A Hybrid and Lightweight Device-to-Server Authentication Technique for the Internet of Things

The Internet of Things(IoT)is a smart networking infrastructure of physical devices,i.e.,things,that are embedded with sensors,actuators,software,and other technologies,to connect and share data with the respective server module.Although IoTs are cornerstones in different application domains,the device’s authenticity,i.e.,of server(s)and ordinary devices,is the most crucial issue and must be resolved on a priority basis.Therefore,various field-proven methodologies were presented to streamline the verification process of the communicating devices;however,location-aware authentication has not been reported as per our knowledge,which is a crucial metric,especially in scenarios where devices are mobile.This paper presents a lightweight and location-aware device-to-server authentication technique where the device’s membership with the nearest server is subjected to its location information along with other measures.Initially,Media Access Control(MAC)address and Advance Encryption Scheme(AES)along with a secret shared key,i.e.,λ_(i) of 128 bits,have been utilized by Trusted Authority(TA)to generate MaskIDs,which are used instead of the original ID,for every device,i.e.,server and member,and are shared in the offline phase.Secondly,TA shares a list of authentic devices,i.e.,server S_(j) and members C_(i),with every device in the IoT for the onward verification process,which is required to be executed before the initialization of the actual communication process.Additionally,every device should be located such that it lies within the coverage area of a server,and this location information is used in the authentication process.A thorough analytical analysis was carried out to check the susceptibility of the proposed and existing authentication approaches against well-known intruder attacks,i.e.,man-in-the-middle,masquerading,device,and server impersonations,etc.,especially in the IoT domain.Moreover,proposed authentication and existing state-of-the-art approaches have been simulated in the real environment of IoT to verify their performance,particularly in terms of various evaluation metrics,i.e.,processing,communication,and storage overheads.These results have verified the superiority of the proposed scheme against existing state-of-the-art approaches,preferably in terms of communication,storage,and processing costs.

Fast Intra Mode Selection in HEVC Using Statistical Model

Comprehension algorithms like High Efficiency Video Coding(HEVC)facilitates fast and efficient handling of multimedia contents.Such algorithms involve various computation modules that help to reduce the size of content but preserve the same subjective viewing quality.However,the brute-force behavior of HEVC is the biggest hurdle in the communication of multimedia content.Therefore,a novel method will be presented here to accelerate the encoding process of HEVC by making early intra mode decisions for the block.Normally,the HEVC applies 35 intra modes to every block of the frame and selects the best among them based on the RD-cost(rate-distortion).Firstly,the proposed work utilizes neighboring blocks to extract available information for the current block.Then this information is converted to the probability that tells which intra mode might be best in the current situation.The proposed model has a strong foundation as it is based on the probability rule-2 which says that the sum of probabilities of all outcomes should be 1.Moreover,it is also based on optimal stopping theory(OST).Therefore,the proposed model performs better than many existing OST and classical secretary-basedmodels.The proposed algorithms expedited the encoding process by 30.22%of the HEVC with 1.35%Bjontegaard Delta Bit Rate(BD-BR).