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NANONETWORK COMMUNICATION: ROUTING PROTOCOLS WITH ENERGY EFFICIENCY.

Byline: I. Iqbal, M. Nazir, M. Rafique, A. Sabah and M. Rafique

Keywords: Nano networking, Nano devices, Wireless Nano sensor Networks (WNSN), Energy Harvesting Aware Routing Protocol.

INTRODUCTION

The devices of a few nanometers are being manipulated in the research domain of nanotechnology which is another area of research among many other research disciplines. This research area aims to let the small scaled nanodevices to communicate at nanoscale (Drexler, 1986). Drexler defined that nanodevices can be proved beneficent with the help of nanoscaled components (Drexler, 1992). It has become impossible to use traditional antennas to let the nanodevices communicate to each other in nanonetworks due to the small size of nanomachines. That is why the idea of nanoantennas (Otsuji et al., 2013; Jornet and Akyildiz, 2013; Tamagnone et al., 2012; Akyildiz, and Jornet, 2010) and nanotranceivers (Deligeorgis et al., 2012; Otsuji et al., 2013; Sensale-Rodriguez et al., 2012) was given, so that the stated problem can be acknowledged. The atennas made for this purpose are graphene based that means they have the capability to let the nanodevices communicate in Terahertz (THz) band.

The progress of nanotechnology making its way in building nanoscaled devices, to fulfill several needs like storing, actuation, computation, and communication. The nanoantennas support Electromagnetic (EM) Communication from Megahertz (MHz) to Terahertz (THz) (Akyildiz and Jornet, 2010). There are certain applications that have become possible due to the Terahertz bands communication of nanonetworks, the applications including health monitoring, plant monitoring, and bio chemical weapon monitoring (Afsana et al., 2018). The maximum range of transmission can be ensured with the help of lowest bandwidth but it didn't provide energy efficient communication which is not acceptable for nanonetworks. The signals having long duration cannot be transmitted at nanoscale due to the limitation of energy and size (Palattella et al., 2013).

The knowledge about energy harvesting mechanism is very important because in most of the cases nanoscaled devices feature minimal power, communication capability, data storage, and processing. Nanobatteries are being used in nanodevices that stores low amount of energy and it is almost impossible to manually replace or recharge the nanobatteries (Wang et al., 2013; Wang, 2008; Gammaitoni et al., 2009; Cottone et al., 2009). That is why energy efficient Medium Access Control (MAC) protocol is a better approach for Wireless Nanosensor Network (WNSN) (Wang et al., 2013). The mechanisms of solar energy and wind power are not feasible in nanonetwoks unlike classical battery powered devices that do not decrease until the battery is empty. Therefore, to provide energy to nanoscaled devices novel methods can be used (Jornet and Akyildiz, 2012).

A model for piezoelectric nanogenerators has already been designed, according to which the energy required for nanomachines is almost equal to 800pJ and the time require to reach the maximum energy capacity is equals to 47s that will tend towards 2361s when the piezoelectric generators are excited by air conditioning (Jornet and Akyildiz, 2012). This study also tells us that 8 packets of 200 bits can be send by a nanodevice with maximum amount of energy harvest which is very scares availability of energy. Nanodevices need much time to recharge its nanobatteries that is why it becomes difficult to reach to the maximum transmission range of Terahertz bands (Piro et al., 2015). Several factors like routing topology, energy predication rate, and energy consumption rate has been deployed in various energy harvesting protocols. The stated factors can affect recharge ability to make the performance of network better and wastage awareness (Anisi et al., 2017).

There are several energy harvesting aware protocols are available in Wireless Sensor Network (WSN) but these are in sufficient for nanoscaled devices as they do not fulfill its specifications. By using energy harvesting systems in nanonetworks the efficiency of energy will fluctuate in both positive and negative manner.

