Data Gathering Scheme Research Articles

Near Optimal Data Gathering in Rechargeable Sensor Networks with a Mobile Sink

We study data gathering problem in Rechargeable Sensor Networks (RSNs) with a mobile sink, where rechargeable sensors are deployed into a region of interest to monitor the environment and a mobile sink travels along a pre-defined path to collect data from sensors periodically. In such RSNs, the optimal data gathering is challenging because the required energy consumption for data transmission changes with the movement of the mobile sink and the available energy is time-varying. In this paper, we formulate data gathering problem as a network utility maximization problem, which aims at maximizing the total amount of data collected by the mobile sink while maintaining the fairness of network. Since the instantaneous optimal data gathering scheme changes with time, in order to obtain the globally optimal solution, we first transform the primal problem into an approximate network utility maximization problem by shifting the energy consumption conservation and analyzing necessary conditions for the optimal solution. As a result, each sensor does not need to estimate the amount of harvested energy and the problem dimension is reduced. Then, we propose a Distributed Data Gathering Approach (DDGA), which can be operated distributively by sensors, to obtain the optimal data gathering scheme. Extensive simulations are performed to demonstrate the efficiency of the proposed algorithm.

RESEARCH IN ONLINE SPACE: THE USE OF SOCIAL MEDIA FOR RESEARCH SETTING

Web 2.0 technology has supported the arrival of various social and collaborative applications. Those applications, which is known as social media, provide an opportunity for users to express their opinions and share their knowledge and thoughts with others in online space. This provides opportunities for reseachers to extend their research setting and generate data within a broader environement. As a result, researchers have increasingly turned to social media sites to practice their research to expand the scope and improve the analytic power of their studies. Yet there is still relatively little direction on and much confusion about how to gather data using qualitative and quantitative approaches on social media sites. Through an exstensive literature review from Information Technology and Information System journals, this studi proposes new data gathering strategies within a broader environment of social media sites. This study contributes to understanding of using social media for new research settings. The limitation and future research of this study is addressed.

Energy Consumption in Wireless Sensor Networks Using Multi Level Clustering Energy Efficient Protocol

A wireless sensor network (WSN) consists of a huge number of sensor nodes that are inadequate in energy, storage and processing power. One of the major tasks of the sensor nodes is the collection of data and forwarding the gathered data to the Base Station (BS). Hence, the network lifetime becomes the major criteria for effective design of the data gathering schemes in WSN .In this paper , every sensor nodes can converse straight with every other or with the base station. This develops into impracticable when the size of the region of interest enlarge. The anticipated protocol uses a multi-hop communication among the cluster-heads to preserve energy and cover up a huge area of interest. To diminish the quantity of information to be sent to the base station, we incorporated data aggregation. Furthermore, the rotation of cluster-heads and the utilize of the low-power sleep mode by the sensor nodes that do not contribute in routing permit to balance the load and reduce energy utilization considerably. Apply the MAT LAB Simulation software Tool to confirm the proposed design, comprehensive simulation has been approved. The NARRATIVE-LEACH schemes recommend reliable wider experience area and longer life span of WSN. To compare the other modules N-LEACH is the best enhanced in terms of stability period while compromise on lifetime.

Introduction of Undergraduate Nursing Students to an Objective Structured Clinical Examination.

The Objective Structured Clinical Examination (OSCE) is used increasingly in nursing education. The purpose of this article is to report on the qualitative evaluation of the first implementation of a medication administration OSCE within an Australian school of nursing. A two-stage data gathering strategy was undertaken. The first stage was to survey participants' pre-OSCE and the second stage involved surveying the same participants' immediately post-OSCE. A constant comparison analysis method was used. The first stage data analysis (pre-OSCE) were classified into three themes: (a) Student Anxiety, (b) Student Preparedness, and (c) The Effectiveness of This Style of Assessment. The three key themes from the second stage (post-OSCE) analysis were: (a) Feelings Toward the OSCE, (b) Assessor Interaction, and (c) The OSCE Environment. Although they found it stressful, the participants valued the OSCE experience. The OSCE gave the students confidence in their capabilities of medication management. [J Nurs Educ. 2017;56(4):231-234.].

