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New Paradigms in Data Sensing and Processing for Edge Computing
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New Paradigms in Data Sensing and Processing for Edge Computing

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

Deadline for manuscript submissions: closed (15 September 2018) | Viewed by 77550

Special Issue Editors

Dr. Mianxiong Dong

E-Mail Website
Guest Editor
Department of Sciences and Informatics, Muroran Institute of Technology, 27-1 Mizumoto-cho, Muroran 050-8585, Hokkaido, Japan
Interests: wireless networks; cloud computing; cyberphysical systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Anfeng Liu

E-Mail Website
Guest Editor
School of Information Science and Engineering, Central South University, Changsha 410083, China
Interests: wireless sensor networks; network security, trust and privacy; green data collection; Internet of Things; mobile crowdsourcing; routing protocols
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Now, with edge computing, we will enter into the post-cloud era, when there is a large quality of data generated by edge networks, such as Internet of Things (IoT), and a number of devices and applications are being deployed in the edge network to sense and process these data. The data sensing and processing in edge computing platforms can leverage the ubiquity of sensor-equipped mobile devices to sense, collect and process data at a low cost, provide new paradigms for solving complex applications from the significant demands of human activities, and/or industrial systems, such as intelligent transportation, surveillance, environment, weather monitoring, and so on. The techniques in big data sensing and processing involved are developing quickly. High-quality data sensing and processing, together with the amount of information of data, information quality, reliability, security and privacy, cost for collection data, data sensing, collection platforms, tools, etc., have been a major objective, and also critical for edge computing platforms. It is a great challenge to ensure rigid big data application, which is one of the emerging paradigms in recent developments of information technology (IT).

The objective of this Special Issue is to publish high-quality research papers, as well as review articles addressing recent advances on high quality data sensing and processing paradigms in edge computing. Potential topics include, but are not limited to:

  • New data sensing and processing paradigms, architectures, techniques, and platforms, such as micro datacenter, cloudlet, and fog computing in edge computing with fast response times for end users.

  • Dynamic data processing offloading architectures or techniques for edge computing.

  • Big data science and foundations for high quality data sensing and processing, including theoretical and computational models, etc.

  • Incentives architectures or techniques for data sensing and processing in heterogeneous edge computing.

  • Algorithms and techniques of energy efficient data sensing and processing for edge computing.

  • Schemes and policies for intrusion and threat detection in edge computing.

  • Solutions to various attacks on data sensing and processing in edge computing.

  • Theories and practices for data sensing and processing with privacy preserving, including source location privacy, communication privacy, and other privacy issues.

  • Techniques and policies of optimization in data sensing and processing for participatory sensing networking as well as Internet of Things (IoT).

  • Platforms and system architectures for high quality data sensing and processing in edge computing.

  • Simulating and emulating environments as well as experimental results on high quality data sensing and processing for edge computing.

  • Other high quality data sensing and processing techniques in big data networks.

Prof. Dr. Mianxiong Dong
Prof. Dr. Anfeng Liu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Edge computing

  • Data sensing and process

  • Offloading architectures or techniques

  • Energy efficient

  • Security and privacy issues

  • Incentives architectures or techniques

  • Platforms or system architectures

Published Papers (19 papers)

