A practical implementation of indoor location-based services using simple wifi

For customers, it can be used to provide people with relevant information to approximately where they are standing — for example, to show them money off coupons or direct them where to go on a map. For brands or venue owners, they are able to see for the first time in most cases a clearer picture of how people are moving in a physical space, even plotting them as dots on a map.

Are there congested points, or areas where nobody goes? How many staff are there in the space? How long are the queue or line times? How could the space be optimised more efficiently? The main benefit of WiFi is that the technology is available and very widely applicable in most parts of the world.

It can already be used with most WiFi hardware and detects all WiFi enabled devices including smartphones and tablets. It is a relatively inexpensive solution. However, WiFi must have a connection to the internet to transfer data. Plus in order to receive permission to interrupt someone with a message, they need to have logged onto the WiFi or given their permission in another way. Currently the accuracy of the device pin-pointing is pretty good — approximately metres, when configured correctly.

For most venues this is adequate enough to give a good picture of what is occurring. A beacon is a small transmitter that can be placed at a known location, which transmits a continuous or periodic radio signal with limited information content e. The physical Beacon itself has not changed. Apple have recently changed the iOS software to deal with Beacons differently — which is what has changed. Apple Inc. Third, Wi-Fi does not require additional special-purpose hardware.

Location estimation can be easily estimated by measuring the received signal strength RSS values from a Wi-Fi access point. Finally, the bandwidth of Wi-Fi systems has increased significantly to meet the requirements of high data rates [ 1 ]. Alternatively, Wi-Fi indoor positioning techniques are classified into two categories, signal propagation models [ 17 , 32 ] and location fingerprinting [ 29 ].

A comparison of signal propagation models and the location fingerprinting method is presented in Table 1. In a signal propagation model, an indoor positioning system using the time-of arrival ToA and time difference of arrival TDoA suffers from multipath fading on several paths [ 5 ] while measuring the distance to the station from mobile devices. Alternatively, the location can be estimated using the angles of the signals received from the mobile user and the Wi-Fi access point; this includes the angle of arrival AoA and angle of departure AoD techniques.

Most systems based on AoA measure the relative angles between signals coming from multiple anchor nodes to estimate the position requiring the antenna directions of both the mobile user and Wi-Fi access point to be known.

By measuring the time-of-flight of the signal traveling from the sender to the user and back, the round-trip time-of-flight RToF of the signal is used to measure a location.

However, this requires the exact delay and processing time. In location fingerprinting, a database containing measurements of the wireless signals at various reference points in a wireless LAN coverage area is established first. Indoor positioning systems using location fingerprinting compare the wireless signal measurements with the reference data [ 29 ].

However, this method requires database generation and maintenance. Compared with location fingerprinting, implementing signal propagation is simple. However, signal propagation as a result of such factors as penetration losses through walls and floors and multipath propagation are still very complicated [ 29 ]. For this reason, a novel Wi-Fi-based indoor positioning system was proposed to achieve a better performance.

In addition, in the era of computing paradigms, cloudlet is known as the technology at the edge of the Internet for deploying mobile cloud services.

The aim of using cloudlets, typically accessed through Wi-Fi connections, is to bring cloud technologies closer to the end-user and provide resource- and latency-sensitive applications [ 33 ]. Moreover, cloudlets are small-scale data center that are designed to provide cloud computing applications quickly to mobile devices such as smartphones, tablets, and wearable devices within close geographical proximity.

This places cloudlets in the following three-tier hierarchy: mobile device, cloudlet, and remote cloud, as shown in Fig. The advantages of cloudlets include the following:. Through Wi-Fi located on a one-hop wireless network [ 35 ], a cloudlet system efficiently provides a powerful computing resource and speeds up mobile application executions. The real-time interactive response can access the cloudlet through a one-hop high-bandwidth wireless to reduce the transmission delay [ 35 , 36 ].

Utilizing mobile device connectivity to nearby cloud servers enables a cloudlet to overcome the distant wide-area network latency and cellular energy consumption problems [ 37 ]. Using cloudlets is more optimized and efficient, enhancing the user experience when computation-intensive tasks offload to nearby cloud servers in a cloudlet-based cloud computing system [ 38 ].

Cloudlets leverage the computational capacity of connected mobile devices [ 39 ]. Cloudlets deployed one wireless hop away from mobile devices can process the computationally intensive tasks offloaded from devices efficiently [ 33 ]. Therefore, cloudlets are typically set up at a public place, such as a shopping center, theater, office building, or assembly room, to enable convenient access for mobile devices [ 35 ].

Compared to the baseline Wi-Fi indoor positioning system, a combination of a cloudlet-based cloud computing system, indoor positioning, and navigation, considered as a single system, is practical.

To achieve that, we designed a model of a cloudlet-based mobile cloud computing system enabling Wi-Fi indoor positioning and navigation, as shown in Fig. The system consists of a self-driving cart, a small-box data center cloudlet available in a wireless access point, and a core cloud. Model of a cloudlet-based mobile cloud computing system enabling Wi-Fi indoor positioning and navigation.

Finally, implementing a cloudlet-based cloud computing system enabling Wi-Fi indoor positioning and navigation is possible for the following reasons. First, because a cloudlet supports resource- and latency-sensitive applications [ 34 ], it can provide location-based services such as indoor navigation for people or robots, personnel, asset tracking, guiding blind people, factory automation, workplace safety, locating patients in a hospital, and location-based advertising [ 1 ].

Second, a cloudlet provides not only the reference access point locations but also all location information of devices in the network to find the route path for an indoor cart. Moreover, the moving edge cloud in an indoor cart can determine the distances between it and the reference access points using a received signal strength indication RSSI -based method.

Similarly, [ 40 ], our system can estimate the location of the indoor cart as the location of the reference access point that is located closest to the indoor cart. We propose a novel cloudlet-based cloud computing system enabling Wi-Fi indoor positioning and navigation. Moreover, with the rapid growth of Internet of Things IoT applications and their deployments on cloud computing, our proposed system brings the cloud closer to IoT devices for providing resource- and latency-sensitive applications.

We define a core cloud, cloudlet, and moving edge cloud. The core cloud is used to store all of the object information, such as global position and status, while the cloudlet stores all specific information for the objects.

The moving edge cloud is embedded to a task-driven indoor mobile robot, referred here to as a self-driving indoor cart. The moving edge cloud determines the route path and makes movement decisions.

We propose a movement decision algorithm for a self-driving cart. A movement decision is made based on measurements of the RSS at a moving edge cloud, which is embedded in the self-driving cart. Consequently, the navigation of the self-driving indoor cart is adjusted in accordance with its current position and the position coordinates of the access points.

In real-world indoor environments, such as a one-floor scenario, although our system uses only one access point to estimate the location of an indoor cart, as contrasted with standard methods using at least three access points, the experimental results for our system are superior to those of the standard methods in terms of the accuracy of navigation.

Our proposals were tested using a self-driving indoor cart and real-world indoor environment on the third floor of the Computer Science and Engineering building at Kyung Hee University, Korea. Wi-Fi survey-based indoor positioning techniques are generally divided into two types, trilateration and triangulation algorithms [ 17 , 43 , 44 ], as shown in Figs.

Trilateration algorithms measure the distance from multiple known access points, while triangulation algorithms compute the angles relative to multiple known access points. A comparison of trilateration and triangulation algorithms is provided in Table 2. The trilateration algorithm calculates the exact location of a user, given the exact location of access points and distances from each access point to the user. As shown in Table 2 , the issues of Wi-Fi-based indoor positioning techniques for use in a real-world indoor environment are as follows.

A complex radio propagation model must be considered with the multipath effect in indoor environments. Triangulation algorithms show poor accuracy of the estimated location because the multipath affects both the time and the angle of an arrival signal.

A typical indoor self-driving cart consists of four modules: perception, localization, navigation, and motion [ 45 ]. One important module known as localization is a key prerequisite for success in navigating the robot, which requires exact identification of current localization [ 46 ]. Moreover, the indoor self-driving cart must know which movements to make until it reaches the goal position when the localization is tested.

This has led to extensive study of the localization and navigation of mobile robots. We summarize a comparison between our solution and other indoor positioning systems in Table 3.

Cordeiro et al. A robot can autonomously move following the desired trajectory while avoiding detected obstacles based on depth images. In addition to odometry, Bessa et al. Zhang et al. The QR codes must be placed on the ceiling. A robot uses a camera at the top to read these QR codes more quickly. Thus, the localization of the robot is estimated based on the positional relationship between the camera and the QR codes when QR codes can be recognized.

