1. Introduction

WSN is a network of wireless nodes that work together to monitor and transmit information about the environment. Sensor nodes, made up of sensors, processors, and transceivers, are powered by batteries and are used to gather data in regions that are difficult for humans to access. These nodes have limited resources such as energy and processing power, making energy conservation a critical aspect of WSNs. Energy consumption is a critical issue in WSNs, affecting the network's lifespan. The versatility of WSNs allows them to be utilized in a wide range of fields, including military operations, environmental monitoring, healthcare, residential settings, commerce, space exploration, chemical processing, and disaster response. Researchers have explored various methods for optimizing energy use, including hierarchical approaches like clustering, which divides the network into small groups, with one node serving as the CH to aggregate data and pass it to the Sink Node. Grid-based clustering is also popular in which the region is partitioned into grids. However, it also has its challenges, such as the hot spot problem, which occurs when CHs near the sink node quickly drain their energy.

Developing efficient routing protocols for WSNs is crucial as routing algorithms significantly impact the performance of WSNs. By utilizing clustering, the network can accommodate a vast quantity of sensors while simultaneously extending its lifespan. Clustering algorithms can be centralized or distributed, with the centralized approach depending on the global network topology and the distributed approach relying on local information. Recently, many clustering algorithms for WSNs have been introduced, with a common feature being the selection of CHs in each round. However, this leads to increased communication between nodes, causing issues such as higher network traffic, faster battery drain in nodes, and heightened collision rates. Techniques like unequal clustering and uniform CH distribution have been suggested in the past to address these issues. In a chain-based topology, the nodes in WSN form chains. The nodes themselves can establish these chains through a greedy approach originating from any sensor in the WSN, or the chains can be created by the sink, which will calculate and disseminate it to all sensors in the WSN. The pioneering routing protocol using a chain-based topology is called PEGASIS ^[3]. PEGASIS works on the principle that every sensor exchanges data with its immediate neighbours and takes turns being the leader. The chain is established using a greedy approach, and the leader assumes the role of transmitting the data to the sink. As each sensor changes its role to become a leader, the energy load becomes uniformly distributed among the nodes in WSN, ensuring a balanced energy expenditure per round. Several variations of chain-based techniques have been suggested, as they effectively balance the power consumption in WSNs.

The proposed approach, EERZCC, is an enhanced protocol that builds on EEARZC ^[1] by introducing a hybrid approach that combines zone-based clustering and a chain approach to improve the lifespan of networks by increasing their energy efficiency. In the first stage of the proposed algorithm, the existing adaptive routing algorithm EEARZC ^[1] is applied to the network, which implements the zone-based clustering technique in the designated region. Consider a practical scenario where a WSN is deployed in a large farmland area. In this context, sensor nodes positioned at various locations within the region are designed to detect specific events, such as the entry of intruders or wild animals. These nodes, referred to as source nodes, are present at different locations in the network and initiate data transmission toward the Sink Node in response to the detected events. These source nodes transmit the sensed data to their CH. Next, the Fuzzy Inference System (FIS) proposed in EEARZC ^[1] determines the Next Hop CH node from among the willing neighbouring candidate CHs for relaying the data to the sink via multiple hops. In EEARZC ^[1], the best next-hop CH node is determined at every hop until the data reaches the Sink Node. This determined path from the source node to the sink is not retained in EEARZC. In the proposed approach, we are retaining this path as a chain for future data transfers by any CH that is part of this chain. This aids in saving the power of nodes, as the CHs don't need to determine the next hop node every time they want to forward data to the sink. In the proposed approach, multiple chains can be present in the network from different event-detecting source nodes for relaying the sensed data to the sink. Also, if a CH that is part of a chain stops relaying data to the sink, the proposed system dynamically replaces it with another CH. This process eliminates the necessity to reconstruct the entire chain and effectively mitigates packet losses, ensuring seamless and uninterrupted data transmission within the network. The EERZCC algorithm has major benefits, such as energy efficiency, improved packet delivery, and an extended network lifespan.

The remaining paper comprises: in Section 2, a survey of related research is explained; Section 3 describes the suggested approach; Section 4 compares with other methods for varying network densities; and Section 5 concludes the study and gives recommendations for further research.

