Abstract: The inability to replace the power supply of nodes in battery-powered sensor networks leads to energy consumption issues that directly impact the network's lifespan. This paper analyzes the energy consumption problem of sensor networks from aspects such as hardware composition and operation mechanism, computational complexity, data communication volume, and energy attack prevention; introduces some energy consumption control strategies; and points out that only by establishing a unified energy consumption quantitative assessment model from various technical levels of hardware and software, analyzing energy consumption problems, and designing holistic control strategies can the energy effectiveness of sensor networks be guaranteed. Keywords: Sensor network, energy consumption control strategy, energy attack. Sensor networks, composed of sensors, microprocessors, and wireless communication interfaces, are increasingly becoming a common computing platform for many monitoring systems and have application value in many fields. Currently, in-depth research has been conducted abroad on various aspects of sensor networks, and they are gradually becoming one of the research hotspots in China. Battery-powered sensor networks typically operate in harsh or even dangerous remote environments that are inaccessible to people such as volcanic areas and war zones, where power replacement or recharging of network nodes is usually impossible. Sensor nodes, widely distributed in the measured environment, are responsible for collecting sensitive data and routing data transmission. Furthermore, attackers may exploit compromised nodes to inject numerous fake data packets into the network, causing the nodes to exhaust their energy and become ineffective while transmitting these packets. Therefore, the irreplaceable nature of network node power supplies makes energy consumption a particularly important issue compared to other key technologies in sensor networks. Designing effective energy consumption control strategies without compromising performance is a core problem in sensor network hardware and software design. 1. Sensor Network Node Composition and Energy Analysis A typical sensor network architecture usually consists of distributed sensor nodes, receivers/transmitters, an internet connection, and a user interface. Sensor nodes, as independent working entities in the network, have basic functional subsystems including a power supply system, a sensing subsystem, a computing subsystem, and a communication subsystem, as shown in Figure 1. 1.1 Power Supply System The power supply system consists of modules such as batteries and AC-DC converters, and its main task is to supply energy to the other subsystems. As the primary energy source for nodes, the performance and capacity of the battery are crucial. While increasing battery capacity can extend the power supply time of the power supply system, employing effective recharging technologies or renewable energy sources such as solar power is more conducive to ensuring the power source of the power supply system and providing continuous power to other subsystems. A novel iBean wireless transmitter, based on iBean wireless technology and "energy harvesting" technology, and operating via an inductive oscillation energy converter, can generate a voltage of 1.2–3.6 mV from 28–30 Hz oscillations under a force of 50–100 mg without battery power, and allows data transmission at a rate of 115 kbps over a distance of 30 m. This provides an effective solution to overcome the problem of short battery life faced by long-range wireless sensor networks. 1.2 Sensing Subsystem The sensing subsystem consists of a set of sensors and an ADC controller, etc. Its main task is to sample/collect sensitive information of the monitored/controlled object and convert it into corresponding digital information. Ideally, when the sensing subsystem automatically detects both periodic and non-periodic events, its total energy consumption can be simply summarized as the product of the energy consumed per sampling and the number of samplings. Therefore, controlling the energy consumption of this subsystem must be done from two aspects: first, controlling the energy consumed in a single data sampling, and second, controlling the sampling frequency. The former can be achieved by using low-power devices to effectively control the energy consumption of a single data sampling from the components themselves. For the latter, since the numerous distributed nodes in a sensor network often monitor the same object or sensitive data in groups, selectively reducing the sampling frequency of a single node will not compromise the validity and integrity of the measured data. As long as the activation principles of the node sampling tasks are reasonably set according to application requirements, the energy consumption of this subsystem can be well controlled while ensuring data accuracy. Figure 1. Sensor Network Node Structure Block Diagram 1.3 Computational Subsystem The computational subsystem includes hardware such as microprocessors/microcontrollers, memory, and I/O interface circuits; software algorithms responsible for controlling sensors, executing communication protocols, and processing sensor data; and is the core of node control and computation. As the functional control center and data computing center of a node, the computing subsystem is complex and closely connected to other subsystems. Therefore, the strength and performance of the computing subsystem, the duration of different working states (active, idle, and hibernation, etc.), and the switching between different states all significantly affect the energy consumption of the entire node. Low-power devices, timely hibernation, and frequency reduction techniques during idle periods are common hardware techniques for reducing the energy consumption of the computing subsystem. Functional rotation between nodes helps to achieve a relatively balanced energy consumption across the network as a whole. Self-organizing cluster generation, encryption/decryption of transmitted data, and the establishment and maintenance of communication links are all accomplished by executing corresponding instruction sequences. The more complex the algorithm, the more instructions are required, and the greater the energy consumption. However, algorithms are a contradictory unity of effectiveness, reliability, and complexity. Effective and reliable algorithms often have high complexity; the effectiveness and reliability of simple algorithms may not be suitable for application requirements. The diversity and uncertainty of application environments make the energy consumption of software algorithms far more difficult to control than that of hardware. It is necessary to meet the requirements of the application environment while minimizing the complexity of the software algorithm. Furthermore, resource-constrained sensor network nodes are vulnerable to physical damage attacks, making control mechanisms and data processing algorithms commonly used in other computer networks, such as asymmetric key management protocols, unsuitable for sensor networks. Depending on the application environment, sensor networks often have different levels of requirements for various control and data processing algorithms. Therefore, each control or data processing algorithm is a highly challenging research area in sensor networks, requiring significant modifications to existing mature algorithms, redesign of new processing algorithms, or even, when necessary, modifications based on node energy development levels and technical characteristics. Additionally, appropriately reducing network or node performance can control node energy consumption and effectively extend the network's lifespan. 