Internet of Agents (IoA) envisions a unified, agent-centric paradigm where heterogeneous large language model (LLM) agents can interconnect and collaborate at scale. Within this paradigm, federated fine-tuning (FFT) serves as a key enabler that allows distributed LLM agents to co-train an intelligent global LLM without centralizing local datasets. However, the FFT-enabled IoA systems remain vulnerable to model poisoning attacks, where adversaries can upload malicious updates to the server to degrade the performance of the aggregated global LLM. This paper proposes a graph representation-based model poisoning (GRMP) attack, which exploits overheard benign updates to construct a feature correlation graph and employs a variational graph autoencoder to capture structural dependencies and generate malicious updates. A novel attack algorithm is developed based on augmented Lagrangian and subgradient descent methods to optimize malicious updates that preserve benign-like statistics while embedding adversarial objectives. Experimental results show that the proposed GRMP attack can substantially decrease accuracy across different LLM models while remaining statistically consistent with benign updates, thereby evading detection by existing defense mechanisms and underscoring a severe threat to the ambitious IoA paradigm.

This paper presents the Mars Dust Storm Detector (MDSD). The system leverages Terahertz (THz) signals between Mars surface assets for environmental sensing, particularly dust storm detection. MDSD exploits THz signal attenuation characteristics with Martian-specific models to monitor storm intensity and particle properties. We develop a link budget analysis for reliable communication under dust conditions and design an Orthogonal Frequency Division Multiplexing (OFDM)-based waveform for joint sensing and communication. System performance is evaluated through numerical calculations at various Node Density Factors (NDFs). Results show linear interpolation achieves correlation coefficients exceeding 0.90 at high NDFs. Nearest-neighbor and Inverse Distance Weighting (IDW) algorithms maintain complete coverage in sparse networks. Error analysis identifies particle size uncertainty as the primary contributor to estimation errors, though the system remains resilient to atmospheric variations. We analyze computational complexity and document system limitations with mitigation strategies. This work extends Integrated Sensing and Communications (ISAC) technology to planetary exploration, enhancing Mars atmospheric monitoring capabilities in the Internet of Space (IoS).

The Internet of Bio-Nano Things (IoBNT) requires mobile nanomachines that navigate fluidic environments while exchanging molecular signals under external control. We propose a chemo-hydrodynamic transceiver for catalytic Janus particles in which a single optical input jointly controls molecular emission and self-propulsion, unlike standard models that decouple communication from mobility. The resulting stochastic channel contains an actuation-dependent variance term scaling asI4, which creates a non-monotonic reliability trade-off. Stronger actuation increases emission but also amplifies motion-induced fading. In a reduced single-link setting, exact-BEP analysis reveals a finite optimal actuation level, and comparison with Brownian-mobility baselines shows that neglecting active motility can markedly underestimate BEP, especially at high actuation and short link distances. These results provide physical-layer guidance for mobility-aware actuation and link design in IoBNT.

Terahertz inter-satellite links (THz-ISL) offer unprecedented bandwidth for future space networks but face fundamental constraints from onboard power and thermal budgets. This paper establishes theoretical performance limits for MIMO Integrated Sensing and Communication (ISAC) systems under per-element constant-envelope (CE) transmission constraints. We demonstrate that hardware distortions, specifically power amplifier nonlinearity, ADC quantization, and oscillator phase noise, impose an achievable-rate ceiling that cannot be overcome by increasing transmit power. A unified link budget framework integrates wideband beam squint, aperture pointing errors, and colored noise sources through a spectral consistency principle that ensures uncompensated phase noise is counted exactly once across communication and sensing analyses. The sensing bounds are derived via the Whittle-Fisher Information Matrix under a Constant Acceleration kinematic model with jerk noise, yielding closed-form scaling laws: uncompensated phase noise variance scales as α−1while dynamic state-estimation error (DSE) variance scales as α−5with pilot overhead α. Numerical results show divergent MIMO scaling: sensing precision improves with array size (RMSE ∝ 1/ √NtNr), while the critical SNR exhibits scale invariance regarding array size, implying that the distortion-limited transition point stabilizes regardless of the array scale. The steep α−5 DSE scaling creates an operationally infeasible region at α∗≈ 0.10, where α∗= (CDSE/CPN)1/4, a constraint-driven threshold under the adopted baseline for LEO operation. These findings provide design guidelines for hardware-efficient THz-ISL constellations.

This article proposes a new cyberattack on decentralized federated learning (DFL), named user isolation poisoning (UIP). While following the standard DFL protocol of receiving and aggregating benign local models, a malicious user strategically generates and distributes compromised updates to undermine the learning process. The objective of the new UIP attack is to diminish the impact of benign users by isolating their model updates, thereby manipulating the shared model to reduce the learning accuracy. To realize this attack, we design a novel threat model that leverages an adversarial message-passing graph (MPG) neural network. Through iterative message passing, the adversarial MPG progressively refines the representations (also known as embeddings or hidden states) of each benign local model update. By orchestrating feature exchanges among connected nodes in a targeted manner, the malicious users effectively curtail the genuine data features of benign local models, thereby diminishing their overall influence within the DFL process. The MPG-based UIP attack is implemented in PyTorch, demonstrating that it effectively reduces the test accuracy of DFL by 49.5% and successfully evades existing cosine similarity- and Euclidean distance-based defense strategies.

This paper focuses on Zero-Trust Foundation Models (ZTFMs), a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems. By integrating core tenets, such as least privilege access, continuous verification, data confidentiality, and behavioral analytics into the design, training, and deployment of FMs, ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversarial IoT environments. We present the first structured synthesis of ZTFMs, identifying their potential to transform conventional trust-based IoT architectures into resilient, self-defending ecosystems. Moreover, we propose a comprehensive technical framework, incorporating federated learning (FL), blockchain-based identity management, micro-segmentation, and trusted execution environments (TEEs) to support decentralized, verifiable intelligence at the network edge. In addition, we investigate emerging security threats unique to ZTFM-enabled systems and evaluate countermeasures, such as anomaly detection, adversarial training, and secure aggregation. Through this analysis, we highlight key open research challenges in terms of scalability, secure orchestration, interpretable threat attribution, and dynamic trust calibration. This survey lays a foundational roadmap for secure, intelligent, and trustworthy IoT infrastructures powered by FMs.

Molecular Communication (MC) leverages the power of diffusion to transmit molecules from a transmitter to a receiver. A wide variety of modulation techniques based on molecule concentration, type, and release time have been extensively studied in the literature. In this paper, we propose a novel modulation technique that encodes the information into the relative concentrations of multiple molecules called Multi Ratio Shift Keying (MRSK) designed for diffusion-based MC without drift. We show that leveraging all possible ratios in a set of molecules can help mitigate the effects of inter-symbol interference (ISI) and provide a flexible communication channel. To evaluate the performance of the MRSK, we develop a mathematical framework for studying the statistics of the ratio of random variables, focusing on noncentral Gaussian distributions. We then assess MRSK performance both analytically and through particle-based simulations under various channel conditions, identifying potential sources of error in our system model. Additionally, we conduct a comparative analysis of commonly used modulation schemes in the literature based on bit error rate (BER). The results show that MRSK significantly outperforms all traditional modulation schemes considered in this study in terms of BER. MRSK offers a promising, flexible, and more reliable communication method for the future of the MC paradigm.

Recent poisoning attacks on federated learning (FL) generate malicious model updates that circumvent widely adopted Euclidean distance-based detection methods. This article proposes a new defense mechanism, namely, GradCAM-AE, against model poisoning attacks on FL, which integrates Gradient-weighted Class Activation Mapping (GradCAM) and autoencoder (AE) to offer a substantially more powerful detection capability compared to existing Euclidean distance-based approaches. Particularly, GradCAM-AE generates a heat map for each uploaded local model update, transforming each local model update into a lower-dimensional, visual representation. An AE further reprojects the GradCAM heat maps of all local module updates with improved distinguishability, thereby accentuating the hidden features of the heat maps and increasing the success rate of identifying anomalous heat maps and malicious local models. A comprehensive evaluation of the proposed GradCAM-AE framework is conducted using the CIFAR-10 and GTSRB datasets under both Independent and Identically Distributed (IID) and Non-IID settings. The ResNet-18 and MobileNetV3-Large models are tested. The results substantiate that GradCAM-AE offers superior detection rates and test accuracy of FL global model, juxtaposed with contemporary state-of-the-art methods. Our code is available at: https://github.com/jjzgeeks/GradCAM-AE .

