Research

18 pages, 4002 KiB

Open AccessArticle

Meta-Heuristic Device-Free Localization Algorithm under Multiple Path Effect

by Huajing Li, Ning Li, Yan Guo, Hao Yuan and Binghan Lei

Entropy 2023, 25(7), 1025; https://doi.org/10.3390/e25071025 - 05 Jul 2023

Viewed by 719

In the scenario of device-free localization under multiple effects, the accuracy of localization based on compressed sensing theory is severely affected. Most existing localization techniques directly ignore multiple path effects. However, it is not practical to ignore the multiple path effect due to [...] Read more.

In the scenario of device-free localization under multiple effects, the accuracy of localization based on compressed sensing theory is severely affected. Most existing localization techniques directly ignore multiple path effects. However, it is not practical to ignore the multiple path effect due to its high signal strength, which can provide localization information. In this paper, we formulate the sensing matrix optimization problem in compressed sensing for device-free localization scenarios based on multiple reflections. To solve this problem, we model it as a constrained combinatorial optimization problem and propose a hybrid meta-heuristic algorithm. First, smart reflection surfaces and virtual node models are used to construct the desired communication links. Second, we iteratively improve the properties of the measurement matrix by using K-means clustering to obtain reasonable thresholds, and use a meta-heuristic algorithm to optimize the sensing matrix. Finally, the simulation results show that the proposed method efficiently optimizes the sensing matrix and achieves fast and high-precision localization while conserving communication resources. Full article

(This article belongs to the Special Issue Wireless Sensor Networks and Their Applications)

► Show Figures

Figure 1

20 pages, 5822 KiB

Open AccessArticle

An SSL-PUF Based Access Authentication and Key Distribution Scheme for the Space–Air–Ground Integrated Network

by Liwei Xu, Han Wu, Jianguo Xie, Qiong Yuan, Ying Sun, Guozhen Shi and Shoushan Luo

Entropy 2023, 25(5), 760; https://doi.org/10.3390/e25050760 - 06 May 2023

Cited by 2 | Viewed by 1280

Abstract

The Space–Air–Ground Integrated Network (SAGIN) expands cyberspace greatly. Dynamic network architecture, complex communication links, limited resources, and diverse environments make SAGIN’s authentication and key distribution much more difficult. Public key cryptography is a better choice for terminals to access SAGIN dynamically, but it [...] Read more.

The Space–Air–Ground Integrated Network (SAGIN) expands cyberspace greatly. Dynamic network architecture, complex communication links, limited resources, and diverse environments make SAGIN’s authentication and key distribution much more difficult. Public key cryptography is a better choice for terminals to access SAGIN dynamically, but it is time-consuming. The semiconductor superlattice (SSL) is a strong Physical Unclonable Function (PUF) to be the hardware root of security, and the matched SSL pairs can achieve full entropy key distribution through an insecure public channel. Thus, an access authentication and key distribution scheme is proposed. The inherent security of SSL makes the authentication and key distribution spontaneously achieved without a key management burden and solves the assumption that excellent performance is based on pre-shared symmetric keys. The proposed scheme achieves the intended authentication, confidentiality, integrity, and forward security, which can defend against masquerade attacks, replay attacks, and man-in-the-middle attacks. The formal security analysis substantiates the security goal. The performance evaluation results confirm that the proposed protocols have an obvious advantage over the elliptic curve or bilinear pairings-based protocols. Compared with the protocols based on the pre-distributed symmetric key, our scheme shows unconditional security and dynamic key management with the same level performance. Full article

(This article belongs to the Special Issue Wireless Sensor Networks and Their Applications)

► Show Figures

Figure 1

13 pages, 1413 KiB

Open AccessArticle

Performance Analysis of IEEE 802.11p MAC with Considering Capture Effect under Nakagami-m Fading Channel in VANETs

by Yang Wang, Jianghong Shi and Lingyu Chen

Entropy 2023, 25(2), 218; https://doi.org/10.3390/e25020218 - 22 Jan 2023

Cited by 3 | Viewed by 1284

Abstract

Vehicular ad hoc networks (VANETs) have recently drawn a large amount of attention because of their enormous potential in road safety improvement and traffic management as well as infotainment service support. As the standard of medium access control (MAC) and physical (PHY) layers [...] Read more.

