The Internet of Things (IoT) is defined as a paradigm in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. In this paper, we survey state-of-the-art methods, protocols, and applications in this new emerging area. This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly. As compared to similar survey papers in the area, this paper is far more comprehensive in its coverage and exhaustively covers most major technologies spanning from sensors to applications.

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Internet of Things: Architectures, Protocols, and Applications

From a Novel

Internet of Things security

With the advances in new-generation information technologies, especially big data and digital twin, smart manufacturing is becoming the focus of global manufacturing transformation and upgrading. Intelligence comes from data. Integrated analysis for the manufacturing big data is beneficial to all aspects of manufacturing. Besides, the digital twin paves a way for the cyber-physical integration of manufacturing, which is an important bottleneck to achieve smart manufacturing. In this paper, the big data and digital twin in manufacturing are reviewed, including their concept as well as their applications in product design, production planning, manufacturing, and predictive maintenance. On this basis, the similarities and differences between big data and digital twin are compared from the general and data perspectives. Since the big data and digital twin can be complementary, how they can be integrated to promote smart manufacturing are discussed.

Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison

In this chapter we deal with the following nonlinear fractional programming problem: 

$$P:\mathop{{\max }}\limits_{{x \in s}} q(x) = (f(x) + \alpha )/((x) + \beta )$$

where f, g: R n → R, α, β ∈ R, S ⊆ R n . To simplify things, and without restricting the generality of the problem, it is usually assumed that, g(x) + β + 0 for all x ∈ S,S is non-empty and that the objective function has a finite optimal value.

Nonlinear Fractional Programming

Massive amounts of multimedia data (ie, text, audio, video, graphics and animation) are being generated everyday Conventionally, multimedia data are managed by the platforms maintained by multimedia service providers, which are generally designed using centralised architecture However, such centralised architecture may lead to a single point of failure and disputes over royalties or other rights It is hard to ensure the data integrity and track fulfilment of obligations listed on the copyright agreement To tackle these issues, in this paper, we present a blockchain-based platform architecture for multimedia data management We adopt self-sovereign identity for identity management and design a multi-level capability-based mechanism for access control We implement a proof-of-concept prototype using the proposed approach and evaluate it using a use case The results show that the proposed approach is feasible and has scalable performance

A blockchain-based platform architecture for multimedia data management

Mobile cloud computing (MCC) is a very hot research focus of both academia and industries, since it can greatly relieve the hardware limitation of mobile devices as well as create new fascinating mobile services with its tremendous storage and processing ability. Moreover, wireless sensor networks (WSNs) have been attracting attention for about two decades, because of its powerful capability to detect physical or environmental conditions. Motivated by incorporating the advantages of both MCC and WSNs, a lot of schemes which integrate MCC with WSNs have been proposed for exploiting the cloud to share the data gathered by WSNs to mobile users. Particularly, all current integration frameworks utilize the always-on WSNs to collect sensory data for cloud clients, since the data requests of mobile users generally require being responded in real-time. However, these MCC and WSNs integration schemes ignore the following two observations: 1) the specific data cloud clients request usually depends on the current location of cloud clients 2) most sensors are usually equipped with non-rechargeable batteries with limited energy. In this paper, motivated by the above two issues, we present two novel collaborative location-based sleep scheduling (CLSS) schemes for WSNs to integrate with MCC. Based on the location of mobile user, CLSS dynamically determines the awake or asleep status of each sensor node to save energy consumption of WSNs. Theoretical and simulation results show that the proposed scheme can achieve a prolonged network lifetime of WSNs while still satisfy the data requests of mobile users.

Collaborative location-based sleep scheduling to integrate wireless sensor networks with mobile cloud computing

The articles in this special section focus on situation awareness in intelligent human-computer interaction for critical decision making (HCI). HCI is recognized as an ctive field that focuses on the various interactions of human with machines. The HCI has been widely applied in multiple domains, such as artificial intelligence, computer vision, image and multimedia analysis, and cognitive and behavioral sciences. The objective of the HCI is to make the computer smart via receiving enough knowledge about the environment where it is deployed and reduce the human intervention aspect toward decision making. This enables development of high-end computers that are context aware and smart in making decisions with reference to the context. Situation awareness of an intelligent HCI will decide the success and application of the solution across the real world environment. The aim of this special issue is to provide a platform on the topic of situation awareness in intelligent HCI for time critical decision making.

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Special Issue on Situation Awareness in Intelligent Human-Computer Interaction for Time Critical Decision Making

Transfer RNA (tRNA), often considered as a housekeeping molecule, mainly participates in protein translation by transporting amino acids to the ribosome. Nevertheless, accumulating evidence has shown that tRNAs are closely related to various physiological and pathological processes. The proper functioning of the immune system is the key to human health. The aim of this review is to investigate the relationships between tRNAs and the immune system. We detail the biogenesis and structure of tRNAs and summarize the pathogen tRNA-mediated infection and host responses. In addition, we address recent advances in different aspects of tRNA-associated dysregulation in immune responses and immune diseases, such as tRNA molecules, tRNA modifications, tRNA derivatives and tRNA aminoacylation. Therefore, tRNAs play an important role in immune regulation. Although our knowledge of tRNAs in the context of immunity remains, for the most part, unknown, this field deserves in-depth research to provide new ideas for the treatment of immune diseases.

The tRNA‐associated dysregulation in immune responses and immune diseases

With the gradual advancement of smart city construction, various information systems have been widely used in smart cities. In order to obtain huge economic benefits, criminals frequently invade the information system, which leads to the increase of malware. Malware attacks not only seriously infringe on the legitimate rights and interests of users, but also cause huge economic losses. Signature-based malware detection algorithms can only detect known malware, and are susceptible to evasion techniques such as binary obfuscation. Behavior-based malware detection methods can solve this problem well. Although there are some malware behavior analysis works, they may ignore semantic information in the malware API call sequence. In this paper, we design a joint framework based on local and global features for malware detection to solve the problem of network security of smart cities, called LGMal, which combines the stacked convolutional neural network and graph convolutional networks. Specially, the stacked convolutional neural network is used to learn API call sequence information to capture local semantic features and the graph convolutional networks is used to learn API call semantic graph structure information to capture global semantic features. Experiments on Alibaba Cloud Security Malware Detection datasets show that the joint framework gets better results. The experimental results show that the precision is 87.76%, the recall is 88.08%, and the F1-measure is 87.79%. We hope this paper can provide a useful way for malware detection and protect the network security of smart city.

Chunsheng Zhu

Papers

A blockchain-based platform architecture for multimedia data management

Collaborative location-based sleep scheduling to integrate wireless sensor networks with mobile cloud computing

Special Issue on Situation Awareness in Intelligent Human-Computer Interaction for Time Critical Decision Making

The tRNA‐associated dysregulation in immune responses and immune diseases

LGMal: A Joint Framework Based on Local and Global Features for Malware Detection