Survey on DAG Based Fault Tolerance in Wireless Multimedia Sensor Network

Blockchain technology relies on decentralized data management across various applications to ensure secure transactions. Consensus algorithms play a critical role in maintaining consistency and trust within decentralized environments, laying the groundwork for further exploration. This paper presents a simulation-based benchmarking framework for evaluating major consensus mechanisms Proof of Work (PoW), Proof of Stake (PoS), Proof of Authority (PoA), Proof of Capacity (PoC), Practical Byzantine Fault Tolerance (PBFT), Directed Acyclic Graph (DAG), and Proof of Biometric Uniqueness (PoBU). A MATLAB-based simulation environment integrated with Docker-emulated blockchain nodes was developed to measure performance across several parameters, including throughput, latency, energy consumption, finality probability, fault tolerance, and resource requirements. Results show that DAG achieves the highest throughput (1600 TPS) and lowest latency (0.8 s), while PoBU offers strong fault tolerance (50%) and energy efficiency (60 J/tx). PBFT and PoA provide ultra-fast confirmations with limited decentralization, whereas PoW demonstrate poor scalability and energy efficiency. These findings highlight the potential for a unified benchmarking framework for consensus mechanisms and provide practical insights for selecting consensus protocols for specific blockchain applications.

We present a novel multiple testing method for testing null hypotheses that are structured in a directed acyclic graph (DAG). The method is a top-down method that strongly controls the familywise error rate and can be seen as a generalization of Meinshausen's procedure for tree-structured hypotheses. Just as Meinshausen's procedure, our proposed method can be used to test for variable importance, only the corresponding variable clusters can be chosen more freely, because the method allows for multiple parent nodes and partially overlapping hypotheses. An important application of our method is in gene set analysis, in which one often wants to test multiple gene sets as well as individual genes for their association with a clinical outcome. By considering the genes and gene sets as nodes in a DAG, our method enables us to test both for significant gene sets as well as for significant individual genes within the same multiple testing procedure. The method will be illustrated by testing Gene Ontology terms for evidence of differential expression in a survival setting and is implemented in the R package cherry.

A new distributed algorithm for the dynamic computation of multiple loop-free paths from source to destination in a computer network or Internet are presented, validated, and analyzed. According to this algorithm, which is called DASM (diffusing algorithm for shortest multipath), each router maintains a set of entries for each destination in its routing table, and each such entry consists of a set of tuples specifying the next router and distance in a loop-free path to the destination. DASM guarantees instantaneous loop freedom of multipath routing tables by means of a generalization of Dijkstra and Scholten's diffusing computations. With generalized diffusing computations, a node in a directed acyclic graph (DAG) defined for a given destination has multiple next nodes in the DAG and is able to modify the DAG without creating a directed loop. DASM is shown to be loop-free at every instant, and its average performance is analyzed by simulation and compared against an ideal link-state algorithm and the diffusing update algorithm (DUAL).

Confounding can significantly distort the findings of studies examining cause-and-effect relationships, especially in etiological research. To mitigate this issue, researchers must carefully assess potential confounding variables that may relate to both the exposure and outcome but are not directly influenced by the exposure itself. It is essential that these variables truly impact the outcome rather than simply being correlated with the exposure to avoid false associations. Strengthening confidence in the actual relationship between exposure and outcome requires an understanding of biological mechanisms and the application of various methods to adjust for confounders. The oversight of confounding often arises from inappropriate statistical tests and the aggregation of data across multiple studies. This scoping review article discusses the challenges posed by confounding and presents machine learning approaches for effective control in health science research. Directed acyclic graphs (DAGs) serve as causal graph tools to identify potential confounding variables in health research. By mapping presumed relationships between variables, DAGs enable researchers to estimate causal effects more accurately. While traditional methods such as randomization, matching, and stratification remain effective for controlling confounding, newer techniques like latent variable modeling with negative controls and machine learning methods such as LASSO, Ridge regression, and random forests offer enhanced flexibility and adaptability.

