Han-You Jeong

Many-to-many core-switch mapping in 2-D mesh NoC architectures

In this paper, we investigate the core-switch mapping (CSM) problem that optimally maps cores onto an NoC architecture such that either the energy consumption or the congestion is minimized. We propose a many-to-many core-switch mapping (mCSM) that allows a switch (core) to have multiple connections to its adjacent cores (switches). We also present decomposition methods that can obtain the suboptimal solutions with enhanced computational efficiency. Our work is the first to provide an exact mixed-integer linear programming (MILP) formulation for the complete CSM problems, including the optimal choice of core placements, switches for each core, and network interfaces for communication flows. Experiments with four random benchmarks show that 4:4 mCSM achieves 81.2% of energy savings and 2.5% of bandwidth savings compared with one-to-one mapping. They also show that, for one-to-one mapping, our optimal solutions obtained by the full MILP save 34.8% of energy consumption and 34.4% of bandwidth requirement compared with those from the existing algorithms.

Key Management for Multiple Multicast Groups in Wireless Networks

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.

Design Optimization of Vehicle Control 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.

A binary (0-1) linear program formulation for the placement of limited-range wavelength converters in wavelength-routed WDM networks

We investigate the problem of optimally placing limited-range wavelength converters at a subset of nodes in wavelength-routed wavelength-division-multiplexing (WDM) networks. We consider two different aspects of the converter-placement problem: 1) the placement minimizing the network-wide blocking probability; and 2) the placement minimizing the number of wavelength-convertible nodes to meet the performance constraints. We present a binary (0-1) linear program (BLP) formulation in which the end-to-end blocking probability is expressed as a linear function of converter locations so that standard linear program (LP) optimization packages can be employed to obtain the optimal solution of the problems. We also present a new analytical model for estimating the end-to-end blocking probabilities. Experiments have been conducted over four network topologies, including 19-node European optical network (EON), 24-node USA backbone network (UBN), 32-node HYPERCUBE, and 36-node MESH-TORUS. We demonstrate that the optimal solutions of the converter-placement problems can be obtained within a reasonable computation time.

Blocking in wavelength-routed optical networks with heterogeneous traffic

In this paper, we present a new analytical model that can give an accurate estimation of the blocking probabilities in wavelength-routed optical networks with heterogeneous traffic. By heterogeneous, we mean that each session offered to the network has its own traffic intensity and burstiness. In such cases, the blocking probability of a session is determined by the busy-wavelength distributions of the links seen at the arrival points of its calls. Thus, we first present two single-link models to estimate the arrival-point busy-wavelength distribution of a link with heterogeneous traffic: the full-population (FP) model and the reduced-population (RP) model. Both models are based on the BPP/M/W/W model, where the first two moments of an arbitrary session are matched by those of a birth-death process whose arrival rate linearly varies with the average number of busy wavelengths occupied by its own calls. We show that different sessions have different arrival-point busy-wavelength distributions depending on the burstiness of their traffic, i.e., a bursty session observes the link more congested than a smooth session. Next, we provide two extensions of the single-link models, the FP-full-load link-correlation model and the RP-reduced-load link-correlation model, to estimate the blocking probabilities of optical networks with heterogeneous traffic and sparse wavelength conversion. Both models employ the existing link-correlation models to take into account the occupied-wavelength-index correlation between two adjacent links. By comparing the results from the models with simulation results, we demonstrate that both models well approximate the blocking probabilities of individual sessions, as well as the network-wide blocking probability, for a wide range of traffic intensity, burstiness, and heterogeneity.

RSU-Based Distributed Key Management (RDKM) For Secure Vehicular Multicast Communications

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.

A Comprehensive Analysis of Beacon Dissemination in Vehicular Networks

In many active safety applications, each vehicle must periodically disseminate a beacon message including the status information, such as position, speed, steering, etc., so that a neighbor vehicle can better perceive and predict the kinematics of the vehicle sending this message. However, a simple broadcasting of beacon message may lead to a low message reception as well as an excessive delay. To resolve this problem, we consider the requirements of an active safety application, a channel load and a vehicle mobility in a highway scenario of vehicular networks. Based on these requirements, we analyze the impact of the following three key parameters of the beacon dissemination on the performance of vehicular networks: beacon period, beacon transmission power, 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 transmission 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 Fusion of Vehicle Sensors and Inter-Vehicle Communications for Vehicular Localizations

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.

Adaptive beacon rate control algorithm for vehicular ad-hoc networks

A key feature of the vehicular ad-hoc networks (VANETs) is to broadcast the beacon message containing the position, speed, heading, etc. However, the fixed-rate scheme in the current standards may incur too large traffic load to the DSRC channel leading to a significant degradation of the network performance. In this paper, we present a new beacon rate control algorithm that succeeds in tracking the movement of nearby vehicles until K consecutive losses of the beacon message. From the numerical results of the simulation, we show that the proposed scheme can achieve much better performance than the fixed-rate scheme in term of the DSRC channel utilization and the successful vehicle tracking probability.

An accurate vehicle positioning algorithm based on vehicle-to-vehicle (V2V) communications

In vehicular safety applications, a vehicular positioning algorithm plays a crucial role in preventing road accidents through the perception of a dangerous situation in advance. However, most of vehicular positioning devices, e.g. a low-cost GPS receiver, have too large position error to be used for these applications. To reduce this error, we present the vehicular positioning accuracy enhancement (VITAE) algorithm which reduces the position error of a vehicle by taking into account the GPS estimate and the sensing estimate of each nearby vehicle; A GPS estimate is obtained from vehicle-to-vehicle communications, whereas a sensing estimate is measured at local sensors, e.g. vehicular radar. We first present a greedy data-association (GDA) algorithm to find a minimal-weight matching between a GPS estimate and a sensing estimate based on an Euclidian distance. We next adjust the position estimate of the vehicle by using the difference in the mass center of GPS estimates and that of sensing estimates. From the law of large numbers, this adjustment leads to the reduction of the position error by 1/√N times, where N is the number of nearby vehicles. To further reduce the position error, we combine our positioning algorithm with the Kalman filter. From the numerical results, we demonstrate that our positioning algorithm can reduce the position error by 81.8% on average.