Lifetime-Aware Scheduling and Power Control for M2M Communications in LTE Networks (original) (raw)
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Lifetime-Aware Scheduling and Power Control for Cellular-based M2M Communications
In this paper the uplink scheduling and transmit power control are investigated to minimize the energy consumption for battery-driven devices deployed in cellular networks. A lifetime metric, based on the accurate energy consumption model for cellular-based machine devices, is provided and used to formulate the uplink scheduling and power control problems as network-lifetime maximization problems. Then, lifetime-aware uplink scheduling and power control protocols which maximize the overall network-lifetime are investigated based on the different lifetime definitions. Besides the exact solution, a low-complexity suboptimal solution is presented in this work which can achieve near optimal lifetime-performance with much lower computational complexity. The performance evaluation shows that the network lifetime is significantly extended under proposed protocols.
Energy Efficient Resource Allocation for M2M Devices in LTE/LTE-A
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Machine-to-machine (M2M) communication consists of the communication between intelligent devices without human intervention. Long term evolution (LTE) and Long-term evolution-advanced (LTE-A) cellular networks technologies are excellent candidates to support M2M communication as they offer high data rates, low latencies, high capacities and more flexibility. However, M2M communication over LTE/LTE-A networks faces some challenges. One of these challenges is the management of resource radios especially on the uplink. LTE schedulers should be able to meet the needs of M2M devices, such as power management and the support of large number of devices, in addition to handling both human-to-human (H2H) and M2M communications. Motivated by the fundamental requirement of extending the battery lives of M2M devices and managing an LTE network system, including both M2M devices and H2H users, in this paper, two channel-aware scheduling algorithms on the uplink are proposed. Both of them conside...
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In Machine-to-Machine (M2M) networks, an energy efficient scalable medium access control (MAC) is crucial for serving massive battery-driven machine-type devices. In this paper, we investigate the energy efficient MAC design to minimize battery power consumption in cellular-based M2M communications. We present an energy efficient MAC protocol that not only adapts contention and reservation-based protocols for M2M communications in cellular networks, but also benefits from partial clustering to handle the massive access problem. Then we investigate the energy efficiency and access capacity of contentionbased protocols and present an energy efficient contention-based protocol for intra-cluster communication of the proposed MAC, which results in huge power saving. The simulation results show that the proposed MAC protocol outperforms the others in energy saving without sacrificing much delay or throughput. Also, the lifetimes of both individual nodes and the whole M2M network are significantly extended.
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In this paper, we introduce uplink scheduling algorithms based on unique data to minimize energy consumption and to succeed data transmission sent by machine-type communication devices (MTCDs) on LTE networks when radio resources are limited. Important data carried by MTCDs are through a form of statistical data analysis termed statistical priority. The statistical priority is based on statistical attributes, such as exceeding a safety threshold, checking data similarity, and observing constant increasing or decreasing trends. The first suggested algorithm focuses on allocating radio resources based on a lifetime metric and controls transmission power to prolong the network lifetime. While the second suggested algorithm is based on a penalty metric. The simulation results show that the proposed algorithms achieve the highest operating lifetime extension on the network and the considerable success rate of sending important data (above 90%) in comparison to the existing MTC algorithm ...
Joint Energy and QoS aware Memetic based Scheduling for M2M Communications in LTE M
IEEE Transactions on Emerging Topics in Computational Intelligence, 2019
As a result of the increasing number of machine-type devices connected through the Internet, several challenges remain to date to support machine-to-machine (M2M) communications over long term evolution (LTE) cellular networks. In this paper, we tackle one important challenge of scheduling M2M traffic in uplink over LTE-M mobile networks. We propose a novel cross-layer scheme that considers packet scheduling in time and frequency domain using a memetic-based algorithm and aims to optimize resource allocation of M2M devices by considering their quality of service (QoS) needs while minimizing at the same time their energy consumption. After integrating an energy module for LTE in NS3 simulator, we perform simulations in a realistic M2M scenario and we evaluate the results which show how our proposed scheduling scheme outperforms other existing scheduling methods from the literature such as round robin and proportional-fair algorithms in terms of throughput, energy consumption and the percentage of satisfied devices with regard to their delay and throughput requirements.
Power Aware Mobility Management of M2M for IoT Communications
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Machine-to-Machine (M2M) communications framework is evolving to sustain faster networks with the potential to connect millions of devices in the following years. M2M is one of the essential competences for implementing Internet of Things (IoT). Therefore, various organizations are now focusing on enhancing improvements into their standards to support M2M communications. Thus, Heterogeneous Mobile Ad Hoc Network (HetMANET) can normally be considered appropriate for M2M challenges. These challenges incorporated when a mobile node (MN) selects a target network in an energy efficient scanning for efficient handover. Therefore, to cope with these constraints, we proposed a vertical handover scheme for handover triggering and selection of an appropriate network. The proposed scheme is composed of two phases. Firstly, the MNs perform handover triggering based on the optimization of the Receive Signal Strength (RSS) from an access point/base station (AP/BS). Secondly, the network selection...