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A road network represents road objects in a given geographic area and their interconnections, and is an essential component of intelligent transportation systems (ITS) enabling emerging new applications such as dynamic route guidance, driving assistance systems, and autonomous driving. As the digitization of geospatial information becomes prevalent, a number of road networks with a wide variety of characteristics coexist. In this paper, we present an area partitioning approach to the conflation of two road networks with a large difference in level of details. Our approach first partitions the geographic area by the Network Voronoi Area Diagram (NVAD) of low-detailed road network. Next, a subgraph of high-detailed road network corresponding to a complex intersection is extracted and then aggregated into a supernode so that a high matching precision can be achieved via 1:1 node matching. To improve the matching recall, we also present a few schemes that address the problem of missing corresponding object and representation dissimilarity between these road networks. Numerical results at Yeouido, Korea's autonomous vehicle testing site, show that our area partitioning approach can significantly improve the performance of road network matching.

Thanks to their multiple sensors, the Android smartphones have drawn significant attention for supporting vehicular sensing apps. These apps usually require continual sampling of sensors to collect the events of driving. However, the standard garbage collection (GC) of the Dalvik virtual machine blocks these apps from collecting sensing information for hundreds of milliseconds, which leads to the failure of detecting vehicular events. To alleviate this problem, we present a scheme that proactively invokes the appdriven GC to reduce the duration of blocking based on free heap memory ratio or periodic calling. The experimental results show that we can significantly reduce 75% of the blocking time.

The highway junction point, where the ramp is merged with the mainstream, is the main traffic congestion point in the highway when handling transport demands. A vehicle driving on the ramp generally attempts to enter the highway without predicting the traffic condition, and commonly causes traffic congestion on highways. Traffic congestion in the highway junction is defined as the traffic situation that the transport demand in the conjunction point exceeds the bottleneck capacity and the accumulated demand remains to the bottleneck. Under traffic congestion, vehicles driving on the ramp cannot efficiently enter the highway. As a result, the waiting time of the vehicle on the ramp becomes long, and fuel consumption and CO2 emission increase due to the frequent acceleration and deceleration. In this paper, we propose a vehicular speed acceleration advisory algorithm for the vehicle on the ramp at the junction point. By recognizing the traffic condition on highways using V2V (Vehicle-to-Vehicle) communication, the proposed algorithm helps to reduce the frequent acceleration and deceleration by drivers. From the simulation results, compared to the conventional driving on the ramp, we show that the proposed algorithm can decrease fuel consumption, CO2 emission under various vehicle densities.

In this paper, we introduce the concept of cyber-physical intersection systems (CPIS) which interconnects roadside units (RSU) located at the intersection, on-board unit s (OBU) of moving vehicles, and the smartphone apps, named the Smartphone Agent (SA). At the pedestrian mode of the SA, the connectivity management schemes, such as a location update and a handover control algorithm, are proposed to better support the CPIS services while minimizing the power consumption of the pedestrian's smartphone. We develop a real prototype of the CPIS, including RSU, OBU, and the SA. Based on the numerical results collected from a pedestrian moving around the Pusan National University campus, we validate that the proposed connectivity management schemes can improve not only the power efficiency of the pedestrian's smartphone, but also the quality of the CPIS services.

With the emergence of diverse group-based services, multiple multicast groups are likely to coexist in a single network, and users may subscribe to multiple groups simultaneously. However, the existing group key management (GKM) schemes, aiming to secure communication within a single group, are not suitable in multiple multicast group environments because of inefficient use of keys, and much larger rekeying overheads. In this paper, we propose a new GKM scheme for multiple multicast groups, called the master-key-encryption-based multiple group key management (MKE-MGKM) scheme. The MKE-MGKM scheme exploits asymmetric keys, i.e., a master key and multiple slave keys, which are generated from the proposed master key encryption (MKE) algorithm and is used for efficient distribution of the group key. It alleviates the rekeying overhead by using the asymmetry of the master and slave keys, i.e., even if one of the slave keys is updated, the remaining ones can still be unchanged by modifying only the master key. Through numerical analysis and simulations, it is shown that the MKE-MGKM scheme can reduce the storage overhead of a key distribution center (KDC) by 75 percent and the storage overhead of a user by up to 85 percent, and 60 percent of the communication overhead at most, compared to the existing schemes.

A vehicle localization technology is an essential component to support many smart-vehicle applications, e.g. collision warning, adaptive cruise control, and so on. In this paper, we present a new vehicle localization algorithm based on the fusion of the sensing estimates from the local sensors and the GPS estimates from the inter-vehicle communications. The proposed algorithm consists of the greedy location data mapping algorithm and the position refinement algorithm. The former maps a sensing estimate with a GPS estimate based on the distance between themselves, and then the latter refines the GPS estimate of the subject vehicle based on the law of large numbers. From the numerical results, we demonstrate that the accuracy of the proposed algorithm outperforms that of the existing GPS estimates by at least 30 % in the longitudinal direction and by at least 60% in the lateral direction.

