Child Nodes Research Articles

Energy and Delay Aware Data Aggregation in Routing Protocol for Internet of Things.

Energy conservation is one of the most critical problems in Internet of Things (IoT). It can be achieved in several ways, one of which is to select the optimal route for data transfer. IPv6 Routing Protocol for Low Power and Lossy Networks (RPL) is a standardized routing protocol for IoT. The RPL changes its path frequently while transmitting the data from source to the destination, due to high data traffic in dense networks. Hence, it creates data traffic across the nodes in the networks. To solve this issue, we propose Energy and Delay Aware Data aggregation in Routing Protocol (EDADA-RPL) for IoT. It has two processes, namely parent selection and data aggregation. The process of parent selection uses routing metric residual energy (RER) to choose the best possible parent for data transmission. The data aggregation process uses the compressed sensing (CS) theory in the parent node to combine data packets from the child nodes. Finally, the aggregated data transmits from a downward parent to the sink. The sink node collects all the aggregated data and it performs the reconstruction operation to get the original data of the participant node. The simulation is carried out using the Contiki COOJA simulator. EDADA-RPL’s performance is compared to RPL and LA-RPL. The EDADA-RPL offers good performance in terms of network lifetime, delay, and packet delivery ratio.

Watermarking in halftone image with maximal child node dependence coefficients in wavelet domain

To achieve high image quality, this paper proposes a novel approach but efficient algorithm for halftoning watermarking. Wavelet coefficients of edge details have strong relations, while the dependence of coefficients of texture is more weak. On the other hand, large wavelet coefficients contain the information of image brink. Considering the relationship of intra-scale and maximal coefficients, the boundary error measure is built. While the gray image turns to halftone image using proposed algorithm, binary watermark is embedded in the edge position. In order to enhance the ability to resist cropping and smearing attack, the watermark image is pre-treated with Arnold transformation processing. The proposed method is effective, because the halftone image is embedded as a watermark sequence during the halftone process. Experiment results show that this algorithm does not cause significant distortion of the image and is good resistance to cropping, daub, JPEG compression and noise attacks.

Integrated Load Balancing and Void Healing Routing with Cuckoo Search Optimization Scheme for Underwater Wireless Sensor Networks

In underwater wireless sensor networks, routing play a vital role in selecting an optimal path for packet forwarding. In routing scheme, most of the existing work is suffering from both load balancing and void node issue. This is due to the environmental interference, overloaded data, energy depletion, random deployment and mobility of the nodes. However, it causes loss of packet, high energy depletion and bad network quality. We have resolved this issue by implementing load balancing and void healing routing using cuckoo search optimization (CSO) scheme. In this scheme, first we placed the parent node and identify their child node within the transmission range in each level of the network. Then, we applied load balancing with priority based packet forwarding to maintain the uneven distribution of the load and reduces the end-to-end delay. Next, void healing routing with CSO scheme is addressed to recover the convex and concave void issue in the network. A novel multi-objective fitness function is also formulated for selecting the optimal number of nodes. In packet routing, each child node is responsible for receiving the packets from their neighbor nodes and transferred to the parent node. After receiving the packets at parent node, autonomous underwater vehicle is used for collecting the relevant packets from each parent node through minimum travelling time and send towards the base station. The performance evaluation of proposed scheme shows better network quality, packet delivery ratio, less energy consumption and delay over the existing solutions.

Discriminative Fast Hierarchical Learning for Multiclass Image Classification.

In this article, a discriminative fast hierarchical learning algorithm is developed for supporting multiclass image classification, where a visual tree is seamlessly integrated with multitask learning to achieve fast training of the tree classifier hierarchically (i.e., a set of structural node classifiers over the visual tree). By partitioning a large number of categories hierarchically in a coarse-to-fine fashion, a visual tree is first constructed and further used to handle data imbalance and identify the interrelated learning tasks automatically (e.g., the tasks for learning the node classifiers for the sibling child nodes under the same parent node are strongly interrelated), and a multitask SVM classifier is trained for each nonleaf node to achieve more effective separation of its sibling child nodes at the next level of the visual tree. Both the internode visual similarities and the interlevel visual correlations are utilized to train more discriminative multitask SVM classifiers and control the interlevel error propagation effectively, and a stochastic gradient descent (SGD) algorithm is developed for learning such multitask SVM classifiers with higher efficiency. Our experimental results have demonstrated that our fast hierarchical learning algorithm can achieve very competitive results on both the classification accuracy rates and the computational efficiency.

