Fractional Artificial Bee Colony Research Articles

Collision Mitigation by Dolphin Ant Lion Optimizer Pre-Packet Scheduling Approach

A wireless sensor network (WSN) consists of distributed autonomous sensors deployed over a large geographical area. Wireless Sensor Networks (WSNs) are composed of large-scale sensors that are specifically assigned to do particular tasks, with a significant portion of these tasks involving reporting and monitoring activities. Nevertheless, because to the potential expansion of the network to several sensor nodes, the likelihood of collision is significantly increased. This research presents an innovative approach for conducting collision detection and mitigation in wireless sensor networks (WSN). The first step involves conducting a simulation of Wireless Sensor Networks (WSN), followed by the utilization of the Fractional Artificial Bee Colony (FABC) algorithm for the selection of cluster heads. In this context, the network-based parameter is derived by considering factors such as the Received Signal Strength Index (RSSI), priority level, delivery rate, and energy consumption. The Deep Recurrent Neural Network (DRNN) has been modified to suit the task of collision detection. The training of the deep recurrent neural network (DRNN) is conducted via the Lion Crow Search optimizer (LCSO). Following the completion of collision detection, the subsequent step involves the implementation of a collision mitigation process utilizing a pre-scheduling technique known as Dolphin Ant Lion Optimizer (Dolphin ALO). In this context, the evaluation of fitness encompasses many factors related to collision mitigation, including energy consumption, Sleep Index (SI), delivery rate, priority level, E-waste management, and E-save measures. The approach presented in this study demonstrated superior performance in terms of energy consumption, throughput, Packet Delivery Ratio (PDR), and collision detection rate. Specifically, it achieved the least energy consumption of 0.185, the greatest throughput of 0.815, the highest PDR of 0.815, and the highest collision detection rate of 0.930.

Detection of collision using optimized deep model and mitigation of collision using dolphin ant lion optimizer in wireless sensor network

SummaryWireless sensor network (WSN) comprises automatic sensors that are dispersed into a huge region. WSN is constructed from huge sensors, which is allocated to a particular task and the majority of task involves reporting and monitoring. However, as the network can be extended to several sensor nodes, there is a high chance of collision. Thus, this paper devises a novel technique for performing both collision detection and mitigation in WSN. Initially, the simulation of WSN is performed, and then the selection of cluster head is done using fractional artificial bee colony (FABC). Here, the network‐based parameter is extracted that involves received signal strength index (RSSI), priority level, delivery rate, and energy consumed. The deep recurrent neural network (DRNN) is adapted for collision detection. Here, the training of DRNN is done using lion crow search optimizer (LCSO). After collision detection, the collision mitigation is performed with a pre‐scheduling algorithm, namely dolphin ant lion optimizer (Dolphin ALO). Here, fitness is considered for collision mitigation that includes energy, sleep index (SI), delivery rate, priority level, E‐waste, and E‐save. The proposed method outperformed with the smallest energy consumption of 0.185, highest throughput of 0.815, highest packet delivery ratio (PDR) of 0.815, and highest collision detection rate of 0.930.

Deep maxout network for lung cancer detection using optimization algorithm in smart Internet of Things

SummaryThe Internet of Things (IoT) has appreciably influenced the technology world in the context of interconnectivity, interoperability, and connectivity using smart objects, connected sensors, devices, data, and appliances. The IoT technology has mainly impacted the global economy, and it extends from industry to different application scenarios, like the healthcare system. This research designed anti‐corona virus‐Henry gas solubility optimization‐based deep maxout network (ACV‐HGSO based deep maxout network) for lung cancer detection with medical data in a smart IoT environment. The proposed algorithm ACV‐HGSO is designed by incorporating anti‐corona virus optimization (ACVO) and Henry gas solubility optimization (HGSO). The nodes simulated in the smart IoT framework can transfer the patient medical information to sink through optimal routing in such a way that the best path is selected using a multi‐objective fractional artificial bee colony algorithm with the help of fitness measure. The routing process is deployed for transferring the medical data collected from the nodes to the sink, where detection of disease is done using the proposed method. The noise exists in medical data is removed and processed effectively for increasing the detection performance. The dimension‐reduced features are more probable in reducing the complexity issues. The created approach achieves improved testing accuracy, sensitivity, and specificity as 0.910, 0.914, and 0.912, respectively.