Fairness in Federated Learning (FL) is imperative not only for the ethical utilization of technology but also for ensuring that models provide accurate, equitable, and beneficial outcomes across varied user demographics and equipment. This paper proposes a new adversarial architecture, referred to as Adversarial Graph Attention Network (AGAT), which deliberately instigates fairness attacks with an aim to bias the learning process across the FL. The proposed AGAT is developed to synthesize malicious, biasing model updates, where the minimum of Kullback-Leibler (KL) divergence between the user's model update and the global model is maximized. Due to a limited set of labeled input-output biasing data samples, a surrogate model is created, which presents the behavior of a complex malicious model update. Moreover, a graph autoencoder (GAE) is designed within the AGAT architecture, which is trained together with sub-gradient descent to reconstruct manipulatively the correlations of the model updates, and maximize the reconstruction loss while keeping the malicious, biasing model updates undetectable. The proposed AGAT attack is implemented in PyTorch, showing experimentally that AGAT successfully increases the minimum value of KL divergence of benign model updates by 60.9% and bypasses the detection of existing defense models. The source code of the AGAT attack is released on GitHub.

This paper introduces DebriSense-THz, an integrated sensing and communications approach for Low Earth Orbit (LEO) satellites, leveraging high-frequency waveforms for debris detection and classification. We develop a complete channel model capturing reflection, scattering, and diffraction from various debris materials, while machine learning algorithms analyze Channel State Information to distinguish debris types. The proposed framework is tested under realistic conditions, including different MIMO configurations and debris densities, demonstrating robust performance even as orbital congestion increases. In particular, the system achieves heightened detection accuracy at higher frequencies, balancing communication reliability with sensing precision. DebriSense-THz thus offers a scalable solution for real-time orbital debris assessment, paving the way for more effective mitigation strategies and sustained satellite operations in crowded LEO environments. Additionally, an adaptive design leverages frequency agility and dynamic MIMO configurations to accommodate varying debris densities and mission priorities. DebriSense-THz significantly advances space situational awareness by integrating THz communications with robust sensing capabilities in LEO’s challenging environment.

Molecular Communications (MC) is a bio-inspired communication paradigm using molecules as information carriers, necessitating novel transceivers and modulation/detection techniques. In realizing practical MC receivers (MC-Rxs), biosensor field-effect transistor (bioFET)-based architectures are promising, having surface receptors that undergo reversible reactions with ligands. These interactions are converted into electrical signals via field effect, enabling the decoding of transmitted information. A significant challenge in these receivers is the limited specificity of receptors to target ligands, which leads to molecular cross-talk from similar interfering ligands co-existing in the MC channel. Decoding transmitted symbols under such interference is challenging in the time domain, especially when MC-Rx lacks prior knowledge of interferer statistics or operates near saturation. To address this, we introduce a frequency-domain detection (FDD) technique for bioFET-based MC-Rxs, which exploits the distinct binding reaction rates of different ligand types, reflected in the power spectrum of binding noise. Compared to conventional time-domain detection (TDD) technique, this method offers improved detection performance under stochastic molecular interference. We analyze the bit error probability (BEP) of FDD, confirming its superior performance in various interference scenarios. Moreover, the theoretical performance limits of FDD are validated through a particle-based spatial stochastic simulator, simulating binding reactions on MC-Rx within microfluidic channels.

This paper proposes a novel, data-agnostic, model poisoning attack on Federated Learning (FL), by designing a new adversarial graph autoencoder (GAE)-based framework. The attack requires no knowledge of FL training data and achieves both effectiveness and undetectability. By listening to the benign local models and the global model, the attacker extracts the graph structural correlations among the benign local models and the training data features substantiating the models. The attacker then adversarially regenerates the graph structural correlations while maximizing the FL training loss, and subsequently generates malicious local models using the adversarial graph structure and the training data features of the benign ones. A new algorithm is designed to iteratively train the malicious local models using GAE and sub-gradient descent. The convergence of FL under attack is rigorously proved, with a considerably large optimality gap. Experiments show that the FL accuracy drops gradually under the proposed attack and existing defense mechanisms fail to detect it. The attack can give rise to an infection across all benign devices, making it a serious threat to FL.

Molecular Communications (MC) is a bio-inspired communication paradigm which uses molecules as information carriers, thereby requiring unconventional transmitter/receiver architectures and modulation/detection techniques. Practical MC receivers (MC-Rxs) can be implemented based on field-effect transistor biosensor (bioFET) architectures, where surface receptors reversibly react with ligands, whose concentration encodes the information. The time-varying concentration of ligand-bound receptors is then translated into electrical signals via field-effect, which is used to decode the transmitted information. However, ligand-receptor interactions do not provide an ideal molecular selectivity, as similar types of ligands, i.e., interferers, co-existing in the MC channel can interact with the same type of receptors, resulting in cross-talk. Overcoming this molecular cross-talk with time-domain samples of the Rx's electrical output is not always attainable, especially when Rx has no knowledge of the interferer statistics or it operates near saturation. In this study, we propose a frequency-domain detection (FDD) technique for bioFET-based MC-Rxs, which exploits the difference in binding reaction rates of different types of ligands, reflected to the noise spectrum of the ligand-receptor binding fluctuations. We analytically derive the bit error probability (BEP) of the FDD technique, and demonstrate its effectiveness in decoding transmitted concentration signals under stochastic molecular interference, in comparison to a widely-used time-domain detection (TDD) technique. The proposed FDD method can be applied to any biosensor-based MC-Rxs, which employ receptor molecules as the channel-Rx interface.

Molecular communication (MC), a new bio-inspired communication paradigm that uses molecules to transfer information, is an important tool for understanding biological communications with many promising applications. The study of biological networks from a communication theoretical perspective is crucial for their introduction to the engineering field. Therefore, this paper aims to provide and analyze a molecular communication model of p-cresol signaling through the human gut-brain axis from a communication theoretical perspective. In this work, the propagation of p-cresol molecules inside the human body and the input-output relationship for p-cresol signaling in autism spectrum disorder (ASD) are investigated. The impulse response of the MC-based p-cresol signaling model system is obtained, and numerical analysis is performed to understand gut-brain communication under different system settings using the presented system model. The effects of system properties, i.e., Rx dimension and Tx-Rx distance, on p-cresol signaling are investigated by simulations.

Molecular communication (MC) is one of the emerging technologies enabling nanonetworks and the Internet of Everything (IoE). The practical implementation of the intra-body MC systems is crucial for realizing smart healthcare applications, i.e., drug delivery, early detection, and health monitoring, through communication between nanomachines. A Flexure field-effect transistor (FET) based MC receiver, providing high sensitivity by utilizing nonlinear electromechanical coupling, has recently been proposed. It can also identify neutral molecules, unlike bioFETs. Thus, virus or pathogen detection can be performed with onboard computing by these receivers placed in the Edge. To date, biosensor-based MC receivers have been analyzed only for concentration shift keying (CSK), although weight shift keying (WSK) is a very robust modulation technique. The Flexure-FET-based MC receiver is a great candidate for use in a WSK-based MC system since its transduction mechanism relies on the molecular weight. This work presents the first practical approach to a WSK-based MC system with an improved Flexure-FET-based MC receiver. Its key performance metrics are analyzed from a theoretical MC perspective, also considering biological interference to obtain a more realistic simulation.

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.

Molecular communication (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, 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 CramérRao Lower Bound 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 scheme to sample the unbound and bound time durations, and a chemical reaction network to perform the required computations in synthetic cells.

