Data Collection Scheme Research Articles

District Partition-Based Data Collection Algorithm With Event Dynamic Competition in Underwater Acoustic Sensor Networks

The advent of underwater acoustic sensor networks (UASNs) has enhanced marine environmental monitoring, auxiliary navigation, and marine military defense. One of the core functions of UASNs is data collection. However, current underwater data collection schemes generally encounter problems such as high energy consumption and high latency. Furthermore, the application of multiple autonomous underwater vehicles (AUVs) has contributed to more problems of task assignment and load balancing. This leads to significant failure in data collections and controlling of spontaneous emergencies. To address these problems, a district partition-based data collection algorithm with event dynamic competition in UASNs has been proposed. In this algorithm, the value of information of the packet determines the priority of its transmission to the cluster head. The navigation position of the mobile sink and the area under the responsibility of each AUV are determined by the spatial region division. The path of the AUV in the subregion is then planned using reinforcement learning. Subsequently, the dynamic competition of multiple AUVs is used to handle emergency tasks. The simulation demonstrates that our proposed algorithm significantly reduces energy consumption to guarantee load balancing while reducing end-to-end transmission delay.

Unmanned aerial vehicle-assisted energy-efficient data collection scheme for sustainable wireless sensor networks

Recently, wireless sensor networks (WSNs), consisted of battery-powered sensor nodes, are widely adopted by various civilian/military applications for implementing real-time monitoring or long-term surveillance tasks. However, due to limited battery capacity, sensor lifetime is limited, which in turn affects the working time of the whole system. Therefore, solving the problem of improving energy efficiency is the premise for constructing a sustainable/energy-efficient WSN. To address this problem, by taking advantage of the emerging unmanned aerial vehicle (UAV) techniques, we propose a novel UAV-assisted data collection scheme for improving the energy efficiency of the deployed WSN. To meet the performance requirements of systems with different scales, our algorithm has two working modes: single- and multiple-UAV scenarios. For the small-scale system, a single UAV considered data-collector is adopted to access and collect data from the deployed nodes. For the large-scale system, based on a newly designed clustering method, multiple data collectors are utilized to gathering sensed data from the deployed nodes for conserving their energy and reducing the data forwarding latency. To evaluate our proposed work, intensive simulation experiments are conducted. The results demonstrate that the proposed algorithm achieved better performance than the control group regarding system-wide energy efficiency and average end-to-end delay.

Social engineering in the national agriculture economy on example of dairy industry development in the Russian Federation

Issues on dairy producers’ motivation are not very obvious at the first sight. The main argument for economic activity is profit. Apparent, a large number of "pitfalls", which are subsequently found in the process of doing business, sometimes lead to a halt in the production process. Periods of change of accents in agricultural business that are typical both for the economic branch as a whole, and specifically for the dairy industry, are considered in this paper. It is worth noting that the quality and quantity of specialized information is the basis of any business. This paper considers aspects of the impact of created, destroyed or modified information modules in the field of economic management in general and the impact of information blocks on the dairy industry. The socio-engineering scheme of data collection, planned for use in the automated system of selection of cattle breeds, depending on the ecological and geographical conditions of the breeding region, is proposed in this work. Algorithm of information processes of the designated system with methods of social engineering is formed. This algorithm is designed to collect data on specific and time-stable features of different cattle breeds.

A Secured Data Retrieval Architecture for WBAN using Elliptic Curve Digital Signature

The big data is proposed a secure scheme of data collection that to deal the problems in WBAN (Wireless Body Area Network). To start with to register the sensor nodes using CA (Certification Authority) connect the Network of Big data center. After the preprocessing stage, the sensors are correlated with big data center through authentication on both sides by ECDSA (Elliptic Curve Digital Signature Algorithm). The sensor node designed using distributed storage and the collected data transfer with improved security protection.

