Data Aggregation Approach Research Articles

Local Policy Synergy with Law Number 6 of 2014 Concerning Village in the Nagari Government in West Sumatera Indonesia

Aim: The practice of decentralization in Indonesia is based on centralistic bureaucratic thinking so that the implementation of government occurs in a uniform manner, such as governance structures, politics, institutions, law, finance in central and regional governments. However, the reality of variations between one region and another does not allow for uniformity. This is based on historical background, rights of regional origin, religion, language, culture, politics, geography, economy, and ethnicity that can give birth to conflicts. The decentralization technique is supposed to establish synergy between local wisdom and the Nagari Ministry in West Sumatra’s fulfillment of Law Number 6 of 2014 addressing communities.Methodology: The methodology used in this research was descriptive in nature with a research design and qualitative in nature. On four research sites, respondents were identified using a purposive approach, namely Nagari Kapau, Nagari Minangkabau, Nagari Mahakarya, and Nagari Anduring in the West Sumatra Province of Indonesia. Surveys, in-depth discussions, and documenting research were used to collect information. Data aggregation approaches were used to check the veracity of the information in this investigation.Findings: The research results prove that the conception of the Nagari governance model can accommodate the ideas of Law Number 6 of 2014 as a bottom-up and top-down government model; however, it is hard to practice the Mixing government model.Implications/Novel Contribution: This study implies that the implementation of local wisdom to Nagari public administration can be accommodated. This study also has suggested that next researchers consider the bottom-up government model and top-down government model that can provide a situation of harmony between the interests of the community or citizens of the village with a government that is structured from the top (top-down) through Law number 6 of 2014 and other legislation. Such efforts are common in many countries, and findings may be generalized to other regional countries for key insights.

False Discovery Rate Control Under General Dependence By Symmetrized Data Aggregation

We develop a new class of distribution-free multiple testing rules for false discovery rate (FDR) control under general dependence. A key element in our proposal is a symmetrized data aggregation (SDA) approach to incorporating the dependence structure via sample splitting, data screening, and information pooling. The proposed SDA filter first constructs a sequence of ranking statistics that fulfill global symmetry properties, and then chooses a data-driven threshold along the ranking to control the FDR. The SDA filter substantially outperforms the Knockoff method in power under moderate to strong dependence, and is more robust than existing methods based on asymptotic p-values. We first develop finite-sample theories to provide an upper bound for the actual FDR under general dependence, and then establish the asymptotic validity of SDA for both the FDR and false discovery proportion control under mild regularity conditions. The procedure is implemented in the R package sdafilter. Numerical results confirm the effectiveness and robustness of SDA in FDR control and show that it achieves substantial power gain over existing methods in many settings.

An Empirical Cluster Head Selection and Data Aggregation Scheme for a Fault-Tolerant Sensor Network

In order to improve the sensor network, the nodes resources should be used in a well-organized way. The new cluster-based routing protocols and data aggregation approach have helped to increase the lifespan of the network. The methods of data aggregation eliminate the network's redundant data packets, which extends the lifetime of the network. A fault tolerant cluster head selection and data aggregation scheme (FT-CHSDA) that performs node clustering and data aggregation in the network is demonstrated in this study. The suggested method uses the energy level of the node to pick the most energy-efficient node as the head of the cluster and executes data aggregation to reduce redundant data packets. In addition, the use of a concept called backup node in a cluster has implemented a novel method to make the network accessible and run without any interruption. In the NS2 simulator, the simulation of the proposed scheme (FT-CHSDA) is being discussed. Using different performance metrics to assess its effectiveness, the proposed scheme (FT-CHSDA) is contrasted with existing proto-cols.

