In this paper, we propose a spatio-temporal data aggregation protocol in Vehicular Delay Tolerant Network (VDTN). We focus on Asynchronous Vehicular Crowdsensing Service (AVCS) to collect volume sensor data (e.g., images captured by on-board cameras) from VDTN-enabled vehicles. In AVCS, it is critical to cope with the huge redundant traffic generated by a large number of vehicles. We propose a novel protocol to aggregate volume spatio-temporal sensor data in Hybrid DTN data collection architecture. By assigning spatio-temporal identifiers (STI) to the aggregation targets in AVCS and extending the message exchange protocol to treat STI in VDTN, the redundant traffic can be significantly improved. Simulation results using a real taxi trace dataset showed the effectiveness of the proposed data aggregation protocol. The coverage of the crowdsensing was improved around 20-35% with 80% traffic reduction compared with the baseline aggregation protocol.

In this paper, we propose a new centralized Vehicle Delay/Disruption Tolerant Network (VDTN) architecture for vehicle volume data collections in the rural areas called "Hybrid DTN". Hybrid DTN applies Software Defined Network (SDN) into VDTN. A centralized controller controls data deliveries by the wide-area cellular network as a control-plane network and collects large volume sensor data from the vehicles to the cloud using VDTN as a data-plane network. We defined an oracle model called Delivery Status Oracle (DSO) for Hybrid DTN, in which the controller manages the delivery status of the distinct messages. By using DSO, Hybrid DTN can achieve the high data delivery success rate even for large volume sensor data collection in the rural cellular network. Simulation results using a real taxi trace dataset showed the practicalness of the Hybrid DTN with DSO; a combination with Epidemic routing algorithm hugely improved delivery completion rate with smaller delivery latency and smaller traffic.

We present the LASK protocol and its platform implementation that supports distributed k-Nearest Service Discovery. LASK achieves scalable and locality-aware name-based service discovery and routing for the target nodes avoiding redundant lookup message exchanges across the edge networks.

This paper presents an overlay network protocol called “LASK: Locality-Aware Service discovery protocol for K-nearest search”, for supporting scalable and locality-aware distributed k-Nearest Service Discovery (kNSD). The kNSD provides a lookup function to find k service nodes located close to the requester. Importance of such function increases under the situation of growth in size and the diversity of the resources in the so-called edge computing environments. In LASK, efficient cooperative routing algorithm is implemented utilizing the two-dimensional structure of key-order preserving structured overlay networks that are constructed according to the network topology and the name of the service. This structure achieves scalable and locality-aware routing of the lookup messages to the matched nodes avoiding redundant data transmissions across the edge networks. Extensive simulation evaluations show that LASK could achieve small discovery latency in the large-scale edge computing environments; a typical kNSD took less than 5 ms in a data center network model and less than 100 ms in the Internet model even when there are 100,000 nodes in the entire network.

In this paper, we propose a novel dynamic data flow platform for Internet of Things (IoT) applications in edge computing environments. To avoid the overloads on network and computational resources that are caused by IoT applications, the proposed platform replicates processes and changes the structure of the data flow dynamically on the distributed computational resources located at network edges and data centers. The proposed platform adds the notion of "index" to the existing distributed topic-based pub/sub (TBPS) messaging method to support flexible data flow definitions and process allocations. In addition, we propose a peer-to-peer-based data stream routing algorithm called "Locality-Aware Stream Routing (LASR)" which can change the data stream destination dynamically on extended TBPS in accordance with structural changes of a data flow, considering network communication localities. By simulation, we confirm that our proposed platform using LASR can change the data stream destination quicker than existing methods with a small overhead in an edge computing environment, even when running applications involving video analysis data flow with a high data rate.