Sensor-enabled nodes support the IoT for smart buildings and smart transport

December 10, 2014 OpenSystems Media

The global Internet of Things (IoT) phenomenon is opening unparalleled opportunities for sensor technology. A recent presentation at the IDTechEx Conference claimed that the bill of materials (BOM) for an IoT node is split evenly between sensors and transceiver (at 45 percent each), with a small part left for the microprocessor (5 percent) and other functions (5 percent). Internet of Things numbers can make heads spin: Cisco IBSG predicts 25 billion IoT devices by 2015 and 50 billion by 2020; Gartner Research values the aggregate number of IoT sensors to reach $10.1 billion by 2020 from $1.3 billion in 2014, with a compound annual growth rate (CAGR) of 41.7 percent. IDTechEx has estimated the market value for IoT IP-addressed sensing nodes to grow from less than $1 billion in 2015 to greater than $48 billion by 2025 (Figure 1).

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Figure 1: IDTechEx is forecasting the value of IP-addressed sensor nodes to increase from $0.68 billion in 2015 to $48 billion in 2025, constituting a 47 percent compound annual growth rate (CAGR).(Click graphic to zoom by 1.7x)

Sensing capabilities are significant in all fields, but smart buildings and smart transportation, referred to as “built infrastructure,” will represent markets of primary importance. In both fields, there’s a need for many different devices that can span from nodes providing basic monitoring to active nodes with a high computational capability. The rationales for the adoption of IoT in these fields are several, from social to environmental to economical. Energy conservation, environmental control, traffic optimization, infrastructure monitoring, accident prevention, and disaster containment are just some of the fields that can benefit from interconnected sensing devices.

Besides a thorough knowledge of sensing capability, understanding the different communication characteristics of IoT nodes is of primary importance. Tradeoffs must be made when developing a solution, and a proper architectural study will enable the minimization of costs, maximizing system performances at the same time.

Characteristic of IoT nodes

The major characteristics of IoT nodes (as shown in Figure 2) include a sensor front-end, low-power signal conditioning electronics (typically an ASIC including a microcontroller with embedded algorithms), power supply/storage/management, and back-end, low-power communications, usually wireless and enclosed in a package (see microelectromechanical systems-based (MEMS-Based) Systems Solutions for more information). The technological challenge for the implementation of such devices is limited to the integration and packaging of different existing components, as well as the availability of energy harvesters to make the node self sufficient.

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Figure 2: MEMS-based Systems Solutions (MBSS) integrate critical elements of the Internet of Things (IoT) including sensing, computing, and communications to provide valuable measurements capability for smart building/smart transport monitoring and control applications.(Click graphic to zoom by 1.7x)

In the IoT domain, networks can be classified as unconstrained (NTU) – characterized by high-speed communication links, offering transfer rates in the Megabit per second (Mbps) range – and constrained (NTC) – characterized by relatively low transfer rates, typically smaller than 1 Mbps.

The network taxonomy is also dependent on the type of terminal used. Unconstrained terminals have high computational power and a
theoretically unlimited energy reserve, allowing them to implement complex tasks such as strong cryptography, HTTP traffic, and high transmission rates typical of NTU networks.

Constrained terminals show important limitations with respect to unconstrained terminals: a reduced transmission capability, smaller than 1 Mbps; a limited energy reserve; a limited data storage capability (typical values are 10 kbytes for RAM and 100 kbytes for ROM); and a limited computational power (less than 100 MHz). Finally, tag-type terminals show extreme limitations in computing power, memory storage, and energy storage.

Smart buildings

Smart buildings provide a quality and comfortable environment, and increased safety and security while operating in an energy-efficient fashion. A typical example is the Nest “learning” thermostat (Figure 3). It consists of seven non-MEMS sensors measuring not only temperature and humidity but also presence, allowing temperature control based on occupant usage history.

