In line with the AI-native 6G vision, explainability and robustness are crucial for building trust and ensuring reliable performance in millimeter-wave (mmWave) systems. Efficient beam alignment is essential for initial access, but deep learning (DL) solutions face challenges, including high data collection overhead, hardware constraints, lack of explainability, and susceptibility to adversarial attacks. This paper proposes a robust and explainable DL-based beam alignment engine (BAE) for mmWave multiple-input multiple-output (MIMO) systems. The BAE uses received signal strength indicator (RSSI) measurements from wide beams to predict the best narrow beam, reducing the overhead of exhaustive beam sweeping. To overcome the challenge of real-world data collection, this work leverages a site-specific digital twin (DT) to generate synthetic channel data closely resembling real-world environments. A model refinement via transfer learning is proposed to fine-tune the pre-trained model residing in the DT with minimal real-world data, effectively bridging mismatches between the digital replica and real-world environments. To reduce beam training overhead and enhance transparency, the framework uses deep Shapley additive explanations (SHAP) to rank input features by importance, prioritizing key spatial directions and minimizing beam sweeping. It also incorporates the Deep k-nearest neighbors (DkNN) algorithm, providing a credibility metric for detecting out-of-distribution inputs and ensuring robust, transparent decision-making. Experimental results show that the proposed framework reduces real-world data needs by 70%, beam training overhead by 62%, and improves outlier detection robustness by up to 8.5×, achieving near-optimal spectral efficiency and transparent decision making compared to traditional softmax based DL models.

Integrated artificial intelligence (AI) and communication has been recognized as a key pillar of 6 G and beyond networks. In line with AI-native 6 G vision, explainability and robustness in AI-driven systems are critical for establishing trust and ensuring reliable performance in diverse and evolving environments. This paper addresses these challenges by developing a robust and explainable deep learning (DL)-based beam alignment engine (BAE) for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems. The proposed convolutional neural network (CNN)-based BAE utilizes received signal strength indicator (RSSI) measurements over a set of wide beams to accurately predict the best narrow beam for each UE, significantly reducing the overhead associated with exhaustive codebook-based narrow beam sweeping for initial access (IA) and data transmission. To ensure transparency and resilience, the Deep k-Nearest Neighbors (DkNN) algorithm is employed to assess the internal representations of the network via nearest neighbor approach, providing human-interpretable explanations and confidence metrics for detecting out-of-distribution inputs. Experimental results demonstrate that the proposed DL-based BAE exhibits robustness to measurement noise, reduces beam training overhead by 75 % compared to the exhaustive search while maintaining near-optimal performance in terms of spectral efficiency. Moreover, the proposed framework improves outlier detection robustness by up to$5 \times$and offers clearer insights into beam prediction decisions compared to traditional softmax-based classifiers.

Ultra-reliable low-latency communications (URLLC) require innovative approaches to modeling channel and interference dynamics, extending beyond traditional average estimates to encompass entire statistical distributions, including rare and extreme events that challenge achieving ultra-reliability performance regions. In this paper, we propose a risk-sensitive approach based on extreme value theory (EVT) to predict the signal-to-interference-plus-noise ratio (SINR) for efficient resource allocation in URLLC systems. We employ EVT to estimate the statistics of rare and extreme interference values, and kernel density estimation (KDE) to model the distribution of non-extreme events. Using a mixture model, we develop an interference prediction algorithm based on quantile prediction, introducing a confidence level parameter to balance reliability and resource usage. While accounting for the risk sensitivity of interference estimates, the prediction outcome is then used for appropriate resource allocation of a URLLC transmission under link outage constraints. Simulation results demonstrate that the proposed method outperforms the state-of-the-art first-order discrete-time Markov chain (DTMC) approach by reducing outage rates up to 100 -fold, achieving target outage probabilities as low as 10−7). Simultaneously, it minimizes radio resource usage$\sim 15 \%$compared to DTMC, while remaining only$\sim 20 \%$above the optimal case with perfect interference knowledge, resulting in significantly higher prediction accuracy. Additionally, the method is sample-efficient, able to predict interference effectively with minimal training data.

This paper centers around a multiple-input-multiple-output (MIMO) visible light communication (VLC) system, where an unmanned aerial vehicle (UAV) benefits from a light emitting diode (LED) array to serve photo-diode (PD)equipped users for illumination and communication simultaneously. Concerning the battery limitation of the UAV and considerable energy consumption of the LED array, a hybrid dimming control scheme is devised at the UAV that effectively controls the number of glared LEDs and thereby mitigates the overall energy consumption. To assess the performance of this system, a radio resource allocation problem is accordingly formulated for jointly optimizing the motion trajectory, transmit beamforming and LED selection at the UAV, assuming that channel state information (CSI) is partially available. By reformulating the optimization problem in Markov decision process (MDP) form, we propose a soft actor-critic (SAC) mechanism that captures the dynamics of the problem and optimizes its parameters. Additionally, regarding the high mobility of the UAV and thus remarkable rearrangement of the system, we enhance the trained SAC model by integrating a meta-learning strategy that enables more adaptation to system variations. By defining energy efficiency as a trade-off between the data rate and power consumption, simulations verify that upgrading a single-LED UAV by an array of 10 LEDs, exhibits 47 % and 34 % improvements in data rate and energy efficiency, albeit at the expense of 8 % more power consumption.

Sensor-based local inference at IoT devices faces severe computational limitations, often requiring data transmission over noisy wireless channels for server-side processing. To address this, split-network Deep Neural Network (DNN) based Joint Source-Channel Coding (JSCC) schemes are used to extract and transmit relevant features instead of raw data. However, most existing methods rely on fixed network splits and static configurations, lacking adaptability to varying computational budgets and channel conditions. In this paper, we propose a novel SNR- and computation-adaptive distributed CNN framework for wireless image classification across IoT devices and edge servers. We introduce a learning-assisted intelligent Genetic Algorithm (LAIGA) that efficiently explores the CNN hyperparameter space to optimize network configuration under given FLOPs constraints and given SNR. LAIGA intelligently discards the infeasible network configurations that exceed computational budget at IoT device. It also benefits from the Random Forests based learning assistance to avoid a thorough exploration of hyperparameter space and to induce application specific bias in candidate optimal configurations. Experimental results demonstrate that the proposed framework outperforms fixed-split architectures and existing SNR-adaptive methods, especially under low SNR and limited computational resources. We achieve a 10% increase in classification accuracy as compared to existing JSCC based SNR-adaptive multilayer framework at an SNR as low as -10dB across a range of available computational budget (1M to 70M FLOPs) at IoT device.

Compared to the generations up to 4G, whose main focus was on broadband and coverage aspects, 5G has expanded the scope of wireless cellular systems toward embracing two new types of connectivity: massive machine-type communications (mMTCs) and ultrareliable low-latency communications (URLLCs). This article discusses the possible evolution of these two types of connectivity within the umbrella of 6G wireless systems. This article consists of three parts. The first part deals with the connectivity for a massive number of devices. While mMTC research in 5G predominantly focuses on the problem of uncoordinated access in the uplink for a large number of devices, the traffic patterns in 6G may become more symmetric, leading to closed-loop massive connectivity. One of the drivers for this type of traffic pattern is distributed/decentralized learning and inference. The second part of this article discusses the evolution of wireless connectivity for critical services. While latency and reliability are tightly coupled in 5G, 6G will support a variety of safety-critical control applications with different types of timing requirements, as evidenced by the emergence of metrics related to information freshness and information value. In addition, ensuring ultrahigh reliability for safety-critical control applications requires modeling and estimation of the tail statistics of the wireless channel, queue length, and delay. The fulfillment of these stringent requirements calls for the development of novel artificial intelligence (AI)-based techniques, incorporating optimization theory, explainable AI (XAI), generative AI, and digital twins (DTs). The third part analyzes the coexistence of massive connectivity and critical services. Specifically, we consider scenarios in which a massive number of devices need to support traffic patterns of mixed criticality. This is followed by a discussion about the management of wireless resources shared by services with different criticality.

