Wireless Network Selection Research Articles

Optimizing relay node selection in cooperative wireless body area networks with modified POA

In the realm of Wireless Body Area Networks (WBANs), cooperative communications emerge as a crucial research focus, lauded for their capacity to mitigate fading and optimize spectral efficiency. Choosing the appropriate relay in cooperative communication is vital for ensuring spatial diversity, as incorrect relay selections can lower the network's overall performance. This study utilizes an optimization method referred to as M-POA(Modified Pelican Optimization Algorithm) to tackle the relay selection problem. This paper introduces a M-POA specifically designed for cooperative networks in Wireless Body Area Networks (WBAN). The core concept behind the M-POA is to mimic the hunting behavior of pelicans in nature. Here, the relay selection is determined using the M-POA, with channel gain as a fitness function. The proposed strategy integrates the Decode-and-Forward (DF) relay protocol to significantly enhance communication efficiency and reliability. The proposed method is assessed in comparison with other relay selection techniques through a Bit Error Rate (BER) analysis, evaluating the effectiveness of each approach across different Signal-to-Noise Ratio (SNR) scenarios. The simulation results analysis elucidates that M-POA provides well-balanced performance in both exploration and exploitation aspects. By calculating the Bit Error Rate (BER), we assessed the effectiveness of the optimized relay selection strategy, with the objective of minimizing BER to enhance overall communication reliability. By studying the convergence curve, we evaluated the relay selection technique's performance and gauged how effectively it improved with each iteration. The proposed scheme effectively addresses both key requirements of WBAN: improving communication system reliability and optimizing relay selection.

Retraction notice: Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Retraction notice: Hybrid artificial bee colony and glow worm algorithm for energy efficient cluster head selection in wireless sensor networks

Heterogeneous wireless network selection using feed forward double hierarchy linguistic neural network

Network selection in heterogeneous wireless networks (HWNs) is a complex issue that requires a thorough understanding of service features and user preferences. This is because the various wireless access technologies have varying capabilities and limitations, and the best network for a voice, video, and data service depends on a variety of factors. For selecting the optimal network in HWNs, varying factors such as the user’s position, accessible network resources, service quality requirements, and user preferences must be considered. The classical decision making procedure is very difficult and uncertain to select the desirable HWNs for voice, video, and data. Therefore, we develop a novel decision making model based on feed-forward neural networks under the double hierarchy linguistic information for the selection of the best HWNs for voice, video, and data. In this article, we introduce a novel feed-forward double hierarchy linguistic neural network using the Hamacher t-norm and t-conorm. Further, the feed-forward double hierarchy linguistic neural network applies to the decision making model for the selection of the best HWNs for voice, video, and data. In this novel decision making model, we first take the given data about HWNs and use the converting function to convert the given data into a double hierarchy linguistic term set. We calculate the hidden layer and output layer information by using Hamacher aggregation operations. Finally, we use the sigmoid activation function on the output layer information to decide on the best HWNs for voice, video, and data according to ranking. The proposed approach is compared with other existing models of decision making and the results of the comparison show that the proposed technique is applicable and reliable for the decision support model.

A Hybrid Approach Combining SAW and TOPSIS for Network Interface Selection in Heterogeneous Wireless Networks

A Hybrid Approach Combining SAW and TOPSIS for Network Interface Selection in Heterogeneous Wireless Networks

Optimizing energy-efficient cluster head selection in wireless sensor networks using a binarized spiking neural network and honey badger algorithm

Wireless Sensor Networks (WSNs) play a vitalparton some applications, like environmental monitoring, healthcare, industrial automation. Optimizing the performance of WSN is essential to ensure efficient data delivery while conserving energy resources. This manuscript addresses the challenges of Multi-Objective Cluster Head Selection and Energy-Aware Routing in WSNs through the development of the Binarized Spiking Neural Network optimized with the Honey Badger Algorithm (EACHS-BzSpNNHBA-WSN). The appropriate cluster heads selection is vital for the overall presentation of WSNs, influencing data aggregation, routing, and energy consumption. Conventional approaches often struggle to balance the conflicting objectives of maximizing network lifetime, minimizing energy consumption, and ensuring timely data delivery. Therefore, there is a pressing need for innovative solutions capable of addressing these multi-objective optimization challenges. In this study, the EACHS-BzSpNNHBA-WSN framework is proposed, leveraging the computational power of Binarized Spiking Neural Networks and the optimization capabilities of the Honey Badger Algorithm to identify optimal cluster heads in WSNs. By formulating the cluster head selection as a multi-objective optimization problem, the aim is to achieve robust data delivery while minimizing energy consumption and extending the network lifetime. The proposed framework is implemented in MATLAB platform. The performance assessment is conducted using various metrics, including the number of active nodes, packet drop rate, energy consumption, network lifetime, delay, throughput, packet delivery ratio. Comparative analysis with existing models demonstrates the effectiveness of the EACHS-BzSpNNHBA-WSN approach.