A Survey on Various Clustering Algorithms in WSN for Optimal Energy Utilisation

Wireless sensor networks comprise of an expansive number of distributed sensor gadgets, which are associated and composed through multi-hop steering. Because of the presence of related data and excess in measuring data, data messages can be joined and converged by performing data aggregation work in the steering procedure. To diminish energy utilization is a noteworthy enhancement target of data aggregation approaches, which can be accomplished by diminishing the mandatory correspondence load of steering. To improvise the network lifetime as much as possible in Wireless Sensor Networks (WSNs) the ways for data move are picked in a way that the aggregate energy used along the way is limited. To help high adaptability and better data aggregation, sensor nodes are routinely collected into disjoint, non-covering subsets called clusters. Clusters make various leveled WSNs which consolidate proficient use of constrained assets of sensor nodes and in this manner broadens network lifetime. The objective of this paper is to demonstrate a forefront survey on clustering calculations announced in the writing of WSNs. This paper presents different energy effective clustering calculations in WSNs. From the hypothetical level, an energy show is proposed to approve the advantages of data aggregation on energy utilization. The key parameters which may affect the aggregation execution are additionally examined.

Selection of industrial and organisational psychology master’s students: Exploring the predictive validity of a person–job fit approach

Orientation: Valid selection of master’s students is essential to the training and development of competent Industrial and Organisational Psychologists. Research purpose: To validate a person-job fit data aggregation approach based on an integration of psychometric results in the selection of Industrial and Organisational Psychology master’s students. Motivation for the study: The Employment Equity Act requires psychological assessment to be valid, reliable, culturally fair and based on the inherent requirements of the job. This mandates context-specific validation research, such as the current study. Research approach/design and method: A non-experimental design was applied to secondary data ( N = 133) derived from the Occupational Personality Questionnaire, Verify Ability Tests and academic success results of 5 student cohorts registered in the years 2012 to 2016. Data was analysed by means of descriptive and correlation statistics. Main findings: The overall Person Job Match score did not show a statistically significant relationship with academic success. A significant relationship was observed between cognitive ability and academic success. Specific Person Job Match competencies presenting a significant relationship with academic success, contained scores from both personality and cognitive measures. Practical implication: Ability tests should be weighted more strongly in selecting master’s students. A review of the master’s students’ competency profile may be needed to align with future world of work demands, and to improve its predictive role in academic success. Contribution/value added: This study contributes to the predictive validity of the selection criteria for Industrial and Organisational Psychology master’s students.

Marginal and average weight-enabled data aggregation mechanism for the resource-constrained networks

In Wireless Sensor Networks (WSNs), data redundancy is a challenging issue that not only introduces network congestion but also consumes a considerable amount of sensor node resources. Data redundancy occurs due to the spatial and temporal correlation among the data gathered by the neighboring nodes. Data aggregation is a prominent technique that performs in-network filtering of the redundant data and accelerates the knowledge extraction by eliminating the correlated data. However, most of the data aggregation techniques have lower accuracy as they do not cater for erroneous data from faulty nodes and pose an open research challenge. To address this challenge, we have proposed a novel, lightweight, and energy-efficient function-based data aggregation approach for a cluster-based hierarchical WSN. Our proposed approach works at two levels, i.e., at the node level and at the cluster head level. At the node level, the data aggregation is performed using Exponential Moving Average (EMA) and a threshold-based mechanism is adopted to detect any outliers for improving the accuracy of aggregated data. At the cluster head level, we have employed a modified version of Euclidean distance function to provide highly-refined aggregated data to the base station. Our experimental results show that our approach reduces the communication cost, transmission cost, energy consumption at the nodes and cluster heads, and delivers highly-refined and fused data to the base station.

