The Internet of Things (IoT) is becoming an attractive system paradigm, in which physical perceptions, cyber interactions, social correlations, and even cognitive thinking can be intertwined in the ...ubiquitous things’ interconnections. It realizes a perfect integration of a new cyber–physical–social–thinking (CPST) hyperspace, which has profound implications for the future IoT. In this article, a novel concept Cybermatics is put forward as a broader vision of the IoT (called hyper IoT) to address science and technology issues in the heterogeneous CPST hyperspace. This article covers a broaden research field and presents a preliminary study focusing on its three main features (i.e., interconnection, intelligence, and greenness). Concretely, interconnected Cybermatics refers to the variants of Internet of anything, such as physical objects, cyber services, social people, and human thinking; intelligent Cybermatics considers the cyber–physical–social–thinking computing to provide algorithmic support for system infrastructures; green Cybermatics addresses energy issues to ensure efficient communications and networking. Finally, open challenging science and technology issues are discussed in the field of Cybermatics.
•We establish a cyber–physical–social–thinking (CPST) hyperspace architecture to explain the Cybermatics.•Interconnected Cybermatics refers to dynamics and variability of Internet of anything.•Intelligent Cybermatics considers computing algorithms for system infrastructure.•Green Cybermatics addresses energy issues to ensure efficient communications and networking.
A recommendation system is an integral part of any modern online shopping or social network platform. The product recommendation system as a typical example of the legacy recommendation systems ...suffers from two major drawbacks: recommendation redundancy and unpredictability concerning new items (cold start). These limitations take place because the legacy recommendation systems rely only on the user's previous buying behavior to recommend new items. Incorporating the user's social features, such as personality traits and topical interest, might help alleviate the cold start and remove recommendation redundancy. Therefore, in this article, we propose Meta-Interest, a personality-aware product recommendation system based on user interest mining and metapath discovery. Meta-Interest predicts the user's interest and the items associated with these interests, even if the user's history does not contain these items or similar ones. This is done by analyzing the user's topical interests and, eventually, recommending the items associated with the user's interest. The proposed system is personality-aware from two aspects; it incorporates the user's personality traits to predict his/her topics of interest and to match the user's personality facets with the associated items. The proposed system was compared against recent recommendation methods, such as deep-learning-based recommendation system and session-based recommendation systems. Experimental results show that the proposed method can increase the precision and recall of the recommendation system, especially in cold-start settings.
Social Internet of Things comes as a new paradigm of Internet of Things to solve the problems of network discovery, navigability, and service composition. It aims to socialize the IoT devices and ...shape the interconnection between them into social interaction just like human beings. In IoT scenarios, a device can offer multiple services and different devices can offer the same services with different parameters and interest factors. The proliferation of offered services led to difficulties during service filtering and customization, this problem is known as services explosion. The selection of a suitable service that fits the requirements of the applications and devices is a challenging task. Several works have addressed service discovery, composition, and selection in IoT. However, these works did not emphasize on the fact that incorporating the users' social features can increase the efficiency of the recommended services and help us to offer context-aware services. In this article, we present a service recommendation system that takes advantage of the social relationships between devices' owners, where the recommendation is based on the different relationships between the service requester and service provider. Experimental results show, in the context of IoT, that incorporating the users' social relationships in service recommendation increases the accuracy and diversity of the offered services.
Accurate speed predictions for urban roads are highly important for traffic monitoring and route planning, and also help relieve the pressure of traffic congestion. Many existing studies on traffic ...speed prediction are based on convolutional neural networks, and these have primarily focused on capturing the spatial proximity among different road segments. However, the real cause of the spread of traffic congestion is the connectivity of these road segments, rather than their spatial proximity. This makes it very challenging to improve prediction accuracy. Using graph neural networks (GNNs), the connectivity of these road segments can be modeled as a graph in which the properties of road segments and the connections between them are embedded as the properties of the nodes and edges, respectively. This paper describes a novel approach that combines the advantages of sequence-to-sequence (Seq2Seq) models and GNNs. Specifically, the evolution of traffic conditions on road networks is modeled as a sequence of graphs. Thus, the proposed SeqGNN model represents both the inputs and outputs as graph sequences. Finally, the extensive experiments using real-world datasets demonstrate the effectiveness of our approach and its advantages over the state-of-the-art methods.
