Data Gathering Scheme Research Articles

Energy-Efficient Data Collection in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are usually self-organized wireless ad hoc networks comprising of a large number of resource constrained sensor nodes. One of the most important tasks of these sensor nodes is systematic collection of data and transmits gathered data to a distant base station (BS). Hence network life- time becomes an important parameter for efficient design of data gathering schemes for sensor networks. In this paper, we benefit both cluster and tree structures for data gathering. In our proposed energy-efficient mechanism, the most appropriates hops for data forwarding will be selected and the lifetime of the whole network will be maximized. The simulation results show that by using the proposed approach, the lifetime and the throughput of the network will be increased.

Compressed sensing for efficient random routing in multi-hop wireless sensor networks

Compressed sensing (CS), as a novel theory based on the fact that certain signals can be recovered from a relatively small number of non-adaptive linear projections, is attracting ever-increasing interests in the areas of wireless sensor networks. However, the applications of traditional CS in such settings are limited by the huge transport cost caused by dense measurement. To solve this problem, we propose several ameliorated random routing methods executed with sparse measurement based CS for efficient data gathering corresponding to different networking topologies in typical wireless sensor networking environment, and analyse the relevant performances comparing with those of the existing data gathering schemes, obtaining the conclusion that the proposed schemes are effective in signal reconstruction and efficient in reducing energy consumption cost by routing. Our proposed schemes are also available in heterogeneous networks, for the data to be dealt with in CS are not necessarily homogeneous.

에너지 효율적 센서 데이터 수집을 위한 리포팅 허용 지연시간 적응형 센서 제어 기법 구현 및 성능평가

Due to the application-specific nature of wireless sensor networks, the sensitivity to such a requirement as data reporting latency may vary depending on the type of applications, thus requiring application-specific algorithm and protocol design paradigms which help us to maximize energy conservation and thus the network lifetime. In this paper, we implement and evaluate a novel delay-adaptive sensor scheduling scheme for energy-saving data gathering which is based on a two phase clustering (TPC), in wireless sensor networks. The TPC is implemented on sensor Mote hardwares. With the help of TPC implemented, sensors selectively use direct links for control and forwarding time critical sensed data and relay links for data forwarding based on the user delay constraints given. Implementation study shows that TPC helps the sensors to increase a significant amount of energy while collecting sensed data from sensors in a real environment.

Efficient Measurement Generation and Pervasive Sparsity for Compressive Data Gathering

We proposed compressive data gathering (CDG) that leverages compressive sampling (CS) principle to efficiently reduce communication cost and prolong network lifetime for large scale monitoring sensor networks. The network capacity has been proven to increase proportionally to the sparsity of sensor readings. In this paper, we further address two key problems in the CDG framework. First, we investigate how to generate RIP (restricted isometry property) preserving measurements of sensor readings by taking multi-hop communication cost into account. Excitingly, we discover that a simple form of measurement matrix [I R] has good RIP, and the data gathering scheme that realizes this measurement matrix can further reduce the communication cost of CDG for both chain-type and tree-type topology. Second, although the sparsity of sensor readings is pervasive, it might be rather complicated to fully exploit it. Owing to the inherent flexibility of CS principle, the proposed CDG framework is able to utilize various sparsity patterns despite of a simple and unified data gathering process. In particular, we present approaches for adapting CS decoder to utilize cross-domain sparsity (e.g. temporal-frequency and spatial-frequency). We carry out simulation experiments over both synthesized and real sensor data. The results confirm that CDG can preserve sensor data fidelity at a reduced communication cost.

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.

Bio-inspired data transmission scheme to multiple sinks for the long-term operation of wireless sensor networks

Wireless sensor networks have a wide range of applications, such as natural environmental monitoring, object tracking, and environmental control in residential spaces or plants. In wireless sensor networks, many sensor nodes with limited resources are placed in an observation area and used to gather information about environments. Therefore, a data gathering scheme (or a routing algorithm) for saving and balancing the energy consumption of each sensor node is needed to prolong the lifetime of wireless sensor networks. This article proposes a new bio-inspired data transmission scheme for the long-term operation of wireless sensor networks. By using the proposed scheme, autonomous load-balancing data transmission to multiple sinks can be actualized. We evaluate the proposed scheme using computer simulations to verify its effectiveness, and also discuss its development potential.

