— A Survey by Efthymios LallasORCIDGeneral Department of Lamia, University of Thessaly, 35100 Lamia, Greece

Appl. Sci. 2019, 9(24), 5488;



Wireless data traffic has experienced an unprecedented boost in past years, and according to data traffic forecasts, within a decade, it is expected to compete sufficiently with wired broadband infrastructure. Therefore, the use of even higher carrier frequency bands in the THz range, via adoption of new technologies to equip future THz band wireless communication systems at the nanoscale is required, in order to accommodate a variety of applications, that would satisfy the ever increasing user demands of higher data rates. Certain wireless applications such as 5G and beyond communications, network on chip system architectures, and nanosensor networks, will no longer satisfy speed and latency demands with existing technologies and system architectures.

Apart from conventional CMOS technology, and the already tested, still promising though, photonic technology, other technologies and materials such as plasmonics with graphene respectively, may offer a viable infrastructure solution on existing THz technology challenges. This survey paper is a thorough investigation on the current and beyond state of the art plasmonic system implementation for THz communications, by providing in-depth reference material, highlighting the fundamental aspects of plasmonic technology roles in future THz band wireless communication and THz wireless applications, that will define future demands coping with users’ needs.

Keywords: wireless NoC (WiNoC); graphene based WiNoCs (GWiNoCs); wireless nanosensor networks (WNSNs); surface plasmon polariton (SPP); GFET; multiple-input-multiple-output (MIMO); graphennas; THz transceiver



