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Exploiting the IoT within Cyber Physical Social System

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (31 March 2019) | Viewed by 40407

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Prof. Dr. Antonio Puliafito

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Guest Editor

Department of Engineering, University of Messina, 98122 Messina, Italy
Interests: cloud; IoT; smart cities; embedded systems; cyberphysical systems
Special Issues, Collections and Topics in MDPI journals

Dr. Francesco Longo

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Department of Engineering, University of Messina, Messina, Italy
Interests: IoT, Fog, Edge, Serverless

Dr. Xianjun Deng

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St. Francis Xavier University, Antigonish, Canada & University of South China, Hengyang, China
Interests: Green Computing, IoT, Coverage optimization

Dr. Xiaokang Wang

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Guest Editor

Department of Computer Science, St. Francis Xavier University, Antigonish B2G 2W5, Canada
Interests: Cyber-Physical-Social Systems, Big Data, Tensor Computing, Parallel and Distributed Computing

Special Issue Information

Dear Colleagues,

Cyber-Physical Systems (CPS) indicate the integration of the physical world with the cyber one, which is also referred as the Internet of Things (IoT). As a further extension, the cyber-physical-social system (CPSS) integrates cyber interactions, physical perceptions and social connections into a ubiquitous hyperspace, and is becoming an attractive network paradigm. CPSS can remarkably enrich and broaden the interactions and connections among human-to-human, human-to-object, and object-to-object in the cyber-physical-social world.

In the following we will indicate as CPSS, a CPS system that also includes social aspects, thus further extending the IoT by including people and their interactions in the loop. Based on the significant development of a wide variety of rich-soured IoT sensing devices, cyber-physical-social sensing and computing technologies, together with some advanced networking and communications technologies, we can obtain an integrated set of data, information and knowledge from the physical world, human society, as well as the virtual world.

However, large scale deployment of cyber-physical-social systems will face a series of challenges and issues (e.g., energy efficiency requirements, architecture, protocol stack design, implementation, security, etc.), which requires more smart sensing and computing methods, advanced networking and communications technologies to provide more pervasive cyber-physical-social services for people.

This Special Issue aims to attract novel contributions to address and deal with current challenges and open issues within the general IoT world, and specifically within CPSS. The authors from both academia and industry are welcome to contribute and demonstrate the latest research results with the design, implementation, deployment, operation and evaluation of smart sensing and computing models, networking methodologies and communications tools and platforms for CPSS.

Topics of interest include, but are not limited to, the following:

Multi-functional IoT sensing devices
Networked smart cyber-physical-social sensing system and platform
Modeling of CPSS
Energy efficient cyber-physical-social sensing architectures
Green computing and sustainable computing for IoT, CPS and CPSS
Cloud computing, fog computing and edge computing for IoT
Routing protocols, data dissemination and offloading algorithms
Community detection and network evolution analysis for CPSS
Localization and node mobility models
Construction technology of dynamics of social groups
Methods for data collection, convergence and storage
Schemes of data mining, processing and analysis
Techniques of Data visualization
Quality of Experience and Quality of Service in CPSS
Social network analysis and social influence analysis
Low-power, distributed data processing in sensor applications
Smart worlds, smart cities and smart healthcare
Security, privacy and trust for the IoT
Energy harvesting communications and networks
Machine learning/deep learning/artificial intelligent approaches
Applications and testbeds of CPSS

Prof. Dr. Antonio Puliafito
Dr. Francesco Longo
Dr. Xianjun Deng
Dr. Xiaokang Wang
Guest Editors

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Published Papers (10 papers)