Vehicular ad hoc networks (VANETs) have recently drawn a large amount of attention because of their enormous potential in road safety improvement and traffic management as well as infotainment service support. As the standard of medium access control (MAC) and physical (PHY) layers for VANETs, IEEE 802.11p has been proposed for more than a decade. Though performance analyses of IEEE 802.11p MAC have been performed, the existing analytical methods still need to be improved. In this paper, to assess the saturated throughput and the average packet delay of IEEE 802.11p MAC in VANETs, a two-dimensional (2-D) Markov model is introduced by considering the capture effect under Nakagami-m fading channel. Moreover, the closed-form expressions of successful transmission, collided transmission, saturated throughput, and average packet delay are carefully derived. Finally, the simulation results are demonstrated to verify the accuracy of the proposed analytical model, which also proves that this analytical model is more precise than the existing ones in terms of saturated throughput and average packet delay. Full article

(This article belongs to the Special Issue Wireless Sensor Networks and Their Applications)

► Show Figures

Figure 1

18 pages, 4346 KiB

Open AccessArticle

Improving Localization Accuracy under Constrained Regions in Wireless Sensor Networks through Geometry Optimization

by Xinpeng Fang, Zhihao He, Shouxu Zhang, Junbing Li and Ranjun Shi

Entropy 2023, 25(1), 32; https://doi.org/10.3390/e25010032 - 23 Dec 2022

Viewed by 1228

Abstract

In addition to various estimation algorithms, the target localization accuracy in wireless sensor networks (WSNs) can also be improved from the perspective of geometry optimization. Note that existing placement strategies are mainly aimed at unconstrained deployment regions, i.e., the positions of sensors are [...] Read more.

In addition to various estimation algorithms, the target localization accuracy in wireless sensor networks (WSNs) can also be improved from the perspective of geometry optimization. Note that existing placement strategies are mainly aimed at unconstrained deployment regions, i.e., the positions of sensors are arbitrary. In this paper, considering factors such as terrain, communication, and security, the optimal range-based sensor geometries under circular deployment region and minimum safety distance constraints are proposed. The geometry optimization problem is modeled as a constrained optimization problem, with a D-optimality-based (maximizing the determinant of FIM matrix) scalar function as the objective function and the irregular feasible deployment regions as the constraints. We transform the constrained optimization problem into an equivalent form using the introduced maximum feasible angle and separation angle, and discuss the optimal geometries based on the relationship between the minimum safety distance and the maximum feasible angle. We first consider optimal geometries for two and three sensors in the localization system, and then use their findings to extend the study to scenarios with arbitrary numbers of sensors and arbitrarily shaped feasible regions. Numerical simulation results are included to verify the theoretical conclusions. Full article

(This article belongs to the Special Issue Wireless Sensor Networks and Their Applications)

► Show Figures

Figure 1

17 pages, 2041 KiB

Open AccessArticle

A Parallelizable Task Offloading Model with Trajectory-Prediction for Mobile Edge Networks

by Pu Han, Lin Han, Bo Yuan, Jeng-Shyang Pan and Jiandong Shang

Entropy 2022, 24(10), 1464; https://doi.org/10.3390/e24101464 - 14 Oct 2022

Viewed by 1074

Abstract

As an emerging computing model, edge computing greatly expands the collaboration capabilities of the servers. It makes full use of the available resources around the users to quickly complete the task request coming from the terminal devices. Task offloading is a common solution [...] Read more.

