Search Results (47)

Numerous protocols have emerged to address the energy depletion problem in Wireless Sensor Networks (WSNs). Among these protocols, the Dynamic Multi-Hop Routing (DMR) protocol adopts a dynamic technique for routing data across the network. The use of the DMR protocol has shown promising results in reducing energy consumption, prolonging the network lifetime, and increasing throughput. To improve the performance of WSNs, this paper proposes the Enhanced Dynamic Multi-Hop Routing (EDMR) protocol as a modification of the DMR protocol. The EDMR protocol introduces an effective sleeping mechanism that selectively deactivates clusters that do not generate significantly updated data for a specific duration. This mechanism reduces redundant transmissions, thereby saving energy and prolonging the network lifetime. The EDMR protocol incorporates static and dynamic approaches to support two major categories of applications: monitoring and event-driven applications. The proposed protocol is evaluated against the DMR protocol, the Enhanced Dynamic Multi-Hop Technique (EMDHT-LEACH) protocol, and the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol. The simulation results demonstrate that the EDMR protocol mitigates energy depletion, extends the network lifetime, increases stability, and improves network throughput toward the Base Station (BS), while reducing packet redundancy compared with the other protocols. Full article

Timely status updates are essential for Internet of Things (IoT) services. The freshness of these updates can be quantified using Age of Information (AoI). The worst-case behavior of AoI is evaluated by peak AoI (PAoI), denoting the maximum AoI just before each successful update. To characterize the time-averaged evolution of the PAoI over a long time horizon, we adopt the average PAoI as a performance metric. In this paper, we consider a two-hop status update system in IoT monitoring networks, where sensors periodically transmit short status packets to a remote edge server via a sink node. The sink node encodes status packets received from multiple sensors into a single longer packet to enhance the transmission reliability of short-packet communications. Here, we analyze the average PAoI in this setup as a function of system parameters and minimize this function by jointly optimizing three key parameters: (i) the number of status packets for joint coding at the sink node, (ii) the blocklength of a status packet in the first hop, and (iii) the blocklength of a coded packet in the second hop. Through numerical studies, we demonstrate the effectiveness of the proposed optimization in reducing the average PAoI. Full article

This paper contributes a two-step approach to monitor clusters of thermal targets on the ground using unmanned aerial vehicles (UAVs) and Gaussian mixture models (GMMs) in a distributed manner. The approach is tailored to networks of UAVs that establish a flying ad hoc network (FANET) and operate without central command. The first step is a monitoring algorithm that determines if the GMM corresponds to the current spatial distribution of clusters of thermal targets on the ground. UAVs make this determination using local data and a sequence of data exchanges with UAVs that are one-hop neighbors in the FANET. The second step is the calculation of a new GMM when the current GMM is found to be unfit, i.e., the GMM no longer corresponds to the new distribution of clusters on the ground due to the movement of thermal targets. A distributed expectation-maximization algorithm is developed for this purpose, and it operates on local data and data exchanged with one-hop neighbors only. Simulation results evaluate the performance of both algorithms in terms of the number of communication exchanges. This evaluation is completed for an increasing number of clusters of thermal targets and an increasing number of UAVs. The performance is compared with well-known solutions to the monitoring and GMM calculation problems, demonstrating convergence with a lower number of communication exchanges. Full article

Hop (Humulus lupulus L.) is a climbing plant that contains essential components for beer production. Although Brazil is the third-largest beer producer in the world, it still relies on imports to meet demand. Some hop varieties have already adapted to the tropical climate, but nitrogen fertilization is essential for the proper development of plants. Digital image processing (DIP) and modeling technologies are emerging as fast and economical alternatives for monitoring the nutritional status of plants. This study evaluated the impact of image quality and the performance of models in classifying hop plants in terms of nitrogen concentration, using predictors extracted from leaf images. A total of 24 plants subjected to six levels of fertilization, ranging from 0 to 200% of the optimal level, were analyzed. The leaves were classified into two nitrogen concentration groups and the data organized into two sets: one containing only significant variables and another including all the variables in the model. Linear discriminant analysis (LDA) and quadratic discriminant analysis (QDA) models were estimated. The QDA models demonstrated great efficacy in classifying plants with a high nitrogen concentration, achieving over 80% accuracy, although performance was lower for plants with a lower nitrogen concentration. Full article