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Research

17 pages, 1453 KiB

Open AccessArticle

Maximizing Coverage Quality with Budget Constrained in Mobile Crowd-Sensing Network for Environmental Monitoring Applications

by Jiaoyan Chen and Jingsen Yang

Sensors 2019, 19(10), 2399; https://doi.org/10.3390/s19102399 - 26 May 2019

Cited by 38 | Viewed by 3717

Abstract

The Mobile Crowd-sensing Network is a novel cyber–physical–social network which has received great attention recently and can be used as a powerful tool to monitor the phenomenon of the field of interest. Due to the limited budget, how to choose appropriate participants to maximize the coverage quality is one of the most important issues when the mobile crowd-sensing network applies to practical application, such as air quality monitoring. In this paper, given the number of available participants, the traverse path and the reward of each participant, we investigate the problem of how to choose suitable participants to monitor an environment of a critical region by a crowd-sensing network, while the total rewards for all selected participants is not larger than the limited budget. In our solution, we first divide a big critical region such as a city into smaller regions of different size, and select some sampling points in the smaller region; the collected data of those sampling points represents the collected data of the whole smaller region. Then, we design a greedy algorithm to select participants to cover the maximum sampling points while the total rewards of selected participants does not exceed the limited budget. Finally, we evaluate the validity and efficiency of the proposed algorithm by conducting extensive simulations. The simulation results show that the greedy algorithm outperforms an existing scheme. Full article

(This article belongs to the Special Issue Exploiting the IoT within Cyber Physical Social System)

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20 pages, 4171 KiB

Open AccessArticle

An Advanced Algorithm for Higher Network Navigation in Social Internet of Things Using Small-World Networks

by Farhan Amin, Rashid Abbasi, Abdul Rehman and Gyu Sang Choi

Sensors 2019, 19(9), 2007; https://doi.org/10.3390/s19092007 - 29 Apr 2019

Cited by 42 | Viewed by 3942

Abstract

The Internet of Things (IoT) is a recent evolutionary technology that has been the primary focus of researchers for the last two decades. In the IoT, an enormous number of objects are connected together using diverse communications protocols. As a result of this massive object connectivity, a search for the exact service from an object is difficult, and hence the issue of scalability arises. In order to resolve this issue, the idea of integrating the social networking concept into the IoT, generally referred as the Social Internet of Things (SIoT) was introduced. The SIoT is gaining popularity and attracting the attention of the research community due to its flexible and spacious nature. In the SIoT, objects have the ability to find a desired service in a distributed manner by using their neighbors. Although the SIoT technique has been proven to be efficient, heterogeneous devices are growing so exponentially that problems can exist in the search for the right object or service from a huge number of devices. In order to better analyze the performance of services in an SIoT domain, there is a need to impose a certain set of rules on these objects. Our novel contribution in this study is to address the link selection problem in the SIoT by proposing an algorithm that follows the key properties of navigability in small-world networks, such as clustering coefficients, path lengths, and giant components. Our algorithm empowers object navigability in the SIoT by restricting the number of connections for objects, eliminating old links or having fewer connections. We performed an extensive series of experiments by using real network data sets from social networking sites like Brightkite and Facebook. The expected results demonstrate that our algorithm is efficient, especially in terms of reducing path length and increasing the average clustering coefficient. Finally, it reflects overall results in terms of achieving easier network navigation. Our algorithm can easily be applied to a single node or even an entire network. Full article

(This article belongs to the Special Issue Exploiting the IoT within Cyber Physical Social System)

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22 pages, 546 KiB

Open AccessArticle

Where There Is Fire There Is SMOKE: A Scalable Edge Computing Framework for Early Fire Detection

by Marios Avgeris, Dimitrios Spatharakis, Dimitrios Dechouniotis, Nikos Kalatzis, Ioanna Roussaki and Symeon Papavassiliou

