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The Internet of Things (IoT) is a key enabler for remote monitoring and control of any medium with wireless devices deployed in substantial numbers. However, these devices often lack the desired lifetimes due to their incompetent batteries. If the envisaged scale of the IoT is realized, replenishing millions of batteries will become impractical. To address this issue, joint utilization of two prominent technologies, energy harvesting (EH) and wireless power transfer (WPT), is explored in this paper. By coupling data from empirical measurements on EH profiles with Federal Communications Commission (FCC) regulations on indoor WPT, we propose and numerically evaluate design guidelines for energy-neutral wireless-powered networks, in which a source first extracts energy from its medium and then uses the collected energy to operate wireless devices via WPT. The initial findings reveal that the IoT devices in a 100m2 office building can be remotely energized by only three EH-enabled wireless power transmitting sources validating the proposed architecture.

The increasing interest on spectrum resources causes various efforts on developing smart and compact solutions as joint radar-communication (JRC) systems. A JRC system can offer cost-effective solution with concurrent operation, as target sensing via radar processing and establishing communication links. JRC capability has been proposed over the years for different types of MIMO radars. However, a JRC capable monostatic coherent MIMO radar system is yet to be developed. These radars offer several advantages as fully coherent signal processing and coherent transmit beamforming which provides beampatterns to minimize probability of intercept. In this paper, two new waveform generation techniques suitable for JRC operation without disturbing transmit beamforming requirements and waveform orthogonality condition in space and time domain are proposed for monostatic coherent MIMO radars. Then, new communication methods are introduced for phase coded monostatic coherent MIMO radars. First method uses chirp-wise information encoding inside the radar pulse as intra-pulse communications. Second rotates the phase of a specific waveform on radiated symbols to a specific direction and the last method applies a small amount of progressive phase shift to the radar waveforms emitted from the antennas to create relative phase modulation between selected radar waveforms. Then, the performance of the proposed communication techniques are investigated in terms of bit error rate (BER) and generated waveforms are examined according to the orthogonality and transmit beamforming requirements., NA

Spinal Cord Injury (SCI) is a severe condition that can result in loss of motor and sensory functions by disrupting communication among neurons, i.e., neuro-spike communication. Future information and communication technology (ICT) based treatment techniques for SCI are expected to rely on nano networks, deployed inside the body. In this respect, modeling neuro-spike communication channels in the spinal cord and revealing the relationship between channel metrics and SCI are required to realize these treatment techniques and diagnosis tools such as replacement neural implants, high-performance diagnosis tools, which are based on ICT metrics instead of large medical data. Therefore, in this study, we focus on a spinal cord network, namely the descending spinal cord pathway, which is responsible for the transmission of brain motor signals to the spinal cord. We aim to analyze the rate of motor information flow to the corresponding muscle. To this end, we model the spinal cord motor network as a layered network consisting of a cascade of two independent neuro-spike channels, which are brain-spinal cord network and spinal cord interneuron-spinal cord motoneuron network. We derive upper and lower bounds for the total rate across the brain-spinal cord network and interneuron-spinal cord network. Our evaluations demonstrate that the total rate in the case of upper motor neuron syndrome (UMNS), which manifests itself with muscle weakness, approaches zero, where the brain-spinal cord network becomes a bottleneck. In lower motor neuron syndrome (LMNS), which results in muscle atrophy, the total rate again approaches zero with the loss of spinal cord motoneurons (MN).

The technical performance requirements from wireless communication networks are continuously rising. One method to satisfy the demands is increasing the carrier frequency to the millimetre wave or low terahertz band spectrum to utilise wider operation bandwidth. In order to facilitate the studies in this frequency range, the corresponding electromagnetic (EM) wave properties, channel attributes and material characteristics need to be analytically formulated. In line with these, this Letter initially presents the theoretical expressions governing the EM wave transmission across a conducting medium. Then, by using the relative S21 parameter quantities in the proposed attenuation constant (alpha) computation technique, the alpha results of the measurements performed between 260 and 350 GHz for the clear window glass samples of different thicknesses are given. This Letter concludes with the evaluation of the outcomes., Scientific and Technological Research Council of Turkey (TÜBİTAK)

The Internet of Things (IoT) is made up of interconnected devices for exchanging information through sensors and actuators. One of the main physical sensors to understand the environment beyond the visible world is a radar. Basically, radars have always been a military tool to investigate the environment. However, with the developing technology, radars have become more compact and affordable to use in a building, in a car, in a drone, or even in a wristwatch. In the near future, radar-equipped IoT platforms will start to appear increasingly. For each IoT platform, dual use of spectrum with dual aperture is required for sensing and communicating when using conventional approaches. Emissions from the radar and communication circuitries are the main causes of the increase in energy consumption for any radar sensing IoT device. Furthermore, an increasing number of radars cause congested spectrum, and RF convergence between radar and communication systems becomes more likely to present itself. In recent years, numerous research works have proposed using the single waveform for perceiving the environment and sending information. They are often called "joint radar-communication' (JRC) systems. As a result of the latest advancements in JRC system designs, radar sensing IoT platforms now can be transformed into an "Internet of Radars" (IoR). This article is an attempt to introduce a prospective research direction in order to develop the architectures necessary to make the IoR concept possible. In this article, we present a short survey of JRC technologies likely to be used on radar-sensing- capable IoT devices. Then possible application areas, challenges to enable JRC, and future research perspectives are proposed., NA

Among the various key networks in the human body, the nervous system occupies central importance. The debilitating effects of spinal cord injuries (SCI) impact a significant number of people throughout the world, and to date, there is no satisfactory method to treat them. In this paper, we review the major treatment techniques for SCI that include promising solutions based on information and communication technology (ICT) and identify the key characteristics of such systems. We then introduce two novel ICT-based treatment approaches for SCI. The first proposal is based on neural interface systems (NIS) with enhanced feedback, where the external machines are interfaced with the brain and the spinal cord such that the brain signals are directly routed to the limbs for movement. The second proposal relates to the design of self-organizing artificial neurons (ANs) that can be used to replace the injured or dead biological neurons. Apart from SCI treatment, the proposed methods may also be utilized as enabling technologies for neural interface applications by acting as bio-cyber interfaces between the nervous system and machines. Furthermore, under the framework of Internet of Bio- Nano Things (IoBNT), experience gained from SCI treatment techniques can be transferred to nano communication research., Comment: IEEE Reviews in Biomedical Engineering

