Data Aggregation Method Research Articles

A review on distributed cluster based routing approaches in mobile wireless sensor networks

Wireless Sensor Networks (WSNs) are class of networks composed of sensor nodes having sensing, communication, and computing capabilities which can monitor various environmental conditions like temperature, pressure, speed, gas, proximity, light, etc. Many applications such as smart transport systems, habitat monitoring, under water monitoring require WSNs to be mobile rather than static. Hence, Mobile Wireless Sensor Networks (MWSN) have become more prospective in human life applications. There exist various methods to organize large number of sensing devices to monitor, gather, aggregate data, and to find the best path to forward the data (routing) to the central node called Base Station. Cluster based routing is one of the efficient methods for data collection and aggregation. Non-centralized approaches for path selection and data forwarding eliminates single point failure and ensures accurate routing decisions. So, distributed cluster based routing prolongs network lifetime, improves Packet Delivery Ratio, and supports scalability in MWSN. A review on MWSN, its applications, issues in mobility management, existing cluster based distributed routing protocols, their features, parameters used for simulation, and limitations are presented in this paper.

Application of the Canadian Council of Ministers of the Environment Water Quality Index to assess and communicate monitoring data from coastal waters in Abu Dhabi, United Arab Emirates

The purpose of this research was to (1) identify an existing water quality index methodology that could be used in Abu Dhabi, United Arab Emirates, and (2) apply the water quality index to Abu Dhabi’s waters to communicate water quality conditions and track changes in conditions over time. The available monitoring data were reviewed to establish the types and frequencies of data available to populate an index, and a literature search was performed to identify and review existing water quality ind in use around the world. The Canadian Council of Ministers of the Environment Water Quality Index was selected and customized for use in Abu Dhabi. Using the Canadian Council of Ministers Water Quality Index, two different data aggregation methods were investigated for developing a single Emirate-wide Water Quality Index score. Finally, an alternative method for calculating indices when there are fewer than four parameters was investigated. Three Water Quality Indexs were developed for use in Abu Dhabi: Eutrophication Index, Microbial Index, and Heavy Metals (in sediment) Index. The Canadian Council of Ministers Water Quality Index methodology was found to be relatively easy to use and flexible as a building block for tailored indices, which are used to communicate marine water quality conditions to various stakeholders, including scientists, managers, policymakers, and the public.

A Swarm Optimization-Enhanced Data Aggregation Tree Based on a Nonuniform Clustering Structure for Long and Linear Wireless Sensor Networks

A reasonable clustering structure can make data aggregation methods run efficiently in most wireless sensor networks (WSNs). However, compared with other WSNs, the energy imbalance problem of long and linear WSNs is more serious, and the delay is higher. It is still impossible to achieve efficient data aggregation by optimizing the clustering structure. Therefore, this letter proposes a novel data aggregation tree based on a clustering structure. First, through the optimization of cluster head selection and multihop path selection, this letter proposes a long and linear nonuniform clustering structure to improve the energy balance. Furthermore, taking the minimum delay as the objective function and the number of fusion nodes and energy balance as the constraint, an aggregation node selection mechanism based on a swarm optimization algorithm is proposed to build a data aggregation tree based on a long and linear nonuniform clustering structure. The simulation results show that the proposed method can effectively reduce the delay and energy consumption and that it is suitable for long and linear WSNs.

A Review on Secure Group Key Management Schemes for Data Gathering in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) is a fast-emerging technology which has become an integral part of the research. WSNs have various applications covering military, environment monitoring, health care, surveillance, national security, etc. Due to the inherent nature of wireless communication, such types of networks are more vulnerable to security attacks, and the authentication and confidentiality of wireless networks are much more critical. WSNs needs include efficient clustering methods, data aggregation methods, data compression methods, data encryption and authentication methods, and data gathering methods. WSNs are more vulnerable to attacks due to their ad hoc nature, so the design of a good key management scheme to provide security is necessary. In this paper, different methods of clustering, data aggregation, data compression, data encryption and authentication, and data gathering are analyzed. A survey is conducted on the key management schemes of WSNs.