Sensors 2019, 19(3), 639; https://doi.org/10.3390/s19030639 - 03 Feb 2019

Cited by 35 | Viewed by 5769

Abstract

A Cyber-Physical Social System (CPSS) tightly integrates computer systems with the physical world and human activities. In this article, a three-level CPSS for early fire detection is presented to assist public authorities to promptly identify and act on emergency situations. At the bottom level, the system’s architecture involves IoT nodes enabled with sensing and forest monitoring capabilities. Additionally, in this level, the crowd sensing paradigm is exploited to aggregate environmental information collected by end user devices present in the area of interest. Since the IoT nodes suffer from limited computational energy resources, an Edge Computing Infrastructure, at the middle level, facilitates the offloaded data processing regarding possible fire incidents. At the top level, a decision-making service deployed on Cloud nodes integrates data from various sources, including users’ information on social media, and evaluates the situation criticality. In our work, a dynamic resource scaling mechanism for the Edge Computing Infrastructure is designed to address the demanding Quality of Service (QoS) requirements of this IoT-enabled time and mission critical application. The experimental results indicate that the vertical and horizontal scaling on the Edge Computing layer is beneficial for both the performance and the energy consumption of the IoT nodes. Full article

(This article belongs to the Special Issue Exploiting the IoT within Cyber Physical Social System)

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18 pages, 3689 KiB

Open AccessArticle

ePhysio: A Wearables-Enabled Platform for the Remote Management of Musculoskeletal Diseases

by Carlo Vallati, Antonio Virdis, Marco Gesi, Nicola Carbonaro and Alessandro Tognetti

Sensors 2019, 19(1), 2; https://doi.org/10.3390/s19010002 - 20 Dec 2018

Cited by 21 | Viewed by 5268

Abstract

Technology advancements in wireless communication and embedded computing are fostering their evolution from standalone elements to smart objects seamlessly integrated in the broader context of the Internet of Things. In this context, wearable sensors represent the building block for new cyber-physical social systems, which aim at improving the well-being of people by monitoring and measuring their activities and provide an immediate feedback to the users. In this paper, we introduce ePhysio, a large-scale and flexible platform for sensor-assisted physiotherapy and remote management of musculoskeletal diseases. The system leverages networking and computing tools to provide real-time and ubiquitous monitoring of patients. We propose three use cases which differ in scale and context and are characterized by different human interactions: single-user therapy, indoor group therapy, and on-field therapy. For each use case, we identify the social interactions, e.g., between the patient and the physician and between different users and the performance requirements in terms of monitoring frequency, communication, and computation. We then propose three related deployments, highlighting the technologies that can be applied in a real system. Finally, we describe a proof-of-concept implementation, which demonstrates the feasibility of the proposed solution. Full article

(This article belongs to the Special Issue Exploiting the IoT within Cyber Physical Social System)

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18 pages, 1663 KiB

Open AccessArticle

Transmission Optimization of Social and Physical Sensor Nodes via Collaborative Beamforming in Cyber-Physical-Social Systems

by Xuecai Bao, Hao Liang and Longzhe Han

Sensors 2018, 18(12), 4300; https://doi.org/10.3390/s18124300 - 06 Dec 2018

Cited by 7 | Viewed by 2911

Abstract

The recently emerging cyber-physical-social system (CPSS) can enable efficient interactions between the social world and cyber-physical system (CPS). The wireless sensor network (WSN) with physical and social sensor nodes plays an important role in CPSS. The integration of the social sensors and physical sensors in CPSS provides an advantage for smart services in different application areas. However, the dynamics of social mobility for social sensors pose new challenges for implementing the coordination of transmission. Furthermore, the integration of social and physical sensors also faces the challenges in term of improving energy efficiency and increasing transmission range. To solve these problems, we integrate the model of social dynamics with collaborative beamforming (CB) technique to formulate the transmission optimization problem as a dynamic game. A novel transmission scheme based on reinforcement learning is proposed to solve the formulated problem. The corresponding implementation of the proposed transmission scheme in CPSS is presented by the design of message exchange processes. The extensive simulation results demonstrate that the proposed transmission scheme presents lower interference to noise ratio (INR) and better signal to noise ratio (SNR) performance in comparison with the existing schemes. The results also indicate that the proposed method has effective adaptation to the dynamic mobility of social sensor nodes in CPSS. Full article

(This article belongs to the Special Issue Exploiting the IoT within Cyber Physical Social System)

