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Decentralized Data Delivery Markets (3DMs) - Open Problem Statement


Short description

With the emergence of Decentralized Storage Networks and the rapid decrease in the price of storage services and hardware, there is a rapidly growing need to leverage the additional storage capacity contributed to Decentralized Storage Networks by new players, including end-users, and use it to deliver reliable and high-quality storage and delivery services. Similarly to Content Delivery Networks (CDNs) for the traditional Cloud Storage market, we now have the opportunity to build Decentralised CDNs for Decentralised Storage Networks. The Decentralized Data Delivery Markets (3DMs) Open Problem covers all the essential areas of work that need to be studied in order to create a fully permissionless free market for data delivery that supports fair data exchange on the service provided.

Long description

Serving content globally at scale is a hard technical problem, as evidenced by the multiple decades of innovation and improvements in the Content Delivery Networks field. This challenge becomes even more interesting once we move away from centralized cloud infrastructure, which is typically managed and monitored by a single party, to a decentralized network that is permissionless, possibly anonymous, constantly changing (i.e. with high node churn) and lacking access to the convenience of a third party mediator system that facilitates the fair exchange of goods (e.g. credit providers). The benefits of moving to a decentralised setting are significant, however: cheaper storage and delivery, resilience against business failures (i.e., the network and all its components are not dependent on business decisions made by a single entity), independence from the personal data-driven business models, as well as a significantly lower barrier to entry for new players who want to contribute.

At the same time, problems already addressed in the past for traditional CDNs reappear, albeit in a different dimension, as we move to a decentralized network design space. These problems include the global orchestration of the system, the efficient allocation and use of the resources, and clear visibility of the content being retrieved from the network.

We introduce Decentralized Data Delivery Markets (3DMs) as a new field of research and innovation and one with rapidly increasing relevance. Decentralised storage networks, such as Filecoin, have reached unprecedented storage capacity commitments (of over 2.5EB of cold storage in Jan 2021) and continue to grow rapidly. There is an urgent need to complement decentralised storage with decentralised data delivery as these storage networks seek a solution to deliver the data stored in their network to end-users while meeting the expectations users have of the centralised services of today.

We envision a 3DM as being:

  • A permissionless and open data delivery market, composed of one or more crypto-economic models
  • A fair exchange relationship between service providers and users
  • A cost efficient way for publishers to distribute their content
  • A unique set of business opportunities including:
    • Delivering data to end users
    • Carrying data between multiple disconnected locations or with high latency between them (Data Muling and/or Package Switching)
    • Creating ephemeral distribution networks for large gatherings
    • Powering the next generation of Web 3.0 applications

This new field is ripe with unique business opportunities given the growing demand from users for access to large datasets, videos, and other data-heavy content, the panoply of powerful mobile devices that users carry, and the limitations imposed by physics in ultimately moving increasingly large data at high speeds.

In this Open Problem definition, we introduce the multiple areas of research (or subOpenProblems), what we know so far for each one of them and link to some new ideas being explored.

Definition of the actors

For convenience and shared understanding, we first define the agents within a 3DM network. These are:

  • Clients - Agents that fetch data from Providers.
  • Providers - Agents that offer data delivery services to Clients.
  • Content Publishers - Agents that create content and want to have it distributed (or intermediaries thereof).

Expected requirements

We present this list as a guide to protocol designers of what we expect a 3DM implementation to meet. This list is non-exhaustive and presents some flexibility between MUST and SHOULD. The requirements identified are:

  • MUST be Decentralized and not just federated
    • Anyone should be able to join and leave at any time, without requiring permission
  • The exchanges of value MUST be verifiable
    • The payment for bandwidth/latency in token should match what has been agreed and fulfilled
    • The payment should be fulfilled if the SLA is fulfilled
    • Parties should be able to verify that the service provided was correct (e.g. that they received the right file)
  • SHOULD be Trustless
    • Ideally, the network can perform the operations without having to leverage trust in third parties and/or the participants of the exchange
    • Nevertheless, designs might benefit from an element of trust to increase the quality of service (e.g. reputation mechanisms may allow for relaxed constraints)
  • The network SHOULD do resource allocation in an efficient way (or as efficiently as possible)
    • Should not be an afterthought
  • Providers SHOULD coordinate and accept pre-fetching of content (warm up the caches)
    • This is in contrast to IPFS’s core principle of replicating only after explicit request by a peer
    • This is done in order to make the network more efficient. However it is not mandatory with the setting being left up to the Provider.

