Using a wearable alarm button and other discrete wireless sensors placed around the home, one can track your daily routine and alerting you to any serious disruptions detected in your normal schedule.
Ideas such as the smart lighting system allows a city to intelligently provide the right level of lighting needed by time of day, season, and weather conditions. Cities have shown a reduction in street lighting energy.
Web enabled lights can be used as multi-functional lights, such as automatically turning off the lights when no one is in a room, or help to secure your home while you are away by turning your lights on and off.
The clothes stream athletic performance biometrics like heart rate, movement intensity, and energy output to the cloud. Integrations give the wearer a unified experience of their athletic performance.
Developer framework for connecting smart objects. Combined with Mobile Apps, smart watches could become the central hub connecting users’ physical lives to the entire ecosystem.
There are sensors and connectivity programs built into tennis rackets. This system allows athletes to track and analyze ball speed, spin, and impact location to improve their game.
When products are embedded with sensors, companies can track the movements of these products and even monitor interactions with them. Business models can be fine-tuned to take advantage of this behavioral data. Some insurance companies, for example, are offering to install location sensors in customers’ cars. That allows these companies to base the price of policies on how a car is driven as well as where it travels. Pricing can be customized to the actual risks of operating a vehicle rather than based on proxies such as a driver’s age, gender, or place of residence.
Or, consider the possibilities when sensors and network connections are embedded in a rental car: it can be leased for short time spans to registered members of a car service, rental centers become unnecessary, and each car’s use can be optimized for higher revenues. Zipcar has pioneered this model, and more established car rental companies are starting to follow. In retailing, sensors that note shoppers’ profile data (stored in their membership cards) can help close purchases by providing additional information or offering discounts at the point of sale. Market leaders such as Tesco are at the forefront of these uses.
In the business-to-business marketplace, one well-known application of the Internet of Things involves using sensors to track RFID (radio-frequency identification) tags placed on products moving through supply chains, thus improving inventory management while reducing working capital and logistics costs. The range of possible uses for tracking is expanding. In the aviation industry, sensor technologies are spurring new business models. Manufacturers of jet engines retain ownership of their products while charging airlines for the amount of thrust used. Airplane manufacturers are building airframes with networked sensors that send continuous data on product wear and tear to their computers, allowing for proactive maintenance and reducing unplanned downtime.
Data from large numbers of sensors, deployed in infrastructure (such as roads and buildings) or to report on environmental conditions (including soil moisture, ocean currents, or weather), can give decision makers a heightened awareness of real-time events, particularly when the sensors are used with advanced display or visualization technologies.
Security personnel, for instance, can use sensor networks that combine video, audio, and vibration detectors to spot unauthorized individuals who enter restricted areas. Some advanced security systems already use elements of these technologies, but more far-reaching applications are in the works as sensors become smaller and more powerful, and software systems more adept at analyzing and displaying captured information. Logistics managers for airlines and trucking lines already are tapping some early capabilities to get up-to-the-second knowledge of weather conditions, traffic patterns, and vehicle locations. In this way, these managers are increasing their ability to make constant routing adjustments that reduce congestion costs and increase a network’s effective capacity. In another application, law-enforcement officers can get instantaneous data from sonic sensors that are able to pinpoint the location of gunfire.
The Internet of Things also can support longer-range, more complex human planning and decision making. The technology requirements—tremendous storage and computing resources linked with advanced software systems that generate a variety of graphical displays for analyzing data—rise accordingly.
In the oil and gas industry, for instance, the next phase of exploration and development could rely on extensive sensor networks placed in the earth’s crust to produce more accurate readings of the location, structure, and dimensions of potential fields than current data-driven methods allow. The payoff: lower development costs and improved oil flows.
As for retailing, some companies are studying ways to gather and process data from thousands of shoppers as they journey through stores. Sensor readings and videos note how long they linger at individual displays and record what they ultimately buy. Simulations based on this data will help to increase revenues by optimizing retail layouts.