Implications of data aggregation method on crop model outputs – The case of irrigated potato systems in Tasmania, Australia

Crop models were originally developed for application at the field scale but are increasingly used to assess the impact of climate and/or agronomic practices on crop growth and yield and water dynamics at larger scales. This raises the question of how data aggregation approaches affect outputs when using crop models at large spatial scales. This study investigates how input and output data aggregation affected simulated rainfed and irrigated potato yield and irrigation water requirement (IWR) across potato production areas in Tasmania, Australia. First, the yield and IWR with aggregated model inputs at 15, 25 and 40 km resolutions (input aggregation) was simulated. Second, simulated model outputs generated with high-resolution input data were aggregated to 15, 25 and 40 km resolutions (output aggregation) and compared to the corresponding yield and IWR with simulations based on input data aggregation. Finally, the differences (D) (DY and DIWR for yield and IWR, respectively) between grids using input and output aggregation were evaluated. The results indicate that the effect of input and output data aggregation on yield depends on water-driven factors including plant available water capacity (PAWC), rainfall and irrigation. Maximum D values were found for rainfed yield (4.4 t ha−1) and IWR (137 mm). DY variations were correlated with the differences of PAWC caused by data aggregation in 82 % of potato production areas. Differences between aggregation methods were reduced when growing season rainfall increased. We conclude that PAWC and the source of water (rainfall or rainfall + irrigation) explained the larger errors associated with the input and output data aggregation on simulated potato yield and IWR. Future studies should consider the data aggregation method in their assessment to minimize errors and therefore produce higher quality advice or farming decisions.

Route Duration Prediction in a Stochastic and Dynamic Vehicle Routing Problem with Short Delivery Deadlines

Abstract We are facing a real-world vehicle routing problem where orders arrive dynamically over the day at an online store and have to be delivered within short time. Stochastic information in form of the expected number and weight of orders and the traffic congestion level is available upfront. The goal is to predict the average time needed to deliver an order for a given time and day. This information is desirable for both routing decisions in the short horizon and planning vehicle drivers’ shifts with just the right capacity prior to the actual day. We compare a white box linear regression model and a neural network based black box model on historic route data collected over three months. We employ a hourly data aggregation approach with sampling statistics to estimate the ground truth and features. The weighted mean square error is used as loss function to favor samples with less uncertainty. A mean validation R2 score over 10 x 5-fold cross-validations of 0.53 indicates a substantial amount of unexplained variance. Both predictors are slightly optimistic and produce median standardized absolute residuals of about one.

An energy-efficient data aggregation approach for cluster-based wireless sensor networks

In wireless sensor networks (WSNs), data redundancy is a challenging issue that not only introduces network congestion but also consumes considerable sensor node resources. Data redundancy occurs due to the spatial and temporal correlations among the data gathered by the neighboring nodes. Data aggregation is a prominent technique that performs in-network filtering of the redundant data and accelerates knowledge extraction by eliminating the correlated data. However, most data aggregation techniques have low accuracy because they do not consider the presence of erroneous data from faulty nodes, which represents an open research challenge. To address this challenge, we have proposed a novel, lightweight, and energy-efficient function-based data aggregation approach for a cluster-based hierarchical WSN. Our proposed approach works at two levels: the node level and the cluster head level. At the node level, the data aggregation is performed using the exponential moving average (EMA), and a threshold-based mechanism is adopted to detect any outliers to improve the accuracy of data aggregation. At the cluster head level, we have employed a modified version of the Euclidean distance function to provide highly refined aggregated data to the base station. Our experimental results show that our approach reduces the communication cost, transmission cost, and energy consumption at the nodes and cluster heads and delivers highly refined, fused data to the base station.

A Novel Spread Spectrum and Clustering Mixed Approach with Network Coding for Enhanced Narrowband IoT (NB-IoT) Scalability

The Narrowband Internet of Things (NB-IoT) is a very promising licensed Internet of things (IoT) technology for accommodating massive device connections in 5G and beyond. To enable network scalability, this study proposes a two-layers novel mixed approach that aims not only to create an efficient spectrum sharing among the many NB-IoT devices but also provides an energy-efficient network. On one layer, the approach uses an Adaptive Frequency Hopping Spread Spectrum (AFHSS) technique that uses a lightweight and secure pseudo-random sequence to exploit the channel diversity, to mitigate inter-link and cross-technology interference. On the second layer, the approach consists of a clustering and network coding (data aggregation) approach based on an energy-signal strength mixed gradient. The second layer contributes to offload the BS, allows for energy-efficient network scalability, helps balance the energy consumption of the network, and enhances the overall network lifetime. The proposed mixed strategy algorithm is modelled and simulated using the Matrix Laboratory (MATLAB) Long Term Evolution (LTE) toolbox. The obtained results reveal that the proposed mixed approach enhances network scalability while improving energy efficiency, transmission reliability, and network lifetime when compared to the existing spread spectrum only, nodes clustering only, and mixed approach with no network coding approaches.