As an emerging computing model, edge computing greatly expands the collaboration capabilities of the servers. It makes full use of the available resources around the users to quickly complete the task request coming from the terminal devices. Task offloading is a common solution for improving the efficiency of task execution on edge networks. However, the peculiarities of the edge networks, especially the random access of mobile devices, brings unpredictable challenges to the task offloading in a mobile edge network. In this paper, we propose a trajectory prediction model for moving targets in edge networks without users’ historical paths which represents their habitual movement trajectory. We also put forward a mobility-aware parallelizable task offloading strategy based on a trajectory prediction model and parallel mechanisms of tasks. In our experiments, we compared the hit ratio of the prediction model, network bandwidth and task execution efficiency of the edge networks by using the EUA data set. Experimental results showed that our model is much better than random, non-position prediction parallel, non-parallel strategy-based position prediction. Where the task offloading hit rate is closed to the user’s moving speed, when the speed is less 12.96 m/s, the hit rate can reach more than 80%. Meanwhile, we we also find that the bandwidth occupancy is significantly related to the degree of task parallelism and the number of services running on servers in the network. The parallel strategy can boost network bandwidth utilization by more than eight times when compared to a non-parallel policy as the number of parallel activities grows. Full article

(This article belongs to the Special Issue Wireless Sensor Networks and Their Applications)

► Show Figures

Figure 1

17 pages, 3098 KiB

Open AccessArticle

A Clustering Scheme Based on the Binary Whale Optimization Algorithm in FANET

by Yonghang Yan, Xuewen Xia, Lingli Zhang, Zhijia Li and Chunbin Qin

Entropy 2022, 24(10), 1366; https://doi.org/10.3390/e24101366 - 27 Sep 2022

Cited by 3 | Viewed by 1552

Abstract

With the continuous development of Unmanned Aerial Vehicle (UAV) technology, UAVs are widely used in military and civilian fields. Multi-UAV networks are often referred to as flying ad hoc networks (FANET). Dividing multiple UAVs into clusters for management can reduce energy consumption, maximize [...] Read more.

With the continuous development of Unmanned Aerial Vehicle (UAV) technology, UAVs are widely used in military and civilian fields. Multi-UAV networks are often referred to as flying ad hoc networks (FANET). Dividing multiple UAVs into clusters for management can reduce energy consumption, maximize network lifetime, and enhance network scalability to a certain extent, so UAV clustering is an important direction for UAV network applications. However, UAVs have the characteristics of limited energy resources and high mobility, which bring challenges to UAV cluster communication networking. Therefore, this paper proposes a clustering scheme for UAV clusters based on the binary whale optimization (BWOA) algorithm. First, the optimal number of clusters in the network is calculated based on the network bandwidth and node coverage constraints. Then, the cluster heads are selected based on the optimal number of clusters using the BWOA algorithm, and the clusters are divided based on the distance. Finally, the cluster maintenance strategy is set to achieve efficient maintenance of clusters. The experimental simulation results show that the scheme has better performance in terms of energy consumption and network lifetime compared with the BPSO and K-means-based schemes. Full article

(This article belongs to the Special Issue Wireless Sensor Networks and Their Applications)

► Show Figures

Figure 1

19 pages, 1971 KiB

Open AccessArticle

Optimal Maneuvering for Autonomous Vehicle Self-Localization

by John L. McGuire, Yee Wei Law, Kutluyıl Doğançay, Sook-Ying Ho and Javaan Chahl

Entropy 2022, 24(8), 1169; https://doi.org/10.3390/e24081169 - 22 Aug 2022

Cited by 3 | Viewed by 1494

Abstract

We consider the problem of optimal maneuvering, where an autonomous vehicle, an unmanned aerial vehicle (UAV) for example, must maneuver to maximize or minimize an objective function. We consider a vehicle navigating in a Global Navigation Satellite System (GNSS)-denied environment that self-localizes in [...] Read more.

