Network Edge Devices Research Articles

Photonic tensor cores for machine learning

With an ongoing trend in computing hardware toward increased heterogeneity, domain-specific coprocessors are emerging as alternatives to centralized paradigms. The tensor core unit has been shown to outperform graphic processing units by almost 3 orders of magnitude, enabled by a stronger signal and greater energy efficiency. In this context, photons bear several synergistic physical properties while phase-change materials allow for local nonvolatile mnemonic functionality in these emerging distributed non-von Neumann architectures. While several photonic neural network designs have been explored, a photonic tensor core to perform tensor operations is yet to be implemented. In this manuscript, we introduce an integrated photonics-based tensor core unit by strategically utilizing (i) photonic parallelism via wavelength division multiplexing, (ii) high 2 peta-operations-per-second throughputs enabled by tens of picosecond-short delays from optoelectronics and compact photonic integrated circuitry, and (iii) near-zero static power-consuming novel photonic multi-state memories based on phase-change materials featuring vanishing losses in the amorphous state. Combining these physical synergies of material, function, and system, we show, supported by numerical simulations, that the performance of this 4-bit photonic tensor core unit can be 1 order of magnitude higher for electrical data. The full potential of this photonic tensor processor is delivered for optical data being processed, where we find a 2–3 orders higher performance (operations per joule), as compared to an electrical tensor core unit, while featuring similar chip areas. This work shows that photonic specialized processors have the potential to augment electronic systems and may perform exceptionally well in network-edge devices in the looming 5G networks and beyond.

An Energy Efficient e-Healthcare Framework Supported by Novel EO-μGA (Extremal Optimization Tuned Micro-Genetic Algorithm)

The edge/fog computing has the potential to gear up the healthcare industry by providing better and faster health services to the patients. In healthcare systems where every second is crucial, the edge computing can be helpful to reduce the time between data capture and analytics in a powerful manner. In edge computing, the network edge devices are configured in such a manner that they can handle critical analysis and make necessary decisions instead of sending the captured health data directly to the cloud. However, lifetime of the edge network is a critical factor and thus an energy efficient network architecture has to be designed to achieve the above mentioned goal. In this regard, this research presents a new extremal optimization tuned micro genetic algorithm (EO-μGA) based clustering technique for the sake of efficient routing and prolonging network lifetime by saving the battery power of network edge devices. Moreover, a novel fitness function with a set of relevant criteria of edge devices such as energy factor, average intra-cluster distance, average distance to cluster leader over data analytics center, average sleeping time, and computational load has been considered for the selection of the cluster leader which will be responsible for managing intra-cluster and inter-cluster data communication. The simulation results show that the proposed EO-μGA based clustering model offers a higher network lifetime and a least amount of transmission energy consumption per node as compared to various state of the art optimization algorithms.

Co-Design of Dual Security Control and Communication for Nonlinear CPS Under DoS Attack

The less conservative co-design problem of dual security control and communication for CPS was studied for a class of nonlinear CPS with an actuator fault and DoS attack based on the DETCS. First, combining with the edge computing thought, data filtering and partial computing tasks were assigned to the network edge devices. The DoS attack with limited energy is converted into a special time delay; thus, the discrete event trigger condition, the actuator fault estimation and regulation, and the robustness for DoS attack can be considered uniformly. A dual security control framework of non-uniformly sampled data system was proposed under the DETCS for CPS, and the T-S fuzzy model of closed-loop nonlinear CPS with the co-existence of fault and attack was established. Second, with the help of time-delay system theory, a robust observer for state and fault estimation and the co-design method of dual security control and communication with the actuator fault and DoS attack were obtained by constructing an appropriate Lyapunov-Krasovskii function and using affine Bessel-Legendre inequality. Finally, the validity and feasibility of the theoretical results were verified by a classical quadruple-tank system.