2. Related Work

Lindsey et al. ^[3] proposed a pioneering chain-based technique called PEGASIS, which prioritizes reducing power consumption to extend WSNs' lifetime. Each node communicates with its neighbours and alternates leadership roles for data transmission to the sink. However, the protocol's selection of nodes to create a chain list is a critical problem. It adds new nodes based solely on the distance from the last sensor in the chain, increasing the distance between nodes, causing higher data transmission costs, and reducing the network's lifetime.

Zhang et al. ^[4] present a novel approach to enhance the energy efficiency of WSNs through a combination of dynamic clustering and a compressive data-gathering algorithm. The paper introduces a dynamic clustering mechanism that efficiently organizes sensor nodes into clusters based on proximity and residual energy. It also presents a compressive data-gathering algorithm, which enhances data transmission efficiency. This algorithm minimizes redundant data transmissions, reducing energy consumption and improving network longevity.

Guo et al. ^[5] suggested an enhanced version of the PEGASIS protocol called PEGant that utilizes an improved ACO algorithm for constructing chains, as opposed to the greedy-based approach employed by PEGASIS. By doing so, PEGant generates more evenly distributed routes with decreased total square data transfer distance. Moreover, PEGant considers the energy factor when creating the chains, ensuring that the power consumed is uniform in the network. Finding the leader during each round depends on the node's residual power, and the chosen leader communicates directly with the Base Station (BS).

Yu et al. ^[6] proposed an algorithm called EECB that adheres to the principles of PEGASIS, but instead of choosing the leader randomly from the chain, it evaluates how far the sensor is to the sink and its remnant power to determine the most suitable node as the chain leader. Additionally, EECB utilizes a distance threshold to avoid creating long links.

Chakraborty et al. ^[7] proposed a routing Protocol called GROUP inspired by a Genetic algorithm. The GROUP algorithm operates as a chain-based approach. It permits a sensor to transfer packets to the sink with a random frequency, dependent on its remnant power and position, improving network longevity. In addition, instead of using PEGASIS's greedy algorithm, which may not always minimize energy depletion, GROUP employs contemporary techniques such as genetic algorithms and SA to establish routes.

Zheng et al. ^[8] proposed a CRBCC protocol that utilizes a hierarchical routing approach with two layers to ensure reduced latency and minimum energy dissipation during data delivery. The chain is divided into multiple chains that can transfer simultaneously to achieve this enhancement. CRBCC uses clustering and chain-based methods using Simulated Annealing. This enables CRBCC to reduce the power consumed while data is aggregated. However, the protocol faces difficulty in deciding leaders, as the chosen ones exhaust their energy fast without an active method to select a new leader. Random leader selection results in bad choices, with some nodes being elected multiple times while others are never chosen. This process could be improved by incorporating suitable parameters like a node's remnant power or considering how far the node is from the sink to improve WSN's efficiency and lifetime.

Chen et al. ^[9] suggested a hierarchical routing technique called CHIRON that aims to achieve energy efficiency while mitigating common drawbacks associated with chain-based methods, such as latency and redundancy in data transfer. The protocol consists of four phases: creating groups, creating chains, electing leaders, and transmitting data. The leader of each chain is elected dependent on the remaining power of the nodes. During data transmission, nodes send the data to the leader, with the cumulative data being propagated from one leader to another toward the BS. This reduces latency and power consumed. However, irregular network partitioning during the initial phase may cause imbalances in the consumed energy and result in latency issues, limiting the scalability of CHIRON.

Huang et al. ^[10] proposed a technique for forming chains to reduce the hop length variability called GRID-PEGASIS protocol, in which the monitoring region is split into smaller cell regions. This minimal hop-distance variance allows for a more balanced power usage amongst the sensors. PEGASIS constructs chains using a greedy method, resulting in longer hops that negatively impact the network lifespan. GRID-PEGASIS employs a novel data acquisition method by forming a chain with minimum hop-distance variance to address this issue. This is based on the observation that a smaller distribution area for the nodes results in a lower probability of longer chain formation. As a result, GRID-PEGASIS effectively prevents longer hops, leading to greater uniformity in consumed power and improved network lifespan.

Linping et al. ^[11] proposed a protocol introducing double CHs to ensure even load distribution and extend the network's lifespan. In PEGASIS, each chain has a single leader, which consumes more energy by relaying all the members' data in every round. It uses a pair of CHs per chain to address this, creating a hierarchical structure to eliminate lengthy chains. The primary CH on the main chain receives data, aggregates it, and transfers the outcome to the secondary CH. The secondary CH relays local data to a higher-level CH towards the sink.