Artificial intelligence (AI) is expected to significantly enhance radio resource management (RRM) in sixth-generation (6G) networks. However, the lack of explainability in complex deep learning (DL) models poses a challenge for practical implementation. This paper proposes a novel explainable AI (XAI)-based framework for feature selection and model complexity reduction in a model-agnostic manner. Applied to a multi-agent deep reinforcement learning (MADRL) setting, our approach addresses the joint sub-band assignment and power allocation problem in cellular vehicle-to-everything (V2X) communications. We propose a novel two-stage systematic explainability framework leveraging feature relevance-oriented XAI to simplify the DRL agents. While the former stage generates a state feature importance ranking of the trained models using Shapley additive explanations (SHAP)-based importance scores, the latter stage exploits these importance-based rankings to simplify the state space of the agents by removing the least important features from the model’s input. Simulation results demonstrate that the XAI-assisted methodology achieves ~97% of the original MADRL sum-rate performance while reducing optimal state features by ~28%, average training time by ~11%, and trainable weight parameters by ~46% in a network with eight vehicular pairs.

In Wireless Networked Control Systems (WNCSs), control and communication systems must be co-designed due to their strong interdependence. This paper presents a novel optimization theory-based safe deep reinforcement learning (DRL) framework for ultra-reliable WNCSs, ensuring constraint satisfaction while optimizing performance, for the first time in the literature. The approach minimizes power consumption under key constraints, including Peak Age of Information (PAoI) violation probability, transmit power, and schedulability in the finite blocklength regime. PAoI violation probability is uniquely derived by combining stochastic maximum allowable transfer interval (MATI) and maximum allowable packet delay (MAD) constraints in a multi-sensor network. The framework consists of two stages: optimization theory and safe DRL. The first stage derives optimality conditions to establish mathematical relationships among variables, simplifying and decomposing the problem. The second stage employs a safe DRL model where a teacher-student framework guides the DRL agent (student). The control mechanism (teacher) evaluates compliance with system constraints and suggests the nearest feasible action when needed. Extensive simulations show that the proposed framework outperforms rule-based and other optimization theory based DRL benchmarks, achieving faster convergence, higher rewards, and greater stability.

The design of Wireless Networked Control System (WNCS) requires addressing critical interactions between control and communication systems with minimal complexity and communication overhead while providing ultra-high reliability. This paper introduces a novel optimization theory based deep reinforcement learning (DRL) framework for the joint design of controller and communication systems. The objective of minimum power consumption is targeted while satisfying the schedulability and rate constraints of the communication system in the finite blocklength regime and stability constraint of the control system. Decision variables include the sampling period in the control system, and blocklength and packet error probability in the communication system. The proposed framework contains two stages: optimization theory and DRL. In the optimization theory stage, following the formulation of the joint optimization problem, optimality conditions are derived to find the mathematical relations between the optimal values of the decision variables. These relations allow the decomposition of the problem into multiple building blocks. In the DRL stage, the blocks that are simplified but not tractable are replaced by DRL. Via extensive simulations, the proposed optimization theory based DRL approach is demonstrated to outperform the optimization theory and pure DRL based approaches, with close to optimal performance and much lower complexity.

Relative vehicle positioning methods can contribute to safer and more efficient autonomous driving by enabling collision avoidance and platooning applications. For full automation, these applications require cm-level positioning accuracy and greater than 50 Hz update rate. Since sensor-based methods (e.g., LIDAR, cameras) have not been able to reliably satisfy these requirements under all conditions so far, complementary methods are sought. Recently, positioning based on visible light communication signals from vehicle head/tail LED lights (VLP) has shown significant promise as a complementary method attaining cm-level accuracy and near-kHz rate in realistic driving scenarios. Vehicular VLP methods measure relative bearing (angle) or range (distance) of transmitters (i.e., head/tail lights) based on received signals from on-board photodiodes and estimate transmitter relative positions based on those measurements. In this survey, we first review existing vehicular VLP methods and propose a new method that advances the state-of-the-art in positioning performance. Next, we analyze the theoretical and simulated performance of all methods in realistic driving scenarios under challenging noise and weather conditions, real asymmetric light beam patterns and different vehicle dimensions and light placements. Our simulation results show that the newly proposed VLP method is the overall best performer, and can indeed satisfy the accuracy and rate requirements for localization in collision avoidance and platooning applications within practical constraints. Finally, we discuss remaining open challenges that are faced for the deployment of VLP solutions in the automotive sector and further research questions.

Attaining ultra-reliable communication (URC) in fifth-generation (5G) and beyond networks requires deriving statistics of channel in ultra-reliable region by modeling the extreme events. Extreme value theory (EVT) has been previously adopted in channel modeling to characterize the lower tail of received powers in URC systems. In this paper, we propose a multivariate EVT (MEVT)-based channel modeling methodology for tail of the joint distribution of multi-channel by characterizing the multivariate extremes of multiple-input multiple-output (MIMO) system. The proposed approach derives lower tail statistics of received power of each channel by using the generalized Pareto distribution (GPD). Then, tail of the joint distribution is modeled as a function of estimated GPD parameters based on two approaches: logistic distribution, which utilizes logistic distribution to determine dependency factors among the Fréchet transformed tail sequence and obtain a bi-variate extreme value model, and Poisson point process, which estimates probability measure function of the Pickands angular component to model bi-variate extreme values. Finally, validity of the proposed models is assessed by incorporating the mean constraint on probability measure function of Pichanks coordinates. Based on the data collected within the engine compartment of Fiat Linea, we demonstrate the superiority of proposed methodology compared to the conventional extrapolation-based methods in providing the best fit to the multivariate extremes.

Minimum length scheduling is used to ensure the strict delay requirements of time-critical applications in wireless powered communications networks (WPCNs). The previous optimal and sub-optimal solutions of the problem suffer from the run-time complexity of the iterative algorithms, which makes real-time applications unpractical. This paper proposes a deep learning based framework for a low-complexity solution to the minimum length scheduling problem in half-duplex WPCNs. The objective of the problem is to minimize the duration of the schedule for energy harvesting (EH) and information transmission (IT), subject to the data demand, energy causality, and maximum transmit power constraints. Multi-input multi-output feed-forward deep neural network (DNN) architecture is considered, where the inputs are channel state information and two parameters derived from the optimality conditions of the problem; and outputs are the transmit powers, EH and IT lengths. To ensure the feasibility of the DNN outputs, we design a final layer which maps the estimated transmit powers to the feasible EH and IT lengths. The DNN is trained offline with both supervised and unsupervised techniques. Simulation results indicate that the proposed DNN-based approaches are up to 8.5 times faster than the benchmark iterative algorithms. These approaches also outperform benchmark sub-optimal algorithms in terms of accuracy with only 0.12% optimality gap and robustness against varying network conditions.

Vehicle-to-Everything Communication (V2X) technologies are provisioned to play an important role in increasing road safety by enabling advanced connected vehicle applications such as cooperative perception, cooperative driving, and remote driving. However, the reliability of the technology is limited mainly due to wireless communication channel characteristics. Therefore, investigation of V2X reliability aspects is crucial to utilize the technology efficiently. In this paper, we provide simulation and measurement-based reliability analysis of Cellular Vehicle-to-Everything (C-V2X) Mode 4 technology for various message sizes and Modulation and Coding Schemes (MCS) selections. We demonstrate that the Packet Delivery Ratio (PDR), a key communication performance metric, heavily depends on message size and selected MCS.

Machine learning (ML) has attracted a great research interest for physical layer design problems, such as channel estimation, thanks to its low complexity and robustness. Channel estimation via ML requires model training on a dataset, which usually includes the received pilot signals as input and channel data as output. In previous works, model training is mostly done via centralized learning (CL), where the whole training dataset is collected from the users at the base station (BS). This approach introduces huge communication overhead for data collection. In this paper, to address this challenge, we propose a federated learning (FL) framework for channel estimation. We design a convolutional neural network (CNN) trained on the local datasets of the users without sending them to the BS. We develop FL-based channel estimation schemes for both conventional and RIS (intelligent reflecting surface) assisted massive MIMO (multiple-input multiple-output) systems, where a single CNN is trained for two different datasets for both scenarios. We evaluate the performance for noisy and quantized model transmission and show that the proposed approach provides approximately 16 times lower overhead than CL, while maintaining satisfactory performance close to CL. Furthermore, the proposed architecture exhibits lower estimation error than the state-of-the-art ML-based schemes.

