Accuracy, latency, and energy cross‐optimization in wireless sensor networks through infection spreading

Bai, Fan; Munasinghe, Kumudu S.; Jamalipour, Abbas

doi:10.1002/dac.1181

Cited by 18 publications

(19 citation statements)

References 27 publications

(50 reference statements)

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“…Along with above mentioned issues, many other issues like, node distortion [16], mobility [17], heterogeneity, accuracy and latency [18], hidden-terminal problem [19] can be handled using CLD. The Table 1 lists some of the CLD approaches for the solve the issues in WSNs.…”

Section: Other Issuesmentioning

confidence: 99%

Challenges and Implementation on Cross Layer Design for Wireless Sensor Networks

Ranjan

Varma

2015

Wireless Pers Commun

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Cross-layer design (CLD) has emerged as an important area in wireless sensor networks (WSNs). Cross-layer enables interaction between different non-adjacent layers and, thereby, exchanging information between layers, which, indeed is not possible in traditional architectures. CLD is used for enhancing the performance of the existing architectures by utilizing the flexible prospects of the protocol layers to improve system performance and to satisfy QoS demands of the applications. The CLD leads to increase in network efficiency and optimized network throughput. In this paper, the various cross-layer design methodologies for WSNs have been reviewed, which have basically been designed to enhance the network performance in WSN. At the end, the paper proposes a CLD based on ongoing research.

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Section: Other Issuesmentioning

confidence: 99%

Challenges and Implementation on Cross Layer Design for Wireless Sensor Networks

Ranjan

Varma

2015

Wireless Pers Commun

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“…Cross-optimization of accuracy, latency, and energy in WSN using infection spreading is investigated in [10]. It has two layer architecture for data harvesting: the lower layer sensors are equipped with a novel adaptive data propagation method inspired by infection spreading and the upper layer consists of randomly roaming data harvesting agents.…”

Section: Related Workmentioning

confidence: 99%

Secured routing in wireless sensor networks using fault‐free and trusted nodes

Devanagavi

Nalini²,

Biradar

2014

Int J Communication

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Wireless sensor network (WSN) should be designed such that it is able to identify the faulty nodes, rectify the faults, identify compromised nodes from various security threats, and transmit the sensed data securely to the sink node under faulty conditions. In this paper, we propose an idea of integrating fault tolerance and secured routing mechanism in WSN named as fault tolerant secured routing: an integrated approach (FASRI) that establishes secured routes from source to sink node even under faulty node conditions. Faulty nodes are identified using battery power and interference models. Trustworthy nodes (non-compromised) among fault-free nodes are identified by using agent-based trust model. Finally, the data are securely routed through fault-free non-compromised nodes to sink. Performance evaluation through simulation is carried out for packet delivery ratio, hit rate, computation overhead, communication overhead, compromised node detection ratio, end-to-end delay, memory overhead, and agent overhead. We compared simulation results of FASRI with three schemes, namely multi-version multi-path (MVMP), intrusion/fault tolerant routing protocol (IFRP) in WSN, and active node-based fault tolerance using battery power and interference model (AFTBI) for various measures and found that there is a performance improvement in FASRI compared with MVMP, IFRP, and AFTBI. IFRP, intrusion/fault tolerant routing protocol; INSENS, intrusion-tolerant routing protocol for wireless sensor networks; MVMP, multi-version multi-path; FDWSN, fault detection of wireless sensor networks; BTRM-WSN, bio-inspired trust and reputation model in wireless sensor network; LSRP, link state routing protocol; AFTBI, active node-based fault tolerance using battery power and interference model; FASRI, fault tolerant secured routing: an integrated approach; CDR, compromised node detection ratio.

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“…Furthermore, our algorithms can be used to gather the conductivity, temperature, and depth data, which come from National Oceanic and Atmospheric Administration's National Data Buoy Center [27], by experiments, and the results are better than those of Bai et al [3]. Meanwhile, the method CSEC in [4] is still effective in our approaches and could be used for robust data transmission because the rank of F is equal to the rank of X.…”

Section: Actual Environmentmentioning

confidence: 99%

Distributed compressed sensing in wireless local area networks

Yang

Huang

et al. 2013

Int J Communication

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SUMMARYIn order to improve energy efficiency in the process of gathering data and transmitting information, compressed sensing (CS) has been applied increasingly in wireless local area networks (WLANs). In traditional usages of CS techniques in previous literatures, the sparsities of the signals have to be known beforehand, which is significant for the recovered results. However, it is difficult to realize precisely the structures of the actual signals in WLANs. Therefore, it is important to further exploit the reasonable practicality of signals in actual applications. In this paper, we present a spatial-temporal correlation model to optimize measure matrix of CS on the basis of a local region. Furthermore, two algorithms based on two kinds of situations are designed, both of which are pervasive data distributions in real environments. Our algorithms have been proven to be valuable and could be considered for actual applications. Experiments show that the signals could be reconstructed accurately and stably even if their sparsities could not be known in advance.

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Accuracy, latency, and energy cross‐optimization in wireless sensor networks through infection spreading

Cited by 18 publications

References 27 publications

Challenges and Implementation on Cross Layer Design for Wireless Sensor Networks

Challenges and Implementation on Cross Layer Design for Wireless Sensor Networks

Secured routing in wireless sensor networks using fault‐free and trusted nodes

Distributed compressed sensing in wireless local area networks

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