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.

A Novel Framework of Multi-Hop Wireless Charging for Sensor Networks Using Resonant Repeaters

Wireless charging has provided a convenient alternative to renew nodes' energy in wireless sensor networks. Due to physical limitations, previous works have only considered recharging a single node at a time, which has limited efficiency and scalability. Recent advances on multi-hop wireless charging is gaining momentum and provides fundamental support to address this problem. However, existing single-node charging designs do not consider and cannot take advantage of such opportunities. In this paper, we propose a new framework to enable multi-hop wireless charging using resonant repeaters. First, we present a realistic model that accounts for detailed physical factors to calculate charging efficiencies. Second, to achieve balance between energy efficiency and data latency, we propose a hybrid data gathering strategy that combines static and mobile data gathering to overcome their respective drawbacks and provide theoretical analysis. Then, we formulate multi-hop recharge schedule into a bi-objective NP-hard optimization problem. We propose a two-step approximation algorithm that first finds the minimum charging cost and then calculates the charging vehicles' moving costs with bounded approximation ratios. Finally, upon discovering more room to reduce the total system cost, we develop a post-optimization algorithm that iteratively adds more stopping locations for charging vehicles to further improve the results while ensuring none of the nodes will deplete battery energy. Our extensive simulations show that the proposed algorithms can handle dynamic energy demands effectively, and can cover at least three times of nodes and reduce service interruption time by an order of magnitude compared to the single-node charging scheme.

Power Allocation in an Asymmetric Wireless Sensor Network

This letter investigates the power allocation problem for an asymmetric wireless senor network, where multiple sensors observe a common binary source and transmit their corrupted observations to a data fusion node. We propose a power allocation scheme by maximizing the weighted channel capacity subject to the sum power constraint and show that this problem is convex. The simulation results verify that the proposed power allocation scheme outperforms the uniform power allocation method. Furthermore, a scheduling scheme for binary data gathering is proposed by determining the sensors that dominate the bit error rate performance.

Graph-Based Data Gathering Scheme in WSNs With a Mobility-Constrained Mobile Sink

Sink mobility can improve the energy efficiency and data gathering performance in wireless sensor networks. However, mobility of the sink station may be constrained in some applications and it becomes a challenge to optimize the network performance for random distribution and hierarchical topology. We focus on prolonging the network lifetime and keep the data gathering performance at an acceptable level. To address this issue, we propose a comprehensive data gathering scheme based on graphing technique, called double optimization of energy efficiency (DOEE) to optimize the energy consumption of all sensor nodes hierarchically. The objective problem of DOEE is solved with a heuristic algorithm. Along with the scheme, we also specialize a route discovery protocol and its corresponding dynamic gateway protocol to balance the energy consumption among sensor nodes. The proposed algorithm and protocols are evaluated in the simulation platform Network Simulation-3 and the experiment results show our technique has superior performance.

Design of a compressive data gathering scheme with an energy efficient reconstruction algorithm for wsn

Design of a compressive data gathering scheme with an energy efficient reconstruction algorithm for wsn

Cooperation forwarding data gathering strategy of wireless sensor networks

Limited energy and easy invalidation are the characteristics of WSNs (Wireless Sensor Networks). To prolong the lifetime of WSNs for building monitoring, an algorithm named CFDGSBRWB is proposed, which is a Cooperation Forwarding Data Gathering Strategy Based on Random Walk Backup in Wireless Sensor Networks. The algorithm consists of data backup based on random walk and cooperation forwarding data gathering by WBA (Wireless Broadcast Advantage). Simulation results show that CFDGSBRWB can reduce packet lost ratio and enhance fault-tolerance significantly under the condition of the insignificant increase of communication traffic, and is suitable for large-scale wireless sensor networks.