We consider the problem of optimal maneuvering, where an autonomous vehicle, an unmanned aerial vehicle (UAV) for example, must maneuver to maximize or minimize an objective function. We consider a vehicle navigating in a Global Navigation Satellite System (GNSS)-denied environment that self-localizes in two dimensions using angle-of-arrival (AOA) measurements from stationary beacons at known locations. The objective of the vehicle is to travel along the path that minimizes its position and heading estimation error. This article presents an informative path planning (IPP) algorithm that (i) uses the determinant of the self-localization estimation error covariance matrix of an unscented Kalman filter as the objective function; (ii) applies an l-step look-ahead (LSLA) algorithm to determine the optimal heading for a constant-speed vehicle. The novel algorithm takes into account the kinematic constraints of the vehicle and the AOA means of measurement. We evaluate the performance of the algorithm in five scenarios involving stationary and mobile beacons and we find the estimation error approaches the lower bound for the estimator. The simulations show the vehicle maneuvers to locations that allow for minimum estimation uncertainty, even when beacon placement is not conducive to accurate estimation. Full article

(This article belongs to the Special Issue Wireless Sensor Networks and Their Applications)

► Show Figures

Figure 1

14 pages, 885 KiB

Open AccessArticle

Gaussian-Based Adaptive Fish Migration Optimization Applied to Optimization Localization Error of Mobile Sensor Networks

by Yong Liu, Wei-Min Zheng, Shangkun Liu and Qing-Wei Chai

Entropy 2022, 24(8), 1109; https://doi.org/10.3390/e24081109 - 12 Aug 2022

Cited by 5 | Viewed by 1225

Abstract

Location information is the primary feature of wireless sensor networks, and it is more critical for Mobile Wireless Sensor Networks (MWSN) to monitor specific targets. How to improve the localization accuracy is a challenging problem for researchers. In this paper, the Gaussian probability [...] Read more.

Location information is the primary feature of wireless sensor networks, and it is more critical for Mobile Wireless Sensor Networks (MWSN) to monitor specific targets. How to improve the localization accuracy is a challenging problem for researchers. In this paper, the Gaussian probability distribution model is applied to randomize the individual during the migration of the Adaptive Fish Migration Optimization (AFMO) algorithm. The performance of the novel algorithm is verified by the CEC 2013 test suit, and the result is compared with other famous heuristic algorithms. Compared to other well-known heuristics, the new algorithm achieves the best results in almost 21 of all 28 test functions. In addition, the novel algorithm significantly reduces the localization error of MWSN, the simulation results show that the accuracy of the new algorithm is more than 5% higher than that of other heuristic algorithms in terms of mobile sensor node positioning, and more than 100% higher than that without the heuristic algorithm. Full article

(This article belongs to the Special Issue Wireless Sensor Networks and Their Applications)

► Show Figures

Figure 1

22 pages, 4814 KiB

Open AccessArticle

An Optimal WSN Node Coverage Based on Enhanced Archimedes Optimization Algorithm

by Thi-Kien Dao, Shu-Chuan Chu, Trong-The Nguyen, Trinh-Dong Nguyen and Vinh-Tiep Nguyen

Entropy 2022, 24(8), 1018; https://doi.org/10.3390/e24081018 - 23 Jul 2022

Cited by 12 | Viewed by 1806

Abstract

Node coverage is one of the crucial metrics for wireless sensor networks’ (WSNs’) quality of service, directly affecting the target monitoring area’s monitoring capacity. Pursuit of the optimal node coverage encounters increasing difficulties because of the limited computational power of individual nodes, the [...] Read more.

