Clustered Sensor Research Articles

Training and Learning Swarm Intelligence Algorithm (TLSIA) for Selecting the Optimal Cluster Head in Wireless Sensor Networks

Background: Wireless sensor networks include a set of non-rechargeable sensor nodes that interact for particular purposes. Since the sensors are non-rechargeable, one of the most important challenges of the wireless sensor network is the optimal use of the energy of sensors. The selection of the appropriate cluster heads for clustering and hierarchical routing is effective in enhancing the performance and reducing the energy consumption of sensors. Aim: Clustering sensors in different groups is one way to reduce the energy consumption of sensor nodes. In the clustering process, selecting the appropriate sensor nodes for clustering plays an important role in clustering. The use of multistep routes to transmit the data collected by the cluster heads also has a key role in the cluster head energy consumption. Multistep routing uses less energy to send information. Methods: In this paper, after distributing the sensor nodes in the environment, we use a Teaching-Learning-Based Optimization (TLBO) algorithm to select the appropriate cluster heads from the existing sensor nodes. The teaching-learning philosophy has been inspired by a classroom and imitates the effect of a teacher on learner output. After collecting the data of each cluster to send the information to the sink, the cluster heads use the Tabu Search (TS) algorithm and determine the subsequent step for the transmission of information. Findings: The simulation results indicate that the protocol proposed in this research (TLSIA) has a higher last node dead than the LEACH algorithm by 75%, ASLPR algorithm by 25%, and COARP algorithm by 10%. Conclusion: Given the limited energy of the sensors and the non-rechargeability of the batteries, the use of swarm intelligence algorithms in WSNs can decrease the energy consumption of sensor nodes and, eventually, increase the WSN lifetime.

A rapid acoustic source localization technique for early warning of building material damage

Structural health monitoring can predict defects at the early stage of their formation and avoid safety hazards or catastrophic accidents caused by structural degradation. However, the cost and the need for additional operating space restricted their wide application for monitoring building materials. The main purpose of this paper is to propose a simple and fast defect localization technique, which can locate the acoustic source using fewer sensors when the operating space outside the structure is limited. With the help of the ‘L’-shaped coupled sensor cluster, only six sensers are required to quickly predict the damage location for both isotropic and anisotropic materials. Experiments were carried out on several building material samples, and the finite element simulation was used to explore the localization relationship of different relative positions of acoustic sources and the cluster. The experimental and simulation results verify the feasibility of the technique.

An Efficient Intra‐Cluster Data Aggregation and finding the Best Sink location in WSN using EEC‐MA‐PSOGA approach

SummaryWireless sensor networks (WSNs) consist of nodes. Issues involved with the use of a sensor network are energy‐saving and the effective use of energy. Clustering in WSNs is a proven technique for energy optimization. The relationship between sensor nodes and cluster heads (CHs) has only been considered prior to cluster‐based routing protocols. However, most clustering algorithms have failed to address the routing overhead and the energy consumption rate between CH nodes and SINK. In this paper, a new framework named as EEC‐MA‐PSOGA ‐ an energy‐efficient (EE) intra‐cluster mobile agent (INC‐MA)‐based particle swarm optimization–genetic algorithm (PSO‐GA), has been presented to initiate the distance communication and place the SINK optimally in WSNs. The cluster members send the collected data towards their respective CHs for aggregation. To find the sink's best position, the PSO‐GA‐based location estimation algorithm is initiated based on the network structure. The limited capability of WSNs makes them more vulnerable to attackers. The prevention mechanism must be less complex to ensure the fair operations in the network. Here, the attack detection ability of the proposed solution has been tested against clone attack. With varied communication range, simulation time, and sensor nodes, the tests are conducted extensively on various scenarios of WSNs. When compared with the prior mechanisms, the framework provides better results and better prevention with less overhead by analyzing the experimental results such as energy consumption, network lifetime, and throughput.

Optimal Array Design and Directive Sensors for Guided Waves DoA Estimation.

