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IoT Monitoring with Blockchain: Generating Smart Contracts from Service Level Agreements

Adam Booth, Awatif Alqahtani, Ellis Solaiman

TL;DR

This work addresses consensus in networks of second-order, non-identical agents governed by non-linear protocols. It proposes decentralized control laws $u_{i1}=-b_i(t)f(q_i)$ and $u_{i2}=\sum_{j\in\mathcal{N}_i} c_{ij} h(p_j-p_i)$ (with leader extensions) and proves convergence on connected graphs using a Lyapunov function that combines kinetic energy with edge-based potential terms, leveraging Barbalat to establish asymptotic behavior. For the leaderless case, it derives an explicit consensus value $p_i(t) \to \dfrac{1}{\beta}\sum_{k=1}^N (b_k p_k(0) + m_k q_k(0))$, highlighting independence from individual masses and gains. In the leader-following scenario, the results guarantee convergence to the leader state $p_i \to p_L$, $q_i \to q_L$ whenever the leader is reachable, with $q_L$ converging to zero under suitable conditions. Numerical simulations with six agents corroborate the theoretical predictions, demonstrating fast consensus and robust leader tracking under the proposed non-linear protocols.

Abstract

A Service Level Agreement (SLA) is a commitment between a client and provider that assures the quality of service (QoS) a client can expect to receive when purchasing a service. However, evidence of SLA violations in Internet of Things (IoT) service monitoring data can be manipulated by the provider or consumer, resulting in an issue of trust between contracted parties. The following research aims to explore the use of blockchain technology in monitoring IoT systems using smart contracts so that SLA violations captured are irrefutable amongst service providers and clients. The research focuses on the development of a Java library that is capable of generating a smart contract from a given SLA. A smart contract generated by this library is validated through a mock scenario presented in the form of a Remote Patient Monitoring IoT system. In this scenario, the findings demonstrate a 100 percent success rate in capturing all emulated violations.

IoT Monitoring with Blockchain: Generating Smart Contracts from Service Level Agreements

TL;DR

This work addresses consensus in networks of second-order, non-identical agents governed by non-linear protocols. It proposes decentralized control laws and (with leader extensions) and proves convergence on connected graphs using a Lyapunov function that combines kinetic energy with edge-based potential terms, leveraging Barbalat to establish asymptotic behavior. For the leaderless case, it derives an explicit consensus value , highlighting independence from individual masses and gains. In the leader-following scenario, the results guarantee convergence to the leader state , whenever the leader is reachable, with converging to zero under suitable conditions. Numerical simulations with six agents corroborate the theoretical predictions, demonstrating fast consensus and robust leader tracking under the proposed non-linear protocols.

Abstract

A Service Level Agreement (SLA) is a commitment between a client and provider that assures the quality of service (QoS) a client can expect to receive when purchasing a service. However, evidence of SLA violations in Internet of Things (IoT) service monitoring data can be manipulated by the provider or consumer, resulting in an issue of trust between contracted parties. The following research aims to explore the use of blockchain technology in monitoring IoT systems using smart contracts so that SLA violations captured are irrefutable amongst service providers and clients. The research focuses on the development of a Java library that is capable of generating a smart contract from a given SLA. A smart contract generated by this library is validated through a mock scenario presented in the form of a Remote Patient Monitoring IoT system. In this scenario, the findings demonstrate a 100 percent success rate in capturing all emulated violations.
Paper Structure (8 sections, 4 theorems, 9 equations, 3 figures, 1 table)

This paper contains 8 sections, 4 theorems, 9 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Problem formulations
  3. Consensus of non-identical agents with non-linear protocols
  4. Consensus of non-identical agents without a leader
  5. Consensus of non-identical agents with a leader
  6. Numerical simulations
  7. Conclusion
  8. Acknowledgments

Key Result

Theorem 1

Consider system (1) with control protocol (2) and assume that the undirected graph $\mathcal{G}$ is connected. Then, $\lim_{t \rightarrow +\infty} (p_{jl}(t)\,{-}\,p_{il}(t))\,{=}\,0$, $\lim_{t \rightarrow +\infty} q_{il}(t)\,{=}\,0$$(i, j\,{\in}\,\bar{N}, l\in\bar{n})$.

Figures (3)

  • Figure 1: Chained interconnection topology with and without a leader
  • Figure 2: Consensus of trajectories of agents without a leader a $\cos({\rm t})\omega_1=0.5$ b $\omega_1=0$ Initial conditions and other parameters are chosen as $(p_i(0),q_i(0))=(0.2i,0.3i)$, $c_{ij}=c_{ji}=0.2(i+j)$ and $m_i=0.1i (i,j=1,2,\ldots,6)$.
  • Figure 3: Consensus of trajectories of agents with a leader a $d_L(t)=0.6+0.15\cos(t),\omega_2=0.5$ b $b_L=0.6, \omega_2=0$ Initial conditions and other parameters are chosen as $(p_i(0),q_i(0))=(-0.3i,0.4i)$, $(p_L(0),q_L(0))=(1,0.3)$, $c_{ij}=c_{ji}=0.3(i+j)$, $b_i(t)=0.2i+0.15\cos(t),\omega_1=0.5$, $c_{1L}=1$ and $m_i=m_L=1$$(i,j=1,2,\ldots,5)$.

Theorems & Definitions (7)

  • Theorem 1
  • Corollary 1
  • Remark 1
  • Remark 2
  • Theorem 2
  • Corollary 2
  • Remark 3