In-Route Task Selection in Spatial Crowdsourcing

Abstract

Consider a city’s road network and a worker who is traveling on a given path from a starting point s to a destination d (e.g., from school or work to home) in said network. Consider further that there is a set of tasks in the network available to be performed, where each such task takes a certain amount of time to be completed and yields a positive reward if completed, and, finally, that the worker is willing to deviate from his/her path as long as the travel time to the selected tasks plus the time taken for completing them does not exceed a given time budget. We call this problem the In-Route Task Selection (IRTS) problem and consider two variants thereof. In the first one, named IRTS-SP, we assume that the worker only specifies s and d and he/she wants to consider alternative paths that deviate (cost-wise) as little as possible from the cost of the shortest path connecting s and d . In the second variant, named IRTS-PP, we assume that the worker has a preferred path from s to d and wants to travel along that one path for as long as possible. The latter is practically relevant in cases where the worker has a path other than the shortest one that is more desirable for non-objective reasons, e.g., availability of public transit, bicycle-friendliness or perceived safety. Common to both variants though, we assume that the worker wants to maximize the rewards collected by completing tasks. Clearly, there are now two conflicting criteria for the worker to contemplate when considering which tasks to perform: minimizing path deviation and maximizing collected reward. In this context, we investigate both IRTS variants using the skyline paradigm in order to obtain the set of non-dominated solutions w.r.t. the tradeoffs between earned rewards and deviation from either the cost of the shortest path, in the case of IRTS-SP, or the actual preferred path, in the case of IRTS-PP. Returning the skyline set of solutions to workers is of practical interest as it empowers them, e.g., it allows them to decide, at query time, which tasks suit them better. We propose exact and heuristic approaches in order to solve both variants of the IRTS problem. Our experiments, using real city-scale datasets, show that while the exact approaches serve as benchmarks, they do not scale due to the NP-hardness of the problems. The overall best heuristic approach, on the other hand, can solve relatively large instances of the IRTS problems within practical query processing time, e.g., at par with less effective greedy heuristics, while still producing very good approximate skyline sets, e.g., often yielding less than 10% relative error w.r.t. the exact solution.

Online In-Route Task Selection in Spatial Crowdsourcing

“…Finally, the most similar works to ours are [7,14]. [7], as mentioned in the previous section, proposes the (offline) IRTS query in which the skyline set of paths is computed considering only a set of static and known tasks, unlike in the Online-IRTS query where tasks are dynamic and skylines are used to compute a path "on-thefly."…”

“…In our previous work [7] we defined a new type of spatial crowdsourcing query, namely In-Route Task Selection (IRTS) query. Consider a city's road network, a worker's given path in such a network, and a static number of known tasks, each yielding a reward and, likely, a detour from the worker's original path.…”

“…At time 0, there are two tasks available to be performed: t 1 and t 2 . This leads to three possible paths 1 shown in the upper half of [3][4][5][6][7][8][9][10][11][12][13],3,$2 Figure 1: Sample scenario where the worker's starting point is s, destination is d, and tasks t 1 , t 2 , t 3 and t 4 (denoted by star vertices) with their corresponding [release time-expiration] range, time for completion and reward. The travel cost for one edge on the grid is one time unit.…”

“…This is graphically illustrated in Figure 2a, in which the shaded area contains the dominated paths. It should be noted, however, that the approach proposed in [7] would completely ignore tasks that could become available at a later time, i.e., t 3 and t 4 in the example above, and would return {P 2 , P 3 } as the query's answer.…”

“…the original (shortest) path. It is important to note that the answer to the Online-IRTS query is a single route, which is built incrementally; whereas in [7] the solution for the offline IRTS is a skyline, i.e., a set of routes, oblivious to tasks that may become available at a later time.…”