For a wheelchair user, a standard blue line on a map is often a broken promise. While platforms like OpenStreetMap (OSM) successfully capture where a path is, they frequently fail to convey how it physically feels to travel on it. This information barrier is problematic for wheelchair users. To solve this issue, we present OmniPath, a system that moves from passive mapping to proactive environmental auditing. Our framework fuses the network topology of OSM with the submeter precision of high-density aerial LiDAR (USGS 3DEP) to create a high-fidelity 3D model of the pedestrian environment. Rather than simply routing a user, our agent virtually traverses the network, analyzing the surface in 0.5 meter increments. It rigorously quantifies physical friction points specifically running slope, cross slope, and vertical discontinuities against ADA compliance standards, calculating a weighted severity score to categorize hazards from ``Mild'' to ``Critical.'' To ensure real world reliability, we validated the system against 200 physical ground truth field surveys across the National Mall using stratified random sampling. The framework demonstrated strong diagnostic reliability for high-severity hazards, achieving F1-scores of 0.60 for Severe and 0.58 for critical categories. By automating this micro-scale inspection, OmniPath identifies the ``invisible'' barriers that standard maps miss, effectively transforming a static dataset into accessibility data source that anticipates accessibility challenges before the user ever leaves home.

Accessible navigation for wheelchair users remains a significant challenge due to obstacles such as uneven sidewalks, steep inclines, and the lack of access ramps. Existing routing systems primarily focus on minimizing travel time or distance, often failing to consider mobility constraints. This paper presents MyPath, an end-to-end accessible navigation system that integrates user-reported data, machine learning, and programmatic analysis to optimize routing for wheelchair users. Additionally, MyPath provides detailed, step-by-step navigation instructions. By unifying these elements within a mobile application, My-Path provides a real-time, personalized, and scalable solution for accessible routing. Our contributions include (1) a novel hybrid routing framework integrating real-world feedback and algorithmic approach, (2) automated detection of environmental barriers, and (3) Step by step detailed navigation. The proposed system has broad implications for urban accessibility, smart mobility, and inclusive navigation solutions.

Wheelchair and mobility aid users often face challenges in navigating the built environment due to uneven sidewalks, temporary barriers, steep inclines, and narrow lanes. To assist these users, accessible routing systems have been introduced that generate wheelchair-accessible paths to facilitate navigation in unfamiliar environments. In general, accessible routing systems rely on surface and path characteristics like surface type, incline, width, etc., and crowd-sourced information about barriers to provide the optimal route. Emerging routing systems even provide personalized routing to users that are catered to the user's specific needs and requirements. However, these types of systems collect crowd-sourced personal/identifiable information which introduces privacy and data heterogeneity concerns that are not addressed by them or elsewhere in the concerned domain. To address these two issues specifically, we propose the novel FedAccess system for accessible routing that utilizes the federated learning paradigm for surface recognition using vibration data. The surface-induced vibrations are captured through smartphone-embedded motion sensors (accelerometers and gyroscopes) from 23 manual wheelchair users during their regular navigation. We have covered 10 distinct surfaces from the USA. As a result, the distribution of the data is naturally non-IID. Empirical evaluation shows that the FedAccess system can protect user data and identity while dealing with non-IID data and still recognize heterogeneous surfaces with higher accuracy than the state-of-the-art.

The Online Food Delivery (OFD) industry, propelled by the recent pandemic, has witnessed substantial growth in the last decade. Major players like Amazon Fresh, GrubHub, UberEats, Postmates, InstaCart, and DoorDash share a common food delivery business model. However, existing methods lack efficiency as deliveries are individually optimized or bundled inefficiently. Recognizing the potential for cost reduction, we model our food delivery problem as a multi-objective optimization, focusing on consumer satisfaction and delivery costs. Taking inspiration from ride-sharing taxis and the prevalent order bundling, we propose DeliverAI - a reinforcement learning-based path-sharing algorithm. Our novel agent interaction scheme dynamically groups deliveries going in the same direction to reduce the total distance traveled while keeping a satisfactory delivery completion time. We test DeliverAI vigorously on a simulation setup using real data from the city of Chicago. Our results show that DeliverAI can reduce the delivery fleet size by 12%, the distance traveled by 13%, and achieve 50% higher fleet utilization compared to the baselines.

