Fault-Tolerant Aggregation: Flow-Updating Meets Mass-Distribution

Flow-Updating (FU) is a fault-tolerant technique that has proved to be efficient in practice for the distributed computation of aggregate functions in communication networks where individual processors do not have access to global information. Previous distributed aggregation protocols, based on repeated sharing of input values (or mass) among processors, sometimes called Mass-Distribution (MD) protocols, are not resilient to communication failures (or message loss) because such failures yield a loss of mass. In this paper, we present a protocol which we call Mass-Distribution with Flow-Updating (MDFU). We obtain MDFU by applying FU techniques to classic MD. We analyze the convergence time of MDFU showing that stochastic message loss produces low overhead. This is the first convergence proof of an FU-based algorithm. We evaluate MDFU experimentally, comparing it with previous MD and FU protocols, and verifying the behavior predicted by the analysis. Finally, given that MDFU incurs a fixed deviation proportional to the message-loss rate, we adjust the accuracy of MDFU heuristically in a new protocol called MDFU with Linear Prediction (MDFU-LP). The evaluation shows that both MDFU and MDFU-LP behave very well in practice, even under high rates of message loss and even changing the input values dynamically.Comment: 18 pages, 5 figures, To appear in OPODIS 201

Network Survivability Performance Evaluation in Underwater Surveillance System Using Markov Model

Underwater Wireless Sensor Network (UWSN) is a useful technology that can be used in Underwater Surveillance System (USS). USSs are mostly used in military purposes for detecting underwater military activities. One of the most important issues in USS is mission reliability or survivability. Due to harsh underwater environment and mission critical nature of military applications, it is important to measure survivability of USS. Underwater sensor node failures can be detrimental for USS. To improve survivability in USS, we propose a fault-tolerant underwater sensor node model. To the best of our knowledge, this is the first fault-tolerant underwater sensor node model in USS that evaluates survivability of an USS.  We develop Markov models for characterizing USS survivability and MTTF (Mean Time to Failure) to facilitate USS. Performance evaluation results show the effectiveness of proposed model

Context-Capture Multi-Valued Decision Fusion With Fault Tolerant Capability For Wireless Sensor Networks

Wireless sensor networks (WSNs) are usually utilized to perform decision fusion of event detection. Current decision fusion schemes are based on binary valued decision and do not consider bursty contextcapture. However, bursty context and multi-valued data are important characteristics of WSNs. One on hand, the local decisions from sensors usually have bursty and contextual characteristics. Fusion center must capture the bursty context information from the sensors. On the other hand, in practice, many applications need to process multi-valued data, such as temperature and reflection level used for lightening prediction. To address these challenges, the Markov modulated Poisson process (MMPP) and multi-valued logic are introduced into WSNs to perform context-capture multi-valued decision fusion. The overall decision fusion is decomposed into two parts. The first part is the context-capture model for WSNs using superposition MMPP. Through this procedure, the fusion center has a higher probability to get useful local decisions from sensors. The second one is focused on multi-valued decision fusion. Fault detection can also be performed based on MVL. Once the fusion center detects the faulty nodes, all their local decisions are removed from the computation of the likelihood ratios. Finally, we evaluate the capability of context-capture and fault tolerant. The result supports the usefulness of our scheme.Comment: 13 pages, 7 figure

Hierarchical fault tolerance in wireless networked control systems

Wireless Networked Control Systems (WNCS) have recently emerged as a replacement for wired control networks. Wireless networked control systems are more suitable for environments that require higher flexibility and robustness. In previous literature a wireless manufacturing line was proposed. The work-cells communication was through IEEE 802.11 technologies and a switched Ethernet backbone. This thesis is aiming to improve the current solution by adding a supervisor to the existing system. The supervisor could be either in passive or active mode. Passive supervisor would intervene when all controllers in the network fail, while active supervisor would act once any controller on the line fail. The system was simulated using OPNET software with 95% confidence analysis. The ability of the system to withstand external interference was assessed through adding a single band jammer to the OPNET simulation. The system was able to hold up to 8KB interfering file sent from a single band jammer affecting the full Wi-Fi spectrum. All results were subjected to a 95% confidence analysis The performability of passive and active supervisor systems was compared. A Markov model of both systems was built. It was shown that by time, the performability of a passive supervisor system is enhanced while that of an active supervisor system degraded. However, the active supervisor showed a better performability in all cases

