Dynamic and Adaptive Scheduling of Cognitive Sensors for collaborative target tracking in energy-efficient IOT environments

초록

In the digital age of the Internet of Things (IoT), there is a significant shift from traditional computing to an ubiquitous, highly connected, and automated world that provides services to anyone, anywhere, and at any time. An IoT-based system is an unstable network characterized by fluctuating dynamics and fragile connections, resulting in lower performance on congestion, latency, and energy consumption. To effectively manage these functional parameters and implement a dynamic scheduling mechanism, this paper presents a novel scheme, "Dynamic and Adaptive Scheduling of Cognitive Sensors (DASCS) for Collaborative Target Tracking in Energy-Efficient IoT Environments". In this approach, cognitive sensors dynamically schedule their functions according to their role in the wireless mesh grid and adapt to new states by checking the network traffic conditions. Its dual goals involve reducing network traffic to significantly decrease energy consumption and enhancing network performance by equally distributing energy resources throughout the grid. Furthermore, it works in object detection and monitors the direction of movement within the IoT environment. DASCS improves network performance by increasing packet delivery ratios by 2.31% at the base station and 27.92% at the cluster head, while adding more live sensors, it improves network stability by 38.46%. DASCS also enhances energy efficiency by increasing the average residual energy by 68.8% compared to other benchmark schemes while maintaining a high event detection rate and a low false alarm rate.

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