SDN-Managed Provisioning of Named Computing Services in Edge Infrastructures

Pushed by the challenging demands of fifth generation (5G) use cases and the recent advancements in virtualization technologies, edge network devices are rapidly evolving from simple forwarders to softwarized infrastructures augmented with computing and storage capabilities. Such a trend blurs the distinction between IT and telco domains and paves the way for the integrated management of network and computing resources. In this paper, we focus on the interplay of the Software Defined Networking (SDN) and Named Data Networking (NDN) paradigms as key drivers for the orchestration of computing services in softwarized edge infrastructures. We propose a new framework that brings out the best of the SDN centralized intelligence to take “smart” decisions and inject rules for service allocation (e.g., retrieve input data, execute a function over them), and the best of the adaptive NDN forwarding plane and its native in-network caching to request and deliver services by name. Within the framework, we devise a service allocation strategy that aims at selecting as an executor, among the potential candidates, the one which is able to guarantee the shortest service provisioning delay for each service request, while accounting for the network topology, the links status, and the available computing resources in the edge nodes. Performance evaluations testify to the superiority of our proposal against benchmarking solutions from the literature under different operation settings.

Design and Implementation of LiFi Communication system

Visible Light Communication technology is considered complimentary to Radio Frequency (RF) technology. More specifically, Light Fidelity (LiFi) based Visible Light Communication system appears to have enhanced wireless capability to realize IoT, 5G Cellular System conforming to requirements such as high data rate, low latency, connectivity etc. Recent developments in solid-state lighting technology have significantly influenced LiFi system Architecture. In view of these developments, we propose to carry out a study on feasibility of deploying LiFi technology along with the existing RF infrastructure. We emphasize to look for designing full-duplex LiFi Indoor communication module, utilizing multiple White Light Emitting Diode (WLED) based lighting equipment and integrate the module with network edge devices such as PC, smart phones for establishing efficient communication systems.

Adaptive resource allocation based on the billing granularity in edge-cloud architecture

With the rapid development and popularization of the Internet of Things technology, the number of network edge devices has increased rapidly, and the Internet of Things perception layer has generated massive data. Cloud-Edge collaboration will be used more and more to solve this problem. When the business reaches its peak, cloud computing capabilities cannot meet business needs. The cloud service provider can apply for resources to meet the requirements of the computing resources. After the peak period of the business, if there are idle computing resources, the resources can be released to the cloud service provider, which can reduce the service cost and save the computing resources. The traditional flexible resource management is mainly used in a single cloud environment, and the prediction of the resource demand is insufficient. The factor of the cloud resource billing granularity is neglected, resulting in high cloud lease cost. Therefore, an adaptive resource allocation algorithm and a data migration algorithm are proposed. The prediction algorithm provides the basis for the adaptive resource allocation of the edge cloud cluster. The adaptive resource allocation algorithm determines the resource allocation scheme of the edge cloud cluster with the lowest service cost. The data migration algorithm guarantees the reliability of data and achieves cluster load balancing. A large number of experimental results show that our newly proposed algorithm can greatly improve system performance in terms of better cost control, higher data integrity and load balancing.

A Cognitive-Inspired Event-Based Control for Power-Aware Human Mobility Analysis in IoT Devices.

Mobile Edge Computing (MEC) relates to the deployment of decision-making processes at the network edge or mobile devices rather than in a centralized network entity like the cloud. This paradigm shift is acknowledged as one key pillar to enable autonomous operation and self-awareness in mobile devices in IoT. Under this paradigm, we focus on mobility-based services (MBSs), where mobile devices are expected to perform energy-efficient GPS data acquisition while also providing location accuracy. We rely on a fully on-device Cognitive Dynamic Systems (CDS) platform to propose and evaluate a cognitive controller aimed at both tackling the presence of uncertainties and exploiting the mobility information learned by such CDS toward energy-efficient and accurate location tracking via mobility-aware sampling policies. We performed a set of experiments and validated that the proposed control strategy outperformed similar approaches in terms of energy savings and spatio-temporal accuracy in LBS and MBS for smartphone devices.

Secure authentication and load balancing of distributed edge datacenters

Edge computing is an emerging research area to incorporate cloud computing into edge network devices. An Edge datacenter, also referred to as EDC, processes data streams and user requests in real-time and is therefore used to decrease the latency and congestion in the network. EDC is usually setup as a distributed system and is accordingly placed between the cloud datacenter and the data source. These EDCs work as an intermediate layer in the fog hierarchy between IoT and Cloud datacenter. EDC’s are aided by load balancers, responsible for distributing the workload amongst multiple EDC, in order to optimize resource utilization and response time. The load balancers make sure that the workload is equally divided amongst the available EDCs to avoid over loading of some EDCs while other remain idle as this directly impacts the user response and real-time event detection. Given the fact that EDCs are deployed in remote environments, the need for secure authentication is of major importance. In this paper we propose a novel load balancing technique that enables EDC authentication as well as identification of idle EDCs for better load balancing. The proposed load balancing technique is also compared with existing approaches and proves to be more efficient in locating EDC’s with less workload. In addition to the improved efficiency, the proposed scheme also strengthens the security of the network by incorporating destination EDC authentication.

Architecting the Web of Things for the fog computing era

Fog computing paradigm is emerging after a decade's dominance of cloud-based system design and architecture. Now, instead of centralising the computation and coordination to remote services, these are deployed and distributed to all over physical surroundings and network nodes, including cloud services, smart gateways, and network edge devices. At the moment, the majority of the Internet of things (IoT) systems and software has built on top of open Web-based technologies. The authors assume that with the ever-growing number and heterogeneity of connected devices, it becomes ever-more crucial to have open standards that support interoperability and enable interactions. They review the current technological space for architecting Web technology-based IoT software in the coming era of fog computing. They focus on fundamental research challenges and discuss the emerging issues.

An Edge Cloud-Assisted CPSS Framework for Smart City

The smart city uses knowledge or rules mined from Internet of Things sensor data to promote the development of the city. This brings new opportunities and challenges, such as low delay and real-time services. This article proposes an edge cloud-assisted CPSS (cyber-physical-social system) framework for smart cities. This framework migrates some tasks from the cloud center to network edge devices and puts the services and resources closer to users, so as to provide lower-latency, real-time, more effective, and proactive services for residents and policymakers.

An Efficient Identity‐Based Proxy Blind Signature for Semioffline Services

Fog computing extends the cloud computing to the network edge and allows deploying a new type of semioffline services, which can provide real‐time transactions between two entities, while the central cloud server is offline and network edge devices are online. For an e‐payment system and e‐voting with such feature, proxy blind signature is a cornerstone to protect users’ privacy. However, the signature based on number theorem, such as hard mathematical problems on factoring problem, discrete logarithm problem, and bilinear pairings, cannot defeat quantum computers attack. Meanwhile, these schemes need to depend on complex public key infrastructure. Thus, we construct an identity‐based proxy blind signature scheme based on number theorem research unit lattice, which can defeat quantum computers attack and does not need to depend on public key infrastructure. The security of the proposed scheme is dependent on Ring‐Small Integer Solution problem over number theorem research unit lattice. The proposed scheme meets the properties of blind signature and proxy signature. Then we compare the proposed scheme with other existing proxy blind signature schemes; the result shows that the proposed scheme outperforms ZM scheme except in proxy signer’s signature size and can be more secure than TA scheme and MMHP scheme.

Micro Operator Design Pattern in 5G SDN/NFV Network

The trend of 5G mobile networks is increasing with the number of users and the transmission rate. Many operators are turning to small cell and indoor coverage of telecom network service. With the emerging Software Defined Networking and Network Function Virtualization technologies, Internet Service Provider is able to deploy their networks more flexibly and dynamically. In addition to the change of the wireless mobile network deployment model, it also drives the development trend of the Micro Operator (μO). Telecom operators can provide regional network services through public buildings, shopping malls, or industrial sites. In addition, localized network services are provided and bandwidth consumption is reduced. The distributed architecture of μO tackles computing requirements for applications, data, and services from cloud data center to edge network devices or to the micro data center of μO. The service model of μO is capable of reducing network latency in response to the low‐latency applications for future 5G edge computing environment. This paper addresses the design pattern of 5G micro operator and proposes a Decision Tree Based Flow Redirection (DTBFR) mechanism to redirect the traffic flows to neighbor service nodes. The DTBFR mechanism allows different μOs to share network resources and speed up the development of edge computing in the future.

