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Wireless-Powered Machine-To-Machine Multicasting in Cellular Networks Abdullah M 1 Wireless-Powered Machine-to-Machine Multicasting in Cellular Networks Abdullah M. Almasoud, and Ahmed E. Kamal, Fellow, IEEE Abstract—In future cellular networks, it is expected that data collected data and communicate with other physical objects traffic will increase significantly due to deployments of large in order to make a decision or accomplish a certain task numbers of Internet of Things (IoT) objects. The IoT objects [3]. It is expected that the number of IoT objects that will operate underlaying a cellular network, and they use Machine-to- Machine (M2M) communication to transmit multicst messages. be deployed in the world will reach 50 billions by 2020 We propose to use Radio Frequency (RF) Energy Transmitters [4]. M2M communication, which is also called machine-type- (ET) to compensate the IoT objects with the energy consumed in communication (MTC), is considered as an important enabling forwarding multicast messages. Our goal is to support multicast technology for IoT, where it allows direct communication service for IoT objects and transmit energy to them such that the between neighboring IoT objects. total transferred energy by the ETs is minimized. We formulated the problem mathematically as a non-convex Mixed Integer Multicasting over cellular networks can be classified based Nonlinear Program (MINLP). Due to the difficulty of solving the on its applications into human oriented and machine oriented problem optimally, we decompose the original problem into two [5]. Multicast service in cellular networks is developed typi- sub-problems using Generalized Bender Decomposition with Suc- cally for human-based applications like video content delivery. cessive Convex programming (GBD-SCP). Although this method On the other hand, machine oriented multicast is designed facilitates finding a solution for the problem, the problem is still hard due to binary variables. Hence, we propose the Constraints to support multicast service for machine-based applications, Decomposition with SCP and Binary Variable Relaxation (CDR) which includes: 1) An IoT object sends software updates to algorithm to solve the problem more efficiently. Simulation results a group of IoT objects, 2) an IoT object sends a multicast show that the proposed algorithm achieves a performance close to messages to a group of IoT actuators to perform controlling the GBD-SCP algorithm while the computation time is reduced actions in a factory and 3) an IoT sensing object that detects significantly when the network size is larger. hazardous events on the road and multicast warning messages Index Terms—Wireless-powered, energy harvesting, power to a groups of IoT objects embedded in Vehicular Ad Hoc transfer, multicast, M2M communication, routing, scheduling. Networks (VANET). Therefore, machine oriented multicasting should address the challenges associated with IoT to enable I. INTRODUCTION its applications in the next generation of the cellular networks. ULTICASTING is an essential service for disseminat- IoT devices are typically designed to use small size batteries M ing a message to a group of recipients. Instead of to satisfy their energy demands. On the other hand, devices sending a message from a source to a group of destinations in wireless-powered networks harvest RF (Radio Frequency) multiple times using unicast communications, multicast ser- energy from dedicated energy transmitters or from ambient RF vice allows addressing a message to a group of destinations radiation. RF energy harvesting is a technology that enables simultaneously. Multicast service becomes more appealing in converting a received RF signal to energy [6]. Hence, wireless- cellular networks due to a rapid growth in data traffic in the powered network has emerged as a candidate solution for some recent years [2]. Multicasting in current cellular networks is applications in future networks [7]. Although wireless energy used for content delivery for typical cellular phones. However, transfer gives the IoT devices an efficient way to satisfy their with the revolution of the Internet of Things (IoT), Machine- energy demands without the need for battery replacement, a to-Machine (M2M) multicast service for large numbers of low- significant portion of the transmitted signal for charging is power IoT devices in cellular networks is required. Therefore, wasted because of signal attenuation and non-optimality of we need to consider several challenges while supporting this the energy harvesters. emerging type of multicast service. There is a trade-off between satisfying the energy demands IoT is a technology that enables physical objects to ob- of the IoT devices using only wireless energy transfer or serve and monitor activities and