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19 pages, 12335 KiB

Open AccessArticle

EmoTour: Estimating Emotion and Satisfaction of Users Based on Behavioral Cues and Audiovisual Data

by Yuki Matsuda, Dmitrii Fedotov, Yuta Takahashi, Yutaka Arakawa, Keiichi Yasumoto and Wolfgang Minker

Sensors 2018, 18(11), 3978; https://doi.org/10.3390/s18113978 - 15 Nov 2018

Cited by 28 | Viewed by 6632

Abstract

With the spread of smart devices, people may obtain a variety of information on their surrounding environment thanks to sensing technologies. To design more context-aware systems, psychological user context (e.g., emotional status) is a substantial factor for providing useful information in an appropriate timing. As a typical use case that has a high demand for context awareness but is not tackled widely yet, we focus on the tourism domain. In this study, we aim to estimate the emotional status and satisfaction level of tourists during sightseeing by using unconscious and natural tourist actions. As tourist actions, behavioral cues (eye and head/body movement) and audiovisual data (facial/vocal expressions) were collected during sightseeing using an eye-gaze tracker, physical-activity sensors, and a smartphone. Then, we derived high-level features, e.g., head tilt and footsteps, from behavioral cues. We also used existing databases of emotionally rich interactions to train emotion-recognition models and apply them in a cross-corpus fashion to generate emotional-state prediction for the audiovisual data. Finally, the features from several modalities are fused to estimate the emotion of tourists during sightseeing. To evaluate our system, we conducted experiments with 22 tourists in two different touristic areas located in Germany and Japan. As a result, we confirmed the feasibility of estimating both the emotional status and satisfaction level of tourists. In addition, we found that effective features used for emotion and satisfaction estimation are different among tourists with different cultural backgrounds. Full article

(This article belongs to the Special Issue Exploiting the IoT within Cyber Physical Social System)

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15 pages, 1511 KiB

Open AccessArticle

Robust Iterative Distributed Minimum Total MSE Algorithm for Secure Communications in the Internet of Things Using Relays

by Zhengmin Kong, Die Wang, Yunjuan Li and Chao Wang

Sensors 2018, 18(11), 3914; https://doi.org/10.3390/s18113914 - 13 Nov 2018

Viewed by 2427

Abstract

In this article, we first investigate secure communications for a two-hop interference channel relay system with imperfect channel estimation in the wireless Internet of Things (IoT), where K source-destination pairs communicate simultaneously when an eavesdropper exists. We jointly conceive source, relay and destination matrices upon minimizing total mean-squared error (MSE) of all legitimate destinations while keeping the MSE at eavesdropper above a given threshold. We illuminate that the design of the source, relay and destination matrices is subject to both transmit power constraints and secrecy requirements. More specifically, we propose an efficient robust iterative distributed algorithm to simplify the process of the joint design for optimal source, relay and destination matrices. Furthermore, the convergence of the iterative distributed algorithm is described. Additionally, the performances of our proposed algorithm, such as its secrecy rate and MSE, are characterized in the form of simulation results. The simulation results reveal that the proposed algorithm is superior to the traditional approach. As a benefit, secure communications can be ensured by using the proposed algorithm for the multiple input multiple output (MIMO) interference relay IoT network in the presence of an eavesdropper. Full article

(This article belongs to the Special Issue Exploiting the IoT within Cyber Physical Social System)

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14 pages, 379 KiB

Open AccessArticle

Combined Channel Estimation with Interference Suppression in CPSS

by Xiaoyang Lai and Huan Wang

Sensors 2018, 18(11), 3823; https://doi.org/10.3390/s18113823 - 08 Nov 2018

Viewed by 2209

Abstract

With social characteristics integrated into cyber-physical systems (CPS), the wireless channel has been a complex electromagnetic environment due to the subjectivity of human behaviour. For the low-power and resource-constrained nodes in cyber-physical-social systems (CPSS), minimum research is available focusing on conquering the issues of computational complexity, external interference and transmission fading simultaneously. This study aims to explore channel estimation with interference suppression based on machine learning. A novel channel estimation scheme is proposed, which combined interference suppression in channel impulse response (CIR) of frequency domain with K-means algorithm and noise cancellation in CIR of time domain with K-nearest neighbor (KNN) algorithm into an integrated process. Complexity analysis and simulation results showed that the proposed scheme has relatively lower complexity and the performance is proven better than traditional schemes, which meets the requirements of CPSS in complex electromagnetic environments. Full article