Detecting disruptive events (DEs), such as riots, protests, and natural calamities, from social media is essential for studying geopolitical dynamics. To automate the process, existing methods rely on classical machine learning (ML) models applied to static datasets, which is counterproductive. To detect DEs from dynamic data streams, this article introduces a novelDiEvD-SFframework, which uses continual machine learning (CML) with selective forgetting. Twitter (currently “X”) is used as a real-time and dynamic data source for validation.DiEvD-SFconsiders the temporal nature of DEs and “selectively forgets” outdated DEs through machine unlearning. To the best of our knowledge, this article is the first to apply CML with selective forgetting to discard outdated DEs and to continue learning about the new DEs. Extensive evaluation using a painstakingly collected Twitter dataset shows that the proposed framework continually identifies new DEs with an average incremental accuracy of 78.942% and successfully forgets old DEs with an average forgetting time of 118.498 seconds, which is better than the state-of-the-art. Additionally, computational analysis is performed to establish the effectiveness of theDiEvD-SFframework by applying various candidate selection strategies.

Delivery of items from the producer to the consumer has experienced significant growth over the past decade and has been greatly fueled by the recent pandemic. Amazon Fresh, GrubHub, UberEats, Postmates, InstaCart, and DoorDash are rapidly growing and are sharing the same business model of consumer items or food delivery. Existing food delivery methods are sub-optimal because each delivery is individually optimized to go directly from the producer to the consumer via the shortest time path. We observe a significant scope for reducing the costs associated with completing deliveries under the current model. For this, we model our food delivery problem as a multi-objective optimization, where consumer satisfaction and delivery costs, both, need to be optimized. Taking inspiration from the success of ride-sharing in the taxi industry, we propose DeliverAI - a reinforcement learning-based path-sharing algorithm. Unlike previous attempts for path-sharing, DeliverAI can provide real-time, time-efficient decision-making using a Reinforcement learning-enabled agent system. Our novel agent interaction scheme leverages path-sharing among deliveries to reduce the total distance traveled while keeping the delivery completion time under check. We generate and test our methodology vigorously on a simulation setup using real data from the city of Chicago. Our results show that DeliverAI can reduce the delivery fleet size by 15%, the distance traveled by 16%, and 50% higher fleet utilization w.r.t point-to-point delivery systems.

Despite the active legislation in many countries supporting the accessibility of public spaces by mobility-impaired users, the reality is far from ideal. Wheelchair users often struggle to navigate the built environment let alone the natural areas. While barriers to wheeled mobility can be caused by broken/uneven surfaces, steep slopes, and unfavorable weather conditions, the effects of many such factors and others are not properly investigated. In this paper, we aim to classify various built and natural surfaces through their characteristic vibration patterns using different deep learning algorithms. The surface vibration data is collected from various cities in Europe (including Paris (FR), Mannheim (DE), Dresden (DE), Munich, Nuremberg (DE), and Salzburg (AT)) while a user drives a manual wheelchair attached with three differently oriented smartphones placed at different heights. Extensive experiments show that a Deep Neural Network model classifies surfaces using a denoised dataset with a 98.9% accuracy which is significantly higher than our previous state-of-the-art.

Given the fast growth of on-demand transportation services and ride-sharing platforms, the concept of private vehicle ownership is rapidly declining. Although there are multiple fully-grown ride-sharing systems, they are proprietary and centrally controlled. Facilitating ride-sharing using a localized distributed coordination between the riders and the drivers is in need. However, fully distributed systems deal with a large number of variables and objectives and are often sub-optimal. In this paper, we propose a distributed ride-sharing system with multiple objectives which are often conflicting to each other. Therefore, we model it as a multi-objective optimization problem and solve it using the Ant Colony optimization technique which sports a multi-agent behavior. We critically analyze the spatio-temporal challenges posed by the ride sharing problem and define novel performance metrics to capture the underlying subtlety of the distributed system performance. An in-depth experimentation with recent large-scale single-ride taxi trip data from Chicago shows that our solution can ensure up to 79.65% success rate of ride sharing. We have shown that ride sharing is more successful during non-peak traffic hours due to less contention and a healthy balance in passenger and taxi numbers. Further, it has been observed that ride-sharing always reduces thetotal distance travelledby all the taxis and thetotal number of taxison-road; both of which positively impact road congestion and environment. The results obtained from the experiments are very much comparable to real time behaviour of taxi networks. Finally, a revenue framework is proposed to analyse nuances of the operating environment.