Tang et al. ^[12] proposed a protocol called CCM that combines the advantages of PEGASIS and LEACH protocols to a significant level, resulting in superior performance outcomes compared to other protocols. CCM splits the WSN into multiple chains and consists of two primary phases. During Phase 1, an updated chain routing method is employed, where the chain members utilize a parallel strategy to forward data to their respective leaders. In the second phase, the leaders of the chains gather to form a cluster, and the combined data is transmitted to an elected cluster. The voted CH then relays the data to the BS. The power consumed for data transmission increases with the spacing between the CH and the BS, which CCM attempts to alleviate by appropriately distributing the nodes. However, CCM has drawbacks, including the resulting delay and uneven energy consumed from distant chains.

Sheikhpour et al. ^[13] developed an ECCP protocol to efficiently spread energy among nodes, reducing energy consumption and improving lifespan. Each node saves details about neighbouring nodes in a table, and it operates in three phases: forming clusters, forming chains, and transferring data. After forming clusters, nodes organize themselves into chains that allow communication and data transfer. During initialization, nodes broadcast information containing their location and remnant power, and neighbouring nodes update their neighbourhood table accordingly. ECCP uses a chain-based forwarding method to transfer data from CHs to the BS, often achieving short-distance transmissions and reducing energy consumption for an extended WSN lifespan.

Despite all of the past research, finding an efficient protocol remains challenging. Hence, this paper suggests an enhanced Routing technique that helps in the efficient use of power in the network while increasing its lifespan.

3. Materials and Methods

The EERZCC algorithm is an enhanced protocol that builds on EEARZC ^[1] by introducing a hybrid approach that combines zone-based clustering and a chain approach to improve the lifespan of networks. The zone-based clustering technique given in EEARZC ^[1] is first applied to the network in the proposed approach. Next, for the event-detecting nodes in the network, optimal paths are identified from these source nodes to the sink using the FIS given in EEARZC ^[1]. In the proposed system, these determined paths are retained as chains in the network to facilitate subsequent data transfers to the sink. This mechanism conserves node energy, as the CHs that are part of a chain need not determine the next hop node each time data is relayed to the sink. Furthermore, when a CH within a chain stops forwarding data to the sink, the proposed system dynamically replaces it with another CH.

The assumptions for the network are as given below:

1. The nodes are uniformly deployed in a $y\mathrm{*}y$ sensing field.

2. All nodes, including the Sink Node, have fixed locations.

3. The Sink Node is placed at the boundary of the region.

4. Every node, including the sink, knows its location.

5. All the nodes have a range of communication with their neighbours.

6. Each node can vary its transmission power

Before explaining the proposed enhancement technique EERZCC, the existing algorithm EEARZC ^[1] is briefly described below. The following steps are applied to the network:

Step 1. Apply to the network the existing Zone-based clustering method proposed in EEZBC ^[2], which is also used in EEARZC ^[1]

The region is partitioned into zones of the same size. A sensor node with the highest remnant power is chosen as the Zone Monitor (ZM) within each zone. Subsequently, multiple CHs are selected in every zone based on the remnant power of nodes. After that, each CH determines its threshold limit, which is half of its remaining power. Then, the remaining sensors in the zone join as members of one of the chosen CHs in the zone. When a CH's remnant power drops beneath its threshold limit, it dispatches a "NoRelay" message to its ZM. Then this CH, to save energy, ceases the relaying of data received from other CHs but continues its other operations of sensing, data consolidation, and relaying of data obtained from its own cluster. A ZM initiates re-clustering in its zone if it receives a 'NoRelay' notification from fifty percent or more of the CHs in its zone or if its remnant power drops beneath its minimal threshold limit, in which case it also selects the next ZM for its zone.

Step 2. Apply the existing FIS-based routing algorithm proposed in EEARZC ^[1]

A CH will send a "RelayRequest" message to neighbouring CHs to send aggregated data to the sink. A neighbour CH obtaining the "RelayRequest" message will respond with an "AcceptReq" response message if it is willing to help relay data. In the response message, the CH will include the following details about itself:

- Remaining Energy

- Distance from the Sink Node

- Location Coordinates

- Capability Factor

The requesting CH will choose a neighbour CH to relay the data depending on the output of a FIS. Four Fuzzy variables are provided to the FIS for each candidate, including the candidate node's residual energy, distance from the sink, distance from the requesting node, and its Capability Factor. The Capability Factor reflects the traffic volume, bandwidth availability, and congestion faced by the node, and using it as a fuzzy input improves routing decisions.

