Data Collection Scheme Research Articles

Energy-Efficient Deadline-Aware Data-Gathering Scheme Using Multiple Mobile Data Collectors.

In wireless sensor networks, the data collected by sensors are usually forwarded to the sink through multi-hop forwarding. However, multi-hop forwarding can be inefficient due to the energy hole problem and high communications overhead. Moreover, when the monitored area is large and the number of sensors is small, sensors cannot send the data via multi-hop forwarding due to the lack of network connectivity. In order to address those problems of multi-hop forwarding, in this paper, we consider a data collection scheme that uses mobile data collectors (MDCs), which visit sensors and collect data from them. Due to the recent breakthroughs in wireless power transfer technology, MDCs can also be used to recharge the sensors to keep them from draining their energy. In MDC-based data-gathering schemes, a big challenge is how to find the MDCs’ traveling paths in a balanced way, such that their energy consumption is minimized and the packet-delay constraint is satisfied. Therefore, in this paper, we aim at finding the MDCs’ paths, taking energy efficiency and delay constraints into account. We first define an optimization problem, named the delay-constrained energy minimization (DCEM) problem, to find the paths for MDCs. An integer linear programming problem is formulated to find the optimal solution. We also propose a two-phase path-selection algorithm to efficiently solve the DCEM problem. Simulations are performed to compare the performance of the proposed algorithms with two heuristics algorithms for the vehicle routing problem under various scenarios. The simulation results show that the proposed algorithms can outperform existing algorithms in terms of energy efficiency and packet delay.

Differential Private Data Collection and Analysis Based on Randomized Multiple Dummies for Untrusted Mobile Crowdsensing

Mobile crowdsensing, which collects environmental information from mobile phone users, is growing in popularity. These data can be used by companies for marketing surveys or decision making. However, collecting sensing data from other users may violate their privacy. Moreover, the data aggregator and/or the participants of crowdsensing may be untrusted entities. Recent studies have proposed randomized response schemes for anonymized data collection. This kind of data collection can analyze the sensing data of users statistically without precise information about other users' sensing results. However, traditional randomized response schemes and their extensions require a large number of samples to achieve proper estimation. In this paper, we propose a new anonymized data-collection scheme that can estimate data distributions more accurately. Using simulations with synthetic and real datasets, we prove that our proposed method can reduce the mean squared error and the JS divergence by more than 85% as compared with other existing studies.

Indicators of wireless technologies for monitoring the quality and management

The problems of integration of the various wireless technologies, for-consists in the provision of required transmission speed and the transmission of information over a considerable distance at low power sensor nodes transmitters wireless Internet-water network for remote monitoring. Analyzed the integration of wireless LAN standard IEEE 802.11n/g and Bluetooth. Determined Graphic analytical dependence-dence, the defining characteristics of the data rate for wireless IEEE 802.11n / g networks. It analyzes the main shortcomings of BSS associated with ef cient use of batteries; the basic Metody reduce energy consumption for WSN based on the ZigBee technology, based on the planning schemes of awakening and data collection schemes. The possibilities MeshLogic technology solutions for the monitoring and control tasks that are critical to BPE-autonomous work Meni sensors.

The negative log-gamma prior distribution for Bayesian assessment of system reliability

This paper presents the negative log-gamma distribution as a prior distribution useful for Bayesian assessment of system reliability. When the scale parameter is held fixed, the negative log-gamma distribution is closed under products, making it convenient for specifying priors for series systems. In particular, for series systems, negative log-gamma component priors can be specified to give an exact desired system prior and vice versa. We consider pass/fail data at the system and component levels for both static and time-varying data collection schemes and propose two new prior distributions for analyzing time-varying reliability. Finally, we consider an application of the negative log-gamma to a missile reliability problem and illustrate diagnostics useful for developing the priors.