A load balancing virtual level routing (LBVLR) using mobile mule for large sensor networks

In a large sensor network, the data are usually routed back to the static base station (BS) using multi-hop communication. The sensor nodes near the BS suffer from an energy hole problem owing to the heavier data traffic load on them. Hence, the nodes near to the BS die early and thereby minimize the network lifetime. The mobile carriers have been considered as a better approach to balance the energy consumption by minimizing the number of transmissions. In spite of several benefits, the mobile carriers have a lot of challenges in data collection from sensor nodes. This paper presents a load balancing virtual level-based routing (LBVLR) using a data mule as a mobile carrier to deal with energy holes and ensure reliable data transfer. Our scheme has attempted to palliate the energy hole problem by applying horizontal level routing in the left and right directions. Fixed virtual rectangular grids of equal size are formed with the help of a mobile data mule and the BS that minimizes the network overheads, which saves a significant amount of energy. This approach uses three different data collection schemes based on the mule speed during trajectory, and we name them uniform speed of mule, variable speed of mule and sojourn point of mule. To compare the performance with the other works, LBVLR is evaluated mathematically and extensive simulations are conducted. The simulation results show a significant improvement in terms of average energy usage, data delivery delay and data delivery ratio compared with the other two similar kinds of well-known routing protocols.

Outliers detection using an iterative strategy for semi‐supervised learning

AbstractAs a direct consequence of production systems' digitalization, high‐frequency and high‐dimensional data has become more easily available. In terms of data analysis, latent structures‐based methods are often employed when analyzing multivariate and complex data. However, these methods are designed for supervised learning problems when sufficient labeled data are available. Particularly for fast production rates, quality characteristics data tend to be scarcer than available process data generated through multiple sensors and automated data collection schemes. One way to overcome the problem of scarce outputs is to employ semi‐supervised learning methods, which use both labeled and unlabeled data. It has been shown that it is advantageous to use a semi‐supervised approach in case of labeled data and unlabeled data coming from the same distribution. In real applications, there is a chance that unlabeled data contain outliers or even a drift in the process, which will affect the performance of the semi‐supervised methods. The research question addressed in this work is how to detect outliers in the unlabeled data set using the scarce labeled data set. An iterative strategy is proposed using a combined Hotelling's T2 and Q statistics and applied using a semi‐supervised principal component regression (SS‐PCR) approach on both simulated and real data sets.

A compressive sensing–based adaptable secure data collection scheme for distributed wireless sensor networks

Toward the goal of high security and efficiency for data collection in wireless sensor network, this article proposed an adaptable secure compressive sensing–based data collection scheme for distributed wireless sensor network. It adopted public key cryptography technology to solve the key distribution problem, and compressive sensing over finite fields to reduce the communication cost of data collection. Under hardness of decisional learning with errors problem on lattice, it can ensure indistinguishability against chosen ciphertext attack (IND-CCA1) security scheme for collected data on the extranet and indistinguishability against chosen plaintext attack security for data during the process of distributed collection on the intranet. Owing to the similar linear structure for lattices and compressive sensing, data encryption collection can be all in the form of efficient linear operations, and internode data aggregation can be in the form of addition operation.

Fast RFID Sensory Data Collection: Trade-off Between Computation and Communication Costs

This paper studies the important sensory data collection problem in the sensor-augmented RFID systems, which is to quickly and accurately collect sensory data from a predefined set of target tags with the coexistence of unexpected tags. The existing RFID data collection schemes suffer from either low time-efficiency due to tag-collisions or serious data corruption issue due to interference of unexpected tags. To overcome these limitations, we propose the hierarchical-hashing data collection (HDC) protocol, which can not only significantly improve the utilization of RFID wireless communication channel by establishing bijective mapping between $k$ target tags and the first $k$ slots in time frame, but also effectively filter out the serious interference of unexpected tags. Although HDC has attractive advantages, the theoretical analysis reveals that the computation cost involved in it is as huge as $\mathcal {O}(k2^{k})$ , where $k$ is normally large in practice. By making some modifications to the basic HDC protocol, we propose the multi-framed hierarchical-hashing data collection (MHDC) protocol to effectively reduce the involved computation complexity. Unlike HDC that only issues a single time frame, MHDC uses multiple time frames to collaboratively collect sensory data from the $k$ target tags. It can be understood as that a big computation task is disintegrated into multiple small pieces and then shared by multiple time frames. As a result, the computation cost involved in MHDC is reduced to $\mathcal {O}(k2^{n})$ , where $n\ll k$ is the expected number of target tags that each time frame handles. Theoretical analysis is given to jointly consider the communication cost and computation cost thereby maximizing the overall time-efficiency of MHDC. Extensive simulation results reveal that the proposed MHDC protocol can correctly collect all sensory data and is always about more than $2\times $ faster than the state-of-the-art RFID sensory data collection protocols.

