River water-level prediction is crucial for mitigating flood damage caused by torrential rainfall. In this paper, we attempt to predict river water levels using a deep learning model based on radar rainfall data instead of data from upstream hydrological stations. A prediction model incorporating a two-dimensional convolutional neural network (2D-CNN) and long short-term memory (LSTM) is constructed to exploit geographical and temporal features of radar rainfall data, and a transfer learning method using a newly defined flow–distance matrix is presented. The results of our evaluation of the Oyodo River basin in Japan show that the presented transfer learning model using radar rainfall instead of upstream measurements has a good prediction accuracy in the case of torrential rain, with a Nash–Sutcliffe efficiency (NSE) value of 0.86 and a Kling–Gupta efficiency (KGE) of 0.83 for 6-h-ahead forecast for the top-four peak water-level height cases, which is comparable to the conventional model using upstream measurements (NSE = 0.84 and KGE = 0.83). It is also confirmed that the transfer learning model maintains its performance even when the amount of training data for the prediction site is reduced; values of NSE = 0.82 and KGE = 0.82 were achieved when reducing the training torrential-rain-period data from 12 to 3 periods (with 105 periods of data from other rivers for transfer learning). The results demonstrate that radar rainfall data and a few torrential rain measurements at the prediction location potentially enable us to predict river water levels even if hydrological stations have not been installed at the prediction location.

We propose a new route guidance system (RGS) to mitigate traffic congestion called Flow-based Capacity-aware Rerouting (FCR), which achieves better global optimality by considering the capacity of detour routes. FCR introduces a flow-based traffic management method that enables us to compute detour paths in units of flow rather than per vehicle processing, which reduces the load to calculate detour paths and makes the centralized system practically feasible. With the flow-based traffic management, FCR also enables us to consider traffic volume that should bypass the congested roads to mitigate the congestion and suggest detour paths to the corresponding traffic volume of vehicles. By prioritizing travel time loss when choosing detour routes to apply, FCR leads to better global optimality with the limited volume of rerouting traffic while considering each rerouted vehicle’s benefit, such as travel time loss. FCR offers a new centralized system architecture for actual real-time traffic control, which improves both global optimality and each vehicle’s benefit within a feasible computational load.

The concept of sensor clouds has been populated for utilizing data from massive amount of IoT devices. In the sensor cloud, a large number of sensors and users are connected and sensor data are traded among them. A number of market frameworks for such data ecosystems have been proposed so far, most of which assumes multiple stakeholders and coordinates their interests using techniques such as the traditional economic theory and game theory. However, because of the duplicability of IoT data, designing a natural pricing scheme based directly on market principles, such as the balance between seller competition and consumer demands, is still a challenge. In this paper, we propose a new pricing scheme for IoT stream data, where prices are determined by the balance between seller competition and consumer demand. Unlike conventional methods, our method is based on simulation. By simulating the market and sellers’ pricing behaviors on the broker’s platform, fair pricing is achieved without causing undesirable phenomena such as price wars. The evaluation results show that the proposed pricing method has desirable characteristics for an IoT data market.

Vehicular Ad hoc NETworks (VANET) has been well studied for a long time as a means to exchange information among moving vehicles. As vehicular networks do not always have connected paths, vehicular networks can be regarded as a kind of delay-tolerant networks (DTNs) when the density of vehicles is not high enough. In this case, packet delivery ratio degrades significantly so that reliability of networks as an information infrastructure is hardly held. Past studies such as SADV (Static-node Assisted Data dissemination protocol for Vehicular networks) and RDV (Reliable Distance-Vector routing) showed that the assistance of low-cost unwired static nodes located at intersections, which work as routers to provide distance-vector or link-state routing functions, significantly improves the communication performance. However, they still have problems: SADV does not provide high-enough delivery ratio and RDV suffers from traffic concentration on the shortest paths. In this paper, we propose MP-RDV (Multi-Path RDV) by extending RDV with multiple paths utilization to improve performance against both of those problems. In addition, we apply a delay routing metric, which is one of the major metrics in this field, to RDV to compare performance with the traffic-volume metric, which is a built-in metric of RDV. Evaluation results show that MP-RDV achieves high load-balancing performance, larger network capacity, lower delivery delay, and higher fault tolerance against topology changes compared to RDV. As for routing metrics, we showed that the traffic-volume metric is better than the delay one in RDV because delay measurement is less stable against traffic fluctuation.

To support various practical applications that are expected to work over vehicular networks, it is important to develop a network infrastructure that provides quality communications on which applications can rely. However, since a vehicular network is essentially a delay tolerant network (DTN) in which connected paths to the destinations do not always exist, providing reliable communications on such a network is a challenging research goal. In this paper, we propose a new table-driven distance-vector routing protocol called RDV (Reliable Distance-Vector routing) for static-node-assisted vehicular networks. RDV provides the guarantee that the expected packet delivery ratio exceeds the preconfigured value by creating the necessary number of packet copies sent on the shortest path. Evaluation results show that RDV provides a stably high packet delivery ratio and outperforms other conventional routing schemes in both packet delivery ratio and overhead of packet copies.