Recently, various approaches have been developed for traffic analysis systems, ranging from Vehicle Detection Systems (VDS) to Mobile Detection Systems (MDS). However, VDS has limitations. For example, in actual driving situations such as intersections or exits, each lane may have different traffic conditions, revealing the limitations of traffic information provided by conventional VDS. In this paper, we propose a methodology to efficiently monitor the traffic condition and safety on the road by utilizing a LiDAR sensor installed on a vehicle to collect continuous traffic information about surrounding vehicles. To implement the Mobile Detection System (MDS), we collect point cloud data from LiDAR and detect the position and size of vehicles using a deep learning-based voxel-RCNN. We then convert the data into traffic information for analysis. Furthermore, we propose an efficient method for analyzing road hazards by introducing the MTTC method based on the TTC for hazard assessment. To evaluate the performance of the proposed method, we compare its reliability with that of conventional VDS and perform road hazards analysis using LiDAR-based probe vehicles with data collected directly from highways in Korea.

By moving network appliance functionality from proprietary hardware to software, Network Function Virtualization promises to bring the advantages of cloud computing to network packet processing. However, the evolution of cloud computing (particularly for data analytics) has greatly benefited from application-independent methods for scaling and placement that achieve high efficiency while relieving programmers of these burdens. NFV has no such general management solutions. In this paper, we present a scalable and application-agnostic scheduling framework for packet processing, and compare its performance to current approaches.

Datacenters have traditionally been architected as a collection of servers wherein each server aggregates a fixed amount of computing, memory, storage, and communication resources. In this paper, we advocate an alternative construction in which the resources within a server are disaggregated and the datacenter is instead architected as a collection of standalone resources.

Commodity-hardware technology has advanced in great leaps in terms of CPU, memory, and I/O bus speeds. Benefiting from the hardware innovation, recent software routers on commodity PC now report about 10 Gbps in packet routing. In this paper we map out expected hurdles and projected speed-ups to reach 100 Gbps in packet routing on a single commodity PC. With careful measurements, we identify two notable bottlenecks for our goal: CPU cycles and I/O bandwidth. For the former, we propose reducing per-packet processing overhead with software-level optimizations and buying extra computing power with GPUs. To improve the I/O bandwidth, we suggest scaling the performance of I/O hubs that limits packet routing speed to well before 50 Gbps.

We present PacketShader, a high-performance software router framework for general packet processing with Graphics Processing Unit (GPU) acceleration. PacketShader exploits the massively-parallel processing power of GPU to address the CPU bottleneck in current software routers. Combined with our high-performance packet I/O engine, PacketShader outperforms existing software routers by more than a factor of four, forwarding 64B IPv4 packets at 39 Gbps on a single commodity PC. We have implemented IPv4 and IPv6 forwarding, OpenFlow switching, and IPsec tunneling to demonstrate the flexibility and performance advantage of PacketShader. The evaluation results show that GPU brings significantly higher throughput over the CPU-only implementation, confirming the effectiveness of GPU for computation and memory-intensive operations in packet processing.

SSL/TLS is a standard protocol for secure Internet communication. Despite its great success, today's SSL deployment is largely limited to security-critical domains. The low adoption rate of SSL is mainly due to high computation overhead on the server side.

The IEEE 802.15.4 standard specifies the physical and medium access control layers designed for low-rate wireless personal area networks. Its operational frequency band includes the 2.4 GHz industrial, scientific and medical band, which is also used by other IEEE 802 wireless standards. This paper presents the coexistence model of IEEE 802.15.4 with IEEE 802.1 lb interference in fading channels and proposes two adaptive channel allocation schemes. The first avoids the IEEE 802.15.4 interference only and the second avoids both of the IEEE 802.15.4 and the IEEE 802.11b interferences. Numerical results show that by selecting a channel which gives the maximum signal to noise ratio to the system, the proposed algorithms are effective for avoiding the interferences and for max-imizing the network capacity.

In this paper, a framework of link capacity analysis for the uplink MCN (Multi-hop Cellular Network) is presented, and the goal of which is to increase link capacity while mitigating the effect of interference. An overlaid network architecture is employed as the network topology : the multi-hop (single-hop) network at the outer (inner) region of the cell. In order to verify the improvement in capacity accrued from the inter-network cooperation, inter-network and intra-network interferences are redefined. In a simulation, the MCN exhibits a significant increase of 1.2 ~ 1.8 times in link capacity compared to a cellular network.