Step 3. The proposed enhancement technique is applied during the routing phase of EEARZC ^[1] to create chains

The FIS output determines which candidate node is to be selected as the Next Hop node for transmitting data to the sink. In the proposed technique, the CH remembers this choice of the Next Hop node for subsequent transfers of aggregated data. This helps conserve the CH's energy as it does not need to find the next hop node by exchanging control messages with its neighbours every time it wants to relay data. The CH stores the unique ID of the determined Next Hop node and marks it as a valid entry.

Data is transmitted from a source to the sink using multiple hops. Whenever a CH receives aggregated data from its neighbouring CH to relay it to the sink, it first checks if it already has a valid entry of the Next Hop node ID stored with it. If a valid entry is available, the CH will forward the data to this Next Hop node; otherwise, the CH uses the EEARZC algorithm to find the optimal Next Hop node using fuzzy inferencing.

In the proposed enhancement, another modification to EEARZC is that whenever the remnant energy of a Cluster Head ch drops beneath its threshold limit, it sends a message "NoRelay" to not only its ZM but also to all its neighbouring CHs. If the unique ID of ch is saved as the Next Hop node entry with any of the neighbouring CHs, then they mark that entry as invalid. The process flow of EERZCC is shown in Fig. 1.

Let us consider an example: Suppose Cluster Head ch1 receives aggregated data from its neighbouring CH to relay it to the sink. Suppose ch1 does not have a valid entry for the Next Hop node. Then ch1 uses the EEARZC algorithm to find the optimal Next Hop node. Suppose the Next hop node determined by FIS is Cluster Head ch2. For subsequent transfers of aggregated data by ch1 to the sink, it uses ch2 as the next hop node till it receives a "NoRelay" message from ch2. If ch2 sends a "NoRelay" message, ch1 will mark its saved Next Hop node entry as invalid for future use.

Another advantage of the proposed enhancement is that, as shown in Fig. 2, whenever a CH that is part of a chain stops serving as a relay device, the whole chain from the source to the sink need not be reconstructed. Only the non-relaying CH will be replaced in the chain with another optimal CH using the FIS given in EEARZC ^[1]. This will help avoid packet losses. In Fig. 2, dotted lines show the alternate path found using the EEARZC algorithm.

Fig. 1. Workflow of the proposed method EERZCC.

Fig. 2. Finding alternate path using EEARZC algorithm when a CH node in a chain sends "NoRelay" message.

4. Results

The suggested technique is simulated using Matlab. The parameters required for simulating in Matlab are specified in Table 1.

The suggested method was evaluated by conducting a simulation on a range of network densities ranging from 150 to 350 nodes. A random deployment of nodes was carried out across a 400 ${\times}$ 400 $m^{2}$ region. The simulation results presented are the mean of a hundred distinct runs, and the trial outcomes demonstrate the effectiveness of the proposed technique. To assess the suggested technique in comparison to available methodologies, we examined the measure of network lifespan and total energy consumed. Fig. 3 shows an area of 400 ${\times}$ 400 $m^{2}$ that is partitioned into uniform zones of 100 ${\times}$ 100 $m^{2}$ .

In the figure, we can see the chains formed from different source nodes to the Sink Node. A comparative study is conducted between the suggested approach EERZCC and the algorithms PEGASIS ^[3], CS-DCES ^[4] and EEARZC ^[1]. The experiment was conducted over 2000 rounds and showed that EERZCC outperforms other existing approaches included in the study. EERZCC was assessed under varying WSN densities to verify its effectiveness. The assessment was made based on the WSN lifespan measures for networks containing 150, 250, and 350 sensors. Comparing the average WSN lifespan obtained by the suggested technique EERZCC with PEGASIS, CS-DCES, and EEARZC for different network densities, Figs. 4-6 show the results for FND, HND, and LND, respectively. These figures show that the suggested approach yielded a better network lifespan for varying network densities and the three measures: FND, HND, and LND.

We make the following observations regarding FND-based network lifetime measure by examining Fig. 4. For a WSN with 150 sensors, the suggested approach outperforms EEARZC by approximately 12%, CS-DCES by about 90%, and PEGASIS by around 192%. Similarly, for a WSN with 250 sensors, the suggested approach outperforms EEARZC by around 16%, CS-DCES by about 57%, and PEGASIS by approximately 175%. Moreover, the suggested approach surpasses EEARZC by about 13%, CS-DCES by around 46%, and PEGASIS by about 143% for a network having 350 nodes.