The nature of the surface has a significant effect on how wheelchair users experience locomotion. The preferred surfaces for wheeled mobility must be even, firm and smooth while generating adequate friction. The development of accessible road maps that include ground conditions is therefore of utmost importance. Our prior work has shown how such maps can be created using surface-induced vibration data collected by motion sensors embedded in smartphones and then classifying them with machine learning algorithms. To make data collection scalable, participatory crowd-sensing can be used, where users collect and transmit sensor data while traveling on wheelchairs. The complexity here is that wheelchairs widely vary in type (manual, power-assist, power), weight, number and nature of wheels, therefore the sensor data generated by different wheelchairs varies greatly. Collecting training data on each individual wheelchair type to develop classification models is not feasible. To address this problem, in this paper we explore the possibility of transferring knowledge from known wheelchairs to unknown types. We develop a transfer learning algorithm to classify 15 surfaces with minimal training data from different wheelchairs. Our experiments with 47 subjects show that surface classification knowledge, learned from sensor data generated by manual wheelchairs, can be transferred to a power wheelchair with up to 90.02% accuracy. This allows crowd-sensing to be used effectively for data collection for generating accessible route maps. We integrate our transfer learning approach into our system for accessible routing, which we developed in previous work.

Named Data Networking (NDN) is a future Internet paradigm which allows user to retrieve and distribute content using their application names. Each NDN router maintains the state of each request packet in the Pending Interest Table (PIT) until corresponding data packet returns. The use of application name, i.e., variable-length key of unbounded length for communication instead of IP address increases memory consumption and lookup cost at the router. Therefore, the PIT should be able to store millions/billions of entries into on-chip memory. However, traditional hash and trie based methods cannot meet these requirements separately. In this paper, we present a scalable and memory-efficient name encoding based lookup scheme (CRT-PIT) leveraging the benefits of both hash and trie data structures for implementing the PIT at NDN forwarding daemon. In CRT-PIT, we calculate the fixed-length encoded names of the content name and then, encoded names are stored in the concurrent path-compressed trie to reduce the storage and lookup latency requirement by not maintaining the redundant information. Extensive experiments show that CRTPIT consumes only 4.84 MB memory for one million names which is an order of magnitude improvement over the baseline solutions.

With the rapid growth of on-demand taxi services, like Uber, Lyft, etc., urban public transportation scenario is shifting towards a personalized transportation choice for most commuters. While taxi rides are comfortable and time efficient, they often lead to higher cost and road congestion due to lower overall occupancy than bigger vehicles. One efficient way to improve taxi occupancy is to adopt ride sharing. Existing ride sharing solutions are mostly centralized and proprietary. Moreover, given the wide spatio-temporal variation of incoming ride requests designing a dynamic and distributed shared-ride scheduling system is NP-hard. In this paper, we have proposed a publisher (passengers) and subscriber (taxis) based ride sharing system that provides effective real-time ride scheduling for multiple passengers. A particle swarm based route optimization strategy has been applied to determine the most preferable route for passengers. Empirical analysis using large scale single-user taxi ride records from Chicago Transit Authority, show that, our proposed system, ensures a maximum of 91.74% and 63.29% overall success rates during non-peak and peak hours, respectively.

The growing popularity of shared transportation enables researchers to explore a wide range of competing solutions. These propositions, in turn, uncover new challenges. Continuous research is done to design systems that are easy to scale and sustain. This survey categorizes ride sharing systems broadly into static and dynamic models. It also presents the system architectures for dynamic ride sharing systems such as central, distributed and hybrid design. This paper outlines the various algorithm designs adopted by the researchers over the years as well as provides an insight into the recent research conducted in this field. It also serves as a guide for potential future research directions towards identified open challenges.

