Optimal Clustering in Wireless Sensor Networks for the Internet of Things Based on Memetic Algorithm: MemeWSN

In wireless sensor networks for the Internet of Things (WSN-IoT), the topology deviates very frequently because of the node mobility. The topology maintenance overhead is high in flat-based WSN-IoTs. WSN clustering is suggested to not only reduce the message overhead in WSN-IoT but also control the congestion and easy topology repairs. The partition of wireless mobile nodes (WMNs) into clusters is a multiobjective optimization problem in large-size WSN. Different evolutionary algorithms (EAs) are applied to divide the WSN-IoT into clusters but suffer from early convergence. In this paper, we propose WSN clustering based on the memetic algorithm (MemA) to decrease the probability of early convergence by utilizing local exploration techniques. Optimum clusters in WSN-IoT can be obtained using MemA to dynamically balance the load among clusters. The objective of this research is to find a cluster head set (CH-set) as early as possible once needed. The WMNs with high weight value are selected in lieu of new inhabitants in the subsequent generation. A crossover mechanism is applied to produce new-fangled chromosomes as soon as the two maternities have been nominated. The local search procedure is initiated to enhance the worth of individuals. The suggested method is matched with state-of-the-art methods like MobAC (Singh and Lohani, 2019), EPSO-C (Pathak, 2020), and PBC-CP (Vimalarani, et al. 2016). The proposed technique outperforms the state of the art clustering methods regarding control messages overhead, cluster count, reaffiliation rate, and cluster lifetime

Review of Optimization Problems in Wireless Sensor Networks

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Metaheuristics Techniques for Cluster Head Selection in WSN: A Survey

In recent years, Wireless sensor communication is growing expeditiously on the capability to gather information, communicate and transmit data effectively. Clustering is the main objective of improving the network lifespan in Wireless sensor network. It includes selecting the cluster head for each cluster in addition to grouping the nodes into clusters. The cluster head gathers data from the normal nodes in the cluster, and the gathered information is then transmitted to the base station. However, there are many reasons in effect opposing unsteady cluster head selection and dead nodes. The technique for selecting a cluster head takes into factors to consider including residual energy, neighbors’ nodes, and the distance between the base station to the regular nodes. In this study, we thoroughly investigated by number of methods of selecting a cluster head and constructing a cluster. Additionally, a quick performance assessment of the techniques' performance is given together with the methods' criteria, advantages, and future directions

Genetical Swarm Optimization of Multihop Routes in Wireless Sensor Networks

In recent years, wireless sensor networks have been attracting considerable research attention for a wide range of applications, but they still present significant network communication challenges, involving essentially the use of large numbers of resource-constrained nodes operating unattended and exposed to potential local failures. In order to maximize the network lifespan, in this paper, genetical swarm optimization (GSO) is applied, a class of hybrid evolutionary techniques developed in order to exploit in the most effective way the uniqueness and peculiarities of two classical optimization approaches; particle swarm optimization (PSO) and genetic algorithms (GA). This procedure is here implemented to optimize the communication energy consumption in a wireless network by selecting the optimal multihop routing schemes, with a suitable hybridization of different routing criteria, confirming itself as a flexible and useful tool for engineering applications

Wireless Sensor Network Optimization Using Genetic Algorithm

Wireless Sensor Network (WSN) is a high potential technology used in many fields (agriculture, earth, environmental monitoring, resources union, health, security, military, and transport, IoT technology). The band width of each cluster head is specific, thus, the number of sensors connected to each cluster head is restricted to a maximum limit and exceeding it will weaken the connection service between each sensor and its corresponding cluster head. This will achieve the research objective which refers to reaching the state where the proposed system energy is stable and not consuming further more cost. The main challenge is how to distribute the cluster heads regularly on a specified area, that’s why a solution was supposed in this research implies finding the best distribution of the cluster heads using a genetic algorithm. Where using an optimization algorithm, keeping in mind the cluster heads positions restrictions, is an important scientific contribution in the research field of interest. The novel idea in this paper is the crossover of two-dimensional integer encoded individuals that replacing an opposite region in the parents to produce the children of new generation. The mutation occurs with probability of 0.001, it changes the type of 0.05 sensors found in handled individual. After producing more than 1000 generations, the achieved results showed lower value of fitness function with stable behavior. This indicates the correct path of computations and the accuracy of the obtained results. The genetic algorithm operated well and directed the process towards improving the genes to be the best possible at the last generation. The behavior of the objective function started to be regular gradually throughout the produced generations until reaching the best product in the last generation where it is shown that all the sensors are connected to the nearest cluster head. As a conclusion, the genetic algorithm developed the sensors’ distribution in the WSN model, which confirms the validity of applying of genetic algorithms and the accuracy of the results

An Overview of Manet Power Management Approaches

One of the primary issues with MANET is power optimization and utilization because it relies on the node's internal battery power to operate the wireless network. The performance of the MANET is also affected by one of the parameters of energy consumption and utilization. Each operation in the MANET requires some amount of energy to complete. This article elaborated on MANET power management from its inception to the present, as well as doing comparison research to recommend new methods for improving MANET power utilization. This study examines MANET power management options in terms of numerous parameter metrics, including Mobility Aware, Clustering, Topology, Transmission Range, QOS, and link-based. Finally, the methodologies used in MANET power management and performance factor improvement were summarised. To surpass all performance indicators in MANET utilization, new manipulative tactics are necessary. The innovative method is the most effective

Sensor Activity Scheduling Protocol for Lifetime prolongation in Wireless Sensor Networks

In Wireless Sensor Network (WSN), the dense sensor nodes deployment in the sensing field can be exploited in conserving the energy of the whole network, where the data of these nodes can be highly correlated. Therefore, it is necessary to turn off the unnecessary nodes that sense similar sensor readings so as to reduce the redundant sensed readings and decrease the communication overhead thus extend the WSN lifetime. This article suggests a Sensor Activity Scheduling (SAS) protocol for lifetime improvement of WSNs. SAS works in a periodic way. It exploits the spatial correlation among sensed sensor data in order to produce the best sensor activities schedule in WSNs. SAS composed of three phases: data collection, decision-based optimization, and sensing. SAS measures the similarity degree among the sensed data that collected in the first phase. It makes a decision of which sensors stay active during the sensing phase in each period and put the other nodes into low power sleep whilst keeping a good accuracy level to the received data at the sink to conserve the power and enhance the lifetime of the WSN. Several experiments based on real sensed data and by using OMNeT++ simulator demonstrate that SAS can save energy and extend the WSN lifetime efficiently compared with the other methods