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Cognitive Radio (CR) promises an efficient utilization of radio spectrum resources by enabling dynamic spectrum access to overcome the spectrum scarcity problem. Cognitive Radio Sensor Networks (CRSNs) are one type of Wireless Sensor Networks (WSNs) equipped with CR capabilities. CRSN nodes need to operate energy-efficiently to extend network lifetime due to their limited battery capacity. In this paper, for the first time in literature, we formulate the problem of finding a common energy-efficient transmission range and transmission duration for all CRSN nodes and network deployment that would minimize the energy consumed per goodput per meter toward the sink in a greedy forwarding scenario. Results reveal non-trivial relations for energy-efficient CRSN transmission range and duration as a function of nine critical network parameters such as primary user activity levels. These relations provide valuable insights for detailed CRSN designs prior to deployment., 12 pages, 8 figures

A well-modeled molecular communication (MC) channel is crucial for the development of required equipment. Previous models, however, have often considered ideal receivers with perfect absorption, which resulted in impractical results. Here, we propose an improved model consisting of a partially absorbing receiver with four unique parameters, whose values are determined using Particle Swarm Optimization (PSO). We evaluate the Root Mean Square Error (RMSE) performance of our model in the estimated received molecules, which shows consistent results for different transmitter-to-receiver distances or receiver radii, besides a five times improvement in the cumulative number of received molecules compared to that of previous models. We also consider two new Concentration Shift Keying (CSK) modulation schemes, in addition to typical ones, and analyze the trend of Bit Error Rate (BER) and detection threshold for these schemes at different channel lengths. Finally, we investigate the feasibility of Hamming code correction on these schemes and show that the third-order Hamming-corrected BCSK (Binary CSK) modulation is the best for the proposed SISO model., This work was supported by the AXA Research Fund (AXA Chair for Internet of Everything at Koç University).

Molecular Communication (MC) is an emerging technology using molecules to transfer information between nanomachines. In this paper, we approach the resource allocation problem in Molecular Nano-networks (MCN) from the perspective of evolutionary game theory. In particular, we consider an MCN as an organism having three types of nodes acting as a sensor, relay, and sink, respectively. The resources are distributed among the nodes according to an evolutionary process, which relies on the selection of the most successful organisms followed by creating their offspring iteratively. In this regard, the success of an organism is measured by the total number of dropped messages during its life cycle. To illustrate the evolution procedure, we design a toy problem, and then solve it analytically and using the evolution approach for comparison. We further simulate the performance of the evolution approach on randomly generated organisms. The results reveal the potential of evolutionary game theory tools to improve the transmission performance of MCNs.

Molecular communication (MC) is one of the most promising technology to enable nanonetworks. Despite many aspects of MC have been investigated broadly, the physical design of the MC receiver has gained little interest. High-performance MC receivers based on bioFETs are proposed and extensively analyzed. However, they have some challenges such as limited detection with charged molecules, Debye screening, and the need for reference electrodes. To overcome these shortcomings, we propose a mechanical-based transducing scheme. In particular, we focus on a Flexure field-effect transistor (FET)-based MC receiver architecture, which provides exponentially high sensitivity by utilizing a nonlinear electromechanical coupling. In addition, the detection of neutral molecules with much simpler instrumentation is possible. In this paper, we analyze its fundamental performance metrics; sensitivity, noise power, signal-to-noise ratio, and the symbol error probability, from an MC theoretical perspective.

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

The New Space Era has increased communication traffic in space by new space missions led by public space agencies and private companies. Mars colonization is also targeted by crewed missions in the near future. Due to increasing space traffic near Earth and Mars, the bandwidth is getting congested. Moreover, the downlink performance of the current missions is not satisfactory in terms of delay and data rate. Therefore, to meet the increasing demand in space links, Terahertz band (0.1-10 THz) wireless communications are proposed in this study. In line with this, we discuss the major challenges that the realization of THz band space links pose and possible solutions. Moreover, we simulate Mars-space THz links for the case of a clear Mars atmosphere, and a heavy dust storm to show that even in the worst conditions, a large bandwidth is available for Mars communication traffic.

