An energy-efficient scheme for WiFi-capable M2M devices in hybrid LTE network

Singh, Shubhranshu; Huang, Kuei-Li; Lin, B-S P.

doi:10.1109/ants.2012.6524242

Cited by 4 publications

(3 citation statements)

References 4 publications

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“…The problem can be severer as the number of devices, either Human-to-Human (H2H) or Machine-to-Machine (M2M), will increase in upcoming years. The overload problem can indirectly affect energy consumption in IoT devices because the overload condition, and hence high network utilization may cause delay and loss for IoT data packets and retransmitting them may incur more battery power consumption [ 30 , 31 , 32 , 33 , 34 , 35 , 56 , 91 ]. In addition to congestion-induced energy consumption issues, another important factor is an energy-efficient allocation of radio resources (in terms of time, frequency, and transmit power) to IoT devices [ 36 , 37 ].…”

Section: Energy Conserving Issues In Wireless Networking-based Iotmentioning

confidence: 99%

“…Singh et al [ 56 ] considered a particular scenario where LTE and WiFi networks coexist in an M2M environment, and suggested device-gateway synchronization as an energy efficient solution. An IoT gateway-calculated Listen Interval (LI) is sent to IoT devices so that they can update their wake up intervals with respect to the gateway.…”

Section: Energy Conserving Solutions In Wireless Networking-based mentioning

confidence: 99%

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A Survey on Energy Conserving Mechanisms for the Internet of Things: Wireless Networking Aspects

Abbas

Yoon

2015

Sensors

110

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The Internet of Things (IoT) is an emerging key technology for future industries and everyday lives of people, where a myriad of battery operated sensors, actuators, and smart objects are connected to the Internet to provide services such as mobile healthcare, intelligent transport system, environmental monitoring, etc. Since energy efficiency is of utmost importance to these battery constrained IoT devices, IoT-related standards and research works have focused on the device energy conserving issues. This paper presents a comprehensive survey on energy conserving issues and solutions in using diverse wireless radio access technologies for IoT connectivity, e.g., the 3rd Generation Partnership Project (3GPP) machine type communications, IEEE 802.11ah, Bluetooth Low Energy (BLE), and Z-Wave. We look into the literature in broad areas of standardization, academic research, and industry development, and structurally summarize the energy conserving solutions based on several technical criteria. We also propose future research directions regarding energy conserving issues in wireless networking-based IoT.

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Section: Energy Conserving Issues In Wireless Networking-based Iotmentioning

confidence: 99%

Section: Energy Conserving Solutions In Wireless Networking-based mentioning

confidence: 99%

A Survey on Energy Conserving Mechanisms for the Internet of Things: Wireless Networking Aspects

Abbas

Yoon

2015

Sensors

110

View full text Add to dashboard Cite

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“…This event is then sent over wired or wireless network to the server/application that translates it into meaningful information for the user. In other words, unlike machine to human communication, in M2M communication information exchanged is over wired, wireless or hybrid network, with minimum possible human interference [1].…”

Section: Introductionmentioning

confidence: 99%

Base Station Selection in M2M Communication Using Q-Learning Algorithm in LTE-A Networks

Mohammed

Khwaja

Anpalagan

et al. 2015

2015 IEEE 29th International Conference on Advanced Information Networking and Applications

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A major problem faced by machine type communication (MTC) devices in machine to machine (M2M) communication is the congestion and traffic overloading when incorporating into LTE Advanced networks. In this paper, we present an approach to tackle this problem by providing an efficient way for multiple access in the network and minimizing network overload. We consider the random access network (RAN) between the LTE base stations and MTC devices in the cell. We propose an unsupervised learning algorithm, based on Q-learning, as a means of base station selection scheme where MTC devices continuously adapt to changing network traffic and decide which base station is to be selected on the basis of QoS parameters. Simulation results demonstrate that the proposed algorithm helps MTC devices achieve better performance and, therefore, enhances the M2M communication performance.

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