Ant Colony Optimization Approaches in Wireless Sensor Network: Performance Evaluation

Within the growing Internet of Things (IoT) paradigm, a Wireless Sensor Network (WSN) is a critical component. In a WSN, sensor node localization is typically utilized to identify the target node’s current location at the sink node (SN). This allows local data to be analysed, making it more meaningful. However, there exists an intrinsic problem with node localization and energy efficiency, as identified in the literature, which has led to poor performance, namely, poor estimation, transmission, and detection of the network. This intrinsic problem also directly affects energy efficiency in a WSN, resulting in energy loss and poor node distribution in the WSN. There seems to be no lasting and reliable solution to this intrinsic node localization problem in WSNs. Hence, this research study proposed hyper-heuristic optimization algorithms to improve node localization and energy efficiency in WSNs. This research adopts the Design Research (DR) methodology and the Theory of Modelling and Simulation as the theoretical frameworks of the study. The hyper-heuristic model designed, was considered the conceptual framework of the study. To validate the novel technique, different sizes of sensor networks, namely: - 100 sensor nodes; 100 to 1 500 nodes and 200 to 450 sensor nodes with 20 anchor nodes were simulated in an area measuring 100m x 100m. The novel hyper-heuristic model was implemented in a MATLAB R2020a environment. The hyper-heuristic optimization algorithm’s substantial simulated experiment results were benchmarked utilizing state-of-the-art (modern) techniques to solve challenges related to node localization error, total energy consumed, average consumed packet energy, network throughput, shortest path, dead nodes, packets dispatched to the base station (BS), and the probability of error within the entire network dependent on size. The Data Energy Efficiency Clustering-Gaussian (DEEC-GAUSS) method was used to provide solutions to challenges related to energy efficiency in WSNs. In addition, this research study explored the use of the novel DEEC-GAUSS Gradient Distance Elimination Algorithm (DGGDEA) as the hyper-heuristic optimisation model for localization of nodes in WSNs. DEEC-GAUSS and DGGDEA were valuable additions to the body of knowledge. The results showed that the novel DEEC-GAUSS was the most energy efficient algorithm for 100 sensor nodes and 1000 to 1500 sensor nodes when compared to other stateof-the-art algorithms. Furthermore, the results showed that the novel DGGDEA was able to drastically minimize the node estimation error for sensor nodes. Reliability, accuracy and convergence using hyper-heuristic models to enhance the communication within WSNs has been simulated with evidence that DEEC-GAUSS and DGGDEA has outperformed other stateof-the-art approaches

Wireless sensor networks (WSNs) function with constraints in energy, computation and storage. The real-time monitored sensor data are transmitted to the base station through intermediate nodes. Application-specific protocols are being developed to enhance the functionality and performance of WSNs. WSN topology is classified into three phases: (i) topology construction; (ii) topology control and (iii) topology maintenance. The authors propose Swarm Intelligence (SI)-based topology maintenance for link failure and congestion control in WSN. SI-based models like Particle Swarm Optimisation (PSO) and Ant Colony Optimisation (ACO) are used in topology maintenance. The proposed model is compared with the existing distributed topology control techniques based on neighbour, location and direction attributes for WSNs. SI-based techniques indicate performance improvement in topology as compared to the existing techniques. The proposed SI model for topology maintenance evaluates WSN attributes based on (i) particle position and particle velocity in PSO; (ii) pheromone cost and (iii) forage success rate in ACO. SI-based topology maintenance is based on finding global and local minima during the search process. Simulation results indicate performance improvement in throughput, data transmission rate and average power efficiency in SI techniques as compared to the existing topology maintenance techniques.

Hybrid meta-heuristic optimization based energy efficient protocol for wireless sensor networks

Applying multimedia to Wireless Sensor Network (WSN) adds more challenges due to WSN resource constraints and the strict Quality of Service (QoS) requirements for multimedia transmission. Different multimedia applications may have different QoS requirements, so routing protocols designed for Wireless Multimedia Sensor Network (WMSN) should be conversant of these requirements and challenges in order to ensure the efficient use of resources to transfer multimedia packets in an utmost manner. The majority of solutions proposed for WMSN depends on traditional layered based mechanisms which are inefficient for multimedia transmission. In this paper we propose a cross-layer Ant based Routing Protocol for WMSN (CARPM) by using modified ant colony optimization (ACO) technique to enhance the routing efficiency. The proposed protocol uses an improved ACO to search for the best path that are satisfied with the multimedia traffic requirements. While making best decision the weightage is given to energy consumption, and queuing delay. The proposed cross layer scheme works between the routing, MAC, and physical layers. Since, the remaining power and timestamp metrics are exchanged from physical layer to network layer. Dynamics duty cycle assignment is proposed at MAC layer which changes according to traffic rate. The presented algorithm is simulated using NS2 and is proven to satisfy its goals through a series of simulations.

