The theme for this week’s sustainability research is SMART CITIES
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Research in Details
Research #1
I-AREOR: An Energy-balanced Clustering Protocol for implementing Green IoT in smart cities
Highlights
To achieve Green IoT implementation, it is important to take necessary measures to prevent energy depletion.
Clustering can extend the lifetime of a green Wireless Sensor Network (WSN).
This paper proposes an Improved-Adaptive Ranking based Energy-efficient Opportunistic Routing protocol (I-AREOR)
The I-AREOR clustering technique shows more efficiency in maximizing the network lifetime.
Authors: Premkumar Chithaluru FadiAl-Turjman, Manoj Kumara, Thompson Stephan
Date of publication: 23 MAY 2020
Summary
This research work adopts the idea of Internet of Things (IoT) for constructing a green Wireless Sensor Network (WSN) for improving sensor based communication in future smart cities. To achieve Green IoT implementation, it is important to take necessary measures to prevent energy depletion and promote energy efficiency techniques. Clustering can extend the lifetime of such networks and its efficiency depends on the selection of quality clustering schemes. To balance the energy consumption for maximizing the network lifetime, this paper proposes an Improved-Adaptive Ranking based Energy-efficient Opportunistic Routing protocol (I-AREOR), based on regional density, relative distance, and residual energy. Importantly, the first node death (FND), half node death (HND), and last node death (LND) are the major challenges for improving the energy efficiency. Therefore, the proposed approach provides a solution to extend the time of FND by considering the regional density, relative distance, and residual energy of the sensor nodes. I-AREOR protocol considers the energy parameters based on dynamic threshold for each round. The demonstrated results show that the I-AREOR clustering technique shows more efficiency in maximizing the network lifetime as compared to the existing algorithms.
Keywords: Wireless sensor networks, Internet of things, Green IoT, Clustering, Network lifetime, Smart cities
Research #2
An Energy Internet DERMS Platform using a Multi-level Stackelberg Game
Highlights
A multi-level sequential DERMS architecture using a noniterative reverse Stackelberg game,
Addressing the balance of supply and demand as a nexus between financial autonomy, service provision, and stakeholder participation.
A Hierarchical control of demand response (DR) and distributed energy resources (DER), and,
A compatible solution with the IEEE2030.5 smart energy application protocol.
Authors: Javad Fattahi, David Wright, Henry Schriemer
Date of publication: 23 MAY 2020
Summary
Realizing environmentally sustainable and socially resilient cities requires clean energy sources and controllable loads as part of the electricity distribution grid. We address the balance of supply and demand as a nexus between financial autonomy, service provision, and stakeholder participation. We consider an energy internet architecture of prosumers, aggregators and DSOs as transactive energy participants. To implement the distributed energy resource management system (DERMS), we develop a transparent decision support system to identify the fair allocation of financial resources. Using a reverse Stackelberg game theoretic approach, we map the multi-level architecture to sub-games interconnected through utility functions that describe realistic price response at each level. The existence and uniqueness of the Nash equilibrium (NE) is proven, and closed form solutions are given for the NE strategies (stakeholder energy contributions). Game dependence on very general parameters is illustrated through a noniterative algorithm. Numerical results shown in scenario assessments demonstrate game outcomes for DERMS operation in both stable and unstable NE situations, revealing stakeholder dependencies on the allocation of financial resources. Robustness and scalability, as necessary requirements for any transactive energy economic mechanism, are also demonstrated. Our approach is compatible with the IEEE2030.5 smart energy profile application protocol needed for DERMS implementation.
Keywords: Transactional Demand Response, IEEE 2030.5, Smart grid, multi-agent system, Neighborhood coordination.
Research #3
One Step Forward Toward Smart City Utopia: Smart Building Energy Management Based on Adaptive Surrogate Modelling
Highlights:
Intelligent supervisory predictive control (ISPC) is introduced and implemented in which energy consumption tends to be minimized
The methodology of ISPC includes building thermal simulation and multi-objective optimization algorithm.
The development of a reliable surrogate model is a key feature to confer greenness to a building.
The implemented energy management can be applied to both new and existing buildings and with any level of HVAC technology.
