For IoT enabled networks, the security and privacy is one of the important research challenge due to open nature of wireless communications, especially for the networks like Vehicular Ad hoc Networks (VANETs). The characteristics like heterogeneity, constrained resources, scalability requirements, uncontrolled environment etc. makes the problems of security and privacy even more challenging. Additionally, the high degree of availability needs of IoT networks may compromise the integrity and confidentially of communication data. The security threats mainly performed during the operations of data routing, hence designing the secure routing protocol main research challenge for IoT networks. In this paper, to design the lightweight security algorithm the use of Named Data Networking (NDN) which provides the benefits applicable for IoT applications like built-in data provenance assurance, stateful forwarding etc. Therefore the novel security framework NDN based Cross-layer Attack Resistant Protocol (NCARP) proposed in this paper. In NCARP, we designed the cross-layer security technique to identify the malicious attackers in network to overcome the problems like routing overhead of cryptography and trust based techniques. The parameters from the physical layer, Median Access Control (MAC) layer, and routing/network layer used to compute and averages the trust score of each highly mobility nodes while detecting the attackers and establishing the communication links. The simulation results of NCARP is measured and compared in terms of precision, recall, throughput, packets dropped, and overhead rate with state-of-art solutions.
Financial Reynolds number works as a proxy for volatility in stock markets. This piece of work helps to identify the predictability and herd behavior embedded in the financial Reynolds number (time series) series for both CNX Nifty Regular and CNX Nifty High Frequency Trading domains. Hurst exponent and fractal dimension have been used to carry out this work. Results confirm conclusive evidence of predictability and herd behavior for both the indices. However, it has been observed that CNX Nifty High Frequency Trading domain (represented by its corresponding financial Reynolds number) is more predictable and has traces of significant herd behavior. The pattern of the predictability has been found to follow a quadratic equation.
The present study assessed the motivation level of nurses working in 3 highly decorated tertiary-level government hospitals of India and also underpins the factors attributing to motivation levels. A sequential mixed-method design was used in this study wherein 400 nurses working in 5 units of nursing care in the hospitals were enrolled based upon proportionate random stratified sampling techniques. A self-administered questionnaire with Likert scale was developed based upon scale used by Mbindyo et al. The attributes of motivation were then categorized into external and internal attributes. For the qualitative component, participants with varied responses in quantitative data were selected and interviewed. Overall mean motivation score of the nursing staff was found 3.57 ± 0.93, which was higher for extrinsic motivational attributes (3.67 ± 0.88) as compared with intrinsic attributes (3.47 ± 0.98). The intrinsic motivational attribute of organizational commitment was rated highest followed by general motivation, conscientiousness, and self-efficacy. Personal issues, timeliness, and burnout were prime discouraging attributes among study participants. Sociodemographic characteristics and work profile characteristics showed significant relationship with the attributes of motivation. This study underscores the significance of different attributes of motivation which needs to be considered while framing administrative strategies and policy guidelines by authorities.
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