Additional expectations (i.e. bonus points):

  • Data retrieval SHOULD be anonymous and privacy-preserving.

Other considerations:

  • It is expected that content will always be identified and retrieved by CIDs
  • The provider network will likely not provide random access to files, i.e., items will be named at the object level.
  • In case there is an auction market, it should always be available and have a fast way to match offers to bids

Areas of Work

Throughout the exploration of the design space, we identified 3 areas of work that need to be explored in order to make 3DMs Fair, Decentralized and Efficient, as well as a bonus opportunity. The areas are listed below.

Data Delivery Metering & Fair Exchange

We believe that this area of work is the main crux for Decentralized Data Delivery Markets. Without it, it will be impossible to have a fully permissionless, decentralized, and open market for data delivery.

How it is done traditionally

In traditional or Web 2.0 setups (e.g. CDNs, Mirrors, Static Servers and so on), the correct metering happens on the server and is handled by the provider, which the clients trust to perform correct measurements. This measurement translates into a statement of how much the service was, ultimately resulting in an invoice and request for payment. This payment is facilitated by a trusted third party (e.g. credit card company) and a legal contract that can be used to resolve disputes in case the client fails to pay to the provider and/or the provider fails to deliver the service promised to the client.

In a decentralized environment that strives to be permissionless, we can’t expect to rely on such legal agreements and third parties and, therefore, need a way to prove fair exchange. That is, we need to be able to prove that the service was delivered correctly and the metering of the provided service was done correctly. This will, in turn, verify the exchange between the two parties and issue the correct payment (i.e. fair exchange).

This trustless property is really important as we know that markets with no mediators, escrows, and other assurance services (e.g. underground markets) are prone to scams, as the users have to trust that the provider will execute on the agreement. In a trustless environment, once the transfer is completed, there is no way to dispute it.

Properties desired

  • The exchanges of value MUST be verifiable and fair
    • Fairness:
      • The payment MUST be fulfilled if the SLA is fulfilled
      • The payment for bandwidth/latency SHOULD match what was agreed and provided
    • Verifiability:
      • Both parties MUST be capable of verifying that the exchange was performed correctly
      • Bonus: Third parties SHOULD be able to verify that the exchange was performed correctly
  • SHOULD be Trustless
    • Ideally, the network can perform the operations without having to leverage Trust
    • Nevertheless in many designs it might be needed to leverage reputation as a trust vehicle to guarantee high quality of service and/or reliability.

How is Data Delivery Metering different from Fair Exchange

The field of Fair Exchange overlaps in part with Service Metering. Some notable differences are:

  • Fair Exchange can be used for non-digital goods also
  • Fair Exchange is traditionally between 2 parties
  • Metering should be available to properly measure the service quality and service provided by one or more parties (e.g. streaming from multiple endpoints)

In review, the Metering & Fair Exchange fields end up contributing to each other and it is likely that the intersection of both is needed for a sound solution.

Known challenges

  • Fairness
  • Making it Trustless * How to verify that the provider has the ability to delivery the file (e.g. has a local copy or is able to pull it from the network) * How to verify that the file being transferred is the one requested, before a large amount of resources has been invested (i.e. before the entirety of the file has been transmitted) * How to verify that the client has received the file
  • How to verify and reward that SLA were met?
  • How to avoid collusion when adding a third-party (e.g. Referee)?
  • How to avoid griefing attacks (make one party pay for fees)?
  • How to avoid a malicious actor causing un-rewarded work, hence wasting others’ resources?
  • Experience
  • How to make the transfers start instantaneously?
  • How to support others (e.g. Content Publishers) paying for the usage?
  • How to make it private (i.e. so that others don’t know which users are requesting what content)?
  • Performance
  • How to overcome send-and-halt in order to maximize bandwidth throughput
    • How to support multipath (i.e. fetching from multiple sources)


When it comes to the state of the art, we found that the existing solutions fall within one of the following categories:


Pay-per-packet solutions offer granular control over what gets paid, enabling the option to verify if the SLA is being fulfilled.

These type of solutions have been found/highlighted:

Known shortcomings of these approaches:

  • send-and-halt - Because the next packet (unit of service) will only be issued after payment for the previous is received, these solutions often fail to max out the bandwidth available in the connection, if the service provider defaults to being conservative, hence increasing the latency of delivery.

