In combination with current sociological trends, the maturing development of Internet of Things devices is projected to revolutionize healthcare. A network of body-worn sensors, each with a unique ID, can collect health data, that is, orders-of-magnitude richer than what is available today from sporadic observations in clinical/hospital environments. When databased, analyzed, and compared against information from other individuals using data analytics, Healthcare Internet of Things data enables the personalization and modernization of care with radical improvements in outcomes and reductions in cost. In this article, we survey the existing and emerging technologies that can enable this vision for the future of healthcare, particularly, in the clinical practice of healthcare. Three main technology areas underlie the development of this field: 1) sensing, where there is an increased drive for miniaturization and power efficiency; 2) communications, where the enabling factors are ubiquitous connectivity, standardized protocols, and the wide availability of cloud infrastructure; and 3) data analytics and inference, where the availability of large amounts of data and computational resources is revolutionizing algorithms for individualizing inference and actions in health management. Throughout this article, we use a case study to concretely illustrate the impact of these trends. We conclude this article with a discussion of the emerging directions, open issues, and challenges.

A Survey of Healthcare Internet of Things (HIoT): A Clinical Perspective

Internet of Things (IoT) is a system that integrates different devices and technologies, removing the necessity of human intervention. This enables the capacity of having smart (or smarter) cities around the world. By hosting different technologies and allowing interactions between them, the internet of things has spearheaded the development of smart city systems for sustainable living, increased comfort and productivity for citizens. The IoT for Smart Cities has many different domains and draws upon various underlying systems for its operation. In this paper, we provide a holistic coverage of the Internet of Things in Smart Cities. We start by discussing the fundamental components that make up the IoT based Smart City landscape followed by the technologies that enable these domains to exist in terms of architectures utilized, networking technologies used as well as the Artificial Algorithms deployed in IoT based Smart City systems. This is then followed up by a review of the most prevalent practices and applications in various Smart City domains. Lastly, the challenges that deployment of IoT systems for smart cities encounter along with mitigation measures.

IoT in Smart Cities: A Survey of Technologies, Practices and Challenges

Privacy-preserving blockchain-based federated learning for traffic flow prediction

Anonymization is a practical solution for preserving user’s privacy in data publishing. Data owners such as hospitals, banks, social network (SN) service providers, and insurance companies anonymize their user’s data before publishing it to protect the privacy of users whereas anonymous data remains useful for legitimate information consumers. Many anonymization models, algorithms, frameworks, and prototypes have been proposed/developed for privacy preserving data publishing (PPDP). These models/algorithms anonymize users’ data which is mainly in the form of tables or graphs depending upon the data owners. It is of paramount importance to provide good perspectives of the whole information privacy area involving both tabular and SN data, and recent anonymization researches. In this paper, we presents a comprehensive survey about SN (i.e., graphs) and relational (i.e., tabular) data anonymization techniques used in the PPDP. We systematically categorize the existing anonymization techniques into relational and structural anonymization, and present an up to date thorough review on existing anonymization techniques and metrics used for their evaluation. Our aim is to provide deeper insights about the PPDP problem involving both graphs and tabular data, possible attacks that can be launched on the sanitized published data, different actors involved in the anonymization scenario, and major differences in amount of private information contained in graphs and relational data, respectively. We present various representative anonymization methods that have been proposed to solve privacy problems in application-specific scenarios of the SNs. Furthermore, we highlight the user’s re-identification methods used by malevolent adversaries to re-identify people uniquely from the privacy preserved published data. Additionally, we discuss the challenges of anonymizing both graphs and tabular data, and elaborate promising research directions. To the best of our knowledge, this is the first work to systematically cover recent PPDP techniques involving both SN and relational data, and it provides a solid foundation for future studies in the PPDP field.

Anonymization Techniques for Privacy Preserving Data Publishing: A Comprehensive Survey

Department of Computer Science and Engineering, Ramco Institute of Technology, Rajapalayam, India Department of Electronics and Communication Engineering, Muthayammal Engineering College, Rasipuram, India School of Computer Science and Engineering, SCE, Taylor's University, Subang Jaya, 47500, Malaysia School of Computer Science and Engineering, Lovely Professional University, Phagwara, India Department of IT, National Engineering College, Kovilpatti, India Department of Computer Science, Vanderbilt University, Nashville, Tennessee, USA

