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Algorithms
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Algorithms for Sensor Networks
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Algorithms for Sensor Networks

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: closed (30 June 2015) | Viewed by 27828

Special Issue Editor

Prof. Dr. Erchin Serpedin

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Texas A&M University, MS 3128, College Station, TX 77843-3128, USA
Interests: signal processing; wireless communications; machine learning; applied mathematics

Special Issue Information

Dear Colleagues,

The Open Access journal Algorithms invites all interested authors to submit papers to the Special Issue entitled: “Algorithms for Sensor Networks”. The aim of this Special Issue is to offer a forum for exchanging intelligent algorithms that find applicability in the deployment and operation of sensor networks. Original contributions and tutorial-style submissions that deal with the development, implementation, performance assessment, or validation of algorithms in the context of sensor networks are welcome in this Special Issue.

Potential topics of interest include, but are not limited to:

  • Distributed signal processing
  • Collaborative information processing
  • Data fusion and information extraction algorithms
  • Machine learning algorithms for sensor networks
  • Outlier detection, cybersecurity, threat detection algorithms
  • Intelligent algorithms for adaptive sensing
  • Big data processing
  • Tracking, localization, monitoring

Prof. Dr. Erchin Serpedin
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 submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms 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 1600 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

  • collaborative signal processing
  • distributed information processing
  • sensor networks
  • algorithms

Published Papers (5 papers)

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Research

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2408 KiB  
Article
Effective Data Acquisition Protocol for Multi-Hop Heterogeneous Wireless Sensor Networks Using Compressive Sensing
by Ahmed M. Khedr
Algorithms 2015, 8(4), 910-928; https://doi.org/10.3390/a8040910 - 16 Oct 2015
Cited by 46 | Viewed by 5393
Abstract
In designing wireless sensor networks (WSNs), it is important to reduce energy dissipation and prolong network lifetime. Clustering of nodes is one of the most effective approaches for conserving energy in WSNs. Cluster formation protocols generally consider the heterogeneity of sensor nodes in [...] Read more.
In designing wireless sensor networks (WSNs), it is important to reduce energy dissipation and prolong network lifetime. Clustering of nodes is one of the most effective approaches for conserving energy in WSNs. Cluster formation protocols generally consider the heterogeneity of sensor nodes in terms of energy difference of nodes but ignore the different transmission ranges of them. In this paper, we propose an effective data acquisition clustered protocol using compressive sensing (EDACP-CS) for heterogeneous WSNs that aims to conserve the energy of sensor nodes in the presence of energy and transmission range heterogeneity. In EDACP-CS, cluster heads are selected based on the distance from the base station and sensor residual energy. Simulation results show that our protocol offers a much better performance than the existing protocols in terms of energy consumption, stability, network lifetime, and throughput. Full article
(This article belongs to the Special Issue Algorithms for Sensor Networks)
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333 KiB  
Article
One-Bit Quantization and Distributed Detection with an Unknown Scale Parameter
by Fei Gao, Lili Guo, Hongbin Li and Jun Fang
Algorithms 2015, 8(3), 621-631; https://doi.org/10.3390/a8030621 - 11 Aug 2015
Cited by 6 | Viewed by 4831
Abstract
We examine a distributed detection problem in a wireless sensor network, where sensor nodes collaborate to detect a Gaussian signal with an unknown change of power, i.e., a scale parameter. Due to power/bandwidth constraints, we consider the case where each sensor quantizes its [...] Read more.
We examine a distributed detection problem in a wireless sensor network, where sensor nodes collaborate to detect a Gaussian signal with an unknown change of power, i.e., a scale parameter. Due to power/bandwidth constraints, we consider the case where each sensor quantizes its observation into a binary digit. The binary data are then transmitted through error-prone wireless links to a fusion center, where a generalized likelihood ratio test (GLRT) detector is employed to perform a global decision. We study the design of a binary quantizer based on an asymptotic analysis of the GLRT. Interestingly, the quantization threshold of the quantizer is independent of the unknown scale parameter. Numerical results are included to illustrate the performance of the proposed quantizer and GLRT in binary symmetric channels (BSCs). Full article
(This article belongs to the Special Issue Algorithms for Sensor Networks)
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359 KiB  
Article
Robust Rank Reduction Algorithm with Iterative Parameter Optimization and Vector Perturbation
by Peng Li, Jiao Feng and Rodrigo C. De Lamare
Algorithms 2015, 8(3), 573-589; https://doi.org/10.3390/a8030573 - 05 Aug 2015
Cited by 2 | Viewed by 4714
Abstract
In dynamic propagation environments, beamforming algorithms may suffer from strong interference, steering vector mismatches, a low convergence speed and a high computational complexity. Reduced-rank signal processing techniques provide a way to address the problems mentioned above. This paper presents a low-complexity robust data-dependent [...] Read more.
In dynamic propagation environments, beamforming algorithms may suffer from strong interference, steering vector mismatches, a low convergence speed and a high computational complexity. Reduced-rank signal processing techniques provide a way to address the problems mentioned above. This paper presents a low-complexity robust data-dependent dimensionality reduction based on an iterative optimization with steering vector perturbation (IOVP) algorithm for reduced-rank beamforming and steering vector estimation. The proposed robust optimization procedure jointly adjusts the parameters of a rank reduction matrix and an adaptive beamformer. The optimized rank reduction matrix projects the received signal vector onto a subspace with lower dimension. The beamformer/steering vector optimization is then performed in a reduced dimension subspace. We devise efficient stochastic gradient and recursive least-squares algorithms for implementing the proposed robust IOVP design. The proposed robust IOVP beamforming algorithms result in a faster convergence speed and an improved performance. Simulation results show that the proposed IOVP algorithms outperform some existing full-rank and reduced-rank algorithms with a comparable complexity. Full article
(This article belongs to the Special Issue Algorithms for Sensor Networks)
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861 KiB  
Article
A Benchmarking Algorithm to Determine Minimum Aggregation Delay for Data Gathering Trees and an Analysis of the Diameter-Aggregation Delay Tradeoff
by Natarajan Meghanathan
Algorithms 2015, 8(3), 435-458; https://doi.org/10.3390/a8030435 - 10 Jul 2015
Cited by 7 | Viewed by 6388
Abstract
Aggregation delay is the minimum number of time slots required to aggregate data along the edges of a data gathering tree (DG tree) spanning all the nodes in a wireless sensor network (WSN). We propose a benchmarking algorithm to determine the minimum possible [...] Read more.
Aggregation delay is the minimum number of time slots required to aggregate data along the edges of a data gathering tree (DG tree) spanning all the nodes in a wireless sensor network (WSN). We propose a benchmarking algorithm to determine the minimum possible aggregation delay for DG trees in a WSN. We assume the availability of a sufficient number of unique CDMA (Code Division Multiple Access) codes for the intermediate nodes to simultaneously aggregate data from their child nodes if the latter are ready with the data. An intermediate node has to still schedule non-overlapping time slots to sequentially aggregate data from its own child nodes (one time slot per child node). We show that the minimum aggregation delay for a DG tree depends on the underlying design choices (bottleneck node-weight based or bottleneck link-weight based) behind its construction. We observe the bottleneck node-weight based DG trees incur a smaller diameter and a larger number of child nodes per intermediate node; whereas, the bottleneck link-weight based DG trees incur a larger diameter and a much lower number of child nodes per intermediate node. As a result, we observe a complex diameter-aggregation delay tradeoff for data gathering trees in WSNs. Full article
(This article belongs to the Special Issue Algorithms for Sensor Networks)
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Review