To help people live better in today's digitally explosive environment, the authors envision a Cyber-Individual (Cyber-I) that is the counterpart of a real individual in the physical world.
The development of intelligent traffic systems can benefit from the pervasiveness of IoT technologies. In recent years, increasing numbers of devices are connected to the IoT, and new kinds of ...heterogeneous data sources have been generated. This leads to traffic systems that exist in extended dimensions of data space. Although cloud computing can provide essential services that reduce the computational load on IoT devices, it has its limitations: high network bandwidth consumption, high latency, and high privacy risks. To alleviate these problems, edge computing has emerged to reduce the computational load for achieving TDaaS in a dynamic way. However, how to drive all edge servers' work and meet data service requirements is still a key issue. To address this challenge, this article proposes a novel three-level transparency-of-traffic-data service framework, that is, a KID-driven TEC computing paradigm. Its aim is to enable edge servers to cooperatively work with a cloud server. A case study is presented to demonstrate the feasibility of the proposed new computing paradigm with associated mechanisms. The performance of the proposed system is also compared to other methods.
The Internet of Things (IoT) computing paradigm and its variants, such as the Social Internet of Things (SIoT) and the Internet of People (IoP), have enabled the interconnection of billions of ...devices with existing computing systems like the social networking platforms. Such interconnection allows IoT applications to offer large-scale context-aware services. On the other hand, the pervasive integration of the IoT and the Cyber-Physical Systems (CPS) have posed many new challenges on the cyber modeling of physical, social and thinking entities. After mapping all the basic elements that form the physical, social and thinking spaces, these elements will be represented in the cyberspace as cyber entities, which are the most elementary particles of the cyberspace. Cybermatics was proposed as a holistic field for the systematic study of cyber entities in the cyberspace and their functions, properties, and conjugations with entities in conventional spaces. In this paper, we emphasize the temporal parts, life cycle and inter-relations of the cyber entities. The potentials of endurance and perdurance cyber mapping are also discussed. Furthermore, a mapping model that incorporates the cyber entity evolution and temporal parts consistency is proposed and presented using a smart home as a use case scenario.
Recommender systems (RSs) have become important tools for solving the problem of information overload. With the advent and popularity of online social networks, some studies on network-based ...recommendation have emerged, raising the concern of many researchers. Trust is one kind of important information available in social networks and is often used for performance improvement in social-network-based RSs. However, most trust-aware RSs ignore the fact that people trust different subsets of friends pertaining to different domains, such as music and movies, because people behave differently in diverse domains according to different interests. This paper proposes a novel recommendation method called TruCom. In a multicategory item recommendation domain, TruCom first generates a domain-specific trust network pertaining to each domain and then builds a unified objective function for improving recommendation accuracy by incorporating the hybrid information of direct and indirect trust into a matrix factorization recommendation model. Through relevant benchmark experiments on two real-world data sets, we show that TruCom achieves better performance than other existing recommendation methods, which demonstrates the effectiveness and reliability of TruCom.
Cooperative data delivery among mobile nodes can improve the performance of data delivery in mobile social networks. However, data routing in the presence of socially selfish (SS) nodes is ...challenging, where they mitigate the degree of their cooperation level based on their social features and ties to achieve their social objectives. This issue becomes more challenging when they prevent revealing their reactions about incoming messages, which leads data forwarding under uncertain behavior. In this paper, we propose a signaling game approach, namely, Sig4UDD, to study the impact of uncertain cooperation among well-behaved and SS nodes on the performance of data forwarding. In Sig4UDD, we employ Bayesian Nash equilibrium to analyze one-stage interactions among nodes. Then, perfect Bayesian equilibrium is applied to analyze their multistage interactions. In this stage, we establish a belief system to help SS nodes predict the type of their opponents and take appropriate actions to maximize their utilities. To update the beliefs of SS nodes, we devised the weighted social distance metric to measure the global social distance among nodes. Finally, we compare the performance of Sig4UDD to some benchmark cooperative and noncooperative data forwarding protocols using Reality Mining and Social Evolution data sets.
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