Data Gathering Wireless Research Articles

Iot based Fire and Gas monitoring System

In infrastructure and industrial plants the rapid growth is creating environmental issues like pollution, climate change and malfunctioning. It has a great consequence for the requirement of an operationally adaptable, efficient, cheap and smart monitoring systems. For this purpose we come up with idea to use these kind of technology i.e the Internet of Things (IoT) in form of a solution. In this paper, we suggest wireless data gathering frameworks that enable each detector node to track the changes in the behavioural pattern of gases and to identify their role in gas leakage problem, whilst at the same time trying to minimize power consumption. In the proposed device, the temperature detector (DHT 11) the gas detector (MQ2,MQ7 and MQ135) and also humidity sensors are used to determine the environment and the undesirable gas within the manufacturing plant, gauged details can be connected to the web. In addition, our research findings demonstrated substantial energy efficiency and high-precision data analysis relative to conventional protection device strategies. For monitoring the fluctuation of parameters like air pollution levels from their normal levels in this case the sensing devices are connected to the embedded computing system.

Communication scheduling in data gathering networks of heterogeneous sensors with data compression: Algorithms and empirical experiments

We consider a communication scheduling problem to address data compression and data communication together, arising from the data gathering wireless sensor networks with data compression. In the problem, the deployed sensors are heterogeneous, in that the data compression ratios, in terms of size reduction, the compression time, and the compression costs, in terms of energy consumption, on different sensors are different. The bi-objective is to minimize the total compression cost and to minimize the total time to transfer all the data to the base station. The problem reduces to two mono-objective optimization problems in two separate ways: in the original problem a time bound is given and the mono-objective is to minimize the total compression cost, and in the complementary problem a global compression budget is given and the mono-objective is to minimize the makespan. We present a unified exact algorithm for both of them based on dynamic programming; this exact algorithm is then developed into a fully polynomial time approximation scheme for the complementary problem, and a dual fully polynomial time approximation scheme for the original problem. All these approximation algorithms have been implemented and extensive computational experiments show that they run fast and return the optimal solutions almost all the time.

An intelligent data aware and energy censoring scheme for wireless sensor networks

Wireless data gathering has accelerated its pace with the advancements in MEMS and Nano technology. Contemporary wireless sensor networks suffer from limited bandwidth, energy constraint and data losses. In this paper, an auction based data gathering approach is proposed for WSN to reduce energy consumption, ultimately to increase the lifetime of the network. In this auction based model, the data are transmitted by a node, only if it differs from the data previously transmitted by other nodes. The order of data transmission becomes directly proportional to the residual energy of the node. Here in this work, an energy aware selective data transmission is introduced to conserve the network energy. Many previous works have considered the data filtering as a better data gathering option. The proposed data gathering considers the energy level of the sensor node to make the transmission decision. This work reduces energy up to 81% and upholds the throughput by preserving life time of the critical paths.

Near Optimal Route Association With Shannon Model in Multi-Drone WSNs

In this paper, we develop a wireless data gathering model for a multi-drone system in traditional wireless sensor networks (WSNs), where each drone serves as a data collector in the extremely large and densely deployed area. Previous solutions usually consider how to schedule the traveling route among nodes, but fail to optimize the data transmission time. We focus on extending the data collection issue over Shannon model which involves the factors such as the transmission bandwidth and the SNR between a drone and a sensor node. A novel and typical system model is formulated and we investigate the corresponding Route Selection and Communication Association (RSCA) problem, that is, given a set of candidate flight routes and a fixed number of deployed nodes, we determine which routes should be selected for traveling and which nodes should be associated such that the overall energy consumption for data gathering could be minimized . We prove the RSCA problem is NP-hard by reduction from the Vertex Cover problem and then devise an efficient and accessible $O(\log \log n)$ approximation algorithm within the time complexity bound by $O(p^{2}\log n\log \log n)$ , where $n$ is the number of sensor nodes and $p$ is the number of routes in WSNs. Extensive simulations are carried out to investigate the performance of our designed algorithm by comparing with the brute-force and random methods. The proposed algorithm achieves 54% more energy consumption at most and 45% more on average comparing with the optimal solution. Furthermore, real-world trace-driven evaluations have been conducted to show that our obtained solution would hold 34% more at most and 25% more on average comparing with the optimal energy consumption and eventually validate our algorithm.

