With the advancement of the 5G network and Internet of Things (IoT) devices, Multi-access Edge Computing (MEC) proposed by ETSI provides multiple devices to access with low latency through heterogeneous networks such as smart factories and vehicular networks. In addition, video streaming and online gaming have become more popular and consume more than half of the traffic on the internet. Thus, there will be more edge servers deployed on the edge of the network for offloading the core network. However, the edge server is more vulnerable because of its proximity to the user equipment (UE). Attackers can quickly launch distributed denial-of-service (DDoS) attacks with plenty of infected IoT devices. In this paper, we propose Tradeoffs Between Security and Application QoE (TBSA) system to solve the security and resource management problems on the edge server. First, we deploy video streaming, online gaming, and network security applications on the edge server. We use Intrusion Detection and Protection Services (IDPS) to perform DDoS mitigation and design resource allocation algorithm to allocate the computing resources to the edge computing applications. Then, we compare different attack rates in the user scenarios and analyze multiple models under the resource limit condition. The experiments show that we can improve the Quality-of-Experience (QoE) of applications by edge computing resources management.

Layout pattern classification, which groups similar layout clips into clusters, underlies a variety of design for manufacturability (DFM) applications, such as hotspot library generation, hierarchical data storage, and yield optimization speedup. The key challenges of layout pattern classification are clip representation and clip clustering. In this paper, we present a fast and general layout pattern classification algorithm considering clip shifting and centroid recreation. Our simple but general clip representation captures both topology and density; we can handle not only rigid area match or edge displacement constraints but also variant edge tolerances and don't care regions. For achieving a small cluster count, our clip clustering is guided by the natural grouping structure of layout clips. The clustering results are further improved by centroid recreation. Our experiments are conducted on 2016 CAD contest at ICCAD benchmark suite. Our results show that our algorithm outperforms the reference solution and all contest winning teams, delivering the smallest cluster count, fastest runtime, and 100% validity. Moreover, our algorithm with clip shifting and centroid recreation further reduces the cluster count effectively and efficiently. In addition to the good solution quality, the interplay between adopted data structures and our algorithm makes it fast and viable to be incorporated into practical DFM flows.

Person identification (PID) is a key issue in many IoT applications. It has long been studied and achieved by technologies such as RFID and face/fingerprint/iris recognition. These approaches, however, have their limitations due to environmental constraints (such as lighting and obstacles) or require close contact to specific devices. Therefore, their recognition rates highly depend on use scenarios. To enable reliable and remote PID, in this work, we present EOY (Eye On You)1, a data fusion approach that combines two kinds of sensors, a 3D depth camera and wearable sensors embedded with inertial measurement unit (IMU). Since these two kinds of data share common features, we are able to fuse them to conduct PID. Further, the result can be transferred to a mobile platform (such as robot) since we have less constraints on devices. To realize EOY, we develop fusion algorithms to address practical challenges, such as asynchronous timing and coordinate calibration. The experimental evaluation shows that EOY can achieve the recognition rate of 95% and is very robust even in crowded areas.

Layout pattern classification, which groups similar layout clips into clusters, underlies a variety of design for manufacturability (DFM) applications such as hotspot library generation, hierarchical data storage, and yield optimization speedup. The key challenges of layout pattern classification are clip representation and clip clustering, while the mutually conflicting concerns are efficiency and solution quality (in terms of cluster count). In this paper, we present a fast and general layout pattern classification algorithm. Our simple but general clip representation captures both topology and density; we can handle not only rigid area match or edge displacement constraints but also variant edge tolerances and don't care regions. On the other hand, for achieving a small cluster count, our clip clustering is guided by the natural grouping structure of layout clips. Our experiments are conducted on 2016 CAD contest at ICCAD benchmark suite; our results show that our algorithm outperforms the reference solution and all contest winning teams, delivering the smallest cluster count, fastest runtime, and 100% validity. In addition to the good solution quality, the interplay between adopted simple and easily manipulated data structures and our algorithm makes it fast and viable to be incorporated into practical DFM flows.

An evolutionary process designed to optimize dynamic composition of Web service components is proposed. The solution proposed in this paper applies evolutionary computing techniques to automatically select optimal combinations of Web service components from available component repositories. Use of the process is illustrated with a computational simulation for optimal component selection.

The evolutionary requirements analyser (ERA) applies evolutionary computing techniques to automatically select optimal combinations of human and machine agents in a system model to match nonfunctional requirements (NFRs). The tool assesses the reliability, performance times and cost of different system models by executing many model variants, as evolving forms, with scenarios and different combinations of environmental variables. Better performing models are selected, to converge on an optimal solution. Use of the tool is illustrated with a case study of requirements analysis for component selection in a command and control system.

We introduce evolutionary computation into requirements engineering and propose a process for selecting an optimal set of components to satisfy fitness criteria of system reliability. The system architecture and algorithms are described with a case study of a command and control system. The results of the evolutionary requirements analysis are discussed.