scholarly journals Bacteria Foraging Algorithm for Optimal Topology Construction in Wireless Sensor Networks

2022 ◽  
Vol 13 (1) ◽  
pp. 0-0
Keyword(s):  
Energy Consumption ◽  
Performance Metrics ◽  
Search Algorithm ◽  
Fitness Function ◽  
Gravitational Search Algorithm ◽  
Residual Energy ◽  
Optimization Techniques ◽  
Wireless Sensor ◽  
Optimal Topology ◽  
Topology Construction

Topology control is a significant method to reduce energy consumption and prolong the network lifetime. Connected Dominated Sets (CDS) are the emerging technologies to construct the energy- efficient optimal topology. Traditional topology construction algorithms are not utilized suitable optimization techniques for finding the optimum location of the active nodes in the networks. In this paper, Bacteria Foraging Algorithm (BFA) identifies the optimal location for active nodes to form the virtual backbone of the network. Residual energy and network connectivity are considered to evaluate the fitness function. The performance of the BFA is compared with other algorithms namely A3, A1, Genetic Algorithm (GA), and Gravitational Search Algorithm (GSA) algorithms for considering the performance metrics of the active nodes, residual energy, and connected sensing area coverage. Simulation results show that the proposed methodology performs well for reducing energy consumption and improving the connected sensing coverage area in the wireless sensor network.

scholarly journals Optimal Selection of the Cluster Head in Wireless Sensor Networks by Combining the Multiobjective Genetic Algorithm and the Gravitational Search Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Seyed Reza Nabavi ◽  
Vahid Ostovari Moghadam ◽  
Mohammad Yahyaei Feriz Hendi ◽  
Amirhossein Ghasemi
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Cluster Head ◽  
Wireless Sensor ◽  
Multiobjective Genetic Algorithm ◽  
Gravitational Search

With the development of various applications of wireless sensor networks, they have been widely used in different areas. These networks are established autonomously and easily in most environments without any infrastructure and collect information of environment phenomenon for proper performance and analysis of events and transmit them to the base stations. The wireless sensor networks are comprised of various sensor nodes that play the role of the sensor node and the relay node in relationship with each other. On the other hand, the lack of infrastructure in these networks constrains the sources such that the nodes are supplied by a battery of limited energy. Considering the establishment of the network in impassable areas, it is not possible to recharge or change the batteries. Thus, energy saving in these networks is an essential challenge. Considering that the energy consumption rate while sensing information and receiving information packets from another node is constant, the sensor nodes consume maximum energy while performing data transmission. Therefore, the routing methods try to reduce energy consumption based on organized approaches. One of the promising solutions for reducing energy consumption in wireless sensor networks is to cluster the nodes and select the cluster head based on the information transmission parameters such that the average energy consumption of the nodes is reduced and the network lifetime is increased. Thus, in this study, a novel optimization approach has been presented for clustering the wireless sensor networks using the multiobjective genetic algorithm and the gravitational search algorithm. The multiobjective genetic algorithm based on reducing the intracluster distances and reducing the energy consumption of the cluster nodes is used to select the cluster head, and the nearly optimal routing based on the gravitational search algorithm is used to transfer information between the cluster head nodes and the sink node. The implementation results show that considering the capabilities of the multiobjective genetic algorithm and the gravitational search algorithm, the proposed method has improved energy consumption, efficiency, data delivery rate, and information packet transmission rate compared to the previous methods.

scholarly journals CDARGA: Cluster-Based Data Aggregation with Genetic Routing Algorithm in Wireless Sensor Networks

2020 ◽  
Vol 8 (6) ◽  
pp. 2976-2982
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Data Aggregation ◽  
Search Algorithm ◽  
Fitness Function ◽  
Base Station ◽  
Wireless Sensor ◽  
Experimental Result ◽  
Genetic Search

In the wireless sensor networks (WSNs), the upholding the energy and routing formation at every sensor node is the major issues. The distance from base station and internal node mainly has imbalanced in the energy consumption during transformation of the data. To reduce the energy upholding and the data aggregation routing issues in Centralized Clustering-Task Scheduling for wireless sensor networks (WSNs), this paper focuses on a Cluster-Based Data Aggregation Routing with Genetic search Algorithm (CDARGA) , which reduces the energy consumption in a hyper round model. The proposed data aggregation routing protocol using the Genetic Algorithm (GA) estimates the fitness function using the three key parameters distance, energy, and Hyper round policy. The proposed methods were compared with RP-MAC and the experimental result shows that the proposed protocol is superior to RP-MAC protocol and the proposed algorithm improves the network lifetime which can used in real time application.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

scholarly journals A New “Good and Bad Groups-Based Optimizer” for Solving Various Optimization Problems

2021 ◽  
Vol 11 (10) ◽  
pp. 4382
Author(s):  
Ali Sadeghi ◽  
Sajjad Amiri Doumari ◽  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Pavel Trojovský ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Gray Wolf ◽  
Good Group ◽  
Good Ability ◽  
Whale Optimization

