A Survey on Fault Management in Wireless Sensor Networks

Submitted by – Atrayee Sarkar

Registration no-161541810005 OF 2016-2017

Roll no-15499016025 Session-2016-2018

Msc in Computer Science

Supervision Under Anurupa Biswas

Dinabandhu Andrews Institute of Technology And Management, Patuli,kol-700084

Abstract

The design of wireless sensor networks (WSNs) is influenced by many factors, including fault

tolerance. Fault tolerance is the ability to ensure the functionality of the network in the events of faults

and failures. Sensor nodes in WSNs are expected to operate autonomously for a long period of time

and may not be easily approachable for battery replacement and maintenance due to their physical

deployment locations. Therefore, in order to guarantee the network quality of service and

performance, it is essential for the WSNs to be able to detect faults and failures, and to perform

something akin to healing and recovering from events that might cause faults or misbehavior in the

network. Therefore, fault tolerance should be seriously considered in many WSNs applications.

Concerning fault management various schemes have been proposed. However, it is evident from the

existing literature that very least attention has been paid to evaluate or analyzed these schemes. The

contribution of this work is to fill this gap by analyzing some of the dominant schemes in this area.

Keywords: Wireless Sensor Networks, Fault Management, Fault Detection, Fault Diagnosis, Fault

Recovery.

Introduction

Fault tolerance has been identified as key challenges in the design and operations of Wireless Sensor Network (WSNs). Failures are inevitable in wireless sensor networks due to inhospitable environment and unattended deployment. Therefore, it is necessary to detect the networks for recovery from the failure to sustain it. WSNs are self-organized using clustering algorithms to conserve energy. LEACH (Low Energy Adaptive Clustering Hierarchy) protocol is one of the significant protocols for routing in WSN. In LEACH, sensor nodes are organized in several small clusters where there are cluster heads in each cluster. These CHs gather data from their local clusters aggregate them & send them to the base station. The proposed scheme is supposed to be an efficient fault detection and recovery mechanism to make the network fault-tolerant and achieve reliability and quality of service.

Performance analysis and evaluation will be made using several scenarios of wireless sensor networks[2].

The development of wireless sensor networks was originally motivated by military applications such as battlefield surveillance. However, wireless sensor networks are now used in many civilian application areas, including environment and habitat monitoring, healthcare applications, home automation, and traffic control [1]. The important operations in a Wireless Sensor Network are data dissemination and data gathering. Data dissemination is the process of routing data or queries throughout the network and data gathering is the collection of observed data from individual sensor node to sink. Due to the nature of these networks a sensor node may fail and hence the route between the source to sink may also fail [7]. Hence providing for fault tolerance is an important requirement of this network. Routing in Wireless Sensor Networks is the process of moving the sensed datafrom source to sink.

A routing protocol should ensure that the WSN can reconfigure be energy efficient and resilient to failures. So far routing in sensor networks has focused upon methods for constructing the best route, or routes, from data source to sink (destination) before sending the data. Moreover, due to the deployment of WSNs in hostile and un-attended environments faults and failures are normal facts, therefore, fault tolerance and reliable data dissemination is also of great importance. Thus, energy-efficient and fault tolerance have been identified as one of the key challenges in the design and operations of WSNs. To address the above mentioned challenges, we proposed a Fault-Management Architecture for WSNs that offers efficient fault detection and recovery mechanisms to make the network fault-tolerant [2]. Now, WSN are permeating a variety of application domains such as avionics, environmental monitoring, structural sensing, telemedicine, space exploration, and command and control. WSN should have a long lifetime to accomplish the application requirements. However, In addition to resource constraints in WSN, the failure of sensor nodes is almost unavoidable due to energy depletion since they have been usually deployed in hostile environments and their batteries cannot be recharged or replaced, hardware failure, communication link errors, and so on . Therefore, in WSN, fault tolerance has become as important as other performance metrics such as energy efficiency, latency and accuracy.

In general, the consequence of these failures is that a node becomes unreachable, violates certain conditions that are essential for providing a service or returns false readings which could cause a disaster especially in critical applications. Furthermore, the above fault scenarios are worsened by the multihop communication nature of WSN. It often takes several hops to deliver data from a source node to the remote base station; therefore, failure of a single node or link may lead to missing reports from the entire region of WSN. Therefore, since sensors are prone to failure, fault tolerance should be seriously considered in many sensing applications which are generally required to be faulttolerant, where any pair of sensors is usually connected by multiple communication paths. Recently, several studies have dealt with fault tolerance in WSN, particularly in the routing process. Moreover, these works focus on the detection and recovery of failures in WSN. We evaluate LEACH in a realistic environment in which sensor nodes can fail and links may be lost. Then, we propose two improved versions FT1-LEACH and FT2LEACH of LEACH so that it becomes a fault tolerant protocol. FT1-LEACH involves two cluster-heads in each cluster: one is primary and the other secondary and FT2LEACH use the checkpoint technique. Moreover, cluster-heads are elected based on their capabilities and in clusters, main cluster-heads and their vice cooperate with each other to reduce extra costs by sending only one copy of sensed data to the sink [3].

