Topic Review
Deep Reinforcement Learning Applications
Deep Reinforcement Learning (DRL) combines Reinforcement Learning and Deep Learning. It is more capable of learning from raw sensors or images as input, enabling end-to-end learning, which opens up more applications in robotics, video games, NLP, computer vision, healthcare, and more. A milestone in value-based DRL is employing Deep Q-Networks (DQN) to play Atari games by Google DeepMindin 2013.
  • 1747
  • 02 Aug 2021
Topic Review
Networked Control System
An NCS consists of control loops joined through communication networks in which both the control signal and the feedback signal are exchanged between the system and the controller.
  • 1632
  • 01 Apr 2021
Topic Review
Wireless Sensor Networks Architecture
Wireless sensor networks (WSNs) have taken a giant leap in scale, expanding their applicability to a large variety of technological domains and applications, ranging from the Internet of things (IoT) for smart cities and smart homes to wearable technology healthcare applications, underwater, agricultural and environmental monitoring and many more. This expansion is rapidly growing every passing day in terms of the variety, heterogeneity and the number of devices which such applications support. Data collection is commonly the core application in WSN and IoT networks, which are typically composed of a large variety of devices, some constrained by their resources (e.g., processing, storage, energy) and some by highly diverse demands. Many challenges span all the conceptual communication layers, from the Physical to the Applicational. In addition, the integrated unit architecture and the platform design can be subject to various stringent constraints. For example, size requirements can impose a strict constraint on the device design; low power consumption, low production cost, and self-operation can represent additional constraints.  Accordingly, the device architecture is fundamental and affects many other factors in the system. For example, power supply affects the life span; it also affects transmission range, memory, and processing unit, which in turn can affect the algorithms that can be executed on the device, etc.
  • 716
  • 12 Apr 2022
Topic Review
Smart Grid Management, Control, and Operation
Smart grid management, control and operation (SGMCO) are key tasks for maintaining their proper functioning as well as for their extension and expansion. The current challenges of power generation, distribution, transmission, and consumption, as well as growing energy demand, facilitate the integration of a large number of smart grids with renewable energy generators and physical information systems, while smart grids are moving toward distribution and decentralization in response to the evolving application of the Internet of Energy (IoE). SGMCO handles not only traditional management, control, and operations, but also the future challenges for smart grids: Collaboration between stakeholders, control of network imbalances (e.g. frequency and voltage regulation), data analysis and management, decentralized network management and operation, and security and privacy.
  • 446
  • 10 Jan 2022
Topic Review
Cloud-Fog-Edge Computing for Smart Agriculture
Cloud Computing is a well-established paradigm for building service-centric systems. However, ultra-low latency, high bandwidth, security, and real-time analytics are limitations in Cloud Computing when analysing and providing results for a large amount of data. Fog and Edge Computing offer solutions to the limitations of Cloud Computing. The number of agricultural domain applications that use the combination of Cloud, Fog, and Edge is increasing in the last few decades.
  • 443
  • 08 Oct 2021
Topic Review
Cybersecurity Economics
Cybersecurity economics can be defined as a field of research that utilizes a socio-technical perspective to investigate economic aspects of cybersecurity such as budgeting, information asymmetry, governance, and types of goods and services, to provide sustainable policy recommendations, regulatory options, and practical solutions that can substantially improve the cybersecurity posture of the interacting agents in the open socio-technical systems.   
  • 420
  • 15 Dec 2021
Topic Review
Wireless Sensor Networks
Wireless sensor networks (WSNs) are spatially scattered networks equipped with an extensive number of nodes to check and record different ecological states such as humidity, temperature, pressure, and lightning states. WSN network provides different services to a client such as monitoring, detection, and runtime decision-making against events occurrence.
  • 388
  • 02 Nov 2021
Topic Review Video
Blockchain Acceptance Models
Blockchain is a promising breakthrough technology that is highly applicable in manifold sectors. The adoption of blockchain technology is accompanied by a range of issues and challenges that make its implementation complicated. To facilitate the successful implementation of blockchain technology, several blockchain adoption frameworks have been developed. However, selecting the appropriate framework based on the conformity of its features with the business sector may be challenging for decision-makers. 
  • 384
  • 18 Feb 2022
Topic Review
Green Wireless Sensor Networks
The issue of energy balancing in Wireless Sensor Networks is a pivotal one, crucial in their deployment. This problem can be subdivided in three areas: (i) energy conservation techniques, usually implying minimizing the cost of communication at the nodes since it is known that the radio is the biggest consumer of the available energy; (ii) energy-harvesting techniques, converting energy from not full-time available environmental sources and usually storing it; and (iii) energy transfer techniques, sharing energy resources from one node (either specialized or not) to another one.
  • 351
  • 01 Jul 2021
Topic Review
Data-Driven Production Logistics
A data-driven approach in production logistics is adopted as a response to challenges such as low visibility and system rigidity. within data-driven production logistics, data is the backbone of the system and all the components are bound together with data. Any decision is made based on data rather than intuition or even experience. All production logistics related activities are supported by data, which is constantly collected from data sources such as machines, human resources, sensors, actuators, etc. A data-driven approach facilitates transition towards a smart, autonomous production logistics system.
  • 345
  • 28 Apr 2021
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