6G Urban Innovation (eBook)

AI and Digital Twin for Next-Gen Sustainable Cities

Ashu Taneja, Abhishek Kumar, Suresh Vishnudas Limkar, Mariya Ouaissa, Mariyam Ouaissa (Herausgeber)

eBook Download: EPUB

2025
355 Seiten
Wiley-Iste (Verlag)
978-1-394-41131-3 (ISBN)

Infos

This book presents the 6G powered integration of Artificial Intelligence (AI) and Digital Twin (DT) technology for sustainable smart cities. In the context of smart cities, 6G, AI and DT hold enormous potential for transformation by boosting city infrastructure and planning, streamlining healthcare facilities, and improving transportation. 6G offers high speed and low latency seamless transfer of vast amounts of data which, when analyzed with sophisticated AI models, enhance the decision-making capabilities for smart city infrastructure and urban planning. DT technology, through continuous monitoring and virtual modeling of urban ecosystems, enables predictive maintenance for energy distribution, water management and waste management in a smart city landscape for environmental sustainability.

6G Urban Innovation covers the 6G technological innovations, trends and concerns, as well as practical challenges encountered in the implementation of AI and DT for transforming smart cities for a sustainable future.

Ashu Taneja is Associate Professor at the Centre for Research Impact and Outcome (CRIO), Chitkara University, India.

Abhishek Kumar is a Senior Member of IEEE and works as Assistant Director and Professor in the Computer Science & Engineering Department at Chandigarh University, India.

Suresh Vishnudas Limkar is Assistant Professor at the Department of Computer Science and Engineering at the Central University of Jammu, India.

Mariya Ouaissa is Professor of Cybersecurity and Networks at the Faculty of Sciences Semlalia, Cadi Ayyad University, Morocco.

Mariyam Ouaissa is Assistant Professor of Networks and Systems at ENSA, Chouaib Doukkali University, Morocco.

Textauszug

1
AI-Enabled Energy Management in Mobile Wireless Sensor Network for 6G Internet-of-Things (IoT)

Mobile wireless sensor networks (MWSNs) within the Internet-of-Things (IoT) are undergoing a significant transformation with the advent of sixth generation (6G) technology, which prioritizes low-latency, high-speed communication, and efficient energy management. This chapter explores the artificial intelligence (AI)-driven strategies that enhance the performance of 6G-enabled MWSNs while optimizing energy consumption and extending network lifespan. The roles of federated learning, deep learning and reinforcement learning are highlighted in addressing energy efficiency challenges. Additionally, innovative approaches for intelligent and sustainable energy management in next-generation wireless networks are introduced, leveraging neuromorphic computing and quantum-inspired algorithms to achieve smarter and more eco-friendly operations. The integration of edge computing and blockchain further strengthens security and data privacy while minimizing energy overhead. Future advancements in 6G MWSNs will continue to rely on AI-driven optimizations to ensure seamless, scalable, and energy-efficient network operations.

1.1. Introduction

Wireless communication and the Internet-of-Things (IoT) are witnessing rapid transformation with the advent of sixth generation (6G) technologies (Mao et al. 2020). The function of mobile wireless sensor networks (MWSNs) becomes ever more important as we move toward this next generation of connection. Comprising many tiny, independent sensors, these networks may gather and broadcast data in many scenarios. However, considering the mobility nature of these sensors and the low-latency, high-speed requirements of 6G networks, energy management becomes somewhat challenging (Maduranga et al. 2024).

This chapter delves into the challenging field of artificial intelligence (AI)-enabled energy management in MWSNs for applications in 6G IoT (Sodhro et al. 2020; Sefati et al. 2024), exploring how the use of AI and machine learning (ML) could provide methods for optimizing lifetime within such networks and reducing the levels of energy usage and increasing performance in these highly demanding systems.

By the end of this chapter, readers will be fully aware of the existing level of the art, challenges and potential routes in this crucial area of research (Alhammadi et al. 2024).

Table 1.1. Evolution of wireless sensor networks (WSNs)

Generation	Key features	Energy management approach

1st Gen

Static nodes, limited resources

Fixed duty cycling

2nd Gen

Improved hardware, longer range

Adaptive duty cycling

3rd Gen

Mobile nodes, dynamic topology

Mobility-aware protocols

4th Gen (5G)

High speed, low latency

AI-assisted optimization

5th Gen (6G)

Ultra-high speed, massive connectivity

Advanced AI/ML techniques

Table 1.1 shows WSN development throughout many generations, therefore stressing the evolving methods of energy management. The complexity of energy management rises as we move toward 6G networks, which calls for the acceptance of powerful AI and ML methods (Manogaran et al. 2021).

1.2. 6G IoT: a new frontier for MWSNs

With unheard-of speeds, ultra-low latency and huge device density, 6G technology promises to bring in a new age of communication. The IoT will be much changed by these developments, allowing a large spectrum of fresh uses and services. In this regard, MWSNs are likely to be rather important in achieving 6G IoT’s full possibilities (Zhu et al. 2023).

