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Indoor localization solutions need to meet several goals if they are to be considered viable candidates for use in indoor environments. Here we review some of the more relevant performance metrics [4] that must be satisfied by any candidate indoor localization solution:

 Accuracy: The location error of a positioning system is one of the most important metrics used to determine the effectiveness of a localization system. In its simplest form, localization accuracy can be reported as an error distance between the estimated location and the actual location of the user or object being tracked. For navigation systems, this may take the form of a running average of errors over a time period of interest, or the error could be calculated using geometric principles, to estimate the deviation of the predicted trajectory from the actual trajectory. Usually, the higher the accuracy, the better the system, but there is often a trade‐off between accuracy and other characteristics. Therefore, a compromise between adequate accuracy and other characteristics described below is essential.

 Timeliness: The timeliness or responsiveness of a solution determines how quickly the location estimate of a target is obtained. For simple indoor localization queries, a fast response to the query is important in most cases, but not crucial. However, for navigation systems, timeliness is a critical measure of effectiveness: if location estimates are not updated quickly in sync with the motion profile of the subject being tracked, the system will be ineffective for the purpose of navigation (regardless of the eventual accuracy of the estimates). Usually, the term location lag is used to refer to the delay between a mobile subject moving to a new location and the new location of that subject being reported by the system.

 Coverage: Any indoor localization solution must work and be usable over the entire indoor environment of interest. Coverage defines the area over which a localization solution can provide estimates of sufficient accuracy, and possibly timeliness, to be considered useful. The physical environment (e.g. obstacles, walls, doors) plays a crucial role in limiting the availability of signals that are used by a given localization technique, consequently impacting the coverage achievable by the technique for that environment. Intuitively, it is possible to extend coverage by altering the physical environment or supplementing it with additional hardware, for example, wireless signal repeaters. Coverage can also be improved by enhancing the hardware carried by the user or object being tracked, for example, using mobile devices with more powerful and capable wireless radio antennas and chipsets.

 Adaptiveness: Often, the physical environment around the subject to be tracked does not stay the same over time. For example, at different times of the day and days of the week, the number of people in a shopping mall varies quite significantly. In some environments, machinery, goods, containers, and other equipment may be repositioned constantly. Sometimes signals from wireless transmitters are temporarily blocked in an environment, or some transmitters may stop functioning due to unpredictable circumstances. These changes create a challenge for any indoor localization solution that relies on these signals. The ability of a solution to cope with these environmental changes represents its adaptiveness, or robustness. Obviously, a solution that is able to adapt to environmental changes can provide better localization accuracy than solutions that cannot adapt. An adaptive system can also prevent the need for repeated calibration of sensors used for localization.

 Scalability: At a system level, solutions for localization may require supporting requests from multiple entities. For instance, a system deployed in a shopping mall needs to be able to handle location queries from a few people, all the way up to thousands of people simultaneously. The ability to “scale up” and quickly respond to multiple location queries is of paramount importance in many indoor environments. Poor scalability can result in poor localization performance, necessitating the reengineering or duplication of systems, which can increase deployment overheads.

 Integrity: The confidence that can be placed in the output of a localization solution can be termed its integrity. A solution with low integrity has a high probability that a malfunction will lead to an estimated position that differs from the required position by more than an acceptable amount and that the user will not be informed within a specified period of time about the malfunction. While regulatory bodies have studied and defined integrity performance parameters in some sectors such as civil aviation, for indoor localization it is more difficult to find well‐quantified integrity parameters. At the very least, an indoor localization solution must provide an indication of some integrity parameters that are related to safety of life, economic factors, or convenience factors; thereby allowing consumers of the solution to understand its limits and capabilities under different usage scenarios.

 Cost: An indoor localization system has costs associated with it that must be as low as possible, to incentivize widespread adoption and ease deployment overheads. These costs may include installation of localization solution‐specific hardware and site survey time during the deployment period. If a positioning system can reuse an existing communication infrastructure (e.g. Wi‐Fi APs already deployed in a building), some part of the infrastructure, equipment, and bandwidth costs can be saved. In addition to the infrastructure, there may also be costs associated with the mobile devices carried by the subject being tracked. For instance, such costs could represent monetary costs of the smartphone and any externally connected hardware sensors. However, the cost could also be calculated by considering other aspects, such as lifetime, weight, and energy consumption. For example, some mobile devices, such as electronic article surveillance (EAS) tags and passive radio frequency identification (RFID) tags, are energy passive (i.e. they only respond to external fields) and thus, can have an unlimited lifetime; however other mobile devices (e.g. smartphones with rechargeable battery) have a limited lifetime of several hours without recharging.

 Complexity: Indoor localization solutions inevitably require hardware and software components that can have different complexities. Solutions may differ in the sophistication required from their associated signal processing software and hardware. While some techniques may involve very simple hardware (e.g. inertial sensors) and software (e.g. to implement simple filtering techniques), other techniques may require more complex custom hardware (e.g. for specialized digital signal processing) and complex software (e.g. sophisticated machine learning techniques). Also, if the computation of the localization algorithm is performed on a centralized server, the localization can be quickly estimated due to the powerful processing capability and the sufficient power supply; however if it is carried out on a mobile device, the effects of complexity can be much more apparent. Inevitably, complexity impacts the cost of the solution, and thus it is common practice to trade off the complexity with the other (non‐cost) metrics.