The highly directional nature of the millimeter wave (mmWave) beams causes several challenges in using that spectrum to meet the communication demands of immersive applications. The mmWave beams are especially susceptible to misalignments and blockages caused by user movements. As a result, mmWave channels are vulnerable to large quality fluctuations, which in turn, degrades the end-to-end performance of immersive applications. In this paper, we propose a reinforcement learning (RL) based application-layer plugin that works in conjunction with the QUIC protocol to combat the challenges of mmWave networks. The plug-in called Millimeter wave based Immersive QUIC Agent (MIQA) uses the RL model to help modulate the sending rate along with the congestion control scheme of QUIC. To evaluate the effectiveness of MIQA, we conduct experiments on a mmWave augmented immersive testbed. The evaluation results show that MIQA significantly improves the immersive experience by increasing the end-to-end throughput and by decreasing the end-to-end latency.

The highly directional nature of the millimeter wave (mmWave) beams pose several challenges in using that spectrum for meeting the communication needs of immersive applications. In particular, the mmWave beams are susceptible to misalignments and blockages caused by user movements. As a result, mmWave channels are vulnerable to large fluctuations in quality, which in turn, cause disproportionate degradation in end-to-end performance of Transmission Control Protocol (TCP) based applications. In this paper, we propose a reinforcement learning (RL) integrated transport-layer plugin, Millimeter wave based Immersive Agent (MIA), for immersive content delivery over the mmWave link. MIA uses the RL model to predict mmWave link bandwidth based on the real-time measurement. Then, MIA cooperates with TCP’s congestion control scheme to adapt the sending rate in accordance with the predictions of the mmWave bandwidth. To evaluate the effectiveness of the proposed MIA, we conduct experiments using a mmWave augmented immersive testbed and network simulations. The evaluation results show that MIA improves end-to-end immersive performance significantly on both throughput and latency.

Millimeter wave (mmWave) access networks have the potential to meet the high-throughput and low-latency needs of immersive applications. However, due to the highly directional nature of the mmWave beams and their susceptibility to beam misalignment and blockage resulting from user movements and rotations, the associated mmWave links are vulnerable to large channel fluctuations. These fluctuations result in disproportionately adverse effects on performance of transport layer protocols such as Transmission Control Protocol (TCP). To overcome this challenge, we propose a network layer solution, COded Taking And Giving (COTAG) scheme to sustain low-latency and high-throughput end-to-end TCP performance in dually connected networks. In particular, COTAG creates network encoded packets at the network gateway and each access point (AP) aiming to adaptively take the spare bandwidth on each link for transmission. Further, if one link bandwidth drops due to user movements, COTAG actively abandons the transmission opportunity by conditionally dropping packets. Consequently, COTAG actively adapts to link quality changes in mmWave access network and enhances the TCP performance without jeopardizing the latency of immersive content delivery. To evaluate the effectiveness of the proposed COTAG, we conduct experiments using off-the-shelf APs and network simulations. The evaluation results show that COTAG improves end-to-end TCP performance significantly on both throughput and latency.

Cloud storage provides the great convenience for people to access their data at anytime from any place. Since cloud storage is usually run by the third-party service provider, keyword search over cloud data with privacy protection is of great importance. Many studies in the literature have proposed keyword search scheme for document search, but, in most schemes, the query keywords must exactly match those in the document indexes. However, it is impractical to restrict query keywords provided by the user when performing the search. This paper proposes the scheme for semantic multi-keyword search over encrypted cloud data. Users are able to select query keywords on their own choice. In addition, the query privacy of the user and the security of the documents are protected simultaneously through encrypted document search to prevent snooping from the cloud service provider. Experiments are conducted using a dataset of massive real world papers. The results show that the proposed scheme can effectively perform the semantic multi-keyword search over encrypted cloud data with great efficiency.

LoRaWAN is a promising solution of Low-power wide area network (LPWAN) operating in unlicensed spectrum to support long range wireless services for Internet of Things (IoTs). However, with the growth of IoT devices deployed in a fixed geographic area, the immunity to interference on the communication increases significantly. Attackers may utilize such situation to jam packet transmission by emitting RF interference signal at the same time when a LoRa end node is sending data to the LoRa gateway. As a consequence, the transmission of the LoRa end node would fail due to collision which would in turn reduce the network performance. In this paper, we implement a LoRa jammer on commercial LoRa devices by modifying the open source and figure out the proper setting of the jammer through three scenarios aiming to evaluate the influence of LoRa transmission configuration on jamming performance. Specifically, the impact of non-orthogonality of LoRa transmission on jamming effect is investigated. Possible countermeasures for LoRaWAN are then presented to alleviate the jamming attacks.

