Internet of Things (IoT) is an inter-connective network system with sensors, actuators, and smart objects to collect, share and process information’s. Most of the time these devices are deployed in power-sensitive scenarios with limited power, computational power, and bandwidth [1]. IoT networks are extensively being established in different businesses such as smart cities, industries, health care, and farming among others where the ability to gather and process real-time information is crucial. However, what is essential for a network of connected things is how data is transmitted from one device to another, this is even looking at the large scale deployments where power consumption and operational life span of the network is of paramount importance [2].

Routing in IoT networks is crucial as it affects directly the network’s energy consumption, reliability and performance. A good routing algorithm helps in preventing the data frame from passing through unnecessary nodes hence helping reduce the energy used by nodes in transmission and reception [3]. It is important to note that most IoT devices are powered through battery which makes energy efficient routing protocols very vital for the network lifetime. In addition, dependency on a dependable routing improves the rate of data delivery, cuts down on latency, and minimizes packet loss so that the IoT applications can run optimally. Hence, efforts to build strong and flexible routing methodologies that meet objective IoT goals are critical for the sustainability of the IoT networks’ application in practical environments [4].

The routing algorithms used in conventional IoT networks have some drawbacks mainly due to the characteristics of resource-constraint IoT settings. Standard protocols such as Distance Vector Routing or Link State Routing are developed for the general-purpose network and do not take into consideration the limitation in energy on the IoT devices [5]. Thus, these algorithms usually result in high energy demands and energy depletion to the sensor nodes’ batteries. Also, these algorithm may not be able to easily cope with the dynamic characteristics of IoT networks where devices can frequently come into the network or leave or may change their places (mobility). Another disadvantage is their scalability with the size of the network they might communicate; route discovery as well as maintenance becomes resource demanding hence slow and costly for a large network [6].

The conventional algorithms fail to address the issue of dynamism in the nodes that mainly control IoT devices as some of the nodes demands may require higher computations, or reserve energy, or even possess better communication interfaces as compared to others [7]. Additionally, they do not take into account relevant features that characterise nodes in an IoT network, such as mobility or the dynamics of traffic, or requirements for real-time transfers of data. Because of these constraints, there has been a continuous realization of the need to develop new, sophisticate, energy-sensitive and adaptive routing protocols that address the need of IoT networks in terms of performance and energy consumption. In the process of addressing the energy optimization in IoT network there are several approaches have been proposed in the past literature. The proposed or existing algorithms can be segregated into multiple types such as deterministic, meta-heuristic, hierarchical, etc. [8].

Energy management in IoT networks has become an important research direction as most IoT devices are energy-limited devices. Such devices commonly work with limited battery energy, hence the efficient energy use is vital in these devices’ performance and durability [15]. Several research works have been done concerning how to conserve energy in different ways such as in routing algorithms and protocols. Solutions including adaptive transmission power control and sleep mode management have been proposed in order to prolong battery lifespan. This literature review focuses on reviewing the above approaches and their efficiency in enhancing energy efficiency in IoT network [16].

Scholars have exploited one of the several important properties of IoT nodes to advance energy efficiency. A salient feature is the node’s state to be in several power modes inclusive of active, sleep, and idle states. It should be noted that these motes may be switched dynamically according to the traffic of the network so that energy is reduced [17]. Further, poor processing capability and memory of the IoT node are used to adopt fewer computational strategies that will somehow decrease power consumption [18]. The behaviours of the data patterns in space and time domain, obtained from the IoT nodes, can also be leverages to inform the best approaches to communications, for instance, avoiding unnecessary re-emission of data. Additionally, using geographical information of the node researchers are able to incorporate the best routing algorithms, hence minimising the distance and energy through which the data will pass through [19]. These properties form the core in establishing policies that maintain an optimal rate between energy usage and network achievement [20].

Smart IoT systems in today’s society are contributing to the interconnectivity of devices and appropriate systems with the aim of sharing important data besides improving automation. In smart homes, IoT devices keep control over the home’s lighting, heating, and security so that living becomes more comfortable. In health care, gadgets that are worn track physiological information and inform clinicians of changes or triggers enhancing health outcomes. The IoT networks also help to run industries efficiently through recording of the working status of machines and determining the time that it would require to have the machines serviced. In the practice of agriculture, IoT sensors detect the state of the soil and the crops to provide the farmers with the best practices concerning irrigation and fertilization. In general, IOT networks improve efficiency and inter connectivity of most industries and activities, and contribute to making these activities much easier to attain.