Flower pollination algorithm‐based energy‐efficient stable clustering approach for WSNs

SummaryThe advances in the size, cost of deployment, and user‐friendly interface of wireless sensor devices have given rise to many wireless sensor network (WSN) applications. WSNs need to use protocols for transmitting data samples from event regions to sink through minimum cost links. Clustering is a commonly used method of data aggregation in which nodes are organized into groups to reduce energy consumption. Nonetheless, cluster head (CH) has to bear an additional load in clustering protocols to organize different activities within the cluster. Proper CH selection and load balancing using efficient routing protocol is therefore a critical aspect for WSN's long‐term operation. In this paper, a threshold‐sensitive energy‐efficient cluster‐based routing protocol based on flower pollination algorithm (FPA) is proposed to extend the network's stability period. Using FPA, multihop communication between CHs and base station is used to achieve optimal link costs for load balancing distant CHs and energy minimization. Analysis and simulation results show that the proposed algorithm significantly outperforms competitive clustering algorithms in terms of energy consumption, stability period, and system lifetime.

SAPDA: Secure Authentication with Protected Data Aggregation Scheme for Improving QoS in Scalable and Survivable UWSNs

Security is one of the main objectives while designing protocols for underwater wireless sensor networks (UWSN), since the sensors in UWSN are vulnerable to malicious attack. So it becomes easy for opponents to manipulate the communication channel of UWSN and its nodes. Authentication and data integrity play important roles in the context of security to make network scalable and survivable. Hence in this paper, a secure authentication and protected data aggregation method for the cluster based structure of UWSN is proposed as because cluster based arrangement produces a concise and stable network. In this technique, the cluster head in each cluster is authenticated by the gateway to ensure that all the clusters are being handled by valid nodes. Also, the data being communicated in the network will be securely handled to ensure that it will not get compromised during network operations. In this way, the security of all the nodes is ensured to maintain safe network communication. The proposed technique improves the data reliability in the network by reducing the energy consumption and delay. Here, the proposed method is moreover compared with the state of the art techniques to prove the validity and effectiveness.

A Novel Low-Complexity Compressed Data Aggregation Method for Energy-Constrained IoT Networks

Sensor nodes used in typical monitoring applications of the Internet of Things (IoT) are an on-board resource (energy, memory, computational capability) constrained devices. The existing data aggregation algorithms have proven that compressed sensing (CS) is promising for energy efficient data aggregation. However, these methods compromise on at least one of energy efficiency, on-node computational complexity and recovery fidelity. In this paper, we propose a novel CS-aided low-complexity compressed data aggregation (LCCDA) method that divides the network into constrained overlapped clusters thereby offering an optimal trade-off among energy consumption, on-node computational complexity and recovery error. We show that the measurement matrix constructed from constrained overlapped clustering satisfies the restricted isometry property (RIP) that guarantees the recovery of the aggregated data. We make use of the graph Laplacian eigenbasis, that is based on the weight adjacency matrix, for finding the sparse representation of the measured data from randomly deployed networks, which enables the high fidelity recovery for aggregated data at the sink node. Through numerical experiments, we demonstrate that the proposed LCCDA method is capable of delivering the data to the sink with high recovery fidelity while achieving significant energy savings.

ЗАБЕЗПЕЧЕННЯ ІНФОРМАЦІЙНОЇ БЕЗПЕКИ В БЕЗДРОТОВИХ СЕНСОРНИХ МЕРЕЖАХ

The problem of information security in wireless sensor networks is considered in this paper. An analysis of existing recommendations for information security in wireless sensor networks. It was found that one of the key problems of information security is the hardware limitations of the sensor nodes of the network. It is substantiated that the use of more complex cryptographic protection mechanisms will increase the load on the network. Safety requirements and their description are given. The main groups and types of information security threats in wireless sensor networks are considered. The classification of attacks and protection of wireless sensor networks according to the OSI model is presented. The existing solutions for information security are considered and analyzed. The shortcomings and vulnerabilities of the considered solutions are revealed. The method of public key cryptography is considered, the main advantages and disadvantages of this method are revealed. The analysis and comparison of ECC and RSA encryption methods are carried out. It is substantiated that the use of ECC in wireless sensor networks is more efficient than RSA. The method of cryptography with a symmetric key is considered, the main advantages and disadvantages of this method are indicated. It was found that cryptographic methods using a symmetric key are more priority for use in wireless sensor networks. Cryptographic key management protocols in wireless sensor networks are considered. The classification of key management protocols is given. Secure routing protocols are considered. The classification of secure routing protocols is given. Methods of secure data aggregation are considered. Contradictions between the requirements for confidentiality and data aggregation have been revealed. The method of intrusion detection is considered, the main advantages and disadvantages of this method are revealed. The results of this work should be used in the design of wireless sensor networks.