An AUV Location Prediction-Based Data Collection Scheme for Underwater Wireless Sensor Networks

In this paper, an autonomous underwater vehicle (AUV) location prediction (ALP)-based data collection scheme (ALP) has been proposed to overcome high and unbalanced energy consumption for underwater wireless sensor networks. In our scheme, an AUV travels around the network, follows a predefined trajectory, and collects data from the sensor nodes. The nodes near the trajectory send their data to the AUV directly, while the others send data to their neighbors that are closer to the trajectory. To overcome the “hot region” problem, which means the nodes near the trajectory of the AUV consume energy faster and die early, a trajectory adjustment mechanism is applied. A mathematical model is proposed to adjust the trajectory periodically. To guarantee an efficient communication between the nodes and the AUV, a reliable time mechanism is proposed. In this mechanism, only the nodes with a sufficient amount of time are capable to communicate and send their data to the AUV directly. The analyses and simulation results validate that our proposed ALP prolongs the network lifetime and has a higher packet delivery ratio than the existing protocols.

Energy Efficient Data Collection in Large-Scale Internet of Things via Computation Offloading

Internet of Things (IoT) can be used to promote many advanced applications by utilizing the sensed data collected from various settings. To reduce the energy consumption of IoT devices, and to extend the lifetime of network, the sensed data are usually compressed before their transmission through compressed sensing theory. By reconstructing the sensed data at the edge of network with more resourceful devices, such as laptops and servers, the intensive computation and energy consumption of the IoT nodes could be effectively offloaded. However, most of the existing data collection schemes are limited in their scalability, because the unified data reconstruction models of them are not suitable for large-scale surveillance scenarios. In our proposed scheme, the whole network is first partitioned into a number of data correlated clusters based on spatial correlation. Then, a data collection tree is built to collect the compressed data in a hybrid mode. Finally, the data reconstruction problem is modelled as a group sparse problem and solved through using an alternating direction method of multiplier-based algorithm. The performance of data communication and reconstruction of the proposed scheme is evaluated through experiments with real data set. The experimental results show that the proposed scheme can indeed lower the amount of data transmission, prolong the network life, and achieve a higher level of accuracy in data collection compared to existing data collection schemes.

An Active Mobile Charging and Data Collection Scheme for Clustered Sensor Networks

Recent years have witnessed the flourish of new promising technologies to prolong the lifetime of wireless sensor networks (WSNs). Employing mobile vehicles for wireless charging and data collection is able to balance the load of WSNs, and meanwhile, provide a reliable energy replenishment for sustainable network operations. Different from traditional charging scheduling policies where sensor nodes passively wait for the arrival of mobile vehicles, a novel dynamic clustering based mobile-to-cluster (M2C) scheme is proposed to optimize the service process for both sensor nodes and the vehicle in an active way. Requiring only local residual energy information, M2C refreshes the cluster-based network topology to ensure no sensor nodes will run out of energy before getting charged. By estimating the energy consumption of being a cluster head, the sensor nodes with residual energy close to the estimation are actively elected as head nodes. More head nodes with low residual energy will be visited, reducing the travel distance along with higher energy efficiency of charging. Furthermore, the convergence and worst-case performance of M2C are theoretically analyzed. Performance evaluation results show that compared with the state-of-the-art schemes, our design reduces the travel distance and charging latency by about 10% and 50%, respectively.

Data Collection with Accuracy-Aware Congestion Control in Sensor Networks

Data collection is a fundamental and critical function of wireless sensor networks (WSNs) for the cyber-physical systems (CPS) to estimate the state of the physical world. However, unstable network conditions impose significant challenges in guaranteeing the data accuracy that is essential for the reliable estimation of physical states. Without efficiently resolving congestion during data transmission in WSNs, packet loss due to congestion can significantly degrade the data quality. Various congestion control schemes have been proposed to address this issue. Most of them rely on reducing transmitted data samples to eliminate the congestion, which, however, could lead to abysmally high estimation error. In this paper, we analyze the impact of congestion control on the data accuracy and propose a Congestion-Adaptive Data Collection scheme (CADC) to efficiently resolve the congestion under the guarantee of data accuracy. CADC mitigates congestion by adaptive lossy compression while ensuring a given overall data estimation error bound in a distributed manner. Considering that for a CPS application different data items may have different priorities, we also propose a weighted CADC scheme such that the data with higher priority has less distortion. We further adapt CADC to guarantee the accuracy of specific aggregate computations. Extensive simulations demonstrate the effectiveness and efficiency of CADC.