Internet of Things (IoT) is an emerging networking paradigm where smart devices generate, aggregate, and seamlessly exchange data over the predominantly wireless medium. The Internet, so far, has played a significant role in connecting the world, but still, IoT-based solutions are suffering from two primary challenges: 1) how to secure the sensors data and 2) how to provide efficient local and global communication among various heterogeneous devices. Recently, named data networking (NDN), a future Internet paradigm is proposed to improve and simplify such IoT communication issues. NDN allowed users to fetch data by names irrespective of the actual hosting entity connected through a host-specific IP address. NDN well suits the contentcentric pattern of machine-to-machine (M2M) communications predominantly used in IoT. In this paper, we leverage the basic feats of NDN architecture for designing and verification of an NDN-based smart health IoT (NHealthIoT) system. NHealthIoT uses pure-NDN-based M2M communication for capturing and transmission of raw sensor data to the home server which can detect emergency healthcare events using Hidden Markov Model. Emergency events are notified to the cloud server using a novel context-aware adaptive forwarding (Cdf) strategy. Post emergency notifications, and user health information is periodically pulled by the cloud server and by other interested parties using NDN-based publish/subscribe paradigm. The cloud server carries out long-term decision making using probabilistic modeling for detecting the possibility of chronic diseases at the early stage. We extend the workflows intuitive formal approach model for verifying the correctness of NHealthIoT during the emergency. We evaluate the cdf strategy using ndnSIM. Moreover, to validate and to show the usability of NHealthIoT, we develop a proofof-concept prototype testbed and evaluate it extensively. We also identify some research challenges of the NDN-IoT for researchers.

Scheduling in distributed computing systems is the process of allocating resources to a computational task. The complexity of this allocation process increases with the amount of criteria that are considered for the scheduling decision. Pervasive computing systems show a high degree of heterogeneity and dynamism. The constant joining and leaving of devices makes the system error-prone and less predictable. The involved devices differ in various properties that we subsume as their context. We argue, that these context dimensions can be used to implement fault-avoidant scheduling strategies. In this paper, we introduce the concept of context-aware scheduling for pervasive computing systems. The schedulers in these systems consider multiple context dimensions to avoid failing resource providers. We discuss relevant context dimensions, develop context-aware scheduling strategies and implement them into an existing distributed computing system. We show how to monitor the context dimensions and evaluate the fault-avoidant scheduling strategies in a large-scale simulation.

Radio Frequency Identification (RFID) is a rapidly developing digital identification technology that employs radio to collect identification information from RFID tags. In a typical RFID identification scenario, an RFID reader sends a request to RFID tags, and the RFID tags reply with the information pre-stored in their storages. In recent decades, many applications such as supply chain management, auto-ticking, human and animal tracking, smart hospital, etc. employ more and more RFID readers.

In recent decades, a growing number of large-scale RFID systems are used in various applications. In such a system, it is not uncommon that multiple concurrent radio communications among RFID readers and tags cause serious inference (called collision in the RFID field). One important kind of method to achieve collision-free communication is to activate RFID readers in different time slots. Existing activation approaches for solving this problem are mainly centralized, which is impractical due to the lack of central server, one-point failure risk, and performance bottleneck. Some distributed algorithms are proposed recently, but failed to consider the adaptiveness of the identification, where all of the RFID readers need to participate in the coordination control even if they do not have communication requirements any more. As a result, the optimal identification performance cannot be achieved. In this paper, we propose an adaptive distributed reader activation approach called ADRA for large-scale RFID systems. We build a fine-grained conflict graph for different kinds of collisions. And then a shared permission based distributed approach is adopted to eliminate those collisions. We guarantee that the RFID readers that do not need to communicate any more are suspended and excluded from the execution of coordination eventually. Extensive simulation results show that our approach outperforms existing approaches in terms of execution time and message overhead.

Recent advances in smartphones and location-aware services necessitate identifying logical locations of users, in terms of their surroundings, instead of raw location coordinates. In this paper, we have proposed CROWD-PAN-360 (CP360), a novel smartphone-based system to generate 360-degree panoramic map of a querying user for his unfamiliar surrounding using crowd-sourced images. The objects (logical locations) appearing in the images are identified using manually or automatically generated tags. The system is context-aware and it intelligently associates user location coordinates with several smartphone contexts, like acceleration and orientation. CP360 can significantly reduce GPS positional errors for even cheap low-end smartphones and can identify the user surroundings very efficiently. We extensively tested the system in both indoor and outdoor environments of IIT Roorkee campus using Android smartphones over a dataset of more than 6,000 crowd-sourced images of nearly 70 objects (departments, hostels, cafeteria, etc.) and CP360 generates the panoramic map with an average accuracy of 92.2 percent.

