The IEEE 802.11 Wireless LAN standards allow multiple non-overlapping frequency channels to be used simultaneously to increase the aggregate bandwidth available to end-users. Such bandwidth aggregation capability is routinely used in infrastructure mode operation, where the traffic to and from wireless nodes is distributed among multiple interfaces of an access point or among multiple access points to balance the traffic load. However, bandwidth aggregation is rarely used in the context of multi-hop 802.11-based LANs that operate in the ad hoc mode. Most past research efforts that attempt to exploit multiple radio channels require modifications to the MAC protocol and therefore do not work with commodity 802.11 interface hardware. In this paper, we propose and evaluate one of the first multi-channel multi-hop wireless ad-hoc network architectures that can be built using standard 802.11 hardware by equipping each node with multiple network interface cards (NICs) operating on different channels. We focus our attention on wireless mesh networks that serve as the backbone for relaying end-user traffic from wireless access points to the wired network. The idea of exploiting multiple channels is particularly appealing in wireless mesh networks because of their high capacity requirements to support backbone traffic. To reap the full performance potential of this architecture, we develop a set of centralized channel assignment, bandwidth allocation, and routing algorithms for multi-channel wireless mesh networks. A detailed performance evaluation shows that with intelligent channel and bandwidth assignment, equipping every wireless mesh network node with just 2 NICs operating on different channels can increase the total network goodput by a factor of up to 8 compared with the conventional single-channel ad hoc network architecture.

Centralized channel assignment and routing algorithms for multi-channel wireless mesh networks

Radio access technologies for cellular mobile communications are typically characterized by multiple access schemes, e.g., frequency division multiple access (FDMA), time division multiple access (TDMA), code division multiple access (CDMA), and OFDMA. In the 4th generation (4G) mobile communication systems such as Long-Term Evolution (LTE) (Au et al., Uplink contention based SCMA for 5G radio access. Globecom Workshops (GC Wkshps), 2014. doi:10.1109/GLOCOMW.2014.7063547) and LTE-Advanced (Baracca et al., IEEE Trans. Commun., 2011. doi:10.1109/TCOMM.2011.121410.090252; Barry et al., Digital Communication, Kluwer, Dordrecht, 2004), standardized by the 3rd Generation Partnership Project (3GPP), orthogonal multiple access based on OFDMA or single carrier (SC)-FDMA is adopted. Orthogonal multiple access was a reasonable choice for achieving good system-level throughput performance with simple single-user detection. However, considering the trend in 5G, achieving significant gains in capacity and system throughput performance is a high priority requirement in view of the recent exponential increase in the volume of mobile traffic. In addition the proposed system should be able to support enhanced delay-sensitive high-volume services such as video streaming and cloud computing. Another high-level target of 5G is reduced cost, higher energy efficiency and robustness against emergencies.

Non-Orthogonal Multiple Access (NOMA) for cellular future radio access

The Internet of Things (IoT) is being hailed as the next wave revolutionizing our society, and smart homes, enterprises, and cities are increasingly being equipped with a plethora of IoT devices. Yet, operators of such smart environments may not even be fully aware of their IoT assets, let alone whether each IoT device is functioning properly safe from cyber-attacks. In this paper, we address this challenge by developing a robust framework for IoT device classification using traffic characteristics obtained at the network level. Our contributions are fourfold. First, we instrument a smart environment with 28 different IoT devices spanning cameras, lights, plugs, motion sensors, appliances, and health-monitors. We collect and synthesize traffic traces from this infrastructure for a period of six months, a subset of which we release as open data for the community to use. Second, we present insights into the underlying network traffic characteristics using statistical attributes such as activity cycles, port numbers, signalling patterns, and cipher suites. Third, we develop a multi-stage machine learning based classification algorithm and demonstrate its ability to identify specific IoT devices with over 99 percent accuracy based on their network activity. Finally, we discuss the trade-offs between cost, speed, and performance involved in deploying the classification framework in real-time. Our study paves the way for operators of smart environments to monitor their IoT assets for presence, functionality, and cyber-security without requiring any specialized devices or protocols.

Classifying IoT Devices in Smart Environments Using Network Traffic Characteristics

Cellular systems evolved from a dedicated mobile communication system to an almost omnipresent system with unlimited coverage anywhere and anytime for any device. The growing ubiquity of the network stirred expectations to determine the location of the mobile devices themselves. Since the beginning of standardization, each cellular mobile radio generation has been designed for communication services, and satellite navigation systems, such as Global Positioning System (GPS), have provided precise localization as an add-on service to the mobile terminal. Self-contained localization services relying on the mobile network elements have offered only rough position estimates. Moreover, satellite-based technologies suffer a severe degradation of their localization performance in indoors and urban areas. Therefore, only in subsequent cellular standard releases, more accurate cellular-based location methods have been considered to accommodate more challenging localization services. This survey provides an overview of the evolution of the various localization methods that were standardized from the first to the fourth generation of cellular mobile radio, and looks over what can be expected with the new radio and network aspects for the upcoming generation of fifth generation.

