Research Related to An Efficient And Adaptive Energy Cooaperation In IoT Using Energy Harvesting

The idea of Internet of things (IoT) originated in 1999. The International Telecommunication Union (ITU) proposed its official name “IoT”, which connects things to each other as well as with the traditional network. It also means extension of Internet from intelligent nodes to the rest of physical world so that they can be accessed and their operation be influenced timely and accurately using Internet [1]. IoT is a growing concept, and it is the upcoming third wave of information after mobile communication and Internet.

In IoT physical state of smart objects are studied and collected data are sent to the network layer with the help of different technologies installed in object environment. These technologies linked with each other’s through RFID, data bus, WiFi, Ethernet, zig bee, 3G/4G/LTE or other modes in the network layer [2] [3].

Recent studies have explored the application of IoT in various fields such as health care, industries, Smart transportation, smart parking and smart grid [4]. In IoT Heterogeneous networks smart gateway are essential for higher bandwidth and high data rates of multi hop heterogeneous network.

[5]. IoT gateway provides connectivity amongst different networks using different protocols e.g. GPRS and Bluetooth [6]. Intelligent transportations system in which user get data from web about vehicles present status and there station information [7]. In order to get data and information about surrounding environment wireless sensor network is becoming the most important part of IoT. For example for smart transportation the whole track will be equipped with sensors to collect data about transportation environment and send it to traditional network.

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The network operational lifetime depends upon batteries attached with the sensors.

Mostly researchers are interested in increasing the lifetime of sensors network by enabling it to harvest energy for WSNs for IoT applications. The Energy harvesting is one of most attractive, emerging and vast field of WSN. The process of energy extraction from suitable sources, some of which are solar energy (solar cell), mechanical energy (PZT material) and wind energy is called energy harvesting or power harvesting. The hidden energy in the sources are non-electrical energy which converted into electrical energy by using special devices design for it. The extracted energy charge the sensor battery directly or stored to use later.

Serbulent Tozlu have specially focused on designing energy efficient Wi Fi sensors having low power and long lifetime of batteries. Energy efficient Wi Fi sensors are more energy efficient than 6LoWPAN for high data rate but not for low date rate [8] . The energy harvesting techniques can be used to recharge the batteries of sensor nodes in communication time to reduce the cost of functioning and avoid downtime of network. Thus, the energy harvested WSNs (EHWSNs) architecture has been attracting significant attention [9]. For variable traffic load of IoT application Thien et al. and there team specially work on energy harvesting roadside IEEE802.15.4 wireless sensor network. They proposed an IEEE 802.15.4 standard protocol which operates at MAC layer for adaptive packet transmission called Adaptive Beacon Order, Super frame Order and Duty cycle (ABSD). The main goal of this protocol is to minimize the network contention to improve energy efficiency and quality of service. They used the same protocol and energy harvesting to propose new algorithm EH- ABSD about proper use of duty cycles and roadside energy harvesting technique (Roadside sensor network used energy which are extracted from traffic load/train using the mechanical pressure of vehicles is converted into electrical energy using PZT material to introduce new concept ‘energy back-off’ mechanism. Both algorithms are only applied in IoT different fields where traffic load varies [10] [11].

On the other hand recently researchers are focusing to enhance the quality, efficiency and throughput of the networks through energy cooperation among different nodes of sensors. In [12] M.A. Mulata proposed model in which reader regularly checked energy of tags/sensors, in case of lack of sufficient energy, they cooperate with each other for prolonging the lifetime of network by mutual sharing. Tag request to another tag through reader for energy sharing if the tag has sufficient energy then they share with other tag through magnetic field but in case of lack of energy the request is ignored. Huijiang Li at al. studied relay based sensor network, such a sensor network model also having energy harvesting capability and cooperative, but relay based communications. In relay based cooperative communications, one sensor uses the services of other sensor called time slotted source-relay-destination system, in this system the source sensor use the service of another sensor (relay) to send data to the desired target. Those nodes which are under observation having the ability to harvest energy.

Performance of network increases but in energy efficiency perspective, the power consumption of a relay sensor node is greater than the power consumption of sensor node to transmit data directly. [13]. In another study the author investigated the transmission of energy and information over a single noisy line. The aim of this tradeoff in communication systems are to perform two function together instantly on a single medium to get energy and information per unit time unlike traditional power transmitting where required. In this work, the fundamental tradeoff is about the rate of transferring energy and information over a single media between one point and another simultaneously [14]. Andr Kurs et al. carried out experimental work in which energy can be transferred between two points with the help of strongly coupled resonant coils. This work is demonstrated experimentally up to distances 8 times the radius of the coils efficiently. The non-radiative power transfer with 40% efficiency over distances up to 2 meters [15].

Ample literature can be found where researchers have focused on improving the quality of service and energy efficiency of the network. Limitations like failure of energy harvesting in bad weather, traffic jam could not be resolved. These limitation in energy harvesting and energy sharing will reduce the efficiency and quality of service and lifetime of wireless sensor network.

