Data Gathering Performance Research Articles

Study on Gibbs Optimization-Based Resource Scheduling Algorithm in Data Aggregation Networks

In data aggregation networks (WSNs, ad hoc, mesh, etc.), it is key to schedule the network resources, such as channels and TDMA time slots, to minimize the communication conflict and optimize the network data-gathering performance. In this paper, the resources scheduling problem is formulated as a vertex coloring problem in graph theory. Then, a multi-channel TDMA scheduling algorithm based on the Gibbs optimization is proposed. By defining the Gibbs energy expression according to the objective function of the problem, the joint probability of channel and time slot can be computed for the optimized selection of channels and time slots. This algorithm is low-complexity and its convergence performance can be proven. Experiments with different network parameters demonstrate that the proposed algorithm can reduce the communication conflict, improve the network throughput, and effectively reduce the network transmission delay and scheduling length for the data aggregation networks.

Adaptive Transmission Design for Rechargeable Wireless Sensor Network With a Mobile Sink

In this article, we aim at maximizing the data gathering performance of the rechargeable wireless sensor network, where a mobile sink moves along the predefined path to charge sensor nodes through a wireless energy transfer technique and gather data from them. First, we show how to transform the original time-average optimization problem into a queue stability one by using the Lyapunov optimization framework, then we show how to decompose it into multiple subproblems by using the optimization decomposition. A distributed speed control and routing algorithm was proposed to reduce the computing load of the mobile sink and to obtain the near-optimal solution for data collection. Our analysis shows that there is an inherent tradeoff between the network utility and the average data queuing size, and the proposed adaptive transmission scheme can achieve the near-optimal network utility when a certain queueing delay can be tolerated.

A Kernel-Based Compressive Sensing Approach for Mobile Data Gathering in Wireless Sensor Network Systems

The recent advances of compressive sensing (CS) have witnessed a great potential of efficient compressive data gathering (CDG) in wireless sensor network systems (WSNSs). However, most existing work on CDG mainly focuses on multihop relaying strategies to improve the performance of data gathering. In this paper, we propose a mobile CDG scheme including a random walk-based algorithm and a kernel-based method for sparsifying sensory data from irregular deployments. The proposed scheme allows a mobile collector to harvest data by sequentially visiting a number of nodes along a random path. More importantly, toward building the gap between CS and machine learning theories, we explore a theoretical foundation for understanding the feasibility of the proposed scheme. We prove that the CS matrices, constructed from the proposed random walk algorithm combined with a kernel-based sparsity basis, satisfy the restricted isometry property. Particularly, we also show that m = O(klog(n/k)) measurements collected by a mobile collector are sufficient to recover a k-sparse signal and t = O(klog(n/k)) steps are required to collect these measurements in a network with n nodes. Finally, we also present extensive numerical results to validate the effectiveness of the proposed scheme by evaluating the performance in terms of energy consumption and the impact of packet losses. The numerical results demonstrate that the proposed scheme is able to not only significantly reduce communication cost but also combat unreliable wireless links under various packet losses compared to the stateof-the-art schemes, which provides an efficient alternative to data relaying approaches for CDG in WSNS.

Coalition Formation Based Compressive Sensing in Wireless Sensor Networks.

Compressive sensing originates in the field of signal processing and has recently become a topic of energy-efficient data gathering in wireless sensor networks. In this paper, we propose an energy efficient distributed compressive sensing solution for sensor networks. The proposed solution utilizes sparsity distribution of signals to group sensor nodes into several coalitions and then implements localized compressive sensing inside coalitions. This solution improves data-gathering performance in terms of both data accuracy and energy consumption. The approach curbs both data-transmission costs and number of measurements. Coalition-based data gathering cuts transmission costs, and the number of measurements is reduced by scheduling sensor nodes and adjusting their sampling frequency. Our simulation showed that our approach enhances network performance by minimizing energy cost and improving data accuracy.

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.

