Internet of Things (IoT), which will create a huge network of billions or trillions of “Things” communicating with one another, are facing many technical and application challenges. This paper introduces the status of IoT development in China, including policies, R&D plans, applications, and standardization. With China's perspective, this paper depicts such challenges on technologies, applications, and standardization, and also proposes an open and general IoT architecture consisting of three platforms to meet the architecture challenge. Finally, this paper discusses the opportunity and prospect of IoT.

A Vision of IoT: Applications, Challenges, and Opportunities With China Perspective

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

Recent emphasis on green communications has generated great interest in the investigations of energy harvesting communications and networking. Energy harvesting from ambient energy sources can potentially reduce the dependence on the supply of grid or battery energy, providing many attractive benefits to the environment and deployment. However, unlike the conventional stable energy, the intermittent and random nature of the renewable energy makes it challenging in the realization of energy harvesting transmission schemes. Extensive research studies have been carried out in recent years to address this inherent challenge from several aspects: energy sources and models, energy harvesting and usage protocols, energy scheduling and optimization, implementation of energy harvesting in cooperative, cognitive radio, multiuser and cellular networks, etc. However, there has not been a comprehensive survey to lay out the complete picture of recent advances and future directions. To fill such a gap, in this paper, we present an overview of the past and recent developments in these areas and highlight a number of possible future research avenues.

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Advances in Energy Harvesting Communications: Past, Present, and Future Challenges

This research study explores the Global Positioning System (GPS), its history, and the process of discovery needed to create the most accurate GPS possible, as well as the contemporary applications of GPS technology. Starting with the first satellite in space, GPS has been a work in progress. Originally pursued by the military for improvements to military tactics, GPS has become integrated into the everyday lives of millions of people around the world. How GPS determines location is a dichotomy, with simplistic theory and complex application. Many factors go into GPS to provide a consistent, accurate location. The orbital planes the satellites are placed in provide 24/7 coverage globally, the L-band frequencies used were chosen specifically for the characteristics they possess, and the multiple atomic clocks installed on each satellite provide incredible accuracy down to the nanoseconds, which is quintessential in GPS accuracy. The applications in GPS are far reaching and more applications are continually being discovered. With as far as GPS technology has progressed, there are still several factors that degrade the signal and are a challenge to overcome. Many of these challenges can be corrected efficiently, however, others, such as scintillation and total electron content variability in the ionosphere, create major hurdles to overcome. Luckily, there are many programs that aid in the correction process of these hurdles. The History of GPS According to R. Saunders’ article ​A Short History of GPS Development,​ The Global Positioning System (GPS) has a long history of trial and error and refinement and improvement. It’s purpose has shifted from being a military strategic asset to commonplace among the general public with its use in traveling, farming, and even banking. The beginning of GPS, introduced with a simple idea, can be traced back to the Soviet Union in the late 1950’s. In 1957, the Soviet Union made history with successfully launching the first satellite in space. To track the satellite Sputnik, Physicists and Scientists at John Hopkins University’s Applied Physics Laboratory listened to the beeps Sputnik’s signals produced. They noticed that the beeps had a Doppler Effect or Doppler Shift as the satellite passed by. Much like the sound a siren makes as a fire truck approaches, then as it passes, the sound of the siren seems different. The change in timing between the beeps let the scientist know Sputnik’s location. This led to the idea of reversing that process, to give a location on the Earth. Using radio frequencies to determine location in a two dimensional plane had been around since WWII, but using satellites would push this technology into the three dimensional realm. The United States Navy, Army, and Air Force all began developing their own GPS satellites in the 1960’s, but this was no small task. In the early 1960’s, the Navy launched its first Transit Satellite. The failure of this satellite, however, was due to

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The Global Positioning System

Internet of Things (IoT) is an emerging concept, which aims to connect billions of devices with each other. The IoT devices sense, collect, and transmit important information from their surroundings. This exchange of very large amount of information amongst billions of devices creates a massive energy need. Green IoT envisions the concept of reducing the energy consumption of IoT devices and making the environment safe. Inspired by achieving a sustainable environment for IoT, we first give the overview of green IoT and the challenges that are faced due to excessive usage of energy hungry IoT devices. We then discuss and evaluate the strategies that can be used to minimize the energy consumption in IoT, such as designing energy efficient datacenters, energy efficient transmission of data from sensors, and design of energy efficient policies. Moreover, we critically analyze the green IoT strategies and propose five principles that can be adopted to achieve green IoT. Finally, we consider a case study of very important aspect of IoT, i.e., smart phones and we provide an easy and concise view for improving the current practices to make the IoT greener for the world in 2020 and beyond.

