Location representations from people’s location-based service data are important and beneficial for urban downstream tasks. Locations usually have complex spatial-temporal contextual semantics, meaning that the same location has variable functionalities in different trajectories. Existing methods are mostly based on sequence or graph, where the former captures accurate temporal but limited spatial information (one trajectory), while the latter obtains a global spatial perspective (upstream and downstream nodes) but ignores the temporal information. Furthermore, the frequencies of visited locations are long-tail distributed, which is disadvantageous for infrequently visited locations. To that end, we propose a spatial-temporal trajectory subgraph contrastive learning framework entitled ST-TGCL, integrating comprehensive spatial-temporal information and relieving the long-tail issue with contrastive learning. Specifically, we construct contrastive trajectory subgraph pairs to stably learn variable functionalities and increase training opportunities for infrequently visited locations. To capture spatial-temporal contextual semantics, we design a trajectory network that formulates trajectories and a trajectory graph convolution network, which has the strengths of both sequence-based and graph-based models. Finally, we apply the location representations for downstream tasks to demonstrate our framework’s effectiveness and generalization. ST-TGCL is evaluated over real-world datasets, and the results demonstrate that our framework significantly outperforms existing methods in location representation learning.

Mobile edge computing (MEC) has been envisioned as a promising distributed computing paradigm where mobile users offload their tasks to edge nodes to decrease the cost of energy and computation. However, most of the existing studies only consider the congestion of wireless channels as a crucial factor affecting the strategy-making process, while ignoring the impact of offloading among edge nodes. In addition, centralized task offloading strategies result in enormous computation complexity in center nodes. Along this line, we take both the congestion of wireless channels and the offloading among multiple edge nodes into consideration to enrich users' offloading strategies and propose the Parallel User Selection Algorithm (PUS) and Single User Selection Algorithm (SUS) to substantially accelerate the convergence. More practically, we extend the users' offloading strategies to take into account idle devices and cloud services, which considers the potential computing resources at the edge. Furthermore, we construct a potential game in which each user selfishly seeks an optimal strategy to minimize its cost of latency and energy based on acceptable latency, and find the potential function to prove the existence of Nash equilibrium (NE). Additionally, we update PUS to accelerate its convergence and illustrate its performance through the experimental results of three real datasets, and the updated PUS effectively decreases the total cost and reaches Nash equilibrium.

Delay tolerant networks (DTNs) are a kind of sparse and highly mobile wireless networks, where no stable connectivity guarantee can be assumed. Most DTN users have several points of interest (PoIs), and they enjoy disseminating messages to the other users of the same PoI through WiFi. In DTNs, some time-sensitive messages (disaster warnings, search notices, etc.) need to be rapidly propagated among specific users or areas. Therefore, finding a path from the source to the destination with the shortest delay is the key problem. Taking the dissemination cost into consideration, we propose an efficient message dissemination strategy for minimizing delivery delay (MDMD) in DTNs, which first defines the user’s activeness according to the transiting habit among different PoIs. Furthermore, depending on the activeness, an optimal user in each PoI is selected to constitute the path with the shortest delay. Finally, the MDMD with inactive state (on the way between PoIs) is further proposed to enhance the applicability. Simulation results show that, compared with other dissemination strategies, MDMD achieves the lowest average delay, and the comparable average hopcounts, on the premise that the delivery ratio is guaranteed to be 100% by the sufficient simulation time.