A Communication Networks Integrated by Data and Energy for Wireless Big Data

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International e-Journal For Technology And Research-2017

A Communication Networks Integrated by Data and Energy for Wireless Big Data Ms. SUMAIYA FARHEN 1, Mrs. SHASHIREKHA H 2 Dept. of Computer Science 1 MTech, Student– VTU PG Center, Mysuru, India 2 Guide, Assistant Professor– VTU PG Center, Mysuru, India SURVEY PAPER 1 ABSTRACT: This survey describes a new type of communication network called data and energy integrated communication networks (DEINs), which integrates the traditionally separate two processes, i.e., wireless information transfer (WIT) and wireless energy transfer (WET), fulfilling co-transmission of data and energy. In particular, the energy transmission using radio frequency is for the purpose of energy harvesting (EH) rather than information decoding. One driving force of the advent of DEINs is wireless big data, which comes from wireless sensors that produce a large amount of small piece of data. These sensors are typically powered by battery that drains sooner or later and will have to be taken out and then replaced or recharged. EH has emerged as a technology to wirelessly charge batteries in a contactless way.

Recent research work has attempted to combine WET with WIT, typically under the label of simultaneous wireless information and power transfer. Such work in the literature largely focuses on the communication side of the whole wireless networks with particular emphasis on power allocation. The DEIN communication network proposed in this paper regards the convergence of WIT and WET as a full system that considers not only the physical layer but also the higher layers, such as media access control and information routing. After describing the DEIN concept and its high-level architecture/protocol stack, this paper presents two use cases focusing on the lower layer and the higher layer of a DEIN network, respectively. The lower layer use case is about a fair resource allocation algorithm, whereas the high-layer section introduces an efficient data forwarding scheme in combination with EH. The two case studies aim to give a better explanation of the DEIN concept. Some future research directions and challenges are also pointed out.

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INTRODUCTION An important form of big data is large amount of small piece of data collected from wireless sensors, namely, wireless big data. These sensors are typically powered by battery that drains sooner or later and will have to be taken out and then replaced or recharged. Energy harvesting (EH) has emerged as a technology to wirelessly charge batteries in a contactless way and thus widely used in wireless sensor networks. EH utilizes radio frequency (RF) rather than the traditional induction principle or other energy sources such as wind, vibration or solar to conduct the charging, making it more controller (usually human cannot control wind or sunshine).

However this RF-based wireless energy transferring (WET) process is independent of wireless information transfer (WIT)and the latter is by far the major objective of radio transmission nowadays. Information and energy are two fundamental notions in nature with critical impact on all aspects of life. All living and machine entities rely on both information and energy for their existence. In wireless communications, the relationship between information and energy is even more apparent as radio waves that carry information also transfer energy. Wireless communication systems employ electromagnetic waves in order to transfer information. Up until recently, the information transmission capacity of these signals has been the main focus of research and applications, neglecting their energy content. However, thanks to recent advances in silicon technology, the energy requirements of embedded systems have been significantly reduced, making electromagnetic waves a potentially useful source of energy. For example, recent experiments show that hundreds of microwatts 1|P a g e

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International e-Journal For Technology And Research-2017 design need to be considered among many other new research issues. The trade-off between information and energy and their effective interaction necessitate novel designs of almost all layers of the network protocol stack. Efficient cross layer design approaches will be necessary, for example, to bring advanced physical layer techniques, such as full duplex and massive multiple-input multiple-output (MIMO), together with dynamic resource allocation algorithms at the data link layer, and even combined with multi-hop data forwarding techniques. As a positioning piece of work, this paper aims to present some high-level concept of DEIN by making the following contributions: To present the DEIN system architecture through redefining the traditional network protocol stack by introducing energy processing alongside the traditional data processing. Explanation to each newly introduced components in the stack and their relationship with other system components is also described.

