Fault Tolerance Model for Efficient Actor Recovery
Paradigm in WSAN
Reem Khalid Mahjoub
Department of Computer Science and Engineering
University of Bridgeport
Department of Computer Science and Engineering
University of Bridgeport
Abstract—Wireless Sensor and Actor Networks (WSAN) is an
area where sensors and actors collaborate to sense, handle and
perform tasks in real-time. Thus, reliability is an important
factor. Due to the natural of WSAN, actor nodes are variable to
failure. Failure of actor nodes degrades the network performance
and may leads to network disjoint. Thus, Fault tolerance
techniques should be applied to insure the efficiency of the
network. In our earlier work we proposed an efficient actor
recovery paradigm (EAR) for WSAN which handles the critical
actor node failure and recovery while maintaining QoS. EAR is
supported with Node Monitoring and Critical Node Detection
(NMCND), Network Integration and Message Forwarding
(NIMF), Priority-Based Routing for Node Failure Avoidance
(PRNFA) and backup Selection Algorithms. In this paper, we
extend the work by adding a fault tolerance mathematical model.
By evaluating the model, EAR shows to manage fault tolerance in
deferent levels. To evaluate the effectiveness, the EAR fault
tolerance is evaluated by simulation using OMNET++
Simulation. In addition, EAR reliability is measured and
compared with RNF, DPCRA, ACR, and ACRA.
Keywords—Wireless sensor and actor network; Actor; Actor
failure; Node Failure; Fault tolerance; Sensor; WSAN;
Wireless Sensor and Actor Networks (WSAN) is a field
where actors and sensors collaborate together in order to
perform specific tasks, or transmit and process information. A
WSAN can be described as a distributed wireless network.
WSAN consists of sensors, actors, and a base station. The
WSAN has many advantages over the regular WSN. One of
the most efficient characteristics is the high energy and low
power consumption. The actor network integrates with the
sensor network to implement the Wireless Sensor and Actor
Network . Sensors are responsible for sensing specific
actions or events and transmitting the sensed event to the actor
node. On the other hand, Actors are high performance nodes
that have the capability to collect, process, transmit data, and
perform actions. Actor are resource-rich nodes equipped with
high capabilities, wide transmission range, and strong
computation power. Also, they run on a high power source[2,
3]. Actors are responsible for collecting and processing the
data which are sent by the sensors. In WSAN, an actor node
can communicate with several sensors. Communication in the
WSAN can be classified as sensor-actor communication,
actor-actor communication, or actor-sink communication.
WSAN can be automated or semi-automated. For the
automated WSAN, the sensor sends the collected data to the
actor node, and the actor node receives, processes and
performs the action needed. For the semi-automated WSAN,
sensors send data to the sink and the sink sends the data to the
actor nodes. In WSAN, sensor nodes are consist of limited
storage. Processing, and power capabilities while actor nodes
are powerful nodes. Due to the architecture, the number of
sensors can range up to thousands while the number of actor
nodes is much lower. WSAN can assist many real-time
applications such as smart energy grids, battlefield
surveillance, and cloud computing, as well as uses in medical,
industrial, and nuclear fields. Significant parameters may
affect the WSAN, including energy efficiency, transmission
media, scalability, and environment. The selection of the
parameters for optimization depends on the application. It is
essential to ensure the communication and efficiency of
WSAN. Thus, it is essential to maintain the fault tolerance of
Fault tolerance is the ability of a network to preserve its
services regardless of the occurrence of faults. The general
taxonomy of fault tolerance techniques consist of :
• Fault prevention.
• Fault detection.
• Fault isolation.
• Fault identification.
• Fault recovery.
Fault tolerant techniques can manage one or more type of
fault in one or more network layer. There are variety of fault
sources such as: nodes mobility, congestion, sensor nodes
resource limitation, communication link failure, and actor
node failure. The impact of failure can be identified in
correspondence to the failure cause and the overall impact
over the network. A failure of an actor may cause losing
communication between nodes. In fact, a failure of a critical
actor had higher impact on the network since it can leads to a
network disjoint. Critical actor is an actor in which its failure
causes network partitioning. In case of critical actor failure,
restoration process should take place. Failure actor restoration
may be take place by replacing it with a redundant backup
actor or by an adjacent neighbor.