Adaptive-Sunflower-Based Grey Wolf Algorithm for Multipath Routing in IoT Networks

This paper devises a routing method for providing multipath routing in an IoT network. Here the fractional artificial bee colony(FABC) algorithm is devised for initiating the clustering process. Moreover, the multipath routing is performed by the newly devised optimization technique, namely adaptive-sunflower-based grey wolf (adaptive-SFG) optimization technique, which is designed by incorporating adaptive idea in sunflower-based grey wolf technique. In addition, the fitness function is newly devised by considering certain factors that involve context awareness, link lifetime energy, trust, and delay. For the computation of the trust, additional trust factors like direct trust, indirect trust, recent trust, and forwarding rate factor are considered. Thus, the proposed adaptive-SFG algorithm selects the multipath for routing based on the fitness function. Finally, route maintenance is performed to ensure routing without link breakage. The proposed adaptive-SFG outperformed other methods with high energy of0.185J minimal delay of 0.765 sec maximum throughput of 47.690% and maximum network lifetime of 98.7%.

Protruder Optimization-Based Routing Protocol for Energy-Efficient Routing in Wireless Sensor Networks

WSNs find valuable application in monitoring purposes, but they suffer from energy-efficiency issues that affect the network lifetime. The energy-efficiency problem is addressed using the cluster head (CH) formation, data aggregation, and routing techniques. Therefore, an energy-aware routing algorithm named protruder optimization algorithm is proposed, which boosts the network lifetime through finding the optimal routing path. The proposed protruder optimization is developed with the hybridization of the wave propagator characteristics and weed characteristics in such a way that the global optimal convergence is boosted while selecting the optimal routing path. Moreover, the communication in the network through the optimal path is progressed through the optimal CHs selection based on fractional artificial bee colony optimization (FABC), and in turn, the energy minimization problem is aided with data aggregation process using sliding window approach that avoids retransmission of the data. The results of the proposed method are compared with the existing methods on the basis of its performance measures, such as energy, alive nodes, and throughput.

Spark Architecture and Fractional Artificial Bee Colony-Chaotic Fruitfly RideNN for Big data Classification in Internet of Things

The typical Internet of Things (IoT) device gathers a huge amount of data specifically termed as big data framework, which transfers the collected data from the sensing layer to the information processing layer. Various big data classification methods are adopted in the industrial applications, and smart cities, but accurately classifying the data in the IoT network poses a challenging task in the research community. Therefore, an effective big data classification model using spark-based architecture is proposed in this research. The big data classification is performed at the master node using the proposed Fractional Artificial Bee Colony- Chaotic Fruitfly Rider Optimization Algorithm (FABC-CFFRideNN). The concept of fictional computing is adopted by the rider optimization algorithm (ROA) to update the position of rider groups based on success rate and the foraging behavior of fruit flies along with the rider parameters is used to enhance to performance of data classification using the proposed CFFRideNN classifier. Moreover, the proposed Fractional Artificial Bee Colony- Chaotic Fruitfly Rider Optimization Algorithm attained better performance using the metrics, namely accuracy, specificity, and sensitivity with the values of 95.382%, 95.81%, and 98.824% for training percentage without node velocity.

Multipath data transmission in WSN using exponential cat swarm and fuzzy optimisation

This study proposes a technique for multipath data transmission in Wireless Sensor Network (WSN) by proposing a novel optimisation algorithm, named exponential cat swarm optimisation (ECSO), by integrating the exponential weighted moving average and CSO. Initially, the CH is selected by the penguin fuzzy-based ant colony optimisation (PFuzzyACO) technique, which is the integration of fuzzy, ACO and penguin search optimisation algorithm (PeSOA). After the selection of the optimal CH, the multipath transmission is done by the proposed ECSO algorithm. Here, an optimal path is selected for transmitting the routeing information from source to destination based on various parameters such as trust, energy, distance, delay, traffic density and link lifetime (LLT). Thus, the CHs with maximum trust, energy and LLT, and minimum distance, delay and traffic density are adapted for multipath data transmission using the proposed ECSO algorithm. The proposed ECSO algorithm shows 18.75, 2.99 and 29.87% improvements in terms of number of alive nodes, throughput and network energy, respectively, than the existing PFuzzyACO, which has high performance than the other comparative methods such as artificial bee colony, ACO, fractional artificial bee colony, Low-Energy Adaptive Clustering Hierarchy (LEACH), FuzzyACO and PeSOA.

Hybrid model for security‐aware cluster head selection in wireless sensor networks

Wireless sensor network (WSN) is considered as the resource constraint network, in which the entire nodes have limited resources. In WSN, prolonging the lifetime of the network remains as the unsolved point. Accordingly, this study intends to propose a hybrid GGWSO (Grouped Grey Wolf Search Optimisation) algorithm to improve the performance of a cluster head selection in WSN, so that the network's lifetime can be extended. The proposed method concerns the main constraints associated with distance, delay, energy, and security. This study compares the performance of the proposed GGWSO with several traditional algorithms like artificial bee colony (ABC), fractional ABC, group search optimisation and Grey Wolf optimisation-based cluster head selection. During the performance analysis, the various ranges of risk, such as 20, 60, and 100% are added to validate the performance variations, by evaluating the number of alive nodes, and normalised network energy remained in the network. The simulation results have shown that there is a need for a hybrid model for attaining the superior results.

