Malicious node detection using machine learning and distributed data storage using blockchain in WSNs

In the proposed work, blockchain is implemented on the Base Stations (BSs) and Cluster Heads (CHs) to register the nodes using their credentials and also to tackle various security issues. Moreover, a Machine Learning (ML) classifier, termed as Histogram Gradient Boost (HGB), is employed on the BSs to classify the nodes as malicious or legitimate. In case, the node is found to be malicious, its registration is revoked from the network. Whereas, if a node is found to be legitimate, then its data is stored in an Interplanetary File System (IPFS). IPFS stores the data in the form of chunks and generates hash for the data, which is then stored in blockchain. In addition, Verifiable Byzantine Fault Tolerance (VBFT) is used instead of Proof of Work (PoW) to perform consensus and validate transactions. Also, extensive simulations are performed using the Wireless Sensor Network (WSN) dataset, referred as WSN-DS. The proposed model is evaluated both on the original dataset and the balanced dataset. Furthermore, HGB is compared with other existing classifiers, Adaptive Boost (AdaBoost), Gradient Boost (GB), Linear Discriminant Analysis (LDA), Extreme Gradient Boost (XGB) and ridge, using different performance metrics like accuracy, precision, recall, micro-F1 score and macro-F1 score. The performance evaluation of HGB shows that it outperforms GB, AdaBoost, LDA, XGB and Ridge by 2-4%, 8-10%, 12-14%, 3-5% and 14-16%, respectively. Moreover, the results with balanced dataset are better than those with original dataset. Also, VBFT performs 20-30% better than PoW. Overall, the proposed model performs efficiently in terms of malicious node detection and secure data storage. © 2013 IEEE

Trustworthy Edge Machine Learning: A Survey

The convergence of Edge Computing (EC) and Machine Learning (ML), known as Edge Machine Learning (EML), has become a highly regarded research area by utilizing distributed network resources to perform joint training and inference in a cooperative manner. However, EML faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EML in the eyes of its stakeholders. This survey provides a comprehensive summary of definitions, attributes, frameworks, techniques, and solutions for trustworthy EML. Specifically, we first emphasize the importance of trustworthy EML within the context of Sixth-Generation (6G) networks. We then discuss the necessity of trustworthiness from the perspective of challenges encountered during deployment and real-world application scenarios. Subsequently, we provide a preliminary definition of trustworthy EML and explore its key attributes. Following this, we introduce fundamental frameworks and enabling technologies for trustworthy EML systems, and provide an in-depth literature review of the latest solutions to enhance trustworthiness of EML. Finally, we discuss corresponding research challenges and open issues.Comment: 27 pages, 7 figures, 10 table

Towards Integrated Traffic Control with Operating Decentralized Autonomous Organization

With a growing complexity of the intelligent traffic system (ITS), an integrated control of ITS that is capable of considering plentiful heterogeneous intelligent agents is desired. However, existing control methods based on the centralized or the decentralized scheme have not presented their competencies in considering the optimality and the scalability simultaneously. To address this issue, we propose an integrated control method based on the framework of Decentralized Autonomous Organization (DAO). The proposed method achieves a global consensus on energy consumption efficiency (ECE), meanwhile to optimize the local objectives of all involved intelligent agents, through a consensus and incentive mechanism. Furthermore, an operation algorithm is proposed regarding the issue of structural rigidity in DAO. Specifically, the proposed operation approach identifies critical agents to execute the smart contract in DAO, which ultimately extends the capability of DAO-based control. In addition, a numerical experiment is designed to examine the performance of the proposed method. The experiment results indicate that the controlled agents can achieve a consensus faster on the global objective with improved local objectives by the proposed method, compare to existing decentralized control methods. In general, the proposed method shows a great potential in developing an integrated control system in the ITSComment: 6 pages, 6 figures. To be published in 2023 IEEE 26th International Conference on Intelligent Transportation Systems (ITSC

A Blockchain Enhanced Framework for Social Networking

Social networking sites have given users unprecedented opportunities for the generation and dissemination of content. A variety of social networking sites exist for different purposes, to afford users a range of anonymous and non-anonymous options for self-expression, and the ability to be a part of a virtual community. These “affordances” enable users to create and share content; however, the ability to partially or wholly detach user identity from the content has resulted in unique challenges for content access and content attribution. This paper proposes a framework for secure, trustworthy social networking that also creates value for user-generated content by using a blockchain-enhanced framework for social networking. This work explains the application of such a framework for collocated spaces of robots and IoT devices and identifies key challenges that result as a consequence of merging social networking sites and blockchain technology

Leveraging Twitter data to understand the dynamics of social media interactions on cryptocurrencies

