# Adapted Diffusion for Energy-Efficient Routing in Wireless Sensor Networks
## 2 Wireless Sensor Networks and Routing--Background/Survey
### Literature Survey
[PERSON] [19] describes DD as a data-centric protocol focusing on desired data rather than individual nodes and extending the original DD to a DD protocol family, which includes: (1) two-phase pull diffusion, (2) one-phase pull diffusion, and (3) push diffusion. The approach can support multiple sinks and sources. The sink sends interest messages to find the source and the source uses exploratory data messages to find sinks as data are transmitted to all neighbors. This is called the two-phase pull. A key feature of DD is that every sensor node can be task-aware. By this we mean that nodes store and interpret interests, rather than simply forwarding them along. Data is cached in neighboring nodes and moves towards the sink. After this, the exploratory data-reinforced paths are used for transmission. All transmission in DD is hop-by-hop or neighbor-to-neighbor. GEAR protocols [19] (Geographic and Energy-Aware Routing) extend diffusion using geographic locations. This paper further discusses the application performance of different diffusion algorithms. Many routing protocols are available for this kind of ad hoc wireless sensor network, depending on the application required, or the longevity/energy requirements. [PERSON] [20] proposed a gradient-based routing protocol considering the minimum hop count and the remaining energy of each node while relaying data from the source node to the sink. A scheme for topology update is provided. The relationship between wireless communication, energy consumption \(E\), and transmission distance \(d=E\propto dk\), where \(k\) is usually 2\(\sim\)4, is presented. Hence, short-distance multiple-hop communication is preferable to long-distance direct communication in sensor networks. Hence, we selected communication with neighboring nodes only. The source node will find the minimum hop count path to the sink. In our protocol, we use the hop count and remaining energy of each node as the metrics. [PERSON] [21] proposes MAC protocols in WSNs, achieving a low duty cycle by employing periodic sleeping and waking. A predictive wakeup MAC (PW-MAC) protocol was made using asynchronous duty cycling, reducing the consumption of the node energy by allowing the senders to predict the receiver's wakeup times. In our paper, the NetLogo [22] simulation environment is used for sending, receiving, forwarding, and sleeping simulations. Sleeping simulation is achieved by having the nodes only transmitting when data are required or retrieved. At other times, no energy is used. [PERSON] [23] developed a simulation model of a WSN with a complex systems approach to study congestion control operations. Testbeds were used for protocol evaluations. [PERSON] [23] considered that this viewpoint provided an alternative way to understand the underlying processes that takes place in wireless sensor deployments. Data packets were regarded as the active entities or agents, which travel across the network to reach an appointed node called the \"sink\". Our model is different in that we use NetLogo agent-based modeling and define the distance from the sink and distance from the source to minimize the system's energy consumption. [PERSON] [23] did not use energy tracking and used a different simulation model. In his paper, [PERSON] [24] proposed a gradient mode which is based on one-phase directed diffusion (DD) [19], which considers the residual energy of the node and the number of hops from the sink to an intermediate node synchronously. The model is designed to achieve a balance of energy consumption and real-time traffic. A mathematical algorithm is used, and the gradient is calculated. In our paper, we set up the gradient or hop count in a NetLogo model rather than mathematically. In [PERSON]'s study, the sink sends a message to all nodes to identify their distance from the sink and the source is selected to be in the upper right corner, whereas in our model the source can be anywhere in the network.