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448 KiB  
Review
An Overview of a Class of Clock Synchronization Algorithms for Wireless Sensor Networks: A Statistical Signal Processing Perspective
by Xu Wang, Daniel Jeske and Erchin Serpedin
Algorithms 2015, 8(3), 590-620; https://doi.org/10.3390/a8030590 - 06 Aug 2015
Cited by 15 | Viewed by 6102
Abstract
Recently, wireless sensor networks (WSNs) have drawn great interest due to their outstanding monitoring and management potential in medical, environmental and industrial applications. Most of the applications that employ WSNs demand all of the sensor nodes to run on a common time scale, [...] Read more.
Recently, wireless sensor networks (WSNs) have drawn great interest due to their outstanding monitoring and management potential in medical, environmental and industrial applications. Most of the applications that employ WSNs demand all of the sensor nodes to run on a common time scale, a requirement that highlights the importance of clock synchronization. The clock synchronization problem in WSNs is inherently related to parameter estimation. The accuracy of clock synchronization algorithms depends essentially on the statistical properties of the parameter estimation algorithms. Recently, studies dedicated to the estimation of synchronization parameters, such as clock offset and skew, have begun to emerge in the literature. The aim of this article is to provide an overview of the state-of-the-art clock synchronization algorithms for WSNs from a statistical signal processing point of view. This article focuses on describing the key features of the class of clock synchronization algorithms that exploit the traditional two-way message (signal) exchange mechanism. Upon introducing the two-way message exchange mechanism, the main clock offset estimation algorithms for pairwise synchronization of sensor nodes are first reviewed, and their performance is compared. The class of fully-distributed clock offset estimation algorithms for network-wide synchronization is then surveyed. The paper concludes with a list of open research problems pertaining to clock synchronization of WSNs. Full article
(This article belongs to the Special Issue Algorithms for Sensor Networks)
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