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Special Issue "Sensor Networks for Collaborative and Secure Internet of Things"

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

Deadline for manuscript submissions: closed (30 June 2018)

Special Issue Editors

Guest Editor
Prof. Dr. Lei Shu

Nanjing Agricultural University, China / University of Lincoln, UK
Website | E-Mail
Interests: wireless sensor networks; multimedia communication; middleware; security
Guest Editor
Prof. Dr. Mohsen Guizani

Department of Electrical and Computer Engineering, University of Idaho, USA
Website | E-Mail
Interests: wireless communications and mobile computing; computer networks; mobile cloud computing; security and smart grid
Guest Editor
Dr. Chunsheng Zhu

Department of Electrical and Computer Engineering, The University of British Columbia, Canada
Website | E-Mail
Interests: wireless sensor networks; cloud computing; Internet of Things; big data; social networks; security

Special Issue Information

Dear Colleagues,

Leveraging various information and communication technologies, our Internet is evolving into the Internet of Things (IoT) which aims to offer various comfortable and convenient services (e.g., smart climate control, smart street lighting, smart transportation, smart parking, and smart grid) to people. However, collaboration and security are identified as two critical issues that go under the radar of enthusiasts championing the IoT concept (i.e., connecting “Things” in the world). Specifically, research has shown that collaboration is the key to an open and accessible IoT. Investigation has found that security is the key to a trustable and robust IoT. In addition, collecting data from the “Things”, sensor networks act as the “eyes” and “ears” of IoT.

Therefore, toward a collaborative and secure IoT from the sensor networks perspective, this Special Issue solicits original technical papers with novel contributions on sensor networks taking into account the collaboration and security issues of IoT. Tutorial or survey papers are also welcome. In addition, selected high quality papers from Collaboratecom 2017 (http://collaboratecom.org/2017/show/home) will be invited for further consideration in this Special Issue for publication. Potential topics include, but are not limited to:

  • Communication in sensor networks for collaborative IoT

  • Communication in sensor networks for secure IoT

  • Computing in sensor networks for collaborative IoT

  • Computing in sensor networks for secure IoT

  • Middleware in sensor networks for collaborative IoT

  • Middleware in sensor networks for secure IoT

  • Cross-layer design in sensor networks for collaborative IoT

  • Cross-layer design in sensor networks for secure IoT

  • Testbed in sensor networks for collaborative IoT

  • Testbed in sensor networks for secure IoT

  • Novel application in sensor networks for collaborative IoT

  • Novel application in sensor networks for secure IoT                                  

Prof. Dr. Lei Shu
Prof. Dr. Mohsen Guizani
Dr. Chunsheng Zhu
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

  • Internet of Things

  • Collaboration

  • Security

  • Sensor network

  • Communication

  • Computing

  • Middleware

Published Papers (19 papers)

