Optimization Problems in Wireless Sensors Networks

Kumar, Sunil; Shivalingagowda, Chaya; Kumar, Dinesh

doi:10.1201/9780429438639-2

Cited by 7 publications

(4 citation statements)

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“…The HexGridClusterization Algorithm takes O(n 2 ) time , LeastMovementConsiderFirst algorithm also takes O(n 2 ) time. For hybridized GA-PSO algorithm,the complexity of GA is depends on chromosome-length, size of population, complexity [6], [46], [47] Consequently, the overall time complexity of proposed MOFAC-GA-PSO algorithm can be expressed as: O(n 2 + n 2 + G.n 2 + I.n 2 ) . In [47], the authors proposed methodology also has the n 2 time complexity for similar WSN network.…”

Section: Time Complexity Of Proposed Mofac-ga-pso Algorithmmentioning

confidence: 99%

“…2. The WSN has been applied for various purposes: environment monitoring, agriculture monitoring, medical surveillance, military surveillance, engineering surveillance, home automation, etc [6]. FIGURE 2: WSN node components with application-specific additional components (Power generator, GPS, Mobilizer) [7] The key obstacles to developing WSN networks include limited battery energy, maximizing coverage area [8], limited processing capabilities, efficient deployment strategy [9]- [11], effective duty cycle optimization, and maintaining the quality of service.…”

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confidence: 99%

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Coverage Area Maximization Using MOFAC-GA-PSO Hybrid Algorithm in Energy Efficient WSN Design

Kumar De,

Banerjee,

Majumder

et al. 2023

IEEE Access

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Coverage area optimization is always a challenging task to configure an efficient Wireless Sensor Network (WSN). This article proposes an energy-efficient coverage area optimization technique of WSN using a novel hybrid algorithm, called MOFAC-GA-PSO (Minimum Overlapped Full Area Coverage using hybridized Genetic Algorithm-Particle Swarm Optimization) algorithm. The objectives of the article are maximization of coverage area, minimization of coverage hole as well as energy requirement. The above-mentioned three objectives had not been yet addressed combinedly with the existing literature. This limitation has been addressed in the proposed work with 100% area coverage. The result of the proposed algorithm is compared with the existing literature as well as with the individual meta-heuristic algorithms (i.e., GA and PSO) to prove the competence of the MOFAC-GA-PSO algorithm. To achieve the benefits of both optimizers, the GA was treated as a global optimizer while the PSO was treated as a local optimizer. The proposed research work achieves 100 percent area coverage with just 25 mobile WSN nodes, but the existing methodology can only provide a maximum of 91.26 percent of area coverage. In terms of energy efficiency, the network built by the proposed algorithm can last 11.06 days as contrasted to the performance of the existing paper, which is 6.33 days. So, a significant improvement concerning the maximization of coverage area as well as minimization of coverage hole, and energy requirement has been observed. Last, but not the least, a statistical analysis is carried out to justify the research for the required number of optimized WSN nodes. INDEX TERMSCoverage area optimization, Energy efficient WSN, Hybrid algorithm, LMCF(Least Movement Consider First), Hexagonal structure, MOFAC-GA-PSO I. INTRODUCTION A Wireless Sensor Node is a battery powered small device with limited computational, transmission and energy capacity. The situation where standard wireless communications networks are difficult or impossible to deploy, the Wireless Sensor Nodes can be deployed to form a collaborative and clustered Wireless Sensor Network(WSN) [1]. Depending on the sensors affixed to the WSN nodes, these WSN nodes collect a variety of physical measurements from their surroundings, including noise intensity, air flow velocity, pollution level, pressure level, temperature, moisture, and surveillance data. After transforming the environmentally acquired data into electrical signals, the WSN nodes send and receive a limited amount of data to other nodes or sink nodes for further processing. The structure of the WSN network is depicted in Fig. 1.Generally, the standard components of a Wireless Sensor Node include a micro-controller unit, a transceiver unit, a