Subsurface Plume Tracking Using Sparse Wireless Sensor Networks

Deployment of sensors for tracking chemical plumes such as those in the subsurface can be quite expensive. A compressed data gathering scheme for chemical plume tracking is presented. With only a fraction (about 25%) of the measurement points required to achieve a given spatial resolution this novel ap-plication of compressed sensing can track the plume within 7% accuracy compared to the case of a full sensor array. The scheme also gathers and re-distributes information of the sensors to the entire network, compress-ing with Discrete Wavelet Transform. The scheme can be used to disseminate global tracking information to sensors as well, with savings in communications by a factor of 5 in average, if such capability is required. The scheme is capable of interpolating randomly missing sensor points with significant accuracy. It also supports data fusion as a simple addition of coefficients requiring no changes to the message length.

Estratégias e metodologias de ajuste de modelos hipsométricos em plantios de Eucalyptus sp.

Conduziu-se este trabalho com o objetivo de testar diferentes estratégias e metodologias de coleta de dados, a fim de realizar ajustes de modelos hipsométricos tradicionais e genéricos para melhor estimar a altura de árvores em plantios de Eucalyptus sp. Foram coletados pares de altura e diâmetro de árvores distribuídas em 36 parcelas, provenientes de plantios pertencentes à empresa Veracel Celulose S.A. Essas foram divididas em diferentes tratamentos conforme idade, região, e combinação de região e idade do plantio, totalizando 14 tratamentos. As metodologias de coleta de dados consistiram na utilização de diferentes números de árvores com medição de altura para formação da base de dados para realização dos ajustes. Foi selecionando como melhor estratégia de ajuste, o modelo de Curtis por parcela e dois modelos genéricos para as diferentes metodologias adotadas, com exceção da metodologia baseada em adição de árvores representativas em todos os quartis diamétricos, que selecionou apenas a estratégia de Curtis por parcela.

An e-voting-based data gathering scheme for decision support systems

In this contribution, a protocol for secure data gathering is introduced that utilises existing e-voting protocols to gather data records instead of votes. The protocol relies on two distinct authorities for the secure gathering of data and makes use of a token for each database record, making unencrypted data accessible only to the system participants. The protocol is suitable for decision support systems among non-mutually trusted parties as it protects the anonymity and privacy of the parties by dissociating the data from their origin and protects both the validity of the data sent by each party and the system by intruders and malicious participants.

Research on the energy hole problem based on unequal cluster-radius for wireless sensor networks

Clustering provides an effective way for prolonging the lifetime of a wireless sensor network. When cluster heads cooperate with each other to forward their data to the base station via multi-hop communication, the cluster head closer to the base station are burdened with heavier relay traffic and trend to die much faster, leaving areas of the energy hole problem and causing network partition. To address the problem, we propose a theoretically analytical method for the energy consumption with different cluster-radius in clustering network. The main conclusions are presented as follows: (1) The expression of cluster-radius τ is given while the network lifetime is maximal. (2) In clustering network, the lifetime is longer than the network with the data transmitted directly. (3) We propose a novel and simple strategy to avoid the energy hole problem for data gathering scheme. Employing an unequal cluster-radius and alternate between dormancy and work is the core idea. That is, the network lifetime depends on the node which has the maximal energy consumption. And it is not the same node in this circumstance. So the total energy consumption is less than the network which employing an optimum and fixed cluster-radius and it can achieve an obvious improvement on the network lifetime. The theoretical analysis and simulation results show that the strategy is very simple and it can effectively avoid the energy hole problem. Therefore, the conclusions can present a better design guideline for wireless sensor networks.

Design and Performance of Wireless Data Gathering Networks Based on Unicast Random Walk Routing

Wireless environment monitoring applications with significantly relaxed quality-of-service constraints are emerging. Hence, the possibility to use rough low knowledge routing in sensor networks to reduce hardware resource and software complexity is questionable. Moreover, low knowledge handling allows better genericity, which is of interest, for instance, for basic operation enabling system set-up. In this framework, this paper revisits stateless unicast random walk routing in wireless sensor networks. Based on random walk theory, original closed-form expressions of the delay, the power consumption and related spatial behaviors are provided according to the scale of the system. Basic properties of such a random routing are discussed. Exploiting its properties, data gathering schemes that fulfill the requirements of the application with rather good energy efficiency are then identified.

Data Gathering Scheme Using Chaotic Pulse-Coupled Neural Networks for Wireless Sensor Networks

Wireless sensor networks (WSNs) have attracted a significant amount of interest from many researchers because they have great potential as a means of obtaining information of various environments remotely. WSNs have a wide range of applications, such as natural environmental monitoring in forest regions and environmental control in office buildings. In WSNs, hundreds or thousands of micro-sensor nodes with such resource limitations as battery capacity, memory, CPU, and communication capacity are deployed without control in a region and used to monitor and gather sensor information of environments. Therefore, a scalable and efficient network control and/or data gathering scheme for saving energy consumption of each sensor node is needed to prolong WSN lifetime. In this paper, assuming that sensor nodes synchronize to intermittently communicate with each other only when they are active for realizing the long-term employment of WSNs, we propose a new synchronization scheme for gathering sensor information using chaotic pulse-coupled neural networks (CPCNN). We evaluate the proposed scheme using computer simulations and discuss its development potential. In simulation experiments, the proposed scheme is compared with a previous synchronization scheme based on a pulse-coupled oscillator model to verify its effectiveness.

