The Industrial Revolution 4.0 (IR 4.0) has drastically impacted how the world operates. The Internet of Things (IoT), encompassed significantly by the Wireless Sensor Networks (WSNs), is an important subsection component of the IR 4.0. WSNs are a good demonstration of an ambient intelligence vision, in which the environment becomes intelligent and aware of its surroundings. WSN has unique features which create its own distinct network attributes and is deployed widely for critical real-time applications that require stringent prerequisites when dealing with faults to ensure the avoidance and tolerance management of catastrophic outcomes. Thus, the respective underlying Fault Tolerance (FT) structure is a critical requirement that needs to be considered when designing any algorithm in WSNs. Moreover, with the exponential evolution of IoT systems, substantial enhancements of current FT mechanisms will ensure that the system constantly provides high network reliability and integrity. Fault tolerance structures contain three fundamental stages: error detection, error diagnosis, and error recovery. The emergence of analytics and the depth of harnessing it has led to the development of new fault-tolerant structures and strategies based on artificial intelligence and cloud-based. This survey provides an elaborate classification and analysis of fault tolerance structures and their essential components and categorizes errors from several perspectives. Subsequently, an extensive analysis of existing fault tolerance techniques based on eight constraints is presented. Many prior studies have provided classifications for fault tolerance systems. However, this research has enhanced these reviews by proposing an extensively enhanced categorization that depends on the new and additional metrics which include the number of sensor nodes engaged, the overall fault-tolerant approach performance, and the placement of the principal algorithm responsible for eliminating network errors. A new taxonomy of comparison that also extensively reviews previous surveys and state-of-the-art scientific articles based on different factors is discussed and provides the basis for the proposed open issues.

Wireless Sensor Networks (WSNs) serve as the backbone for the Internet of Things (IoT) and smart cities, enabling the gathering of essential data and vital information. The widespread deployment of sensors created new scenarios that presented new domains for various applications. Due to their numerous advantages, WSN’s simulators are still gaining considerable attention as the primary method for testing and evaluating new protocols and approaches for WSN. The scientific community has developed various simulators, some tailored explicitly for WSNs and others for general use. In addition, an old but renovated direction has been enormously grown recently, representing the researcher’s attention to building its own simulator. However, many researchers struggle to select the most appropriate tool for performance analysis, which requires extensive research into available options. Choosing a suitable simulator to meet the necessary simulation criteria necessitates exhaustive research into the available options. The published studies on WSN’s simulators have limitations, such as the limited number of simulators under examination, ignoring the essential functions of the simulators, and inadequate performance criteria for precise comparison. To get beyond these restrictions in previous studies and offer a fresh study that compares the simulation tools comprehensively, an analysis of thirty-three different simulators was conducted based on a new taxonomy. Furthermore, this study examines the advantages and constraints of each simulator regarding many specific academic areas in WSNs. Moreover, the study presented a detailed comparison among the performance analysis tools according to many famous performance metrics.

Event-driven Wireless Sensor Networks (WSNs) consist of thousands of tiny nodes. Sensor nodes are prone to faults because of their fragility and the fact that they are typically placed in harsh environments. Erroneous readings pose a high risk in many situations and affect the network’s reliability, necessitating a solution to distinguish between true and faulty events. In response to this challenge, this work proposes the Friendship Degree and Tenth Man Strategy (FD-TMS) method for true event detection in WSNs. This new method can differentiate between erroneous readings and true events in a distributed manner. The FD idea has previously been used to solve security problems, while military intelligence operations have inspired the TMS and have never been used in WSNs. The FD-TMS consists of two stages. In the first stage, it employs a majority voting approach considering the friendship degree among voters. Voting among only trustworthiness nodes with high FD values will effectively differentiate true events and incorrect measurements. The second stage will validate the voting process through a novel perspective based on the TMS. TMS will check the voters’ replies based on the event’s location. The proposed method will delete erroneous readings, while only the true event reports will be reported. FD-TMS was comprehensively assessed in a simulation environment utilizing a performance analysis tool constructed on Java. The results were compared to the baseline algorithm, highlighting key parameters like false alarms and event detection accuracy. The simulation results demonstrated the proposed approach significantly enhanced the performance of the baseline works.

The wide deployment of wireless sensor networks is the primary reason behind the advancement of numerous routing protocols. In this context, sensors need to be remotely conveyed in unattended situations. Further, propels in wireless sensor network innovation have ensured accessibility and minimal effort in terms of sensor hubs with a capacity to sense various physical and natural environment conditions, in addition to wireless communication and data processing. Moreover, wireless sensor networks are rather different from regular and cellular networks, which comprise nodes with restricted bandwidth and energy. However, the most important limitation of wireless sensor networks is their characteristic limited energy resources. Furthermore, there is an imbalance of power consumption in their sensor nodes. Therefore, it is preferable to distribute the amount of energy throughout a wireless sensor network to minimize the maintenance and maximize the overall system performance. To this end, this paper suggests a new strategy in wireless sensor network dissemination and routing protocols referred to as the Fast Simple Flooding Strategy, which focuses on energy efficiency as a crucial design objective for routing protocols used in wireless sensor networks, without the overhead of other design factors, and reduces end-to-end latency. Additionally, this new strategy can be listed under flat approaches; it is simple and very fast in delivering the packet to the sink and does not require additional equipment or complex mathematical operations. It also resolves the major drawbacks of classic flooding and gossiping.

Event-driven Wireless Sensor Networks (WSNs) are a fundamental component of the Internet of Things (IoT), reporting data only upon event occurrence to conserve energy. A critical aspect of these networks is geographic routing, where routing decisions are based on node locations rather than identifiers, reflecting the spatial relevance of detected events. However, conventional geographic routing algorithms suffer from workload imbalance, congestion, and high energy consumption, limiting network lifetime and reliability. To address these issues, this study proposes a novel geographic routing algorithm based on the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The algorithm introduces a new decision metric, Exclusive Routing Share (ERS), that identifies and prevents node overutilization, thereby mitigating bottlenecks. It selects optimal forwarding nodes based on multiple criteria: residual energy on the path, hop count, delivery ratio, distance, and ERS, enabling more balanced and energy-efficient routing than traditional shortest-path methods. The algorithm’s effectiveness was evaluated through extensive simulations under varying network conditions and compared with the benchmark Fault-Tolerant Routing (FTR) algorithm. Quantitative results show a 15.4% reduction in packet error rate, a 14.9% increase in network lifetime, and a 1.46% improvement in packet delivery ratio. These findings demonstrate that the proposed TOPSIS-based approach significantly enhances energy efficiency, reliability, and workload balance in event-driven WSNs.