Nhu-Ngoc Dao

A feasible method to combat against DDoS attack in SDN network

In Software Defined Network, the controller is so vulnerable to flooding attack. By injecting spoofed request packets continuously, attackers make a burdensome process to the controller, cause bandwidth occupation in the controller-switch channel, and overload the flow table in switch. The final target of attackers is to downgrade or even shutdown the stability and quality of service of the network. In this paper, we introduce a feasible method to protect the network against Distributed Denial of Service attacks more effectively.

Adaptive Resource Balancing for Serviceability Maximization in Fog Radio Access Networks

Serviceability is the ability of a network to serve user equipments (UEs) within desired requirements (e.g., throughput, delay, and packet loss). High serviceability is considered as one of the key foundational criteria toward a successful fog radio access infrastructure satisfying the Internet of Things paradigm in the 5G era. In this paper, we propose an adaptive resource balancing (ARB) scheme for serviceability maximization in fog radio access networks wherein the resource block (RB) utilization among remote radio heads (RRHs) are balanced using the backpressure algorithm with respect to a time-varying network topology issued by potential RRH mobilities. The optimal UE selection for service migration from a high-RB-utilization RRH to its neighboring low-RB-utilization RRHs is determined by the Hungarian method to minimize RB occupation after moving the service. Analytical results reveal that the proposed ARB scheme provides substantial gains compared with the standalone capacity-aware, max-rate, and cache-aware UE association approaches in terms of serviceability, availability, and throughput.

Mitigating wifi interference to improve throughput for in-vehicle infotainment networks

In recent years, in-vehicle infotainment networks (IVINs) have rapidly become one of the most valuable features auto makers have used to promote their flagship models as an advanced competitive marketing weapon. IVINs can provide passengers with multimedia services locally as well as Internet connectivity through a gateway known as a mobile hotspot. The in-vehicle mobile hotspot is embedded in the car and supports cellular connection. Utilizing this system, mobile devices can access the in-vehicle unified infotainment framework to comfortably enjoy streaming services, online games, online commerce, social network services, and so on. However, because of wireless access characteristics, if a significant number of Wi-Fi mobile hotspots are densely located, the throughput of the mobile devices will be tremendously diminished due to the interference among the mobile hotspots of IVINs, as well as with existing fixed office or residential APs along the road. In this article, we discuss the interference problems of Wi-Fi access in IVINs, provide effective solutions to these problems, and present the performance of each proposed approach within typical case studies.

Adaptive MCS selection and resource planning for energy-efficient communication in LTE-M based IoT sensing platform

As an important part of IoTization trends, wireless sensing technologies have been involved in many fields of human life. In cellular network evolution, the long term evolution advanced (LTE-A) networks including machine-type communication (MTC) features (named LTE-M) provide a promising infrastructure for a proliferation of Internet of things (IoT) sensing platform. However, LTE-M may not be optimally exploited for directly supporting such low-data-rate devices in terms of energy efficiency since it depends on core technologies of LTE that are originally designed for high-data-rate services. Focusing on this circumstance, we propose a novel adaptive modulation and coding selection (AMCS) algorithm to address the energy consumption problem in the LTE-M based IoT-sensing platform. The proposed algorithm determines the optimal pair of MCS and the number of primary resource blocks (#PRBs), at which the transport block size is sufficient to packetize the sensing data within the minimum transmit power. In addition, a quantity-oriented resource planning (QORP) technique that utilizes these optimal MCS levels as main criteria for spectrum allocation has been proposed for better adapting to the sensing node requirements. The simulation results reveal that the proposed approach significantly reduces the energy consumption of IoT sensing nodes and #PRBs up to 23.09% and 25.98%, respectively.

Adaptive Suspicious Prevention for Defending DoS Attacks in SDN-Based Convergent Networks

The convergent communication network will play an important role as a single platform to unify heterogeneous networks and integrate emerging technologies and existing legacy networks. Although there have been proposed many feasible solutions, they could not become convergent frameworks since they mainly focused on converting functions between various protocols and interfaces in edge networks, and handling functions for multiple services in core networks, e.g., the Multi-protocol Label Switching (MPLS) technique. Software-defined networking (SDN), on the other hand, is expected to be the ideal future for the convergent network since it can provide a controllable, dynamic, and cost-effective network. However, SDN has an original structural vulnerability behind a lot of advantages, which is the centralized control plane. As the brains of the network, a controller manages the whole network, which is attractive to attackers. In this context, we proposes a novel solution called adaptive suspicious prevention (ASP) mechanism to protect the controller from the Denial of Service (DoS) attacks that could incapacitate an SDN. The ASP is integrated with OpenFlow protocol to detect and prevent DoS attacks effectively. Our comprehensive experimental results show that the ASP enhances the resilience of an SDN network against DoS attacks by up to 38%.