The cryo-EM method microcrystal electron diffraction (MicroED).

In 2013 we established a cryo-electron microscopy (cryo-EM) technique called microcrystal electron diffraction (MicroED). Since that time, data collection and analysis schemes have been fine-tuned, and structures for more than 40 different proteins, oligopeptides and organic molecules have been determined. Here we review the MicroED technique and place it in context with other structure-determination methods. We showcase example structures solved by MicroED and provide practical advice to prospective users.

Anonymous data collection scheme for cloud-aided mobile edge networks

With the rapid spread of smart sensors, data collection is becoming more and more important in Mobile Edge Networks (MENs). The collected data can be used in many applications based on the analysis results of these data by cloud computing. Nowadays, data collection schemes have been widely studied by researchers. However, most of the researches take the amount of collected data into consideration without thinking about the problem of privacy leakage of the collected data. In this paper, we propose an energy-efficient and anonymous data collection scheme for MENs to keep a balance between energy consumption and data privacy, in which the privacy information of senors is hidden during data communication. In addition, the residual energy of nodes is taken into consideration in this scheme in particular when it comes to the selection of the relay node. The security analysis shows that no privacy information of the source node and relay node is leaked to attackers. Moreover, the simulation results demonstrate that the proposed scheme is better than other schemes in aspects of lifetime and energy consumption. At the end of the simulation part, we present a qualitative analysis for the proposed scheme and some conventional protocols. It is noteworthy that the proposed scheme outperforms the existing protocols in terms of the above indicators.

Data collection scheme with minimum cost and location of emotional recognition edge devices

This paper develops a real-time and reliable data collection system for big scale emotional recognition systems. Based on the data sample set collected in the initialization stage and by considering the dynamic migration of emotional recognition data, we design an adaptive Kth average device clustering algorithm for migration perception. We define a sub-modulus weight function, which minimizes the sum of the weights of the subsets covered by a cover to achieve high-precision device positioning. Combining the energy of the data collection devices and the energy of the wireless emotional device, we balance the data collection efficiency and energy consumption, and define a minimum access number problem based on energy and storage space constraints. By designing an approximate algorithm to solve the approximate minimum Steiner point problem, the continuous collection of emotional recognition data and the connectivity of data acquisition devices are guaranteed under the energy constraint of wireless devices. We validate the proposed algorithms through simulation experiments using different emotional recognition systems and different data scale. Furthermore, we analyze the proposed algorithms in terms of topology for devices classification, location accuracy, and data collection efficiency by comparing with the Bayesian classifier-based expectation maximization algorithm, the background difference-based moving target detection arithmetic averaging algorithm, and the Hungarian algorithm for solving the assignment problem.

EPIC: Efficient Privacy-Preserving Scheme With EtoE Data Integrity and Authenticity for AMI Networks

In this paper, we propose EPIC, an efficient and privacy-preserving data collection scheme with EtoE data integrity verification for advanced metering infrastructure networks. Using efficient cryptographic operations, each meter should send a masked reading to the utility such that all the masks are canceled after aggregating all meters’ masked readings, and thus the utility can only obtain an aggregated reading to preserve consumers’ privacy. The utility can verify the aggregated reading integrity without accessing the individual readings to preserve privacy. It can also identify the attackers and compute electricity bills efficiently by using the fine-grained readings without violating privacy. Furthermore, EPIC can resist collusion attacks in which the utility colludes with a relay node to extract the meters’ readings. A formal proof and probabilistic analysis are used to evaluate the security of EPIC, and ns-3 is used to implement EPIC and evaluate the network performance. In addition, we compare EPIC to existing data collection schemes in terms of overhead and security/privacy features.

An energy-efficient and adaptive data collection scheme for multisensory wireless sensor networks

With the development of sensed technology, more and more sensor nodes carry multiple sensors in information collection wireless sensor networks. As a result, there are always a large number of corr...