Wireless Sensor Networks (WSNs) are important in data acquisition in IoT devices comprising of small sensors which are battery operated and work in synergy [21]. As already mentioned, in densely deployed IoT networks based on WSNs, sensors may duplicate their data, which makes it difficult to address the problem of resource limitation, most notably energy. To overcome this, the proposed EFUCSS protocol incorporates energy efficient fuzzy based unequal clustering with sleep scheduling. This approach helps in increasing the Network life span and at the same time minimizing energy consumption in clustering; scheduling and transmission of data. Algorithms show that the advantage of using EFUCSS in the network is a twofold increase in energy storage retention as well as an Increase in the network life cycle compared to the traditional policies [22]. Authors also have focused on the energy consumption during state transition where it has been found that substantial amount of energy getting consumed during the on/off and sleep-mode energy management schemes [23]. The rise in the adoption of IoT devices both themselves and diverse and energy-constrained has driven an increased awareness of the need to be more vigilant about data security in IoT environments. In order to solve this problem, the authors of [24] have extended light-weight encryption-based solutions using traditional algorithms. The study presents a new IoT paradigm developed through the integration of lightweight crypto-graphic ciphers and Autonomic Computing by saving energy and providing different protection levels suitable to IoT application requirements and device behavior [25].

In this line of context, localization is one of the proven method for energy management in IoT network. The selection of appropriate mobile technique improves energy efficiency, enlarges sensor lifetime and consequently enables precise location-aware IoT applications, however open research challenges still must be addressed in face of new needs [26]. Table 1 presents the critical analysis of the proposed routing protocol against existing deterministic approaches. Energy optimization can also be effectively achieved by accurate localization estimation.

This paper [39] presents a cluster-based routing protocol and Modified Bat for Node Optimization to improve the coverage of nodes in terms of redundancy and energy-efficient symmetrical localization. A reinforcement-based Q-learning algorithm constructs, optimizes, and localizes the unknown nodes in a local fashion [40]. This work aims at improving the accuracy estimation between anchor nodes and their neighboring nodes, in order to optimize coverage of nodes and improve localization across the network [41]. Energy-efficient protocols are more vital as the number of sensor nodes rises. In particular, wireless communication typically has high energy costs. In this paper [42], a routing algorithm is proposed to reduce the energy consumption of the network by exploiting both localization and clustering. Table 2 presents the critical analysis of the proposed routing protocol against existing metaheuristic approaches.

The current work focuses on reducing sensor energy consumption in IoT networks to extend network lifetime. It employs a hybrid metaheuristic algorithm combining Whale Optimization Algorithm (WOA) with Simulated Annealing (SA) to select the optimal Cluster Head (CH) [52]. IoT-enabled Wireless Sensor Networks are increasingly used for disaster management in smart cities, including applications like emergency medical services and flood control. However, these networks face challenges such as high energy consumption from communication, cluster overlapping, and large communication distances, which make efficient data collection difficult. To address the mentioned issues, an integrated modified Genetic Algorithm (GA) for Cluster Head (CH) election in wireless sensor networks (WSNs) is proposed, known as ModifyGA, which aims to maximize network lifetime [53].

To achieve the optimization of a set of design variables this research combines a meta-heuristic approach with a deterministic approach that is the proposed method. The following Fig. 2 shows the classification of algorithms that are exist in the past literature. In the previous works several deterministic approaches have been premeditated in order to carry out improved energy management in IoT network. This is the Deterministic Energy-efficient Clustering protocol (DEC), a self organizing and adaptive method to minimize energy quality consumed in networks while choosing CHs, relative to residual energy. Nevertheless, problems arise with DEC that includes a failure to account for intra-cluster distance and node degree. In order to overcome these drawbacks, this paper presents PSO based Deterministic Energy Efficient Clustering (PSO-DEC) protocol by incorporating PSO to make the existing protocol more efficient [54].

