Wireless Sensor Networks (WSNs) have acquired intensive popularity due to the wide range of applications in different fields [1]. A recent emerging application is the Internet of Things (IoT), which allows the interconnection of different objects or devices through the world of the internet [2]. About 5 billion intelligent devices are connected, and the number is quickly increasing worldwide [3]. The number of people interacting can exceed the number of virtual devices that connect to them. As a result, significant traffic will be generated, and humans are the slightest contributor to this traffic [4].

WSNs are of two types: homogenous and heterogeneous [5]. In a homogeneous network, nodes are identical. Resource heterogeneity in WSN can be divided into computational, link, and energy. Computational heterogeneity describes those networks where nodes differ in microprocessor's power and storage capacity. Hence, the powerful nodes can perform complex data processing and long-term storage. When nodes differ with bandwidth, the networks come under the link heterogeneity category and are suitable for reliable data transmission. Both link and computational heterogeneity consume a considerable amount of energy, reducing the network lifetime. Hence, the type of WSN must be worked out, where nodes are differentiated in terms of battery power.

The energy source consists of limited battery power, which is one of the major challenges in designing any sensor network [6]. The nodes may be deployed over a hostile location because the application makes the battery recharging almost unmanageable. Moreover, the nodes are expected to perform data acquisition indefinitely to achieve the application requirements. Hence, many researchers are currently exploring various techniques to extend the network lifetime to achieve high quality of service by balancing the energy consumption over the network.

When WSN is applied on larger platforms, topology control becomes an important parameter in balancing the network load to enhance the network lifetime and scalability. Clustering is an energy-efficient method for the hierarchical organization of sensor nodes in a network [7]. However, each node in an ad hoc network communicating directly with the sink node leads to problems such as data collision, network congestion, and unnecessary drainage of power. Low Energy Adaptive Clustering Hierarchy (LEACH) [8] is a classical cluster-based protocol proposed to minimize energy consumption by efficiently selecting CHs. Forming small clusters within the network helps overcome these crucial issues through efficient resource utilization. For each cluster, a CH is elected to act as a hop between the sensing nodes and the sink, thereby reducing the transmission distance. The CHs are elected dynamically after a specific interval to reduce the overhead. Once the CHs are elected, they broadcast an ADV message using the CSMA MAC protocol. Based on the RSSI of the ADV message, the nodes decide which CH wants to join for the current round and send a REQ message back to it using the CSMA MAC protocol.

The clustered network helps the system maintain a longer life term by scheduling a duty cycle between nodes in a cluster without affecting the normal functionalities of the network. The CH sets a Time Division Multiple Access (TDMA) schedule for data transmission to prevent any collision of messages. The non-CH node sends its data to the respective CHs through Direct Sequence Spread Spectrum (DSSS) communication, in which each cluster has its unique propagation code to reduce interference. The CHs add received data and send it to BS through a fixed propagation code with CSMA.

Energy-saving is the utmost priority of a sensor network. Different methods have been adopted according to the requirements of the application and user. The characteristics of WSN-based IoT diverge from traditional network paradigms such as the internet and ad hoc networks. Unlike ad hoc networks, WSNs designed for IoT applications confront a slew of additional hurdles, including node density, hardware, communication mode, battery capacity, and computational cost, to mention a few. In an IoT paradigm, nodes are given additional functions and must overcome new hurdles in terms of security, QoS, and energy management. Ring routing, as proposed by Maurya et al. [9], targets to reduce overhead by introducing mobile sink nodes. These concerns can be alleviated by implementing technology upgrades in traditional WSN methods and schemes. The potential applications of WSN-based IoT required data to be sent to the user with a minimum delay to provide an immediate response to an event, as in the case of tracking. The query response time in a WSN is another key factor in applications such as forest fire detection. Mobile forest fire patrol units can inject queries into the WSN to monitor environmental variables such as temperature and humidity. The WSN must respond to the inquiries as soon as feasible to speed up response to a fire and enhance fire prevention efforts. The monitoring applications require successful data reception by the server irrespective of the transmission time.

WSNs were first used for military and defense applications, further motivating researchers to explore new technologies that could enhance their performance. The main domains of applications of the WSNs are as follows:

Environment monitoring: Since the attention to sustainable energy solutions has increased substantially, smart technologies for energy conservation have gained importance. Monitoring environmental conditions has been one of the popular applications of WSN, which controls and manages services such as air, water, and soil monitoring. This network allows the end-user to gather data at a resolution in areas where accessing data is otherwise difficult.

Home application: With the expansion of WSN-integrated IoT, tiny sensor nodes can be implanted into household equipment such as electric appliances and furniture to access from a remote place [10]. For instance, the nodes can be incorporated into microwave ovens, vacuum cleaners, washing machines, and air conditioners connected to a room server.

Healthcare application: Driven by the convergence of data collection regarding people's health and maintaining accuracy in collected information with minimal cost, WSN for healthcare has evolved in recent years. Researchers have invented a new branch of WSN exclusive for healthcare, Body Area Network (BAN), that provides better treatment at a lesser cost. BAN is administered to monitor patients' physiological, cognitive, and psychological information.

