Viability Bounds of M2M Communication Using Energy-Harvesting and Passive Wake-Up Radio

Rinne, J.; Keskinen, Jari; Berger, Paul R.; Lupo, Donald; Valkama, Mikko

doi:10.1109/access.2017.2713878

Cited by 16 publications

(18 citation statements)

References 29 publications

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“…Substituting (F.1) into problem (29), we show that problem (29) is equivalent to problem (34). Then, we show that problem (34) is a convex problem.…”

Section: Appendix F Proof Of Theoremmentioning

confidence: 91%

“…wheret t t = [t 1 , · · · , t M+N ] T . Obviously, problem (29) is nonconvex due to nonconvex objective function (29a) and constraints (29b)-(29d). In the following, we first provide the optimal conditions for problem (29), and then we propose a low-complexity algorithm to solve problem (29).…”

Section: Energy Efficient Resource Allocation For Tdmamentioning

confidence: 99%

“…We first show that problem (29) can be equivalently transformed into problem (34). We introduce a set of new nonnegative variables: p j = t j p j ,q i = t M+i q i , ∀i ∈ N , j ∈ J i .…”

Section: Appendix F Proof Of Theoremmentioning

confidence: 99%

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Energy Efficient Resource Allocation in Machine-to-Machine Communications With Multiple Access and Energy Harvesting for IoT

Yang

Pan

et al. 2018

IEEE Internet Things J.

172

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Abstract-This paper studies energy efficient resource allocation for a machine-to-machine (M2M) enabled cellular network with non-linear energy harvesting, especially focusing on two different multiple access strategies, namely non-orthogonal multiple access (NOMA) and time division multiple access (TDMA). Our goal is to minimize the total energy consumption of the network via joint power control and time allocation while taking into account circuit power consumption. For both NOMA and TDMA strategies, we show that it is optimal for each machine type communication device (MTCD) to transmit with the minimum throughput, and the energy consumption of each MTCD is a convex function with respect to the allocated transmission time. Based on the derived optimal conditions for the transmission power of MTCDs, we transform the original optimization problem for NOMA to an equivalent problem which can be solved suboptimally via an iterative power control and time allocation algorithm. Through an appropriate variable transformation, we also transform the original optimization problem for TDMA to an equivalent tractable problem, which can be iteratively solved. Numerical results verify the theoretical findings and demonstrate that NOMA consumes less total energy than TDMA at low circuit power regime of MTCDs, while at high circuit power regime of MTCDs TDMA achieves better network energy efficiency than NOMA.Index Terms-Internet of Things (IoT), machine-to-machine (M2M), non-orthogonal multiple access (NOMA), energy harvesting, resource allocation.

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“…Substituting (F.1) into problem (29), we show that problem (29) is equivalent to problem (34). Then, we show that problem (34) is a convex problem.…”

Section: Appendix F Proof Of Theoremmentioning

confidence: 91%

Section: Energy Efficient Resource Allocation For Tdmamentioning

confidence: 99%

See 1 more Smart Citation

Energy Efficient Resource Allocation in Machine-to-Machine Communications With Multiple Access and Energy Harvesting for IoT

Yang

Pan

et al. 2018

IEEE Internet Things J.

172

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“…Energy harvesting applying diverse methods [ 25 , 26 , 27 , 28 , 29 ] like RF energy harvesting, solar energy harvesting, thermal energy harvesting allows almost perpetual use of devices and hence helps in maintaining very large systems as, e.g., batteries are not needed to be maintained or changed regularly.…”

Section: Rf and Photovoltaic Energy Harvestingmentioning

confidence: 99%

M2M Communication Assessment in Energy-Harvesting and Wake-Up Radio Assisted Scenarios Using Practical Components

Rinne

Keskinen

Berger

et al. 2018

Sensors

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Techniques for wireless energy harvesting (WEH) are emerging as a fascinating set of solutions to extend the lifetime of energy-constrained wireless networks, and are commonly regarded as a key functional technique for almost perpetual communications. For example, with WEH technology, wireless devices are able to harvest energy from different light sources or Radio Frequency (RF) signals broadcast by ambient or dedicated wireless transmitters to support their operation and communications capabilities. WEH technology will have increasingly wider range of use in upcoming applications such as wireless sensor networks, Machine-to-Machine (M2M) communications, and the Internet of Things. In this paper, the usability and fundamental limits of joint RF and solar cell or photovoltaic harvesting based M2M communication systems are studied and presented. The derived theoretical bounds are in essence based on the Shannon capacity theorem, combined with selected propagation loss models, assumed additional link nonidealities, diversity processing, as well as the given energy harvesting and storage capabilities. Fundamental performance limits and available capacity of the communicating link are derived and analyzed, together with extensive numerical results evaluated in different practical scenarios, including realistic implementation losses and state-of-the-art printed supercapacitor performance figures with voltage doubler-based voltage regulator. In particular, low power sensor type communication applications using passive and semi-passive wake-up radio (WuR) are addressed in the study. The presented analysis principles and results establish clear feasibility regions and performance bounds for wireless energy harvesting based low rate M2M communications in the future IoT networks.

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“…Among the hardware solutions, wake-up radio technology is a way for maximizing the energy efficiency and is based on the utilization of a near-zero consumption secondary transceiver which triggers the main radio interface when a specific signal is detected [7]. In the Passive configuration, the wake-up radio harvests energy from the RF signals, and hence introduces fullzero consumptions [8]. While there is plenty of research on the circuitery design of a Wake-Up receiver (WuRx) (e.g.…”

Section: Introductionmentioning

confidence: 99%

Dual-Mode Wake-Up Nodes for IoT Monitoring Applications: Measurements and Algorithms

Bedogni

Bononi

Canegallo

et al. 2018

2018 IEEE International Conference on Communications (ICC)

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Internet of Things (IoT)-based monitoring applications usually involve large-scale deployments of battery-enabled sensor nodes providing measurements at regular intervals. In order to guarantee the service continuity over time, the energyefficiency of the networked system should be maximized. In this paper, we address such issue via a combination of novel hardware/software solutions including new classes of Wake-up radio IoT Nodes (WuNs) and novel data-and hardware-driven network management algorithms. Three main contributions are provided. First, we present the design and prototype implementation of WuN nodes able to support two different energy-saving modes; such modes can be configured via software, and hence dynamically tuned. Second, we show by experimental measurements that the optimal policy strictly depends on the application requirements. Third, we move from the node design to the network design, and we devise proper orchestration algorithms that select both the optimal set of WuN to wake-up and the proper energy-saving mode for each WuN, so that the application lifetime is maximized, while the redundancy of correlated measurements is minimized. The proposed solutions are extensively evaluated via OMNeT++ simulations under different IoT scenarios and requirements of the monitoring applications. • First, we describe the architecture and the implementation of a prototype IoT Wake-Up node (WuN), which

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Viability Bounds of M2M Communication Using Energy-Harvesting and Passive Wake-Up Radio

Cited by 16 publications

References 29 publications

Energy Efficient Resource Allocation in Machine-to-Machine Communications With Multiple Access and Energy Harvesting for IoT

Energy Efficient Resource Allocation in Machine-to-Machine Communications With Multiple Access and Energy Harvesting for IoT

M2M Communication Assessment in Energy-Harvesting and Wake-Up Radio Assisted Scenarios Using Practical Components

Dual-Mode Wake-Up Nodes for IoT Monitoring Applications: Measurements and Algorithms

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