Wireless powered communication networks (WPCNs) will act as a major enabler of massive machine type communications (MTCs), which is a major service domain for 5G and beyond systems. The MTC networks will be deployed by using low-power transceivers with finite discrete configurations. This paper considers minimum length scheduling problem for full-duplex WPCNs, where users transmit information to a hybrid access point at a rate chosen from a finite set of discrete-rate levels. The optimization problem considers energy causality, data and maximum transmit power constraints, and is proven to be NP-hard. As a solution strategy, we define the minimum length scheduling (MLS) slot, which is slot of minimum transmission completion time while starting transmission at anytime after the decision time. We solve the problem optimally for a given transmission order based on the optimality analysis of MLS slot. For the general problem, we categorize the problem based on whether the MLS slots of users overlap over time. We propose optimal algorithm for non-overlapping scenario by allocating the MLS slots, and a polynomial-time heuristic algorithm for overlapping scenario by allocating the transmission slot to the user with earliest MLS slot. Through simulations, we demonstrate significant gains of scheduling and discrete rate allocation.

A key building block in the design of ultra-reliable communication systems is a wireless channel model that captures the statistics of rare events occurring due to the significant fading. In this paper, we propose a novel methodology based on extreme value theory (EVT) to statistically model the behavior of extreme events in a wireless channel for ultra-reliable communication. This methodology includes techniques for fitting the lower tail distribution of the received power to the generalized Pareto distribution (GPD), determining the optimum threshold over which the tail statistics are derived, ascertaining the optimum stopping condition on the number of samples required to estimate the tail statistics by using GPD, and finally, assessing the validity of the derived Pareto model. Based on the data collected within the engine compartment of Fiat Linea under various engine vibrations and driving scenarios, we demonstrate that the proposed methodology provides the best fit to the collected data, significantly outperforming the conventional extrapolation-based methods. Moreover, the usage of the EVT in the proposed method decreases the required number of samples for estimating the tail statistics significantly.

Heterogeneous vehicular communications aim to improve the reliability, security and delay performance of vehicle-to-vehicle (V2V) communications, by utilizing multiple commu-nication technologies. Predicting the path loss through conventional fitting based models and radio frequency (RF) jamming detection through rule based models of different communication schemes fail to address comprehensive mobility and jamming scenarios. In this paper, we propose a machine learning based adaptive link quality estimation and jamming detection scheme for the optimum selection and aggregation of IEEE 802.11p and Vehicular Visible Light Communications (V-VLC) technologies targeting reliable V2V communications. We propose to use Random Forest regression and classifier based algorithms, where multiple individual learners with diversity are trained by using measurement data and the final result is obtained by averaging outputs of all learners. We test our framework on real-world road measurement data, demonstrating up to 2.34 dB and 0.56 dB Mean Absolute Error (MAE) improvement for V-VLC and IEEE 802.11p path loss prediction compared to fitting based models, respectively. The proposed jamming presence detection scheme yields 88.3% accuracy to detect noise interference injection for IEEE 802.11p links, yielding 3% better prediction performance than previously proposed deep convolutional neural network (DCNN) based scheme.

Millimeter wave multiple-input multiple-output (mmWave-MIMO) systems with small number of radio-frequency (RF) chains have limited multiplexing gain. Spatial path index modulation (SPIM) is helpful in improving this gain by utilizing additional signal bits modulated by the indices of spatial paths. In this paper, we introduce model-based and model-free frameworks for beamformer design in multi-user SPIM-MIMO systems. We first design the beamformers via model-based manifold optimization algorithm. Then, we leverage federated learning (FL) with dropout learning (DL) to train a learning model on the local dataset of users, who estimate the beamformers by feeding the model with their channel data. The DL randomly selects different set of model parameters during training, thereby further reducing the transmission overhead compared to conventional FL. Numerical experiments show that the proposed framework exhibits higher spectral efficiency than the state-of-the-art SPIM-MIMO methods and mmWave-MIMO, which relies on the strongest propagation path. Furthermore, the proposed FL approach provides at least 10 times lower transmission overhead than the centralized learning techniques.

ARINC 664, which is a heavily used protocol for modern avionics networks, is preferred due to its simplicity although its mixed-criticality support is limited. Time Triggered Ethernet (TTEthernet), and IEEE Time Sensitive Networking (TSN), which utilize time synchronized schedule, are more suitable for supporting mixed-criticality applications; however, both require a fault tolerant time synchronization that makes the certification process more challenging. In this paper, we propose a novel dynamic priority assignment (DPA) concept together with the burst limiting shaper (BLS) from the IEEE TSN standard to enhance the schedulability and the mixed-criticality support of ARINC 664. The decision of flow re-assignment to a new priority class is done by calculating the high priority (HP) and low priority (LP) class worst-case delays using the network calculus framework. The numerical results show that the class utilization rates can be significantly increased by using the DPA concept with and without the BLS while the deadline constraints for all classes are satisfied. Thus, the DPA can improve the schedulability and mixed-criticality of ARINC 664 without using any time synchronization mechanism.

Cooperative communication using energy harvesting (EH) nodes promises significant improvement in network throughput, coverage, and reliability. This work studies a full-duplex (FD) wireless powered cooperative communication network (WPCCN), in which the users communicate to a hybrid access point (HAP) via decode-and-forward (DF) EH relay nodes. We present an optimization framework for the relay selection problem with an objective of maximizing the sum throughput of the FD-WPCCN, for the first time in the literature. The formulated optimization problem is a mixed integer non-linear programming problem (MINLP), which is difficult to solve for global optimal solution. As a solution strategy, we first analyze the problem for a given relay allocation and prove that it is a convex problem. We solve the problem for the optimal solution by using the Karush-Kuhn-Tucker conditions. Then, for the relay selection problem, we present a polynomial time heuristic algorithm based on the allocation of relay with the best channel conditions to each user. Through extensive simulations, we show that the proposed algorithm performs close to the optimal solution for various network parameters such as different HAP transmission power values, network sizes, and initial battery level of the nodes. We observe that the sum throughput of the network can be increased significantly by using a proper relay selection technique.

Vehicle systems currently utilize wired networks for power delivery and communication between nodes. Wired networks cannot practically accommodate nodes in moving parts and with the increasing functional complexity in vehicles, they require kilometer-long harnesses, significantly increasing fuel consumption, manufacturing, and design costs. Alternatively, energy harvesting intravehicular wireless sensor networks (IVWSNs) can accommodate nodes in all locations and they obviate the need for wiring, significantly lowering costs. This article empirically analyzes the feasibility of such an IVWSN framework via extensive in-vehicle measurements for communications at 2.4 GHz, ultrawideband (UWB), and millimeter-wave (mmWave) together with radio frequency (RF), thermal, and vibration energy harvesting. Our analyses indicate mmWave performs best for short Line-of-Sight (LoS) links in the engine compartment with performance close to UWB for LoS links in the chassis and passenger compartments considering worst case signal-to-interference-and-noise ratio (SINR). For non-LoS links, which appear mostly in the engine compartment and chassis, UWB provides the highest security and reliability. 2.4 GHz suffers heavily from interference in all compartments while UWB utilizes narrowband suppression techniques at the cost of lower bandwidth; mmWave inherently experiences very low interference due to its propagation characteristics. On the other hand, RF energy harvesting provides up to 1 mW of power in all compartments. Vibration and thermal energy harvesters can supply nodes consuming <; 10 mW in the engine compartment and <; 5 mW nodes in the chassis. In the passenger compartment, thermal harvesting is not available due to low temperature gradients, but vibration and RF sources can supply <; 1 mW nodes.