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.

Internet of Things (IoT) is a perfect candidate to realize efficient observation and management for Smart City concept. This requires deployment of large number of wireless devices. However, replenishing batteries of thousands, maybe millions of devices may be hard or even impossible. In order to solve this problem, Internet of Energy Harvesting Things (IoEHT) is proposed. Although the first studies on IoEHT focused on energy harvesting (EH) as an auxiliary power provisioning method, now completely battery-free and self-sufficient systems are envisioned. Taking advantage of diverse sources that the concept of Smart City offers helps us to fully appreciate the capacity of EH. In this way, we address the primary shortcomings of IoEHT; availability, unreliability, and insufficiency by the Internet of Hybrid EH Things (IoHEHT). In this paper, we survey the various EH opportunities, propose an hybrid EH system, and discuss energy and data management issues for battery-free operation. We mathematically prove advantages of hybrid EH compared to single source harvesting as well. We also point out to hardware requirements and present the open research directions for different network layers specific to IoHEHT for Smart City concept.

Social Internet of Vehicles (SIoV) is a new paradigm that enables social relationships among vehicles via the Internet. People in the vehicles using online social networks (OSNs) can be an integral part of SIoV that enables the collection of data for sensing a physical phenomenon, i.e., social sensing. In this paper, we study the main social sensing mechanism in Facebook, comment thread network (CTN), which is based on the interactions of users through user walls in Facebook for SIoV. After seeing their commuters' contents about an event, users either add comments or like these posts, and Facebook CTN emerges as a social sensing medium in estimation of an event through social consensus. For the first time, this paper investigates the social sensing capability of Facebook CTN, i.e., the accuracy of collective observations for SIoV. The accuracy depends on the user characteristics and the features of the OSN, since perceptions of the users and how they use Facebook may manipulate their observation signals. We analyze the reliability of Facebook CTN for varying user behaviors, user relationships, Facebook features, and network size. The results indicate that the polarized weighting of the observations and the use of less reliable post types in CTN deteriorate the accuracy of the estimate signal, i.e., social consensus. Furthermore, the selection of users is likely to be an important factor in social sensing.

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.

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.

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 reinvestigation 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.

The emerging technology Terahertz Band (0.3-10 THz) communication is envisioned to accommodate high speed wireless communication. Large bandwidth makes it a good candidate for 5G mobile networks. In this paper, fundamental experiments on channel modeling at THz Band are presented with detailed analysis of the setup. The measurement setup consisted of subharmonic mixer and vector network analyzer. Path loss and phase delay measurements from 260 GHz to 400 GHz for different distances, angles of arrival and objects acting as reflectors were examined along with their capacity limits. We have shown that LOS link can reach speeds of terabits per second. In addition, reflections from materials were also examined and results indicated that, in case of signal obstruction, a reflector can be used for establishing NLOS link.

We utilize the well known Additive Inverse Gaussian Noise (AIGN) communication channel to investigate the effect of diversity in diffusion-based molecular communication with drift, where the transmitter releases different types of molecules to the fluid medium by encoding the information onto the release time and type of molecules. The fluid channel imposes extra delay on the communication, and the receiver decodes the encoded information by solely utilizing the molecular arrival times. In this paper, simple receiver models based on maximum likelihood estimation (MLE) are investigated. Furthermore, upper and lower bounds on the capacity of AIGN communication channel with molecular diversity are derived.

Network size and number of mobile users are ever-increasing with the advancements in cellular network technologies. Hence, this situation makes the network monitoring highly complex. Although there are numerous network tomography approaches, service providers need real-time network monitoring tools to provide better network utilization. In this paper, we propose a crowdsourcing-based real-time network tomography framework. In the proposed framework, channel condition and user data usage are monitored via an application at the mobile terminals, and then the mobile terminals transmit their data to the server. In this way, the network and user behavior can be continuously monitored, and real-time actions can be implemented to improve the network performance. By using the proposed framework, we propose an optimization framework for the amount and reporting frequency of the transmitted data to avoid battery drain at the mobile terminal and network congestion. At the end, we provide simulation results for the proposed optimization framework.

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.

Molecular communication (MC) is a bio-inspired communication method based on the exchange of molecules for information transfer among nanoscale devices. MC has been extensively studied from various aspects in the literature; however, the physical design of MC transceiving units is largely neglected with the assumption that network nodes are entirely biological devices, e.g., engineered bacteria, which are intrinsically capable of receiving and transmitting molecular messages. However, the low information processing capacity of biological devices and the challenge to interface them with macroscale networks hinder the true application potential of nanonetworks. To overcome this limitation, recently, we proposed a nanobioelectronic MC receiver architecture exploiting the nanoscale field-effect transistor-based biosensor (bioFET) technology, which provides noninvasive and sensitive molecular detection while producing electrical signals as the output. In this paper, we introduce a comprehensive model for silicon nanowire FET-based MC receivers by integrating the underlying processes in MC and bioFET to provide a unified analysis framework. We derive closed-form expressions for the noise statistics, the signal-to-noise ratio (SNR) at the receiver output, and the symbol error probability (SEP). Performance evaluation in terms of SNR and SEP reveals the effects of individual system parameters on the detection performance of the proposed MC receiver.

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.

Considering the inability of the existing energy resources to satisfy the current needs, the right and efficient use of the energy has become compulsory. To make energy sustainability permanent, management and planning activities should be carried out by arranging the working hours and decreasing the energy wasting. For all these, power metering, managing and controlling systems or plugs has been proposed in recent efforts. Starting from this point, a new DASH7-based Smart Plug (D7SP) is designed and implemented to achieve a better structure compared to ZigBee equipped models and reduce the drawbacks of current applications. DASH7 technology reaches nearly 6 times farther distances in comparison with 2.4 GHz based protocols and provides multi-year battery life as a result of using limited energy during transmission. Performing in the 433 MHz band prevents the possible interference from overcrowded 2.4 GHz and the other frequencies which helps to gather a more reliable working environment. To shorten the single connection delays and human oriented failures, the MCU was shifted directly into the plug from the rear-end device. Working hours arrangement and standby power cutting off algorithms are implemented in addition to these energy saving targeted improvements to enhance more efficient systems. With the collaboration of the conducted hardware and software oriented adjustments and DASH7-based improvements, a more reliable, mobile and efficient system has been obtained in this work.

Wireless networked control system (WNCS) is a control system that a wireless network closes the control loop. WNCS estimator, i.e., Kalman filter, estimates the system state according to the observations of sensors. These observations which are from N independent subnetworks are conveyed to the Kalman filter through vacant bands opportunistically with cognitive radio capability of the nodes. We characterize the successful packet delivery probability and study the maximization of energy-efficiency of overall system under the convergence constraint of the Kalman filter by defining an optimization problem. We also find a lower bound on maximum total coverage area. Furthermore, we perform numerical analysis to observe the effects of system parameters such as number of subnetworks, average ON probability of primary users, transmission ranges and densities of sensor nodes and primary users, and false alarm probability.

As the demand for wireless bandwidth rapidly increases, alternative methods to radio frequency-based communication are investigated to overcome the limited bandwidth problem. Visible light communication (VLC) using light emitting diodes (LEDs) is one of these alternatives. LEDs are estimated to replace the incandescent bulbs within the decade. Since, LEDs can be intensity modulated faster than the human eye can detect, illumination and communication can both be provided by the same lighting system. Indoors communication constitutes 70% of the overall traffic, and VLC is a promising technology to complement Wi-Fi and cellular wireless systems. However, proper handover mechanism should be developed for VLC to be a complete indoors solution. In this paper, we present two soft handover methods for VLC. Simulation results indicate our solutions provide higher data rate for both the overall system and individual users in the handover region.

