobility. However, because much of the data collected by WBAN are related to personal information of the user, the sensitive private data may be at risk of leakage or malicious modification in the actual process of application and deployment. In order to assure the security and privacy of user's data in the environment of WBAN, this paper presents a Data Privacy Protective Mechanism for WBAN. In order to secure data and secure transmission, this mechanism combines symmetric key with an asymmetric key to transmit user's data. Then, it cuts and reorganizes the data in the process of transmission to better capture defense and the attacks of the nodes. Ultimately transmits the user's data securely under the condition that the data collected by the nodes are confidential and secure. Copyright © 2015 John Wiley & Sons, Ltd.

This paper proposes a Contrarian Probabilistic Model (CPM) to evaluate the effectiveness of contrarians' investment in preferred stocks using big data from Tradeline. CPM accommodates the unique features of investment data which are often correlated, nested, heterogeneous, non-normal with missing values. The clustering and statistical inference are integrated in CPM, which enables joint investment behavior trajectory pattern recognition and risk analyses based on the entire variance-covariance structure between and within clusters. The empirical study using CPM provides a finer and comprehensive evaluation of contrarian investment in preferred stocks. Two distinctive investment behavior trajectory clusters were identified, showing a few high-risk-seeking contrarians achieved high returns over five year long-term investment, while the majority of contrarians did not outperform glamour stockholders in preferred stock investment. Although CPM was developed using historical data, it could be developed into an analytical tool for online near real time big investment data analyses.

Based on empirical studies, the feature of random initialization in Particle Swarm Optimization (PSO) based Fuzzy c-means (FCM) methods affects the computational performance especially in big data. As the data points in high-density areas are more likely near the cluster centroids, we design a new algorithm to guide the initialization according to the data density patterns. Our algorithm is initialized by fusing the data characteristics near the cluster centers. Our evaluation results from real data show that our approach can significantly improve the computational performance of PSO-based Fuzzy clustering methods, while preserving comparable clustering performance.

Cooperative spectrum sharing increases the spectrum efficiency and improves the performance of primary users (PUs) in cognitive radio domain. This paper proposes an energy-aware dynamic spectrum sharing framework, named Cooperative Spectrum Sharing with Energy-save(CSSE), which maximizes energy saving while ensures communication QoS (i.e. transmission rate) of primary transmitter (PT). In CSSE, the PT leverages a proper set of secondary transmitters (STs) as cooperative relays for its transmission and releases a proportion of bandwidth to the cooperative STs. Under the restriction of energy budget, each ST decides its power density allocation (including relaying power density and transmit power density for its own transmission) to maximize its transmission rate. Taking the users' selfishness and intellectuality into consideration, we formulate the above optimal problem as a Stackelberg game (SG) and prove that a Unique Nash Equilibrium (UNE) point exists among the non-cooperative STs. Theoretical analysis and simulation results show that the PU can obtain maximum benefit. Meanwhile, the relaying STs can get acceptable benefits under CSSE.

Opportunistic networks are a emerging networks characterized by frequent network partitions, high bit error ratio and random topology instability, where the message propagation depends on the cooperation of nodes to fulfill a "store-carry-forward" fashion. Due to the constrained energy, memory and processing capacity, some individual nodes may behave selfishly, or even maliciously, which will introduce damage into the existing routing schemes based on cooperation and degrade the performance (lower delivery ratio, longer latency etc.,) of opportunistic networks greatly. In order to address the above issues, the current price-based incentive strategy, Credit relies on a fixed management-center which is rare in the realistic opportunistic networks with little infrastructure to manage the transaction that the source of messages pays virtual credits to nodes that relay messages for it. So this paper proposes a novel Game based Incentive Strategy (GIS) which utilizes three-time bargaining model based on two-person transaction and allows the sending nodes to pay the relay nodes directly according to the optimal price drawn by game without any third party. GIS stimulates the cooperation of selfish nodes to forward messages effectively, while holds back the deceptive price stemmed from the malicious intermediary nodes to facilitate deals. From the extensive simulations results, GIS can optimize the average latency and the delivery ratio to the greatest extent. Additionally, effectiveness and fairness can be guaranteed.