Impact of redundant sensor deployment over data gathering performance: A model based approach

This paper proposes an efficient sensor deployment model, called ‘MOdel Driven GRAdient Deployment for Irregular Terrain’ (MoDGraDIT), for increasing network lifetime and data gathering accuracy. MoDGraDIT uses two steps for computing the sensor deployment density. First, the number of children for any intermediate node has been estimated based on the connected-coverage criteria. Second, sensors are deployed in high redundancy to sustain node failures. The density of deployment has been estimated that depends on the computed ‘Energy Dissipation Factor’ (EDF), considering the gradient effect of energy depletion. The performance of MoDGraDIT has been analyzed using theoretical and simulation results.

Design and Analysis of Novel Modified Cross Layer Controller for WMSN

Wireless Multimedia Sensor Networks (WMSNs) are swell systems of wirelessly networked systems that consent reclaiming moving images, still images, scalar sensor information and audio information. It is an essential factor of grave networks to safeguard the function of tactical national transportation, afford maintain to work against emergencies and coercion and improve transportation for tactical armed functions. It needs the sensor network customary to be re-consideration in sight of necessitate for technique to fetch multimedia data with a pre-described stage of Quality of Service (QoS) to permit these functions. By believing MIMO modulator, Architecture of the Modified Cross-Layer Controller is proposed. Then using this concept, Signal-to-Noise Ratio (SNR), Bit Error Rate (BER), Symbol Error Rate (SER) and Average Mean Square Error (AMSE) are measured with using MATLAB program and the same is compared with other signal processing approaches named Least Square method and Minimum Mean Square Error method. The same concept is used to reduce area, power and to increase speed of the system with the help of VHDL program. Simulation results show that the proposed Modified Cross-Layer Controller can successfully improve the data gathering performance in wireless multimedia sensor networks. In this research, life cycle improvement of the system is proposed. The goal of our proposed work is to vigorously review the routing method outflow and the constraint of the multimedia function to make sure an adequate quality of service. Simulation results show that our solution is efficient and gives better performance than other methods. Keywords: Life Cycle Improvement and Performance Estimation, MIMO Modulator, Modified Cross-Layer Controller, Quality of Service

A Hybrid Security and Compressive Sensing-Based Sensor Data Gathering Scheme

The use of cryptographic techniques such as encryption and hashing largely increases the energy consumption of sensors, which aggravates the original critical energy constraint problem of wireless sensor networks (WSNs). To reduce the burden of sensors, compression can be utilized. Since the traditional chaos-based schemes are not directly applicable for WSNs, we present a hybrid security solution. The hybrid security consists of 8-bit integer chaotic block encryption and a chaos-based message authentication codes. It aims to promote the security and performance of data gathering. In this paper, a hybrid security and compressive sensing-based scheme for multimedia sensor data gathering is presented. It has light security mechanism and thus decreases the complexity and energy consumption of system. Performance analysis about security and compression is carried out. The results show that our scheme is more applicable for WSNs multimedia data gathering from security and compression efficiency.

A Framework of Joint Mobile Energy Replenishment and Data Gathering in Wireless Rechargeable Sensor Networks

Recent years have witnessed the rapid development and proliferation of techniques on improving energy efficiency for wireless s`ensor networks. Although these techniques can relieve the energy constraint on wireless sensors to some extent, the lifetime of wireless sensor networks is still limited by sensor batteries. Recent studies have shown that energy rechargeable sensors have the potential to provide perpetual network operations by capturing renewable energy from external environments. However, the low output of energy capturing devices can only provide intermittent recharging opportunities to support low-rate data services due to spatial-temporal, geographical or environmental factors. To provide steady and high recharging rates and achieve energy efficient data gathering from sensors, in this paper, we propose to utilize mobility for joint energy replenishment and data gathering. In particular, a multi-functional mobile entity, called SenCar in this paper, is employed, which serves not only as a mobile data collector that roams over the field to gather data via short-range communication but also as an energy transporter that charges static sensors on its migration tour via wireless energy transmissions. Taking advantages of SenCar’s controlled mobility, we focus on the joint optimization of effective energy charging and high-performance data collections. We first study this problem in general networks with random topologies. We give a two-step approach for the joint design. In the first step, the locations of a subset of sensors are periodically selected as anchor points, where the SenCar will sequentially visit to charge the sensors at these locations and gather data from nearby sensors in a multi-hop fashion. To achieve a desirable balance between energy replenishment amount and data gathering latency, we provide a selection algorithm to search for a maximum number of anchor points where sensors hold the least battery energy, and meanwhile by visiting them, the tour length of the SenCar is no more than a threshold. In the second step, we consider data gathering performance when the SenCar migrates among these anchor points. We formulate the problem into a network utility maximization problem and propose a distributed algorithm to adjust data rates at which sensors send buffered data to the SenCar, link scheduling and flow routing so as to adapt to the up-to-date energy replenishing status of sensors. Besides general networks, we also study a special scenario where sensors are regularly deployed. For this case we can provide a simplified solution of lower complexity by exploiting the symmetry of the topology. Finally, we validate the effectiveness of our approaches by extensive numerical results, which show that our solutions can achieve perpetual network operations and provide high network utility.