Optimization is the science that presents a solution among the available solutions considering an optimization problem’s limitations. Optimization algorithms have been introduced as efficient tools for solving optimization problems. These algorithms are designed based on various natural phenomena, behavior, the lifestyle of living beings, physical laws, rules of games, etc. In this paper, a new optimization algorithm called the good and bad groups-based optimizer (GBGBO) is introduced to solve various optimization problems. In GBGBO, population members update under the influence of two groups named the good group and the bad group. The good group consists of a certain number of the population members with better fitness function than other members and the bad group consists of a number of the population members with worse fitness function than other members of the population. GBGBO is mathematically modeled and its performance in solving optimization problems was tested on a set of twenty-three different objective functions. In addition, for further analysis, the results obtained from the proposed algorithm were compared with eight optimization algorithms: genetic algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), teaching–learning-based optimization (TLBO), gray wolf optimizer (GWO), and the whale optimization algorithm (WOA), tunicate swarm algorithm (TSA), and marine predators algorithm (MPA). The results show that the proposed GBGBO algorithm has a good ability to solve various optimization problems and is more competitive than other similar algorithms.

An Energy-Balance Based Distributed Topology Control Algorithm for Wireless Sensor Networks

2012 ◽  
Vol 490-495 ◽  
pp. 1392-1396 ◽  
Author(s):  
Chu Hang Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Topology Control ◽  
Control Algorithm ◽  
Path Loss ◽  
Network Connectivity ◽  
Premature Death ◽  
Residual Energy ◽  
Wireless Sensor

Topology control is an efficient approach which can reduce energy consumption for wireless sensor networks, and the current algorithms mostly focus on reducing the nodes’ energy consumption by power adjusting, but pay little attention to balance energy consumption of the whole network, which results in premature death of many nodes. Thus, a distributed topology control algorithm based on path-loss and residual energy (PRTC) is designed in this paper. This algorithm not only maintains the least loss links between nodes but also balances the energy consumption of the network. The simulation results show that the topology constructed by PRTC can preserve network connectivity as well as extend the lifetime of the network and provide good performance of energy consumption.

scholarly journals Systematic Literature Review on Metaheuristic Optimization Techniques in WSNs

2020 ◽  
Vol 14 ◽  
Keyword(s):  
Systematic Review ◽  
Wireless Sensor Networks ◽  
Exact Solution ◽  
Energy Consumption ◽  
Optimization Techniques ◽  
Metaheuristic Algorithms ◽  
Wireless Sensor ◽  
Time Constraints ◽  
Metaheuristic Optimization ◽  
New Algorithms

Metaheuristic algorithms are recognized for developing new algorithms and optimizing various aspects in Wireless Sensor Networks (WSNs). Evaluating a multitude of possible modes is required, in most complicated problems, to obtain an exact solution. Metaheuristic algorithms can obtain solutions in acceptable time constraints. These algorithms play an operational role in solving such problems by optimizing the different metrics such as coverage rate and energy consumption of the networks. These metrics have valuable impact on network lifetime as well. This systematic review focuses on the published work from 2010 to 2020 in metaheuristic optimization in WSN. Furthermore, the systematic review will answer multiple questions that will be discussed in the methodology section.

scholarly journals Optimized Wireless Sensor Node Multidimensional Routing using Fuzzy Clustering and Chaotic Gravitational Search Algorithm

2021 ◽  
Vol 3 (1) ◽  
pp. 40-48
Author(s):  
Sivaganesan D
Keyword(s):  
Sensor Node ◽  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Base Station ◽  
Wireless Sensor ◽  
Destination Node ◽  
Wireless Sensor Node ◽  
Search Spaces ◽  
Gravitational Search

A network of tiny sensors located at various regions for sensing and transmitting information is termed as wireless sensor networks. The information from multiple network nodes reach the destination node or the base station where data processing is performed. In larger search spaces, the clustering mechanisms and routing solutions provided by the existing heuristic algorithms are often inefficient. The sensor node resources are depleted by un-optimized processes created by reduced routing and clustering optimization levels in large search spaces. Chaotic Gravitational Search Algorithm and Fuzzy based clustering schemes are used to overcome the limitations and challenges of the conventional routing systems. This enables effective routing and efficient clustering in large search spaces. In each cluster, among the available nodes, appropriate node is selected as the cluster head. Reduction in delay, increase in energy consumption, increase in network lifetime and improvement of the network clustering accuracy are evident from the simulation results.

scholarly journals Cluster Routing Algorithm for Ring Based Wireless Sensor Network Using Particle Swarm And Lion Swarm Optimization

