This paper proposes strategies to improve the IOTA tangle in terms of resilience to splitting attacks. Our contribution is two fold. First, we define the notion of confidence fairness for tips selection algorithms to guarantee the first approval for all honest tips. Then, we analyze IOTA-tangle from the point of view of confidence fairness and identify its drawbacks. Second, we propose a new selection mechanism, G-IOTA, that targets to protect tips left behind. G-IOTA therefore has a good confidence fairness. G-IOTA lets honest transactions increase their confidence efficiently. Furthermore, G-IOTA includes an incentive mechanism for users who respect the algorithm and punishes conflicting transactions. Additionally, G-IOTA provides a mutual supervision mechanism that reduces the benefits of speculative and lazy behaviours. To evaluate the performances of G-IOTA, we implemented G-IOTA and compared it with the original IOTA. In our simulations, G-IOTA presents more confirmed transactions and fewer left-behind transactions than the original IOTA.

A desirable flooding-based time synchronization protocol in Wireless Sensor Networks (WSNs) should neither demand fast propagation of up-to-date time information nor keeping track of the neighboring nodes. Moreover, such a protocol is strictly required to have low computational and communication overhead as well as small memory footprint. Would there be a protocol which meets these requirements' We answer this question positively by introducing a novel time synchronization protocol whose main component is “adaptive-value tracking”. Thanks to this component, each sensor node synchronizes the rate of its clock to that of a reference clock through successive feedbacks with a considerably low computational and memory overhead. By adjusting time offset of the rate-synchronized clocks, the network-wide synchronization is established even without demanding rapid propagation of the reference clock and keeping track of the neighboring nodes. In the light of our experimental evaluation in a testbed of 20 MICAz sensor nodes, we observed that the proposed protocol provides similar synchronization under the same communication frequency with an approximately 97% less CPU overhead and 80% less memory allocation compared to the recent flooding based time synchronization protocols in WSNs.

Energy conservation measurements in buildings are more and more popular as they benefit from an intelligent contractual framework called Energy Performance Contracts (EPC), where energy savings are measured as the difference between a predictive baseline model and the actual consumption. While modern predictive models make use of large amounts of data from external sources and increasingly complex algorithms, these two aspects make their use difficult in practice because they need mutual understanding and transparency, requiring the involvement of a third-party for auditing. In this sense, we designed and developed a prototype that overcomes these issues by storing the predictive models and the data in an immutable blockchained data structure using the Ethereum framework. To the best of our knowledge, this is the first working prototype using the blockchain technology applied to EPCs. This paper presents and discusses the technical solutions and best-practice guidelines adopted in this prototype.

Blockchain is a very attractive technology since it maintains a public, append-only, immutable and ordered log of transactions which guarantees an auditable ledger accessible by anyone. Blockchain systems are inherently interdisciplinary since they combine various fields such as cryptography, multi-agent systems, distributed systems, social systems, economy, and finance. Furthermore, they have a very active and dynamic ecosystem where new blockchain platforms and algorithms are developed continuously due to the interest of the public and the industries to the technology. Consequently, we anticipate a challenging and interdisciplinary research agenda in blockchain systems, built upon a methodology that strives to capture the rich process resulting from the interplay between the behavior of agents and the dynamic interactions among them. To be effective, however, modeling studies providing insights into blockchain systems, and appropriate description of agents paired with a generic understanding of their components are needed. Such studies will create a more unified field of blockchain systems that advances our understanding and leads to further insight. According to this perspective, in this study, we propose using a generic multi-agent organizational modeling for studying blockchain systems, namely AGR4BS. Concretely, we use the Agent/Group/Role (AGR) organizational modeling approach to identify and represent the generic entities which are common to blockchain systems. We show through four real case studies how this generic model can be used to model different blockchain systems. We also show briefly how it can be used for modeling three well-known attacks on blockchain systems.

Fairness is a crucial property for blockchain systems since it affects the participation: the ones that find the system fair tend to stay or enter, the ones that find the system unfair tend to leave. While current literature mainly focuses on fairness for Bitcoin-like blockchains, little has been done to analyze Tendermint. Tendermint is a blockchain technology that uses a committee-based consensus algorithm, which finds an agreement among a set of block creators (called validators), even if some are malicious. Validators are regularly selected to the committee based on their investments. When a validator does not have enough asset to invest, it can increase it with the help of participants that delegate their assets to the validators (called delegators). In this paper, we implement the default Tendermint model and a Tendermint model for fairness in a multi-agent blockchain simulator where participants are modeled as rational agents who enter or leave the system based on their utility values. We conducted experiments for both models where agents have different investment strategies and with various numbers of delegators. In the light of our experimental evaluation, we observed that while, for both models, the fairness decreases and the system shrinks in the absence of delegators, the fairness increases, and the system expands for the second model in the presence of delegators.

Smart contracts are programs stored on a blockchain that run when predetermined conditions are met. However, designing and implementing a smart contract is not trivial since upon deployment on a blockchain, it is no longer possible to modify it (neither for improving nor for bug fixing). It is only possible by deploying a new version of the smart contract which is costly (deployment cost for the new contract and destruction cost for the old contract). To this end, there are many solutions for testing the smart contracts before their deployment. Since realizing bug-free smart contracts increase the reliability, as well as reduce the cost, testing is an essential activity. In this paper, we group the existing solutions that attempt to tackle smart contract testing into following categories: public test networks, security analysis tools, blockchain emulators and blockchain simulators. Then, we analyze these solutions, categorize them and show what their pros and cons are.