(This article belongs to the Special Issue Exploiting the IoT within Cyber Physical Social System)

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20 pages, 441 KiB

Open AccessArticle

Resource Allocation in the Cognitive Radio Network-Aided Internet of Things for the Cyber-Physical-Social System: An Efficient Jaya Algorithm

by Xiong Luo, Zhijie He, Zhigang Zhao, Long Wang, Weiping Wang, Huansheng Ning, Jenq-Haur Wang, Wenbing Zhao and Jun Zhang

Sensors 2018, 18(11), 3649; https://doi.org/10.3390/s18113649 - 27 Oct 2018

Cited by 7 | Viewed by 3272

Abstract

Currently, there is a growing demand for the use of communication network bandwidth for the Internet of Things (IoT) within the cyber-physical-social system (CPSS), while needing progressively more powerful technologies for using scarce spectrum resources. Then, cognitive radio networks (CRNs) as one of those important solutions mentioned above, are used to achieve IoT effectively. Generally, dynamic resource allocation plays a crucial role in the design of CRN-aided IoT systems. Aiming at this issue, orthogonal frequency division multiplexing (OFDM) has been identified as one of the successful technologies, which works with a multi-carrier parallel radio transmission strategy. In this article, through the use of swarm intelligence paradigm, a solution approach is accordingly proposed by employing an efficient Jaya algorithm, called PA-Jaya, to deal with the power allocation problem in cognitive OFDM radio networks for IoT. Because of the algorithm-specific parameter-free feature in the proposed PA-Jaya algorithm, a satisfactory computational performance could be achieved in the handling of this problem. For this optimization problem with some constraints, the simulation results show that compared with some popular algorithms, the efficiency of spectrum utilization could be further improved by using PA-Jaya algorithm with faster convergence speed, while maximizing the total transmission rate. Full article

(This article belongs to the Special Issue Exploiting the IoT within Cyber Physical Social System)

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18 pages, 1799 KiB

Open AccessArticle

An IoT-Oriented Offloading Method with Privacy Preservation for Cloudlet-Enabled Wireless Metropolitan Area Networks

by Zhanyang Xu, Renhao Gu, Tao Huang, Haolong Xiang, Xuyun Zhang, Lianyong Qi and Xiaolong Xu

Sensors 2018, 18(9), 3030; https://doi.org/10.3390/s18093030 - 10 Sep 2018

Cited by 21 | Viewed by 3229

Abstract

With the development of the Internet of Things (IoT) technology, a vast amount of the IoT data is generated by mobile applications from mobile devices. Cloudlets provide a paradigm that allows the mobile applications and the generated IoT data to be offloaded from the mobile devices to the cloudlets for processing and storage through the access points (APs) in the Wireless Metropolitan Area Networks (WMANs). Since most of the IoT data is relevant to personal privacy, it is necessary to pay attention to data transmission security. However, it is still a challenge to realize the goal of optimizing the data transmission time, energy consumption and resource utilization with the privacy preservation considered for the cloudlet-enabled WMAN. In this paper, an IoT-oriented offloading method, named IOM, with privacy preservation is proposed to solve this problem. The task-offloading strategy with privacy preservation in WMANs is analyzed and modeled as a constrained multi-objective optimization problem. Then, the Dijkstra algorithm is employed to evaluate the shortest path between APs in WMANs, and the nondominated sorting differential evolution algorithm (NSDE) is adopted to optimize the proposed multi-objective problem. Finally, the experimental results demonstrate that the proposed method is both effective and efficient. Full article

(This article belongs to the Special Issue Exploiting the IoT within Cyber Physical Social System)

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