Lock/Unlock access to the resource

Solutions of this type enable the client and the provider to verify the actual service before unlocking the payment. One of the main tools used is locking the access to the service by encrypting it with a key and then performing an exchange of the key that gives access to the service.

These solutions can be found in several forms:

Known shortcomings of these approaches:

  • Vulnerable to griefing attacks.
  • Generally, these solutions focus on the fulfilment of the delivery, more so than measuring the speed and rate of the transfer.

Optimistic Fair Exchange

These solutions build on top of the Fair Exchange ones and adopt an Optimistic approach where some sort of Reputation or Stake is used to give both parties trust that the exchange will occur correctly. If one misbehaves, they can still rely on a dispute system.

Known shortcomings of these approaches:

  • Setup + Dispute resolution is non-trivial and takes significant time

Reputation based

Reputation-based solutions give the possibility to speed up the transfer and improve the quality of the service by leveraging the trust built over time between clients & providers.

Known shortcomings of these approaches:

  • Not a direct shortcoming, but a challenge is that each participant my have different levels of sensitivity with regards to how much they are willing to rely on trust vs. strict verifiability.

Privacy focused

There are multiple solutions with the goal of delivering reader privacy and writer privacy. These incur an additional cost in setup and/or latency. Disclaimer: At the time of writing, we haven't dived deep into this set of solutions.

Traditional approaches (close to Web 2.0 world)

These approaches suggest novel ways to add integrity checks to data transferred or to promote new ways for content distribution, but they don’t depart from the Web 2.0 world of assumptions such as trusted third parties and central points of control.

Known shortcomings of these approaches:

  • Not fully aware of the challenges of building a decentralized network

Known attacks to be mitigated

Here we list the attacks to consider when designing a solution. These are:

  • Malicious actor forces provider to spend bandwidth without issuing payment (also known as Griefing Attack)
    • Type: client fraud
    • Consequence: bandwidth is spent
  • Malicious actor forces provider to pay to retrieve the file from storage point (e.g. Filecoin, Cloud Storage, etc) without the provider itself ever getting paid
    • Type: client fraud
    • Consequence: currency is spent
  • Provider claims it has sent the file, but actually never did
    • Type: Provider fraud
    • Consequence: Client gets penalized without receiving the service
  • Metering Inflation
    • Providers report to have used more resources to serve content to clients than what they have actually done.
  • Sybil / Throttle attacks
    • Description: A set of nodes collude against a content-provider to try and make him go bankrupt (loss of all its “SLA stake”). They all try to download content from that content provider at the same time, so that Providers start consuming the stake from the content providers. If clients are not paying for the service we take the risk of enabling this attack.

New ideas being explored

ResNetLab organized a Research Intensive Workshop on 3DMs, out of which, the following ideas, structured as RFCs emerged:

Distribution Graph Forming

The area of graph forming for Decentralised Data Delivery Markets revolves around three main areas, which together compose the different goals of the Graph Forming problem:

  • Content discovery & routing: the system can forward requests to find content
  • Content placement: the system should have ways to proactively distribute/replicate copies of data across different Providers in different geographic regions in order to be able to serve content fast.
  • Content copy selection: the system can choose the optimal copy of the content to serve if multiple copies exist in several Providers, where potentially:
    • every copy has a different “asking” price (defined by the cryptoeconomic model) and delivery latency, and
    • every miner achieves different performance and has a different reputation profile.

In order to achieve the above goals, we need to answer certain architectural questions: where do end-users connect (e.g., to a Provider, or to a separate content resolution system), which entity in the architecture makes content resolution and request forwarding decisions and finally, which entity(ies) has(ve) the required knowledge to forward requests to the closest (to the requesting user) copy, aka nearest replica routing.

We generally consider that Providers are relatively powerful devices with local storage dedicated to storing hot copies of the content, high uptime (close to zero churn) and, ideally, a public IP address (reachable from anywhere). The architecture should be able to take advantage of storage in less powerful and intermittently-connected end-user devices, such as laptops and mobile phones - this is discussed as a different area further down. Although not a strict requirement, this is what will make the decentralised storage network take full benefit of planetary-scale unused storage.

It is worth highlighting that the system should be able to serve different use-cases and therefore different setups and architectures could apply depending on the use-case. For instance, the content resolution system can be different between the cases where: i) the application operates based on a closed-content ecosystem (e.g., subscriber-based music or video streaming, where control of the content is solely with the content publisher) and ii) the application operates on a totally open content space, e.g., web.