Secure hierarchical Bitcoin wallet scheme against privilege escalation attacks

As the rising popularity of Bitcoin, people tend to use Bitcoin wallets to manage the keys for spending or receiving funds. Instead of generating randomly pairs of keys, which may need higher space complexity for key management, hierarchical deterministic (HD) wallets derive all the keys from a single seed, which is sufficient to recover all the keys, to reduce the complexity of key management. In an HD wallet, it allows users to generate child public keys from the parent public keys without knowing any of the corresponding private keys. This feature allows a permitted auditor to derive all the public keys for auditing. However, this feature makes HD wallets suffered from so-called privilege escalation attacks, where the leakage of any child private key along with its parent public key will expose the other child private keys. To confront with this security flaw, we propose a novel HD wallet scheme that gives out a signature with trapdoor hash functions instead of directly giving private keys for signing. Since it conceals private keys from any child nodes, it can prevent from privilege escalation attacks. Nevertheless, the proposed scheme also provides unlinkability between two public keys to achieve anonymity of user identities and high scalability to the derivations of huge amount of keys. Thus, the proposed scheme achieves user anonymity, public key derivation, and high scalability.

RPL Enhancement for a Parent Selection Mechanism and an Efficient Objective Function

In routing protocol (RPL), a child node exclusively makes the decision in selecting a parent based on the Rank value. In this paper, we make the parent selection mechanism, a mutual decision between the potential parent node and the child node, which significantly improves the network throughput under a heavy and highly dynamic load. To demonstrate the effectiveness of our parent selection mechanism, we used it to balance the load to avoid congestion by preventing the child node from using the congested parent node. The proposed parent selection mechanism is implemented as an add-on feature in RPL and hence is called enhanced RPL (EN-RPL). Furthermore, we introduce a new objective function for efficient routing (OF-ER) that is based on the newly defined composite efficient routing (CER) metric. CER metric takes important metrics, namely, the link quality, queue utilization, lifetime of node, delay, and number of bottleneck nodes, into account for optimal parent selection. The simulation results demonstrate that EN-RPL lowers the packet loss by up to 37% and reduces energy consumption by up to 46.5% compared with the standard RPL. The evaluation results of OF-ER outperform the state-of-the-art load balancing-based OF with respect to the network stability, reliability, and energy efficiency.

A solution for synchronous incremental maintenance of materialized views based on SQL recursive query

Materialized views are excessively stored query execution results in the database. They can be used to partially or completely answer queries which will be further appeared instead of re-executing query from the scratch. There is a large number of published works that address the maintenance, especially incremental update, of materialized views and query rewriting for using those ones. Some of them support materialized views based on recursive query in datalog language. Although most of datalog queries can be transferred into SQL queries and vise versa but it is not the case for recursive queries. Recursive queries in the data log try to find all possible transitive closures. Recursive queries in SQL (Common Table Expression – CTE) return direct links but not transitive closures. In this paper, we propose efficient methods for incremental update of materialized views based on CTE; and then propose an algorithm for generating source codes in C language for any input SQL recursive queries. The synthesized source codes implement our proposed incremental update algorithms according to inserted/deleted/updated record set in the base tables. This paper focuses mainly on the recursive queries whose execution results are directed tree-structured data. The two cases of tree node are considered. In the first case, a child node has only one parent node and in the second case, a child node can have many parent nodes. Those two cases represent the two types of relationships between entities in real world, that are one–to–many and many–to–many, respectively. For the one–to–many relationships, the relationship data is accompanied with the records describing the child using some fields. Those fields are set as null in deleting a concrete relationship. For the many–to–many relationships, it is stored in a separate table and the concrete relationships are removed by deleting describing records from that table. Considering of enforcing referential integrity may help to reduce the searching space and therefore, help to improve the performance. However, the set of tree nodes or tree edges can be manipulated. All those combinations lead to different algorithms. The experimental results are provided and discussed to confirm the effectiveness of our proposed methods

