ORCID as entered in ROS

Select Publications
2018, Internet of Things Meets Brain-Computer Interface: A Unified Deep Learning Framework for Enabling Human-Thing Cognitive Interactivity, http://dx.doi.org/10.48550/arxiv.1805.00789
,2018, A Blockchain Based Liability Attribution Framework for Autonomous Vehicles, http://dx.doi.org/10.48550/arxiv.1802.05050
,2018, MOF-BC: A Memory Optimized and Flexible BlockChain for Large Scale Networks, http://dx.doi.org/10.48550/arxiv.1801.04416
,2017, LSB: A Lightweight Scalable BlockChain for IoT Security and Privacy, http://dx.doi.org/10.48550/arxiv.1712.02969
,2017, MindID: Person Identification from Brain Waves through Attention-based Recurrent Neural Network, http://dx.doi.org/10.48550/arxiv.1711.06149
,2017, Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals, http://dx.doi.org/10.48550/arxiv.1709.08820
,2017, BlockChain: A distributed solution to automotive security and privacy, http://dx.doi.org/10.48550/arxiv.1704.00073
,2017, Crowdsourcing with Tullock contests: A new perspective, http://dx.doi.org/10.48550/arxiv.1701.01216
,2017, Sustainable Incentives for Mobile Crowdsensing: Auctions, Lotteries, and Trust and Reputation Systems, http://dx.doi.org/10.48550/arxiv.1701.00248
,2016, Towards Policy Enforcement Point as a Service (PEPS), http://dx.doi.org/10.48550/arxiv.1610.02526
,2016, Blockchain in internet of things: Challenges and Solutions, http://dx.doi.org/10.48550/arxiv.1608.05187
,2016, SenseFlow: An Experimental Study for Tracking People, http://dx.doi.org/10.48550/arxiv.1606.03713
,2016, Fair Scheduling for Data Collection in Mobile Sensor Networks with Energy Harvesting, http://arxiv.org/abs/1603.02476v1
,2013, Providing Trustworthy Contributions via a Reputation Framework in Social Participatory Sensing Systems, http://arxiv.org/abs/1311.2349v1
,2013, Ear-Phone: A Context-Aware Noise Mapping using Smart Phones, http://dx.doi.org/10.48550/arxiv.1310.4270
,2013, A Trust-based Recruitment Framework for Multi-hop Social Participatory Sensing, http://dx.doi.org/10.48550/arxiv.1306.0193
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