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Modeling, Analysis, and Performance Evaluation of Wireless Ad Hoc Networks
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Modeling, Analysis, and Performance Evaluation of Wireless Ad Hoc Networks

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".

Deadline for manuscript submissions: closed (20 December 2022) | Viewed by 6099

Special Issue Editor

Prof. Dr. Hongju Cheng

E-Mail Website
Guest Editor
College of Mathematics and Computer Science, Fuzhou University, Fuzhou, China
Interests: mobile ad hoc networks; wireless sensor networks; wireless mesh networks

Special Issue Information

Dear Colleagues,

A wireless ad hoc network is built to enable many wireless devices connected without infrastructure equipment, such as a wireless router or unplanned access point. These special characteristics are very important in emergent or restricted situations. However, as such networks are increasingly complex, performance modeling and evaluation play a crucial part in their design process to ensure their successful deployment and exploitation in practice. There are also other challenging issues, such as mobility, security, and QoS performance, which shall be considered when designing wireless ad hoc networks, especially in the coming digital age.

The aim of this Special Issue is to share novel approaches for monitoring, measuring, modeling, optimizing, simulating, analyzing, and case studying the characteristics of ad hoc, sensor, pervasive, and ubiquitous networks, as well as exploring and developing new ad hoc networking protocols and tools. It aims to bring together researchers and industry professionals to report recent research advances on wireless ad hoc networks. We invite academia and industry to contribute to the latest research advances that help to solve the challenges associated with wireless ad hoc networks.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Infrastructure and system model of wireless ad hoc networks;
  • Performance analysis of wireless ad hoc networks;
  • Simulation and evaluation of wireless ad hoc networks;
  • Security in wireless ad hoc networks;
  • QoS supporting in wireless ad hoc networks;
  • Media access in wireless ad hoc networks;
  • Routing protocols in wireless ad hoc networks;
  • Traffic control in wireless ad hoc networks;
  • Coordination between wireless ad hoc networks and edge computing;
  • Data analysis in smart city, smart grid, and other applications.

We look forward to receiving your contributions.

Prof. Dr. Hongju Cheng
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

20 pages, 3909 KiB  
Article
IACRA: Lifetime Optimization by Invulnerability-Aware Clustering Routing Algorithm Using Game-Theoretic Approach for Wsns
by Jun Wang, Yadan Zhang, Chunyan Hu, Pengjun Mao and Bo Liu
Sensors 2022, 22(20), 7936; https://doi.org/10.3390/s22207936 - 18 Oct 2022
Cited by 4 | Viewed by 1336
Abstract
Energy limitation is one of the intrinsic shortcomings of wireless sensor networks (WSNs), although it has been widely applied in disaster response, battlefield surveillance, wildfire monitoring, radioactivity detection, etc. Due to the large amount of energy consumed for data transmission, how to prolong [...] Read more.
Energy limitation is one of the intrinsic shortcomings of wireless sensor networks (WSNs), although it has been widely applied in disaster response, battlefield surveillance, wildfire monitoring, radioactivity detection, etc. Due to the large amount of energy consumed for data transmission, how to prolong the network lifespan by designing various hierarchical routing protocols has attracted more and more attention. As a result, numerous achievements have emerged successively. However, these presented mechanisms can rarely guarantee the satisfactory quality of service (QoS), while lowering the energy cost level of WSNs. Meanwhile, invulnerability is undoubtedly an excellent quantitative index to assess QoS. Therefore, it is critical to develop a practical routing method to optimize network lifetime by considering both invulnerability and energy efficiency. Game theory is suitable for such a critical problem as it can be used in node or at network level to encourage the decision-making capabilities of WSNs. In this paper, a novel invulnerability-aware clustering routing algorithm (IACRA) using game-theoretic method is proposed to solve the predicament. The core features of the addressed game-theory-based routing protocol include integral invulnerability awareness, optimal cluster head selection in hierarchical routing, distance-aware cluster head discovery, and cluster rotation update mechanism for lifetime optimization. Particularly, the integral network invulnerability based on weighted fusion is constructed for further defining the profit model by combining the invulnerability indicators used to evaluate the local and whole network. Meanwhile, the optimal probability function of every node elected as CH in per cluster is established through the game between invulnerability and node energy consumption. In addition, the cluster update mechanism base on cluster rotation is proposed to avoid the rapid death of nodes with large energy consumption for maximizing network lifetime. The experimental results indicated a significant improvement in energy balance as well as in invulnerability compared with the other three kinds of well-known clustering routing protocols including GEEC (Game-theory-based energy efficient clustering routing protocol), HGTD (Hybrid, game-theory-based distributed clustering protocol), and EEGC (Efficient energy-aware and game-theory-based clustering protocol). Concretely, at the 400 communication rounds, the invulnerability of IACRA was higher than that of GEEC, HGTD, and EEGC by 77.56%, 29.45% and 15.90%, respectively, and the average residual energy of IACRA was 8.61%, 18.35% and 6.36% larger than that of GEEC, HGTD, and EEGC, respectively. Based on these results, the proposed protocol can be utilized to increase the capability of WSNs against deterioration of QoS and energy constraints. Full article
(This article belongs to the Special Issue Modeling, Analysis, and Performance Evaluation of Wireless Ad Hoc Networks)
►▼ Show Figures

