sensors-logo

Journal Browser

Journal Browser

Special Issue "Data Collection in Wireless Sensor Networks (WSN) and Internet of Things (IoT)"

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

Deadline for manuscript submissions: 30 September 2020.

Special Issue Editor

Dr. Omer Gurewitz
Website
Guest Editor
Department of Communication Systems Engineering, Ben-Gurion University of the Negev, Beer Sheba 8410501, Israel
Interests: wireless sensor networks; Internet of Things; wireless security and privacy; next-generation wireless communication (5G and beyond)

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) aims at improving day-to-day life, ranging from smart cities to smart homes, pervasive health care, assisted living, environmental monitoring, and surveillance. The IoT paradigm relies on interconnecting a large number of assorted devices (things) linked by the Internet via heterogeneous access networks, through which they can exchange information with one or more internet gateways or sinks that can process the data, take action, and forward it to another destination if needed. The number of devices connected to the Internet utilizing the IoT paradigm has dramatically increased over recent years and is expected to exceed 30 billion connected devices by 2020 (the actual number of devices is hard to predict, and different studies envision different figures which typically span between 30 to over 40 billion devices by 2020).

Data collection and dissemination in a highly dense network such as the IoT network that spans highly heterogeneous devices, a great percentage of which are expected to be small, with very constrained processing, storage, and energy resources and with very limited network capabilities. It is highly challenging and draws significant attention from both industrial and academic communities. Some of these challenges include (i) information management—the amount of information collected or needing to be distributed between the relevant entities is enormous, and some of it is expected to be redundant, both in terms of the information sent by each device, which can be highly compressed, and in terms of duplicate information received by different entities. Accordingly, innovative techniques are required for data compression to reduce transmitted data over wireless channels, as well as aggregation techniques that exploit the redundancy between information sent by the different entities. (ii) Connectivity—collecting and disseminating data from and to so many devices will be one of the biggest challenges of the future of IoT; accordingly, novel MAC protocols and coding schemes should be devised to comply with this challenge. (iii) Data analysis and reaction—the expected vast data exchange and the low latency requirement (at least for some of the information collected) requires processing and analysis of data in real-time or near real-time, to enable timely decision making and instantaneous action taken. (iv) Security—connecting enormous numbers of devices to the internet exposes the IoT network to serious security vulnerabilities, all the more so since the relevant entities are highly limited. Accordingly, issues such as authenticity, data encryption, and vulnerability to attacks (e.g., device impersonation) are highly important for the IoT paradigm’s continuous growth. (v) Privacy—the IoT creates unique privacy challenges. Since the information transmitted over the IoT network can be highly confidential (e.g., health reports, device tracking, and activity monitoring), the collection and dissemination of this information creates challenges related to data protection and privacy.

Potential topics include but are not limited to the following:

  • Architecture and protocols for data collection and dissemination in IoT
  • Data acquisition
  • Data reduction and compression for IoT
  • Storage and management for IoT
  • Big data for IoT
  • MAC protocols and massive access scheme for IoT
  • Network coding techniques and relay utilization for IoT networks
  • IoT storage and processing techniques
  • Analytic tools for IoT
  • Computational and artificial intelligence algorithms for IoT
  • Security, privacy, and trust in IoT

Assoc. Prof. Omer Gurewitz
Guest Editor

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 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 2000 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.

Published Papers (4 papers)

Order results
Result details
Select all
Export citation of selected articles as:
Open AccessArticle
Adaptive Data Acquisition with Energy Efficiency and Critical-Sensing Guarantee for Wireless Sensor Networks
Sensors 2019, 19(12), 2654; https://doi.org/10.3390/s19122654 - 12 Jun 2019
Cited by 4
Abstract
Due to the limited energy budget, great efforts have been made to improve energy efficiency for wireless sensor networks. The advantage of compressed sensing is that it saves energy because of its sparse sampling; however, it suffers inherent shortcomings in relation to timely [...] Read more.
Due to the limited energy budget, great efforts have been made to improve energy efficiency for wireless sensor networks. The advantage of compressed sensing is that it saves energy because of its sparse sampling; however, it suffers inherent shortcomings in relation to timely data acquisition. In contrast, prediction-based approaches are able to offer timely data acquisition, but the overhead of frequent model synchronization and data sampling weakens the gain in the data reduction. The integration of compressed sensing and prediction-based approaches is one promising data acquisition scheme for the suppression of data transmission, as well as timely collection of critical data, but it is challenging to adaptively and effectively conduct appropriate switching between the two aforementioned data gathering modes. Taking into account the characteristics of data gathering modes and monitored data, this research focuses on several key issues, such as integration framework, adaptive deviation tolerance, and adaptive switching mechanism of data gathering modes. In particular, the adaptive deviation tolerance is proposed for improving the flexibility of data acquisition scheme. The adaptive switching mechanism aims at overcoming the drawbacks in the traditional method that fails to effectively react to the phenomena change unless the sampling frequency is sufficiently high. Through experiments, it is demonstrated that the proposed scheme has good flexibility and scalability, and is capable of simultaneously achieving good energy efficiency and high-quality sensing of critical events. Full article
Show Figures