Reliable multicasting service for densely deployed military sensor networks

Wireless sensor networks (WSNs) have drawn attention for military applications where a large number of light-weight sensor nodes can detect enemies and intruders in the battlefield and alert a command post (CP) to our forces. In particular, in the military sensor networks, QoS (quality-of-service) is one of the most important requirements to support their mission-critical applications. However, achieving reliable data transmission is not an easy task because sensor nodes are densely deployed and wireless channel is unpredictable due to high mobility. Also, in the multihop deployed sensor networks, redundant data forwarding occurs frequently and it causes interference among the nodes. In this paper, we propose an efficient reliable multicast scheme using pre-ACK frames to provide fully reliable multicasting and decrease unnecessary data forwarding in the military sensor networks. When a receiver node overhears a multicast frame, the pre-ACK is used to announce its reception of the multicast frame to neighboring nodes which then do not need to duplicate the same multicast frame to it. The performance evaluation shows that the proposed scheme provides full reliability (100%) and outperforms the existing schemes in terms of aggregate throughput (612%) and energy consumption (514%) when the number of nodes is 512 with full buffer.

Using social Internet of Things (SIoT) demand side management on the plant

Demand-side management program is important for future smart grid. Most of the DSM program developed in the past interact customer and utility company. We suggest the Factory Management System using Social Internet of Things. It is not interaction between customer and utility company but interaction between things and things. Therefore we propose a autonomous demand-side management system and a system architecture.

Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver.

Sensor fusion techniques have made a significant contribution to the success of the recently emerging mobile applications era because a variety of mobile applications operate based on multi-sensing information from the surrounding environment, such as navigation systems, fitness trackers, interactive virtual reality games, etc. For these applications, the accuracy of sensing information plays an important role to improve the user experience (UX) quality, especially with gyroscopes and accelerometers. Therefore, in this paper, we proposed a novel mechanism to resolve the gyro drift problem, which negatively affects the accuracy of orientation computations in the indirect Kalman filter based sensor fusion. Our mechanism focuses on addressing the issues of external feedback loops and non-gyro error elements contained in the state vectors of an indirect Kalman filter. Moreover, the mechanism is implemented in the device-driver layer, providing lower process latency and transparency capabilities for the upper applications. These advances are relevant to millions of legacy applications since utilizing our mechanism does not require the existing applications to be re-programmed. The experimental results show that the root mean square errors (RMSE) before and after applying our mechanism are significantly reduced from 6.3×10-1 to 5.3×10-7, respectively.

Pattern-Identified Online Task Scheduling in Multitier Edge Computing for Industrial IoT Services

In smart manufacturing, production machinery and auxiliary devices, referred to as industrial Internet of things (IIoT), are connected to a unified networking infrastructure for management and command deliveries in a precise production process. However, providing autonomous, reliable, and real-time offloaded services for such a production is an open challenge since these IIoT devices are assumed lightweight embedded platforms with limited computing performance. In this paper, we propose a pattern-identified online task scheduling (PIOTS) mechanism for the networking infrastructure, where multitier edge computing is provided, in order to handle the offloaded tasks in real time. First, historical IIoT task patterns in every timeslot are used to train a self-organizing map (SOM), which represents the features of the task patterns within defined dimensions. Consequently, offline task scheduling among edge computing-enabled entities is performed on the set of all SOM neurons using the Hungarian method to determine the expected optimal task assignments. In real-time context, whenever a task arrives at the infrastructure, the expected optimal assignment for the task is scheduled to the appropriate edge computing-enabled entity. Numerical simulation results show that the proposed PIOTS mechanism overcomes existing solutions in terms of computation performance and service capability.

Trend analyses of authentication in peer aware communication (PAC)

In this paper, we introduced Authentication trends on IEEE 802.15 Task Group 8 which aims to develop a Standard for wireless Medium Access Control (MAC) and Physical Layer (PHY) specification for Peer Aware Communication (PAC). PAC terminals which have only low power and low computational power can make a network without infrastructure using ad-hoc searching. Because of no infrastructure, there are many challenges to overcome, especially, in security part. So we introduced Authentication trends on PAC As the number of PDs (PAC Devices) continues to grow, the need to deal with security issues increases consistently.