1. Introduction

Wireless data traffic has experienced an unprecedented boost in the past years, and it is expected to increase sevenfold up to 2021 [1]. Data traffic forecasts in wireless communication networks will account for more than 60% of the overall internet traffic by then [2]. Current wireless communications handle data rates of tens of Gbps per link or even more, and the prospect for the future demands will be 100 Gbit/s within 10 years [3], with multiplexed rates well beyond 100 Gbit/s, and eventually Tbit/s. Wireless communications seem to be in advance against conventional wired communications. By 2030, wireless data rates will be sufficient enough to compete with wired broadband rates [4]. Therefore, the use of even higher carrier frequency bands in the THz range is required, via adoption of new technologies equipping future THz band wireless communication systems at the nanoscale, in order to accommodate a variety of applications, that would satisfy the ever increasing user demands for higher data rates. Certain wireless applications such as 5G and beyond communications, NoC system architectures and nanosensor networks, will no longer satisfy their speed and latency demands with existing technologies and system architectures. Apart from conventional CMOS technology, and the already tested, still promising, photonic technology, other technologies and materials such as plasmonics with graphene material, may offer a viable solution on existing THz technology challenges.
At the moment, wireless traffic in the access 5G networks exploits millimeter wave (mmW) bands. In order to accommodate the continuously increasing traffic demands of 5G and beyond communications, researchers have been focused on taking advantage of higher regions in the radio spectrum, pointing to the THz band communication and infrastructure, as a promising solution to equip 5G plus networks, thus enabling efficient operation of bandwidth hungry applications, that are not feasible at the moment with current infrastructure.
Wireless NoC (WiNoC) [5] with its inherent broadcast capability, appears as a promising approach to overcome all abovementioned bottlenecks of ancestor technologies. Ultra small miniature sizes of plasmon based antennas and other nanolink components as well, with considerably much less wiring, are the desired features of this technology, in order to enable the integration of one or multiple antennas per core, paving the way for dense, scalable NoC schemes, as required by future applications. Graphene based WiNoCs (GWiNoCs) is probably the most updated promising approach for THz nanoscale wireless communications, and it is therefore considered to be the basis for implementing future on chip network architectures. Alternatively, hybrid optical wireless schemes, may be also proved to be a promising NoC solution [6], by combining the best assets of these two worlds: low loss dielectric waveguide media, and miniature sized plasmonic material oscillating at THz rates.
Last, wireless nanosensor networks (WNSNs) is another established THz nanoscale application, with basic similarities as in WiNoCs, such as core to core or to memory communication, but also with other unique characteristic types of communication, mainly between nanosensors and nanomachines in the THz band [7]. Such EM communication in the THz band, is usually enabled by plasmonic materials, as graphene for implementing plasmonic nanotransceivers and nanoantennas, as in the WiNoC case. These three important wireless THz nanocommunication applications can be seen altogether in Figure 1.
Figure 1. Applications for wireless THz nanocommunications.
Nonetheless, the THz band as the last undiscovered frontier of the total EM spectra range, and THz wireless nanoscale communications, are still urging for an efficient, compact and standardized interconnect solution for generating, transmitting, propagating, and detecting the THz wave information. Despite the fact that new efficient methods have been introduced based on modern system architectures via the utilization of new technologies for manipulating THz Band signals by the research academia, still there are many challenges to face, such as the very high propagation signal loss, the impedance mismatch between THz link components, the limited size restrictions along with integration potentials, associated with high bandwidth availability and ultrafast operating data rates with minimum latency requirements. Conventional CMOS based electronic interconnects are definitely far from the target to meet THz speed, low propagation signal loss, and the impedance match between THz link components. Current CMOS scaling technology growth has restricted the cut-off frequency and the maximum oscillation frequency of the device to several hundreds of a GHz. Traditionally, the operating frequency should be much below the cut-off frequency when designing mixed-signal circuits at high frequencies, and the CMOS device technology applied, should be capable of continuously scaling down the size and values of on-chip components (e.g., transistors, capacitors, and inductors), in order to achieve higher throughput and reduced circuit footprints [8]. Evidently, at THz frequencies this approach has weak potential for improvements, which are further limited by the loss encountered in on-chip metal structures [9]. The alternatives for such a case, would be either to search for a new implementation technology that offers better scaling prospects and a higher intrinsic transit frequency, or to exploit the non-linearities of the device for efficient power generation at higher-order harmonics. As concerns the latter, there are approaches that use novel circuit to generate, radiate, and control THz frequencies widely adopted in academia and industry [10], however it is high time for other technologies to take this on, at this critical stage.
Artificial intelligence (AI) computing with its hot AI chip topic may be an alternative for CMOS wired interconnection and NoC architectures, against CMOS process and device bottleneck and Von Neumann and memory wall bottlenecks. In a broader sense, the AI chip is the adoption of AI principles in computing processing systems in the form of accelerators, in order to boost their computing performance. AI approaches such as processing-in-memory (PIM), machine learning (ML), and especially neural network (NN)-based accelerators, such as FPGA, GPU and ASIC, are considered as mature solutions for speeding up computing performance [11]. PIM or near data computing (NDC), is a promising solution to tackle with the memory wall bottleneck. PIM architectures put additional computation logic in or near memory by leveraging 3D memory technologies to integrate computation logic with memory, thus speeding up NN computations on larger memory capacity and bandwidth, via in-memory data communication, at the same time. The metal-oxide resistive random access memory (ReRAM) has showed great potential to be used for main memory, with its crossbar array structure, capable for performing matrix vector multiplication efficiently, and accelerating NN computations [12].