MATERIALS AND METHODS

Communication in any form requires energy, and nanonode has to harvest the energy from several sources like heat, vibration, and light. Due to this energy harvesting procedures, it gained attraction in this domain. The amount of harvested energy depends on various factors like time, and location. For example, nanonodes have to operate in the fluidic environment with no light. Here, thermal energy will not work because it has issues of downsizing. These limitations give motivation to explore other alternative sources that should be compatible for nanonode requirements like chemical and mechanical or biochemical sources. This energy can be harvested through biofuel cells (BFCs); these may address many incompatibilities that traditional fuel cells may bear. BFCs are potential sources to harvest energy in nanonodes. Traditional fuel cells harvest energy by converting chemical energy into electricity by a chemical reaction.

This technology is well known for the macroscale but cannot be directly used for microscale or nanoscale because of fabrication cost, lack of material that can be used to develop them. Unlike fuel cells the BFCs are much suitable for intra Body Area Nanonetwork (BANN), because it substitute biological enzymes instead of metals. These biological enzymes work as cathode, anode or both that is why they are efficient and biologically compatible to work and provide power. Still the existing BFCs do not provide stable behavior (Hansen et al., 2010). The mechanical energy is in the form of motion or vibration and it presents in most of the applications of human body or even home appliances (Piro et al., 2015). For example energy generated from heartbeat, running or walking may be used to fuel many nanonodes. This energy range may vary from few hertz to several kilohertz in frequency and that would yield the power densities of several microwatts to few mill watts (Jornet and Akyildiz, 2012).

Traditional lead zirconate titanate is not useful for energy harvesting because they are not reliable, safe and durable for nanonodes. Piezoelctrical wires are being used to make nanogenerators and become a source of energy harvesting (Wang, 2008). It would be inefficient to rely only on bio chemical or bio mechanical energy harvesting process. To fill this gap scientists are now exploring another technique that would be used for energy harvesting from several sources. Harvesting energy from sources concurrently will be advantageous to handle instability at nanoscale (Anisi et al., 2017). Physical activities like muscle stretching, walking or exercising are the process that increase the metabolism level of body and are sources of bio chemical or mechanical energy (Piro et al., 2015). The hybrid of these two forms is emerging and can power nanonodes in biomedical domain (Hansen et al., 2010).

The size of nanonodes makes them compatible to integrate in different objects like clothes (Palattella et al., 2013). Moreover its low energy consumption makes it more convenient in different applications of IoNT. Several protocols have been developed for nanonetworks and IoNT will be helpful to evaluate their performances based on connected objects. Furthermore, issues like mobility, security, and scalability can also be checked. Nanonetwork applications can also take help from Wireless Network on Chip (WNoC). An ad hoc network may use any cast or broadcast communication with point to point communication for mesh or ring network but a nanonetwork may not form a mesh or a grid. Moreover, nanonodes may vary in their ranges to reach various nanonodes (Drexler, 1992). So, there would be some tailoring methods to use for traditional MAC protocols that can be altered for WNoC applications.

This change will bring more sophisticated ways of communication in which increasing or decreasing range of broadcasting messages will be deployed for nanonodes (Wang et al., 2013). Nanonetwork can also be used as collection of tiny nanorobots that are also known as utility fogs. These robots can be used to fulfill the desired task with coordination. The nanorobots can be used where self-reconfiguration and self-assembly is needed because of their feature of coordination (Wang et al., 2013). These would be specifically helpful when the situations are un-predictable beforehand like deep space exploration, missions of search and rescue in unstructured environment. The THz communication of nanonetwork satteled well in dense networking of nanobots where the distance between each nanorobot is a few centimeters and the energy consumption will also be low (Boillot et al., 2013).

New methods and protocols are needed for self-organized network of nanorobots and if they will be used for long mission then energy harvesting mechanisms will also be required. The impact of mobility of energy consumption would be a greater concern.

