Multi-mobile agent itinerary planning-based energy and fault aware data aggregation in wireless sensor networks

Fissaoui, Mohamed El; Beni‐Hssane, Abderrahim; Saadi, Mostafa

doi:10.1186/s13638-018-1099-0

Cited by 31 publications

(23 citation statements)

References 36 publications

(32 reference statements)

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“…Different approaches have been proposed to group sensor nodes into clusters. In [4] [6] and [7] authors chooses to create the clusters based on nodes density impact factor. Each sensor node will receives the impact factors from other sensor neighbors, then compares the accumulated impact factors and decides either to be elected or not as CH.…”

Section: Related Workmentioning

confidence: 99%

“…This approach is not suitable in a large network because it is difficult for the MDC to visits all the deployed sensor nodes if the number of sensors increases. Due to node failure, authors in [6] and [7] propose to construct a dynamic path for the MDC, based on fault tolerant. This technique calculates alternative itineraries for the MDC in case of losing the dispatched Mobile Agent, if sensor nodes fail.…”

Section: Related Workmentioning

confidence: 99%

“…This technique calculates alternative itineraries for the MDC in case of losing the dispatched Mobile Agent, if sensor nodes fail. The clustering method used in [6] and [7] consists on a weight based approach that selects the best sensor nodes as CHs according to the node's impact factor.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

An Efficient Mobile Data Gathering Method with Tree Clustering Algorithm in Wireless Sensor Networks Balanced and Unbalanced Topologies

Meddah

HADDAD²,

EZZEDDINE

2021

Preprint

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Mobile Data Collector device (MDC) is adopted to reduce the energy consumption in Wireless Sensor Networks. This device travels the network in order to gather the collected data from sensor nodes. This paper presents a new Tree Clustering algorithm with Mobile Data Collector in Wireless Sensor Networks, which establishes the shortest travelling path passing throw a subset of Cluster Heads (CH). To select CHs, we adopt a competitive scheme, and the best sensor nodes are elected according to the number of packets forwarded between sensor nodes, the number of hops to the tree’s root, the residual energy, and the distance between the node and the closest CH. In simulation results, we adopt the balanced and unbalanced topologies and prove the efficiently of our proposed algorithm considering the network lifetime, the fairness index and the energy consumption in comparison with the existing mobile data collection algorithms.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Efficient Mobile Data Gathering Method with Tree Clustering Algorithm in Wireless Sensor Networks Balanced and Unbalanced Topologies

Meddah

HADDAD²,

EZZEDDINE

2021

Preprint

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“…Multi-mobile Agent itinerary planning-based Energy and Fault aware (MAEF) was proposed by [21]. Similar to (OMIP), MAEF also groups the source nodes into clusters and each cluster has a Cluster Head (CH).…”

Section: Related Workmentioning

confidence: 99%

CMIP: Clone Mobile-Agent Itinerary Planning Approach for Enhancing Event-to-Sink Throughput in Wireless Sensor Networks

et al. 2018

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In order to mitigate the problem of data congestion, increased latency, and high energy consumption in Wireless Sensor Networks (WSNs), Mobile Agent (MA) has been proven to be a viable alternative to the traditional client-server data gathering model. MA has the ability to migrate among network nodes based on an assigned itinerary, which can be formed via Single Itinerary Planning (SIP) or Multiple Itinerary Planning (MIP). MIP-based data gathering approach solves problems associated with SIP in terms of task duration, energy consumption, and reliability. However, the majority of existing MIP approaches focus only on reducing energy consumption and task duration, while the Event-to-sink throughput has not been considered. In this paper, a Clone Mobile-agent Itinerary Planning approach (CMIP) is proposed to reduce task duration while improving the Event-to-sink throughput in real-time applications, especially when the MA is assigned to visit a large number of source nodes. Simulation results show that the CMIP approach outperforms both Central Location-based MIP (CL-MIP) and Greatest Information in Greatest Memory-based MIP (GIGM-MIP) in terms of reducing task duration by about 56% and 16%, respectively. Furthermore, CMIP improves the Event-to-sink throughput by about 93% and 22% as compared to both CL-MIP and GIGM-MIP approaches, respectively.

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“…Many algorithms have been developed for data aggregation and enlarging the network lifetime. In [1],A Multi-Mobile Agent Itinerary Planning-Based Energy and Fault Aware data aggregation (MAEF) was introduced. But, the network lifetime was not improved using MAEF.…”

Section: Introductionmentioning

confidence: 99%

Ensembled Elbow and Bray-Curtis Fuzzy C-Means Clustering For Energy Efficient Data Aggregation in WSN

Kokilavani¹,

Nitesh²

2021

IRJASH

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Wireless sensor network (WSN) comprises the distributed sensors for aggregating and organizing the data. Data aggregation is the major concern in WSN since it relies on several factors, namely energy constraints of sensors, network topology, links conditions and so on. The conventional approach does not perform efficient data aggregation due to their battery power of nodes and degrade the network lifetime. To improve data aggregation and network lifetime, An Energy-Efficient Ensembled Elbow Fuzzy C-means Clustering based Data Aggregation (EEEEFCC-DA) method is designed. Initially, residual energy of each sensor node (SN) is calculated. To determine the number of clusters, the elbow method is used in fuzzy c-means clustering algorithm. Then, Centroids value is calculated for every cluster to group SNs. Bray-Curtis Similarity Index is used to compute the similarity between the SN and Centroids value of cluster. SNs are grouped depends on the similarity value. The process gets iterated until every SNs gets clustered to the suitable clusters. After that, the SN with higher residual energy is selected as cluster head (CH). CH gathers data from each SNs and send to sink node. This, assist to enhance the data gathering accuracy and lessen the energy consumption. Simulation of EEEEFCC-DA method is carried out with various metrics namely energy consumption, network lifetime, data aggregation accuracy (DAA) and data aggregation time with number of SNs and number of data packets (DP). Results show that EEEEFCC-DA method provides better performance in term of DAA , network lifetime , energy consumption and data aggregation time than the conventional methods.

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Multi-mobile agent itinerary planning-based energy and fault aware data aggregation in wireless sensor networks

Cited by 31 publications

References 36 publications

An Efficient Mobile Data Gathering Method with Tree Clustering Algorithm in Wireless Sensor Networks Balanced and Unbalanced Topologies

An Efficient Mobile Data Gathering Method with Tree Clustering Algorithm in Wireless Sensor Networks Balanced and Unbalanced Topologies

CMIP: Clone Mobile-Agent Itinerary Planning Approach for Enhancing Event-to-Sink Throughput in Wireless Sensor Networks

Ensembled Elbow and Bray-Curtis Fuzzy C-Means Clustering For Energy Efficient Data Aggregation in WSN

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