Understanding the Impact of Rural Electrification in Uttar Pradesh and Bihar, India: Evidence from The Rockefeller Foundation's Smart Power for Rural Development Initiative

Launched in 2015, Smart Power for Rural Development (SPRD) is a $75 million Rockefeller Foundationinitiative aimed at accelerating development in India's least electrified states. Through the deploymentof decentralized, renewable energy mini-grids, SPRD has supported the Foundation's vision of speedingthe growth of rural economies, while at the same time improving the lives and livelihoods of poor andmarginalized families and communities.A monitoring and evaluation (M&E) grantee, Sambodhi, was funded to work alongside implementingpartners to measure and document the changes that the initiative is having in people's lives. Sambodhialso collected data to inform decision making and support course correction throughout the initiative'simplementation.This report summarizes M&E data collected in late 2016, covering the period March 2016–August 2016.The sample for this report is 39 sites across Uttar Pradesh and Bihar, consisting of 1,000 households and320 micro-enterprises. Together, these constitute nearly 10 percent of SPRD customers. Another 328non-customer households were consulted to provide a comparative perspective

Providing Long-Term Participation Incentive in Participatory Sensing

Providing an adequate long-term participation incentive is important for a participatory sensing system to maintain enough number of active users (sensors), so as to collect a sufficient number of data samples and support a desired level of service quality. In this work, we consider the sensor selection problem in a general time-dependent and location-aware participatory sensing system, taking the long-term user participation incentive into explicit consideration. We study the problem systematically under different information scenarios, regarding both future information and current information (realization). In particular, we propose a Lyapunov-based VCG auction policy for the on-line sensor selection, which converges asymptotically to the optimal off-line benchmark performance, even with no future information and under (current) information asymmetry. Extensive numerical results show that our proposed policy outperforms the state-of-art policies in the literature, in terms of both user participation (e.g., reducing the user dropping probability by 25% to 90%) and social performance (e.g., increasing the social welfare by 15% to 80%).Comment: This manuscript serves as the online technical report of the article published in IEEE International Conference on Computer Communications (INFOCOM), 201