Researchers have proposed several ensemble methods for the data stream environments including online bagging and boosting. These studies show that bagging methods perform better than boosting methods although the opposite is known to be true in the batch setting environments. The reason behind the weaker performance of boosting methods in the streaming environments is not clear. We have taken advantage of the algorithmic procedure of meta expert learnings for the sake of our study. The meta expert learning differs from the classic expert learning methods in that each expert starts to predict from a different point in the history. Moreover, maintaining a collection of base learners follows an algorithmic procedure. The focus of this paper is on studying the pruning function for maintaining the appropriate set of experts rather than proposing a competitive algorithm for selecting the experts. It shows how a well-structured pruning method leads to a better prediction accuracy without necessary higher memory consumption. Next, it is shown how pruning the set of base learners in the meta expert learning (in order to avoid memory exhaustion) affects the prediction accuracy for different types of drifts. In the case of time-locality drifts, the prediction accuracy is highly tied to the mathematical structure of the pruning algorithms. This observation may explain the main reason behind the weak performance of previously studied boosting methods in the streaming environments. It shows that the boosting algorithms should be designed with respect to the suitable notion of the regret metrics.

The performance gain of cooperative communications depends heavily on the selection of relay. Most of existing relay selection methods aim at maximizing cooperative gain by selecting appropriate relay, without taking into account the adverse effect brought by cooperative communications: extra interferences introduced by relay transmission (called cooperation interference). Thus the derived performance gain could be inaccurate and/or the selected relay may be not optimal. In this paper, we address the assignment of relays for multiple communication sessions using cooperative communications in a wireless network. We first thoroughly investigate the adverse effect brought by using relays, and derive the cooperation gain with consideration of cooperation interference. Based on the insights of our investigation, we propose a method of assigning relays to individual transmission flows while taking into account cooperation interference in cooperative communications. In order to tradeoff the advantage and adverse effect caused by relay transmissions, we use an auction approach to address relay assignment of cooperative communications. Specifically, we propose a Single round double Auction Scheme (SAS) for centralized wireless network and a Multiple rounds sequential Auction Scheme (MAS) for decentralized wireless network for relay assignment. We conduct extensive simulation experiments to validate the effectiveness of SAS and MAS. The significance of the impact of cooperation interference, improvement of system throughput and energy efficiency are demonstrated by numerical results.

In this paper, we propose a relay selection method, Maximum Throughput Relay Selection Algorithm (MTRSA) for wireless networks. Based on the derivation of direct and cooperative communications, MTRSA takes both MAC overhead and collision into consideration for maximizing the system throughput. In addition, the analytical derivation and proposed relay selection algorithm support both amplify-and-forward (AF) and decode-and-forward (DF). Numerical results and simulations are provided to validate the efficiency of our algorithm.

Opportunistic network is a type of challenged network where an end-to-end path between the source and the destination doesn't exist. The dissemination of the data relies on the encounters of nodes. Link duration time is a main factor in determining the transmission capacity between two encounter nodes in the opportunistic network. Besides, inter-contact time plays a key role in forwarding algorithms and has an obvious effect on the delivery delay. In this paper, according to statistical analysis and numerical methods, statistical characteristics of wireless link in random waypoint (RWP) are analyzed from aspects of contact duration time and inter-contact time with different moving speed and transmission radiuses of the nodes. Complementary Cumulative Distribution Functions (CCDF) of the contact duration time and inter-contact time of the nodes are provided by numerical methods.

Opportunistic networks are the emerging networks featured by partitions, long disconnections, and topology instability, where the message propagation depends on the cooperation of nodes to fulfill a “store-carry-and-forward” fashion. But due to constrained energy and buffer, some nodes may behave selfishly, which will involve damage to the existing routing approaches and seriously degrade the performance of opportunistic networks. Aiming at the above problem, this paper proposes a novel bargaining based incentive protocol (BIP) for opportunistic networks, which exploits two-person bargaining model and allows a node to pay and charge according to its state and the attributes of messages. In addition, the proposed BIP protocol can tackle the issue of blind cooperation when the resources are very scarce. Extensive simulation results demonstrate the effectiveness and the practicality of the proposed BIP protocol in terms of high delivery ratio, low energy consumption and small average delay.

In order to overcome the phenomenon that the existence of end-to-end path is no longer guaranteed because of intermittent connection in the opportunistic network, it is desirable to employ a special node called ferry, which moves in a specific trace and forwards data for disconnected nodes, to provide communication opportunities. In this paper, Global Ferry Scheme (GFS), which exploits multiple local ferries and a global ferry to deliver messages, is proposed to minimize the average message delivery delay. The performance of GFS is evaluated and compared to the existing ferry-based routing method by analysis and simulation. The results show that GFS can improve the performance of opportunistic networks in terms of average delay and delivery ratio.

