Paniz Abedin’s research while affiliated with Florida Polytechnic University and other places

In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with user engagement due to inadequate incentives and privacy concerns. In this paper, we use a dynamic incentive mechanism based on a recurrent reverse auction model, incorporating vehicular mobility patterns and realistic urban scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator and OpenStreetMap (OSM). By selecting a representative subset of vehicles based on their locations within a fixed budget, our mechanism aims to improve coverage and reduce data redundancy. We evaluate the applicability of successful participatory sensing approaches designed for pedestrian data and demonstrate their limitations when applied to VCS. This research provides insights into adapting greedy algorithms for the particular dynamics of vehicular crowdsensing.

In this paper, we investigate whether greedy algorithms, traditionally used for pedestrian-based crowdsensing, remain effective in the context of vehicular crowdsensing (VCS). Vehicular crowdsensing leverages vehicles equipped with sensors to gather and transmit data to address several urban challenges. Despite its potential, VCS faces issues with user engagement due to inadequate incentives and privacy concerns. In this paper, we use a dynamic incentive mechanism based on a recurrent reverse auction model, incorporating vehicular mobility patterns and realistic urban scenarios using the Simulation of Urban Mobility (SUMO) traffic simulator and OpenStreetMap (OSM). By selecting a representative subset of vehicles based on their locations within a fixed budget, our mechanism aims to improve coverage and reduce data redundancy. We evaluate the applicability of successful participatory sensing approaches designed for pedestrian data and demonstrate their limitations when applied to VCS. This research provides insights into adapting greedy algorithms for the particular dynamics of vehicular crowdsensing.

This paper presents an incentive mechanism for vehicular crowdsensing (VCS). Here, a platform selects a set of spots or Places of sensing Interest (PsI) and outsources the collection of data from these places. In particular, the platform is interested in collecting data from most of the PsIs (spatial coverage) at regular and well-spread time intervals (temporal coverage). Although spatial coverage is a natural by-product of this approach, our main focus is to reach temporal coverage. To this goal, we model the interaction between participants (vehicles) as a non-cooperative game in which vehicles are the players, and the time to sample at a given PsI is the players’ strategy. Here, vehicles are rewarded for deviating from their pre-planned paths and visiting a set of PsIs. The rewarding formula is designed such that selfish vehicles trying to maximize their reward will collect high temporal coverage data. In particular, this paper analyses the effects of increasing the number of vehicle deviations on the utilities of both vehicles and the platform.

The purpose of this article is to illustrate an investigation of methods that can be effectively used to predict the data incompleteness of a dataset. Here, the investigators have conceptualized data incompleteness as a random variable, with the overall goal behind experimentation providing a 360-degree view of this concept conceptualizing incompleteness of a dataset both as a continuous, discrete random variable depending on the aspect of the required analysis. During the course of the experiments, the investigators have identified Kolomogorov–Smirnov goodness of fit, Mielke distribution, and beta distributions as key methods to analyze the incompleteness of a dataset for the datasets used for experimentation. A comparison of these methods with a mixture density network was also performed. Overall, the investigators have provided key insights into the use of methods and algorithms that can be used to predict data incompleteness and have provided a pathway for further explorations and prediction of data incompleteness.

Let T1 and T2 be two rooted trees with an equal number of leaves. The leaves are labeled, and the labeling of the leaves in T2 is a permutation of those in T1. Nodes are associated with weight, such that the weight of a node u, denoted by W(u), is more than the weight of its parent. A node x∈T1 and a node y∈T2 are induced, iff their subtrees have at least one common leaf label. A heaviest induced ancestor query HIA(u1,u2) with input nodes u1∈T1 and u2∈T2 asks to output the pair (u1∗,u2∗) of induced nodes with the highest combined weight W(u1∗)+W(u2∗), such that u1∗ is an ancestor of u1 and u2∗ is an ancestor of u2. This is a useful primitive in several text processing applications. Gagie et al. (Proceedings of the 25th Canadian Conference on Computational Geometry, CCCG 2013, Waterloo, Ontario, Canada, 2013) introduced this problem and proposed three data structures with the following space-time trade-offs: (i) O(nlog2n) space and O(lognloglogn) query time, (ii) O(nlogn) space and O(log2n) query time, and (iii) O(n) space and O(log3+ϵn) query time. Here n is the number of nodes in both trees combined and ϵ>0 is an arbitrarily small constant. We present two new data structures with better space-time trade-offs: (i) O(nlogn) space and O(lognloglogn) query time, and (ii) O(n) space and O(log2n/loglogn) query time. Additionally, we present new applications of these results.

The non-overlapping indexing problem is defined as follows: pre-process a given text T[1,n] of length n into a data structure such that whenever a pattern P[1,m] comes as an input, we can efficiently report the largest set of non-overlapping occurrences of P in T. The best-known solution is by Cohen and Porat [ISAAC 2009]. The size of their structure is O(n) words and the query time is optimal O(m+nocc), where nocc is the output size. Later, Ganguly et al. [CPM 2015 and Algorithmica 2020] proposed a compressed space solution. We study this problem in the cache-oblivious model and present a new data structure of size O(nlog⁡n) words. It can answer queries in optimal O(mB+logB⁡n+noccB) I/O operations, where B is the block size. The space can be improved to O(nlogM/B⁡n) in the cache-aware model, where M is the size of main memory. Additionally, we study a generalization of this problem with an additional range [s,e] constraint. Here the task is to report the largest set of non-overlapping occurrences of P in T, that are within the range [s,e]. We present an O(nlog2⁡n) space data structure in the cache-aware model that can answer queries in optimal O(mB+logB⁡n+nocc[s,e]B) I/O operations, where nocc[s,e] is the output size.

Let T[1,n] be a string of length n and T[i,j] be the substring of T starting at position i and ending at position j. A substring T[i,j] of T is a repeat if it occurs more than once in T; otherwise, it is a unique substring of T. Repeats and unique substrings are of great interest in computational biology and information retrieval. Given string T as input, the Shortest Unique Substring problem is to find a shortest substring of T that does not occur elsewhere in T. In this paper, we introduce the range variant of this problem, which we call the Range Shortest Unique Substring problem. The task is to construct a data structure over T answering the following type of online queries efficiently. Given a range [α,β], return a shortest substring T[i,j] of T with exactly one occurrence in [α,β]. We present an O(nlogn)-word data structure with O(logwn) query time, where w=Ω(logn) is the word size. Our construction is based on a non-trivial reduction allowing for us to apply a recently introduced optimal geometric data structure [Chan et al., ICALP 2018]. Additionally, we present an O(n)-word data structure with O(nlogϵn) query time, where ϵ>0 is an arbitrarily small constant. The latter data structure relies heavily on another geometric data structure [Nekrich and Navarro, SWAT 2012].

The shortest unique substring (SUS) problem is an active line of research in the field of string algorithms and has several applications in bioinformatics and information retrieval. The initial version of the problem was proposed by Pei et al. [ICDE’13]. Over the years, many variants and extensions have been pursued, which include positional-SUS, interval-SUS, approximate-SUS, palindromic-SUS, range-SUS, etc. In this article, we highlight some of the key results and summarize the recent developments in this area.