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Research

16 pages, 3555 KiB  
Article
EARS-DM: Efficient Auto Correction Retrieval Scheme for Data Management in Edge Computing
by Kai Fan, Jie Yin, Kuan Zhang, Hui Li and Yintang Yang
Sensors 2018, 18(11), 3616; https://doi.org/10.3390/s18113616 - 24 Oct 2018
Cited by 5 | Viewed by 2738
Abstract
Edge computing is an extension of cloud computing that enables messages to be acquired and processed at low cost. Many terminal devices are being deployed in the edge network to sense and deal with the massive data. By migrating part of the computing [...] Read more.
Edge computing is an extension of cloud computing that enables messages to be acquired and processed at low cost. Many terminal devices are being deployed in the edge network to sense and deal with the massive data. By migrating part of the computing tasks from the original cloud computing model to the edge device, the message is running on computing resources close to the data source. The edge computing model can effectively reduce the pressure on the cloud computing center and lower the network bandwidth consumption. However, the security and privacy issues in edge computing are worth noting. In this paper, we propose an efficient auto-correction retrieval scheme for data management in edge computing, named EARS-DM. With automatic error correction for the query keywords instead of similar words extension, EARS-DM can tolerate spelling mistakes and reduce the complexity of index storage space. By the combination of TF-IDF value of keywords and the syntactic weight of query keywords, keywords who are more important will obtain higher relevance scores. We construct an R-tree index building with the encrypted keywords and the children nodes of which are the encrypted identifier FID and Bloom filter BF of files who contain this keyword. The secure index will be uploaded to the edge computing and the search phrase will be performed by the edge computing which is close to the data source. Then EDs sort the matching encrypted file identifier FID by relevance scores and upload them to the cloud server (CS). Performance analysis with actual data indicated that our scheme is efficient and accurate. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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16 pages, 1360 KiB  
Article
The Improved Image Scrambling Algorithm for the Wireless Image Transmission Systems of UAVs
by Jie Dong, Guowei Wu, Tingting Yang and Yangyang Li
Sensors 2018, 18(10), 3430; https://doi.org/10.3390/s18103430 - 12 Oct 2018
Cited by 7 | Viewed by 3182
Abstract
With the deepening of modern military reforms, information has become the key to winning modern warfare. The use of unmanned aerial vehicle (UAV) to capture image information has become an important means of reconnaissance in modern warfare and plays an irreplaceable role. The [...] Read more.
With the deepening of modern military reforms, information has become the key to winning modern warfare. The use of unmanned aerial vehicle (UAV) to capture image information has become an important means of reconnaissance in modern warfare and plays an irreplaceable role. The image information usually uses a wireless image transmission system, since image information is intercepted or stolen easily during the information transmission, encrypting an image is a common method for ensuring image security. However, traditional encryption algorithms have some deficiencies in terms of efficiency and security. In order to overcome these shortcomings, a new algorithm is proposed in this paper-an improved image scrambling encryption algorithm based on Fibonacci-p coding. The first new idea of the algorithm is to separate the positive and negative signs and data of the scrambled DCT coefficients, then form the symbol matrix and the data matrix respectively, perform the scrambling encryption operation on the symbol matrix. The second new idea is to encrypt the color RGB image by converting the R, G, and B colors into Y, Cb, and Cr, and converting the normal image operation into operations on Y, Cb, and Cr, thereby implementing the encryption operation. The comprehensive performance of the algorithm is optimal with different image information. Experiments results validate the favorable performance of the proposed improved encryption algorithm. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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28 pages, 5606 KiB  
Article
BMCGM: A Behavior Economics-Based Message Transmission Cooperation Guarantee Mechanism in Vehicular Ad-hoc NETworks
by Jiaqi Liu, Nan Zhong, Deng Li and Hui Liu
Sensors 2018, 18(10), 3316; https://doi.org/10.3390/s18103316 - 03 Oct 2018
Cited by 8 | Viewed by 2494
Abstract
Vehicular Ad-hoc NETwork (VANET) is a special mobile ad hoc network that composed of facilities such as vehicle nodes and roadside units. Message transfer among vehicle nodes has been a great challenge due to the network’s highly variable topology and the selfish nature [...] Read more.
Vehicular Ad-hoc NETwork (VANET) is a special mobile ad hoc network that composed of facilities such as vehicle nodes and roadside units. Message transfer among vehicle nodes has been a great challenge due to the network’s highly variable topology and the selfish nature of vehicle nodes. Thus, it is very necessary to propose a mechanism to improve the cooperation among vehicle nodes to guarantee the effective message transmission. Currently, incentive-based cooperation mechanisms are commonly used to encourage nodes to participate in message transmission. Those mechanisms are based on traditional economics and generally assume that the decision-making behavior of nodes is completely independent. Also, the cooperation of nodes depends on whether the cooperation behavior can obtain the higher utility. But researches in behavioral economics have shown that due to the existence of altruistic reciprocity, the behavior of nodes is affected by not only their utility but also the behavioral motives of other nodes, so as to obtain different results from traditional incentive-based mechanisms. Therefore, the paper introduces the reciprocal altruistic from behavioral economics and proposes the reciprocal altruistic factor to reconstruct the utility function of nodes. The reconstructed utility function reflects the interaction of behavioral motives among nodes, which promotes the node’s cooperative behavior. Also, since the Network Formation Game (NFG) is a common mathematical model for studying the interaction and communication links formation among network nodes, hence the paper regards NFG in traditional economics as the research object. A Behavior Economics-based Message Transmission Cooperation Guarantee Mechanism named BMCGM is proposed, which motivates nodes to participate in the message transmission to reduce the transmission delay ratio. The simulation results show that the BMCGM reduces message transmission delay by at least 30.3% compared with the recent representative cooperation transmission mechanism. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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16 pages, 1903 KiB  
Article
SWIPT-Aware Fog Information Processing: Local Computing vs. Fog Offloading
by Haina Zheng, Ke Xiong, Pingyi Fan, Li Zhou and Zhangdui Zhong
Sensors 2018, 18(10), 3291; https://doi.org/10.3390/s18103291 - 30 Sep 2018
Cited by 24 | Viewed by 3022
Abstract
This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing by using a simple model, where a sensor harvests energy and receives information from a hybrid access point (HAP) through power splitting (PS) receiver architecture. Two information processing modes, local [...] Read more.
This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing by using a simple model, where a sensor harvests energy and receives information from a hybrid access point (HAP) through power splitting (PS) receiver architecture. Two information processing modes, local computing and fog offloading modes are investigated. For such a system, two optimization problems are formulated to minimize the sensor’s required power for the two modes under the information rate and energy harvesting constraints by jointly optimizing the time assignment and the transmit power, as well as the PS ratio. The closed-form and semi-closed-form solutions to the proposed optimization problems are derived based on convex optimization theory. Simulation results show that neither mode is always superior to the other one. It also shows that when the number of logic operations per bit associated with local computing is less than a certain value, the local computing mode is a better choice; otherwise, the fog offloading mode should be selected. In addition, the mode selection associated with the positions of the user for fixed HAP and fog server (FS) is also discussed. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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19 pages, 3126 KiB  
Article
A Secure Multi-Tier Mobile Edge Computing Model for Data Processing Offloading Based on Degree of Trust
by Francisco José Mora-Gimeno, Higinio Mora-Mora, Diego Marcos-Jorquera and Bruno Volckaert
Sensors 2018, 18(10), 3211; https://doi.org/10.3390/s18103211 - 23 Sep 2018
Cited by 24 | Viewed by 3584
Abstract
Current mobile devices need to run applications with high computational demands and critical response times. The mobile edge computing (MEC) paradigm was developed to improve the performance of these devices. This new computation architecture allows for the mobile devices to execute applications on [...] Read more.
Current mobile devices need to run applications with high computational demands and critical response times. The mobile edge computing (MEC) paradigm was developed to improve the performance of these devices. This new computation architecture allows for the mobile devices to execute applications on fog nodes at the network edge; this process is called data processing offloading. This article presents a security model for the externalization of application execution in multi-tier MEC environments. The principal novelty of this study is that the model is able to modify the required security level in each tier of the distributed architecture as a function of the degree of trust associated with that tier. The basic idea is that a higher degree of trust requires a lower level of security, and vice versa. A formal framework is introduced that represents the general environment of application execution in distributed MEC architectures. An architecture is proposed that allows for deployment of the model in production environments and is implemented for evaluation purposes. The results show that the security model can be applied in multi-tier MEC architectures and that the model produces a minimal overhead, especially for computationally intensive applications. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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17 pages, 1280 KiB  