In vehicle-to-vehicle (V2V) communications, each vehicle should periodically disseminate a beacon message including the kinematics information, such as position, speed, steering, etc., so that a neighbor vehicle can better perceive and predict the movement of the vehicle. However, a simple broadcasting of such messages may lead to a low reception probability as well as an excessive delay. In this paper, we attempt to analyze the impact of the following key parameters of the beacon dissemination on the performance of vehicular networks: beacon period, carrier-sensing range, and contention window (CW) size. We first derive a beacon period which is inversely proportional to the vehicle speed. Next, we mathematically formulate the maximum beacon load to demonstrate the necessity of the transmit power control. We finally present an approximate closed-form solution of the optimal CW size that leads to the maximum throughput of beacon messages in vehicular networks.

A Wi-Fi mesh network providing multi-hop wireless connections based on IEEE 802.11 PHY/MAC technology has recently received a significant attention as a network infrastructure that interconnects RFID systems and wireless sensor networks (WSNs). However, the current IEEE 802.11 contention-based MAC protocol cannot fully utilize the network capacity due to eithor frame collisions or unused network resources. In this paper, we propose a novel multi-channel scheduling MAC (MCS-MAC) protocol for Wi-Fi mesh networks. Under the secondary interference model of Wi-Fi mesh networks, the MCS-MAC protocol can maximize the network throughput via activation of collision-free links that has a maximal link weight. Through the simulations, we show that the throughput of the MCS-MAC protocol is at least three times higher than that of existing MAC protocols in Wi-Fi mesh networks.

The advancement of electronic technology has made significant contributions to the safety and convenience of modern vehicles. New intelligent functionalities of vehicles have been implemented in a number of electronic control units (ECUs) that are connected to each in vehicle control networks (VCNs). However, with the rapid increase in the number of ECUs, VCNs currently face several challenges, e.g., design complexity, space constraints, system reliability, and interdependency. Considering these factors, the complexity of the VCN design problem exponentially increases, which means that the problem cannot be solved within a reasonable time using conventional optimization techniques. In this paper, we report a new methodology for the optimal design of VCNs. An analytical model was derived to examine the fundamental characteristics of the VCN design problem. Compared with the case of a conventional data network, which typically considers temporal scheduling over a fixed physical topology, the VCN design problem should also consider spatial constraints, e.g., volume, position, and weight. Moreover, the spatial constraints change during the solving procedure. Such temporal and spatial joint optimization problems with varying constraints incur extremely high computational complexity. To tackle the high complexity, this paper proposes a fast solution based on a repeated-matching method, which reduces the problem complexity from O(NNN) to O(NN3). By applying our methodology to a number of different real-world VCN design scenarios, this proposal can produce a 1% near-optimal design within a significantly reduced time.

Although lots of research efforts have focused on group key management (GKM) for secure multicast, existing GKM schemes are inadequate for vehicle communication (VC) systems since they incur unnecessary rekeying overhead without considering the characteristics of VC systems such as Vehicle-to-Infrastructure communications and a great number of high mobility vehicles. Therefore, we propose a GKM scheme, called RSU-based decentralized key management (RDKM), dedicated for the multicast services in the VC systems. The RDKM scheme significantly reduces the rekeying overhead through delegating a part of the key management functions to the road-side infrastructure units (RSUs) and through updating the key encryption keys (KEKs) within a RSU. The performance of the RDKM scheme is analyzed in terms of communication overhead and storage overhead each of which has a strong impact on the performance of GKM. Furthermore, we propose an optimization algorithm that minimizes the weighted sum of the communication and the storage overhead, called the GKM overhead (GKMO), by appropriately determining the design parameters. The numerical results from the extensive analysis demonstrate that the RDKM scheme outperforms the existing GKM schemes in terms of the GKMO.

Due to the increasing number of vehicles in recent years, traffic congestion problem is a common issue for residents of metropolises. For a better understanding of traffic congestion, the analyzed data from big data technology can be provided as timeline information. However, a scalability problem would occur when we convert raw traffic data into the timeline information due to the volume and complexity of traffic data. In this paper, we present two MapReduce-based approaches which extract the timeline information of road traffic data. By utilizing the distribute processing strategy, we can resolve the scalability problem. We propose an iterative approach of MapReduce as a baseline approach and a single iteration approach as an efficient solution. The single iteration easily extended to support various and/or complex analytic queries by providing proper codes at a reducer. We validate experimentally our MapReduce-based approaches on real traffic dataset from a Busan Intelligent Transport System (ITS) center.