Due to the ever-increasing number and diversity of data sources, and the continuous flow of data that are inevitably redundant and unused to the cloud, the Internet of Things (IoT) brings several problems including network bandwidth, the consumption of network energy, cloud storage, especially for paid volume, and I/O throughput as well as handling huge amount of stored data in the cloud. These call for data pre-processing at the network edge before data transmission over the network takes place. Data reduction is a method for mitigating such problems. Most state-of-the-art data reduction approaches employ a single tier, such as gateways, or two tiers, such gateways and the cloud data center or sensor nodes and base station. In this paper, an approach for IoT data reduction is proposed using in-networking data filtering and fusion. The proposed approach consists of two layers that can be adapted at either a single tier or two tiers. The first layer of the proposed approach is the data filtering layer that is based on two techniques, namely data change detection and the deviation of real observations from their estimated values. The second layer is the data fusion layer. It is based on a minimum square error criterion and fuses the data of the same time domain for specific sensors deployed in a specific area. The proposed approach was implemented using Python and the evaluation of the approach was conducted based on a real-world dataset. The obtained results demonstrate that the proposed approach is efficient in terms of data reduction in comparison with Least Mean Squares filter and Papageorgiou’s (CLONE) method.

The advances in underwater sensor communication has become imperative getting up-to-date information about underwater happenings, especially when world has already faced the calamity like Tsunami. The underwater environment possessed freak and unpredictable movements which becomes more harsh time to time. The sensor nodes deployed under such juncture are the main source of information which in fact, facing numerous challenges. These nodes are mainly energy-constrained and rely on limited battery source. Due to most intricated underwater routing architecture, the biggest detriment is the limited battery lifespan. Therefore, it is imperative to adopt the pragmatic and possible alternate to improve the life expectancy of these sensor nodes. The solution of such shortcomings and identifying the varieties of impingements impelled by forwarding node on battery lifespan during packet transmission course are meticulously explored by developing an Underwater Pragmatic Routing Approach through Packet Reverberation mechanism (UPRA-PR). It is a novel approach and never considered in past. Through Packet Reverberation technique, the use of energy has been confined and the desired outcomes are achieved in four phases. In the first phase, the eligibility criteria for both packet and nodes have been computed by setting the Node Depth Factor (Ndf). Second phase formulates the forwarding relay node mechanism and rummage out the path failure by complying a Data Rate criterion D0. The selection of the shrewd communication link is established in third phase by considering an Accepted Link Quality (ALQ) factor. The fourth phase where most prominent developments has been made regarding impingements effects on the battery lifespan left by the forwarding node after the packet transmission. The UPRA-PR performance metrics are assessed by staging extensive NS2 simulation with AquaSim 2.0 and compared to state-existing routing protocols i.e., DBR, H2DAB, GEDAR and FBR for Packet dissemination ratio, Path failure, Point-to-point delay estimation, System energy consumption, Network lifespan, Forwarding node impingement and Network throughput. The simulation results have ratified the UPRA-PR performance and justified the statements made in this respect.

Websites can increase their security and prevent harmful Internet attacks by providing CAPTCHA verification for determining whether end-user is a human or a robot. Text-based CAPTCHA is the most common and designed to be easily recognized by humans and difficult to identify by machines or robots. However, with the dramatic advancements in deep learning, it becomes much easier to build convolutional neural network (CNN) models that can efficiently recognize text-based CAPTCHAs. In this study, we introduce an efficient CNN model that uses attached binary images to recognize CAPTCHAs. By making a specific number of copies of the input CAPTCHA image equal to the number of characters in that input CAPTCHA image and attaching distinct binary images to each copy, we build a new CNN model that can recognize CAPTCHAs effectively. The model has a simple structure and small storage size and does not require the segmentation of CAPTCHAs into individual characters. After training and testing the proposed CAPTCHA recognition CNN model, the achieved experimental results reveal the strength of the model in CAPTCHA character recognition.

The selection of optimal relay node ever remains a stern challenge for underwater routing. Due to a rigid and uncouth underwater environment, the acoustic channel faces inevitable masses that tarnish the transmission cycle. None of the protocols can cover all routing issues; therefore, designing underwater routing protocol demands a cognitive coverage that cannot be accomplished without meticulous research. An angle-based shrewd technique is being adopted to improve the data packet delivery, as well as revitalize the network lifespan. From source to destination, one complete cycle comprises three phases indeed; in the first phase, the eligibility of data packet belonging to the same transmission zone is litigated by Forwarder Hop Angle (FHA) and Counterpart Hop Angle (CHA). If FHA value is equal or greater than CHA, it presages that the generated packet belongs to the same transmission zone; otherwise, it portends that packet is maverick from other sectors. The second phase picks out the best relay node by computing a three-state link quality with prefix values using the Additive-Rise and Additive-Fall method. Finally, the third phase renders a decisive solution regarding exorbitant overhead fistula; a packet holding time is contemplated to prevent the packet loss probability. Simulation results using NS2 have been analyzed, regarding packet delivery ratio, packet error rate, communication overhead, and end-to-end delay. Comparing to HHVBF and GEDAR, USPF indeed has outperformed, leading into the evidence of applicability’s favor.