Reliable Multicasting Service for Densely Deployed Military Sensor Networks

Na, Woongsoo; Dao, Nhu‐Ngoc; Cho, Sungrae

doi:10.1155/2015/341912

Cited by 12 publications

(5 citation statements)

References 9 publications

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“…In this se ing we have the following upper-bound on the broadcast capacity of the network. 1. e broadcast capacity of the 3 × 3 grid network is at most 1 3 .…”

Section: Network With Interference Constraintsmentioning

confidence: 99%

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Throughput-Optimal Broadcast in Wireless Networks with Point-to-Multipoint Transmissions

Sinha

Modiano

2021

IEEE Trans. on Mobile Comput.

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We consider the problem of e cient packet dissemination in wireless networks with point-to-multi-point wireless broadcast channels. We propose a dynamic policy, which achieves the broadcast capacity of the network.is policy is obtained by rst transforming the original multi-hop network into a precedence-relaxed virtual single-hop network and then nding an optimal broadcast policy for the relaxed network. e resulting policy is shown to be throughput-optimal for the original wireless network using a sample-path argument. We also prove the NP-completeness of the nite-horizon broadcast problem, which is in contrast with the polynomial time solvability of the problem with point-to-point channels. Illustrative simulation results demonstrate the e cacy of the proposed broadcast policy in achieving the full broadcast capacity with low delay.

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“…In this se ing we have the following upper-bound on the broadcast capacity of the network. 1. e broadcast capacity of the 3 × 3 grid network is at most 1 3 .…”

Section: Network With Interference Constraintsmentioning

confidence: 99%

“…Corollary: As a direct consequence of the above reduction, it follows that the problem Wireless Broadcast remains NP-complete even with the following additional restrictions: (1) e wireless transmissions are non-interfering. (2) e graph G(V , E) is a DAG (c.f.…”

mentioning

confidence: 93%

Throughput-Optimal Broadcast in Wireless Networks with Point-to-Multipoint Transmissions

Sinha

Modiano

2021

IEEE Trans. on Mobile Comput.

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“…As shown in Fig. 1.2, there are varieties of applications benefiting from WSNs, such as building monitoring [6], health care [7], smart agriculture [8,9], military surveillance [10], environment monitoring [11,12], and detection issues [13,14,15].…”

Section: Global View Of Wireless Sensor Networkmentioning

confidence: 99%

“…Finally, we investigate the impact of step S. We set S ∈ [1,10] and W varies from 10 to 25. As shown in Fig.…”

Section: Selection For W and Smentioning

confidence: 99%

Sensor Network Security

Dinker¹,

Sharma²

2017

Energy-Efficient Wireless Sensor Networks

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Wireless Sensor Networks (WSNs) have been regarded as an emerging and promising field in both academia and industry. Currently, such networks are deployed due to their unique properties, such as self-organization and ease of deployment. However, there are still some technical challenges needed to be addressed, such as energy and network capacity constraints. Data aggregation, as a fundamental solution, processes information at sensor level as a useful digest, and only transmits the digest to the sink. The energy and capacity consumptions are reduced due to less data packets transmission. As a key category of data aggregation, aggregation function, solving how to aggregate information at sensor level, is investigated in this thesis.We make four main contributions: firstly, we propose two new networking-oriented metrics to evaluate the performance of aggregation function: aggregation ratio and packet size coefficient. Aggregation ratio is used to measure the energy saving by data aggregation, and packet size coefficient allows to evaluate the network capacity change due to data aggregation. Using these metrics, we confirm that data aggregation saves energy and capacity whatever the routing or MAC protocol is used. Secondly, to reduce the impact of sensitive raw data, we propose a data-independent aggregation method which benefits from similar data evolution and achieves better recovered fidelity. Thirdly, a property-independent aggregation function is proposed to adapt the dynamic data variations. Comparing to other functions, our proposal can fit the latest raw data better and achieve real adaptability without assumption about the application and the network topology. Finally, considering a given application, a target accuracy, we classify the forecasting aggregation functions by their performances.The networking-oriented metrics are used to measure the function performance, and a Markov Decision Process is used to compute them. Dataset characterization and classification framework are also presented to guide researcher and engineer to select an appropriate functions under specific requirements.Key-words : wireless sensor networks; data aggregation; aggregation functions; energy consumption; capacity saving; classification; performance evaluation. Global view of Wireless Sensor Networksperformance of wireless sensor network. How to deal with the above constraints has become a hot topic for researchers. Data aggregation [3, 4], which is a way for saving energy and network capacity, has been frequently investigated. By studying the correlations of raw data, data aggregation reduces the data packets in the network, thereby saving communication cost and easing network congestion. In this manuscript, we place our focus on data aggregation of wireless sensor network. However, due to the limited battery power of sensor nodes, the network lifetime and performance are restricted. Meanwhile, in several applications (e.g. temperature monitoring), sensor nodes are prone to transmit redundant or correlated inform...

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“…According to this, MTC is considered as a promising approach for the spreading of Internet of things (IoT) sensing platforms that generally involve small data communication [ 1 ]. Utilizing the benefits of the LTE-M infrastructure, IoT sensing platforms (IoTSPs) are expected to cover a larger geographical area with an economically feasible cost since MTC has a close relationship with IoT devices [ 2 ]. In the scope of this paper, the term IoT sensing platform is equivalent to the term IoT sensing system.…”

Section: Introductionmentioning

confidence: 99%

Adaptive MCS selection and resource planning for energy-efficient communication in LTE-M based IoT sensing platform

et al. 2017

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As an important part of IoTization trends, wireless sensing technologies have been involved in many fields of human life. In cellular network evolution, the long term evolution advanced (LTE-A) networks including machine-type communication (MTC) features (named LTE-M) provide a promising infrastructure for a proliferation of Internet of things (IoT) sensing platform. However, LTE-M may not be optimally exploited for directly supporting such low-data-rate devices in terms of energy efficiency since it depends on core technologies of LTE that are originally designed for high-data-rate services. Focusing on this circumstance, we propose a novel adaptive modulation and coding selection (AMCS) algorithm to address the energy consumption problem in the LTE-M based IoT-sensing platform. The proposed algorithm determines the optimal pair of MCS and the number of primary resource blocks (#PRBs), at which the transport block size is sufficient to packetize the sensing data within the minimum transmit power. In addition, a quantity-oriented resource planning (QORP) technique that utilizes these optimal MCS levels as main criteria for spectrum allocation has been proposed for better adapting to the sensing node requirements. The simulation results reveal that the proposed approach significantly reduces the energy consumption of IoT sensing nodes and #PRBs up to 23.09% and 25.98%, respectively.

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Reliable Multicasting Service for Densely Deployed Military Sensor Networks

Cited by 12 publications

References 9 publications

Throughput-Optimal Broadcast in Wireless Networks with Point-to-Multipoint Transmissions

Throughput-Optimal Broadcast in Wireless Networks with Point-to-Multipoint Transmissions

Sensor Network Security

Adaptive MCS selection and resource planning for energy-efficient communication in LTE-M based IoT sensing platform

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