The Internet of Everything (IoE) is a recently introduced information and communication technology (ICT) framework promising for extending the human connectivity to the entire universe, which itself can be regarded as a natural IoE, an interconnected network of everything we perceive. The countless number of opportunities that can be enabled by IoE through a blend of heterogeneous ICT technologies across different scales and environments and a seamless interface with the natural IoE impose several fundamental challenges, such as interoperability, ubiquitous connectivity, energy efficiency, and miniaturization. The key to address these challenges is to advance our communication technology to match the multi-scale, multi-modal, and dynamic features of the natural IoE. To this end, we introduce a new communication device concept, namely the universal IoE transceiver, that encompasses transceiver architectures that are characterized by multi-modality in communication (with modalities such as molecular, RF/THz, optical and acoustic) and in energy harvesting (with modalities such as mechanical, solar, biochemical), modularity, tunability, and scalability. Focusing on these fundamental traits, we provide an overview of the opportunities that can be opened up by micro/nanoscale universal transceiver architectures towards realizing the IoE applications. We also discuss the most pressing challenges in implementing such transceivers and briefly review the open research directions. Our discussion is particularly focused on the opportunities and challenges pertaining to the IoE physical layer, which can enable the efficient and effective design of higher-level techniques. We believe that such universal transceivers can pave the way for seamless connection and communication with the universe at a deeper level and pioneer the construction of the forthcoming IoE landscape.Index Terms– Internet of Everything, Universal IoE Transceiver, Interoperability, Multi-modality, Hybrid Energy Harvesting, Molecular Communications, THz Communications, Graphene and related nanomaterials.

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).

Advent of nanoscale sensors has paved the way for countless applications envisioned in the concept of a Smart City. In this chapter, we are focusing on one of the most fundamental requirements of the smart city, that is, smart health care. Great advancements in personal health care are expected with the emergence of nanosensing devices; however, single nanosensor is limited in its processing power and storage; thus we need to form network of nanosensors for any health-care application. In this chapter, we first elaborate the communication paradigms for nanosensor network. Moreover, we discuss various smart health-care applications such as smart drug delivery, body area network, implantable devices to treat injuries or malfunctions, and Internet of Nano Things. In the end, we highlight the implementation challenges for the nanosensor network for biomedical applications.

The Internet of Things (IoT) offers enhanced connectivity so that any system, being, or process can be reached from anywhere at any time by perpetual surveillance, which results in very large and complex data sets, i.e., Big Data. Despite numerous advantages, IoT technology comes with some unavoidable drawbacks. Considering the number of devices to be added to the current electromagnetic spectrum, it is a fact that wireless communications will severely suffer and eventually become inoperable. Furthermore, as wireless devices are equipped with limited capacity batteries, frequent replenishments and/or maintenance will be needed. However, this is neither practical nor achievable due to the excessive number of devices envisioned by the IoT paradigm. Here, the unification of Energy Harvesting (EH) and Cognitive Radio (CR) stands highly promising to alleviate the current drawbacks, enabling more efficient data generation, acquisition, and analysis. This chapter outlines a new vision, namely Internet of Energy Harvesting Cognitive Radios (IoEH-CRs), to take the IoT-enabled Big Data paradigm a step further. It discusses the basics of the EH-assisted spectrum-aware communications and their implications for the IoT, as well as the challenges posed by the unification of these techniques. An operational framework together with node and network architectures is also presented.

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)

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).

In this paper, we focus on an indoor application of THz communication and the effect of human blockage on performance of the system. We investigate the impact of using two transmitters to overcome the human blockage and provide direct line of sight (LoS) between transmitters and the receiver to improve coverage, reliability and capacity of the transmission. We derive an analytical model to estimate the probability of a receiver, carried on person, having LoS with deployed antennas inside a room crowded with people. Based on this probability we derive an expression for the average number of people with reception, the average coverage, in a given setup. Our findings support the following conjecture: The gain in average coverage due to deployment of many antennas compared to a single one is bounded from above by the number of antennas deployed.

In this chapter, the authors put forward the notion of internet of everything (IoE) as an effort to maximally connect our communication infrastructure to the universe, which can itself be regarded as the real IoE, an interconnected network of physical phenomena (i.e., Everything we perceive as independent wholes that persist through time, such as molecules, light, waves, living organisms, and celestial objects, with the purpose of gaining better understanding of its mechanisms and manipulating them to enable novel technologies via a networked sensing, analysis, and actuation approach). The strategy to outline the IoE effort is by dissecting the vast IoE landscape into IoXs according to their various application domains (Xs), for each of which the authors give an up-to-date account of the state-of-the-art in related fields and point out the challenges in contemporary research faces. They also discuss a wide spectrum of challenges and future research directions (e.g., ubiquitous connectivity, security, big data, etc., which are common to many IoXs and penetrate into the IoE effort in general).

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

Communication among neurons is the most promising technique for biocompatible nanonetworks. This 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 their 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. Hence, in this paper, we provide a comprehensive model for multiple-input single-output (MISO) neuro-spike communication by integrating the spike timing dependent plasticity (STDP) into existing channel model. We simulate this model for a realistic scenario with correlated inputs and varying spiking threshold. We show that plasticity is strengthening the correlated input synapses at the expense of weakening the synapses with uncorrelated inputs. Moreover, a nonlinear behavior in signal transmission is observed with changing spiking threshold.

© 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

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

© 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.