To give two use cases of DEIN. The first one gives a simple example of the functionalities of the DEIN lower layer, namely, power allocation to maximize the minimum data rate of user devices. And the user devises are powered by the same antenna via WET and their batteries respect the basic energy harvesting rules. The second use case presents a high-level perspective of DEIN, namely, multi-hop data forwarding in combination with energy harvesting. To identify some research challenges of DEIN and to point out some future research directions in pursuit of this new and exciting type of future wireless communication networks as DEIN[1].”

3 SURVEY 3.1 A Fair Resource Allocation Algorithm for Data and Energy Integrated Communication Networks “In this survey we study about strength of network virtualization such as software defined networking (SDN) and collaborative radio access networks (CRAN). For instance, SDN is expected to transform the way services are created, sourced, deployed, and supported. This paper discusses another type of virtualization, namely, a base station, being virtualized into providing not only information transferring but also energy transferring to charge mobile devices. This is largely driven by the fact that mobile devices, while IDL - International Digital Library

getting more powerful in processing and networking, exhaust their battery more quickly.

To address this challenge, this paper utilizes energy harvesting into wireless communications, namely, the so-called DEINs (Data and energy integrated communication networks). With the development of energy harvesting (EH) technologies and wireless energy transfer (WET) techniques, the DEIN becomes an emerging trend focusing on the study of wireless power and information cooperation communications.

Compared with simultaneous wireless information and power transfer (SWIPT) which mainly focuses on the physical layer, DEINs focus on the whole network system, resource allocation, and protocols design in different layers. The typical architecture of a DEIN is shown in Figure 1, which has three major components, that is, a virtual energy base station (eBS), a data base station (dBS), and massive mobile users/mobile devices. The wireless communication is divided into two parts. The virtual eBS first transfers energy to multiple mobile users (MUs) that do not have embedded energy sources via downlink (DL), and then MUs use the harvested energy to perform uplink (UL) wireless information transmission (WIT) to the dBS. No existent works have taken into account the power sensitivity of RF-DC circuits when DL transfer energy in DEINs, which can lead to a falsely higher data rate when received RF signals cannot be converted into DC (i.e., energy transfer) if their power level is lower than the power sensitivity of an RF-DC circuit. Besides, none of these works have considered the possibility of energy overflow or the opportunities for users to optimize the use of harvested energy across UL WIT slots. It has been shown that a user using all available energy for WIT in each slot achieves a lower data rate than uniformly distributing energy between energy arrivals. Therefore, new dynamic time allocation schemes are needed since not all MUs can harvest energy in every slot, which take into consideration the policy that the energy harvesting of every user should not overflow in every DL WET phase and the energy harvested in the DL WET phase of former slot may be used in the UL WIT of the next.”

3.2 The Internet of Energy: Smart Sensor Networks and Big Data Management for Smart Grid “The electricity demand globally is expected to increase more than two-thirds by the year of 2035 according to the International Energy Agency

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International e-Journal For Technology And Research-2017 intelligent power infrastructure. Smart grid technology promises to make the world power systems more secure, reliable, efficient, flexible, and sustainable 2. It achieves these goals through the integration of information and communication networks. Intelligent algorithms for information collection and processing are being and will be developed to provide automated control over the power grid. In smart grid, a reliable and real-time monitoring is highly required to provide solutions quickly when natural accidents or catastrophes occur to prevent power disturbance and outage. Hence, intelligent monitoring and sensing capabilities to insure real-time response from the power grid are necessary. Wireless sensor networks (WSNs) can be used as compared to traditional communication technologies because of its low-cost, rapid deployment, flexibility, and aggregated intelligence via parallel processing 3. However, better security solutions are needed to prevent the network from any malicious behaviors, sniffing, or attackers. The integration of WSNs, actuators, smart meters, and other components of the power grid with together with information and communication technology (ICT), is referred to as the Internet of Energy (IoE). IoE uses the bidirectional flow of energy and information within the smart grid to gain deep insights on power usage and predicts future actions to increase energy efficiency and low overall cost.