Error control coding improves reliability and efficiency of wireless communication systems. This research delves into the latest advancements in error control coding schemes for wireless communication systems, with a specific focus on their application within the domain of Low Power Wide Area Networks (LPWANs) such as Narrowband Internet of Things (NB-IoT) systems, LoRa energy-efficient hardware design, random access control systems, and wireless sensor networks. In the context of LPWANs, particularly NB-IoT systems, we investigate the adaptive coding and modulation (ACM) in NB-IoT systems, which adapts coding rates and modulation techniques to changing channel circumstances. Comprehensive analysis of the literature review shows that the proposed approaches are more energy efficient and less error-prone than fixed coding and modulation schemes. Using Cell Design Methodology (CDM), Single Error Correction (SEC) codes are optimized for energy efficiency. Power and energy efficiency have improved in standard CMOS and CDM logic systems. This impacts IoT devices, memory storage, and security systems. Next, Forward Error Correction (FEC) is examined for ALOHA-based random-access control systems wherein packet loss rate and spectral efficiency metrics are mathematically calculated. FEC is beneficial, especially when time and frequency synchronization diverge. Monte Carlo simulations studied in the literature shows that FEC improves satellite communication systems. This review establishes benefits of FEC, notably in satellite communications systems. In addition, Wireless Sensor Networks (WSNs) error control techniques and network reliability and performance are examined along with Convolutional, Turbo, Low density parity check codes(LDPC), and Polar error correction coding (ECC) methods. We learn about their benefits, drawbacks, and implementation challenges by assessing their efficacy, efficiency, and applicability in wireless sensor networks (WSN) applications. This study further highlights advancements of energy-efficient error control coding when, integrated into network designs alongwith the trade-offs between efficacy and intricacy to help improve wireless communication networks. Finally, this study summarizes current error control coding approaches for wireless communication systems. We provide insights into improving reliability, energy conservation, and efficacy by examining ACM, CDM, FEC, and error correction coding (ECC) across varied domains including LPWAN and IoT technologies with earnest hope to facilitate researchers, engineers, and professionals working in the field of error control coding in wireless communication.

Wireless sensor network (WSN) is an emerging technology which find useful in several application areas such as healthcare, environmental monitoring, border surveillance, etc. Several issues that exist in the designing of WSN are node localization, coverage, energy efficiency, security, and so on. In spite of the issues, node localization is considered an important issue, which intends to calculate the coordinate points of unknown nodes with the assistance of anchors. The efficiency of the WSN can be considerably influenced by the node localization accuracy. Therefore, this paper presents a modified search and rescue optimization based node localization technique (MSRO-NLT) for WSN. The major aim of the MSRO-NLT technique is to determine the positioning of the unknown nodes in the WSN. Since the traditional search and rescue optimization (SRO) algorithm suffers from the local optima problem with an increase in number of iterations, MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the technique. The application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO algorithm. In order to validate the effective node localization performance of the MSRO-NLT algorithm, a set of simulations were performed to highlight the supremacy of the presented model. A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.

