Consensus of Multi-Agent Networks in the Presence of Adversaries Using Only Local Information

This paper addresses the problem of resilient consensus in the presence of misbehaving nodes. Although it is typical to assume knowledge of at least some nonlocal information when studying secure and fault-tolerant consensus algorithms, this assumption is not suitable for large-scale dynamic networks. To remedy this, we emphasize the use of local strategies to deal with resilience to security breaches. We study a consensus protocol that uses only local information and we consider worst-case security breaches, where the compromised nodes have full knowledge of the network and the intentions of the other nodes. We provide necessary and sufficient conditions for the normal nodes to reach consensus despite the influence of the malicious nodes under different threat assumptions. These conditions are stated in terms of a novel graph-theoretic property referred to as network robustness.Comment: This report contains the proofs of the results presented at HiCoNS 201

Coordination and Self-Adaptive Communication Primitives for Low-Power Wireless Networks

The Internet of Things (IoT) is a recent trend where objects are augmented with computing and communication capabilities, often via low-power wireless radios. The Internet of Things is an enabler for a connected and more sustainable modern society: smart grids are deployed to improve energy production and consumption, wireless monitoring systems allow smart factories to detect faults early and reduce waste, while connected vehicles coordinate on the road to ensure our safety and save fuel. Many recent IoT applications have stringent requirements for their wireless communication substrate: devices must cooperate and coordinate, must perform efficiently under varying and sometimes extreme environments, while strict deadlines must be met. Current distributed coordination algorithms have high overheads and are unfit to meet the requirements of today\u27s wireless applications, while current wireless protocols are often best-effort and lack the guarantees provided by well-studied coordination solutions. Further, many communication primitives available today lack the ability to adapt to dynamic environments, and are often tuned during their design phase to reach a target performance, rather than be continuously updated at runtime to adapt to reality.In this thesis, we study the problem of efficient and low-latency consensus in the context of low-power wireless networks, where communication is unreliable and nodes can fail, and we investigate the design of a self-adaptive wireless stack, where the communication substrate is able to adapt to changes to its environment. We propose three new communication primitives: Wireless Paxos brings fault-tolerant consensus to low-power wireless networking, STARC is a middleware for safe vehicular coordination at intersections, while Dimmer builds on reinforcement learning to provide adaptivity to low-power wireless networks. We evaluate in-depth each primitive on testbed deployments and we provide an open-source implementation to enable their use and improvement by the community

Network Robustness: Diffusing Information Despite Adversaries

In this thesis, we consider the problem of diffusing information resiliently in networks that contain misbehaving nodes. Previous strategies to achieve resilient information diffusion typically require the normal nodes to hold some global information, such as the topology of the network and the identities of non-neighboring nodes. However, these assumptions are not suitable for large-scale networks and this necessitates our study of resilient algorithms based on only local information. We propose a consensus algorithm where, at each time-step, each normal node removes the extreme values in its neighborhood and updates its value as a weighted average of its own value and the remaining values. We show that traditional topological metrics (such as connectivity of the network) fail to capture such dynamics. Thus, we introduce a topological property termed as network robustness and show that this concept, together with its variants, is the key property to characterize the behavior of a class of resilient algorithms that use purely local information. We then investigate the robustness properties of complex networks. Specifically, we consider common random graph models for complex networks, including the preferential attachment model, the Erdos-Renyi model, and the geometric random graph model, and compare the metrics of connectivity and robustness in these models. While connectivity and robustness are greatly different in general (i.e., there exist graphs which are highly connected but with poor robustness), we show that the notions of robustness and connectivity are equivalent in the preferential attachment model, cannot be very different in the geometric random graph model, and share the same threshold functions in the Erdos-Renyi model, which gives us more insight about the structure of complex networks. Finally, we provide a construction method for robust graphs

New Distributed Byzantine Fault Detection & Data Integrity Scheme for WANET

Wireless ad-hoc networks (WANET) with multi-hop communication are subject to a variety of faults and attacks, and detecting the source of any fault is highly important to maintain the quality of service, confidentiality, and reliability of an entire network operation. Intermediate byzantine nodes in WANET could subvert the system by altering sensitive routed information unintentionally due to many reasons such as power depletion, software bug, malware, and environmental obstacles. This thesis highlights some of the research studies done in the area of distributed fault detection (DFD) and proposes a solution to detect Byzantine behavior cooperatively. The present research will focus on designing a scalable distributed fault detection (DFD) algorithm to detect byzantine nodes who permanently try to distort or reroute information while relaying a message from one node to another, complimentary to that, a symmetric distributed cryptography scheme will be employed to continuously validates the data integrity of a routed message. The main hypothesis of the research is that if a wireless ad-hoc network is been divided into N number of groups (classes) with relatively equal number of members, each group of nodes can cooperatively protect the network from every other group. Practically, each group of nodes will be assigned to a distinct shared key; nodes with similar group assignment shall guard the integrity of a routing path by incorporating their own secret message authentication code (MAC) that can be only validated by nodes belonging to the same group contributing to the same routing path. If a node from Group(i) detects a tampering event, it should either store and delay a fault report or embed a fault report to the same routed message and forward it to the Master Node (Destination) if applicable. Further report message overhead optimization has been devised to reduce the energy cost. Moreover, the empirical results have shown that the more reported evidence the master node can collect, the more accuracy of detection can be reached based on an incremental stream of evidence that contains information about both healthy and unhealthy nodes; so that every healthy report type can justify the unhealthy false report. The heuristic simulation based study considered many different aspects of the system for evaluation such as detection accuracy, fault model, the optimal number of classes, energy consumption, the impact of mobility, and network lifetime. The iGraph network simulation tool has been employed for visualization and graph manipulation, whereas, Python programming language has been utilized in conjunction to implement and simulate the DFD algorithm and generate the results

SoK: A Consensus Taxonomy in the Blockchain Era

Consensus (a.k.a. Byzantine agreement) is arguably one of the most fundamental problems in distributed systems, playing also an important role in the area of cryptographic protocols as the enabler of a (secure) broadcast functionality. While the problem has a long and rich history and has been analyzed from many different perspectives, recently, with the advent of blockchain protocols like Bitcoin, it has experienced renewed interest from a much wider community of researchers and has seen its application expand to various novel settings. One of the main issues in consensus research is the many different variants of the problem that exist as well as the various ways the problem behaves when different setup, computational assumptions and network models are considered. In this work we perform a systematization of knowledge in the landscape of consensus research starting with the original formulation in the early 1980s up to the present blockchain-based new class of consensus protocols. Our work is a roadmap for studying the consensus problem under its many guises, classifying the way it operates in many settings and highlighting the exciting new applications that have emerged in the blockchain era