Mankind has experienced several deadly disease outbreaks, such as, cholera, plague, yellow fever, SARS, and dengue. Researchers need to study disease propagation data in order to understand patterns of disease outbreaks, their nature, symptoms, and ways of containment and cure. Though our healthcare establishments record and maintain patient information, they fail to detect a pandemic at an early stage due to the following challenges. Firstly, modern people are too busy to visit a doctor at the early stage of their symptoms which along with their high degree of mobility fuels the risk of contagion. Secondly, even for the recorded cases of a disease, quickly consolidating all local information to detect disease propagation over a large area is nontrivial using today's technology. Finally, all existing methods of outbreak detection identifies a single day of outbreak which is less realistic considering that outbreak happens over a period of time. In this paper, we introduce a wearable sensor based mobile application to capture early symptoms of a disease and to ensure faster consolidation of isolated cases over large areas. We then apply a purely novel technique based on discrepancy scores to detect disease outbreak-period. Experiments and prototypes show the usability and efficiency of our solution.

An important category of mobile RFID systems is the RFID system with mobile RFID tags. The mobility of RFID tags poses new challenges to designing RFID anti-collision protocols. Existing RFID anti-collision protocols cannot support high tag moving speed and high identification rate simultaneously. These protocols do not distinguish the identification deadlines of moving tags. Also, when tags move fast, they cannot determine the number of unidentified tags in the interrogation area of an RFID reader. In this paper, we propose a schedule-based RFID anti-collision protocol which, given a high identification rate, achieves the maximal tag moving speed. The protocol, without the need to estimate the number of unidentified tags, schedules an optimal number of tags to compete for the channel according to their identification deadlines, so as to achieve the optimal identification performance. The simulation and experiment results show that our approach can increase the moving speed of tags significantly compared with existing approaches, while achieving a high identification rate.

Mobile social network (MoSoN) signifies an emerging area in the social computing research built on top of the mobile communications and wireless networking. It allows virtual community formation among like minded users to share data and to organize collaborative social activities at commonly agreed upon places and times. Such an activity scheduling in real-time is non-trivial as it requires tracing multiple users' profiles, preferences, and other spatio-temporal contexts, like location, and availability. Inherent conflicts among users regarding choices of places and time slots further complicates unanimous decision making. In this paper, we propose an autonomic system for activity scheduling in MoSoN communities. Our system allows flexible activity proposition while efficiently handling the user conflicts. As evident from our simulation and testbed results and analysis, our system can schedule multiple simultaneous activities in real-time while incurring low message and time cost.

Vehicular Ad hoc Networks (VANETs) are a killer application of Mobile Ad hoc Networks (MANETs), which exchange data among vehicles and vehicles to roadside infrastructures by routing. To save energy, various routing protocols for VANETs have been proposed in recent years. However, VANETs impose challenging issues to routing. These issues consist of dynamical road topology, various road obstacles, high vehicle movement and the fact that the vehicle movement is constrained on roads and traffic conditions. Moreover, the movement is significantly influenced by driving behaviors and vehicle categories. To this end, we incorporate them into routing and propose energy-efficient routing using movement trends (ERBA) for VANETs—an energy-efficient routing protocol. ERBA classifies vehicles into several categories, and then leverages vehicle movement trends to make routing recommendation. It predicts the movement trends by current directions and next directions after going through the road intersections. With the vehicular category information, the driving behavior patterns, the distance between the current sections and the next intersections, ERBA propagates information among vehicles with less energy consumption. The proposed scheme is validated by real urban scenarios extracted from ShanghaiGrid project. Experimental results show that ERBA outperforms the compared routing protocols with respect to the end-end delay, the packet delivery ratio and the path duration time.