PLANET: A radial layout algorithm for network visualization

Tree layouts are among the key approaches for network visualization, and are of particular importance for exploring the hierarchical structure of networks. However, visualizations of the overall network structure and its hierarchical relationships can rarely be optimized simultaneously. This paper presents a radial layout algorithm called PLANET that enables users to explore the network structure from a root node, while maintaining readability. In order to distribute the nodes evenly and minimize edge crossings, we define a list of angle assignment rules for displaying child nodes which can automatically maximize the tunable angles between parent and child nodes, and to uniformly divide the angles of child nodes. Using these rules, the structural properties of the network such as hubs can be properly conveyed, and the readability of nodes that are far from the root can be guaranteed. Our experimental results show that PLANET is comparable to similar algorithms in terms of execution time, and gives better performance in terms of node distribution, variance of edge length and number of edge crossing; these advantages become greater for networks with large diameters.

Exploiting Related and Unrelated Tasks for Hierarchical Metric Learning and Image Classification.

In multi-task learning, multiple interrelated tasks are jointly learned to achieve better performance. In many cases, if we can identify which tasks are related, we can also clearly identify which tasks are unrelated. In the past, most researchers emphasized exploiting correlations among interrelated tasks while completely ignoring the unrelated tasks that may provide valuable prior knowledge for multi-task learning. In this paper, a new approach is developed to hierarchically learn a tree of multi-task metrics by leveraging prior knowledge about both the related tasks and unrelated tasks. First, a visual tree is constructed to hierarchically organize large numbers of image categories in a coarse-to-fine fashion. Over the visual tree, a multi-task metric classifier is learned for each node by exploiting both the related and unrelated tasks, where the learning tasks for training the classifiers for the sibling child nodes under the same parent node are treated as the interrelated tasks, and the others are treated as the unrelated tasks. In addition, the node-specific metric for the parent node is propagated to its sibling child nodes to control inter-level error propagation. Our experimental results demonstrate that our hierarchical metric learning algorithm achieves better results than other state-of-the-art algorithms.

Taxonomy Proposal for Research on Reversible Logic

Reversible Logic has become a topic of interest among global researchers for its heat arresting attribute, the very reason for which CMOS devices are witnessing performance thresholds. Within a decade of substantial growth, reversible logic is quoted as one of the valued emerging technologies. The growth of research in this domain has exhibited enormous volumes of research publications. Searching the perfect published article of interest among the plethora of publications demand an enormous amount of literature study and hence is highly time-consuming. After accessing a substantial amount of articles, only a very few tend to address our needs. Hence, we propose a taxonomy for research on reversible logic in this paper which concentrates articles of relevant/similar nature into nodes so that researchers can get a first-hand reference for their choice of interest. Knowledge of pre-requisites for gaining insights into a particular domain also mandates a thorough amount of literature survey. Our taxonomy takes care of those issues by concentrating the nodes into a tree-like structure thereby providing the pre-requisite (parent node) for the node of interest (child node). For generating the taxonomy, we have used the Cosine Similarity function for measuring the distance between two author keywords. The taxonomy proposal in this paper will serve upcoming researchers in getting references for their choice of the domain rather than a comprehensive survey of the higher magnitude of published data. Researchers pursuing research in a certain domain too will benefit from the taxonomy.

A novel Error-Correcting Output Codes algorithm based on genetic programming

Error-Correcting Output Codes (ECOC) is widely used in the field of multiclass classification. As an optimal codematrix is key to the performance of an ECOC algorithm, this paper proposes a genetic programming (GP) based ECOC algorithm (GP-ECOC). In the design of individual of our GP, each terminal node represents a class, and nonterminal nodes combine the classes in their child nodes. In this way, an individual is a class combination tree, and represents an ECOC codematrix. A legality checking process is embedded in our algorithm to check each codematrix, so as to ensure each codematrix satisfying ECOC constraints. Those violating the constraints will be corrected by a proposed Guided Mutation operator. Before fitness evaluation, a local optimization algorithm is proposed to append new columns for tough classes, so as to improve the generalization ability of each individual and accelerate the evolutionary speed. In this way, our GP can evolve optimal codematrices through the evolutionary process. Experiments show that compared with other ensemble algorithms, our algorithm can achieve stable and high performances with relatively small ensemble scales on various UCI data sets. To the best of our knowledge, it is the first time that GP has been applied to implement the ECOC encoding algorithm. Our Python code is available at https://github.com/samuellees/gpecoc.