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Research

Open AccessArticle A Robust Predicted Performance Analysis Approach for Data-Driven Product Development in the Industrial Internet of Things
Sensors 2018, 18(9), 2871; https://doi.org/10.3390/s18092871
Received: 23 June 2018 / Revised: 28 August 2018 / Accepted: 29 August 2018 / Published: 31 August 2018
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Abstract
Industrial Internet of Things (IoT) is a ubiquitous network integrating various sensing technologies and communication technologies to provide intelligent information processing and smart control abilities for the manufacturing enterprises. The aim of applying industrial IoT is to assist manufacturers manage and optimize the
[...] Read more.
Industrial Internet of Things (IoT) is a ubiquitous network integrating various sensing technologies and communication technologies to provide intelligent information processing and smart control abilities for the manufacturing enterprises. The aim of applying industrial IoT is to assist manufacturers manage and optimize the entire product manufacturing process to improve product quality and production efficiency. Data-driven product development is considered as one of the critical application scenarios of industrial IoT, which is used to acquire the satisfied and robust design solution according to customer demands. Performance analysis is an effective tool to identify whether the key performance have reached the requirements in data-driven product development. The existing performance analysis approaches mainly focus on the metamodel construction, however, the uncertainty and complexity in product development process are rarely considered. In response, this paper investigates a robust performance analysis approach in industrial IoT environment to help product developers forecast the performance parameters accurately. The service-oriented layered architecture of industrial IoT for product development is first described. Then a dimension reduction approach based on mutual information (MI) and outlier detection is proposed. A metamodel based on least squares support vector regression (LSSVR) is established to conduct performance prediction process. Furthermore, the predicted performance analysis method based on confidence interval estimation is developed to deal with the uncertainty to improve the robustness of the forecasting results. Finally, a case study is given to show the feasibility and effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle A Hypergraph-Based Blockchain Model and Application in Internet of Things-Enabled Smart Homes
Sensors 2018, 18(9), 2784; https://doi.org/10.3390/s18092784
Received: 11 July 2018 / Revised: 20 August 2018 / Accepted: 21 August 2018 / Published: 24 August 2018
PDF Full-text (2191 KB) | HTML Full-text | XML Full-text
Abstract
With the fast development and expansion of the Internet of Things (IoT), billions of smart devices are being continuously connected, and smart homes, as a typical IoT application, are providing people with various convenient applications, but face security and privacy issues. The idea
[...] Read more.
With the fast development and expansion of the Internet of Things (IoT), billions of smart devices are being continuously connected, and smart homes, as a typical IoT application, are providing people with various convenient applications, but face security and privacy issues. The idea of Blockchain (BC) theory has brought about a potential solution to the IoT security problem. The emergence of blockchain technology has brought about a change of decentralized management, providing an effective solution for the protection of network security and privacy. On the other hand, the smart devices in IoT are always lightweight and have less energy and memory. This makes the application of blockchain difficult. Against this background, this paper proposes a blockchain model based on hypergraphs. The aims of this model are to reduce the storage consumption and to solve the additional security issues. In the model, we use the hyperedge as the organization of storage nodes and convert the entire networked data storage into part network storage. We discuss the design of the model and security strategy in detail, introducing some use cases in a smart home network and evaluating the storage performance of the model through simulation experiments and an evaluation of the network. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle TLTD: A Testing Framework for Learning-Based IoT Traffic Detection Systems
Sensors 2018, 18(8), 2630; https://doi.org/10.3390/s18082630
Received: 27 June 2018 / Revised: 3 August 2018 / Accepted: 3 August 2018 / Published: 10 August 2018
PDF Full-text (1482 KB) | HTML Full-text | XML Full-text
Abstract
With the popularization of IoT (Internet of Things) devices and the continuous development of machine learning algorithms, learning-based IoT malicious traffic detection technologies have gradually matured. However, learning-based IoT traffic detection models are usually very vulnerable to adversarial samples. There is a great
[...] Read more.
