Proof of Location: Geospatial data on blockchains
Proof of Location services are working on open source maps and verifiable and tamper-proof geospatial data. These maps are intended for humans, machines (e.g. autonomous cars), and code (e.g. dapps or smart contracts). Currently, there are only a few Proof of Location services of which FOAM might be the most prominent one.
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However, all of them are still in the experimentation phase. Nevertheless, this post describes Proof of Location services, compares them against existing solutions, lists potential application fields for them and concludes with some open issues. Implementation examples are provided based on FOAM.
In the context of Proof of Location services, three types of maps exist:
- Commercial maps: Commercial maps are centrally maintained by organizations. Examples are Google Maps and Foursquare.
- Non-blockchain-based open-source maps: Non-blockchain-based open-source maps (hereinafter referred to as just open-source maps) are maintained by a community of voluntary individuals (cartographers). The most prominent example is OpenStreetMap.
- Blockchain-based open-source maps: Like open-source maps blockchain-based open-source maps (hereinafter referred to as just blockchain-based maps or Proof of Location services) are maintained by a community of voluntary individuals.
Based on that, the differences between open-source and blockchain-based maps as well as the issues of commercial and open-source maps that blockchain-based maps try to solve, are explained below.
Blockchain-based maps are community-driven, open source, and semi-free
Blockchain-based maps are community-driven, open source, and semi-free. Through that, they are maintained by a broader range of people, have fewer restrictions and distribute rents more fairly. The following section explains each of these concepts in more detail.
Community-driven allows more people to curate and verify maps
One of the focal points of Proof of Location services is that their maps are community-driven.
This allows more people to curate and verify maps. As such, this stands in contrast to commercial maps where only the governing organizations can curate and verify geospatial data.
Less restrictive through open-source
Moreover, because blockchain-based maps are open-source, hardly any usage restrictions exist. Thus, for instance, developers can access the raw geodata to experiment with new routing algorithms for navigation software. Again, this stands in contrast to commercial maps. Commercial maps are proprietary and come with restrictions and requirements. Besides individual usage preconditions (i.e. the Terms and Conditions), there are copyright restrictions, inextricably linkage to certain applications (e.g. Foursquare’s POI cannot be used outside Foursquare) or the lack of access to the raw geodata.
Fairer rent distribution through semi rent-free payment model
Further, commercial and blockchain-based maps have different economic models. Commercial maps are either fully paid or semi-free. Semi-free commercial maps like Foursquare are semi-free because they do not cost money, but people pay indirectly through their data. In contrast, the payment model for blockchain-based maps differs based on the accessing actor. Generally speaking, there are three actors: users, curators, and members.
- Users: Users are those actors that use maps to look up objects. For them, blockchain-based maps are free.
- Members: Members are entities that want to be included on the maps as POIs. The POIs in blockchain-based maps constitute an exclusive list. In order to become part of this list, POIs must pay a fee. The concept behind such exclusive lists is called a token-curated registry. [1]
- Curators: Curators are entities (people or machines) that contribute something and are rewarded for their work. A common contribution is the curation of POIs (see below).
Thus, although Proof of Location services per se or their creators do not incur rents, blockchain-based maps are still only semi rent-free because curators (who might be the creators) earn rents. However, because curators are rewarded and the creators not necessarily, one might argue that blockchain-based maps still distribute rents more fairly than commercial maps where all rents are collected by the map creators.
Furthermore, this incentivization of curators is one of the crucial aspects that differentiates blockchain-based maps from open-source maps.
Incentivisation in Proof of Location services
As indicated above, Proof of Location services incentivizes curators to maintain them. This is an essential difference to open-source maps whose adoption struggle exactly due to this lack of inherent incentives.
Asides that, in Proof of Location services curation is needed in three areas:
Curation areas in Proof of Location services
- Community-based curation of static geospatial data (POIs)
- Maintaining hardware nodes that enable alternative navigation systems for dynamic geospatial data
- Validating dynamic geospatial data
Four pillars that constitute Proof of Location services
These three curation areas are also three of the four pillars that make up Proof of Location services:
- Community-based curation of static geospatial data (POIs)
- Maintaining hardware nodes that enable alternative navigation systems for dynamic geospatial data
- Validating the location of dynamic objects
- Location encoding to bring geospatial data to blockchains
The following section explains each of these building blocks in more detail.