Communication system design for wireless networked control systems (WNCSs) requires strict timing, reliability and lifetime guarantees despite limited battery resources and the non-idealities introduced by wireless networking such as delays. In this paper, we introduce radio frequency (RF) energy harvesting paradigm into WNCS framework for the first time in the literature. We study the optimal power control, energy harvesting and scheduling problem with the objective of providing maximum level of adaptivity under periodicity, delay and reliability requirements. We show that the power allocation problem is separable from the scheduling problem at optimality and provide the exact expression for optimal power control. The scheduling problem is then formulated as a mixed integer linear programming (MILP) problem and proven to be NP-Hard. For the scheduling, we propose polynomial-time heuristic algorithms motivated by the analogy between scheduling sensor nodes with energy harvesting requirements over time units and jobs with sequence dependent setup times on identical machines. We prove the theoretical worst-case bound for the performance of these heuristics. We show via extensive simulations that the proposed algorithms perform close-to-optimal and significantly better than Earliest Deadline First (EDF) algorithm in terms of adaptivity, delay, reliability and average runtime.

In this paper, we consider a full duplex wireless powered communication network where multiple users with RF energy harvesting capabilities communicate to a hybrid energy and information access point. An optimization framework is proposed with the objective of maximizing the sum throughput of the users subject to energy causality and maximum transmit power constraints considering a realistic energy harvesting model incorporating initial battery levels of the users. The joint optimization of power control, time allocation and scheduling is mathematically formulated as a mixed integer non linear programming problem which is hard to solve for a global optimum. The optimal power and time allocation and scheduling decisions are investigated separately based on the optimality analysis on the optimization variables. Optimal power and time allocation problem is proven to be convex for a given transmission order. Based on the derived optimality conditions, we propose a fast polynomial-time complexity heuristic algorithm. We illustrate that the proposed algorithm performs very close-to-optimal while significantly outperforming an equal time allocation based scheduling scheme.

We consider a wireless powered, harvest-then-transmit communication network, which consists of multiple, single antenna, energy and information access points (APs) and multiple, single antenna users with energy harvesting capabilities and rechargeable batteries, and allows simultaneous information transmission. We formulate the joint power control and scheduling problem with the objective of minimizing the total schedule length, subject to the constraints on the minimum amount of data to be sent by the users to the APs, and the maximum transmit power for the information transmission. This problem is a nonlinear and non-convex, mixed integer programming problem for which there is no known polynomial time algorithm. The proposed heuristic algorithm is based on, first, finding the solution for a fixed energy harvesting time and then searching for the optimal energy harvesting time that minimizes the total schedule length. For the former, a scheduling problem is formulated as an integer programming problem, which we solve with Branch and Price based methods upon solving the power control problem separately. Simulation results demonstrate that the proposed algorithm outperforms previously proposed time minimization algorithms that do not consider simultaneous transmission scenarios up to 3.5% for larger AP power, 25.4% for tighter maximum transmit power limit, and 6.5% for greater number of users per AP.

Dynamic Spectrum Access (DSA) improves spectrum utilization by allowing secondary users (SUs) to opportunistically access temporary idle periods in the primary user (PU) channels. Previous studies on utility maximizing spectrum access strategies mostly require complete network state information, therefore, may not be practical. Model-free reinforcement learning (RL) based methods, such as Q-learning, on the other hand, are promising adaptive solutions that do not require complete network information. In this paper, we tackle this research dilemma and propose deep Q-learning originated spectrum access (DQLS) based decentralized and centralized channel selection methods for network utility maximization, namely DEcentralized Spectrum Allocation (DESA) and Centralized Spectrum Allocation (CSA), respectively. Actions that are generated through centralized deep Q-network (DQN) are utilized in CSA whereas the DESA adopts a non-cooperative approach in spectrum decisions. We use extensive simulations to investigate spectrum utilization of our proposed methods for varying primary and secondary network sizes. Our findings demonstrate that proposed schemes outperform model-based RL and traditional approaches, including slotted-Aloha and Whittle index policy, while % 87 of optimal channel access is achieved.

Radio frequency (RF) energy harvesting is key in attaining perpetual lifetime for time-critical wireless powered communication networks (WPCNs) due to full control on energy transfer, far field region, small and low-cost circuitry. In this paper, we propose a novel minimum length scheduling problem to determine the optimal power control, time allocation and schedule subject to data, energy causality and maximum transmit power constraints in a full-duplex WPCN. We first formulate the problem as a mixed integer non-linear programming problem and conjecture that the problem is NP-hard. As a solution strategy, we demonstrate that the power control and time allocation, and the scheduling problems can be solved separately in the optimal solution. For the power control and time allocation problem, we derive the optimal solution by evaluating Karush-Kuhn-Tucker conditions. For the scheduling, we introduce a penalty function allowing reformulation of the problem as a sum penalty minimization problem. Upon derivation of the optimality conditions based on the characteristics of the penalty function, we propose two polynomial-time heuristic algorithms and a reduced-complexity exact algorithm employing smart pruning techniques. Via extensive simulations, we illustrate that the proposed heuristic schemes outperform the schemes for predetermined transmission order of users and achieve close-to-optimal solutions.

Relay nodes are used to improve the throughput, delay and reliability performance of energy harvesting networks by assisting both energy and information transfer between sources and access point. Previous studies on radio frequency energy harvesting networks are limited to single-source-single/multiple-relay networks. In this paper, a novel joint relay selection, scheduling and power control problem for multiple-source-multiple-relay network is formulated with the objective of minimizing the total duration of wireless power and information transfer. The formulated problem is non-convex mixed-integer non-linear programming problem, and proven to be NP-hard. We first formulate a sub-problem on scheduling and power control for a given relay selection. We propose an efficient optimal algorithm based on a bi-level optimization over power transfer time allocation. Then, for optimal relay selection, we present optimal exponential-time Branch-and-Bound (BB) based algorithm where the nodes are pruned with problem specific lower and upper bounds. We also provide two BB-based heuristic approaches limiting the number of branches generated from a BB-node, and a relay criterion based lower complexity heuristic algorithm. The proposed algorithms are demonstrated to outperform conventional harvest-then-cooperate approaches with up to 87% lower schedule length for various network settings with at least 7.88 times higher algorithm runtime.

We consider a wireless powered, harvest-then-transmit communication network, which consists of a single antenna, energy and information access point (AP) and multiple, single antenna, batteryless users with energy harvesting capabilities. At the beginning of a time frame, the AP broadcasts energy in the downlink to the users. Then, users transmit their data to the AP in the uplink, using their harvested energy. We formulate the optimization problem with the objective of minimizing the total schedule length, subject to the constraints on the minimum amount of data to be sent to the AP, and unlike previous studies, the maximum transmit power for the information transmission. This problem is nonlinear and non-convex. The solution is based on bi-level optimization, consisting of optimizing the transmit power allocation of the nodes for a given energy harvesting time and searching over harvesting time allocation. We also propose a heuristic algorithm in which we incorporate the optimal solution of a single user network. Simulation results demonstrate that under appropriate network conditions, our proposed algorithms provide close-to-optimal results with a reasonable run time compared to a previously proposed time minimization algorithm that does not integrate the uplink power constraint.

Vehicle-to-vehicle (V2V) communication and positioning systems are expected to play an important role in the development of future automated and autonomous vehicle safety concepts. Visible light communication and positioning (VLC and VLP) promise high data rates and cm-level positioning accuracy, respectively, with vehicle head/tail lights. Existing methods for vehicular VLP often require multiple spatially-separated co-operating nodes with either tightly synchronized clocks or precisely known relative locations and they dictate certain modulation schemes or message content for the VLC subsystem. The proposed novel VLP method utilizes a single VLC receiver capable of measuring angle-of-arrival (AoA) on a receiving vehicle (RXV). The method dictates no modulation constraints on the VLC subsystem and no co-operation is required from the transmitting vehicle (TXV) other than disseminating its real-time speed and heading information via VLC. The method uses speed and heading data and two consecutive AoA samples from the same receiver to deduce 2D position of the TXV relative to the RXV with triangulation. Simulation results show the method performs cm-level positioning accuracy at >50Hz rates under realistic road and VLC channel conditions. With such performance, the proposed VLP method enables time-critical traffic safety applications like collision avoidance.