Survey of Cellular Mobile Radio Localization Methods: From 1G to 5G

Many network applications, eg, industrial control, demand ultra-low latency (ULL) However, traditional packet networks can only reduce the end-to-end latencies to the order of tens of milliseconds The IEEE 8021 time sensitive networking (TSN) standard and related research studies have sought to provide link layer support for ULL networking, while the emerging IETF deterministic networking (DetNet) standards seek to provide the complementary network layer ULL support This paper provides an up-to-date comprehensive survey of the IEEE TSN and IETF DetNet standards and the related research studies The survey of these standards and research studies is organized according to the main categories of flow concept, flow synchronization, flow management, flow control, and flow integrity ULL networking mechanisms play a critical role in the emerging fifth generation (5G) network access chain from wireless devices via access, backhaul, and core networks We survey the studies that specifically target the support of ULL in 5G networks, with the main categories of fronthaul, backhaul, and network management Throughout, we identify the pitfalls and limitations of the existing standards and research studies This survey can thus serve as a basis for the development of standards enhancements and future ULL research studies that address the identified pitfalls and limitations

Ultra-Low Latency (ULL) Networks: The IEEE TSN and IETF DetNet Standards and Related 5G ULL Research

Many industrial automation systems rely on time-synchronized (and timely) communication among sensing, computing, and actuating devices. Advances in Ethernet enabled by time-sensitive networking (TSN) standards, being developed by the IEEE 802.1 TSN Task Group, are significantly improving time synchronization as well as worst case latencies. Next-generation industrial systems are expected to leverage advances in distributed time coordinated computing and wireless communications to enable greater levels of automation, efficiency, and flexibility. Significant progress has been made in extending accurate time synchronization over the air (e.g., 802.1AS profile for IEEE 802.11/Wi-Fi). Given the inherently unreliable, varying capacity and latency prone characteristics associated with wireless communications, proving the feasibility of worst case latency performance over the wireless medium is a major research challenge. More specifically, understanding what levels of capacity, reliability, and latency could be guaranteed over wireless links with high reliability are important research questions to guide the development of new radios, protocols, and time coordinated applications. This paper provides an overview of the potential applications, requirements, and unique research challenges to extend TSN capabilities over wireless. The paper also describes advances in wireless technologies (e.g., next-generation 802.11 and 5G standards) toward achieving reliable and accurate time distribution and timeliness capabilities. It also provides a classification of wireless applications and a reference architecture for enabling the integration of wired and wireless TSN capabilities in future industrial automation systems.

Extending Accurate Time Distribution and Timeliness Capabilities Over the Air to Enable Future Wireless Industrial Automation Systems

The IEEE 802.1 Audio/Video Bridging Task Group has created a series of IEEE standards that specify methods used to provide the appropriate quality of service (QoS) for audio/video (A/V) streams in a heterogeneous network. This paper describes the requirements for such a network and summarizes the methods described in these standards and how they are used in some example higher layer protocols.

Heterogeneous Networks for Audio and Video: Using IEEE 802.1 Audio Video Bridging

A new economy built on the massive growth of endpoints on the internet will require precise and verifiable timing in ways that current systems do not support. Applications, computers, and communica ...

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Time-Aware Applications, Computers, and Communication Systems (TAACCS)

Real-time cooperation among agents requires sharing of certain temporal information. That information might take the form of "take action X right now!" or it might better be achieved in some cases by establishing an a priori agreement by which each action should be taken and when, together with a shared understanding of what time it is. Distributed applications increasingly rely on this latter synchronized time approach rather than explicit "right now!" communication for cooperative sense and control tasks. Such a capability is foundational to the field of cyber physical systems (CPS) where computation and communication interact with the physical world in real time. While multiple historical approaches exist for time-synchronized (and timely) communication among sensing, computing, and actuating devices, the addition of robust temporal performance characteristics to standard Ethernet through the work of the Time Sensitive Networking (TSN) Task Group of IEEE 802.1 is bringing together the benefits of standard IT networking with excellent worst case latency and synchronization performance that was previously available only to these specialized and sometimes proprietary networks. Such advances will be important in next-generation applications as diverse as CPS/real-time distributed control, smart factories, sensor fusion, autonomous vehicles, and even cloud applications [1]. As we show, precision network time synchronization can dramatically enhance the operation of these applications and is a requirement of certain new network latency enhancements as well.

Distributing Deterministic, Accurate Time for Tightly Coordinated Network and Software Applications: IEEE 802.1AS, the TSN profile of PTP

A bridge mechanism enables efficient communication between first and second devices, e.g., processors, via an interface, such as PCI express.

Kevin Stanton

Papers

Extending Accurate Time Distribution and Timeliness Capabilities Over the Air to Enable Future Wireless Industrial Automation Systems

Heterogeneous Networks for Audio and Video: Using IEEE 802.1 Audio Video Bridging

Time-Aware Applications, Computers, and Communication Systems (TAACCS)

Distributing Deterministic, Accurate Time for Tightly Coordinated Network and Software Applications: IEEE 802.1AS, the TSN profile of PTP

Method and apparatus for memory interface