4. According to reports, 800 millions smart meters are expected to be installed globally by 20205. In order to achieve fine-grain monitoring and scheduling, information from the power grid needs to be collected within short intervals. Assuming that smart meters take one record every 15 minutes, this leads to about 77 billions of readings globally during one day. Such huge amount of data could overwhelm existing processing and storage techniques and systems. Utility companies need these readings to have a better understanding of end usersâ€&#x; behaviors regarding consumption and pricing policies.

To manage the massive amounts of data generated from smart meters and other components of the grid, utility companies can use services provided by cloud infrastructures. Services that are provided by cloud enterprises and infrastructures for smart grid users include: provide a storage space, energy resources management, and virtual power plant. Latency and security of cloud computing might be the reasons for utility companies not to adopt cloud systems.

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However, Fog computing or edge computing deals with the IoT in a distributed manner rather than using the centralized cloud computing model. It aims to minimize latency and improve the bandwidth usage.�

3.3 Big Data Network Architecture and Monitoring Use Wireless Technology Big Data in Information and Communication Technology (ICT) is data collection in large number and complex data transfer / transaction that need a good data management system or application to process of those data sets because current data management have difficulties to handle it. Some challengers for data collection such as data storage and need large database capacity, data capture and sharing that required high speed infrastructure system, data visual and analysis required good management system tools to do that. With high volume and numbers of data need to collect and large number data traffic, a new method of information system is required to process of that ability to enhancement decision and management system also optimization performance.

Billions number of data transaction and streams coming from devices worldwide, one of the challenges for the current data management to serve without any losses and low throughput also latency. Although, after enabling and integration of cloud management system still having difficulties to serve all the data streams and transaction. Cloud based Big Database and Data centre techniques offer some promise to overcome issues mentions above. Mobile of people with carrying device such as smart phone or tablet is extending of Internet of Things (IoT), everyone has their own unique IP/IPs address with hold device. The future of IoT is to manage devices carrying by people around the world with unique IP as hub and interface to others devices. Billions number of devices with IoT has capability to sense, communicate and detect that allow efficient communication and data collection thru the device. Introduction of Internet Protocol version 6 (IPv6) and low power wireless network would able to do sensing and communication through devices. With the IPv6 has billions number of unique IPs for device and sensor that apply for house application such as monitoring of cameras, manage assets, house equipments controlling and security application. Fifth Generation (5G) is promising technology to use for future development in telecommunication infrastructure. Currently, the use of radio frequency spectrum is in wide area, information system, industry, 3|P a g e

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International e-Journal For Technology And Research-2017 medical, education and so on than the spectrum a bit mess. By 2020 is estimate that 50 billion devices will be connected to the internet, so alternative of the use spectrum for connections of devices to internet need to be attention else system unable to serve of high demand. The large number of devices and machines connected to internet will be due to a boom inanimate object using the 5G network as known as the Internet of Things (IoT). In this paper, discuss more on the use of 5G technology and wireless communication system to accommodate large number of devices and capacity for Big Data Centre (BDC). The promising technology to use 5G technology as alternative system in backbone and framework for Big Data infrastructure, 3 Big Data Network Architecture because of standard sets in 5G technology able to running in high speed Gigabits data connection. High speed data transfer and connection is required in Big Data Centre to serve large number of clients and high volume of data storage, else many issues facing up and also slow system will happen.

