-Numerous key management schemes have been proposed for sensor networks. The objective of key management is to dynamically establish and maintain secure channels among communicating nodes. Many schemes, referred to as static schemes, have adopted the principle of key predistribution with the underlying assumption of a relatively static short-lived network (node replenishments are rare, and keys outlive the network). An emerging class of schemes, dynamic key management schemes, assumes long-lived networks with more frequent addition of new nodes, thus requiring network rekeying for sustained security and survivability. This paper proposes a dynamic key management scheme by combining the advantages of simple cryptography and random key distribution schemes. When the hamming distance between the two nodes is found high, the unique key is changed instead of changing the set of keys and the communication takes place by using any one of the set of key x-oring with the new unique key. The security and performance of the proposed algorithm is compared with the existing dynamic key management scheme based on Exclusion Basis System and prove that the proposed scheme performs better when compared to existing scheme by considering the number of nodes colluded with time. The result obtained by simulation also shows that the proposed scheme provides security solution and performs better than the existing scheme.Index Terms -WSN's, dynamic key management, collusion, hamming distance, security.--------------------
. I NTRODUCTIONHE envisioned growth in utilizing sensor networks in a wide variety of sensitive applications ranging from healthcare to warfare is stimulating numerous efforts to secure these networks. Sensor networks comprise a large number of tiny sensor nodes that collect and (partially) process data from the surrounding environment. The data is then communicated, using wireless links, to aggregation and forwarding nodes (or gateways) that may further process the data and communicate it t o t h e ou t s id e w o rld t h ro u g h on e o r m o re b ase st at i on s ( o r command nodes). Base stations are the entry points to the network where user requests begin and network responses are received. Typically, gateways and base stations are higher-end nodes. It is to be noted, however, that various sensor, gateway, and base station functions can be performed by the same or different nodes. The sensitivity of collected data makes encryption keys essential to secure sensor networks.
In this era of artificial intelligence, a wide variety of techniques are available in healthcare industry especially to study about various changes happening in the human body. Intelligent assistance using brain-like framework helps to understand and analyze various types of complex data by utilizing most recent innovations such as deep learning and computer vision. Activities are complex practices, including continuous actions as well as interleaved actions that could be processed with fully interconnected neuron-like processing machine in a way the human brain works. Human postures have the ability to express different body movements in different environments. An optimal method is required to identify and analyze different kinds of postures so that the recognition rate has to be increased. The system should handle ambiguous circumstances that include diverse body movements, multiple views and changes in the environments. The objective of this research is to apply real-time pose estimation models for object detection and abnormal activity recognition with vision-based complex key point analysis. Object detection based on bounding box with a mask is successfully implemented with detectron2 deep learning model. Using PoseNet model, normal and abnormal activities are successfully distinguished, and the performance is evaluated. The proposed system implemented a state of the art computing model for the development of public healthcare industry. The experimental results show that the models have high levels of accuracy for detecting sudden changes in movements under varying environments.
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