The dramatic development of shared mobility in food delivery, ridesharing, and crowdsourced parcel delivery has drawn great concerns. Specifically, shared mobility refers to transferring or delivering more than one passenger/package together when their traveling routes have common sub-routes or can be shared. A core problem for shared mobility is to plan a route for each driver to fulfill the requests arriving dynamically with given objectives. Previous studies greedily and incrementally insert each newly coming request to the most suitable worker with a minimum travel cost increase, which only considers the current situation and thus not optimal. In this paper, we propose a demand-aware route planning (DARP) for shared mobility services. Based on prediction, DARP tends to make optimal route planning with more information about requests in the future. We prove that the DARP problem is NP-hard, and further show that there is no polynomial-time deterministic algorithm with a constant competitive ratio for the DARP problem unless P=NP. Hence, we devise an approximation algorithm to realize the insertion operation for our goal. With the insertion algorithm, we devise a prediction based solution for the DARP problem. Extensive experiment results on real datasets validate the effectiveness and efficiency of our technique.

No abstract

No abstract

Contactless RF-based sensing techniques are emerging as a viable means for building gesture recognition systems. While promising, existing RF-based gesture solutions have poor generalization ability when targeting new users, environments or device deployment. They also often require multiple pairs of transceivers and a large number of training samples for each target domain. These limitations either lead to poor cross-domain performance or incur a huge labor cost, hindering their practical adoption. This paper introduces Wi-Learner, a novel RF-based sensing solution that relies on just one pair of transceivers but can deliver accurate cross-domain gesture recognition using just one data sample per gesture for a target user, environment or device setup. Wi-Learner achieves this by first capturing the gesture-induced Doppler frequency shift (DFS) from noisy measurements using carefully designed signal processing schemes. It then employs a convolution neural network-based autoencoder to extract the low-dimensional features to be fed into a downstream model for gesture recognition. Wi-Learner introduces a novel meta-learner to "teach" the neural network to learn effectively from a small set of data points, allowing the base model to quickly adapt to a new domain using just one training sample. By so doing, we reduce the overhead of training data collection and allow a sensing system to adapt to the change of the deployed environment. We evaluate Wi-Learner by applying it to gesture recognition using the Widar 3.0 dataset. Extensive experiments demonstrate Wi-Learner is highly efficient and has a good generalization ability, by delivering an accuracy of 93.2% and 74.2% - 94.9% for in-domain and cross-domain using just one sample per gesture, respectively.

No abstract