Moreover, a robot uses a laser ranger finder to avoid collisions. The authors also use the Dijkstra algorithm to plan a global path and the dynamic window approach to plan local paths. Mota et al. First, cards with RFID technology were placed at each intersection of the pathways of a structured environment labyrinth.

Second, a robot was equipped with an RFID reader on its bottom. A robot moves until it passes over cards with RFID. At each intersection, the robot performs actions, such as turning right or left according to the map defined in its algorithm.

Next, it goes straight to the next card. Moreover, the authors use a black line to connect each card to its neighbor cards. The robot is equipped with three infrared sensors to detect and follow these lines. In the mobile edge computing environment, a multi-modal framework for indoor localization tasks was proposed in [ 51 ]. In this system, machine learning models are used for processing RSS based indoor localization tasks. Nevertheless, the presence of unstable factors that affect RSS is a major drawback.

It will be failed to repeat the same performance in practical situations. On the other hand, smartphone-based indoor localization and navigation systems rely on RSSI from BLE beacons and inbuilt sensors of smartphones for localizing the user in indoor area, such as Bluetooth receivers, accelerometers, and barometers [ 52 ]. Lee et al. Satan et al. To remove the noise signal, the positioning algorithm [ 55 ] estimates the distance between client and beacon, based on Bluetooth RSSI values and log-distance path loss model.

Yu et al. This algorithm includes multisensor-based position estimation, Bluetooth model-based position estimation, and Kalman filter fusion. However, Bluetooth beacon-based indoor localization and navigation systems require for client-based solutions and have a relatively small range up to 30m. Especially, Sadowski et al. They proved Wi-Fi to be the most accurate [ 57 ]. To reduce the negative effects arising from the propagation model, we propose a new system structure that combines a cloudlet-based cloud computing system and indoor positioning and navigation techniques using an RSS-based method.

Our system can estimate the position accurately and navigate the task-driven, self-driving indoor cart, as discussed in the next section. For integration of the Wi-Fi access point with the cloud network, we propose a three-tier architecture for a self-driving indoor cart, as shown in Fig. In the first tier, a core cloud connects with cloudlets that are placed on the corridors.

The core cloud stores all information collected from cloudlets. In the second tier, the cloudlets manage local information such as the specific position and status. Finally, the third tier is the moving edge cloud, which is placed in the self-driving indoor cart.

Based on the information requested from the cloudlet or core cloud, the moving edge cloud calculates the route path for reaching a destination point. The route path includes the names of the access points APs that the cart will be moved past.

Note that the route path provides the optimized shortest distance from the starting point to the destination point. There are three alternatives depending on the route path: go straight Fig. Note that the AP must be placed in corridor intersections to make the movement decision. In real-world indoor environments, such as a one-floor scenario, APs are deployed at corridor ceilings, especially at intersections. The movement decision is based on the previous position, current AP position, and next position, as shown in Fig.

Additionally, positioning estimates are used for correcting the movement of the self-driving indoor cart due to avoid the obstacle. For example, the cart moves from position A 3, 4 to position B 10, 4 in the deployed grid map, and it estimates that the movement time is 10 s. However, the cart avoided something in the corridors; hence, 10 s later, it is not at point B 10, 4 , and we assume it is at point C.

These A, B, and C points are shown in Fig. The cart has to estimate its current position and move to point B 10, 4 using positioning estimation. As described in Fig. In the indoor environment, we divide the distance from the beginning point to the destination point into segments according to the respective AP locations. The self-driving cart will move among APs in accordance with movement decisions based on its current position and AP positions known as target positions.

The shortest path between two vertices is defined as a path with the shortest length, called link-distance. The drawback of using Wi-Fi is that the accuracy of location will solely depend on signal strength which may suffer sometimes but with recent developments in Wi-Fi speed and SDKs like android 9 and 10 it is a problem that can be tackled easily. The latitude and longitude The relative position of our device to each Wi-Fi. It is calculated by making use of Wi-Fi frequency usually 2. This is modified free-space path loss formula where the frequency is in dBm and signal strength in megahertz.

The absolute position of a WIFI tower is also calculated using the above information point 1 and 2. This information is then integrated into a code created in the Android studio. The following flowchart explains the working of an indoor positioning system. Skip to content. Change Language. Related Articles.