Aggregation Tree Research Articles

A hierarchical secure data aggregation method using the dragonfly algorithm in wireless sensor networks

A wireless sensor network (WSN) consists of a set of sensor nodes that are widely scattered in inaccessible areas. When deployed in large areas, WSNs generate a large volume of the data. Therefore, efficient methods are needed to process the data. One solution to minimize traffic on large-scale wireless sensor networks is to use data aggregation schemes. In this paper, a secure data aggregation method is proposed. The proposed secure data aggregation scheme has three phases: intra-cluster data aggregation, inter-cluster data aggregation, and data transfer. In the intra-cluster data aggregation phase, a fuzzy scheduling system is designed to adjust the appropriate data transmission rates of the cluster member nodes. In the inter-cluster data aggregation phase, an aggregation tree is created between the cluster head nodes. The dragonfly algorithm (DA) is used to find the optimal aggregation tree between cluster head nodes. In the data transfer phase, the columnar transposition cipher method is used to establish a secure connection between cluster member nodes and their cluster head node. Also, a symmetric and lightweight encryption method based on the residue number system (RNS) is utilized to provide secure communications between the cluster head nodes. We modify RNS and call it RNS+. Finally, the simulation results of the proposed scheme are compared to three data aggregation methods including Sign-share, Sham-share, and RCDA. The results show that the proposed data aggregation scheme outperforms other data aggregation methods in terms of network lifetime, delay and packet delivery rate.

Community‐level responses to increasing dryness vary with plant growth form across an extensive aridity gradient

AbstractAimPerennial plant communities are crucial for regulating ecological processes and maintaining ecosystem functions. Variation in community structure is driven by both biotic and abiotic factors, reflecting adaptation strategies of plant communities to various environments. Although much is known about the response of individual perennial plants to drier climates, empirical evidence of the community‐level responses to increasing dryness is limited.LocationEastern Australia; 1500 km gradient.TaxonPerennial plants.MethodsWe measured the abundance and size distribution (median, skewness and variance) of perennial plant communities from different growth forms (trees, shrubs and grasses), the spatial arrangement of trees in the overstorey and both biotic (competition) and abiotic (climate, soil properties) factors at 150 sites along an extensive aridity gradient from humid to arid areas. We used regression analyses and linear models to explore variation in community structure with increasing aridity and key driving factors for different perennial plant communities.ResultsVariation in community structure differed with growth form. As aridity increased, trees had wider canopies and were spatially aggregated, shrubs became miniaturised, but highly variable in size, and grasses comprised more larger individuals. Biotic and abiotic factors exerted different effects on different growth forms, with trees and shrubs consistently affected by competition and aridity, respectively, whereas grasses were weakly affected by aridity, summer rainfall and soil texture.Main conclusionsOur study highlights the idiosyncratic adaptation strategies used by trees, shrubs and grasses in response to drying climates at the community level through their effect on the size distribution or spatial aggregation. The structure of different perennial growth forms was influenced by different effects from either biotic (competition) or abiotic (climate, soil) factors. Under forecasted drier climates, canopy expansion and greater aggregation of trees might enhance resource sinks and shelter for diverse biota, potentially shielding plant communities against predicted aridification.

A Cost-Efficient Framework for Crowdsourced Data Collection in Vehicular Networks

Vehicular networks, which are recognized as an innovative technology for information collection due to the powerful sensing capability and strong mobility, have been an important platform for geographic crowdsourcing services and are particularly useful in environmental data collection. Crowdsourced data collection in vehicular networks must be performed in a communication-efficient manner due to the resource-constrained wireless communication links. Moreover, it is a pressing problem to improve the quality of responses because workers may provide data of poor quality or even fabricate data to defraud rewards. While the existing work has focused on spatial/temporal task coverage and monetary rewards, in this work, we propose a cost-efficient framework for crowdsourced data collection in vehicular networks while ensuring the accuracy of the response. Crowdsourced data collection consists of two main steps: 1) task assignment and 2) crowdsourced answer gathering. We first propose a task assignment scheme that maximizes the overall data quality and reduces the amount of data transmission. We estimate the data quality level based on a Gaussian mixture model and reduce the amount of data transmission by carefully selecting a subset of vehicles for crowdsourced tasks. We then design an answer gathering scheme that considers both the length of the aggregation tree and data delivery and minimizes the communication cost for collecting answers from participants. Extensive experiments on both synthetic data sets and real data sets show that our proposed framework achieves promising results.