High throughput capability of the terahertz band (0.3-10 THz) wireless communications is expected to be utilized by the fifth generation of mobile telecommunication systems and enable a plethora of new applications. Supporting devices will transfer large amounts of data in both directions, causing high energy consumption by the electronic circuitries of the equipment in use. Therefore, physical layer for these systems must be designed carefully in order to reduce energy consumption per bit. In this paper, the best performing modulation scheme and hardware parameters that minimize the energy consumption without affecting the system throughput are determined. THz band device technologies are outlined and a complete survey of the state-of-the-art low-THz band circuit blocks which are suitable for mass market production is given. It is shown that for short-range communications, M-ary quadrature amplitude modulation is the most energy-efficient technique that can lead up to 90% reduction in consumed energy. Moreover, optimal transceiver parameters which can be used to further minimize the energy consumption of the THz band system are examined., Scientific and Technological Research Council of Turkey (TÜBİTAK)

Communication among neurons, known as neuro-spike communication, is the most promising technique for realization of a bio-inspired nanoscale communication paradigm to achieve biocompatible nanonetworks. In neuro-spike communication, the information, encoded into spike trains, is communicated to various brain regions through neuronal network. An output neuron needs to receive signal from multiple input neurons to generate a spike. Hence, in this paper, we aim to quantify the information transmitted through the multiple-input single-output (MISO) neuro-spike communication channel by considering models for axonal propagation, synaptic transmission, and spike generation. Moreover, the spike generation and propagation in each neuron requires opening and closing of numerous ionic channels on the cell membrane, which consumes considerable amount of ATP molecules called metabolic energy. Thus, we evaluate how applying a constraint on available metabolic energy affects the maximum achievable mutual information of this system. To this aim, we derive a closed form equation for the sum rate of the MISO neuro-spike communication channel and analyze it under the metabolic cost constraints. Finally, we discuss the impacts of changes in number of pre-synaptic neurons on the achievable rate and quantify the tradeoff between maximum achievable sum rate and the consumed metabolic energy.

Using the advances in molecular communications (MCs), nanomachines as a group can undertake complex tasks. With the emergence of Internet of molecular things (IoMT), such nanomachine groups are now larger than ever. However, the minimal design of nanomachines makes cooperation difficult. In this paper, we investigate the performances of anarchic and cooperative transmitters in IoMT. We design an MC game in which nanomachines choose to cooperate or confront. We discuss the advantages and disadvantages of cooperation and state the possible transmitter personalities using game theoretic principles. Moreover, we focus on methods to ensure cooperation and we explore the optimal transmitter behavior if its partner rejects cooperation. Finally, we deduce that although ensuring cooperation may be done effectively with minimum hardware, anarchy is not necessarily a bad result. We also realize that in case a transmitter rejects cooperation, perpetual confrontation is not a good approach.

Understanding the fundamentals of communication among neurons, known as neuro-spike communication, leads to reach bio-inspired nanoscale communication paradigms. In this paper, we focus on a part of neuro-spike communication, known as axonal transmission, and propose a realistic model for it. The shape of the spike during axonal transmission varies according to previously applied stimulations to the neuron, and these variations affect the amount of information communicated between neurons. Hence, to reach an accurate model for neuro-spike communication, the memory of axon and its effect on the axonal transmission should be considered, which are not studied in the existing literature. In this paper, we extract the important factors on the memory of axon and define memory states based on these factors. We also describe the transition among these states and the properties of axonal transmission in each of them. Finally, we demonstrate that the proposed model can follow changes in the axonal functionality properly by simulating the proposed model and reporting the root mean square error between simulation results and experimental data.

Molecular communication (MC) is the most promising communication paradigm for nanonetwork realization since it is a natural phenomenon observed among living entities with nanoscale components. Since MC significantly differs from classical communication systems, it mandates re-investigation of information and communication theoretical fundamentals. The closest examples of MC architectures are present inside our own body. Therefore, in this paper, we investigate the existing literature on intrabody nanonetworks and different MC paradigms to establish and introduce the fundamentals of molecular information and communication science. We highlight future research directions and open issues that need to be addressed for revealing the fundamental limits of this science. Although the scope of this development encompasses wide range of applications, we particularly emphasize its significance for life sciences by introducing potential diagnosis and treatment techniques for diseases caused by dysfunction of intrabody nanonetworks., ERC project MINERVA; European Union project CIRCLE; Scientific and Technological Research Council of Turkey (TÜBİTAK)

Internet of Things (IoT) is envisioned to bring the Internet connection to every object/service/process to seamlessly and efficiently observe, manage, and control pervasive systems. This necessitates the employment of wireless standalone devices in excessive numbers. However, periodic maintenance of thousands, maybe millions of batteries will add massive workload and replenishment costs to the operation. In order to alleviate this problem, we introduce a totally new energy harvesting paradigm based on utilizing ambient electric-field in the vicinity of lighting elements. A low voltage prototype is designed, constituted, and evaluated on a generic 4 x 18W-T8 ceiling-type fluorescent troffer. Empirical results disclose the availability of 1.5 J of energy that can be gathered in 30 min when a copper plate, i.e., the harvester, covered by a reflective dielectric is employed. The design issues to achieve the best performance attainable are addressed in both theoretical and experimental manners. The physical model of the proposed technique and an applicable circuit diagram for its execution are provided. We also point out possible application areas, and protocol stack requirements specific to our proposal to conveniently enable self-configuring IoT services, which are free from battery constraints., NA

