Continuous growth in software, hardware and internet technology has enabled the growth of internet-based sensor tools that provide physical world observations and data measurement. The Internet of Things(IoT) is made up of billions of smart things that communicate, extending the boundaries of physical and virtual entities of the world further. These intelligent things produce or collect massive data daily with a broad range of applications and fields. Analytics on these huge data is a critical tool for discovering new knowledge, foreseeing future knowledge and making control decisions that make IoT a worthy business paradigm and enhancing technology. Deep learning has been used in a variety of projects involving IoT and mobile apps, with encouraging early results. With its data-driven, anomaly-based methodology and capacity to detect developing, unexpected attacks, deep learning may deliver cutting-edge solutions for IoT intrusion detection. In this paper, the increased amount of information gathered or produced is being used to further develop intelligence and application capabilities through Deep Learning (DL) techniques. Many researchers have been attracted to the various fields of IoT, and both DL and IoT techniques have been approached. Different studies suggested DL as a feasible solution to manage data produced by IoT because it was intended to handle a variety of data in large amounts, requiring almost real-time processing. We start by discussing the introduction to IoT, data generation and data processing. We also discuss the various DL approaches with their procedures. We surveyed and summarized major reporting efforts for DL in the IoT region on various datasets. The features, application and challenges that DL uses to empower IoT applications, which are also discussed in this promising field, can motivate and inspire further developments.

In today’s world, diabetic retinopathy is a very severe health issue, which is affecting many humans of different age groups. Due to the high levels of blood sugar, the minuscule blood vessels in the retina may get damaged in no time and further may lead to retinal detachment and even sometimes lead to glaucoma blindness. If diabetic retinopathy can be diagnosed at the early stages, then many of the affected people will not be losing their vision and also human lives can be saved. Several machine learning and deep learning methods have been applied on the available data sets of diabetic retinopathy, but they were unable to provide the better results in terms of accuracy in preprocessing and optimizing the classification and feature extraction process. To overcome the issues like feature extraction and optimization in the existing systems, we have considered the Diabetic Retinopathy Debrecen Data Set from the UCI machine learning repository and designed a deep learning model with principal component analysis (PCA) for dimensionality reduction, and to extract the most important features, Harris hawks optimization algorithm is used further to optimize the classification and feature extraction process. The results shown by the deep learning model with respect to specificity, precision, accuracy, and recall are very much satisfactory compared to the existing systems.

Abstract:We can realize instant joint group communication by forming Mobile Ad Hoc Networks without demanding any pre-plan or pre-existing infrastructure setup. Conversely, the curbs of these networks such as unreliable wireless medium, unpredictable topology, no central administration, fuel the compulsion of a key based cryptographic algorithm to defend data traffic. In this perspective, substantial research work has been done in the last decade or so and ascertained that the trust based frameworks for group key management deliver superior performance than others. Since the nodes in ad hoc networks have limited computing resources, the overall performance of the system depends on how effectively and securely designed the system. This encourages us to work on a framework which consumes less computing power and also invulnerable to internal as well as external attacks. We propose a framework which reduces the network resource consumption for the trust request and collection using game theory concept. The energy of the wireless nodes are significantly saved by choosing the novel strategy called as finding optimal set of remote nodes to send the response for trust request using game theory. Choosing local optimal at each stage eventually leads to global optimal. Later, synthesize the collected trust and handle the attacks using fuzzy concept in order to get the degree of trustworthiness instead of binary classification. We prove with our simulation results that our proposed scheme reduces overhead of the network significantly while without compromising security aspects.