Abasic human instinct is to get oriented in unfamiliar surroundings. Our early ancestors used the sun and stars to find their way, while the quadrant and sextant guided European explorers in the Age of Discovery over 500 years ago. Now we use smartphone apps to achieve the same thing: positioning.
The modern world, especially IoT, has created more scenarios where it’s useful to know where things are. Factory managers expect to track materials and equipment, and fire-fighters need to find people in smoke-filled buildings. Everyone wants to locate parking spaces quickly and easily and, in malls, quickly find shops, products or curious children who have wandered off. Likewise, a smart home needs to know where its occupants are to perform even simple tasks like turning the lights on and off.
Whenever objects are connected to a network, there’s a need for positioning.
What’s so great about Wi-Fi?
Recent research into IoT has covered positioning technologies such as cellular networks, satellite, ZigBee, Bluetooth, ultra-wideband, and Wi-Fi.
However, each technology has its own disadvantages. Cellular networks are only accurate to a few hundred meters, making precision positioning impossible for IoT; satellite signals cannot be acquired indoors; and short-range wireless communications technologies like ZigBee, Bluetooth, and ultra-wideband are very scenario-specific. Moreover, ultra-wideband is too costly to popularize, the positioning accuracy of ZigBee and Bluetooth is just 5 to 10 meters, and the limited bandwidth of all three hinders widespread application.
Bandwidth is where Wi-Fi has the upper hand. Wi-Fi bandwidth is constantly increasing, and we’re seeing multiple antenna technology more widely used. With higher bandwidth, timing and range resolution improves, and multiple antenna technology allows multiple angulations, significantly increasing positioning accuracy.
Wi-Fi’s other advantages is its real-world commercial success in numerous sectors, which will make it easier to drive use in other scenarios.
Wi-Fi infrastructure already exists: Wi-Fi chips have long been standard in smart phones, and there’s no shortage of Wi-Fi access points. This will facilitate the commercial adoption of Wi-Fi for positioning and, as its use increases, costs will fall further.
Indoor pain points for IoT
Lack of a universal solution. In malls, exhibition halls, and other crowded public places, positioning signals are vulnerable to interference and attenuation, which affects precision. Factors such as differences in the accuracy of equipment and the placement of objects in indoor locations can also cause positioning errors. These kinds of issues require complex and diverse solutions, increasing costs.
Excessive solution maintenance costs. Most positioning solutions are based on either ranging or fingerprinting technologies. The former requires the additional deployment of anchors and the recording of anchor positions, often necessitating a large amount of testing for channel modeling, which drives up costs.
The latter depends on the advance collection of a huge quantity of field data in regard to the positioning environment. Because fingerprint collection is difficult to automate, this frequently makes this process extremely labor-intensive.
Higher positioning accuracy means higher data maintenance costs due to the correlation between positioning precision and fingerprint collection granularity.
Even slight changes to the environment or anchors require the fingerprint database to be maintained, further increasing costs.
Immature industry chain raises costs. The industry chain ecosystem for positioning applications is immature, so costs are high. Today, the industry chain consists of many, uncoordinated links; for example, positioning equipment manufacturers, map management companies, overall solution integration providers, back-end application developers, and positioning service operators.
Poor accuracy for IoT. The current level of accuracy is insufficient, mainly because Wi-Fi was designed for communication and not positioning. The cost issues highlighted above will be resolved, so accuracy remains the major hurdle.
Getting precise
With the rapid growth of new applications, especially IoT applications, poor positioning accuracy is a technological constraint. However, new technology may offer a solution.
MMW band: Millimeter waves occupy the electromagnetic frequency spectrum from 30 GHz to 300 GHz. MMW is unsuitable for long-distance wireless communication due to high attenuation and because some bands are easily absorbed by the atmosphere. However, MMW is fine for short distance communication due to its large spectrum and the short transmission range needed in today’s dense cellular networks. This is why MMW is touted as a major 5G technology.
MMW is also an IEEE 802.11 standard for next-gen 60G communications. As a result, anticipation is heavy for Wi-Fi-based MMW technology. MMW's super-high bandwidth ensures higher timing resolution, and its narrow beam and high angular resolution make it well suited for high-precision indoor positioning.
The positioning accuracy and stability of MMW can be affected by its short transmission distance, sensitivity to blocking and movement, and interference due to the lack of networking management; however, it will be possible to overcome these drawbacks when MMW is integrated with Wi-Fi technology.
Fine Timing Measurement (FTM): TOA/TDOA timing/ranging-based technology is another current positioning method. In theory, it can achieve high positioning accuracy, but it relies on relatively high signal bandwidth.
In the recently revised IEEE 802.11 standards that define FTM, the timing unit is 0.1 ns for use in accurate time measurements. This means that Wi-Fi technology based on this protocol through accurate timing enables ranging granularity of up to 3 cm. As the new standards evolve over time, more accurate timing measurement mechanisms will be considered.
CSI: CSI is an Orthogonal Frequency Division Multiplexing (OFDM) technology that describes the physical layer of Wi-Fi.
It provides information at the subcarrier level about amplitude and phase variation after the wireless signal undergoes spatial transmission. This creates lower-level and more stable channel information and higher spatial resolution, which leads to better positioning. CSI is not affected by signal instability that hinders the widely used RSSI positioning method.
But, CSI technology has technical drawbacks. The ideal CSI value accurately reflects the time-frequency response of the spatial channel the signal passes through. However, it is impossible to perfectly synchronize the timing, frequency, and phase between the receiving and sending end.
Synchronization errors lead to contaminated CSI values, preventing their use as location values. Purifying CSI values is a current, promising area of focus, and lab teams have got it down to about 1 m using CSI fingerprints.
MMW and FTM Wi-Fi technologies will make centimeter-level positioning accuracy possible. No doubt, Wi-Fi will attract more interest from other sectors besides the consumer domain.
Industry and security, which have much higher requirements for positioning accuracy, are likely to be attracted to applications such as high-precision excavators for mining or pinpointing small products in large warehouses.
CSI technology will improve the accuracy of Wi-Fi positioning based on fingerprint technology to sub-meter levels for applications like locating products in supermarkets, indoor navigation, and security monitoring solutions. These sorts of applications will take user experience to new heights.
Emerging technologies will necessitate modifications to the lowest layer of Wi-Fi, which will allow the technology's potential as a high-precision positioning solution to be fully exploited.
Thanks to new MMW, accurate timing, and CSI technologies, Wi-Fi is set to become the preferred universal positioning solution. As Wi-Fi positioning technology improves, it will give rise to countless new applications for IoT.