Rapid Acquisition, Management, and Analysis of Spatial Maize (Zea mays L.) Phenological Data—Towards ‘Big Data’ for Agronomy Transformation in Africa

Mobile smartphones, open-source set tools, and mobile applications have provided vast opportunities for timely, accurate, and seamless data collection, aggregation, storage, and analysis of agricultural data in sub-Saharan Africa (SSA). In this paper, we advanced and demonstrated the practical use and application of a mobile smartphone-based tool, i.e., the Open Data Kit (ODK), to assemble and keep track of real-time maize (Zea mays L.) phenological data in three SSA countries. Farmers, extension agents, researchers, and other stakeholders were enlisted to participate in an initiative to demonstrate the applicability of mobile smartphone-based apps and open-source servers for rapid data collection and management. A pre-installed maize phenology data application based on the ODK architecture was provided to the participants (n = 75) for maize data collection and management over the maize growing season period in 2015–2017. The application structure was custom designed based on maize developmental stages such as planting date, date of emergence, date of first flowering, anthesis, grain filling, and maturity. Results showed that in Ethiopia, early maturing varieties took 105 days from sowing to maturity in low altitudes, whereas late-maturing varieties took up to 190 days to complete developmental stages in high altitude areas. In Tanzania, a similar trend was observed, whereas in Nigeria, most existing varieties took an average of 100 days to complete their developmental stages. Furthermore, the data showed that the durations from sowing to emergence, emergence to flowering, flowering to maturity were mainly dependent on temperature. The values of growing degree for each phase of development obtained from different planting dates were almost constant for each maize variety, which showed that temperature and planting time are the main elements affecting the rate of maize development. The data aggregation approach using the ODK and on-farm personnel improved efficiency and convenience in data collection and visualization. Our study demonstrates that this system can be used in crop management and research on many spatial scales, i.e., local, regional, and continental, with relatively high data collation accuracy.

A new data aggregation approach for WSNs based on open pits mining

Wireless sensor networks consist of a large number of sensor nodes with limited energy, which is widely used in various Internet of things scenarios in recent years. Regarding the vast use of smart objects and applications, one of the big challenges is to collect and analyse the data. Sensor energy limitations and data redundancy are the primary challenges in these networks and reduce network lifetime as well. Therefore, the nodes try to eliminate redundant data, before transferring it to the central station. Data aggregation in IoT such as wireless sensor network plays an important role because in IoT there are heterogeneous data collected from different sources which need more energy to send data. One of the solutions to reduce energy, in this case, is to process and aggregate data prior to sending it. Data aggregation is an effective technique in reducing the data redundancy as well as improving energy efficiency; It also increases the lifespan of Wireless Sensor Networks. Integrating and combining relevant and identical data prevents sending additional packets, and minimizes the redundancy, saves energy, and increases network lifetime. The main purpose of this paper is to provide a new data aggregation method based on the open-pit mining idea efficiently. In this approach, the wireless sensor network is divided into several clusters, and in each cluster, a central node is specified, around which some hypothetical pits are considered to aggregate and send data.