The estimation of Direction of Arrival (DoA) of guided ultrasonic waves is an important task in many Structural Health Monitoring (SHM) applications. The aim is to locate sources of elastic waves which can be generated by impacts or defects in the inspected structures. In this paper, the array geometry and the shape of the piezo-sensors are designed to optimize the DoA estimation on a pre-defined angular sector, from acquisitions affected by noise and interference. In the proposed approach, the DoA of a wave generated by a single source is considered as a random variable that is uniformly distributed in a given range. The wave velocity is assumed to be unknown and the DoA estimation is performed by measuring the Differences in Time of Arrival (DToAs) of wavefronts impinging on the sensors. The optimization procedure of sensors positioning is based on the computation of the DoA and wave velocity parameters Cramér-Rao Matrix Bound (CRMB) with a Bayesian approach. An efficient DoA estimator is found based on the DToAs Gauss-Markov estimator for a three sensors array. Moreover, a novel directive sensor for guided waves is introduced to cancel out undesired Acoustic Sources impinging from DoAs out of the given angles range. Numerical results show the capability to filter directional interference of the novel sensor and a considerably improved DoA estimation performance provided by the optimized sensor cluster in the pre-defined angular sector, as compared to conventional approaches.

High-throughput and high-content bioassay enables tuning of polyester nanoparticles for cellular uptake, endosomal escape, and systemic in vivo delivery of mRNA.

Nanoparticle-based mRNA therapeutics hold great promise, but cellular internalization and endosomal escape remain key barriers for cytosolic delivery. We developed a dual nanoparticle uptake and endosomal disruption assay using high-throughput and high-content image-based screening. Using a genetically encoded Galectin 8 fluorescent fusion protein sensor, endosomal disruption could be detected via sensor clustering on damaged endosomal membranes. Simultaneously, nucleic acid endocytosis was quantified using fluorescently tagged mRNA. We used an array of biodegradable poly(beta-amino ester)s as well as Lipofectamine and PEI to demonstrate that this assay has higher predictive capacity for mRNA delivery compared to conventional polymer and nanoparticle physiochemical characteristics. Top nanoparticle formulations enabled safe and efficacious mRNA expression in multiple tissues following intravenous injection, demonstrating that the in vitro screening method is also predictive of in vivo performance. Efficacious nonviral systemic delivery of mRNA with biodegradable particles opens up new avenues for genetic medicine and human health.

Data Reliability and Sensors Lifetime in Bridge Health Monitoring using LoRaWAN-Zigbee

The Wireless Sensor Network (WSN) is regarded as the fastest expanding technological trend in recent years due its application in a variety of sectors. In the monitoring region, several sensor nodes with various sensing capabilities are installed to gather appropriate data and communicate it to the gateway. The proposed system of the heterogeneous WSN employing LoRaWAN-Zigbee based hybrid communication is explored in this research study. To communicate in a network, two Long–Range Wide Area Network (LoRaWAN) sensor clusters and two Zigbee sensor clusters are employed, together with two Zigbee and LoRaWAN converters. The suggested Golden eagle shepherd optimization (GESO) method then forms Zigbee as well as LoRaWAN networking clusters. Furthermore, depending on energy usage and data packet size, the fitness of each sensor node is assessed using the Dynamic Intelligent Reasoning Based Neural (DIRN) approach. MATLAB software is used to implement and execute this study. When the Zigbee network’s transmission distance is 650 m and the LoRaWAN network’s transmission range is 3.5 km, the system can function with a packet loss rate of less than 0.04 percent. This study shows significant gains in the performance of the system when compared to traditional approaches based on digital findings obtained on software solutions.

Multivariate Outlier Detection for Forest Fire Data Aggregation Accuracy

Wireless sensor networks have been a very important means in forest monitoring applications. A clustered sensor network comprises a set of cluster members and one cluster head. The cluster members are normally located close to each other, with overlaps among their sensing coverage within the cluster. The cluster members concurrently detect the same event to send to the Cluster Head node. This is where data aggregation is deployed to remove redundant data at the cost of data accuracy, where some data generated by the sensing process might be an outlier. Thus, it is important to conserve the aggregated data’s accuracy by performing an outlier data detection process before data aggregation is implemented. This paper concerns evaluating multivariate outlier detection (MOD) analysis on aggregated accuracy of data generated by a forest fire environment using OMNeT++ and performing the analysis in MATLAB R2018b. The findings of the study showed that the MOD algorithm conserved approximately 59.5% of aggregated data accuracy, compared with an equivalent algorithm, such as the FTDA algorithm, which conserved 54.25% of aggregated data accuracy for the same event.