Probabilistic principal component analysis-based dimensionality reduction and optimization for arrhythmia classification using ECG signals

Abstract Electrocardiogram (ECG) is an electrical signal that contains data about the state and functions of the heart and can be used to diagnose various types of arrhythmias effectively. The modeling and simulation of ECG under different conditions are significant to understand the function of the cardiovascular system and in the diagnosis of heart diseases. Arrhythmia is a severe peril to the patient recovering from acute myocardial infarction. The reliable detection of arrhythmia is a challenge for a cardiovascular diagnostic system. As a result, a considerable amount of research has focused on the development of algorithms for the accurate diagnosis of arrhythmias. In this paper, a system for the classification of arrhythmia is developed by employing the probabilistic principal component analysis (PPCA) model. Initially, the cluster head is selected for the effective transmission of ECG signals of patients using the adaptive fractional artificial bee colony algorithm, and multipath routing for transmission is selected using the fractional bee BAT algorithm. Features such as wavelet features, Gabor transform, empirical mode decomposition, and linear predictive coding features are extracted from the ECG signal with high dimension (which are reduced using PPCA) and finally given to the proposed classifier called adaptive genetic-bat (AGB) support vector neural network (which is trained using the AGB algorithm) for arrhythmia detection. The experimentation of the proposed system is done based on evaluation metrics, such as the number of alive nodes, normalized network energy, goodput, and accuracy. The proposed method obtained a classification accuracy of 0.9865 and a goodput of 0.0590 and provides a better classification of arrhythmia. The experimental results show that the proposed system is useful for the classification of arrhythmias, with a reasonably high accuracy of 0.9865 and a goodput of 0.0590. The validation of the proposed system offers acceptable results for clinical implementation.

Energy-aware multi-path transmission of ECG signals for the classification of arrhythmia in wireless sensor network

Electrocardiogram (ECG) transmission and classification of arrhythmia stand as an effective area for dealing with the cardiac-related diseases since the world is reporting a higher rate of heart patients. Remote monitoring of the patients, being an effective solution in providing an effective diagnosis solution, wireless technology plays a significant role in ECG transmission. However, routing seems to be a hectic challenge, and hence, the paper proposes an effective routing protocol, termed as fractional artificial bee colony BAT (FBeeBAT) algorithm that is the integration of fractional concept, artificial bee colony (ABC) algorithm, and bat optimisation algorithm. The proposed algorithm enables the energy-aware multi-path routing in the wireless body area network (WBAN), in which the ECG signal of the patient is transmitted to the destination. The received ECG signal is subjected to arrhythmia classification using the genetic bat-support vector neural network (GB-SVNN). The effectiveness of the proposed algorithm is analysed by establishing the simulation environment using 50 and 100 nodes in transmitting and classifying the ECG signal. The proposed method assured a classification accuracy of 0.98 and the goodput of 0.058 that is better compared with the existing methods.

Energy-aware multi-path transmission of ECG signals for the classification of arrhythmia in wireless sensor network

Electrocardiogram (ECG) transmission and classification of arrhythmia stand as an effective area for dealing with the cardiac-related diseases since the world is reporting a higher rate of heart patients. Remote monitoring of the patients, being an effective solution in providing an effective diagnosis solution, wireless technology plays a significant role in ECG transmission. However, routing seems to be a hectic challenge, and hence, the paper proposes an effective routing protocol, termed as fractional artificial bee colony BAT (FBeeBAT) algorithm that is the integration of fractional concept, artificial bee colony (ABC) algorithm, and bat optimisation algorithm. The proposed algorithm enables the energy-aware multi-path routing in the wireless body area network (WBAN), in which the ECG signal of the patient is transmitted to the destination. The received ECG signal is subjected to arrhythmia classification using the genetic bat-support vector neural network (GB-SVNN). The effectiveness of the proposed algorithm is analysed by establishing the simulation environment using 50 and 100 nodes in transmitting and classifying the ECG signal. The proposed method assured a classification accuracy of 0.98 and the goodput of 0.058 that is better compared with the existing methods.