Energy aware forwarder selection in wireless body area networks to enhance stability and lifetime

Energy aware forwarder selection in wireless body area networks to enhance stability and lifetime

An innovative approach for cluster head selection and Energy Optimization in wireless sensor networks using Zebra Fish and Sea Horse Optimization techniques

In recent times, Wireless Sensor Networks (WSNs) have become an indispensable technology across various industries, offering diverse applications and services. Among the crucial performance metrics for WSNs, optimal cluster head (CH) selection and energy efficiency are paramount for cost-effective network operations. This paper proposes a novel approach for WSNs that tackles both challenges using Zebra Fish Optimization (ZFO) and Sea Horse Optimization (SHO) algorithms. The proposed approach focuses on dynamic cluster formation and CH selection. The ZFO algorithm, enhanced with a new multi-level threading technique, dynamically selects the most suitable CH based on a fitness function. Subsequently, the SHO algorithm, equipped with an innovative adaptive parameter tuning mechanism, optimizes energy consumption within the network. This two-phased approach ensures balanced performance. Performance evaluation is validated using key metrics like packet delivery ratio (PDR), throughput, network lifetime, and residual energy. Experimental results and statistical analysis demonstrate that the proposed hybrid scheme outperforms existing popular algorithms in all these metrics. The improvements range from 1.8 % to 6.9 % for PDR, 6.7 % to 24 % for throughput, 1.86 % to 7.40 % for network lifetime, and 9.65 % to 37.95 % for residual energy. These advancements are attributed to the innovative modifications introduced in both ZFO and SHO algorithms, ultimately contributing to the enhanced performance of the entire system.

IDA: Improved dragonfly algorithm for load balanced cluster heads selection in wireless sensor networks

IDA: Improved dragonfly algorithm for load balanced cluster heads selection in wireless sensor networks

On the Performance of the Relay Selection in Multi-hop Cluster-based Wireless Networks with Multiple Eavesdroppers Under Equally Correlated Rayleigh Fading

The performance of multi-hop cluster-based wireless networks under multiple eavesdroppers is investigated in the present work. More precisely, we derive the outage probability (OP) of the considered networks under two relay selection schemes: the channel-gain-based scheme and the random scheme. Although equally correlated Rayleigh fading is taken into consideration, the derived mathematical framework remains tractable. Specifically, we represent the exact expression of the OP under the channel-based scheme in series form, while the OP under the random scheme is computed in a closed-form expression. Additionally, we propose a novel power allocation for each transmitter that strictly satisfies the given intercept probability. Numerical results based on the Monte Carlo method are provided to verify the correctness of the derived framework. These results are also used to identify the influences of various parameters, such as the number of clusters, the number of relays per cluster, and the transmit power.

A multi-agent deep Q-learning-based joint relay and jammer selection in dual-hop wireless networks

A multi-agent deep Q-learning-based joint relay and jammer selection in dual-hop wireless networks

An enhanced sea-horse optimizer for solving global problems and cluster head selection in wireless sensor networks

An efficient variant of the recent sea horse optimizer (SHO) called SHO-OBL is presented, which incorporates the opposition-based learning (OBL) approach into the predation behavior of SHO and uses the greedy selection (GS) technique at the end of each optimization cycle. This enhancement was created to avoid being trapped by local optima and to improve the quality and variety of solutions obtained. However, the SHO can occasionally be vulnerable to stagnation in local optima, which is a problem of concern given the low diversity of sea horses. In this paper, an SHO-OBL is suggested for the tackling of genuine and global optimization systems. To investigate the validity of the suggested SHO-OBL, it is compared with nine robust optimizers, including differential evolution (DE), grey wolf optimizer (GWO), moth-flame optimization algorithm (MFO), sine cosine algorithm (SCA), fitness dependent optimizer (FDO), Harris hawks optimization (HHO), chimp optimization algorithm (ChOA), Fox optimizer (FOX), and the basic SHO in ten unconstrained test routines belonging to the IEEE congress on evolutionary computation 2020 (CEC’20). Furthermore, three different design engineering issues, including the welded beam, the tension/compression spring, and the pressure vessel, are solved using the proposed SHO-OBL to test its applicability. In addition, one of the most successful approaches to data transmission in a wireless sensor network that uses little energy is clustering. In this paper, SHO-OBL is suggested to assist in the process of choosing the optimal power-aware cluster heads based on a predefined objective function that takes into account the residual power of the node, as well as the sum of the powers of surrounding nodes. Similarly, the performance of SHO-OBL is compared to that of its competitors. Thorough simulations demonstrate that the suggested SHO-OBL algorithm outperforms in terms of residual power, network lifespan, and extended stability duration.