River Water Temperature in Relation to Local Air Temperature in the Mackenzie and Yukon Basins

Water temperature has an important impact on many aspects of basin hydrology and ecology. In the northern regions, investigation of river thermal regimes and their changes over space and time is a challenge because of data limitations. This study determines the water temperature regimes at several locations within the Yukon and Mackenzie River basins and examines their relationship with air temperature. The Yukon and Mackenzie Rivers have distinct water temperature dynamics. They remain near zero from freeze-up in the fall to ice breakup in the spring and reach their peak temperature during mid-summer. For the locations examined, peak mean monthly water temperatures ranged from 9˚ to 15˚C, and mean July air temperatures ranged from 13˚ to 16˚C. The lags between water and air temperatures ranged from 1 to 40 days. The largest lag was found at the Great Bear River monitoring location, since water temperature at this site is strongly influenced by the heat storage of Great Bear Lake. Tests of three models, linear regression, logical regression (s-shape), and the physically based air2stream model, show that the air2stream model provided the best results, followed by logical regression. Linear regression gave the poorest result. Model estimates of water temperature from air temperature were slightly improved by the inclusion of discharge data. The water temperature sampling regimes had a considerable effect on model performance; long-term data provide a more robust test of a model. Comparisons of mean monthly water temperatures suggest significant spatial variability and some inconsistency between upstream and downstream sites that is due mainly to differences in data collection schemes. This study strongly demonstrates the need to improve water temperature monitoring in the northern regions.

Multi-scale Perimeter Control Approach in a Connected-Vehicle Environment

This paper proposes a novel approach to integrate optimal control of perimeter intersections (i.e. to minimize local delay) into the perimeter control scheme (i.e. to optimize traffic performance at the network level). This is a complex control problem rarely explored in the literature. In particular, modeling the interaction between the network level control and the local level control has not been fully considered. Utilizing the Macroscopic Fundamental Diagram (MFD) as the traffic performance indicator, we formulate a dynamic system model, and design a Model Predictive Control (MPC) based controller coupling two competing control objectives and optimizing the performance at the local and the network level as a whole. To solve this highly non-linear optimization problem, we employ an approximation framework, enabling the optimal solution of this large-scale problem to be feasible and efficient. Numerical analysis shows that by applying the proposed controller, the protected network can operate around the desired state as expressed by the MFD, while the total delay at the perimeter is minimized as well. Moreover, the paper sheds light on the robustness of the proposed controller. This multi-scale hybrid controller is further extended to a stochastic MPC scheme, where connected vehicles (CV) serve as the only data source. Hence, low penetration rates of CVs lead to strong noises in the controller. This is a first attempt to develop a network-level traffic control methodology by using the emerging CV technology. We consider the stochasticity in traffic state estimation and the shape of the MFD. Simulation analysis demonstrates the robustness of the proposed stochastic controller, showing that efficient controllers can indeed be designed with this newly-spread vehicle technology even in the absence of other data collection schemes (e.g. loop detectors).

Mobility for an Optimal Data Collection in Wireless Sensor Networks

Sensor nodes located in the vicinity of a static sink drain rapidly their batteries since they have to carry more traffic burden. This situation results in network partition, holes as well as data losses. To mitigate this issue, many research proposed the use of mobile sink in data collection as a potential solution. However, due to its speed, the mobile sink has very short communication time to pick up all data from the sensor nodes within the network, therefore the sink is forced to return back to gather the remaining data. In this paper, we propose a new data collection scheme that aims to decrease the latency and enlarge the staying time between the mobile sink and the meeting points that buffer data originated from the other sensor nodes. We have also handled the case of urgent data so that they can be delivered without any delay. Our proposed scheme is validated via extensive simulations using NS2 simulator. Our approach significantly decreases the latency and prolongs the contact time between the mobile sink and sensor nodes.

A GEOMORPHOLOGICAL APPROACH TO THE ASSESSMENT OF RADIOACTIVE CONTAMINATION IN SMALL LOWLAND AGRICULTURAL CATCHMENTS

Large fallout of 137Cs caused by the Chernobyl accident resulted in radioactive contamination of vast areas in the East-European Plain. Adsorption of the radionuclide by soil particle makes erosion processes the main driver of 137Cs redistribution in the landscape. In turn, radioisotope 137Cs is widely used to evaluate soil erosion rates and erosion-driven transport of sediments and associated contaminants. The paper presents the overview of more than two-decade long investigations of erosion and associated 137Cs migration in Chernobyl-affected areas in European part of Russia as well as the specific scheme of field data collection required for proper assessment of modern radioecological risks. The main result is that the most part of the transported 137Cs was deposited in the upper reaches of fluvial networks.