Improving Wireless Sensor Network Lifetime Using Self-Organizing Protocol

This paper proposes a E-cient Tree-based Self-organizing Protocol to improving wireless sensor network lifetime. all nodes are divided into two kinds: network nodes and non-network nodes Network nodes broadcast packets to select child nodes and non-network nodes collect packets to join in network. During the self-organization process we take hop, residual energy, number of child node and communication distance into account to calculate the weight of available sink nodes, and then select the node with max weight as sink node. After a non-network node joins the network successful it will work as a network node to searching child nodes. A tree network can be constructed one layer by one layer. For balancing energy consumption and prolonging network lifetime we adjust the topology dynamic. All experiments were done with NS2 Furthermore, the success rate of packet and Life time is much higher compared with LBT.

Geometry Coding for Dynamic Voxelized Point Clouds Using Octrees and Multiple Contexts.

We present a method to compress geometry information of point clouds that explores redundancies across consecutive frames of a sequence. It uses octrees and works by progressively increasing resolution of the octree. At each branch of the tree, we generate an approximation of the child nodes by a number of methods which are used as contexts to drive an arithmetic coder. The best approximation, i.e. the context that yields the least amount of encoding bits, is selected and the chosen method is indicated as side information for replication at the decoder. The core of our method is a context-based arithmetic coder in which a reference octree is used as reference to encode the current octree, thus providing 255 contexts for each output octet. The 255×255 frequency histogram is viewed as a discrete 3D surface and is conveyed to the decoder using another octree. We present two methods to generate the predictions (contexts) which use adjacent frames in the sequence (inter-frame) and one method that works purely intra-frame. The encoder continuously switches the best mode among the three and conveys such information to the decoder. Since an intra-frame prediction is present, our coder can also work in purely intra-frame mode, as well. Extensive results are presented to show the method's potential against many compression alternatives for the geometry information in dynamic voxelized point clouds.

The Organization of Point Cloud Data Based on the Compact Octree Model

While using the octree model to organize a huge amount of scattered point cloud data, it is easy to generate an unbalanced tree structure. Thus, a compact octree model is proposed. It reconstructs the minimum bounding cuboid of each child node to shrink its spatial scope after each division, which effectively decreases the tree depth of scattered point cloud data. Through a series of tests, we verify the feasibility of our proposed spatial index model.

A novel cascaded deep neural network for analyzing smart phone data for indoor localization

This paper proposes and develops a cascaded deep neural network (CDNN) to analyze data, collected using the sensors of smart-phones, to accurately localize an object in an indoor environment. The indoor localization of an object (living or non-living) is important now a days, especially for those living alone or for securing valuable things. There are many existing studies that have attempted to identify the location of an inhabitant in a room through the analysis of the radio signal strength (RSS), with varying success. The strength of the RSS varies with distance and the presence of obstacles within the line of sight. As a result, an automated system using RSS signal in one environment might not work in another one. In this paper therefore, we propose and develop a different localization method based on data collected from different sensors embedded in a smart-phone. To analyze and predict the exact location within a very short distance (say a 1 to 1.5 m radius), we develop a novel CDNN. In this model, several deep neural networks (DNNs) are used in a tree structure where each node of the tree is an independent DNN. The output of any parent DNN at a specific node is used to decide which child node will be used to localize the position of an object. The combination of smart phone sensor data and the CDNN is applied in an academic building with an area of 175m2. The experimental results show that the proposed method can localize the subject/IoT object with a 74.17% accuracy within a 1.5 m radius and a 53% (approx.) accuracy within a 1 m radius. These performances are higher than those reported in many recent studies.

Weighted Oblique Decision Trees

Decision trees have attracted much attention during the past decades. Previous decision trees include axis-parallel and oblique decision trees; both of them try to find the best splits via exhaustive search or heuristic algorithms in each iteration. Oblique decision trees generally simplify tree structure and take better performance, but are always accompanied with higher computation, as well as the initialization with the best axis-parallel splits. This work presents the Weighted Oblique Decision Tree (WODT) based on continuous optimization with random initialization. We consider different weights of each instance for child nodes at all internal nodes, and then obtain a split by optimizing the continuous and differentiable objective function of weighted information entropy. Extensive experiments show the effectiveness of the proposed algorithm.

