A traditional distinction that often comes up for users considering HyperTrack (especially business users) is – should I build location tracking in-house or outsource? The thought process then moves on to whether this is a core feature in the product. If so, “we are going to build it in-house”, they say. If not, “we are looking to partner with someone to implement it for us”, they conclude and that usually means an enterprise-y application with customizations, integration services and operational roll-outs. Continue reading
One of the major problems that affects the operation of real-time location tracking is patchy mobile network connectivity. Our users trust our SDKs by plugging them into their apps that are out in the big bad world. We guarantee uninterrupted operation of the app regardless of network connectivity. Our SDKs are built to be offline-first. Collecting and handling location data on the smartphone is resilient against bad network. As a result, it is considered to be the source of truth for all data including time – a crucial dimension for real-time location tracking.
As engineers we want life to be easier than breathing. We want apps to replace unnecessary (random) human interactions, bots to replace assistants, even swiping left or right over the awkward effort of initiating contact the 90’s way. Usually, these thoughts find little resonance with the ‘others’ but here’s a use case where we finally felt accepted, understood, at one at last with those who think Calculus was primarily a Tintin character.
Every other evening, we’re out for drinks, or a movie or some other shindig. More often than not, one person in the group reaches the venue first and then either waits around thinking about the cosmos or for the more frantic kind, starts calling everyone else with the usual questions, “where are you at?”, “how long are you going to take?” and so on. This is usually frustrating for both parties – the one that needs to chill and the one that needs to be more punctual.
In a previous post, we talked about our end customer tracking experience. HyperTrack’s Android and iOS Consumer SDKs enable developers to implement a smooth real-time location tracking experience in their consumer apps. Until now the SDKs were designed to view only one task at a time on the map. And then developers at echo plans requested a feature to view multiple tasks simultaneously on the same map view. echo plans is an app for groups of friends to plan meetups. Check out their blog post to learn more about echo plans and how they implemented location tracking with HyperTrack.
We redesigned the Consumer SDK to enable the feature for echo plans. In this post, we elaborate on the design pattern we followed. UI elements on the Consumer SDK – map markers, info layouts – can be customized to fit into the theme of the host app. Our goal was to make the new code design extensible and comprehensible, and thus make the Consumer SDK integration easy for the developers.
We have built HyperTrack’s dashboard ground up using angular2. Oh and we released a brand spanking new version yesterday that you should check out if you haven’t already. Our dashboard provides realtime visualization of geospatial data for business users. One of the more popular views is the task page that lets you track all live tasks being performed by the workforce. Continue reading
We received a ton of feature requests and feedback on the dashboard that we released along with the HyperTrack public Beta release a couple of months ago. Many iterations, beers, Murphy interventions and comfort food later, we are now announcing the release of HyperTrack dashboard v2. Here is what’s new. Continue reading
One of the biggest challenges with continuous location tracking is dealing with volatile quality of smartphone’s GPS readings. Numerous factors affect GPS accuracy such as:
- Quality of GPS receiver
- Source of signal (GPS, WiFi, cell tower triangulation)
- Environment (weather, skyline visibility, enclosed spaces, multipath reception)
- Device state (low power mode, flight mode, initial fix)
Due to the error introduced by all these factors it becomes essential to carefully process the location stream in order to accurately predict the path taken by a driver.