Q-learning based routing for in-network aggregation in wireless sensor networks

In-network data aggregation is an inherent paradigm that extends the lifetime of resource-constrained wireless sensor networks (WSNs). By aggregating sensor data at intermediate nodes, it eliminates data redundancy, minimizes the number of transmissions and saves energy. A key component of in-network data aggregation is the design of an optimal routing structure. However, when the monitoring environment is highly dynamic, the conventional in-network aggregation routing algorithms lead to unnecessary redesign, high overhead and inferior performance, and make in-network aggregation a challenging task. This paper proposes a novel adaptive routing algorithm for in-network aggregation (RINA) in wireless sensor networks. The proposed approach employs a reinforcement learning method called Q-learning to build a routing tree based on minimal information such as residual energy, distance between nodes and link strength. In addition, it finds the aggregation points in the routing structure to maximize the number of overlapping routes in order to increase the aggregation ratio. Theoretical analysis proves the feasibility of the proposed approach. Simulation results show that the aggregation tree constructed by RINA increases the network lifetime by achieving optimum data aggregation and outperform other state-of-the-art approaches in terms of different significant features under different simulation scenarios.

Data-driven tree structure for PIN models

Probability of informed trading (PIN) models characterize trading with certain types of information through a tree structure. Different tree structures with different numbers of groups for market participants have been proposed, with no clear, consistent tree used in the literature. One of the main causes of this inconsistency is that these trees are artificially proposed through a bottom-up approach rather than implied by actual market data. Therefore, in this paper, we propose a method that infers a tree structure directly from empirical data. More precisely, we use hierarchical clustering to construct a tree for each individual firm and then infer an aggregate tree through a voting mechanism. We test this method on US data from January 2002 for 7608 companies, which results in a tree with two layers and four groups. The characteristics of the resulting aggregate tree are between those of several proposed tree structures in the literature, demonstrating that these proposed trees all reflect only part of the market, and one should consider the proposed empirically driven method when seeking a tree representing the whole market.

Structure-Free General Data Aggregation Scheduling for Multihop Battery-Free Wireless Networks

With advances in wireless power transfer techniques, battery-free wireless sensor networks (BF-WSNs) which can support long-term applications, has been attracting increasing interests in recent years. Unfortunately, the problem of minimum latency aggregation scheduling (MLAS) is not well studied in BF-WSNs. Existing works always have a rigid assumption that there is only one single query which is targeted at the whole network. Aiming at making the work more practical and general, we investigate the general MLAS problem in BF-WSNs, which is targeted at any subset of nodes in the network and aimed for an arbitrary number of aggregation queries. First, the general MLAS problem when there is one single query is studied. To control the number of nodes participating in the aggregation process, a node selection algorithm is proposed to cover and connect the whole target nodes. Then, a latency and energy aware scheduling algorithm is proposed to integrate the construction of aggregation tree with the chosen nodes, and the computation of a conflict-free schedule simultaneously, relying on non-predetermined structures. Second, the general MLAS problem when there is a group of aggregation queries is studied. Through designing some special structures to avoid collisions between both current and existing aggregation schedules, an algorithm without any waiting time is proposed. Additionally, the algorithm under physical interference model and dynamic energy arrival model are also presented. The theoretical analysis and simulation results verify that the proposed algorithms have high performance in terms of latency and energy efficiency.

EWPS: Emergency Data Communication in the Internet of Medical Things

In this article, we explore the remote monitoring of patients in an on-demand service that can be implemented using the Internet of Medical Things (IoMT). Network congestion and delay are important issues that must be resolved to handle emergencies in IoMT. Backpressure scheduling is a well-known scheme to resolve network congestion and improve network throughput for emergency data packets. Traditional backpressure scheduling is known to categorize each packet and schedules emergency packets to be forwarded faster than regular packets. However, in large-scale networks, these scheduling algorithms find unnecessarily long paths. This leads to high end-to-end delay and decreases the performance of guaranteed delivery. To resolve these issues, this article proposes an event-aware priority scheduling algorithm for data packets. This model follows a single priority queue model to manage all packets and emergency packets have been identified by the threshold values. Also, a separate communication path has been identified as well to reduce the waiting time for each category of the packet. Meanwhile, to reduce delay in packet communication, a delay-efficient data aggregation tree is constructed, which is combined with the priority queue model. Our in-depth simulation results of the proposed model prove the novel contribution to reducing delay in emergency packet delivery and avoiding network congestion. Moreover, the proposed model is also compared with the existing state-of-the-art models to show its ability to outperform similar methodologies.

Liver Tumor Segmentation Based on Dilated Convolution of Stacked Tree Aggregation Structure

Liver Tumor Segmentation Based on Dilated Convolution of Stacked Tree Aggregation Structure

Routing and data aggregation in wireless sensor networks by using clusters

Due to the non-unique node distribution energy consumption existsdifferenceon the basis of clusters networks for wireless sensors. Based on this problem, we propose an effective communication and routing data aggregation tree based on the previous clustering architecture. Using fuzzy logic technology, cluster heads are chosen based on residual capacity, node density and heads for load clusters. Via modifying inter-cluster energy consumption, the algorithm for the energy usage of cluster heads between cluster routing balancing. Using the data correlation model, data compression is then applied to minimise energy consumption.

Tree Node Switching Algorithm for Minimum Energy Cost Aggregation Tree Problem in Wireless Sensor Networks

Tree Node Switching Algorithm for Minimum Energy Cost Aggregation Tree Problem in Wireless Sensor Networks

PERFORMANCE EVALUATION OF PHY-MA IN WIRELESS NETWORKS

In the paper we evaluate the performance of thePhy-MA(closet to Mac layer) in wireless networks with the increasing the throughput. Load balancing and data aggregation plays a vital role in wireless communication technology for enhancing the battery life cycle time. We utilize the concept sleep mode scheduling scheme to preserve energy consumption for whole life cycle. In the work, the DAT (Data Aggregation Trees) model ensures the basic topological arrangement for enhancing the lifetime networks.

Effects of small aggregates of mature tree retention on adjacent planted coastal Douglas-fir growth and survival

Long-term retention of mature trees at the time of harvesting, called variable retention, has increased significantly since the 1990s as a silvicultural practice for maintaining stand-level biodiversity. A common approach is aggregated retention where groups of trees are left to increase structural diversity and provide forest influence over the adjacent harvested area. Despite the increased global application of aggregated retention, the growth and survival of adjacent trees has received less research effort than biodiversity studies. This knowledge gap was addressed with a study of small (0.01 to 0.58 ha, mean 0.3 ha) retained mature tree aggregates in harvested areas on southeast Vancouver Island, British Columbia, Canada. Coastal Douglas-fir (Pseudotsuga menziesii var. menziesii (Mirb.) Franco.) seedlings were planted in ~10-m-wide belt transects (three planting rows), extending up to 10 m into and 50 m away from aggregate edges in four cardinal directions. Nitrogen fertilizer was applied at planting to some transects. Basal diameter, height and survival of the planted Douglas-fir were measured for an average of 11 years. We used nonlinear mixed-effects models to assess the relationships of planted-tree basal diameter and height growth to distance from aggregate edge, edge orientation, planted-tree age, fertilization and aggregate characteristics (basal area and height). We extended planting into the aggregates to model tree response on both sides of edges. Modeled growth rates were slower for 10-year-old planted trees under the aggregate canopy and along the edges of the harvested area compared to growth at about 10 m and greater distance from aggregate edges; i.e. trees achieved 90% of their potential height and diameter by about 10 m from the aggregate canopy. Impacts were greatest on north-facing edges and least on south-facing edges, although differences were very small. We examined planted-tree survival by proximity to aggregate edges using a Cox proportional hazards model. Survival was lower directly beneath the aggregate canopy and up to approximately 5 m into the harvested area and was also slightly lower adjacent to south-facing edges. Fertilized trees showed a minor increase in survival versus unfertilized trees. Structural characteristics of aggregates had only a minimal impact on planted-tree growth and survival. This study helps provide a better understanding of the relationships among planted-tree growth and survival and edge effects for implementation of aggregated retention.

Data validation and integrity verification for trust based data aggregation protocol in WSN

In Wireless Sensor Networks, during data fragmentation, there are chances for dropping some blocks and injecting forged data by the attackers. So the integrity of these fragmented blocks and correctness of data, need to be verified. Also, DoS attacks needs to be prevented. In order to consider the above points, we proposed a design trust based data aggregation protocol using data validation and integrity verification in WSN. In this protocol, aggregation tree is formed by using the trust value which is evaluated for each node based on the trust features and the sensed data is encrypted with the shared symmetric key. Then the encrypted data are fragmented and signed with homomorphic MAC tag. Once the signed blocks are received, the aggregator performs SUM aggregation function after checking the correctness of data and verifying integrity of each block. The aggregated result is then transmitted to the sink, where it is again verified by the sink. This process is repeated by each sensor for the sensed results. By comparing the values obtained by simulation, we exhibit that the presented technique improves the data correctness.

Construction of multi-level data aggregation trees for energy efficiency and delivery delay in machine-to-machine communications

Machine-to-Machine (M2M) communications have gone forth as the newest technology for succeeding in communication generations. The M2M connections use the sensor nodes to capture an event into data packets and relayed through a network. The sensor nodes consume more energy whenever the increase in data packets transmitted from the sensor nodes in the system. To reduce the energy utilization applying the data aggregation is essential. We proposed a comprehensive model for calculating energy utilization and delay-tolerance by using Multi-Level Data Aggregation Trees (MLDAT). In the proposed scheme, the first stage is about the construction of Multi-Level Data Aggregation Tree, which aggregates the data originated from various wireless sensor nodes in the communication network. In the second stage, a delay-tolerant scheduling algorithm for controlling the delivery delay for user queries presented. Ultimately, the simulation results of the proposed scheme show that the suggested algorithms have better performance than the existing state-of-the-art approaches significantly.

On contact tracing in COVID-19 (SARS-CoV-2) pandemic using lowest common ancestor in m-ary data aggregation tree in the fog-computing enhanced internet of things

PurposeThe purpose of this study is to exploit the lowest common ancestor technique in an m-ary data aggregation tree in the fog computing-enhanced IoT to assist in contact tracing in COVID-19. One of the promising characteristics of the Internet of Things (IoT) that can be used to save the world from the current crisis of COVID-19 pandemic is data aggregation. As the number of patients infected by the disease is already huge, the data related to the different attributes of patients such as patient thermal image record and the previous health record of the patient is going to be gigantic. The authors used the technique of data aggregation to efficiently aggregate the sensed data from the patients and analyse it. Among the various inferences drawn from the aggregated data, one of the most important is contact tracing. Contact tracing in COVID-19 deals with finding out a person or a group of persons who have infected or were infected by the disease.Design/methodology/approachThe authors propose to exploit the technique of lowest common ancestor in an m-ary data aggregation tree in the Fog-Computing enhanced IoT to help the health-care experts in contact tracing in a particular region or community. In this research, the authors argue the current scenario of COVID-19 pandemic, finding the person or a group of persons who has/have infected a group of people is of extreme importance. Finding the individuals who have been infected or are infecting others can stop the pandemic from worsening by stopping the community transfer. In a community where the outbreak has spiked, the samples from either all the persons or the patients showing the symptoms are collected and stored in an m-ary tree-based structure sorted over time.FindingsContact tracing in COVID-19 deals with finding out a person or a group of persons who have infected or were infected by the disease. The authors exploited the technique of lowest common ancestor in an m-ary data aggregation tree in the fog-computing-enhanced IoT to help the health-care experts in contact tracing in a particular region or community. The simulations were carried randomly on a set of individuals. The proposed algorithm given in Algorithm 1 is executed on the samples collected at level-0 of the simulation model, and to aggregate the data and transmit the data, the authors implement Algorithm 2 at the level-1. It is found from the results that a carrier can be easily identified from the samples collected using the approach designed in the paper.Practical implicationsThe work presented in the paper can aid the health-care experts fighting the COVID-19 pandemic by reducing the community transfer with efficient contact tracing mechanism proposed in the paper.Social implicationsFighting COVID-19 efficiently and saving the humans from the pandemic has huge social implications in the current times of crisis.Originality/valueTo the best of the authors’ knowledge, the lowest common ancestor technique in m-ary data aggregation tree in the fog computing-enhanced IoT to contact trace the individuals who have infected or were infected during the transmission of COVID-19 is first of its kind proposed. Creating a graph or an m-ary tree based on the interactions/connections between the people in a particular community like location, friends and time, the authors can attempt to traverse it to find out who infected any two persons or a group of persons or was infected by exploiting the technique of finding out the lowest common ancestor in a m-ary tree.

Can’t see the Woodlark for the trees? Commercial forests as a habitat for a bird of conservation concern

This study investigated the extent to which intensive forest management practices influence a widespread bird of conservation concern, the Woodlark Lullula arborea. We compared trends of the studied population in Notec Forest, Poland during 2010–2014 against country-wide monitoring data, and populations of the forest interior versus forest edge dominated landscapes (forest-agricultural area mosaic). We also studied the influences of forest management practice (clearing size, stand structure, forestry activities on restock sites, harvesting in the neighboring habitat, and retaining tree aggregates) on the population of this species. Changes in woodlark numbers, habitat availability, and quality differences among studied sites and years were assessed using ANOVA. The influence of habitat characteristics on woodlark occupancy and abundances were tested by Generalized Estimation Equations, while the magnitude of influences in the study years was compared by Random Forest algorithm (as were population trends). Over the four study years, 419 woodlark pairs occupied 336 patches, with a maximum of 127 pairs. The number of woodlark pairs fell by 40% on the studied areas over the period, in line with declines across Poland as a whole. In our study system, population trends did not differ between forest and forest- agro mosaic landscapes. Size, age and number of available habitats did not differ either temporally or spatially during the study. Retention of unfelled trees in clearfell areas and forest works in the nearby sites, however, showed slight differences among the sampling polygons. Larger habitat patch size and restock areas under five years favored woodlarks. Less important but still significant positive influences were a higher proportion of retained trees and elongated shape of the habitat patches. Forestry activities on the neighbor stands were correlated with increased woodlark abundances, which is an unexpected result. Moreover, forestry provides suitable habitats for woodlarks in a dynamically changing arrangement. The magnitude of the studied factors’ influences on woodlarks showed a very high difference between study years, with the highest values in the years where woodlarks are the most numerous. In conclusion, we found no evidence that contemporary forestry adversely affects Woodlark habitat occupancy. Our study suggests that while managing land for one ecosystem service (timber), an apparent dis-benefit (loss of recreational attractiveness) might benefit some semi-open habitat species.

WDAT‐OMS: A two‐level scheme for efficient data gathering in mobile‐sink wireless sensor networks using compressive sensing theory

This study addresses the problems of energy and delay in wireless sensor networks equipped with mobile sinks. The authors jointly consider the compressive sensing (CS) theory, cluster-based routing, and sink mobility to propose a data collection method named ‘weighted data aggregation trees with optimal mobile sink(s) (WDAT-OMS)’. The proposed scheme relies on a two-level architecture in which sensors are clustered at the first level. WDAT-OMS uses the CS theory along with load-balanced data aggregation trees to route packets from sensors to the corresponding cluster heads (CHs). In this regard, they present an efficient metric named ‘energy-and distance-aware CH selection’ to fairly distribute the energy consumption among different sensors. At the second level, one or more sinks traverse the network to collect the aggregated data of CHs. As an advantage, WDAT-OMS not only balances the energy consumption among different sensors but also increases the network scalability. Numerical results demonstrate that the proposed algorithm reduces energy consumption in comparison with ‘centralised clustering algorithm’, ‘energy-aware CS-based data aggregation’, and ‘energy-balanced high-level data aggregation tree ‘by 66%, 62%, and 63% for an average number of clusters, respectively. It also decreases the sink delay in comparison with the ‘single-hop data-gathering problem’ by 10%.

