## 1 Introduction
In this case, the receiver node knows whether its neighbors have been covered based on the global information obtained in the neighbors' control packet list information. The sender node with the auto-self-awareness back-off information nowhas a higher priority to forward the network packet to the destination. This approach will provide resiliency to errors and failures, minimize the end-to-end network delay, and improve the overall network routing performance and energy efficiency. Quite a few number of approaches to adaptive and fault tolerance in sensor networks are given in the literature [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53]. However, most proposed approaches do not provide quantitative techniques for autonomic self-aware and adaptive fault tolerant routing features in a wireless sensor network. Common approaches to multi-hop routing employ routing tables that show the path from sources to destinations. The most popular of these are the Dynamic Source Routing (DSR) [54], _Ad-hoc_ On Demand Distance Vector (AODV) [55], MintRoute [25], Low Energy Adaptive Clustering Hierarchy (LEACH) [5] and Directed Diffusion (DD) [56]. These approaches consider the state information of the sensor node neighbor as active or sleeping in order to make routing decisions. These routing schemes incur more overhead if autonomic self-aware and adaptive features are not integrated. Therefore, providing an autonomic self-awareness and adaptive mechanism to fault tolerant routing is a better way for addressing the challenges of inhospitable and hostile environments. Self-Selective Routing (SSR) [40, 41, 51, 52] and Self-Healing Routing (SHR) [24, 53] are other approaches for providing fault tolerance and recovery techniques to sensor networks. In SHR, a sensor node broadcasts its packets to all its neighbors within its communication range. Based on hop distance knowledge and a priority schedule coupled with a back-off delay scheme, the neighbors ensure that the network packet is transmitted by only one of the contending neighbors. However, the lack of an autonomic self-awareness and adaptive feature makes these two proposed fault tolerant routing algorithms prone to errors, failures, and associated routing overhead. In this research paper, we present an autonomous self-aware and adaptive fault tolerant routing technique that addresses the limitations of self-healing and self-selective routing schemes for reduced packet contention, route repair, and efficient energy conservation in the presence of transient and permanent node failure rates and in a highly congested and scalable sensor networks. The salient contributions of this research paper are as follows:  
1. We propose an autonomous self-aware and adaptive fault tolerant routing technique for efficient and reliable route formation and faster routing decision-making for wireless sensor networks. This is achieved by combining both continuous and prioritized slotted back-off delay and multiple randomize function techniques for speedy routing convergence to obtain local and global network state information for the efficient and reliable routing of sensor data. 2. We propose a route repair technique using the greedy algorithmic approach for reliable transmission of sensor data in the presence of permanent and transient node failure rates, and efficient adaptation to simultaneous network topology changes. 3. We conducted an extensive simulation to demonstrate the routing performance, flexibility and efficiency of the autonomous self-aware and adaptive fault tolerant routing technique (ASAART) under five different simulated scenarios. The simulation results show that the ASAART performs better in terms of high resiliency against errors and failure, and has better routing performance and energy efficiency compared with SSR and SHR protocols in the presence of transient and permanent node failure rates and in highly congested, faulty, and scalable sensor networks.