# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 3 Classification of Energy Management Techniques for Prolonging WSN's Lifetime
### A Review of Taxonies for Classifying Energy Management Techniques in WSN  
A number of earlier studies have reviewed the energy management techniques for prolonging the WSN's lifetime as follows: [34; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 59; 60; 61; 62; 63; 64; 65]. Most of these studies have developed a taxonomy for classifying the energy management techniques for prolonging the WSN's lifetime. However, there is no generally accepted taxonomy for classifying energy management techniques prolonging the WSN's lifetime. In this subsection, we discuss the existing studies that have reviewed the energy management techniques for prolonging the WSN's lifetime and their taxonomies, and finally, we present an adapted taxonomy. Our adapted taxonomy will then permit us to classify the energy management techniques that have been used in existing studies to prolong the lifespan of LWSN monitoring systems. [PERSON] et al. [36] conducted a review on energy conservation in WSNs. They developed a taxonomy that broadly classified energy conservation schemes into techniques for minimizing energy consumption at the node level and techniques for minimizing \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Affected** & **Parameters** & **Influence in LWSN Configuration** \\ \hline Physical layer & Transmission power, transmission range, packet size, data rate, modulation scheme & The influence of these parameters on energy consumption is in much the same way as in traditional WSNs \\ \hline Data link layer & Topology, duty cycle, synchronization, MAC protocol, error control mechanism, frame size, acknowledgment mechanism, neighbor discovery & The influence of these parameters on energy consumption may differ slightly from traditional WSNs due to the fixed linear topology of LWSNs since each node typically has two neighbors (one preceding and one following), making the topology \\  & & One preceding the workings and one following), making the topology \\  & & One preceding this, since the data link layer primarily handles point-to-point communication, its overall influence on energy consumption remains similar to that in traditional WSNs. \\ \hline Network layer & Routing protocol, route discovery and maintenance, routing metric, multicast and broadcast operations, quality of service (QoS) requirements, maximum jump factor (MJF), fault detection and recovery & The influence of these parameters on energy consumption significantly differs from traditional WSNs. The fixed routing structure in LWSNs eliminates the flexibility found in traditional WSNs, where nodes can dynamically choose among multiple neighbors. Furthermore, broadcast operations, which are commonly used for route discovery and network maintenance in traditional WSNs, are largely absent in LWSNs due to the linear structure. \\ \hline Transport layer & Retransmission rate, timeout interval, retransmission limit, window size, protocol overhead, security mechanism, traffic pattern & These parameters remain relevant in LWSNs, similar to traditional WSNs. However, the one-dimensional nature of LWSNs can limit the effectiveness of certain optimization techniques. \\ \hline Application layer & Message size, task scheduling, data fusion and aggregation, data compression, data filtering, application protocol, data collection frequency, quality of service (QoS) requirements & Functions similar to those in traditional WSNs. Data fusion and aggregation strategies are still relevant and effective, but they are tailored to the specific roles of the different node types within the linear structure. \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Parameters influencing energy consumption at the network-wide level and their associated layers. energy consumption during network activities.