Data Collection Path Research Articles

EAPC: Energy-Aware Path Construction for Data Collection Using Mobile Sink in Wireless Sensor Networks

Data collection is one of the paramount concerns in wireless sensor networks. Many data collection algorithms have been proposed for collecting data in particular monitoring regions. However, the efficiency of the paths for such data collection can be improved. This paper proposes an energy-aware path construction (EAPC) algorithm, which selects an appropriate set of data collection points, constructs a data collection path, and collects data from the points burdened with data. EAPC is intended to prolong the network lifetime, it accounts for the path cost from its current data collection point to the next point and the forwarding load of each sensor node. Performance evaluation reveals that the proposed EAPC has more efficient performance than existing data collection mechanisms in terms of network lifetime, energy consumption, fairness index, and efficiency index.

A Lifetime-Enhanced Data Collecting Scheme for the Internet of Things

A backpressure-based data collecting scheme has been applied in the Internet of Things, which can control the network congestion effectively and increase the network throughput. However, there is an obvious shortcoming in the traditional backpressure data collecting scheme for the network service chain. It attempts to search all possible paths between source node and destination node in the networks, causing an unnecessary long path for data collection, which results in large end-toend delay and redundant energy consumption. To address this shortcoming of backpressure data collecting scheme in the Internet of Things, this article proposes an energy-aware and distance-aware data collecting scheme to enhance the lifetime of backpressure-based data collecting schemes. We propose an energy- and distance-based model that combines the factors of queue backlog, hop counts, and residual energy for making routing decisions. It not only reduces the unnecessary energy consumption, but also balances the residual energy. The experiment results show that the proposed scheme can reduce unnecessary energy consumption and end-to-end delay compared to the traditional and LIFO-based schemes. Meanwhile, it balances the energy of nodes and extends the lifetime of an Internet of Things.

Efficient Data Collection by Mobile Sink to Detect Phenomena in Internet of Things

With the rapid development of Internet of Things (IoT), more and more static and mobile sensors are being deployed for sensing and tracking environmental phenomena, such as fire, oil spills and air pollution. As these sensors are usually battery-powered, energy-efficient algorithms are required to extend the sensors’ lifetime. Moreover, forwarding sensed data towards a static sink causes quick battery depletion of the sinks’ nearby sensors. Therefore, in this paper, we propose a distributed energy-efficient algorithm, called the Hilbert-order Collection Strategy (HCS), which uses a mobile sink (e.g., drone) to collect data from a mobile wireless sensor network (mWSN) and detect environmental phenomena. The mWSN consists of mobile sensors that sense environmental data. These mobile sensors self-organize themselves into groups. The sensors of each group elect a group head (GH), which collects data from the mobile sensors in its group. Periodically, a mobile sink passes by the locations of the GHs (data collection path) to collect their data. The collected data are aggregated to discover a global phenomenon. To shorten the data collection path, which results in reducing the energy cost, the mobile sink establishes the path based on the order of Hilbert values of the GHs’ locations. Furthermore, the paper proposes two optimization techniques for data collection to further reduce the energy cost of mWSN and reduce the data loss.

Preparing Students for a Diverse, Deliberative Democracy: College Diversity Experiences and Informed Citizenship after College

