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Chapter One: Introduction 1.1 Background of the Study
Wireless sensor networks (WSNs) are made up of a number of sensor nodes that are primarily used to collect essential data in certain locations (Chen et al., 2012).

WSNs have a wide range of applications, including battlefield (Gangadharaiah et al, 2014), military intelligence sensing and tracking, environmental tracking (Mei et al., 2010), emergency response and disaster management (Guo et al, 2014), bio-complexity mapping of the environment, flood detection, precision agriculture, medical telemonitoring, and chemical and structural monitoring (Shi et al., 2012).

WSNs are also useful in areas including the Internet of Things (IoT), cyber-physical systems, intelligent transportation systems, and smart cities (Zhang & Long, 2017). WSNs are recognised to have a significant restriction, which is the low power consumption of sensor nodes (Guo et al, 2015b).

Parallel processing amongst sensor nodes is a novel method that supports WSNs’ critical compute capability (Guo et al, 2014, 2015b) while maintaining minimal power consumption by the sensor nodes.

Parallel processing relies heavily on task allocation. Parallel processing requires assigning tasks to appropriate sensor nodes while also balancing network load in uncertain and dynamic network contexts (Guo et al, 2014).

However, studies on the problem of work allocation in distributed systems have shown that the limitations of job allocation in WSNs differ from those of ordinary distributed systems (Guo et al, 2014, 2015b).

In WSNs, task allocation entails distributing tasks logically inside sensor nodes in order to reduce overall power consumption while ensuring that tasks are completed before the given deadlines, hence extending the sensor network’s lifetime (Guo et al, 2011, 2015b). Load balancing is a critical aspect in extending network lifetime (Suganya & Jayanthi, 2016; Guo et al, 2015b).

Without proper job allocation, each sensor node will function independently (Guo et al, 2015b). WSNs face problems such as unstable wireless communication networks and dynamically changing topologies.

As a result, there may be additional uncertainties and vulnerabilities in real-time applications (Suganya & Jayanthi, 2016; Guo et al, 2015b). A sensor node failure should not necessarily influence the sensor network’s overall job processing, particularly for safety and security essential applications.

Fault tolerance is a crucial endeavour for maintaining sensor network performance without interruption owing to sensor node failures (Suganya & Jayanthi, 2016; Guo et al, 2015b).

For example, if sensor nodes are installed in a battlefield or military camp for monitoring and detection, fault tolerance must be strong since the sensed data is vital for security and safety (Priyanka et al., 2016; Guo et al., 2015b).

As a result, a fault-tolerant method is required for such safety and time-critical applications (Guo et al, 2015b; Zhu et al, 2011). Table 1.1 summarises fault-tolerant innovation at several layers of the WSN abstraction.

Table 1: Fault-Tolerant Innovation at Different Layers of WSN Abstraction (Guo et al, 2015b)

Abstraction Structure Goals

Methods Used

Application layer

To improve reliability, consider minimising data redundancy through backup, primary copy, or data aggregation.

Transport layer improves monitoring quality by identifying defective nodes.

Fault detection and isolation


Network layer improves resilience and manages traffic congestion.

Fault-tolerant routing.

Data link layer improves coverage with a strong link.

Fault-tolerant coverage and topological control

The physical layer improves dependability by leveraging inherent information redundancy.

Hardware redundancy, many sensor investigation.

Task allocation is handled at the application layer, as shown in Table 1.1, with a focus on decreasing data redundancy while increasing dependability (Guo et al., 2015b).

Priyanka et al. (2016) employed information aggregation to reduce redundancy and ensure accurate data. The replication strategy, which employs a primary/backup (P/B) system, is the most commonly used method for fault-tolerant task allocation because it allows copies of a task to be processed on distinct sensor nodes (Guo et al, 2015b).

Backup schemes are classified as either passive or active (Priyanka et al, 2016). The active process executes both the primary and backup copies continuously, whereas the passive process activates the backup copies only when the primary copies produce an inaccurate result (Guo et al, 2015b).

The real-time fault-tolerant task allocation scheme is a type of method used to prevent system failure or breakdown, and it mostly use passive replication backup technology.

However, the passive replication technique suffers from a delay in task processing time (Han, 2015). Delays in task processing time can be detrimental for real-time systems, which are crucial in terms of safety.

1.2 The Significance of Research

Task allocation and scheduling are critical for WSNs to maximise network lifetime by decreasing energy consumption or processing time. Recently, task allocation research has been conducted with the goal of producing fault-tolerant systems with an emphasis on energy reduction and less attention paid to the total time required to execute activities.

There is a requirement for a real-time WSN that is fault-tolerant and safety critical, necessitating the creation of a modified real-time fault-tolerant task allocation mechanism, such as to avoid system breakdown due to sensor node failure. The updated real-time fault-tolerant task allocation algorithm enables a system to continue working even in the presence of 4.

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