RESULTS

Efficient energy usage in nanonetworks not only require new energy aware models but the protocols too that should be harvesting and location aware to use the energy and distance to the best of the knowledge available. The size and communication constraints of nanonodes in THz are the unique properties to efficiently consume and harvest energy while communicating to each other that is why new ideas regarding protocol development are needed. These layers of nanonetwork are still to be explored; all those protocols that need to be developed should consider scalable energy efficient solutions. The protocols for network layer of nanonetwork must be built by considering a hierarchal architecture for scalability of nanonodes, information routing and addressing. These properties will affect the performance of upper layers like application layer that deals with requirements of delay and reliability of application. Another thing that has to be deal with while making the design of protocol is mobility.

The method should be dynamic and it will affect address routing and every other layers of nanonetwork specifically MAC layer (Jornet et al., 2012). Dynamicity will bring variability in data traffic and that will eventually lead towards different energy efficient models that will satisfy the need of mobility like success ratio or delay of packet delivery. Protocol design for this layer must be light weight to adjust implementation details of scalability or resource limitations of nanonodes. A centralized topology works well on this layer because a nanocontroller can give equal rights of channel access to all nanonodes (Jornet et al., 2012). The proposed research will provide fair access of medium to all nanonodes keeping in mind the energy efficiency while harvesting or consuming and to provide interruption free communication.

There are many other energy harvesting mechanisms like in Receiver Initiated Harvesting Medium Access Control (RIH MAC), the receiver nanonodes generate an alert that it is ready to receive the message (Wang et al., 2013; Jornet et al., 2012). By this mechanism both the sender and receiver nanonode make the best use of harvested energy. Moreover, the RIH MAC is centralized and distributed too. The Distributed Receiver Initiated Harvesting Medium Access Control (DRIH MAC) use coloring graph edge procedure to get the time slots booked for a particular information flow. This process will check the energy level of nanonodes on both the sides that will eventually minimize the possibility of neighboring nanonode have less energy level. There are many challenging domain areas that have been identified of nanonetworking with efficient energy consumption and harvesting.

Like; 1) there should be economical processes for efficient energy storage and harvest; 2) new applications should be identified to improve the performance requirements, 3) to cope up with distance and THz frequency new ways should be paved out. The objective of efficient energy usage in nanonetwork is way different than traditional energy efficient protocols in wireless networks, because nanonodes are expected to refresh the energy level they harvested and their main aim to achieve the maximum output from the available energy. The traditional energy efficient models majorly rely on balanced energy consumption, data compression, data aggregation and duty cycles among devices. It is yet to be seen whether any of the traditional models work well with the features of nanonodes. The communication tends to be unsuccessful if a nanonode does not use the harvested energy efficiently.

The thermal effects and molecular absorption may cause loss in signals and unsuccessful packet transmission, all of these issues may arise due to the extreme sensitive range of frequency i.e; 0.1-10THz. For example if the communication is in the airy environment and the water vapors are 10 percent then the probability of path loss would be around 100dB and it would be 50dB if the distance would be 1cm. That is why at various distances the power requirement would vary and it should be considered while creating the new design scheme for communication between nanonodes. A model has been developed to compensate the loss in THz and this has become possible due to pulse based communication (Jornet and Akyildiz, 2011). It introduces On Off Key (OOK) scheduling based on time division in which 1 represent the presence of data signals and 0 represents no data signal i.e; silence. This OOK scheduling is a simple modulation technique and suitable for nanonetworks as more complex are not feasible for them.

This concept has been emerged because of the small sized nanonodes and immature energy harvesting protocols available in literature. The level of energy harvested is probabilistic because the energy harvested at a particular time depends on the type of activity performed by a body or the activity that is going to be performed by that body. According to the activity type and intensity of workout the amount of harvested energy will also differ (Boisseau et al., 2012). As it is discussed in the table-1, the size of nanonode energy harvesting, energy consumption and energy storage rate will vary. Energy harvesting and storage decreases faster comparative to energy consumption. So these parameters should be considered intelligently while making design of energy efficient protocol for nanonetwork.

Application requirements like throughput and delay should be accommodated as well as energy efficient policies while deigning nanonetwork. It would become more challenging while having large number of nanonodes because complex solutions won't be scalable for large nanonetwork.