An Optimal Scheme for WSN Based on Compressed Sensing

Wireless sensor networks (WSNs) is one of the renowned ad hoc network technology that has vast varieties of applications such as in computer networks, bio-medical engineering, agriculture, industry and many more. It has been used in the internet-of-things (IoTs) applications. A method for data collecting utilizing hybrid compressive sensing (CS) is developed in order to reduce the quantity of data transmission in the clustered sensor network and balance the network load. Candidate cluster head nodes are chosen first from each temporary cluster that is closest to the cluster centroid of the nodes, and then the cluster heads are selected in order based on the distance between the determined cluster head node and the undetermined candidate cluster head node. Then, each ordinary node joins the cluster that is nearest to it. The greedy CS is used to compress data transmission for nodes whose data transmission volume is greater than the threshold in a data transmission tree with the Sink node as the root node and linking all cluster head nodes. The simulation results demonstrate that when the compression ratio is set to ten, the data transfer volume is reduced by a factor of ten. When compared to clustering and SPT without CS, it is reduced by 75% and 65%, respectively. When compared to SPT with Hybrid CS and Clustering with hybrid CS, it is reduced by 35% and 20%, respectively. Clustering and SPT without CS are compared in terms of node data transfer volume standard deviation. SPT with Hybrid CS and clustering with Hybrid CS were both reduced by 62% and 80%, respectively. When compared to SPT with hybrid CS and clustering with hybrid CS, the latter two were reduced by 41% and 19%, respectively.

Tourism cloud management system: the impact of smart tourism

This study investigates the possibility of supporting tourists in a foreign land intelligently by using the Tourism Cloud Management System (TCMS) to enhance and better their tourism experience. Some technologies allow tourists to highlight popular tourist routes and circuits through the visualisation of data and sensor clustering approaches. With this, a tourist can access the shared data on a specific location to know the sites of famous local attractions, how other tourists feel about them, and how to participate in local festivities through a smart tourism model. This study surveyed the potential of smart tourism among tourists and how such technologies have developed over time while proposing a TCMS. Its goals were to make physical/paper tickets redundant via the introduction of a mobile app with eTickets that can be validated using camera and QR code technologies and to enhance the transport network using Bluetooth and GPS for real-time identification of tourists’ presence. The results show that a significant number of participants engage in tourist travels, hence the need for smart tourism and tourist management. It was concluded that smart tourism is very appealing to tourists and can improve the appeal of the destination if smart solutions are implemented. This study gives a first-hand review of the preference of tourists and the potential of smart tourism.

Electrophysiological disruptions and cerebrospinal fluid biomarkers in mild cognitive impairment patients

AbstractBackgroundAlzheimer's Disease (AD) is a progressive neurodegenerative disorder characterized, at the neuropathological level, by the accumulation of amyloid‐β (Aβ) neuritic plaques and neurofibrillary tangles formed by hyperphosphorylated tau protein. A key aspect of AD’s is focused on its early detection. In recent years, cerebrospinal fluid (CSF) has been established as a sensible and validated biomarker that could help to provide an accurate diagnosis of the disease. It is known that lower CSF Aβ‐42 concentrations and higher CSF levels of total and phosphorylated tau (t‐tau and p‐tau respectively), have been extensively replicated parameters among AD patients. Similarly, electrophysiological signatures, as analyzed by the electroencephalography (EEG) technique, seem to be a non‐invasive useful tool for evaluating the progressive loss of efficiency of neuronal networks. Given that, the purpose of this study was to explore the correlation between power EEG values derived from clusters of sensors and CSF markers in a sample of mild cognitive impairment (MCI) patients.Method7 MCI participants (aged from 61 to 85) were recruited from the Cognitive Disorders Unit of the Hospital Universitari Santa Maria (Lleida, Spain). All of them underwent a neuropsychological evaluation, CSF lumbar puncture, and EEG recordings.ResultAmyloid. Male population showed a significant negative correlation (rho = ‐0.754, p < 0.003) between amyloid load and theta power in antero‐posterior regions of the brain. The average power of the cluster correlated significatively with ADAS (rho = 0.562, p < 0.037), total tau (rho = 0.538, p < 0.049) and delayed recall (rho = ‐0.612, p < 0.020). Total tau. A widespread alpha power cluster was found in the male population whose oscillatory activity correlated negatively with t‐tau CSF load (rho = ‐0.653, p < 0.014). When assessing the female population, we found a relationship (rho = ‐0.864, p < 0.001) between total‐tau and beta power in a widespread cluster. Additionally, the beta cluster average power significatively correlated with p‐tau load (rho = ‐0.800, p < 0.005) and age (rho = ‐0.621, p < 0.041).ConclusionElectrophysiological activity seems to be depicting a clear sex‐based pattern of associations with neuropathological markers of AD.