The capacity gain of MIMO systems significantly depends on the fading correlation between antennas, and there is no analytical study which considers the fading correlation in the troposcatter communications. In this paper, we develop an analytical model, ring scatter model (RSM), to derive the fading correlation in the troposcatter systems as a function of spatial, frequency and angular separations for the first time in the literature. In addition, we compare the effects of the diversity techniques that are suitable for troposcatter communications: space, frequency, angle and space-frequency diversity techniques by deriving the distribution of their achievable data rates with transmit beam-forming. To this end, we extend our previously introduced troposcatter channel model [1] for the implementation of MIMO-OFDM and the diversity techniques.

Troposcatter can be used as a communication medium for beyond-Line-of-Sight (b-LoS) links. However, available troposcatter channel models do not provide comprehensive channel modeling especially at high frequencies. Therefore, the main motivation of this study is to develop a ray-based MIMO troposcatter channel model to analyze transmission-loss characteristics, coherence bandwidth and correlation between antennas for the first time in the literature for troposcatter communications. In addition, the link budget calculations and the distribution of capacity in troposcatter links are provided by using real world water vapor mixing ratio measurements.

To perform spectrum handoff, cognitive radio (CR) nodes communicating with each other need to exchange licensed user detection information, i.e., perform spectrum coordination, over a common control channel. The spectrum coordination can be fulfilled either via existing cognitive radio interface with time division or via a separate dedicated radio, i.e., a common control interface (CCI), continuously. CR nodes with CCI can instantly exchange licensed user detection information and cease frame transmission, while spectrum coordination can only be performed after the frame transmission period without CCI. Nevertheless, the impact of CCI incorporation into CR nodes in terms of common performance metrics must be thoroughly assessed to evaluate the worthiness of additional radio cost. In this paper, an analytical framework is presented to assess the impact of CCI incorporation into CR nodes for spectrum handoff. The developed framework enables analyzing potential benefits and disadvantages of employing CCI for spectrum handoff, in terms of achievable delay, energy consumption, spectrum utilization and event estimation performance. Extensive performance evaluations are presented to illustrate the impact of CCI utilization on efficiency of spectrum handoff. The network and communication regimes that would yield having CCI favorable are characterized in terms of spectrum conditions and CR parameters.

The number of applications that use industrial, scientific and medical (ISM) radio bands increase every day, creating interference problem for the wireless sensor networks (WSN) that generally operate on these bands. Cognitive radio sensor network (CRSN) has been proposed as a promising solution to this problem. However, since sensor nodes are energy-constrained devices energy efficient spectrum sensing methods are needed for CRSN. To address this need, we propose a novel cooperative coarse sensing scheme for CRSN (CC4C). CC4C is based on sequential sensing, thus, it is simple and fast. Simulation results show that CC4C incurs less sensing delay and provides significant energy conservation compared to energy detection based coarse sensing schemes, and single stage sensing schemes where no coarse sensing is performed.

Nanonetworks constructed by interconnecting nanodevices using wireless communication allow the nanodevices to perform more complex functions by means of cooperation between them. For the first time in the literature, a novel and physically realizable nanoscale communication technique is introduced: Nanoscale Heat Communication (NHC) in which the heat transfer is used for communication at the nanoscale. The transmitted information is encoded in temperature signals using Magneto-Caloric Effect (MCE) which is the change in temperature of a magnetic material exposed to a varying magnetic field. Thermal energy emitted or absorbed by a transmitter nanodevice is subject to the laws of thermal diffusion which changes the temperature of the communication medium. The transmitted information is decoded by a receiver nanodevice that senses the temperature variations. Using information theoretical analysis, a closed-form expression for the channel capacity is obtained. According to the performance evaluation of the channel capacity, NHC provides a significantly higher capacity communication compared with the existing molecular communication techniques. Therefore, NHC stands as a promising solution to nanoscale communication between nanomachines based on its channel capacity performance, advantages, and possible applications for the emerging field of nanonetworks.

Virtual Multiple-Input Single-Output (MISO) is recently proposed to extend the benefits of transmitter space diversity to networks in which the deployment of antenna arrays on individual nodes is infeasible from a practical point of view. Ad-hoc and sensor networks are examples of these type of networks. In these scenarios, nodes equipped with single antenna can cooperatively transmit to emulate an antenna array. However, cooperative transmissions require knowledge of the channel state either at the transmitter side or the receiver side in order to achieve full performance gains. Several solutions are proposed in the literature under these assumptions, but at the expense of increased overhead and energy consumption. In this paper, the array gain at the receiver from the non-coherent combining of the signals from multiple transmitters is analyzed in statistical sense, under the assumption that the channel knowledge is unavailable. The transmitters are assumed to be randomly spread over a circular region. More specifically, exact or very accurate closed-form expressions for the expectation and variance of the array gain are obtained, and then a complete statistical distribution is postulated and validated by means of heuristic procedures, goodness-of-fit tests and specialized software. The results obtained in this paper can be especially useful for the implementation of two-tiered wide area sensor networks.

The integration of wireless communication and control systems revealed wireless networked control systems (WNCSs). One fundamental problem in WNCSs is to have a wide coverage area. For the first time in the literature, we address this problem and we obtain the maximum coverage area by solving an optimization problem. In this paper, we consider a WNCS where the output sensor measurements are transmitted over separate multi-hop wireless ad-hoc subnetworks. The system state is estimated using the Kalman filter. We present the critical arrival probability for a sensor measurement packet such that if the packet arrival probability is larger than the critical value, it is guaranteed that the expected state estimation error covariance is bounded, and hence the WNCS is stable. We find the optimum hop-diameter of a multi-hop wireless ad-hoc subnetwork under the constraints of both the stability of the WNCS and the cost-efficiency of the multi-hop wireless network. Furthermore, under these constraints, we derive the maximum total coverage area of the wireless subnetworks. The numerical analyses show that the maximum total coverage area can be increased by appropriately adjusting the number of sensors, the successful packet transmission probability between relay nodes, and the eigenvalues of the system matrix.

In this paper, we propose a spectrum-aware cluster-based routing (SCR) protocol for cognitive radio sensor networks (CRSN) that jointly overcomes the formidable limitations of energy and spectrum. Clustering is exploited to provide energy efficient routing by limiting the nodes to participate in route establishment and also to ensure the smooth provision of data delivery to the sink in dynamic spectrum access. In SCR, the cluster-head selection is based on the energy and relative spectrum awareness such that non-contiguous available spectrum bands are clustered and scheduled to provide continuous transmission opportunity. For data routing, SCR employs the hybrid medium access by combining carrier sense multiple access (CSMA) and time division multiple access (TDMA) in which TDMA operates for intra-cluster transmission while CSMA is used for inter-cluster routing. Thus, a cross-layer design of routing, MAC and physical layer provides efficient routing in CRSN prevailing over the energy and spectrum issues, which is revealed through simulation experiments.

Wireless sensor networks (WSN) with dynamic spectrum access (DSA) capability, namely cognitive radio sensor networks (CRSN), is a promising solution for spectrum scarcity problem. Despite improvement in spectrum utilization by DSA capability, energy-efficient solutions for CRSN are required due to resource-constrained nature of CRSN inherited from WSN. Clustering is an efficient way to decrease energy consumption. Existing clustering approaches for WSN are not applicable in CRSN and existing solutions for cognitive radio networks are not suitable for sensor networks. In this paper, we propose an event-driven clustering protocol which forms temporal cluster for each event in CRSN. Upon detection of an event, we determine eligible nodes for clustering according to local position of nodes between event and sink. Cluster-heads are selected among eligible nodes according to node degree, available channels and distance to the sink in their neighborhood. They select one-hop members for maximizing the number of two-hop neighbors that are accessible by one-hop neighbors through cluster channels to increase connectivity between clusters. Clusters are between event and sink and are no longer available after the end of the event. This avoids energy consumption due to unnecessary cluster formation and maintenance overheads. Performance evaluation reveals that our solution is energy-efficient with a delay due to spontaneous cluster formation.