Cooperation forwarding data gathering strategy of wireless sensor networks

Cooperation forwarding data gathering strategy of wireless sensor networks

An efficient privacy-preserving compressive data gathering scheme in WSNs

Because of the strict energy limitation and the common vulnerability of Wireless Sensor Networks (WSNs), providing efficient and secure data gathering in WSNs becomes an essential problem. Compressive data gathering, which is based on the recent breakthroughs in compressive sensing theory, has been proposed as a viable approach for data gathering in WSNs at low communication overhead. Nevertheless, compressive data gathering is susceptible to various attacks in the presence of the open wireless medium. In this paper, we propose a novel Efficient Privacy-Preserving Compressive Data Gathering Scheme, which exploits homomorphic encryption functions in compressive data gathering to thwart the traffic analysis/flow tracing and realize the privacy preservation. This allows the proposed scheme to possess the two important privacy-preserving features of message flow untraceability and message content confidentiality. Extensive performance evaluations and security analyses demonstrate the validity and efficiency of the proposed scheme.

Compressed sensing based acoustic event detection in protected area networks with wireless multimedia sensors

Wireless multimedia sensors have been frequently used for detecting events in acoustic rich environments such as protected area networks. Such areas have diverse habitat, frequently varying terrain and are a source of very large number of acoustic events. This work is aimed at detecting the tree cutting event in a forest area, by identifying the acoustic pattern generated due to an axe hitting a tree bole, with the help of wireless multimedia sensors. A series of operations using the hamming window, wiener filter, Otsu thresholding and mathematical morphology are used for removing the unwanted clutter from the spectrogram obtained from such events. Using the sparse nature of the acoustic signals, a compressed sensing based energy efficient data gathering scheme is devised for accurate event reporting. A network of Mica2 motes is deployed in a real forest area to test the validity of the proposed scheme. Analytical and experimental results proves the efficacy of the proposed event detection scheme.

“Comparative Silence” Still?

This paper revisits the longstanding debate about journalism, academic scholarship, and their connections with the powerful forces of surveillance that shape the lives of contemporary democracies. Drawing critically on the practical findings of Edward Snowden and others, we offer an analysis of the “Five Eyes” intelligence collection and sharing arrangements between the United States, United Kingdom, Australia, New Zealand, and Canada, and the responses it has elicited from journalists and academic researchers. We show how and why journalists and academics have been deterred from researching and reporting on the significance of Five Eyes activities to the public. The paper provides a new account of the Five Eyes project, helped along by the findings of journalists, including the NSA data-gathering schemes exposed by Edward Snowden. Then we examine the uneven outputs of journalists and academics. Finally, we will show why Edward Snowden’s revelations must be seen as just one contribution to our understanding of a much longer historical trend; and we show why the work of other, less well-known journalists is vital for explaining and understanding a surveillance programme that arguably has profound threatening implications for the future of journalism, university scholarship, and the ideals and institutions of democratic citizenship.

Data-Gathering Scheme Using AUVs in Large-Scale Underwater Sensor Networks: A Multihop Approach.

In this paper, we propose a data-gathering scheme for hierarchical underwater sensor networks, where multiple Autonomous Underwater Vehicles (AUVs) are deployed over large-scale coverage areas. The deployed AUVs constitute an intermittently connected multihop network through inter-AUV synchronization (in this paper, synchronization means an interconnection between nodes for communication) for forwarding data to the designated sink. In such a scenario, the performance of the multihop communication depends upon the synchronization among the vehicles. The mobility parameters of the vehicles vary continuously because of the constantly changing underwater currents. The variations in the AUV mobility parameters reduce the inter-AUV synchronization frequency contributing to delays in the multihop communication. The proposed scheme improves the AUV synchronization frequency by permitting neighboring AUVs to share their status information via a pre-selected node called an agent-node at the static layer of the network. We evaluate the proposed scheme in terms of the AUV synchronization frequency, vertical delay (node→AUV), horizontal delay (AUV→AUV), end-to-end delay, and the packet loss ratio. Simulation results show that the proposed scheme significantly reduces the aforementioned delays without the synchronization time-out process employed in conventional works.

