Aggregation Tree Research Articles

A survey on network lifetime maximization using data aggregation trees

A survey on network lifetime maximization using data aggregation trees

Improved Fast-Response Consensus Algorithm Based on HotStuff.

Recent Byzantine Fault-Tolerant (BFT) State Machine Replication (SMR) protocols increasingly focus on scalability and security to meet the growing demand for Distributed Ledger Technology (DLT) applications across various domains. Current BFT consensus algorithms typically require a single leader node to receive and validate votes from the majority process and broadcast the results, a design challenging to scale in large systems. We propose a fast-response consensus algorithm based on improvements to HotStuff, aimed at enhancing transaction ordering speed and overall performance of distributed systems, even in the presence of faulty copies. The algorithm introduces an optimistic response assumption, employs a message aggregation tree to collect and validate votes, and uses a dynamically adjusted threshold mechanism to reduce communication delay and improve message delivery reliability. Additionally, a dynamic channel mechanism and an asynchronous leader multi-round mechanism are introduced to address multiple points of failure in the message aggregation tree structure, minimizing dependence on a single leader. This adaptation can be flexibly applied to real-world system conditions to improve performance and responsiveness. We conduct experimental evaluations to verify the algorithm's effectiveness and superiority. Compared to the traditional HotStuff algorithm, the improved algorithm demonstrates higher efficiency and faster response times in handling faulty copies and transaction ordering.

Data aggregation algorithm for wireless sensor networks with different initial energy of nodes.

Data aggregation plays a critical role in sensor networks for efficient data collection. However, the assumption of uniform initial energy levels among sensors in existing algorithms is unrealistic in practical production applications. This discrepancy in initial energy levels significantly impacts data aggregation in sensor networks. To address this issue, we propose Data Aggregation with Different Initial Energy (DADIE), a novel algorithm that aims to enhance energy-saving, privacy-preserving efficiency, and reduce node death rates in sensor networks with varying initial energy nodes. DADIE considers the transmission distance between nodes and their initial energy levels when forming the network topology, while also limiting the number of child nodes. Furthermore, DADIE reconstructs the aggregation tree before each round of data transmission. This allows nodes closer to the receiving end with higher initial energy to undertake more data aggregation and transmission tasks while limiting energy consumption. As a result, DADIE effectively reduces the node death rate and improves the efficiency of data transmission throughout the network. To enhance network security, DADIE establishes secure transmission channels between transmission nodes prior to data transmission, and it employs slice-and-mix technology within the network. Our experimental simulations demonstrate that the proposed DADIE algorithm effectively resolves the data aggregation challenges in sensor networks with varying initial energy nodes. It achieves 5-20% lower communication overhead and energy consumption, 10-20% higher security, and 10-30% lower node mortality than existing algorithms.

An Analysis of Hierarchical Routing Strategy with Advanced Additional Sensors in WSNs

Two kinds of sensors will be discussed in some sort of wireless sensor networks (WSNs). One is named a normal sensor (called A_nodes) with fixed initial energy, which can get perception data from the surrounding environment and have functions of storage and forwarding. The other is named relay sensor (called B_node) with sufficient energy, which only can store data and forward data. Cluster heads (called A_heads) are chosen among A_nodes by probability mode to create clusters first. Local adjustments would be done inter-cluster. Then the sink is selected as the root and a backbone shortest path tree with hops limited is built dynamically from A_heads, B_Nodes and sink. Inside the backbone shortest path aggregation tree, two steps are done. The first step, select some B_node as cluster head (denoted as B_node) and make local adjustment intra-clusters if it is possible. The second step, adjust the shortest path aggregation tree. By using both particle swarm optimization (PSO) and ant colony optimization (ACO), a traveling salesman problem (TSP) cycle with shorter path length is obtained (this method is noted as TSP_PSO_ACO). This TSP cycle consists of all nodes in the aggregation backbone tree. Along this cycle, energy is offered to nodes except sink. The above strategy would be discussed for its feasibility and time complexity. The experimental results imply that the energy consumption is reduced by using local adjustment. The relative failure rate of nodes can be reduced in the later stage of WSN survival. Offering energy to a small number of nodes several times has no obvious impact on the overall energy consumption and survival time of WSN. The case with some small-sized obstacles in WSN area is also discussed. There are little affections on TSP cycles both from results on theoretical analysis and simulation if there are some obstacles with small size in WSN area.