How Do Gender and Anxiety Affect Students’ Self-Assessment and Actual Performance on a High-Stakes Clinical Skills Examination?

Research suggests that medical students are not accurate in self-assessment, but it is not clear whether students over- or underestimate their skills or how certain characteristics correlate with accuracy in self-assessment. The goal of this study was to determine the effect of gender and anxiety on accuracy of students' self-assessment and on actual performance in the context of a high-stakes assessment. Prior to their fourth year of medical school, two classes of medical students at Johns Hopkins University School of Medicine completed a required clinical skills exam in fall 2010 and 2011, respectively. Two hundred two students rated their anxiety in anticipation of the exam and predicted their overall scores in the history taking and physical examination performance domains. A self-assessment deviation score was calculated by subtracting each student's predicted score from his or her score as rated by standardized patients. When students self-assessed their data gathering performance, there was a weak negative correlation between their predicted scores and their actual scores on the examination. Additionally, there was an interaction effect of anxiety and gender on both self-assessment deviation scores and actual performance. Specifically, females with high anxiety were more accurate in self-assessment and achieved higher actual scores compared with males with high anxiety. No differences by gender emerged for students with moderate or low anxiety. Educators should take into account not only gender but also the role of emotion, in this case anxiety, when planning interventions to help improve accuracy of students' self-assessment.

Traffic-Distributed Clustering Scheme for Cluster-Based WSNs with Various Non-Uniform Traffic Fluctuations

In wireless sensor networks (WSNs), it is essential to save energy consumption at sensor nodes (SNs). A clustering technique is one of the approaches to save energy consumption, where several neighboring SNs form a cluster and transmit the sensed data to their cluster head (CH), and then the CH sends the aggregated data to a sink node. Under spatial non-uniform traffic environments, the clustering technique causes the non-uniformity in data gathering performance and energy consumption between clusters in WSNs. In this paper, we propose a clustering scheme for the WSNs employing IEEE802.15.4 beacon enabled mode under various non-uniform traffic environments. The proposed scheme distributes network traffic uniformly to the clusters through cluster area control by adjusting beacon transmission power, and thereby achieves uniform and improved data gathering performance. In the clusters with expanded area, however, the performance degradation arises from long distance communications. To solve this problem, the proposed scheme controls transmission power at SNs. In addition, to reduce energy consumption the proposed scheme sets the appropriate active period length in duty cycle operation to the current traffic condition. The performance evaluations by computer simulation show the effectiveness of the proposed scheme for the WSNs under various non-uniform traffic environments.

Optimization-Based Distributed Algorithms for Mobile Data Gathering in Wireless Sensor Networks

Recent advances have shown a great potential of mobile data gathering in wireless sensor networks, where one or more mobile collectors are employed to collect data from sensors via short-range communications. Among a variety of data gathering approaches, one typical scheme is called anchor-based mobile data gathering. In such a scheme, during each periodic data gathering tour, the mobile collector stays at each anchor point for a period of sojourn time, and in the meanwhile the nearby sensors transmit data to the collector in a multihop fashion. In this paper, we focus on such a data gathering scheme and provide distributed algorithms to achieve its optimal performance. We consider two different cases depending on whether the mobile collector has fixed or variable sojourn time at each anchor point. We adopt network utility, which is a properly defined function, to characterize the data gathering performance, and formalize the problems as network utility maximization problems under the constraints of guaranteed network lifetime and data gathering latency. To efficiently solve these problems, we decompose each of them into several subproblems and solve them in a distributed manner, which facilitates the scalable implementation of the optimization algorithms. Finally, we provide extensive numerical results to demonstrate the usage and efficiency of the proposed algorithms and complement our theoretical analysis.