Network virtualization is a promising technology for the emerging 5G and cloud computing networks where the virtual network is a logical topology consisting of virtual nodes and virtual links. In network virtualization, how to efficiently assign resources of the physical network to the virtual networks is of great significance and is known as the Virtual Network Embedding (VNE) problem. This paper presents an efficient algorithm tackling with the coordinated VNE problem. Specifically, a Mod-MaxMatch approach is presented which takes the global link resources into considerations when mapping the virtual nodes. Furthermore, a path splitting scheme based on the genetic algorithm is proposed while mapping the virtual links. The proposed algorithm minimizes the redundant reutilization of physical links and mitigates the demand for network bandwidths. A well-known link cost function is used to evaluate the network performance. The experimental results show that the link cost for the proposed approach is reduced by 77% compared to the traditional methodology and by 21% compared to the state-of-art design.

In this paper, we propose a user centric bandwidth allocation scheme for low latency data transmission in ultra dense vehicular networks. Various mobile devices such as mobile phones, sensors of vehicles or autonomous driving systems,require low latency and bandwidth intensive packet delivery between the devices and the Internet aiming to support real-time applications. In the ultra dense vehicular network, small base stations (SBSs) are densely deployed in a fixed geographic area to provides higher date rate. In further, each SBS cooperates with others to form clusters to better support seamless wireless data transmissions, and each mobile device dynamically plans its wireless connectivity for data transmission when it moves in the network. Specifically, each mobile device pre-allocates the amount of bandwidth from a cluster, formed by several SBSs, based on its expected movement, the delay and band-width requirement of the packet transmission and the resource availability of each cluster. Moreover, to effectively utilize the available network resource, each cluster also redistributes its residual bandwidth to the mobile devices pre-allocate bandwidth from it. Consequently, the latency of the data transmission can be better sustained in the ultra dense vehicular network.

Earthquake early warning system uses high-speed computer network to transmit earthquake information to population center ahead of the arrival of destructive earthquake waves. This short (10 s of seconds) lead time will allow emergency responses such as turning off gas pipeline valves to be activated to mitigate potential disaster and casualties. However, the excessive false alarm rate of such a system imposes heavy cost in terms of loss of services, undue panics, and diminishing credibility of such a warning system. At the current, the decision algorithm to issue an early warning of the onset of an earthquake is often based on empirically chosen features and heuristically set thresholds and suffers from excessive false alarm rate. In this paper, we experimented with three advanced machine learning algorithms, namely, K-nearest neighbor (KNN), classification tree, and support vector machine (SVM) and compared their performance against a traditional criterion-based method. Using the seismic data collected by an experimental strong motion detection network in Taiwan for these experiments, we observed that the machine learning algorithms exhibit higher detection accuracy with much reduced false alarm rate.

To assist various intelligent applications, IoT devices send their monitored data to Internet for further process. WiFi access points (APs) would be a candidate to support data transmission in IoTs due to its large wireless service coverage and low cost. An AP connects to the Internet through Ethernet to provide Internet connectivity to its connected IoT devices. However, data transmission may be failed when an AP loses its Ethernet connectivity. To resolve this problem, a software define networking (SDN) and wireless distribution system (WDS) assisted fault recovery automation scheme, SDWDS, is proposed. In SDWDS, packets can be directly and wirelessly forwarded between two APs when the Ethernet link fails at one of the APs. Specifically, when the Ethernet link failure of an AP is detected by the SDN controller, the AP will switch to WDS mode to wirelessly connect to its neighboring APs. The SDN controller in further reroutes corresponding packets through the newly associated AP. Consequently, the IoT devices can sustain Internet access even though their associated AP cannot directly communicate with the Internet via Ethernet. Additionally, the proposed SDWDS is implemented in commercial APs for performance evaluation. Preliminary results show the SDWDS can automatically recover more than one Ethernet link failures in the network. However, due to the limitation of the commercial AP, the creation of WDS requires rebooting the AP which costs about 20 seconds recovery latency. In the future, we will introduce virtualization or multi- connectivity techniques to more efficiently support the fault recovery automation in IoTs.

IoT devices monitor and provide information to a control system which manages the operation of an intelligent building. Due to the limited capability of wireless coverage and battery life, IoT devices may have not direct Internet access to the control system. Instead, they first connect to WiFi access points (APs) for data transmission. However, when abnormal situation such as attacks occurs, the performance of packet transmission between the IoT devices and the control system degrade. To eliminate this problem, we utilize the dual-band wireless networking technology and propose a Stay Connected (SCONN) mechanism. When the software defined networking (SDN) controller detects the abnormal situation occurs between an AP and the control system, with SCONN, packets will be adaptively rerouted through the backup wireless mesh network consisted by dual-band APs. Hence, the performance in data transmission can be enhanced. To further validate the capability of SCONN, we implement it in commercial APs. Experimental results show the improvement of the proposed SCONN to support data transmission.

