Adeel Ansari

Analysis of MAC Strategies for Underwater Applications

Abstract This study aims at analyzing the performance of protocols designed for the control of Medium Access in the underwater environment using Underwater Acoustic Wireless Sensor Networks (UW-ASNs). Oceans today, are playing an eminent part in monitoring climate, storing and releasing Carbon dioxide, supplying foodstuffs, and shipping. The sectors that can benefit most from this research are industries dealing with oil and gas, fisheries, UW instrumentation, armed forces, research and exploration bureaus, etc. Existing terrestrial Wireless Sensor Networks (WSN) Medium Access Control (MAC) protocol which mostly use radio waves for communications are unsuitable for underwater atmosphere. Underwater sensor networks (UWSNs) using acoustic wireless networking finds application in the supporting tools for such applications. The unique properties of the UW acoustic communication path necessitate the need for new efficient, reliable MAC protocols to meet the challenges of propagation delay, multipath and fading and excessive power absorption at higher frequencies etc. Owing to the typical properties of acoustic wave propagation in the underwater atmosphere, the energy efficiency in UW-ASN is badly affected by the long propagation delay and data packet collisions, which hinders the transmission of the data packets that enables us to achieve collective monitoring tasks. This paper, present MAC protocols tailored for UWANs, the classification of MAC protocols, the state of the art, environment characteristics, challenges, and performance metrics in terms of throughput, propagation delay, energy consumption and quantitative analysis of some selected protocols. It also gives an insight of some challenges and open issues for future works. The analysis was made on UAN-CW-MAC protocol and UAN-Aloha, to understand the shape of the throughput curves for the protocol. The simulations were performed on network simulator ns3 to obtain the throughput curves for uan-cw mac with 20 nodes and uan-Aloha with 2–15 sensor nodes with one sink. The results obtained are shown for 20 simulation runs for each node.

Infomax and FASTICA using principle component analysis as preprocessor for airwave removal in seabed logging

This research aims to apply the FASTICA and Infomax algorithm in the field of seabed logging, by utilizing the Principal Component Analysis (PCA) as preprocessor. All the three algorithms are statistical algorithms used for signal deconvolution and are respectively in the field of Independent Component Analysis (ICA). In seabed logging (SBL) implies the marine controlled source electromagnetic (CSEM) technique for the detection of hydrocarbons underneath the seabed floor. The results from SBL, indicate the presence of Hydrocarbon, but due to the presence of noise, in the form of airwaves, interfere with the signals from the subsurface and tend to dominate the receiver response. Hence, the Infomax and FASTICA de-convolution algorithms are used, considering PCA as a pre-processor to filter out the airwaves which disrupt the subsurface signals within the receiver response. The results obtained from simulations and their comparative analysis, indicate that the results from the infomax algorithm are better.

Subsurface exploration of seabed using electromagnetic waves for the detection of hydrocarbon layers

This paper focuses on the detection of hydrocarbon layers under the seabed using Electromagnetic methods and to prove the relationship between the thickness and resistivity constrast of the hydrocarbon. Simulations have been carried out by varying the depth of seawater from 1000m to 100m and the resistivity contrast and thickness for each level of depth is also varied. The electric field is also measured using various simulation models and graphs over different offsets. The results obtained prove that the resistivity property of Hydrocarbon is directly proportional to the thickness, and at particular points the presence of hydrocarbon layer is clearly significant.