In this paper we investigate the problem of delivering real time multimedia streams in a MAN environment. Most research in this area presumes the scenario where all packets of a stream are transmitted along a single path from the sender to a receiver In this paper, we propose a new application layer multicast scheme, namely ROME, to better fulfill heterogeneous QoS requirements from various receivers simultaneously by setting up multiple paths. Instead of enforcing a multicast tree structure, a multicast DAG (Directed Acyclic Graph) is constructed in ROME, where multiple nodes in the multicast group may share the responsibility to deliver the stream to a node. To achieve this each stream is partitioned into a set of stream shares. A QoS oriented share assignment technique is proposed to distribute stream shares progressively among delivery paths to maximize the number of fulfilled clients while minimizing the overall bandwidth usage. Moreover, both the delivery paths and the stream partition can be adapted easily on the fly to accommodate multicast group membership change during the real time delivery. Last but not least, we evaluate our scheme via simulations and show that the proposed scheme can fulfill a larger number of clients with higher QoS requirements than existing approaches under the same network capacity.

Before an application modelled as a directed acyclic graph (DAG) is executed on a heterogeneous system, a DAG mapping policy is often enacted. After mapping, the tasks (in the DAG-based application) to be executed at each computational resource are determined. The tasks are then sent to the corresponding resources, where they are orchestrated in the pre-designed pattern to complete the work. Most DAG mapping policies in the literature assume that each computational resource is a processing node of a single processor, i.e. the tasks mapped to a resource are to be run in sequence. Our studies demonstrate that if the resource is actually a cluster with multiple processing nodes, this assumption will cause a mis-perception in the tasks' execution time and execution order. This will disturb the pre-designed cooperation among tasks so that the expected performance cannot be achieved. In this paper, a DAG mapping algorithm is presented for multicluster architectures. Each constituent cluster in the multicluster is shared by background workload (from other users) and has its own independent local scheduler. The multicluster DAG mapping policy is based on theoretical analysis and its performance is evaluated through extensive experimental studies. The results show that compared with conventional DAG mapping policies, the new scheme that we present can significantly improve the scheduling performance of a DAG-based application in terms of the schedule length.

Fault tolerant aggregation in heterogeneous sensor networks

A formal treatment to the security of Concealed Data Aggregation (CDA) and the more general Private Data Aggregation (PDA) is given. While there exist a handful of constructions, rigorous security models and analyses for CDA or PDA are still lacking. Standard security notions for public key encryption, including semantic security and indistinguishability against chosen ciphertext attacks, are refined to cover the multisender nature and aggregation functionality of CDA and PDA in the security model. The proposed security model is sufficiently general to cover most application scenarios and constructions of privacy-preserving data aggregation. An impossibility result on achieving security against adaptive chosen ciphertext attacks in CDA/PDA is shown. A generic CDA construction based on public key homomorphic encryption is given, along with a proof of its security in the proposed model. The security of a number of existing schemes is analyzed in the proposed model.

In hop-by-hop networks, provision of multipath routes for all nodes can improve fault tolerance and performance. In this paper we study the multipath route calculation by constructing a directed acyclic graph (DAG) which includes all edges in the network. We define new DAG construction problems with the objectives of 1) maximizing the minimum connectivity, 2) maximizing the minimum max-flow, and 3) max- imizing the minimum max-flow as an extension of shortest path routing. A family of new algorithms called Maximum Alternative Routing Algorithms (MARAs) is described, proven formally to solve the problems optimally, and contrasted with existing multipath algorithms. MARAs are evaluated for the number of paths, the length of paths, the computational complexity, and the computation time, using simulations based on several real Internet Autonomous System (AS) network topologies. We show that MARAs run in sub-second times on moderate-speed processors and achieve a significant increase in the number of paths compared to existing multipath routing algorithms. These results should help further the process of deploying multipath routing in real-world networks.

Modern enterprise data platforms increasingly operate under conditions of extreme scale, heterogeneity, and uncertainty. Traditional data pipeline orchestration frameworks rely on static Directed Acyclic Graphs (DAGs) and deterministic retry semantics, which are fundamentally misaligned with environments characterized by schema volatility, infrastructure churn, and non-stationary workloads. This paper presents a comprehensive architectural model for Multi-Agent Orchestrated Data Pipelines (MODP), where autonomous agents replace task-centric orchestration with goal-driven reasoning.The architecture integrates four primary subsystems: an Agent Orchestrator, a Knowledge Plane grounded in Retrieval-Augmented Generation (RAG), a Unified Feature Store, and a Causal Tracing Engine. Together, these components enable self-healing execution, dynamic schema adaptation, and causal observability across the data lifecycle. Empirical evidence from large-scale distributed systems research demonstrates that agent-based orchestration improves fault tolerance, reduces mean time to recovery (MTTR), and significantly enhances developer productivity. This work formalizes agentic data engineering as a shift from procedural execution to intent-based systems, positioning autonomous multi-agent orchestration as a foundational design principle for next-generation data platforms.

