M-MAC: Mobility-Based Link Management Protocol for Mobile Sensor Networks

Choi, Lynn; Lee, Sang Hoon; Choi, Hyo Hyun

doi:10.1109/stfssd.2009.47

Cited by 8 publications

(9 citation statements)

References 11 publications

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“…Both algorithms only require send, receive primitives and the link quality indicator. The send and receive primitives are important services required from MAC and the physical layer [18,19,[23][24][25]. IEEE 802.15.4 provides the link quality indicator (LQI) which can be calculated by the combination of the signal-to-noise ratio and the detected energy.…”

Section: Discussionmentioning

confidence: 99%

“…In S-MAC and the TRAMA, probe packets and the overhearing method are used to obtain link quality [44]. B-MAC [25], M-MAC [24] and the WMEWMA [45] are the other underlying protocols for the multi-hop routing network layers which provide link quality estimation.…”

Section: Discussionmentioning

confidence: 99%

“…In conclusion, the number and sizes of clusters should be controllable by the algorithms to adjust them to the required values. (4) The clustering algorithms should be independent from the underlying protocols as much as possible to interface with various MAC and physical layer standards such as in [18,19,[23][24][25][26]. (5) The algorithms should be efficient in terms of time and message complexity to provide low energy consumptions of sensor networks.…”

Section: The Clustering Problemmentioning

confidence: 99%

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Graph Matching-Based Distributed Clustering and Backbone Formation Algorithms for Sensor Networks

Dağdeviren

Erciyes

2010

The Computer Journal

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Clustering is a widely used technique to manage the essential operations such as routing and data aggregation in wireless sensor networks (WSNs). We propose two new graph-theoretic distributed clustering algorithms for WSNs that use a weighted matching method for selecting strong links. To the best of our knowledge, our algorithms are the first attempts that use graph matching for clustering. The first algorithm is divided into rounds; extended weighted matching operation is executed by nodes in each round; thus the clusters are constructed synchronously. The second algorithm is the enhanced version of the first algorithm, which provides not only clustering but also backbone formation in an energy-efficient and asynchronous manner. We show the operation of the algorithms, analyze them, provide the simulation results in an ns2 environment. We compare our proposed algorithms with the other graph-theoretic clustering algorithms and show that our algorithms select strong communication links and create a controllable number of balanced clusters while providing lowenergy consumptions. We also discuss possible applications that may use the structure provided by these algorithms and the extensions to the algorithms.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: The Clustering Problemmentioning

confidence: 99%

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Graph Matching-Based Distributed Clustering and Backbone Formation Algorithms for Sensor Networks

Dağdeviren

Erciyes

2010

The Computer Journal

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“…(4) Bridge detection algorithm should be independent from the underlying protocols as much as possible to interface to various MAC and physical layer standards such as in [25][26][27][28][29][30].…”

Section: The Bridge Detection Problemmentioning

confidence: 99%

Energy-Efficient Bridge Detection Algorithms for Wireless Sensor Networks

Dağdeviren

Akram

2013

International Journal of Distributed Sensor Networks

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A bridge is a critical edge whose fault disables the data delivery of a WSN component. Because of this, it is important to detect bridges and take preventions before they are corrupted. Since WSNs are battery powered, protocols running on WSN should be energy efficient. In this paper, we propose two distributed energy-efficient bridge detection algorithms for WSNs. The first algorithm is the improved version of Pritchard's algorithm where two phases are merged into a single phase and radio broadcast communication is used instead of unicast in order to remove a downcast operation and remove extra message headers. The second algorithm runs proposed rules on 2-hop neighborhoods of each node and tries to detect all bridges in a breadth-first search (BFS) execution session using O( ) messages with O(Δ(log 2 ( ))) bits where is the node count and Δ is the maximum node degree. Since BFS is a natural routing algorithm for WSNs, the second algorithm achieves both routing and bridge detections. If the second proposed algorithm is not able to to classify all edges within the BFS phase, improved version of Turau's algorithm is executed as the second phase. We show the operation of the algorithms, analyze them, and provide extensive simulation results on TOSSIM environment. We compare our proposed algorithms with the other bridge detection algorithms and show that our proposed algorithms provide less resource consumption. The energy saving of our algorithms is up to 4.3 times, while it takes less time in most of the situations.

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“…By analyzing previous papers [5], [7] related to mobility aware MAC protocol, it was found that the relay nodes are selected randomly. But there may be a situation that randomly selected relay node may be overloaded or it can be moving, leading to data loss and repeated retransmissions.…”

Section: Problem Identificationmentioning

confidence: 99%

Optimal Load and Mobility Aware MAC Protocol for Wireless Sensor Networks

MadhaviD¹,

Swamy²

2013

IJCA

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In mobile environment of wireless sensor networks (WSN), the fixed frame of MAC protocol causes performance degradation such as packet collision due to overloading etc. In this paper an optimal load and mobility aware MAC protocol for WSN is proposed. Each node measures the received signal strength (RSS) from the receiver by monitoring the acknowledgement packets and based on the RSS value, the link quality and mobility of the node can be predicted. Depending on the predicted link quality and mobility, the relay node selection process is initiated. In relay node selection process the neighbors with minimum load and good RSSI values is selected as the relay node. The moving source then transmits the packets through this relay node. This protocol reduces the packet drop due to overload and weak link quality. By simulation results, it is shown that the proposed approach minimizes the energy consumption.

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M-MAC: Mobility-Based Link Management Protocol for Mobile Sensor Networks

Cited by 8 publications

References 11 publications

Graph Matching-Based Distributed Clustering and Backbone Formation Algorithms for Sensor Networks

Graph Matching-Based Distributed Clustering and Backbone Formation Algorithms for Sensor Networks

Energy-Efficient Bridge Detection Algorithms for Wireless Sensor Networks

Optimal Load and Mobility Aware MAC Protocol for Wireless Sensor Networks

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