Fault Tolerance In Wireless Sensor Networks Research Articles

Multi‐objective cluster head‐based energy aware routing using optimized auto‐metric graph neural network for secured data aggregation in Wireless Sensor Network

SummaryThe data are combined and transmitted through wireless sensor network (WSN) in internet of things applications. Multi‐Objective Cluster Head Based Energy Aware Routing using Auto‐Metric Graph Neural Network with Hybrid Balancing Composite Motion and Border Collie Optimization is proposed in this article for secured data aggregation (SDA) in WSN. The proposed method activates routing procedure via cluster head (CH). Hence, data aggregation basis Auto‐Metric Graph Neural Network selects CH depending upon multi‐objective fitness function. This fitness function deems factors, such as energy, delay, throughput, distance amid the nodes, capacity, collision, traffic rate, and cluster density. After CH selection, malicious node is present in the cluster. The optimum path is depending on three parameters: trust, connectivity, and quality of service (QoS). These three parameters have optimized under Hybrid Balancing Composite Motion and Border Collie Optimization method for ideal path selection. The ideal path is employed to transmit the gathered data to base station. The proposed technique is executed in network simulator, and its efficiency is evaluated under performance metrics, like network lifetime, packet delivery ratio, and delay. The proposed technique achieves 19.67%, 22.38%, and 27.45% better network lifetime; 28.92%, 25.83%, and 19.46% better packet delivery ratio; and 12.32%, 34.54%, and 22.33% lower delay while compared to the existing models: Taylor‐spotted hyena optimization for dependable and energy‐efficient CH selection‐based safe data routing and fault tolerance in WSN (T‐SHO‐DAWSN), multi‐objective cluster head utilizing SAPGAN for secured data aggregation (SAPGAN‐DAWSN), and energy efficient data accumulation design for protected routing protocol in WSN (MDAS‐DAWSN) methods, respectively.

A Survey of Data Aggregation Protocols for Energy Conservation in WSN and IoT

Conservation of energy has been a major concern for Wireless Sensor Networks (WSNs) and IoT applications. Several strategies were devised, aimed at optimizing energy consumption in these applications, based on: (a) use of low-powered hardware devices, (b) deploying mobile/relay agents for data collection, (c) clustering, and (d) data aggregation. Amongst these, data aggregation is widely acknowledged as an important tool to conserve energy in WSN and IoTs. The paper provides a comprehensive survey of various data aggregation strategies, discusses the efficacy of these strategies in handling issues that are typical to WSN and IoT applications. These issues severely impact the performance metrics such as: energy efficiency, latency, fault-tolerance, network throughput, and network lifetime. Therefore, to optimize the data aggregation approach, an application developer needs to arrive at optimal tradeoffs between these parameters. A major contribution of the paper is to present a holistic review of data aggregation approaches emphasizing the effect of topology, security, mobility, interference, and fault-tolerance in WSN and IoTs. Based on gap areas in literature, we throw open few challenges and present them as “posers”, and put-forth suggestions for further research.

Self-configuring BLE deep sleep network for fault tolerant WSN

This paper is focused on Wireless Sensor Network (WSN) leveraging on Bluetooth Low Energy (BLE) connectivity for low energy applications which is fault tolerant versus communication path failures. The topic is important to create a robust sensorized environment to be applied in industrial context or smart infrastructure to enable scheduled monitoring with low power consumption applications. Currently BLE applications are mainly thought for smart home solutions, health care and positioning systems. In those applications the BLE nodes are continuously supplied by external power suppliers. Our goal is to design a self-configuring network with a synchronized deep sleep behavior, aimed to optimize the energy consumption, with an overall active time interval constraint optimized with a data-driven method. The aim is to find a tradeoff between the on time and the ability to collect all the nodes data, pursuing a low power consumption. Our research is based on BLE protocols, interaction between edge systems for data collection and cloud system for data analysis and software agent optimization system. The paper analyses different configurations and describes the possible optimization algorithm to be used for the software agent design, in order to reach a fine-tuned control to improve the fault tolerance and fault diagnosis of the system. Finally experimental results are compared with the estimates obtained via a software simulation tool implemented for this architectural pattern.