DISCUSSION

Ferranti stated different concepts, requirements, and challenges of nanonetwork (Ferranti and Cuomo, 2017). His work is emphasizing on Internet of Things (IoT) and in body nanocommunication that is Body Area Nanonetwork (BANNet). In his research he exhibits several state of the art applications in the area of nanonetworks like bio medical but still there exist many challenges. Function centric nanonetworking (FCNN) has also been discussed and compared with the similar ones. These functional capabilities of FCNN are used for location information, so the nanoscaled devices can exchange information and communicate with each other in a better and progressive way. The exclusivity and resilience of nanonodes do not impact the FCNN. Energy lost due to the EM channel and it eventually lessens the overall performance of nanonetworks and it decreases the throughput of nanodevices. To improve the communication energy reaping can be useful without considering the life time of batterie.

The Af protocol can maintain the performance constraint and can help to maintain the distance between nanodevices. This protocol proposed power transfer in nanonetworks while combining the wireless information in Terahertz bands within the range of 0.1THz to 10 THz. The performance of Af relaying protocol was measured in the zones of time switching and power splitting, and it has been witnessed from the proposed research work of Ferranti and his colleagues that power splitting gives better results than time switching (Ferranti and Cuomo, 2017). Assembling Micro Electro-Mechanical System (MEMS) and microrobots together to assess their behaviors and make a communication between nanoscale devices and microrobots (Boillot et al., 2015) is possible due to Claytronics project (Amiri et al., 2017). In these projects cylinder microrobot Catom (Claytronics atom) has been designed which can act more collectively with other catoms but has limited functionality as individual catom.

These catoms are mutually known as walker, each walker explore and communicate with each other using electromagnetic wireless nanonetwork. For claytronics project Dynamic physical rendering simulator (DPRSIM) has been used that was original simulator designed by Intel in 2006. Boillot and his colleagues have proposed a practical application of microrobots (catoms) which can detect map surface and walk collectively using programmable matters (Boillot et al., 2013). Packet routing at nanoscale in nanonetwork is a complicated job due to the extreme restrictions of wireless channels and hardware limitation. To overcome such obstacle static 3D nanonetwork can be used. In this research work each node has to deduce whether the sending node connected to the receiver with linear segment routing solution using virtual coordinate and an addressing mechanism has been proposed (Tsioliaridou et al., 2017).

The node can maintain the width of the linear path that can be used in future to discover new approaches towards adaptive routing schemes and overall an energy efficient way.

Conclusion: In this research work different energy efficient routing protocols have been discussed. Nanodevices in terahertz band introduce many challenges in the design and implementation of energy harvesting aware routing protocols. Different energy management techniques have been reviewed and then a comparison study has been made depends on several parameters of nanonetwork. Issues like optimization, scalability, energy transfer efficiency, energy harvesting efficiency and time synchronization have major role in the literature work. In this paper it has been observed that considerable research is still needed to increase the optimization of energy efficient routing protocols. There should be the effective control over the harvested energy so that better decisions can be taken to route the data for extra harvested energy.

Our future work, we will optimize the current methodologies, study the dense environment of IoNT, investigate it and design the protocol that will use the harvested energy more effectively and greedily to meet the networking requirements in more realistic scenario.

REFERENCES

Afsana, F., M. Asif-Ur-Rahman, M.R. Ahmed, M. Mahmud and M.S. Kaiser (2018). An Energy Conserving Routing Scheme for Wireless Body Sensor Nanonetwork Communication. IEEE Access. 6: 9186-9200.

Akyildiz, I.F. and J.M. Jornet (2010). Electromagnetic wireless nanosensor networks. Nano Communication Networks. 1(1): 3-19.

Amiri, A., S. Salehkalaibar and B. Maham (2017). Detection in neuronal communications with finite channel state. Nano Communication Networks. 13: 60-69.

Angelopoulos C.M., S. Nikoletseas and T.P. Raptis (2014). Wireless energy transfer in sensor networks with adaptive, limited knowledge protocols. Computer Networks. 70: 113-141.

