## 2 Related Works  
There have been many strategies used recently to locate the buried land mine. The dog's primary training involves finding the RDX-exploded substance in the target area. it has a 60-centimeter detecting range for objects buried in the ground. The rats were taught to scratch a hidden area on their foot to indicate the presence of land mines by rewarding them with food every time they did so. A catastrophic loss could occur if buried weapons are not precisely located due to poorly trained dogs and rats. Additionally, various types of explosive material in the affected area were found using bees, bacteria, and plants [9; 10; 11]. Radio waves were used by the ground penetrating radar to examine the characteristics of a return signal from the explosive components. Infrared detection picks up the electromagnetic variation in the buried explosive surface due to the signal's reflection and emission. The temperature variation close to the explosive's buried surface was recorded by the thermal-based detector. Due to the fact that different materials exhibit different types of signal reflection, the ultrasound-based detection generates a variety of acoustic properties depending on the materials [9; 10]. One of the most significant challenges in real-time data collection is determining the exact position of an event's occurrence, hence many applications employ the camera and GPS to determine the location. However, the use of GPS and image processing techniques increases computing complexity, memory, and bandwidth, which are directly related to the energy consumption in WSNs. A number of camera-based technologies have been developed with image compression methods in order to improve network longevity for energy-constrained WSNs. These techniques increase the rate of packet delivery by reducing the amount of bits in the image signal. The optimal zonal 2 \(\times\) 2 BinnDCT was proposed for WSNs in [12], in which a shift and add function is used to extract the coefficients of an 8 \(\times\) 8 image block. A Discrete Wavelet Transform (DWT) based distributed image compression technique was proposed in [13] to share the processing tasks and aimed to reduce the computational complexity and energy consumption. A distributed picture compression method based on the JPEG 2000 standard was presented in [14], in which parallel DWT and tiling techniques were utilized to exchange information and distribute the processing workload between the different nodes. The Lapped Bi-orthogonal Transform (LBT) based image compression was introduced in [15] which utilized a clustering technology to distribute the processing load across the nodes and lower the large amount of energy consumption. Numerous routing schemes were proposed to gather the information and transmit it to the Base Station (BS) [16; 17; 18]. For instance, the Unmanned Aerial Vehicle (UAV) or Unmanned Surface Vehicle (USV) was employed to monitor nuclear radiation in a specific area and identify regions of radiation that were highly concentrated. The Internet of Things (IoT) has been designed with the standard Transmission Control Protocol (TCP) to offer secure data connections without traffic congestion. However, it could not be used directly to WSNs due to the restrictions imposed by sensor nodes. For the analysis of mobile ad-hoc networks, a probabilistic, energy-aware broadcast calculus is proposed in [19; 20] to enhance topology control and lessen interference. The formulation and analysis of the connectivity and energy consumption is a challenging research topic in MANET. This can lead to design an automatic tool, as described in [21]. Also, a design level formal model is described in [22; 23] to avoid the collisions and interference. An appropriate routing and topology structure is required to prevent the collision, enhance the packet delivery rate and throughput. In traditional WSNs, the sensor nodes were manually or randomly placed in remote areas to sense the necessary events and send the data directly to the BS.