Tracking and military application: Battlefield surveillance was the most critical application of wireless mediums' sensor networks. Characteristics such as self-association, fast organization, and good adaptation to node failures make WSN an extremely desired sensing technique for military applications. Sensor nodes could be located in remote areas to sense and receive data as long as possible that can alert the user about possible blast locations, enemy positions, and chemical attacks.

Transportation: WSN helps drivers alert any congestion or traffic problems by regularly monitoring traffic statistics. Vehicular motions can be tracked instantaneously to avoid blockage or accidents. WSN also reduces the length of the wiring harness and saves time when it comes to the installation cost.

Industrial applications: WSNs offer momentous cost reductions and investments that allow innovative functionalities for industry-based technologies. Continuous environmental sensing, condition monitoring, and process automation are some of the requirements of Industrial WSNs. The solutions need to be simple to use and install yet versatile, with low-cost devices and a long lifetime.

WSN is a bridge element that combines the digital virtual world with the real world. A WSN is formed by collecting many sensors called nodes, which have limited computing, sensing, and communication functions. The sensor nodes are implemented in a geographical area to monitor physical phenomena such as humidity, temperature, and vibrations. The nodes are small devices with essential components, such as subsystems' detection, processing, and communication and a power supply unit, as shown in Fig. 2.

The node architecture shown in Figure 4 has four important units. Sensors and an ADC (analog to digital converter) constitute the sensing unit. The analog signals generated by sensors are converted to their digital form by the ADC. They are then forwarded to the processing unit, which consists of a memory-equipped microprocessor or microcontroller. The processing unit is also responsible for controlling the sensor nodes intelligently. The communication unit handles data transmission and reception via a Radio Frequency (RF) channel. It also connects the nodes to the rest of the network. The most vital component is the power unit, containing a battery that supplies energy to all the system components. Considering the low cost of production and energy consumption, these units are integrated into a small module. There can be other sub-units of the node that are application dependents, such as a power generator, location-finding system, and a mobilizer.

Due to the network being homogenous, nodes will be identical regarding hardware complexity and battery energy. In these networks, static clustering has been used only. This implementation utilizes a single network topology, and it is not complicated. However, the most significant drawback of a homogeneous sensor network is that all network nodes may be able to act as CHs, which means they must have the hardware capabilities to meet the hardware requirements. Since energy is an extremely critical resource, various routing protocols have been proposed to minimize energy consumption at different levels of the network.

Routing techniques differ depending on the applications and dependability of the node’s hazardous condition. The robustness of the routing strategy is dependent on the network architecture and design of the WSN. The design challenges for routing protocols in WSNs are outlined. Numerous clustering strategies are highlighted and projected with comprehensive routing techniques, improving performance. Fully demonstrated and tested LEACH extends network longevity while eliminating intra-cluster collisions. Researchers have mostly focused on enhancing the CH selection criteria, which have been shown to save energy more efficiently. Owing to challenges such as power, network scalability, security, and packet-delivery ratio, researchers have modified the LEACH protocol over time. This paper intends to present a more comprehensive survey of LEACH-based classical and bio-inspired protocols to help researchers understand routing protocols with diverse architectures, novel strategies, and enhanced performance. The LEACH-MAC protocol can be used in networks where life is a significant issue. I-LEACH protocol can be beneficial on large-scale as well as small networks. Similarly, the LEACH-KH protocol yields a high PDR and can be adopted in networks where reliability is the prime factor.

Research possibilities in real-time applications still exist that solve difficulties with video and image sensors. The network must include node mobility because stationary sensor nodes cannot match demand. The primary issue is that the frequent updating locations of the sensor nodes and BS’s position deplete the network energy. As a result, the overhead of mobility and topological fluctuations necessitates the invention of unique protocols. Additionally, the wireless connection technology used in a WSN commonly facilitates various cyber-attacks when transferring data packets. The randomly moving sensors provide hackers with several opportunities to launch denial-of-service attacks, thus decreasing the WSN’s security. Despite significant research efforts and advances in recent years, many unanswered research concerns with routing in WSNs need to be addressed. The development of cryptographic methods for authenticated encryption in WSNs that enable privacy and network security can be explored.

I want to express my heartfelt gratitude to Dr. Hitesh Mahapatra for his invaluable guidance and support throughout our research on energy-efficient protocols for WSNs in IoT applications. His deep knowledge, constructive feedback, and dedication have been instrumental in shaping the direction and quality of this work. Dr. Mahapatra’s mentorship has enhanced my understanding of cutting-edge technologies and encouraged me to think critically and innovatively. I deeply appreciate his encouragement and contributions, which have significantly enhanced my research experience. Thank you for being an inspiring mentor.

Anish Bhargav was solely responsible for the conception, design, execution, and analysis of the research presented in this paper.

The author conducted all aspects of writing, data collection, and interpretation.

This research received no external funding.

The data used and analyzed during the current study are available from the corresponding author upon reasonable request.

The authors declare no conflicts of interest regarding the publication of this paper.

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