Distributed homology-based sensor selection and scheduling in wireless sensor networks

Distributed homology-based sensor selection and scheduling in wireless sensor networks

Entropy Weighted TOPSIS Based Cluster Head Selection in Wireless Sensor Networks under Uncertainty

In recent decades, wireless sensor networks (WSNs) have become a popular ambient sensing and model-based solution for various applications. WSNs are now achievable due to the developments of micro electro mechanical and semiconductors logic circuits with rising computational power and wireless communication technology. The most difficult issues concerning WSNs are related to their energy consumption. Since communication typically requires a significant amount of energy, there are some techniques/ways to reduce energy consumption during the operation of the sensor’s communication systems. The topology control technique is one such effective method for reducing WSNs’ energy usage. A cluster head (CH) is usually selected using a topology control technique known as clustering to control the entire network. A single factor is inadequate for CH selection. Additionally, with the traditional clustering method, each round exhibits a new batch of head nodes. As a result, when using conventional techniques, nodes decay faster and require more energy. Furthermore, the inceptive energy of nodes, the range between sensor nodes and base stations, the size of data packets, voltage and transmission energy measurements, and other factors linked to sensor nodes are also completely unexpected due to irregular or hazardous natural circumstances. Here, unpredictability represented by Triangular Fuzzy Numbers (TFNs). The associated parameters of nodes were converted into crisp ones via the defuzzification of fuzzy numbers. The fuzzy number has been defuzzified using the well-known signed distance approach. Here, we have employed a multi-criteria decision-making (MCDM) approach to choosing the CHs depending on a bunch of characteristics of each node (i) residual energy, (ii) the number of neighbors, (iii) distance from the sink, (iv) average distance of cluster node, (v) distance ratio, and (vi) reliability. This study used the entropy-weighted Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) approach to select the CH in WSNs. For experiments, we have used the NSG2.1 simulator, and based on six characteristics comprising residual energy, number of neighbor nodes, distance from the sink or base station (BS), average distance of cluster nodes, distance ratio, and reliability, optimal CHs have been selected. Finally, experimental results have been presented and compared graphically with the existing literature. A statistical hypothesis test has also been conducted to verify the results that have been provided.

Feature Selection and Energy Management in Wireless Sensor Networks using Deep Learning

In wireless sensor networks, when the available energy sources and battery capacity are extremely constrained, energy efficiency is a major issue to be adressed. One of the main goals in the design of wireless sensor networks (WSNs) is to maximize longevity of battery life. Designers can benefit from the use of intelligent power utilization models to accomplish this goal. These models seek to decrease the number of chosen sensors used to record environmental measures in order to minimize power utilization while retaining the acceptable level of measurement accuracy. In order to simulate wireless sensor networks, we looked at real world datasets. Our simulation findings demonstrate that the suggested strategy can be used to accomplish significant goals by using the right number of sensors using deep learning, extend the lifespan of the wireless sensor networks.

Reinforcement learning-based secure joint relay and jammer selection in dual-hop wireless networks

Reinforcement learning-based secure joint relay and jammer selection in dual-hop wireless networks

Deep Learning-based Prioritized Packet Classification and Optimal Route Selection in Wireless Multimedia Sensor Networks

Wireless multimedia sensor networks suffer network congestion due to increased traffic leading to data packet loss and increased energy consumption. Thus, it is essential to have a mechanism for priority-based packet classification to improve the performance and decrease the delay in real-time packets. This paper introduces priority-based packet classification using a word embedding mechanism to extract packet semantics and classification using the long short-term memory model that works based on the different characteristics of the packet header to decide the priority of each packet. Furthermore presents a hybrid meta-heuristic algorithm termed butterfly-based rider optimization algorithm, which considers the packet priority and other parameters in optimal route selection. This hybrid meta-heuristic algorithm is developed by merging rider optimization algorithm, and butterfly optimization algorithm. Finally, the experimental results validate the analytical result as well as the performance comparison between the proposed optimal route selection model and the conventional models.

Access selection in heterogeneous wireless networks based on user preferences

Access selection is an important key in heterogeneous networks, and the design of a new algorithm for decision is not a trivial task. Different aspects must be taken into consideration while designing a new decision algorithm, including both users' requirements (in terms of resources, QoS, user's preferences), and operator policies that aim to maximise the utilisation of its network capacity and to deliver services with acceptable QoS levels for the largest number of customers. Thus, in this paper, we propose a new selection algorithm based on user's preferences. The comparison between the proposed scenarios is given based on several performance indicators. The results show the improvement achieved by increasing the resource utilisation and therefore the overall system capacity.

Access selection in heterogeneous wireless networks based on user preferences

Access selection in heterogeneous wireless networks based on user preferences

A modified grasshopper optimization algorithm based on levy flight for cluster head selection in wireless sensor networks

A modified grasshopper optimization algorithm based on levy flight for cluster head selection in wireless sensor networks

Optimal trajectory UAV path design based on bezier curves with multi-hop cluster selection in wireless networks

Optimal trajectory UAV path design based on bezier curves with multi-hop cluster selection in wireless networks