SenPUI: Solutions for Sensing and Primary User Interference in Cognitive Radio Implementation of a Wireless Sensor Network

Besher, Kedir Mamo; Hipólito, Juan Iván Nieto; Vazquez-Briseno, Mabel; Buenrostro-Mariscal, Raymundo

doi:10.1155/2019/2405141

Cited by 8 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to have an estimation of the device lifetime for a given battery capacity, network configuration, and transmission frequency, we need to understand the power consumption of an IoT device during (1) initial turn-on, (2) connected state, (3) re-transmission and (4) sleep/listen state. To the best of our knowledge, literature research is more focused on optimizing energy efficiency through different scheduling sachems, which is not on solving the cause [4,5,[10][11][12]23,24]. Instead, this work is based on empirical evaluation of real deployments, which shed light on the relation between power consumption and spectrum management in congested IoT environment.…”

Section: Related Workmentioning

confidence: 99%

Spectrum Based Power Management for Congested IoT Networks

Besher

Hipólito

Buenrostro-Mariscal

2021

Sensors

Self Cite

View full text Add to dashboard Cite

With constantly increasing demand in connected society Internet of Things (IoT) network is frequently becoming congested. IoT sensor devices lose more power while transmitting data through congested IoT networks. Currently, in most scenarios, the distributed IoT devices in use have no effective spectrum based power management, and have no guarantee of a long term battery life while transmitting data through congested IoT networks. This puts user information at risk, which could lead to loss of important information in communication. In this paper, we studied the extra power consumed due to retransmission of IoT data packet and bad communication channel management in a congested IoT network. We propose a spectrum based power management solution that scans channel conditions when needed and utilizes the lowest congested channel for IoT packet routing. It also effectively measured power consumed in idle, connected, paging and synchronization status of a standard IoT device in a congested IoT network. In our proposed solution, a Freescale Freedom Development Board (FREDEVPLA) is used for managing channel related parameters. While supervising the congestion level and coordinating channel allocation at the FREDEVPLA level, our system configures MAC and Physical layer of IoT devices such that it provides the outstanding power utilization based on the operating network in connected mode compared to the basic IoT standard. A model has been set up and tested using freescale launchpads. Test data show that battery life of IoT devices using proposed spectrum based power management increases by at least 30% more than non-spectrum based power management methods embedded within IoT devices itself. Finally, we compared our results with the basic IoT standard, IEEE802.15.4. Furthermore, the proposed system saves lot of memory for IoT devices, improves overall IoT network performance, and above all, decrease the risk of losing data packets in communication. The detail analysis in this paper also opens up multiple avenues for further research in future use of channel scanning by FREDEVPLA board.

show abstract

Section: Related Workmentioning

confidence: 99%

Spectrum Based Power Management for Congested IoT Networks

Besher

Hipólito

Buenrostro-Mariscal

2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Because the objective is for cognitive-based delay analysis, energy detection is performed when every packet reception is completed. The cognitive algorithm presented in our previous work [3] is applied. Spectrum sensing, selects the available licensed channel and broadcasts selected channel to all MNs through the periodical beacon frame is the responsibility of CN.…”

Section: General Scenariosmentioning

confidence: 99%

“…We measured the service delay which is described as the continuation that unlicensed user data packet is successfully transmitted to its sink node. Regarding licensed once they are served as soon as they need without any service delay time [3]. Figure 2 Explains the most common steps that an unlicensed user packet transmission transmitted successfully on channel CH (11, 12, .…”

Section: S Delay Service Delay Derivationmentioning

confidence: 99%

“…The inactive portion of communication is used for sensing, channel energy level detection, updating and results saving in a table, TSR, so that we can use it in the future. The whole process is described in our previous work [3]. Using cognitive channels resulted in effective ATMLT due to the fact that it effectively manages the communication spectrum.…”

Section: Cogitative Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Cognition-Based Delay Analysis to Determine the Average Minimum Time Limit for Wireless Sensor Communications

Besher¹,

Hipólito²,

Sanchez-Lopez³

et al. 2020

IEICE Trans. Inf. & Syst.

Self Cite

View full text Add to dashboard Cite

End-to-end delay, aiming to realize how much time it will take for a traffic load generated by a Mobile Node (MN) to reach Sink Node (SN), is a principal objective of most new trends in a Wireless Sensor Network (WSN). It has a direct link towards understanding the minimum time delay expected where the packet sent by MN can take to be received by SN. Most importantly, knowing the average minimum transmission time limit is a crucial piece of information in determining the future output of the network and the kind of technologies implemented. In this paper, we take network load and transmission delay issues into account in estimating the Average Minimum Time Limit (AMTL) needed for a health operating cognitive WSN. To further estimate the AMTL based on network load, an end-to-end delay analysis mechanism is presented and considers the total delay (service, queue, ACK, and MAC). This work is proposed to answer the AMTL needed before implementing any cognitive based WSN algorithms. Various time intervals and cogitative channel usage with different application payload are used for the result analysis. Through extensive simulations, our mechanism is able to identify the average time intervals needed depending on the load and MN broadcast interval in any cognitive WSN.

show abstract

“…Therefore, some solutions need to be deployed to achieve viable CRSN based SG communications. Minimum hardware, for example using single radio, and less advanced spectrum sensing functionalities, can be used to lower the complexity level of the sensing operations and reduce energy consumption [12], [17]. Reducing the sensing durations to an appreciable level can be a good solution.…”

Section: Spectrum Sensingmentioning

confidence: 99%

Radio Resource Allocation Improvements in CRSN for Smart Grid: A Survey

Ogbodo¹,

Dorrell²,

Abu‐Mahfouz³

2019

Preprint

View full text Add to dashboard Cite

A cognitive radio sensor network (CRSN) based Smart Grid (SG) is a new paradigm for a modern SG. It is totally different from the traditional power grid and also different from the conventional SG that uses a static resource allocation technique to allocate resources to sensor nodes and communication devices in the SG network. Due to the challenges associated with competitive sensor nodes and communication devices in accessing and utilizing radio resources, the need for dynamic radio resource allocation (RRA) has been proposed as a solution for allocating radio resources to sensor nodes in a CRSN based smart grid ecosystem (network). These challenges include energy/power constraints, poor quality of service (QoS), interference, delay, spectrum efficiency issues, and excessive spectrum hand-offs. Hence, the optimization of resource allocation criteria, such as energy efficiency, throughput maximization, QoS guarantee, fairness, priority, interference mitigation/avoidance, etc., will go a long way in addressing the problems of RRA in a CRSN based SG. Consequently, this work explores RRA in CRSNs for SGs. Various resource allocation schemes, as well as its architecture in a CRSN for SG environment, are presented. The work reported in this paper introduces a model called the “guaranteed network connectivity channel allocation” for throughput maximization (GNC-TM) and optimal spectrum band determination in RRA for improved throughput criteria in CRSNs for SGs. The results show that the model outperforms the existing protocol in terms of throughput and error probability. Finally, the contribution to knowledge and future research direction, such as energy efficiency and hybrid energy harvesting schemes are highlighted.

show abstract

SenPUI: Solutions for Sensing and Primary User Interference in Cognitive Radio Implementation of a Wireless Sensor Network

Cited by 8 publications

References 17 publications

Spectrum Based Power Management for Congested IoT Networks

Spectrum Based Power Management for Congested IoT Networks

Cognition-Based Delay Analysis to Determine the Average Minimum Time Limit for Wireless Sensor Communications

Radio Resource Allocation Improvements in CRSN for Smart Grid: A Survey

Contact Info

Product

Resources

About