Exploring the new policy framework of environmental performance management for shipping: a pilot study

Changes in weather patterns are now recorded more and more frequently in various locations of the Earth. Collective action is needed to deal with the challenges of climate change; the maritime industry must contribute its fair share to those activities, aiming to mitigate a risk that can threaten humanity’s own existence. On the positive side, both the European Union (EU) and the International Maritime Organization (IMO) have clear ambitions to reduce greenhouse gas (GHG) emissions from ships that are linked to climate change, and two similar albeit separate regimes have been introduced. EU’s monitoring, reporting, and verification (MRV) regulation of CO2 emissions has already been influencing shipping activities, since the related data collection has started on the 1st of January 2018. The second initiative is the IMO’s data collection system (DCS), which revolves primarily around fuel consumption; the necessary data collection started on the 1st of January 2019. To support an effective environmental performance management framework for the maritime industry, it is necessary to create a robust database that describes all aspects of energy efficient ship operations, with an emphasis on the inventory of CO2 emitted from ships. The analysis at hand examines the challenges in the relevant data collection scheme, with a particular focus on MRV, considering that the specific regulation is already operationalized. An online survey was conducted among maritime professionals in Singapore and Bangladesh to examine the gap between current energy-related regulations/policies and standing operational practices. The findings address the issues relating to shipping operations and certain compliance, institutional and management matters. They also identify the industry’s preparedness for this new policy framework of environmental performance management as well as the impact on existing energy efficiency practices. A very important conclusion derived is that the quality of data collected is crucial for the effective management of emissions related to shipping and the associated fuel consumption.

An energy-efficient data collection scheme using denoising autoencoder in wireless sensor networks

As one of the key operations in Wireless Sensor Networks (WSNs), the energy-efficient data collection schemes have been actively explored in the literature. However, the transform basis for sparsifing the sensed data is usually chosen empirically, and the transformed results are not always the sparsest. In this paper, we propose a Data Collection scheme based on Denoising Autoencoder (DCDA) to solve the above problem. In the data training phase, a Denoising AutoEncoder (DAE) is trained to compute the data measurement matrix and the data reconstruction matrix using the historical sensed data. Then, in the data collection phase, the sensed data of whole network are collected along a data collection tree. The data measurement matrix is utilized to compress the sensed data in each sensor node, and the data reconstruction matrix is utilized to reconstruct the original data in the sink. Finally, the data communication performance and data reconstruction performance of the proposed scheme are evaluated and compared with those of existing schemes using real-world sensed data. The experimental results show that compared to its counterparts, the proposed scheme results in a higher data compression rate, lower energy consumption, more accurate data reconstruction, and faster data reconstruction speed.

Secured Data Collection With Hardware-Based Ciphers for IoT-Based Healthcare

There are tremendous security concerns with patient health monitoring sensors in Internet of Things (IoT). The concerns are also realized by recent sophisticated security and privacy attacks, including data breaching, data integrity, and data collusion. Conventional solutions often offer security to patients’ health monitoring data during the communication. However, they often fail to deal with complicated attacks at the time of data conversion into cipher and after the cipher transmission. In this paper, we first study privacy and security concerns with healthcare data acquisition and then transmission. Then, we propose a secure data collection scheme for IoT-based healthcare system named SecureData with the aim to tackle security concerns similar to the above. SecureData scheme is composed of four layers: 1) IoT network sensors/devices; 2) Fog layers; 3) cloud computing layer; and 4) healthcare provider layer. We mainly contribute to the first three layers. For the first two layers, SecureData includes two techniques: 1) light-weight field programmable gate array (FPGA) hardware-based cipher algorithm and 2) secret cipher share algorithm. We study KATAN algorithm and we implement and optimize it on the FPGA hardware platform, while we use the idea of secret cipher sharing technique to protect patients’ data privacy. At the cloud computing layer, we apply a distributed database technique that includes a number of cloud data servers to guarantee patients’ personal data privacy at the cloud computing layer. The performance of SecureData is validated through simulations with FPGA in terms of hardware frequency rate, energy cost, and computation time of all the algorithms and the results show that SecureData can be efficient when applying for protecting security risks in IoT-based healthcare.