The provisioned hybrid algorithm mainly targets the identification of better routes in both global and local arenas. This target is achieved by utilizing the IoT nodes’ properties in global and local space using meta-heuristic approach in harmony with the deterministic approach. The choice of ACO is, doing what ants do, find the shortest path when looking for a food source they drop pheromone trails. When employed in an IoT network, ACO can help locate the best path between nodes, and simultaneously, share the energy usage load among nodes. Agents are ants, the search is the path, and pheromone is the marking of good low-energy paths for future reference. In incorporation with Greedy Algorithm its next step best decision is taken to be the locally optimum one like choosing the next node with maximum residual energy or minimum distance from the source. As much as it is efficient and fast it does not guarantee optimization right from the global level down to the individual networks. The combination of these two, ACO can be used for global search and optimization, the GA provides for rapid, local decision making. This integrated approach is useful to explore and exploit knowledge to prevent convergence to local optima early on in the process.

As energy consumption of individual nodes has to be minimized in an IoT network, routing is the most critical factor affecting how much energy their data consumes. Here, the ACO provides the envisioned way of how to look for energy-efficient paths, and GA contributes toward choosing the nodes in a short amount of time with more energy left or shorter paths to the destination. In the long run, the two guarantees that the network load is equally distributed hence enhancing the network durability. The total energy consumed in the IoT network can be represented as Eq. (1): (1)Etotal=∑i=1NPi×Eiwhere:

Etotal:Total energy consumed

N:Number of nodes in the network

Pi:Probability of choosing the ith node (determined by ACO)

Ei:Energy consumed by the ith node

This Eq. (1) shows the total energy which is consumed in an IoT network where a large number of nodes are participating to have transmission of data in that network, ETotal shown as the total energy consumed by all the nodes in the network, where N is the total number of nodes in that network. In IoT networks, nodes can be sensors, actuators or any of the devices that get connected to others. Pi represents the probability of choosing the i-th node to forward or transmit data in the entire network, which is the sum of the energy consumed by individual nodes. N denoted the number of nodes in the network. In IoT networks, nodes can be sensors, actuators, or any device communicating with others. Pi denoted the This is the probability of selecting the i-th node for data routing or transmission. In an ACO framework this probability is defined with reference to the pheromones deposited with relation to this node and the heuristic values which exist with reference to this node. Ei refers to the total energy expended by the i-th node for its data transmission or data processing. Specifically the energy consumed by a node depends on a number of factors including the distance over which the nodes communicates and its residual energy. (2)Pi=[τi]α×[ηi]β∑j∈S[τj]α×[ηj]βwhere:

τi:Pheromone level on the path to node i

ηi:Heuristic value (e.g., inverse of distance or residual energy)

α:Parameter controlling the influence of pheromone factor

β:Parameter controlling the influence of heuristic factor

S:Set of available nodes

The probability Pi of choosing the node i in ACO is given by Eq. (2). Calculate the probability Pi of the path leading to node i in ACO for routing purposes. Where τi mean the pheromonetrail strength on the path that leads to the node i. In ACO, the pheromone is a grephererpherereal agent that leaves scent by ants on the paths to be used, and the magnitude of the pheromone determines the number of times the path has been used. In IoT networks, the level of the pheromone is higher in the path (or node) that has been successful in terms of energy efficiency and reliability is identified by ηi which indicates the heuristic value for node i. This heuristic could mean that the path between the nodes is shortest or the remaining energy of a node and so on. The greater the residual energy or distance of the node, the higher the heuristic values. The pheromone levels τi and heuristic values ηi are the factors with the tuning parameters α and β, respectively. If α is high, then pheromone’s strength dominates; if β is high, then heuristic value takes the precedence ∑j∈S[τj]α×[ηj]β the denominator is used to normalize the probability.mone is a substance that ants deposit on the paths they travel, and the strength of the pheromone represents how often that path has been used. In IoT networks, higher pheromone levels indicate that a path (or node) has been successful in terms of energy efficiency and reliability. ηi represents the heuristic value for node i. This heuristic could be the inverse of the distance between nodes (i.e., shorter paths are preferred) or it could depend on other factors like the remaining energy of a node. Nodes with more residual energy or closer proximity have higher heuristic values. α and β are the tunning parameters that control the influence of the pheromone levels τi and heuristic values ηi, respectively. If α is high, the pheromone’s influence dominates; if β is high, the heuristic value is prioritized. ∑j∈S[τj]α×[ηj]β the denominator ensures that the probability is normalized. It adds the pheromone and heuristic values of all nodes available in the set S so as to normalize the final probability Pi between 0 and 1.

