Intelligent city transportation systems are one of the core infrastructures of a smart city. The true ingenuity of such an infrastructure lies in providing the commuters with real-time information about citywide transport like public buses, allowing them to pre-plan their travel. However, providing prior information for transportation systems like public buses in real-time is inherently challenging because of the diverse nature of different stay-locations where a public bus stops. Although straightforward factors like stay duration extracted from unimodal sources like GPS at these locations look erratic, a thorough analysis of public bus GPS trails for 1,335.365 km at the city of Durgapur, a semi-urban city in India, reveals that several other fine-grained contextual features can characterize these locations accurately. Accordingly, we develop BuStop , a system for extracting and characterizing the stay-locations from multi-modal sensing using commuters’ smartphones. Using this multi-modal information BuStop extracts a set of granular contextual features that allows the system to differentiate among the different stay-location types. A thorough analysis of BuStop using the collected in-house dataset indicates that the system works with high accuracy in identifying different stay-locations such as regular bus stops, random ad hoc stops, stops due to traffic congestion, stops at traffic signals, and stops at sharp turns. Additionally, we develop a proof-of-concept setup on top of BuStop to analyze the potential of the framework in predicting expected arrival time, a critical piece of information required to pre-plan travel at any given bus stop. Subsequent analysis of the PoC framework, through simulation over the test dataset, shows that characterizing the stay-locations indeed helps make more accurate arrival time predictions with deviations less than 60 seconds from the ground-truth arrival time.

Efficient air quality sensing serves as one of the essential services provided in any recent smart city. Mostly facilitated by sparsely deployed Air Quality Monitoring Stations (AQMSs) that are difficult to install and maintain, the overall spatial variation heavily impacts air quality monitoring for locations far enough from these pre-deployed public infrastructures. To mitigate this, we in this article propose a framework named AQuaMoHo that can annotate data obtained from a low-cost thermo-hygrometer (as the sole physical sensing device) with the AQI labels, with the help of additional publicly crawled Spatio-temporal information of that locality. At its core, AQuaMoHo exploits the temporal patterns from a set of readily available spatial features using an LSTM-based model and further enhances the overall quality of the annotation using temporal attention. From a thorough study of two different cities, we observe that AQuaMoHo can significantly help annotate the air quality data on a personal scale.

Unmanned Aerial Vehicles (UAVs) can be utilized as aerial base stations to establish wireless communication networks in various challenging scenarios, such as emergency disaster areas and rural areas. Under large regions, the aerial communication networks would require UAVs to form wireless (backhaul) links among each other to provide end-to-end wireless services between two or more ground users (via one or more UAVs). Such UAV backhauling in aerial communication networks may be severely compromised if one or more UAVs are knocked off during the time of operation – it may be due to UAV hardware/software faults, limited battery, malicious attacks, etc. Deep reinforcement learning (DRL) has emerged as a powerful tool for learning tasks with large state and continuous action spaces. In this paper, we leverage emerging DRL to achieve reliable backhauling in an aerial communication network that remains functional and supports end-to-end wireless services even under various random and/or targeted UAV node failures. The proposed method (i) maximizes the reliability of UAV backhauling with joint consideration for communication coverage, (ii) learns the complex environment and its dynamics, and (iii) makes 3D positioning decisions for each UAV under the guidance of two deep neural networks. Our performance evaluation reveals that the proposed DRL approach outperforms the baseline method in terms of wireless coverage and network reliability against UAV failures.

Unmanned Aerial Vehicles (UAVs), as aerial base stations, is a promising solution for providing end-to-end wireless communications to ground users, thanks to its positioning, flexibility, and autonomy. However, to provide end-to-end wireless communication services, all UAVs must ensure a reliable multi-UAV network topology, even when one or more UAVs are knocked off the network due to hardware/software faults, unreliable wireless connections, etc. Hence, how to design a reliable Multi-UAV network with a minimum number of UAVs becomes a key design issue, which is largely unaddressed in the literature. In this paper, we propose exploring biological robustness to design a reliable MuLtI-UAV NetworK, termed, bio-LINK, which is resilient against the UAV node failures and thus, ensures reliable end-to-end communication services to ground users. We first formulate the above bio-LINK problem as an integer linear programming (ILP) optimization problem and show it is NP-Hard. Next, we propose a polynomial-time heuristic that employs an iterative UAV positioning inspired by Markov Chain Monte Carlo (MCMC) random sampling approach. Our extensive simulation study shows that the proposed algorithm outperforms three baseline algorithms in terms of several considered robustness metrics (e.g., motif count, network efficiency, etc.) and ground user coverage, notwithstanding the random and targeted failure of UAV nodes. When compared with a baseline algorithm with the same number of UAVs, the proposed algorithm retains the motif count by 5-6 folds and improves network efficiency by 39 - 95% and ground user coverage by 2-18%.