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Section: Time Complexity Of Proposed Mofac-ga-pso Algorithmmentioning

confidence: 99%

mentioning

confidence: 99%

Coverage Area Maximization Using MOFAC-GA-PSO Hybrid Algorithm in Energy Efficient WSN Design

Kumar De,

Banerjee,

Majumder

et al. 2023

IEEE Access

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“…Cloud computing acquired tremendous popularity in recent years, mainly due to advancements in virtualization technology. This technology has shown a new era to many applications that applies speech and signal processing (1,2) with 5g, (3) which provides higher bandwidth and flexibility. To get pay-per-use services, many applications were forwarded to data centers.…”

Section: Introductionmentioning

confidence: 99%

Resource allocation on periotity based schuduling and improve the security using DSSHA-256

Prathap Kumar,

Rohini

2024

Data and Metadata

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Cloud computing has gained popularity with advancements in virtualization technology and the deployment of 5G. However, scheduling workload in a heterogeneous multi-cloud environment is a complicated process. Users of cloud services want to ensure that their data is secure and private, especially sensitive or proprietary information. Several research works have been proposed to solve the challenges associated with cloud computing. The proposed Adaptive Priority based scheduling (PBS) focuses on reducing data access completion time and computation expense for task scheduling in cloud computing. PBS assigns tasks depending on its size and selects the minimum cost path for data access. It contains a task register, scheduler, and task execution components for efficient task execution. The proposed system also executes a double signature mechanism for data privacy and security in data storage. This study correlates the perfo}rmance of three algorithms, PBS, (Task Requirement Degree) TRD and (recommended a Risk adaptive Access Control) RADAC, in terms of task execution time and makespan time. The experimental results demonstrate that PBS outperforms TRD and RADAC in both metrics, as the number of tasks increases. PBS has a minimum task execution time and a lower makespan time than the othertwo algorithms

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“…The latest advances in virtualization technology have elevated it to a significant position currently. Cloud computing has given new life to a wide range of applications, including speech and signal processing [ 1 , 2 ], thanks to the increased bandwidth and flexibility provided by 5G [ 3 ]. A variety of applications are submitted to data centers to obtain pay-per-use services.…”

Section: Introductionmentioning

confidence: 99%

An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing

Srivastava

Tiwari

Srivastava

et al. 2022

Computational Intelligence and Neuroscience

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One of the important and challenging tasks in cloud computing is to obtain the usefulness of cloud by implementing several specifications for our needs, to meet the present growing demands, and to minimize energy consumption as much as possible and ensure proper utilization of computing resources. An excellent mapping scheme has been derived which maps virtual machines (VMs) to physical machines (PMs), which is also known as virtual machine (VM) placement, and this needs to be implemented. The tremendous diversity of computing resources, tasks, and virtualization processes in the cloud causes the consolidation method to be more complex, tedious, and problematic. An algorithm for reducing energy use and resource allocation is proposed for implementation in this article. This algorithm was developed with the help of a Cloud System Model, which enables mapping between VMs and PMs and among tasks of VMs. The methodology used in this algorithm also supports lowering the number of PMs that are in an active state and optimizes the total time taken to process a set of tasks (also known as makespan time). Using the CloudSim Simulator tool, we evaluated and assessed the energy consumption and makespan time. The results are compiled and then compared graphically with respect to other existing energy-efficient VM placement algorithms.

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Optimization Problems in Wireless Sensors Networks

Cited by 7 publications

References 1 publication

Coverage Area Maximization Using MOFAC-GA-PSO Hybrid Algorithm in Energy Efficient WSN Design

Coverage Area Maximization Using MOFAC-GA-PSO Hybrid Algorithm in Energy Efficient WSN Design

Resource allocation on periotity based schuduling and improve the security using DSSHA-256

An Energy-Efficient Strategy and Secure VM Placement Algorithm in Cloud Computing

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