Location encoding to bring geospatial data to blockchains
Similar to an oracle, Proof of Location services enable the integration of verifiable and tamper-proof geospatial data to blockchains. Prior to them, such an integration was impossible.
The following list shows applications where verifiable and tamper-proof geospatial data is useful. It must be noted that the list does not contain new applications, but rather improved versions of existing ones.
Ride-sharing: Verifying driver locations
In vehicle sharing networks drivers can spoof their GPS-based location to charge higher fees. Verifiable and tamper-proof locations would prevent such fraud.
Data and supply chain management: Verifying the location of data and products
Verifiable and tamper-proof locations could verify the true location of goods along the supply chain.
Similarly, Proof of Location services could ensure that data (e.g. evidence pictures) was truly captured where it is claimed.
Compliance with local laws
When securely knowing customers’ locations, businesses could fully comply with local laws.
Location-based business cases
Through tamper-proof locations, companies can offer location-based products without “location fraud”. One example are location-based car insurances.
Location-based rewards
Verifiable and tamper-proof locations enable a more trustful collection of location-based rewards. Examples include cryptocollectibles, wages, loyalty points, and toll reductions.
Location-based cryptocollectibles for games
Proof of Location Services can help build games where users hunt for cryptocollectibles (unique digital collectibles, and one of many cryptoeconomic primitives) constrained to specific locations. Because the gamer’s location is verifiable and cannot be spoofed unlike with GPS, games are truly location-based. As such, fully tamper-proof Pokemon Go variations are finally possible.
More about cryptocollectibles here.
More about cryptoeconomic primitives here.
Location-based wages
Another type of location-based reward is wages. Payments in form of cryptocurrencies can be designed in a way that they can only be collected at a defined location (e.g. workplace). Again, as the worker’s location is verifiable and tamper-proof, fraud is minimized.
Location-based rewards to incentivize store visits
Companies can steer customers to certain stores by attaching rewards such as loyalty points to predetermined stores.
Location-based rewards to manage traffic flows
Similar to the incentivization of store visits, city planners can steer traffic to specific routes by placing rewards (such as fractional toll reductions) along them.
Community-based curation of static geospatial data (POIs)
Static geographical data or POIs refer to hardly changing concrete or virtual locations. Concrete locations are objects like stores or restaurants, and virtual locations are classified based on the activities happening there. Examples for virtual locations are traffic bottlenecks or areas with increased criminal activity.
Commercial and open-source maps have troubles curating such POIs (due to lacking incentives and the sheer amount of POIs). Proof of Location services, by combining financial incentives and open-source, try to combat these downsides.
Non-blockchain-based maps have troubles maintaining POIs due constantly changing POIs and lack of incentives
As indicated above, POIs are mostly centrally curated by commercial companies such as Google and Foursquare. As POIs change constantly (e.g. businesses move), centralized entities have troubles keeping them up-to-date. Open-source alternatives, which are maintained by a larger group of curators, could combat this through greater manpower. However, they fall behind because incentivizing curators, like in any open-source project, is difficult.
In this regard, blockchain-based maps are similar to open-source maps; they can keep up with constantly changing POIs through their large member bases. However, in contrast to open-source maps, with token-curated registries, blockchain-based maps have found a way to incentivize curators.
Token-curated registries are blockchain-based methods (one of many cryptoeconomic primitives) to incentivize the curation of high-quality lists by decentralized entities. Such lists contain items that meet certain criteria. In the case of Proof of Location services, the lists are the POIs, each item on this list is one POI, the criteria can be the POI type (e.g. sights) and the decentralized entities are the cartographers. As such, combing combining financial incentives and open-source, Proof of Location services could theoretically motivate enough people to keep POIs up-to-date.
More about token-curated registries here.
More about cryptoeconomic primitives here
Related to the curation of static geospatial data, Proof of Location services are working on navigation system alternative to GPS which are tamper-proof and whose location information can be validated.
Building tamper-proof and verifiable navigation system alternatives to GPS for dynamic geospatial data
Besides the fairly static POIs, Proof of Location Services want to capture dynamic geographical data as well. Dynamic geographical data refers to moveable objects such as cars or people.