Distributed Self-Stabilizing Capacitated Maximal Independent Set Construction in Wireless Sensor Networks

Wireless sensor networks (WSNs) are composed of a large number of wireless self-organized sensor nodes connected through a wireless decentralized distributed network without the aid of a predefined infrastructure. Fault-tolerance and power management are fundamental challenges in WSNs. A WSN is self-stabilizing if it can initially start at any state and obtain a legitimate state in a finite time without any external intervention. Self-stabilization is an important method for providing fault-tolerance in WSNs. Maximal independent set (MIS) is an extensively used structure for many important applications such as clustering (Randhawa and Jain in Wirel Personal Commun 97(3):3355, 2017. https://doi.org/10.1007/s11277-017-4674-5 ) and routing (Attea et al. in Wirel Personal Commun 81(2):819, 2015. https://doi.org/10.1007/s11277-014-2159-3 ; Lipinski in Wirel Personal Commun 101(1):251, 2018. https://doi.org/10.1007/s11277-018-5686-5 ) in WSNs. The capacitated MIS (CapMIS) problem is an extension of MIS in that each node has a capacity that determines the number of nodes it may dominate. In this paper, we propose a distributed self-stabilizing capacitated maximal independent set algorithm (CapMIS) in order to reduce energy consumption and support load balancing in WSNs. To the best of our knowledge, this is the first algorithm in this manner. The algorithm is validated through theoretical analysis as well as testbed implementations and simulations.

Distributed $k$-Connectivity Restoration for Fault Tolerant Wireless Sensor and Actuator Networks: Algorithm Design and Experimental Evaluations

Connectivity maintenance is an important requirement in wireless sensor and actuator networks (WSANs) because node failures can, potentially, lead to destructive changes in the network topology, which, in turn, can create a partitioned network. Preserving k-connectivity in a WSAN is important for keeping stable connections. A k-connected network is a network that remains connected after removing any k-1 nodes. Higher k values provide more reliable connectivity and a higher level of fault tolerance. In this article, we present a distributed k-connectivity restoration approach for heterogeneous WSANs where the nodes can be static or mobile. In the proposed algorithm, each node identifies the mobile nodes in the network and its 2-hop local subgraph. After a node is incapacitated, a neighbor of the failed node calls a mobile node with minimum moving cost to the location of the failed node if the failure reduces k. A minimum cost movement path between a neighbor of the failed node and a mobile node is constructed by considering the locations of the nodes, moving costs, and obstacles. Testbed experiments and comprehensive simulations reveal that the proposed distributed algorithm is capable of restoring k-connectivity with up to 35.5% lower sent Bytes and up to 40.9% lower movement cost than the existing algorithms.

Relay Node Placement in Wireless Sensor Networks: From Theory to Practice

The increasingly wide utilization of Wireless Sensor Networks (WSNs) in industrial applications outstands the significance of the Delay Constrained Relay Node Placement (DCRNP) problem. Existing algorithms to the DCRNP problem are designed based on the ideal geometric disk wireless channel model, and no real-world deployments are performed to verify the effectiveness of these algorithms. However, the unreliable and unpredictable wireless links in WSNs may lead these algorithms to fail in practice. Therefore, we first conduct extensive real-world deployments under the guidance of existing algorithms to evaluate their performance and to gain some insights for designing practical deployment algorithms. The results exhibit that the WSNs built by existing algorithms have a favorable performance in end-to-end delay but a poor performance in reliability, which is mainly due to the lack of methods ensuring high-quality links. To this end, we first devise a Set-Covering-based Algorithm (SCA) which figures out the DCRNP problem while ensuring the quality of each link better than a given threshold. As our experiments also show that the fault-tolerant topology can significantly improve network reliability, we then design a $k$ k -Set-Covering-based Algorithm ( $k$ k SCA) to build fault-tolerant WSNs based on the methodology of SCA. Furthermore, the elaborate analysis proves that both SCA and $k$ k SCA are polynomial-time algorithms, and their approximation ratios are both O( $\ln n$ ln n ), where $n$ n is the number of sensor nodes. Finally, extensive experiments are performed under the guidance of SCA and $k$ k SCA to demonstrate the effectiveness of these two algorithms.