The realization of bio-compatible nanomachines would pave the way for developing novel diagnosis and treatment techniques for the dysfunctions of intra-body nanonetworks and revolutionize the traditional healthcare methodologies making them less invasive and more efficient. The network of these nanomachines is aimed to be used for treating neuronal diseases such as developing an implant that bridges over the injured spinal cord to regain its normal functionality. Thus, nanoscale communication paradigms are needed to be investigated to facilitate communication between nanomachines. Communication among neurons is one of the most promising nanoscale communication paradigm, which necessitates the thorough communication theoretical analysis of information transmission among neurons. The information flow in neuro-spike communication channel is regulated by the ability of neurons to change synaptic strengths over time, i.e. synaptic plasticity. Thus, the performance evaluation of the nervous nanonetwork is incomplete without considering the influence of synaptic plasticity. In this paper, we focus on information transmission among hippocampal pyramidal neurons and provide a comprehensive channel model for MISO neuro-spike communication, which includes axonal transmission, vesicle release process, synaptic communication and spike generation. In this channel, the spike timing dependent plasticity (STDP) model is used to cover both synaptic depressiofan and potentiation depending on the temporal correlation between spikes generated by input and output neurons. Since synaptic strength changes depending on different physiological factors such as spiking rate of presynaptic neurons, number of correlated presynaptic neurons and the correlation factor among them, we simulate this model with correlated inputs and analyze the evolution of synaptic weights over time. Moreover, we calculate average mutual information between input and output of the channel and find the impact of plasticity and correlation among inputs on the information transmission. The simulation results reveal the impact of different physiological factors related to either presynaptic or postsynaptic neurons on the performance of MISO neuro-spike communication. Moreover, they provide guidelines for selecting the system parameters in a bio-inspired neuronal network according to the requirements of different applications.

Molecular Communications (MC) uses molecules as information carriers between nanomachines. MC channel in practice can be crowded with different types of molecules, i.e., ligands, which can have similar binding properties causing severe cross-talk on ligand receptors. Simultaneous sensing of multiple ligand types provides opportunities for eliminating interference of external molecular sources and multi-user interference (MUI), and developing new multiple access techniques for MC nanonetworks. In this paper, we investigate channel sensing methods that use only a single type of receptors and exploit the amount of time receptors stay bound and unbound during ligand-receptor binding reaction to concurrently estimate the concentration of multiple types of ligands. We derive the Cram\'er-Rao Lower Bound (CRLB) for multi-ligand estimation, and propose practical and low-complexity suboptimal estimators for channel sensing. We analyze the performance of the proposed methods in terms of normalized mean squared error (NMSE), and show that they can efficiently estimate the concentration of ligands up to $10$ different types with an average NMSE far below $10^{-2}$. Lastly, we propose a synthetic receptor design based on modified kinetic proofreading (KPR) scheme to sample the unbound and bound time durations, and a Chemical Reaction Network (CRN) to perform the required computations in synthetic cells., This work was supported in part by the ERC projects MINERVA (ERC-2013-CoG #616922), and MINERGRACE (ERC-2017- PoC #780645).

Inspired by nature, molecular communications (MC), i.e., the use of molecules to encode, transmit, and receive information, stands as the most promising communication paradigm to realize the nanonetworks. Even though there has been extensive theoretical research toward nanoscale MC, there are no examples of implemented nanoscale MC networks. The main reason for this lies in the peculiarities of nanoscale physics, challenges in nanoscale fabrication, and highly stochastic nature of the biochemical domain of envisioned nanonetwork applications. This mandates developing novel device architectures and communication methods compatible with MC constraints. To that end, various transmitter and receiver designs for MC have been proposed in the literature together with numerable modulation, coding, and detection techniques. However, these works fall into domains of a very wide spectrum of disciplines, including, but not limited to, information and communication theory, quantum physics, materials science, nanofabrication, physiology, and synthetic biology. Therefore, we believe it is imperative for the progress of the field that an organized exposition of cumulative knowledge on the subject matter can be compiled. Thus, to fill this gap, in this comprehensive survey, we review the existing literature on transmitter and receiver architectures toward realizing MC among nanomaterial-based nanomachines and/or biological entities and provide a complete overview of modulation, coding, and detection techniques employed for MC. Moreover, we identify the most significant shortcomings and challenges in all these research areas and propose potential solutions to overcome some of them., This work was supported in part by the European Research Council (ERC) Projects MINERVA under Grant ERC-2013-CoG #616922 and MINERGRACE under Grant ERC-2017-PoC #780645.

Atmospheric ducts that are caused by the rapid decrease in the refractive index of the lower atmosphere can trap the propagating signals. The trapping effect of atmospheric ducts provides significant received power enhancements and enables signals to reach beyond-line-of-sight distances. Although wave propagation in atmospheric ducts are well studied, there is no work that provides fading and correlation analysis for atmospheric ducts. Therefore, the main motivation of this communication is to develop a large-scale path-loss model, estimate the distribution of fading, and analyze fading correlation for space and polarization diversity systems in the presence of surface ducts. Lastly, the achievable data rate results are presented to show the performance improvement with space and polarization diversity techniques., NA

Cognitive radio sensor networks (CRSNs) are event-based systems such that sensor nodes detect events and the event readings of the sensors are collaboratively conveyed in a multi-hop manner through vacant channels from event regions to a sink. Hence, the event-to-sink communication and the dynamic radio environment require a coordination scheme in CRSNs. In this paper, we propose a spectrum-aware clustering protocol to address the event-to-sink communication coordination issue in mobile CRSNs. Our clustering scheme consists of two phases. The first phase is the determination of nodes eligible for clustering, and the second phase is to form clusters among those nodes according to vacant spectrum bands. Clusters are temporary and they are not preserved after the end of events. Furthermore, we find average re-clustering probability, expected cluster coverage area, and find maximum event generation frequency for energy-efficient operation of our protocol. We study performance of our protocol in terms of control and data packet exchange, time steps required for clustering, connectivity of clusters, energy consumed for clustering, and re-clustering ratio due to the mobility. Performance comparison simulations show that our algorithm has better performance in terms of connectivity and energy consumption., Scientific and Technological Research Council of Turkey (TÜBİTAK)

Fog Computing is a new paradigm that has been proposed by CISCO to take full advantage of the ever growing computational capacity of the near-user or edge devices (e.g., wireless gateways and sensors). The paradigm proposes an architecture that enables the devices to host functionality of various user-centric services. While the prospects of Fog Computing promise numerous advantages, development of Fog Services remains under-investigated. This article considers an opportunity of Fog implementation for Alert Services on top of Wireless Sensor Network (WSN) technology. In particular, we focus on targeted WSN-alert delivery based on spontaneous interaction between a WSN and hand-held devices of its users. For the alert delivery, we propose a Gravity Routing concept that prioritizes the areas of high user-presence within the network. Based on the concept, we develop a routing protocol, namely the Gradient Gravity Routing (GGR) that combines targeted delivery and resilience to potential sensor-load heterogeneity within the network. The protocol has been compared against a set of state-of-the-art solutions via a series of simulations. The evaluation has shown the ability of GGR to match the performance of the compared solutions in terms of alert delivery ratio, while minimizing the overall energy consumption of the network.

Molecular devices, which consist of single or a few molecules, are envisioned to perform advanced tasks such as molecular information processing and collaborative sensing/actuating if they are operated in a cooperative manner. To connect these nanoscopic primitive devices with each other and with macroscale networks, and thus, to realize the internet of molecular devices, requires fundamentally different and novel approaches, other than the molecular or electromagnetic nanocommunications. Recently, we proposed and studied the use of Förster resonance energy transfer (FRET), which is a short-range nonradiative energy transfer process between fluorophores, as a high-rate and reliable wireless communication mechanism to connect fluorophore-based photoactive molecular devices. In this paper, we provide an in-depth architectural view of this new communication paradigm with a focus on its peculiarities, fundamental principles, and design requirements by comprehensively surveying the theoretical and experimental positions and ideas. We give an overview of networking opportunities offered by the intrinsic capabilities of fluorophores under the novel concept of Internet of Molecular Things. We present some prospective applications, theoretical modeling approaches, and experimental opportunities, and finally discuss the implementation challenges., European Research Council (ERC); Minerva; Scientific and Technological Research Council of Turkey (TÜBİTAK); Turkish National Academy of Sciences Distinguished Young Scientist Award Program (TÜBA-GEBİP); IBM Faculty Award

Cognitive radio (CR) is proposed to use the spectrum opportunistically. Spectrum opportunity (SOP) can be defined as the possibility of a spectrum-aware communication between a CR transmitter and a CR receiver. Successful spectrum-aware communication between the communicating CRs, which is utilization of SOP (USOP), depends on the SOP detection and the correct transmission of a packet. Spectrum sensing performance, physical channel, and network parameters affect the probability of USOP. In this letter, we characterize the probability of the USOP under different network topologies. The network topology is due to the relation between transmission ranges of licensed users and CRs. We numerically study this probability for different network parameters and topologies. We find that the characteristics of USOP highly depend on the network topology, CR sensing performance and licensed users' activities., Scientific and Technological Research Council of Turkey (TÜBİTAK)

Recently, huge attention is attracted to the concept of integrating communication and radar missions within the same platform. Joint Radar-Communications (JRC) system gives an important opportunity to reduce spectrum usage and product cost while doing concurrent operation, as target sensing via radar processing and establishing communication links. A JRC-capable coherent MIMO radar system have been proposed recently in the literature. Several methods are introduced to reach dual goal as a notable null level towards the direction of interest of the radar and MIMO radar waveform orthogonality. Due to the limitations originated form the JRC operation, communication channel may encounter unwanted amplitude variations. This unwanted modulation normally affects the communication performance by its nature, due to the fades on radiated signal amplitude towards the direction of communication. However, the effect of this unintentional modulation on communication channel is yet to be investigated. In this paper, the communication channel for JRC capable phase-coded coherent MIMO radars is analyzed and investigated under additive white Gaussian noise and Rayleigh/Rician fading conditions. Communication capacity is evaluated for each channel condition. The results reveal that, using the single-side limited null direction fixed waveform generation method displays the best capacity performance under all channel conditions., NA

In a natural disaster such as an earthquake, it is vital to know the number of people trapped under the ruins. To address this problem, we propose RESCUE - wiREless backScattering CommUnication based disastEr recovery system. RESCUE is composed of special Radio-frequency identification (RFID) readers and sensors that are used to determine the total number of people under the ruins. Passive wireless sensor nodes are placed inside the building during construction and are equipped with a camera that is activated during an earthquake. After the earthquake, communication to the passive tags of sensors is achieved by wireless power transfer from a reader located outside the ruins. Tags harvest this energy and send the image data stored by the camera. We also design an antenna structure to maximize simultaneous wireless information and power transfer (SWIPT) for devices under ruins. We analyze the communication channel between reader and sensors and derive a channel model over ruins. Furthermore, we obtain the results of experimental study where we validate the derived channel model. Results show that RESCUE can collect the desired data in a relatively short time, and hence, is a promising disaster recovery system architecture., Scientific and Technological Research Council of Turkey (TÜBİTAK); Turkish National Academy of Sciences Distinguished Young Scientist Award Program (Turkish Academy of Sciences (TÜBA)-GEBIP)

© 2017 Elsevier B.V. Due to the opportunistically resource usage of users in cognitive radio sensor networks (CRSNs), the availability of network resources is highly variable. Therefore, admission control is an essential mechanism to manage the traffic of cognitive radio users in order to satisfy the quality of service (QoS) requirements of applications. In this study, a connection admission control (CAC) mechanism is introduced to satisfy the requirements of delay sensitivity and power consumption awareness. This proposed mechanism is modeled through a semi Markov decision process (SMDP) and a linear programming problem is derived with the aim of obtaining the optimal policy to control the traffic of CRSNs and achieving maximum reward. The number of required channels at each network state is estimated through a graph coloring approach. An end to end delay constraint is defined for the optimization problem which is inspired from Kleinrock independence approximation. Furthermore, a power-aware weighting method is proposed for this mechanism. We conduct different simulation-based scenarios to investigate the performance of the proposed mechanism. The experimental results demonstrate the efficiency of this SMDP-based mechanism in comparison to the last CAC mechanism in CRSNs.

Communication theoretical understanding of healthy and diseased connections in the spinal cord motor system is crucial for realizing future information and communication technology (ICT) based diagnosis and treatment techniques for spinal cord injuries (SCI). A spinal cord motor nucleus associated with a particular muscle constitutes an ideal candidate for studying to have an understanding of SCI. Typical spinal cord motor nucleus system contains pool of lower motor neurons (MNs) controlling a muscle by integrating synaptic inputs from spinal interneurons (INs), upper motor neurons (DNs) and sensory neurons (SNs). In this study, we consider this system from ICT perspective. Our aim is to quantify the rate of information flow across a spinal cord motor nucleus. To this end, we model an equivalent single-hop multiterminal network, where multiple transmitting nodes representing heterogeneous population of DNs, INs and SNs sen information to multiple receiving nodes corresponding to MNs. To identify the outputs at receiving nodes, we define corresponding neurospike communication channel and then find the bound on total rates across this network. Based on the network model, we analyze achievable rates for a particular motor nucleus system called Tibialis Anterior (TA) motor nucleus in the spinal cord numerically and simulate several spinal cord dysfunction scenarios. The numerical results reveal that decrease in the maximum total rates with the lower motor neuron injury causes weakness in the affected muscle., European Union (European Union); European Research Council (ERC) Projects MINERVA; European Research Council (ERC) Proof of Concept Project MINERGRACE

© 2018 IEEE. Sensor nodes, one of the most crucial elements of Internet of Things (IoT), sense the environment and send their observations to a remote Access Point (AP). One drawback of sensor nodes in an IoT setting is their limited battery supply. Hereby, energy harvesting (EH) stands as a promising solution to reduce or even completely eliminate lifetime constraints of sensors with exploitation of available resources. In this paper, we propose an electric-field EH (EFEH) method to enable battery-less execution of sensor-based IoT services for Smart Grid (SG) context. For this purpose, for the first time in the literature, harvestable energy through EFEH method is investigated with a transformer room experimental set-up. Our experiments reveal that 40 mJ of energy can be harvested in a period of 900 sec with the proposed EFEH method. Building on this energy profile, we define a throughput objective function θ for a «harvest-then-transmit» type system model, to shed light on the harvesting- throughput trade-off specific to IoT-assisted SG applications. Numerical results disclose non- trivial relationships between optimal harvesting period T-H, optimal transmission period T-T and critical network parameters such as node-AP hop distance, path loss exponent and minimum reporting frequency requirement.

Communication among neurons is the highly evolved and efficient nanoscale communication paradigm, hence the most promising technique for biocompatible nanonetworks. This necessitates the understanding of neuro-spike communication from information theoretical perspective to reach a reference model for nanonetworks. This would also contribute towards developing ICT-based diagnostics techniques for neuro-degenerative diseases. Thus, in this paper, we focus on the fundamental building block of neuro-spike communication, i.e., signal transmission over a synapse, to evaluate its information transfer rate. We aim to analyze a realistic synaptic communication model, which for the first time, encompasses the variation in vesicle release probability with time, synaptic geometry and the re-uptake of neurotransmitters by pre-synaptic terminal. To achieve this objective, we formulate the mutual information between input and output of the synapse. Then, since this communication paradigm has memory, we evaluate the average mutual information over multiple transmissions to find its overall capacity. We derive a closed-form expression for the capacity of the synaptic communication as well as calculate the capacity-achieving input probability distribution. Finally, we find the effects of variation in different synaptic parameters on the information capacity and prove that the diffusion process does not decrease the information a neural response carries about the stimulus in real scenario., European Research Council (ERC); MINERVA; European Union (EU); CIRCLE; Horizon 2020; Scientific and Technological Research Council of Turkey (TÜBİTAK)

Extensive use of amateur drones (ADrs) poses a threat to the public safety due to their possible misuse. Hence, surveillance drones (SDrs) are utilized to detect and eliminate potential threats. However, limited battery, and lack of efficient communication and networking solutions degrade the quality of surveillance. To this end, we conceptualize the Energy Neutral Internet of Drones (enIoD) to enable enhanced connectivity between drones by overcoming energy limitations for autonomous and continuous operation. Power provisioning with recharging stations is introduced by wireless power transfer to energize the drones. Renewable energy harvesting is utilized to realize energy neutrality, which is minimization of deficit in harvested and consumed energy in enIoD. Communication and networking architectures and protocols for realization of multi-dimensional objectives are presented. Finally, possible application areas are explained with a case study to show how enIoD operates.

CR enables dynamic spectrum access to utilize licensed spectrum when it is idle. CR technology is applied to wireless ad hoc and sensor networks to form CRAHNs and CRSNs, respectively. Clustering is an efficient topology management technique to regulate communication and allocate spectrum resources by CR capabilities of nodes in CRAHNs and CRSNs. In this article, we thoroughly investigate the benefits and functionalities of clustering such as topology, spectrum, and energy management in these networks. We also overview motivations for and challenges of clustering in CRAHNs and CRSNs. Existing clustering schemes are reviewed and compared. We conclude by revealing key considerations and possible solutions for spectrum-aware clustering in multi-channel CRAHNs and CRSNs., NA

We consider a microfluidic molecular communication (MC) system, where the concentration-encoded molecular messages are transported via fluid flow-induced convection and diffusion, and detected by a surface-based MC receiver with ligand receptors placed at the bottom of the microfluidic channel. The overall system is a convection-diffusion-reaction system that can only be solved by numerical methods, e.g., finite element analysis (FEA). However, analytical models are key for the information and communication technology (ICT), as they enable an optimisation framework to develop advanced communication techniques, such as optimum detection methods and reliable transmission schemes. In this direction, we develop an analytical model to approximate the expected time course of bound receptor concentration, i.e., the received signal used to decode the transmitted messages. The model obviates the need for computationally expensive numerical methods by capturing the nonlinearities caused by laminar flow resulting in parabolic velocity profile, and finite number of ligand receptors leading to receiver saturation. The model also captures the effects of reactive surface depletion layer resulting from the mass transport limitations and moving reaction boundary originated from the passage of finite-duration molecular concentration pulse over the receiver surface. Based on the proposed model, we derive closed form analytical expressions that approximate the received pulse width, pulse delay and pulse amplitude, which can be used to optimize the system from an ICT perspective. We evaluate the accuracy of the proposed model by comparing model-based analytical results to the numerical results obtained by solving the exact system model with COMSOL Multiphysics., European Union (EU); H2020; ERC project MINERVA; EU project CIRCLE

Molecular Communication (MC) is a bioinspired communication technique that uses molecules as a method of information transfer among nanoscale devices. MC receiver is an essential component having profound impact on the communication system performance. However, the interaction of the receiver with information bearing molecules has been usually oversimplified in modeling the reception process and developing signal detection techniques. In this paper, we focus on the signal detection problem of MC receivers employing receptor molecules to infer the transmitted messages encoded into the concentration of molecules, i.e., ligands. Exploiting the observable characteristics of ligand-receptor binding reaction, we first introduce a Maximum Likelihood (ML) detection method based on instantaneous receptor occupation ratio, as aligned with the current MC literature. Then, we propose a novel ML detection technique, which exploits the amount of time the receptors stay unbound in an observation time window. A comprehensive analysis is carried out to compare the performance of the detectors in terms of bit error probability. In evaluating the detection performance, emphasis is given to the receptor saturation problem resulting from the accumulation of messenger molecules at the receiver as a consequence of intersymbol interference. The results reveal that detection based on receptor unbound time is quite reliable even in saturation, whereas the reliability of detection based on receptor occupation ratio substantially decreases as the receiver gets saturated. Finally, we also discuss the potential methods of implementing the detectors., European Research Council (ERC); European Union (European Union); H2020; MINERVA; CIRCLE

Understanding the communication theoretical capabilities of information transmission among neurons, known as neuro-spike communication, is a significant step in developing bio-inspired solutions for nanonetworking. In this paper, we focus on a part of this communication known as synaptic transmission for pyramidal neurons in the Cornu Ammonis area of the hippocampus location in the brain and propose a communication-based model for it that includes effects of spike shape variation on neural calcium signaling and the vesicle release process downstream of it. For this aim, we find impacts of spike shape variation on opening of voltage-dependent calcium channels, which control the release of vesicles from the pre-synaptic neuron by changing the influx of calcium ions. Moreover, we derive the structure of the optimum receiver based on the Neyman-Pearson detection method to find the effects of spike shape variations on the functionality of neuro-spike communication. Numerical results depict that changes in both spike width and amplitude affect the error detection probability. Moreover, these two factors do not control the performance of the system independently. Hence, a proper model for neuro-spike communication should contain effects of spike shape variations during axonal transmission on both synaptic propagation and spike generation mechanisms to enable us to accurately explain the performance of this communication paradigm.

The Internet of Things (IoT) provides connectivity to the objects that monitor and sense the environment to integrate physical world with digital world. If IoT is enabled in the Smart Grid (SG), it can benefit from advantages of the IoT such as interoperability, connectivity, etc. By combining the IoT with energy harvesting (EH) and cognitive radio (CR) techniques, the problems of SG, such as harsh channel conditions and limited battery power, may be resolved. Hence, incorporation of EH and CR reveals a new networking paradigm for IoT-enabled SG. To this end, we first introduce CR usage in the IoT-enabled SG, and explain the advantages and challenges of CRs. Furthermore, we propose EH approaches for the resource constraint of wireless devices in the IoT-enabled SG. Operation and node architecture of energy harvesting cognitive radios (EH-CR), and network architecture of the IoT-enabled SG are described to explain details of our networking paradigm. Open issues and future research directions are discussed to enable this new paradigm., NA

Spatial correlation between densely deployed sensor nodes in a wireless sensor network (WSN) can be exploited to reduce the power consumption through a proper source coding mechanism such as distributed source coding (DSC). In this paper, we propose the Decoding Delay-based Distributed Source Coding (D-DSC) to improve the energy efficiency of the classical DSC by employing the decoding delay concept which enables the use of the maximum correlated portion of sensor samples during the event estimation. In D-DSC, network is partitioned into clusters, where the clusterheads communicate their uncompressed samples carrying the side information, and the cluster members send their compressed samples. Sink performs joint decoding of the compressed and uncompressed samples and then reconstructs the event signal using the decoded sensor readings. Based on the observed degree of the correlation among sensor samples, the sink dynamically updates and broadcasts the varying compression rates back to the sensor nodes. Simulation results for the performance evaluation reveal that D-DSC can achieve reliable and energy-efficient event communication and estimation for practical signal detection/estimation applications having massive number of sensors towards the realization of Internet of Sensing Things (loST)., Aselsan Inc.; Scientific and Technological Research Council of Turkey (TÜBİTAK); Turkish Academy of Sciences (TÜBA) Young Outstanding Researcher Support Programme (GEBIP)

The nervous system holds a central position among the major in-body networks. It comprises of cells known as neurons that are responsible to carry messages between different parts of the body and make decisions based on those messages. In this work, further to the extensive theoretical studies, we demonstrate the first controlled information transfer through an in vivo nervous system by modulating digital data from macro-scale devices onto the nervous system of common earthworms and conducting successful transmissions. The results and analysis of our experiments provide a method to model networks of neurons, calculate the channel propagation delay, create their simulation models, indicate optimum parameters such as frequency, amplitude and modulation schemes for such networks, and identify average nerve spikes per input pulse as the nervous information coding scheme. Future studies on neuron characterization and artificial neurons may benefit from the results of our work., The ERC project MINERVA; CIRCLE; European Union (European Union); Horizon 2020; European Research Council (ERC)

© 1997-2012 IEEE. Information transmission in the nervous system is performed through the propagation of spikes among neurons, which is done by vesicle release to chemical synapses. Understanding the fundamentals of this communication can lead to the implementation of bio-inspired nanoscale communication paradigms. In this letter, we utilize a realistic pool-based model for vesicle release and replenishment in hippocampal pyramidal neurons and evaluate the capacity of information transmission in this process by modeling it as a binary channel with memory. Then, we derive a recurrence relation for the number of available vesicles, which is used to find successful bit transmission probabilities and mutual information between input and output. Finally, we evaluate the spiking probability that maximizes mutual information and derive the capacity of the channel.

Wireless sensors equipped with cognitive radio, i.e., cognitive radio sensor networks (CRSN), can access the spectrum in an opportunistic manner and coexist with licensed users to mitigate the crowded spectrum problem and provide ubiquitous remote event monitoring and tracking for cyber-physical systems. In this paper, a novel transport layer protocol for CRSN, spectrum-aware energy-adaptive reliable transport protocol is presented to enable energy-adaptive collaborative event sensing in spectrum-scarce cyber-physical systems. To the best of our knowledge, this is the first attempt to specifically devise a reliable event transport scheme for CRSN., Scientific and Technological Research Council of Turkey (TÜBİTAK); Turkish National Academy of Sciences Distinguished Young Scientist Award Program

Summary In the near future, billions of wireless devices are expected to be operational. To enable the required machine to machine communications, two major problems must be addressed. How to obtain the required spectrum efficiency, and how to deliver the required power to these devices. The most promising answers to these questions are cognitive radio and energy harvesting, respectively. Energy harvesting enables deployment of sensors and devices without having to worry about their battery lifetime. Cognitive radio increases the utilization of spectrum by accessing unused spectrum dynamically. Energy harvesting from electromagnetic waves is suitable for these low power, low cost devices used in machine to machine communications because only minimal additional hardware is required for such energy harvesting. With this idea as the starting point, we first present an analysis on how much throughput can be obtained from a cognitive, electromagnetic energy harvesting wireless network. Then, we show when and how cooperation among network nodes may increase performance. We believe that our results will provide insight for the development of future cooperative cognitive energy harvesting networks. Copyright © 2015 John Wiley & Sons, Ltd.

Molecular communication is an important tool to understand biological communications with many promising applications in Internet of Bio-Nano Things (IoBNT). The insulin-glucose system is of key significance among the major intra-body nanonetworks, since it fulfills metabolic requirements of the body. The study of biological networks from information and communication theoretical (ICT) perspective is necessary for their introduction in the IoBNT framework. Therefore, the objective of this paper is to provide and analyze for the first time in the literature, a simple molecular communication model of the human insulin-glucose system from ICT perspective. The data rate, channel capacity, and the group propagation delay are analyzed for a two-cell network between a pancreatic beta cell and a muscle cell that are connected through a capillary. The results point out a correlation between an increase in insulin resistance and a decrease in the data rate and channel capacity, an increase in the insulin transmission rate, and an increase in the propagation delay. We also propose applications for the introduction of the system in the IoBNT framework. Multi-cell insulin glucose system models may be based on this simple model to help in the investigation, diagnosis, and treatment of insulin resistance by means of novel IoBNT applications., European Research Council (ERC); European Union (European Union); H2020; MINERVA; CIRCLE

© 2017 IEEE. A joint radar-communication (JRC) system can provide cost-effective and spectrum-efficient platform solution with simultaneous operation, while accomplishing important tasks, sensing via radar processing and allocation of communication links. Existing modulation techniques where information embedding is achieved using sidelobe Amplitude-Shift Keying (ASK) for the JRC system are not investigated so far under fading channels and an optimum threshold estimation algorithm is yet to be developed. Specifying an optimum threshold level under fading can become a comprehensive problem, especially for mobile communication systems. In this paper, a novel non-data aided (NDA) threshold estimation technique and a receiver design are introduced. Furthermore, a new sidelobe ASK modulation technique is proposed for utilizing JRC system for mobile platforms under fading. Proposed modulation technique implements dual Sidelobe Level (SLL) ASK with waveform diversity by exploiting multiple orthogonal waveforms. One pair is modulated with dual SLL in amplitude rotational manner and initiates NDA threshold estimation process at the receiver. This method utilizes K bits of information using only K + 1 orthogonal waveform. The performance of the proposed technique is investigated in terms of the bit error rate (BER) and data rate. Simulations reveal that the operation of proposed method coupled with NDA threshold estimation process can reach more data rate, since it exhibits almost the same BER performance as existing methods under fading channel without requiring more orthogonal waveform.

In this paper, we analyze molecular communications (MCs) in a proposed artificial synapse (AS), whose main difference from biological synapses (BSs) is that it is closed, i.e., transmitter molecules cannot diffuse out from AS. Such a setup has both advantages and disadvantages. Besides higher structural stability, being closed, AS never runs out of transmitters. Thus, MC in AS is disconnected from outer environment, which is very desirable for possible intra-body applications. On the other hand, clearance of transmitters from AS has to be achieved by transporter molecules on the presynaptic membrane of AS. Except from these differences, rest of AS content is taken to be similar to that of a glutamatergic BS. Furthermore, in place of commonly used Monte Carlo-based random walk experiments, we derive a deterministic algorithm that attacks for expected values of desired parameters such as evolution of receptor states. To assess validity of our algorithm, we compare its results with average results of an ensemble of Monte Carlo experiments, which shows near exact match. Moreover, our approach requires significantly less amount of computation compared with Monte Carlo approach, making it useful for parameter space exploration necessary for optimization in design of possible MC devices, including but not limited to AS. Results of our algorithm are presented in case of single quantal release only, and they support that MC in closed AS with elevated uptake has similar properties to that in BS. In particular, similar to glutamatergic BSs, the quantal size and the density of receptors are found to be main sources of synaptic plasticity. On the other hand, the proposed model of AS is found to have slower decaying transients of receptor states than BSs, especially desensitized ones, which is due to prolonged clearance of transmitters from AS.

The aim of this paper is tracking Parkinson's disease (PD) progression based on its symptoms on vocal system using Unified Parkinsons Disease Rating Scale (UPDRS). We utilize a standard speech signal feature set, which contains 6373 static features as functionals of low-level descriptor (LLD) contours, and select the most informative ones using the maximal relevance and minimal redundancy based on correlations (mRMR(C)) criteria. Then, we evaluate performance of Gaussian mixture regression (GMR) and support vector regression (SVR) on estimating the third subscale of UPDRS, i.e., UPDRS: motor subscale (UPDRS-III). Among the most informative features, a list of features are selected after redundancy reduction. The selected features depict that LLDs providing information about spectrum flatness, spectral distribution of energy, and hoarseness of voice are the most important ones for estimating UPDRS-III. Moreover, the most informative statistical functions are related to range, maximum, minimum and standard deviation of LLDs, which is an evidence of the muscle weakness due to the PD. Furthermore, GMR outperforms SVR on compact feature sets while the performance of SVR improves by increasing number of features.

