Cluster head selection using RF signal strength

Fazackerley, Scott; Paeth, Alan W.; Lawrence, Ramon

doi:10.1109/ccece.2009.5090148

Cited by 22 publications

(11 citation statements)

References 19 publications

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“…A high number of cluster members would also increase channel contentions amongst nodes within each cluster thus reducing the benefit of clustering. SumRSSI resulted in seven clusters formed in the network at transmission range r from 300 to 350 m, at which point n o is roughly n=2, this is consistent with the expected EOEn o derived in (9). The Dunn index metric [26] attempts to quantify the compactness, favourable distribution and separation of clusters.…”

Section: Clustering Performance With Varying Transmission Range Rsupporting

confidence: 69%

Reinforcement learning‐based clustering protocols for a self‐organising cognitive radio network

Ramli

Grace

2015

Trans. Emerging Tel. Tech.

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This paper presents a methodology on how cognitive radio networks can form clusters by exploiting reinforcement learning-based principles. Each node repeatedly senses the received signal strength indicator beacons given off by other nodes in the network. This information can be used by the nodes to learn about its positioning significance within the network and whether to become cluster heads thus forming a cluster. Extensive simulation results are presented, and it is shown that on average, the clustering performance (packing efficiency) can be improved when nodes have the ability to learn. The results are compared with that of a k-means and node degree-based approach for the formation of clusters. It is found that the node degree scheme can result in the clustering performance deteriorating, which is undesirable as it can lead to an increase in the total energy consumption of the network over time. It is shown that in a shadowing environment, that clusters formed via learning through received signal strength indicator can reduce their transmission power by up to 2 dBW (achieving a potential power saving of 37 per cent) while achieving the same signal-to-noise ratio as that of the no learning and node degree schemes. Further energy conservation can be obtained by restricting beacon transmissions to immediate neighbouring nodes. In certain geographical node distributions, a no learning scheme is preferred for the formation of clusters due to its lower overhead.

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Section: Clustering Performance With Varying Transmission Range Rsupporting

confidence: 69%

Reinforcement learning‐based clustering protocols for a self‐organising cognitive radio network

Ramli

Grace

2015

Trans. Emerging Tel. Tech.

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“…Hence, the LEACH-C model requires a Global Positioning System (GPS) for this case. In another way, in [10], the selection of the CHs depends upon the node's remaining energy that makes the decision if the normal node becomes CH or not after completion of the first round. E-LEACH is the approach named there, that elects the node having higher energy as the CH and considers the remaining nodes having lesser energy as cluster heads.…”

Section: Related Workmentioning

confidence: 99%

Systematic Cluster Head (CH) Selection Through Node-Grade Based Clustering (NGBC) in WSN

Keynes¹,

Punithavathani²

2018

IJET

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As it is well known, in Wireless Sensor Networks, the sensor nodes will be either mobile or static. When mobility is concerned, on the whole network performance could be degraded, since the sensor nodes are furnished with restricted battery power, restricted memory, less computational ability and lower range of communication. So, a mechanism which is effective is needed there for forwarding the data packets with efficient energy management and coverage. With that note, the principle target of this work is to propose systematic method of CH selection based on the factors such as low mobility, density of the nodes and their remaining energy. Moreover, an innovative method called Node-Grade Based Clustering (NGBC) is proposed in this paper so as to select the CHs, studying the node’s energy and position regarding to their Base Station (BS), which will act as a sink for collected information. The CHs are replaced in every round based on its duty cycle on sensor nodes and Threshold Energy Rate (TER). Since the BS evaluates the quantity of every round a CH (Cluster Head) can sustain, it minimizes the quantity of energy consumed and increases the WSN’s lifetime. The results of the simulation demonstrate that the proposed algorithm attains higher coverage, efficiency in energy and network lifetime. Furthermore, the performance results in the work which is proposed, are distinguished with the algorithms proposed previously such as LEACH and HEED using some evaluation metrics like packet delivery ratio, throughput, energy consumption and end-to-end delay to prove the efficiency of energy efficient NGBC.

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“…The LEACH [5] algorithm for selecting clusterheads is a probabilistic method which produces clusters with a large variation of link distances and uneven energy consumption during the data transmission phase. To address this issue, a distributed algorithm is presented in [6] in which nodes attempt to form favourable clusters based on Received Signal Strength indication (RSSI) density and quality. These papers do not take into account multihop clustering aspects.…”

Section: State Of the Artmentioning

confidence: 99%

Using LQI to improve clusterhead locations in dense zigbee based wireless sensor networks

Diallo¹,

Marot²,

Becker

2010

2010 IEEE 6th International Conference on Wireless and Mobile Computing, Networking and Communications

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International audienceIn WSN, it is not often desirable to use the GPS technology. Indeed, the use of GPS is expensive and may reduce the overall network performance. Moreover, indoor reception of the GPS signal is not possible. The Link Quality Indicator (LQI) is defined in the 802.15.4 standard, but its context of use is not specified in this standard. Some works on the LQI, few of which are field experiments, have shown that the LQI decreases as the distance increases. However, the challenge of clustering mechanisms is to form the smallest number of clusters by maximizing distances separating cluster heads to provide an efficient cover of the network and also minimizes the cluster overlaps. This reduces the amount of channel contention between clusters, and also improves the efficiency of algorithms that run at the level of the cluster heads. Therefore we propose an analytical model based on the use of the LQI in order to derive an optimally one-dominating set in which the smaller distance separating two cluster heads is improve

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Cluster head selection using RF signal strength

Cited by 22 publications

References 19 publications

Reinforcement learning‐based clustering protocols for a self‐organising cognitive radio network

Reinforcement learning‐based clustering protocols for a self‐organising cognitive radio network

Systematic Cluster Head (CH) Selection Through Node-Grade Based Clustering (NGBC) in WSN

Using LQI to improve clusterhead locations in dense zigbee based wireless sensor networks

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