Probabilistic Analysis of Hierarchical Cluster Protocols for Wireless Sensor Networks

Kaj, Ingemar

doi:10.1007/978-3-642-10406-0_10

Cited by 3 publications

(9 citation statements)

References 6 publications

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“…Clustering is a key technique to reduce energy consumption and it increases the scalability and lifetime of the network [7], [8]. A Low-Energy Adaptive Clustering Hierarchy protocol (LEACH) to evenly distribute the energy load among the sensors and enable scalability for dynamic networks by incorporating localized coordination is proposed [9], [10]. A low overhead protocol that periodically selects cluster-heads according to the node residual energy to support scalable data aggregation is established in [11].…”

Section: A Motivation and Related Workmentioning

confidence: 99%

“…Given a maximum transmitter power constraint, P Tmax for each device, the maximum transmission range is given by (P Tmax /P R min ) 1/α . Dividing L(λ a ) by N a and taking its expectation with respect to the distribution of N a , we obtain the mean length of connections 10 , and dividing this ratio by the maximum transmission range, we obtain the desired result.…”

Section: Lemma 4 a Lower Bound On K The Number Of Stages Is Lower Bmentioning

confidence: 99%

“…Total energy density, with optimal fraction of aggregators, γopt, marked for PLO = PRX/4[28]. Rate coverage distribution with maximum power constraint PT max /P T =[1,5,10,20].…”

mentioning

confidence: 99%

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Optimizing Data Aggregation for Uplink Machine-to-Machine Communication Networks

Malak

Dhillon

Andrews

2016

IEEE Trans. Commun.

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Machine-to-machine (M2M) communication's severe power limitations challenge the interconnectivity, access management, and reliable communication of data. In densely deployed M2M networks, controlling and aggregating the generated data is critical. We propose an energy efficient data aggregation scheme for a hierarchical M2M network. We develop a coverage probability-based optimal data aggregation scheme for M2M devices to minimize the average total energy expenditure per unit area per unit time or simply the energy density of an M2M communication network. Our analysis exposes the key tradeoffs between the energy density of the M2M network and the coverage characteristics for successive and parallel transmission schemes that can be either half-duplex or full-duplex. Comparing the rate and energy performances of the transmission models, we observe that successive mode and half-duplex parallel mode have better coverage characteristics compared to full-duplex parallel scheme. Simulation results show that the uplink coverage characteristics dominate the trend of the energy consumption for both successive and parallel schemes.2 Later in Sect. V, in evaluating the SIR-based coverage probability, we also incorporate the small-scale fading into the analysis that is assumed to be independent and identically distributed (iid) with unit mean. Therefore, incorporating fading yields the same average energy analysis. To keep the notation simple, we do not incorporate fading in Sects. II, III and IV.3 Na (random variable) denotes the number of devices in the Voronoi cell of a typical aggregator, and is detailed in Sect. III. 4 The interference is due to simultaneously active aggregator cells. Users within each Voronoi cell are assumed to use TDMA for access, and at a particular time slot, there is only one active transmitting device in each cell. For the sequential mode, the interference is due to the active transmitters outside the typical cell. On the other hand, for the parallel transmission mode, since all the stages are simultaneously active, there is both intra cell and out of cell interferences., which is detailed in Sect. VI. 5 In Sect. III, we will motivate the choice of γ < 0.5 in our setup.

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Section: A Motivation and Related Workmentioning

confidence: 99%

Section: Lemma 4 a Lower Bound On K The Number Of Stages Is Lower Bmentioning

confidence: 99%

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Optimizing Data Aggregation for Uplink Machine-to-Machine Communication Networks

Malak

Dhillon

Andrews

2016

IEEE Trans. Commun.

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“…They analytically determined the optimal value of k in LEACH using the computation and communication energy models and shown that the number of CHs depend on the distance between BS and sensor network system. Ingemar Kaj et al [6] presented a probabilistic analysis of hierarchical cluster protocols for LEACH and provided a solution for low-energy and limited-loss optimization. He also extended his results to a multi-level version of LEACH, where clusters of nodes again self-organize to form cluster of clusters, and so on.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…Different communication techniques are adopted to save the available resources, however, cluster based communication technique is best to save resources as defined in [7]. Ingemar Kaj [6]…”

Section: F Multi-level Hierarchal Cluster Based Routingmentioning

confidence: 99%

Analyzing Energy-Efficiency and Route-Selection of Multi-level Hierarchal Routing Protocols in WSNs

Fareed

Javaid

Ahmed

et al. 2012

2012 Seventh International Conference on Broadband, Wireless Computing, Communication and Applications

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The advent and development in the field of Wireless Sensor Networks (WSNs) in recent years has seen the growth of extremely small and low-cost sensors that possess sensing, signal processing and wireless communication capabilities. These sensors can be expended at a much lower cost and are capable of detecting conditions such as temperature, sound, security or any other system. A good protocol design should be able to scale well both in energy heterogeneous and homogeneous environment, meet the demands of different application scenarios and guarantee reliability. On this basis, we have compared six different protocols of different scenarios which are presenting their own schemes of energy minimizing, clustering and route selection in order to have more effective communication. This research is motivated to have an in-sight that which of the under consideration protocols suit well in which application and can be a guide-line for the design of a more robust and efficient protocol. MATLAB simulations are performed to analyze and compare the performance of LEACH, multi-level hierarchal LEACH and multi-hop LEACH.

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Optimal Number of Cluster Head Selection for Efficient Distribution of Sources in WSNs

Fareed

Javaid

Akbar

et al. 2012

2012 Seventh International Conference on Broadband, Wireless Computing, Communication and Applications

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In this paper, we compare problems of cluster formation and cluster-head selection between different protocols for data aggregation and transmission. We focus on two aspects of the problem: (i) how to guess number of clusters required to proficiently consume available sources for a sensor network, and (ii) how to select number of cluster-heads to cover up sensor networks more proficiently. A sensor in Wireless Sensor Networks (WSNs) can communicate directly only with other sensors that are within a radio range in a cluster. However, in order to enable communication between sensors not within communication range, they must form new clusters in distributed sensors. Several clustering algorithms such as LEACH, DEEC, and SEP have been proposed with the objectives of energy minimization, route-path selection, increased connectivity and network longevity. LEACH protocol and the similar ones assume an energy homogeneous system where a node is not likely to fail due to failure in connectivity and packet dropping. More recent protocols like SEP and TEEN considered the reverse that is energy heterogeneity which is more applicable to case of WSNs. We developed a bi-dimensional chain model to select average number of for DEEC. Simulation results are used to compare performance of different protocols to found optimal solutions of above mentioned problems.

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Probabilistic Analysis of Hierarchical Cluster Protocols for Wireless Sensor Networks

Cited by 3 publications

References 6 publications

Optimizing Data Aggregation for Uplink Machine-to-Machine Communication Networks

Optimizing Data Aggregation for Uplink Machine-to-Machine Communication Networks

Analyzing Energy-Efficiency and Route-Selection of Multi-level Hierarchal Routing Protocols in WSNs

Optimal Number of Cluster Head Selection for Efficient Distribution of Sources in WSNs

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