Aim of this paper is proposing a stochastic model for vesicle release process, a part of neuro-spike communication. Hence, we study biological events occurring in this process and use microphysiological simulations to observe functionality of these events. Since the most important source of variability in vesicle release probability is opening of voltage dependent calcium channels (VDCCs) followed by influx of calcium ions through these channels, we propose a stochastic model for this event, while using a deterministic model for other variability sources. To capture the stochasticity of calcium influx to pre-synaptic neuron in our model, we study its statistics and find that it can be modeled by a distribution defined based on Normal and Logistic distributions.

© 2017 IEEE. In this paper, we investigate the communication channel capacity among hippocampal pyramidal neurons. To this aim, we study the processes included in this communication and model them with realistic communication system components based on the existing reports in the physiology literature. We consider the communication between two neurons and reveal the effects of the existence of multiple terminals between these neurons on the achievable rate per spike. To this objective, we derive the power spectral density (PSD) of the signal in the output neuron and utilize it to calculate the rate region of the channel. Moreover, we evaluate the impacts of vesicle availability on the achievable rate by deriving the expected number of available vesicles in input neuron using a realistic vesicle release model. Simulation results show that number of available vesicles for release does not affect the achievable rate of neuro-spike communication with univesicular release model. However, in neurons that multiple vesicles can release from each synaptic terminal, achievable rate is significantly affected by depletion of vesicles. Moreover, we show that increasing the number of synaptic terminals between two neurons makes the synaptic connection stronger. Hence, it is an important factor in learning and memory, which occur in the hippocampal region of the brain based on the synaptic connectivity.

In nano-bio networks, multiple transmitter-receiver pairs will operate in the same medium. Both inter-symbol interference and multi-user interference can cause saturation at the receiver side, and this effect may cause an outage. Thus, we propose a tractable framework to calculate the theoretical operating points for fully absorbing receiver.

Computational methods have been extensively used to understand the underlying dynamics of molecular communication methods employed by nature. One very effective and popular approach is to utilize a Monte Carlo simulation. Although it is very reliable, this method can have a very high computational cost, which in some cases renders the simulation impractical. Therefore, in this paper, for the special case of an excitatory synaptic molecular communication channel, we present a novel mathematical model for the diffusion and binding of neurotransmitters that takes into account the effects of synaptic geometry in 3-D space and re-absorption of neurotransmitters by the transmitting neuron. Based on this model we develop a fast deterministic algorithm, which calculates expected value of the output of this channel, namely, the amplitude of excitatory postsynaptic potential (EPSP), for given synaptic parameters. We validate our algorithm by a Monte Carlo simulation, which shows total agreement between the results of the two methods. Finally, we utilize our model to quantify the effects of variation in synaptic parameters, such as position of release site, receptor density, size of postsynaptic density, diffusion coefficient, uptake probability, and number of neurotransmitters in a vesicle, on maximum number of bound receptors that directly affect the peak amplitude of EPSP.

© 2002-2012 IEEE. Electric-field energy harvesting (EFEH) can be considered as an emerging and promising alternative for self-sustainable next-generation WSNs. Unlike conventional harvesting methods that rely on ambient variables, EFEH provides more reliable and durable operation as it is operable with any voltage-Applied conductive material. Therefore, it is better suited for advanced throughput and applications requiring a certain QoS. In this article, we introduce this newly emerging WSN paradigm, and focus on enabling EFEH technology for smart grid architectures, such as home, building, and near area networks, where the field intensity is relatively low. To this end, a practical methodology and a general use implementation framework have been developed for low-voltage applications by regarding compelling design issues and challenging source scarcity. The proposed double-layer harvester model is experimentally evaluated. Its performance in terms of implementation flexibility, sensor lifetime, and communication throughput is investigated. In addition, current challenges, open issues, and future research directions are discussed for the design of more enhanced EFEH wireless networks.

Molecular communication (MC) is a bio-inspired communication method based on the exchange of molecules for information transfer among nanoscale devices. Although MC has been extensively studied from various aspects, limitations imposed by the physical design of transceiving units have been largely neglected in the literature. Recently, we have proposed a nanobioelectronic MC receiver architecture based on the nanoscale field effect transistor-based biosensor (bioFET) technology, providing noninvasive and sensitive molecular detection at nanoscale while producing electrical signals at the output. In this paper, we derive analytical closed-form expressions for the capacity and capacity-achieving input distribution for a memoryless MC channel with a silicon nanowire (SiNW) FET-based MC receiver. The resulting expressions could be used to optimize the information flow in MC systems equipped with nanobioelectronic receivers., European Research Council (ERC) under grant ERC-CoG; Minerva

© 2016 IEEE. Network coding is mostly used to achieve the capacity of communication networks. In this paper, motivated by the nanoscale communications where the energy cost for the channel symbols is asymmetric due to the widely employed on-off keying modulation, we design energy-minimizing network codes. We develop the best mapping between the input and output symbols at the network coding node that minimizes the average codeword energy using Latin squares, which we call the minimum energy network code (MENC). We define the class of networks composed of coding nodes with N incoming and 1 outgoing symbols as in-N networks. First, we derive the condition on the network code to minimize the average energy in in-two networks and propose two linear MENCs. Later, we investigate the MENCs for in-N networks using the Latin hypercubes and propose a low-energy network code (LENC) to reduce the average energy with network coding. We compare MENC with the classical XOR and random network codes for in-two networks. The performance comparison between LENC and random network codes for in-N networks shows that the proposed network codes provide significant energy gains.

© 2017 IEEE. Current and proposed Internet of things (IoT) applications are expected to bring about a major technological revolutions. Next-generation wireless communications in such devices are expected to support high speed data transfers. Among different candidate technologies, terahertz (THz) band communication seems to be a promising direction due to availability of high bandwidth in the electromagnetic spectrum around this frequency range and its directional nature governed by the directive antennas. In this paper, we look into some networking scenarios of full-dimension multiple-input multiple-output (FD-MIMO) based THz Band indoor wireless networks to determine the number of nodes that can be connected to a base station as a function of the antenna characteristics. Furthermore, we analyze the performance of the users and network based on their ergodic capacity. Our results suggest fundamental parameters that can be used in future THz Band analysis and implementations.