With the popularization of IoT (Internet of Things) devices and the continuous development of machine learning algorithms, learning-based IoT malicious traffic detection technologies have gradually matured. However, learning-based IoT traffic detection models are usually very vulnerable to adversarial samples. There is a great need for an automated testing framework to help security analysts to detect errors in learning-based IoT traffic detection systems. At present, most methods for generating adversarial samples require training parameters of known models and are only applicable to image data. To address the challenge, we propose a testing framework for learning-based IoT traffic detection systems, TLTD. By introducing genetic algorithms and some technical improvements, TLTD can generate adversarial samples for IoT traffic detection systems and can perform a black-box test on the systems. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle A Collaborative Data Collection Scheme Based on Optimal Clustering for Wireless Sensor Networks
Sensors 2018, 18(8), 2487; https://doi.org/10.3390/s18082487
Received: 25 June 2018 / Revised: 25 July 2018 / Accepted: 26 July 2018 / Published: 1 August 2018
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Abstract
In recent years, energy-efficient data collection has evolved into the core problem in the resource-constrained Wireless Sensor Networks (WSNs). Different from existing data collection models in WSNs, we propose a collaborative data collection scheme based on optimal clustering to collect the sensed data
[...] Read more.
In recent years, energy-efficient data collection has evolved into the core problem in the resource-constrained Wireless Sensor Networks (WSNs). Different from existing data collection models in WSNs, we propose a collaborative data collection scheme based on optimal clustering to collect the sensed data in an energy-efficient and load-balanced manner. After dividing the data collection process into the intra-cluster data collection step and the inter-cluster data collection step, we model the optimal clustering problem as a separable convex optimization problem and solve it to obtain the analytical solutions of the optimal clustering size and the optimal data transmission radius. Then, we design a Cluster Heads (CHs)-linking algorithm based on the pseudo Hilbert curve to build a CH chain with the goal of collecting the compressed sensed data among CHs in an accumulative way. Furthermore, we also design a distributed cluster-constructing algorithm to construct the clusters around the virtual CHs in a distributed manner. The experimental results show that the proposed method not only reduces the total energy consumption and prolongs the network lifetime, but also effectively balances the distribution of energy consumption among CHs. By comparing it o the existing compression-based and non-compression-based data collection schemes, the average reductions of energy consumption are 17.9% and 67.9%, respectively. Furthermore, the average network lifetime extends no less than 20-times under the same comparison. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle GWS—A Collaborative Load-Balancing Algorithm for Internet-of-Things
Sensors 2018, 18(8), 2479; https://doi.org/10.3390/s18082479
Received: 29 June 2018 / Revised: 26 July 2018 / Accepted: 30 July 2018 / Published: 31 July 2018
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Abstract
This paper firstly replaces the first-come-first-service (FCFS) mechanism with the time-sharing (TS) mechanism in fog computing nodes (FCNs). Then a collaborative load-balancing algorithm for the TS mechanism is proposed for FCNs. The algorithm is a variant of a work-stealing scheduling algorithm, and is
[...] Read more.
This paper firstly replaces the first-come-first-service (FCFS) mechanism with the time-sharing (TS) mechanism in fog computing nodes (FCNs). Then a collaborative load-balancing algorithm for the TS mechanism is proposed for FCNs. The algorithm is a variant of a work-stealing scheduling algorithm, and is based on the Nash bargaining solution (NBS) for a cooperative game between FCNs. Pareto optimality is achieved through the collaborative working of FCNs to improve the performance of every FCN. Lastly the simulation results demonstrate that the game-theory based work-stealing algorithm (GWS) outperforms the classical work-stealing algorithm (CWS). Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle QoS-Driven Adaptive Trust Service Coordination in the Industrial Internet of Things
Sensors 2018, 18(8), 2449; https://doi.org/10.3390/s18082449
Received: 21 June 2018 / Revised: 25 July 2018 / Accepted: 26 July 2018 / Published: 27 July 2018
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Abstract
The adaptive coordination of trust services can provide highly dependable and personalized solutions for industrial requirements in the service-oriented industrial internet of things (IIoT) architecture to achieve efficient utilization of service resources. Although great progress has been made, trust service coordination still faces
[...] Read more.