Cellular Vehicle to Everything (C - V2X) is the technology, which incorporates V2X communication and vehicular user (VUE) to network communication, and enables the system to select the communication interface, which can provide safe, reliable and energy efficient communication for VUEs. We study energy efficient VUE association problem, which aims to balance the power consumption of VUEs and cellular infrastructure with the help of the selection of the appropriate communication interface. The problem aims to minimize power consumption of the network for Uplink (UL) and Downlink (DL) decoupled UE association scheme, considers switching on/off the Small Base Stations (SBS)s and supports C-V2X link formation either over LTE-Uu conventional cellular radio interface and 5G or over Sidelink (SL), PC5 radio interface. We additionally incorporate realistic power consumption model for BSs, VUEs in UL and Fron-thaul (FH) links, centralized controller and Transmit (TX) and Receive (RX) operations of VUEs. Simulation results demonstrate that enabling the selection of communication interface provides decrease in power consumption of the network instead of forcing VUEs to form communication links over LTE-Uu conventional cellular radio interface for varying number of distance thresholds and VUEs.

Today’s vehicles are no longer stand-alone transportation means, due to the advancements on vehicle-tovehicle (V2V) and vehicle-to-infrastructure (V2I) communications enabled to access the Internet via recent technologies in mobile communications, including WiFi, Bluetooth, 4G, and even 5G networks. The Internet of vehicles was aimed toward sustainable developments in transportation by enhancing safety and efficiency. The sensor-enabled intelligent automation of vehicles’ mechanical operations enhances safety in on-road traveling, and cooperative traffic information sharing in vehicular networks improves traveling efficiency.

The dramatic growth of machine-to-machine (M2M) communication in cellular networks brings the challenge of satisfying the quality of service (QoS) requirements of a large number of M2M devices with limited radio resources. In this paper, we propose an optimization framework for the semi-persistent scheduling of M2M transmissions based on the exploitation of their periodicity with the goal of reducing the overhead of the signaling required for connection initiation and scheduling. The goal of the optimization problem is to minimize the number of frequency bands used by the M2M devices to allow fair resource allocation of newly joining M2M and human-to-human communications. The constraints of the problem are delay and periodicity requirements of the M2M devices. We first prove that the optimization problem is NP-hard and then propose a polynomial-time heuristic algorithm employing a fixed priority assignment according to the QoS characteristics of the devices. We show that this heuristic algorithm provides an asymptotic approximation ratio of 2.33 to the optimal solution for the case where the delay tolerances of the devices are equal to their periods. Through extensive simulations, we demonstrate that the proposed algorithm performs better than the existing algorithms in terms of frequency band usage and schedulability.

Emerging technologies enforce strict requirements on future wireless networks such as massive connectivity that cannot be supported with scheduled access. Contention based Non-Orthogonal Multiple Access is a novel technique to overcome strict massive connectivity requirements by efficient use of wireless resources. However, most of the solutions proposed in this direction assumes different loads which would degrade the performance significantly if they would not hold. To stress these assumptions a resource efficiency metric is defined and state of the art solutions are evaluated for varying load regarding this metric. It is shown that the resource efficiency problem in the state of the art can be improved with multiplicity estimation, and hence, we propose Multiplicity estimating Random Access protocol, that adapts to the dynamic loads. This adaptation is evaluated through analytical calculation against the state of the art and it is shown that resource efficiency against with a slight decrease in the metric any load from 1 up to > 103users is supported. In addition, we show how this protocol can be dimensioned and integrated to contention based NOMA.

In this paper, we propose a multiple-input multiple-output (MIMO) uplink transmission scheme for optical wireless communication applications. The transmission is based on optical single-carrier frequency domain equalization (SC-FDE) due to its low complexity where the signal is transmitted over infrared communication channels. Based on non-sequential ray tracing, we first obtained realistic infrared MIMO channel impulse responses including low-pass filter effect of infrared light-emitting-diodes. We then investigate the performance of bit-error-rate (BER) and peak to average power ratio (PAPR) with respect to different modulation orders using spatial multiplexing.

Estimation of the time-varying optical wireless channel response is crucial in order to decode received signals coherently. In this work, we investigate symbol-error-rate and mean absolute error performance of different interpolation techniques including linear, nearest, spline, and piece-wise cubic Hermite interpolating polynomial (pchip), which are used in pilot-aided channel estimation process for visible light communication. The performance of interpolators is evaluated in realistic time-varying channel model, generated on Zemax software and compared with each other under the consideration of different modulation orders, different pilot symbol periods, and different user equipment (UE) speeds through Monte Carlo simulations. The results reveal that spline and pchip techniques are more robust to low pilot symbol transmission rate and fast time-varying channel conditions as a consequence of high UE speeds. However, low complex linear interpolation technique can be chosen for highly rated pilot signal transmission cases or when optical channel varies slowly over the time.

Communication system design for wireless networked control systems requires satisfying the high reliability and strict delay constraints of control systems for guaranteed stability, with the limited battery resources of sensor nodes, despite the wireless networking induced non-idealities. These include non-zero packet error probability caused by the unreliability of wireless transmissions and non-zero delay resulting from packet transmission and shared wireless medium. In this paper, we study the joint optimization of control and communication systems incorporating their efficient abstractions practically used in real-world scenarios. The proposed framework allows including any non-decreasing function of the power consumption of the nodes as the objective, any modulation scheme and any scheduling algorithm. We first introduce an exact solution method based on the analysis of the optimality conditions and smart enumeration techniques. Then, we propose two polynomial-time heuristic algorithms based on intelligent search space reduction and smart searching techniques. Extensive simulations demonstrate that the proposed algorithms perform very close to optimal and much better than previous algorithms at much smaller runtime for various scenarios.

The automotive industry is under a major change and new vehicles are being enriched by the recent advances in communication. Not only business plans are changing due to connected and urbanized lifestyle, but also transportation is becoming more intelligent with smart roads that connect smart cars. Technology coined as the vehicular ad-hoc network (VANET) is harmonizing with Intelligent Transportation System (ITS) and Intelligent Traffic System (ITF). However, ITS and ITF systems suffer from the scarcity of radio frequency spectrum. Visible light communication (VLC) that uses modulated optical radiation in the visible light spectrum is an alternative medium being researched. To date, the majority of research on vehicular VLC was aimed at achieving high data rates provided that high lighting quality is achieved without any concern on dimmable LED lights. Auto-dimmable headlights gain attention due to danger caused by sudden glare on drivers at night conditions which makes dimming in VLC necessary. In this paper, we first present the latest concept of vehicular VLC on ITS and ITF systems and address dimming utility. We then demonstrate experimentally that dimming is a key parameter in VLC which affects data dissemination and received power signal strength.

Vehicular hotspots for on-board Internet access using Long Term Evolution (LTE) as the backhaul network has recently gained popularity. Currently, Wi-Fi is the most common technology to provide in-vehicle access, where data has been relayed through on board LTE receiver. Despite its wide acceptance, coexistence and contention based data rate limitations with Wi-Fi necessitates alternatives for in-vehicle data access schemes. This paper investigates the performance of hybrid LTE and visible light communication (VLC) networks using LTE as the backhaul and VLC as the on-board access network.Under the consideration of vehicle interior unique channel characteristics and light emitting diode (LED) deployment flexibility, best transmitter configuration using repetition coding (RC) and spatial multiplexing (SM) multiple input multiple output (MIMO)modes is determined. Proposed configurations based on direct current biased optical orthogonal frequency-division multiplexing(DCO-OFDM) are compared with respect to their bit-error-rate (BER) performances. Furthermore, the performance of intravehicular VLC networks for single and multi-user scenarios is investigated.

Visible light communication (VLC) has recently gained popularity as a complementary technology to radio frequency (RF) based alternatives for vehicular communications as a low-cost, secure and RF interference free technology. In this paper, we propose IEEE 802.15.7 standard-compliant physical layer (PHY) implementation and experimental evaluation, using commercial off-the-shelf (COTS) automotive light emitting diode (LED) fog light for the purpose of low-latency safety message dissemination. We first show that the standard is applicable to line of sight (LoS) vehicle-to-vehicle (V2V) VLC. We then demonstrate that the proper selection of modulation coding schemes (MCS) plays an important role in order to minimize bit-error- rate (BER) for the reliable transmission with varying inter-vehicle distances. We also addressed the angular limitations of COTS automotive LED light for viable vehicular VLC.

Designing communication system for wireless networked control systems requires overcoming the additional challenge of maintaining a guaranteed performance for the control system in the presence of wireless network induced imperfections including packet error, delay, sampling and quantization errors compared to traditional wireless sensor networks. The joint optimization of controller and communication systems encompassing efficient abstractions of each system and taking into account all wireless induced imperfections, the parameters of the wireless communication system including the transmission power, rate and scheduling and the parameters of the control system including the sampling period has been studied for the objective of minimizing the average power consumption of the network and the MQAM modulation scheme. In this paper, we extend the joint optimization problem for a generalized power cost function that represents many power-related objectives including minimization of total power consumption of the network and minimization of maximum power consumption among the nodes in the network and for any modulation scheme that satisfies certain properties including MQAM and MFSK. The optimization problem is formulated as a Mixed-Integer Programming problem thus difficult to solve for the global optimum. However, upon determining the optimality conditions for the optimization variables, the problem reduces to an Integer Programming problem for which we propose an optimal fast enumeration algorithm. Simulations demonstrate that the proposed optimal solution method outperforms the traditional separate design of control and communication systems.

Communication system design for wireless networked control systems (WNCSs) is very challenging since the strict timing and reliability requirements of control systems should be met by the wireless communication systems that introduce non-zero packet error probability and non-zero delay at all times. Particularly, the scheduling algorithms for WNCSs should be designed to provide maximum level of adaptivity accommodating packet losses and changes in network topology while exploiting periodic nature of the sensor node transmissions. Creating such a schedule has been previously studied for an Ultra Wide Band (UWB) based WNCS. In this paper, we extend the joint optimization problem of power control, rate adaptation and scheduling with the objective of providing maximum adaptivity for general WNCSs employing continuous rate transmission model in which Shannon's channel capacity formulation is used for the achievable transmission rate. Upon proving the NP-hardness of the problem, we provide a framework for the design of a heuristic algorithm for scheduling and propose an optimal polynomial time algorithm for the power control and rate adaptation problem following the derivation of the optimality conditions. We demonstrate via extensive simulations that the proposed algorithms outperform the existing algorithms with performance close to optimal solution and average runtime admissible for practical WNCSs.

Traditional approach to the minimum length scheduling problem ignores the packet level details of transmission protocols, meaning that a packet transmission can be divided into several data chunks each of which is transmitted at a different rate due to the difference in the set of concurrently transmitting nodes. This solution requires including packet headers for each data chunk resulting in both an increase in the system overhead and underutilization of the time slots. In this paper, we extend the previous works on minimum length scheduling by considering the transmission of the packets of arbitrary sizes in the time slots of arbitrary lengths. Given the packet traffic demands on the links, we formulate the joint optimization of the power control, rate adaptation and scheduling for minimizing the schedule length of a wireless ad hoc network and demonstrate the hardness of this problem. Upon solving the power control and rate adaptation problem separately, we formulate the scheduling problem as an integer programming (IP) problem where the number of variables is exponential in the number of the links. In order to solve this large-scale IP problem fast and efficiently, we propose Branch and Price Method and Column Generation Method based heuristic algorithms.

The communication protocol design for wireless networked control systems brings the additional challenge of providing the guaranteed stability of the closed-loop control system compared to traditional wireless sensor networks. In this paper, we provide a framework for the joint optimization of controller and communication systems encompassing efficient abstractions of both systems. The objective of the optimization problem is to minimize the power consumption of the communication system due to the limited lifetime of the battery-operated wireless nodes. The constraints of the problem are the schedulability and maximum transmit power restrictions of the communication system, and the reliability and delay requirements of the control system to guarantee its stability. The formulation comprises communication system parameters including transmission power, rate and scheduling, and control system parameters including sampling period. The resulting problem is a Mixed-Integer Programming problem. However, analyzing the optimality conditions on the variables of the problem allows us to reduce it to an Integer Programming problem for which we propose an efficient solution method based on its relaxation. Simulations demonstrate that the proposed method performs very close to optimal and much better than the traditional separate design of these systems.

Developing real-time safety and non-safety applications for vehicular ad hoc networks (VANET) requires understanding the dynamics of the network topology characteristics since these dynamics determine both the performance of routing protocols and the feasibility of an application over VANET. Using various key metrics of interest including node degree, number of clusters, link duration and link quality, we provide a realistic analysis of the VANET topology characteristics. In this analysis, we integrate real-world road topology and real-time data extracted from Freeway Performance Measurement System database into the microscopic mobility model in order to generate realistic traffic flows along the highway. Moreover, we use more realistic, recently proposed, obstacle-based channel model and compare the performance of this sophisticated model to the most commonly used more simplistic channel models including unit disc and log-normal shadowing model. Our investigation on the key system metrics reveal that largely used unit disc model fails to realistically model communication channel, while parameters of simplistic models like log normal can be adjusted to match the corresponding system metrics of more complex and hard to implement obstacle based model.

We study the optimal power control, rate adaptation and scheduling for energy minimization subject to delay, traffic demand, transmit power and SNIR constraints in UltraWideband wireless networks. We first show that power control is not required for delay constrained energy minimization. We then formulate optimal scheduling problem as an exponential size Linear Programming (LP) problem for which we propose the Pricing Minimization based Column Generation Method (PMCGM). PM-CGM decomposes the exponential size LP problem into two sub-problems Restricted Master Problem (RMP) and Pricing Problem (PP) and solves it iteratively. We solve the corresponding delay minimization problem for the initialization of the RMP and propose a pricing minimization based polynomial time algorithm to solve the non-linear integer PP formulation. Simulations illustrate that PM-CGM algorithm decreases the runtime required to solve the large scale LP problem considerably while performing very close-to-optimal for different network scenarios.

Clustering is an effective mechanism to handle the fast changes in the topology of vehicular ad hoc networks (VANET) by using local coordination. Constructing stable clusters by determining the vehicles sharing similar mobility pattern is essential in reducing the overhead of clustering algorithms. In this paper, we introduce VMaSC: Vehicular Multi-hop algorithm for Stable Clustering. VMaSC is a novel clustering technique based on choosing the node with the least mobility calculated as a function of the speed difference between neighboring nodes as the cluster head through multiple hops. Extensive simulation experiments performed using ns-3 with the vehicle mobility input from the Simulation of Urban Mobility (SUMO) demonstrate that novel metric used in the evaluation of the least mobile node and multi-hop clustering increases cluster head duration by 25% while decreasing the number of cluster head changes by 10%.

Most applications of wireless sensor networks require reliable and timely data communication with maximum possible network lifetime under low traffic regime. These requirements are very critical especially for the stability of wireless sensor and actuator networks. Designing a protocol that satisfies these requirements in a network consisting of sensor nodes with traffic pattern and location varying over time and space is a challenging task. We propose an adaptive optimal duty-cycle algorithm running on top of the IEEE 802.15.4 medium access control to minimize power consumption while meeting the reliability and delay requirements. Such a problem is complicated because simple and accurate models of the effects of the duty cycle on reliability, delay, and power consumption are not available. Moreover, the scarce computational resources of the devices and the lack of prior information about the topology make it impossible to compute the optimal parameters of the protocols. Based on an experimental implementation, we propose simple experimental models to expose the dependency of reliability, delay, and power consumption on the duty cycle at the node and validate it through extensive experiments. The coefficients of the experimental-based models can be easily computed on existing IEEE 802.15.4 hardware platforms by introducing a learning phase without any explicit information about data traffic, network topology, and medium access control parameters. The experimental-based model is then used to derive a distributed adaptive algorithm for minimizing the power consumption while meeting the reliability and delay requirements in the packet transmission. The algorithm is easily implementable on top of the IEEE 802.15.4 medium access control without any modifications of the protocol. An experimental implementation of the distributed adaptive algorithm on a test bed with off-the-shelf wireless sensor devices is presented. The experimental performance of the algorithms is compared to the existing solutions from the literature. The experimental results show that the experimental-based model is accurate and that the proposed adaptive algorithm attains the optimal value of the duty cycle, maximizing the lifetime of the network while meeting the reliability and delay constraints under both stationary and transient conditions. Specifically, even if the number of devices and their traffic configuration change sharply, the proposed adaptive algorithm allows the network to operate close to its optimal value. Furthermore, for Poisson arrivals, the duty-cycle protocol is modeled as a finite capacity queuing system in a star network. This simple analytical model provides insights into the performance metrics, including the reliability, average delay, and average power consumption of the duty-cycle protocol.