NLFFT: A Novel Fault Tolerance Model Using Artificial Intelligence to Improve Performance in Wireless Sensor Networks

A wireless sensor network (WSN) is a collection of various tiny devices known as sensor nodes, which are also called motes. Due to high-energy consumption, the possibility of hardware, link or node failure, and some malicious attacks, sensor networks are considered error-prone networks. Hence, fault tolerance (FT) in WSN is one of the prominent issues. This article presents a novel FT approach named node-link failure fault tolerance model (NLFFT Model) in WSN, to handle the faults that occur either by link or node failure during data transmission from the sensor to the sink or base station. The NLFFT model consists of an improved quadratic minimum spanning tree (Imp-QMST) approach. This approach helps in finding the alternate link whenever it fails due to various situations and also an improved-handoff (Imp-Handoff) algorithm to support the node failure to the fault tolerance. Improved QMST presents a novel mechanism to find an alternate edge in place of the broken or failed edge in the spanning tree, to improve the fault tolerance in WSN. Imp-Handoff suggests a novel way to find the faulty node owing to less battery power and replaces a defective node by an appropriate neighbor to shift the tasks performed by a faulty node in WSN. Simulation results clearly state that as compared to the basic techniques i.e. Q-MST and Handoff algorithm, the proposed NLFFT model improvises the performance of WSN around by 7%. The results prove that the Imp-QMST gives about 6% improved throughput, 5% less end-to-end delay, and 6% less power consumption than the QMST algorithm. Similarly, Imp-Handoff improves about 4% throughput, 6% less end-to-end delay, and utilizes 7% less power consumption.

Fault tolerance in WSN through PE‐LEACH protocol

The fault is inevitable in case of the wireless sensor network (WSN) because of its remote deployment and constrained architecture. In literature, many algorithms have been proposed to address several types of fault in the WSN life-cycle. In the context of fault tolerance, the clustering method has been adopted as a proven technique. Heintzelman et al. first considered the clustering technique and proposed the seminal low energy adaptive clustering hierarchy (LEACH) algorithm for fault handling in WSN. In this study, the authors propose a LEACH variant clustering convention called partitioned-based energy-efficient – LEACH (PE-LEACH) protocol which tends to the energy-based fault-tolerant technique. They also study and present a taxonomy on LEACH variants. The execution of PE-LEACH is analysed against its predecessors; hard-computing based LEACH, energy-efficient LEACH (E-LEACH) convention and soft-computing-based energy-swarm-optimisation LEACH (ESO-LEACH). They have found through their recreation that PE-LEACH outperforms than LEACH and E-LEACH separately whereas, for ESO-LEACH the PE-LEACH is a tough competitor.

Detection and avoidance of water loss through municipality taps in India by using smart taps and ICT

Global climate change and the rapid population growth have been seriously impacting water sources. The existing traditional water distribution network (WDN) fails to manage proper water distribution according to the consumption rate. The current situation demands an intelligent water management and treatment model to keep water available in the long run. Information and Communications Technology (ICT) is a combination of the Internet of Things and information which introduces machine-to-machine communication with data for management. An ICT-based WDN which helps in monitoring excessive water supply and preventing water loss is proposed. The designed architecture is based on a heterogeneous wireless sensor network (WSN) and a smart tap which detects water loss and communicates the report to the utility centre. A fault-tolerant WSN routing scheme to handle the faulty situation during the transmission process is also proposed. The district of Sambalpur in Odisha, India is the chosen location for this study.

An energy-efficient, self-stabilizing and distributed algorithm for maximal independent set construction in wireless sensor networks

Maximal independent set (MIS) is a very important structure that provides data aggregation, topology control and routing for wireless sensor networks (WSNs). Energy-efficient and fault-tolerant construction of MIS on WSNs is one of the vital tasks. A distributed sensor network is self-stabilizing if it can initially start at any state and regain a legal state in a finite time without any external intervention. Self-stabilization is a considerable method to provide fault tolerance in WSNs. This paper presents a distributed self-stabilizing MIS algorithm which is an improved version of Turau’s algorithm under a fully distributed scheduler for WSNs. The proposed algorithm is theoretically analyzed and evaluated with its counterparts. The proposed algorithm is compared with the other studies through testbed experiments on IRIS nodes and simulations on TOSSIM environment. It is shown that the proposed algorithm outperforms other algorithms in terms of move count and energy consumption.

A fault tolerance based route optimisation and data aggregation using artificial intelligence to enhance performance in wireless sensor networks

In the on-demand usage of wireless sensor networks over the internet, fault tolerance is an exigent task to improve the overall performance of service computing. In the proposed research work, an attempt has been made to make use of an artificial bee colony approach to find data aggregation for providing fault tolerance in wireless sensor networks (WSNs) and to make effective utilisation of the existing resources over the internet. In this paper, it is tried to apply quadratic minimum spanning tree (Q-MST) which is an artificial intelligence technique to provide fault tolerance along with data aggregation in WSN. Q-MST is used to improve the fault tolerance in WSN to transmit data packets from the source node to sink node. Ant colony, PRIMS and Particle Swarm Optimisation (PSO) algorithms are used for generating minimum spanning tree (MST) which can be used for data aggregation. The Q-MST is an improved version of minimum spanning tree where an ordered pair of distinct edges would be considered for implementing an alternative edge for the existing edge failure in MST.