The actor nodes may include high performance features
which can increase the power and enhance the usage of the
network in general. Maintaining the inter-actor connectivity is
essential in WSAN. Thus, we intend to focus on fault
tolerance in reference to critical actor node failure. In our
earlier study  we introduced an efficient actor recovery
paradigms (EAR) for WSAN. Despite to earlier models, EAR
handle actor node failure and recovery while maintaining
quality of service (QoS). In this paper, we're proposing a fault
tolerance model plus extending the evaluation of fault
tolerance in EAR. To ensure the efficiency of the fault
tolerance model in EAR, reliability is measured using
In the following sections, related work is presented in
section II, the EAR and fault tolerance mathematical model is
shown in section III. Simulation and results are analyzed in
section IV while the conclusion is provided in section V.
II. RELATED WORK
In WSAN, nodes are variable to failure due to the harsh
deployment environment, hardware failure, attacks,
communication links issue, and energy depletion. As we
discussed earlier, fault tolerance taxonomy is classified to fault
prevention, fault detection, fault isolation, fault identification,
and fault recovery. Researches in sensors networks mainly
focused on failure detection and recovery [8-26]. In fault
detection mechanisms are classified as proactive and reactive
methods. In proactive methods, the fault and restoration
mechanisms are addressed during network setup. Some
mechanisms implement fault tolerance topologies during
network setup, while others use redundant and backup nodes
to ensure fault tolerance .
On the other hand, reactive schemes attempt to utilize
network resources and perform recovery through node
repositioning. Backup selection is performed if failures occur
within the network lifetime. Reactive schemes require network
monitoring to maintain the statuses of nodes. The network
status, recovery algorithm, and recovery scope are important
factors in reactive schemes.
Distributed recovery from network partitioning in movable
sensor/actor networks via controlled mobility (ACR) on
critical actor failure detection and recovery while minimizing
nodes' movements . ACR assigns a failure handler (FH)
node for each critical actor, cut-vertex. FH is the neighbor
node with the nearest distance from the critical node. The FH
is responsible for the network recovery when critical node
failure occur. The main objective of the work is to localize and
minimize node movement distance during the recovery
Recovering from a node failure in wireless sensor-actor
networks with minimal topology changes (LeDiR) (RNF)
focusses on critical actor failure detection and recovery while
minimizing path length amongs nodes . In RNF, each
node calculates the shortest path. The 1-hop neighbors identify
if the node is critical or non-critical using the shortest path
routing table. Recovery take place by selecting the smallest
block of neighbors.
A hybrid timer based single node failure recovery
approach for wsans (DPCRA) is proposed for critical actor
failure . DPCRA recovers the partitions and reinstate the
node connectivity by using small number of nodes. Multiple
bake-up failure handlers (FH) are assigned for each actor.
Recovery process is done locally while storing minimum
information stored in each node. The main focus of the work
is to use multiple backup nodes for the partitioned recovery.
Recovery of lost connectivity in wireless sensor and actor
networks using static sensors as bridge routing (ACRA) uses a
sensor node to recover from critical actor failure . ACRA
is based on two point crossover GA to reconnect the
partitioned network. The main focus of the work is to measure
total travel distance and number of the messages. If cut-vertex
failure is detected, recovery phase take place. The cluster head
(CH) find stable sensor with high transmission power and
higher coverage. The stable sensor CHs neighbor node is
selected as bridging router for connecting disjoint network.
Efficient actor recovery (EAR) paradigm is proposed in
. The main objective of the paradigm is to enhance the
overall network performance by provide critical actor failure
detection and recovery mechanism while maintaining network
quality of service. The evaluation matrix used includes,
network life time, actor/ sensor recovery time, data recovery,
residual energy and time complexity. Brief description is
provided in section III.
Fig. 1. Fault Detection and recovery Mechanzm considrations
In conclusion, It is essential to develop techniques which
support the heterogeneity of WSAN. Even though, some
existing mechanisms manage to handle the failure
detection/recovery but failed to manage the overhead from
such event. Although, applying these mechanisms may
degrade the overall quality of service. Even if failure occurs,
their impact should be minimized. Thus, techniques should
consider (figure 1):
• Self monitoring, and self healing.
• The implementation of robust failure
detection/recovery technique while maintaining
network quality of service .
• Mechanisms that reduce and prevent packet lost; as
well as failure.
Even though, some existing mechanisms manage to handle
the failure detection/recovery but failed to manage the
overhead from such event. Although, applying these
mechanisms may degrade the overall quality of service.