Intra-vehicular wireless sensor networks is a promising new research area that can provide part cost, assembly, maintenance savings and fuel efficiency through the elimination of the wires, and enable new sensor technologies to be integrated into vehicles, which would otherwise be impossible using wired means, such as Intelligent Tire. The most suitable technology that can meet high reliability, strict energy efficiency and robustness requirements of these sensors in such a harsh environment at short distance is Ultra-Wideband (UWB). However, there are currently no detailed models describing the UWB radio channel for intra-vehicular wireless sensor networks making it difficult to design a suitable communication system. We analyze the small-scale and large-scale statistics of the UWB channel based on a measurement campaign for a variety of sensor locations beneath the chassis of a vehicle. The analysis for large-scale statistics show that the characteristics of the channel around the tires is very different from the other parts under the chassis. The path loss exponents around the tires and under chassis are 4 and 2.2 respectively. The clustering phenomenon observed in the averaged power delay profile can be well-modeled by Saleh-Valenzuela model. The clusters decay exponentially with arrival time but with a smaller decay constant after 30ms. The decay rate of ray amplitudes is increasing with delay and can be modeled using a dual slope linear model in logarithmic scale. The best fit for inter-cluster arrival time is Weibull distribution. The analysis for small-scale statistics on the other hand show that the best fit for the received energies in each bin at 81 locations of the measurement grid is lognormal distribution with decreasing μ and almost constant σ parameters. Moreover, different bins of the delay can be assumed to fade independently. This is the first work to model small-scale channel characteristics for intra-vehicular wireless sensor networks.

Modeling the link quality is essential in achieving and maintaining stable communication and minimizing energy consumption by controlling packet transmissions across a wireless sensor network. The quality of the communication links is a function of many variables including location, distance, direction and time. In this paper, we investigate the statistical channel models for spatial and temporal characteristics of link quality in different environments. These investigations are based on three metrics: received signal strength indicator (RSSI), packet loss rate (PLR) and link quality index (LQI). Statistical models are offered for all three parameters for both indoor and outdoor cases. The best distributions modeling PLR, LQI and RSSI at a certain distance are exponential, Weibull and normal respectively. The best fit for the parameters of these distributions are the same functions but with different constants for indoor and outdoor environments. Moreover, the correlations in different directions have normal distributions for all three metrics with absolute means and variances less than 0.4. The variations of the link characteristics over time on the other hand depends on the average quality of the link which should be taken into account in the design of upper layers. The temporal correlation of a link is modeled by using a sinusoidal function the parameters of which depend on the quality of the link. This is the first work to perform a detailed quantification of time and space dependencies on the link quality by using statistical models.

Algorithms for scheduling TDMA transmissions in multi-hop networks usually determine the smallest length conflict-free assignment of slots in which each link or node is activated at least once. This is based on the assumption that there are many independent point-to-point flows in the network. In sensor networks however often data are transferred from the sensor nodes to a few central data collectors. The scheduling problem is therefore to determine the smallest length conflict-free assignment of slots during which the packets generated at each node reach their destination. The conflicting node transmissions are determined based on an interference graph, which may be different from connectivity graph due to the broadcast nature of wireless transmissions. We show that this problem is NP-complete. We first propose two centralized heuristic algorithms: one based on direct scheduling of the nodes or node-based scheduling, which is adapted from classical multi-hop scheduling algorithms for general ad hoc networks, and the other based on scheduling the levels in the routing tree before scheduling the nodes or level-based scheduling, which is a novel scheduling algorithm for many-to-one communication in sensor networks. The performance of these algorithms depends on the distribution of the nodes across the levels. We then propose a distributed algorithm based on the distributed coloring of the nodes, that increases the delay by a factor of 10–70 over centralized algorithms for 1000 nodes. We also obtain upper bound for these schedules as a function of the total number of packets generated in the network.

We present a novel approach for Medium Access Control (MAC) protocol design based on protocol engine. Current way of designing MAC protocols for a specific application is based on two steps: First the application specifications (such as network topology and packet generation rate), the requirements for energy consumption, delay and reliability, and the resource constraints from the underlying physical layer (such as energy consumption and data rate) are specified, and then the protocol that satisfies all these constraints is designed. Main drawback of this procedure is that we have to restart the design process for each possible application, which may be a waste of time and efforts. The goal of a MAC protocol engine is to provide a library of protocols together with their analysis such that for each new application the optimal protocol is chosen automatically among its library with optimal parameters. We illustrate the MAC engine idea by including an original analysis of IEEE 802.15.4 unslotted random access and Time Division Multiple Access (TDMA) protocols, and implementing these protocols in the software framework called SPINE, which runs on top of TinyOS and is designed for health care applications. Then we validate the analysis and demonstrate how the protocol engine chooses the optimal protocol under different application scenarios via an experimental implementation.

We consider the problem of deploying relay nodes to achieve connectivity with minimum cost in a sensor network of unknown radio propagation characteristics. For a network where a certain number of targets or sensing nodes have already been deployed in fixed and known positions, we aim at efficiently adding communication or relay nodes to guarantee connectivity with minimum cost, between any sensor node and a base station. The communication cost of a wireless link is defined as the expected number of retransmissions over that link and is modeled using an underlying Gaussian process (GP) between the nodes. We propose an iterative sensor deployment approach that learns the parameters of the underlying GP while deploying the additional nodes in the best positions possible at each step. Our deployment algorithm is more powerful with respect to the ones found in literature since: 1) we do not assume fixed communication range, i.e., we do not assume that nodes can perfectly communicate within a fixed range and will not communicate at all outside that range (this assumption is not realistic for the wireless channel); 2) we do not assume the existence of a pilot deployment aimed at learning the radio propagation characteristics because of the high cost of the deployment process and of the sensor nodes themselves.

Accurate analytical expressions of delay and packet reception probabilities, and energy consumption of duty-cycled wireless sensor networks with random medium access control (MAC) are instrumental for the efficient design and optimization of these resource-constrained networks. Given a clustered network topology with unslotted IEEE 802.15.4 and preamble sampling MAC, a novel approach to the modeling of the delay, reliability, and energy consumption is proposed. The challenging part in such a modeling is the random MAC and sleep policy of the receivers, which prevents to establish the exact time of data packet transmission. The analysis gives expressions as function of sleep time, listening time, traffic rate and MAC parameters. The analytical results are then used to optimize the duty cycle of the nodes and MAC protocol parameters. The approach provides a significant reduction of the energy consumption compared to existing solutions in the literature. Monte Carlo simulations by ns2 assess the validity of the analysis.

Active safety systems are based upon the accurate and fast estimation of the value of important dynamical variables such as forces, load transfer, actual tire-road friction (kinetic friction) muk, and maximum tire-road friction available (potential friction) mup. Measuring these parameters directly from tires offers the potential for improving significantly the performance of active safety systems. We present a distributed architecture for a data-acquisition system that is based on a number of complex intelligent sensorsinsidethetirethat form a wireless sensor network with coordination nodes placed on the body of the car. The design of this system has been extremely challenging due to the very limited available energy combined with strict application requirements for data rate, delay, size, weight, and reliability in a highly dynamical environment. Moreover, it required expertise in multiple engineering disciplines, including control-system design, signal processing, integrated-circuit design, communications, real-time software design, antenna design, energy scavenging, and system assembly.