Extracting Patterns from Large Movement Datasets

Extracting useful information from large spatiotemporal datasets is a challenging task that requires suitable visual data representations. Big movement data are particularly hard to visualize since they are prone to visual clutter caused by overlapping and crisscrossing trajectories. Different data aggregation approaches have been developed to address this challenge and to provide analysts with better visualizations for data exploration and data-driven hypothesis generation. However, most approaches for extracting patterns, such as mobility graphs or generalized flow maps, cannot handle large input datasets. This paper presents a flow extraction algorithm that can be used in distributed computing environments and thus make it possible to explore movement patterns in large datasets. We demonstrate its usefulness in a use case exploring maritime vessel movements

A Two-Hop Real-Time LoRa Protocol for Industrial Monitoring and Control Systems

A single-hop LoRa ( Lo ng Ra nge) network suffers from a limitation in coverage caused by high signal attenuation when deployed in industrial areas, which often include wireless unfriendly zones (WUZs). Thus, some end nodes in WUZs may fail to transmit data to the gateway (GW). This paper proposes a reliable two-hop real-time LoRa protocol in which the nodes in WUZs transmit data to another node that relays the received data to the gateway for reliable transmission. The proposed protocol allocates transmission slots to nodes with different data transmission periods, so the nodes not only achieve high reliability but also meet time constraints or periods for delivering data to a gateway. In this case, 1-hop nodes (one hop away from GW) may consume more energy than 2-hop nodes (two hops away from GW). For energy balancing, the protocol uses a data aggregation approach to minimize the number of transmissions. We show by analysis that the proposed protocol can support up to 200 nodes using a single-channel GW when every node sends one packet with a payload of 30 bytes within 17 seconds, and 30% of the nodes are 2-hop nodes. In addition, we show by experiment that the proposed protocol can achieve high reliability in data transmission.

Real time energy efficient data aggregation and scheduling scheme for WSN using ATL

Wireless Sensor Network (WSN) has been used for data collection in different situations. Various methods are described earlier for the problem, but they consider only limited parameters in data collection which introduces poor lifetime, throughput and energy holes. To overcome this issue, a real time energy efficient data aggregation approach is presented here. The method schedules the nodes of the network for their working based on the parameters (Availability-Throughput-Lifetime) ATL. Using all these parameters, the method estimates Data Availability Support, Network Throughput Support and Network Lifetime Support measures. Using all these support measures, the method computes the Data Aggregation Support (DAGS) measure. Using the measure estimated, the nodes are scheduled for their working. Similarly, the nodes are selected for data aggregation based on the above support measures estimated. The ATL approach achieves higher data aggregation performance with higher lifetime maximization.

Improved priority-based data aggregation congestion control protocol

With advancement in multimedia applications, Wireless sensor networks (WSNs) are becoming popular due to their inherent characteristics and wide range of applications. However, WSNs contain very small sensor nodes, these nodes are battery constrained. Also, the batteries of these sensor nodes are not either replaceable or rechargeable. Therefore, many energy efficient protocols have been implemented so far to improve the network lifetime. However, the data aggregation at sink may suffer from data flooding issue, which reduces the network lifetime of WSNs. For handling this issue, in this paper, an effective data aggregation approach is designed. We have designed a priority-based data aggregation control protocol, which considers token bucket, Lempel–Ziv–Welch (LZW) compression and a hybrid of ant colony optimization and particle swarm optimization-based soft computing approach. Extensive experiments reveal that the presented protocol provides better network lifetime in contrast to the existing energy efficient protocols.

A TTL-based Approach for Data Aggregation in Geo-distributed Streaming Analytics

Streaming data analytics has been an important topic of research in recent years. Large quantities of data are generated continuously over time across a variety of application domains such as web and social analytics, scientific computing and energy analytics. One of the key requirements in modern data analytics services is the real-time analysis of these data streams to extract useful and timely information for the analyst. Several distributed data analytics platforms have been developed in recent times to meet this growing requirement of real-time streaming analytics. Nowadays, a large amount of data is generated continuously by geographically distributed sources (e.g., agents, sensors, mobile devices, edge nodes, etc.) in many streaming applications. For instance, services like Facebook, Twitter and Netflix continuously gather data from the end users for a variety of analytical purposes such as finding the popular web content amongst their users or monitoring the QoS metrics. Large content delivery networks (CDNs) like Akamai that serve a significant fraction of content on the Internet continuously collect data from their edge servers and clients from around the globe to understand what, where and how content is accessed for the purpose of providing content analytics insights to businesses.