Prediction of double stacking hydrocarbon using Marine Controlled Source Electromagnetic method

In Marine Control Source Electromagnetic (MCSEM) method for Seabed Logging (SBL), different antenna orientations varies the sensitivity in detecting hydrocarbon layer. Up to the recent works, double stacking of hydrocarbon sensitivity has not been addressed. In this article, first we present the challenging oil detect-ability using different orientations of the electric dipole antenna. Our simulation results prevails that the Ex field response by Horizontal Electric Dipole, in-line with receiver (HED-R), is found to be the most sensitive in detecting one hydrocarbon layer, compared to the Vertical Electric Dipole (VED) in the given conditions. Furthermore for every 50m increment of oil layer thickness, 20% of the E-field magnitude is found to be increased. For deep target oil reservior, VED antenna gives the most optimum sensitivity in detecting double stacking of hydrocarbon layer with the highest percentange difference increment of 11.21% compared to both orientations of (HED). Thus with the above contributions, this research opens new doors in the field of efficient oil detection using the respective dipole antenna orientation and challenging resistive layer detection in the offshore environment.

Comparative Analysis of MAC Protocols and Strategies for Underwater Applications

AbstractThis research focuses on the comparison of the throughput performance of MAC protocols designed for underwater acoustic networks. Our emphasis was to study the key features of the existing MAC protocols for underwater acoustic communications and provide analytical analysis where feasible. We compared some selected underwater MAC protocols like UAN-ALOHA, CSMA, MACA, MACA-EA and S-FAMA and analyzed their throughputs. We chose to evaluate possible improvements in the throughput of S-FAMA by using the retry mechanism. We found the retry mechanism only showed marginal improvement in the throughput. The proposed mechanisms may not have practical efficacy, however, this mechanism may be helpful in saving energy of the sensor nodes by preventing the repetition of the entire transmission cycle.

Airwaves estimation in shallow water CSEM data: Multi-layer perceptron versus multiple regression

In this study, a Multi-Layer Perceptron Neural Network and Multiple Regression techniques are used to estimate airwaves associated with shallow water Controlled-Source Electro-Magnetic (CSEM) data. Both techniques are appropriate for the development of estimation models. However, multiple regression models make some assumptions about the underlying data. These assumptions include independence, normality and homogeneity of variance. Conversely, neural network based models are not constrained by such assumptions. The performance of the two techniques is calculated based on coefficient of determination (R2) and mean square error (MSE). The results indicate that MLP produced better estimate for the airwaves with MSE of 0.0113 and R2 of 0.9935.

A multiple regression model for predicting airwaves in shallow water sea bed logging data

This paper focuses on formulating a multiple regression model using matrix notation that can be used to predict the magnitude of airwaves in Shallow Water Sea Bed Logging (SBL) Data. The term airwaves refer to the propagated EM signals from the source antenna via atmosphere that is induced along air/sea surface and interferes with the subsurface signal. In shallow water, the airwaves have the ability to mask other subsurface responses possibly containing valuable information about subsurface resistive structure such as hydrocarbon reservoir. A fair representation of SBL environments was simulated to generate the airwaves data. Magnitude of airwaves at selected offset is used as the dependent variable. Whereas the predictor variables (independent variables) for the proposed multiple regression model are the frequency, seawater depth, seawater conductivity, sediment conductivity and offset. Akaikes Information Criterion (AIC) is used for selecting the multiple regression models. The formulated regression m...

Filtration of Airwave in Seabed Logging Using Principal Component Analysis

In this research, Independent component analysis using Principal Component Analysis (ICA-PCA) technique has been applied in the field of seabed logging application for the filtration of airwaves. Independent component analysis (ICA) is a statistical approach for transforming data of multivariate nature into its constituent components (sources) which are considered to be statistically independent of each other. ICA-PCA is applied in the domain of marine controlled source electromagnetic (CSEM), called seabed logging (SBL) sensing method used for the detection of hydrocarbons based reservoirs in SBL application. ICA-PCA has not been applied before in SBL application, and therefore may reduce exploration costs in deep sea areas. The task is to identify the air waves and to filter them out, hence, the ICA-PCA algorithm is carried out for airwave filtration, at varying seawater depth from 100 m to 3000 m. It is observed that the results are favorable upto 2500 m depth. Upon increasing seawater depth, the component representing the presence of hydrocarbon becomes more dispersed, vague and indistinguishable.