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

https://www.mdpi.com/1424-8220/19/13/2946/pdf

An AutoEncoder and LSTM-Based Traffic Flow Prediction Method

The emergence of wireless body area network (WBAN) technology has brought hope and dawn to solve the problems of population aging, various chronic diseases, and medical facility shortage. The increasing demand for real-time applications in such networks, stimulates many research activities. Designing such a scheme of critical events while preserving the energy efficiency is a challenging task, due to the dynamic of the network topology, severe constraints on the power supply, and the limited computation power. The design of routing protocols becomes an essential part of WBANs and plays an important role in the communication stacks and has a significant impact on the network performance. In this paper, we briefly introduce WBAN and focus on the analysis of the routing protocol, classify, and compare the advantages and disadvantages of various routing protocols. Lastly, we put forward some problems and suggestions, which provides ideas for the follow-up routing design.

https://www.mdpi.com/1424-8220/19/7/1638/pdf

A Survey of Routing Protocols in WBAN for Healthcare Applications.

The precision of the conventional user identification algorithm is not satisfactory because it ignores the role of user-generated data in identity matching. In this paper, we propose a frequent pattern mining-based cross-social network user identification algorithm that analyzes user-generated data in a personalized manner. We adopt the posterior probability-based information entropy weight allocation method that improves the precision rate and recall rate compared to the empirical weight allocation method. The extensive simulations are provided to demonstrate that the proposed algorithm can enhance the precision rate, recall rate, as well as the F-Measure (F1).

/pdf/a-user-identification-algorithm-based-on-user-behavior-3jpurdw3p0.pdf

A User Identification Algorithm Based on User Behavior Analysis in Social Networks

Wireless body area networks will inevitably bring tremendous convenience to human society in future development, and also enable people to benefit from ubiquitous technological services. However, one of the reasons hindering development is the limited energy of the network nodes. Therefore, the energy consumption in the selection of the next hop must be minimized in multi-hop routing. To solve this problem, this paper proposes an energy efficient routing protocol for reliable data transmission in a wireless body area network. The protocol takes multiple parameters of the network node into account, such as residual energy, transmission efficiency, available bandwidth, and the number of hops to the sink. We construct the maximum benefit function to select the next hop node by normalizing the node parameters, and dynamically select the node with the largest function value as the next hop node. Based on the above work, the proposed method can achieve efficient multi-hop routing transmission of data and improve the reliability of network data transmission. Compared with the priority-based energy-efficient routing algorithm (PERA) and modified new-attempt routing protocol (NEW-ATTEMPT), the simulation results show that the proposed routing protocol uses the maximum benefit function to select the next hop node dynamically, which not only improves the reliability of data transmission, but also significantly improves the energy utilization efficiency of the node and prolongs the network lifetime.

https://www.mdpi.com/1424-8220/19/19/4238/pdf

An Energy-Efficient Routing Protocol for Reliable Data Transmission in Wireless Body Area Networks.

In recent years, with the emergence of UAVs(Unmanned Aerial Vehicles) in military and civil applications, the FANETs(Flying Ad-Hoc Networks) composed of multiple UAVs has attracted extensive attention from researchers. As a new type of airborne self-organizing network, the particularity in FANETs such as time-varying network topology and dynamic link makes it difficult to maintain continuous communication when performing tasks. Therefore, it is challenging to design a routing protocol for FANETs to guarantee the quality of data transmission and make communication more effective. In this article, we propose a new opportunistic routing protocol based on trajectory prediction, named EORB-TP. To be specific, we first predict the position of nodes in three-dimensional space and solve the problem of uncertainty of node contact in opportunistic communication. Secondly, we define the node’s trajectory metric value to measure the node’s trajectory characteristics and effectively avoid the excessive consumption of edge nodes. In addition, when choosing relay nodes, an energy-saving data forwarding strategy is designed to deal with the limited energy resources and storage space of UAVs. Simulation results show that compared with the state-of-the-art protocols, our protocol can increase the delivery rate by approximately 40% at best and can reduce the delay by approximately 80%.

/pdf/an-energy-efficient-opportunistic-routing-protocol-based-on-10n39fia8s.pdf

Honghai Wu

Papers

An AutoEncoder and LSTM-Based Traffic Flow Prediction Method

A Survey of Routing Protocols in WBAN for Healthcare Applications.

A User Identification Algorithm Based on User Behavior Analysis in Social Networks

An Energy-Efficient Routing Protocol for Reliable Data Transmission in Wireless Body Area Networks.

An Energy-Efficient Opportunistic Routing Protocol Based on Trajectory Prediction for FANETs