Monitoring data outliers comprises isolated mode outliers, cluster mode outliers, and normal points. To identify and distinguish the data hopping problems caused by outliers and environmental mutations in the displacement monitoring data of concrete AMS, this paper proposes a method based on wavelet transform, DBSCAN clustering algorithm combined with isolated forest and reinforcement learning algorithm to identify outliers in concrete dam monitoring data. In this paper, the trend line of measuring point data are extracted by the wavelet transform algorithm, and the residual data are obtained by subtracting it from the original process line. Subsequently, the DBSCAN clustering algorithm divides the residual data according to density. Therewith, the outlier scores of different data clusters are calculated, the iterative Q values are updated, and the threshold values are set. The data exceeding the threshold are finally marked as outliers. Finally, the water level and displacement data were compared by drawing the trend to ensure that the water level change did not cause the final identified concrete dam displacement data outliers. The results of the example analysis show that compared with the other two outlier detection methods (“Wavelet transform combined with DBSCAN clustering” or “W-D method”, “Wavelet transform combined with isolated forest method” or “W-IF method”). The method has the lowest error rate and the highest precision rate, recall rate, and F1 score. The error rate, precision rate, recall rate, and F1 score were 0.0036, 0.870, 1.000, and 0.931, respectively. This method can effectively identify data jumps caused by an environmental mutation in deformation monitoring data, significantly improve the accuracy of outlier identification, reduce the misjudgement rate of outliers, and have the highest detection accuracy. Full article

Broad-spectrum crop protection technologies, such as abamectin and bifenthrin, are globally relied upon to curb the existential threats from economic crop pests such as the generalist herbivore Tetranychus urticae Koch (TSSM). However, the rising cost of discovering and registering new acaricides, particularly for specialty crops, along with the increasing risk of pesticide resistance development, underscores the urgent need to preserve the efficacy of currently registered acaricides. This study examined the overall genetic mechanism underlying adaptation to abamectin and bifenthrin in T. urticae populations from commercial hop fields in the Pacific Northwestern region of the USA. A transcriptomic study was conducted using four populations (susceptible, abamectin-resistant, and two bifenthrin-resistant populations). Differential gene expression analysis revealed a notable disparity, with significantly more downregulated genes than upregulated genes in both resistant populations. Gene ontology enrichment analysis revealed a striking consistency among all three resistant populations, with downregulated genes predominately associated with chitin metabolism. In contrast, upregulated genes in the resistant populations were linked to biological processes, such as peptidase activity and oxidoreductase activity. Proteolytic activity by peptidase enzymes in abamectin- and bifenthrin-resistant TSSM populations may suggest their involvement in acaricide metabolism. These findings provide valuable insights into the molecular mechanisms underlying acaricide resistance in the TSSM. This knowledge can be utilized to develop innovative pesticides and molecular diagnostic tools for effectively monitoring and managing resistant TSSM populations. Full article

Underwater wireless sensor networks have a wide range of application prospects in important fields such as ocean exploration and underwater environment monitoring. However, the influence of complex underwater environments makes underwater wireless sensor networks subject to many limitations, such as resource limitation, channel openness, malicious attacks, and other problems. To address the above issues, we propose a routing scheme for underwater wireless networks based on a trust model and Void-Avoided algorithm. The proposed scheme establishes a trust model, evaluates the behavior of underwater nodes through direct trust, indirect trust, and environmental trust, and finds malicious nodes while taking into account evaluation of the channel, which provides support for the next data transmission event. The proposed scheme prioritizes the total cabling distance and introduces a two-hop availability checking model for data transmission, checking the nodes for voids and avoiding the void areas, to find the transmission path with the lowest energy consumption and lowest latency as much as possible. In this study, simulation experiments were conducted on the proposed scheme, and the results showed that the target scheme can effectively detect malicious nodes through anomalous behaviors and outperforms existing work in terms of malicious node detection rate, energy consumption, and end-to-end latency, and network performance. Full article

In this paper, we demonstrate the realistic test of a 2.4 GHz multi-hop wireless network for mountainous forest and watercourse environments. A multi-hop network using IEEE 802.15.4 XBee3 micro-modules and a communication protocol among nodes were developed. A wireless node deployment solution was introduced for practical testing. The proposed system’s communication reliability was tested in two different scenarios: a mountainous forest with sloping areas and trees and a watercourse, which referred to environmental and flooding monitoring applications. Wireless network performances were evaluated through the received signal strength indicator (RSSI) level of each wireless link, a packet delivery ratio (PDR), as the successful rate of packet transmission, and the end-to-end delay (ETED) of all data packets from the transmitter to the receiver. The experimental results demonstrate the success of the multi-hop WSN deployment and communication in both scenarios, where the RSSI of each link was kept at the accepted level and the PDR achieved the highest result. Furthermore, as a real-time response, the data from the source could be sent to the sink with a small ETED. Full article