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Figure 3: The Nest thermostat uses seven different sensors, including temperature, humidity, and presence to determine the conditions of a home and its occupants resulting in “self programming” room temperatures to maximize comfort and save energy. It uses an embedded microcontroller with proprietary algorithms and communications to the cloud for remote programming.(Click graphic to zoom by 1.7x)

The Bob and Betty Beyster Computer Science Building at the University of Michigan was recently instrumented by Professor Jerry Lynch of the Center for Wireless Integrated MicroSensing and Systems (WIMSS) with 15 Martlet wireless sensor nodes consisting of 45 channels of temperature, humidity, and CO2 sensors. The objective of the project, states Professor Lynch, is to “deploy a sensor network and model the environmental conditions as they relate to heating, ventilation, and air conditioning (HVAC) performance. The next steps include monitoring occupant’s behavior/presence and connecting the network directly to the control system of the HVAC system to achieve optimum performance versus cost.”

Smart transport

Major drivers for IoT adoption in transport are safety, convenience, fuel efficiency, and environmental pollution. Libelium has developed a system of sensor platforms measuring the presence of parked vehicles in Santander, Spain. This 400-node monitoring system includes magnetic sensors, signal conditioning electronics, a 7- to 10-year battery life, and a radio in a 12 cm diameter package (Figure 4). Data is transmitted to an access point on a nearby lamppost and relayed to the parking department headquarters where it gets analyzed and then sent to displays on the street. It can also be accessed by Internet-connected devices to direct vehicles to the appropriate available parking spots. Additionally, another 600-node system is mounted on lampposts and uses CO2 sensors to measure air quality.

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Figure 4: Housed in a 12 cm package and shown embedded in the street pavement, the vehicle-presence parking node consists of a magnetic sensor, signal conditioning electronics, a communications IC, and 7 to 10 years of battery life.(Click graphic to zoom by 1.7x)

Sensys Networks has developed a similar magnetic sensor-based system for use in traffic intersections. The system consists of a three-axis magnetometer, signal conditioning electronics with embedded software, and a radio in a 3″ x 3″ x 3″ package that gets embedded at traffic roadways and intersections (Figure 5). This is clearly a lower cost solution to today’s large, 6′ diameter magnetic loops. This package will be enhanced with a low noise floor, high-sensitivity accelerometer to determine vehicle classification based on axle counts and spacing using vibration signature analysis.

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Figure 5: Smart transport applications include traffic lane monitoring, intersection monitoring for vehicles, and bike lane/intersection monitoring, as well as parking space occupancy status. Magnetic sensors are the sensor of choice for vehicle detection and radar sensors for pedestrian and bicycle detection.(Click graphic to zoom by 1.7x)

Sensys has also introduced “micro radar” sensor systems, installed at intersections and bike lanes. Consisting of a highly directive radar antenna operating at 6.3 GHz, the system can determine the presence of bicycles in a range from 1.2 to 3.0 m. The radar approach was adopted because a magnetic sensor can’t adequately detect the presence of people and composite materials of bicycles. Similar functionalities including signal processing, battery, and a radio are employed.

The U.S. highway system is a prime example of how a valuable asset has been permitted to slowly deteriorate to the point where several bridges have collapsed, notably, the I-35W bridge over the Mississippi River in Minneapolis, resulting in 13 casualties. Many of the original highway roads and bridges constructed from the 1950s to the 1970s as part of the interstate highway system have exceeded their design life and traffic expectancy. Public funding has been limited to support adequate maintenance and repair. A recent study, Federal National Bridge Inventory, showed that 65,605 of 607,380 bridges were classified as “structurally deficient” (in need of rehabilitation or replacement because at least one of the major components of the span has advanced deterioration), and 20,808 were classified as “fracture critical” (without redundant protections and at risk of collapse if a single, vital component fails).

To directly address this severe situation, Michigan’s Professor Lynch has instrumented two bridges – the Monroe Michigan Telegraph Road Bridge and the New Carquinez California Bridge – with sensor nodes to determine the bridge’s structural status under dynamic conditions. Built in 2003, the New Carquinez Bridge has 31 wireless sensor nodes deployed across the 1.056 km structure. A total of 87 channels of tri-axis accelerometers, strain gages, wind velocity, temperature, and potentiometer displacement sensors are measured using a proprietary Narada 4″ by 4″ printed circuit board platform that can accept up to four channels of sensor data.