Fault tolerance in WBAN applications

One of the most promising applications of IoT is Wireless Body Area Net-works (WBANs) in medical applications. They allow physiological signals monitoring of patients without the presence of nearby medical personnel. Furthermore, WBANs enable feedback action to be taken either periodically or event-based following the Networked Control Systems (NCSs) techniques. This thesis first presents the architecture of a fault tolerant WBAN. Sensors data are sent over two redundant paths to be processed, analyzed and monitored. The two main communication protocols utilized in this system are Low power Wi-Fi (IEEE 802.11n) and Long Term Evolution (LTE). Riverbed Modeler is used to study the system’s behavior. Simulation results are collected with 95% confidence analysis on 33 runs on different initial seeds. It is proven that the system is fully operational. It is then shown that the system can withstand interference and system’s performance is quantified. Results indicate that the system succeeds in meeting all required control criteria in the presence of two different interference models. The second contribution of this thesis is the design of an FPGA-based smart band for health monitoring applications in WBANs. This FPGA-based smart band has a softcore processor and its allocated SRAM block as well as auxiliary modules. A novel scheme for full initial configuration and Dynamic Partial Reconfiguration through the WLAN network is integrated into this design. Fault tolerance techniques are used to mitigate transient faults such as Single Event Upsets (SEUs) and Multiple Event Upsets (MEUs). The system is studied in a normal environment as well as in a harsh environment. System availability is then obtained using Markov Models and a case study is presented

A Smart Checkpointing Scheme for Improving the Reliability of Clustering Routing Protocols

In wireless sensor networks, system architectures and applications are designed to consider both resource constraints and scalability, because such networks are composed of numerous sensor nodes with various sensors and actuators, small memories, low-power microprocessors, radio modules, and batteries. Clustering routing protocols based on data aggregation schemes aimed at minimizing packet numbers have been proposed to meet these requirements. In clustering routing protocols, the cluster head plays an important role. The cluster head collects data from its member nodes and aggregates the collected data. To improve reliability and reduce recovery latency, we propose a checkpointing scheme for the cluster head. In the proposed scheme, backup nodes monitor and checkpoint the current state of the cluster head periodically. We also derive the checkpointing interval that maximizes reliability while using the same amount of energy consumed by clustering routing protocols that operate without checkpointing. Experimental comparisons with existing non-checkpointing schemes show that our scheme reduces both energy consumption and recovery latency

An ANFIS estimator based data aggregation scheme for fault tolerant Wireless Sensor Networks

AbstractWireless Sensor Networks (WSNs) are used widely in many mission critical applications like battlefield surveillance, environmental monitoring, forest fire monitoring etc. A lot of research is being done to reduce the energy consumption, enhance the network lifetime and fault tolerance capability of WSNs. This paper proposes an ANFIS estimator based data aggregation scheme called Neuro-Fuzzy Optimization Model (NFOM) for the design of fault-tolerant WSNs. The proposed scheme employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) estimator for intra-cluster and inter-cluster fault detection in WSNs. The Cluster Head (CH) acts as the intra-cluster fault detection and data aggregation manager. It identifies the faulty Non-Cluster Head (NCH) nodes in a cluster by the application of the proposed ANFIS estimator. The CH then aggregates data from only the normal NCHs in that cluster and forwards it to the high-energy gateway nodes. The gateway nodes act as the inter-cluster fault detection and data aggregation manager. They pro-actively identify the faulty CHs by the application of the proposed ANFIS estimator and perform inter-cluster fault tolerant data aggregation. The simulation results confirm that the proposed NFOM data aggregation scheme can significantly improve the network performance as compared to other existing schemes with respect to different performance metrics