We present a novel approach for minimizing the energy consumption of medium access control (MAC) protocols developed for duty-cycled wireless sensor networks (WSN) for the unslotted IEEE 802.15.4 standard while guaranteeing delay and reliability constraints. The main challenge in this optimization is the random access associated with the existing IEEE 802.15.4 hardware and MAC specification that prevents controlling the exact transmission time of the packets. Data traffic, network topology, MAC, and the key parameters of duty cycles (sleep and wake time) determine the amount of random access, which in turn determines delay, reliability and energy consumption. We formulate and solve an optimization problem where the objective function is the total energy consumption in transmit, receive, listen and sleep states, subject to constraints of delay and reliability of the packet delivery and the decision variables are the sleep and wake time of the receivers. The optimal solution can be easily implemented on existing IEEE 802.15.4 hardware platforms, by storing light look-up tables in the receiver nodes. Numerical results show that the protocol outperforms significantly existing solutions.

Monitoring its health by detecting its failed sensors is essential to the reliable functioning of any sensor network. This paper presents a distributed, online, sequential algorithm for detecting multiple faults in a sensor network. The algorithm works by detecting change points in the correlation statistics of neighboring sensors, requiring only neighbors to exchange information. The algorithm provides guarantees on detection delay and false alarm probability. This appears to be the first work to offer such guarantees for a multiple sensor network. Based on the performance guarantees, we compute a tradeoff between sensor node density, detection delay and energy consumption. We also address synchronization, finite storage and data quantization. We validate our approach with some example applications.

Advances in low-power and low-cost sensor networks have led to solutions mature enough for use in a broad range of applications, requiring various degrees of reliability. To facilitate this, a broad range of options are possible to tune reliability, throughput or energy cost in the IEEE 802.15.4 standard defining the medium access control (MAC) and physical layer for sensor networks. Knowing how to tune those knobs however requires detailed models of the protocol behavior under different conditions. In our earlier work, we have proposed a very accurate model for the slotted Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) access scheme of the IEEE 802.15.4 standard for the unacknowledged transmission mode. Because of the design of the 802.15.4 carrier sensing mechanism, modeling the performance of the network in case of acknowledged transmissions is not a trivial extension. In this paper, we hence derive such model and illustrate through simulations that it is extremely accurate. Next, using the model, guidelines are derived to optimize the energy or throughput performance of sensor networks using the IEEE 802.15.4 standard.

Advances in low-power and low-cost sensor networks have led to solutions mature enough for use in a broad range of applications varying from health monitoring to building surveillance. The development of those applications has been stimulated by the finalization of the IEEE 802.15.4 standard, which defines the medium access control (MAC) and physical layer for sensor networks. One of the MAC schemes proposed is slotted carrier sense multiple access with collision avoidance (CSMA/CA), and this paper analyzes whether this scheme meets the design constraints of those low-power and low-cost sensor networks. The paper provides a detailed analytical evaluation of its performance in a star topology network, for uplink and acknowledged uplink traffic. Both saturated and unsaturated periodic traffic scenarios are considered. The form of the analysis is similar to that of Bianchi for IEEE 802.11 DCF only in the use of a per user Markov model to capture the state of each user at each moment in time. The key assumptions to enable this important simplification and the coupling of the per user Markov models are however different, as a result of the very different designs of the 802.15.4 and 802.11 carrier sensing mechanisms. The performance predicted by the analytical model is very close to that obtained by simulation. Throughput and energy consumption analysis is then performed by using the model for a range of scenarios. Some design guidelines are derived to set the 802.15.4 parameters as function of the network requirements.

The IEEE 802.15.4 standard defines the medium access control (MAC) and physical layer for sensor networks. One of the MAC schemes proposed is slotted carrier sense multiple access with collision avoidance (CSMA/CA), and this paper analyzes whether this scheme meets the design constraints of low-power and low-cost sensor networks. The paper provides a detailed analytical evaluation of its performance in a star topology network for both saturated and unsaturated periodic traffic.The form of the analysis is similar to that of Bianchi for IEEE 802.11 DCF only in the use of a per user Markov model to capture the state of each user at each moment in time. The key assumptions to enable this important simplification and the coupling of the per user Markov models are however different, as a result of the very different designs of the 802.15.4 and 802.11 carrier sensing mechanisms. The performance predicted by the analytical model is very close to that obtained by simulation. Throughput and energy consumption analysis is then performed and design guidelines are derived.

Energy efficient system design in wireless sensor networks has been previously discussed at different levels of the network protocol stack so as to provide the maximum possible lifetime of a given network. This paper proposes a novel idea to save energy through extra re lay nodes by eliminating geometric deficiencies of the given topology. Given the sensing locations, the problem is to determine the optimal locations of relay nodes together with the optimal energy provided to them so that the network is alive during the desired lifetime with minimum total energy. We first formulate the problem as a nonlinear programming problem. We then propose an approximation algorithm based on restricting the locations where the relay nodes are allowed to a square lattice. This algorithm approximates the original problem with performance ratio of as low as 2 by trading complexity. For the parking lot application we consider, the relay nodes provide a significant decrease in the total energy required to achieve a specific lifetime.

We address the effect of the errors occurring at the analog-to-digital converter (ADC), from quantization noise, circuit noise, aperture uncertainty and comparator ambiguity, on the accuracy of sensor field reconstruction. We focus on the oversampling of bandlimited sensor fields in a distributed processing environment. It has previously been shown that pulse code modulation (PCM) style sampling fails to decrease the quantization error above some finite sampling rate. We show that the dither-based scheme, developed to decrease the quantization error, fails to decrease random errors associated with circuit noise, aperture uncertainty and comparator ambiguity. We propose an advanced dither-based sampling scheme with the goal of reducing both kinds of errors by increasing the density of the sensor nodes. It is based on distributing the task of improving the quantization error and random error among the nodes. The error of the scheme is shown to be O(1/r/sup 1/2 /) for oversampling rate r. The maximum energy consumption per node is O(log(r)). Finally, the bit rate of the scheme is O(1/r/sup 1/2 /log(r)) and it offers robustness to node failures in terms of a graceful degradation of reconstruction error.

In recent years, there has been a lot of interest in using wireless local area networks (WLAN) for voice communications with the expanded coverage of hot spots. This paper describes packet aggregation method that increases the throughput of WLAN for voice communication by decreasing the overhead of backoff at the beginning of each packet transmission. Aggregation allows AP to acquire the channel and send its packets inside a multicast packet or back-to-back. Implementation and analysis of frame aggregation show that it improves the performance of voice over IP (VoIP) operating on IEEE 802.11 considerably

We study the use of multi-cast probes to infer network internal loss behavior from losses observed in multi-cast receivers. First part of the paper analyzes the estimation problem based on the assumption that there is no temporal correlation between link losses for different probes. We have applied expectation-maximization (EM) algorithm to this problem. We compared the results of EM with a direct approach developed in R. Cacerer et al. (1999). The second part of the paper is based on the assumption of Markov temporal correlation between packet losses. We applied EM algorithm to this problem with E-step approximated by Gibbs sampling, completely factorized variational and structured variational approximation. We observed from simulation that the links closer to observed nodes give more accurate results and that structured variational approximation improves simple variational approximation results considerably

PEDAMACS is a Time Division Multiple Access (TDMA) scheme that extends the common single hop TDMA to a multihop sensor network, using a high-powered access point to synchronize the nodes and to schedule their transmissions and receptions. The protocol first enables the access point to gather topology (connectivity) information. A scheduling algorithm then determines when each node should transmit and receive data, and the access point announces the transmission schedule to the other nodes. The performance of PEDAMACS is compared to existing protocols based on simulations in TOSSIM, a simulation environment for TinyOS, the operating system for the Berkeley sensor nodes. For the traffic application we consider, the PEDAMACS network provides a lifetime of several years compared to several months and days based on random access schemes with and without sleep cycles, respectively, making sensor network technology economically viable.

We propose a power efficient system architecture that exploits the characteristics of sensor networks in order to decrease the power consumption in the network. The primary characteristic of sensor networks is that the destination of all the data packets in the network is a central data collector, which is usually denoted as access point (AP), has unlimited transmission power and energy whereas the sensor nodes have one battery energy to remain alive fro several years. Our system uses the AP to directly synchronize and explicitly schedule the nodes' transmissions over time division multiple access (TDMA) time slots. Simulations performed in TOSSIM, a simulation environment for the TinyOS, show that the battery lifetime of the network with this scheme can be increased to 1-2 years from 10 days that can be obtained from a general random access network.