We make the following observations regarding HND-based network lifetime measure by examining Fig. 5. For a WSN with 150 sensors, the suggested approach outperforms EEARZC by approximately 8%, CS-DCES by about 46%, and PEGASIS by around 186%. Similarly, for a WSN with 250 sensors, the suggested approach outperforms EEARZC by around 5%, CS-DCES by about 55%, and PEGASIS by approximately 168%. Moreover, the suggested approach surpasses EEARZC by about 6%, CS-DCES by around 55%, and PEGASIS by about 132% for a network having 350 nodes.

We make the following observations regarding LND-based network lifetime measure by examining Fig. 6. For a WSN with 150 sensors, the suggested approach outperforms EEARZC by approximately 10%, CS-DCES by about 47%, and PEGASIS by around 97%. Similarly, for a WSN with 250 sensors, the suggested approach outperforms EEARZC by around 8%, CS-DCES by about 44%, and PEGASIS by approximately 92%. Moreover, the suggested approach surpasses EEARZC by about 8%, CS-DCES by around 46%, and PEGASIS by about 95% for a network having 350 nodes.

The effectiveness of the suggested approach EERZCC in enhancing network lifespan measure, as given by FND, HND, and LND, is demonstrated in Fig. 7 compared to other algorithms, namely PEGASIS, CS-DCES, and EEARZC, across various network densities. Figs. 4-7 depict EERZCC's superior performance compared to the methods mentioned above.

Fig. 8 plots the count of alive nodes for different approaches in a network with 250 nodes against the number of rounds. EEARZC algorithm has 134 nodes alive, CS-DCES has 74, PEGASIS has 57, and the proposed method has 154 alive nodes after 1200 rounds. The proposed method outperforms the other techniques, with an improvement of about 15% over EEARZC, 108% over CS-DCES, and 170% over PEGASIS in terms of the Alive Nodes measure at 1200 rounds.

Fig. 9 illustrates the amount of packets received by the sink for a WSN containing 250 sensors. It is evident from the graph that EERZCC enhances the number of packets received at the sink, outperforming EEARZC, CS-DCES, and PEGASIS. Specifically, after 1000 rounds, the Packet Delivery Rate in EERZCC is boosted by 8%, 40%, and 115% compared to EEARZC, CS-DCES, and PEGASIS, respectively.

Fig. 10 portrays the proportion of remnant energy per round. It can be observed from the figure that EERZCC preserves a greater amount of energy compared to the other three methods.

Fig. 3. Simulation of the proposed routing algorithm EERZCC.

Fig. 4. Comparison of First Node Dies (FND).

Fig. 5. Comparison of Half Nodes Die (HND).

Fig. 6. Comparison of Last Node Dies (LND).

Fig. 7. Average improvement achieved for WSN lifespan.

Fig. 8. Comparison of Alive Nodes per round.

Fig. 9. Amount of packets received at the sink.

Fig. 10. Comparison of total residual energy per round.

5. Conclusion

In WSNs, where sensor nodes possess limited energy and computational resources, it's critical to use efficient energy conservation strategies. Our proposed enhancement, EERZCC, introduces a novel approach to optimize energy utilization and network performance. Utilizing a FIS, EERZCC selects optimal CHs from a pool of candidate CHs for forwarding data to the sink. Unlike traditional routing methods in WSNs, EERZCC retains routing paths as chains within the network, minimizing the need for frequent determination of next-hop nodes during data transmission. This innovative approach reduces latency and enhances energy conservation. Furthermore, EERZCC incorporates dynamic replacement of relaying nodes within routing chains, ensuring uninterrupted data transmission and mitigating packet losses. By considering various network parameters, such as residual energy and proximity to the sink, EERZCC adapts to changing network conditions and optimizes routing paths in real time. Our simulation results, conducted using Matlab, demonstrate the superior performance of EERZCC compared to existing approaches. EERZCC achieves significant improvements in energy conservation, data delivery rates, and network lifetime, making it a valuable contribution to the field of energy-efficient routing algorithms for WSNs. The algorithm EERZCC, proposed for WSNs with stationary nodes, could be enhanced to include mobile sensor nodes in the future.

6. Conflict of Interest

The author declares no potential conflict of interest.

7. Abbreviations

In this paper, the subsequent acronyms are utilized: