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Special Issue "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)

Special Issue Editors

Guest Editor
Dr. Mianxiong Dong

Department of Information and Electronic Engineering, Muroran Institute of Technology, Hokkaido, Japan
Website | E-Mail
Interests: software-defined networks; big data, mobile networks, fog computing
Guest Editor
Prof. Dr. Anfeng Liu

School of Information Science and Engineering, Central South University, Changsha, China
Website | E-Mail
Interests: wireless sensor networks; network security, trust and privacy; green data collection; Internet of Things; mobile crowdsourcing; routing protocols

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 papers will be 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 monthly 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 1800 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 (14 papers)

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Research

Open AccessArticle A Distributed and Context-Aware Task Assignment Mechanism for Collaborative Mobile Edge Computing
Sensors 2018, 18(8), 2423; https://doi.org/10.3390/s18082423
Received: 2 July 2018 / Revised: 19 July 2018 / Accepted: 23 July 2018 / Published: 25 July 2018
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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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Open AccessArticle Compressive Sensing Based Multilevel Fast Multipole Acceleration for Fast Scattering Center Extraction and ISAR Imaging
Sensors 2018, 18(7), 2024; https://doi.org/10.3390/s18072024
Received: 9 March 2018 / Revised: 19 April 2018 / Accepted: 6 June 2018 / Published: 25 June 2018
PDF Full-text (3539 KB) | HTML Full-text | XML Full-text
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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Open AccessArticle A General Cross-Layer Cloud Scheduling Framework for Multiple IoT Computer Tasks
Sensors 2018, 18(6), 1671; https://doi.org/10.3390/s18061671
Received: 12 April 2018 / Revised: 14 May 2018 / Accepted: 17 May 2018 / Published: 23 May 2018
PDF Full-text (930 KB) | HTML Full-text | XML Full-text
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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Open AccessArticle RCSS: A Real-Time On-Demand Charging Scheduling Scheme for Wireless Rechargeable Sensor Networks
Sensors 2018, 18(5), 1601; https://doi.org/10.3390/s18051601
Received: 19 April 2018 / Revised: 9 May 2018 / Accepted: 13 May 2018 / Published: 17 May 2018
Cited by 2 | PDF Full-text (1676 KB) | HTML Full-text | XML Full-text
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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Open AccessArticle LiteNet: Lightweight Neural Network for Detecting Arrhythmias at Resource-Constrained Mobile Devices
Sensors 2018, 18(4), 1229; https://doi.org/10.3390/s18041229
Received: 1 April 2018 / Revised: 12 April 2018 / Accepted: 12 April 2018 / Published: 17 April 2018
Cited by 1 | PDF Full-text (4815 KB) | HTML Full-text | XML Full-text
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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Open AccessArticle Edge-Based Efficient Search over Encrypted Data Mobile Cloud Storage
Sensors 2018, 18(4), 1189; https://doi.org/10.3390/s18041189
Received: 7 March 2018 / Revised: 1 April 2018 / Accepted: 10 April 2018 / Published: 13 April 2018
Cited by 9 | PDF Full-text (5909 KB) | HTML Full-text | XML Full-text
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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Open AccessArticle Explicit Content Caching at Mobile Edge Networks with Cross-Layer Sensing
Sensors 2018, 18(4), 940; https://doi.org/10.3390/s18040940
Received: 16 February 2018 / Revised: 15 March 2018 / Accepted: 18 March 2018 / Published: 22 March 2018
PDF Full-text (945 KB) | HTML Full-text | XML Full-text
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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Open AccessArticle A Comparative Study on Two Typical Schemes for Securing Spatial-Temporal Top-k Queries in Two-Tiered Mobile Wireless Sensor Networks
Sensors 2018, 18(3), 871; https://doi.org/10.3390/s18030871
Received: 17 January 2018 / Revised: 9 March 2018 / Accepted: 12 March 2018 / Published: 15 March 2018
PDF Full-text (7216 KB) | HTML Full-text | XML Full-text
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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Open AccessArticle Service Migration from Cloud to Multi-tier Fog Nodes for Multimedia Dissemination with QoE Support
Sensors 2018, 18(2), 329; https://doi.org/10.3390/s18020329
Received: 28 November 2017 / Revised: 15 January 2018 / Accepted: 19 January 2018 / Published: 24 January 2018
Cited by 4 | PDF Full-text (738 KB) | HTML Full-text | XML Full-text
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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Open AccessArticle Fog-Based Two-Phase Event Monitoring and Data Gathering in Vehicular Sensor Networks
Sensors 2018, 18(1), 82; https://doi.org/10.3390/s18010082
Received: 10 October 2017 / Revised: 22 December 2017 / Accepted: 26 December 2017 / Published: 29 December 2017
Cited by 1 | PDF Full-text (1180 KB) | HTML Full-text | XML Full-text
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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Open AccessArticle Sparse Adaptive Iteratively-Weighted Thresholding Algorithm (SAITA) for L p -Regularization Using the Multiple Sub-Dictionary Representation
Sensors 2017, 17(12), 2920; https://doi.org/10.3390/s17122920
Received: 19 October 2017 / Revised: 9 December 2017 / Accepted: 13 December 2017 / Published: 15 December 2017
Cited by 3 | PDF Full-text (3067 KB) | HTML Full-text | XML Full-text
Abstract
Both L1/2 and L2/3 are two typical non-convex regularizations of Lp (0<p<1), which can be employed to obtain a sparser solution than the L1 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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Open AccessArticle MinT: Middleware for Cooperative Interaction of Things
Sensors 2017, 17(6), 1452; https://doi.org/10.3390/s17061452
Received: 14 April 2017 / Revised: 16 June 2017 / Accepted: 19 June 2017 / Published: 20 June 2017
Cited by 3 | PDF Full-text (9096 KB) | HTML Full-text | XML Full-text
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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Open AccessArticle An Energy-Aware Hybrid ARQ Scheme with Multi-ACKs for Data Sensing Wireless Sensor Networks
Sensors 2017, 17(6), 1366; https://doi.org/10.3390/s17061366
Received: 30 April 2017 / Revised: 2 June 2017 / Accepted: 9 June 2017 / Published: 12 June 2017
PDF Full-text (8794 KB) | HTML Full-text | XML Full-text
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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Open AccessArticle Optimal Time Allocation in Backscatter Assisted Wireless Powered Communication Networks
Sensors 2017, 17(6), 1258; https://doi.org/10.3390/s17061258
Received: 17 April 2017 / Revised: 17 May 2017 / Accepted: 22 May 2017 / Published: 1 June 2017
Cited by 5 | PDF Full-text (432 KB) | HTML Full-text | XML Full-text
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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