The spatial features of emitted wireless signals are the basis of location distinction and determination for wireless indoor localization. Available in mainstream wireless signal measurements, the Received Signal Strength Indicator (RSSI) has been adopted in vast indoor localization systems. However, it suffers from dramatic performance degradation in complex situations due to multipath fading and temporal dynamics. Break-through techniques resort to finer-grained wireless channel measurement than RSSI. Different from RSSI, the PHY layer power feature, channel response, is able to discriminate multipath characteristics, and thus holds the potential for the convergence of accurate and pervasive indoor localization. Channel State Information (CSI, reflecting channel response in 802.11 a/g/n) has attracted many research efforts and some pioneer works have demonstrated submeter or even centimeter-level accuracy. In this article, we survey this new trend of channel response in localization. The differences between CSI and RSSI are highlighted with respect to network layering, time resolution, frequency resolution, stability, and accessibility. Furthermore, we investigate a large body of recent works and classify them overall into three categories according to how to use CSI. For each category, we emphasize the basic principles and address future directions of research in this new and largely open area.

From RSSI to CSI: Indoor Localization via Channel Response, A survey on indoor localization using PHY-layer information

As new fabrication and integration technologies reduce the cost and size of micro-sensors and wireless interfaces, it becomes feasible to deploy densely distributed wireless networks of sensors and actuators. These systems promise to revolutionize biological, earth, and environmental monitoring applications, providing data at granularities unrealizable by other means. In addition to the challenges of miniaturization, new system architectures and new network algorithms must be developed to transform the vast quantity of raw sensor data into a manageable stream of high-level data. To address this, we propose a tiered system architecture in which data collected at numerous, inexpensive sensor nodes is filtered by local processing on its way through to larger, more capable and more expensive nodes.We briefly describe Habitat monitoring as our motivating application and introduce initial system building blocks designed to support this application. The remainder of the paper presents details of our experimental platform.

Habitat monitoring: Application driver for wireless communications technology

A Comprehensive Survey of Network Function Virtualization

The Industrial Internet of Things (IIoT) is a crucial research field spawned by the Internet of Things (IoT). IIoT links all types of industrial equipment through the network; establishes data acquisition, exchange, and analysis systems; and optimizes processes and services, so as to reduce cost and enhance productivity. The introduction of edge computing in IIoT can significantly reduce the decision-making latency, save bandwidth resources, and to some extent, protect privacy. This paper outlines the research progress concerning edge computing in IIoT. First, the concepts of IIoT and edge computing are discussed, and subsequently, the research progress of edge computing is discussed and summarized in detail. Next, the future architecture from the perspective of edge computing in IIoT is proposed, and its technical progress in routing, task scheduling, data storage and analytics, security, and standardization is analyzed. Furthermore, we discuss the opportunities and challenges of edge computing in IIoT in terms of 5G-based edge communication, load balancing and data offloading, edge intelligence, as well as data sharing security. Finally, we introduce some typical application scenarios of edge computing in IIoT, such as prognostics and health management (PHM), smart grids, manufacturing coordination, intelligent connected vehicles (ICV), and smart logistics.

Edge Computing in Industrial Internet of Things: Architecture, Advances and Challenges

is a network or group of networks under a common administration and with common routing policies. BGP is used to exchange routing information for the Internet and is the protocol used between Internet service providers (ISP). Customer networks, such as universities and corporations, usually employ an Interior Gateway Protocol (IGP) such as RIP or OSPF for the exchange of routing information within their networks. Customers connect to ISPs, and ISPs use BGP to exchange customer and ISP routes. When BGP is used between autonomous systems (AS), the protocol is referred to as External BGP (EBGP). If a service provider is using BGP to exchange routes within an AS, then the protocol is referred to as Interior BGP (IBGP).

Border Gateway Protocol

The emergence of fog computing has brought unprecedented opportunities to the Internet-of-Things (IoT) field, and it is now feasible to incorporate deep learning at the edge of the IoT network to provide a wide range of highly tailored services. In this article, we present a fog-based democratically collaborative learning scheme in which fog nodes collaborate on the model training process even without the support of the cloud, contributing to the advances of IoT in terms of realizing a more intelligent edge. To achieve that, we design a voting strategy so that a fog node could be elected as the coordinator node based on both distance and computational power metrics to coordinate the training process. Also, a collaborative learning algorithm is proposed to generalize the training of different deep learning models in the fog-enabled IoT environment. We then implement two popular use cases, including a user trajectory prediction and a distributed image recognition, to demonstrate the feasibility, practicality, and effectiveness of the scheme. More importantly, the experiments on both use cases are conducted through a real world, in-door fog deployment. The result shows that the scheme can utilize fog to obtain a well-performing deep learning model in the cloudless IoT environment while mitigating the data locality issue for each fog node.

Achieving Democracy in Edge Intelligence: A Fog-Based Collaborative Learning Scheme

Information Centric Networking (ICN) is a new paradigm where the
network provides users with named content, instead of communication
channels between hosts This document outlines some research
directions for Information Centric Networking with respect to applying
ICN approaches for coping with natural or human-generated, large-scale
disasters

Using ICN in disaster scenarios

-Information Centric Networking (ICN) is a new paradigm where the network provides users with named content, instead of communication channels between hosts. This document outlines some research directions for Information Centric Networking with respect to applying ICN approaches for coping with natural or human- generated, large-scale disasters. We argue that ICN approaches have many benefits for enabling (or continuing) communication after a disaster has impaired a communication network. In this context, we present key research challenges and an overview of our ongoing research activities for applying Information Centric Networking to address these challenges.

The Benefit of Information Centric Networking for Enabling Communications in Disaster Scenarios

We tightly integrate caching and congestion control into a unified approach to enhance user-centric performance in information-centric networking. To do that, we take advantage of the available congestion price fed back by congestion control to guide caching decision at each content router. The point is to let caching work in a longer time scale to alleviate congestion retrieving expensive content in terms of congestion cost. As a consequence, not only does caching increase throughput for users who request the cached content, but also it contributes to a large extent towards reducing network congestion, which benefits all other users who share the same congested parts of the network. Our use of existing congestion feedback signals eliminates the need for out-of-band congestion measurement such as bandwidth share computation which in reality might not reflect the true congestion state of the delivery paths. The proposed method yields positive performance gain in simulations compared with existing cache schemes.

Congestion price for cache management in information-centric networking

Forthcoming 5G networks raise an important research issue of seamless mobility management of cellular and non-cellular networks. Adopting Information Centric Networking (ICN) architecture as common mobility management is promising because existing mobility management mechanisms are complicated and incur large signaling overhead. This paper designs a routing-based mobility architecture to provide seamless mobility for the both networks. The architecture consists of routing during longer duration and anchor-less forwarding during shorter duration. A main contribution of the paper is that the architecture is designed by carefully considering how to port it to cellular networks with as few modifications to them as possible.

Atsushi Tagami

Papers

Achieving Democracy in Edge Intelligence: A Fog-Based Collaborative Learning Scheme

Using ICN in disaster scenarios

The Benefit of Information Centric Networking for Enabling Communications in Disaster Scenarios

Congestion price for cache management in information-centric networking

Proposal on routing-based mobility architecture for ICN-based cellular networks