3.4 Wireless Communications in the Era of Big Data Decades of exponential growth in commercial data services has ushered in the so-called “big data” era, to which the expansive mobile wireless network is a critical data contributor. As of 2014, the global penetration of mobile subscribers has reached 97%, producing staggeringly 10.7 Extra Bytes (10.7× 1018) of mobile data worldwide. The surge of mobile data traffic in recent years is mainly attributed to the popularity of smart phones, phone cameras, mobile tablets and other smart mobile devices that support mobile broadband applications, e.g., online music, video and gaming as shown in Fig. 1. With a compound annual growth rate of over 40%, it is expected that the mobile data traffic will increase by 5 times from 2015 to 2020 .In addition to the vast amount of wireless source data, modern wireless signal processing often amplifies the system‟s pressure from big data in pursuit of higher performance gain. For instance, MIMO antenna technologies are now extensively used to boost throughput and reliability at both mobile terminals (MTs) and base stations (BSs) of high speed wireless services. This, however, also increases the system data traffic to be processed in proportion to the number of antennas in use. Moreover, the 5G (the fifth generation) wireless network presently under IDL - International Digital Library

development is likely to migrate the currently hierarchical, BS-centric cellular architecture to a cloud-based layered network structure, consisting of a large number of cooperating wireless access points (APs) connected by either wire line or wireless front haul links to big data capable processing central unit (CU). New wireless access structures, such as coordinated multipoint (CoMP or networked MIMO), heterogeneous network (HetNet) and cloud-based radio access network (C-RAN), are under development to achieve multi-standard, interferenceaware and energy-friendly (green) wireless communications. In practice, the use of cooperating wireless APs could easily generate multiple Gbps data from a single user‟s front haul links due to the need for baseband joint processing, such that the high traffic load may overwhelm the front haul link or the system computing unit for signal processing and coordination.

Such intensely high system traffic volume, together with the rapidly growing mobile data source volume, surpasses both the processing power improvement speed four current computing capabilities and the front haul/backhaul link rate increase pace of our networking systems. It necessitates a new wireless architecture along with efficient signal processing methods to make wireless systems scalable to continued growth of data traffic. On the other hand, timely and cost-efficient information processing is made possible by the fact that the vast-volume mobile data traffics are not completely chaotic and hopelessly beyond management. Rather, they often exhibit strong insightful features, such as user mobility pattern, spatial, temporal and social correlations of data contents. These special characteristics of mobile traffic present us with opportunities to harness and exploit big data for potential performance gains in various wireless services. To effectively utilize and exploit these characteristics, they should be identified, extracted, and efficiently stored. For instance, caching popular contents at wireless hot spots could effectively reduce the real-time traffic in the front haul links.

Additionally, network control decisions, such as routing, resource allocation, and status reporting, instead of being rigidly programmed, could be made data-driven to fully capture the interplay between big data and network structure. Presently, however, these advanced data-aware features could not be efficiently implemented in current wireless systems, which are mainly designed for content delivery, instead of analyzing and making use of the data traffic.

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International e-Journal For Technology And Research-2017 3.5 Big Data Storage Method in Wireless Communication Environment One buzzword that has been popular in the last couple of years is Big Data. Big data phenomenon refers to the practice of collection and processing of very large data sets and associated systems and algorithms used to analyze these massive datasets. Architectures for big data usually range across multiple machines and clusters, and they commonly consist of multiple special purpose sub-systems. In simplest terms, Big Data symbolizes the aspiration to build platforms and tools to collect, store and analyze data that can be voluminous, diverse, and possibly fast changing. As a Specific kind of application for big data, spatial application such as Mobile-based Geographical Information System (Mobile-GIS) has become more and more important in both scientific research and industry. With the development of earth observation technologies, the spatial data are growing the proposed framework is indeed effective in addressing the crosssite cold-start product recommendation problem.

Currently, only simple neutral network architecture has been employed for user and product embedding learning. In the future, more advanced deep learning models such as Convolution Neural Networks13 can be study for learning.

CONCLUSION This survey proposes a new network architecture / protocol stack called DEIN where traditional information transmission is fully or partially powered

by RF EH. The cooperation between WIT and WET in DEIN is described. Two DEIN use cases are then presented focusing on the lower layer and the higher layer of a DEIN network respectively. The lower layer use case is about a fair resource allocation algorithm whereas the high-layer section introduces an efficient data forwarding scheme in combination with EH. Further next article we describe operation and implementation process on this same topic.

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