Spatio Temporal Error Adaptation Model (STEAM) for Energy Efficient Wireless Data Gathering

Redundant Data communication greatly downgrades the performance and reliability of Wireless Sensor Network's (WSN). Spatial similarity and temporal similarity are the intrinsic features of sensory data. By reducing this spatio-temporal data redundancy, large amount of residual energy and bandwidth can be conserved. Very few methods use both spatial and temporal correlation to reduce data redundancy, where the system's error threshold is divided between spatial and temporal error thresholds that are fixed throughout the network's life span. In Spatio-Temporal Error Adaptation Model (STEAM), the spatio-temporal correlation between sensory data is exploited towards data reduction. The spatial redundancy is reduced by a data aware clustering and temporal redundancy is reduced by model based prediction approach. In the proposed work, the proportion of spatial and temporal error thresholds on total error is adapted based on data dynamics and spatial pattern changes of the network. The proposed work decreases considerable amount of data communication, while maintaining the data accuracy within user defined error tolerance. The proposed work follows a distributed approach, hence it is highly scalable. Since the work adapts the threshold based on network parameters, the performance is improved on diverse network conditions. The work attains up to 89% communication reduction in wireless data gathering system with an error tolerance of 0.4°C on collected data.

Startling Starts: Smart Contact Lenses and Technogenesis

Startling Starts: Smart Contact Lenses and Technogenesis

Distributed Similarity based Clustering and Compressed Forwarding for wireless sensor networks

Wireless sensor networks are engaged in various data gathering applications. The major bottleneck in wireless data gathering systems is the finite energy of sensor nodes. By conserving the on board energy, the life span of wireless sensor network can be well extended. Data communication being the dominant energy consuming activity of wireless sensor network, data reduction can serve better in conserving the nodal energy. Spatial and temporal correlation among the sensor data is exploited to reduce the data communications. Data similar cluster formation is an effective way to exploit spatial correlation among the neighboring sensors. By sending only a subset of data and estimate the rest using this subset is the contemporary way of exploiting temporal correlation. In Distributed Similarity based Clustering and Compressed Forwarding for wireless sensor networks, we construct data similar iso-clusters with minimal communication overhead. The intra-cluster communication is reduced using adaptive-normalized least mean squares based dual prediction framework. The cluster head reduces the inter-cluster data payload using a lossless compressive forwarding technique. The proposed work achieves significant data reduction in both the intra-cluster and the inter-cluster communications, with the optimal data accuracy of collected data.

Technology Focus: Production Monitoring/Surveillance

Technology Focus As we take a look over the last year, the industry has seen further advancements in the areas of production monitoring, surveillance, analysis, and optimization. We continue to find new ways of data gathering, processing, and interpretation. We are optimizing our operations and even predicting future production behavior in a new manner. Innovative intervention methods have also followed with creative methods of addressing a variety of performance issues. We have become more efficient at using existing technology to improve performance in mature fields and in full-field management strategies for new developments. Technology is improving continuously to help us with production optimization through new approaches, including downhole multiphase-flow measurement, wireless data gathering, wireless power-transfer methods, and new fluids technology. We have seen enhancement in predictive analysis with regard to determination of production performance as well as equipment failure for multiple types of artificial-lift methods. The last year witnessed further studies and improvements in the understanding of heavy-oil and unconventional reservoirs. While production optimization is improving continuously, growing environmental concerns are also being addressed with better understanding and technology. The contributions allowing these steps forward are being generated from large fields, offshore developments, smaller land-based operations, and all sectors of our business. New ideas and methods are being shared from all regions of the globe, demonstrating the diversity and strength of the industry as well as the people making these contributions. This efficiency and innovation should help us continue on a path of success as global markets change in the future. The articles selected for review are intended to show some examples of implementing multiple concepts and methodologies for production evaluation while applying the data to achieve better performance. While demonstrating the full system overview, also included is an insight into the enabling technology allowing these improvements and possibly a glimpse into what may lie ahead as we continue down the path toward further understanding of our reservoirs. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 171101 An Integrated Approach Using Commercial Monitoring and Surveillance Tools Coupled With Statistical Analysis Techniques To Optimize Oil, Heavy-Oil, and Extraheavy- Oil Reservoirs by D. Lee, DL Petroleum Engineering and Consulting, et al. SPE 170864 Mature Fractured Reservoir Meets the Era of Young Minds and the New Age of Technology Digital Oil Field by Mariam Abdul-Raheem Jamal, KOC, et al. SPE 164854 Downhole Multiphase-Flow Measurement in Horizontal Wellbores by o. Haldun unalmis, Weatherford, et al.