A Cross-Layer Fault Tolerant Protocol with Recovery Mechanism for Clustered Sensor Networks

This article describes how fault tolerance is an essential issue for many WSN (Wireless Sensor Network) applications such as wildlife monitoring, battlefield surveillance and health monitoring. It represents a great challenge for researchers regarding to the characteristics of sensor nodes which are prone to failures due essentially to their limited resources. Faults may occur, not only when sensor nodes exhaust their energy, but also when the congestion phenomenon emerges, because of a high traffic in the network and limited storage capacity of the sensor nodes. In order to support fault tolerance in WSNs, the authors propose a new scheme which incorporates a link quality estimation algorithm and a congestion detection mechanism to enable nodes that present high quality links to be chosen for routing in a non-congested area in case of faults. Evaluations through simulations under NS2 show that our proposed protocol tolerates faults with a minimum cost relatively to HEEP protocol and improves network's performances comparatively to other fault tolerant protocols such as EF-LEACH.

Trust Awaken Routing for Improving The Reliable Communication in WSN

Wireless Sensor Networks (WSN) is of ubiquitous importance in the present day wireless communication. While disseminated architecture delights a more security and fault-tolerant WSN, such architecture also places a number of security challenges particularly when employed in vital applications such as health care, environment monitoring and battlefield. In this paper, we propose Trust Awaken Routing for Improving the Reliable Communication in WSN. The main objective of this paper is to develop trust management for WSNs, and this technique is used to improve the reliability of data transmission in the network. In this scheme, the trust Awaken Factor is used to select the reliable path from source to destination in the system. trust Awaken Factor computed by node interaction, node recommendation, quick response and node selfishness. The simulation results show the better improvement in throughput and reduce the network delay in the network.

Response time optimization with enhanced fault-tolerant wireless sensor network design for on-board rapid transit applications

In the recent decades, rapid transit system (RTS) plays a significant key role to form most affordable and comfort travel zone to humans. With increase in population and usage of RTS, safety measures are concerned to be most notable factor in the world. Moreover, during emergency cases internal wired safety setups of any RTS does not prevents drastic disaster and allows negative impact for on-demand reliable changes. As an alternative to other expensive technologies, wireless sensor network (WSN) system provides fast communicative and compact applications to RTS. Inorder to reduce causalities, designing such a network application to RTS always poses challenging problems in terms of fault tolerance and time bounded packet delivery. Integrated fuzzy-logic based synchronized data transmission (IFSDT) algorithm has been designed to optimize those challenges, especially in RTS. IFSDT is a synchronized energy aware algorithm that prevents the occurrence of potential faults during unexpected emergencies in the coaches of RTS and ensures the fast transmission of critical broadcast signal and response in a limited time span. Furthermore, the results of the proposed design performs better than the existing reliable and energy optimized WSN design and exhibits the optimized response time for broadcasting emergency signals which is below 0.02 s.

Exploiting self-organization and fault tolerance in wireless sensor networks: A case study on wildfire detection application

The wildfires caused by human occupation is one of the factors that most contributes to deforestation of conservation areas, resulting in a number of issues for ecological systems. Premature fire detections lead to the elimination or reduction of the damage that will be caused by a fire incident. Wireless sensor networks have been shown to be a good alternative for environmental monitoring applications, as they can collect and send information in real time, such as humidity, wind, and temperature of various parts of the forest. Due to problems such as power limitation, communication failure, and loss of nodes, the network topology is constantly changing, requiring mechanisms to achieve self-organization and fault tolerance. This article proposes the development of a model and application in self-organizing and fault-tolerant wireless sensor networks for fire detection in conservation areas. To achieve self-organization and fault tolerance, we rely on encouraged local interactions between neighboring nodes that monitor the same region and the coordination of tasks, through a supervisor node, equipped with a framework for developing component-based fault-tolerant applications. For the analysis and validation of both model and application, it was simulated 60 events in the network of sensors in a real scenario using the simulator Sinalgo. The results were classified as True (partial or absolute) or False (partial or absolute). In all, 45% of consensus identified a possible fault in the application and in only 35% there was absolute consensus.

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

Wireless 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.

