A hybrid data collection scheme to achieve load balancing for underwater sensor networks

Underwater wireless sensor networks possess considerable potential to monitor large and hostile underwater environments by reliably sensing, collecting, and forwarding data toward the surface sinks. Although the research community has made promising efforts, barriers such as continuous node mobility, longer delays, unavailability of location information, and energy limitations must be addressed. Taking this into account, this research aims to develop a hybrid and intelligent data collection scheme that considers node position and network characteristics during data forwarding. To accomplish the objective, the network is divided into two layers. The top layer, considered more dynamic, follows a hop-by-hop data forwarding scheme. The lower layer, experiencing stable water currents, follows a clustering-based data collection method. The proposed scheme, called Multilayer Dynamic Data Forwarding (MD2F), is suitable for large and deep underwater areas. MD2F is scalable as it uses a multi-sink architecture, while single or multiple autonomous underwater vehicles (AUVs) can be utilized depending on the area being monitored. Implementing hop-by-hop transmission and clustering-based data collection at different layers balances the network load, thereby increasing the network life. Results show that MD2F exhibits better performance when compared with Multilayer Cluster-based Energy Efficient (MLCEE) and Energy efficient and link reliable routing (E2LR) schemes, both are very close in working behavior. The results are encouraging in terms of delivery ratio, network throughput, and end-to-end delays. Alongside achieving these targets, the network also exhibits less energy consumption through load balancing

Agriculture internet of things: AG-IoT

International audienceThe Internet of Things (IoT) for agriculture is a rapidly emerging technology where seamless connected sensors device make it possible to monitor and control crop parameters to get quality and quantity of food. This research proposes a new dynamic clustering and data gathering scheme for harnessing the IoT in agriculture. In this paper, an Unmanned Aerial Vehicle (UAV) is used to locate and assist ground IoT devices to form themselves in cluster formation then establishes a reliable uplink communication backbone for data transmission. Use of multifrequency, multi power transmission, and mobile sink make it possible to reduce power utilization of IoT devices as much as possible. The proposed scheme is evaluated by using simulation models and practical experiments. It is found working outclass as compare to all existing systems

Wireless Sensor’s Civil Applications, Prototypes, and Future Integration Possibilities: A Review

International audienceAdvances in wireless communication are forging new possibilities for sensors. New sensors are equipping major systems around us with unparalleled intelligence as in the case of smart grids, smart homes, and driverless vehicles. Considering the current developments in the field of sensor networks, one feels that it has reached an interesting stage, where the role of the sensors becoming crucial in numerous applications. This all speaks volumes of the fact that sensors are going to be at the front and center of most of future technologies, needless to say the Internet of Things. Considering their vital role from futuristic perspective this survey reports variety of sensors along with their characteristics and applications, which impact human life and well-being. In addition, this survey considers recent prototypes, leading sensor manufacturers as well as major projects that have made use of sensors since the last decade. Moreover, significance of this effort is that integration possibilities of sensors with other networks and major technologies are discussed, while possible challenges and key benefits are highlighted. This research effort focuses the latest developments in the area of sensors and sensor networks as research gears up to meet the challenges of the emerging technologies and their applications particularly those that emphasize smart sensors

Direction of Arrival of Narrowband Signals Based on Virtual Phased Antennas

International audienceData collection from field sensors by using Unmanned Aerial Vehicle (UAV) is the application taken in consideration in this paper. All the sensor nodes are kept location unaware to reduce their cost and energy utilization. The issue addressed in this paper is localization of sensor nodes by UAV to collect data in efficient way. ULA of multiple antennas are used to measure the Angle of Arrival (AoA) of incoming signals. However, the drawbacks of mounting such multiple antennas on an Unmanned Aerial Vehicle (UAV) outweigh the benefits. The challenge is to affix multiple antennas and receivers on an UAV, increase its weight which ultimately decrease its payload capacity, flight time, speed and agility. In this paper, we are proposing a new method to estimate the AoA, called Virtual Phase Array (VPA) antenna system. A single moving antenna installed over an UAV taking snapshots every fixed time periods forms an antenna array virtually. This VPA has enable us to introduce two new concepts of adaptive staring precision and multiple frequency use. All these became reality only because number and spacing between antenna elements can be adjusted, which is not easy to implement in physical antenna array especially when antenna is onboard. The proposed system is evaluated by simulation model. Suggested modifications and additions in classical MUSIC algorithm make it possible to operate the virtual antenna system with the same precision as the physical antenna may have, but adding more flexibility, ease of use, cost economy, more reliability and better throughput

Cloud-connected flying edge computing for smart agriculture

International audienceDue to recent advancements and the success of versatile mobile applications, more and more services around the globe are being moved to the cloud. As a result, its limitations have become more evident. The major issues that cloud-based applications face include large latency, bottlenecks because of central processing, compromised security, and lack of offline processing. The drawbacks of cloud computing are reduced by fog and edge computing, where data are processed near the places where it is generated—at network edges or fog nodes—most importantly in a distributed way. Smart agriculture is an approach based on the Internet of Things (IoT) where cloud computing is not an option as the internet is usually not available at remote sites. In addition, pure edge computing also is not practical, as most sensor nodes are very small and they do not have enough computing power. Intermediate fog computing also is not a good choice, as fixed fog nodes (getaway nodes) do not work well with high node fluctuation caused by bad weather or harsh conditions. Considering these issues and limitations, we have proposed the idea of flying edge computing where an unmanned aerial vehicle (UAV) acts as an edge-computing machine. This can be an ideal solution for smart agriculture, given the size and remoteness of many agricultural areas. This technique can be called “wind or breeze computing” because the data are “blown” or moved by the current of computing. The Flying-Edge offers fast deployment of edge facilities in challenging locations and it can be a major step to accomplish the goal of IoT-based smart agriculture