Article
A Distributed and Context-Aware Task Assignment Mechanism for Collaborative Mobile Edge Computing
by Bo Gu, Yapeng Chen, Haijun Liao, Zhenyu Zhou and Di Zhang
Sensors 2018, 18(8), 2423; https://doi.org/10.3390/s18082423 - 25 Jul 2018
Cited by 51 | Viewed by 4463
Abstract
Mobile edge computing (MEC) is an emerging technology that leverages computing, storage, and network resources deployed at the proximity of users to offload their delay-sensitive tasks. Various existing facilities including mobile devices with idle resources, vehicles, and MEC servers deployed at base stations [...] Read more.
Mobile edge computing (MEC) is an emerging technology that leverages computing, storage, and network resources deployed at the proximity of users to offload their delay-sensitive tasks. Various existing facilities including mobile devices with idle resources, vehicles, and MEC servers deployed at base stations or road side units, could act as edges in the network. Since task offloading incurs extra transmission energy consumption and transmission latency, two key questions to be addressed in such an environment are (i) should the workload be offloaded to the edge or computed in terminals? (ii) Which edge, among the available ones, should the task be offloaded to? In this paper, we formulate the task assignment problem as a one-to-many matching game which is a powerful tool for studying the formation of a mutual beneficial relationship between two sets of agents. The main goal of our task assignment mechanism design is to reduce overall energy consumption, while satisfying task owners’ heterogeneous delay requirements and supporting good scalability. An intensive simulation is conducted to evaluate the efficiency of our proposed mechanism. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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14 pages, 3539 KiB  
Article
Compressive Sensing Based Multilevel Fast Multipole Acceleration for Fast Scattering Center Extraction and ISAR Imaging
by Wei Zhu, Ming Jiang, Xin He and Jun Hu
Sensors 2018, 18(7), 2024; https://doi.org/10.3390/s18072024 - 25 Jun 2018
Cited by 2 | Viewed by 2639
Abstract
In recent years, Compressive Sensing (CS) theory has been very popular in the data sensing and process area as it utilizes the sparsity and measurement matrix to reconstruct the compressible signal from limited samples successfully. In this paper, CS is introduced into an [...] Read more.
In recent years, Compressive Sensing (CS) theory has been very popular in the data sensing and process area as it utilizes the sparsity and measurement matrix to reconstruct the compressible signal from limited samples successfully. In this paper, CS is introduced into an efficient numerical method, multilevel fast multipole acceleration (MLFMA), for the electromagnetic (EM) scattering problem over a wide incident angle. This allows composition of a new kind of incident wave, which obtains efficient and reliable data for scattering centers extraction with low complexity. The resulting data from CS-based MLFMA are processed for ISAR) imaging. Simulation results show the received data for ISAR imaging from MLFMA with CS can outperform the data from MLFMA, which achieves a similar quality of ISAR imaging. Additionally, the computation complexity is improved by CS through the reduced matrix computation for fewer incident waves. It makes ISAR imaging using real data feasible and meaningful. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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20 pages, 930 KiB  
Article
A General Cross-Layer Cloud Scheduling Framework for Multiple IoT Computer Tasks
by Guanlin Wu, Weidong Bao, Xiaomin Zhu and Xiongtao Zhang
Sensors 2018, 18(6), 1671; https://doi.org/10.3390/s18061671 - 23 May 2018
Cited by 7 | Viewed by 3023
Abstract
The diversity of IoT services and applications brings enormous challenges to improving the performance of multiple computer tasks’ scheduling in cross-layer cloud computing systems. Unfortunately, the commonly-employed frameworks fail to adapt to the new patterns on the cross-layer cloud. To solve this issue, [...] Read more.
The diversity of IoT services and applications brings enormous challenges to improving the performance of multiple computer tasks’ scheduling in cross-layer cloud computing systems. Unfortunately, the commonly-employed frameworks fail to adapt to the new patterns on the cross-layer cloud. To solve this issue, we design a new computer task scheduling framework for multiple IoT services in cross-layer cloud computing systems. Specifically, we first analyze the features of the cross-layer cloud and computer tasks. Then, we design the scheduling framework based on the analysis and present detailed models to illustrate the procedures of using the framework. With the proposed framework, the IoT services deployed in cross-layer cloud computing systems can dynamically select suitable algorithms and use resources more effectively to finish computer tasks with different objectives. Finally, the algorithms are given based on the framework, and extensive experiments are also given to validate its effectiveness, as well as its superiority. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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18 pages, 1676 KiB  
