## 1 Introduction  
Wireless sensor networks (WSNs) have received significant attention from the research community in past years; they are used for various applications in several fields [1]. However, the search for deploying a self-adaptable network and successfully prolonging its lifetime still needs further investigation. A network usually consists of battery-powered wireless devices with sensing and communication capabilities that collect data about the environment or an event of interest (e.g., light, temperature, and movement) and relay data toward a \"sink\" in a multi-hop manner [2]. Research in this field has been dynamic for years, and several open issues await valid solutions [3]. Aside from constraints in terms of processing power and storage capacity, other significant issues in WSNs are the limited battery duration of sensors and the consequent limited network lifetime.  
The sensors' lifetime is still the main factor in WSN; addressing this issue requires energy to be almost always replenished. The ideal solution is to make the sensors harvest from their surroundings [4, 5]. However, energy harvesting is, unfortunately, not steady and the total amount of energy harvested is barely predictable. For example, many factors affect solar energy harvesting, including the weather, the season, and time (weather exposed under the sun). This constitutes a great challenge in the design of energy efficient protocols for wireless sensor networks (in order to maintain them, improve their energy efficiency, and extend the lifetime of the networks) [6].  
Rechargeable WSNs (R-WSNs) are a promising technology for addressing energy problems in WSNs, and therefore attract an increasing number of researchers and manufacturers to discussthe potential for actual industrial applications [7; 8]. This technology possesses high-energy transfer efficiency, even under omni-directional transmission; it does not require line of sight, and it is insensitive to the neighboring environment [9; 10]. The charging energy of R-WSNs is reliable and stable.  
Progress in wireless power transfer techniques has advanced research activities in the direction of battery recharging, with high expectations for its application to WSNs. A hybrid charging scheme combines charging by a mobile charger with a directional antenna and energy trading (e.g., transferring and harvesting between neighboring sensor nodes) [11]. Sensor replacement techniques have also been widely studied as a means of providing service continuity in the network. Recharging or replacing the sensor nodes that are running out of energy can be identified and researched as two main objectives. Mobile robots are used as solutions for the execution of either recharging or replacing tasks automatically and autonomously to maintain the WSN, thus reducing human intervention [12].  
This study focuses on the manner in which R-WSNs are deployed and efficiently serve a complex geography. R-WSNs can collect information, monitor, and perform other multiple tasks. They are sustainable networks and have high performance, which are important factors for WSNs. For example, in a forest-monitoring system, we need to deploy numerous sensor nodes to cover the entire sensing field. We assume that all nodes have the same amount of energy, capacity, and transmission capabilities. These nodes can determine their location automatically from the first time after deployment. In the case of detecting failure or damaged nodes in the network, several problems may affect network sustainability, data collection, and field-monitoring results. Our approach shows good results in detecting and handling this type of failure in a large monitoring field (such as forest fire monitoring).