Cognitive radio sensor network (CRSN) is an emerging sensor networking paradigm that aims to incorporate opportunistic spectrum access capability to the wireless sensor networks. Since sensor nodes are energy-constrained devices, design of efficient spectrum sensing schemes is imperative for the implementation of CRSNs. In order to address this need, a cooperative spectrum sensing scheme (CSS), specifically designed for CRSNs, is presented in this paper. CSS aims to minimize power consumption and delay during spectrum sensing, while meeting the performance requirements in terms of accuracy with minimal complexity. Simulation results indicate that significant power savings can be achieved with the proposed solution.

Troposcatter communications provide a good alternative for beyond-Line-of-Sight (b-LoS) communication because it can provide reliable high data rate applications with the advancement in the modem and high power amplifiers. The employment of the high data rate applications with troposcatter communications requires the investigation of the troposcatter channels. However, available channel models for the troposcatter communications are not able to take the non-homogeneities of the air turbulence into account. Therefore, the main motivation of this paper is to develop a ray-based MIMO troposcatter channel model in which the beamwidths of the antennas are divided to small parts and, the associated delay and power of the rays are calculated in order to consider the non-homogeneities and time variations of the channel. Also, in order to show the time varying behaviour of the channel this paper provides the simulation results for the maximum data rate of the channel by using real world measurements first time in the literature.

In the Molecular Communication (MC), molecules are utilized to encode, transmit, and receive information. Transmission of the information is achieved by means of diffusion of molecules and the information is recovered based on the molecule concentration variations at the receiver location. The MC is very prone to intersymbol interference (ISI) due to residual molecules emitted previously. Furthermore, the stochastic nature of the molecule movements adds noise to the MC. For the first time, we propose four methods for a receiver in the MC to recover the transmitted information distorted by both ISI and noise. We introduce sequence detection methods based on maximum a posteriori (MAP) and maximum likelihood (ML) criterions, a linear equalizer based on minimum mean-square error (MMSE) criterion, and a decision-feedback equalizer (DFE) which is a nonlinear equalizer. We present a channel estimator to estimate time varying MC channel at the receiver. The performances of the proposed methods based on bit error rates are evaluated. The sequence detection methods reveal the best performance at the expense of computational complexity. However, the MMSE equalizer has the lowest performance with the lowest computational complexity. The results show that using these methods significantly increases the information transmission rate in the MC.

Nanoscale communication based on Forster Resonance Energy Transfer (FRET) is a promising paradigm that allows future molecular-size machines to communicate with each other over distances up to 10 nm using the excited state energies of fluorescent molecules. In this study, we propose a novel nanoscale communication method based on multi-step FRET using identical fluorophores as relay nodes between communicating nanomachines, and utilizing multi-exciton transmission scheme in order to improve the limited range of the communication and achievable transmission rate over the nanoscale channel. We investigate two communication scenarios: immobile nanomachines communicating through a channel in a host material with linearly located relay nodes, and mobile nanomachines communicating through a channel in a 3-dimensional aqueous environment with randomly deployed relay nodes. We simulate the communication over these channels with realistic algorithms considering the high degree of randomness intrinsic to FRET phenomenon. Using the simulation results and following a Monte Carlo approach, we evaluate the performance of the channels by means of information theoretical capacity and interference probability. We show that multi-step FRET-based communication significantly outperforms the other biologically inspired nanocommunication techniques proposed so far in terms of maximum achievable data transmission rates. The results underline the compatibility and practicality of the FRET-based communication for several applications ranging from molecular computers to nanosensor networks.

Nanoscale communications is an appealing domain in nanotechnology. Novel nanoscale communications techniques are currently being devised inspired by some naturally existing phenomena such as the molecular communications governing cellular signaling mechanisms. Among these, neuro-spike communications, which governs the communications between neurons, is a vastly unexplored area. The ultimate goal of this paper is to accurately investigate nanoscale neuro-spike communications characteristics through the development of a realistic physical channel model between two neurons. The neuro-spike communications channel is analyzed based on the probability of error and delay in spike detection at the output. The derived communication theoretical channel model may help designing novel artificial nanoscale communications methods for the realization of future practical nanonetworks, which are the interconnections of nanomachines.

Communication between neurons occurs via transmission of neural spike trains through junctional structures, either electrical or chemical synapses, providing connections among nerve terminals. Since neural communication is achieved at synapses, the process of neurotransmission is called synaptic communication. Learning and memory processes are based on the changes in strength and connectivity of neural networks which usually contain multiple synaptic connections. In this paper, we investigate multiple-access neuro-spike communication channel, in which the neural signal, i.e., the action potential, is transmitted through multiple synaptic paths directed to a common postsynaptic neuron terminal. Synaptic transmission is initiated with random vesicle release process from presynaptic neurons to synaptic paths. Each synaptic channel is characterized by its impulse response and the number of available postsynaptic receptors. Here, we model the multiple-access synaptic communication channel, and investigate the information rate per spike at the postsynaptic neuron, and how postsynaptic rate is enhanced compared to single terminal synaptic communication channel. Furthermore, we analyze the synaptic transmission performance by incorporating the role of correlation among presynaptic terminals, and point out the postsynaptic rate improvement.

Electromagnetic interference, equipment noise, multi-path effects and obstructions in harsh smart grid environments make the quality-of-service (QoS) communication a challenging task for WSN-based smart grid applications. To address these challenges, a cognitive communication based cross-layer framework has been proposed. The proposed framework exploits the emerging cognitive radio technology to mitigate the noisy and congested spectrum bands, yielding reliable and high capacity links for wireless communication in smart grids. To meet the QoS requirements of diverse smart grid applications, it differentiates the traffic flows into different priority classes according to their QoS needs and maintains three dimensional service queues attributing delay, bandwidth and reliability of data. The problem is formulated as a Lyapunov drift optimization with the objective of maximizing the weighted service of the traffic flows belonging to different classes. A suboptimal distributed control algorithm (DCA) is presented to efficiently support QoS through channel control, flow control, scheduling and routing decisions. In particular, the contributions of this paper are three folds; employing dynamic spectrum access to mitigate with the channel impairments, defining multi-attribute priority classes and designing a distributed control algorithm for data delivery that maximizes the network utility under QoS constraints. Performance evaluations in ns-2 reveal that the proposed framework achieves required QoS communication in smart grid.

It is essential to develop energy-efficient communication techniques for nanoscale wireless communications. In this paper, a new modulation and a novel minimum energy coding scheme (MEC) are proposed to achieve energy efficiency in wireless nanosensor networks (WNSNs). Unlike existing studies, MEC maintains the desired code distance to provide reliability, while minimizing energy. It is analytically shown that, with MEC, codewords can be decoded perfectly for large code distances, if the source set cardinality is less than the inverse of the symbol error probability. Performance evaluations show that MEC outperforms popular codes such as Hamming, Reed-Solomon and Golay in the average codeword energy sense.

Nanoscale communication based on Förster Resonance Energy Transfer (FRET) enables nanomachines to communicate with each other using the excited state of the fluorescent molecules as the information conveyer. In this study, FRET-based nanoscale communication is further extended to realize FRET-based nanoscale broadcast communication with one transmitter and many receiver nanomachines, and the performance of the broadcast channel is analyzed information theoretically. Furthermore, an electrically controllable routing mechanism is proposed exploiting the Quantum Confined Stark Effect (QCSE) observed in quantum dots. It is shown that by appropriately selecting the employed molecules on the communicating nanomachines, it is possible to control the route of the information flow by externally applying electric field in FRET-based nanonetworks.

In this paper, we propose a cross-layer design to meet the QoS requirements for smart grids employing the cognitive radio sensor networks for their control and monitoring operations. Existing routing protocols pertaining to QoS support are not able to simultaneously handle traffic of different characteristics present in smart grids. Therefore, considering the traffic heterogeneity of smart grid applications exhibiting diverse QoS requirements, a set of priority classes is defined in order to differentiate the traffic for the respective service. Specifically, the problem is formulated as a weighted network utility maximization (WNUM) whose objective is to maximize the weighted sum of flows service. A cross-layer heuristic solution is provided to solve the utility optimization problem by performing joint routing, dynamic spectrum allocation and medium access. Performance of the proposed protocol is evaluated using ns-2, which shows that the number of flows belonging to each class are served according to their weight fraction with their respective data rate, latency and reliability requirement.

Wireless nanosensor networks (WNSNs), which are collections of nanosensors with communication units, can be used for sensing and data collection with extremely high resolution and low power consumption for various applications. In order to realize WNSNs, it is essential to develop energy-efficient communication techniques, since nanonodes are severely energy-constrained. In this paper, a novel minimum energy coding scheme (MEC) is proposed to achieve energy-efficiency in WNSNs. Unlike the existing minimum energy codes, MEC maintains the desired Hamming distance, while minimizing energy, in order to provide reliability. It is analytically shown that, with MEC, codewords can be decoded perfectly for large code distance, if source set cardinality, M is less than inverse of symbol error probability, 1/ps. Performance analysis shows that MEC outperforms popular codes such as Hamming, Reed-Solomon and Golay in average energy per codeword sense.

Energy limitation of sensor nodes is the main constraint to be addressed while designing and implementing algorithms for wireless sensor networks (WSN). Recently, to mitigate battery depletion problem and extend network lifetime, wireless passive sensor networks (WPSN) have become a new field of interest. Modulated backscattering is an important communication technique for WPSN to enable unlimited lifetime for sensor nodes. Determination of required number and power level of RF sources for wireless power transfer to sensor nodes is crucial for energy-efficient distributed sensing operation. Furthermore, deployed RF sources can share spectrum opportunistically via incorporation of cognitive radio capability such that desired distributed estimation distortion can be achieved with minimum spectrum utilization by WPSN. Employment of RF sources that radiate power only when spectrum opportunities are available unveils passive opportunistic distributed sensing (PODS). In this paper, first, we model intercepted power by passive sensor from RF sources and reflected power by passive sensor at the sink, and effect of opportunistic access to licensed spectrum bands on instantaneous throughput of sensor nodes. Then, a power level control scheme for RF sources is proposed to achieve desired distortion level with minimum energy consumption while using opportunistic distributed sensing in WPSN. Achieved estimation distortion at sink with respect to number and power level of RF sources, and available spectrum opportunities is investigated, and energy saving provided by proposed power control scheme is assessed for various distortion requirements, channel noise levels, and available spectrum opportunities via simulation experiments.

Network virtualization has been proposed as a powerful and flexible way to support multiple heterogeneous architectures on a shared infrastructure. A major challenge in this respect lies in the resource allocation problem that allocates the resources of Infrastructure Providers (InPs) among Service Providers (SPs) fairly and efficiently. Because of the complexity in the interaction between InPs and SPs, this problem is more complicated and needs more investigation. This paper introduces an InP-SP-End user (ISE) system model in network virtualization environment, in which we present a bandwidth allocation scheme based on VCG (Vickrey-Clarke-Groves) to maximize the total revenue of SPs by inhibiting selfish action of SPs, and design a Q-learning strategy algorithm in order to obtain optimal bidding strategies for SPs. Simulations and experimental studies demonstrate the effectiveness and the fairness of the proposed scheme, and the convergence of the proposed algorithm.

Recent developments in nanotechnology have enabled the fabrication of nanomachines with very limited sensing, computation, communication, and action capabilities. The network of communicating nanomachines is envisaged as nanonetworks that are designed to accomplish complex tasks such as drug delivery and health monitoring. For the realization of future nanonetworks, it is essential to develop novel and efficient communication and networking paradigms. In this paper, the first step toward designing a mobile ad hoc molecular nanonetwork (MAMNET) with electrochemical communication is taken. MAMNET consists of mobile nanomachines and infostations that share nanoscale information using electrochemical communication whenever they have a physical contact with each other. In MAMNET, the intermittent connectivity introduced by the mobility of nanomachines and infostations is a critical issue to be addressed. An analytical framework that incorporates the effect of mobility into the performance of electrochemical communication among nanomachines is presented. Using the analytical model, numerical analysis for the performance evaluation of MAMNET is obtained. Results reveal that MAMNET achieves adequately high throughput to enable frontier nanonetwork applications with acceptable communication latency.

Cognitive radio (CR) technology helps mitigate wireless resource scarcity problem by dynamically changing frequency spectrum, power and modulation type. Opportunistic spectrum access increases the network capability and quality. Recently, CR applied to wireless sensor networks (WSNs) generated the paradigm of cognitive radio sensor networks (CRSNs) overcoming the challenges posed by event-driven traffic demands of WSNs. To realize advantages of CRSN, spectrum and power allocation, and routing must be jointly considered to maximize the information capacity, resource utilization and the lifetime. In this paper, power and rate adaptation problem is analyzed for a multi-hop CRSN in an information theoretical (IT) capacity maximization framework combined with energy adaptive (EA) mechanisms and utilization of sensor data information correlations (ICs). CRSN characteristics, i.e., fast data aggregation, bursty traffic and node failures, are considered. The capacity optimization problem is defined analytically and practical local schemes are presented showing the superiority of objective functions utilizing ICs and EA mechanisms in terms of the resulting maximum information rate at sink, i.e., Rmax, lifetime, and energy utilization. Furthermore, dependence of performance on total bandwidth and various relay energy distributions is explored observing the logarithmic dependence of Rmaxon total bandwidth.

Underwater physical medium is a challenging environment for communication using radio frequency (RF) or acoustic waves due to strong attenuation, delay, multi-path fading, power and cost limitations. Discovered a century ago, magneto-inductive (MI) communication technique stands as a strong alternative paradigm due to its independence of environmental impairments including multi-path fading, dynamic channels and high propagation delays experienced by acoustic waves. Furthermore, MI technique yields networking solutions exploiting low-cost, easily-deployable and flexible antenna structures, and the possibility of forming networks of magnetic waveguides defeating path loss. In this work, highly power efficient and fully connected underwater communication networks (UWCNs) composed of transceiver and relay induction coils are presented. Three dimensional (3D) UWCNs are analysed in terms of basic communication metrics, i.e, signal-to-noise ratio, bit-error rate, connectivity and communication bandwidth. The performance studies of realistic 3D networks covering hundreds of meters sea depths and a few km2areas show that fully connected multi-coil networks with communication bandwidths extending from a few to tens of KHz are possible. Furthermore, the performance dependence on coil properties and network size is theoretically modelled. Results show that MI wireless communication is a promising alternative for UWCNs and future research challenges are pointed out.

Cognitive Radio (CR) and its dynamic spectrum access capabilities can be exploited by many wireless network architectures including sensor networks. Thus, cognitive radio sensor networks (CRSN) has emerged as a promising solution to address the spectrum-related challenges of wireless sensor networks (WSN). Among others, determination of the optimal packet size is one of the most fundamental problems to be addressed for the practical realization of CRSN. The existing optimal packet size solutions devised for wireless, sensor, and CR networks are not applicable in CRSN regime. Hence, the objective of this paper is to determine the optimal packet size for CRSN that maximizes energy-efficiency while maintaining acceptable interference level for licensed primary users (PU) and achieving reliable event detection at the sink. The energy-efficient optimal packet size is analytically formulated and its variation with respect to different network parameters is observed. Results reveal that PU behavior and channel BER are the most critical parameters in determining the energy-efficient optimal packet size for CRSN.

Entanglement distribution over long distances is one of the main problems in the existing quantum communication networks. Most of the existing methods of establishing entanglement paired link (Einstein, Podolsky, Rosen - EPR pairs) between distant nodes assume symmetric network topologies comprised of links with identical EPR generation capacities. In this work, the entanglement rate capacity of randomly distributed quantum ad hoc networks is investigated. To this end, constant fidelity maximum flow (CFMF) of entanglement problem is defined, and its theoretical analysis is presented. A new heuristic algorithm, i.e., Entanglement Swapping Scheme Search (ESSS), is presented to find the best possible swapping scheme over a multi-hop entanglement path. Furthermore, Shortest Path Entanglement Flow (SPEF) algorithm is introduced as an effective heuristic solution for this problem. Analysis shows that there is a trade-off between the desired constant target fidelity and the entanglement generation rate (maximum flow) of the network.