Optimal cluster head selection by hybridisation of firefly and grey wolf optimisation

Clustering is one of the fundamental techniques for prolonging the life expectancy of Wireless Sensor Networks (WSNs). However, cluster head selection remains the major challenge in WSN concerning energy stabilisation. This paper intends to propose the Firefly Cyclic Grey Wolf Optimisation (FCGWO) to simulate the optimal cluster head selection framework. The main objective of this paper is to select the cluster head optimally by focusing on the stabilisation of energy, minimisation of distance between nodes and minimisation of delay. It hybridises the Firefly (FF) and Grey Wolf Optimisation (GWO) algorithms to attain the best performance. After the simulation, it compares the performance of the FCGWO-based cluster head selection with the traditional algorithms like Genetic Algorithm (GA), Group Search Optimisation (GSO), Artificial Bee Colony (ABC), Fractional Artificial Bee Colony (FABC), Firefly (FF) with Cyclic Randomisation (FCR) and GWO based cluster head selection. The performance comparison appears to analyse the network lifetime, energy efficiency and statistics of dead nodes. The simulation outcomes show that the proposed cluster head selection model is more efficient to prolong the lifetime of the network.

Optimal cluster head selection framework to support energy aware routing protocols of wireless sensor network

Data transfer to the base station from the sensor node through the choice of an optimal cluster head is a huge challenge posed for the routing protocol, in the case of the wireless sensor network. An energy proficient clustering scheme relying on artificial bee colony algorithm and fractional artificial bee colony algorithm tends to maximise the network energy and lifetime of nodes through the selection of an optimal cluster head. However, the growing complexity of the network architecture requires substantial improvement. This paper develops a weighing model as a cost function to represent the optimal cluster head selection problem. Further, the traditional ABC is enhanced by adopting a novel adaptive concept called dynamic scout bee, and hence ABC-DS algorithm is derived in this paper. According to the concept of ABC-DS, multiplicating the scout bee generation enhances the number of alive nodes and the energy of cluster head. Comparative study between the performance of the proposed ABC-DS routing mechanism against that of ABC and FABC-based routing is conducted. The simulation results show that the proposed scheme increases the number of alive nodes with 25% of maximum normalised network energy than the existing protocols.

Dynamicity of the scout bee phase for an Artificial Bee Colony for optimized cluster head and network parameters for energy efficient sensor routing

Data transmitted to the base station from the sensor node by selecting an optimal cluster head is a massive challenge subjected to the routing protocol, in the case of the wireless sensor network. An energy efficient clustering method, depending on the Artificial Bee Colony (ABC) algorithm and the Fractional Artificial Bee Colony (FABC) algorithm, has a propensity to maximize the energy of the network and life time of nodes by the optimal cluster head selection (CHS). When conveying data, an ABC-Dynamic Scout bee algorithm is presented that multiplies the scout bee production to enlarge the number of alive nodes and the of CH energy. An assessment among the performances of the implemented ABC-based Dynamic Scout bee algorithm routing mechanism in opposition to that of ABC-based CHS and FABC-based CHS routing is done. The experimental outcome demonstrates that the implemented method increases the quantity of alive nodes with 25% of maximum energy for the normalized network compared with conventional protocols.

EACO and FABC to multi‐path data transmission in wireless sensor networks

Owing to the extensive growth of wireless technology for sending and collecting a variety of information for the different applications, routing is a major challenge to find the optimal path for the data transmission. In this study, the authors have developed a new algorithm called, exponential ant colony optimisation (EACO) to route discovery problem in wireless sensor network after finding the cluster heads (CHs) using fractional artificial bee colony (FABC) algorithm. In the first step, CHs are found out using the FABC algorithm with fitness function considering the distance, energy and delay. In the second phase, ACO algorithm is modified with exponential smoothing model for multi-path route discovery. This new algorithm called, EACO found the optimal routes among CHs to transmit a data from any source node to base station with multiple objectives including energy, distance, intra-cluster delay and intercluster delay. These objectives are effectively formulated as new fitness function to find the optimal route path. From the experimentation, the outcome showed that the cumulative energy kept after 2000 round of experiments is 0.2039 for the proposed algorithm but the existing approach (threshold + ACO) kept only 0.0338.

Multi-objective fractional artificial bee colony algorithm to energy aware routing protocol in wireless sensor network

Due to the promising application of collecting information from remote or inaccessible location, wireless sensor networks pose big challenge for data routing to maximize the communication with more energy efficient. Literature presents different cluster-based energy aware routing protocol for maximizing the life time of sensor nodes. Accordingly, an energy efficient clustering mechanism, based on artificial bee colony algorithm and factional calculus is proposed in this paper to maximize the network energy and life time of nodes by optimally selecting cluster-head. The hybrid optimization algorithm called, multi-objective fractional artificial bee colony is developed to control the convergence rate of ABC with the newly designed fitness function which considered three objectives like, energy consumption, distance travelled and delays to minimize the overall objective. The performance of the proposed FABC-based cluster head selection is compared with LEACH, PSO and ABC-based routing using life time, and energy. The results proved that the proposed FABC maximizes the energy as well as life time of nodes as compared with existing protocols.