The proposed model enhance and improve the energy efficiency, quality of service and lifetime of network by integrating both energy harvesting and energy cooperation techniques among different tags to recharge directly the batteries of network sensors or store it for later use. The stored energy can be shared with other nodes when needed. Energy harvested from ambient environment for recharging the batteries of already installed sensors by using most widely and environment friendly energy harvesting devices e.g. solar cell and piezoelectric material from sources of energy.  Photovoltaic cells have highest output power efficiency to convert light energy into electricity as compared to other energy harvesting devices. The stored energy of batteries decreases when they consumed power during communication and sending collected data to central unit for communication. In IoT network sensors the batteries of sensors also can be replaced by those harvesting devices whose 24 hours operation guaranteed. Sensor node also share (cooperate) the stored energy with those nodes which having insufficient energy during communication and they demand energy from central unit. The key points of our research are following,

1. Integrate both techniques, energy harvesting and energy sharing.

2. The sharing energy must be on priority bases.

3. Sharing takes place between nearest nodes.

By integrating energy harvesting and energy sharing the lifetime of network can be prolonged. Each tags send collected data and share their stored energy level information with central unit which coordinate with all tags means central unit have all information about energy of tags at any instant, each tags harvest energy independently. If tag has sufficient energy then they share with neighbor tags through magnetic field pointed by central unit when needed. After completion of this research work the lifetime of wireless sensor network will increase, which causes to enhance the lifetime, efficiency and the quality of network.

AIM AND OBJECTIVES

1. To design energy harvesting circuits for different scenario like for solar cell, PZT material and then simulate it in MATLAB to get output characteristic curve.

2. To simulate energy cooperation among tags in MATLAB also finding lifetime enhancement and QoS of network.

3. Integrate both techniques of energy harvesting and energy cooperation and find lifetime enhancement and QoS of network.

RESEARCH METHODOLOGY

The proposed research work will be carried out in Research Laboratory, Department of Electronics, University of Peshawar. Simulation based experimental research work will be conducted in high speed computing software’s to harvest energy by using energy harvesting devices (Solar cell, PZT material). After necessary circuitry and management the energy provided to batteries of wireless sensor network. In case of lack of harvested energy, energy cooperation takes place among different tags by resonance magnetic field through central unit. This work will compiled in the following optimum software’s.

1. MATLAB SIMULINK.

2. Lab VIEW.

3. MICROSOFT VISIO.

4. SMARTDRAW.

PLAN OF WORK

The research work will be carried out in three phases:

Phase 1:

In the first phase the literature will be reviewed properly.

Phase 2:

In the 2nd phase

System model will be proposed by mathematical technique and then its simulation will be conducted through the simulator and result will be recorded.

Phase 3:

In the 3rd and final phase, an efficient and adaptive energy cooperative system will finalized and research report will be compiled according to the University format.

References

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Chen, H.; Jia, X.; Li, H. A brief introduction to IoT gateway. In Communication Technology and Application IET 2011, 610-613.
Zhu, Q.; Wang, R.; Chen, Q.; Liu, Y.; Qin, W. IoT gateway: Bridging wireless sensor networks into internet of things. In Embedded and Ubiquitous Computing (EUC), IEEE/IFIP 8th International Conference IEEE 2010, 347-352.
Khanna, A.; Anand, R. IoT based smart parking system. In Internet of Things and Applications (IOTA) IEEE 2016, 266-270.
Guoqiang, S.; Yanming, C.; Chao, Z.; Yanxu, Z. Design and implementation of a smart IoT gateway. In Green Computing and Communications (GreenCom), and Internet of Things (iThings/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing 2013, 720-723.
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Wang, Y.; Qi, H. Research of intelligent transportation system based on the internet of things frame. Wireless Engineering and Technology 2012, 3(03), 160.
Tozlu, S. Feasibility of Wi-Fi enabled sensors for Internet of Things. In Wireless Communications and Mobile Computing Conference (IWCMC), IEEE. 2011, 291-296.
Tentzeris, M.M.; Georgiadis, A.; Roselli, L. Energy harvesting and scavenging [scanning the issue]. Proceedings of the IEEE. 2014, 102(11), 1644-1648.
Mineto, A.T.; Braun, M.P.D.; Navarro, H.A. Varoto, P.S. Modeling of a cantilever beam for piezoelectric energy harvesting. In DINCON10, 9th Brazilian Conference on Dynamics Control and their Applications 2010.
Erturk, A.; Inman, D.J. Piezoelectric energy harvesting. John Wiley & Sons 2011.
Mulatu, M.A. Energy cooperation in communication of energy harvesting tags.  International Journal of Electronics and Communications AEU 2017, 71, 145-151.
Li, H.; Jaggi, N.; Sikdar, B. Relay scheduling for cooperative communications in sensor networks with energy harvesting. Transactions on Wireless Communications IEEE 2011, 10(9), 2918-2928.
Varshney, L.R. Transporting information and energy simultaneously. In Information Theory, ISIT, International Symposium IEEE 2008, 1612-1616.
Kurs, A.; Karalis, A.; Moffatt, R.; Joannopoulos, J.D.; Fisher, P.; Soljačić, M. Wireless power transfer via strongly coupled magnetic resonances Science 2007, 317(5834), 83-86.
Nguyen, T.D.; Khan, J.Y.; Ngo, D.T. Energy harvested roadside IEEE 802.15. 4 wireless sensor networks for IoT applications. Ad Hoc Networks 2017, 56, 109-121.