Last but not least, the system of overlay Provider nodes has to be permissionless and decentralised. No entity has full control of the network of nodes, as is the case with traditional CDNs, where a single entity is in charge of the network setup and the content served by the system.

How it is done traditionally (CDNs, P2P CDNs)

Traditionally, content is stored, hosted, replicated and delivered by centralised CDNs. In those setups, the CDN entity has full control over: i) the formation of their network of servers, e.g., in which geographic areas to place servers and how to interconnect them, both in terms of topology and in terms of bandwidth deployed between servers, ii) the placement and replication of content, i.e., where to put hot copies of content, as well as iii) the network elements that are responsible for content resolution.

In a centralized setup it is much easier to optimise performance and provide guarantees, compared to the case where every network node is operated by a separate (legal) entity that operates rationally in a profit-maximizing way.

Closer to our setup are P2P CDNs, where end-user equipment is “employed” to contribute to the storage and dissemination of content. Node churn, unreliable connectivity and low bandwidth speeds become a reality in P2P CDNs, but the operation of a P2P CDN is still largely controlled and monitored by a single entity. This makes decisions with regard to content placement and replication easier. The most important advantage of a single entity taking care of the P2P CDN setup is monitoring, that is, being able to observe the performance of peers and make decisions on where and when to replicate content or assign content distribution decisions.


  • The system MUST always be able to discover content and satisfy content requests
    • There should never be a “404 Content not Found” error for content available anywhere on the network
  • The system MUST replicate content to different storage points in order to reduce delivery times and maximize performance.
  • Providers MUST follow the crypto-economic model and the system MUST make sure that Providers do not misbehave.
  • The network MUST be content-addressable and operate based on content identifiers.
  • The system MUST be permissionless and trustless
    • Anyone should be free to join and set up a Provider node to contribute to the network.


The target area and expected outcome of this topic is to form the network, the interactions between different network entities, as well as the basic protocols through which network entities will communicate. Given this target, we split our investigation in the following three main areas:

P2P CDNs: the network architecture setup in these systems is similar in some ways to that of a 3DM

P2P VoD: video delivery is an ideal target use-case, with very strict requirements; therefore, the mechanisms proposed in this field and used for P2P VoD can be inspiring

Content-Centric Networking: there are several network architectures and protocols proposed for content-/information-centric networks which can be great inspiration for a permissionless, content-addressable P2P network.

Our initial, but thorough investigation resulted in the following potential groups of designs for content discovery and resolution:


DHTs are very popular constructions in P2P networks. The DHT system is used as a content resolution mechanism, as well as a content and peer routing system. The routing table is split between peers that participate in the DHT, providing higher resilience and scalability. In a DHT-based system, clients “walk” the DHT (iteratively, or recursively) to find the provider record and then directly connect to the peer included in the provider record.

  • Pros: tested in the past, engineers have lots of experience with these structures, can become faster and less expensive than the IPFS DHT setup, if: i) re-publishing is done at coarser granularities, which is reasonable if we assume stable connectivity, i.e., low peer churn (reasonable to assume for Providers), and ii) some payment is associated with publishing. It is also reasonable to assume public IP connectivity for Providers.
  • Cons: slow, several round-trips needed to resolve content (especially in case of iterative DHT lookup). The solution will not scale in the longer term.

Name-based routing:

In Name-based routing systems, routing hints are integrated as part of the content name; routing tables are filled with routing hints at network setup time by a routing protocol. Routers make hop-by-hop forwarding decisions based on content names seen in requests and routing hints in their routing tables. Matching between hints and names depend on the structure of the names, e.g., hierarchical vs flat.

  • Pros: very fast, enables multicast-like opportunities and on-path caching, which can result in significant savings in case of popular and heavy content (e.g., HD VoD). Also makes routing from browser/limited connectivity clients more feasible.
  • Cons: design can be(come) complex, security properties not fully studied in the literature.

DNS-style, object-level indexing service:

The Domain Name System (DNS) uses index tables to resolve names (URLs) to IP addresses of hosts that store the requested name. It is considered by many to be a decentralised system, in the sense that DNS servers can be deployed by anyone running an ISP. At the top-level, however, it is administered by ICANN which is in charge of assigning names and therefore, does not comply with the permissionless nature of 3DMs. We could envision a similar, index-based naming system, which is governed by the Ethereum Name Service (ENS) to bypass centralization concerns. Cloudflare has recently introduced a Name Resolver for the Distributed Web, where IPFS content can be accessed through ENS.

