Fault Tolerant Techniques in USNs: History
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Subjects: Automation & Control Systems
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Lauri Vihman

Sensor networks provide services to a broad range of applications ranging from intelligence service surveillance to weather forecasting. While most of the sensor networks are terrestrial, Underwater Sensor Networks (USN) are an emerging area. One of the unavoidable and increasing challenges for modern USN technology is tolerating faults, i.e., accepting that hardware is imperfect, and coping with it. Fault Tolerance tends to have more impact in underwater than in terrestrial environment as the latter is generally more forgiving. Moreover, reaching the malfunctioning devices for replacement and maintenance under water is harder and more costly.

  • underwater sensor network
  • fault tolerance
  • cross-layer fault tolerance
  • fault management

1. Introduction

Underwater Sensor Networks (USNs) have become widespread and are being deployed in a wide range of applications ranging from harbor security to monitoring underwater pipelines and fish farms. Due to the fact that USNs often operate in an extremely harsh environment, and many of their applications are safety-critical, it is imperative to develop techniques enabling these networks to tolerate faults. Moreover, USNs face many challenges that are not present in terrestrial networks, such as virtual inapplicability of the wireless radio communication under water and limitations of the acoustic means, for example.

2. Specifics of Underwater Sensor Networks

Environmental and engineering challenges for sensor networks in underwater environments are shown on Figure 1. An underwater environment is mostly different from a terrestrial one due to the harsh physical conditions—high pressure and hard accessibility, as well as limited communication and energy resources. Depending on the specific location, the temperature may fall with increasing depth, which may affect, e.g., the battery lifetime. In underwater environments, faults can be caused over time by ambient flowing water generated by surface waves or other reasons that shake the components of the sensor networks. Moreover, faults can be introduced by humans or aquatic organisms.
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Figure 1. Environmental and engineering challenges in USNs.
Many communication methods are unavailable underwater, and there are multiple phenomena [1][2] that obstruct communication there. Because of the possibility of flooding the hardware due to water leakage, more attention and resources should be paid to the physical integrity of sensor nodes. On the other hand, faults from excessive heat should be rare and avoidable underwater. In the underwater context, Fault Tolerance has been so far addressed for reliant UWSN networking [1][3][4][5], space localization [6], and monitoring underwater pipelines [7]. While it should be possible to adapt most of the generic Fault Tolerance concepts for the underwater use, the environment is more demanding and unforgiving, and faults are more costly. Some more demanding approaches, like cloud computing, may not make sense to be implemented in USNs. However, the authors cannot see any obstacles for applying those fault tolerant approaches that yield appropriate communication methods, low network bandwidths, and power requirements in the underwater domain.
Last but not least, one of the promising approaches that could be adapted successfully within the underwater environment’s constraints appears to be cross-layer resilience, which is an open research topic and lacking in recent research works, even for the terrestrial implementations.

3. Taxonomy of Faults and Fault Tolerance Tasks

In the following, we present the taxonomy of the sources of faults, as well as of the Fault Tolerance tasks. The objective of describing and representing these taxonomies is to categorize the articles for the current survey.

3.1. Sources of Faults

A fault is defined [8] as an underlying defect of a system that leads to an error. An error is a faulty system state, which may lead to failure, and failure is an error that affects system functionality. Faults may occur in different components and layers of systems for different reasons. The only type of fault possible in software is a design fault introduced during the software development, i.e., a bug [9]. Software bugs can be addressed separately and will not be covered further in the current paper.
Fault sources can be categorized by components where they occur. In sensor networks, they can occur in sensor nodes, in the communication network, and in the data sink [10]. Sensor networks share common failure issues with traditional networks, as well as introduce node failures as new fault sources [11].
USNs additionally introduce faults caused by environmental conditions, such as pressure, currents, underwater obstacles, etc. Those conditions may cause physical damage that may result in failures, as well as obstruct the system’s functionality. For instance, in underwater acoustic networks, loss of connection and high bit error rate may be caused by shadow zones [2] formed by different physical reasons. Domingo and Vuran distinguish up to five different underwater propagation phenomena which may obstruct communication [1].
Faults can either be permanent or temporary [12]. Permanent faults may be caused by manufacturing defects, as variances of the hardware components are inevitable due to physical reasons [13]. One of the other factors that can introduce faults is aging and wear-out of the hardware components [14]. In addition to the components themselves, the interconnections between them are also affecting the reliability and may cause faults [15].
One of the challenges of fault management is temporary faults, especially soft errors. Soft error is a temporary change of signal value due to ionizing particles [12] that may lead to failure. Due to high integration density, it is estimated that soft failure rate is increasing in the future [16]. Another potential source of temporary faults is electromagnetic interference [17].

3.2. Fault Tolerance Tasks

The objective of the current section is to define a taxonomy of Fault Tolerance tasks to help categorize the identified papers. The Fault Tolerance tasks are based on more general Fault Tolerance principles from References [18][19]. Figure 2. shows the taxonomy of Fault Tolerance tasks applicable in USNs and how they affect each other. While the design and initial deployment of USNs contribute to Fault Prevention and Prediction abilities, data collecting techniques at the run-time contribute also to Fault Detection and Fault Recovery stages of the system, all of which are going to be discussed in the current paper.