Figure 1

Figure 1
<p>Flowchart of routing scheme.</p> Full article ">Figure 2
<p>Relationships between differing <span class="html-italic">e</span> and invulnerability under distinct attacks.</p> Full article ">Figure 3
<p>Effects of changing <span class="html-italic">k</span> on invulnerability for varied attacks.</p> Full article ">Figure 4
<p>Impacts of altering <span class="html-italic">r</span> on invulnerability for various attacks.</p> Full article ">Figure 5
<p>Influences of varying <span class="html-italic">N</span> on invulnerability for different attacks.</p> Full article ">Figure 6
<p>Relationships between differing <span class="html-italic">e</span> and average residual energy under distinct attacks.</p> Full article ">Figure 7
<p>Effects of changing <span class="html-italic">k</span> on average residual energy for varied attacks.</p> Full article ">Figure 8
<p>Impacts of altering <span class="html-italic">r</span> on average residual energy for various attacks.</p> Full article ">Figure 9
<p>Influences of varying <span class="html-italic">N</span> on average residual energy for different attacks.</p> Full article ">Figure 10
<p>Comparisons of invulnerability of IACRA, GEEC, HGTD, and EEGC.</p> Full article ">Figure 11
<p>Comparisons of average residual energy of IACRA, GEEC, HGTD, and EEGC.</p> Full article ">
24 pages, 2564 KiB  
Article
Traffic Aware Scheduler for Time-Slotted Channel-Hopping-Based IPv6 Wireless Sensor Networks
by Diana Deac, Eden Teshome, Roald Van Glabbeek, Virgil Dobrota, An Braeken and Kris Steenhaut
Sensors 2022, 22(17), 6397; https://doi.org/10.3390/s22176397 - 25 Aug 2022
Cited by 7 | Viewed by 1971
Abstract
Wireless sensor networks (WSNs) are becoming increasingly prevalent in numerous fields. Industrial applications and natural-disaster-detection systems need fast and reliable data transmission, and in several cases, they need to be able to cope with changing traffic conditions. Thus, time-slotted channel hopping (TSCH) offers [...] Read more.
Wireless sensor networks (WSNs) are becoming increasingly prevalent in numerous fields. Industrial applications and natural-disaster-detection systems need fast and reliable data transmission, and in several cases, they need to be able to cope with changing traffic conditions. Thus, time-slotted channel hopping (TSCH) offers high reliability and efficient power management at the medium access control (MAC) level; TSCH considers two dimensions, time and frequency when allocating communication resources. However, the scheduler, which decides where in time and frequency these communication resources are allotted, is not part of the standard. Orchestra has been proposed as a scheduler which allocates the communication resources based on information collected through the IPv6 routing protocol for low-power and lossy networks (RPL). Orchestra is a very elegant solution, but does not adapt to high traffic. This research aims to build an Orchestra-based scheduler for applications with unpredictable traffic bursts. The implemented scheduler allocates resources based on traffic congestion measured for the children of the root and RPL subtree size of the same nodes. The performance analysis of the proposed scheduler shows lower latency and higher packet delivery ratio (PDR) compared to Orchestra during bursts, with negligible impact outside them. Full article
(This article belongs to the Special Issue Modeling, Analysis, and Performance Evaluation of Wireless Ad Hoc Networks)
►▼ Show Figures