The above titled paper was mistakenly omitted from the June 2010 JSAC issue (Vol. 28, No. 5) on Mission Critical Networking and appears here in its entirety.

Wireless sensor networks (WSN) have thus far been used for detection and tracking of static and mobile targets for mission critical surveillance applications. However, detection and tracking do not suffice for a complete and accurate target classification. In fact, surveillance target imaging yields the most valuable information. Current techniques mainly aim to provide images of static environment in a sensor network. Nevertheless, imaging of mobile targets requires networked and collaborative detection, tracking and imaging capabilities. With this regard, ultra-wideband (UWB) radar technology stands as a promising approach for networked target imaging due to its unique features such as having no line-of-sight (LoS) requirement. However, UWB wireless radar sensor network (WRSN) is yet to be developed for imaging of mobile targets. In this paper, an architecture and a new collaborative mobile target imaging (CMTI) algorithm for WRSN are presented. The objective is to efficiently obtain an accurate image of mobile targets based on the collaborative effort of deployed radar sensor nodes. CMTI enables detection, tracking and imaging of mobile targets as a complete WRSN solution. Performance evaluations reveal that CMTI yields high quality radar image of mobile targets inWRSN with very low communication overhead regardless of the target shape and velocity.

Wireless sensor networks (WSN) are event-based systems based on the collaboration of several microsensor nodes. Due to the limited supply of energy at sensor nodes, energy- efficient configuration of WSN has become a major design goal to improve the lifetime of the network. Many clustering algorithms have been proposed as energy-efficient, however, the existing classical pre-event clustering solutions form clusters in the entire network unnecessarily that brings significant overheads in maintaining the network configuration. Unlike pre- event clustering, energy-efficient operation of WSN requires the event-to-sink directed clustering notion, which forms clusters when and where they are needed and in the direction of data flow from event location to the sink. To the best of our knowledge, energy-efficient clustering in WSN has not been studied from this perspective before. In this paper, we propose a novel Event- to-Sink Directed Clustering (ESDC) protocol for WSN. ESDC realizes energy efficiency in sensor network configuration by employing two techniques: (1) clustering of the nodes only within the event-to-sink data flow corridor to avoid unnecessary cluster formation, (2) directional clustering to minimize the number of hops for data forwarding. The directional clustering process in ESDC also sets up the routing path of the event flows over the clusters. Performance results reveal that the ESDC protocol achieves the energy-efficiency objectives and outperforms the existing conventional pre-event clustering approaches.

The most difficult challenge for the design of wireless sensor networks (WSN) is to maintain long network lifetimes since the sensor nodes are severely energy-constrained. Traditional WSN assumes employment of conventional RF transmitters which consume most of the stored power on the sensor node. In this regard, modulated backscattering (MB) emerges as a promising communication technique alternative, in which the sensor nodes reflect the incident signal of an RF source and modulate their data on the reflected signal. With the use of MB, the power consumption of the nodes reduce drastically since it replaces the most power consuming component of a typical sensor node, i.e., the RF transmitter. In addition, the nodes acquire relatively long-range communication ability through MB. Furthermore, the incident RF power can be converted into DC power in order to drive the sensing and processing circuitries. This, in turn,i leads to the design of battery-free wireless passive sensor networks (WPSN), which stands as a radically distinct solution approach for the energy problems of WSN. The objective of this paper is to revisit the main design challenge of WSN from entirely different perspective. To this end, the fundamental principles of WPSN are first introduced. In addition, in order to realize the potential benefits of WPSN, a new clustering-based energy-efficient communication protocol, i.e., PADRE (PAssive Data REtrieval), is presented for WPSN operating via MB technique. Simulations show that PADRE protocol achieves high performance in terms communication reliability and network lifetime.

Although many localization protocols have been proposed for terrestrial sensor networks in recent years, the unique characteristics of the underwater acoustic communication channel, such as high and variable propagation delay and the three dimensional volume of the environment make it necessary to design and develop new localization algorithms. In this paper, a localization algorithm called three-dimensional underwater localization (3DUL) is introduced. 3DUL achieves networkwide robust 3D localization by using a distributed and iterative algorithm. Most importantly, 3DUL exploits only three surface buoys for localization initially. The sensor nodes leverage the low speed of sound to accurately determine the inter-node distances. Performance evaluations show that 3DUL algorithm provides high accuracy in underwater localization, which does not degrade with network size.

Cognitive radio is the promising radio technology, which aims to detect and utilize the temporally unused spectrum bands by sensing its radio environment in order to enhance spectrum utilization. However, these objectives bring significant challenges and required functionalities such as spectrum sensing, sharing, management and mobility for the realization of cognitive radio networks (CRN). In particular, efficient spectrum sharing problem in cognitive radio communication is one of the most important problem which must be addressed in order to enhance the overall spectrum utilization in dynamic spectrum access environments. In this paper, we introduce a new BIOlogically-inspired spectrum sharing (BIOSS) algorithm which is based on the adaptive task allocation model in insect colonies. Without need for any coordination among the unlicensed users, BIOSS enables each unlicensed user to distributively determine the appropriate channel(s) over which it can communicate. Performance evaluations clearly reveal that BIOSS achieves efficient dynamic spectrum sharing with high spectrum utilization and without any coordination among the users and hence yielding no spectrum handoff latency overhead due to coordination.

Wireless Sensor Networks (WSN) are comprised of densely deployed sensor nodes collaboratively observing and communicating extracted information about a physical phenomenon. Dense deployment of sensor nodes makes the sensor observations highly correlated in the space domain. In addition, consecutive samples obtained by a sensor node are also temporally correlated for the applications involving the observation of the variation of a physical phenomenon. Based on the physical characteristics and dispersion pattern over the area, the phenomenon to be observed can be modeled as point source or field source. Clearly, understanding the spatio-temporal correlation characteristics of the point and field sources brings potential advantages to be exploited in the design of efficient communication protocols. In this paper, a theoretical analysis of spatio-temporal correlation in WSN is carried out. The objective of this analysis is to capture the spatio-temporal characteristics of point and field sources in WSN. First, the model for point and field sources are developed and their spatio-temporal characteristics are analytically derived along with the distortion functions. Based on the theoretical analysis, numerical simulations are performed. This analytical work provides tools for finding the feasible operating region in terms of spatial and temporal resolution for a specific distortion constraint considering spatio-temporal correlation, signal properties, and network variables in WSN.

Wireless sensor networks (WSNs) are event-based systems that rely on the collective effort of several microsensor nodes. Reliable event detection at the sink is based on collective information provided by source nodes and not on any individual report. However, conventional end-to-end reliability definitions and solutions are inapplicable in the WSN regime and would only lead to a waste of scarce sensor resources. Hence, the WSN paradigm necessitates a collective event-to-sink reliability notion rather than the traditional end-to-end notion. To the best of our knowledge, reliable transport in WSN has not been studied from this perspective before. In order to address this need, a new reliable transport scheme for WSN, the event-to-sink reliable transport (ESRT) protocol, is presented in this paper. ESRT is a novel transport solution developed to achieve reliable event detection in WSN with minimum energy expenditure. It includes a congestion control component that serves the dual purpose of achieving reliability and conserving energy. Importantly, the algorithms of ESRT mainly run on the sink, with minimal functionality required at resource constrained sensor nodes. ESRT protocol operation is determined by the current network state based on the reliability achieved and congestion condition in the network. This self-configuring nature of ESRT makes it robust to random, dynamic topology in WSN. Furthermore, ESRT can also accommodate multiple concurrent event occurrences in a wireless sensor field. Analytical performance evaluation and simulation results show that ESRT converges to the desired reliability with minimum energy expenditure, starting from any initial network state.

Currently there is no control for real-time traffic sources in IP networks. This is a serious problem because real-time traffic can not only congest the network but can also cause unfairness and starvation of TCP traffic. However, it is not possible to apply current solutions for Internet to the networks with high bandwidth-delay products and high bit error rates. The channel errors may result in inaccurate congestion control decisions and unnecessary rate throttles leading to severe performance degradation. This problem is amplified in the links with high bandwidth-delay products, since the link is inefficiently utilized for a very long time until the unnecessary rate throttle is recovered. In this paper, a new Rate Control Scheme, RCS, is introduced for real-time interactive applications in networks with high bandwidth-delay products and high bit error rates. RCS is based on the concept of using dummy packets to probe the availability of network resources. Dummy packets are treated as low priority packets and consequently they do not affect the throughput of actual data traffic. Therefore, RCS requires all the routers in the connection path to support some priority policy. A new algorithm is also proposed to improve the robustness of the RCS to temporal signal loss conditions. The delay-bound considerations for real-time traffic sources using RCS rate control scheme are also investigated. Simulation experiments show that in environments with high bandwidth-delay products and high bit error rates, RCS achieves high throughput performance without penalizing TCP connections.

The next-generation wireless Internet (NGWI) is expected to provide a wide range of services including high-speed data and real-time multimedia to mobile users. To realize this expectation, a diverse set of challenges need to be addressed, which are posed by heterogeneous wireless networking environments within NGWI and the according application requirements. Furthermore, the architectural heterogeneities must be captured dynamically, while mobile users may roam during their connection duration. Current existing transport layer protocols have been developed for a specific network paradigm in mind, e.g., for wireless local area networks (WLANs), micro/macro wireless systems, or for satellite systems. Using these existing different transport layer protocols for NGWI to support global roaming of mobile users is not a practical solution due to processing and memory constraints of wireless terminals. Thus, there is a need for a unified adaptive transport layer protocol suite which can address the architectural heterogeneities for roaming mobile users and achieve the best performance for NGWI. In this paper, a unified adaptive transport layer (ATL) suite is introduced for NGWI which incorporates a new adaptive transport protocol (TCP-ATL) for reliable data transport and a new adaptive rate control protocol (RCP-ATL) for multimedia delivery in the NGWI. According to the requested service type, i.e., reliable data or multimedia, ATL selects the appropriate protocol. Both TCP-ATL and RCP-ATL, deploy a new adaptive congestion control method that dynamically adjusts the protocol configurations according to the current wireless network paradigms depending where the mobile user currently resides. Hence, the unified adaptive ATL protocol suite achieves high-throughput performance in all of underlying heterogeneous wireless architectures, i.e., WLANs, micro, macro, or satellite environments. Moreover, the developed adaptive congestion control explicitly takes fairness into consideration. Performance evaluation via simulation experiments reveals that the ATL protocol suite addresses the challenges posed by the NGWI and significantly improves the performance for reliable data and multimedia transport in NGWI.

Space exploration missions are crucial for acquisition of information about space and the Universe. The entire success of a mission is directly related to the satisfaction of its communications needs. For this goal, the challenges posed by the InterPlaNetary (IPN) Internet need to be addressed. Current transmission control protocols (TCPs) have very poor performance in the IPN Internet, which is characterized by extremely high propagation delays, link errors, asymmetrical bandwidth, and blackouts. The window-based congestion control, which injects a new packet into the network upon an ACK reception, is responsible for such performance degradation due to high propagation delay. Slow start algorithms of the existing TCPs further contribute to the performance degradation by wasting long time periods to reach the actual data rate. Moreover, wireless link errors amplify the problem by misleading the TCP source to unnecessarily throttle the congestion window. The recovery from erroneous window decrease takes a certain amount of time, which is proportional to the round-trip time (RTT) and further decreases the network performance. In this paper, a reliable transport protocol (TP-Planet) is presented for data traffic in the IPN Internet. It is intended to address the challenges and to achieve high throughput performance and reliable data transmission on deep-space links of the IPN Backbone Network. TP-Planet deploys a rate-based additive-increase multiplicative-decrease (AIMD) congestion control, whose AIMD parameters are tuned to help avoid throughput degradation. TP-Planet replaces the inefficient slow start algorithm with a novel Initial State algorithm, which allows the capture of link resources in a very fast and controlled manner. A new congestion detection and control mechanism is developed, which decouples congestion decisions from single packet losses in order to avoid the erroneous congestion decisions due to high link errors. In order to reduce the effects of blackout conditions on the throughput performance, TP-Planet incorporates the blackout state procedure into the protocol operation. The bandwidth asymmetry problem is addressed by the adoption of delayed selective acknowledgment (SACK). Simulation experiments show that the TP-Planet significantly improves the throughput performance and addresses the challenges posed by the IPN Backbone Network.

Next-generation wireless Internet (NGWI) is expected to provide a wide range of services including real-time multimedia to mobile users. However, the real-time multimedia traffic transport requires rate control deployment to protect shared Internet from unfairness and further congestion collapse. The transmission rate control method must also achieve high throughput and satisfy multimedia requirements such as delay or jitter bound. However, the existing solutions are mostly for the wired Internet, and hence, they do not address the challenges in the wireless environments which are characterized by high bit error rates. In this paper, a new analytical rate control (ARC) protocol for real-time multimedia traffic over wireless networks is presented. It is intended to achieve high throughput and multimedia support for real-time traffic flows while preserving fairness to the TCP sources sharing the same wired link resources. Based on the end-to-end path model, the desired behavior of a TCP source over lossy links is captured via renewal theory. The resulting asymptotic throughput equation is designated as the driving equation for the proposed rate control method. Performance evaluation via simulation experiments reveals that ARC achieves high throughput and meets multimedia traffic expectations without violating good citizenship rules for the shared Internet.

The handoff call blocking probability minimization is an important issue for seamless wireless network operation. Handoff delay minimization is in parallel with this task. Mobile stations with QoS requirements should be able to receive the same service level during and after handoff. This constraint amplifies the handoff latency caused forced termination problem. This research introduces a handoff method, which performs some preparation phase tasks in advance, in wireless networks. With the expected visitor list (EVL) deployment at base stations and RSVP, QoS requirements of an incoming mobile station is obtained and examined, and hence overall handoff latency minimization is achieved. A virtual QoS provisioning method for call admission control of the mobile is presented. Performance evaluation with simulation experiments shows that the proposal can be a promising methodology for QoS provisioning in the nest generation (NG) wireless networks.

Wireless sensor networks (WSN) are event based systems that rely on the collective effort of several microsensor nodes. Reliable event detection at the sink is based on collective information provided by source nodes and not on any individual report. Hence, conventional end-to-end reliability definitions and solutions are inapplicable in the WSN regime and would only lead to a waste of scarce sensor resources. However, the absence of reliable transport altogether can seriously impair event detection. Hence, the WSN paradigm necessitates a collective phevent-to-sink reliability notion rather than the traditional end-to-end notion. To the best of our knowledge, reliable transport in WSN has not been studied from this perspective before.In order to address this need, a new reliable transport scheme for WSN, the event-to-sink reliable transport (ESRT) protocol, is presented in this paper. ESRT is a novel transport solution developed to achieve reliable event detection in WSN with minimum energy expenditure. It includes a congestion control component that serves the dual purpose of achieving reliability and conserving energy. Importantly, the algorithms of ESRT mainly run on the sink, with minimal functionality required at resource constrained sensor nodes. ESRT protocol operation is determined by the current network state based on the reliability achieved and congestion condition in the network. If the event-to-sink reliability is lower than required, ESRT adjusts the reporting frequency of source nodes aggressively in order to reach the target reliability level as soon as possible. If the reliability is higher than required, then ESRT reduces the reporting frequency conservatively in order to conserve energy while still maintaining reliability. This self-configuring nature of ESRT makes it robust to random, dynamic topology in WSN. Analytical performance evaluation and simulation results show that ESRT converges to the desired reliability with minimum energy expenditure, starting from any initial network state.