Enhancing broadcast authentication in sensor networks

Due to the nature of wireless sensor networks, security is a critical problem since resource constrained and usually unattended sensors are much vulnerable to malicious attackers that may impersonate the sender. Therefore authenticating received messages is a crucial matter to protect the system integrity. Generally used TESLA (Timed Efficient Stream Loss-tolerant Authentication) based authentication techniques involve consecutive delays for decryption purposes. These delays render the network vulnerable to different malicious attacks such as Denial of Service attack. As several techniques try to achieve immediate authentication to alleviate these threats, other factors such as reliability and buffer requirements may have been compromised. This project proposes an integration of Low Buffer ,uTESLA protocol and an immediate authentication protocol to achieve a new refined scheme in broadcast authentication in sensor networks. Performance analysis and simulation results demonstrate that the proposed method succeeds to achieve immediate authentication while preserving desired security and low memory requirements in sensor nodes.

Enhancing broadcast authentication in sensor networks

Due to the nature of wireless sensor networks, security is a critical problem since resource constrained and usually unattended sensors are much vulnerable to malicious attackers that may impersonate the sender. Therefore authenticating received messages is a crucial matter to protect the system integrity. Generally used TESLA (Timed Efficient Stream Loss-tolerant Authentication) based authentication techniques involve consecutive delays for decryption purposes. These delays render the network vulnerable to different malicious attacks such as Denial of Service attack. As several techniques try to achieve immediate authentication to alleviate these threats, other factors such as reliability and buffer requirements may have been compromised. This project proposes an integration of Low Buffer ,uTESLA protocol and an immediate authentication protocol to achieve a new refined scheme in broadcast authentication in sensor networks. Performance analysis and simulation results demonstrate that the proposed method succeeds to achieve immediate authentication while preserving desired security and low memory requirements in sensor nodes.

Research on Indoor Scene Classification Mechanism Based on Multiple Descriptors Fusion

This study aims at the great limitations caused by the non-ROI (region of interest) information interference in traditional scene classification algorithms, including the changes of multiscale or various visual angles and the high similarity between classes and other factors. An effective indoor scene classification mechanism based on multiple descriptors fusion is proposed, which introduces the depth images to improve descriptor efficiency. The greedy descriptor filter algorithm (GDFA) is proposed to obtain valuable descriptors, and the multiple descriptor combination method is also given to further improve descriptor performance. Performance analysis and simulation results show that multiple descriptors fusion not only can achieve higher classification accuracy than principal components analysis (PCA) in the condition with medium and large size of descriptors but also can improve the classification accuracy than the other existing algorithms effectively.

A Blockchain-Based Hybrid Incentive Model for Crowdsensing

Crowdsensing is an emerging paradigm of data aggregation, which has a pivotal role in data-driven applications. By leveraging the recruitment, a crowdsensing system collects a large amount of data from mobile devices at a low cost. The critical issues in the development of crowdsensing are platform security, privacy protection, and incentive. However, the existing centralized, platform-based approaches suffer from the single point of failure which may result in data leakage. Besides, few previous studies have addressed the considerations of both the economic incentive and data quality. In this paper, we propose a decentralized crowdsensing architecture based on blockchain technology which will help improve the attack resistance. Furthermore, we present a hybrid incentive mechanism, which integrates the data quality, reputation, and monetary factors to encourage participants to contribute their sensing data while discouraging malicious behaviors. The effectiveness our of proposed incentive model is verified through a combination of the theory of mechanism design. The performance analysis and simulation results illustrate that the proposed hybrid incentive model is a reliable and efficient mean to promote data security and incentivizing positive conduct on the crowdsensing application.