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Special Issue "Wireless Sensor Network, Smart Grid & Analytic Considerations for Smart Cities and Environmental Sustainability"

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

Deadline for manuscript submissions: 31 December 2023 | Viewed by 1905

Special Issue Editors

Institute of Innovation, Science and Sustainability (IISS), Federation University, Berwick, VIC 3805, Australia
Interests: smart city; sensor networks; health informatics; crime informatics; artificial intelligence and machine learning; Internet of Things
Institute of Innovation, Science and Sustainability (IISS), Federation University, Gippsland, VIC 3842, Australia
Interests: mechatronics; Internet of Things; control systems; artificial intelligence and machine learning
School of Education, Swansea University, Swansea SA2 8PP, UK
Interests: data science; AI; computational intelligence; intelligent systems; smart infrastructure; cybersecurity; digital education; digital society; digital economy
Institute of Innovation, Science and Sustainability (IISS), Federation University, Mt Helen, VIC 3350, Australia
Interests: smart grids; future power grids; control theory; distribution systems; energy management
Centre for Smart Analytics, Federation University Australia, Ballarat, VIC 3842, Australia
Interests: Internet of Things; machine learning; data analytics; cybersecurity
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A smart city implements a wide range of electronic and digital technologies to communities and cities. Importantly these include wireless sensor networks that are capable of detecting states and events in the urban environment and intelligent and considered responses to this gathered data. Wireless sensor networks therefore play a foundational role in developing advanced techniques and implementing intelligent systems for smart cities. These intelligent responses are broad and include enhancing quality, performance and interactivity of urban services such as environmental monitoring and public health surveillance, reducing costs and resource consumption such as smart energy planning, or smart grids, and increasing contact between citizens and government. The latter raises many public policy and ethical considerations, related to gathering of data, algorithmic bias and privacy protecting of citizens. In relation to the analytics side there are real issues related to combining scalability, real-time processing, to develop subsequent decision making and recommender systems.

This Special Issue aims to address important features of these different layers within smart cities, focusing on the sensors, and data mining for decision support systems and recommender systems, which include use cases such as smart grids and environmental factors, without compromising privacy and ethical considerations.  It solicits the state-of-the-art theoretical, as well as practical works on a broad range of issues important for sensor-analytic smart cities for researchers, developers, and practitioners from both academia and industry.

Topics of primary interest include, but are not limited to:

  • Wireless Sensor Networks
  • Decision support systems for smart cities
  • Smart Grid
  • Analytic Considerations for Smart Cities
  • Environmental Sustainability
  • Ethical and public policy issues of AI applications for development of smart cities
  • Ethical and public policy issues related to wifi-sensing
  • Crime prevention and detection in smart cities
  • Human AI interaction for smart city
  • Energy economics and security for smart city
  • Cyber security considerations in smart city
  • Smart grids,
  • Future Power Grids (i.e., Renewable energy integration, wide-area control).
  • Asynchronous Grid Connection through VSC-HVDC.
  • Power System Stability and Dynamics.
  • Application of Data Mining in Power System.
  • Application of Control Theory in Power System.
  • Distribution System Energy Management and low carbon energy system.
  • Smart microgrid for smart cities
  • Energy storage solutions for smart city
  • Sustainable energy management practices for smart city
  • Intelligent energy monitoring
  • Energy sustainability

Dr. Giles Oatley
Dr. Tanveer Choudhury
Prof. Dr. Tom Crick
Dr. Rakibuzzaman Shah
Prof. Dr. Joarder Kamruzzaman
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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. 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 2600 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.


  • wireless sensor networks
  • smart grids
  • smart cities
  • environmental sustainability
  • cybersecurity

Published Papers (1 paper)

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18 pages, 366 KiB  
Obfuscated Memory Malware Detection in Resource-Constrained IoT Devices for Smart City Applications
Sensors 2023, 23(11), 5348; - 05 Jun 2023
Cited by 2 | Viewed by 1218
Obfuscated Memory Malware (OMM) presents significant threats to interconnected systems, including smart city applications, for its ability to evade detection through concealment tactics. Existing OMM detection methods primarily focus on binary detection. Their multiclass versions consider a few families only and, thereby, fail [...] Read more.
Obfuscated Memory Malware (OMM) presents significant threats to interconnected systems, including smart city applications, for its ability to evade detection through concealment tactics. Existing OMM detection methods primarily focus on binary detection. Their multiclass versions consider a few families only and, thereby, fail to detect much existing and emerging malware. Moreover, their large memory size makes them unsuitable to be executed in resource-constrained embedded/IoT devices. To address this problem, in this paper, we propose a multiclass but lightweight malware detection method capable of identifying recent malware and is suitable to execute in embedded devices. For this, the method considers a hybrid model by combining the feature-learning capabilities of convolutional neural networks with the temporal modeling advantage of bidirectional long short-term memory. The proposed architecture exhibits compact size and fast processing speed, making it suitable for deployment in IoT devices that constitute the major components of smart city systems. Extensive experiments with the recent CIC-Malmem-2022 OMM dataset demonstrate that our method outperforms other machine learning-based models proposed in the literature in both detecting OMM and identifying specific attack types. Our proposed method thus offers a robust yet compact model executable in IoT devices for defending against obfuscated malware. Full article
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