Use of Remote Sensing for Population Number Determination

Ideally, in a country the population censuses are held regularly (five or ten-year intervals), population surveys, called “control surveys” are then conducted during the intercensal period. The latter, as well as the registers of civil status (information on the movements of the population), help determining a representative sample, called “scale model of the population.” Random, stratified and weighted, it has the advantage of providing a good statistical database for any generalizations about the target population with relatively little risk of error. Our study area, Bukavu city, doesn't comply with the classical scheme of data collection for two main reasons: - there are more than twenty years that real demographic censuses have been carried out in the province, the records of the `civil status is poorly maintained and often incomplete—if any!—Especially during this post-conflict period. A study was conducted in Bukavu City to determine the number of people living in this city. Two GeoEye satellite images of 50 cm resolution captured in July 2012 were used. A net of 200 × 200 meters was created with ArcGIS to divide the satellite images into regular cells. In total 2772 cells were created to cover the two satellite images but only 2353 cells were considered for classification. Three classes were identified in the satellite images according to houses density: High density, medium density and low density zones. Three samples were selected and for each different density type, a point map was created covering each house of the selected sample zones received a point. Using the three different density patterns, 95 highly populated zones were identified, 307 medium density zones and 800 low density zones having each respectively a total of 30'400, 46'050, and 40'000 houses. The population of the city was obtained by taking the number of houses times an average of 8, 7 and 6 habitants per house respectively in high, medium and low density zones. A total of 805550 habitants was obtained for Bukavu city which is almost the same number of people estimated (830'000) by the Inspection Provinciale de la Sante which is the health office in charge of vaccination campaign in South-Kivu Province. This method can be used whenever there is a need to quickly estimate the number of the population in a region where there is no census data.

Effective data collection scheme for real-spatial group communication over hybrid infra-ad hoc wireless networks

Effective data collection scheme for real-spatial group communication over hybrid infra-ad hoc wireless networks

A novel compressive sensing method based on SVD sparse random measurement matrix in wireless sensor network

Purpose The performance of the measurement matrix directly affects the quality of reconstruction of compressive sensing signal, and it is also the key to solve practical problems. In order to solve data collection problem of wireless sensor network (WSN), the authors design a kind of optimization of sparse matrix. The paper aims to discuss these issues. Design/methodology/approach Based on the sparse random matrix, it optimizes the seed vector, which regards elements in the diagonal matrix of Hadamard matrix after passing singular value decomposition (SVD). Compared with the Toeplitz matrix, it requires less number of independent random variables and the matrix information is more concentrated. Findings The performance of reconstruction is better than that of Gaussian random matrix. The authors also apply this matrix to the data collection scheme in WSN. The result shows that it costs less energy and reduces the collection frequency of nodes compared with general method. Originality/value The authors design a kind of optimization of sparse matrix. Based on the sparse random matrix, it optimizes the seed vector, which regards elements in the diagonal matrix of Hadamard matrix after passing SVD. Compared with the Toeplitz matrix, it requires less number of independent random variables and the matrix information is more concentrated.

Magic-angle turning with double acquisition

The double-acquisition scheme for efficient data collection of hypercomplex data (the States method) of a two-dimensional experiment is adopted to magic-angle hopping (MAH) and magic-angle turning (MAT) experiments, which are powerful methods to measure the principal values of the chemical shift anisotropy (CSA) in a powder sample. It is shown that the double acquisition MAT (DAMAT) sequence realizes the S/N ratio comparable to or better than those of other variants of the MAH/MAT sequences. In addition, we show that DAMAT has preferable features that there are no spinning sidebands in the indirect dimension, and no spectral shearing is necessary.

Cohort study investigating the relationship between cholesterol, cardiovascular risk score and the prescribing of statins in UK primary care: study protocol

IntroductionRisk scoring is an integral part of the prevention of cardiovascular disease (CVD) and should form the basis for the decision to offer medication to reduce cholesterol (statins). However, there...