Pervasive computing environments are composed of numerous smart entities (objects and human alike) which are interconnected through contextual links in order to create a Web of physical objects. The contextual links can be based on matching context attribute-values (e.g., co-location) or social connections. We call such a Web of smart physical objects as context map. Context maps can be used for context-aware search and browse of the physical world. However, changes of dynamic context values over time may render a context map inconsistent. So, it is important to update contextual links with changes in specific context values. Given the asynchronous nature of pervasive environments, it is non-trivial to detect events generated by contextual changes in real time. We propose two algorithms for instantaneous and periodic detection of events with concurrent timing relations. Our algorithms have low time complexity and they can address the needs of different types of pervasive computing applications. We have evaluated our proposed algorithms through simulations as well as test bed experiments.

Event aggregation in Wireless Sensor Networks (WSNs) is a process of combining several low-level events into a high-level event to eliminate redundant information to be transmitted and thus save energy. Existing works on event aggregation consider either latency constraint or aggregation function, but not both. A solution jointly considering the two issues will be desirable. Moreover, existing works only consider optimal aggregation for single high-level event type, but many applications are composed of multiple types of high-level events. This paper studies the problem of aggregating multiple high-level events in WSNs with different latency constraints and aggregation functions. We first propose an event aggregation algorithm considering the two issues for single high-level event, and then extend it for multiple high-level events. The simulation results show that our algorithm outperforms existing approaches and saves significant amount of energy (up to 35% in our system).

Service unavailability frequently occurs in dynamic environments, like mobile ad hoc networks (MANET), due to service provider failure, network partitioning, or service scope outage by service provider or user mobility. Service handoff is needed to provide users with alternate matching services in case the original service becomes unavailable. However, existing service discovery solutions for MANET do not address this problem. In this paper, we propose a novel service discovery solution which employs service handoff to facilitate seamless service access for mobile users. The major concerns of service handoff are -- reducing handoff frequency, message cost, and delay as well as balancing loads on service providers. Addressing the above issues are challenging in the dynamic MANET environment. We design a reliable and seamless service discovery solution which employs two different service handoff protocols for different situations. Simulation results show that our protocols can support seamless service access for mobile users at low message cost and time delay while achieving high load balance among service providers.

Collaborative filtering (CF) has achieved widespread success in recommender systems such as Amazon and Yahoo! music. However, CF usually suffers from two fundamental problems - data sparsity and limited scalability. Among the two broad classes of CF approaches, namely, memory-based and model-based, the former usually falls short of the system scalability demands, because these approaches predict user preferences over the entire item-user matrix. The latter often achieves unsatisfactory accuracy, because they cannot capture precisely the diversity in user rating styles. In this paper, we propose an efficient collaborative filtering approach using smoothing and fusing (CFSF) strategies. CFSF formulates the CF problem as a local prediction problem by mapping it from the entire large-scale item-user matrix to a locally reduced item-user matrix. Given an active item and a user, CFSF dynamically constructs a local item-user matrix as the basis of prediction. To alleviate data sparsity, CFSF presents a fusion strategy for the local item-user matrix that fuses ratings of the same user makes on similar items, and ratings of like-minded users make on the same and similar items. To eliminate diversity in user rating styles, CFSF uses a smoothing strategy that clusters users over the entire item-user matrix and then smoothes ratings within each user cluster. Empirical study shows that CFSF outperforms the state-of-the-art CF approaches in terms of both accuracy and scalability.

In this paper we propose a fault tolerant service discovery protocol for MANET using quorum of directories. Directory nodes are selected considering their weight values. We have implemented the protocol using MANET testbed.

In this paper, we propose a distributed algorithm to elect the top K leaders among the nodes in a wireless ad hoc network. Leader election is a fundamental distributed coordination problem arising from many applications, e.g. token regeneration, directory service. However, there is no deterministic algorithm proposed for electing k leaders. In our algorithm, election is based on the weight values of the nodes, which can represent any performance related attribute such as the node's battery power, computational capabilities etc. To achieve message efficiency, coordinator nodes are first selected locally and then the coordinator nodes collect the weight information of other nodes using a diffusing computation approach. The coordinator nodes collaborate with each other to further reduce the message cost. Node failures are also considered in our design. The simulation results show that, compared with a naive solution, our proposed algorithm can elect top K leaders with much less message cost.