Figure 1

Figure 1
<p>Example of routing table created by the IPv6 routing protocol for low-power and lossy networks (RPL) in storing mode for node 3.</p> Full article ">Figure 2
<p>Example of scheduled Rx and Tx cells for nodes 1 to 6 in the time/channel offset space. The Rx cells are highlighted with red and the Tx cells with green. (<b>a</b>) Receiver-based shared (RBS) cell allocation. (<b>b</b>) Sender-based shared (SBS) cell allocation.</p> Full article ">Figure 3
<p>Overview of discussed schedulers.</p> Full article ">Figure 4
<p>Initialization phase: allocation of Rx and Tx slots. (<b>a</b>) Initialization phase step 1: Rx slots are allocated for each node. (<b>b</b>) Initialization phase step 2: Tx slots are allocated for children to communicate with parent. (<b>c</b>) Initialization phase step 3: Tx slots are allocated for the parent to communicate with children.</p> Full article ">Figure 5
<p>The structure of the DAO message and the RPL target option. The fields used in our implementation are marked. (<b>a</b>) Destination advertisement object (DAO) message structure. The <span class="html-italic">reserved</span> field is used for inserting the subtree size. (<b>b</b>) The structure of the RPL target option.</p> Full article ">Figure 6
<p>Example of allocation of Rx and Tx slots with the new scheduler. (<b>a</b>) The placement of nodes results in a topology with 3 children for the root. Each child has a different subtree size. (<b>b</b>) Allocation of Tx and Rx slots based on the topology in (<b>a</b>).</p> Full article ">Figure 7
<p>Summary of the allocation and deallocation phases for the root and its children. (<b>a</b>) The algorithm for allocating and deallocating Tx slots for the root’s children. (<b>b</b>) The algorithm for allocating and deallocating Rx slots for the root.</p> Full article ">Figure 8
<p>Possible placements of nodes. The green area represents the transmission and interference ranges, both equal to 50 m. The size of the grid is 200 m × 200 m. (<b>a</b>) Placement 1: the sink node is situated at the top of the grid. (<b>b</b>) Placement 2: the sink node is situated in the middle of the grid.</p> Full article ">Figure 9
<p>We observe that when it comes to average packet delivery ratio (PDR), the new scheduler performs better than Orchestra, in high traffic situations.</p> Full article ">Figure 10
<p>We observe that when it comes to average number of packets dropped from queues, the new scheduler performs better than Orchestra, in high traffic situation.</p> Full article ">Figure 11
<p>We observe that when it comes to average delay, the new scheduler performs better than Orchestra, in high traffic situation.</p> Full article ">Figure 12
<p>Average PDR for each sender node in low and high traffic situations. (<b>a</b>) We observe that in normal traffic situation, the average PDR is high for both schedulers. (<b>b</b>) We observe that in a high traffic situation for RBS Orchestra, the average PDR is lowest for the root’s children, being nodes 2–5.</p> Full article ">Figure 13
<p>Average delay for each sender node in low and high traffic situations. (<b>a</b>) We observe that in normal traffic situation, the average delay is comparable for the two schedulers. (<b>b</b>) We observe that in high traffic situation for RBS Orchestra the average delay is highest for the root’s children, being nodes 2–5.</p> Full article ">Figure 14
<p>We observe that when it comes to average radio duty cycle, the new scheduler performs better than Orchestra in a high-traffic situation.</p> Full article ">Figure 15
<p>We observe that when it comes to stability, the new scheduler performs better than Orchestra, in high traffic situations.</p> Full article ">Figure 16
<p>Comparison between OSCAR and the new scheduler considering different topology sizes. (<b>a</b>) We observe that the new scheduler has a higher PDR when the number of nodes is less than 100. (<b>b</b>) We observe that the new scheduler has a lower delay when the number of nodes is less than 80. (<b>c</b>) We observe that the new scheduler has a higher radio duty cycle.</p> Full article ">Figure 16 Cont.
<p>Comparison between OSCAR and the new scheduler considering different topology sizes. (<b>a</b>) We observe that the new scheduler has a higher PDR when the number of nodes is less than 100. (<b>b</b>) We observe that the new scheduler has a lower delay when the number of nodes is less than 80. (<b>c</b>) We observe that the new scheduler has a higher radio duty cycle.</p> Full article ">
19 pages, 1362 KiB  
Article
A Time-Driven Cloudlet Placement Strategy for Workflow Applications in Wireless Metropolitan Area Networks
by Jianshan Zhang, Ming Li, Xianghan Zheng and Ching-Hsien Hsu
Sensors 2022, 22(9), 3422; https://doi.org/10.3390/s22093422 - 29 Apr 2022
Cited by 4 | Viewed by 2119
Abstract
With the rapid development of mobile technology, mobile applications have increasing requirements for computational resources, and mobile devices can no longer meet these requirements. Mobile edge computing (MEC) has emerged in this context and has brought innovation into the working mode of traditional [...] Read more.
With the rapid development of mobile technology, mobile applications have increasing requirements for computational resources, and mobile devices can no longer meet these requirements. Mobile edge computing (MEC) has emerged in this context and has brought innovation into the working mode of traditional cloud computing. By provisioning edge server placement, the computing power of the cloud center is distributed to the edge of the network. The abundant computational resources of edge servers compensate for the lack of mobile devices and shorten the communication delay between servers and users. Constituting a specific form of edge servers, cloudlets have been widely studied within academia and industry in recent years. However, existing studies have mainly focused on computation offloading for general computing tasks under fixed cloudlet placement positions. They ignored the impact on computation offloading results from cloudlet placement positions and data dependencies among mobile application components. In this paper, we study the cloudlet placement problem based on workflow applications (WAs) in wireless metropolitan area networks (WMANs). We devise a cloudlet placement strategy based on a particle swarm optimization algorithm using genetic algorithm operators with the encoding library updating mode (PGEL), which enables the cloudlet to be placed in appropriate positions. The simulation results show that the proposed strategy can obtain a near-optimal cloudlet placement scheme. Compared with other classic algorithms, this algorithm can reduce the execution time of WAs by 15.04–44.99%. Full article
(This article belongs to the Special Issue Modeling, Analysis, and Performance Evaluation of Wireless Ad Hoc Networks)
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Figure 1

Figure 1
<p>Cloudlet placement and WA offloading in a WMAN.</p> Full article ">Figure 2
<p>Diagram of the WA.</p> Full article ">Figure 3
<p>Crossover operator in the personal cognitive update operation and the social cognitive update operation.</p> Full article ">Figure 4
<p>Mutation operator in the inertial update operation.</p> Full article ">Figure 5
<p>Traditional crossover operator. (The red parts indicate the crossover and adjustment, and the arrows indicate the crossover and adjustment operator.)</p> Full article ">Figure 6
<p>Traditional mutation operator. (The red parts indicate the mutation and adjustment, and the arrows indicate the mutation and adjustment operator.)</p> Full article ">Figure 7
<p>Comparison of local cloudlet placement results in typical WMANs. (<b>a</b>) Huangpu. (<b>b</b>) Xuhui. (<b>c</b>) Minhang. (<b>d</b>) Pudong.</p> Full article ">Figure 8
<p>Comparison of execution times of WAs in typical WMANs.</p> Full article ">Figure 9
<p>The impact of WMAN changes on the performance of PGEL. (<b>a</b>) APs’ topology changes. (<b>b</b>) CPU clock frequency changes. (<b>c</b>) WA changes.</p> Full article ">Figure 10
<p>Comparison of cloudlet placement. (<b>a</b>) Huangpu. (<b>b</b>) Xuhui. (<b>c</b>) Minhang. (<b>d</b>) Pudong.</p> Full article ">
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