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Figure 2. Taxonomy of Fault Tolerance tasks in USNs.

The techniques under consideration can be categorized into the following groups:

  • Fault Prediction and Prevention
    This task is about both preventing a fault to happen, as well as about proactive fault avoidance. Sensor networks can prevent certain faults from happening by design and/or deployment aspects. A disadvantage of fault prevention is a potentially increased system complexity. Fault avoidance, in turn, includes manufacturing testing and verification, which have a high cost often exceeding that of the entire design process.
  • Fault Detection and Identification
    One of the central parts of Fault Tolerance is Fault Detection and Fault Identification of affected components which can, for instance, be performed by utilizing data collection with ping messages. Without Fault Identification, for instance, sensor node and network faults may be hard to distinguish. A disadvantage of Fault Detection and Fault Identification may be additional energy requirements and network congestion.
  • Fault Isolation, Masking, and Recovery
    Isolation, masking, and recovery are different techniques for repairing a fault, minimizing the effect of a fault, or avoiding it to turn to system failure. Identified faults can be isolated, masked, and sensor network recovered, for instance, redirecting traffic through healthy backup components. Fault Recovery can ensure overall system operation even when components degrade. The downside may be the cost of adding components to ensure redundancy.

The overview of fault tolerant techniques presented in the following section follows the above-described taxonomy.

4. Overview of Techniques by Fault Tolerance Tasks

4.1. Fault Prevention and Prediction

Fault prevention and prediction in sensor networks are dependent on the architectural design of the system and the initial deployment method of the sensor network. These will be discussed in the following subsections. In addition, data collection in USNs and testing frameworks for UWSNs are presented.

4.1.1. Design of the Sensor Network

In Wireless Sensor Networks (WSN), instead of a centralized homogeneous topology, dividing nodes into clusters is an energy efficient and resilient method [20], where dedicated cluster head nodes may have more energy and communication capabilities to effectively act as mediators between regular nodes and data sinks.
To overcome the issues caused by varying environmental challenges of Underwater Wireless Sensor Networks (UWSN), natural algorithms may be utilized. For instance, clustering and routing can be done utilizing Cuckoo Search algorithm and Particle Swarm Optimization [21], which have behaved more resiliently in underwater conditions than more usual terrestrial Low Energy Adaptive Clustering Hierarchy (LEACH) protocol [22]. Pressure measurements have been used for UWSN routing [23] with floating depth-controlling sensors. Fault Management tasks can also be distributed across the whole network. In WSN with enough spare nodes energy efficient grid can be formed [24], changing the node manager, gateway and sensing nodes selected and spare nodes put to sleep. This results in energy-efficient and lightweight network but requires excess nodes.
However, existing UWSN protocols have not been adequately compared in underwater field trials yet [25].

4.1.2. Sensor Network Deployment

Sensor network deployment techniques are important for WSNs where deployment may directly affect the nodes’ locations and networking availability. Even for terrestrial wireless sensor networks, to obtain a satisfactory network performance, an adaptable deployment method is essential [26]. Usually, the sensor placement for WSNs utilizes, for redundancy reasons, more sensors than the minimum required number [27]. The deployment costs and energy efficiency of WSNs have been investigated in Reference [28], and it has been found that there is no single solution that can easily be applied in practice [29].
Wired sensor network deployment is less researched, possibly because wired sensor networks’ node deployment locations are limited by the cables, their locations are more predetermined, and node connectivity is not directly related to the location.

4.1.3. Data Collection

Sensor networks tend to have limited network bandwidth, energy, and storage capabilities. Thus, filtering and aggregating sensor information may be a way to meet those requirements. Raw sensor data near the source can be divided into informative, non-informative, and outlier groups [30], and only the needed data could be communicated or stored. Outlier data may result from noise, failures, disturbances, etc., and may be useful for Fault Tolerance purposes.
Different techniques to compress and aggregate collected information in UWSNs are investigated in Reference [31]. It was found that aggregation is justified, and cluster-based aggregation techniques are performing better than non-cluster-based ones. For instance, cluster head (CH) switching to backup (BCH) technique was proposed [32] for cluster-based UWSNs.
Moreover, security challenges need to be addressed. One way to minimize the risk of data tampering and/or interference is to ensure that the data is processed locally or, if that is not possible, then communicated end-to-end encrypted [33].

4.1.4. UWSN Testing Frameworks

Wireless networking protocols are one of the key research areas in UWSNs. To evaluate the implementation of underwater wireless protocols, simulation is often used. Due to the specifics of underwater environments (See Section 3), generic simulation environments are not able to capture some of the relevant features. Frameworks covered in the current section are useful for underwater acoustic protocols’ simulation and evaluation.
Frameworks, such as DESERT version 1 and 2 [34] and SUNSET [35], that allow simulation, emulation, and testing of the sensor networks, have been developed for UWSNs. An analysis conducted in Reference [36] shows that SUNSET represents a more mature, flexible, and robust framework for in-field testing than DESERT. However, DESERT v2 was released subsequently. For acoustic UWSN security testing, SecFUN framework [37] has been proposed.

