Distributed Artificial Intelligence Empowered Sustainable Cognitive Radio Sensor Networks: A Smart City on-demand Perspective

Manman, Li; Goswami, Pratik; Mukherjee, Proshikshya; Mukherjee, Amrit; Yang, Lixia; Ghosh, Uttam; Menon, Varun G.; Qi, Yong; Nkenyereye, Lewis

doi:10.1016/j.scs.2021.103265

Cited by 24 publications

(8 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…"Environmental benefits arise from enhanced rational use and reduction of the extraction of natural resources, reduction of water and energy consumption, conscious and orderly development" [148]. It has been argued that robotic systems can enhance the construction environment by reducing fatalities and liberating employees from performing hazardous assignments [16]. In this regard, the implementation of robotics is a possible solution to expand environmental resources and sustainability in many ways, including by reducing construction waste, saving natural resources, improving the safety of the workplace, and supporting an improved living atmosphere [21].…”

Section: Discussionmentioning

confidence: 99%

“…Considering the low level of investment in this sector, many projects have been cancelled or suspended [14]. In all developing countries, the building industry meets the demands of the government demand, society, and consumers, consequently lagging other comparable businesses [15] Furthermore, the problem of sustainability in the low-income building industry has not been solved and considered [16]. As a result, the necessity of "sustainable buildings" that are ecologically friendly and resource-efficient has been stressed in the literature.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Partial Least Squares Structural Equation Modeling of Robotics Implementation for Sustainable Building Projects: A Case in Nigeria

et al. 2022

View full text Add to dashboard Cite

Sustainability concepts should be adopted via new technologies to achieve the greatest possible gains without compromising the objectives of projects. In this research, we empirically investigated the influence of identified drivers on the implementation of robotics in the building sector of developing countries. To this end, with a view to sustainable building projects, the drivers of robotics were derived from the literature, which were subsequently contextually adjusted using a survey method through the exploratory factor analysis (EFA) method. The results of EFA revealed that the drivers of robotics can be classified into three primary constructs: technology, industry, and culture. However, the benefits of implementing robotics can be grouped into two primary constructs: resources and environment. Therefore, in this study, we employed partial least squares structural equation modeling (PLS-SEM) to evaluate the connections amongst drivers and applications of robotics in Nigeria’s building industry. The results indicated that the input to the implementation of robotics in Nigeria’s building industry via the drivers of robotics has a considerable influence at a small scale, with an impact of 14.5%. The findings of this study can serve as a guide for policymakers looking to improve their projects and increase sustainability by using robotics in the building sector.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Partial Least Squares Structural Equation Modeling of Robotics Implementation for Sustainable Building Projects: A Case in Nigeria

et al. 2022

View full text Add to dashboard Cite

show abstract

“…For example, SmartME, a smart home battery proposed by Bruneo et al [57], uses AI to optimize energy storage and distribution, reducing overall energy consumption. Similarly, the study by Hazarika et al [60], Manman et al [66] suggests the use of Cognitive Radio Sensor Networks (CRSNs) for energy-efficient industrial IoT applications.…”

Section: Green Aiotmentioning

confidence: 99%

Combining Multi-Agent Systems and ArtificialIntelligence of Things: Technical Challenges andGains