An unequal redundancy level-based mechanism for reliable data collection in wireless sensor networks

Most applications require high reliability with low delay in wireless sensor networks (WSNs). Unfortunately, it is a challenge issue to achieve reliable communication because of the lossy nature of wireless channels. In this paper, a novel unequal redundancy level (URL) data collection scheme is proposed to achieve high reliability for wireless sensor networks while long lifetime can be still guaranteed. Aiming at the imbalance of energy consumption in WSNs, a URL data collection scheme adopts network coding-based mechanism with high redundancy level in non-hotspot areas while adopting low redundancy level in hotspot areas. The theoretical analysis and experimental results show that, comparing with equal redundancy level (ERL) approach, the URL approach improves the lifetime by 15–50 % under the same reliability, and it can also improve the reliability by 14.1–28.3 % under the same lifetime. Comparing with an SW-ARQ approach, the URL approach improves the lifetime by 7–30 % under the same reliability, and it can also improve the reliability by 8–23 % under the same lifetime.

Secure Energy Efficient Data Collection Scheme Over Wireless Multimedia Sensor Networks

A secure data collection in wireless multimedia sensor networks (WMSNs) has given attention to one of security issues. WMSNs pose unique security challenges due to their inherent limitations in communication and computing, which makes vulnerable to various attacks. For the energy efficiency, WMSNs adopt mobile sinks to collect data from sensor nodes. Thus, how to gather data securely and efficiently is an important issue WMSNs. In this paper, we propose a secure energy efficient data collection scheme over WMSNs, which are based on Bilinear pairing and symmetric key cryptosystem. First of all, we devise a security model based on a hierarchical key structure for the security mechanisms, authentication, key agreement, confidentiality, and integrity. Based on the model, we propose a secure energy efficient data collection scheme, which could establish secure session in one round. The proposed scheme could efficiently remedy security and efficiency problems in the previous data collection schemes over WMSNs. It has only about 18% of overhead for the security but also has energy efficiency compared with the other related schemes.

Energy and Load based Emergency Data Collection Scheme using Mobile Sink for Wireless Sensor Networks

The extensive Wireless Sensor Networks (WSN), used for environmental monitoring, human body observation, military surveillance, tracking objects and applications. Multi-hop transmission can lead to packet losses, delay, and energy waste due to a large number of re-transmissions and packet (P) drops. The weakness of using a immobile sinks are well known. Advantage of sink mobility for data gathering has drawn a big attention in recent years. Various routing protocols had been developed for achieving network load management in data collecting wireless sensor networks. Mobile sink (MS) reduces the network load and data loss compared to a immobile sink. The main contribution of this paper is to provide a simulation-based study of network load based emergency data collection from harsh environment using mobile sinks. Aim to reduce data loss, minimize energy consumption while establishing path through MS, control emergency mobile sink arrival message storm by zone based WSN. Emergency nodes be capable of minimizing queue overflow through sensor self aggregation and load balancing with neighbors. The focus is on three important configuration specifications: Mobile sink-based data collection, emergencymobile sink arrival message supervision, and sensor self aggregation. General Terms Wireless Sensor Networks, Routing protocols.

Incentives for Delay-Constrained Data Query and Feedback in Mobile Opportunistic Crowdsensing.

In this paper, we propose effective data collection schemes that stimulate cooperation between selfish users in mobile opportunistic crowdsensing. A query issuer generates a query and requests replies within a given delay budget. When a data provider receives the query for the first time from an intermediate user, the former replies to it and authorizes the latter as the owner of the reply. Different data providers can reply to the same query. When a user that owns a reply meets the query issuer that generates the query, it requests the query issuer to pay credits. The query issuer pays credits and provides feedback to the data provider, which gives the reply. When a user that carries a feedback meets the data provider, the data provider pays credits to the user in order to adjust its claimed expertise. Queries, replies and feedbacks can be traded between mobile users. We propose an effective mechanism to define rewards for queries, replies and feedbacks. We formulate the bargain process as a two-person cooperative game, whose solution is found by using the Nash theorem. To improve the credit circulation, we design an online auction process, in which the wealthy user can buy replies and feedbacks from the starving one using credits. We have carried out extensive simulations based on real-world traces to evaluate the proposed schemes.