phenomena, processes the batteries. The former approach helps the IoT to satisfy their energy demands without the need for battery replacement or Abdullah M. Almasoud and Ahmed E. Kamal are with the Department suffering from energy outage. However, part of the transmitted of Electrical and Computer Engineering, Iowa State University, Ames, IA 50011, USA. Abdullah M. Almasoud is now with the Department of Electrical energy can be lost due to the warless medium and imperfect Engineering, College of Engineering in Al-Kharj, Prince Sattam bin Abdulaziz energy harvesters. Therefore, using only conventional batteries University, Al-Kharj 11942, Saudi Arabia. to power IoT devices eliminates wasting energy that happens E-mails: [email protected], [email protected]. Part of this work has been accepted for publication in 2018 IEEE Global during wireless energy transfer. Accordingly, it may not be Communications Conference [1]. feasible to power the whole M2M devices (IoT objects) in This research was supported in part by grant 1827211 from the National the network using only RF energy harvesting technology. Science Foundation, USA. This publication was supported by the Deanship of Scientific Research at However, wireless energy transfer can power the M2M devices Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia. partially by supporting the M2M devices with the required 2 energy to transmit the multicast messages. opportunistically. The proposed work aimed at enhancing In this paper, we consider wirelessly powered M2M multi- spectrum and energy efficiencies. casting underlaying cellular networks. Due to the high cost of 3) Internet of Things: In [17], we proposed a cognitive powering the multicast communication using wireless energy mobile base station that transmits data and energy to IoT transfer, the goal is that we minimize the total required energy devices. To transfer energy to the IoT devices within a certain to be transferred from the Energy Transmitters (ET) to the tolerable time, the mobile base station adjusts its location M2M devices. Since M2M devices are compensated for the and transmission power such that the IoT devices are charged energy consumed in sending multicast messages, they should without delay. To optimize the operation of the mobile base minimize their total consumed energy for transmission to station, we showed how to minimize the total energy consumed reduce the total transmitted energy from the ETs. As the in energy transfer and the mobility of the base station. The M2M devices operate underlaying a cellular network, they paper in [16] studied an energy efficient resource allocation must keep their interference under certain thresholds to protect for M2M communication and energy harvesting for IoT. Joint the regular cellular users and the other M2M devices. Within power allocation and time allocation are considered in order any time slot, the M2M devices can either: 1) transmit data, to minimize total energy consumption. The authors in [18] 2) receive data, 3) harvest energy or 4) stay idle. Hence, we studied full-duplex M2M communication for wireless-powered show how to schedule multicast message transmission and IoT. The idea of the paper is to utilize the extra energy not reception and RF energy harvesting for the M2M devices. The used by receivers, and hence, receiving IoT devices transfer scheduling process aims in supporting the multicast services energy to the transmitting IoT device. while minimizing the required transmitted energy by the ETs. 4) Multicasting: An M2M multicast service for transferring data and energy to a large number of users is proposed in [19]. It is shown that the proposed scheme reduces energy A. Related Works consumption and delay while reducing the control overhead. In 1) Wireless-Powered Networks: In [8], the authors studied [20], the authors introduced a reliable multicast and broadcast the beamforming in multicast wirelessly powered networks. method for energy harvesting network. The proposed method They formulated the problem mathematically and proposed a guarantees reliable multicast service for the energy harvesting fast parallel iterative algorithm that converges to a KKT point. nodes which suffers from energy deficiency. In [21], algo- The paper in [9] considered energy efficiency optimization for rithms for routing multimedia multicast in IoT is studied. It machined-to-machined communication. The proposed work is shown that the speed and the accuracy of the proposed considered a joint optimization for channel selection, power algorithm outperforms a representative multicast routing algo- control and time allocation. Moreover, the authors in [12] rithm. Wireless-powered multicast and unicast services with investigated maximum energy efficiency in wireless powered full duplex self-energy recycling
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