Fault Detection

The power supply is the most critical restriction, given that it is typically not rechargeable. For this reason faults are likely to occur frequently and will not be isolated events. Besides, large-scale deployment of cheap individual nodes means that node failures from fabrication defects will not be uncommon. Attacks by adversaries could happen because these networks will be often embedded in critical applications. Worse, attacks could be facilitated because these networks will be often deployed in open spaces or enemy territories, where adversaries can not only manipulate the environment (so as to disrupt communication, for example, by jamming), but have also physical access to the nodes.

At the same time, ad-hoc wireless communication by radio frequencies means that adversaries can easily put themselves in the network and disrupt infrastructure functions (such as routing) that are performed by the individual nodes themselves. Finally, the sensors nodes are susceptible to natural phenomenons like rain, fire, or even falls of trees since they are commonly used to monitor external environments. For all these reasons faults in WSNs need to be tackled differently than in traditional networks. Fault management, an essential component of any network management system, will play an equal, if not more, crucial role in WSNs. Failure detection, in particular, is vital not only for fault management, but also for security and performance. If, in addition to detecting a failure, the management application can also determine (or gather indicatives) that it has malicious origin, then the management application can trigger security management services[4]. On the other hand, if it has not a malicious origin, i.e., if itis an accidental or natural failure, \backup nodes" could be activated in order to substitute the unavailable nodes, hence maintaining quality of service.

WSNs are embedded in applications to monitor the environment and sometimes, act upon it. In applications where we are interested in the conditions of the environment at all times, sensor nodes will be programmed to sense and send back their measurements at regular intervals or continuously. We call these networks programmed and continuous, respectively. In other applications (probably a large class of them), we are only interested in hearing from the network when certain events occur. We call these networks event-driven networks. On the other hand, when the network is able to answer to queries of the observers, we refer to this network as on demand. Configuring the network as event-driven is an attractive option for a large class of applications since it typically sends far fewer messages. This is translated into a significant energy saving, since message transmissions are much more energy-intensive when compared to sensing and (CPU) processing. For instance, if the application is temperature monitoring, it could be possible just to report data when the temperature of the area being monitored goes above or below certain thresholds. In terms of failure detection, event-driven networks present challenges not found in continuous networks. Under normal conditions, the observer of a continuous network receives sensing data at regular intervals. This stream of data not only delivers the content we are interested in, but also works as an indicative of the network operation quality.

If data are received from every single node, then it knows that all is well (of course, assuming that the messages are authenticated, and cannot be spoofed). If, however, the management application stops receiving data from certain nodes or entire regions of the network, it cannot distinguish if a failure has occurred or if no application event has occurred. Leveraging precisely on this indication, and supposing that nodes periodically send messages to the base station, Staddon et al. [5] proposed a scheme for tracing failed nodes in continuous sensor networks. Their scheme takes advantages of periodic transmission of sensor reports to do the tracing. Because we consider event-driven networks, their solution is not directly applicable. In [4], it is proposed a scheme where nodes police each other in order to detect faults and misbehaviour. More specifically, nodes listen-in on the neighbour it is currently routing to, and can determine whether the message it sent was forwarded. If the message was not forwarded, the node concludes that its neighbour has failed and chooses a new neighbour to route to. Unfortunately, this scheme does not help in cases in which an entire region is compromised.In our work, we study the problem of failure detection for an event-driven WSN and propose a fault management solution using some management services, management functions, and WSN models which are part of the MANNA architecture [8]. In MANNA management services for WSNs are defined.

Realated Works

LEACH is a clustering-based protocol that minimizes energy dissipation in sensor networks. LEACH randomly selects nodes as cluster-heads & periodic reelection for CHs is performed. It helps in energy dissipation among nodes in the network instead of depending on only one node .although some protocols have been independently developed. Since sensor nodes are often placed in harsh zone , sensor nodes area prone to failure. Supposed if a cluster-head is faulty it can leave a cluster disconnected from the base station until the network reorganizes again.

The idea proposed in LEACH has been an inspiration for many hierarchical routing protocols,heads must be an autonomous, distributed process. This is achieved through LEACH.

although some protocols have been independently developed. Since sensor nodes are often placed in harsh zone , sensor nodes area prone to failure. Supposed if a cluster-head is faulty it can leave a cluster disconnected from the base station until the network reorganizes again.