Break-and-join tree construction for latency-aware data aggregation in wireless sensor networks

Emerging applications require processing a huge amount of environmental data from wireless sensor networks, and then triggering appropriate actions in response to the detected events. To this end, it is desirable to minimize the time needed for data aggregation. This paper investigates the minimum-latency aggregation scheduling problem in wireless sensor networks. We propose an aggregation tree construction algorithm called Break-and-Join which adjusts any aggregation tree toward a smaller delay one. In order to perform tree adjustments, the algorithm iteratively changes parent of some nodes in the tree, using a novel numerical metric as a tree quality guideline. Each node determines if it can adopt an additional child in the neighborhood in order to relax the aggregation load at some bottleneck node in the network, thereby improving the overall aggregation tree quality. We performed the algorithm on several state-of-the-art aggregation schemes, and the results shows that final aggregation delay is quite indifferent to choice of initial tree and the tree quality can be significantly improved (e.g. 7 times for shortest path tree). Scheduling on the obtained trees also outperforms the best known scheme up to 13% in terms of delay.

Scheduling data aggregation trees to extend network lifetime in sensor networks

SummaryIncreasing network lifetime (NL) is an important requirement in wireless sensor networks (WSNs). One of the techniques to extend NL is to use Data Aggregation Trees (DATs). DATs improve NL by combining the energy efficiency benefits of both Data Aggregation (DA) and tree‐based routing. While centralized and distributed strategies for DAT construction are widely used, we propose a combined approach for DAT construction to improve NL. The approach reduces the communication overhead and relaxes the requirement of complete network information at the sink. In the proposed work, this collaborative approach is termed as Extended Local View (ELV) approach. Two ELV‐based DAT construction algorithms termed as ELV with Fixed sink (ELVF) and ELV with Random sink (ELVR) are proposed. Both ELVF and ELVR use heuristics‐based technique of Local Path Reestablishment (LPR) and greedy‐based technique of Extended Path Reestablishment (EPR). Using these techniques a sequence of DATs are scheduled that collectively improve NL and also reduce the associated DAT reconstruction overhead. Performance of ELVF and ELVR is evaluated with rigorous experiments, and the simulation results show that the proposed algorithms have improved NL and are scalable across different DA ratio values. DAT schedule analysis further demonstrates reduced DAT reconstruction overhead of the proposed algorithms which illustrates its suitability for hostile and critical environments.

Spatiotemporal Spread of Abnormal Vertical Growth of Macadamia in Australia Informs Epidemiology.

Australian macadamia production is threatened by a disorder known as abnormal vertical growth (AVG), for which the etiology is unknown. AVG is characterized by vigorous upright growth and reduced lateral branching, flowering, and nut set that results in over 70% yield loss annually. Six commercial macadamia orchards were surveyed in 2012 and again in 2018 to examine spatiotemporal dynamics of the epidemic. Data were subjected to point-pattern and geostatistical analyses. AVG incidence in all orchards showed a better fit to the beta-binomial distribution than the binomial distribution. AVG incidence in the different orchards varied between 5 and 47% in 2012, and 13 and 55% in 2018 and the rate of spread was slow, averaging at about 2% increase in disease incidence per annum. Spatial patterns of AVG were highly aggregated on both survey years and spread was mainly between neighboring trees in a row or trees that were opposite to each other in different rows. Semivariograms showed large range values (approximately 15 to 120), indicating aggregation of AVG-affected trees beyond quadrat levels. Furthermore, clusters of disease were mainly at the edge of the orchard on the first survey date and the disease progressed toward the center of the orchard over time. It is concluded that AVG is caused by an infectious agent, and based on patterns of spread, we hypothesize that spread is facilitated by root grafting or root-to-root contact. Furthermore, a vascular-limited pathogen could be involved that modulates plant hormone production.