Wireless Sensor Networks (WSNs) being special type of wireless communication networks, characterized via various specific features like Limited Memory, Energy, Less Processing power. In Wireless Sensor Networks, every sensor node actively participates in routing work by forwarding the packets from sender to receiver and the packet forwarding is entirely based on network topology. One of the most important issue surrounding WSNs is Energy Efficiency and Efficient Routing mechanism which should be dynamic and efficient enough to handle changing topologies. So, there is utmost need for optimization techniques which can lay the foundation of development of suitable routing protocol to attain energy efficiency and routing in sensor networks. Swarm Intelligence is one the most important technique which is highly considered for developing energy efficient routing protocols. Considering Swarm Intelligence, Ant Colony Optimization (ACO) technique is utilized to propose Energy Efficient protocol for WSN. In this paper, a Novel Energy Efficient Routing Protocol based on ACO for WSN is proposed i.e. IEEMARP (Improvised Energy Efficient Multipath Ant Based Routing Protocol). Hardcore testing of protocol proposed i.e. IEEMARP is done in different simulation scenarios using NS-2 simulator on varied parameters like Packet Delivery Ratio, Throughput, Routing Overhead, Energy Consumption and End-To-End Delay and performance is compared with other routing protocols like Basic ACO, DSDV, DSR, ACEAMR, Ant Chain, EMCBR and IACR. The results states that IEEMARP is almost 7 to 10 times better in different parameters. It has also been observed that IEEMARP routing protocol is also efficient in transmitting TCP packets.

Energy efficiency is critical in Wireless Sensor Networks (WSNs), pivotal for Internet of Things (IoT) applications like environmental monitoring and smart cities. Clustering protocols like Low-Energy Adaptive Clustering Hierarchy (LEACH) extend network lifetime through Cluster Head (CH) rotation, but random CH selection often leads to suboptimal energy use. This study evaluates eight hybrid Particle Swarm Optimization-based LEACH (PSO-LEACH) variants to optimize CH selection and clustering: FA-PSO-LEACH (Firefly Algorithm), CSA-PSO-LEACH (Crow Search Algorithm), PSO-LEACH with CBTEERA, Adaptive PSO-LEACH, ABC-PSO-LEACH (Artificial Bee Colony), ACO-PSO-LEACH (Ant Colony Optimization), WOA-PSO-LEACH (Whale Optimization Algorithm), and Hybrid GA-PSO-LEACH (Genetic Algorithm). The primary research objective is to compare these variants in terms of energy efficiency, network lifetime, and clustering quality to identify the most effective strategies for prolonging WSN operation in IoT environments. Specifically, this paper addresses the research question: Which hybrid PSO-LEACH variant provides the optimal balance between early-stage stability and long-term energy conservation in a simulated WSN with 50 nodes? Each variant uses a unique metaheuristic for CH selection based on residual energy and distance, with PSO refining clustering. A Python simulation modelled a 100m × 100m WSN with 50 nodes, assessing network lifetime (First Node Died, FND; Half Nodes Died, HND; Last Node Died, LND), energy consumption, and clustering quality. Results show ABC-PSO-LEACH leading with FND at 57 rounds and LND at 462 rounds, followed by WOA-PSO-LEACH (HND 148, LND 406), surpassing others (e.g., PSO-LEACH with CBTEERA: FND 10, LND 280). WOA-PSO-LEACH’s balanced energy distribution excels in mid-to-late stages, despite computational overhead. These bio-inspired hybrids outperform traditional LEACH, with ABC-PSO-LEACH and WOA-PSO-LEACH ideal for prolonged WSN operation. Future work aims to enhance early-stage performance and explore dynamic topologies.

Sensor networks are collection of sensor nodes which co-operatively send sensed data to base station. The major problem in the field of wireless network is draining of sensor's battery. As the senor nodes are driving high power so there is a need of an intelligent algorithm which could enable the sensors to utilize the energy in an energy efficient manner. It will also enable the network to work for longer duration of time as well as reduces trafficking inside network. Data Aggregation is a technique which can solve these problems. The main goal of data aggregation algorithms is to gather and aggregate data in an energy efficient manner so that network lifetime is enhanced. It involves the selective transfer of data from the nodes to the base station. Wireless sensor networks (WSN) offer an increasingly Sensor nodes need less power for processing as compared to transmitting data. It is preferable to do in network processing inside network and reduce packet size. One such approach is data aggregation which attractive method of data gathering in distributed system architectures and dynamic access via wireless connectivity. In this paper, a data aggregation framework on wireless sensor networks is presented. The framework works as a middleware for aggregating data measured by a number of nodes within a network. Also the paper briefly discusses a case of sending the temperature reading from the source node to the destination. It also provides techniques in order to send the minimum data at the cost of a small error. The aim of the proposed work is to compare the performance of SPIN in terms of energy efficiency in comparison with and without data aggregation in wireless sensor networks and to assess the suitability of the protocol in an environment where resources are limited.