Intelligent Approaches for Enhancing Networked Routing Protocol

The rapid evolution of wireless networking technologies opens the door to the evolution of the Wireless Sensor Networks (WSNs) and their applications in different fields. The WSN consists of small energy sensor nodes used in a harsh environment. The energy needed to communicate between the sensors networks can be identified as one of the major challenges. It is essential to avoid massive loss, or loss of packets, as well as rapid energy depletion and grid injustice, which lead to lower node efficiency and higher packet delivery delays. For this purpose, it was very important to track the usage of energy by nodes in order to improve general network efficiency by the use of intelligent methods to reduce the energy used to extend the life of the WSN and take successful routing decisions. For these reasons, designing an energy-efficient system that utilizes intelligent approaches is considered as the most powerful way to prolong the lifetime of the WSN. The proposed system is divided into four phases (sensor deployment phase, clustering phase, intra-cluster phase, and inter-cluster phase). Each of these phases uses a different intelligent algorithm with some enhancements. The performance of the proposed system was analyzed and evaluations were elaborated with well-known existing routing protocols. To assess the proficiency of the proposed system and evaluate the endurance of the network, efficiency parameters such as network lifetime, energy consumption, and packet delivery to the Sink (Base station) were exploited. The experimental outcomes justify that the proposed system surpasses the existing mechanisms by 50%.

A bio-inspired adaptive model for search and selection in the Internet of Things environment.

The Internet of Things (IoT) is a paradigm that can connect an enormous number of intelligent objects, share large amounts of data, and produce new services. However, it is a challenge to select the proper sensors for a given request due to the number of devices in use, the available resources, the restrictions on resource utilization, the nature of IoT networks, and the number of similar services. Previous studies have suggested how to best address this challenge, but suffer from low accuracy and high execution times. We propose a new distributed model to efficiently deal with heterogeneous sensors and select accurate ones in a dynamic IoT environment. The model’s server uses and manages multiple gateways to respond to the request requirements. First, sensors were grouped into three semantic categories and several semantic sensor network types in order to define the space of interest. Second, each type’s sensors were clustered using the Whale-based Sensor Clustering (WhaleCLUST) algorithm according to the context properties. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was improved to search and select the most adequate sensor matching users’ requirements. Experimental results from real data sets demonstrate that our proposal outperforms state-of-the-art approaches in terms of accuracy (96%), execution time, quality of clustering, and scalability of clustering.

Calibration of CO, NO2, and O3 Using Airify: A Low-Cost Sensor Cluster for Air Quality Monitoring.

During the last decade, extensive research has been carried out on the subject of low-cost sensor platforms for air quality monitoring. A key aspect when deploying such systems is the quality of the measured data. Calibration is especially important to improve the data quality of low-cost air monitoring devices. The measured data quality must comply with regulations issued by national or international authorities in order to be used for regulatory purposes. This work discusses the challenges and methods suitable for calibrating a low-cost sensor platform developed by our group, Airify, that has a unit cost five times less expensive than the state-of-the-art solutions (approximately €1000). The evaluated platform can integrate a wide variety of sensors capable of measuring up to 12 parameters, including the regulatory pollutants defined in the European Directive. In this work, we developed new calibration models (multivariate linear regression and random forest) and evaluated their effectiveness in meeting the data quality objective (DQO) for the following parameters: carbon monoxide (CO), ozone (O3), and nitrogen dioxide (NO2). The experimental results show that the proposed calibration managed an improvement of 12% for the CO and O3 gases and a similar accuracy for the NO2 gas compared to similar state-of-the-art studies. The evaluated parameters had different calibration accuracies due to the non-identical levels of gas concentration at which the sensors were exposed during the model’s training phase. After the calibration algorithms were applied to the evaluated platform, its performance met the DQO criteria despite the overall low price level of the platform.

