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

Offloading traffic through Opportunistic Mobile Networks, also known as opportunistic offloading is a promising way to relieve the overload of cellular networks. The efficiency of such opportunistic offloading is highly determined by the selection of initial seeds. With considering both the freshness of the content and the cost of transmission from the cellular network to the initial seeds, this paper defines a novel freshness-aware seed selection optimization problem to find K initial seeds to maximize the overall content utility. To solve the optimization problem, we propose two seed selection methods: the greedy seed selection method and the decay-based seed selection method. The greedy seed selection method iteratively selects nodes with the maximum Freshness Centrality value as initial seeds. To further improve the performance, the decay-based seed selection method selects initial seeds who are far apart and important in theirs local structure. Extensive real trace-driven simulations are conducted to evaluate the performance of our proposed seed selection methods. The results show that as expected the proposed decay-based seed selection method is superior to the proposed greedy seed selection method and the random seed selection method.

Freshness-aware initial seed selection for traffic offloading through opportunistic mobile networks

Making a computer to understand, analyze and interpret the meaning for human communication language is Natural Language Processing (NLP). Language in general has been defined as the means of communication between two or more individuals to share their ideologies and feelings. Traditionally, the communication between human is realized throughvarious natural languages.However,when this is extended to human computer interaction, the scenario changes distinctly with human communicating in their own natural language and this communication should be interpreted such that the computer understands the appropriate meaning communicated through the natural language with an eye on not losing some essential properties such as emotions, feelings and context. This aspect of training the computer system to understand and learn natural languages has made NLP an interesting, challenging and demanding research area especially when it comes to artificial intelligence and human computer interaction. Algorithms of NLP are basically derived from machine learning approaches, where it uses the machine learning approaches to learn the rules automatically for analyzing large volume of data. A fast-automatic processing for natural languages is very impossible by machine learning approaches. Hence, it is very necessary to find a more advanced approach to replace machine learning for providing fast-automatic NLP in various real time applications. Deep learning is one of the more advanced machine learning approaches that extends the features of artificial neural networks.Deep learning can extract and classify features automatically and fast. The primary objective of deep learning is to classify and analyze the different patterns generated out of natural languages. Deep learning provides a multi-layer abstraction approach towards non-linear feature and pattern

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Special issue on deep learning for natural language processing

Human xanthine oxidase is considered to be a target for therapy of hyperuricemia. Cichorium intybus is a Chinese plant medicine which widely used in Xinjiang against various diseases. In order to screen the inhibitors of xanthine oxidase from C. intybus and to explore main pharmacological actions of cichory a compound collection of C. intybus was built via consulting related references about chemical research on cichory. The three-dimensional crystal structure of xanthine oxidase (PDB code: 1N5X) from Protein Data Bank was downloaded.. Autodock 4.2 was employed to screen the inhibitors of xanthine oxidase from cichory 70 compounds were found to possess quite low binding free energy comparing with TEI (febuxostat). C. intybus contains constituents possessing potential inhibitive activity against xanthine oxidase. It can explain the main pharmacological actions of cichory which can significantly lower the level of serum uric acid.

Molecular docking analysis of xanthine oxidase inhibition by constituents of cichory

This paper focuses on improving the geographic routing performance of the two-phase geographic greedy forwarding (TPGF) in duty-cycled wireless sensor networks (WSNs) and proposes a geographic routing oriented sleep scheduling (GSS) algorithm. The algorithm analysis and simulation results show that GSS can shorten the length of the first explored transmission path of TPGF, compared with the connected-k neighborhood (CKN) algorithm.

Sleep scheduling towards geographic routing in duty-cycled sensor networks

In Delay Tolerant Networks (DTN), the resource utilization is decreased because of the limited resources and redundant copies This paper proposes an improved Socially Aware Feedback Mechanism (SAFM) In this mechanism, the historical information of the encountered nodes are utilized to construct social links which indicates the level of social relationship between nodes In the feedback process, acknowledgements are forwarded to the nodes whose Social Link (SL) is higher than a given threshold α After getting the acknowledgements, nodes will delete the copies of messages which have been received by the destination nodes, so as to reduce the redundancy In simulation, the threshold α is obtained to reach the best performance of SAFM Compared with active and passive receipt approaches in an acceptable range of delay, SAFM improves the delivery probability, decreases the buffer occupancy and reduces the overhead

Chunsheng Zhu

Papers

Freshness-aware initial seed selection for traffic offloading through opportunistic mobile networks

Special issue on deep learning for natural language processing

Molecular docking analysis of xanthine oxidase inhibition by constituents of cichory

Sleep scheduling towards geographic routing in duty-cycled sensor networks

A Social Awareness based Feedback Mechanism for delivery reliability in Delay Tolerant Networks