Spanning Multi-Tree Algorithm for node and traffic Balancing in multi-sink wireless sensor networks

Sia, Yeong Kang; Goh, Hock Guan; Liew, Soung-Yue; Gan, Ming-Lee

doi:10.1109/fskd.2015.7382292

Cited by 4 publications

(3 citation statements)

References 8 publications

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“…If the keys are biased, a grouping with more trie nodes might perform better when the trie data structure is unbalanced. As needed, we can employ tree balance techniques [6,11,18,23,41,43] to rebalance the data structure. Thus, when the number of groupings is fixed, fewer trie nodes always perform better.…”

Section: Genmatcher Grouping Algorithmmentioning

confidence: 99%

GenMatcher

Wang

McHale

Gratz

et al. 2018

ACM Trans. Archit. Code Optim.

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Packet classification methods rely upon packet content/header matching against rules. Thus, throughput of matching operations is critical in many networking applications. Further, with the advent of Software Defined Networking (SDN), efficient implementation of software approaches to matching are critical for the overall system performance. This article presents 1 GenMatcher, a generic, software-only, arbitrary matching framework for fast, efficient searches. The key idea of our approach is to represent arbitrary rules with efficient prefix-based tries. To support arbitrary wildcards, we rearrange bits within the rules such that wildcards accumulate to one side of the bitstring. Since many non-contiguous wildcards often remain, we use multiple prefix-based tries. The main challenge in this context is to generate efficient trie groupings and expansions to support all arbitrary rules. Finding an optimal mix of grouping and expansion is an NP-complete problem. Our contribution includes a novel, clustering-based grouping algorithm to group rules based upon their bit-level similarities. Our algorithm generates near-optimal trie groupings with low configuration times and provides significantly higher match throughput compared to prior techniques. Experiments with synthetic traffic show that our method can achieve a 58.9X speedup compared to the baseline on a single core processor under a given memory constraint.

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Section: Genmatcher Grouping Algorithmmentioning

confidence: 99%

GenMatcher

Wang

McHale

Gratz

et al. 2018

ACM Trans. Archit. Code Optim.

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“…Sia, Y.K. et al [ 25 ] presented a Spanning Multi-Tree Algorithm for Load Balancing (SMT-LB). SMT-LB aims to balance the load in multi-sink wireless sensor networks with heterogeneous traffic generating nodes.…”

Section: Related Workmentioning

confidence: 99%

A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks

Sha

Qiu

et al. 2016

Sensors

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To balance energy consumption and reduce latency on data transmission in Wireless Sensor Networks (WSNs), a type of low-latency data gathering method with multi-Sink (LDGM for short) is proposed in this paper. The network is divided into several virtual regions consisting of three or less data gathering units and the leader of each region is selected according to its residual energy as well as distance to all of the other nodes. Only the leaders in each region need to communicate with the mobile Sinks which have effectively reduced energy consumption and the end-to-end delay. Moreover, with the help of the sleep scheduling and the sensing radius adjustment strategies, redundancy in network coverage could also be effectively reduced. Simulation results show that LDGM is energy efficient in comparison with MST as well as MWST and its time efficiency on data collection is higher than one Sink based data gathering methods.

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“…To address the above problems, many studies on data collection have explored the use of multiple sinks. (6,7) The use of multiple sinks can decrease the delay by selecting the sink having the minimum number of hops from among the sinks and increase the network lifetime through load balancing among the sensor nodes. However, it requires an appropriate sink deployment strategy to avoid redundant sink coverage.…”

Section: Introductionmentioning

confidence: 99%

Regional Density-aware Data Collection Using Unmanned Aerial Vehicle in Large-scale Wireless Sensor Networks

Kim¹,

Choi²,

Kwon³

2018

Sensors and Materials

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This paper presents a regional density-aware data collection (RDDC) using unmanned aerial vehicles (UAVs) in large-scale wireless sensor networks (WSNs). The RDDC is designed to support fast data collection by adaptively adjusting the value of the minimum contention window (CW min ) of the sensor nodes depending on the regional node density of sensing fields. The operation of RDDC consists of the region-partitioning, node-scanning, and data-collecting phases. In the region-partitioning phase, the RDDC partitions the sensing field into multiple regions and assigns a region ID to each sensor node according to the region to which it belongs. In the node-scanning phase, the RDDC calculates the node density of each region. Finally, in the data-collecting phase, the RDDC assigns different CW min values to each sensor node considering the regional node density of each region. The experimental simulation results show that the RDDC obtains higher aggregate throughput and shorter delay than the existing data collection method.

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Spanning Multi-Tree Algorithm for node and traffic Balancing in multi-sink wireless sensor networks

Cited by 4 publications

References 8 publications

GenMatcher

GenMatcher

A Type of Low-Latency Data Gathering Method with Multi-Sink for Sensor Networks

Regional Density-aware Data Collection Using Unmanned Aerial Vehicle in Large-scale Wireless Sensor Networks

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