Another research work on fault tolerance is discussed in [8]. Here a Dynamical Jumping Real-time Fault-tolerant Routing Protocol (DMRF) has been proposed. Once node failure, network congestion or void region occurs then the transmission mode will switch to jumping transmission mode leading to reduced transmission delay and guarantees the data packet to be sent to its desired destination within the specified time limit. Each node can dynamically adjust the jumping probabilities to increase the ratio of successful data transmission by using feedback mechanism. This mechanism results in reduced effect of failure nodes, congestion and void region and reduced transmission delay, reduced number of control packets and higher ratio of successful transmission.

One more fault tolerant work is discussed in [9]. Basically, WSNs faces resource limitations, high failure rates and fault caused by wireless channels & wireless sensor nodes. It increases the reliability & robustness of the network by creating a backup path for every node on a main path of data delivery. When a node gets failure it immediately applies its backup path as the main path for data delivery of next incoming packets. This protocol reduces the number of dropped data packets and increases robustness of the entire network by maintaining the continuity of data packet transmission even in presence of faults

FAULT DIAGNOSIS

Fault diagnosis is a stage that the causes of detected fault can be properly identified and

distinguished from the other irrelevant or spurious alarms.However, there is still no

comprehensive model or description of faults in sensor networks to support the network

system for accurate fault diagnosis. Most existing approaches address the fault models only

on the individual node level (including its hardware components malfunction). In particular,

both [10, 9] assume the system software (including sensor application software) are already

fault tolerant. They focus on hardware level faults of a sensor node, especially on sensor and

actuator which are most prone to malfunctioning. Farinaz et al., [2] adopt two fault models.

The first one is related to sensors that produce binary outputs. The second fault model is

related to the sensors with continuous (analog) or multilevel digital outputs. In [5], Thomas et

al., only consider faulty nodes are due to harsh environmental conditions. In their work,

faulty nodes are assumed to send inconsistent and arbitary values to other nodes during

information sharing phase. While, Min Ding et al., [9] model the “event” (or abnormal

behaviour of a sensor node) by real numbers such as sensor readings instead of 0/1 decision

model. As a result, this algorithm is generic enough as long as the thresholds and real number

of events can be specified by fault tolerance requirements from various sensor applications.

Note that, apart from fault tolerance models for hardware components (e.g. sensor, actuator)

or abnormal behaviours (i.e. arbitrary sensor readings) of a node, there is still a need of the

development of fault models for sensor networks, which are various due to their types,

environment, and also at the network and distributed system level.

FAULT RECOVERY

Ideally, the failure recovery phase is the stage at which the sensor network is restructured or

reconfigured, in such a way that failures or faulty nodes do not impact further on network

performance. Most existing approaches in WSNs isolate failed (or misbehaving) nodes

directly from the network communication layer (e.g. the routing layer). For example, in the

technique of Marti et al. [6], after the faulty neighbour is detected, a node chooses a new

neighbour to route to. Instead of taking passive recovery actions as mentioned before, the

proactive action is also considered as a novel approach to prevent the potential faults in the

network. As from the approach of WinMS [4], the central manager detects the network region

with weak health (e.g. low battery power) by comparing the current network state (including

individual nodes) with a historical network information model (e.g. an energy map or

topology map). It takes proactive action by instructing nodes in that area to send data less

frequently for node’s energy conservation. The key idea is to adapt application algorithms

and/or operating system to match the available hardware and the applications needs. Their

current approach focuses on five primary types of resource: computing, storage,

communication, sensing and actuating, which can replace each other via suitable changes in

system and application software. Although this solution is not directly relevant to fault

recovery in respect of the network system management, it still provides us with a useful

vision of a future design to reconfigure or update the management functionality of a node

when its management responsibility has changed due to fault occurred in the network.

FAULT MANAGEMENT ARCHITECTURE

Efficient architecture is usually on-demand to support the execution of management activities.

Generally, these architectures can be classified into centralized, distributed and hierarchical models.