Adaptive scheme to Control Power Aware for PDR in Wireless Sensor Networks

Nowadays Wireless sensor networks playing vital role in all area. Which is used to sense the environmental monitoring, Temperature, Soil erosin etc. Low data delivery efficiency and high energy consumption are the inherent problems in Wireless Sensor Networks. Finding accurate data is more difficult and also it will leads to more expensive to collect all sensor readings. Clustering and prediction techniques, which exploit spatial and temporal correlation among the sensor data, provide opportunities for reducing the energy consumption of continuous sensor data collection and to achieve network energy efficiency and stability. So as we propose Dynamic scheme for energy consumption and data collection in wireless sensor networks by integrating adaptively enabling/disabling prediction scheme, sleep/awake method with dynamic scheme. Our framework is clustering based. A cluster head represents all sensor nodes within the region and collects data values from them. Our framework is general enough to incorporate many advanced features and we show how sleep/awake scheduling can be applied, which takes our framework approach to designing a practical dynamic algorithm for data aggregation, it avoids the need for rampant node-to-node propagation of aggregates, but rather it uses faster and more efficient cluster-to-cluster propagation. To the best of our knowledge, this is the first work adaptively enabling/disabling prediction scheme with dynamic scheme for clustering-based continuous data collection in sensor networks. When a cluster node fails because of energy depletion we need to choose alternative cluster head for that particular region. It will help to achieve less energy consumption. Our proposed models, analysis, and framework are validated via simulation and comparison with Static Cluster method in order to achieve better energy efficiency and PDR.

An Intelligent Authentication Based Vehicle Initiated Broadcast-Dynamic Path Data Collection Scheme in VANET

Objectives: The major focus of this research paper is to propose an Intelligent Authentication based Vehicle Initiated Broadcast-Dynamic Path (IAVIB-DP) data collection scheme with the aim of increasing the effectiveness of existing Vehicle Initiated Broadcast-Complete Path (VIB-CP) data collection scheme which is considered to be the best and most commonly used way that is opted for data collection in VANET and is evaluated in terms of packet delivery ratio, average latency and communication overhead. Methods/Analysis: Simulation is conducted by using OMNet++ to compare the performance of IAVIB-DP with one of the best data collection scheme working on single RSU, VIB-CP. Performance Index (PI) is measured by evaluating the Packet Delivery Ratio (PDR), average latency and Communication Overhead (CO) for proposed and existing scheme of collecting data. Best scheme will be decided on the basis of calculated PI. Other parameters for simulation such as the minimum speed, space dimensions and the maximum speed of moving vehicles remain fixed. Findings: VIB-CP and IAVIB-DP Data Collection Schemes (DCSs) are compared and analysis is done on the calculated value of Performance Index. PI decides whether a data collection scheme is effective or not. PI is calculated on different factors like PDR, Latency and CO. The simulation results show that PI of proposed IAVIB-DP data collection scheme is more as compared to PI of VIBCP, as it has high PDR, low latency and less CO. Application/Improvements: VIB-CP and IAVIB-DP are compared on same factors and are used for PI calculation. On the basis of the simulation results it is evaluated that IAVIB-DP performs better.

An apparatus to measure frictional, anelastic, and viscous behavior in ice at temperate and planetary conditions.

In this paper, we describe a cryogenic, servo-controlled biaxial friction apparatus designed to measure the deformational behaviors of ice. The apparatus is specifically designed to accurately achieve and measure the low differential stresses applicable to deforming ice on earth and on icy satellites. We can apply loads in the range ∼2-1800 kPa and velocities up to 4 mm/s, with resolution of 39 Pa and 0.7 μm, respectively. Precise temperature control, measurement, and insulation allow testing at constant temperature (from -2 to -30 °C) for prolonged periods of time. The apparatus is tested with various plastics as well as with polycrystalline ice samples and the results are consistent with previously published values. Critical components of the instrument are described along with examples of data collection schemes and preliminary results. The flexibility of the design allows for both glaciological and planetary applications over a range of deformational behaviors including friction, anelastic, and viscous.