BoSS: a moving strategy for mobile sinks in wireless sensor networks

Since sink mobility can alleviate the effects of the hotspot problem in wireless sensor networks and improve network performance, it has attracted much research interest in recent years. Considering the unawareness of energy efficiency in random moving strategies and the lack of flexibility in fixed-track moving strategies, we propose an autonomous moving strategy for the mobile sinks in periodically data-gathering applications in this paper. We first suggest a data-gathering scheme for low-duty cycle sensor networks and then present a mechanics-inspired moving strategy, called BoSS. The moving strategy drives a sink to move towards the nodes with high residual energy and meanwhile, balances energy consumption among the nodes with low energy. Finally, we compare our moving strategy with three near-optimal fixed-track strategies by simulations. The results show that our strategy can not only extend network lifetime notably, but also provide scalability and topology adaptability.

The extraction and utilization of local and scientific geospatial knowledge within the Bluff oyster fishery, New Zealand

This paper assesses the Bluff oyster fishery in New Zealand as a case study in common pool resource management. It discusses ways in which modern information technology, augmented by low-tech data gathering strategies and community ethnography, can be used to produce an integrated scientific and local knowledge-inspired fishery database that lends itself to fostering collaboration in resource management and planning. The specific context and state of the oyster fishery in Bluff are described. Issues regarding undocumented and ephemeral intergenerational knowledge, much of which is geospatial in nature, on the fishery, the current crisis that many see in the future of the fishery, and a lack of cohesion or common sense of purpose between the stakeholder groups are discussed. It is argued that the digital resource that results from the integration of local and scientific knowledge and the potential community building processes that can ensue from collaboration and dialogue around this centrepiece are of central importance in developing an oyster fishery management plan that is holistic in concept and sustainable in purpose.

Epidemiology of male same-sex behaviour and associated sexual health indicators in low- and middle-income countries: 2003-2007 estimates.

Objectives:To conduct a systematic review of published and unpublished data from research and public health information systems on the prevalence of male-to-male sex in the total male population; as well as among men who have sex with men (MSM), data on prevalence of heterosexual activity and heterosexual unions; prevalence of condom use with male and female partners; and prevalence of HIV infection and other sexually transmitted infections (STIs).Methods:Key indicators were defined (a) among men in the general population: prevalence of sex with a man ever and last year; (b) among MSM: prevalence of heterosexual experiences ever and last year; proportion of male-female transgenders; proportion of sex workers; prevalence of HIV and other STIs, condom use in last sexual encounter; consistent condom use with men last year; never used a condom with a man. With help from key informants, study searches were conducted in Pubmed, LILLACS, institutional databases, conference records and other sources. Methodology and quality of information were assessed, and the best data available for 2003–7 were selected. Indicator estimates from each study were used to propose regional estimate ranges.Results:A total of 83 new entries were entered into the database in addition to the previous 561, totalling 644. Of these, 107 showing 2003–7 data were selected. Many new studies came from sub-Saharan Africa, portraying hidden HIV epidemics among MSM. The most frequently reported estimate was HIV infection, with high estimate ranges in most of the regions, except for Middle East and North Africa and Eastern Europe. The next most frequently reported was lifetime frequency of heterosexual sex, showing that roughly 50% of MSM ever had sex with a woman. The small number of newer studies reporting prevalence of “sex with a man in last 12 months” between 2003 and 2007, did not warrant enough new evidence to revise our 2005 size estimates for MSM populations.Conclusions:A considerable number of new studies with estimates of relevance to understanding sexual behaviour and HIV among MSM were identified, with an encouraging amount of new data coming from sub-Saharan Africa. However, limitations in the quality, utility and comparability of available information persist. At least three measures could be promoted for use in surveillance and academic studies: standardised indicators for MSM studies; standardised operational definitions of, and instructions to describe, variables; and standardised research designs and data gathering strategies. A prerequisite for this all is intense advocacy to ensure a social climate in which research into such matters is prioritised, resources are made available as needed and the human rights of MSM are respected.