The Greedy selection rule determines inext through Eq. (3), while inext is the next node in the route and determined by the GA. arg min This notation stand for “argument of the minimum” here it means that the selected node is the one that minimize the expression that is inside the parenthesis D(i,j)—This is the distance between the current node i and the candidate node j. In IoT networks, minimizing the distance between nodes is important because long distance transmission will consume more energy. Eresidual(j) refers to the energy that is left in node j. The greedy algorithm selects nodes with relatively high residual energy in order to avoid overloading particular nodes which will quickly get depleted and create partitions in a network. (3)inext=arg⁡min(D(i,j)Eresidual(j))where:

D(i,j):Distance between nodes i and j

Eresidual(j):Residual energy of node j

Eq. (3) is used to determine the next node that has the least distance and within that least energy. The algorithm identifies the node j with the shortest distance to the node i and node j has enough energy level. That is how the Greedy Algorithm selects the nodes: in such a manner to distribute the energy intake the most and not let nodes exhaust their energy sources too quickly, thus extending the lifetime of the network.

The use of the proposed hybrid approach to prolong the IoT network lifetime follows several main activities. When comparing the efficiency of the method, input parameters for the algorithms which are implemented are assessed in addition to various deployment scenarios. The present section discusses the requirements of the data set, the network architecture, and the validation.

The ACOGA algorithm yields a minimum dead node ratio and therefore proves that it is more energy efficient since less number of nodes exhaust their energy. However, the ratio of that algorithm is lower than the Hybrid energy-efficient (HEE) algorithm [55] and ALDC algorithm [56], which suggests that these two algorithms achieve less efficient energy distribution. The evaluation of the proposed algorithm shows that it increases the network lifetime more than HEE and ALDC causing the first node die after a significantly large time. This is because the ACO selects the path out of the two available paths and the Greedy algorithm concentrates on the equal energy consumption. The proposed algorithm also denotes a higher capability in the preservation of a functional network relative to HEE and ALDC in that fifty percent of nodes live longer than their counterparts in those algorithms. This has proven the fact that hybrid architecture has better load balancing and energy saving mechanisms compared to the other options out of the four. ACOGA algorithm consumes 15% less energy than HEE illustrating optimization in path selection and the minimum number of transmissions. However, even better than ALDC, which outperforms HEE, the proposed method is better. ALDC performs marginally better in the realm of delay due to the fact that the proposed method was designed to compress data for efficient time transfer. But the presented ACOGA approach proposed compromise delay of 1.5 s although it balances energy usage and enhances network live time. Routing of a call is more inefficient in HEE case and hence the delays tend to be higher than when using intelligent electronic agent.

In the Greedy selection, the proposed algorithm guarantees that nodes utilize nearly an equal energy until the battery is depleted. While HEE delivers moderate performance, ALDC exhibits energy load imbalance where few nodes drain energy more quickly because of uneven compression loads. This proposed method a PDR of almost 98 % which shows that reliable packet delivery of packets is highly probable. HEE and ALDC PDR values are significantly lower than the other values because they encounter node failures more often, and paths are selected less efficiently. A major advantage of the proposed ACOGA algorithm is the path selection strategy, where the ACO showed the probabilistic path selection, while Greedy algorithm provided the optimal energy nodes selection. While implementing energy-awareness tasks, HEE shows reasonably good results but still cannot reach the level of optimization provided by ACO. ALDC optimizes the data compression field over routing which makes the path selection process less efficient. According to the analysis based on the significant industry parameters, it is clear that the proposed ACOGA algorithm is better than both HEE and ALDC in terms of network lifetime, dead node ratio, total energy consumption and path selection ratio. With a slight lower delay because of its compression, it causes less foldability in energy efficiency and network durability in ALDC. HEE had also been less optimal in terms of both energy and delay and hence the proposed method outperforms in enhancing energy efficiency in IoT networks. Table 4 presents the trade-off between proposed and existing methods.