Fog Computing Based Face Identification and Resolution Scheme in Internet of Things

The identification and resolution technology are the prerequisite for realizing identity consistency of physical–cyber space mapping in the Internet of Things (IoT). Face, as a distinctive noncoded and unstructured identifier, has especial advantages in identification applications. With the increase of face identification based applications, the requirements for computation, communication, and storage capability are becoming higher and higher. To solve this problem, we propose a fog computing based face identification and resolution scheme. Face identifier is first generated by the identification system model to identify an individual. Then, a fog computing based resolution framework is proposed to efficiently resolve the individual's identity. Some computing overhead is offloaded from a cloud to network edge devices in order to improve processing efficiency and reduce network transmission. Finally, a prototype system based on local binary patterns (LBP) identifier is implemented to evaluate the scheme. Experimental results show that this scheme can effectively save bandwidth and improve efficiency of face identification and resolution.

AirNet: An Edge‐Fabric abstraction model to manage software‐defined networks

SummarySoftware‐defined networking (SDN) is one of the most significant initiatives to make current networks easier to configure, test, debug, and evolve. Within an SDN ecosystem, the Northbound interface is used by operators to define policies and to program the control plane, it thus represents a major challenge. Ideally, this northbound interface should allow administrators to describe, as simply as possible, network services and their interactions, rather than specifying how and on what physical device they need to be deployed. In this article, we propose a new network control language, called AirNet, which is built on top of an abstraction model whose main feature is to provide a clear separation between edge and core network devices. Thus, logical boundaries between different types of policies will exist (control, data, and transport services), ensuring modularity and reusability of the control program. An implementation of the AirNet language has also been done, which includes in particular a hypervisor that achieves the composition of the control policies on the virtual network and their mapping on the physical infrastructure. Finally, several experiments showing promising results have been conducted on different use cases.

Design and performance evaluation of an adaptive FPGA for network applications

This paper presents the design, implementation and performance evaluation of a coarse-grain dynamically reconfigurable FPGA platform for multi-service edge and access network devices. The platform consists of two MicroBlaze RISC processors and a number of hardware co-processors used for the processing of packet payloads (Data Encryption Standard (DES) and Lempel–Ziv Compression). The co-processors can be connected either directly to the processors or using a shared bus. The functionality of the co-processors is dynamically reconfigured to meet the requirements of the network workload. The system has been implemented on the Xilinx Virtex II Pro platform and the network traces from real passive measurements have been used for performance evaluation. The use of dynamically reconfigurable co-processors for network applications shows that the performance speedup versus a static version varies from 12% to 35% in the best case and from 10% to 15% on average, depending on the network traffic fluctuation.

Guest Editors' Introduction: Wireless Grids--Distributed Resource Sharing by Mobile, Nomadic, and Fixed Devices

Wireless grids, a new type of resource-sharing network, connect sensors, mobile phones, and other edge devices with each other and with wired grids. Ad hoc distributed resource sharing allows these devices to offer new resources and locations of use for grid computing. This article places wireless grids in context, explains their basic requirements, and provides an example implementation that uses a wireless grid for distributed audio recording. Finally, it introduces articles in this special issue on wireless grid architectures and applications.

Evolution on the network edge: intelligent devices

A network's edge is arguably one of its most important areas, because this is where it delivers services to network users-or, from the Internet service provider perspective, to subscribers. Devices at the network's edge classify, prioritize, and mark packets for the rest of the network to understand, and ultimately allow them into the network. Given modern networks' complex internetworking, it is not surprising that developing and maintaining edge devices are tasks that head the agendas of most of today's network equipment manufacturers, carriers, service providers, and network administrators. This article examines key elements of current network edge devices and the network topologies that work best with them. It also discusses Multiprotocol Label Switching (MPLS), a standardized solution that combines the performance and virtual circuit capabilities of data-link-layer switching with the proven scalability of network layer routing used by today's edge devices.