Professor Lynch states that the purpose of the implementation was to determine the cost-effective deployment and robustness of the Narada sensor nodes and their remote sensors. Installed in 2011, the system is currently collecting data and is supported by the California Department of Transportation. Data taken by the system will be used to validate the models developed by the WIMSS team and will be used to better understand the response of the bridge under conditions including high wind loading and earthquakes.

Professor Bill Spencer of the University of Illinois Urbana-Champagne and his team have instrumented the Jindo Island Bridge in Korea with 113 nodes (the largest deployment of its type for bridge monitoring) over the 344 m span. The 659 data channels are comprised of sensors including accelerometers to measure vibration in the bridges stay cables, strain gauges, anemometers for wind speed and direction, and temperature and light level sensors. The system was installed in 2010 and operated until 2012.

Professor Spencer stated, “In conjunction with our colleagues at Seoul National University, we have demonstrated that we can deploy a wireless autonomous measurement solution that’s robust and significantly lower in cost at about $100 per channel. This project has returned results as expected, and we’ve been able to better understand the wind loading algorithms and validate our models.”

New developments

A solution for smart buildings currently being developed by Innoveering uses accelerometers and strain gages connected to a node/access point. The Enhanced Structural Collapse Awareness and Prediction Equipment (ESCAPE) application measures the structural integrity of a building during a fire and warns first responders of the building’s condition to keep them from harm’s way. This program is in the early stages of hardware and algorithm development.

Professors Babak Moaveni and Usman Khan of Tufts University are developing drone-based optical systems for the inspection of bridges. Such inspection is currently conducted by engineers and maintenance personnel using visual methods. The Tufts researchers are exploring the instrumentation of drones with HD and IR camera to take pictures of the structures, store the information on the drone’s memory system, and download the information when the drone returns to base. Using the drone pictures to detect cracks in the structure coupled with vibration signature analysis is expected to achieve higher accuracy assessments of deteriorating structures. A major advantage of this approach is that it uses a historically and highly acceptable approach of determining bridge structural deterioration – visual – which is expected to facilitate its acceptance by the maintenance community.

A cost/benefit analysis of IoT sensor nodes

Wireless autonomous sensor networks/IoT nodes have two main components: sensors and communication modules. It’s possible to classify communication devices according to their capabilities: unconstrained, constrained, and tags and system architectures must integrate various IoT nodes seamlessly. They’ve been in operation over the past years in different domains, but mainly in pilot projects. Based on research results, the major barrier to their widespread adoption is funding. Although many studies have established the aging nature of our roadways and bridges and their constant deterioration, this isn’t sufficient to motivate government agencies to address these problems structurally. These conditions exist in Japan, China, and Vietnam, as well as the U.S.

We believe that a cost/benefit ratio should be used as a primary index for developing IoT nodes and monitoring systems. The replacement cost of the I-35W Mississippi Bridge was $234 million, which is enough to instrument more than 20,000 bridges.

[1] Note that Roger Grace will be collaborating with MEPTEC in organizing the inaugural IoT Technologies and Applications Symposium, May 21, 2015 in San Jose, Calif. Alessandro Bassi will be making a presentation on IoT at the Tronshow in Tokyo, Japan on December 12, 2014.

Roger Grace is the president of Roger Grace Associates, a strategic marketing consulting firm specializing in high technology.

Alessandro Bassi is currently an independent consultant, working on topics related to the Internet of Things (IoT), cloud storage, and smart cities, and has been a keynote speaker in more than 100 events.

Roger Grace Associates

www.rgrace.com

rgrace@rgrace.com

Alessandra Bassi Consulting

www.bassiconsulting.eu

info@bassiconsulting.eu

@bassiconsulting

 

 

Roger Grace, Roger Grace Associates
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