However, as processing system scalability increases, AI processing loads are getting more and more data-intensive and demand higher bandwidth and heavy data movement between computing logic and memory. Hence, when the scale of the NN computation and accelerator increases, the NoC-based data communication within NN accelerators would evidently have to deal with a performance bottleneck. In addition to performance, the energy consumption of the NoC in an NN accelerator may be also a big challenge to deal with. Particularly, when all this AI processing is done at the edge level, embedded in sensors, smartphones or general IOT equipment, there are more strict power requirements and the need for much more specific hardware implementations [13]. Compared with cloud applications, the application requirements and constraints of edge devices are much more complex, and special architecture design may be needed for handling different situations. Among them, the most important feature and at the same time, request for current edge IOT devices, is their ability to locally perform “inference”, relieving thus processing burden from cloud servers and reducing delay [13]. However, in such a case, the demand for training in edge embedded devices is not very clear, given that in the future, all these wearable IOT devices should be capable to perform efficient inference computing, which in turn, requires them to have sufficient inference computing ability, so as to achieve a certain intelligence threshold under the strict power and cost constraints of the edge area, in order to meet the challenges of various different AI application scenarios. Efforts from the research community have been made towards the direction of locally reducing accuracy, and computational complexity, by combining some data structure transformations, such as FFTs to reduce the multiplication in matrix operations, or table lookup to simplify the implementation of multiply-and-accumulate (MAC) operations. Moreover, various low power methods have been applied to AI chips of edge devices to further reduce the overall power consumption, such as the clock-gating applied to MAC. Nowadays, industry has been focused in developing specialized AI chips and all kinds of IOT devices with enhanced inference capabilities at low power and costs. The collaborative training and inference among cloud and edge devices would be an interesting direction to be explored by research academia [13].
As current NN training accelerators relied on conventional wired NoCs, seem to have to deal with certain limitations, especially as time goes by, WiNOC on the other hand, with its inherent features, as broadcast support and multiple access to the shared medium by beamforming and antenna beam narrowing, spatial multiplexing within package, reduced latency, as wireless channel is a distance independent communication means, flexibility in a sense of virtually mapping different topologies within a cycle, and scalability potential as systems scale linearly with the number of cores, may be also considered as an alternative, for being exploited as an interconnection means for these accelerators [14]. Since NN training accelerator parallelized computation nature in a many-core-like environment, is similar to one-to-all, or all-to-all WiNoC communication nature, focusing on exploring the matching points between these two pillars, may be an interesting direction to be investigated by research academia. In this way, the improvement of the efficiency of a WiNoC architecture should be sought not only on the implementation of the miniature antennas and transceiver wireless equipment, but also on the proper design of the NN architecture, as concerns the intensive data movement between processing core and memory units.
In general, the AI chip concept is a complex multi-variable issue, lying in the middle of a whole layer stack, with demanding tasks ranging from providing efficient support for higher layer cloud applications and algorithms, up to orchestrating entities based on AI principles in low level architectures, consisting of devices and circuits, processes and materials, and hence there are a lot of unsolved issues and unanswered uncertainties that may be set under consideration [13], which is out of the scope of this work. Despite the fact that AI chips have made significant progress in the area of ML and NN computations, it is still in its infancy stage, and there seems to be a long way to go, before achieving a generic standardized AI framework; the so called artificial general intelligence (AGI), capable for solving out heterogeneous nature AI applications, especially at the edge network [13].
The photonic based interconnect solution is undoubtedly a viable approach for providing high data rates at low propagation losses, still, their component size is one with two orders of magnitude larger than what is required for the THz band case. Plasmon based THz link components on the other hand, due to their extremely small size and their ability to operate at ultra-high rates, may be a promising approach for equipping wireless THz nanoscale communication systems. Moreover, they could be perfectly combined with photonic technology, and particularly with dielectric waveguiding, as plasmonic waveguiding is quite lossy for long interconnect distances.
To this end, there is still an urge to have a comprehensive view on the current progress and recent advances in the wireless THz communications field, that would help researchers to have a reference point, and based on that, to expand their own ideas and directions, and find motivations to further develop research in this field. This work is a thorough investigation on current and beyond state of the art plasmonic system implementation for THz communications, by identifying the target nanoscale applications and major open research challenges, as well as the recent research achievements. It is the aim of this comprehensive survey then, to highlight the key roles of plasmon based technologies on equipping future competitive THz nanoscale communication systems hosting wireless THz nanoapplications, namely NoCs, WNSNs and beyond 5G communications. This survey paper may be well considered as a complementary work of [15], which had emphasized on key roles of plasmonics and silicon photonics, on equipping wired ultra-high bit rate interconnects, ranging from nanoscale intra and inter-chip interconnections, up to board to board and rack to rack interconnections between data centers (DCs).Particularly, the current work aims to complete the fundamental roles of plasmonic elements and mechanisms referred in the previous work, by associating the currently under investigation, THz system infrastructure in wireless communications. The potential of the THz communications is highlighted by illustrating the basic design issues in equipping these three important THz applications, that will define future wireless application demands coping with users’ needs. Moreover, key roles of plasmonics for equipping each single, individual part of a future wireless THz nanocommunication link, namely the antennas and the transceiver parts, are also highlighted.
The rest of this paper is structured according to these two pillars, wireless THz nanoapplications and the implementation of future THz transceiver components. Section 2, Section 3 and Section 4 are each dedicated accordingly on these three major wireless THz nanoapplications, namely NoCs, WNSNs and beyond 5G communications. In each of these sections, in-depth reference material is provided, which includes the latest literature findings regarding the fundamental aspects of plasmonic technology roles and accompanied photonic technologies whenever required, for each one of these wireless THz nanoapplications, respectively. In Section 5, we focus individually on each critical plasmon based, or hybrid component part of a wireless THz nanocommunication link, namely the antennas and the transceiver parts. Finally, we conclude the paper in Section 6, by also providing two summary tables, with all the information aggregated, including all, state of the art and beyond state of the art characteristic plasmon based and hybrid achievements, for equipping future competitive THz nanoscale communication systems and wireless THz nanoapplications, accordingly.