--Centralized model, is designed ideally for the centralized fault detection approaches. A central

controller is usually responsible for fault maintenance of the overall network. In order to construct

the global view of the network, central controller keeps updating nodes’ states by message

exchange. The central controller aggregates these data into information models, in terms of metrics,

topology model topology and energy map , WSN models , and cluster topology model . The central

node identifies any faulty or suspicious nodes by comparing the current node states against those

historical information models. It is seen to be accurate solution to identify fault. However, this

centralized architecture will become less efficient and more energy-expensive in consideration of

large-scale sensor networks. The message flooding of such approach may greatly consume node

energy because of frequent in-network message exchanges. --Distributed model, splits the entire

network into several sub-regions, and distributes fault management tasks evenly into individual

regions. Each region employs a central manager. Manager is responsible for monitoring and

detecting failure in its region. It is also able to directly communicate with other managers in a

coordination fashion for fault detection. As a result, the central controller of the overall network

only needs to monitor a very small number of sensors in the network. This design conserves node

energy by lessening in-network communication messages, and also enhances the system response

time towards events occurred in the network. Many existing approaches have adopted Clustering to

group nodes into different regions.Clustering allows sensors to efficiently coordinate their local

interactions in order to achieve global goals. In particular, localized clustering can contribute to

more scalable behaviours as number of nodes increase, improved robustness, and more efficient

resource utilization for many distributed sensor coordination tasks. As mentioned in Section 3,

cluster-based failure detection supports the cluster head (CH) to efficiently identify suspicious nodes

within a small region of the network by exchanging heartbeat messages.

--Hierarchical model, it is a hybrid between the centralized and distributed approach. It uses intermediate managers to distribute manager functions. However, these intermediate managers do not directly communicate with each other. Each manager is responsible to manage nodes in its sub-network and report to its high-level central manager. In a three-layered hierarchical sensor network structure , cluster is adopted to reduce the number of sensor nodes monitoring the events occurred in the network. They believe only a tiny fraction of the sensors in the network can accurately detect the target events; while most sensors’ measurements (which are far away from the target) are just pure noises

COMPARATIVE STUDY

Table shows the overall classification and comparison of existing fault management approaches and

architecture. The table describes the approaches with their operation organization and types of

faults they detect, diagnosis and recover from.

Fault management in WSNs is different from traditional networks. Recently, researchers have

developed various techniques and approaches to deal with various types of faults at different layers

of the network. To provide resilience in faulty situations these three main actions (fault detection,

fault diagnosis and fault recovery) must be performed [5, 6]. We categorize these existing

approaches according to different phases of the fault management architecture, i.e. fault detection,

fault diagnosis and fault recovery.

Schemes Management System Organization

Types of faults & failures addressed

Action taken

Sympathy [2] Centralized Hierarchical, Pro-active monitoring

Node self, Sink fault, Crash & time-out omission failures

faults Detection & Diagnosis

MANNA[3] Centralized + Distributed Passive monitoring

Node faults Detection, Diagnosis & Recovery

WinMS [7] Centralized + Distributed (Hierarchical) Pro-active monitoring

Node faults (week or faulty)

Detection & Recovery

WSNMP [6] Centralized + Distributed (Hierarchical Clustering based)

Node faults, Network faults

Detection & Recovery

Cluster-Based approach [8]

Centralized + Distributed

Node faults (energy failures), Network faults (network connectivity), Permanent faults

Detection & Recovery

Passive Diagnosis of WSNs [8]

Centralized + Hierarchical, Probabilistic approach Passive monitoring

Node faults, Network faults, Transient fault

Detection, Diagnosis & Recovery

Efficient Tracing of failed nodes [9]

Centralized & Active monitoring

Node faults, Route Faults

Detection, Diagnosis & Recovery

CONCLUSION

Wireless sensor network has gradually emerged as a cutting-age technology to develop new wireless

applications for pervasive computing in the 21st centaury. One of challenges to success this vision is

robust fault management. In this paper, we provided a comprehensive overview of fault

management in WSNs, and surveyed existing approaches. We classified them in three phases, such

as fault detection, diagnosis and recovery. As we have seen, the fundamental requirement of self-

managed WSN solutions is that of providing an efficient management architecture. However, there

is a trade-off between the complexity of architecture design for fault management in WSNs and the

resourceconsumption of sensor networks. More specifically, reconfiguration of fault management

functionality for a sensor node is one main challenge when node management responsibilities

change. So far, mobile code technology has been proposed as an efficient scheme. It selectively

chooses and updates the parts of code that are needed to support adaptation and reconfiguration of

node functionality. In order to achieve this, sensor nodes in the network have to distribute and

forward the on-demand mobile code to the destination nodes. These actions may consume limited

resource of sensor nodes, such as battery energy. Thus, there is still a need to design an alternate

solution to energy-efficiently support the functionality reconfiguration of sensor nodes. Moreover,

selfmanageable WSNs are also required to have a certain wisdom, which clearly identifies various

faults in the network. In order to distinguish between different faults in the network and

subsequently take the appropriate actions, the development of theoretical and realistic fault models

for WSNs becomes another key technique.

Acknowledgements

We express our sincere gratitude to the Head of the Dept.-of-Computer Science, Dinabandhu

Andrews Institute Of Technology And Management for promoting research activities. And also

special thanks to Prof. Anurupa Biswas for constant support and encouragement.

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