# Adapted Diffusion for Energy-Efficient Routing in Wireless Sensor Networks
## 2 Wireless Sensor Networks and Routing--Background/Survey  
### Literature Survey  
Many routing protocols are available depending on the application required. In our situation, we selected DD as our baseline routing protocol. A number of papers have been published with new protocols comparing them to DD and achieving reduced energy usage. For example [PERSON] [3] used Resource-Aware Information Collection (RAIC), first using Utility-Based Flooding (UBF). The authors compared Resource-Aware Information Collection (RAIC), a distributed two-phased resource-aware approach to information collection in ad hoc networks to DD. Similarly, we are comparing AD to DD. Wireless sensor networks are networks of sensing nodes that communicate by wireless means with other neighboring nodes and have large areas of use in many industries, the environment, defense, mining, and healthcare. In recent years, nodes/sensors have become cheaper, more efficient, and smarter. They are now able to implement complex routing protocols to forward data to a central location for analysis. The key issues with WSNs relate to their life being limited by the power availability from the batteries, and hence, the restricted life of the network. Energy is used in transmitting and receiving data, and computation. The biggest energy consumer is transmission. When a node uses up its energy, sensor data can no longer be transmitted. Neither can the node forward the sensor data from other nodes. Thus, whole portions of the network can become isolated. The way to extend the life of the network is by optimizing the energy consumption of the routing protocols used to transmit and receive data from the sink (requesting data) to the source (data requested) and back. In this paper, we determine the number of source data retrievals until the data can no longer reach the sink due to insufficient energy for transmission in nodes for particular protocols. Most of the research on WSNs has concentrated on the design of energy- and computationally efficient algorithms and protocols. The purpose of the paper is to describe the new agent-based model of a data-centric protocol, the AD protocol for wireless sensor networks, and a newly developed derivative of the AD protocol. The models are tested for longevity and energy efficiency. In future research, all models willallow for energy harvesting. Our models then test all the protocols to extract time-to-live statistics for various parameters. Changes to several aspects including the routing protocol, the ability of nodes to perform energy harvesting, and the ability to enhance the energy of selected nodes are made to the models. The results of energy consumption and network longevity are plotted. Substantial research has and is being conducted to optimize the lifetime of WSNs. Robot path planning bears similarity to WSN networks in optimizing travel path to a source. A number of papers [4; 5] have been published on robot path planning. Most path-planning algorithms combine deep reinforcement learning (DRL) and path-planning algorithms to achieve obstacle avoidance and path shortening. These path-planning papers use mathematical modeling. [PERSON] considers a 3D network of autonomous underwater vehicles (AUVs). The simulation results in [PERSON]'s paper demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path-planning problem. Target tracking with multiple AUVs is undertaken. The Dubins path typically refers to the shortest curve that connects two points in the two-dimensional Euclidean plane. [PERSON] also looks at AUV path planning in a complex underwater environment. These are three-dimensional networks where robots such as drones or underwater vehicles travel. The algorithm considers flight paths with the intent of preventing two agents colliding. In our case, the messages travel in a way to prevent revisiting previously visited locations.