Existing studies have demonstrated that uneven and dynamic usage patterns by the primary users of license-based wireless communication systems can often lead to temporal and spatial spectrum underutilization. This provides an opportunity for the secondary users (SUs) to tap into underutilized frequency bands provided that they are capable of cognitively accessing systems without colliding or impacting the performance of the primary users (PUs). When there are multiple networks with spare spectrum, secondary users can opportunistically choose the best network to access, subject to certain constraints. In cognitive radio systems, this is referred to as the network selection problem for secondary users. This paper develops a Markov queuing model to obtain the maximum allowable arrival rate of secondary users subject to a target collision probability for the primary users. Based on this model, we design a novel Collision-Constrained Network Selection (CCNS) method that maximizes secondary users' throughput subject to a given PU collision probability. Further, we propose two approaches, referred as CCNS-Greedy and CCNS-Energy, which target to reduce collision probability and to decrease energy consumption of secondary users when the system is underloaded. This, however, has one practical drawback in that the proposed CCNS method depends on PU and SU traffic characteristics such as inter-arrival time and service time, which might not be available in real scenario. We next illustrate that a MEAsurement-based Networks Selection (MEANS) scheme can be used to perform network selection for secondary users based on online measurement of PU collision probability of each network. We evaluated the performance based on extensive simulation, which conclusively shows that the proposed schemes achieve the best performance in terms of resulting PU collision probability, SU throughput, and SU energy consumption, when compared to both Random and Greedy strategies.

Intelligent Operations and Management, in general, and, Intelligent Network Operations and Management, in particular, is increasingly about End-to-End control across multiple networks/services; across multiple layers in network, computing, and software stacks; and, across a variety of time-frames. It is, therefore, a problem of integration and analysis of huge amounts of very heterogeneous data in real time. This talk will contain an overview of AT&T Shannon Labs research, and a discussion of our work in Information Mining and Software Research with applications in telecommunications industry including network operations, network security, IP network management, fraud detection, marketing, and business & consumer markets analysis. The emphasis will be on near real-time analysis and mining of large scale data streams. Selected future research direction will be presented.

Measurement studies have shown that uneven and dynamic usage patterns by the primary users of license based wireless communication systems often lead to temporal and spatial spectrum underutilization. This provides an opportunity for secondary users to tap into underutilized frequency bands provided that they are capable of cognitively accessing without colliding or impacting the performance of the primary users. When there are multiple networks with spare spectrum, secondary users can opportunistically choose the best network to access, subject to certain constraints. In cognitive radio systems, this is referred to as the network selection problem. In this paper, multiple network selection strategies namely, random, weighted, and greedy, are comprehensively evaluated. It is found that without adequate admission control, those methods cannot provide sufficient service protection for the primary users. Next, a Markov decision model is applied to obtain the maximum allowable arrival rate for secondary users subject to a target collision probability for the primary users. Based on this model, a Collision-Constrained Network Selection (CCNS) method is proposed that maximizes system throughput subject to a given collision probability. Simulations show that comparing to random, weighted, and greedy strategies CCNS achieves an improved performance in terms of system throughput and collision probability.

This paper investigates the performance of EPC-gloabl Generation-2 (Gen-2) ultra high frequency (UHF) radio frequency identification (RFID) protocol. Gen-2 protocol has a critical parameter Q that plays an important role in resolving tag collisions. Gen-2 protocol proposes an adaptive slot-count algorithm to adjust Q dynamically based on the type of replies from tags. In this paper, we define two performance metrics for tag identification: Query Success Rate (QSR) and tag identification speed (TIS). We establish a Discrete-Time Markov Chain (DTMC) model for the Gen-2 and accordingly obtain quantitative results of QSR and TIS. Extensive simulations validate our theoretical analysis and demonstrate that the number of tags has little impact on the performance. In other words, QSR and TIS do not nearly decrease even though the number of tags may be increasing. Our model for Gen-2 protocol is also useful to study the performance of other RFID protocols.

Radio frequency identification (RFID) provides a non-line-of-sight and contactless approach for object identification. But if there are multiple tags in the range of an RFID reader, tag collision can take place due to radio signal interference and therefore an anti-collision algorithm is required to

Congestion in wireless sensor networks not only causes packet loss, but also leads to excessive energy consumption. Therefore congestion in WSNs needs to be controlled in order to prolong system lifetime. In addition, this is also necessary to improve fairness and provide better quality of service (QoS), which is required by multimedia applications in wireless multimedia sensor networks. In this paper, we propose a novel upstream congestion control protocol for WSNs, called priority-based congestion control protocol (PCCP). Unlike existing work, PCCP innovatively measures congestion degree as the ratio of packet inter-arrival time along over packet service time. PCCP still introduced node priority index to reflect the importance of each sensor node. Based on the introduced congestion degree and node priority index, PCCP utilizes a cross-layer optimization and imposes a hop-by-hop approach to control congestion. We have demonstrated that PCCP achieves efficient congestion control and flexible weighted fairness for both single-path and multi-path routing, as a result this leads to higher energy efficiency and better QoS in terms of both packet loss rate and delay.