Self-Adapting MAC Layer for Wireless Sensor Networks

Sha, Mo; Dor, Rahav; Hackmann, Gregory; Lu, Chenyang; Kim, Tae-Suk; Park, Taerim

doi:10.1109/rtss.2013.27

Cited by 31 publications

(25 citation statements)

References 23 publications

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“…NEURAL NETWORK [79] [23], [99], [100] [26] [29], [30] [35], [36] SUPPORT VECTOR MACHINES [49], [50], [51] [33], [34], [46], [47], [48] DECISION TREE [80] [101], [102] [27] [115] [37] GAUSSIAN PROCESSES [81], [82] [103], [104] [127]…”

Section: Discussionmentioning

confidence: 99%

“…This introduces new design challenges that are related to the dynamic communication patterns and service requirements over time. Sha et al [115] studied this problem at the MAC layer, hence proposing the "Self-Adapting MAC Layer" (SAML) design. SAML is composed of two main components: The "Reconfigurable MAC Architecture" (RMA) to switch between the different MAC protocols, and the MAC engine that is used to learn the suitable MAC protocol for the current network conditions.…”

Section: ) Adaptive Mac Layermentioning

confidence: 99%

“…The learning features of the decision tree are: inter packet interval (IPI) and received signal strength indication (RSSI) statistical parameters (i.e., the mean and the variance), the application QoS requirements (reliability, energy usage, Fig. 13: The decision tree classifier used to select the optimal MAC algorithm in the SAML architecture [115]. and latency), packet delivery rate (PDR), and the traffic pattern.…”

Section: ) Adaptive Mac Layermentioning

confidence: 99%

See 2 more Smart Citations

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Alsheikh

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Niyato

et al. 2014

IEEE Commun. Surv. Tutorials

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Abstract-Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.

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Section: Discussionmentioning

confidence: 99%

Section: ) Adaptive Mac Layermentioning

confidence: 99%

Section: ) Adaptive Mac Layermentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Alsheikh

Lin

Niyato

et al. 2014

IEEE Commun. Surv. Tutorials

788

406

View full text Add to dashboard Cite

show abstract

“…Studies on addressing external disturbances are relatively few and mostly rely on centralized decision making. The approach in [11] stores a predetermined number of link layer schedules in the system and chooses the appropriate one when disturbances are detected. However, this approach is either incapable of handling arbitrary 1.…”

Section: Related Workmentioning

confidence: 99%

Fully Distributed Packet Scheduling Framework for Handling Disturbances in Lossy Real-Time Wireless Networks

Zhang

Gong

Han

et al. 2021

IEEE Trans. on Mobile Comput.

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Along with the rapid growth of Industrial Internet-of-Things (IIoT) applications and their penetration into many industry sectors, real-time wireless networks (RTWNs) have been playing a more critical role in providing real-time, reliable and secure communication services for such applications. A key challenge in RTWN management is how to ensure real-time Quality of Services (QoS) especially in the presence of unexpected disturbances and lossy wireless links. Most prior work takes centralized approaches for handling disturbances, which are slow and subject to single-point failure, and do not scale. To overcome these drawbacks, this paper presents a fully distributed packet scheduling framework called FD-PaS. FD-PaS aims to provide guaranteed fast response to unexpected disturbances while achieving minimum performance degradation for meeting the timing and reliability requirements of all critical tasks. To combat the scalability challenge, FD-PaS incorporates several key advances in both algorithm design and data link layer protocol design to enable individual nodes to make on-line decisions locally without any centralized control. Our extensive simulation and testbed results have validated the correctness of the FD-PaS design and demonstrated its effectiveness in providing fast response for handling disturbances while ensuring the designated QoS requirements.

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“…Over the last years, it has gained popularity due to its success in enhancing network-wide performance ( i.e ., the Quality of Service, QoS) [ 26 ], facilitating intelligent behavior by adapting to complex and dynamically changing (wireless) environments [ 27 ] and its ability to add automation for realizing concepts of self-healing and self-optimization [ 28 ]. During the past years, different learning approaches have been applied in various wireless networks schemes such as medium access control [ 29 , 30 ], routing [ 9 , 10 ], data aggregation and clustering [ 31 , 32 ], localization [ 33 , 34 ], energy harvesting communication [ 35 ], cognitive radio [ 36 , 37 ], etc . These schemes apply to a variety of wireless networks such as: mobile ad hoc networks [ 38 ], wireless sensor networks [ 18 ], wireless body area networks [ 39 ], cognitive radio networks [ 20 , 40 ] and cellular networks [ 41 ].…”

Section: Introduction To Data Science In Wireless Networkmentioning

confidence: 99%

Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial

Kulin

Fortuna

Poorter

et al. 2016

Sensors

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Data science or “data-driven research” is a research approach that uses real-life data to gain insight about the behavior of systems. It enables the analysis of small, simple as well as large and more complex systems in order to assess whether they function according to the intended design and as seen in simulation. Data science approaches have been successfully applied to analyze networked interactions in several research areas such as large-scale social networks, advanced business and healthcare processes. Wireless networks can exhibit unpredictable interactions between algorithms from multiple protocol layers, interactions between multiple devices, and hardware specific influences. These interactions can lead to a difference between real-world functioning and design time functioning. Data science methods can help to detect the actual behavior and possibly help to correct it. Data science is increasingly used in wireless research. To support data-driven research in wireless networks, this paper illustrates the step-by-step methodology that has to be applied to extract knowledge from raw data traces. To this end, the paper (i) clarifies when, why and how to use data science in wireless network research; (ii) provides a generic framework for applying data science in wireless networks; (iii) gives an overview of existing research papers that utilized data science approaches in wireless networks; (iv) illustrates the overall knowledge discovery process through an extensive example in which device types are identified based on their traffic patterns; (v) provides the reader the necessary datasets and scripts to go through the tutorial steps themselves.

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Self-Adapting MAC Layer for Wireless Sensor Networks

Cited by 31 publications

References 23 publications

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Fully Distributed Packet Scheduling Framework for Handling Disturbances in Lossy Real-Time Wireless Networks

Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial

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