Heterogeneous Data Aggregation and Normalization in Information Security Monitoring and Intrusion Detection Systems of Large-scale Industrial CPS

Monitoring of industrial cyber-physical systems (CPS) is an ongoing process necessary to ensure their security. The effectiveness of information security monitoring depends on the quality and speed of collection, processing, and analyzing of heterogeneous CPS data. Today, there are many methods of analysis for solving security problems of distributed industrial CPS. These methods have different requirements for the input data characteristics, but there are common features in them due to the subject area. The work is devoted to preliminary data processing for the security monitoring of industrial CPS in modern conditions. The general architecture defines the use of aggregation and normalization methods for data preprocessing. The work includes the issue from the requirements for the preprocessing system, the specifics of the subject area, to the general architecture and specific methods of multidimensional data aggregation.

Quantum Data Aggregation Using Secret Sharing and Genetic Algorithm

In the process of data aggregation, intruder nodes and compromised nodes may decrease the system performance. Hence, security plays an important role. Secret sharing is a method to find the compromised nodes. The previous researches in secure data aggregation concentrated on key authentication process and little research is done in N-party secret sharing methods. In this article, we proposed quantum data aggregation using secret sharing and genetic algorithm, where a node forwards data to its most trusted neighbor only. In the proposed method, a node selects n 1-hop neighbors from a list of its m 1-hop neighbors, which well behaved over a period and forwards data to the most trusted amongst them. We simulated quantum teleportation using SimulaQron tool for establishing secure communication. However secured is the communication channel the compromised nodes may decrease the system performance by exposing network to malicious activities. Each node creates n shares of a secret and forwards a share on a secured quantum channel to the n 1-hop neighbors in such a way that from any t neighbors share(t<n) the secret is reconstructed. The data aggregation security increases since a group of t users can reconstruct the key but no single user has the complete authority on the secret. The proposed method ensures that each node will be able to aggregate the captured message securely without any interventions from the compromised/intruder nodes. The proposed system applies genetic method to improve the efficiency of the process of detection of the compromised nodes based on the behavioral history of the node. While detecting the malicious nodes the proposed method accuracy rate is 98.8 and false rate is 0.15, which is almost equal to secure and energy-efficient data aggregation method based on an access control method. However, in terms of the size of the network considered, the percentage of malicious nodes assumed and from the basic principles of quantum teleportation, the proposed method proves to be a better protocol for implementing secure data aggregation.

A Differentially Private Data Aggregation Method Based on Worker Partition and Location Obfuscation for Mobile Crowdsensing

With the popularity of sensor-rich mobile devices, mobile crowdsensing (MCS) has emerged as an effective method for data collection and processing. However, MCS platform usually need workers’ precise locations for optimal task execution and collect sensing data from workers, which raises severe concerns of privacy leakage. Trying to preserve workers’ location and sensing data from the untrusted MCS platform, a differentially private data aggregation method based on worker partition and location obfuscation (DP-DAWL method) is proposed in the paper. DP-DAWL method firstly use an improved K-means algorithm to divide workers into groups and assign different privacy budget to the group according to group size (the number of workers). Then each worker’s location is obfuscated and his/her sensing data is perturbed by adding Laplace noise before uploading to the platform. In the stage of data aggregation, DP-DAWL method adopts an improved Kalman filter algorithm to filter out the added noise (including both added noise of sensing data and the system noise in the sensing process). Through using optimal estimation of noisy aggregated sensing data, the platform can finally gain better utility of aggregated data while preserving workers’ privacy. Extensive experiments on the synthetic datasets demonstrate the effectiveness of the proposed method.

A Reliability-Aware Vehicular Crowdsensing System for Pothole Profiling

Accurately profiling potholes on road surfaces not only helps eliminate safety related concerns and improve commuting efficiency for drivers, but also reduces unnecessary maintenance cost for transportation agencies. In this paper, we propose a smartphone-based system that is capable of precisely estimating the length and depth of potholes, and introduce a holistic design on pothole data collection, profile aggregation and pothole warning and reporting. The proposed system relies on the built-in inertial sensors of vehicle-carried smartphones to estimate pothole profiles, and warn the driver about incoming potholes. Because of the difference in driving behaviors and vehicle suspension systems, a major challenge in building such system is how to aggregate conflicting sensory reports from multiple participating vehicles. To tackle this challenge, we propose a novel reliability-aware data aggregation algorithm called Reliability Adaptive Truth Discovery (RATD). It infers the reliability for each data source and aggregates pothole profiles in an unsupervised fashion. Our field test shows that the proposed system can effectively estimate pothole profiles, and the RATD algorithm significantly improves the profiling accuracy compared with popular data aggregation methods.