Accurate localization of devices within Internet of Things (IoT) networks is driven by the emergence of novel applications that require context awareness to improve operational efficiency, resource management, automation, and safety in industry and smart cities. With the Integrated Localization and Communication (ILAC) functionality, IoT devices can simultaneously exchange data and determine their position in space, resulting in maximized resource utilization with reduced deployment and operational costs. Localization capability in challenging scenarios, including harsh environments with complex geometry and obstacles, can be provided with robust, reliable, and energy-efficient communication protocols able to combat impairments caused by interference and multipath, such as the IEEE 802.15.4 Time-Slotted Channel Hopping (TSCH) protocol. This paper presents an enhancement of the TSCH protocol that integrates localization functionality along with communication, improving the protocol’s operational capabilities and setting a baseline for monitoring, automation, and interaction within IoT setups in physical environments. A novel approach is proposed to incorporate a hybrid localization by integrating Direction of Arrival (DoA) estimation and Multi-Carrier Phase Difference (MCPD) ranging methods for providing DoA and distance estimates with each transmitted packet. With the proposed enhancement, a single node can determine the location of its neighboring nodes without significantly affecting the reliability of communication and the efficiency of the network. The feasibility and effectiveness of the proposed approach are validated in a real scenario in an office building using low-cost proprietary devices, and the software incorporating the solution is provided. The experimental evaluation results show that a node positioned in the center of the room successfully estimates both the DoA and the distance to each neighboring node. The proposed hybrid localization algorithm demonstrates an accuracy of a few tens of centimeters in a two-dimensional space. Full article

With the development of the Internet of Things (IoT) technology, massive amounts of sensor data in applications such as fire monitoring need to be transmitted to edge servers for timely processing. However, there is an energy-hole phenomenon in transmitting data only through terrestrial multi-hop networks. In this study, we focus on the data collection task in an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) network, where a UAV is deployed as the mobile data collector for the ground sensor nodes (SNs) to ensure high information freshness. Meanwhile, the UAV is equipped with an edge server for data caching. We first establish a rigorous mathematical model in which the age of information (AoI) is used as a measure of information freshness, related to both the data collection time and the UAV’s flight time. Then a mixed-integer non-convex optimization problem is formulated to minimize the peak AoI of the collected data. To solve the problem efficiently, we propose an iterative two-step algorithm named the AoI-minimized association and trajectory planning (AoI-MATP) algorithm. In each iteration, the optimal SN–collection point (CP) associations and CP locations for the parameter $\epsilon$ are first obtained by the affinity propagation clustering algorithm. The optimal UAV trajectory is found using an improved elite genetic algorithm. Simulation results show that based on the optimized $\epsilon$, the AoI-MATP algorithm can achieve a balance between data collection time and flight time, reducing the peak AoI of the collected data. Full article

The AQUASENSE project is a multi-site Innovative Training Network (ITN) that focuses on water and food quality monitoring by using Internet of Things (IoT) technologies. This paper presents the communication system suitable for supporting the pollution scenarios examined in the AQUASENSE project. The proposed system is designed and developed in the SimuLTE/OMNeT++ simulation for simulating an LTE network infrastructure connecting the Wireless Sensors Network (WSN) with a remote server, where data are collected. In this frame, two network topologies are studied: Scenario A, a single-hop (one-tier) network, which represents a multi-cell network where multiple sensors are associated with different base stations, sending water measurements to the remote server through them, and Scenario B, a two-tier network, which is again a multi-cell network, but this time, multiple sensors are associated to local aggregators, which first collect and aggregate the measurements and then send them to the remote server through the LTE base stations. For these topologies, from the network perspective, delay and goodput parameters are studied as representative performance indices in two conditions: (i) periodic monitoring, where the data are transmitted to the server at larger intervals (every 1 or 2 s), and (ii) alarm monitoring, where the data are transmitted more often (every 0.5 or 1 s); and by varying the number of sensors to demonstrate the scalability of the different approaches. Full article

In order to achieve more effective online monitoring of distribution cables, a deployment scheme of the distribution cable condition monitoring devices based on a wireless sensor network (WSN) has been proposed. The proposed deployment scheme can improve the coverage rate and extend the lifetime of the sensor network. Moreover, the clustering method, node density, and node deployment method of the wireless sensor routing algorithm are improved, and based on isosceles triangle non-uniform deployment, a routing optimization algorithm has been proposed to achieve the balance of the energy consumption of each node in the network. Firstly, according to the energy consumption model of the network, the energy consumption of each cluster is calculated, and then by taking the minimum energy consumption of data transmission between clusters as the constraint condition, the optimal clustering distance of the network is solved. Then, according to the scale of network nodes, the density of routing nodes in each cluster is calculated, and the routing nodes in the cluster are deployed by an isosceles triangle. Finally, according to the cluster head election formula, the routing cluster head nodes in each cluster are selected, and the network data information is transmitted to the base station in a multi-hop manner through the routing cluster head nodes. The simulation results indicated that, compared with the traditional routing optimization algorithms, the proposed algorithm could keep the routing coverage at more than 200% all the time, and can effectively balance the energy consumption of nodes, improve the energy utilization efficiency of the routing nodes, and at least extend the lifetime of the network by two times. Moreover, the longer the cables, the more cost savings and the better the life cycle optimization effect of the proposed scheme. In addition, the proposed scheme can greatly reduce the economic cost of network investment, compared with using the demodulator to obtain monitoring data. Full article