Background/Context The role of race in the university continues to be a contentious issue. Proponents of college diversity often cite the importance of fostering a diverse and deliberative democratic society, but the link between student experiences and postcollege citizenship has received limited attention. Purpose/Objective This study explores the extent to which two types of college diversity experiences (cross-racial interaction and curricular/co-curricular diversity engagement) predict aspects of informed citizenship associated with supporting a deliberative democracy six years after graduation (i.e., following the news, discussion of racial issues, and importance of keeping up to date with politics). Participants The dataset for this study came from UCLA's Higher Education Research Institute. We utilized the 1994–1998–2004 cohort of students/alumni, which included a postcollege survey administered six years after graduation. The total sample consisted of 8,634 alumni from 229 institutions. Research Design This study utilized secondary data analysis of the 1994–1998–2004 CIRP dataset. Data Collection and Analysis Path analysis was particularly useful for this study to examine the direct and indirect effects of the college diversity experiences on senior-year and longer-term outcomes. Results College diversity experiences have direct effects on postcollege discussions of racial issues, which suggests that these forms of engagement may have long-lasting effects on college graduates. Moreover, curricular/co-curricular diversity engagement also has positive, indirect effects on keeping up to date with politics, news consumption, and discussing racial issues well after graduation. The pattern of findings differed when analyzed separately by racial/ethnic group (i.e., Whites/Caucasians, Asian Americans, and underrepresented students of color). Conclusions/Recommendations This study adds to the existing knowledge base by making a key contribution to the limited research on the long-term benefits of diversity experiences as well as the dimensions of higher education that inform active citizenship in a deliberative democracy. This study examined the complex relationships—both direct and indirect effects—associated with these college diversity experiences and outcomes after college and how these relationships vary by racial/ethnic group. The current findings point to the particular importance of maximizing opportunities for cross-racial interaction and curricular/co-curricular diversity engagement for all students regardless of their race/ethnicity.

Tracing the Potential Flow of Consumer Data: A Network Analysis of Prominent Health and Fitness Apps

BackgroundA great deal of consumer data, collected actively through consumer reporting or passively through sensors, is shared among apps. Developers increasingly allow their programs to communicate with other apps, sensors, and Web-based services, which are promoted as features to potential users. However, health apps also routinely pose risks related to information leaks, information manipulation, and loss of information. There has been less investigation into the kinds of user data that developers are likely to collect, and who might have access to it.ObjectiveWe sought to describe how consumer data generated from mobile health apps might be distributed and reused. We also aimed to outline risks to individual privacy and security presented by this potential for aggregating and combining user data across apps.MethodsWe purposively sampled prominent health and fitness apps available in the United States, Canada, and Australia Google Play and iTunes app stores in November 2015. Two independent coders extracted data from app promotional materials on app and developer characteristics, and the developer-reported collection and sharing of user data. We conducted a descriptive analysis of app, developer, and user data collection characteristics. Using structural equivalence analysis, we conducted a network analysis of sampled apps’ self-reported sharing of user-generated data.ResultsWe included 297 unique apps published by 231 individual developers, which requested 58 different permissions (mean 7.95, SD 6.57). We grouped apps into 222 app families on the basis of shared ownership. Analysis of self-reported data sharing revealed a network of 359 app family nodes, with one connected central component of 210 app families (58.5%). Most (143/222, 64.4%) of the sampled app families did not report sharing any data and were therefore isolated from each other and from the core network. Fifteen app families assumed more central network positions as gatekeepers on the shortest paths that data would have to travel between other app families.ConclusionsThis cross-sectional analysis highlights the possibilities for user data collection and potential paths that data is able to travel among a sample of prominent health and fitness apps. While individual apps may not collect personally identifiable information, app families and the partners with which they share data may be able to aggregate consumer data, thus achieving a much more comprehensive picture of the individual consumer. The organizations behind the centrally connected app families represent diverse industries, including apparel manufacturers and social media platforms that are not traditionally involved in health or fitness. This analysis highlights the potential for anticipated and voluntary but also possibly unanticipated and involuntary sharing of user data, validating privacy and security concerns in mobile health.

Stochastic Heuristic Optimization based Multi-Query Processing in Wireless Sensor Network using Genetic Algorithm

