Fast Aggregation Scheduling in Wireless Sensor Networks

Yousefi, Hamed; Malekimajd, Marzieh; Ashouri, Majid; Movaghar, Ali

doi:10.1109/twc.2015.2405060

Cited by 57 publications

(24 citation statements)

References 24 publications

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“…The effect of the scheduling method largely depends on the routing structures formed. Later works simultaneously executed the aggregation scheduling and the tree construction processes [40]- [42]. Bagaa et al [40] [41] have addressed the problem of building the minimum latency schedule in duty-cycle sensor networks.…”

Section: ) Minimum Latency Aggregation Scheduling (Mlas)mentioning

confidence: 99%

“…Li et al [43] proposed a distributed scheduling method on a cluster-based constructed tree called Clu − DDAS, which is declared to provide an upper bound of 4R + 2∆ − 2 on delay. Later, this upper bound developed by Li [43] is proved not correct [42]. Yousefi et al [42] made amends and invented an efficient distributed scheduling method named FAST with the latency bounded by 12R + ∆ − 2 time slots.…”

Section: ) Minimum Latency Aggregation Scheduling (Mlas)mentioning

confidence: 99%

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Median Access Control Protocols for Sensor Data Collection: A Review

Zhao

Yan

et al. 2020

IEEE Access

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Median Access Control (MAC) protocols are designed to mitigate collisions and enhance the energy efficiency for sensor data collection. This paper reviews two basic categories of MAC protocols. The first class is the contention-based protocols, where nodes randomly compete for channel access. The second class is the schedule-based MAC protocols, in which nodes access the channel on the basis of the predetermined schedules. We focus on discussing the Time Division Multiple Access (TDMA) protocols and classify different TDMA schedulings into three categories according to the communication patterns in the network, i.e., the link scheduling, aggregate scheduling and non-aggregate scheduling. Link scheduling deals with the peer to peer communication pattern, where there is no central node in the network. In comparison, both the aggregate and non-aggregate schedulings handle the convergecast communication pattern, in which all traffic are destined to the sink. This survey provides a comprehensive overview of these three categories and provided a detailed briefing on how the TDMA schedules handle the network traffic dynamics in the network. Compared with other surveys in this domain, this review does not confine itself to deal with collecting a particular form of data, but provides a unified framework to integrate the data semantics in a broader sense into the design of TDMA scheduling algorithms.

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Section: ) Minimum Latency Aggregation Scheduling (Mlas)mentioning

confidence: 99%

Section: ) Minimum Latency Aggregation Scheduling (Mlas)mentioning

confidence: 99%

Median Access Control Protocols for Sensor Data Collection: A Review

Zhao

Yan

et al. 2020

IEEE Access

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“…Because the nodes are powered by batteries, it is not realistic to replace the dead nodes with batteries in a wide range of application scenarios. Therefore, the energy problem is a key factor limiting the reliability of WSNs [17,18,19,20]. The low-power design of the node is an effective way to solve the energy problem.…”

Section: Introductionmentioning

confidence: 99%

Marine Observation Beacon Clustering and Recycling Technology Based on Wireless Sensor Networks

Zhang

2019

Sensors

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Monitoring of marine polluted areas is an emergency task, where efficiency and low-power consumption are challenging for the recovery of marine monitoring equipment. Wireless sensor networks (WSNs) offer the potential for low-energy recovery of marine observation beacons. Reducing and balancing network energy consumption are major problems for this solution. This paper presents an energy-saving clustering algorithm for wireless sensor networks based on k-means algorithm and fuzzy logic system (KFNS). The algorithm is divided into three phases according to the different demands of each recovery phase. In the monitoring phase, a distributed method is used to select boundary nodes to reduce network energy consumption. The cluster routing phase solves the extreme imbalance of energy of nodes for clustering. In the recovery phase, the inter-node weights are obtained based on the fuzzy membership function. The Dijkstra algorithm is used to obtain the minimum weight path from the node to the base station, and the optimal recovery order of the nodes is obtained by using depth-first search (DFS). We compare the proposed algorithm with existing representative methods. Experimental results show that the algorithm has a longer life cycle and a more efficient recovery strategy.

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“…At present, due to obstacles of the computing strength and electricity helpful resource of detector nodes, knowledge is aggregative by victimization very simple algorithms which has averaging. But, such aggregation is assumed to be terribly prone to faults, and bigger significantly, malicious attacks [1]. This cannot be remedied by suggests that of science methods, as a result of the attackers sometimes gain whole get admission to statistics hold on at intervals the compromised nodes.…”

Section: Introductionmentioning

confidence: 99%

Secure Data Aggregation Technique for Wireless Sensor Networks in the Presence of Collusion Attacks

2017

IJAERD

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Fast Aggregation Scheduling in Wireless Sensor Networks

Cited by 57 publications

References 24 publications

Median Access Control Protocols for Sensor Data Collection: A Review

Median Access Control Protocols for Sensor Data Collection: A Review

Marine Observation Beacon Clustering and Recycling Technology Based on Wireless Sensor Networks

Secure Data Aggregation Technique for Wireless Sensor Networks in the Presence of Collusion Attacks

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