Mobile edge computing (MEC) is a promising edge technology to provide high bandwidth and low latency shared services and resources to mobile users. However, the MEC infrastructure raises major security concerns when the shared resources involve sensitive and private data of users. This paper proposes a novel blockchain-based key management scheme for MEC that is essential for ensuring secure group communication among the mobile devices as they dynamically move from one subnetwork to another. In the proposed scheme, when a mobile device joins a subnetwork, it first generates lightweight key pairs for digital signature and communication, and broadcasts its public key to neighbouring peer users in the subnetwork blockchain. The blockchain miner in the subnetwork packs all the public key of mobile devices into a block that will be sent to other users in the subnetwork. This enables the mobile device to communicate with its peers in the subnetwork by encrypting the data with the public key stored in the blockchain. When the mobile device moves to another subnetwork in the tree network, all the mobile devices of the new subnetwork can quickly verify its identity by checking its record in the local or higher hierarchy subnetwork blockchain. Furthermore, when the mobile device leaves the subnetwork, it does not need to do anything and its records will remain in the blockchain which is an append-only database. Theoretical security analysis shows that the proposed scheme can defend against the 51 percent attack and malicious entities in the blockchain network utilizing Proof-of-Work consensus mechanism. Moreover, the backward and forward secrecy is also preserved. Experimental results demonstrate that the proposed scheme outperforms two baselines in terms of computation, communication and storage.

Existing occluded person re-identification (re-ID) methods mainly learn limited visual information for occluded pedestrians from images. However, textual information, which can describe various human appearance attributes, is rarely fully utilized in the task. To address this issue, we propose a Text-guided Hierarchical Context Blending Network ( THCB-Net) for occluded person re-ID. Specifically, at the data level, informative multi-modal inputs are first generated to make full use of the auxiliary role of textual information and make image data have a strong inductive bias for occluded environments. At the feature expression level, we design a novel Hierarchical Context Blending (HCB) module that can adaptively integrate shallow appearance features obtained by CNNs and multi-scale semantic features from visual transformer encoder. At the model optimization level, a Multi-modal Feature Interaction (MFI) module is proposed to learn the multi-modal information of pedestrians from texts and images, then guide the visual transformer encoder and HCB module to further learn discriminative identity information for occluded pedestrians through Image-Multimodal Contrastive (IMC) learning. Extensive experiments on standard occluded person re-ID benchmarks demonstrate that the proposed THCB-Net outperforms state-of-the-art methods.

The first-mile transportation provides a transit service using ridesharing-based vehicles, e.g., feeder buses, for passengers to travel from their homes, workplaces, or public institutions to the nearest public transportation depots (rapid-transit metro or appropriated bus stations) which are located beyond comfortable walking distance. This paper studies the vehicle routing problem (VRP) for the first-mile transportation, which aims at finding the optimal travel routes for a vehicle fleet to deliver passengers from their doorstep to the depots, where the passengers can continue their journeys using fixed-route buses or trains. We focus on the Peak-Hour VRP (PHVRP) for a limited vehicle fleet capacity to serve a large volume of travel requests, with the aim of maximizing the number of served passengers. The PHVRP generalizes the VRP with time window by considering multiple alternative depots for each travel request, such that a request is satisfied if the passenger is taken to one of his/her nearest depots. We formally formulate the PHVRP with constraints on vehicle capacity, pickup time windows, and quality of service regarding riding time, where a novel trip-based constraint model is used. We proposed an ant-colony optimization algorithm for the PHVRP, which is initialized with pheromone information that jointly considers the temporal-spatial distance as well as depot similarity among different travel requests. We introduced a novel scheme (called trip-by-trip scheme) to construct the travel routes by repeatedly forming a single trip for the vehicle with earliest end time until no vehicle can accept any more trips. In constructing a single trip, the algorithm intelligently decides whether or not to end the trip instead of taking more passengers. The effectiveness of the proposed methods is evaluated by comparing with optimal solutions on small size instances and with heuristic solutions on large-size instances, using road network in Singapore and synthetic travel requests that are generated based on real bus travel demands.