Node coverage is one of the crucial metrics for wireless sensor networks’ (WSNs’) quality of service, directly affecting the target monitoring area’s monitoring capacity. Pursuit of the optimal node coverage encounters increasing difficulties because of the limited computational power of individual nodes, the scale of the network, and the operating environment’s complexity and constant change. This paper proposes a solution to the optimal node coverage of unbalanced WSN distribution during random deployment based on an enhanced Archimedes optimization algorithm (EAOA). The best findings for network coverage from several sub-areas are combined using the EAOA. In order to address the shortcomings of the original Archimedes optimization algorithm (AOA) in handling complicated scenarios, we suggest an EAOA based on the AOA by adapting its equations with reverse learning and multidirection techniques. The obtained results from testing the benchmark function and the optimal WSN node coverage of the EAOA are compared with the other algorithms in the literature. The results show that the EAOA algorithm performs effectively, increasing the feasible range and convergence speed. Full article

(This article belongs to the Special Issue Wireless Sensor Networks and Their Applications)

► Show Figures

Figure 1

25 pages, 1495 KiB

Open AccessArticle

Energy-Efficient Clustering Mechanism of Routing Protocol for Heterogeneous Wireless Sensor Network Based on Bamboo Forest Growth Optimizer

by Qing Feng, Shu-Chuan Chu, Jeng-Shyang Pan, Jie Wu and Tien-Szu Pan

Entropy 2022, 24(7), 980; https://doi.org/10.3390/e24070980 - 15 Jul 2022

Cited by 13 | Viewed by 1499

Abstract

In wireless sensor networks (WSN), most sensor nodes are powered by batteries with limited power, meaning the quality of the network may deteriorate at any time. Therefore, to reduce the energy consumption of sensor nodes and extend the lifetime of the network, this [...] Read more.

In wireless sensor networks (WSN), most sensor nodes are powered by batteries with limited power, meaning the quality of the network may deteriorate at any time. Therefore, to reduce the energy consumption of sensor nodes and extend the lifetime of the network, this study proposes a novel energy-efficient clustering mechanism of a routing protocol. First, a novel metaheuristic algorithm is proposed, based on differential equations of bamboo growth and the Gaussian mixture model, called the bamboo growth optimizer (BFGO). Second, based on the BFGO algorithm, a clustering mechanism of a routing protocol (BFGO-C) is proposed, in which the encoding method and fitness function are redesigned. It can maximize the energy efficiency and minimize the transmission distance. In addition, heterogeneous nodes are added to the WSN to distinguish tasks among nodes and extend the lifetime of the network. Finally, this paper compares the proposed BFGO-C with three classic clustering protocols. The results show that the protocol based on the BFGO-C can be successfully applied to the clustering routing protocol and can effectively reduce energy consumption and enhance network performance. Full article

(This article belongs to the Special Issue Wireless Sensor Networks and Their Applications)

► Show Figures

Figure 1

18 pages, 394 KiB

Open AccessArticle

Outage Performance of Wireless Powered Decode-and-Forward Relaying Networks in Rician Fading

by Zhifei Zhang, Peng Dong, Xinlu Tan, Yaping Li and Ke Xiong

Entropy 2022, 24(6), 763; https://doi.org/10.3390/e24060763 - 28 May 2022

Cited by 1 | Viewed by 1455

Abstract

This paper investigates the outage performance of simultaneous wireless information and power transfer (SWIPT)-enabled relay networks with the decode-and-forward relaying protocol, where the effect of the energy triggering threshold at the relay on the system performance is considered. The closed-form expressions of the [...] Read more.

This paper investigates the outage performance of simultaneous wireless information and power transfer (SWIPT)-enabled relay networks with the decode-and-forward relaying protocol, where the effect of the energy triggering threshold at the relay on the system performance is considered. The closed-form expressions of the system outage probability and throughput are derived in Rician channel fading. Monte Carlo Simulation method is used to verify the accuracy of the derived closed-form expressions. The effects of some system parameters on the system performances are discussed via simulations, which show that the system outage probability increases with the increase of the minimum transmission rate required by the users and also decreases with the increase of the energy conversion efficiency. Besides, the system throughput increaseswith the increment of the transmit power of the source node, as well as the energy conversion efficiency. Additionally, the outage performance of the system with the equal two-hop distance is better than that of the system with unequal two-hop distance. Full article