Anisi M.H., G. Abdul-Salaam, M.Y.I. Idris, A.W. Wahab and I. Ahmedy (2017). Energy harvesting and battery power based routing in wireless sensor networks. Wireless Networks. 23(1): 249-266.

Boillot, N., D. Dhoutaut and J. Bourgeois (2013). Efficient simulation environment of wireless radio communications in mems modular robots. In Green Computing and Communications (GreenCom), 2013 IEEE and Internet of Things (iThings/CPSCom), IEEE International Conference on and IEEE Cyber, Physical and Social Computing. 638-645). IEEE.

Boillot, N., D. Dhoutaut and J. Bourgeois (2015). Large scale MEMS robots cooperative map building based on realistic simulation of nano-wireless communications. Nano Communication Networks. 6(2): 51-73.

Boisseau, G.D.S., G. Despesse and B.A. Seddik (2012). Electrostatic Conversion for Vibration Energy Harvesting. Small-Scale Energy Harvesting. InTech.

Chen, Z., D. Li, Y. Huang and C. Tang (2015). Event-triggered communication for distributed time synchronization in wsns. Control Conference (CCC), 2015 34th Chinese. IEEE. 7789-7794.

Chi, K., Y.-h. Zhu, X. Jiang and X. Tian (2013). Optimal coding for transmission energy minimization in wireless nanosensor networks. Nano Communication Networks. 4(3): 120-130.

Cottone, F., H. Vocca and L. Gammaitoni (2009). Nonlinear energy harvesting. Physical Review Letters, 102(8): 080601.

Dai H., X. Wu, G. Chen, L. Xu and S. Lin (2014). Minimizing the number of mobile chargers for large-scale wireless rechargeable sensor networks. Computer Communications. 46: 54-65.

Deligeorgis, G., F. Coccetti, G. Konstantinidis and R. Plana (2012). Radio frequency signal detection by ballistic transport in y-shaped graphene nanoribbons. Applied Physics Letters. 101(1): 013502.

Dhondge, K., R. Shorey and J. Tew (2016). Hola: Heuristic and opportunistic link selection algorithm for energy efficiency in industrial internet of things (iiot) systems. Communication Systems and Networks (COMSNETS), 2016 8th International Conference on. IEEE. 1-6.

Drexler, K.E. (1992). Nanosystems: Molecular machinery, manufacturing, and computation. NY: Wiley. 12(1): 160-162.

Farris I., L. Militano, A. Iera, A. Molinaro and S. C. Spinella (2016). Tagbased cooperative data gathering and energy recharging in wide area rfid sensor networks. Ad Hoc Networks. 36: 214-228.

Ferranti, L. and F. Cuomo (2017). Nano-wireless communications for microrobotics: An algorithm to connect networks of microrobots. Nano communication networks, 12: 53-62.

Gammaitoni, L., I. Neri and H. Vocca (2009). Nonlinear oscillators for vibration energy harvesting. Applied Physics Letters. 94(16): 164102.

Hansen, B.J., Y. Liu, R. Yang and Z.L. Wang (2010). Hybrid Nanogenerator for Concurrently Harvesting Biomechanical and Biochemical Energy. ACS Nano. 4(7): 3647-3652.

Jornet, J.M. and I.F. Akyildiz (2011). Information Capacity of Pulse-Based Wireless Nanosensor Networks. Proc. IEEE Int'l Conf. Sensing, Communication and Networking (SECON 11). 80-88.

Jornet, J.M. and I.F. Akyildiz (2012). Joint energy harvesting and communication analysis for perpetual wireless nanosensor networks in the terahertz band. IEEE Transactions on Nanotechnology. 11(3): 570.

Jornet, J.M., and I.F. Akyildiz (2013). Graphene-based plasmonic nano-antenna for terahertz band communication in nanonetworks. IEEE Journal on selected areas in communications. 31(12): 685-694.