Distributed Joint Resource and Routing Optimization in Wireless Sensor Networks via Alternating Direction Method of Multipliers

We consider a distributed total transmit power minimization in a multi-hop single-sink data gathering wireless sensor network by jointly optimizing the resource allocation and the routing with given source rates. An inherent coupling in optimal routing and resource allocation is taken into account via cross-layer optimization to increase the energy efficiency of the network. Instead of distributing the solution process horizontally by commonly used dual decomposition, we apply consensus optimization in conjunction with the alternating direction method of multipliers (ADMM). By duplicating flow variables, the problem decomposes into node specific subproblems with local variables. These variables are iteratively driven into consensus via the ADMM. Numerical examples show that the proposed algorithm converges significantly faster as compared to the state of the art methods based on the dual decomposition. Additionally, the algorithm is appealing for practical implementation due to its low local communication overhead, robust operation in slightly changing channel conditions and scalability to large networks.

Mixed Cooperation MAC Protocol with Sleep Mechanism for Data Acquisition in Wireless Machine-to-Machine Networks

By exploring the idle state of each sensor node, a mixed sleep-cooperative time division multiple access (TDMA) media access control (MAC) protocol (MS-CTDMA) is proposed for the wireless data gathering network in wireless machine-to-machine (M2M) networks. The basic idea is that in the idle state, each sensor node dynamically goes into sleep state or cooperative state to maximize the network lifetime. A single-hop network delay model of MS-CTDMA is established by the [Formula: see text] N-policy queue with multiple exhaustive vacations, which provides a way to balance energy consumption in network and the delay of each node with the delay sensitive traffic constraint. Furthermore, based on some reasonable assumptions, the proposed MS-CTDMA analysis model can be extended to the whole M2M network. Consequently, we propose an optimal source node selection strategy from the perspective of the relay node during its idle time, regarding the traffic load, residual energy, and channel state. Numerical results reveal that with proper tradeoff between delays and energy conservations, MS-CTDMA can significantly prolong the network lifetime in Rayleigh fading channels compared to pure sleep scheme.

Fault Tolerance in Data Gathering Wireless Sensor Networks

In data gathering wireless sensor networks, data loss often happens due to external faults such as random link faults and hazard node faults, since sensor nodes have constrained resources and are often deployed in inhospitable environments. However, already known fault tolerance mechanisms often bring new internal faults (e.g. out-of-power faults and collisions on wireless bandwidth) to the original network and dissipate lots of extra energy and time to reduce data loss. Therefore, we propose a novel Dual Cluster Heads Cooperation (CoDuch) scheme to tolerate external faults while introducing less internal faults and dissipating less extra energy and time. In CoDuch scheme, dual cluster heads cooperate with each other to reduce extra costs by sending only one copy of sensed data to the Base Station; also, dual cluster heads check errors with each other during the collecting data process. Two algorithms are developed based on the CoDuch scheme: CoDuch-l for tolerating link faults and CoDuch-b for tolerating both link faults and node faults; theory and experimental study validate their effectiveness and efficiency.