In health care, sensors and data links offer possibilities for monitoring a patient’s behavior and symptoms in real time and at relatively low cost, allowing physicians to better diagnose disease and prescribe tailored treatment regimens. Patients with chronic illnesses, for example, have been outfitted with sensors in a small number of health care trials currently under way, so that their conditions can be monitored continuously as they go about their daily activities. One such trial has enrolled patients with congestive heart failure. These patients are typically monitored only during periodic physician office visits for weight, blood pressure, and heart rate and rhythm. Sensors placed on the patient can now monitor many of these signs remotely and continuously, giving practitioners early warning of conditions that would otherwise lead to unplanned hospitalizations and expensive emergency care. Better management of congestive heart failure alone could reduce hospitalization and treatment costs by a billion dollars annually in the United States.
Making data the basis for automation and control means converting the data and analysis collected through the Internet of Things into instructions that feed back through the network to actuators that in turn modify processes. Closing the loop from data to automated applications can raise productivity, as systems that adjust automatically to complex situations make many human interventions unnecessary. Early adopters are ushering in relatively basic applications that provide a fairly immediate payoff. Advanced automated systems will be adopted by organizations as these technologies develop further.
1. Process optimization
The Internet of Things is opening new frontiers for improving processes. Some industries, such as chemical production, are installing legions of sensors to bring much greater granularity to monitoring. These sensors feed data to computers, which in turn analyze them and then send signals to actuators that adjust processes—for example, by modifying ingredient mixtures, temperatures, or pressures. Sensors and actuators can also be used to change the position of a physical object as it moves down an assembly line, ensuring that it arrives at machine tools in an optimum position (small deviations in the position of work in process can jam or even damage machine tools). This improved instrumentation, multiplied hundreds of times during an entire process, allows for major reductions in waste, energy costs, and human intervention.
In the pulp and paper industry, for example, the need for frequent manual temperature adjustments in lime kilns limits productivity gains. One company raised production 5 percent by using embedded temperature sensors whose data is used to automatically adjust a kiln flame’s shape and intensity. Reducing temperature variance to near zero improved product quality and eliminated the need for frequent operator intervention.
2. Optimized resource consumption
Networked sensors and automated feedback mechanisms can change usage patterns for scarce resources, including energy and water, often by enabling more dynamic pricing. Utilities such as Enel in Italy and Pacific Gas and Electric (PG&E) in the United States, for example, are deploying “smart” meters that provide residential and industrial customers with visual displays showing energy usage and the real-time costs of providing it. (The traditional residential fixed-price-per-kilowatt-hour billing masks the fact that the cost of producing energy varies substantially throughout the day.) Based on time-of-use pricing and better information residential consumers could shut down air conditioners or delay running dishwashers during peak times. Commercial customers can shift energy-intensive processes and production away from high-priced periods of peak energy demand to low-priced off-peak hours.
Data centers, which are among the fastest-growing segments of global energy demand, are starting to adopt power-management techniques tied to information feedback. Power consumption is often half of a typical facility’s total lifetime cost, but most managers lack a detailed view of energy consumption patterns. Getting such a view isn’t easy, since the energy usage of servers spikes at various times, depending on workloads. Furthermore, many servers draw some power 24/7 but are used mostly at minimal capacity, since they are tied to specific operations. Manufacturers have developed sensors that monitor each server’s power use, employing software that balances computing loads and eliminates the need for underused servers and storage devices. Greenfield data centers already are adopting such technologies, which could become standard features of data center infrastructure within a few years.
3. Complex autonomous systems
The most demanding use of the Internet of Things involves the rapid, real-time sensing of unpredictable conditions and instantaneous responses guided by automated systems. This kind of machine decision making mimics human reactions, though at vastly enhanced performance levels. The automobile industry, for instance, is stepping up the development of systems that can detect imminent collisions and take evasive action. Certain basic applications, such as automatic braking systems, are available in high-end autos. The potential accident reduction savings flowing from wider deployment could surpass $100 billion annually. Some companies and research organizations are experimenting with a form of automotive autopilot for networked vehicles driven in coordinated patterns at highway speeds. This technology would reduce the number of “phantom jams” caused by small disturbances (such as suddenly illuminated brake lights) that cascade into traffic bottlenecks.