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Challenges, Trade-offs, and Experiences for Edge Computing in Wireless Sensor Networks

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

Deadline for manuscript submissions: closed (15 September 2020) | Viewed by 6895

Special Issue Editors


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Guest Editor
Advanced Digital Image Processing, Department of Information Engineering and Mathematics, University of Siena, via Roma 56, 53100 Siena, Italy
Interests: computer vision, pattern recognition, autonomous learning systems, homeland safety, videosurveillance, cultural heritage preservation

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Guest Editor
Department of Information Engineering and Mathematics, University of Siena, via Roma 56, 53100 Siena, Italy
Interests: high-efficiency computer architecture, parallel processing, produtivity-oriented parallel programming, information security, service-oriented architectures

Special Issue Information

Dear Colleagues,

The vast diffusion of wirelessly connected intelligent devices is rapidly changing the approach towards the design of monitoring infrastructures in several application fields. The availability of low-cost data-processing devices is moving part of the computation load towards the edge of the network. This leads to the definition of a wide range of new application scenarios. In particular, intelligent wireless sensor networks open up the way for the realization of monitoring infrastructures based on sensors that require mixed use of computationally light sensors (e.g., pressure sensors, thermal sensors, sound sensors), up to computationally heavy ones (e.g., cameras and 3D range sensors), to implement distributed, scalable, flexible, and resilient systems.

Data processing at the edge of the network, nevertheless, poses several technological challenges, in terms of security and reliability, as well as energy efficiency and management, network connectivity, timing constraints, and orchestration. And, crucially, in the vast majority of situations, multiple facets need to be addressed together and induce the design and management of nontrivial trade-offs.

This Special Issue invites original contributions on topics including, but not limited to, the following arguments:

 

  • Wireless data transmission techniques for intelligent sensor networks;
  • Data summarization and data interpolation that also accounts for spatial and temporal information;
  • Integration between fixed and mobile sensors, including drone-based ones;
  • Imaging and multimedia data management in wireless sensor networks;
  • Energy policies and trade-offs;
  • Security and reliability in the architectural design of wireless sensor networks;
  • Energy harvesting techniques and implications for intelligent sensor nodes and networks;
  • Hierarchical design of the architecture and functions;
  • Dynamic role reconfiguration in nodes and networks;
  • Programmability and performance/energy efficiency;
  • Timing constrains in surveillance and monitoring; and
  • Applications, best practices, and real-world examples based on intelligent sensor nodes/networks.

Prof. Dr. Allessandro Mecocci
Prof. Dr. Sandro Bartolini
Dr. Alessandro Pozzebon
Guest Editors

Manuscript Submission Information

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Keywords

  • wireless sensor networks
  • edge computing
  • fog computing
  • security
  • reliability
  • energy harvesting
  • power management

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Published Papers (2 papers)

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Research

24 pages, 1072 KiB  
Article
A Novel Spread Spectrum and Clustering Mixed Approach with Network Coding for Enhanced Narrowband IoT (NB-IoT) Scalability
by Emmanuel Migabo, Karim Djouani and Anish Kurien
Sensors 2020, 20(18), 5219; https://doi.org/10.3390/s20185219 - 13 Sep 2020
Cited by 1 | Viewed by 2673
Abstract
The Narrowband Internet of Things (NB-IoT) is a very promising licensed Internet of things (IoT) technology for accommodating massive device connections in 5G and beyond. To enable network scalability, this study proposes a two-layers novel mixed approach that aims not only to create [...] Read more.
The Narrowband Internet of Things (NB-IoT) is a very promising licensed Internet of things (IoT) technology for accommodating massive device connections in 5G and beyond. To enable network scalability, this study proposes a two-layers novel mixed approach that aims not only to create an efficient spectrum sharing among the many NB-IoT devices but also provides an energy-efficient network. On one layer, the approach uses an Adaptive Frequency Hopping Spread Spectrum (AFHSS) technique that uses a lightweight and secure pseudo-random sequence to exploit the channel diversity, to mitigate inter-link and cross-technology interference. On the second layer, the approach consists of a clustering and network coding (data aggregation) approach based on an energy-signal strength mixed gradient. The second layer contributes to offload the BS, allows for energy-efficient network scalability, helps balance the energy consumption of the network, and enhances the overall network lifetime. The proposed mixed strategy algorithm is modelled and simulated using the Matrix Laboratory (MATLAB) Long Term Evolution (LTE) toolbox. The obtained results reveal that the proposed mixed approach enhances network scalability while improving energy efficiency, transmission reliability, and network lifetime when compared to the existing spread spectrum only, nodes clustering only, and mixed approach with no network coding approaches. Full article
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29 pages, 712 KiB  
Article
An Energy-Efficient and Adaptive Channel Coding Approach for Narrowband Internet of Things (NB-IoT) Systems
by Emmanuel Migabo, Karim Djouani and Anish Kurien
Sensors 2020, 20(12), 3465; https://doi.org/10.3390/s20123465 - 19 Jun 2020
Cited by 12 | Viewed by 3565
Abstract
Most of the current research work on the Narrowband Internet of Things (NB-IoT) is focused on enhancing its network coverage. Many of the existing NB-IoT channel coding techniques are based on repeating transmission data and control signals as a way of enhancing the [...] Read more.
Most of the current research work on the Narrowband Internet of Things (NB-IoT) is focused on enhancing its network coverage. Many of the existing NB-IoT channel coding techniques are based on repeating transmission data and control signals as a way of enhancing the network’s reliability and therefore, enabling long-distance transmissions. Although most of these efforts are made at the expense of reducing the energy consumption of the NB-IoT network, they do not always consider the channel conditions. Therefore, this work proposes a novel NB-IoT Energy-Efficient Adaptive Channel Coding (EEACC) scheme. The EEACC approach is a two-dimensional (2D) approach which not only selects an appropriate channel coding scheme based on the estimated channel conditions (dynamically classified as bad, medium or good from initial based on a periodically assessed BLER performance outcome) but also minimizes the transmission repetition number under a pre-assessed probability of successful transmission (based on the ratio of previous successful transmissions over the total number of transmissions). This results in creating a single mixed gradient based on which a higher or lower Modulating Coding Scheme (MCS) is selected on each transmission. It is aimed at enhancing the overall energy efficiency of the network by dynamically selecting the appropriate Modulation Coding Scheme (MCS) number and efficiently minimizing the transmission repetition number. Link-level simulations are performed under different channel conditions (good, medium, or bad) considerations to assess the performance of the proposed up-link adaptation technique for NB-IoT. The obtained results demonstrate that the proposed technique outperforms the existing Narrowband Link Adaptation (NBLA) as well as the traditional repetition schemes in terms of the achieved energy efficiency as well as network reliability, latency, and scalability. Full article
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