4.2. Fault Detection and Identification

In essence, Fault Detection means determining that one or more bits in the computation differ from their correct value [19]. This can be detected via continuous monitoring of the network and nodes’ status. Some sources also use the word “Diagnosis” in a broader meaning than just detection and identification. Diagnosis has been defined as “characterizing the system’s state to locate the causes of errors, determine how the system is changing over time, and predict errors before they occur [19]”. The current section covers different techniques to execute the previously mentioned concepts.
A distributed hierarchical fault management [38] has been used for WSNs, where agent Fault Detection devices collect information from the power modules and sensors to determine failure conditions and sequentially diagnose the nature of the detected failure.
At higher abstraction levels, there has been a wide use of the SNMP protocol [39] by the industry for Fault Detection querying and triggering in IP networked devices. There are multiple commercial tools for generating failures, e.g., Chaos Monkey from Netflix [40], that randomly terminate services in production environments, to ensure their resiliency. The latter does not manage the occurring faults but ensures that the repairing mechanisms are in place and operable. Intelligent Platform Management Interface (IPMI) [41] is an industrial technology specification for hardware system management and monitoring.
A neural-network-based scheme for sensor failure detection, identification, and accommodation can be used which may allow the conditions to deviate to greater extent from theoretical models and estimation. A relatively simple and computationally light approach has been presented [42], where a neural network is used as an online learning state estimator for detecting faults. The neural network itself can be built as fault-tolerant [43], so that failing nodes have the least impact on result data.
Situational Awareness approach, using a mechanism that has been borrowed from humans, can be applied in sensor data interpretation for Internet of Things (IoT), specifically, regarding processes of sensation, perception and cognition. In addition to specification-based and learning-based approaches, a perception-based approach utilizing Fuzzy Formal Concept was proposed [44] for Situational Awareness identification.
Semantic Sensor Network Ontology has been proposed in Reference [45] for managing interoperability between sensing systems. The Semantic Ground describes information for interoperability and cooperation among agents [46]. To enhance resilience in Semantic Sensor Networks, monitoring nodes may forward observations to association nodes, which develop Situational Awareness by mining association rules, for example, via a natural Artificial Bee Colony algorithm [46].
Electric Power Grids need efficient monitoring since, for outage detection, environmental monitoring, and fault diagnostics, different WSN-based approaches are reviewed [47]. Most of these approaches are also applicable in other kinds of applications.

4.3. Fault Isolation, Masking and Recovery

Subsequent to Fault Detection, Fault Identification, and Fault Diagnosis, a fault handling stage can be entered [38] to prevent further data corruption and system deterioration. The fault handling consists of Fault Isolation, Masking, and Recovery. Fault handling can hide the fault occurrence from other components by applying Fault Masking; the key techniques for such masking are informational, time, and physical redundancy [18]. Proposed masking technique For Underwater Vehicles is Triple Modular Redundancy (TMPR) [48], which is also one of the most commonly used Fault Masking techniques. Isolating a faulty component from the others can be facilitated by using virtualization [18]. In large scale distributed systems, frozen virtual images of healthy services have been used as checkpoints [49] for rolling back in case of a fault occurrence.

Fault Recovery ensures that the fault does not propagate to visible results, for instance, by rolling back to a previous healthy state (checkpointing) or re-trying failed operations (time redundancy). Some of the techniques for Fault Recovery can be Reconfiguration, which is changing the system’s state so that the same or similar error is prevented from occurring again, and Adaptation, which is re-optimizing the system, for instance, after Reconfiguration task [19].

In Sensor Networks, different approaches for Fault Recovery have been used, that have different resource overheads, energy-efficiencies, scalabilities and network types. For both network and node Fault Recovery in wireless sensor networks, Mitra et al. (2016) [50] compares techniques, such as checkpoint-based recovery (CRAFT), agent-based recovery (ABSR), fault node recovery (FNR), cluster-based and hierarchical fault management (CHFM), and Failure Node Detection and Recovery algorithm (FNDRA). While some of those are specific to terrestrial wireless usage, some principles (e.g., checkpointing, etc.) can also be used in wired and/or underwater environments. To reduce the network bandwidth requirements, checkpoint backup can be mobile to nearby nodes [51] and used for recovering from fault situations.

In network protocols, Fault Masking and Fault Recovery are handled by error control schemes that are commonly categorized into the following three groups [1]:

  • Automatic Repeat Request (ARQ)—re-transmission of corrupted data is asked;
  • Forward Error Correction (FEC)—data corruption can be detected and corrected by the receiving end; and
  • Hybrid ARQ (HARQ)—a combination of FEC and ARQ.

The cross-layer approach benefits Fault Recovery significantly since single-layer redundancy, such as hardware redundancy and application checkpointing, have very high costs, and latency between fault occurrence and detection makes the recovery difficult [19].

This entry is adapted from the peer-reviewed paper 10.3390/s21093264

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