Sensor networks are collection of sensor nodes which co-operatively send sensed data to base station. The major problem in the field of wireless network is draining of sensor's battery. As the senor nodes are driving high power so there is a need of an intelligent algorithm which could enable the sensors to utilize the energy in an energy efficient manner. It will also enable the network to work for longer duration of time as well as reduces trafficking inside network. Data Aggregation is a technique which can solve these problems. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. It involves the selective transfer of data from the nodes to the base station. Wireless sensor networks (WSN) offer an increasingly Sensor nodes need less power for processing as compared to transmitting data. It is preferable to do in network processing inside network and reduce packet size. One such approach is data aggregation which attractive method of data gathering in distributed system architectures and dynamic access via wireless connectivity. In this paper, a data aggregation framework on wireless sensor networks is presented. The framework works as a middleware for aggregating data measured by a number of nodes within a network. Also the paper briefly discusses a case of sending the temperature reading from the source node to the destination. It also provides techniques in order to send the minimum data at the cost of a small error. The aim of the proposed work is to compare the performance of SPIN in terms of energy efficiency in comparison with and without data aggregation in wireless sensor networks and to assess the suitability of the protocol in an environment where resources are limited.

We propose a Gibbs sampler for structure learning in directed acyclic graph (DAG) models. The standard Markov chain Monte Carlo algorithms used for learning DAGs are random-walk Metropolis-Hastings samplers. These samplers are guaranteed to converge asymptotically but often mix slowly when exploring the large graph spaces that arise in structure learning. In each step, the sampler we propose draws entire sets of parents for multiple nodes from the appropriate conditional distribution. This provides an efficient way to make large moves in graph space, permitting faster mixing whilst retaining asymptotic guarantees of convergence. The conditional distribution is related to variable selection with candidate parents playing the role of covariates or inputs. We empirically examine the performance of the sampler using several simulated and real data examples. The proposed method gives robust results in diverse settings, outperforming several existing Bayesian and frequentist methods. In addition, our empirical results shed some light on the relative merits of Bayesian and constraint-based methods for structure learning.

The quality of shortwave channels may vary considerably due to the time-specific changes of the transmission conditions. So the correct transmission of a continuous data stream, e.g. digitized speech signals output by a vocoder cannot be guaranteed under adverse conditions even by means of complex error correction measures or costly high transmitter power. This results in the loss of vocoder data-the transmission of speech information is partly or fully interrupted which impairs intelligibility. For this reason we propose a voice transmission system as a solution to the problem of correct voice data transmission. It is a method that offers quasi-real-time behaviour for channels that are subject to no interference or only slight interference, and that offers complete and correct data transmission with reduced real-time behaviour (mailbox operation) in the presence of strong or long-term interference and fading. To reach intelligibility at the transmitter end, the vocoder data are first divided into intelligible blocks, e.g. into coherent words between two speech pauses and stored in a buffer. The blocks are then transferred to the lower layers of the ISO/OSI reference model where an ARQ protocol is employed which leads to correct data transmission. At the receiver end, the blocks can then be listened to individually or as a whole and, if necessary, repeated once or several times (mailbox operation). This method was tested over a radio link of 800 km between Rostock University and Rohde & Schwarz Munich laboratory. 1200 bps vocoders from Comsat Co. and XK 2100 transceivers with packet radio protocol (PRP) RSX.25 from Rohde & Schwarz were used.

Preface

The paper presents ServeR – the system for enabling remote statistical calculation supported by R-project. Multilayer architecture and Windows Communication Foudantion technology usage allow to deploy the system in many configurations adapted to network infrastructure. This makes the system scalable and fault tolerant in some aspect. Usage of ServeR in two modes – off-line (batch mode) and on-line (remote R-sesssion) is shown in detailed examples of statistical tasks. A new feature for increasing system security – an isolated storage mechanism used by statistical task processes – is considered.