Data aggregation is an essential operation in many applications of Wireless Sensor Networks (WSNs). For energy constrained and delay sensitive WSNs, how to prolong the lifetime and reduce the latency of the network are two critical problems. We deal with these two problems together in data aggregation by proposing a Load Balanced and Delay Efficient Algorithm (LBDEA), which consists of two phases. First, construct a load-balanced data aggregation tree based on Semi-Matching algorithm in the graph theory. Second, seek a collision-free scheduling based on greedy strategy for the transmission links in the aggregation tree. Through balancing the load of the sensor nodes, the lifetime of the network is optimized. By avoiding collisions and retransmissions of data delivery, the total latency is decreased. Simulations show that LBDEA not only improves the lifetime but also cuts down the delay for data aggregation in WSNs, compared with other related algorithms.

Data aggregation in wireless sensor networks using ant colony algorithm

Wireless sensor networks (WSNs) are becoming more prevalent in a wide range of uses, including tracking the environment, medical care, and military monitoring. Yet, security, energy efficiency, and overall efficiency are important concerns for these networks. Ensuring the security of data transmission while maintaining energy efficiency is critical for the longevity and reliability of WSNs. This paper proposes a novel method to enhance both security and efficiency in WSNs done the integration of metaheuristic optimization algorithms. The proposed method leverages advanced optimization techniques such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO) to dynamically optimize key parameters, including node deployment, routing protocols, and encryption schemes. By optimizing the network topology, routing paths, and energy consumption strategies, the algorithm significantly improves data security, minimizes energy usage, and enhances network lifespan. The simulation results show that the suggested methodology beats standard methods in terms of throughput, security resilience and energy efficiency. The findings highlight the potential of metaheuristic optimization algorithms as a robust tool for improving WSN security and operational efficiency in real-world applications

Directional antennas in wireless sensor networks (WSNs) have several advantages including the enhanced capacity of the network, longer range of transmission, better spatial reuse and lower interference. By enhancing the alleviating contention and the range of communication, the directional antennas improve the performance of WSNs. In common, various methods like localisation, power management, data aggregation and optimisation obtain these advantages in WSNs; however, the reliability requirements and the energy concerns make the antenna technology more advantageous. When compared with methods, in which the directional antennas provide advantages like the extended range of transmission, energy savings, better prevention of wormhole attack, long coverage and high interference tolerance. This review provides a detailed study of recent improvements and open research problems on directional antennas in WSN applications. First, the introduction and the background of directional antennas briefly based on their classification radiation patterns and advantages in directional communication in WSNs. During the past years, a number of directional antennas are designed for the applications of WSNs. In this study, recently developed directional antennas and their applications in WSNs are reviewed. The applications of directional antenna in WSNs are classified into security benefits, localisation, convergecast, contention reduction and energy efficiency.

To realize efficient performance in industrial applications, wireless sensor networks (WSNs) have witnessed routes developed. In WSN operations, it is important to note that they rely upon and operate on battery, which implies that there is energy restriction. In the current study, the aim was to design a routing protocol through which improved WSN energy conservation could be achieved, hence preserving the battery life. In the study, three variables were on the focus and aided in making informed decisions about routes that would be deemed appropriate. These parameters included the distance needed for the successful sending of packets to the destination node (from the source and in meters), the traffic amount in Erlang, and the sensor energy in joules. In the proposed routing protocol, it is important to note that it was based on FACO (fuzzy logic and ant colony optimization). Indeed, the role of employing fuzzy logic lay in the calculation of the total cost of the node–gateway intersection relative to the node’s energy, as well as its traffic load. Similarly, the implementation of ACO was informed by the need for searching and establishing distances that would prove the shortest between the sources to the destination sensor nodes, with the shortest distances aiding in system performance evaluation and inference-making. With MATLAB simulation adopted, findings demonstrated significant improvements in system performance, especially in terms of energy conservation. Particularly, results from FACO implementation, relative to the energy conservation parameter, suggested its superiority, as it outperformed ACO, having implemented the two algorithms under the same experimental conditions and with the same experimental parameters. The future implication for industrial applications is that the routing algorithm associated with system improvements via more energy conservation could be implemented via the use of WSN.KeywordsRouting optimizationEnergy conservationACOFACOWSN

Adaptive data aggregation scheme in clustered wireless sensor networks