A Distance-Aware Replica Adaptive Data Gathering Protocol for Delay Tolerant Mobile Sensor Networks

In Delay Tolerant Mobile Sensor Networks (DTMSNs) that have the inherent features of intermitted connectivity and frequently changing network topology it is reasonable to utilize multi-replica schemes to improve the data gathering performance. However, most existing multi-replica approaches inject a large amount of message copies into the network to increase the probability of message delivery, which may drain each mobile node’s limited battery supply faster and result in too much contention for the restricted resources of the DTMSN, so a proper data gathering scheme needs a trade off between the number of replica messages and network performance. In this paper, we propose a new data gathering protocol called DRADG (for Distance-aware Replica Adaptive Data Gathering protocol), which economizes network resource consumption through making use of a self-adapting algorithm to cut down the number of redundant replicas of messages, and achieves a good network performance by leveraging the delivery probabilities of the mobile sensors as main routing metrics. Simulation results have shown that the proposed DRADG protocol achieves comparable or higher message delivery ratios at the cost of the much lower transmission overhead than several current DTMSN data gathering schemes.

Remote Monitoring System Design of Distribution Transformer Operating Status Based on GSM

In this paper, a remote Distribution Transformer (DT) monitoring system is presented and developed to solve the problems of existing remote monitoring system wiring too much, too complicated, fault diagnosing and maintaining difficulty. The system hardware design based on 16 bit PIC18F458 chip and GSM module TC35i is proposed. The system software design with MySQL and Visual C++6.0 are discussed in detail. A real-time monitoring of parameters such as three-phase voltage, current, power factor and transformer temperature is finally realized. Experiment results show that the system possesses accurate data gathering, strong anti-interference and good operation performance.

On the data gathering capacity and latency in wireless sensor networks

In this paper, we investigate the fundamental properties of data gathering in wireless sensor networks, in terms of both capacity and latency. We consider a scenario in which s(n) out of n total network nodes have to deliver data to a set of d(n) sink nodes at a constant rate λ(n, s(n), d(n)). The goal is to characterize the maximum achievable rate, and the latency in data delivery. We present a simple data gathering scheme that achieves asymptotically optimal data gathering capacity and latency with arbitrary network deployments when d(n) = 1, and for most scaling regimes of s(n) and d(n) when d(n) > 1 in case of square grid and random node deployments. Differently from most previous work, our results and the presented data gathering scheme do not sacrifice energy efficiency to the need of maximizing capacity and minimizing latency. Finally, we consider the effects of a simple form of data aggregation on data gathering performance, and show that capacity can be increased by a factor f(n) with respect to the case of no data aggregation, where f(n) is the node density. To the best of our knowledge, the ones presented in this paper are the first results showing that asymptotically optimal data gathering capacity and latency can be achieved in arbitrary networks in an energy efficient way.

Cross Layer Optimization for Data Gathering in Wireless Multimedia Sensor Networks within Expected Network Lifetime

The use of multimedia sensor nodes can significantly enhance the capability of wireless sensor networks (WSNs) for event description. In a number of scenarios, e.g., an erupting volcano, the WSNs are not deployed to work for an extremely long time. Instead, the WSNs aim to deliver continuous and reliable multimedia data as much as possible within an expected lifetime. In this paper, we focus on the efficient gathering of multimedia data in WSNs within an expected lifetime. An adaptive scheme to dynamically adjust the transmission Radius and data generation Rate Adjustment (RRA) is proposed based on a cross layer design by considering the interaction among physical, network and transport layers. We first minimize the end-to-end transmission delay in WSNs while using the minimum data generation rate. In this phase, an optimal transmission radius can be derived. Then, using this transmission radius, we adaptively adjust the data generation rate to increase the amount of gathered data. Simulation results show that the proposed RRA strategy can effectively enhance the data gathering performance in wireless multimedia sensor networks (WMSNs) by dynamically adjusting the transmission radius of sensor nodes and the data generation rate of source nodes.