III. FAULT TOLERANCE IN EAR
Existing approaches either attempt to recover the failure
actor or try to reduce the. We conclude that existing
approaches attempted to replace the critical node with another
backup node, but they failed to maintain the QoS and energy
consumption. Despite previous studies, EAR craves for
providing efficient failure detection and recovery mechanism
while harvesting maintains the Quality of service. Thus, EAR
maintains to cover most aspects of the fault tolerance
taxonomy. To have a clear understanding of EAR, we'll cover
the overall model of EAR first, then we'll introduce our
proposed fault tolerance model.
A. EAR Model Overview
EAR model handle to manage critical actor node detection
and recovery for WSAN . In addition, it's is supported with
algorithms that manage to handle fault isolation and fault
prevention. For ensuring QoS in EAR algorithm, Node
Monitoring and Critical Node Detection (NMCND) algorithm
that monitors the activities of the nodes to determine the nodes
types and distinguish critical nodes. Additionally, the
proposed approach not only determines the critical node, but
handles the packet forwarding process when critical node fails.
To handle packet forwarding, Network Integration and
Message Forwarding (NIMF) message was introduced. In
addition, process -Based Routing for Node Failure Avoidance
algorithm (PRNFA) is developed in order to handle the
routing process and to eliminate routing process of the
redundant packets to other node in order to avoid the network
congestion and reducing the latency. Moreover, the RSSI
model aims to ensure the contention-free forwarding
capability that minimizes packets lost in case of node failure.
EAR system model is shown in figure 2. The overall processes
are summarized as following:
• The critical actor failure process stops the entire
working process so that proper node replacement could
continue the functional-process of the network. Thus,
backup node process is initiated to make the necessary
replacement prior to failure the node using Backup
Node Selection Process. In addition, secondary and
tertiary backup nodes are introduced in case failure of
first and second backup nodes.
• Determining the critical node is the tricky and difficult
process. The NMCND algorithm enables the Sink node
to broadcast the message to all actor nodes to send the
report of the pre-failure actor nodes.
• Network Integration and Message Forwarding
algorithm determines the legitimacy of actor nodes and
base station. If node is declared as a legitimate node,
then data packets forwarded by this actor node are
accepted. Furthermore, different data forwarding
packets transmitted by multiple actors are also dealt
with simultaneously after node validation process.
Thus algorithm is capable to stop routing process of the
redundant packets in order to avoid the network
congestion and reducing the latency.
• Priority is assigned to those actor nodes that have to
forward the data packets of high significance. In
addition, energy-level of data forwarding nodes is
checked. If a node possess the low energy level than
the set criteria, as that node is not entertained because
there is probability that node may not be able to destine
the packets to appropriate actor node. As a result, we
maintain the QoS provisioning and power efficiency.
• Node forwarding capability, correct node-location and
distance between either actor nodes or sensor nodes are
determined using optimized RSSI approach that helps
decide the distance covered by each packet and proper
delivery to appropriate node when being forwarded by
an actor node.
Fig. 2. EAR System Model
In NMCND algorithm, critical actor nodes are identified.
When an actor node is defined as critical, back-up node
selection among neighbors take place. Figure 3 demonstrates
the backup assignment in addition to failure detection/
recovery of the critical node in EAR. Backup selection is
based on: critical/noncritical, weight-based criteria, distance,
energy, buffer size, and neighbor node degree. The critical
node is notified with its backup. The critical node will
broadcast a message to its neighbors notifying them about its
backup. In case failure of critical node is detected, the backup
node handles the recovery process and move towards critical
node location to recover the network. Simultaneously,
neighbor nodes will stop forwarding packets to the failed
critical actor and redirect the packets towards its backup.
Fig. 3. EAR Failure detection/ recovery
Network integration message (NIM) is set to interconnect
the entire network and destination address of the base station
is saved for packet using NIMF algorithm. After the actor
node failure and node assignment processes completion, the
actor nodes should be linked to forward the collected data.
When actor node receives NIM from the base station, it saves
the destination address of the base station for packet
forwarding (PF). NIM is broadcasted in the network among all
the actors. The NIM Stop routing process of the redundant
Priority is assigned to those actor nodes that have to
forward the data packets of high significance using PRNFA
algorithm. energy-level of nodes data forwarding is checked.
