Differential Evolution-Based 3-D Directional Wireless Sensor Network Deployment Optimization

Cao, Bin; Kang, Xinyuan; Zhao, Jianwei; Yang, Po; Lv, Zhihan; Liu, Xin

doi:10.1109/jiot.2018.2801623

Cited by 31 publications

(17 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A promising way to collect such a huge volume of data in IoVs is through mobile crowdsensing (MCS) [3]- [11], which outsources the sensing tasks to the sensors of vehicles. MCS usually involves an IoT center for receiving data collection requests and delegating the data sensing tasks to the participating devices like the vehicles in the IoV environment.…”

Section: An Efficient Collaboration and Incentive Mechanismmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Collaboration and Incentive Mechanism for Internet of Vehicles (IoV) With Secured Information Exchange Based on Blockchains

Yin

et al. 2020

IEEE Internet Things J.

View full text Add to dashboard Cite

He received the Ph.D. degree in Computing and Mathematics and the B.Sc. degree (1st Class Hons.) in Computer Science from the University of Bradford, United Kingdom, in 2010 and 2006, respectively. His expertise is on networking and his main research interests include intelligent networking technologies, network slicing and softwarization, future Internet architecture and technologies, Green networking, wireless networks, network security and privacy, and analytical modelling and performance optimization. He is an Editor of IEEE Transactions on Network and Service Management, Elsevier Computer Networks and IEEE Access. He contributes to major conferences on networking as various roles including a Steering Committee Chair, a General Chair, a Program Chair, and a Technical Program Committee Member.

show abstract

Section: An Efficient Collaboration and Incentive Mechanismmentioning

confidence: 99%

“…The starting time of task T j is the same as Equation (10). The starting time of T 0 is changed because of inserting the task T j before it, and it is shown in Equation (11).…”

Section: Task Processing Timementioning

confidence: 99%

An Efficient Collaboration and Incentive Mechanism for Internet of Vehicles (IoV) With Secured Information Exchange Based on Blockchains

Yin

et al. 2020

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…An example of a multi-objective problem is the case where DE was employed to optimize resource utilization, energy consumption and data transmission with security controls as constraints [31]. The multi-objective problem is known as deployment issues which include variables such as connectivity, coverage, lifetime, clustering and reliability [32], construction cost and total head loss in the network [33].…”

Section: Differential Evolution In Wireless Communicationmentioning

confidence: 99%

Quantile Approximation of the Erlang Distribution using Differential Evolution Algorithm

Okagbue¹

2020

IJATCSE

View full text Add to dashboard Cite

Erlang distribution is a particular case of the gamma distribution and is often used in modeling queues, traffic congestion in wireless sensor networks, cell residence duration and finding the optimal queueing model to reduce the probability of blocking. The application is limited because of the unavailability of closed-form expression for the quantile (inverse cumulative distribution) function of the distribution. The problem is primarily tackled using approximation since the inversion method cannot be applied. This paper extended a six parameter quantile model earlier proposed to the Nakagami distribution to the Erlang distributions. Consequently, the established relationship between the two distributions is now extended to their quantile functions. The quantile model was used to fit the machine (R software) values with their corresponding quartiles in two ways. Firstly, artificial neural network (ANN) was used to establish that a curve fitting can be achieved. Lastly, differential evolution (DE) algorithm was used to minimize the errors obtained from the curve fitting and hence estimate the values of the six parameters of the quantile model that will ensure the best possible fit, for different values of the parameters that characterize Erlang distribution. Hence, the problem is constrained optimization in nature and the DE algorithm was able to find the different values of the parameters of the quantile model. The simulation result corroborates theoretical findings. The work is a welcome result for the quest for a universal quantile model that can be applied to different distributions.

show abstract

“…In [13,18,19], the authors have better solved the problem of mobile energy consumption, but these are based on 2D plane verification and are not suitable for 3D environments. Therefore, the research of the literature [20][21][22] has successively proposed the orientation sensor model and algorithm for the 3D coordinate system. For example, the authors studied the low-power green communication of 3D DSNs and proposed the space-time coverage optimisation scheduling (STCOS) algorithm to obtain the maximum network coverage in [21].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Adjustment Optimisation Algorithm in 3D Directional Sensor Networks Based on Spherical Sector Coverage Models

2019

View full text Add to dashboard Cite

In directional sensor networks research, target event detection is currently an active research area, with applications in underwater target monitoring, forest fire warnings, border areas, and other important activities. Previous studies have often discussed target coverage in two-dimensional sensor networks, but these studies cannot be extensively applied to three-dimensional networks. Additionally, most of the previous target coverage detection models are based on a circular or omnidirectional sensing model. More importantly, if the directional sensor network does not design a better coverage algorithm in the coverage-monitoring process, its nodes’ energy consumption will increase and the network lifetime will be significantly shortened. With the objective of addressing three-dimensional target coverage in applications, this study proposes a dynamic adjustment optimisation algorithm for three-dimensional directional sensor networks based on a spherical sector coverage model, which improves the lifetime and coverage ratio of the network. First, we redefine the directional nodes’ sensing model and use the three-dimensional Voronoi method to divide the regions where the nodes are located. Then, we introduce a correlation force between the target and the sensor node to optimise the algorithm’s coverage mechanism, so that the sensor node can accurately move to the specified position for target coverage. Finally, by verifying the feasibility and accuracy of the proposed algorithm, the simulation experiments demonstrate that the proposed algorithm can effectively improve the network coverage and node utilisation.

show abstract

Differential Evolution-Based 3-D Directional Wireless Sensor Network Deployment Optimization

Cited by 31 publications

References 43 publications

An Efficient Collaboration and Incentive Mechanism for Internet of Vehicles (IoV) With Secured Information Exchange Based on Blockchains

An Efficient Collaboration and Incentive Mechanism for Internet of Vehicles (IoV) With Secured Information Exchange Based on Blockchains

Quantile Approximation of the Erlang Distribution using Differential Evolution Algorithm

Dynamic Adjustment Optimisation Algorithm in 3D Directional Sensor Networks Based on Spherical Sector Coverage Models

Contact Info

Product

Resources

About