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Software Defined Networking for Improved Wireless Sensor Network Management: A Survey
Wireless sensor networks (WSNs) are becoming increasingly popular with the advent of the Internet of things (IoT). Various real-world applications of WSNs such as in smart grids, smart farming and smart health would require a potential deployment of thousands or maybe hundreds of thousands of sensor nodes/actuators. To ensure proper working order and network efficiency of such a network of sensor nodes, an effective WSN management system has to be integrated. However, the inherent challenges of WSNs such as sensor/actuator heterogeneity, application dependency and resource constraints have led to challenges in implementing effective traditional WSN management. This difficulty in management increases as the WSN becomes larger. Software Defined Networking (SDN) provides a promising solution in flexible management WSNs by allowing the separation of the control logic from the sensor nodes/actuators. The advantage with this SDN-based management in WSNs is that it enables centralized control of the entire WSN making it simpler to deploy network-wide management protocols and applications on demand. This paper highlights some of the recent work on traditional WSN management in brief and reviews SDN-based management techniques for WSNs in greater detail while drawing attention to the advantages that SDN brings to traditional WSN management. This paper also investigates open research challenges in coming up with mechanisms for flexible and easier SDN-based WSN configuration and management.
Journal Article
Efficient network management and security in 5G enabled internet of things using deep learning algorithms
The rise of fifth generation (5G) networks and the proliferation of internet-of-things (IoT) devices have created new opportunities for innovation and increased connectivity. However, this growth has also brought forth several challenges related to network management and security. Based on the review of literature it has been identified that majority of existing research work are limited to either addressing the network management issue or security concerns. In this paper, the proposed work has presented an integrated framework to address both network management and security concerns in 5G internet-of-things (IoT) network using a deep learning algorithm. Firstly, a joint approach of attention mechanism and long short-term memory (LSTM) model is proposed to forecast network traffic and optimization of network resources in a, service-based and user-oriented manner. The second contribution is development of reliable network attack detection system using autoencoder mechanism. Finally, a contextual model of 5G-IoT is discussed to demonstrate the scope of the proposed models quantifying the network behavior to drive predictive decision making in network resources and attack detection with performance guarantees. The experiments are conducted with respect to various statistical error analysis and other performance indicators to assess prediction capability of both traffic forecasting and attack detection model.
Journal Article
Self-Diagnostic Advanced Metering Infrastructure Based on Power-Line Communication: A Study Case in Spanish Low-Voltage Distribution Networks
The transformation of low-voltage distribution grids toward decentralized, user-centric models has increased the need for advanced metering infrastructures capable of ensuring both visibility and control. This paper presents a self-diagnostic advanced metering solution based on power-line communication deployed in a segment of the Spanish distribution network. The proposed infrastructure leverages the existing power network as a shared-media communication channel, reducing capital expenditures while enhancing system observability. A methodology is introduced for integrating smart metering data with topological and operational analytics to improve network monitoring and energy management. This study details the proposed metering infrastructure, highlighting its role in enhancing distribution network resilience through asynchronous energy measurements, event-driven analytics, and dynamic grid management strategies. The self-diagnostic module enables the detection of non-technical losses, identification of congested areas, and monitoring of network assets. Furthermore, this paper discusses the regulatory and technological challenges associated with scaling metering solutions, particularly in the context of increasing distributed energy resource penetration and evolving European Union regulatory frameworks. The findings demonstrate that a well-integrated advanced metering infrastructure system significantly improves distribution network efficiency, enabling proactive congestion detection and advanced load management techniques. However, this study also emphasizes the limitations of PLC in high-noise environments and proposes enhancements such as hybrid communication approaches to improve reliability and real-time performance. The insights provided contribute to the ongoing evolution of metering infrastructure technologies, offering a path toward more efficient and resource-optimized smart grids.
Journal Article