Sensor Network is an infrastructure comprising of sensing, and computing. The communication elements in sensor network give capability to instrument, watch, and respond to events and phenomenon in a particular situation. Query processing in sensor network first transfers the query generated position to the node where the result obtained is similar to this query. Collaborative Query-Centric Framework (COSE) heterogeneous sensor networks are effectual and well-organized for processing of queries. Query processing with respect to energy efficiency attain single pipeline of query processing but fails to address the issues related to multiple pipeline of query processing. COSE major drawback is that it is unable to attain an optimal solution for multiple pipelines. Next, Pocket Driven Trajectories (PDT) algorithm monitors the query processing based on spatial layout of the selected nodes. PDT algorithm efficiently adapts to different types of data collection paths but not effective for multicast query phase. To develop a multiple query processing strategy in wireless sensor network, Stochastic Heuristic Optimization using Genetic Algorithm (SHO-GA) is introduced. The SHO- GA framework process the multiple query plans based on the closeness of nodes required to answer the user query. The multi-query processing using Genetic Algorithm (GA) takes the associations with FROM clause and other multiple query operators. The SHO-GA framework carries the probability of crossover, mutation and the pre-specified number of generations as input, and produces the top 'n' multi-query processing as output. The stochastic heuristic optimization fitness value provides the best fit chromosome to find the improvement in its query processing parameters. The stochastic heuristic for multi-query called base station optimization (BSO) eliminates the redundancy from the original set. The multi-query plans processed involve minimum processing time for answering the user query leading to efficient query processing sensor network system. Experimental evaluation is performed on factors such as average processing cost, cumulative distribution, multi-query processing time, query answer transmitted speed, query processing delay and user accessibility level. KeywordsNetwork, Genetic Algorithm, Multi-Query Processing, Plans Formation, FROM clause, Crossover, Mutation rate

Multi-track Mobile Data Collection Mechanism for Wireless Sensor Networks

Wireless sensor networks (WSNs) have been widely researched by countries and institutes. The limited node energy has already become a constraint of large-scale applications. Seeking the energy-balance features at sensor nodes to prolong the network lifetime, a multi-track mobile data collecting mechanism (MTMDC) was proposed in this paper. The MTMDC mechanism was composed of three major phases: the Nodes Estimation Phase, the Energy Estimation Phase and the Multi-track Energy-balance Phase. Based on the three phases, a data collection path for mobile data collector or mobile sink (MS) was built to balance the energy consume of the sensor nodes. Theoretical analysis and performance simulations indicated that the proposed MTMDC mechanism has prominent features compared with the existing approaches in terms of the energy-balance performance and network lifetime. DOI : http://dx.doi.org/10.11591/telkomnika.v12i2.3736

A fast algorithm for data collection along a fixed track

Recent research shows that significant energy saving can be achieved in wireless sensor networks (WSNs) with a mobile base station that collects data from sensor nodes via short-range communications. However, a major performance bottleneck of such WSNs is the significantly increased latency in data collection due to the low movement speed of mobile base stations. In this paper we study the problem of finding a data collection path for a mobile base station moving along a fixed track in a wireless sensor network to minimize the latency of data collection. The main contribution is an O(mnlog⁡n) expected time algorithm, where n is the number of sensors in the networks and m is the complexity of the fixed track.

An artificial bee colony algorithm for data collection path planning in sparse wireless sensor networks

In sparse wireless sensor networks, a mobile robot is usually exploited to collect the sensing data. Each sensor has a limited transmission range and the mobile robot must get into the coverage of each sensor node to obtain the sensing data. To minimize the energy consumption on the traveling of the mobile robot, it is significant to plan a data collection path with the minimum length to complete the data collection task. In this paper, we observe that this problem can be formulated as traveling salesman problem with neighborhoods, which is known to be NP-hard. To address this problem, we apply the concept of artificial bee colony (ABC) and design an ABC-based path planning algorithm. Simulation results validate the correctness and high efficiency of our proposal.

Optimizing data collection path in sensor networks with mobile elements

Exploiting mobile elements (MEs) to accomplish data collection in wireless sensor networks (WSNs) can improve the energy efficiency of sensor nodes, and prolong network lifetime. However, it will lead to large data collection latency for the network, which is unacceptable for data-critical applications. In this paper, we address this problem by minimizing the traveling length of MEs. Our methods mainly consist of two steps: we first construct a virtual grid network and select the minimal stop point set (SPS) from it; then, we make optimal scheduling for the MEs based on the SPS in order to minimize their traveling length. Different implementations of genetic algorithm (GA) are used to solve the problem. Our methods are evaluated by extensive simulations. The results show that these methods can greatly reduce the traveling length of MEs, and decrease the data collection latency.