Authors: Diogo Goncalves, Yahya Sheikhnejad, Mónica Oliveira, Nelson Martins
Date of publication: 22 May 2020
Summary
This study steps into the roadmap of agenda 2030 to mitigate the human footprint on an environment with the aim of management of energy consumption in residential/commercial buildings. In order to materialize this concept, a new generation of adaptable systems of intelligent supervisory predictive control (ISPC) is introduced and implemented in which energy consumption tends to be minimized without sacrificing occupants thermal comfort. The methodology of ISPC includes building thermal simulation and multi-objective optimization algorithm that interact with conventional machine-level controllers of HVAC systems, to define optimized setpoints considering current and forecasted operation conditions. The development of a reliable surrogate model, based on robust machine learning techniques, is a key feature to confer greenness to a building in order to promote sustainability in the built environment and finally to have a smart green building. It is showed that the proposed ISPC is capable of delivering a robust, energy- and cost-effective decision while being independent of the HVAC system. The implemented energy management, as a non-destructive retrofitting procedure, can be applied to both new and existing buildings and with any level of HVAC technology.
Keywords: Intelligent energy management, machine learning, supervisory predictive control, adaptive surrogate modelling, smart green buildings.
Research #4
A Deep Learning-based IoT-oriented Infrastructure for Secure Smart City
Highlights:
Smart Manufacturing, Smart Industries, and Cyber-Physical System (CPS) are part of IoT-oriented infrastructure.
The IoT aims to integrate the physical world to computational facilities as cyberspace.
Security and Privacy, Centralization, Communication Latency, Scalability is a challenge in such an environment.
To mitigate these challenges, a Deep Learning-based IoT-oriented infrastructure was introduced.
Authors: Sushil Kumar Singh, Young-Sik Jeong, Jong Hyuk Park
Date of publication: 22 MAY 2020
Summary
In recent years, the Internet of Things (IoT) infrastructures are developing in various industrial applications in sustainable smart cities and societies such as smart manufacturing, smart industries. The Cyber-Physical System (CPS) is also part of IoT-oriented infrastructure. CPS has gained considerable success in industrial applications and critical infrastructure with a distributed environment. This system aims to integrate the physical world to computational facilities as cyberspace. However, there are many challenges, such as security and privacy, centralization, communication latency, scalability in such an environment. To mitigate these challenges, we propose a Deep Learning-based IoT-oriented infrastructure for a secure smart city where Blockchain provides a distributed environment at the communication phase of CPS, and Software-Defined Networking (SDN) establishes the protocols for data forwarding in the network. A deep learning-based cloud is utilized at the application layer of the proposed infrastructure to resolve communication latency and centralization, scalability. It enables cost-effective, high-performance computing resources for smart city applications such as the smart industry, smart transportation. Finally, we evaluated the performance of our proposed infrastructure. We compared it with existing methods using quantitative analysis and security and privacy analysis with different measures such as scalability and latency. The evaluation of our implementation results shows that performance is improved.
Keywords: Deep Learning, IoT-oriented Infrastructure, CPS, Blockchain, SDN, Smart City, Security and Privacy
Research #5
Data Mining and Machine Learning Methods for Sustainable Smart Cities Traffic Classification: A Survey
Highlights
This paper study Data Mining and Machine Learning Methods for Sustainable Smart Cities Traffic Classification: A Survey.
This survey paper describes the significant literature survey of Sustainable Smart Cities (SSC), Machine Learning (ML), Data Mining (DM), datasets, feature extraction and selection for network traffic classification.
In this paper, most cited methods and datasets of features were identified, read and summarized.
In this paper, different classification techniques for SSC network traffic classification are presented.
In this paper, in the end, challenges and recommendations for SSC network traffic classification with the dataset of features are presented.
Authors: Muhammad Shafiq, Zhihong Tian, Ali Kashif Bashir, Alireza Jolfaei, Xiangzhan Yud
Date of publication: 17 May, 2020
Summary
This survey paper describes the significant literature survey of Sustainable Smart Cities (SSC), Machine Learning (ML), Data Mining (DM), datasets, feature extraction and selection for network traffic classification. Considering relevance and most cited methods and datasets of features were identified, read and summarized. As data and data features are essential in Internet traffic classification using machine learning techniques, some well-known and most used datasets with details statistical features are described. Different classification techniques for SSC network traffic classification are presented with more information. The complexity of data set, features extraction and machine learning methods are addressed. In the end, challenges and recommendations for SSC network traffic classification with the dataset of features are presented.
Keywords: Sustainable Smart Cities, Security, Traffic, Classification, Data Mining, Machine Learning, A Survey.