  • Pros: can be very fast
  • Cons: need to rely on ENS (which is fine given the ENS system is deployed and tested in the wild, albeit in smaller scales), or build a similar service


In pubsub systems information propagates in the network based on topics that nodes subscribe to. Notifications about new content being published in the system can propagate through a dedicated channel, or more realistically, applications can have their own topics to disseminate information about content published within the remit of their application.

  • Pros: simple approach, lots of literature and testing in dozens of systems, engineers have lots of experience with similar protocols
  • Cons: too slow for real-time content delivery, can take seconds to find content, can’t scale in the longer term

Known attacks to be mitigated

  • Providers provide false content discovery and routing information
  • Providers serve bogus content
  • Providers provide false provider records (i.e., they claim that they have and can serve content that they actually do not have)
  • Cache poisoning
  • Privacy attacks: there are a number of privacy considerations and threat vectors to be taken into account

New ideas being explored

ResNetLab organized a Research Intensive Workshop on 3DMs, from which the following ideas emerged and were structured as RFCs:

CryptoEconomic model for Data Delivery

The aim of the cryptoeconomic model is to build an incentive system to ensure that all actors in the network are aligned towards the same goal: to deliver data efficiently. The cryptoeconomic model is tightly coupled to all of the areas presented above, offering a way to incentivize desired behaviors in the network. Ideally, the economic model should offer the substrate to build a decentralized and trustless infrastructure for data delivery able to compete against traditional CDN infrastructures in terms of cost, scalability, and price.

How it is done traditionally

In traditional and centralized setups, a set of legal agreements is used to govern the relationships between entities, align the incentives of all participants, and discourage disruptive behaviour. These agreements offer a basic level of trust to ensure good behavior by all entities in the system (clearly stating the SLA, and punishments for misbehaving or not fulfilling the contract). We currently see this setup in the multilateral agreements between CDNs, ISPs, and Cloud Providers to share resources from their infrastructures for a certain price in order to build a network of relationships that allow them to provide their content delivery services efficiently.

In decentralized setups, it is not possible to use legal agreements to orchestrate the relationship between entities in the system. In recent deployments of hybrid CDNs, traditional, centralised CDNs leverage the resources of end users to deliver data with enhanced QoS reducing the load on their infrastructure and as a result the cost of upgrading/extending their infrastructure. End users are incentivized to contribute to the system with their upload bandwidth by making them eligible for better service. This additional reward is used to incentivize a behavior in the system (in this case users contributing with bandwidth in hybrid CDNs). However, the fact that there is still a centralized infrastructure managed by an entity with greater power over the rest of actors is enough to prevent misbehavior. This removes the need to design stronger reputation or reward systems. This does not represent a trustless setup.

For decentralized and trustless setups, two main schemes have been traditionally used to align the incentive of the system’s participants and prevent misbehaviors:

  • Trustless networks governed by a common consensus, like public blockchains, use economic models based on token rewards and clear punishments built upon a secure consensus algorithm. The consensus algorithm gives a basic layer of trust where all participants “keep an eye on one another” preventing misbehaviors, and upon which the economic model is built to align all their goals. A good example of this model is Bitcoin, where the Proof of Work consensus algorithm prevents attacks, while the mining rewards incentivize miners to keep serving the network.

  • For trustless P2P networks where there is no common protocol run by all the participants of the network, other schemes such as reputation systems, or credit networks need to be implemented to orchestrate the relationships and good behaviors of all entities in the system.

In the same way public blockchains leverage the schemes and security properties in place to design their economic model, 3DMs will have to leverage the metering and graph forming designs in place to design the right economic model.

Finally, auction markets and game theoretical models have been traditionally used to study and achieve efficient resource allocation in P2P networks and decentralized systems.

Properties desired

  • The model MUST foster fair competition and avoid the creation of monopolies.
    • We should prevent superlinear advantages from winning/completing a high percentage of retrieval deals in the system.
  • The model SHOULD discourage, prevent, and punish misbehaviors.
    • Or the other way around, good behaving entities should be rewarded by the model.
    • In its different layers, the system will include schemes to prevent attacks in the network (client metering, sybil attacks, data-ransoming, etc.). However, this economic model should disincentivize these kinds of misbehavior in advance (from a game theoretical approach, the system should target a Nash Equilibrium in the system where misbehaviors are disincentivized).
    • It should also avoid “malicious economic attacks” such as a content provider trying to bankrupt its competition by draining the stake of his retrieval deal.
  • The model SHOULD foster collaboration between entities to benefit users perceived QoS / QoE.
    • The model should reward entities that collaborate to serve content efficiently and with high QoS to users.
    • This will also result in an efficient allocation of resources in the network.
  • If any part of the economics of the system ends up being orchestrated by an auction market, this market MUST be always available.
    • And should match offers to bids in a fast and efficient way.