Horizontal pattern of trees in the cenopopulation of <i>Larix gmelinii</i> (Rupr.) Rupr. in the forest tundra ecotone on the Taimyr Peninsula

The features of the horizontal spatial structure of multi-aged open forests of Gmelin larch (Larix gmelinii (Rupr.) Rupr.), forming the northern limit of tree vegetation in the forest-tundra ecotone in the eastern part of the Taimyr Peninsula, are considered. It was found that all studied tree stands are characterized by a mosaic structure, in which areas occupied by woody vegetation alternate with open spaces, the area of which ranges from 37 to 50%. In all cases, the composition of the tree layer includes both single trees and their aggregations (biogroups), share of which varies from 17 to 32%. Pair aggregation of trees predominates in biogroups, amounting to 83%. The predominance of the random nature of the location of trees in the formation of the horizontal pattern has been snown, but the specific type of spatial distribution is also determined by the stage of ontogenesis in which the cenopopulation is located.

Exploring the solution space for adaptive curriculum sequencing: Study of a multi-objective approach

Adaptive Curriculum Sequencing (ACS) is an important issue in personalized learning. In ACS problems, one desires the best sequence of learning materials that meet the profile of a given student. To do so, multiple features of the students and the materials used are necessary to generate good solutions. In fact, understanding the students’ goals, motivation, and preferences is not an easy task and, consequently, different Internet of Things (IoT) approaches to gather this information during the learning process have been proposed. Actually, some works from the literature consider five objectives and, in this case, one has a many-objective optimization problem. Instead of solving the optimization problem considering the multiple objectives individually, the usual approach is to obtain solutions for a weighted sum of the objective values using search approaches for mono-objective optimization problems. However, this kind of approach may bias the search and limits the capacity of finding good results. Here, we solve the multi-objective ACS problem considering five objective functions. NSGA-II, a well-known Genetic Algorithm for multi-objective optimization problems, was used. In addition, the aggregation trees were employed to reduce the number of objectives to two and three due to the large number of objectives in the original problem. ACS problems from the literature were used to comparatively evaluate the proposed methods and the results obtained were compared to those found by the traditional approach of summing the objective values. According to these results, the best curriculum sequences were reached when using the proposal.

Electric field induced associations in the double layer of salt-in-ionic-liquid electrolytes.

Ionic liquids (ILs) are an extremely exciting class of electrolytes for energy storage applications. Upon dissolving alkali metal salts, such as Li or Na based salts, with the same anion as the IL, an intrinsically asymmetric electrolyte can be created for use in batteries, known as a salt-in-ionic liquid (SiIL). These SiILs have been well studied in the bulk, where negative transference numbers of the alkali metal cation have been observed from the formation of small, negatively charged clusters. The properties of these SiILs at electrified interfaces, however, have received little to no attention. Here, we develop a theory for the electrical double layer (EDL) of SiILs where we consistently account for the thermoreversible association of ions into Cayley tree aggregates. The theory predicts that the IL cations first populate the EDL at negative voltages, as they are not strongly bound to the anions. However, at large negative voltages, which are strong enough to break the alkali metal cation-anion associations, these IL cations are exchanged for the alkali metal cation because of their higher charge density. At positive voltages, we find that the SiIL actually becomes more aggregated while screening the electrode charge from the formation of large, negatively charged aggregates. Therefore, in contrast to conventional intuition of associations in the EDL, SiILs appear to become more associated in certain electric fields. We present these theoretical predictions to be verified by molecular dynamics simulations and experimental measurements.

DYNATE: Localizing rare-variant association regions via multiple testing embedded in an aggregation tree.

Rare-variants (RVs) genetic association studies enable researchers to uncover the variation in phenotypic traits left unexplained by common variation. Traditional single-variant analysis lacks power; thus, researchers have developed various methods to aggregate the effects of RVs across genomic regions to study their collective impact. Some existing methods utilize a static delineation of genomic regions, often resulting in suboptimal effect aggregation, as neutral subregions within the test region will result in an attenuation of signal. Other methods use varying windows to search for signals but often result in long regions containing many neutral RVs. To pinpoint short genomic regions enriched for disease-associated RVs, we developed a novel method, DYNamic Aggregation TEsting (DYNATE). DYNATE dynamically and hierarchically aggregates smaller genomic regions into larger ones and performs multiple testing for disease associations with a controlled weighted false discovery rate. DYNATE's main advantage lies in its strong ability to identify short genomic regions highly enriched for disease-associated RVs. Extensive numerical simulations demonstrate the superior performance of DYNATE under various scenarios compared with existing methods. We applied DYNATE to an amyotrophic lateral sclerosisstudy and identified a new gene, EPG5, harboring possibly pathogenic mutations.

