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Despite comprising only one per cent of global forests, mangroves provide vital ecological and economic benefits to their ecosystems. Due to its decreasing extent over the past decade, there is a rise in research innovations supporting mangrove conservation. Specifically, consumer-grade Unmanned Aerial Vehicles (UAV) were proven effective as potential remote sensing alternatives to support mangrove research and monitoring in recent studies. As most studies use custom UAV-mounted sensors for mangrove species classification, similar studies using a UAV’s default red–green–blue (RGB) cameras were scarce. This study explores the potential of high-resolution RGB aerial images through state-of-the-art object detection algorithm, YOLOv5 to detect the dominant Rhizophora mangroves in Sarawak, Malaysia. A total of 400 RGB images were equally selected from two study areas and allocated into three datasets, two corresponding to each study area and one combining all images. The annotation process was performed using a previously proposed novel method, assisted by YOLOv5 for a semi-automated annotation process with expert verification. Systematic training experiments were conducted to select an optimal epoch size across models trained with each dataset. The final models produced an average true positive rate of 73.8% and 71.7% for each study site, while the combined dataset model produced an average true positive rate of 73.7%. Overall, this study demonstrated the potential of UAV-based RGB images and deep learning object detection architectures to identify specific mangrove objects, while also highlighting key considerations for similar future research.

Devices in a visual sensor network (VSN) are mostly powered by batteries, and in such a network, energy consumption and bandwidth utilization are the most critical issues that need to be taken into consideration. The most suitable solution to such issues is to compress the captured visual data before transmission takes place. Compressive sensing (CS) has emerged as an efficient sampling mechanism for VSN. CS reduces the total amount of data to be processed such that it recreates the signal by using only fewer sampling values than that of the Nyquist rate. However, there are few open issues related to the reconstruction quality and practical implementation of CS. The current studies of CS are more concentrated on hypothetical characteristics with simulated results, rather than on the understanding the potential issues in the practical implementation of CS and its computational validation. In this paper, a low power, low cost, visual sensor platform is developed using an Arduino Due microcontroller board, XBee transmitter, and uCAM-II camera. Block compressive sensing (BCS) is implemented on the developed platform to validate the characteristics of compressive sensing in a real-world scenario. The reconstruction is performed by using the joint multi-phase decoding (JMD) framework. To the best of our knowledge, no such practical implementation using off the shelf components has yet been conducted for CS.

In this article, amultiview image compression framework, which involves the use of Block-based Compressive Sensing (BCS) and Joint Multiphase Decoding (JMD), is proposed for a Visual Sensor Network (VSN). In the proposed framework, one of the sensor nodes is configured to serve as the reference node, the others as nonreference nodes. The images are encoded independently using the BCS to produce two observed measurements that are transmitted to the host workstation. In this case, the nonreference nodes always encoded the images (INR) at a lower subrate when compared with the images from the reference nodes (IR). The idea is to improve the reconstruction of INR using IR. After the two observed measurements are received by the host workstation, they are first decoded independently, then image registration is applied to align IR onto the same plane of INR. The aligned IR is then fused with INR, using wavelets to produce the projected image IP. Subsequently, the difference between the measurements of the IP and INR is calculated. The difference is then decoded and added to IP to produce the final reconstructed INR. The simulation results show that the proposed framework is able to improve the quality of INR on average by 2dB to 3dB at lower subrates when compared with other Compressive Sensing (CS)-based multiview image compression frameworks. [ABSTRACT FROM AUTHOR]

The main problem for event gathering in wireless sensor networks (WSNs) is the restricted communication range for each node. Due to the restricted communication range and high network density, event forwarding in WSNs is very challenging and requires multihop data forwarding. Currently, the energy-efficient ant based routing (EEABR) algorithm, based on the ant colony optimization (ACO) metaheuristic, is one of the state-of-the-art energy-aware routing protocols. In this paper, we propose three improvements to the EEABR algorithm to further improve its energy efficiency. The improvements to the original EEABR are based on the following: (1) a new scheme to intelligently initialize the routing tables giving priority to neighboring nodes that simultaneously could be the destination, (2) intelligent update of routing tables in case of a node or link failure, and (3) reducing the flooding ability of ants for congestion control. The energy efficiency improvements are significant particularly for dynamic routing environments. Experimental results using the RMASE simulation environment show that the proposed method increases the energy efficiency by up to 9% and 64% in converge-cast and target-tracking scenarios, respectively, over the original EEABR without incurring a significant increase in complexity. The method is also compared and found to also outperform other swarm-based routing protocols such as sensor-driven and cost-aware ant routing (SC) and Beesensor. [ABSTRACT FROM AUTHOR]