In a world where resources are scarce and urban areas consume the vast majority of these resources, it is vital to make cities greener and more sustainable. Advanced systems to improve and automate processes within a city will play a leading role in smart cities. From smart design of buildings, which capture rain water for later use, to intelligent control systems, which can monitor infrastructures autonomously, the possible improvements enabled by sensing technologies are immense. Ubiquitous sensing poses numerous challenges, which are of a technological or social nature. This paper presents an overview of the state of the art with regards to sensing in smart cities. Topics include sensing applications in smart cities, sensing platforms and technical challenges associated with these technologies. In an effort to provide a holistic view of how sensing technologies play a role in smart cities, a range of applications and technical challenges associated with these applications are discussed. As some of these applications and technologies belong to different disciplines, the material presented in this paper attempts to bridge these to provide a broad overview, which can be of help to researchers and developers in understanding how advanced sensing can play a role in smart cities.

The Role of Advanced Sensing in Smart Cities

Metasurfaces are two-dimensional arrangements of subwavelength scatterers that control the propagation of optical waves. Here, we show that cascaded metasurfaces, each performing a predefined mathematical transformation, provide a new optical design framework that enables new functionalities not yet demonstrated with single metasurfaces. Specifically, we demonstrate that retroreflection can be achieved with two vertically stacked planar metasurfaces, the first performing a spatial Fourier transform and its inverse, and the second imparting a spatially varying momentum to the Fourier transform of the incident light. Using this concept, we fabricate and test a planar monolithic near-infrared retroreflector composed of two layers of silicon nanoposts, which reflects light along its incident direction with a normal incidence efficiency of 78% and a large half-power field of view of 60°. The metasurface retroreflector demonstrates the potential of cascaded metasurfaces for implementing novel high-performance components, and enables low-power and low-weight passive optical transmitters.

Planar metasurface retroreflector

This paper reports a 128-channel neural recording integrated circuit (IC) with on-the-fly spike feature extraction and wireless telemetry. The chip consists of eight 16-channel front-end recording blocks, spike detection and feature extraction digital signal processor (DSP), ultra wideband (UWB) transmitter, and on-chip bias generators. Each recording channel has amplifiers with programmable gain and bandwidth to accommodate different types of biological signals. An analog-to-digital converter (ADC) shared by 16 amplifiers through time-multiplexing results in a balanced trade-off between the power consumption and chip area. A nonlinear energy operator (NEO) based spike detector is implemented for identifying spikes, which are further processed by a digital frequency-shaping filter. The computationally efficient spike detection and feature extraction algorithms attribute to an auspicious DSP implementation on-chip. UWB telemetry is designed to wirelessly transfer raw data from 128 recording channels at a data rate of 90 Mbit/s. The chip is realized in 0.35 mum complementary metal-oxide-semiconductor (CMOS) process with an area of 8.8 times 7.2 mm2 and consumes 6 mW by employing a sequential turn-on architecture that selectively powers off idle analog circuit blocks. The chip has been tested for electrical specifications and verified in an ex vivo biological environment.

A 128-Channel 6 mW Wireless Neural Recording IC With Spike Feature Extraction and UWB Transmitter

Wireless sensor networks are poised to become a very significant enabling technology in many sectors. Already a few very low power wireless sensor platforms have entered the marketplace. Almost all of these platforms are designed to run on batteries that have a very limited lifetime. In order for wireless sensor networks to become a ubiquitous part of our environment, alternative power sources must be employed. This paper reviews many potential power sources for wireless sensor nodes. Well established power sources, such as batteries, are reviewed along with emerging technologies and currently untapped sources. Power sources are classified as energy reservoirs, power distribution methods, or power scavenging methods, which enable wireless nodes to be completely self-sustaining. Several sources capable of providing power on the order of 100 μW/cm3 for very long lifetimes are feasible. It is the authors’ opinion that no single power source will suffice for all applications, and that the choice of a power source needs to be considered on an application-by-application basis.

Power Sources for Wireless Sensor Networks

Power sources for wireless sensor networks

A 16 mm/sup 3/ autonomous solar-powered sensor node with bidirectional optical communication for distributed sensor networks has been demonstrated. The device digitizes integrated sensor signals and transmits/receives data over a free-space optical link. The system consists of three die - a 0.25 /spl mu/m CMOS ASIC, a 2.6 mm/sup 2/ SOI solar cell array, and a micromachined four-quadrant corner-cube retroreflector (CCR), allowing it to be used in a one-to-many network configuration. The CMOS ASIC includes a photosensor integrated 3 MHz oscillator, 69 pJ/bit optical receiver and 31 pJ/sample ADC.

/pdf/an-autonomous-16-mm-sup-3-solar-powered-node-for-distributed-4xv603f9n0.pdf

An autonomous 16 mm/sup 3/ solar-powered node for distributed wireless sensor networks

A successive approximation ADC targeted for use in distributed sensor networks is presented. The individual nodes in these sensor networks are very energy constrained. Typical use of the individual nodes will include long periods of idle time in a low power standby mode followed by a period of activity that may include sampling of the sensors, computation, and communication. The ADC reported here consumes only 3.1 µW, resulting in 31 pJ/8-bit sample at 1V supply with a maximum sampling rate slightly over 100 kS/s. The standby power consumption at 1 V supply is 41 pW. The µ-power consumption makes this one of the lowest power ADCs ever reported.

https://www-bsac.eecs.berkeley.edu/publications/search/send_publication_pdf2client.php?pubID=1023737719

An ultra&#8211;low power ADC for distributed sensor networks

A low-energy successive approximation analog-to-digital converter (ADC) targeted for use in distributed sensor networks is presented The individual nodes combine sensing, computation, communications, and power into a tiny volume Energy is extremely limited, forcing the nodes to operate with very low duty cycles This paper describes the design and implementation of an ADC to meet the unique requirements of sensor networks The ADC reported here consumes 31 pJ/8-bit sample at 1-V supply and 100 kS/s, with a standby power consumption of 70 pW This energy consumption is one of the lowest ever reported

/pdf/an-ultralow-energy-adc-for-smart-dust-48szy51ijj.pdf

An ultralow-energy ADC for Smart Dust

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An autonomous 16 mm/sup 3/ solar-powered node for distributed wireless sensor networks

An ultra–low power ADC for distributed sensor networks

An ultralow-energy ADC for Smart Dust

M.D. Scott

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Papers

An autonomous 16 mm/sup 3/ solar-powered node for distributed wireless sensor networks

An ultra&#8211;low power ADC for distributed sensor networks

An ultralow-energy ADC for Smart Dust

An ultra–low power ADC for distributed sensor networks