Luzolo,

Elrawashdeh,

Outay

et al. 2024

Preprint

View full text Add to dashboard Cite

A Multiagent System (MAS) refers to a network of agents collaborating to achieve same objective. This system comprises numerous individual programs or hardware components (agents) that are simpler to construct and manage. Additionally, these agents can dynamically and swiftly adapt to changes in their environment. The MAS proves advantageous in addressing intricate issues by employing the divide-and-conquer approach. It finds application in diverse fields where the emphasis is on distributed computing and control, enabling the development of resilient, adaptable, and scalable systems. The Multiagent System (MAS) is not a substitute or rival for Artificial Intelligence (AI) methods. Instead, AI techniques can be integrated within the agents to enhance their computational and decision-making capabilities. The diversity or uniformity of goals, actions, domain knowledge, sensor inputs, and outputs among the agents in the MAS can determine whether each agent is heterogeneous or homogeneous. The Internet of Things (IoT) and Artificial Intelligence (AI) are two technologies that have long been applied to the development of smart systems. These systems cover various areas, such as smart cities, energy management, autonomous cars, etc. Intelligence, autonomy, and real-time monitoring are the fundamental elements that characterize these application areas. The convergence of artificial intelligence (AI) and IoT, known as AIOT, Allows those electronic devices to make intelligent, autonomous and more automatic decisions. This integration leverages the power of MAS to enable intelligent communication and collaboration among various entities, while IoT provides a vast network of interconnected sensors and devices that collect and transmit real-time data. On the other hand, AI algorithms process and analyze this data to derive valuable insights and make informed decisions. The authors devoted efforts on the critical analysis of AIOT research, highlighting specific areas with insufficient solutions and pointing out gaps for future advances. Essentially, the authors contribution is in the formulation of innovative research directions, outlining a clear guide for researchers and professionals in the expansion of knowledge in AIOT integration. Research results in a significant contribution to the continuous advance of the area, enriching the understanding of challenges and boosting the development of solutions and strategies in this technological convergence. Eleven research questions are considered at the beginning of the review, including typical research topics and application domains. From the SLR results the research directions are: (i) Development of a methodology that shows how to integrate the different applications independent of the scenarios that are deployed in. Additionally, elaboration of the tools used in the integration process; (ii) Deployment of an agent in a microprocessor; (iii) How to implement and connect Multi-agent systems (MAS) technology and Internet of Things (IoT) devices (processors, controllers, sensors, and actuators).

show abstract

“…Reference [35] and [36] employed backpropagation neural networks and convolutional neural networks, respectively, for IoT performance optimization. Reference [37] used distributed artificial intelligence for active resource allocation in IoT, the results of which suggest that high-performance resource management is achieved by merging cognitive radio with wireless sensor networks (WSNs). References [38] and [39] used simulated annealing (SA) to improve the performance of MEC and photovoltaic systems, respectively.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Latency- and Power-Aware Approach for Beyond Fifth-Generation Internet-of-Things Edge Systems

Kaushik

Al-Raweshidy

2022

IEEE Access

View full text Add to dashboard Cite

Fifth-generation (5G) empowered internet of things (IoT) edge networks suffer from latency in delay-sensitive applications. To fulfil the low latency requirements of beyond fifth-generation (B5G)-IoT applications and provide quality of service (QoS) to IoT-edge communication, it is important to minimize server delay. Furthermore, 5G-IoT systems consume more power than their predecessors, which is a concern given the growing size of future IoT networks. This research presents a hybrid latency and power-aware approach for B5G-IoT networks (HLPA B5G-IoT) that minimizes latency with minimum overhead on battery-constrained IoT nodes while simultaneously providing a power-efficient solution for B5G-IoT-edge networks. HLPA B5G-IoT has a novel algorithm classifier tool (ACT) for selecting appropriate optimization algorithms based on the characteristics and requirements of B5G-IoT systems. The ACT matrix not only parametrically compares HLPA B5G-IoT with existing approaches but also identifies crucial parameters that enable algorithm selection for load balancing and energy efficiency. In this paper, metaheuristic algorithms, i.e., biogeography-based optimization (BBO) and grey wolf optimization (GWO), are tailored to meet the requirements of load balancing and power efficiency in IoT-edge systems. The proposed load-balancing algorithm reduces latency and improves overall network performance by 33.33%, 27.45%, 23.52%, 21.56%, 13.72%, 11.76%, and 7.84% compared with simulated annealing (SA), genetic algorithm (GA), particle swarm optimization (PSO), bacteria foraging algorithm (BFA), ant colony optimization (ACO), bat algorithm (BA), and genetic SA PSO (GSP), respectively. The power-efficiency algorithm consumes 46.6%,

show abstract

Distributed Artificial Intelligence Empowered Sustainable Cognitive Radio Sensor Networks: A Smart City on-demand Perspective

Cited by 24 publications

References 34 publications

A Partial Least Squares Structural Equation Modeling of Robotics Implementation for Sustainable Building Projects: A Case in Nigeria

A Partial Least Squares Structural Equation Modeling of Robotics Implementation for Sustainable Building Projects: A Case in Nigeria

Combining Multi-Agent Systems and ArtificialIntelligence of Things: Technical Challenges andGains

A Hybrid Latency- and Power-Aware Approach for Beyond Fifth-Generation Internet-of-Things Edge Systems

Contact Info

Product

Resources

About