# Evaluation of Green Strategies for Prolonging the Lifespan of Linear Wireless Sensor Networks
## 4 Energy Conservation Techniques
### Node-Level-Focused Energy Conservation Schemes
#### 4.1.1 Power Management Techniques  
A sensor node exhibits various operational modes, which are determined by the states of its individual components, namely the processor, sensing subsystem, and radio transceiver. Each of these states is associated with distinct levels of power consumption. The term duty cycle is defined as the fraction of time a node is active during its lifetime. Thus, duty cycling techniques reduce the sensor node's energy consumption by turning off the sensor node's hardware components when they are not needed and waking them up whenever necessary [81]. This establishes a small duty cycle for the nodes based on the events occurring in the monitored environment [61]. Thus, techniques based on duty cycling rely on the fact that active nodes do not need to maintain their radios, processors, and sensing devices as being continuously on. According to the survey by [PERSON] et al. [36], duty cycling is achieved by two complementary approaches, with one approach taking advantage of the redundancy in WSNs by adaptively selecting only a minimum subset of nodes to remain active for maintaining connectivity while the other approach ensures that the active nodes do not maintain their radio and sensors as being continuously on by constantly switching them off (i.e., placed in the low-power sleep mode) when there is no network activity. The authors termed the former topology control and the latter power management. Energy savings in WSNs via power management can be achieved through strategies such as adaptive sleep/wake-up, radio optimization, energy-efficient MAC protocols, and processor power management. The strategy adopted by sleep/wake-up schemes is to reduce the energy consumption of the sensor node by applying duty cycling on the MCU, radio, and sensor, switching them between active and sleep modes. As generally known in the literature, the communication unit (radio module) consumes most of the sensor node's energy [1; 47]. By reducing the activities (transmission, reception, idle listening) of the radio module, higher energy savings can be achieved at the node level. Detailed information about this strategy is found in [36; 57; 59; 61]. Several factors affect the power consumption characteristics of a radio module, including the radio duty cycle, modulation scheme, data rate, and transmission distance [59]. To optimize the radio and minimize sensor node energy consumption, researchers have developed radio optimization techniques that focus on optimizing radio parameters such as radio coding and modulation, transmission power, and antenna direction [37; 38], using techniques such as adaptive transmission power control, dynamic frequency selection, duty cycling and low-power listening, smart antenna systems, etc. These techniques are implemented in energy-efficient cognitive radio [57]. Detailed information about these techniques is found in [36; 38; 82; 83; 84; 85]. Energy-efficient MAC protocols impact various aspects, such as radio idle listening, overhearing, over-transmitting, error control, retransmission rate, channel collisions, and medium access [34]. Notably, their focus is often directed towards optimizing communication links between neighboring nodes rather than considering the broader network context. Consequently, mechanisms ensuring data reliability, including error detection and correction techniques, can be leveraged to achieve energy savings. The MAC layer protocols can be categorized into contention-less, contention-based, and hybrid protocols [36]. [PERSON] et al. [65] presented a review of energy-efficient MAC layer protocols that have been used in WSNs. Processor management techniques reduce the energy consumption of the microprocessor by dynamically adjusting the power with respect to the workload. They enable intelligent trade-offs between energy consumption and operational fidelity using techniques such as dynamic voltage scaling (DVS), dynamic voltage frequency scaling (DVFS),dynamic power management (DPM), hardware acceleration, etc. [55].