Public city bus services across various developing cities inhabit multiple stay-locations on the routes due to ad-hoc bus stops to provide on-demand passenger boarding and alighting services. Characterizing these stay-locations is essential to correctly develop models for bus transit patterns used in various digital navigation services. In this poster, we create a deep learning-driven methodology to characterize ad-hoc stay-locations over bus routes based on crowd-sensing contextual information. Experiments over 720km of bus travel data in a semi-urban city in India indicate promising results from the model in terms of good detection accuracy.

Facebook’s “Mark Yourself Safe” or Google Person Finder are quite popular nowadays. Such applications generate crisis maps based on crowdsourced information during or after disasters. Crisis maps are inevitably an extremely effective digital dashboard application for rescue and reliefs. But what if there is even a partial Internet blackout after the disaster strikes? This is indeed a common scenario, but today’s crisis mapping solutions heavily depend on the Internet. In this paper, we discuss a thorough background study and design details of Soteria, an end-to-end solution for smartphone-based opportunistic crisis mapping in the fate of Internet blackouts. Soteria uses intelligent and energy-efficient mechanisms for opportunistic ad-hoc information collection and filtering along with data summarization and dashboard application for crisis mapping over end-users’ smartphones. The smartphone application intelligently incorporates and tunes the existing network systems and services at the backend to make the system work even when the conventional network infrastructure fails. We evaluate the performance of Soteria from multiple field-trials for over five years, and the observed quantitative and qualitative performance is extremely promising for its mass-scale adoption at the disaster-prone areas.

Existing street light monitoring systems use vehicle-borne sensor platforms, LiDAR etc. which are obtrusive for in-the-wild deployments. In this paper, we propose BikeL; a crowd sensed system to monitor street lighting conditions in a novel approach using smartphone sensors during Bike navigation. We identify the underlying issues and challenges from pilot experiments to make the system phone-invariant, robust, and user-friendly. We used regression models and unsupervised clustering to resolve these issues. We have carried out extensive experiments under various road type illumination scenarios and phones type covering more than 400 km. Over 80 night trips collecting 10,000 functional light pole samples to tune the system parameters. Results show that the overall system successfully detects both functioning and non-functioning light poles with good accuracy (F1 score > 0.85) and can produce uniformly calibrated illumination levels. This viable, economical, and easy to deploy solution can work effectively for under-developed regions of low and middle-economy countries.

Although public transport vehicles such as buses have always been an economical means of commuting in the cities of many developing countries, it is always considered as a secondary mode of transport owing to poor infrastructure, chaotic and reckless driving habits, and absence of any proper information system in buses. Based on rigorous experiments carried out over a period of two years and multiple surveys, we have tried to learn the problems faced by bus commuters. As a solution, in this article, we develop a novel energy-efficient system which would help commuters navigate through their journey safely. Along with making them aware of any upcoming points of concerns (PoCs) such as sudden bumps, sharp turns, and bad roads, we also inform commuters about the expected time of arrival at the destination. The system makes use of several landmarks such as speed breakers, turns, and bus stops on a trail stored in a specialized data structure, the probabilistic timed automata. We conducted extensive experiments using 25 volunteers over 50 trails. The system showed an average localization error of only 50 m and mean estimated time of arrival (ETA) error of 2.5 mins and a fairly high alert prediction accuracy while consuming significantly less energy when compared to GPS.

This work presents a software-centric visible light communication (VLC) system in full duplex mode with CSMA/CA and RTS-CTS in mobile environment. We focus on channel access mechanism problems in the same environment. To solve these we modify Distributed Coordination function (DCF) by adding ambient light measurement slot. The result shows modified DCF can handle the problems.

Public transport in suburban cities (covers 80% of the urban landscape) of developing regions suffer from the lack of information in Google Transit, unpredictable travel times, chaotic schedules, absence of information board inside the vehicle. Consequently, passengers suffer from lack of information about the exact location where the bus is at present as well as the estimated time to be taken to reach the desired destination. We find that off-the-shelf deployment of existing (non-GPS) localization schemes exhibit high error due to sparsity of stable and structured outdoor landmarks (anchor points). Through rigorous experiments conducted over a month however, we realize that there are a certain class of volatile landmarks which may be useful in developing efficient localization scheme. Consequently, in this paper, we design a novel generalized energy-efficient outdoor localization scheme - UrbanEye, which efficiently combines the volatile and non-volatile landmarks using a specialized data structure, the probabilistic timed automata. UrbanEye uses speed-breakers, turns and stops as landmarks, estimates the travel time with a mean accuracy of ±2.5 mins and produces a mean localization accuracy of 50 m. Results from several runs taken in two cities, Durgapur and Kharagpur, reveal that UrbanEye provides more than 50% better localization accuracy compared to the existing system Dejavu [1], and consumes significantly less energy.