If a node possess the low energy level than the set node’s
criteria. As a result, we maintain the QoS provisioning and
In summary, for ensuring QoS in EAR algorithm, Node
Monitoring and Critical Node Detection (NMCND) algorithm
monitors the activities of the nodes to determine their types
and distinguish critical nodes. Additionally, the proposed
approach not only determines the critical node, but handles the
packet forwarding process in case of critical node failure. To
handle packet forwarding, Network Integration and Message
Forwarding (NIMF) message was introduced. In addition,
process -Based Routing for Node Failure Avoidance algorithm
(PRNFA) was developed in order to handle the routing
process and to eliminate routing process of the redundant
packets to other node in order to avoid the network congestion
and reducing the latency.
B. Fault Tolerance Mathimatical Model
Fault tolerance is essential in WSAN application.
Considering different component of the WSAN, actor and
sensor nodes plays main role in such network. In this section,
we’ll calculate the average of actor/ sensor node corrected and
not corrected fault. Thus, considering fault tolerance model:
Let us consider the probability ‘Pr’ that is estimated when
sensor /actor are not faulty as given by:
Where Bi: Binary variable with decoder value, Sre: Sensor-
reading, Tg: truth ground
γ (0,k): K of the sensor/actor nodes that have same
reading, Pc: Conditional probability, σ Not faulty neighbors.
Similarly, we can determine the expressions for
Thus, expected number of decoded errors ‘β’ can be
obtained by disregarding over values for Sre
β: An average number of error after decoding, δ: Number of
other nodes, Ψ: nodes in the affected region, : expected
faulty nodes, tk: total deployed nodes in the network.
Therefore, the reduced errors are given as
Thus, we can show explicitly β, the average number of
corrected faults ‘μ’ in sensor/actors can be obtained by
combining the conditional probabilities of equations (2) and
The number of uncorrected faults can be given by
where μ¯: uncorrected faults
IV. SIMULATION AND MODEL EVALUATION
Simulation is conducted using OMNET++ simulator. EAR
system model and fault tolerance model were implemented. In
addition, ACR, RNF, DPCRA, and ACRA. We applied the
same parameter that used in our previous work. Table 1
summarizes the simulation parameters and setup.
Sensing Range (sensor)
Initial energy (sensor)
Initial energy of an
Sensing Range of an
Bandwidth of sensor
Bandwidth of actor
Number of actors
Number of sensors
Number of hops in
EAR, RNF, DPCRA, ACR, and
Buffering capacity at
sensor and actor
50 & 300 Packets buffering
capacity at each sensor and
0 m/s to 12 m/s
Data Packet size
Initial pause time
−14 dBm to 13 dBm.
Total simulation time
The simulation scenarios include up to 400 nodes ranging
from 27–54 actor nodes and 173–356 sensor nodes with a
transmission range of 70 m. The sensor/actor nodes are
deployed randomly. The initial energy of the actor nodes is set
20 J and sensor nodes have 4 J. EAR system model and fault
tolerance model were Fault tolerance is evaluated for EAR. In
addition, the performance of EAR reliability after deploying
our fault tolerance model is compared with of schemes such as
ACR, RNF, DPCRA, and ACRA. Simulation results prove
that EAR performs effectively among those schemes.
A. Fault Tolerance in EAR
EAR main objective is to handle critical node failure
detection and recovery while maintaining QoS. Thus, this
consideration take place while implementing each and every
component in the model. Figure 4 illustrates EAR in regards
to fault tolerance. EAR strive to handle different level of fault
tolerance taxonomy as well.
• Fault prevention: The NIFM eliminates redundant
packet forwarding. Thus, its enhance the resource
usage of the nodes. In addition, PRNFA check's nodes
power level. If a node possess the low energy level
than the set node’s criteria. PRNFA also checks the
significant of the event in order to generate and
forward it over the link. PRNFA reduce packet
forwarding through critical nodes to maintain its
energy as long as possible.
• Fault detection: the NMCND algorithm identifies
critical nodes assign backup node as a pre-failure.
NMCND is supported with failure detection
mechanism using exchange heartbeat messages.
• Fault isolation: The process of identifying the backup
and informing the neighbors with the critical actor's
backup improved the fault isolation. This process leads
to stop forwarding messages toward the critical actor
once detecting its failure. Thus, failed node is isolated
as well as packets lost is prevented.
• Fault identification: the integration of NMCND helps
in this process.
• Fault recovery is done using the reassigned backup
Fig. 4. EAR and Fault Tolerance
We extended our simulation to examine how tolerance is
EAR in reference to number of events compared to total
packet lost. EAR shows the there is no packet lost up to 13
events. The model tolerance up to total average of 40 events,
figure 5. The average tolerance of network can be improved
in the future while taking into consideration application
Fig. 5. Number of events vs. tata lost Fault tolerance
Reliability is very essential for networks systems in
general. It's one of the most important factors especially in
WSAN where most of the MAC protocols used lack to offer
reliability measurement. An actor failure can degrade network
reliability in such uneven environment. Thus, when the actor
fails, then it is important to initiate the prompt recovery
process to avoid the reduction in the network performance.