Study on the Capacity Benefit of Physical-layer Network Coding for Wireless Data-gathering Ad hoc Networks

Study on the Capacity Benefit of Physical-layer Network Coding for Wireless Data-gathering Ad hoc Networks

An Energy Conservation MAC Protocol in Wireless Sensor Networks

Wireless sensor networks use battery-operated computing and sensing devices. Because of the limitation of battery power in the sensor nodes, energy conservation is a crucial issue in wireless sensor networks. Consequently, there is much literature presenting energy-efficient MAC protocols based on active/sleep duty cycle mechanisms to conserve energy. Convergecast is a common communication pattern across many sensor network applications featuring data gathering from many different source nodes to a single sink node. This leads to high data collision rates, high energy consumption, and low throughput near the sink node. This paper proposes an efficient slot reservation MAC protocol to reduce energy consumption and to make transmission more efficient in data gathering wireless sensor networks. The simulation results show that our protocol provides high throughput, low delivery latency and low energy consumption compared to other methods.

An Approach for Constructing Load-Balancing Networks for Data Gathering Wireless Sensor Networks

Wireless sensor networks are characterized by centralized data gathering, multi-hop communication and many-to-one traffic pattern. These three characteristics may give rise to funneling effects that can lead to severe packet collision, network congestion, packet loss and even congestion collapse. This can also result in hotspots of energy consumption that may cause premature death of sensor nodes and even premature death of entire network. Load-Balancing techniques can effectively avoid the occurrence of funneling effects. Based on the static data gathering wireless sensor networks and motivated by the idea of supply and demand network, a distributed algorithm is proposed in this paper to organize the sensor node into a balanced deal network that can balance the load of nodes. The network structure constructed by this approach is not a load-balancing tree, but a load-balancing network. Experimental results validate the effectiveness of this approach.

On the Scaling Laws of Dense Wireless Sensor Networks: The Data Gathering Channel

We consider dense wireless sensor networks deployed to observe arbitrary random fields. The requirement is to reconstruct an estimate of the random field at a certain collector node. This creates a many-to-one data gathering wireless channel. In this note, we first characterize the transport capacity of many-to-one dense wireless networks subject to a constraint on the total average power. In particular, we show that the transport capacity scales as /spl Theta/(log(N)) when the number of sensors N grows to infinity and the total average power remains fixed. We then use this result along with some information-theoretic tools to derive sufficient and necessary conditions that characterize the set of observable random fields by dense sensor networks. In particular, for random fields that can be modeled as discrete random sequences, we derive a certain form of source/channel coding separation theorem. We further show that one can achieve any desired nonzero mean-square estimation error for continuous, Gaussian, and spatially bandlimited fields through a scheme composed of single-dimensional quantization, distributed Slepian-Wolf source coding, and the proposed antenna sharing strategy. Based on our results, we revisit earlier conclusions about the feasibility of data gathering applications using dense sensor networks.

Correlated Sources Over Wireless Channels: Cooperative Source-Channel Coding

We consider wireless sensor networks deployed to observe arbitrary random fields. The requirement is to reconstruct an estimate of the random field at a certain collector node. This creates a many-to-one data gathering wireless channel. One of the main challenges in this scenario is that the source/channel separation theorem, proved by Shannon for point-to-point links, does not hold any more. In this paper, we construct novel cooperative source-channel coding schemes that exploit the wireless channel and the correlation between the sources. In particular, we differentiate between two distinct cases. The first case assumes that the sensor nodes are equipped with receivers and, hence, every node can exploit the wireless link to distribute its information to its neighbors. We then devise an efficient deterministic cooperation strategy where the neighboring nodes act as virtual antennas in a beamforming configuration. The second, and more challenging, scenario restricts the capability of sensor nodes to transmit only. In this case, we argue that statistical cooperative source-channel coding techniques still yield significant performance gains in certain relevant scenarios. Specifically, we propose a low complexity cooperative source-channel coding scheme based on the proper use of low-density generator matrix codes. This scheme is shown to outperform the recently proposed joint source-channel coding scheme (Garcia-Frias et al., 2002) in the case of highly correlated sources. In both the deterministic and statistical cooperation scenarios, we develop analytical results that guide the optimization of the proposed schemes and validate the performance gains observed in simulations.