International students' motivations for studying in UK HE

PurposeInternational students' HE decision making is a high stakes process. There is an insufficient evidence base that would aid university level strategic planning in areas of recruitment from the African continent and in supporting its students to maximise the benefits from a UK HE experience. This paper aims to explore the decision making and experience of African students in UK HE and provides hypothesis for re‐conceptualising these processes.Design/methodology/approachThe research was exploratory and part of a bigger project on international students' experience of UK HE. It employed focus group interviews with 28 students studying in two universities in the South of England together with semi structured discussion with staff in those institutions with a specific remit for recruiting from Africa.FindingsA six element model of decision making was developed from the data which identifies a range of push and pull factors operating within constraints of fears and anxieties about studying in UK HE. The data suggest that African students come to study in England on the promise of getting a truly international HE experience. Questions are however raised about whether this promise is delivered in full.Research limitations/implicationsThe sample size and use of focus groups as a single data gathering strategy does not allow broad generalisation of findings. However, the evidence obtained enabled the generation of useful hypothesis to stimulate further research in this area. The research identifies implications for strategic decisions for recruitment, student support and curriculum and for future research in this area.Originality/valueThis is an area with patchy research and the research reported here provides a good basis for developing a broader research agenda in Africa to support decision making on a wider scale.

Hypercube-based data gathering in wireless sensor networks

Sensor nodes, charged with battery power and capable of wireless communications, are distributed around to survey the environments and to send the needed data to the base station. The limited energy resources of sensor nodes are consumed in computation and especially in communication. To reduce power consumption for the sensor network, this paper presents a new data gathering scheme based on the hypercube topology. The hypercube-based data gathering scheme collects data from all sensor nodes to the base station through the communication tree of the constructed hypercube. It shortens communication delay by parallel transmission and replaces dead nodes through reconfiguration. Data gathering by the distributed hypercube and binary tree is also provided.

DAR: An energy-balanced data-gathering scheme for wireless sensor networks

A wireless sensor network faces special challenges due to its inherent features, such as the limited energy. The energy constraint drives research on how to utilize energy efficiently to prolong the lifetime of the network. Because a sink node takes the responsibility of collecting data from other nodes, a usual conception is to transfer data towards the sink node by multihop. However, conventional data-gathering schemes based on the conception give rise to the hotspot problem because of the nodes that run out of their energy sooner than other nodes, which results in accelerating the end of the whole network. The closer sensor nodes are to the sink, the more quickly they exhaust their energy, which leaves an upper bound to the lifetime of the whole network. Because of the bottleneck nodes, the network loses its service ability regardless of a large amount of residual energy of the other nodes. In this paper, we propose a novel data-gathering scheme, DAR, to handle the hotspot problem, in which all the nodes participate in the workload of gathering data from the whole network and transferring the data directly to the sink. In our scheme, the forwarding behavior of all the nodes is scheduled to balance their burden of aggregating and transmitting the network data and the nodes may send their data back against the sink, which differs from the conventional schemes. We performed simulation experiments to evaluate the performance of the DAR scheme, and the results show that our data-gathering scheme can balance the energy consumption among all the nodes and extend the network lifetime notably.

HUMS: An Autonomous Moving Strategy for Mobile Sinks in Data-Gathering Sensor Networks

Sink mobility has attracted much research interest in recent years because it can improve network performance such as energy efficiency and throughput. An energy-unconscious moving strategy is potentially harmful to the balance of the energy consumption among sensor nodes so as to aggravate the hotspot problem of sensor networks. In this paper, we propose an autonomous moving strategy for the mobile sinks in data-gathering applications. In our solution, a mobile sink approaches the nodes with high residual energy to force them to forward data for other nodes and tries to avoid passing by the nodes with low energy. We performed simulation experiments to compare our solution with other three data-gathering schemes. The simulation results show that our strategy cannot only extend network lifetime notably but also provides scalability and topology adaptability.

Movement-Assisted Data Gathering Scheme with Load-Balancing for Sensor Networks

Data gathering is the basic function of the sensor networks. However, the existing gathering schemes are almost based on the architecture with a static base station which results in the quick death of nodes around the base station. The reason is that the sensor nodes located near a base station have to relay data for a large part of the network and thus deplete their batteries very quickly. This paper discusses how to use the mobile base station for data gathering with load-balancing. A data gathering scheme MADG (movement-assisted data gathering), which makes use of the mobile base station for data collection, is presented. In this scheme, the base station moves in a stationary annularity area exploited for data buffering. The gathered data are firstly forwarded into the buffering area and then collected by the mobile base station. It is theoretically proved that the location 2 / 2R away from the center is the optimal location for minimizing the energy consumption for transmitting data and that there exists a location which can make the maximal node load minimize. This paper then considers the optimum location jointing the energy consumption and load-balancing based on above analyses. Compared with the static base station scheme and the existing mobile base station scheme, MADG reduces the load by over 95% and 80%, respectively.