If the network works efficiently, and all of the components
should operate properly, then the reliability of the network Rws
is obtained as
Where k(τ) : Functioning probability of either actor/sensor
node, Rk * ω (t): Reliability of total components used in the
EAR manage to handle actor failure while maintaining
QoS in compared to existing actor failure/recovery algorithms.
Thus, this total improvement reflects positively over the EAR
reliability. Figures 6-9 show the network reliability of the
proposed EAR algorithm along with other competing
approaches: RNF, DPCRA, ACR, and ACRA. In these
experiments, different network topologies are used: 600 × 600
m² , 800 × 800 m², 1200 × 1200 m² and 1400 × 1400 m². The
network consist of 54 actor nodes and 346 sensor nodes. In
Figure 6, we used 600 × 600 m². EAR shows slight
improvement over competing techniques with 0.07-0.2%
Fig. 6. Reliability of EAR, RNF, DPCRA, ACR, and ACRA approaches with
600 X 600m²
Figure 7 shows the result of reliability in 800 × 800 m².
EAR reliability is 99.93 while competing algorithms average
from 99.75- 99.35 slight improvement over competing
techniques with 0.2-0.9% improvement.
Fig. 7. Reliability of EAR, RNF, DPCRA, ACR, and ACRA 800 × 800 m²
While in figure 8, we used 1000 × 1000 m². EAR
reliability is 99.8 while competing algorithms average from
99.1- 98.25 slight improvement over competing techniques
with 0.7-1.55% improvement.
Fig. 8. Reliability of EAR, RNF, DPCRA, ACR, and ACRA 1000 × 1000 m²
Reliability for EAR, RNF, DPCRA, ACR, and ACRA
approaches with 1400 X1400 m² indicates that EAR
reliability is 99.6. The total improvement of EAR over
competing techniques with 1.1-2.1 % improvement.
Fig. 9. Reliability for approaches with 1400 X1400 m²
Reliability can be affected with the occurrence of
malicious nodes. EAR manage to handle the possibility of this
scenario via the network integration message (NIM). NIM
determines the legal position of either sensor or actor node in
the network. If the node is identified as legal through NIM,
then sensed data is routed through that node to the next hop.
Similarly, the multiple actor nodes are checked through NIM
in order to validate their status (in the network. If illegitimate
node is found erroneously as the legitimate node, then there is
possibility of malicious attacks that lead to network failure. As
this situation happens once all the backup nodes get failure at
the same time, then illegitimate node replaces the legitimate
backup node and declares itself as legitimate node and gains
the access to the confidential data. However, this condition
can happen in worst-case when getting failure the all backup
Hence, the reliability where malicious nodes involved was
also addressed in EAR. In this experiment there were total
number of 400 nodes including 50 actors and 330 sensors
along with 4 malicious actors and 16 malicious sensors.
Simulation results in figure 10 demonstrate that EAR
reliability 99.3 with total improvement of 2.19-2.7 %.
Fig. 10. Reliability with occurrence of malicious nodes.
As we can observe increasing number of nodes improves
the overall performance of EAR reliability. Thus, the
algorithms are behaving differently in changed network size.
If size of the network is larger then there is possibility of QoS
reduction if sufficient (sensors/actor) are not deployed.
The total improvement in EAR reflects positively over the
overall network reliability. Results confirm the improvement
of EAR with verity of network sizes, with total improvement
Wireless sensor and actor network is getting more
attention considering its deployment in mission critical
environments. This topology of WSAN may change during the
network life with reference to environmental change, response
to event detection, and lost actor restoration process. Thus, it’s
essential to maintain fault tolerance of such network. In this
paper, we presented a fault tolerance mathematical model for
our EAR paradigms and evaluated the overall EAR
performance in reference to the fault tolerance taxonomy. In
addition, we extended parameter measurement for the
reliability. EAR shows promising results compared to existing
algorithms. Moreover, the overall fault tolerance evaluation of
EAR ensure that EAR manage to handle almost all fault
tolerance taxonomy levels. In future work, we plan to
evaluate, adjust and manage EAR fault tolerance taking in
consideration specific application requirements in respond to
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