Green IoT: An Investigation on Energy Saving Practices for 2020 and Beyond

In this paper, we present an overview of the time-reversal (TR) wireless paradigm for green Internet of Things (IoT) It is shown that the TR technique is a promising technique that focuses signal waves in both time and space domains The unique asymmetric architecture significantly reduces the cost of the terminal devices, the total number of which is expected to be very large for IoT The focusing effect of the TR technique can harvest the energy of all the multi-paths at the receiver, which improves the energy efficiency of the wireless transmission and thus the battery life of terminal devices in IoT Facilitated by the high-resolution spatial focusing, the TR division multiple access scheme leverages the uniqueness of the multi-path profiles in the rich-scattering environment and maps them into location-specific signatures, so that spatial multiplexing can be achieved for multiple users operating on the same spectrum In addition, the TR system can easily support heterogeneous terminal devices by providing various quality-of-service (QoS) options through adjusting the waveform and rate backoff factor Finally, the unique location-specific signature in TR system can provide additional physical-layer security and thus can enhance the privacy and security of customers in IoT All the advantages show that the TR technique is a promising paradigm for IoT

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Time-Reversal Wireless Paradigm for Green Internet of Things: An Overview

Indoor positioning systems (IPSs) are attracting more and more attention from the academia and industry recently. Among them, approaches based on WiFi techniques are more favorable since they are built upon the WiFi infrastructures available in most indoor spaces. However, due to the bandwidth limit in mainstream WiFi systems, the IPS leveraging WiFi can hardly achieve centimeter localization accuracy under strong nonline-of-sight (NLOS) conditions which is common for indoor environment. In this paper, to achieve the centimeter-level accuracy, we present a WiFi-based IPS that exploits the frequency diversity via frequency hopping. In the offline phase, the system collects channel frequency responses (CFRs) from multiple channels and from a number of locations-of-interest. Then, the CFRs are post-processed to mitigate the synchronization errors as well as interference from other WiFi networks. Then, using bandwidth concatenation, the CFRs from multiple channels are combined into location fingerprints which are stored into a local database. During the online phase, CFRs are formulated into the location fingerprint and is compared against the fingerprints in the database via the time-reversal resonating strength (TRRS). Finally, the IPS determines the location according to the TRRS. Extensive experiment results demonstrate a perfect centimeter-level accuracy in an office environment with strong NLOS using only one pair of single-antenna WiFi devices.

Achieving Centimeter-Accuracy Indoor Localization on WiFi Platforms: A Frequency Hopping Approach

A time-reversal positioning system includes a storage storing first data representing channel impulse responses derived from probe signals sent from a plurality of positions and second data representing coordinates of the positions. A data processor determines a position of a terminal device based on the stored channel impulse responses and a time-reversed signal determined based on a time-reversed version of a channel impulse response that is estimated based on a channel probing signal sent from the terminal device.

Wireless positioning systems

In this paper, we introduce TR-BREATH, a time-reversal (TR)-based contact-free breathing monitoring system. It is capable of breathing detection and multiperson breathing rate estimation within a short period of time using off-the-shelf WiFi devices. The proposed system exploits the channel state information (CSI) to capture the miniature variations in the environment caused by breathing. To magnify the CSI variations, TR-BREATH projects CSIs into the TR resonating strength (TRRS) feature space and analyzes the TRRS by the Root-MUSIC and affinity propagation algorithms. Extensive experiment results indoor demonstrate a perfect detection rate of breathing. With only 10 s of measurement, a mean accuracy of $99\%$ can be obtained for single-person breathing rate estimation under the non-line-of-sight (NLOS) scenario. Furthermore, it achieves a mean accuracy of $98.65\%$ in breathing rate estimation for a dozen people under the line-of-sight scenario and a mean accuracy of $98.07\%$ in breathing rate estimation of nine people under the NLOS scenario, both with 63 s of measurement. Moreover, TR-BREATH can estimate the number of people with an error around 1. We also demonstrate that TR-BREATH is robust against packet loss and motions. With the prevailing of WiFi, TR-BREATH can be applied for in-home and real-time breathing monitoring.

TR-BREATH: Time-Reversal Breathing Rate Estimation and Detection

Channel frequency response (CFR) is a fine-grained location-specific information in WiFi systems that can be utilized in indoor positioning systems (IPSs). However, CFR-based IPSs can hardly achieve an accuracy at the centimeter level due to the limited bandwidth in WiFi systems. To achieve such accuracy using WiFi devices, we propose an IPS that fully harnesses the spatial diversity in multiple-input–multiple-output WiFi systems, which leads to a much larger effective bandwidth than the bandwidth of a WiFi channel. The proposed IPS obtains CFRs associated with locations-of-interest on multiple antenna links during the training phase. In the positioning phase, the IPS captures instantaneous CFRs from a location to be estimated and compares it with the CFRs acquired in the training phase via the time-reversal resonating strength with residual synchronization errors compensated. Extensive experiment results in an office environment with a measurement resolution of 5 cm demonstrate that, with a single pair of WiFi devices and an effective bandwidth of 321 MHz, the proposed IPS achieves detection rates of 99.91% and 100% with false alarm rates of 1.81% and 1.65% under the line-of-sight (LOS) and non-LOS (NLOS) scenarios, respectively. Meanwhile, the proposed IPS is robust against environment dynamics. Moreover, experiment results with a measurement resolution of 0.5 cm demonstrate a localization accuracy of 1–2 cm in the NLOS scenario.

Hung-Quoc Lai

Papers

Time-Reversal Wireless Paradigm for Green Internet of Things: An Overview

Achieving Centimeter-Accuracy Indoor Localization on WiFi Platforms: A Frequency Hopping Approach

Wireless positioning systems

TR-BREATH: Time-Reversal Breathing Rate Estimation and Detection

Achieving Centimeter-Accuracy Indoor Localization on WiFi Platforms: A Multi-Antenna Approach