Expectations for 6G networks include data speeds of up to 1 Tbps, latency as low as 1 microsecond and capacity for up to ten million devices per square kilometer. Among the many fresh uses these features will allow are brain–computer connections, tactile Internet and holographic communications. As the eyes and ears of the 6G IoT ecosystem, MWSNs will be indispensable in gathering the enormous volumes of data needed to enable these uses (Dubey et al. 2023; Abbas et al. 2024).

However, the higher performance standards of 6G networks also provide major difficulties for MWSNs, especially with relation to energy usage. 6G applications’ high data rates and low latency will demand sensor nodes to process and transmit data at hitherto unheard-of rates, hence possibly fast depleting energy resources. Moreover, the great device density allowed by 6G networks will lead to higher network complexity, which emphasizes even more effective energy management (Guo et al. 2021).

Figure 1.1 shows the projected growth in IoT devices and the corresponding increase in data generation until 2030.

Figure 1.1. Projected growth of IoT devices and data generation.

This graph shows the predicted exponential expansion in data produced as well as IoT device count. Over 75 billion IoT devices are expected to be running by 2030, producing roughly 600 zettabytes of data yearly (Verma et al. 2020). This enormous scale emphasizes how urgently effective energy management in MWSNs supports the 6G IoT ecosystem (Gera et al. 2023).

The value of MWSNs to 6G IoT transcends simple data collection and dissemination. Difficult tasks such as autonomous decision-making, edge computing and cooperative sensing will fall to these systems. For smart city applications tracking public safety, air quality and traffic flow, MWSNs might be used, for instance. Apart from data collecting, the sensor nodes would localize it, make real-time decisions, and coordinate with other nodes to optimize city operations.

In healthcare environments, MWSNs might be used for remote treatment as well as for ongoing patient monitoring in hospitals (Singh et al. 2024). Wearable sensors would gather vital signs and other health-related data, analyze it at the edge and forward only pertinent information to medical professionals. This method would need advanced energy management to guarantee long-term functioning of the wearable gadgets while preserving the great dependability expected by medical uses (Lv et al. 2021).

Environmental monitoring is another area where MWSNs will be very vital in the 6G IoT ecosystem. Sensor nodes placed in polar areas, seas or forests would have to run for long stretches of time free from human interference. These nodes would gather information on pollution levels, animal migrations or climate change; so, strong energy management techniques would be necessary to live in demanding surroundings and maintain long-term data collecting capacity (Logeshwaran et al. 2024).

Integration of MWSNs with other developing technologies will increase their possibilities in the environment of the 6G IoT. For dynamic 3D sensing and network reconfiguration, for instance, the mix of MWSNs with unmanned aerial vehicles (UAVs) enables their use as mobile base stations or data mules, UAVs gather data from ground-based sensors and convey it to central processing units (Dabas et al. 2024). This hybrid method would provide new dimensions to energy management and call for plans considering the energy limitations of ground sensors and flying vehicles.

Deeper into the 6G era, the distinction between sensing and communication will keep blurring. Ideas such as ambient backscatter communications and combined radar-communication systems will become increasingly frequent (Chen and Okada 2020). Under these conditions, MWSNs will not only detect the surroundings but also use the same signals for communication, thus possibly improving the energy-efficient operations. Realizing these advantages, nevertheless, will depend on advanced energy management strategies able to accommodate the dual character of these systems (Chen et al. 2020).

Furthermore, allowing new paradigms in distributed intelligence and collaborative sensing will be the great connection promised by 6G networks. Large-scale sensing systems will be built on MWSNs, where hundreds or even millions of nodes cooperate to accomplish challenging sensing missions (Xu 2021). By spreading the burden across many nodes, this cooperative strategy might perhaps result in more energy-efficient activities. It does, however, also create fresh difficulties with regard to network coordination and energy balance (Gong et al. 2022).

Examining the following breakdown of energy consumption in a standard MWSN node shown in Figure 1.2 helps us better grasp the energy consumption trends in 6G IoT applications.

Figure 1.2. Energy consumption distribution in a 6G IoT MWSN node.

With 40% of the total energy used, Figure 1.2 shows that for MWSN nodes, communication is still the most energy-intensive activity. Sensing and movement, each using 20% of the energy, come next (Letaief et al. 2021). Processing contributes for 15%; the node uses only 5% of its energy in sleep mode. These ratios demonstrate the need for energy management strategies, emphasizing increasing mobility and communication while also improving the efficiency of sensing and processing operations (Kamruzzaman...

Produkt-Details

• Erscheint lt. Verlag: 17.9.2025
• Reihe/Serie: ISTE Invoiced
• Sprache: englisch
• Themenwelt: Technik ► Architektur
• Schlagworte: 6G • Artificial Intelligence (AI) • continuous monitoring • Digital Twin (DT) • energy distribution • Environmental sustainability • Infrastructure • smart cities • urban planning • virtual modeling • waste management • Water Management
• ISBN-10: 1-394-41131-6 / 1394411316
• ISBN-13: 978-1-394-41131-3 / 9781394411313