1.4 Communication Subsystem The communication subsystem, composed of wireless transceiver components, is responsible for the node's communication tasks. The modulation mode, data rate, transmit power, and operating cycle used by the wireless transceiver components are all key factors affecting the energy consumption of the communication subsystem. Furthermore, due to the physical characteristics of the communication components themselves, the communication subsystem consumes almost the same amount of energy as during the receiving period, even when idle. Therefore, when there are no communication tasks, the communication subsystem should be put into a dormant state as much as possible, rather than left idle. Short-range wireless communication and reducing network traffic are the main means of controlling energy consumption in communication subsystems. Hop-hopping communication, commonly used in sensor networks, achieves energy savings by shortening communication distance and reducing transmission power; data fusion reduces energy consumption by decreasing network traffic. Data redundancy is an effective means of ensuring that the base station can still obtain complete data even if individual nodes or parts of the communication link fail; however, directly transmitting raw data will significantly increase network traffic and cause a large amount of unnecessary energy consumption. Cluster head data fusion is one of the effective means of eliminating redundant data and reducing network traffic. In traditional cluster head data fusion, the cluster head node receives data from each node in the cluster, then checks the content and eliminates redundancy before uploading the result data to the base station. This method only reduces energy consumption during data routing and has no impact on the energy consumption of nodes transmitting data within the cluster. As shown in Figure 2, the data fusion mechanism based on a secure template further reduces network traffic within the cluster by replacing a large amount of data transmission with a small amount of data transmission. In this method, sensor nodes do not directly transmit collected data. Instead, they generate combined codes for the collected data using a secure template received from the cluster head node before uploading them. The cluster head node receives the uploaded code data from the sensor nodes, checks for redundancy, and selectively requests the transmission of actual data from some sensor nodes to effectively reduce network communication within the cluster. Finally, the cluster head node receives the non-redundant collected data from the selected sensor nodes and directly uploads it to the base station. Figure 2 illustrates data fusion based on secure templates. This secure template-based data fusion mechanism is a beneficial supplement to traditional data fusion mechanisms, making the energy consumption of the entire network more reasonable. Secure templates can also simplify data encryption algorithms, further reducing energy consumption. However, too slow a template seed replacement frequency can severely impact network security, while too fast a frequency may cause unnecessary template data transmission, frequently waking up sensor nodes for template data processing, leading to unnecessary energy consumption. Therefore, the effectiveness of this method depends on the amount of network data redundancy and the energy consumption ratio between redundant data transmission and template data transmission/processing. 2. Energy Attack Prevention The inherent characteristics of sensor network nodes—unattended operation and limited resources—make them more vulnerable to a wider range and more diverse forms of attacks. Unlike conventional resource-consuming attacks, energy attacks target the limited energy resources of nodes. They don't aim to deplete the nodes' computing and storage resources, but rather focus on exhausting their energy. Attackers infiltrate nodes and inject large amounts of fake data into the network, causing nodes, especially routing nodes, to run out of energy and fail due to heavy data communication, ultimately paralyzing the entire network. Therefore, the intruder's primary objective is to deplete the energy of routing nodes; the farther the injected fake data travels, the more nodes are affected. Because intruders may gain complete control of the compromised nodes, standard authentication mechanisms are ineffective against this type of internal network attack. Fake data detection mechanisms involve setting up converging nodes in the network to authenticate sensor nodes and integrate data packets. The base station and converging nodes perform effective analysis and interactive verification to detect fake data packets. The key to this mechanism is for the base station to detect the fake data injected by the intruder to prevent decision-making errors. However, because it cannot reduce the transmission distance of fake data packets, it cannot be used as a defense against energy attacks. To detect and discard spoofed data packets injected by attacked nodes as early as possible, thereby meeting security requirements and reducing energy consumption, the concept of interactive authentication is further extended. Associations are established between nodes on the data transmission link from the cluster head node to the base station, as shown in Figure 3. All nodes then verify the data packets they wish to transmit in an interleaved, hop-by-hop manner. Only when all t+1 nodes (t is a set security upper limit, representing the number of nodes in the cluster) pass authentication can the data packet be transmitted to the base station. Therefore, as long as the number of attacked nodes is less than or equal to t, the base station or unattacked nodes can detect and discard the spoofed data packets injected by the intruder. Figure 3: Node Association Diagram (t=3) 3. Conclusion Energy consumption issues involving all levels of sensor network software and hardware are crucial to the network lifecycle. From the perspective of network structure and operation, the energy consumption of each subsystem of a node is interconnected, with some increasing and others decreasing. Energy consumption control strategies targeting a single subsystem cannot fundamentally solve the problem. Therefore, it is essential to consider the network's application environment, taking into account the functional characteristics and performance requirements of each subsystem, including device selection, the effectiveness and complexity of data processing algorithms, data communication volume, and network operation mechanisms. A holistic assessment of energy consumption is crucial, and if necessary, performance standards should be appropriately lowered to design corresponding energy consumption control strategies that effectively extend the network's lifespan. Generally speaking, energy consumption control strategies for sensor networks should focus on quantifying and designing aspects such as the specific power consumption characteristics of the devices themselves, sleep entry principles, shortening communication distances, and reducing network traffic. However, to date, the energy efficiency of sensor networks has not been modeled or quantified, nor does it possess a universally accepted standard, requiring further in-depth research.