The composite graph in Fig. 8 provides a visual comparison of the proposed ACOGA algorithm against the HEE and ALDC algorithms across key performance factors: as primary features of comparison - Dead Node Ratio, Network Lifetime, Energy Consumption, and Delay per Round. In the Dead Node Ratio subplot the energy efficient algorithm proposed proved to be more efficient where only 5% were dead after certain rounds while in HEE and ALDC 15% and 20% respectively were dead. This could be explained by the fact that the distribution of energy consumption across the proposed algorithm is also very efficient. The Network Lifetime subplot shows that the proposed algorithm significantly outperforms in both metrics: the first node dies after passing 1200 rounds, the probabilities of 50% of nodes surviving reach up to 1500 rounds while nodes in HEE and ALDC fail early. This means that proposed method has longer operating cycle of network as compared to the traditional method of merging.

Based on Energy Consumption, the proposed ACOGA algorithm outperforms the baseline (HEE) with 15% less energy consumption as compared to ALDC with a lower energy consumption profiling. This case of reduction in the energy utilization is an indication of the efficiency that is realized by the hybrid mode of operation. Lastly, the Delay per Round subplot represents the last aspect with ALDC having the lowest delay (1.3 s) however, the proposed method yields a good balance of mean delay (1.5 s). While HEE has higher delays of 2.0 s, thus it is less efficient than ACOGA. On the whole, the supplied composite graph evidently shows that the proposed ACOGA algorithm outperforms the other algorithms with respects to the energy efficiency factor, network lifetime, and energy consumption factor and similarly in a manner as far as, delay factor is concerned.

In the contemporary society especially in both urban and rural areas, the proactivity of SWMS are mostly desirable in that they help in preventing wastage of water. These systems are based on IoT technology including sensors and intelligent devices for continual observation and data gathering for wise decision making. One of the primary issues in such systems is to transmit data between the IoT devices in an energy efficient manner so that the sensor nodes are not depleted often and need replacement of batteries frequently. Based on the proposed ACOGA algorithm, it can work on the routing paths with an optimal manner to allocate the energy and network lifetime and that’s why is suitable for SWMS.

The ACOGA algorithm is developed here through combining the Ant Colony Optimization (ACO) technique and Greedy algorithm for the detection of data routing between IoT sensors used in a water management network. The ACO component guarantees that all the paths around the world for communication are explored and the Greedy Algorithm used will select the node according to energy available in that node thus helping in equal energy distribution around the network.

Table 5 formulated by the ACOGA algorithm the optimization of energy consumption and network lifetime is highly efficient for the SWMS. Its service of carrying loads of data also prolongs the functional years of the network and perpetuates data stream from the peripheral nodes such as the sensors to the hub node.

IoT networks face major challenges in energy efficiency, network lifetime, and reliable data delivery. To address this, we proposed the ACOGA algorithm. Compared to HEE and ALDC, ACOGA delivers better results in several key areas. As per results, the dead node ratio stays below 5% even after 500 rounds. In contrast, HEE and ALDC show 15% and 20%, respectively. The network lifetime is also extended, with the first node dying after 1200 rounds. HEE and ALDC show lifespans of 900 and 850 rounds. This is mainly due to balanced energy use in ACOGA, leading to a 15% drop in total energy consumption. It also achieves a high packet delivery rate of 98%. Despite these strong results, the current work has limitations. The experiments use small, static networks. Real-world IoT networks are larger and more dynamic. The model also assumes ideal conditions, ignoring interference, packet loss, and node diversity. In the future, we plan to make the algorithm more dynamic using machine learning. This will help in predicting traffic, node failures, and adapting to changes. ACOGA can also be extended for heterogeneous IoT systems, where nodes differ in energy and communication range. Real-time data compression could further reduce energy use and delay. Another direction is adding adaptive mechanisms that adjust to network density and mobility. We also aim to test ACOGA on larger platforms like iFogSim or Cooja to study its behavior under real-world conditions.