Effects of dispersed and aggregated retention on epiphytic microarthropod assemblages: A northern hardwood forest case study

Epiphytic lichens and bryophytes provide important habitat for microarthropods, yet little work has documented epiphytic microarthropod assemblages. Further, the response of epiphytes and their associated microarthropod communities to varying levels of forest disturbance and cutting intensity are not well known. We conducted a case study to provide an initial community description of epiphyte-dwelling microarthropods in Adirondack northern hardwoods, and to assess potential for impacts of aggregated and dispersed patterns of reserve tree retention to these communities. In total, 79 families of microarthropods were identified in the 0.14 m2 of samples. Late summer average daily maximum temperatures were 3.2 °C higher in the shelterwood, and three years following treatments the per-unit area dry weight of the bole-associated bryophyte Neckera pennata was 34 % lower in the shelterwood. Microarthropod richness and density were distinctly greater in epiphyte substrates at the base of trees, and were reduced in the shelterwood. Several indicator taxa were identified within the basal bryophytes in the reserve treatment, and in both bryophytes the microarthropod assemblages were more homogenous in the shelterwood. A few mobile and heavily sclerotized taxa persisted in shelterwood substrates, especially in the lichens which lacked 3-dimensional structure. This case study revealed a diverse community of microarthropods in northern hardwood forests that were structured by epiphyte type, height on tree and, potentially, by canopy removal, and suggests conservation value of aggregated tree retention when applying even-age regeneration methods with respect to epiphytic bryophytes, lichens, and their associated invertebrates.

Deformable Part Region Learning and Feature Aggregation Tree Representation for Object Detection.

Region-based object detection infers object regions for one or more categories in an image. Due to the recent advances in deep learning and region proposal methods, object detectors based on convolutional neural networks (CNNs) have been flourishing and provided promising detection results. However, the accuracy of the convolutional object detectors can be degraded often due to the low feature discriminability caused by geometric variation or transformation of an object. In this article, we propose a deformable part region (DPR) learning in order to allow decomposed part regions to be deformable according to the geometric transformation of an object. Because the ground truth of the part models is not available in many cases, we design part model losses for the detection and segmentation, and learn the geometric parameters by minimizing an integral loss including those part losses. As a result, we can train our DPR network without extra supervision, and make multi-part models deformable according to object geometric variation. Moreover, we propose a novel feature aggregation tree (FAT) so as to learn more discriminative region of interest (RoI) features via bottom-up tree construction. The FAT can learn the stronger semantic features by aggregating part RoI features along the bottom-up pathways of the tree. We also present a spatial and channel attention mechanism for the aggregation between different node features. Based on the proposed DPR and FAT networks, we design a new cascade architecture that can refine detection tasks iteratively. Without bells and whistles, we achieve impressive detection and segmentation results on MSCOCO and PASCAL VOC datasets. Our Cascade D-PRD achieves the 57.9 box AP with the Swin-L backbone. We also provide an extensive ablation study to prove the effectiveness and usefulness of the proposed methods for large-scale object detection.

An energy-efficient scheme for maximizing data aggregation tree lifetime in wireless sensor network

An energy-efficient scheme for maximizing data aggregation tree lifetime in wireless sensor network

Multi-Tenancy- and Redundancy-Aware In-Network Aggregation using Programmable Switches

Recent advances in programmable switches make it possible to aggregate data in partition-aggregation applications (e.g., MapReduce applications) at programmable switches. However, a well-designed aggregation scheme is indispensable for aggregating as much data as possible under the limited resources of programmable switches in an environment where multiple applications are running concurrently. In this article, we propose a multitenancy- and redundancy-aware in-network aggregation (MARINA) scheme that preferentially aggregates highly redundant data at a programmable switch and improves aggregation performance by constructing a multi-tenancyaware aggregation tree. Evaluation results demonstrate that MARINA can improve data aggregation performance by up to 81% compared with the conventional approach using statically partitioned resources and sequential aggregation.

