After any natural disaster the availability of existing conventional communication infrastructure almost gets ruled out. After the devastation, to restore the communication system in ad hoc basis; ensuring almost 100% packet delivery within acceptable latency with optimal utilization of resources are prime design motives. Our work proposes a four tier planned hybrid architecture, which conforms the aforesaid motives yielding a desired performance in terms of delivery probability within least latency, for a given disaster hit area map with a suitable heuristic algorithm. Our study also reveals that there exists no deterministic polynomial time solution that can implement the desired design motives as well as the feasibility of our planned methodology. Compared to any brute force strategy, as per the simulation results, our approach shows 42% higher delivery probability and 49% lower latency.
Public bus services in many cities in countries like India are controlled by private owners, hence, building up a database for all the bus routes is non-trivial. In this paper, we leverage smart-phone based sensing to crowdsource and populate the information repository for bus routes in a city. We have developed an intelligent data logging module for smart-phones and a server side processing mechanism to extract roads and bus routes information. From a 3 month long study involving more than 30 volunteers in 3 different cities in India, we found that the developed system, CrowdMap, can annotate bus routes with a mean error of 10m, while consuming 80% less energy compared to a continuous GPS based system. CCS Concepts •Human-centered computing → Ubiquitous and mobile computing systems and tools;
City transit maps are one of the important resources for public navigation in today's digital world. However, the availability of transit maps for many developing countries is very limited, primarily due to the various socio-economic factors that drive the private operated and partially regulated transport services. Public transports at these cities are marred with many factors such as uncoordinated waiting time at bus stoppages, crowding in the bus, sporadic road conditions etc., which also need to be annotated so that commuters can take informed decision. Interestingly, many of these factors are spatio-temporal in nature. In this paper, we develop CityMap, a system to automatically extract transit routes along with their eccentricities from spatio-temporal crowdsensed data collected via commuters' smart-phones. We apply a learning based methodology coupled with a feature selection mechanism to lter out the necessary information from raw smart-phone sensor data with minimal user engagement and drain of ba ery power. A thorough evaluation of CityMap, conducted for more than two years over 11 di erent routes in 3 di erent cities in India, show that the system e ectively annotates bus routes along with other route and road features with more than 90% of accuracy.
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