The adaptive coordination of trust services can provide highly dependable and personalized solutions for industrial requirements in the service-oriented industrial internet of things (IIoT) architecture to achieve efficient utilization of service resources. Although great progress has been made, trust service coordination still faces challenging problems such as trustless industry service, poor coordination, and quality of service (QoS) personalized demand. In this paper, we propose a QoS-driven and adaptive trust service coordination method to implement Pareto-efficient allocation of limited industrial service resources in the background of the IIoT. First, we established a Pareto-effective and adaptive industrial IoT trust service coordination model and introduced a blockchain-based adaptive trust evaluation mechanism to achieve trust evaluation of industrial services. Then, taking advantage of a large and complex search space for solution efficiency, we introduced and compared multi-objective gray-wolf algorithms with the particle swarm optimization (PSO) and dragonfly algorithms. The experimental results showed that by judging and blacklisting malicious raters quickly and accurately, our model can efficiently realize self-adaptive, personalized, and intelligent trust service coordination under the given constraints, improving not only the response time, but also the success rate in coordination. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle Energy Modeling of IoT Mobile Terminals on WiFi Environmental Impacts
Sensors 2018, 18(6), 1728; https://doi.org/10.3390/s18061728
Received: 15 March 2018 / Revised: 16 May 2018 / Accepted: 16 May 2018 / Published: 28 May 2018
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Abstract
With the popularity of various IoT mobile terminals such as mobile phones and sensors, the energy problems of IoT mobile terminals have attracted increasingly more attention. In this paper, we explore the impacts of some important factors of WiFi environments on the energy
[...] Read more.
With the popularity of various IoT mobile terminals such as mobile phones and sensors, the energy problems of IoT mobile terminals have attracted increasingly more attention. In this paper, we explore the impacts of some important factors of WiFi environments on the energy consumption of mobile phones, which are typical IoT end devices. The factors involve the WiFi signal strength under good signal conditions, the type and the amount of protocol packets that are initiated by WiFi APs (Access Points) to maintain basic network communication with the phones. Controlled experiments are conducted to quantitatively study the phone energy impacts by the above WiFi environmental factors. To describe such impacts, we construct a time-based signal strength-aware energy model and packet type/amount-aware energy models. The models constructed in the paper corroborate the following user experience on phone energy consumption: (1) a phone’s energy is drawn faster under higher WiFi signal strengths than under lower ones even in normal signal conditions; (2) phones consume energy faster in a public WiFi network than in a private one even in the basic phone state. The energy modeling methods proposed in the paper enable ordinary developers to analyze phone energy draw conveniently by utilizing inexpensive power meters as measurement tools. The modeling methods are general and are able to be used for phones of any type and any platform. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle PS-CARA: Context-Aware Resource Allocation Scheme for Mobile Public Safety Networks
Sensors 2018, 18(5), 1473; https://doi.org/10.3390/s18051473
Received: 30 March 2018 / Revised: 1 May 2018 / Accepted: 4 May 2018 / Published: 8 May 2018
PDF Full-text (4419 KB) | HTML Full-text | XML Full-text
Abstract
The fifth-generation (5G) communications systems are expecting to support users with diverse quality-of-service (QoS) requirements. Beside these requirements, the task with utmost importance is to support the emergency communication services during natural or man-made disasters. Most of the conventional base stations are not
[...] Read more.
The fifth-generation (5G) communications systems are expecting to support users with diverse quality-of-service (QoS) requirements. Beside these requirements, the task with utmost importance is to support the emergency communication services during natural or man-made disasters. Most of the conventional base stations are not properly functional during a disaster situation, so deployment of emergency base stations such as mobile personal cell (mPC) is crucial. An mPC having moving capability can move in the disaster area to provide emergency communication services. However, mPC deployment causes severe co-channel interference to the users in its vicinity. The problem in the existing resource allocation schemes is its support for static environment, that does not fit well for mPC. So, a resource allocation scheme for mPC users is desired that can dynamically allocate resources based on users’ location and its connection establishment priority. In this paper, we propose a public safety users priority-based context-aware resource allocation (PS-CARA) scheme for users sum-rate maximization in disaster environment. Simulations results demonstrate that the proposed PS-CARA scheme can increase the user average and edge rate around 10.3% and 32.8% , respectively because of context information availability and by prioritizing the public safety users. The simulation results ensure that call blocking probability is also reduced considerably under the PS-CARA scheme. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle Privacy-Preserving Authentication Using a Double Pseudonym for Internet of Vehicles
Sensors 2018, 18(5), 1453; https://doi.org/10.3390/s18051453
Received: 14 March 2018 / Revised: 21 April 2018 / Accepted: 1 May 2018 / Published: 7 May 2018
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Abstract
The Internet of Vehicles (IoV) plays an important role in smart transportation to reduce the drivers’s risk of having an accident and help them manage small emergencies. Therefore, security and privacy issues of the message in the tamper proof device (TPD) broadcasted to