Mobile Filter: Exploring Filter Migration for Error-Bounded Continuous Sensor Data Collection

In wireless sensor networks, filters that suppress data update reports within predefined error bounds effectively reduce the traffic volume for continuous data collection. All prior filter designs, however, are stationary in the sense that each filter is attached to a specific sensor node and remains stationary over its lifetime. In this paper, we propose a mobile filter, i.e., a novel design that explores the migration of filters to maximize overall traffic reduction. A mobile filter moves upstream along the data-collection path, with its residual size being updated according to the collected data. Intuitively, this migration extracts and relays unused filters, leading to more proactive suppression of update reports. While extra communications are needed to move filters, we show through probabilistic analysis that the overhead is outrun by the gain from suppressing more data updates. We present an optimal filter-migration algorithm for a chain topology. The algorithm is then extended to general multichain and tree topologies. Extensive simulations demonstrate that, for both synthetic and real data traces, the mobile filtering scheme significantly reduces data traffic and extends network lifetime against a state-of-the-art stationary filtering scheme. Such results are also observed from experiments over a Mica-2 sensor network testbed.

Corrected High–Frame Rate Anchored Ultrasound With Software Alignment

To improve lingual ultrasound imaging with the corrected high frame rate anchored ultrasound with software alignment (CHAUSA; Miller, 2008) method. A production study of the IsiXhosa alveolar click is presented. Articulatory-to-acoustic alignment is demonstrated using a Tri-Modal 3-ms pulse generator. Images from 2 simultaneous data collection paths, using dominant ultrasound technology and the CHAUSA method, are compared. The probe stabilization and head movement correction paradigm is demonstrated. The CHAUSA method increases the frame rate from the standard National Television System Committee (NTSC) video rate (29.97) to the ultrasound internal machine rate--in this case, 124 frames per second (fps)--by using Digital Imaging and Communications in Medicine (DICOM; National Electrical Manufacturers Association, 2008) data transfer. DICOM avoids spatiotemporal inaccuracies introduced by dominant ultrasound export techniques. The data display alignment of the acoustic and articulatory signals to the correct high-frame rate (FR) frame (± 4 ms at 124 fps). CHAUSA produces high-FR, high-spatial-quality ultrasound images, which are head corrected to 1 mm. The method reveals tongue dorsum retraction during the posterior release of the alveolar click and tongue tip recoil following the anterior release of the alveolar click, both of which were previously undetectable. CHAUSA visualizes most of the tongue in studies of dynamic consonants with a major reduction in field problems, opening up important areas of speech research.

Optimizing query processing using selectivity-awareness in Wireless Sensor Networks

Monitoring queries are fundamental for Wireless Sensor Networks (WSNs) that collect data for physical phenomena. In this work we address three key characteristics of monitoring queries. First, a monitoring query can be selective, i.e., it requests readings only from parts of a WSN. Second, a monitoring query can be continuous, i.e., it draws sensor readings for long periods of time. Finally, since physical phenomena are spatially correlated, a monitoring query selects spatially co-located nodes. In our earlier work, we proposed the Pocket Driven Trajectories (PDT) algorithm; a selectivity-aware data collection technique that tailors data collection paths for a monitoring query based on the spatial layout of selected nodes. In this work, we extend the basic PDT algorithm with an adaptive behavior. We show that the enhanced PDT algorithm is ideal for real world WSNs due to its two major strengths; the PDT algorithm is local, i.e., it does not require any global information about node locations or network connectivity. Furthermore, the PDT algorithm efficiently adapts its data collection paths over the lifetime of a query as changes in the spatial layout of selected nodes occur. Using extensive simulations, we show that in terms of energy efficiency the PDT algorithm clearly outperforms well-known WSN data collection algorithms.

900 MHz radio signals operational control

The use of 900 MHz radio for supervisory control and data acquisition applications was investigated by the Houston Lighting and Power Company (HL&P). Multiple address system applications in the 928/952 MHz band were evaluated. The purpose of this application was to replace leased telephone lines with 928/952 MHz radio channels to achieve lower costs and improved operational reliability. A second application with distribution automation and control pilot test programs on the HL&P distribution system was explored. The purpose was reliable, cost-effective, bidirectional communications at a reasonable bit rate for control and monitoring of distribution feeders and related sectionalizing facilities. A third application of 900 MHz involving real-time monitoring of high-voltage transmission line conductors was investigated. In this instance, the radio provided a safe and reliable path for data collection between power donuts and master controller.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>