The Listen-Before-Talk (LBT) is the main procedure for Licensed Assisted Access (LAA) to accomplish fair and friendly coexistence with other operators or technologies operating over unlicensed spectrum. However, in LBT, the lack of coordination with the other existing systems brings challenges in sustaining performance in the coexistence of LAA and WiFi networks. Specifically, the hidden node situation (HNS) and exposed node situation (ENS) cannot be effectively handled when both LAA and WiFi attempt to access the unlicensed spectrum at the same time. Thus, transmission failure might occur and the network performance would be degraded. In order to mitigate the influences caused by HNS and ENS, based on LBT, a Give-And-Take (GAT) mechanism with the Listen-Before-Receive (LBR) procedure is proposed in this paper. With GAT, the LAA sender conditionally gives up or takes back transmission opportunities, and thus the unlicensed spectrum could be efficiently shared between LAA and WiFi in terms of energy usage and throughput. Evaluation results show that the proposed GAT could conditionally obtain better network performance comparing to legacy LBT.

Real-time immersive content delivery requires a lot of bandwidth. Wireless backhaul can provide high link bandwidth and is a possible approach supporting the deployment of a UDN. With wireless backhaul, the UDN could provide higher bandwidth for UEs without expensive deploying cost. However, the spatial and temporal fluctuation of traffic flows in the UDN could cause congestion at some links, and then packet loss may occur. Under this circumstance, the quality of real-time immersive content would be degraded significantly. To overcome above mentioned problem, a Take-And-Give (TAG) mechanism is proposed to adaptively and reliably manage packet forwarding in the UDN with wireless mesh backhaul. With TAG, packets of several traffic flows are linearly combined at the eNB before forwarding to SBSs. Each SBS individually and inearly combines the combined packets again and manages number of packets forwarded to next hop according to the available link bandwidth. When spare bandwidth exists, TAG mechanism ''takes'' the available bandwidth to send additional linearly combined packets. TAG mechanism ''gives'' the transmission opportunity when no spare bandwidth is left. The SBS with direct connection to destinated UEs recover received packets, and then forwards packets to the corresponding UEs. As a consequence, the content can be delivered more reliably. Evaluation results show the effectiveness of the proposed TAG in delivering real-time immersive content in the network.

Next generation wireless networks (WMNs) are designed to provide better performance than other existing personal, local, and metropolitan wireless networks, such as Wireless Local Area Networks and WiFi. In WMNs, each link has different amount of available bandwidth, and the bandwidth fluctuates dynamically based on the wireless environment. When the available bandwidth fluctuates, Transmission Control Protocol (TCP) flows experience packet losses, packet re-ordering, and timeouts, resulting in low end-to-end throughput. To alleviate the problem of low TCP throughput, we propose a Spare-bandwidth Rate-adaptive Network Coding (SRNC) scheme. In SRNC scheme, each gateway node forwards packets after network coding. Each intermediate node also adaptively uses network coding before forwarding the packets to the outgoing links. Each mesh access node decodes the network coded packets before forwarding them to the destinations. The key feature of SRNC is that each node adapts its network coding rate based on the available bandwidth on the outgoing links, such that the access nodes can decode the packets with higher probability without significantly affecting the cross-traffic. Effectiveness of the proposed scheme is evaluated using simulation. The simulation results show that the proposed scheme uses the available bandwidth on each link efficiently and it significantly improves end-to-end throughput of TCP flows.

In this paper, we propose a strategy for individual users to purchase frequency spectrum in cognitive radio based wireless networks. From the user's perspective, the goal is to reduce the cost of acquiring the spectrum needed to meet the quality of service needs of their real-time streaming applications. The key aspect of our proposed strategy is that the users can purchase spectrum for use in future time instances based on stochastic predictions of their locations and the prices set by competing wireless service providers. Another important aspect of the proposed strategy is that the tuning of many design parameters is not required as the tuning is often needed in conventional schemes. To evaluate our proposed scheme, we model a user utility function based on the cost of spectrum purchase and its impact on the quality of service. Simulation results show that the proposed strategy performs better than baseline schemes in terms of user utility.

Due to the growing importance of coastline surveillance and protection, underwater communication is playing an increasingly important role in military networks. As compared to terrestrial networks, large propagation delays and low data rates are fundamental characteristics of underwater communication. Furthermore, due to some key differences in the factors causing fluctuations in the quality of the underwater channels, the corresponding communication links experience more prolonged data rate changes as compared to those in terrestrial networks. Large propagation delays and prolonged link data rate deteriorations severely degrade the end-to-end performance of Transmission Control Protocol (TCP) based applications. Since military applications often require the reliable data delivery provided by TCP, it is important to devise solutions to alleviate this problem. In this paper, we propose a new routing scheme called Linear Coded Digraph Routing (LCDR) to enhance the end-to-end throughput of TCP based packet flows in underwater mesh networks. LCDR is a fully distributed scheme designed to locally respond to changes in the link data rates. In LCDR, each ingress node forwards packets after network coding. Each intermediate node adaptively uses network coding before forwarding the packets to the outgoing links. Each terrestrial gateway decodes the network coded packets before forwarding them to terrestrial networks. Each node adapts its packet forwarding rate based on the available bandwidth on the outgoing links, such that the terrestrial gateway can successfully receive packets with higher probability without significantly affecting cross-traffic. The effectiveness of the proposed scheme is evaluated using simulation. The simulation results show that the proposed scheme uses the spare bandwidth on each link efficiently and it significantly improves end-to-end throughput of TCP flows.