Article
RCSS: A Real-Time On-Demand Charging Scheduling Scheme for Wireless Rechargeable Sensor Networks
by Ping Zhong, Yiwen Zhang, Shuaihua Ma, Xiaoyan Kui and Jianliang Gao
Sensors 2018, 18(5), 1601; https://doi.org/10.3390/s18051601 - 17 May 2018
Cited by 37 | Viewed by 3919
Abstract
With the emergence of edge computing, a large number of devices such as sensor nodes have been deployed in the edge network to sense and process data. However, how to provide real-time on-demand energy for these edge devices is a new challenge issue [...] Read more.
With the emergence of edge computing, a large number of devices such as sensor nodes have been deployed in the edge network to sense and process data. However, how to provide real-time on-demand energy for these edge devices is a new challenge issue of edge networks. In real-world applications of edge computing, sensor nodes usually have different task burdens due to the environmental impact, which results in a dynamic change of the energy consumption rate at different nodes. Therefore, the traditional periodical charging mode cannot meet the nodes charging demand that have dynamic energy consumption. In this paper, we propose a real-time on-demand charging scheduling scheme (RCSS) under the condition of limited mobile charger capacity. In the process of building the charging path, RCSS adequately considers the dynamic energy consumption of different node, and puts forward the next node selection algorithm. At the same time, a method to determine the feasibility of charging circuit is also proposed to ensure the charging efficiency. During the charging process, RCSS is based on adaptive charging threshold to reduce node mortality. Compared with existing approaches, the proposed RCSS achieves better performance in the number of survival nodes, the average service time and charging efficiency. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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18 pages, 4815 KiB  
Article
LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices
by Ziyang He, Xiaoqing Zhang, Yangjie Cao, Zhi Liu, Bo Zhang and Xiaoyan Wang
Sensors 2018, 18(4), 1229; https://doi.org/10.3390/s18041229 - 17 Apr 2018
Cited by 35 | Viewed by 6238
Abstract
By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher [...] Read more.
By running applications and services closer to the user, edge processing provides many advantages, such as short response time and reduced network traffic. Deep-learning based algorithms provide significantly better performances than traditional algorithms in many fields but demand more resources, such as higher computational power and more memory. Hence, designing deep learning algorithms that are more suitable for resource-constrained mobile devices is vital. In this paper, we build a lightweight neural network, termed LiteNet which uses a deep learning algorithm design to diagnose arrhythmias, as an example to show how we design deep learning schemes for resource-constrained mobile devices. Compare to other deep learning models with an equivalent accuracy, LiteNet has several advantages. It requires less memory, incurs lower computational cost, and is more feasible for deployment on resource-constrained mobile devices. It can be trained faster than other neural network algorithms and requires less communication across different processing units during distributed training. It uses filters of heterogeneous size in a convolutional layer, which contributes to the generation of various feature maps. The algorithm was tested using the MIT-BIH electrocardiogram (ECG) arrhythmia database; the results showed that LiteNet outperforms comparable schemes in diagnosing arrhythmias, and in its feasibility for use at the mobile devices. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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14 pages, 5909 KiB  
Article
Edge-Based Efficient Search over Encrypted Data Mobile Cloud Storage
by Yeting Guo, Fang Liu, Zhiping Cai, Nong Xiao and Ziming Zhao
Sensors 2018, 18(4), 1189; https://doi.org/10.3390/s18041189 - 13 Apr 2018
Cited by 29 | Viewed by 5356
Abstract
Smart sensor-equipped mobile devices sense, collect, and process data generated by the edge network to achieve intelligent control, but such mobile devices usually have limited storage and computing resources. Mobile cloud storage provides a promising solution owing to its rich storage resources, great [...] Read more.