Intelligent Dust Monitoring Application in Patient Room

Due to an ongoing epidemic, the number of hospitalized bedridden patients has increased. It is imperative to closely monitor the hospital room and maintain and clean it regularly to avoid harm to the patients. The accommodations may not be within the standards for a bed-bound patient’s room. The patient has a risk of contracting a respiratory disease, or having an asthmatic attack, if exposed to high levels of PM1, PM2.5, or PM10 dust that cannot be seen with bare eyes so the risk factor is not easy to notice. The goal of this study was to develop a dust monitoring system for hospital bedrooms using IoT, so that the caregivers can monitor air quality in the room. By applying the Internet of Things (IoTs) technology to communicate between sensors and mobile phones, the internet serves as the medium for communication. The demonstration system in the room was equipped with 5 sensor cluster, each measuring: temperature, humidity and dust sensors for PM1, PM2.5, and PM10. Decision trees were trained to predict the outcome of cases after collecting data. The final decision tree model reached an overall classification accuracy of 92.8%. The system could alert for housekeeping or turn on or off an automatic dust remover based on the amount of dust in the room. It also supports cleaning and dust removal to ensure that the bed patient’s room is appropriate and reduces the risk of respiratory diseases caused by dust.

Coal mine personnel positioning algorithm based on improved adaptive unscented Kalman filter with wireless channel fading and unknown noise statistics

This paper is concerned with the problem of personnel localization in the complex coal mine environment with wireless channel fading and unknown noise statistics. Considering the random channel fading caused by signal fluctuation and transmission fault, an improved adaptive unscented Kalman filter (IAUKF) algorithm is proposed. The mean and error covariances of noise are estimated adaptively by adopting the improved Sage–Husa noise estimation method. In order to save energy and improve energy utilization, the multi-sensor clustering is performed to divide the spatial distribution of sensors into multiple clusters. The sensors in the same cluster can communicate with each other to maintain the consistency of estimation. The simulation results show that the IAUKF algorithm is better than extended Kalman filter (EKF), unscented Kalman filter (UKF), and improved unscented Kalman filter (IUKF) algorithms.

Mate preference and brain oscillations: Initial romantic attraction is associated with decreases in alpha- and lower beta-band power.

Initial romantic attraction (IRA) refers to a series of positive reactions (such as feelings of exhilaration and compulsive thinking) toward desirable potential partners, usually at initial or early‐stage encounters when no close relationship has yet been established. After decades of effort, the evolutionary value and key characteristics of IRA are well understood. However, the brain mechanisms associated with IRA are unclear. To address this question, we simulated a mate selection platform similar to that of Tinder. When participants assessed their romantic interest in potential partners on the platform, their electroencephalogram (EEG) signals were recorded in real time. The behavioral data demonstrated that IRA to ideal potential partners mainly reflects the dimensions of arousal and domination. The main study finding was that processing of the individual preference faces that resulted in IRA was associated with a decrease in power in the alpha and lower beta bands over the posterior and anterior sensor clusters; this occurred between 870 and 2,000 ms post‐stimulus. Key findings regarding event‐related potentials (ERPs) sensitive to individual stimuli preferences were replicated. The results support the hypothesis that brain oscillations in the alpha and lower beta range may reflect modulation in cortical activity associated with individual mate preferences.

Congestion control method for Clustered Sensor Networks Based on T-S fuzzy model

The many to one communication mode of clustered sensor network is prone to information transmission interference, which leads to network data transmission congestion, resulting in frequent network dynamic changes and resource constraints. In order to solve the above problems, this paper proposes a congestion control method based on T-S fuzzy model for Clustered Sensor Network, so as to achieve reasonable control and processing of massive data in the congested network environment.Based on the analysis of the characteristics of clustering sensor networks, this paper describes the strategies of congestion detection and congestion avoidance, and focuses on the introduction and analysis of typical congestion relief algorithms based on rate control, traffic scheduling and transmission scheduling. Finally, the development trend of congestion control technology is prospected. Experiments show that clustering based on T-S fuzzy model is feasible The congestion control method of sensor network has high control effect in the practical application process, and fully meets the research requirements.