Incorporating aggregation trees in two-archive algorithm 2 for solving high dimensional multi-objective electromagnetic optimization

The optimization designs of modern electromagnetic devices are often modeled as high dimensional multi-objective problems due to their high-quality demands in multiple functions and systemization. To identify and remove the redundant objectives, an interactive aggregation trees method embedded into the two-archive algorithm 2 is proposed to solve the high dimensional multi-objective optimizations. To balance the speed and the accuracy in finding the redundant objectives, conflict degree, conflict list and conflict flag are developed in an interactive aggregation trees frame. The two-archive algorithm 2 is modified to adjust the updated model obtained from the objective-reduction frame. Mathematical test functions and TEAM Workshop Problem 22 are used to test the performance of the proposed method. Numerical results demonstrate that the proposed method can remove the redundant objectives effectively and provide qualified solutions for the electromagnetic optimization designs.

TEAM: A MULTIPLE TESTING ALGORITHM ON THE AGGREGATION TREE FOR FLOW CYTOMETRY ANALYSIS.

In immunology studies, flow cytometry is a commonly used multivariate single-cell assay. One key goal in flow cytometry analysis is to detect the immune cells responsive to certain stimuli. Statistically, this problem can be translated into comparing two protein expression probability density functions (pdfs) before and after the stimulus; the goal is to pinpoint the regions where these two pdfs differ. Further screening of these differential regions can be performed to identify enriched sets of responsive cells. In this paper, we model identifying differential density regions as a multiple testing problem. First, we partition the sample space into small bins. In each bin, we form a hypothesis to test the existence of differential pdfs. Second, we develop a novel multiple testing method, called TEAM (Testing on the Aggregation tree Method), to identify those bins that harbor differential pdfs while controlling the false discovery rate (FDR) under the desired level. TEAM embeds the testing procedure into an aggregation tree to test from fine- to coarse-resolution. The procedure achieves the statistical goal of pinpointing density differences to the smallest possible regions. TEAM is computationally efficient, capable of analyzing large flow cytometry data sets in much shorter time compared with competing methods. We applied TEAM and competing methods on a flow cytometry data set to identify T cells responsive to the cytomegalovirus (CMV)-pp65 antigen stimulation. With additional downstream screening, TEAM successfully identified enriched sets containing monofunctional, bifunctional, and polyfunctional T cells. Competing methods either did not finish in a reasonable time frame or provided less interpretable results. Numerical simulations and theoretical justifications demonstrate that TEAM has asymptotically valid, powerful, and robust performance. Overall, TEAM is a computationally efficient and statistically powerful algorithm that can yield meaningful biological insights in flow cytometry studies.

Efficient Tree Aggregation and Processing Time for Wireless Sensor Networks

Efficient Tree Aggregation and Processing Time for Wireless Sensor Networks

Hybrid Delay‐Minimization Scheduling Algorithm of FT and MPTS in WSN Data Aggregation

The data acquisition of Internet of Things (IoT) is mostly brought out by wireless sensor networks (WSNs), and the efficiency of IoT is directly affected by the time delay in the process of data acquisition, which is researched mainly by the data aggregation of WSNs. Minimizing the delay in the data aggregation process is one of the most important operations. In order to reduce the data aggregation delay, a hybrid delay‐minimization data aggregation scheduling (HDDAS) algorithm is proposed, which optimizes the delay from the two main aspects that affect the delay, namely, the transmission path and the collision rate. Accordingly, the algorithm is divided into two phases: the aggregation tree construction and the data transmission scheduling. In the aggregation tree construction phase, the fat tree (FT) is introduced to generate an optimal aggregation tree with the shortest path; in the data transmission scheduling phase, the maximum parallel transmission set (MPTS) is adopted to increase the amount of the collision‐free data transmission. The simulation experiment results demonstrate that the HDDAS algorithm proposed in the paper has performed favorably in terms of the data aggregation delay.