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.

A methodology to prioritize spatio-temporal monitoring of drinking water quality considering population vulnerability

The optimization of drinking water monitoring becomes increasingly complex with the size of a water distribution system. Municipal water managers have to combine their experiences with different types of information (historical water quality variability, infrastructure, water residence times, sociodemographic profiles, etc.) that are available in different forms (qualitative, quantitative, geographical, etc.) to be able to select the monitoring locations for regulatory compliance and routine water quality management and control. Therefore, the integration of such information requires to select suitable variables and use the appropriate data mining and aggregation methods. This work aims to develop a methodology that helps optimize drinking water quality monitoring programs by considering the different components of population vulnerability that vary both spatially and temporally. This project was conducted in a distribution system that supplies approximately 510 000 citizens. Due to the high seasonal climatic variations and the size of the network, there are also considerable spatial and temporal variations in water quality throughout the year. An index representing the spatio-temporal population vulnerability (combination of population exposure, sensitivity and adaptation capacity) to the degradation of drinking water quality was developed by selecting the relevant parameters and aggregation methods. The population vulnerability index was calculated by aggregating spatio-temporal water quality data (representing microbiological and chemical risks) and distribution network characteristics (number of leakages, pipes type and age). This information was then compared with sociodemographic data related to population sensitivity (percentage of children and the elderly, and the number of health care centers) and the population's adaptive capacity (social and material deprivation). A fuzzy synthetic evaluation method is used for parameter aggregation and to calculate the different indexes. By considering variable locations and periods of time that may better represent the population vulnerability, the results of this project are useful for drinking water managers to optimize their drinking water monitoring strategies.

Entropy-driven data aggregation method for energy-efficient wireless sensor networks

Data aggregation is one of the essential and fundamental processes in Wireless Sensor Networks (WSNs). When and how to gather data from the sensors to the sink has a direct impact on the lifetime of the WSNs because the energy consumption is proportion to the frequency of data transmission. In general, sensors in a WSN are randomly distributed for creating a massive coverage WSN environment within a short period. Because the sensors nearby the sink are responsible for more data forwarding tasks, they normally have much shorter lifetime than those located away from the sink. Once all sensors nearby the sink run out of energy, the data collected from the terminal sensors can not reach the sink because all established connections to the sink are broken, which is called the termination of a WSN lifetime. This paper proposes a strategy with multiple algorithms for deploying sensors aiming at maximizing a WSN lifetime. The proposed Entropy-driven Data Aggregation with Gradient Distribution (EDAGD) deployment strategy contains three algorithms: (1) multihop tree-based data aggregation, (2) entropy-driven aggregation tree-based routing algorithm and (3) gradient deployment algorithm. The numerical and experimental results indicating that the proposed EDAGD method outperforms the conventional algorithm with the random deployment strategy.

Analysis of acoustic signals in increments for functional diagnostics of multi-drive units

The current trend in the development of mechatronic systems is the use of multi-drive frame configurations. Such objects, as a rule, consist of a large number of interacting elements. The relative displacements of these elements generate vibrations that can critically affect the operation of precision mechatronic systems. This can lead to limiting operating conditions of the equipment, and in some cases, its failure. An effective means of preventing accidents, identifying critical modes of operation, diagnosing faults in equipment of electromechanical systems are the methods of functional diagnostics.Methods of functional diagnostics in real time require a large number of calculations, mathematical modeling of the object, processing a large amount of information, which leads to the mandatory use of computer technology. The main methods of processing the measurement results are the Fourier and Laplace transforms, classical methods for analyzing time series, multiple-scale wavelet analysis, etc. The disadvantage of the approaches used is computational complexity in the implementation of discrete transformations and the need for significant amounts of memory to store reference values. The considered methods are based on the analysis of the original signal, however, the increments of the signal of different orders may also be informative.The aim of the research is to develop methods for analyzing acoustic signals generated by working equipment of electromechanical complexes, in increments suitable for use in functional diagnostics systems when monitoring the operating modes of electric drives.In the process of research, an analysis of acoustic noise signals from a working multi-drive unit, obtained as a result of a series of experiments at two high-speed modes, was performed. To speed up the process of analyzing signals and eliminate their redundancy, a data aggregation method has been applied. The degree of permissible aggregation is determined using a multi-scale analysis. The developed method for analyzing aggregated acoustic signals in increments in phase space can be used for functional diagnostics of multi-drive units.