Jornet, J.M., J.C. Pujol, and J.S. Pareta (2012). PHLAME: A Physical Layer Aware MAC Protocol for Electromagnetic Nanonetworks in the Terahertz Band. Nano Communication Networks. 3(1): 74-81.

Ma, Z. and G.A. Vandenbosch (2013). Optimal solar energy harvesting efficiency of nano-rectenna systems. Solar Energy. 88: 163-174.

Ma, Z., X. Zheng, G. A. Vandenbosch and V. Moshchalkov (2013). On the efficiency of solar energy harvesting with nano-dipoles. Antennas and Propagation (EuCAP), 2013 7th European Conference on. IEEE. 2856-2859.

Madhja, A., S. Nikoletseas and T.P. Raptis (2015). Distributed wireless power transfer in sensor networks with multiple mobile chargers. Computer Networks. 80: 89-108.

Mohrehkesh, S. and M.C. Weigle (2014). RIH-MAC: Receiver-Initiated Harvesting-Aware MAC for Nanonetworks. Proc. 1st ACM Int'l Conf. Nanoscale Computing and Communication (NanoCom 14). 1-9.

Niyato, D., P. Wang, H.P. Tan, W. Saad and D.I. Kim (2015). Cooperation in delay-tolerant networks with wireless energy transfer: Performance analysis and optimization. IEEE Transactions on Vehicular Technology. 64(8) 3740-3754.

Otsuji, T., S. Boubanga Tombet, A. Satou, M. Ryzhii and V. Ryzhii (2013). Terahertz-wave generation using graphene- toward new types of terahertz lasers. IEEE Journal of Selected Topics in Quantum Electronics. 19(1).

Palattella, M.R., N. Accettura, X. Vilajosana, T. Watteyne, L.A. Grieco, G. Boggia and M. Dohler, (2013). "Standardized protocol stack for the internet of (important) things. IEEE communications surveys and tutorials, 15(3): 1389-1406.

Piro, G., G. Boggia and L.A. Grieco (2015). On the design of an energy-harvesting protocol stack for Body Area Nano-NETworks. Nano Communication Networks. 6(2): 74-84.

Sensale-Rodriguez, B., R. Yan, M.M. Kelly, T. Fang, K. Tahy, W.S. Hwang, D. Jena, L. Liu and H.G. Xing (2012). Broadband graphene terahertz modulators enabled by intraband transitions. Nature Communications.

Tamagnone, M., J.S. Gomez-Diaz, J.R. Mosig and J. Perruisseau-Carrier (2012). Reconfigurable terahertz plasmonic antenna concept using a graphene stack. Applied Physics Letters. 101(21): 214102.

Tsioliaridou, A., C. Liaskos, E. Dedu and S. Ioannidis (2017). Packet routing in 3D nanonetworks: A lightweight, linear-path scheme. Nano communication networks. 12: 63-71.

Vicarelli, L., M.S. Vitiello, D. Coquillat, A. Lombardo, A.C. Ferrari, W. Knap, M. Polini, V. Pellegrini and A. Tredicucci (2012). Graphene field-effect transistors as room-temperature terahertz detectors. Nature Materials. 11(10): 865-871.

Wang, P., J.M. Jornet, M.A. Malik, N. Akkari and I.F. Akyildiz (2013). Energy and spectrum-aware MAC protocol for perpetual wireless nanosensor networks in the Terahertz Band. Ad Hoc Networks. 11(8): 2541-2555.

Wang, Z.L. (2008). Towards self-powered nanosystems: from nanogenerators to nanopiezotronics. Advanced Functional Materials. 18(22):3553-3567.

Xie, L., Y. Shi, Y.T. Hou, W. Lou, H.D. Sherali, H. Zhou and S.F. Midkiff (2015). A mobile platform for wireless charging and data collection in sensor networks. IEEE Journal on Selected Areas in Communications. 33(8): 1521-1533.
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Author:I. Iqbal, M. Nazir, M. Rafique, A. Sabah and M. Rafique
Publication:Pakistan Journal of Science
Date:Dec 31, 2019
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