Smart sensor-equipped mobile devices sense, collect, and process data generated by the edge network to achieve intelligent control, but such mobile devices usually have limited storage and computing resources. Mobile cloud storage provides a promising solution owing to its rich storage resources, great accessibility, and low cost. But it also brings a risk of information leakage. The encryption of sensitive data is the basic step to resist the risk. However, deploying a high complexity encryption and decryption algorithm on mobile devices will greatly increase the burden of terminal operation and the difficulty to implement the necessary privacy protection algorithm. In this paper, we propose ENSURE (EfficieNt and SecURE), an efficient and secure encrypted search architecture over mobile cloud storage. ENSURE is inspired by edge computing. It allows mobile devices to offload the computation intensive task onto the edge server to achieve a high efficiency. Besides, to protect data security, it reduces the information acquisition of untrusted cloud by hiding the relevance between query keyword and search results from the cloud. Experiments on a real data set show that ENSURE reduces the computation time by 15% to 49% and saves the energy consumption by 38% to 69% per query. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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21 pages, 945 KiB  
Article
Explicit Content Caching at Mobile Edge Networks with Cross-Layer Sensing
by Lingyu Chen, Youxing Su, Wenbin Luo, Xuemin Hong and Jianghong Shi
Sensors 2018, 18(4), 940; https://doi.org/10.3390/s18040940 - 22 Mar 2018
Cited by 5 | Viewed by 3314
Abstract
The deployment density and computational power of small base stations (BSs) are expected to increase significantly in the next generation mobile communication networks. These BSs form the mobile edge network, which is a pervasive and distributed infrastructure that can empower a variety of [...] Read more.
The deployment density and computational power of small base stations (BSs) are expected to increase significantly in the next generation mobile communication networks. These BSs form the mobile edge network, which is a pervasive and distributed infrastructure that can empower a variety of edge/fog computing applications. This paper proposes a novel edge-computing application called explicit caching, which stores selective contents at BSs and exposes such contents to local users for interactive browsing and download. We formulate the explicit caching problem as a joint content recommendation, caching, and delivery problem, which aims to maximize the expected user quality-of-experience (QoE) with varying degrees of cross-layer sensing capability. Optimal and effective heuristic algorithms are presented to solve the problem. The theoretical performance bounds of the explicit caching system are derived in simplified scenarios. The impacts of cache storage space, BS backhaul capacity, cross-layer information, and user mobility on the system performance are simulated and discussed in realistic scenarios. Results suggest that, compared with conventional implicit caching schemes, explicit caching can better exploit the mobile edge network infrastructure for personalized content dissemination. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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13 pages, 7216 KiB  
Article
A Comparative Study on Two Typical Schemes for Securing Spatial-Temporal Top-k Queries in Two-Tiered Mobile Wireless Sensor Networks
by Xingpo Ma, Xingjian Liu, Junbin Liang, Yin Li, Ran Li, Wenpeng Ma and Chuanda Qi
Sensors 2018, 18(3), 871; https://doi.org/10.3390/s18030871 - 15 Mar 2018
Cited by 10 | Viewed by 2987
Abstract
A novel network paradigm of mobile edge computing, namely TMWSNs (two-tiered mobile wireless sensor networks), has just been proposed by researchers in recent years for its high scalability and robustness. However, only a few works have considered the security of TMWSNs. In fact, [...] Read more.
A novel network paradigm of mobile edge computing, namely TMWSNs (two-tiered mobile wireless sensor networks), has just been proposed by researchers in recent years for its high scalability and robustness. However, only a few works have considered the security of TMWSNs. In fact, the storage nodes, which are located at the upper layer of TMWSNs, are prone to being attacked by the adversaries because they play a key role in bridging both the sensor nodes and the sink, which may lead to the disclosure of all data stored on them as well as some other potentially devastating results. In this paper, we make a comparative study on two typical schemes, EVTopk and VTMSN, which have been proposed recently for securing Top-k queries in TMWSNs, through both theoretical analysis and extensive simulations, aiming at finding out their disadvantages and advancements. We find that both schemes unsatisfactorily raise communication costs. Specifically, the extra communication cost brought about by transmitting the proof information uses up more than 40% of the total communication cost between the sensor nodes and the storage nodes, and 80% of that between the storage nodes and the sink. We discuss the corresponding reasons and present our suggestions, hoping that it will inspire the researchers researching this subject. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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17 pages, 738 KiB  
Article
Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support
by Denis Rosário, Matias Schimuneck, João Camargo, Jéferson Nobre, Cristiano Both, Juergen Rochol and Mario Gerla
Sensors 2018, 18(2), 329; https://doi.org/10.3390/s18020329 - 24 Jan 2018
Cited by 54 | Viewed by 5984
Abstract
A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for [...] Read more.