An efficient route planning model for mobile agents on the internet of things using Markov decision process

Data aggregation on the Internet of Things (IoT) needs context-aware routing protocols to satisfy the Quality of Services (QoS) requirements. Developing energy-efficient, delay-aware, and reliable route planning, in particular, is one of the significant challenges in the scenarios of data aggregation for IoT applications. The established data aggregation approaches, which exploit the client-server-based methods, struggle with the short lifetime of the network, and high data transmission delay. It does not cover the variety of applications, which developed through IoT. To address these challenges, in this paper, the concept of mobile software agents for data aggregation on the IoT is used. The paper proposes a novel route planning mechanism for data aggregation on IoT using mobile agents. The proposed mechanism is composed of two main stages. The first stage clusters the IoT devices; then, organizes the cluster-heads into some groups by using an angle-based process for the assignment of mobile agents. The main purpose of the second stage is to provide route planning for each mobile agent in each group of the cluster-heads for efficient data aggregation through Markov Decision Process (MDP). The results show that the proposed mechanism improves energy consumption, data transmission delay, and the reliability of the IoT.

Causal Loop Diagram Aggregation Towards Model Completeness

Experienced system dynamicists commonly conceptualise causal relationships and feedback loops using Causal Loop Diagrams (CLDs). In adhering to best practice, multiple data collection activities may be required (e.g. multiple group model building sessions), resulting in multiple CLDs. To achieve covariation that correctly attributes cause and effect from multiple data sets, aggregation of CLDs may be necessary. Such aggregation must adequately account for attribution variations across constructed CLDs to produce a coherent view of a phenomenon of interest in a ‘complete’ model. Discourse concerning model completeness should account for the potential for method bias. The data collection method chosen for CLD development will influence the ability to create a model that is fit for the purposes of the study and influence the likelihood of achieving model completeness. So too does the method chosen for model aggregation. Little processual guidance exists on a method for data aggregation in system dynamics studies. This paper examines three data aggregation approaches, based on existing qualitative analysis methods, to determine the suitability of each method. The approaches considered include triangulation, includes all data in the aggregation process; grounded theory, bases aggregation on frequency of occurrence; and synthesis, extends aggregation to include variables based on magnitude of occurrence. Comments are made regarding the relevance of each method for different study types, with final remarks reiterating the consideration of equifinality and multifinality in research and their impact on method selection. This paper enhances the rigour of research aiming at facilitating greater success in studies utilising CLDs.

An Enhanced Distributed Data Aggregation Method in the Internet of Things.

“Internet of Things (IoT)” has emerged as a novel concept in the world of technology and communication. In modern network technologies, the capability of transmitting data through data communication networks (such as Internet or intranet) is provided for each organism (e.g., human beings, animals, things, and so forth). Due to the limited hardware and operational communication capability as well as small dimensions, IoT undergoes several challenges. Such inherent challenges not only cause fundamental restrictions in the efficiency of aggregation, transmission, and communication between nodes; but they also degrade routing performance. To cope with the reduced availability time and unstable communications among nodes, data aggregation, and transmission approaches in such networks are designed more intelligently. In this paper, a distributed method is proposed to set child balance among nodes. In this method, the height of the network graph increased through restricting the degree; and network congestion reduced as a result. In addition, a dynamic data aggregation approach based on Learning Automata was proposed for Routing Protocol for Low-Power and Lossy Networks (LA-RPL). More specifically, each node was equipped with learning automata in order to perform data aggregation and transmissions. Simulation and experimental results indicate that the LA-RPL has better efficiency than the basic methods used in terms of energy consumption, network control overhead, end-to-end delay, loss packet and aggregation rates.