[...] Read more.
The Internet of Vehicles (IoV) plays an important role in smart transportation to reduce the drivers’s risk of having an accident and help them manage small emergencies. Therefore, security and privacy issues of the message in the tamper proof device (TPD) broadcasted to other vehicles and roadside units (RSUs) have become an important research subject in the field of smart transportation. Many authentication schemes are proposed to tackle the challenges above and most of them are heavy in computation and communication. In this paper, we propose a novel authentication scheme that utilizes the double pseudonym method to hide the real identity of vehicles and adopts the dynamic update technology to periodically update the information (such as member secret, authentication key, internal pseudo-identity) stored in the tamper-proof device to prevent the side-channel attack. Because of not using bilinear pairing, our scheme yields a better performance in terms of computation overhead and communication overhead, and is more suitable to be applied in the Internet of Vehicles. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle Classification of Incomplete Data Based on Evidence Theory and an Extreme Learning Machine in Wireless Sensor Networks
Sensors 2018, 18(4), 1046; https://doi.org/10.3390/s18041046
Received: 31 January 2018 / Revised: 27 March 2018 / Accepted: 29 March 2018 / Published: 30 March 2018
Cited by 1 | PDF Full-text (1148 KB) | HTML Full-text | XML Full-text
Abstract
In wireless sensor networks, the classification of incomplete data reported by sensor nodes is an open issue because it is difficult to accurately estimate the missing values. In many cases, the misclassification is unacceptable considering that it probably brings catastrophic damages to the
[...] Read more.
In wireless sensor networks, the classification of incomplete data reported by sensor nodes is an open issue because it is difficult to accurately estimate the missing values. In many cases, the misclassification is unacceptable considering that it probably brings catastrophic damages to the data users. In this paper, a novel classification approach of incomplete data is proposed to reduce the misclassification errors. This method uses the regularized extreme learning machine to estimate the potential values of missing data at first, and then it converts the estimations into multiple classification results on the basis of the distance between interval numbers. Finally, an evidential reasoning rule is adopted to fuse these classification results. The final decision is made according to the combined basic belief assignment. The experimental results show that this method has better performance than other traditional classification methods of incomplete data. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle Sensor-Based Optimization Model for Air Quality Improvement in Home IoT
Sensors 2018, 18(4), 959; https://doi.org/10.3390/s18040959
Received: 31 January 2018 / Revised: 19 March 2018 / Accepted: 19 March 2018 / Published: 23 March 2018
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Abstract
We introduce current home Internet of Things (IoT) technology and present research on its various forms and applications in real life. In addition, we describe IoT marketing strategies as well as specific modeling techniques for improving air quality, a key home IoT service.
[...] Read more.
We introduce current home Internet of Things (IoT) technology and present research on its various forms and applications in real life. In addition, we describe IoT marketing strategies as well as specific modeling techniques for improving air quality, a key home IoT service. To this end, we summarize the latest research on sensor-based home IoT, studies on indoor air quality, and technical studies on random data generation. In addition, we develop an air quality improvement model that can be readily applied to the market by acquiring initial analytical data and building infrastructures using spectrum/density analysis and the natural cubic spline method. Accordingly, we generate related data based on user behavioral values. We integrate the logic into the existing home IoT system to enable users to easily access the system through the Web or mobile applications. We expect that the present introduction of a practical marketing application method will contribute to enhancing the expansion of the home IoT market. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessCommunication A Proposal for IoT Dynamic Routes Selection Based on Contextual Information
Sensors 2018, 18(2), 353; https://doi.org/10.3390/s18020353
Received: 5 December 2017 / Revised: 19 January 2018 / Accepted: 23 January 2018 / Published: 26 January 2018
PDF Full-text (2339 KB) | HTML Full-text | XML Full-text
Abstract
The Internet of Things (IoT) is based on interconnection of intelligent and addressable devices, allowing their autonomy and proactive behavior with Internet connectivity. Data dissemination in IoT usually depends on the application and requires context-aware routing protocols that must include auto-configuration features (which
[...] Read more.
The Internet of Things (IoT) is based on interconnection of intelligent and addressable devices, allowing their autonomy and proactive behavior with Internet connectivity. Data dissemination in IoT usually depends on the application and requires context-aware routing protocols that must include auto-configuration features (which adapt the behavior of the network at runtime, based on context information). This paper proposes an approach for IoT route selection using fuzzy logic in order to attain the requirements of specific applications. In this case, fuzzy logic is used to translate in math terms the imprecise information expressed by a set of linguistic rules. For this purpose, four Objective Functions (OFs) are proposed for the Routing Protocol for Low Power and Loss Networks (RPL); such OFs are dynamically selected based on context information. The aforementioned OFs are generated from the fusion of the following metrics: Expected Transmission Count (ETX), Number of Hops (NH) and Energy Consumed (EC). The experiments performed through simulation, associated with the statistical data analysis, conclude that this proposal provides high reliability by successfully delivering nearly 100% of data packets, low delay for data delivery and increase in QoS. In addition, an 30% improvement is attained in the network life time when using one of proposed objective function, keeping the devices alive for longer duration. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle Secure Transmission of Cooperative Zero-Forcing Jamming for Two-User SWIPT Sensor Networks
Sensors 2018, 18(2), 331; https://doi.org/10.3390/s18020331
Received: 3 January 2018 / Revised: 21 January 2018 / Accepted: 22 January 2018 / Published: 24 January 2018
PDF Full-text (1008 KB) | HTML Full-text | XML Full-text
Abstract
In this paper, the secrecy performance of the two-user simultaneous wireless information and power transfer (SWIPT) sensor networks is studied and a novel secure transmission scheme of cooperative zero-forcing (ZF) jamming is proposed. The two sensors opportunistically conduct the SWIPT and cooperative ZF
[...] Read more.
In this paper, the secrecy performance of the two-user simultaneous wireless information and power transfer (SWIPT) sensor networks is studied and a novel secure transmission scheme of cooperative zero-forcing (ZF) jamming is proposed. The two sensors opportunistically conduct the SWIPT and cooperative ZF jamming, respectively, where the energy required for jamming the eavesdropper is provided by the SWIPT operation so as to keep the energy balance at the sensors in the long run. By deriving the exact closed-form expressions of the secrecy outage probability and the secrecy throughout, we provide an effective approach to precisely assess the impacts of key parameters on the secrecy performance of the system. It has been shown that the secrecy outage probability is a monotonically increasing function of the growth of secrecy rate ( R s ), and a monotonically decreasing function of the increase of the transmit signal-to-noise ratio ( γ S ), and energy conversion efficiency ( η ). Furthermore, the secrecy throughput could be enhanced when η increases, which becomes especially obvious when a large γ S is provided. Moreover, the existence of an optimum R s maximizing the secrecy throughput is depicted, which also grows with the increase of γ S . Simulations are provided for the validation of the analysis. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle Social Incentive Mechanism Based Multi-User Sensing Time Optimization in Co-Operative Spectrum Sensing with Mobile Crowd Sensing
Sensors 2018, 18(1), 250; https://doi.org/10.3390/s18010250
Received: 10 December 2017 / Revised: 12 January 2018 / Accepted: 14 January 2018 / Published: 16 January 2018
PDF Full-text (3427 KB) | HTML Full-text | XML Full-text
Abstract
Co-operative spectrum sensing emerging as a significant method to improve the utilization of the spectrum needs sufficient sensing users to participate. Existing related papers consider only the limited secondary users in current sensing system and assume that they will always perform the co-operative
[...] Read more.
Co-operative spectrum sensing emerging as a significant method to improve the utilization of the spectrum needs sufficient sensing users to participate. Existing related papers consider only the limited secondary users in current sensing system and assume that they will always perform the co-operative spectrum sensing out of obligation. However, this assumption is impractical in the realistic situation where the secondary users are rational and they will not join in the co-operative sensing process without a certain reward to compensate their sensing energy consumption, especially the ones who have no data transmitting in current time slot. To solve this problem, we take advantage of the mobile crowd sensing to supply adequate co-operative sensing candidates, in which the sensing users are not only the secondary users but also a crowd of widely distributed mobile users equipped with personal spectrum sensors (such as smartphones, vehicle sensors). Furthermore, a social incentive mechanism is also adapted to motivate the participations of mobile sensing users. In this paper, we model the interactions among the motivated sensing users as a co-operative game where they adjust their own sensing time strategies to maximize the co-operative sensing utility, which eventually guarantees the detection performance and prevents the global sensing cost being too high. We prove that the game based optimization problem is NP-hard and exists a unique optimal equilibrium. An improved differential evolution algorithm is proposed to solve the optimization problem. Simulation results prove the better performance in our proposed multi-user sensing time optimization model and the proposed improved differential evolution algorithm, respectively compared with the non-optimization model and the other two typical equilibrium solution algorithms. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle Optimal Pricing and Power Allocation for Collaborative Jamming with Full Channel Knowledge in Wireless Sensor Networks
Sensors 2017, 17(11), 2697; https://doi.org/10.3390/s17112697
Received: 18 October 2017 / Revised: 16 November 2017 / Accepted: 19 November 2017 / Published: 22 November 2017
PDF Full-text (1318 KB) | HTML Full-text | XML Full-text
Abstract
This paper presents a price-searching model in which a source node (Alice) seeks friendly jammers that prevent eavesdroppers (Eves) from snooping legitimate communications by generating interference or noise. Unlike existing models, the distributed jammers also have data to send to their respective destinations
[...] Read more.
This paper presents a price-searching model in which a source node (Alice) seeks friendly jammers that prevent eavesdroppers (Eves) from snooping legitimate communications by generating interference or noise. Unlike existing models, the distributed jammers also have data to send to their respective destinations and are allowed to access Alice’s channel if it can transmit sufficient jamming power, which is referred to as collaborative jamming in this paper. For the power used to deliver its own signal, the jammer should pay Alice. The price of the jammers’ signal power is set by Alice and provides a tradeoff between the signal and the jamming power. This paper presents, in closed-form, an optimal price that maximizes Alice’s benefit and the corresponding optimal power allocation from a jammers’ perspective by assuming that the network-wide channel knowledge is shared by Alice and jammers. For a multiple-jammer scenario where Alice hardly has the channel knowledge, this paper provides a distributed and interactive price-searching procedure that geometrically converges to an optimal price and shows that Alice by a greedy selection policy achieves certain diversity gain, which increases log-linearly as the number of (potential) jammers grows. Various numerical examples are presented to illustrate the behavior of the proposed model. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle Secure Communications in CIoT Networks with a Wireless Energy Harvesting Untrusted Relay
Sensors 2017, 17(9), 2023; https://doi.org/10.3390/s17092023
Received: 8 August 2017 / Revised: 31 August 2017 / Accepted: 1 September 2017 / Published: 4 September 2017
Cited by 2 | PDF Full-text (1837 KB) | HTML Full-text | XML Full-text
Abstract
The Internet of Things (IoT) represents a bright prospect that a variety of common appliances can connect to one another, as well as with the rest of the Internet, to vastly improve our lives. Unique communication and security challenges have been brought out
[...] Read more.
The Internet of Things (IoT) represents a bright prospect that a variety of common appliances can connect to one another, as well as with the rest of the Internet, to vastly improve our lives. Unique communication and security challenges have been brought out by the limited hardware, low-complexity, and severe energy constraints of IoT devices. In addition, a severe spectrum scarcity problem has also been stimulated by the use of a large number of IoT devices. In this paper, cognitive IoT (CIoT) is considered where an IoT network works as the secondary system using underlay spectrum sharing. A wireless energy harvesting (EH) node is used as a relay to improve the coverage of an IoT device. However, the relay could be a potential eavesdropper to intercept the IoT device’s messages. This paper considers the problem of secure communication between the IoT device (e.g., sensor) and a destination (e.g., controller) via the wireless EH untrusted relay. Since the destination can be equipped with adequate energy supply, secure schemes based on destination-aided jamming are proposed based on power splitting (PS) and time splitting (TS) policies, called intuitive secure schemes based on PS (Int-PS), precoded secure scheme based on PS (Pre-PS), intuitive secure scheme based on TS (Int-TS) and precoded secure scheme based on TS (Pre-TS), respectively. The secure performances of the proposed schemes are evaluated through the metric of probability of successfully secure transmission ( P S S T ), which represents the probability that the interference constraint of the primary user is satisfied and the secrecy rate is positive. P S S T is analyzed for the proposed secure schemes, and the closed form expressions of P S S T for Pre-PS and Pre-TS are derived and validated through simulation results. Numerical results show that the precoded secure schemes have better P S S T than the intuitive secure schemes under similar power consumption. When the secure schemes based on PS and TS polices have similar P S S T , the average transmit power consumption of the secure scheme based on TS is lower. The influences of power splitting and time slitting ratios are also discussed through simulations. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle A Practical Evaluation of a High-Security Energy-Efficient Gateway for IoT Fog Computing Applications
Sensors 2017, 17(9), 1978; https://doi.org/10.3390/s17091978