Achieving Online and Scalable Information Integrity by Harnessing Social Spam Correlations

Malicious web links, social rumors, fraudulent advertisements, faked comments, and biased propaganda are overwhelmingly influencing online social networks. Enabling information integrity is a hot topic in both academia and industry. Traditional social spam detection techniques rely on centralized processing, focusing only on one specific set of data sources, thereby ignoring the social spam correlations between distributed data sources. In this paper, we propose an online and scalable misinformation detection system, named Spiral, to uncover social spam by leveraging the correlations between different social data sources in geo-distributed sites. The key insight in our approach is to amplify the effectiveness of state-of-the-art techniques to detect inappropriate posts by enabling the efficient large-scale propagation of detection information across domains. The novelty of our design lies in three key components: (1) a decentralized distributed hash-table-based tree overlay deployment for harvesting and uncovering deceptive information spreading in multiple online social networks communities; (2) a progressive aggregation tree for collecting the properties of these posts and creating new classifiers to actively filter out the propagation of inappropriate posts; and (3) a group communication structure that allows multiple groups to exchange the correlations among distributed social data sources. We designed and implemented a prototype of the Spiral system. Our large-scale experiments, using real-world social data, demonstrate Spiral’s scalability, effective load-balancing, and efficiency in online spam detection for social networks.

Intelligent Water Drops Algorithm-Based Aggregation in Heterogeneous Wireless Sensor Network

This paper provides a novel implementation of the intelligent water drops (IWD) method for resolving data aggregation issues in heterogeneous wireless sensor networks (WSN). When the aggregating node is utilized to transmit the data to the base station, the research attempts to show that the traffic situations of WSN may be modified appropriately by parameter tuning and algorithm modification. IWD is used to generate an optimum data aggregation tree in WSN as one of its applications. IWD assumes that all nodes in the environment are identical, resulting in identical parameter updates for all nodes. In practical scenarios, however, diverse nodes with variable beginning energy, communication range, and sensing range characteristics are deployed. In order to replicate the influence of heterogeneity in the environment, improved IID (IIWD) is offered as an enhancement to the original IID. The suggested enhancement is appropriate for scenarios in which the aggregation node is utilized to transmit data to the base station in heterogeneous configurations. In terms of residual energy, dead nodes, payload, and network lifespan, a series of simulation results demonstrates that the proposed IIWD significantly improves the accuracy and effectiveness of the IWD method in comparison.

Accelerating model synchronization for distributed machine learning in an optical wide area network

Geo-distributed machine learning (Geo-DML) adopts a hierarchical training architecture that includes local model synchronization within the data center and global model synchronization (GMS) across data centers. However, the scarce and heterogeneous wide area network (WAN) bandwidth can become the bottleneck of training performance. An intelligent optical device (i.e., reconfigurable optical all-drop multiplexer) makes the modern WAN topology reconfigurable, which has been ignored by most approaches to speed up Geo-DML training. Therefore, in this paper, we study scheduling algorithms to accelerate model synchronization for Geo-DML training with consideration of the reconfigurable optical WAN topology. Specifically, we use an aggregation tree for each Geo-DML training job, which helps to reduce model synchronization communication overhead across the WAN, and propose two efficient algorithms to accelerate GMS for Geo-DML: MOptree, a model-based algorithm for single job scheduling, and MMOptree for multiple job scheduling, aiming to reconfigure the WAN topology and trees by reassigning wavelengths on each fiber. Based on the current WAN topology and job information, mathematical models are built to guide the topology reconstruction, wavelength, and bandwidth allocation for each edge of the trees. The simulation results show that MOptree completes the GMS stage up to 56.16% on average faster than the traditional tree without optical-layer reconfiguration, and MMOptree achieves up to 54.6% less weighted GMS time.

Cautious weighted random forests

Random forest is an efficient and accurate classification model, which makes decisions by aggregating a set of trees, either by voting or by averaging class posterior probability estimates. However, tree outputs may be unreliable in presence of scarce data. The imprecise Dirichlet model (IDM) provides workaround, by replacing point probability estimates with interval-valued ones. This paper investigates a new tree aggregation method based on the theory of belief functions to combine such probability intervals, resulting in a cautious random forest classifier. In particular, we propose a strategy for computing tree weights based on the minimization of a convex cost function, which takes both determinacy and accuracy into account and makes it possible to adjust the level of cautiousness of the model. The proposed model is evaluated on 25 UCI datasets and is demonstrated to be more adaptive to the noise in training data and to achieve a better compromise between informativeness and cautiousness.