A lightweight key renewal scheme based authentication protocol with configurable RO PUF for clustered sensor networks

In clustered sensor networks, an adversary can easily capture and damage a member node. Therefore, it is important to detect the compromised nodes and renew the keys of all other nodes. Although many key renewal schemes have been proposed, the detection of compromised nodes is still difficult to achieve. Furthermore, due to the resources limitation, conventional encryption and authentication protocols are impractical when we take the power consumption and resource usage into consideration. In this paper, a lightweight and energy-efficient authentication protocol and key renewal scheme for clustered sensor networks was proposed, in which the configurable ring oscillator (CRO) physical unclonable function (PUF) is adopted to enhance the security. Typical CRO PUFs, including multiplexer (MUX) based PUF and XOR gate based CRO PUF, are analyzed and compared. The proposed scheme is implemented and evaluated on Zynq 7000 series SoC FPGAs. The results indicate that the configurable PUF structure is cost efficient and the proposed protocol is secure against major attacks on clustered sensor networks.

Recommended System for Cluster Head Selection in a Remote Sensor Cloud Environment Using the Fuzzy-Based Multi-Criteria Decision-Making Technique

Clustering is an energy-efficient routing algorithm in a sensor cloud environment (SCE). The clustering sensor nodes communicate with the base station via a cluster head (CH), which can be selected based on the remaining energy, the base station distance, or the distance from the neighboring nodes. If the CH is selected based on the remaining energy and the base station is far away from the cluster head, then it is not an energy-efficient selection technique. The same applies to other criteria. For CH selection, a single criterion is not sufficient. Moreover, the traditional clustering algorithm head nodes keep changing in every round. Therefore, the traditional algorithm energy consumption is less, and nodes die faster. In this paper, the fuzzy multi-criteria decision-making (F-MCDM) technique is used for CH selection and a threshold value is fixed for the CH selection. The fuzzy analytical hierarchy process (AHP) and the fuzzy analytical network process (ANP) are used for CH selection. The performance evaluation results exhibit a 5% improvement compared to the fuzzy AHP clustering method and 10% improvement compared to the traditional method in terms of stability, energy consumption, throughput, and control overhead.

Spatio-temporal soil moisture retrieval at the catchment scale using a dense network of cosmic-ray neutron sensors

Abstract. Cosmic-ray neutron sensing (CRNS) is a powerful technique for retrieving representative estimates of soil water content at a horizontal scale of hectometres (the “field scale”) and depths of tens of centimetres (“the root zone”). This study demonstrates the potential of the CRNS technique to obtain spatio-temporal patterns of soil moisture beyond the integrated volume from isolated CRNS footprints. We use data from an observational campaign carried out between May and July 2019 that featured a dense network of more than 20 neutron detectors with partly overlapping footprints in an area that exhibits pronounced soil moisture gradients within one square kilometre. The present study is the first to combine these observations in order to represent the heterogeneity of soil water content at the sub-footprint scale as well as between the CRNS stations. First, we apply a state-of-the-art procedure to correct the observed neutron count rates for static effects (heterogeneity in space, e.g. soil organic matter) and dynamic effects (heterogeneity in time, e.g. barometric pressure). Based on the homogenized neutron data, we investigate the robustness of a calibration approach that uses a single calibration parameter across all CRNS stations. Finally, we benchmark two different interpolation techniques for obtaining spatio-temporal representations of soil moisture: first, ordinary Kriging with a fixed range; second, spatial interpolation complemented by geophysical inversion (“constrained interpolation”). To that end, we optimize the parameters of a geostatistical interpolation model so that the error in the forward-simulated neutron count rates is minimized, and suggest a heuristic forward operator to make the optimization problem computationally feasible. Comparison with independent measurements from a cluster of soil moisture sensors (SoilNet) shows that the constrained interpolation approach is superior for representing horizontal soil moisture gradients at the hectometre scale. The study demonstrates how a CRNS network can be used to generate coherent, consistent, and continuous soil moisture patterns that could be used to validate hydrological models or remote sensing products.