2. WiNoCs

2.1. WiNoC Architectures Potential

As the number of processing cores within a chip area increases in pace with the increased requirements of running applications, communication needs increase as well, and given the fact that the chip area is limited, the node architecture complexity increases accordingly. More complex and sophisticated system designs are required, not only for cores, but also for memory parts to communicate with each other efficiently. Evidently, multi-level memory hierarchy communication needs with multi-core architectures, may be causing a communication bottleneck, as they grow in size.
Nowadays, state of the art multi-core architectures are based on wired NoC paradigm designs [16]. The first multi-processing core interconnections were shared bus architectures [17] which later on were replaced by on-chip CMOS electrical wired interconnections, according to the NoC framework. These were originally implemented via metal traces over a substrate forming a PCB [18]. In the last decade, many alternative fabrics and technologies have been progressively proposed in order to deal effectively with the NoC communication bottleneck, such as 3D NoC [19], RF signals over on-chip transmission lines [20], FSO communication systems at IR frequencies and above [21], photonic NoC [22], nanophotonic NoC [23], and recently WiNoC [24,25], or hybrid WiNoC [26,27]. Three dimensional NoCs are definitely an advantageous network architecture with desired features such as low distance, multiple variety horizontal or vertical interconnections, allowing integration of different technologies at different layers. This technology, however, requires thermal management to deal with the increased heat density due to the superposition of active layers and complex alignment methodologies for the precise positioning of the vertical interconnects. On chip RF schemes allow the interconnection of multiple cores over the same channel with dynamic bandwidth allocation, but they don’t have much scalability potential, as they require an increased area and power overhead for the implementation of complex multi signal transceivers, and they also have to deal with the energy reflections at the line terminals. FSO systems on the other hand, may be a promising solution for providing high data rates at large bandwidth and operating at high frequencies accordingly, and they still have to tackle with a few issues such as the low transmission power budget due to eye-safety limits, the impact of several atmospheric effects (e.g., fog, rain, etc.) on signal propagation, and the strict alignment between transmitter and receiver that limits the achievable data rates [28]. Photonic and nanophotonic NoCs are definitely suggested for providing ultra-high data rates and bandwidths, they are CMOS compatible, but there are some parts within a NoC chip area, that are difficult to be implemented all optically, such as buffers, memories, and header controllers.
In general, it seems that as the number of cores on a chip increases and hence the communication performance requirements increase accordingly, all conventional wired interconnection and NoC schemes are inadequate to provide at the same time guaranteed desired latency, throughput, bandwidth, and energy efficiency, while wireless or hybrid wireless optical NoC solutions may be proved to be more promising alternatives. Specifically, WiNoCs, due to its inherent broadcast and multicast features, should be capable of providing improved performance in terms of scalability, flexibility and area overhead for multi-core systems. Only a single wireless transceiver along with integrated antennas and considerably less wiring equipment are required for interconnecting and sharing resources, among all the chip components, instead of many individual wired connections that would otherwise be required in conventional wired NoCs. It is a critical target for WiNoCs, to be able to manage efficiently wireless communication requirements at the core level, by exploiting miniature sizes of plasmon based antennas and other transceiver parts, in order to enable the integration of one or multiple antennas per core, as seen in Figure 2.
Figure 2. Wireless NoC(WiNoC) critical target—one nanoantenna per core.
Such antennas are mostly graphene based planar antennas, which radiate signals at the THz band, and utilize the minimum chip area than other conventional metallic counterparts [29]. Evidently, wireless interconnects are feasible to reduce wire equipment and parasitic and area occupation, as well as the power dissipation of long global, or multi hop short wires that would normally be required at wired competitors, providing the same high bandwidth and low latency communication [5]. Moreover, as wireless schemes natively enable all-to-all communication, they deal effectively with many other interconnection related issues such as multi-core interconnection with single memory, data coherency, consistency, and synchronization. Indeed, memory ordered execution operations, and cache coherence operations which involve a single memory image accessible to all processors, are critical in terms of latency, especially as the number of cores on a chip increases, in which case, traditional wired NoCs would be insufficient for guaranteeing such latency conditions, while wireless NoCs may do offer a promising solution [5].

2.2. GWiNoCs

WiNoC’s main enabler is considered to be its on chip antenna, which it is integrated with a proper transceiver. Originally, WiNoC implementations were based on millimeter on-chip antennas radiating in the GHz band, integrated with adequate high frequency transceivers [30]. Nowadays, the research community has been focused on advanced wireless communication at the THz era. By increasing the communication frequency from GHz to THz domain, first, we anticipate for smaller footprints of the transceiver and the antenna, thus improving the integration potential of the system, and second, we anticipate for larger available transmission bandwidth and higher achievable data rates. At this critical crossroad there seem to be two reasonably strong trends to act jointly as a promising solution: the considerable reduction of the size of the current metallic antennas and other transceiver components, so as to operate at very high resonant THz frequencies, which are mostly implemented via graphene material [31], and the adoption of an hybrid, wireless optical approach for WiNoCs, based on seamless integration of optical and wireless links on chip, enabling wireless multicasting and broadcasting of data, at optical frequencies [32].
GWiNoC, is a relatively recent approach that relies on graphene material for implementing not only nanoantennas [33], but also any other THz wireless transceiver part, for fully equipping the interconnection between the cores of a multiprocessor. As mentioned above, it is not feasible to reach extremely high resonant THz band frequencies by simply scaling down current metallic antennas [34], at the expected size of a nanosystem (a few μm) [35], as it would result in a huge channel attenuation. Graphene based nanoantennas on the other hand, are inherently just a few micrometers in size, i.e., two orders of magnitude below the dimensions of future metallic on-chip antennas, and hence they could provide inter-core communication in the THz band (usually between 0.1–10 THz). These graphene inherent features would offer both size compatibility with each continuously shrunk processor core, as well as adequate bandwidth for massively parallel processing.It seems that the ultimate WiNoC design target has been already set, consisting of a single graphene based nanoantenna and a nanotransceiver interconnected for each individual processing core, for managing the data of outgoing and incoming transmissions to the antenna respectively.
Graphene based antennas or graphennas, have shown excellent behavior as far as concerns the propagation of surface plasmon polariton (SPP) waves in the THz band. SPPs are coupled electron-light oscillations at the interface between a dielectric and a metal, that can propagate at the speed of light. SPPs in graphene are confined much more strongly than those in conventional noble metals, and they are electrically and chemically tunable by electrical gating and doping [8]. Hence, graphene can be considered as an appropriate THz tunable material for building THz resonator devices [36]. Graphene based nanoantennas and transceivers are a hundred times smaller in size than conventional microstrip antennas, with equal or higher bandwidth and gain [37]. Its long plasmon lifetime and the very high propagation velocity characterize it as an ideal material for implementing plasmonic waveguides for on-chip communication. Moreover, graphene has been found to be an appropriate material to enable the elaboration of GFET, providing higher speed and lower energy than conventional CMOS devices [38], and what’s more, it is CMOS compatible. All graphene THz transceiver components can be combined with graphene-based THz antenna arrays, to achieve dynamic beam forming and steering enabling all wireless NoC scenarios. Therefore, apart from antennas, graphene has been equivalently proposed to build all types of THz transceiver components, as described in [39], and will be discussed in the next subsection.

2.3. Hybrid Optical Wireless NoCs

On the other hand, optical technology when combined with chip scale wireless interconnections may be as well considered as a promising hybrid NoC solution to overcome the performance bottlenecks of the current state of the art NoC architectures. Unfortunately, all plasmonic based solutions proposed in the literature for wireless applications do not overcome the problem of integration with SOI based NoC platforms. Moreover, plasmonic waveguides display high propagation losses and, therefore, they are not suitable for implementing long range on chip interconnections. An appropriate solution would be based on the adoption of the hybrid combination of plasmonic resonators as nanoantennas, while keeping dielectric waveguides as the feeding elements [6]. Hence, the employment of plasmonic nanoantennas adjusted to dielectric waveguides for building nano-optical wireless links instead of conventional plasmonic waveguide links, with short range propagation limitations would be a promising solution.
Another key design issue for building successfully such hybrid NoC architectures, is the implementation of the perfect coupling of plasmonic antennas with conventional silicon waveguides, guaranteeing full compatibility with Si photonic and nanophotonics circuitry standards. Waveguide coupled plasmonic antennas may become a drastic solution for a successful coupling without losses [40,41], enabling a hybrid optical wireless approach in the NoC design. The efficient coupling between plasmonic antennas and SOI waveguides is a non-trivial issue, as an on-chip, point-to-point connection normally requires matched directive nanoantennas. The nanoantenna shape and size should be properly designed so as to ensure impedance matching to the waveguide, and directional emission in the desired direction [42].
Moreover, hybrid wireless optical on chip communication takes advantage of the entire WDM spectrum when propagating in the optical wired links, guaranteeing even higher multiple capacities, as required by intra-chip communications [43]. Various configurations of plasmonic nanoantennas for supporting wireless-optical on chip communication have been proposed in the literature, such as plasmonic horn nanoantennas [44], a directional plasmonic Yagi-Uda nanoantenna placed on a dielectric waveguide [45], or a plasmonic nanoantenna array on a dielectric waveguide [46], or various configurations of plasmonic Vivaldi antennas (double, or an array of them) to name but a few [47,48]. Plasmonic antennas will be described more analytically in the upcoming section.

3. Wireless Nano Sensor Networks

WNSNs is another established THz nanoscale application under the internet of nanothings (IoNT) framework [49], which encourages, not only the core to core or to memory communication as in the WiNoC case, but also the interconnection between other nanoscale components, mainly nanosensors and nanomachines. These nanoscale networks rely on the THz band communication between its different components, which as mentioned, could be either nanosensors or nanomachines [7]. Nanosensors are capable of detecting events with unprecedented accuracy, while nanomachines are dedicated to tasks ranging from computing and data storing to sensing and actuation [50]. Hence, WNSNs are composed by integrated nanomachines and nanosensors, which interact with each other through EM communication [51]. EM communication in the THz band are mostly enabled by graphene based plasmonic nanotransceivers and nanoantennas, as in the WiNoC case.
Main features of the WNSNs are: (i) the size of nano-devices, which range from one to a few hundred nanometers, (ii) the exploitation of graphene based nanoantennas for THz band communication, (iii) extremely high bit rates (Tbit/s), and (iv) very short transmission ranges (tens of millimeters) [51]. Evidently, the THz band is considered as the natural domain for the operation of nanosensor components, as this frequency range supports very high transmission bandwidths within a short range. Alternatively, in the event of transmitting at lower frequencies (e.g., the MHz range), nanosensor devices would have to communicate over longer distances, but the energy efficiency of such a process to mechanically generate EM waves for remote control of these devices would be very low, and hence, communication by using the MHz frequencies wouldn’t be an appropriate solution. Consequently, nanosensor devices would properly communicate with each other in the THz band [35].
As mentioned, apart from graphene-based THz antennas, graphene is also preferred for the development of other transceiver components in a scale ranging from one to a few hundreds of nanometers, such as: nanoscale FET transistors, nanosensors, nanoactuators, and nanobatteries. With the exploitation of graphene material, the integration of these nano-components in a single device of just a few micrometers in size is feasible, and will result in implementing autonomous nano-devices, able to perform specific tasks at the nanoscale, such as computing, data storing, sensing or actuation [35].
Depending on the measured parameters, nanosensors could be categorized in three types; namely physical, chemical and biological nanosensors [35]. Physical nanosensors such as pressure nanosensors [52], force nanosensors [53] or displacement nanosensors [54], are used to measure magnitudes such as mass, pressure, force, or displacement accordingly. Chemical nanosensors are used to measure magnitudes such as the concentration of a given gas, the presence of a specific type of molecules, or the molecular composition of a substance. Their working principle of both types is more or less the same and it is usually based on the change of the electronic properties of nanotubes and nanoribbons when they are used in a FET configuration, whose on/off threshold voltage changes as well by alteration of the value of each measured magnitude.
Last, biological nanosensors are used to monitor biomolecular processes such as antibody/antigen interactions, DNA interactions, enzymatic interactions or cellular communication processes. A biological nanosensor is usually composed of a biological recognition system or bioreceptor, such as an antibody, an enzyme, a protein or a DNA strain, and a transduction mechanism, e.g., an electrochemical detector, an optical transducer, or an amperometric, voltaic or magnetic detector [55]. There are mainly two subtypes of biological nanosensors based on their working principle: electrochemical biological nanosensors which work in a similar way to chemical nanosensors and photometric biological nanosensors. The latter subtype working principle is based on the use of noble metal nanoparticles and the excitation using optical waves of surface plasmons.
More specifically, a typical generic architecture of a WNSN node as seen in Figure 3 [35], would be consisted of: (i) Sensing unit: graphene material and its derivatives, namely, graphene nanoribbons (GNRs) and carbon nanotubes (CNTs) [56], provide outstanding sensing capabilities and they are the basis for implementing many types of sensors [57]. (ii) Actuation unit: an actuation unit will allow nanosensors to interact with their close environment. Several nanoactuators have also been designed and implemented so far [58]. (iii) Processing unit: nanoscale processors are being enabled by the development of different forms of miniature FET transistors in the nanometer scale. They were mostly implemented via CNTs and GNRs nanomaterials. (iv) Storage unit: graphene has shown excellent performance in a number of applications from supercapacitors [59] to photomechanical actuators [60], however, so far, its potential in nanomemory construction has not been adequately explored. (v) Power Unit: there are two types of nanobatteries [61] for feeding nanomachines: (a) harvesting the energy from the environment via nanoscale energy harvesting systems [62] and (b) wireless energy induced from an external power source [63]. (vi) Communication unit: this consists of nanoantennas and transceivers for guaranteeing EM communication between nanosensors. The working principle of energy harvesting is based on the conversion of mechanical or vibrational or hydraulic energy into electrical energy. The mechanical energy is produced by the human body movements, or muscle stretching, the vibrational energy is generated by acoustic waves or structural vibrations of buildings, and finally the hydraulic energy is produced by body fluids, or the blood flow. This energy conversion is achieved by the piezoelectric effect seen in zinc oxide (ZnO) nanowires, as they are bent, when a voltage appears in the nanowires (Figure 4) [35].