UAV-Assisted Dynamic Clustering of Wireless Sensor Networks for Crop Health Monitoring

In this study, a crop health monitoring system is developed by using state of the art technologies including wireless sensors and Unmanned Aerial Vehicles (UAVs). Conventionally data is collected from sensor nodes either by fixed base stations or mobile sinks. Mobile sinks are considered a better choice nowadays due to their improved network coverage and energy utilization. Usually, the mobile sink is used in two ways: either it goes for random walk to find the scattered nodes and collect data, or follows a pre-defined path established by the ground network/clusters. Neither of these options is suitable in our scenario due to the factors like dynamic data collection, the strict targeted area required to be scanned, unavailability of a large number of nodes, dynamic path of the UAV, and most importantly, none of these are known in advance. The contribution of this paper is the formation of dynamic runtime clusters of field sensors by considering the above mentioned factors. Furthermore a mechanism (Bayesian classifier) is defined to select best node as cluster head. The proposed system is validated through simulation results, lab and infield experiments using concept devices. The obtained results are encouraging, especially in terms of deployment time, energy, efficiency, throughput and ease of use

Affordable Broad Agile Farming System for Rural and Remote Area

International audienceWe develop a fast-deployed crop health monitoring system using state-of-the-art technologies to collect data from crop fields in order to take appropriate and timely actions. For the proposed resource optimized system, Saudi Arabian agriculture is taken as a case study. To achieve the desire goals, we harness IoT (Internet of Things) and drones in agriculture to establish rapid system deployment. This paper focuses on the data collection from crop field by organizing heterogeneous IoT devices in clusters and localise them for data harvesting. Clusters are formed by considering the path of UAVs, sensors heterogeneity, weather conditions, fluctuation of sensor nodes, and the communication cost of IoT devices. For localisation, carrying larger or heavier arrays of antennas and receivers with a small size UAV is also a major issue considered in this paper. Hence, we introduce a dynamic clustering and virtual antenna array to develop a complete data collection scheme supported by simulations and experimental tests with proof-of-concept devices. The results are analysed and found promising in terms of energy efficiency, throughput, ease of use, and deployment time. Whole the system is developed with the concept that it can install in rural and remote area with minimum deployment time and agile enough that can collect data in worst conditions (bad weather, hostile environment, fluctuating nodes, poor infrastructure, with or without an established network). In broader sense it can map easily in many similar applications where data is needed to be harvested from a wide range of heterogeneous sensors without existing any infrastructure and ground topology

Sustainable seawater desalination: Current status, environmental implications and future expectations

International audienceDesalination is the process of making the saltwater of the Earth's oceans drinkable and could be a solution to droughts, especially in warmer and drier regions. However, the concerns of high cost and ecosystem degradation have always restricted the growth of the desalination industry and necessitated further studies and corrective actions. As desalination is becoming essential for our planet, the development of sustainable and energy-efficient technologies to produce clean water is a key research topic. Considering the worth of seawater desalination to fulfill the needs of the Earth habitants, this article explores how this process can be made less problematic to use as a real solution, not just a temporary opportunity. Desalination methods will play a key role, so all the major options and their environmental footprints are discussed, considering the energy requirements in particular of each process. A brief but up-to-date summary of the current status and future trends of desalination technologies is provided. Available equipment and procedures that are being developed to make desalination a sustainable process are explored. Finally, based on a detailed review, we highlight the future trends and issues to make informed decisions pertaining to the future research and development projects of this sector

HLASwin-T-ACoat-Net Based Underwater Object Detection

Due to the limited light penetration in underwater environments, sonar equipment plays a crucial role in various commercial and military operations. However, underwater images often suffer from degradation due to scattering and absorption phenomena, resulting in poor visibility of submerged objects. To address this challenge, image enhancement techniques are essential for enhancing the appearance and visibility of underwater objects. This research proposes a novel approach called HLAST-ACNet, which combines the advantages of a hybrid Local Acuity Swin Transformer and an Adapted Coat-Net for Underwater Object Detection (UOD). The HLASwin-T-ACoat-Net leverages Contrast Limited Adaptive Histogram Equalization (CLAHE) to increase the quality of images. Additionally, it incorporates a path aggregation network to integrate deep and shallow feature maps and utilizes online complicated example mining to improve training efficiency. Furthermore, the algorithm improves Region of Interest (ROI) pooling by introducing ROI alignment, which mitigates quantization errors and enhances object detection accuracy. Compared to existing algorithms, the algorithms based on HLASTACNet demonstrate significant improvements in the URPC2018 and OUC datasets, achieving precision rates of 91.25% and 92.36%, respectively. The research model has a higher computational complexity than four existing methods, as evidenced by its GFLOPs, per-image processing time with a speed of 20ms, and the FPS measures for average processed frames per second reaching 2.28s. The research model effectively addressed the challenges and false detection with varying sizes of objects in complicated underwater environments