(This article belongs to the Special Issue Wireless Sensor Networks and Their Applications)

► Show Figures

Figure 1

18 pages, 2887 KiB

Open AccessArticle

Task Offloading Strategy Based on Mobile Edge Computing in UAV Network

by Wei Qi, Hao Sun, Lichen Yu, Shuo Xiao and Haifeng Jiang

Entropy 2022, 24(5), 736; https://doi.org/10.3390/e24050736 - 22 May 2022

Cited by 6 | Viewed by 2149

Abstract

When an unmanned aerial vehicle (UAV) performs tasks such as power patrol inspection, water quality detection, field scientific observation, etc., due to the limitations of the computing capacity and battery power, it cannot complete the tasks efficiently. Therefore, an effective method is to [...] Read more.

When an unmanned aerial vehicle (UAV) performs tasks such as power patrol inspection, water quality detection, field scientific observation, etc., due to the limitations of the computing capacity and battery power, it cannot complete the tasks efficiently. Therefore, an effective method is to deploy edge servers near the UAV. The UAV can offload some of the computationally intensive and real-time tasks to edge servers. In this paper, a mobile edge computing offloading strategy based on reinforcement learning is proposed. Firstly, the Stackelberg game model is introduced to model the UAV and edge nodes in the network, and the utility function is used to calculate the maximization of offloading revenue. Secondly, as the problem is a mixed-integer non-linear programming (MINLP) problem, we introduce the multi-agent deep deterministic policy gradient (MADDPG) to solve it. Finally, the effects of the number of UAVs and the summation of computing resources on the total revenue of the UAVs were simulated through simulation experiments. The experimental results show that compared with other algorithms, the algorithm proposed in this paper can more effectively improve the total benefit of UAVs. Full article

(This article belongs to the Special Issue Wireless Sensor Networks and Their Applications)

► Show Figures

Figure 1

18 pages, 616 KiB

Open AccessArticle

A Mahalanobis Surrogate-Assisted Ant Lion Optimization and Its Application in 3D Coverage of Wireless Sensor Networks

by Zhi Li, Shu-Chuan Chu, Jeng-Shyang Pan, Pei Hu and Xingsi Xue

Entropy 2022, 24(5), 586; https://doi.org/10.3390/e24050586 - 22 Apr 2022

Cited by 5 | Viewed by 1610

Abstract

Metaheuristic algorithms are widely employed in modern engineering applications because they do not need to have the ability to study the objective function’s features. However, these algorithms may spend minutes to hours or even days to acquire one solution. This paper presents a [...] Read more.

Metaheuristic algorithms are widely employed in modern engineering applications because they do not need to have the ability to study the objective function’s features. However, these algorithms may spend minutes to hours or even days to acquire one solution. This paper presents a novel efficient Mahalanobis sampling surrogate model assisting Ant Lion optimization algorithm to address this problem. For expensive calculation problems, the optimization effect goes even further by using MSAALO. This model includes three surrogate models: the global model, Mahalanobis sampling surrogate model, and local surrogate model. Mahalanobis distance can also exclude the interference correlations of variables. In the Mahalanobis distance sampling model, the distance between each ant and the others could be calculated. Additionally, the algorithm sorts the average length of all ants. Then, the algorithm selects some samples to train the model from these Mahalanobis distance samples. Seven benchmark functions with various characteristics are chosen to testify to the effectiveness of this algorithm. The validation results of seven benchmark functions demonstrate that the algorithm is more competitive than other algorithms. The simulation results based on different radii and nodes show that MSAALO improves the average coverage by 2.122% and 1.718%, respectively. Full article

(This article belongs to the Special Issue Wireless Sensor Networks and Their Applications)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Wireless Sensor Networks and Their Applications

Share This Special Issue

Special Issue Editors

Special Issue Information

Published Papers (13 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI