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Record W2020263958 · doi:10.1002/wcm.1126

Architectures and protocols for wireless mesh, ad hoc, and sensor networks

2011· article· en· W2020263958 on OpenAlexaff
Farid Naït‐Abdesselam, Kwang‐Cheng Chen, Ehab S. Elmallah, Matthias Frank

Bibliographic record

VenueWireless Communications and Mobile Computing · 2011
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsComputer scienceWireless mesh networkWireless ad hoc networkComputer networkAd hoc wireless distribution serviceWireless sensor networkVehicular ad hoc networkKey distribution in wireless sensor networksWireless networkOptimized Link State Routing ProtocolTelecommunicationsWireless

Abstract

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Welcome to this special issue of the Wiley's Wireless Communications and Mobile Computing Journal. This special issue is devoted to the topic of the latest research and development in the field of wireless communications and networking. During the last few years, we have witnessed a tremendous interest from academia, industry, and standardization bodies in wireless mesh, ad hoc, and sensor networking. With several appealing characteristics, such as dynamic self-configuration, self-organization, self-healing, easy maintenance, and high scalability, wireless mesh networks have been prodded as a cost-effective approach to support high-speed last mile connectivity and ubiquitous broadband access in the context of home, enterprise, and community networking. Wireless ad hoc networks have also shown applications in a variety of situations, such as battlefields, disaster recovery/rescue operations, and entertainment. At the same time, wireless sensor networks are being deployed and actively researched for various forms of environmental monitoring, home automation, military, and civilian applications. Despite recent advances, and the technical accumulations from more than a decade's research effort in wireless networking, many research issues remain open in all protocol layers of wireless mesh, ad hoc, and sensor networks. For example, the foreseen multi-channel, multi-radio, and multi-antenna hybrid architectures (infrastructure and ad hoc) have brought new challenges in the design of physical, MAC, and routing protocols. New application scenarios are urging researchers to address enhanced quality of service support and various security issues in the design of different protocol layers for wireless mesh, ad hoc, and sensor networks. This special issue is dedicated to various aspects of wireless mesh, ad hoc, and sensor networks. We selected eight papers to show the recent advances in architectures and protocols. The papers cover both topical and innovative areas. A detailed overview of the selected papers is given below. In “Radio resource management of self-organizing OFDMA wireless mesh networks,” Chu et al. present a cognitive resource management system for wireless mesh networks with self-organizing base stations to optimize the available spectrum. The authors first present the concept of radio resource management based on an extensive literature survey and then highlight the challenges to achieve the best performance of such systems in future wireless mesh networks. In “IP address assignment in wireless mesh networks,” extending a work that received a best paper award in LCN 2008, Zimmermann et al. introduce and evaluate a novel auto-configuration protocol for wireless mesh networks, dubbed DWCP for Dynamic WMN Configuration Protocol. The proposed protocol deals with assigning unique addresses, managing the free and assigned addresses, reacting autonomously to failures and features support of conventional Dynamic Host Configuration Protocol (DHCP) clients. The proposed protocol has been deployed and evaluated in a real testbed and showed a good potential. In “CORE: centrally optimized routing extensions for efficient bandwidth management and network coding in the IEEE 802.16 MeSH mode,” Mogre et al. propose CORE, a new system that targets the optimization of routing, scheduling and bandwidth savings via network coding. Based on new heuristics, CORE has been evaluated through simulations and the obtained results demonstrated its performance. In “Study of Patching-based and Caching-based video-on-demand in mutli-hop WiMax mesh networks,” Xie et al. provide a study of two cross layer techniques, the Patching-based scheme and the Caching-based scheme, to provide video-on-demand in multi-hop WiMax mesh networks. Both approaches employ a novel joint solution of admission control and channel scheduling for video streams in the lower layers. This joint solution guarantees the data rate for the admitted video streams, which is crucial for real-time video streaming application. Their extensive simulations showed a good performance of this joint solution under real system settings. In “A security framework for wireless mesh networks,” Mogre et al. develop a holistic approach toward securing the wireless mesh networks with a particular focus on the network layer. They provide a set of solutions that guarantee the integrity and authenticity of routing messages, detect misbehaviors in forwarding data or routing messages, and dynamically manage reputation of nodes throughout the network. The combination of these building blocks enables a secure and self-organizing wireless mesh networks as they demonstrated it through their implementation and tests of a realistic IEEE 802.16 mesh network. In “M-DART: multi-path dynamic address routing,” Caleffi et al. propose a Distributed Hash Table (DHT)-based multi-path routing protocol for scalable ad hoc networks. The resulting protocol, based on the well known DART protocol with a multi-path routing extension, guarantees multi-path forwarding without introducing any additional communication or coordination overhead with respect to DART. The performance of M-DART has been evaluated by means of numerical simulations across a wide range of environments and workloads. The authors showed good results of the M-DART in comparison to the widely adopted routing protocols in all considered scenarios. In “SAUCeR: a QoS-aware slotted-aloha based UWB MAC with cooperative transmissions,” Tan et al. present a study of how cooperative communication can be applied to achieve differentiated QoS in a sensor network that uses Ultra-Wideband (UWB) as its underlying PHY layer technology. They present SAUCeR, a slotted-aloha based ultra-wideband medium access protocol with cooperative retransmissions that provides differentiated QoS with varying traffic classes. A QoS-aware cooperative retransmission technique and two distributed relay selection schemes are also introduced to improve overall traffic throughput and reduce end to end delays while preventing the starvation of any traffic class. In “A simple learning automata-based solution for intrusion detection in wireless sensor networks,” Misra et al. propose a simple, low complexity, and energy-aware protocol for intrusion detection in wireless sensor networks. The protocol is self-learning and distributed in nature. The distributed nature of the proposed protocol avoids all other nodes being sacrificed when a single node is compromised. The protocol juxtaposes the concept of stochastic learning automata on packet sampling mechanism to achieve energy-aware intrusion detection system. The authors have evaluated the performance of their protocol by performing a variety of experiments and found their solution very promising. Finally, we would like to express our gratitude to the Editor-in-Chief, Professor Mohsen Guizani, for his advices patience, and encouragements since the beginning until the final stage. We thank the Steering Committee of the IEEE LCN for their support and encouragement in preparing this special issue, and all the anonymous reviewers who spent much of their precious time reviewing the papers. Their timely reviews and comments greatly helped us select the best papers in this special issue. We also thank all authors who have submitted their papers for consideration in this issue. A special thank goes to Ms. Jovelyne P. Sotoya who made a great effort on the production of this issue. We hope you will enjoy reading the selection of papers in this issue.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.990
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.037
GPT teacher head0.288
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2011
Admission routes1
Has abstractyes

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