Received: 28 July 2017 / Revised: 16 August 2017 / Accepted: 19 August 2017 / Published: 29 August 2017
Cited by 14 | PDF Full-text (16784 KB) | HTML Full-text | XML Full-text
Abstract
Fog computing extends cloud computing to the edge of a network enabling new Internet of Things (IoT) applications and services, which may involve critical data that require privacy and security. In an IoT fog computing system, three elements can be distinguished: IoT nodes
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Fog computing extends cloud computing to the edge of a network enabling new Internet of Things (IoT) applications and services, which may involve critical data that require privacy and security. In an IoT fog computing system, three elements can be distinguished: IoT nodes that collect data, the cloud, and interconnected IoT gateways that exchange messages with the IoT nodes and with the cloud. This article focuses on securing IoT gateways, which are assumed to be constrained in terms of computational resources, but that are able to offload some processing from the cloud and to reduce the latency in the responses to the IoT nodes. However, it is usually taken for granted that IoT gateways have direct access to the electrical grid, which is not always the case: in mission-critical applications like natural disaster relief or environmental monitoring, it is common to deploy IoT nodes and gateways in large areas where electricity comes from solar or wind energy that charge the batteries that power every device. In this article, how to secure IoT gateway communications while minimizing power consumption is analyzed. The throughput and power consumption of Rivest–Shamir–Adleman (RSA) and Elliptic Curve Cryptography (ECC) are considered, since they are really popular, but have not been thoroughly analyzed when applied to IoT scenarios. Moreover, the most widespread Transport Layer Security (TLS) cipher suites use RSA as the main public key-exchange algorithm, but the key sizes needed are not practical for most IoT devices and cannot be scaled to high security levels. In contrast, ECC represents a much lighter and scalable alternative. Thus, RSA and ECC are compared for equivalent security levels, and power consumption and data throughput are measured using a testbed of IoT gateways. The measurements obtained indicate that, in the specific fog computing scenario proposed, ECC is clearly a much better alternative than RSA, obtaining energy consumption reductions of up to 50% and a data throughput that doubles RSA in most scenarios. These conclusions are then corroborated by a frame temporal analysis of Ethernet packets. In addition, current data compression algorithms are evaluated, concluding that, when dealing with the small payloads related to IoT applications, they do not pay off in terms of real data throughput and power consumption. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT
Sensors 2017, 17(9), 1967; https://doi.org/10.3390/s17091967
Received: 16 July 2017 / Revised: 22 August 2017 / Accepted: 22 August 2017 / Published: 26 August 2017
Cited by 3 | PDF Full-text (4732 KB) | HTML Full-text | XML Full-text
Abstract
The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host’s network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the
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The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host’s network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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Open AccessArticle Lifetime Maximization via Hole Alleviation in IoT Enabling Heterogeneous Wireless Sensor Networks
Sensors 2017, 17(7), 1677; https://doi.org/10.3390/s17071677
Received: 20 June 2017 / Revised: 11 July 2017 / Accepted: 12 July 2017 / Published: 21 July 2017
Cited by 2 | PDF Full-text (503 KB) | HTML Full-text | XML Full-text
Abstract
In Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs), there are two major factors which degrade the performance of the network. One is the void hole which occurs in a particular region due to unavailability of forwarder nodes. The other is the
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In Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs), there are two major factors which degrade the performance of the network. One is the void hole which occurs in a particular region due to unavailability of forwarder nodes. The other is the presence of energy hole which occurs due to imbalanced data traffic load on intermediate nodes. Therefore, an optimum transmission strategy is required to maximize the network lifespan via hole alleviation. In this regard, we propose a heterogeneous network solution that is capable to balance energy dissipation among network nodes. In addition, the divide and conquer approach is exploited to evenly distribute number of transmissions over various network areas. An efficient forwarder node selection is performed to alleviate coverage and energy holes. Linear optimization is performed to validate the effectiveness of our proposed work in term of energy minimization. Furthermore, simulations are conducted to show that our claims are well grounded. Results show the superiority of our work as compared to the baseline scheme in terms of energy consumption and network lifetime. Full article
(This article belongs to the Special Issue Sensor Networks for Collaborative and Secure Internet of Things)
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