Research of a resource-efficient, real-time and fault-tolerant wireless sensor network system

Memory optimization, energy conservation, real-time scheduling and fault tolerance are the essential research topics in the wireless sensor network (WSN). In this paper, a memory-efficient, energy-efficient, real-time and fault-tolerant WSN system LiveOS is designed and implemented. Compared to the other systems, LiveOS has several typical features. Firstly, the optimized Rate-Monotonic Scheduling (RMS) scheduling algorithm is implemented. By doing this, the memory cost of the multithreaded real-time operating system (RTOS) can be 47.3% optimized if compared to the traditional single-core node. Consequently, the RTOS becomes appropriate to run even on the memory-constrained WSN nodes. Secondly, the new research approach that addresses the WSN challenges by combining both the multi-core hardware technique and the software technique is applied in LiveOS. By means of the multi-core hardware support, the energy cost of LiveOS can be 35% optimized compared to the traditional single-core platform. Moreover, the real-time scheduling performance and the fault-tolerant ability of the WSN nodes can be optimized significantly. Due to the above features, LiveOS becomes the WSN system which has small memory footprint, long lifetime, high real-time scheduling performance and upstanding fault-tolerant ability. The evaluation on the different prototype nodes proves that LiveOS is appropriate to run on the resource-constrained WSN platforms and is competent to execute the outdoor real-time WSN applications.

Optimizing the Reliability and Performance of Service Composition Applications with Fault Tolerance in Wireless Sensor Networks.

The services composition technology provides flexible methods for building service composition applications (SCAs) in wireless sensor networks (WSNs). The high reliability and high performance of SCAs help services composition technology promote the practical application of WSNs. The optimization methods for reliability and performance used for traditional software systems are mostly based on the instantiations of software components, which are inapplicable and inefficient in the ever-changing SCAs in WSNs. In this paper, we consider the SCAs with fault tolerance in WSNs. Based on a Universal Generating Function (UGF) we propose a reliability and performance model of SCAs in WSNs, which generalizes a redundancy optimization problem to a multi-state system. Based on this model, an efficient optimization algorithm for reliability and performance of SCAs in WSNs is developed based on a Genetic Algorithm (GA) to find the optimal structure of SCAs with fault-tolerance in WSNs. In order to examine the feasibility of our algorithm, we have evaluated the performance. Furthermore, the interrelationships between the reliability, performance and cost are investigated. In addition, a distinct approach to determine the most suitable parameters in the suggested algorithm is proposed.

Fault Tolerant Multi-Criteria Multi-Path Routing in Wireless Sensor Networks

The Ad Hoc On-Demand Multi-Path Distance Vector (AOMDV) routing protocol allows the transport of data along one or more paths in wireless sensor networks (WSNs). The path chosen is based on a single shortest path hop count metric. The data on some WSNs is mission critical, for example, military and health care applications. Hence, fault tolerance in WSNs is becoming increasingly important. To improve the fault tolerance of WSNs in lossy environments, this work adds to the AOMDV routing protocol as it incorporates an additional packet loss metric. This Multi-criteria AOMDV or M-AOMDV is evaluated using the ns2 simulator. Simulations show that M- AOMDV maintains relatively low packet loss rates when the WSN is experiencing loss.

Modeling and Analysis of Fault Detection and Fault Tolerance in Wireless Sensor Networks

Technological advancements in communications and embedded systems have led to the proliferation of Wireless Sensor Networks (WSNs) in a wide variety of application domains. These application domains include but are not limited to mission-critical (e.g., security, defense, space, satellite) or safety-related (e.g., health care, active volcano monitoring) systems. One commonality across all WSN application domains is the need to meet application requirements (e.g., lifetime, reliability). Many application domains require that sensor nodes be deployed in harsh environments, such as on the ocean floor or in an active volcano, making these nodes more prone to failures. Sensor node failures can be catastrophic for critical or safety-related systems. This article models and analyzes fault detection and fault tolerance in WSNs. To determine the effectiveness and accuracy of fault detection algorithms, we simulate these algorithms using ns-2. We investigate the synergy between fault detection and fault tolerance and use the fault detection algorithms’ accuracies in our modeling of Fault-Tolerant (FT) WSNs. We develop Markov models for characterizing WSN reliability and Mean Time to Failure (MTTF) to facilitate WSN application-specific design. Results obtained from our FT modeling reveal that an FT WSN composed of duplex sensor nodes can result in as high as a 100% MTTF increase and approximately a 350% improvement in reliability over a Non-Fault-Tolerant (NFT) WSN. The article also highlights future research directions for the design and deployment of reliable and trustworthy WSNs.