Class based dynamic priority scheduling for uplink to support M2M communications in LTE

Giluka, Mukesh Kumar; Rajoria, Nitish; Kulkarni, Ashish C.; Sathya, Vanlin; Tamma, Bheemarjuna Reddy

doi:10.1109/wf-iot.2014.6803179

Cited by 46 publications

(26 citation statements)

References 6 publications

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“…Multiclass scheduling can be performed when various data to be scheduled have different characteristics [29,30]. The Multiclass Based Dynamic Priority (MBDP) scheduling algorithm [30] classifies messages used for M2M communication in LTE environments into four types: very small-sized delay-intolerant messages (Type 1), messages requiring a minimum guaranteed bit rate (Type 2), bulk-sized delay-tolerant messages (Type 3), and very small-sized delay-tolerant messages (Type 4).…”

Section: Related Studiesmentioning

confidence: 99%

“…The Multiclass Based Dynamic Priority (MBDP) scheduling algorithm [30] classifies messages used for M2M communication in LTE environments into four types: very small-sized delay-intolerant messages (Type 1), messages requiring a minimum guaranteed bit rate (Type 2), bulk-sized delay-tolerant messages (Type 3), and very small-sized delay-tolerant messages (Type 4). Type 1 messages are classified as class 1 and their delay times are 0.…”

Section: Related Studiesmentioning

confidence: 99%

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An IoT System for Remote Monitoring of Patients at Home

Park

Lee

2017

Applied Sciences

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Application areas that utilize the concept of IoT can be broadened to healthcare or remote monitoring areas. In this paper, a remote monitoring system for patients at home in IoT environments is proposed, constructed, and evaluated through several experiments. To make it operable in IoT environments, a protocol conversion scheme between ISO/IEEE 11073 protocol and oneM2M protocol, and a Multiclass Q-learning scheduling algorithm based on the urgency of biomedical data delivery to medical staff are proposed. In addition, for the sake of patients' privacy, two security schemes are proposed-the separate storage scheme of data in parts and the Buddy-ACK authorization scheme. The experiment on the constructed system showed that the system worked well and the Multiclass Q-learning scheduling algorithm performs better than the Multiclass Based Dynamic Priority scheduling algorithm. We also found that the throughputs of the Multiclass Q-learning scheduling algorithm increase almost linearly as the measurement time increases, whereas the throughputs of the Multiclass Based Dynamic Priority algorithm increase with decreases in the increasing ratio.

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Section: Related Studiesmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

An IoT System for Remote Monitoring of Patients at Home

Park

Lee

2017

Applied Sciences

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“…In the previous 2 categories, the M2M scheduling is isolated from UE scheduling. The third method schedules MTCDs together with UEs in what is called hybrid scheduling [9].…”

Section: Classification Of M2m Uplink Schedulersmentioning

confidence: 99%

“…In [9], a hybrid scheduling algorithm called "Class Based Dynamic Priority (CBDP)" is proposed. The M2M devices and UEs are divided into classes based on Remaining Time to Serve (RTTS), which is the remaining time till the deadline.…”

Section: Related Workmentioning

confidence: 99%

“…This algorithm is application-specific for Wireless Sensor Networks (WSN) or cascaded alarming systems. Delay has more priority than channel [8] QoS Dedicated QoS class is defined by delay and bit rate [9] Delay Hybrid Classes based on delay [10] Channel, Utlity Hybrid Utility is based on required bit rate [11] Fairness, QoS, Channel Hybrid QoS class is defined by delay and bit rate [12] Packet Arrival N/A* Groups based on packet arrival and jitter [13] Packet Arrival, Data Size Dedicated Groups based on packet arrival and data size [14] Delay …”

Section: Related Workmentioning

confidence: 99%

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BAT: A Balanced Alternating Technique for M2M Uplink Scheduling over LTE

Elhamy

Gadallah

2015

2015 IEEE 81st Vehicular Technology Conference (VTC Spring)

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Machine-to-Machine (M2M) scheduling over Long Term Evolution (LTE) networks is an essential research area for future communications. This is due to the strong expectations that M2M communications will be a main element of the overall traffic over 5G networks. The diversity of M2M applications strongly motivates studying the problem of resource allocation in the uplink direction where the M2M traffic is dominant. M2M communications impose requirements that differ from those required by Human-to-Human (H2H) communications. We present a classification of M2M scheduling techniques from the perspective of these requirements. We then propose an M2M uplink scheduling algorithm that offers a balance between throughput and delay requirements. It is also adaptive to traffic characteristics since it considers both channel state and system deadlines in an adjustable manner according to network needs. Finally, we conduct experiments to compare the performance of the proposed technique to that of other schedulers that belong to the different M2M scheduler categories.

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Performance evaluation of a priority‐based resource allocation scheme for multiclass services in IoT

AlQahtani

2019

Int J Communication

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Summary Recently, the deployment of novel smart network concepts, such as the Internet of things (IoT) or machine‐to‐machine communication, has gained more attention owing to its role in providing communication among various smart devices. The IoT involves a set of IoT devices (IoTDs) such as actuators and sensors that communicate with IoT applications via IoT gateways without human intervention. The IoTDs have different traffic types with various delay requirements, and we can classify them into two main groups: critical and massive IoTDs. The fundamental promising technology in the IoT is the advanced long‐term evolution (LTE‐A). In the future, the number of IoTDs attempting to access an LTE‐A network in a short period will increase rapidly and, thus, significantly reduce the performance of the LTE‐A network and affect the QoS required by variant IoT traffic. Therefore, efficient resource allocation is required. In this paper, we propose a priority‐based allocation scheme for multiclass service in IoT to efficiently share resources between critical and massive IoTD traffic based on their specific characteristics while protecting the critical IoTDs, which have a higher priority over the massive IoTDs. The performance of the proposed scheme is analyzed using the Geo/G/1 queuing system focusing on QoS guarantees and resource utilization of both critical and massive IoTDs. The distribution of service time of the proposed system is determined and, thus, the average waiting and service times are derived. The results indicate that the performance of the massive IoTDs depends on the data traffic characteristics of the critical IoTDs. Furthermore, the results emphasize the importance of the system delay analysis and demonstrate its effects on IoT configurations.

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Class based dynamic priority scheduling for uplink to support M2M communications in LTE

Cited by 46 publications

References 6 publications

An IoT System for Remote Monitoring of Patients at Home

An IoT System for Remote Monitoring of Patients at Home

BAT: A Balanced Alternating Technique for M2M Uplink Scheduling over LTE

Performance evaluation of a priority‐based resource allocation scheme for multiclass services in IoT

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