The interest in the production of hops in Brazil, motivated by the third position in the world ranking of beer producers and the growth of the craft brewery business, justifies the intensification of studies into its adaptation to local growing conditions. Due to the high internal demand, the aim of this study was to evaluate the phytochemical profiles of hop varieties grown in subtropical conditions under different cropping systems. Studies that promote the expansion of cultivation areas in distinct climate conditions and ensure quality are very important. A randomized block design was adopted with a 2 × 5 subdivided plot. The main factor was the cropping system (organic and conventional), and the secondary factor was the hop variety (Columbus, Chinook, Nugget, Cascade and Hallertau Mittelfrüeh), with four blocks and four plants per plot. The quality parameters monitored in this work were the contents of alpha and beta acids, and xanthohumol in the inflorescences of hops, as well as the relative composition of their essential oils. The variations in the chemical profiles of essential oils showed differences between some varieties, and the different compositions and levels resulting from the two cropping systems show that management and cultural practices can influence the aromatic characteristics of hops; in total, 23 compounds were found. The terpene fraction represented 79.67% of the oil in Hallertau and 93.63% in Cascade, with myrcene being the main compound. The levels of bitter acids and xanthohumol did not differ statistically as a function of the treatments. This study contributes the first records of the chemical profiles of hops grown in subtropical conditions in Brazil, in general, the Nugget variety had the highest qualitative potential Full article

Several industries use wireless sensor networks (WSN) for various tasks such as monitoring, data transmission, and data gathering. They find applications in the industrial internet of things (IIoT). WSNs are utilized to track and monitor changes in the environment. Since they include multiple small sensor nodes (SN), they are severely constrained, so resource management geared toward energy efficiency is crucial in this kind of network. Minimizing the power to interpret, transmit, and store data between various sensors poses important challenges. Experts have considered various ways to address these issues that unavoidably affect the network’s performance: reducing energy usage while maintaining system throughput remains the primary research issue. Another important concern relates to network security. Specifically, intrusion detection and avoidance are major concerns. In this work, we introduce the meta-heuristic-based secure and energy-efficient routing (MHSEER) protocol for WSN-IIoT. The protocol learns the forwarding decisions using the number of hops, connection integrity characteristics, and accumulated remaining energy. To make the method more secure, the protocol also employs counter-encryption mode (CEM) to encrypt the data. A meta-heuristics study designed to achieve reliable learning is used in the suggested protocol. The protocol consists of two stages. The first stage uses a heuristics method to improve the option for dependable data routing. Security based on a computationally simple and random CEM is accomplished in the second stage. The proposed MHSEER protocol has been compared to the secure trust routing protocol for low power (Sectrust-RPL), heuristic-based energy-efficient routing (HBEER), secure and energy-aware heuristic-based routing (SEHR), and secure energy-aware meta-heuristic routing (SEAMHR) in terms of packet drop ratio, throughput, network delay, energy usage, and faulty pathways. The proposed protocol increases throughput to 95.81% and decreases the packet drop ratio, packet delay, energy consumption, and faulty pathways to 5.12%, 0.10 ms, 0.0102 mJ, and 6.51%, respectively. Full article

Crude oil leakages and spills (OLS) are some of the problems attributed to pipeline failures in the oil and gas industry’s midstream sector. Consequently, they are monitored via several leakage detection and localisation techniques (LDTs) comprising classical methods and, recently, Internet of Things (IoT)-based systems via wireless sensor networks (WSNs). Although the latter techniques are proven to be more efficient, they are susceptible to other types of failures such as high false alarms or single point of failure (SPOF) due to their centralised implementations. Therefore, in this work, we present a hybrid distributed leakage detection and localisation technique (HyDiLLEch), which combines multiple classical LDTs. The technique is implemented in two versions, a single-hop and a double-hop version. The evaluation of the results is based on the resilience to SPOFs, the accuracy of detection and localisation, and communication efficiency. The results obtained from the placement strategy and the distributed spatial data correlation include increased sensitivity to leakage detection and localisation and the elimination of the SPOF related to the centralised LDTs by increasing the number of node-detecting and localising (NDL) leakages to four and six in the single-hop and double-hop versions, respectively. In addition, the accuracy of leakages is improved from 0 to 32 m in nodes that were physically close to the leakage points while keeping the communication overhead minimal. Full article