The internet of things & Big data analytics in eLearning brings tremendous challenges & opportunities to educational institutions & students. In recent trends, the growth of Pervasive computing, Social media, evolving IoT capabilities, technologies such as cloud computing, and big data and analytics are improving the core values of teaching and conducting research but also instilling a new digital culture and developing an IoT-centric society. The primary purpose of this paper is to provide an impact of IoT & Big data analytics in the area of E-learning and study on different E-learning approaches.
Clustered sensor networks have shown to increase system throughput, decrease system delay and save energy. In this paper, we propose a bio-inspired clustering protocol inheriting the social behavior of Rhesus Macaque monkeys, targeting prolonged network lifetime. The behavioral features are added to the basic LEACH, thereby reducing the energy overhead involved in the set-up phase. The simulation results prove that implanting these kinds of bio-inspired intelligence into the pre-existing protocols will tremendously increase its performance. d = [(x 2 -x 1 ) 2 + (y 2 -y 1 ) 2 ] 1/2 . . . (10) Where (x 1 , y 1 ) and (x 2 , y 2 ) are locations of nodes.Step 3. Find the redundant CHs without any member nodes: identify the redundant cluster heads without any nodes attached to it.Step 4. Attach redundant CHs to other nearest cluster and check the cluster size of the attached cluster: in this step, all the cluster heads that are without any nodes attached to it, is broken from the contact of BS and made to attach to some other nearest cluster. Its cluster head designation is removed and demoted to a non-CH node. Later the cluster size is checked with the threshold, which is set in each node by the network deployer. If the cluster head is having a count greater than the threshold, the cluster associated with that CH splits. The cluster splits in accordance with the number of nodes greater than the threshold electing a new cluster head for the split-new clusters. The nodes chosen for splitting are the nodes farthest from CH.Step 5. Choose nodes randomly for communication and re-elect cluster head in that cluster: in this step, a non-CH node is randomly chosen for communication. Once they participate in communication, they drain their energy with
Wireless sensor networks are associated with risk due to the threats of security vulnerabilities. In this context, we propose a scheme, which uses random key distribution based Artificial Immune System (AIS) for detecting spoofing attacks. The prospective method is for clustered sensor networks and as an example, the algorithm is executed on LEACH protocol. The simulation results prove that the design is energy efficient than the other widely used cryptographic methods while providing robust security in the network. Index Terms-ArtificialImmune System, cryptography, LEACH protocol, clustered sensor networks This packet consists of the details regarding randomly picked keys by the node and the trailer. K1 K2 CRITICAL INFO k Where, K1, K2 keys randomly picked by the node for communication with its CH. CRITICAL INFO consists of various fields including, preamble, sync bits, destination address, type, group identity, length of message, counter for message sent, source address, error checking bits, payload. k prime number picked by the node for communication with CH, which are the trailer bytes iv) Packet sent from CH to the BS W1 W2 CRITICAL INFO m Where, W1, W2 keys randomly picked by the CH for communication with its node. CRITICAL INFOconsists of various fields including, preamble, sync bits, destination address, type, group identity, length of message, counter for message sent, source address, error checking bits, payload containing aggregated data from one or more ordinary nodes. m prime number picked by the CH for communication with the BS, which are the trailer bytes.
Deep learning technology is often used for object detection. It has received attention recently because to the intimate connections between object detection, video analysis, and picture understanding. The goal of object detection has been pursued using a variety of models, and this is immensely beneficial to humanity. The most recent technical developments have helped the computational experiments, which would not have been conceivable if they had been tried using the conventional techniques. The powerful approaches employed in deep learning can show noticeably higher efficiency when compared to conventional designs and architectures. Numerous strategies and techniques have been used in deep learning to boost accuracy, and their drawbacks have also been somewhat addressed in order to lessen them. This study's main objective is to give an overview of several object detection procedures and approaches based on deep learning. Additionally, it lists the benefits and drawbacks of various object identification systems based on their potential applications and limitations.
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