Data aggregation in sensor networks using learning automata

Esnaashari, Mehdi; Meybodi, M. R.

doi:10.1007/s11276-009-0162-5

Cited by 45 publications

(19 citation statements)

References 23 publications

(51 reference statements)

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“…The work of this paper is compared to data aggregation using Learning Automata protocol [6], and the default routing protocol using the shortest path to the sink. Learning Automata protocol relies on gathering similar sensors' data only, and it consists of two phases: route discovery and route selection.…”

Section: Related Workmentioning

confidence: 99%

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Dynamic Aggregation Protocol for Wireless Sensor Networks

Said

Ibrahim

Soua

et al. 2013

2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA)

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Abstract-Sensor networks suffer from limited capabilities such as bandwidth, low processing power, and memory size. There is therefore a need for protocols that deliver sensor data in an energy-efficient way to the sink. One of those techniques, it gathers sensors' data in a small size packet suitable for transmission.In this paper, we propose a new Effective Data Aggregation Protocol (DAP) to reduce the energy consumption in Wireless Sensor Networks (WSNs), which prolongs the network lifetime. This work uses in-network aggregation approach to distribute the processing all over the aggregation path to avoid unbalanced power consumption on specific nodes until they run out.Simulation results prove that DAP, compared to other protocols, achieves more data aggregation percentage and less power consumption for a one data harvesting round.

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Section: Related Workmentioning

confidence: 99%

“…Performance evaluation for the proposed protocol is compared with two other protocols, namely: i) Data Aggregation using Learning Automata protocol [6], and ii) the conventional routing protocol.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Aggregation Protocol for Wireless Sensor Networks

Said

Ibrahim

Soua

et al. 2013

2013 IEEE 27th International Conference on Advanced Information Networking and Applications (AINA)

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“…For some of these algorithms, each node may have more than one route to sink node that one of them is selected on the basis of a series of criteria, among which the level of energy consumption along the route can be a proper criterion. Energy saving can be taken into account in two ways: (1) energy consumption is calculated for any separate routes, then the route with minimal energy consumption is chosen (Shah and Rabaey, 2002); and (2) data aggregation is based on provided learning automata, which prevents extra packets from being sent in networks by identifying sensors generating identical data and by activating sensor nodes periodically, thus saving a large amount of energy while increasing network lifetime (Esnaashari and Meybodi, 2010). A solution has been provided in AlKaraki et al (2009) for data aggregating and routing with internetwork aggregations in wireless sensor networks in order to maximize network lifetime by using internetwork processing techniques and data aggregation.…”

Section: Literature Reviewmentioning

confidence: 99%

A learning automata-based algorithm using stochastic minimum spanning tree for improving life time in wireless sensor networks

Asgari¹,

Torkashvand²,

Barati³

2013

Int. J. Phys. Sci.

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Several algorithms have already been provided for problems of data aggregation in wireless sensor networks, which somehow tried to increase networks lifetimes. In this study, we dealt with this problem using a more efficient method by taking parameters such as the distance between two sensors into account. In this paper, we presented a heuristic algorithm based on distributed learning automata with variable actions set for solving data aggregation problems within stochastic graphs where the weights of edges change with time. To aggregate data, the algorithm, in fact, creates a stochastic minimum spanning tree (SMST) in networks where variable distances of links are considered as edges, and sends data in the form of a single packet to central node after data was processed inside networks. To understand this subject better, we modeled the problem for a stochastic graph having edges with changing weights. Although this assumption that edges weights change with time makes our task difficult, the results of simulations indicate relatively optimal performance of this method.

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“…It has been shown in [14,18,20] that the learning automata are capable of solving the NP-hard problems. Recently, several learning automata-based protocols have been designed for improving the performance of the wireless ad hoc networks [15,16,17] or sensor networks [21,22,23].…”

Section: R Andom Environm Entmentioning

confidence: 99%

A mobility-based cluster formation algorithm for wireless mobile ad-hoc networks

Torkestani

Meybodi

2011

Cluster Comput

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In the last decade, numerous efforts have been devoted to design efficient algorithms for clustering the wireless mobile ad-hoc networks (MANET) considering the network mobility characteristics. However, in existing algorithms, it is assumed that the mobility parameters of the networks are fixed, while they are stochastic and vary with time indeed. Therefore, the proposed clustering algorithms do not scale well in realistic MANETs, where the mobility parameters of the hosts freely and randomly change at any time. Finding the optimal solution to the cluster formation problem is incredibly difficult, if we assume that the movement direction and mobility speed of the hosts are random variables. This becomes harder when the probability distribution function of these random variables is assumed to be unknown. In this paper, we propose a learning automata-based weighted cluster formation algorithm called MCFA in which the mobility parameters of the hosts are assumed to be random variables with unknown distributions. In the proposed clustering algorithm, the expected relative mobility of each host with respect to all its neighbors is estimated by sampling its mobility parameters in various epochs. MCFA is a fully distributed algorithm in which each mobile independently chooses the neighboring host with the minimum expected relative mobility as its cluster-head. This is done based solely on the local information each host receives from its neighbors and the hosts need not to be synchronized. The experimental results show the superiority of MCFA over the best existing mobilitybased clustering algorithms in terms of the number of clusters, cluster lifetime, reaffiliation rate, and control message overhead.

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Data aggregation in sensor networks using learning automata

Cited by 45 publications

References 23 publications

Dynamic Aggregation Protocol for Wireless Sensor Networks

Dynamic Aggregation Protocol for Wireless Sensor Networks

A learning automata-based algorithm using stochastic minimum spanning tree for improving life time in wireless sensor networks

A mobility-based cluster formation algorithm for wireless mobile ad-hoc networks

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