Rapid technological change in the last decades has led to the emergence of new platforms and fields such as cryptocurrencies and social media data. Cryptocurrencies are decentralized digital currencies that use blockchain technology to create a secure and decentralized environment. In the decade since the inception of social media, it has created revolutions and connected people with interests. Social media platforms such as Twitter allow users worldwide to share opinions, emotions, and news. Twitter is one of the most used social media platforms worldwide. The social media platform has millions of users where tweets are continuously shared every second. Therefore, tweets are useful when a large amount of data is generated to conduct a social media analysis. In addition, Twitter is broadly utilized by investors and financial analysts to gather valuable information. Several studies have shown that the content posted on Twitter can predict the movement of cryptocurrency prices. However, limited research has been conducted on the dynamics of Twitter interactions on cryptocurrencies among users. By leveraging 1724328 tweets, this research aims to understand the dynamics of social media users’ interactions on cryptocurrencies. Essentially by shedding light on larger cryptocurrencies contrary to smaller. The findings reveal that Twitter users are more positive than negative about cryptocurrencies. The analysis also shows an existing relationship between events and the interaction of users, where cryptocurrency-related events shift the emotion, sentiment, and discussion topics of the users. The thesis contributes to demonstrating the effectiveness of the Social set analysis framework to analyze and visualize a massive amount of social media data and user-generated data created on social media platforms such as Twitter

Development of a cryptocurrency bot

As an emerging market and research direction, cryptocurrencies and cryptocurrency trading have seen considerable progress and a notable upturn in interest and activity, even entering the market people without experience or sufficient knowledge. In addition, the tremendous volatility and the fact that this market never closes make the human factor affect crypto asset trading too much. Hence, in this project a cryptocurrency trading bot is developed. To be exact, the algorithm consists of two distinguishable parts: the bot itself and the backtesting. Notwithstanding that both parts departs from the analysis of financial markets in general, and cryptocurrencies in particular, both present clear differences in terms of code and pretext. On the one hand, the bot’s algorithm is used to trade in reality, specifically through the Binance exchange. Here the user plays risks their monetary capital. On the other hand, backtesting consists of verifying the trading strategy based on historical data. Backtesting serves, then, as validation of the strategy to be followed by the bot. Thus, all the necessary fundamentals to understand both the general cryptocurrency context and technical analysis relevant concepts are presented, along with a detailed explanation of the implemented algorithm and a proper analysis of the obtained results. Finally, further code improvements and new ideas to develop in the future are suggested, apart from presenting the code developed

Blockchain and Smart Contracts: The Need for Better Education

The study aims at understanding the current scenario of blockchain education and whether it is meeting the increasing demand of blockchain professionals in the job market. In addition, it also provided a comparison between Ethereum and Hyperledger and analyzed the one best suited for better academic curriculum design. By drawing from various sources of data, including journals, articles, reports, the study provided critical insights into the various aspects of a blockchain education. It assesses the existing curriculum on the blockchain that includes courses and programs from various renowned universities and business schools. The study reveals that although the courses are comprehensive in developing theoretical knowledge of the learner, it does not provide the scope for practical skill development. This gap reflects the skill-shortage of blockchain professionals in the job market. To address this, the research also provides some guidelines for developing a comprehensive pedagogical structure using Hyperledger technology. The discussion also highlights the benefits associated with Hyperledger and the way it can foster active learning among the learners. Finally, the researcher also provided the practical and theoretical implications of the study and assessed its limitations directing on the future course of research

RRP: a reliable reinforcement learning based routing protocol for wireless medical sensor networks.

Wireless medical sensor networks (WMSNs) offer innovative healthcare applications that improve patients' quality of life, provide timely monitoring tools for physicians, and support national healthcare systems. However, despite these benefits, widespread adoption of WMSN advancements is still hampered by security concerns and limitations of routing protocols. Routing in WMSNs is a challenging task due to the fact that some WMSN requirements are overlooked by existing routing proposals. To overcome these challenges, this paper proposes a reliable multi-agent reinforcement learning based routing protocol (RRP). RRP is a lightweight attacks-resistant routing protocol designed to meet the unique requirements of WMSN. It uses a novel Q-learning model to reduce resource consumption combined with an effective trust management system to defend against various packet-dropping attacks. Experimental results prove the lightweightness of RRP and its robustness against blackhole, selective forwarding, sinkhole and complicated on-off attacks

Policy-aware Distributed and Dynamic Trust based Access Control Scheme for Internet of Things

 The use of smart devices is driving the Internet of Things (IoT) trend today. Day by day IoT helps to support more services like car services, healthcare services, home automation, and security services, weather prediction services, etc, to ease user’s life. Integration of heterogeneous IoT devices and social resources sometimes creates many problems like the privacy of data. To avoid privacy issues, an appropriate access control mechanism is required to check authorized and trusted devices, so that only valid devices can access the data which is only required.  In the sequel, this paper presents implementation of distributed and dynamic trust based access control mechanism (DDTAC) for secure machine to machine communication or distributed IoT environment. Novelty of this mechanism is that, it uses trust calculation and device classification for dynamic access control. The proposed scheme is implemented, tested and deployed on Node MCU and same mechanism is also simulated on NS-2 for large number of nodes. This access control model support Scalability, Heterogeneity, Privacy, Trust, Selective disclosure, Principle of least privileges, and lightweight calculation features. Results of this models proves that it gives good performance as compared to existing scheme in terms of scalability, throughput and delay. As number of devices increase it does not degrade performance. This mechanism is also protected against the Man-in-the-Middle attack, Sniffing attack, Session Hijacking attacks and Injection attacks. It required less time to detect and resist those attacks