Energy efficient data collection in sparse sensor networks using multiple Mobile Data Patrons

Prabu, P.; Ahmed, Ahmed Najat; Venkatachalam, K.; Nalini, S.; Manikandan, R.

doi:10.1016/j.compeleceng.2020.106778

Cited by 19 publications

(8 citation statements)

References 9 publications

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“…Assume that packet P is located at node i. If pr represents the packet priority queue value and t represents the elapsed time, the overall priority of the queue will be as Equation (1).…”

Section: Fairness Weighted Scheduling Algorithmmentioning

confidence: 99%

“…Increasing advances in electronics, computers, and telecommunications have made it possible to build sensors with high power, reasonable prices, small dimensions, and very low consumption. Depending on their type and application, these sensors can sense information from the environment, collect them and then send this collected information to the sink [1]. Some of these sensor networks just receive information from the environment and transmit it to the analysis centre, while others are intelligent and they are capable of cooperating and coordinating with each other.…”

Section: Introductionmentioning

confidence: 99%

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A priority‐based congestion avoidance scheme for healthcare wireless sensor networks

Mazloomi

Gholipour

Zaretalab

2022

IET Wireless Sensor Systems

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The Wireless Sensor Network (WSN) is a self-organizing network consisting of a number of sensor nodes located in the monitoring area. One of the main applications of wireless sensor network is in the field of health care and remote patient monitoring. One of the most important challenges of such networks is controlling network congestion and transmitting data in a way that improves quality of service (QoS) parameters. Thus, it increasing grid performance and reducing energy consumption. Energy consumption increases due to various reasons such as unsuccessful delivery of packets to the receiver, congestion in the network, retransmission of packets, and delay in delivery packets to the sink, low received signal strength and so on. Given the importance of some data in the field of health, congestion should be avoided and secure data transmission should be ensured. This study divides the collected data into two groups based on their intrinsic characteristics by presenting a congestion management protocol: 1) critical data 2) non-critical data. The proposed protocol provides a dynamic routing algorithm based on the TOPSIS model for non-critical data transmission. In addition, an algorithm for transmitting critical data through the shortest possible path is also provided based on support vector machines. This improves the network performance through using multi-classification that is obtained from Support Vector Machines. The simulation results indicate that the proposed method works better than other methods and leads to better performance in delay, network performance and power consumption.

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“…Assume that packet P is located at node i. If pr represents the packet priority queue value and t represents the elapsed time, the overall priority of the queue will be as Equation (1).…”

Section: Fairness Weighted Scheduling Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A priority‐based congestion avoidance scheme for healthcare wireless sensor networks

Mazloomi

Gholipour

Zaretalab

2022

IET Wireless Sensor Systems

View full text Add to dashboard Cite

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“…In [31], the authors utilize multiple mobile DCs called mobile data patrons that are responsible to collect data from the sensor nodes in a sparse network and transfer to the BS, where, mobile data patrons require high amounts of energy to do transmission. In that work, authors attempt to improve the data transmission rate in such a way that the energy consumption of mobile data patrons can be minimized as much as possible.…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Static Data Collector-based Scheme in Smart Cities

Rajab¹

2022

Computers, Materials &Amp; Continua

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In the Internet of Things (IoT)-based smart city applications, employing the Data Collectors (DC) as the data brokers between the nodes and Base Station (BS) can be a promising solution to enhance the energy efficiency of energy-constrained IoT sensor nodes. There are several schemes that utilize mobile DCs to collect the data packets from sensor nodes. However, moving DCs along the hundreds of thousands of sensors sparsely distributed across a smart city is considered a design challenge in such schemes. Another concern lies in how these mobile DCs are being powered. Therefore, to overcome these limitations, we exploit multiple energy-limited Static Data Collectors (SDC) which are deployed at the locations optimized so that the energy consumption of both nodes and SDCs is minimized. Likewise, an Unmanned Aerial Vehicle (UAV)-enabled wireless power transfer is considered for sustaining the SDCs to avoid their energy depletion and data packet loss. The sustainable charging process operates periodically in each cycle so that the UAV flies from a charging station and after recharging the SDCs, it comes back to the station to be recharged. In this study, we formulate a problem to optimize the movement trajectory and charging time of UAV so that sustainable operation of SDCs during a cycle can be achieved. Unlike the prior studies, our proposed scheme determines the optimal trajectory and charging time at the beginning of each cycle which leads to increase accuracy in comparison with long-term optimization-based schemes. The outcomes of simulation experiments show that the proposed scheme achieves improved network performance in terms of data delivery ratio (12.5%); system throughput (6.6%); total energy consumption (59.19%); and network lifetime (58%) as compared to previous related works.

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“…Unfortunately, the data aggregation in such networks requires significant improvement in low-powered low-resourced SN devices. Since data is forwarded in a multi-hop fashion, it not only generates unwanted traffic congestion but also depletes the energy of intermediate nodes, resulting in minimized network lifetime [59][60][61]. Therefore, the main objective is to minimize the network traffic, message overhead, and the power used in SNs, while aggregating information to enhance the network lifetime.…”

Section: Mobile Code and Itinerary Planningmentioning

confidence: 99%

“…It is harnessed in a distributed environment to overcome issues found in centralized environments, such as flexibility and scalability. It also conserves energy at the node level and enhances overall network lifetime and performance [59][60][61]. There could be several MAs crawling over many network clusters.…”

Section: Mobile Code and Itinerary Planningmentioning

confidence: 99%

A Blockchain-Based Multi-Mobile Code-Driven Trust Mechanism for Detecting Internal Attacks in Internet of Things

Tariq

Asim

Khan

et al. 2020

Sensors

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A multitude of smart things and wirelessly connected Sensor Nodes (SNs) have pervasively facilitated the use of smart applications in every domain of life. Along with the bounties of smart things and applications, there are hazards of external and internal attacks. Unfortunately, mitigating internal attacks is quite challenging, where network lifespan (w.r.t. energy consumption at node level), latency, and scalability are the three main factors that influence the efficacy of security measures. Furthermore, most of the security measures provide centralized solutions, ignoring the decentralized nature of SN-powered Internet of Things (IoT) deployments. This paper presents an energy-efficient decentralized trust mechanism using a blockchain-based multi-mobile code-driven solution for detecting internal attacks in sensor node-powered IoT. The results validate the better performance of the proposed solution over existing solutions with 43.94% and 2.67% less message overhead in blackhole and greyhole attack scenarios, respectively. Similarly, the malicious node detection time is reduced by 20.35% and 11.35% in both blackhole and greyhole attacks. Both of these factors play a vital role in improving network lifetime.

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Energy efficient data collection in sparse sensor networks using multiple Mobile Data Patrons

Cited by 19 publications

References 9 publications

A priority‐based congestion avoidance scheme for healthcare wireless sensor networks

A priority‐based congestion avoidance scheme for healthcare wireless sensor networks

Energy-Efficient Static Data Collector-based Scheme in Smart Cities

A Blockchain-Based Multi-Mobile Code-Driven Trust Mechanism for Detecting Internal Attacks in Internet of Things

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