Energy Efficient Strategies with Mobile Sink for WSNs: A Survey

Sink mobility has been widely used to reduce the energy consumption of sensor nodes, which can prolong the lifetime of nodes in wireless sensor networks (WSNs). Researchers found that sink mobility can improve the stability, throughput and lifetime of the very large-scale WSNs. However, sink mobility also causes issues such as data latency and communication overhead. With these properties sink mobility remains more research efforts under different requirements. In this paper we present a survey of energy efficient strategies in WSNs with mobile sink. Firstly, we investigate the existing issues in WSNs. Then we focus on the problem of energy efficient data gathering strategy with mobile sink and present some existing solutions. Finally, we conclude this paper with some potential extensions which need to be further explored.

A honeycomb structure based data gathering scheme with a mobile sink for wireless sensor networks

With the widespread use of wireless sensor networks, more and more applications require energy efficient and low packet loss rate data collection methods. Recently, the concept of ‘mobile’ is introduced in various mechanisms to meet the needs of this kind. In this paper, a honeycomb structure based data gathering scheme, HSDG, is proposed for wireless sensor networks with a mobile sink. By partitioning the network into a honeycomb structure and giving each partition a direction value, every sensor node can obtain the latest location of the mobile sink dynamically with a small amount of broadcasting overhead. HSDG uncouples the moving strategy of mobile sink from the data forwarding mechanism, and three subscheme HSDG_RM, HSDG_DGM, and HSDG_EGM are proposed. Our schemes are investigated from average energy consumption, maintenance cost, packet loss rate and the number of packets collected. Compared with BTDG and ALURP, HSDG_DGM is the most energy efficient with a low packet loss rate.

Zero-effort projection for sensory data reconstruction in wireless sensor networks

Compressive sensing is a promising technique for data gathering in large-scale wireless sensor networks. Existing compressive sensing–based data gathering techniques still follow sampling than compression paradigm. In this article, we proposed a random sampling zero-encoding data gathering scheme for wireless sensor networks, which exploits virtual Gaussian energy diffusion model to obtain sampling and compression data gathering. Our proposed data gathering model not only can make simultaneous sampling and compression but also do not need to assign projection matrix to each sensor node. Our scheme can efficiently resolve two types of sensor networks’ data gathering problems: recover missing sensory data and extend monitoring field using incomplete random sampling. Extensive experimental results show that our proposed random sampling zero-encoding data gathering model has good performance for reconstructing the sensory data in wireless sensor networks.

Auction-Based Data Gathering Scheme for Wireless Sensor Networks

This letter proposes a novel data gathering scheme for wireless sensor networks (WSNs) that limits the energy expenditure, and hence, prolongs network lifetime. Data gathering is modeled as an auction where a node broadcasts its own result only if it is higher than the maximum already-broadcasted result by other nodes. For a WSN of 100 nodes, the mathematical and simulation results show that the proposed scheme can save up to 70% of the energy consumption with <1% performance loss, compared with the conventional scheme.

An Adaptive Data Gathering Scheme for Multi-Hop Wireless Sensor Networks Based on Compressed Sensing and Network Coding.

Joint design of compressed sensing (CS) and network coding (NC) has been demonstrated to provide a new data gathering paradigm for multi-hop wireless sensor networks (WSNs). By exploiting the correlation of the network sensed data, a variety of data gathering schemes based on NC and CS (Compressed Data Gathering—CDG) have been proposed. However, these schemes assume that the sparsity of the network sensed data is constant and the value of the sparsity is known before starting each data gathering epoch, thus they ignore the variation of the data observed by the WSNs which are deployed in practical circumstances. In this paper, we present a complete design of the feedback CDG scheme where the sink node adaptively queries those interested nodes to acquire an appropriate number of measurements. The adaptive measurement-formation procedure and its termination rules are proposed and analyzed in detail. Moreover, in order to minimize the number of overall transmissions in the formation procedure of each measurement, we have developed a NP-complete model (Maximum Leaf Nodes Minimum Steiner Nodes—MLMS) and realized a scalable greedy algorithm to solve the problem. Experimental results show that the proposed measurement-formation method outperforms previous schemes, and experiments on both datasets from ocean temperature and practical network deployment also prove the effectiveness of our proposed feedback CDG scheme.