Figure 1

Open AccessArticle
A Charging-Aware Multi-Mode Routing Protocol for Data Collection in Wireless Rechargeable Sensor Networks
Sensors 2019, 19(15), 3338; https://doi.org/10.3390/s19153338 - 30 Jul 2019
Abstract
This paper proposes a charging-aware multi-mode routing protocol (CMRP) to collect data in the wireless rechargeable sensor networks. The routing mechanism in CMRP is not steady but changes according to the energy charging status of sensors. Sensors that cannot replenish their energy efficiency [...] Read more.
This paper proposes a charging-aware multi-mode routing protocol (CMRP) to collect data in the wireless rechargeable sensor networks. The routing mechanism in CMRP is not steady but changes according to the energy charging status of sensors. Sensors that cannot replenish their energy efficiency use the routing protocol with less energy consumption. On the contrary, sensors that can replenish their energy use the low propagation delay routing protocol. A novel heuristic chaining mechanism based on multi-level convex hull circle (MCC) is also proposed. Simulation results show that CMRP not only has longer operation time than LEACH and PEGASIS but also has the shortest propagation delay time. The lifetime of CMRP is less than the minimum spanning tree by about 1%, but the propagation delay is shorter than MSTP about 21–28%. In addition, CMRP considers both reducing energy consumption and shortening the propagation delay at the same time. The life-delay rate of the CMRP is close to the optimal results. Full article
Show Figures

Figure 1

Open AccessArticle
Cross-Layer Routing for a Mobility Support Protocol Based on Handover Mechanism in Cluster-Based Wireless Sensor Networks with Mobile Sink
Sensors 2019, 19(13), 2843; https://doi.org/10.3390/s19132843 - 26 Jun 2019
Cited by 2
Abstract
Wireless sensor networks with mobile collectors or sinks face some challenges regarding the data collection process and the continuous connectivity and delivering of data while the mobile sink is moving throughout the network. These challenges increase as the network grows. For this aim, [...] Read more.
Wireless sensor networks with mobile collectors or sinks face some challenges regarding the data collection process and the continuous connectivity and delivering of data while the mobile sink is moving throughout the network. These challenges increase as the network grows. For this aim, we propose in this paper a cross-layer routing protocol which supports mobility for large-scale wireless sensor networks, which we name CLR-MSPH. We adapt CLR-MSPH for the hierarchical architecture of the network, and it performs on cluster-based wireless sensor networks where the network is organized in clusters. Our proposed protocol deals with the problem of handover data after the mobile sink leaves the radio range of cluster head without sending all data stored in the cluster head’s buffer. We also introduce a mobility model for the mobile sink for a better data collection process. CLR-MSPH is considered as an extending implementation of BMAC protocol with handover mechanism (BMAC-H). In order to prove the efficiency of the proposed protocol, we compare CLR-MSPH to BMAC-H, where we adapted BMAC-H to perform in cluster-based wireless sensor networks. The simulation results show that CLR-MSPH performs better than BMAC-H in terms of packets reception rate, energy, and latency. Full article
Show Figures

Figure 1

Open AccessArticle
Error-Aware Data Clustering for In-Network Data Reduction in Wireless Sensor Networks
Sensors 2020, 20(4), 1011; https://doi.org/10.3390/s20041011 - 13 Feb 2020
Cited by 1
Abstract
A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to [...] Read more.
A wireless sensor network (WSN) deploys hundreds or thousands of nodes that may introduce large-scale data over time. Dealing with such an amount of collected data is a real challenge for energy-constraint sensor nodes. Therefore, numerous research works have been carried out to design efficient data clustering techniques in WSNs to eliminate the amount of redundant data before transmitting them to the sink while preserving their fundamental properties. This paper develops a new error-aware data clustering (EDC) technique at the cluster-heads (CHs) for in-network data reduction. The proposed EDC consists of three adaptive modules that allow users to choose the module that suits their requirements and the quality of the data. The histogram-based data clustering (HDC) module groups temporal correlated data into clusters and eliminates correlated data from each cluster. Recursive outlier detection and smoothing (RODS) with HDC module provides error-aware data clustering, which detects random outliers using temporal correlation of data to maintain data reduction errors within a predefined threshold. Verification of RODS (V-RODS) with HDC module detects not only random outliers but also frequent outliers simultaneously based on both the temporal and spatial correlations of the data. The simulation results show that the proposed EDC is computationally cheap, able to reduce a significant amount of redundant data with minimum error, and provides efficient error-aware data clustering solutions for remote monitoring environmental applications. Full article
Show Figures

Figure 1

Back to TopTop