2021 ◽  
Author(s):  
Huangshui Hu ◽  
Yuxin Guo ◽  
Jinfeng Zhang ◽  
Chunhua Yin ◽  
Dong Gao
Keyword(s):  
Wireless Sensor Networks ◽  
Energy Consumption ◽  
Sensor Networks ◽  
Hot Spot ◽  
Fitness Function ◽  
Routing Algorithm ◽  
Particle Swarm ◽  
Relay Node ◽  
Wireless Sensor ◽  
Swarm Optimization

Abstract In order to solve the problem of hot spot caused by uneven energy consumption of nodes in Wireless Sensor Networks (WSNs) and reduce the network energy consumption, a novel cluster routing algorithm called CRPL for ring based wireless sensor networks using Particle Swarm Optimization (PSO) and Lion Swarm Optimization (LSO) is proposed in this paper. In CRPL, the optimal cluster head (CH) of each ring are selected by using LSO whose fitness function is composed of energy,number of neighbor nodes, number of cluster heads and distance. Moreover, PSO with a multi-objective fitness function considering distance, energy and cluster size is used to find the next hop relay node in the process of data transmission, and the optimal routing paths are obtained, so as to alleviate the hot spot problem as well as decrease the energy consumption in the routing process. The simulation results show that, compared with some existing optimization algorithms, CRPL has better effects in balancing the energy consumption of the network and prolonging the life cycle of the network.

Energy and Quality Aware Multi-Objective Resource Allocation Algorithm in Cloud

2021 ◽  
pp. 2150052
Author(s):  
Koné Kigninman Désiré ◽  
Eya Dhib ◽  
Nabil Tabbane ◽  
Olivier Asseu
Keyword(s):  
Resource Allocation ◽  
Energy Consumption ◽  
Search Algorithm ◽  
Fitness Function ◽  
Video Quality ◽  
Minimum Energy ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Energy Aware ◽  
Resource Allocation Algorithm

Cloud gaming has become the new service provisioning prototype that hosts the video games in the cloud and broadcasts the interactive game streaming to the players through the Internet. Here, the cloud must use massive resources for video representation and its streaming when several simultaneous players reach a particular point. Alternatively, various players may have separate necessities on Quality-of Experience, like low delay, high-video quality, etc. The challenging task is providing better service by the fixed cloud resource. Hence, there is a necessity for an energy-aware multi-resource allocation in the cloud. This paper devises a Fractional Rider-Harmony search algorithm (Fractional Rider-HSA) for resource allocation in the cloud. The Fractional Rider-HSA combines fractional calculus, Rider Optimization algorithm (ROA), and HSA. Moreover, the fitness function, like mean opinion score (MOS), gaming experience loss, fairness, energy consumption, and network parameters, is considered to determine the optimal resource allocation. The proposed model produces the maximal MOS of 0.8961, maximal gaming experience loss (QE) of 0.998, maximal fairness of 0.9991, the minimum energy consumption of 0.3109, and minimal delay 0.2266, respectively.

Design and Implementation of Tuning PID Controller using Modern Optimization Techniques for Micro-Robotics System

2021 ◽  
Vol 10 (11) ◽  
pp. 51-68
Author(s):  
Ehab S. Ghith ◽  
◽  
Mohamed Sallam ◽  
Islam S. M. Khalil ◽  
Mohamed Youssef Serry ◽  
...  
Keyword(s):  
Steady State ◽  
Optimization Problems ◽  
Model Simulation ◽  
Search Algorithm ◽  
Optimal Parameter ◽  
Fitness Function ◽  
Optimization Techniques ◽  
Control Technique ◽  
Pid Controllers ◽  
Flower Pollination Algorithm

One of the main difficult tasks in the field of micro-robotics is the process of the selection of the optimal parameters for the PID controllers. Some methods existed to solve this task and the common method used was the Ziegler and Nichols. The former method require an accurate mathematical model. This method is beneficial in linear systems, however, if the system becomes more complex or non-linear the method cannot produce accurate values to the parameters of the system. A solution proposed for this problem recently is the application of optimization techniques. There are various optimization techniques can be used to solve various optimization problems. In this paper, several optimization methods are applied to compute the optimal parameter of PID controllers. These methods are flower pollination algorithm (FPA), grey wolf optimization (GWO), sin cosine algorithm (SCA), slime mould algorithm (SMA), and sparrow search algorithm (SSA). The fitness function applied in the former optimization techniques is the integral square Time multiplied square Error (ISTES) as the performance index measure. The fitness function provides minimal rise time, minimal settling time, fast response, and no overshoot, Steady state error equal to zero, a very low transient response and a non-oscillating steady state response with excellent stabilization. The effectiveness of the proposed SSA-based controller was verified by comparisons made with FPA, GWO, SCA, SMA controllers in terms of time and frequency response. Each control technique will be applied to the identified model (simulation results) using MATLAB Simulink and the laboratory setup (experimental results) using LABVIEW software. Finally, the SSA showed the highest performance in time and frequency responses.

Sign in / Sign up

Export Citation Format

Share Document