COMPLIANCE ASSESSMENT OF AQUACULTURE DEVELOPMENT TRENDS IN THE RUSSIAN FEDERATION TO UNIVERSAL TRENDS

Purpose of the study: The relevance of the research topic is due to the fact that the development of aquaculture is one of the key tools to ensure the necessary level of food security both in Russia in particular and on a global scale in general. The purpose of the article is to assess the development of aquaculture in Russia, in the context of global trends.&#x0D; Methodology: To analyze the existing problem, theoretical and empirical methods, data aggregation methods, expert evaluation, classification and structuring of information, reference and statistical data, and a comparison method were used.&#x0D; Results: The paper summarized the scientific and theoretical foundations of the definition’s essence of the “aquaculture” concept, as a result of which this concept was clarified, a classification of the main sub-complexes of aquaculture was given. The main vectors for the implementation of a systemic approach to the development of aquaculture, which have their relevance both nationally and globally, are highlighted. The article analyzes the current state and development trends of world aquaculture and fisheries, as well as the development of domestic aquaculture. The authors share the position of most scientists that in the medium term, aquaculture will occupy a dominant position compared to fishing, not only for environmental but also for economic reasons. According to the results of the study, it was concluded that in Russia, despite the presence of huge resource potential for the development of aquaculture, the main indicators for the functioning of this industry are significantly lagging behind global trends.&#x0D; Applications of this study: Measures aimed at the formation of the growing trends of aquaculture development in Russia were proposed.&#x0D; Novelty/Originality of this study: It can be noted that the implementation of the presented set of measures will contribute to the formation of growing trends in the development of aquaculture in Russia, which will correspond to the main global trends in this field.

Statistical Measurement of Poverty and Inequality in the CIS Countries

This article deals with methodological, organizational and practical issues of measuring poverty and inequality in the countries of the Commonwealth of Independent States in the context of international statistical recommendations, in particular, the Agenda for Sustainable Development. The paper provides basic conceptual approaches to measuring monetary and non-monetary poverty, as reflected in the national practices of statistical offices of the CIS countries.The authors determine the role of income and expenditure indicators in measuring poverty and inequality in the CIS region. They also identify the main challenges and issues of income and expenditure statistics arising from differences in data sources, applied classifications and aggregation methods. The paper includes some recommendations on the unification of methods for collecting income and expenditure statistics, developed as part of the joint UNECE, Rosstat and CIS-Stat project on harmonization of poverty indicators for monitoring sustainable development in CIS Countries.The article addresses national practical applications of poverty measuring concepts concerning absolute, relative and subjective poverty.Another important matter that this paper discusses is the comparability of poverty statistics to review progress on Sustainable Development Goals (SDGs) in the CIS region. It is associated with the use of different poverty lines and welfare indicators, that serve as a basis for constructing population distribution series. The authors provide a method for calculating poverty indicators using the international and national poverty lines.The article formulates ways to further improve poverty statistics that are aimed at developing integral indicators and criteria to measure poverty not only on the basis of income and expenditure, but also taking into account factors affecting the population’s quality of life (primarily, the possibility of obtaining health care services, education, access to clean drinking water and adequate sanitary conditions), environmental situation, life safety.

Methods and Application of Archeological Cloud Platform for Grand Sites Based on Spatio-Temporal Big Data

Grand sites are important witnesses of human civilization. The archeology of grand sites has the characteristics of a long period, interdisciplinary study, irreversibility and uncertainties. Because of the lack of effective methods and valid tools, large amounts of archeological data cannot be properly processed in time, which creates many difficulties for the conservation and use of grand sites. This study provides a method of integrating spatio-temporal big data of grand sites, including classification and coding, spatial scales and a spatio-temporal framework, through which the integration of archeological data of multiple sites or different archeological excavations is realized. A system architecture was further proposed for an archeological information cloud platform for grand sites. By providing services such as data, visualization, standardizations, spatial analysis, and application software, the archeological information cloud platform of grand sites can display sites, ruins, and relics in 2D and 3D according to their correlation. It can also display the transformation of space and time around archeological cultures, and restored ruins in a 3D virtual environment. The platform provides increased support to interdisciplinary study and the dissemination of research results. Taking the Origin of Chinese Civilization Project as a case study, it shows that the method for data aggregation and fusion proposed in this study can efficiently integrate multi-source heterogeneous archeological spatio-temporal data of different sites or different periods. The archeological information cloud platform has great significance to the study of the origin of Chinese civilization, dissemination of Chinese civilization, and the public participation in archeology, which would promote the sustainable development of the conservation and use of grand sites.

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.