A wide range of multimedia services is expected to be offered for mobile users via various wireless access networks. Even the integration of Cloud Computing in such networks does not support an adequate Quality of Experience (QoE) in areas with high demands for multimedia contents. Fog computing has been conceptualized to facilitate the deployment of new services that cloud computing cannot provide, particularly those demanding QoE guarantees. These services are provided using fog nodes located at the network edge, which is capable of virtualizing their functions/applications. Service migration from the cloud to fog nodes can be actuated by request patterns and the timing issues. To the best of our knowledge, existing works on fog computing focus on architecture and fog node deployment issues. In this article, we describe the operational impacts and benefits associated with service migration from the cloud to multi-tier fog computing for video distribution with QoE support. Besides that, we perform the evaluation of such service migration of video services. Finally, we present potential research challenges and trends. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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1180 KiB  
Article
Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks
by Yongxuan Lai, Fan Yang, Jinsong Su, Qifeng Zhou, Tian Wang, Lu Zhang and Yifan Xu
Sensors 2018, 18(1), 82; https://doi.org/10.3390/s18010082 - 29 Dec 2017
Cited by 30 | Viewed by 4274
Abstract
Vehicular nodes are equipped with more and more sensing units, and a large amount of sensing data is generated. Recently, more and more research considers cooperative urban sensing as the heart of intelligent and green city traffic management. The key components of the [...] Read more.
Vehicular nodes are equipped with more and more sensing units, and a large amount of sensing data is generated. Recently, more and more research considers cooperative urban sensing as the heart of intelligent and green city traffic management. The key components of the platform will be a combination of a pervasive vehicular sensing system, as well as a central control and analysis system, where data-gathering is a fundamental component. However, the data-gathering and monitoring are also challenging issues in vehicular sensor networks because of the large amount of data and the dynamic nature of the network. In this paper, we propose an efficient continuous event-monitoring and data-gathering framework based on fog nodes in vehicular sensor networks. A fog-based two-level threshold strategy is adopted to suppress unnecessary data upload and transmissions. In the monitoring phase, nodes sense the environment in low cost sensing mode and generate sensed data. When the probability of the event is high and exceeds some threshold, nodes transfer to the event-checking phase, and some nodes would be selected to transfer to the deep sensing mode to generate more accurate data of the environment. Furthermore, it adaptively adjusts the threshold to upload a suitable amount of data for decision making, while at the same time suppressing unnecessary message transmissions. Simulation results showed that the proposed scheme could reduce more than 84 percent of the data transmissions compared with other existing algorithms, while it detects the events and gathers the event data. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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3067 KiB  
Article
Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for L p -Regularization Using the Multiple Sub-Dictionary Representation
by Yunyi Li, Jie Zhang, Shangang Fan, Jie Yang, Jian Xiong, Xiefeng Cheng, Hikmet Sari, Fumiyuki Adachi and Guan Gui
Sensors 2017, 17(12), 2920; https://doi.org/10.3390/s17122920 - 15 Dec 2017
Cited by 13 | Viewed by 4016
Abstract
Both L 1 / 2 and L 2 / 3 are two typical non-convex regularizations of L p ( 0 < p < 1 ), which can be employed to obtain a sparser solution than the L 1 regularization. Recently, the multiple-state sparse [...] Read more.
Both L 1 / 2 and L 2 / 3 are two typical non-convex regularizations of L p ( 0 < p < 1 ), which can be employed to obtain a sparser solution than the L 1 regularization. Recently, the multiple-state sparse transformation strategy has been developed to exploit the sparsity in L 1 regularization for sparse signal recovery, which combines the iterative reweighted algorithms. To further exploit the sparse structure of signal and image, this paper adopts multiple dictionary sparse transform strategies for the two typical cases p ∈ { 1 / 2 ,   2 / 3 } based on an iterative L p thresholding algorithm and then proposes a sparse adaptive iterative-weighted L p thresholding algorithm (SAITA). Moreover, a simple yet effective regularization parameter is proposed to weight each sub-dictionary-based L p regularizer. Simulation results have shown that the proposed SAITA not only performs better than the corresponding L 1 algorithms but can also obtain a better recovery performance and achieve faster convergence than the conventional single-dictionary sparse transform-based L p case. Moreover, we conduct some applications about sparse image recovery and obtain good results by comparison with relative work. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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Article
MinT: Middleware for Cooperative Interaction of Things
by Soobin Jeon and Inbum Jung
Sensors 2017, 17(6), 1452; https://doi.org/10.3390/s17061452 - 20 Jun 2017
Cited by 16 | Viewed by 6465