As described above, the outcome of this topic should be to design an economic model that aligns the goals of all profit-maximizing entities in the system while preventing misbehaviors. Given this target, we split our investigation in the following areas:

Economics of Hybrid CDNs and P2P networks

The economics of current Hybrid CDN deployments give a good understanding of the system and cost models involved in the delivery of content at scale. This, added to the different analysis and design proposals of economic models for content delivery in P2P networks represent a good foundation to understand the complexity of the problem and potential ways to tackle it.

Related Papers:


  • Protocols and systems involved in the cost model of a Hybrid-CDN.
  • Models of P2P content delivery networks.
Game theoretical models and reward/reputation systems in P2P networks

How to design the right incentive and reputation models to incentivize certain behaviors in P2P networks has been a widely studied problem for some time now. From reputation systems where nodes track the level of good behavior of their peers and cooperate with them according to their grade; to incentive systems where good behavior is explicitly rewarded. Game-theoretical models are a way to evaluate the strength of these schemes and to understand if the proposals can achieve Nash Equilibrium.

Related Papers:


  • Use different metrics to evaluate the level of good behavior of peers.
  • Examples of game theoretic models to approach the evaluation of these protocols.
  • Schemes to avoid attacks to which these systems can be vulnerable (such as sybil attacks).
Credit networks and Token Designs

Credit networks provide liquidity in markets where transaction volumes are close to balanced in both directions. They offer a way of performing payments and rewarding behaviors without the need of a common consensus or dedicated agreements between all the entities in the systems. Credit networks require no “a priori” trust relationships when they are backed by on-chain escrows, so they represent an interesting approach to tackle the economics of 3DMs.

Related Papers:


  • Economics of credit networks.
  • Using credit networks for fast payments and reputation systems.
  • Credit networks for content distribution in P2P networks.
Auctions, decentralized markets, and efficient resource allocation

Auctions and decentralized markets have traditionally been a good way of allocating resources efficiently in a decentralized manner. These papers analyze different proposals of auction systems and decentralized markets that can aid the design of efficient resource allocation in 3DMs.

Related Papers:


  • Use of auctions as an efficient way to allocate resources in a decentralized system.
  • Local auction protocols. to drive the offer and demand of resources.
  • Decentralized market protocols for efficient pricing and distribution of resources.

Known attacks to be mitigated

  • Sybil attacks: Actors trying to game the system by indiscriminately generating a large amount of entities and gaining large influence over the system (forging client retrievals, overestimating the use of resources of a provider, preventing access from some resource in the network, making actors in the system dedicate resources to useless work, etc.).
  • Collusion attacks: Several independent actors in the system colluding to perform attacks in order to gain large influence in the network. The type of attacks that can be performed with a collusion attack are similar to the ones performed in a sybil attack, but without having to generate “pseudonymous identities”.
  • Data ransoming: This attack takes place when a provider agrees to serve some data from a publisher, but ends up preventing its retrieval from clients until a ransom is paid. An alternative form of this attack occurs when a provider is serving chunks of content to a client, and doesn’t deliver the last couple of chunks until it pays a ransom.
  • Malicious economic attacks: Attacks aimed at economically harming actors in the systems. For instance, clients trying to get a provider or content publisher bankrupt, providers offering abusive prices to content publishers, providers and clients colluding to avoid the economic rewards of target entities, etc.

New ideas being explored

ResNetLab organized a Research Intensive Workshop on 3DMs, out of it, the following ideas, structured as RFCs emerged:

Additional: Opportunistic deployment

An area adjacent to the Distribution Graph Forming one is that of extending the network beyond Providers, as defined earlier, to also include more ephemerally-connected, end-user devices. Such devices can include laptops, desktops, (futuristic) storage-equipped WiFi Access Points, or even mobile smartphone and tablet devices.