Traditionally the position of such objects is captured with the
navigation system GPS. However, Proof of Location services consider GPS unsuitable for blockchain-based location data, because, among other things, the following reasons:
- Insecure and prone to failure: GPS is insecure and prone to failure due to its centralized organization
- Limited range: GPS does not work indoors or underground
- Energy-intensive: The battery drain of GPS makes it unsuitable for small devices such as microcontrollers. This is particularly important in IoT-related applications such as access controls for cars and many other areas in the Automotive & Mobility industry.
- Not tamper-proof: GPS is susceptible to spoofing
- Inaccurate: GPS’s 5 to 15 meter precision is considered too inaccurate
Thus, Proof of Location services are working on navigation system alternatives to GPS which ought to be tamper-proof (e.g. insusceptible to spoofing), more reliable (due to decentralized hardware), wider-reaching (e.g. also indoors or underground), less energy hungry, and more accurate.
As such, FOAM, for instance, proposes LoRa as an alternative.
LoRA is a low-energy wireless transmission technology for long-distances. A LoRa-based navigation system would more reliable (because nodes are maintained by multiple entities and because LoRa nodes are easily replaced), wider-reaching (as it is satellite-free it works indoors or underground), and suitable for small devices (due to LoRa’s low-energy consumption).
However, to build a LoRa-based navigation system, FOAM needs multiple LoRa sensors (nodes). Instead of setting up and maintaining all nodes themselves, FOAM’s goal is to have node operators maintain them. As indicated above, FOAM will reward node operators via FOAM tokens.
Validating the location of dynamic geospatial data
Finally, FOAM wants the LoRa network to be tamper-proof. Similar to the maintenance of LoRa nodes, FOAM uses financial incentives to ensure verified locations of moving objects (so-called Verifiers receive FOAM tokens for verifying locations).
Open issues
Having said all that, one must not forget to weight in open issue for Proof of Locations services. The following issues are very broad and do not deal with implementation aspects. Thus if you have technical experience with blockchains or even Proof of Locations services, you might already be familiar.
User adoption required
Most obviously the success of Proof of Location services depends on whether enough users, curators (cryptographers or node operators), and members can be acquired.
In part, this depends on the height of the incentives curators earn. For instance, node operators (such as LoRa nodes in the case of FOAM) will expect a return at least equal to the costs of nodes.
Another reason hindering the acquisition of curators is simply lack of curators. If community-based blockchain applications where volunteers for maintenance are needed keep growing, we might encounter worker shortages. Similarly, depending on the complexity of curation tasks, Proof of Location services might simply not find enough qualified people.
Proof of Locations services still in the experimentation phase
For the sake of completion it must be mentioned that by looking at the different approaches of competing Proof of Locations services, one can observe that they are still in the experimentation phase. Among other things they are experimenting in regards to location sensors, security and privacy configurations, and bootstrapping methods,
Untested building blocks
Similarly, one must not forget that the building blocks of Proof of Locations services such as token-curated registries or blockchain governance models are also still in the experimentation phase.
Competing data and long-term oligopoly
Although the currently available Proof of Location services still show little to no traction, it is foreseeable that they will compete against each other. This competition also implies that competing geospatial datasets will exist. However, as there is little use in having different geospatial data (after all geospatial data is subjective — an object is either there or not), in the long-run the industry will lead consist of oligopolistic data providers. This again would result in a return to today’s situation where only a few companies govern the world’s maps.
Conclusion and final thoughts
Proof of location services serve two purposes. On the one side, they improve geospatial data (through more openness, fairer distribution of rents, incentivization and accountability, and verifiable truths). On the other side, they serve as blockchain building blocks by bringing geospatial data to blockchains. As such blockchain building blocks, they are not enabling new applications but rather improve existing ones. This might change in the future. However, currently, Proof of location services are still in experimentation phase and must first bootstrap their navigation networks and the required geospatial data.
Notes
[1] Token-curated registries are blockchain-based methods (one of many cryptoeconomic primitives) to incentivize the curation of high-quality lists by decentralized entities. Such lists contain items that meet certain criteria. In the case of Proof of Location services, the lists are the POIs, each item on this list is one POI, the criteria can be the POI type (e.g. sights) and the decentralized entities are the cartographers.
More about token-curated registries here.
Originally published at researchly.leobosankic.com on June 19, 2018.