Abstract
This paper proposes an Internet of Things (IoT) middleware called Middleware for Cooperative Interaction of Things (MinT). MinT supports a fully distributed IoT environment in which IoT devices directly connect to peripheral devices easily construct a local or global network, and share their [...] Read more.
This paper proposes an Internet of Things (IoT) middleware called Middleware for Cooperative Interaction of Things (MinT). MinT supports a fully distributed IoT environment in which IoT devices directly connect to peripheral devices easily construct a local or global network, and share their data in an energy efficient manner. MinT provides a sensor abstract layer, a system layer and an interaction layer. These enable integrated sensing device operations, efficient resource management, and active interconnection between peripheral IoT devices. In addition, MinT provides a high-level API to develop IoT devices easily for IoT device developers. We aim to enhance the energy efficiency and performance of IoT devices through the performance improvements offered by MinT resource management and request processing. The experimental results show that the average request rate increased by 25% compared to Californium, which is a middleware for efficient interaction in IoT environments with powerful performance, an average response time decrease of 90% when resource management was used, and power consumption decreased by up to 68%. Finally, the proposed platform can reduce the latency and power consumption of IoT devices. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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8794 KiB  
Article
An Energy-Aware Hybrid ARQ Scheme with Multi-ACKs for Data Sensing Wireless Sensor Networks
by Jinhuan Zhang and Jun Long
Sensors 2017, 17(6), 1366; https://doi.org/10.3390/s17061366 - 12 Jun 2017
Cited by 4 | Viewed by 3419
Abstract
Wireless sensor networks (WSNs) are one of the important supporting technologies of edge computing. In WSNs, reliable communications are essential for most applications due to the unreliability of wireless links. In addition, network lifetime is also an important performance metric and needs to [...] Read more.
Wireless sensor networks (WSNs) are one of the important supporting technologies of edge computing. In WSNs, reliable communications are essential for most applications due to the unreliability of wireless links. In addition, network lifetime is also an important performance metric and needs to be considered in many WSN studies. In the paper, an energy-aware hybrid Automatic Repeat-reQuest protocol (ARQ) scheme is proposed to ensure energy efficiency under the guarantee of network transmission reliability. In the scheme, the source node sends data packets continuously with the correct window size and it does not need to wait for the acknowledgement (ACK) confirmation for each data packet. When the destination receives K data packets, it will return multiple copies of one ACK for confirmation to avoid ACK packet loss. The energy consumption of each node in flat circle network applying the proposed scheme is statistical analyzed and the cases under which it is more energy efficiency than the original scheme is discussed. Moreover, how to select parameters of the scheme is addressed to extend the network lifetime under the constraint of the network reliability. In addition, the energy efficiency of the proposed schemes is evaluated. Simulation results are presented to demonstrate that a node energy consumption reduction could be gained and the network lifetime is prolonged. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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432 KiB  
Article
Optimal Time Allocation in Backscatter Assisted Wireless Powered Communication Networks
by Bin Lyu, Zhen Yang, Guan Gui and Hikmet Sari
Sensors 2017, 17(6), 1258; https://doi.org/10.3390/s17061258 - 01 Jun 2017
Cited by 27 | Viewed by 4906
Abstract
This paper proposes a wireless powered communication network (WPCN) assisted by backscatter communication (BackCom). This model consists of a power station, an information receiver and multiple users that can work in either BackCom mode or harvest-then-transmit (HTT) mode. The time block is mainly [...] Read more.
This paper proposes a wireless powered communication network (WPCN) assisted by backscatter communication (BackCom). This model consists of a power station, an information receiver and multiple users that can work in either BackCom mode or harvest-then-transmit (HTT) mode. The time block is mainly divided into two parts corresponding to the data backscattering and transmission periods, respectively. The users first backscatter data to the information receiver in time division multiple access (TDMA) during the data backscattering period. When one user works in the BackCom mode, the other users harvest energy from the power station. During the data transmission period, two schemes, i.e., non-orthogonal multiple access (NOMA) and TDMA, are considered. To maximize the system throughput, the optimal time allocation policies are obtained. Simulation results demonstrate the superiority of the proposed model. Full article
(This article belongs to the Special Issue New Paradigms in Data Sensing and Processing for Edge Computing)
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