Efficient two-dimensional Haar$$^+$$ synopsis construction for the maximum absolute error measure

Several wavelet synopsis construction algorithms were previously proposed for optimal Haar\(^+\) synopses. Recently, we proposed the OptExtHP-EB algorithm to find an optimal one-dimensional \(\hbox {Haar}^+\) synopsis. By utilizing the novel properties of optimal synopses, OptExtHP-EB represents the set of optimal synopses in a node of a \(\hbox {Haar}^+\) tree by a set of extended synopses. While it is much faster than the previous \(\hbox {Haar}^+\) synopsis construction algorithms, it can handle only one-dimensional data. In this paper, we propose the OptExtHP-EB2D algorithm for two-dimensional \(\hbox {Haar}^+\) synopses by extending OptExtHP-EB. While a one-dimensional \(\hbox {Haar}^+\) tree has only two child nodes and three coefficients in a node, a two-dimensional \(\hbox {Haar}^+\) tree is much more complex in that it has four child nodes and seven coefficients per node. Thus, for each possible subset of the coefficients selected in a node, we develop the efficient methods to compute a set of optimal synopses denoted by extended synopses. Our experiments confirm the effectiveness of our proposed OptExtHP-EB2D algorithm.

Task Scheduling in Graphic Processing Units Heterogeneous With Density of The Graph

An efficient scheduling algorithm is significant to achieve high performance in heterogeneous consisted by the central processing units (CPU) and the graphic processing unit (GPU). However, most of the scheduling algorithms are not suitable to meet the present demands of the heterogeneous. According to the characteristics of the direction acyclic graph, the direction acyclic graph nodes density list scheduling algorithm (DAG-NDLS) was proposed. The algorithm had two important phase, the first phase was task prioritizing, selected the ready task with highest priority, defined by the summation of the node density and the transmission between the node and child nodes, and the second phase was processor selection, selected the processors for the tasks with the earliest finish time minimum. In the end, compared the performances of the proposed algorithm with the heterogeneous earlier finish time algorithm and the high performance task scheduling algorithm, the results of the proposed algorithm were the best in case of the efficiency and utilization.

Local Barycenter Based Efficient Tree-Support Generation for 3D Printing

3D printing technology that enables fabrication of custom objects for home users has become popular in recent years. However, additional support structures have to be generated to support the overhanging models. As the critical step of 3D printing, the support generation technique plays an important role in 3D printing software in engineering applications. In this paper, a new Local Barycenter based Tree Support (LBTS) algorithm is developed with good property of higher efficiency and less material using to generate support structures for overhang regions acquired from the mesh solid. Based on a divide and conquer strategy, supporting points generated from an overhang region can be divided into sub-regions iteratively. In each sub-region, a new tree node is calculated based on the local barycenter of children nodes, then new tree pillars are created. Based on the constructed tree topology, variable diameters of tree nodes are calculated to ensure the tree stability during the printing. Moreover, with regard to the interference search, which is an inevitable and extraordinarily time-consuming process in conventional support approaches, an efficient interference search algorithm with constant time complexity is developed based on the proposed Double-Voxel based Interference Search Tool (DVIST). Experimental results show that the proposed LBTS approach outperforms the previous methods in algorithm execution efficiency and support structure material saving.

One Core Task of Interpretability in Machine Learning — Expansion of Structural Equation Modeling

Structural equation modeling (SEM) is a system of two kinds of equations: a linear latent structural model (SM) and a linear measurement model (MM). The latent structure model is a causal model from the latent parent node to the latent child node. Meanwhile, MM’s link is from latent variable parent node to observed variable child node. However, researchers should determine the initial causal order between variables based on experience when applying SEM. The main reason is that SEM does not fully construct causal models between observed variables (OVs) from big data. When the artificial causal order is contrary to the fact, the causal inference from SEM is doubtful, and the implicit causal information between the OVs cannot be extracted and utilized. This study first objectively identifies the causal order of variables using the DirectLiNGAM method widely accepted in recent years. Then traditional SEM is converted to expanded SEM (ESEM) consisting of SM, MM and observation model (OM). Finally, through model testing and debugging, ESEM with good fit with data is obtained.