A Wireless Sensor Networks for IoT and 6G: An Analysis of Intelligence and Energy Efficiency

Abstract

Wireless Sensor Networks (WSNs) have emerged as a core technology for making intelligent environments available in healthcare monitoring, smart cities, industrial automation, and environmental monitoring applications. Increasing pressure for low-power, autonomous sensing systems and the explosive growth in Internet-of-Things (IoT) connected devices with corresponding pressure to improve the energy efficiency of WSNs make WSN architectures smarter and more energy-friendly. In addition, the development of next-generation paradigms of communication like 6G imposes new challenges as well as prospects for WSN integration.Intelligent and efficient WSN is the goal to be achieved while this survey takes into consideration innovation in system structure, communication, energy harvesting technology, and adoption of artificial intelligence and machine learning at the edge. The aim is to synthesize and critically review the state-of-the-art currently and determine gaps and possible areas of future work. Major challenges like energy limitations, secure data communication, interoperability, and scalability are examined in relation to contemporary application requirements.The originality of this work is that it has a holistic and futuristic approach, in which WSNs are studied not only in conventional applications but also in new areas motivated by smart decision-making and cross-layer optimization techniques. Moreover, this survey addresses the integration of WSNs with blockchain, edge computing, and green energy systems, providing insights into how these technologies together determine the future of autonomous sensor networks in the IoT and 6G era.

Introduction

1. Introduction & Motivation

Wireless Sensor Networks (WSNs) are foundational to the Internet of Things (IoT), used in areas like healthcare, agriculture, smart cities, and environmental monitoring. A key challenge in WSNs is energy efficiency, due to limited battery capacity in sensor nodes.

To support long-term deployment, innovations are focusing on:

• Energy-efficient hardware and protocols

• Energy harvesting (solar, thermal, vibration)

• AI/ML for adaptive and intelligent control

• 6G integration for ultra-low latency and scalability

2. Challenges in WSN Design

• WSNs operate in remote or harsh environments.

• Battery replacement is often not feasible.

• The demand for low-power, high-performance, and scalable systems increases with the IoT and 6G expansion.

3. Energy-Efficient Routing Protocols

Efficient data transmission is essential for conserving energy and extending network life.

Types of protocols:

• Proactive (e.g., DSDV, OLSR): Fast delivery but energy-intensive.

• Reactive (e.g., AODV, DSR): Energy-saving but higher latency.

• Hybrid (e.g., HEED): Combines both for balanced performance.

Clustering-based routing:

• Nodes are grouped; cluster heads transmit aggregated data.

• Protocols like LEACH distribute energy load efficiently but face scalability issues.

• Multi-hop and hierarchical clustering improve efficiency further.

4. Energy Harvesting Techniques

Essential for sustainable WSNs in inaccessible areas.

• Solar energy is the most reliable source, enhanced by better panels and storage.

• Thermal and vibration energy (via TEGs and piezoelectric devices) are less efficient but useful as supplements.

5. AI and ML Integration

AI/ML enhances WSN adaptability, efficiency, and intelligence.

• Energy optimization: Techniques like reinforcement learning adjust power levels dynamically.

• Smart data aggregation: ML reduces redundant transmissions, forecasts network behavior, and tunes parameters proactively.

6. Security via Blockchain

WSNs are vulnerable to attacks (e.g., eavesdropping, spoofing).

• Blockchain technology enhances security through:

  • Decentralized authentication

  • Tamper-evident data logging

  • Secure data aggregation

Conclusion

The future uses are being combined with future technologies like 6G, edge computing, and IoT. With ultra-low latency and high speed enabled by 6G networks, they will be a perfect facilitator of high-performance massive-scale WSNs. The combination with edge computing will also facilitate near-data processing, hence minimizing communication overhead and energy efficiency [24]. In this survey of literature, we have presented the trends and development in energy-efficient Wireless Sensor Networks (WSNs) for IoT and 6G networks. The survey highlights some of the most important strategies including energy-efficient routing mechanisms, data aggregation mechanisms, and security communication mechanisms. Some major research pieces by Gopinath et al. [24], Zhang and Wang [29], and Kumari et al. [30] all pointed towards the importance of energy management in WSNs in maximizing the lifetime, reliability, and scalability. Different new techniques have been devised, including federated learning-based energy optimization [24], fault tolerance using reinforcement learning [33], and secure data aggregation with blockchain [29]. These techniques help address some of the current issues of data security, network lifespan, and rising complexity of IoT-based WSN. Further, the blending of Artificial Intelligence (AI), machine learning, and metaheuristic algorithms has been remarkable in boosting the flexibility and performance of WSNs. Rajendran and Kumar\'s study [24], for example, looks into how much 6G-integrated WSNs can use such intelligent systems to get optimal performance in various applications. Aside from this, future developments in AI-based clustering, data gathering using UAVs [30], and AI-based intrusion detection [24] are yet to redefine the role of WSNs in complex environments like smart cities, health, and the military. Apart from this, there are some issues that are still pending to be solved with precise context to energy optimization, security in aggregation of data, and integration with emerging wireless technologies.With the onset of 6G and onward, WSNs are becoming much bigger, and therefore there is an urgent need to devise more efficient, scalable, and powerful methods able to manage the ginormous quantity of devices in the network. R&D has to continue to hone such methods, making them more energy efficient with hybrid protocols, and closing security loopholes through more sophisticated cryptography protocols like homomorphic encryption [24]. Moreover, fault-tolerant systems and secure data transfer are of top priority, especially for dynamic and heterogeneous networks such as disaster relief and military applications [32]. A. Future Directions 1) AI-Optimization: As the sophistication in AI and machine learning deepens, there are potential areas to enhance energy efficiency for WSN using this domain. Inclusion of deep models towards anticipating trends of energy consumption as well as real-time aggregation and routing optimization may be a field opened by potential future work. More study involving the implementation of clustering algorithms within AI to handle adaptability against change in the status of the network may be what enhances WSN\'s energy efficiency. 2) 6G and Beyond: With the world progressing towards 6G, WSNs will be faced with new challenges like connectivity, data rate, and interconnecting an unprecedented level of IoT devices. The work must be focused on the integrated integration of WSNs within 6G networks for ultra-low latency, high reliability, and low energy consumption. Literature [24] suggests that AI-based integration of 6G will be the driving force. 3) Advanced Security Mechanisms: As the need for data aggregation security grows, promising cryptographic techniques such as homomorphic encryption [24] have to ensure data privacy without being energy consumption. Future work can include using advanced cryptography protocols using blockchain to ensure security for data streams in large-scale sensor networks, especially for mission-sensitive applications such as the military and health care. 4) Energy Harvesting Technologies: Technology research would be focused more on the establishment of energy harvesting technologies that have the ability to power WSNs independently in remote and harsh environments. Power management strategies together with energy harvesting technologies would prolong sensor node lifetime to a high extent without recharge or maintenance.Fog and Edge Computing Convergence: Future research can investigate the convergence of edge computing and fog computing with WSNs to enhance data processing and decision-making capabilities at the network edge. It can alleviate communication overhead and WSN wastage by offloading computationally expensive tasks to local processing units to enable real-time data processing.

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Copyright

Copyright © 2025 Sahana S, Raksitha R, Mohana Priya S, Pranitha B. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.