We call these environments “opportunistic deployments” to reflect their unpredictability in terms of availability, uptime, resource quality and quantity. In contrast to Providers, as defined earlier that are expected to have stable, public and high-bandwidth connectivity, 3DM opportunistic deployments can utilise everyday user devices to extend the storage footprint of Provider nodes and create a wealth of new network connectivity and business opportunities.

Although this area seems to sit on the periphery of 3DMs, it is actually a highly impactful area, as it realises the vision of Decentralised Storage Networks in general and Filecoin, in particular, of regular end-users sharing their own resources to contribute to the network. That said, we place high value in capturing this opportunity and offering to end-users the opportunity of being rewarded for their contribution to the network.


The literature in the field of Opportunistic and Delay-Tolerant Networks is vast. We have narrowed down the scope of the literature that we reviewed and identified the following most-promising 3DM applicability areas.

Transient Providers

We consider end-users that store content in their ephemerally connected devices (laptops, or mobile phones) and provide them to the network through (one or more) Providers, as defined in the “Distribution Graph Forming” area. These service providers are called Transient Providers (TPs) to depict their ephemeral nature in resource availability and time. TPs can have some economic relationship with one or more Providers, e.g., Providers can “recruit” TPs to expand their storage capacity and footprint. In this case, Provider nodes have to maintain their own monitoring mechanisms and do resource allocation to ensure that they are getting desirable levels of service from their group of TPs.

Opportunistic D2D

This area of research and deployment is closer to traditional Delay-Tolerant Networks (DTNs). We envision applications where mobile devices contribute to the distribution of content in the mobile domain. These can be akin to previously proposed User-Operated Mobile Content Distribution Networks, or applications to realise concepts such as “Floating Content”.

Known shortcomings

User Mobility

When a user is “registered” under one address/network location to serve content (at least as far as its Provider is concerned) and then moves to another network location, the content is not discoverable/retrievable anymore.

  • Brings up content mobility issues and “content churn”
  • Similar to node churn, this is a very tricky problem: how can you find content that was linked to some network address, when this address is not valid anymore. You can, of course, update the record if you know where the record lives, but if the record is “allowed” to be provided by anyone, then it’s difficult to even find it.
  • Simple solution: route through “home” gateway, similar to Mobile IP. Increases delay, is not ideal, but can work
  • There is a lot of work on “producer mobility” in the literature that we should look at.

Privacy - Efficiency Tradeoff

In mobile opportunistic communications, the two are often conflicting. On one extreme, you have efficient (low-replication) solutions that make use of a lot of node data (geolocation history/patterns, contact history/patterns, current geographic destination, current velocity vector). On the other extreme, you have naive epidemic dissemination solutions that require no node profiling but introduce data redundancy. There is a continuum in between, as well as a third variable, which is QoS: in the extreme, if willing to wait forever for delivery, you can blindly pass a message around without replication.

Keeping with the ethos of our projects, we believe that the most suitable solutions are those that don’t require sensitive data, including data related to location or contact history, from which identity and behaviour can be easily extracted. Aggregate transitive metrics (e.g. total expected time to destination measures) can provide a measure of delivery likelihood without disclosing the full contact vector or the nature of the contact (direct vs. indirect), and may be a useful compromise. Other data, including interests/subscriptions, is akin to what is already used e.g. in pubsub protocols and not considered to be any more privacy-invading.


Like with any other distributed routing/forwarding approach, malicious nodes can interfere with message propagation and delivery. Because of the disconnected nature of the network, such attacks are harder (or at least slower) to detect and counteract. Secure-by-design approaches are likely to suffer from additional overhead (either in data or complexity) and may be precocious when compared to the security model of other, more critical, parts of the network.

Energy Efficiency and Battery Consumption

Opportunistic communications that rely heavily (or exclusively) on user devices always come with the challenge of usability from the energy/battery perspective. Of course, incentives through FIL will alleviate some of it, but even then the incentive would have to be such that it overcomes the “running out of battery” shock. Techniques to overcome this can be as simple as disabling user participation when the battery level is below X%.


The incentive to participate and the cryptoeconomic model behind the opportunistic part of the network is quite likely going to be incompatible with the one in the main Distribution Graph Forming area that applies to large/stable Providers. Nevertheless, incentives is a core part of this area and has traditionally been a stumbling block for the deployment of opportunistic networks.

New ideas being explored

ResNetLab organized a Research Intensive Workshop on 3DMs, out of which, the following ideas, structured as RFCs emerged: