Emerging industrial and massive Internet of Things (IoT) applications increasingly rely on private 5G New Radio (NR) deployments operating in the 3.5 GHz Citizens Broadband Radio Service (CBRS) band. General Authorized Access (GAA) provides a low-cost spectrum opportunity for IoT-specific NR networks, but the absence of interference protection makes uplink reliability highly vulnerable to co-channel interference (CCI), especially in dense IoT deployments. Ensuring dependable IoT connectivity requires interference-aware channel pattern allocation coordinated by the Spectrum Access System (SAS) while accounting for IoT traffic characteristics and gateway constraints. This paper presents a game-theoretic optimization framework for channel pattern allocation for IoT NR small cells operating under the CBRS GAA tier. We first formulate CCI minimization as a binary quadratic program (BQP) and show its nonconvex and NP-hard nature. To address this complexity, we develop two complementary solutions: (i) a cooperative hedonic coalition formation algorithm that enables SAS-assisted clustering of IoT NR gateways, and (ii) a distributed potential game approach where IoT gateways autonomously select channel patterns using log-linear learning. Numerical evaluations under realistic IoT deployment and propagation models demonstrate significant gains in packet delivery ratio (PDR), reduced energy consumption of IoT devices through fewer retransmissions, and improved spectrum reliability compared to existing CBRS coexistence mechanisms. The proposed framework is shown to be robust under asymmetric interference conditions and achieves more equitable per-user reliability than existing coexistence mechanisms. The proposed approaches provide practical and scalable solutions for interference-aware private IoT NR networks in shared mid-band spectrum.

This paper studies the performance improvement of a multi-user downlink system via polarization design at the transmitter such that the gains at the desired locations are maximized. To this end, a weighted sum rate maximization problem is formulated, constrained under total power, polarization norm, and a minimum signal-to-interference-and-noise ratio guarantee to the users for simultaneous transmission (ST), and its performance is compared with time-shared transmission (TT). As a solution strategy, weighted minimum mean square error based and fair resource allocation algorithms have been proposed to optimize ST and TT respectively with only long-term channel statistics as well as with channel state information (CSI) at the transmitter. Weighted sum rate performance is theoretically studied for long-term channel statistics. Branch-and-bound approach and the duality gap are considered to validate the optimal resource allocation with CSI for small-scale and large scale scenarios, respectively. Simulation results show that the proposed joint phase and polarization beamforming consistently enhances the multiaccess performance with respect to only phase beamforming, with average gain of about 23% and 6% with CSI and 89% and 4% with long-term channel statistics for ST and TT, respectively. We also study the performance of the proposed beamforming strategies with imperfect CSI and non-orthogonal multiple access-based ST to capture practical scenarios.

In 6G era, Satellite-Ground (S-G) free-space optical (FSO) links will be critical in achieving global coverage and high data rate communication. Key 6G applications like quantum key distribution and distributed computing will depend heavily on the reliability of these S-G FSO connections. However, atmospheric turbulence remains a significant challenge. This paper introduces a novel Optimal Aerial Nodes Placement (OAP) framework to enhance the reliability of S-G FSO links with an objective of turbulence aware path length balancing. We explore three deployment configurations, namely, serial, parallel, and OAP. We evaluate two cooperation protocols, amplify-and-forward (AF) and decode-and-forward (DF). Comparative analysis reveals that the proposed OAP framework performs better under DF mode compared to AF and achieves a 20 dB performance improvement over traditional parallel transmission and a 6 dB improvement over serial transmission in DF mode, thus demonstrating the effectiveness of OAP in boosting FSO link reliability.

ALOHA can be a viable solution as a light-weight medium access control (MAC) protocol in low power wide area networks (LPWANs) for Internet of Things (IoT). However, the maximum throughput of traditional ALOHA is too low to accommodate a large number of IoT devices. To address this limitation, this work proposes an enhanced ALOHA, where successive interference cancellation (SIC) aids in collision resolution. In the proposed system, each user measures the time interval from their transmission epoch to the end of a collision using a collision timer. Upon a collision, the access point (AP) with SIC and the users’ collision timer work jointly to resolve the collision. This work characterizes the throughput of the proposed system and further proposes an online backoff algorithm to maximize the throughput. Numerical results demonstrate that the proposed ALOHA with SIC-aided collision resolution (SACR) can offer significantly improved throughput compared to slotted ALOHA and the other systems.

Grid connected and solar powered base stations (BSs) acting as distributed energy sources are increasingly becoming a popular solution to mobile operators. These networks experience double stochasticity due to the space-time variations in energy harvest and BS traffic. Hence, accurate and efficient green energy outage estimation in such networks is a challenging task. In this work, we propose a complutationally efficient cooperative energy transfer based distributed energy bank strategy to alleviate green energy outage and design energy sustainable networks. We first develop low-complexity Markovian frameworks to estimate green energy outage in a standalone BS without energy cooperation (WEC) and a multi-BS energy-cooperative (EC) setting, respectively. For the WEC system, we present a computationally efficient three-state discrete time Markovian statistical model, while the multi-BS EC framework is characterized by a two-state Markov model. The energy outage is studied as a function of capital expenditure (CAPEX), manifesting engineering insights from a service provider's perspective. Subsequently for the EC framework, we formulate a CAPEX optimization problem by jointly optimizing the BS cluster size and solar provisioning on individual BSs. Our results demonstrate that the proposed EC framework alleviates the green energy outage significantly, providing computational efficiency gains and CAPEX savings over the state-of-art approaches.

Realizing energy sustainability is a key theme in sixth generation communications. Along with the emphasis on energy efficiency, operator revenue has emerged as a crucial aspect to make the networks scalable. In this paper, we propose a high altitude platform station (HAPS) aided and power grid connected green communication framework. To design green network, the proposed framework aims to offload excess users with the solar powered terrestrial macro base station (tMBS) to the HAPS mounted MBS (hMBS) in the event of high traffic or low energy harvest. The solar powered tMBS utilizes the power grid connectivity purely for energy selling rather than energy procurement. The inherent communication and energy networks in the proposed system are studied and modeled jointly as a six state discrete time Markov chain. The paper also provides analytical bounds on the solar provisioning required at the hMBS for radio access network functions. The proposed framework is compared with a without offloading and grid energy procurement based competitive state of art, in terms of network quality of service (QoS) and annual operator profit. Our simulation based performance studies demonstrate that the proposed framework under limited hMBS offloading capability offers gains compared to the competitive state of the art, up to 21% enhanced network QoS and 64% increased operator profit.

Collision-free trajectory planning of UAVs is a challenging task. In this paper, we address the UAV collision avoidance problem in the presence of wind, which cannot be ignored in practical aerial-aided communication infrastructure deployment scenarios. In order to avoid a collision with an obstacle, the UAV needs to decide to make a turn from a sufficient distance at an appropriate angle, and this phenomenon is characterized by the two parameters termed as turning radius and semiapex angle. First, the effect of wind speed on the UAV movement is modeled, which is used to estimate the turning radius and semiapex angle in the presence of wind. The wind data is random in nature, so the collision avoidance parameters are also random. To this end, a discrete-time Markov chain model is used to analyze the collision avoidance parameters in a statistical sense. Finally, the probability of collision avoidance is estimated. For performance evaluation, the wind data of the last four years with five minutes resolution from three cities of the USA situated at diverse geographical locations is used. Through system simulations, the severity of collision performance in the presence of random wind is captured. Further, as a measure of collision avoidance, the turning radius and simplex angle are estimated as a function of wind velocity.

In the post-fifth generation (5G) era, escalating user quality of service (QoS) strains terrestrial network capacity, especially in urban areas with dynamic traffic distributions. This paper introduces a novel cooperative unmanned aerial vehicle relay-based deployment (CUD) framework in satellite air-ground integrated networks (SAGIN). The CUD strategy deploys an unmanned aerial vehicle-based relay (UAVr) in an amplify-and-forward (AF) mode to enhance user QoS when terrestrial base stations fall short of network capacity. By combining low earth orbit (LEO) satellite and UAVr signals using cooperative diversity, the CUD framework enhances the signal to noise ratio (SNR) at the user. Comparative evaluations against existing frameworks reveal performance improvements, demonstrating the effectiveness of the CUD framework in addressing the evolving demands of next-generation networks.

Intelligent spectrum sharing is one of the key enablers of upcoming sixth generation (6G) communications. Unmanned aerial vehicles (UAVs) have emerged as an attractive low altitude aerial base station (BS), providing on demand capacity especially in urban areas. This work aims to demonstrate the feasibility of coexisting UAV to UAV communication based adhoc network over digital television (DTV) bands governed through latest ATSC 3.0 standards. We propose an adaptive modulation and dynamic subcarrier (AMDS) allocation framework to intelligently allocate the resources at the UAV network through adaptive bit loading and frequency allocations. The work aims to maximize the capacity of the coexisting UAV network in addition to protecting the performance of the TV-receiver from the resultant coexisting network interference. A rate maximization problem is formulated and solved using a low computation complexity based bi-section method. Extensive simulation results indicate that the connected UAV link can achieve up to 40 Mbps capacity when 1 km apart, while coexisting and guaranteeing the performance of the DTV network.

This paper characterizes the impact of polarization on sustainable wireless power transfer (WPT) to the mobile Internet-of-Thing (IoT) devices, focusing on various transmitter and receiver antenna polarization states. It investigates both line-of-sight (LOS) and controlled non-LOS (NLOS) reflections via reconfigurable intelligent surface (RIS), deriving closed-form expressions for received power to identify optimal antenna polarization. Theoretical findings are experimentally validated in an anechoic chamber, showing strong agreement with a normalized root mean square error ≤ 0.08. The research also assesses the necessity of RIS's polarization handling, concluding its non-essentiality in single source-receiver setups. Simulations reveal that horizontal polarization boosts received power by ≤ 28% compared to other polarizations. Furthermore, incorporating RIS in green communication significantly enhances received power.

Renewable power provisioning of the base stations (BS) in addition to the traditional power grid connectivity presents an interesting prospect towards realizing green future network services. Designing such dual-powered systems is challenging due to the presence of space-time varying stochasticity in traffic and green energy harvest at each BS. These traffic and green energy imbalances result in non-optimal network green energy utilization and thus resulting in a higher grid energy purchase to the mobile operator. In this paper, we present a novel coverage adjustment and sharing of energy (CASE) framework that exploits the user traffic load and green energy availability imbalances across the networked BSs towards maximizing the operator profit and designing energy sustainable system. The profit maximization problem is formulated considering the networked BSs to have the flexibility of load aware coverage adjustment and green energy sharing capability among themselves, in addition to trading energy with the grid. The proposed CASE framework first leverages the spatio-temporal traffic and energy inhomogeneities and performs load management for maximizing user quality of service (QoS). The CASE strategy then distributes the residual energy imbalance across the BSs and maximizes the utilization of temporal green energy harvest across the BSs. The proposed strategy is compared with only coverage adjustment, only sharing of energy, and a benchmark without CASE based framework. Our simulation results indicate significant improvement in user QoS and operator profit, up to 18% and 39% respectively at high skewness scenario, in addition to fully utilizing the green energy potential in the network.

The field-deployed Internet-of-Things (IoT) sensor nodes are powered by rechargeable batteries. The nodes are equipped with energy harvesters to harvest energy from the environment to replenish the batteries and continue the sensing operations. However, due to the high energy consumption of the power-hungry sensors, the nodes still suffer from energy depletion issues. Towards a green IoT system, an energy aware adaptive sensing algorithm is proposed in this paper. For a multi-sensing node, a learning-aided smart sensing strategy is developed to find a set of optimal sensors to be activated in the next measurement cycle depending on the cross-correlation factors and the sensing energy consumption. The parameters of inactive sensors are predicted from cross-correlated parameters of active sensors using Gaussian process regressor model. Further, the algorithm is implemented in a solar powered air pollution monitoring system to analyze the performance of this method. The proposed method saves 68% energy of the node compared to the nearest competitive method, while the sensing error is within the limit.

The utility of unmanned aerial vehicle (UAV)-based data collection for massive sensor networks has received great attention. However, limited onboard battery capacity poses a constraint on its operation. In this paper, we model data collection of backscatter nodes using UAVs that are supported by wireless energy transfer from LASER based charging stations (LCS) for sustainable operations. Under the assumption that UAV to backscatter node channel experiences Nakagami-$m$fading, we derive the expression for signal-to-noise ratio (SNR). We define expressions for overall backscatter energy outage probability and overall backscatter SNR outage probability as the metrics of sustainable operations. Our numerical simulations reveal that, at optimal transmit power up to around 55% of UAV operations are reliable and completely sustainable via LCS.

Distributed beamforming is a promising technique for transferring power to a node wirelessly, as it allows optimal design of waveforms at each transmitter such that they interact constructively at the receiver node. In this paper, we propose a distributed RF beamforming scheme which involves phase detection and prediction for the sources participating in beamforming process. The proposed method is capable of tracking source drifts and statistical fluctuations of the channel. The phase detection process requires one feedback for synchronizing all the sources to the receiver pairs and provides a closed-form solution for the phase offsets between a pair of received beams under the presence of RF power measurement errors. Autoregressive (AR) model is used for phase prediction which avoid feedback in synchronization process all together. Performance of the proposed detection and prediction model is quantified in terms of average received power, efficiency, and number of message exchange between the sources and the receiver.

Owing to large spectrum availability, millimeter wave (mmWave) communications are being proposed for 5th generation and beyond networks. However, the mmWaves are easily obstructed by objects, resulting in complete link blockage, which is more common in indoor communication. In this study, we propose to use the existing infrastructure of passive backscatter devices to establish links between the access point and a blocked legacy mobile user, with uncertain coordinates, in an indoor mmWave communication system. We set up backscatter devices in retro-reflective mode for the user to estimate the direction of arrival from them. To estimate the angles, we propose an estimator and derive its Cramer-Rao lower bound. Furthermore, while accounting for the antenna array configuration at both the user and the backscatter devices, we maximize the rate support at the user by optimizing the backscatter device reflection coefficient and the user's steering angle. The simulation results show that, by exploiting the backscatter devices already present in the environment with a sufficient number of antenna elements at the user, higher capacity is achieved as compared to that achieved by using a fixed re-configurable intelligent surface.

The accuracy of localization using global positioning system (GPS) data plays a key role in reliable positioning and control of unmanned aerial vehicles (UAVs). This paper proposes a novel statistically-aided earning-based localization approach, called filtered neural network (FNN) for high-precision localization of UAVs. The proposed FNN framework utilizes an entropy adaptive Kalman filter to fine-tune the inputs to a recurrent neural network, which works in a loop with the filter to generate subsequent robust position estimates. The proposed framework outperforms the state-of-the-art techniques with an nRMSE of ≈ 10−6, ≈ 97% reduced estimation delay, ≈ 73% reduced modeling time, ≤ 100 lag samples for FNN training, and only 4-6 overall model retraining instances per flight trajectory. The results are verified over a wide range of mean GPS noise power.

The need for digital multimedia has surged with advanced and affordable mobile devices. Offering multimedia via free space optical (FSO) systems is possible but doing so requires a lot of energy. Also, for optical wireless communication systems, signal strength degradation brought on by atmospheric turbulence is an unavoidable drawback. In this paper we present an energy-efficient turbulence-regime based adaptive FSO broadcast scheme, that optimally combines the turbulence-regime information at the receiver with a particular multimedia service (e.g., video quality profile) for adaptive scalable multimedia encoding of the broadcast content. The strength of turbulence at the receiver is obtained a priori based on rytov parameter at that location. Based on this information, in order to optimally use the base station power reserve the transmitter may decide to send turbulence adapted multimedia content quality namely, a fair signal strength instead of good/excellent for weak turbulence, good signal strength instead of excellent for moderate turbulence and excellent signal strength for strong turbulence . By incorporating the turbulence information in the multimedia encoding, the proposed scheme helps to achieve a better quality of experience and higher energy saving both in terms of base station power reserve and user equipment energy saving as compared to the pure quality maximizing scheme or fixed power allocation scheme. The simulation results also show that the churn rate is minimum in the proposed scheme.

To improve the throughput per channel in multichannel nonorthogonal multiple access (NOMA) random access (RA) system, users (re)transmit their packet to one of the channels using transmit power control such that the receive power of the packets at the base station (BS) can be one of the predefined levels called target receive power (TRP). The BS decodes the received packets in the descending order of the TRPs at each slot using successive interference cancellation (SIC). This work proposes the real-time transmission algorithm for users to (re)transmit their packet for maximization of the system throughput. To do this, the BS estimates the number of backlogged users in real time and adjusts and broadcasts the throughput-optimal (re)transmission probability in the algorithm. We analyze the average RA delay performance of the proposed algorithm and demonstrate its performance even with time-varying traffic.

Wireless communication in power grid environment faces peculiar power system environment specific noise. Owing to its impulsive nature, the characterization of such noise is critically important and fundamentally different from the existing analysis. This paper presents a theoretical analysis of the impact of power system electromagnetic impulse noise in smart grid environment on the performance of wireless communication between the grid health monitoring devices (e.g., Phasor Measurement Units) and the local data concentrator. It uses a classical approach to the derivation of a re-parameterized impulse noise model from its characteristic function. A comparison of outage probability between the corona impulse noise (CIN)-ridden and noise-free channel is studied. The study is extended for comparison with co-channel interference (CCI) for establishing a better understanding of the power specific electromagnetic impulse noise. A modified performance metric called signal-to-pulse-noise ratio is defined and its probability distribution is derived and compared with the additive white Gaussian noise, CIN and CCI-ridden channel statistics. The physical layer wireless channel characterization analysis is extended further to study the average system throughput via Markov modeling of the channel.

This paper proposes a novel machine learning-based multivariate real-time data pruning and prediction framework for smart PMU (phasor measurement unit) communication. In an Internet-of-Things (IoT) enabled smart grid monitoring application, the proposed data-driven pruning technique exploits cross- and auto-correlation in multiple attributes sensed by a PMU (IoT node). The attributes are classified into base and nonbase groups based on their ability to aid prediction of the remaining attributes. The idea of transmitting only base attributes reduces the data dimensionality significantly. A reconstruction algorithm is designed for the edge node (local Phasor Data Concentrator) for efficient data reconstruction. The performance of the proposed framework is evaluated on large-scale real-time data from the PMUs. Comparison of the proposed technique with the closest state-of-the-art multi-threaded uni-variate data pruning algorithm in literature demonstrates around 40% more bandwidth saving and ∼42% reduction in retraining count.

Achieving self-sustainability has been one of the key challenges in designing smart grid connected residential systems. Solar enabled and power grid connected, dual-powered residential systems is an attractive solution, but it is not carbon free and cost optimal for the end user. This paper proposes temporal energy cooperation among the dual-powered residences as a potential cost and energy efficient solution. Through this paper, we present a microgrid based, multi-residence cooperative energy transfer mechanism to offset the power grid dependency. The developed analytical framework characterizes the green energy storage as a discrete time Markov model and aims to exploit the temporal residential load variations, towards designing self-sustainable systems at a much lower capital expenditure (CAPEX). Our simulation results capture the variation of optimum residence cluster size as a function of energy sharing price and load skewness to become cost profitable. The results also demonstrate a significant reduction in CAPEX, achieved through the proposed energy cooperative framework, over a non-cooperative residential system.

One of the pivotal services of the fifth generation (5G) of cellular technology is massive Machine-type Communications (mMTCs), which is intended to support connecting high density of machine-type devices (MTDs). Random access channel of long-term evolution (LTE)/LTE advanced needs to be modified in order to support the large simultaneous arrival of MTDs. 3GPP suggested access class barring (ACB) as a mechanism to inhibit network congestion in the mMTC or massive Internet of Things (IoT) scenario. Consequently, in devices that are repeatedly ignored by ACB, the queue of data packets keeps growing. In storage-constrained IoT nodes with limited buffer, this may lead to packet drop due to buffer overflow, causing a decline in the overall throughput of the system. To address this issue, a novel queue-aware prioritized access classification (QPAC)-based ACB technique is proposed in this article, where MTDs having data queue size close to its buffer limit are dynamically given higher priority in ACB. To study the queue build-up at each MTC device, a node-centric analysis of ACB in the buffer-constrained scenario is performed using a 2-D Markov chain. It is shown that the proposed QPAC scheme, with optimal model parameters obtained by maximizing overall system utility, offers up to 70% gain in throughput compared to the nearest competitive dynamic ACB scheme.

In this article, an energy-efficient temporal sensing framework is proposed for a wireless sensor node (SN) monitoring a temporal process. A concept of Age of Sample (AoS) is introduced, and expressions for the AoS and average AoS functions are derived that capture freshness of sensed samples (or intersample time). To incorporate the effect of process variations, weighted AoS (WAoS) and its average functions are developed. The framework solves a multiobjective optimization problem (MOP) that optimizes this average WAoS function and energy efficiency of the SN to select a few temporal sensing instances of a sensing window while maintaining a predefined sensing quality. An upper bound on the average WAoS is derived, which makes the MOP solvable. Using these few measurements, the process signal across entire sensing window is estimated by leveraging inherent temporal correlation. The idea of adapting sensing window according to the changing correlation of the process is also presented, which addresses the nonstationary aspect of the monitored process. Simulation studies on real data sets illustrate that on comparison with the closest existing scheme, the proposed scheme provides 30.1% gain in sensing quality while consuming nearly same sensing energy. Furthermore, it consumes 22.4% lesser sensing energy to maintain nearly same sensing quality.

Designing solar-enabled and power grid connected, ‘dual-powered’, cellular networks is challenging due to the double stochasticity arising from energy harvest and user traffic, resulting in spatio-temporally varying traffic-energy imbalances. Improper strategy to optimize the power grid connectivity results in generation of significant carbon footprint. In this paper, we present an analytical framework to mathematically capture the traffic-energy imbalances in such a dual-powered network and propose to improve the temporal network energy utilization by exploiting these imbalances through a cooperative energy sharing mechanism among the base stations (BSs), via the grid infrastructure itself. The cooperative communication system is designed and optimized independently from two perspectives, namely, grid energy procurement and carbon emission minimization (in carbon free ‘energy producer’ mode) and operator revenue maximization (in ‘energy prosumer’ mode). The energy producer mode involves the BSs, without the flexibility to procure energy and acting as distributed energy source to the power grid. The energy prosumer mode provides additional flexibility of grid energy procurement to the BSs in addition to energy sharing and selling. For a given capital expenditure (CAPEX), both the optimization problems are reformulated into convex quadratic problems and closed form expressions for the optimal quanta of energies to be shared/procured through/from the grid are obtained. The optimal CAPEX for the proposed modes of network operation are obtained via linear revenue maximization problem formulation. The results demonstrate that the proposed cooperative energy framework significantly improves the temporal network energy utilization, thereby reducing the grid energy procurement and providing significant revenue gains compared to the state-of-art.

Hovering inaccuracy of unmanned aerial vehicle (UAV) degrades the performance of UAV-aided radio frequency energy transfer (RFET). Such inaccuracy arises due to positioning error and rotational motion of UAV, which lead to localization mismatch (LM) and orientation mismatch (OM). In this paper, antenna array beam steering based UAV hovering inaccuracy mitigation strategy is presented. The antenna beam does not accurately point towards the field sensor node due to rotational motion of the UAV along with pitch, roll, and yaw, which leads to deviation in the elevation angle. An analytical framework is developed to model this deviation, and its variation is estimated using the data collected through an experimental setup. Closed-form expressions of received power at the field node are obtained for the four cases arising from LM and OM. An optimization problem to estimate the optimal system parameters (transmit power, UAV hovering altitude, and antenna steering parameter) is formulated. The problem is proven to be nonconvex. Therefore, an algorithm is proposed to solve this problem. Simulation results demonstrate that the proposed framework significantly mitigates the hovering inaccuracy; compared to reported state-of-the-art the same performance can be achieved with substantially less transmit power.

The existing literature on cellular multi-user mmWave communication focus on joint baseband and RF precoding designs to enable spatial multiple access with minimum interference. These studies either assume the number of users$M$is less than the number of RF units$N_{RF}$or schedule the users in time domain by dedicating one RF unit to each user if$M >N_{RF}$. It is expected that serving multiple users over OFDMA in each analog beam will offer better utilization of the wideband channel. To this end, for the scenario with$M \gg N_{RF}$we propose a sectored-cell model that is supported by multi-RF chains over the wideband mmWave channel, with each beam serving multiple users within a sector and the sectors being scheduled in round-robin fashion. We also propose a variable time frame structure that conducts sector-wise initial access, with simultaneous access to all users within a sector. It provides improved spectrum efficiency and decreased initial access delay as compared to the initial access using exhaustive beam search method. We then jointly estimate the optimum beamwidth and optimum$N_{RF}$that offer maximum average long-run user rate. Further, we introduce a reduced-complexity sector sojourn time optimization for non-homogeneously distributed users, that improves fairness of long-run user rates leveraging the variable time frame structure. The numerical results show that, while a high value of$N_{RF}$causes more interference to peak data rate, the average long-run user rate improves. Additionally, using a very narrow beam is also not optimal for providing maximum rate support. The proposed beamforming method offers a higher average long-run user rate over the competitive beamforming schemes while the complexity of user scheduling is independent of$M$.

Solar enabled and grid connected “dual-powered” base stations (BSs) have developed as a cost effective solution to network operators. While these networks prevent energy outages and are effective in providing seamless operation for catering to the user quality of service (QoS), they still rely significantly on the power-grid, generating carbon footprint. In this paper, we propose a profitable energy sharing based cooperative framework to reduce the grid energy consumption and to facilitate better utilization of network energy. Traffic-energy imbalances in a dual-powered network often result in some BSs being energy deficient due to spatio-temporal variation of traffic. In such an event, it is proposed that the energy deficient BS, through the proposed energy cooperation (EC) framework, interact with the other networked BSs and requests them to share the deficient energy or a portion of it at a much lower price than the grid price via grid itself. We compare the proposed EC model with a without energy cooperation (WEC) model, where the BSs do not interact with one another and release energy above their storage capacity only to the power-grid for selling. Our results demonstrate that for a two BS network, the EC model provides a significant reduction in grid consumption up to 50% with a 38% gain in operator's revenue at 80% traffic skewness.

Wireless sensors networks (WSNs) are gaining enormous attention for monitoring physical conditions in various application. WSNs equipped with power-hungry senors often suffer from energy sustainability. Hence, an efficient smart sensing approach is required to enhance the energy sustainability of such WSNs. A wireless node equipped with a sensor monitoring the variation of a particular parameter in time often exhibits high temporal correlation that can be studied to smartly sense the parameter. To optimize the energy consumption of these sensors and increase the network lifetime, this paper presents a learning- based adaptive sampling framework that explores the sparsity in the time series data and finds optimal sampling instants for the next measurement cycle. Principal component analysis (PCA) is used to sparsify the time domain signal and the sparse signal is reconstructed from its low-dimensional signal using the sparse Bayesian learning (SBL) method. An optimization function is formed that solves the trade-off between accuracy and energy consumption and finds the optimal sampling instants for the next measurement cycle. The performance of the proposed adaptive sampling framework is tested on air pollution monitoring dataset. The simulation results validate the energy efficiency of the proposed method. Compared to the existing adaptive sampling algorithms the proposed learning-based algorithm saves up to 58% energy with a marginally higher computational complexity while maintaining an acceptable range of sensing error.

Unmanned aerial vehicle (UAV)-enabled cellular architecture over the millimeter-wave (mmWave) frequency band is likely to be the best solution for on-demand high data rate service provisioning in the next generation communication networks. The beam formed by the mmWave antenna array is highly directional and requires multiple-beam scans to cover the entire area. This work presents a novel sectoring approach to ensure coverage of the whole area. The side lobe gain of the antenna array is taken into consideration, which generates substantial interference in other sectors. The expression for the probability distribution of the signal-to-interference-plus-noise ratio due to simultaneous transmissions in different sectors is derived in the downlink communication scenario. To limit interference in the concurrent transmission strategy, a threshold on power spillage from adjacent sectors is placed. For this topology, a resource allocation problem is formulated aiming to maximize the sum rate while ensuring a minimum rate guarantee to each user. It is observed that sum-rate variation with height is unimodal. The sum power and backhaul capacity constraints are accounted. This optimization problem is mixed-integer nonconvex programming. Hence, it is solved using the Lagrangian dual decomposition method, which provides an asymptotic global optimal solution. Since this method is computationally intensive, a suboptimal solution is proposed. Simulation results demonstrate convergence to an optimal solution, and it is observed that backhaul link capacity restricts the sum rate. Numerical results are presented for multiple representative field environments consisting of different types of built-up areas. It is observed that the transmitter antenna array sidelobe has a strong impact on the performance as compared to the ideal scenario without sidelobe, which overestimates the total sum rate by a factor of 3.

IoT-driven smart societies are modern service-networked ecosystems, whose proper functioning is hugely based on the success of supply chain relationships. Robust security is still a big challenge in such ecosystems, catalyzed primarily by naive cyber-security practices (e.g., setting default IoT device passwords) on behalf of the ecosystem managers, i.e., users and organizations. This has recently led to some catastrophic malware-driven DDoS and ransomware attacks (e.g., the Mirai and WannaCry attacks). Consequently, markets for commercial third-party cyber-risk management (CRM) services (e.g., cyber-insurance) are steadily but sluggishly gaining traction with the rapid increase of IoT deployment in society, and provides a channel for ecosystem managers to transfer residual cyber-risk post attack events. Current empirical studies have shown that such residual cyber-risks affecting smart societies are often heavy-tailed in nature and exhibit tail dependencies. This is both, a major concern for a profit-minded CRM firm that might normally need to cover multiple such dependent cyber-risks from different sectors (e.g., manufacturing and energy) in a service-networked ecosystem, and a good intuition behind the sluggish market growth of CRM products. In this article, we provide: 1) a rigorous general theory to elicit conditions on (tail-dependent) heavy-tailed cyber-risk distributions under which a risk management firm might find it (non)sustainable to provide aggregate cyber-risk coverage services for smart societies and 2) a real-data-driven numerical study to validate claims made in theory assuming boundedly rational cyber-risk managers, alongside providing ideas to boost markets that aggregate dependent cyber-risks with heavy-tails. To the best of our knowledge, this is the only complete general theory till date on the feasibility of aggregate CRM.

As authors of our recently accepted article:Aggregate Cyber-Risk Management in the IoT Age: Cautionary Statistics for (Re)Insurers and Likes, published in the IEEE IoT Journal, we regret that we have found a few errors in the numerical evaluation setup of the works in[1]and[2]that we had borrowed for our accepted paper. In this correction statement, we describe the errors in detail, correct it, and present our revised results with a renewed experimental setup, hoping it to replace the existing incorrect numerical results in the accepted paper. We apologize for the inconvenience caused to the reader. We emphasize that the numerical evaluation section does not in any way hamper the theoretical contributions in this article, and was initially only meant to provide some empirical evidence for whether the theory proposed in this article generalizes to behavioral settings introduced in[2].

In this paper, performance of unmanned aerial vehicle (UAV)-aided RF energy transfer (RFET) in presence of hovering inaccuracy is investigated. Hovering inaccuracy of UAV comprises of two types of mismatches: Localization mismatch (LM) and Orientation mismatch (OM). Thus, a total of four combinations arise. Their impact on received power at ground deployed sensor node is characterized. For this purpose, a generalized radiation pattern of UAV-mounted transmitter antenna is considered. A closed-form expression of received power at the sensor node is obtained for each of these four cases. An optimization problem is formulated with the objective of optimizing the system parameters, such as transmit power, hovering altitude, and antenna exponent. This problem contains mixed nature of variables, i.e., continuous as well as discrete. To solve this problem, an algorithm, called Hovering Inaccuracy-aware Optimal Charging System Design (HI-OCSD), is proposed to find the optimal system parameters. Through system simulations it is demonstrated that, hovering inaccuracy has notable impact on the performance, as received power at the sensor node reduces significantly in presence of hovering inaccuracy compared to ideal scenario. The effect of LM is more severe than that of OM. Further, a scenario with different level of hovering inaccuracy accounting for different deployment scenarios is considered, and the optimal system parameters are also evaluated. This study reveals that, UAV needs to hover at a relatively higher altitude to overcome the severity of hovering inaccuracy.

With increasing density of data-hungry devices per unit area, allocating single highly-directed beam per user in millimeter-wave communications is not practical. Therefore, the requirement is to serve multiple users over a single beam. Considering a single-cell scenario with a fixed number of users, this paper addresses the problem of selection of optimal beamwidth depending on user density and distribution. First, by considering fixed beam service time in each sector, optimal beamwidth is estimated using exhaustive search for average long-run user rate and base station energy efficiency maximization. Based on the results of the average long-run user rate maximization using an exhaustive search, another method of reduced complexity is proposed to find sub-optimal beamwidth. Subsequently, optimum beamwidth is estimated with user density dependent variable time scheduling in a sector, that offers improved performances over fixed time scheduling. An efficient algorithm on variable time scheduling is also provided. Finally, the effect of localization error on optimal beamwidth estimation is investigated. The numerical results show that using the narrowest beam does not necessarily result in achieving a better average long-run user rate. Further, localization error does not affect the selection of optimal beamwidth, however, user Quality-of-Service degrades.

Recent advances in electric metering infrastructure have given rise to the generation of gigantic chunks of data. Transmission of all of these data certainly poses a significant challenge in bandwidth and storage constrained Internet of Things (IoT), where smart meters act as sensors. In this work, a novel multivariate data compression scheme is proposed for smart metering IoT. The proposed algorithm exploits the cross correlation between different variables sensed by smart meters to reduce the dimension of data. Subsequently, sparsity in each of the decorrelated streams is utilized for temporal compression. To examine the quality of compression, the multivariate data is characterized using multivariate normal-autoregressive integrated moving average modeling before compression as well as after reconstruction of the compressed data. Our performance studies indicate that compared to the state-of-the-art, the proposed technique is able to achieve impressive bandwidth saving for transmission of data over communication network without compromising faithful reconstruction of data at the receiver. The proposed algorithm is tested in a real smart metering setup and its time complexity is also analyzed.

This paper addresses the problem of decentralized sensor selection in an energy-constrained wireless sensor network-based Internet-of-Things, for monitoring a spatio-temporally varying process. To do so, an adaptive edge computing framework and its variants are proposed which distributedly optimize a critical trade-off between sensing quality and remaining energy of the sensor nodes (SNs). Unlike the existing distributed sensing approaches, the proposed one aims to maintain energy balance among the SNs. The original sensor selection problem is decoupled into multiple sub-problems, each solvable at an edge node elected as head of a coverage region containing a set of SNs. The sub-problem in each coverage region is adapted to variations of the underlying process. In each region, the process is estimated using PCA-SBL (principal component analysis-sparse Bayesian learning) on noisy signal measured by the respective active SNs. Further, to correctly adapt to the process and estimate the signal, a novel logic is designed that indicates requirement of network retraining in the next measurement cycle. The results from extensive simulation studies illustrate improved energy efficiency and network energy balance of the proposed framework over the existing closest competitive centralized and decentralized approaches. The proposed framework is tested on synthetic as well as real data-sets of a sensor network.

Solar-powered and power grid connected green cellular networks are becoming attractive due to low carbon footprint and cost-effectiveness in providing uninterrupted service. In this paper, we analyze the performance of such dual-powered multi-cell network in presence of skewed traffic load across the different base stations (BSs). Cell coverage is decided at the network design stage based on long-term average traffic intensity across the various regions of a multi-cell coverage area. In presence of dynamically-changing skewness of traffic loads across different cells, we propose to adjust the cell coverage to accommodate the traffic and energy availability imbalance in the cells, while the demand for residual energy deficiency for serving the customers is fulfilled through the power grid connectivity. Network service provider's cost with the proposed coverage adjustment based strategy is compared with that of the conventional approach where the individual BSs do not undergo any cell coverage adjustment and seek to provide the maximum network performance. Our analysis and simulation-based performance results demonstrate, that the network performance as well as monetary gains of the service provider are significantly higher with our proposed strategy. For example, at a moderate (30%) traffic skewness, the proposed strategy offers about 4% gain in operator's annual profit, while serving about 8% more users on average at the peak hour. At a very high (80%) skewness, these numbers are respectively about 50% and 39%.

This article presents a new paradigm for channel dynamics adaptive transmission of intermittent data in smart grid IoT communication networks, wherein novel channel prediction frameworks using stochastic modeling as well as data-driven learning of channel variability are proposed. A probing-based transmission is also proposed as a benchmark. These prediction frameworks are complemented with an adaptive channel coding scheme to increase the transmission reliability of time-critical grid monitoring data over a wireless channel. Through analyzing the prediction and packet loss performance at varying SNR and fading conditions, it is noted that the stochastic modeling framework is efficient when the fading correlation in the channel is high while the learning-based approach is more adaptive to channel dynamics as the correlation reduces. The proposed frameworks are easily implementable on low-cost end nodes, owing to the optimal selection of parameters for low runtime complexity. When compared to probing-based data transmission for a given fading in the channel, the packet loss probability of the learning-based transmission closely matches while with stochastic model loss probability is found to be 12.3% higher. However, their respective signaling overheads are 38% and 98% lower with respect to the probing-based approach, which is a significant gain at the cost of marginally additional computation complexity.

Solar-enabled systems are becoming popular for provisioning pollution-free and cost-effective energy solution. Dimensioning of a solar-enabled system requires estimation of appropriate size of photovoltaic (PV) panel as well as storage capacity while satisfying a given energy outage constraint. Dimensioning has strong impact on the user's quality of experience and network operator's interest in terms of energy outage and revenue. In this paper, dimensioning problem of solar-enabled communication nodes is analyzed in order to reduce the computation overhead, where stand-alone solar-enabled base station (SS-BS) is considered as a case study. For this purpose, hourly solar data of last 10 years has been taken into consideration for analysis. First, the power consumption model of BS is revised to save energy and increase revenue. Using the hourly solar data and power consumption profile, the lower bounds on panel size and storage capacity are obtained using the Gaussian mixture model, which provides a reduced search space for cost-optimal system dimensioning. Then, the cost function and energy outage probability are modeled as functions of panel size and number of battery units using curve fitting technique. The cost function is proven to be quasiconvex, whereas energy outage probability is proven to be convex function of panel size and number of battery units. These properties transform the cost-optimal dimensioning problem into a convex optimization framework, which ensures a global optimal solution. Finally, a Computationally-efficient Energy outage aware Cost-optimal Dimensioning Algorithm (CECoDA) is proposed to estimate the system dimension without requiring exhaustive search. The proposed framework is tested and validated on solar data of several cities; for illustration purpose, four cities, New Delhi, Itanagar, Las Vegas, and Kansas, located at diverse geographical regions, are considered. It is demonstrated that, the presented optimization framework determines the system dimension accurately, while reducing the computational overhead up to 94 percent and the associated energy requirement for computation with respect to the exhaustive search method used in the existing approaches. The proposed framework CECoDA takes advantage of the location-dependent unique solar profile, thereby achieving cost-efficient solar-enabled system design in significantly less time.

This paper proposes a novel multi-sensing framework for an energy harvesting heterogeneous wireless sensor network. This framework has utility in smart environment application of Internet-of-Things. It considers a network of multiple wireless nodes, each having heterogeneous sensors to sense slowly- varying environment parameters. For monitoring each parameter, the developed strategy adaptively selects the corresponding sensors of a few neighboring nodes for activation, which jointly optimizes the sensing accuracy and energy efficiency. This process effectively activates a subset of sensors at each node. The subset of selected field nodes as well as the activated sensors at each node in the field is dynamically updated, as a function of harvested energy availability and sensing quality. Simulation results demonstrate the energy efficiency and sensing quality of the proposed framework.

This paper studies transmission performance in a coexistence scenario when a secondary communication network is deployed in co-channel mode within TV broadcast bands. Keeping in view the differences in timing and spectral characteristics between the primary and secondary transmissions, subcarrier-level interference at the primary is computed in presence of time-and-frequency overlapped secondary transmission. This estimation is validated experimentally as well as via simulations. Specifically, the possibility of coexistent secondary transmission near the primary broadcast receivers is experimentally evaluated in an emulated DVB-T2 transmission environment. The analytical and experimental studies demonstrate that the effects of interference at the primary receiver are within tolerable range even when the secondary transmitter transmits at high power with appropriately chosen time-frequency occupancy overlaps. The benefits are further enhanced when the agility of frequency and temporal overlaps are combined with power control in the secondary network.

To move towards low-latency wireless communication while taking care of elastic traffic, proper resource allocation is very important to provide acceptable quality of service (QoS) to all users. This work solve two optimization problems which aim at utility-proportional fairness maximization by optimizing power and bandwidth (BW) allocation among heterogeneous users having real-time and elastic traffic. Noting that both problems are non-convex, local optimal solutions in terms of BW and power are found by alternating optimization algorithm. Numerical results validate analysis, represent schedulability and average utility of real-time traffic user with respect to available resources, and compare utility fairness performance of both schemes.

In this paper a cross-layer adaptive scheme is presented for transmission of H.264 encoded video over wireless channels, under a specified delay constraint. Adaptive modulation and coding (AMC) is considered at the physical layer, accompanied by type-I hybrid automatic repeat request (HARQ) at the link layer. The HARQ scheme is cognizant of the video characteristics. It prioritizes the important independent (I) frames at the cost of dependent (P) frames, maintaining the group of pictures (GOP) deadline boundaries. At the application layer, quantization parameter (QP) variation is carried out to control the rate and quality requirements. This multi-layer adaptive scheme is modelled analytically as a multivariate nested Markov chain (MC). The proposed approach takes into account both fading and shadowing effects of the wireless channel. Numerical results highlight performance benefits of the proposed model. Realistic simulation results indicate improved performance of the proposed approach over similar multi-layer adaptation schemes in terms of video quality metrics.

State‐of‐the‐art variants of slotted Aloha (S‐Aloha) for underwater acoustic networks do not consider the joint effect of retransmission and distance‐dependent propagation delay variability, which is one of the most important parameters that affect the performance of any medium access control protocol. In this study, the authors analyse the stability of S‐Aloha in a network environment, such as underwater, where propagation delay and its variance are not negligible. A finite number of unbuffered transmitters are considered. Through mathematical analysis, supported by discrete event simulations, they compare the utilisation and stability with S‐Aloha in radio frequency environments where propagation delay, as well as delay variance, are negligible. The comparison results indicate that the stability region is appreciably shifted and also there is a significant decrease of utilisation in the presence of retransmission and distance‐dependent propagation delay variability.

This paper presents an innovative multi-faceted architecture, called UE-TV, for user-centric, revenue aware, and energy-efficient TV broadcast. Scalable high-efficiency video coded high definition television (HDTV) content is broadcast over LTE multicast/broadcast single frequency network (MBSFN) along with the available Wi-Fi access points (APs). The proposed framework adaptively encodes the TV content and allocates radio resource based on current network. Stackelberg two-stage game theoretic approach discerns an optimal transmit power at the LTE base stations (eNodeBs) and the proportion of subscribers that are respectively served by eNodeBs and available Wi-Fi APs. Varied user equipment (UE) resolutions, users' energy/price sensitivities, and channel conditions govern the service options and user satisfaction. Our analysis and simulations show that, in comparison with the broadcast schemes over MBSFN without or with adaptive video coding, UE-TV framework significantly enhances the user satisfaction via optimized price/quality trade-off as well as energy saving at the eNodeBs and UEs.

One of the main goals of 5G networks is to support the technological and business needs of various industries (the so-called verticals), which wish to offer to their customers a wide range of services characterized by diverse performance requirements. In this context, a critical challenge lies in mapping in an automated manner the requirements of verticals into decisions concerning the network infrastructure, including VNF placement, resource assignment, and traffic routing. In this paper, we seek to make such decisions jointly, accounting for their mutual interaction, efficiently. To this end, we formulate a queuing-based model and use it at the network orchestrator to optimally match the vertical's requirements to the available system resources. We then propose a fast and efficient solution strategy, called MaxZ, which allows us to reduce the solution complexity. Our performance evaluation, carried out an accounting for multiple scenarios representing the real-world services, shows that MaxZ performs substantially better than the state-of-the-art alternatives and consistently close to the optimum.

In this paper, we consider a Multiple Input Single Output (MISO) multicasting Internet of Things (IoT) system comprising of a multiantenna Transmitter (TX) that simultaneously transfers information and power to low power and data hungry IoT devices. Each IoT device is assumed to be equipped with Power Splitting (PS) hardware that enables Energy Harvesting (EH) and imposes an individual Quality of Service (QoS) constraint to the downlink communication. We study the joint design of TX precoding and IoT PS ratios for the considered MISO Simultaneous Wireless Information and Power Transfer (SWIPT) multicasting IoT with the objective of maximizing the minimum harvested energy among IoT, while satisfying their individual QoS requirements. In our novel EH fairness maximization formulation, we adopt a generic Radio Frequency (RF) EH model capturing practical rectification operation, and resulting in a nonconvex optimization problem. For this problem, we first present an equivalent Semi- Definite Relaxation (SDR) for the considered design problem and prove that it possesses unique global optimality. Then, capitalizing on our derived tight upper and lower bounds on the optimal solution, we present an efficient algorithmic implementation for the jointly optimal TX precoding and IoT PS ratio parameters. Insights on the optimal TX precoding structure are also presented. Representative numerical results including comparisons with benchmark schemes corroborate the usefulness of the proposed design and provide useful insights on the interplay of critical system parameters on the optimized power vs achievable rate trade off.

Limited battery capacity is one of the major hurdles towards perpetual operation of wireless sensor networks (WSNs). In this work, a framework for Unmanned Aerial Vehicle (UAV) based wireless charging of sensor nodes using radio frequency energy transfer (RFET) is presented. First, RFET zone is conceptualized where energy transfer is possible such that the received power is above a sensitivity level of power harvester. Next, two novel strategies Static Charging Time Allocation (SCTA) and Optimal Charging Time Allocation (OCTA) are proposed for charging the sensors. The UAV remains static in SCTA, whereas in OCTA it hovers above each sensor node and replenishes depleted energy of the sensors that are within its RFET zone. Further, a model for evaluating the energy consumption of UAV is presented in order to determine the number of rounds required by the UAV for charging. Different gas sensors are planted emulating a practical deployment scenario, and with this arrangement charging time with the proposed strategies are evaluated. Our numerical results justify applicability of the UAV-assisted RFET.

Thanks to network slicing, 5G networks will support a variety of services in a flexible and swift manner. In this context, we seek to make high-quality, joint optimal decisions concerning the placement of VNFs across the physical hosts for realizing the services, and the allocation of CPU resources in VNFs sharing a host. To this end, we present a queuing-based system model, accounting for all the entities involved in 5G networks. Then, we propose a fast and efficient solution strategy yielding near-optimal decisions. We evaluate our approach in multiple scenarios that well represent real-world services, and find it to consistently outperform state-of-the-art alternatives and closely match the optimum.

In low power wide area (LPWA) networks, low-cost end devices (EDs) such as sensors communicate with a central base station (BS) using pure ALOHA protocol. In particular, half-duplex is considered wherein the BS switches from receiving to transmitting mode after receiving a packet successfully from an ED in order to send the acknowledgement. Since the EDs can not know at the moment when the BS turns to the receiving mode, the packets transmitted by the EDs during the receiving mode of the BS are lost. To investigate performance of such half-duplex pure ALOHA systems, this paper develops a semi-Markov (SM) process model for pure ALOHA channel with infinite population. Furthermore, the SM process is approximated with an M/M/1 system in order to analyze the system with finite population. As results, this paper presents how the performance of half-duplex system is influenced by switching time, backoff interval of EDs, and stability criteria of the system, which are verified through system simulations.

Wireless sensor networks (WSNs) often have limited battery capacity-sensor nodes (SNs), which severely limit continuous monitoring-based Internet-of-Things applications. To substantially increase the lifetime of a densely deployed WSN for monitoring spatio-temporally varying signal, this paper presents a novel sparse Bayesian learning-based adaptive sensor selection framework. The developed strategy selects an active sensor set and turns off the remaining SNs by jointly optimizing two conflicting performance measures: 1) sensing quality and 2) energy efficiency, while considering prevailing energy parameters of the network. To achieve this, a multiobjective optimization problem is formulated. Further, a joint principal component analysis-sparse Bayesian learning (PCA-SBL) scheme is presented which uses PCA-based estimated transformation matrix to sparsify the data sensed by sensors, and subsequently uses approximate overcomplete dictionary-based SBL scheme to estimate it. Employing PCA-SBL-based signal estimate, a closed loop adaptive mechanism is developed which estimates variations of the monitored signal to predict the number of active sensors for next measurement cycle such that the sensing error remains within an acceptable range. This predicted value is then used in sensor selection problem to dynamically select the active set. The sensor selection, signal recovery, and feedback loop use spatial and temporal correlation inherent in the monitored phenomenon. Extensive simulation studies validate the energy efficiency and stable sensing performance of the proposed framework using both synthetic and real data of a WSN.

Self-sustainability of wireless nodes in Internet-of-Things applications can be realized with the help of controlled radio frequency energy transfer (RF-ET). However, due to significant energy loss in wireless dissipation, there is a need for novel schemes to improve the end-to-end RF-ET efficiency. In this paper, first we propose a new channel model for accurately characterizing the harvested dc power at the receiver. This model incorporates the effects of nonline of sight (NLOS) component along with the other factors, such as radiation pattern of transmit and receive antennas, losses associated with different polarization of transmitting field, and efficiency of power harvester circuit. Accuracy of the model is verified via experimental studies in an anechoic chamber (a controlled environment). Supported by experiments in controlled environment, we also formulate an optimization problem by accounting for the effect of NLOS component to maximize the RF-ET efficiency, which cannot be captured by the Friis formula. To solve this nonconvex problem, we present a computationally efficient golden section-based iterative algorithm. Finally, through extensive RF-ET measurements in different practical field environments we obtain the statistical parameters for Rician fading as well as path loss factor associated with shadow fading model, which also asserts the fact that Rayleigh fading is not well suited for RF-ET due to presence of a strong line of sight component.

Enabling technologies for energy sustainable Internet of Things (IoT) are of paramount importance since the proliferation of high data rate demands of low power network devices. In this paper, we consider a multiple input single output (MISO) multicasting system comprising of a multiantenna transmitter (TX) simultaneously transferring information and power to data hungry IoT nodes. Each IoT device is assumed to be equipped with power splitting (PS) hardware that enables energy harvesting (EH) and imposes an individual quality of service (QoS) constraint to the downlink communication. We study the joint design of TX precoding and IoT PS ratios for the considered MISO simultaneous wireless information and power transfer multicasting system with the objective of maximizing the minimum harvested energy among IoT, while satisfying their individual QoS requirements. In our novel EH fairness maximization formulation, we adopt a generic EH model capturing practical rectification operation, and resulting in a nonconvex optimization problem. For this problem, we first present an equivalent semi-definite relaxation formulation and then prove it possesses unique global optimality. We also derive tight upper and lower bounds on the globally optimal solution that are exploited in obtaining low complexity algorithmic implementations for the targeted joint design. Analytical expressions for the optimal TX beamforming directions, power allocation, and PS ratios are also presented. Representative numerical results including comparisons with benchmark designs corroborate the utility of proposed framework and provide useful insights on the interplay of key system parameters.

Energy efficiency plays a critical role in widespread deployability of Internet of Things (IoT) applications. While there have been some prior studies on channel-adaptive communication strategies, these approaches do not fully exploit the channel knowledge on increasing energy efficiency in link-layer communications. In this paper, we consider point-to-point IoT communication over wireless channel. We propose a generalized channel-aware link-layer communication strategy that exploits temporal variations of the communication channel to improve on energy efficiency. Specifically, we propose a generalized channel dependent communication strategy that just depends on rate of variation of the channel and not on its underlying fading distribution. We utilize this generalized information on channel state variation in dynamically deciding on the transmission and waiting windows in link-layer transmission, which gives significant benefits in terms of system throughput as well as energy efficiency by optimally using the good channel states and avoiding redundant transmission of acknowledgements. Our analytical claims and numerical results are verified through extensive simulations. Our numerical results further demonstrate that, the proposed dynamic window protocol offers a significant gain of about 40% in terms of data throughput and about 41% in terms of energy efficiency in comparison to its nearest competitive existing benchmark scheme.

This paper presents a novel data driven framework based on $\epsilon$ -Support Vector Regression to reduce the bandwidth requirement for transmission of phasor measurement unit (PMU) data. This is achieved by judicious elimination of redundant data at the PMU before transmission. Simultaneously, the missing samples are predicted at PDC to ensure faithful identification of impending disturbances in the power system. Due to inherent nonstationary nature of PMU data, the hyperparameters are dynamically recomputed as necessary, thereby maintaining the accuracy of prediction and robustness of the algorithm. Performance of the proposed algorithm is evaluated via large scale simulations using powerline frequency data. A trade-off between prediction quality and runtime of the algorithm is observed, which is addressed by suitable selection of hyperparameters. Compared to the competitive data reduction scheme, the proposed algorithm saves around 60% bandwidth and identifies power system disturbances 73% more accurately.

In this paper, a novel characterization of smart meter data based on Gaussian mixture (GM) model is presented. It is shown that compared to the existing characterization models, the proposed GM model provides a significantly better fit for smart meter data. Furthermore, at each smart meter, sparsity of data is exploited to devise an adaptive data reduction algorithm using compressive sampling technique such that the bandwidth requirement for smart meter data transmission is reduced with minimum loss of information. When compared to the closest competitive scheme, the proposed compressive sampling based data reduction algorithm is found to be noise robust and offers 12.8% and 7.4% higher bandwidth saving, respectively, at 1 s and 30 s sampling intervals for comparable reconstruction accuracy. Proposed scheme is tested in real-time using RT-LAB.

Doubly-near-far problem in RF-powered networks can be mitigated by choosing appropriate device-to-device (D2D) communication mode and implementing energy-efficient information transfer (IT). In this work, we present a novel RF energy harvesting architecture where each transmitting-receiving user pair is allocated a disjoint channel for its communication which is fully powered by downlink energy transfer (ET) from hybrid access point (HAP). Considering that each user pair can select either D2D or cellular mode of communication, we propose an optimized transmission protocol controlled by the HAP that involves harvested energy-aware jointly optimal mode selection (MS) and time allocation (TA) for ET and IT to maximize the sum-throughput. Jointly global optimal solutions are derived by efficiently resolving the combinatorial issue with the help of optimal MS strategy for a given TA for ET. Closed-form expressions for the optimal TA in D2D and cellular modes are also derived to gain further analytical insights. Numerical results show that the joint optimal MS and TA, which significantly outperforms the benchmark schemes in terms of achievable RF-powered sum-throughput, is closely followed by the optimal TA scheme for D2D users. In fact, about 2/3 fraction of the total user pairs prefer to follow the D2D mode for efficient RF-powered IT.

Performance of RF powered communication network is bottlenecked by short downlink energy transfer range and doubly-near-far problem faced in uplink information transfer to Hybrid Access Point (HAP). Theses problems can be resolved by cooperation of an RF energy harvesting node R present between HAP and RF energy harvesting information source S. However, there lies a dilemma at R on whether to transfer its harvested energy to S or to act as an information relay for transferring its data to HAP in a two-hop fashion. This paper resolves this dilemma of R by proposing a novel integrated information and energy relaying (i2ER) protocol. It also provides insights on the relay positions suited for either energy relaying (ER), or information relaying (IR), or where neither ER nor IR will be useful. In this regard, while considering Rician fading channels, we derive the expression for mean harvested dc power at S via energy transfer from HAP and ER from R. We also derive the closed-form outage probability expression for decode-and-forward relaying with maximal-ratio-combining at HAP over Rician channels. Finally numerical results validating the analytical results and providing insights on the relaying role (ER and/or IR) of RF harvesting helping node are presented.

Air pollution poses disproportionate health hazard in developing countries, due to juxtaposition of industrial units and residences. While excessive emission is routinely detected, enforcement of emission norms remains rare due to present technological limitations in pinpointing sources. To fill this gap, we propose a method of source localization and emission rate estimation via AERMOD-based simulation under mean squared error criterion. Further, we validate the method using field data collected from an industrial cluster in India.

In this paper, we propose an opportunistic scheduling algorithm for secondary users (SUs) in a dynamic spectrum access network. Scheduling is performed at the beginning of each super-frame where appropriate channel(s) are assigned to the SUs based on the expected throughput that each channel provides. We compute the expected throughput for each channel on specific time slots in a super-frame based on three factors: i) primary channel occupancy, ii) signal-to-interference-and-noise ratio, and iii) back-logged queue length of the SUs. We consider scheduling both on a frame-by-frame basis and also on a slot-by-slot basis. The state transitions are modeled using a discrete-time Markov chain (DTMC) process and their probabilities are calculated. The state transitions occur at the frame/slot boundaries. Multiple SUs are allocated the same channel on the same time slot as long as there is no interference between them. Such non-interfering sets of SUs is found by considering the expected throughput of each user on all candidate channels. In order to maximize spatial and temporal reuse of channels, the non-interfering sets are assigned to as many channels as possible on maximum possible time slots. Performance of the proposed scheduling schemes is validated using simulations where we measure metrics such as throughput, number of slots allocated, fairness, and blocking probability. We also show the efficacy of the queue-aware scheduling by comparing with one that does not consider the queues.

Solar-enabled cellular base stations are getting significant attention because they avoid greenhouse gas emission as well as easily available. Dimensioning of base station is a very important issue where the photo-voltaic (PV) panel size and storage capacity are determined to operate the system for a long time with minimum energy black out. In this work we look into energy outage aware system cost as well as utility of solar-enabled base stations. Hourly harvested energy and traffic dependent hourly consumed energy are considered for analysis. First, the quantized profile of harvested and consumed energy are obtained. Subsequently, the lower bound on PV panel and storage capacity is determined which provides dimensioning information a priori and at reduced complexity. To calculate the outage probability, a discrete-time Markov chain is formed with different energy levels of the battery as the system states. The state transition probabilities depend on the harvested and consumed energy profile. It is observed that, with more stringent outage constraint, the harvested energy which is neither utilized nor stored increases significantly. To this end, we suggest the system other than cost optimal where the excess energy is sold to the intelligent smart power grid to generate revenue. Harvested and consumed energies of the three cities situated at different geographical locations are considered to verify the results.

To overcome finite lifetime bottleneck in the ubiquitous deployment of low-power wireless devices in Internet-of-Things, we propose a novel integrated information relay and energy supply (i2RES)-assisted RF harvesting co-operative communication model. i2RES aids the communication between two distant energy-constrained wireless nodes by: 1) RF energy transfer to the source and 2) relaying source data along with supplying energy to the destination. To enable efficient i2RES-powered information transfer to the destination, we first derive and then maximize the delay-limited achievable throughput over Rician channels by jointly optimizing time allocation for information and energy transfer along with relative position of i2RES between source and destination. Although the throughput maximization problem is nonconvex and highly nonlinear, we prove its generalized-convexity and obtain the global-optimal numerical solutions. To gain analytical insights, we also derive tight closed-form approximation for the optimized solutions. Numerical results validate the analysis and demonstrate significant gain in throughput performance via our proposed optimization schemes under practical hardware constraints. Finally, we discuss how the analysis and optimization results can be extended to general RF-EH system settings with relaxed constraints.

The performance of RF powered communication networks is bottlenecked by the short downlink energy transfer range and the doubly near-far problem faced in uplink information transfer to hybrid access point (HAP). These problems can be resolved by cooperation of an RF energy harvesting node R present between HAP and RF energy harvesting information source S. However, there lies a dilemma at R on whether to transfer its harvested energy to S or to act as an information relay for transferring its data to HAP in a two-hop fashion. This paper resolves this dilemma at R by providing insights into its optimal positions suited for either energy relaying (ER) or information relaying (IR). It also investigates the possibilities of integrated ER and IR along with the regions where neither ER nor IR will be useful. In this regard, while considering Rician fading channels and practical nonlinear RF energy harvesting model, the expression for mean harvested dc power at S via energy transfer from HAP and ER from R is first derived. The closedform outage probability expression is also derived for decodeand-forward relaying with maximal-ratio-combining at HAP over Rician channels. Using these expressions, insights into optimal relaying mode is obtained along with global-optimal utilization of harvested energy at R for ER and IR to maximize the delaylimited RF-powered throughput. Numerical results validate the analysis and provide insights into the optimal relaying mode.

We propose a relay-assisted RF harvesting sensor network (R2FSN) model, where the field nodes having finite battery capacity are powered by RF energy transfer (RFET) from a single relay, thus leading to perpetual sensor network operation. The relay node also forwards the field data to the information sink in a two-hop, half-duplex decode-and-forward fashion. We derive closed-form expression of the average end-to-end signal-to-noise ratio (SNR) for each source-destination link in cooperative R2FSN. The performance improvement provided by R2FSN over conventional RF-powered networks is numerically investigated along with the impact of RFET time and relay location on the maximum and minimum average end-to-end SNR over all links.

In this paper, we develop an optimization framework to jointly optimize the efficiency of wireless information and RF power transfer to the destination of a two hop decode-and-forward source-relay-destination network. In particular, we investigate the performance of three optimization schemes: (i) optimal power allocation (PA) with fixed relay placement (RP), (ii) optimal RP with fixed PA, and (iii) joint optimization of PA and RP, for minimizing the outage probability under the harvested power constraint at the destination. In absence of direct source to destination reachability, closed-form global-optimal solutions are obtained for all three optimization schemes. Numerical results show that the optimized schemes significantly outperform fixed allocation and the joint optimal PA and RP provides an outage improvement of about 35%. Also, a tradeoff exists between minimized outage probability and minimum required average harvested power at destination for its uninterrupted operation.

In energy‐constrained cognitive radio networks (CRNs), the choice on single channel access versus switched multichannel access is critical for energy saving and sustainable network operation. In this study, the authors study and compare the energy efficiency of switched (probabilistic) multichannel access ( p MCA) and fixed single‐channel access (SCA) in CRNs. In p MCA, a secondary user (SU) switches channel with certain probability whenever it encounters a busy channel. In SCA on the other hand, the SU stays on the same channel for its usage and waits for its availability. Via an analytical framework the authors derive the channel utilisation and energy efficiency of the two schemes. From the results the authors examine the primary user (PU) traffic dependent optimum switching probability in p MCA and the regime of PU activity dynamics where SCA outperforms p MCA.

Simultaneous wireless information and power transfer (SWIPT) can lead to uninterrupted network operation by integrating radio frequency (RF) energy harvesting with data communication. In this paper, we consider a two-hop source-relay-destination network and investigate the efficient usage of a decode-and-forward (DF) relay for SWIPT toward the energy-constrained destination. In particular, by assuming a Rician fading environment, we jointly optimize power allocation (PA), relay placement (RP), and power splitting (PS) so as to minimize outage probability under the harvested power constraint at the destination node. We consider the two possible cases of source-to-destination distance: (1) small distance with direct information transfer link; and (2) relatively large distance with no direct reachability. Analytical expressions for individual and joint optimal PA, RP, and PS are obtained by exploiting convexity of outage minimization problem for the no direct link case. In case of direct source-to-destination link, multipseudoconvexity of joint-optimal PA, RP, and PS problem is proved, and alternating optimization is used to find the global optimal solution. Numerical results show that the joint optimal solutions, although strongly influenced by the harvested power requirement at the destination, can provide respectively 64% and 100% outage improvement over the fixed allocation scheme for without and with direct link.

In this paper, we consider the problem of resource allocation in the orthogonal frequency division multiple access system with single source and M untrusted users in presence of a friendly jammer. The jammer is used to improve either the weighted sum secure rate or the overall system fairness. The formulated optimization problem in both the cases is a mixed integer non-linear programming problem, belonging to the class of NP-hard. In the sum secure rate maximization scenario, we decouple the problem and first obtain the subcarrier allocation at source and the decision for jammer power utilization on a per-subcarrier basis. Then, we do joint source and jammer power allocation using primal decomposition and alternating optimization framework. Next, we consider fair resource allocation by introducing a novel concept of subcarrier snatching with the help of jammer. We propose two schemes for jammer power utilization, called proactively fair allocation (PFA) and on-demand allocation (ODA). PFA considers equitable distribution of jammer power among the subcarriers, while ODA distributes jammer power based on the user demand. In both cases of jammer usage, we also present suboptimal solutions that solve the power allocation at a highly reduced complexity. Asymptotically optimal solutions are derived to benchmark optimality of the proposed schemes. We compare the performance of our proposed schemes with equal power allocation at source and jammer. Our simulation results demonstrate that the jammer can indeed help in improving either the sum secure rate or the overall system fairness.

In this paper, we propose a class of energy-efficient dynamic spectrum access (DSA) protocols for secondary user (SU) communication over a single primary user (PU) channel. The proposed variants of DSA can be optimized with respect to different backoff strategies and SU packet lengths. Via Markov chain models and numerical analysis, we derive the optimal SU packet length and inter-sensing time for optimal SU performance in the DSA variants. We evaluate the protocol performance in terms of SU goodput, SU energy efficiency, and PU collision ratio. Adaptability of the proposed SU operation protocol in practical scenarios is tested over cellular GSM band as well as under real-time video over IP based PU traffic in ISM band. Our performance studies demonstrate that the proposed protocols offer significantly high channel utilization while keeping the PU collisions below an acceptable threshold. The proposed protocol operation is also outlined, where the protocol adapts to the changing PU traffic load for optimized performance.

We consider the problem of resource allocation among M untrusted mobile users with a source and a friendly jammer in a secure OFDMA system. The jammer can be utilized to improve either the sum secure rate or the fairness of the system. Since the optimization problem is combinatorial and belongs to the class of NP hard, we propose two independent suboptimal schemes to solve the resource allocation problem. Our simulation results demonstrate that the use of jammer helps trade between secure rate and fairness performance.

This paper presents an innovative multi-faceted architecture, named U-TV, that provides user-centric and adaptive digital television (DTV) broadcast for heterogeneous users over heterogeneous wireless networks. The service providers (SPs) are the DTV base station (DTV BS) and Wi-Fi access points (Wi-Fi APs). The Bertrand duopoly game theoretic approach determines the pricing policy of the SPs and also the proportion of subscribers each of these SPs serve. The proposed framework incorporates the user-end as well as system utility definitions based on the users' energy-saving and price trade-off. The joint-optimization solution facilitates scalable video encoding subject to the device display resolutions, and energy and price sensitivities. The proposed solution results in an increased user satisfaction by providing them with optimized video content over the price competent SPs. Additionally, the SPs are able to serve more customers with acceptable user experience, as compared to the conventional as well as adaptive DTV service networks.

Recently, wireless radio frequency energy transfer (RFET) has emerged as an effective technology for prolonging lifetime of the energy-limited wireless sensor networks. However, low RFET efficiency is still a fundamental bottleneck in its widespread usage. Multi-hop RF energy transfer (MHET) can improve the RFET efficiency by deploying relay nodes that scavenge the dispersed energy and transfer it to the nearby sensor node. The efficiency of MHET is strongly influenced by the relay node's placement. To maximize the RFET efficiency for a two-hop scenario, in this paper a novel optimization model is proposed to determine the optimal relay placement (ORP) on an Euclidean x-y plane. Nontrivial tradeoff between the energy scavenged at the relay versus the effective energy delivered by the relay to the target node is investigated. Due to the nonconvex and highly nonlinear nature of the optimization problem, an α-based branch and bound algorithm has been used. The proposed optimization model is further extended by incorporating distributed beamforming to enhance the RFET efficiency. Numerical results illustrate that the proposed algorithm provides convergence to the ϵ-global optimal solution in a few iterations, and ORP provides significant energy saving over arbitrary relay positions for commercial RF energy harvesting systems.

Wireless transfer of energy through directed radio frequency waves has the potential to realize perennially operating sensor nodes by replenishing the energy contained in the limited on-board battery. However, the high power energy transfer from energy transmitters (ETs) interferes with data communication, limiting the coexistence of these functions. This paper provides the first experimental study to quantify the rate of charging, packet loss due to interference, and suitable ranges for charging and data communication of the ETs. It also explores how the placement and relative distances of multiple ETs affect the charging process, demonstrating constructive and destructive energy aggregation at the sensor nodes. Finally, we investigate the impact of the separation in frequency between data and energy transmissions, as well as among multiple concurrent energy transmissions. Our results aim at providing insights on radio frequency-based energy harvesting wireless sensor networks for enhanced protocol design and network planning.

This paper presents an adaptive SVC (scalable video coding) multimedia broadcast framework for the heterogeneous wireless mobile receivers with varying display resolutions, battery constraints, channel conditions, and service subscription priorities. In a bid to achieve optimal performance two variants of optimizations, namely, Subscriber-count Maximizing Adaptive SVC Rate and Energy Saving (SM-ASRES) scheme and Revenue Maximizing ASRES (RM-ASRES) are formulated. In SM-ASRES, the optimization of multimedia encoding parameters is focused toward maximizing the number of served subscribers based only on user capability and channel conditions. On the other hand, RM-ASRES additionally considers subscribers' priority for multimedia broadcast adaptation. Energy saving at the user equipment is a result of time slicing approach at the transmission stage. As compared to the conventional broadcast schemes, the proposed user adaptive framework shows an appreciable improvement in quality of user experience and increased energy saving for hand-held mobile users, along with an increased revenue when employing RM-ASRES.

Harvesting energy from radio frequency (RF) waves brings us closer to achieving the goal for perpetual operation of a wireless sensor network (WSN) by replenishing the batteries of the sensor nodes. However, due to restrictions on the maximum transmitted power, path loss, and receiver sensitivity, only a small amount of energy can be harvested. While a dedicated RF source alleviates the problem to some extent, novel techniques are required to boost the energy transfer efficiency of the source. In this paper, we provide the first experimental demonstration of multi-path energy routing (MPER) for the case of a sparsely distributed WSNs and show its improved performance over direct energy transfer (DET). In addition, we extend this concept to the case of densely distributed WSNs and experimentally demonstrate and compare the gains obtained by 2- and 3-path energy routing over DET. Our experimental results show that significant energy gains can be achieved in a dense network deployment even when the node to be charged is partially blocked by the neighboring nodes.

The reduction of energy consumption is a major concern in the current telecommunications environment - especially with the growth in usage of energy-hungry multimedia-centric applications on high-end mobile devices. In this context, this paper proposes eSMART, an Energy-efficient Scalable Multimedia Broadcast Transmission mechanism, that considers the energy-quality trade-off to reduce battery power consumption (increase energy saving) of heterogeneous mobile devices while maintaining acceptable perceived quality levels of received video. A real experimental test-bed has been built to analyze the impact of different multimedia scalability factors on the energy consumption of various mobile devices receiving broadcast content. Overall mobile device energy-saving is modeled using the accumulative effect of adaptive scalable video playback energy saving and time-sliced broadcast reception based radio-receiver's energy saving. eSMART's optimization framework performs user-centric adaptive encoding of scalable video that is broadcast to heterogeneous user equipments. eSMART serves more users at improved quality of experience levels and achieves up to 69% increase in mobile device energy savings as compared to a non-adaptive time-slicing scheme from the literature.

Design of an efficient medium access control protocol is critical for proper functioning of a distributed cognitive radio network and better utilization of the channels not being used by primary users. In this paper, we design a contention based distributed medium access control (MAC) protocol for the secondary users' channel access. The proposed MAC protocol allows collision-free access to the available data channels and eventually their utilization by secondary users, with spectrum sensing part being handled by exclusive sensing nodes. We further introduce the provision of reservation of free channels by secondaries for extended periods to increase utilization without causing harmful interference to primaries. We demonstrate how such extended access to resources can be tuned to provide differential quality of service to the secondary users. The effectiveness of the protocol is evaluated by performing analysis and simulation. We use blocking probability, secondary usage of a secondary user and performance degradation caused to primary incumbents as performance metrics. We obtain the conditions for such extended access and try to gauge the resulting increase in utilization. Under optimal conditions, the proposed scheme enables the secondary network to utilize all available channels. The proposed scheme is shown to outperform the most sophisticated existing MAC schemes for distributed secondary networks.

This paper presents a novel cross-layer optimization framework to improve the quality of user experience (QoE) and energy efficiency of the heterogeneous wireless multimedia broadcast receivers. This joint optimization is achieved by grouping the users based on their device capabilities and estimated channel conditions experienced by them and broadcasting adaptive content to these groups. The adaptive multimedia content is obtained by using scalable video coding (SVC) with optimal source encoding parameters resulted from an innovative cooperative game. Energy saving at user terminals results from using a layer-aware time slicing approach in the transmission stage. A trade-off between energy saving and QoE is observed, and is incorporated in the definition of a utility function of the players in the formulated heterogeneous user composition and physical channel aware game. An adaptive modulation and coding scheme is also optimally incorporated in order to maximize the reception quality of the broadcast receivers, while maximizing the network broadcast capacity. Compared to the conventional broadcast schemes, the proposed framework shows an appreciable improvement in QoE levels for all users, while achieving higher energy-savings for the energy constrained users.

In this paper, we present a new resource allocation scheme for cell-edge active users to achieve improved performance in terms of a higher system capacity and better quality-of-service (QoS) guarantee of the users, where we utilize the two-dimensional resource allocation flexibility of orthogonal frequency division multiple access (OFDMA) networks. Here, the mobile stations (MSs) at the cell-edge can maintain parallel connections with more than one base station (BS) when it is in their coverage area. A MS, before handoff to a new BS, seeks to utilize additional resources from the other BSs if the BS through which its current session is registered is not able to satisfy its requirements. The handoff procedure is termed as split handoff. The BSs participate in split handoff operation while guaranteeing that they are able to maintain QoS of the existing connections associated with them. In this study, first, we present the proposed shared resource allocation architecture and protocol functionalities in split handoff, and give a theoretical proof of concept of system capacity gain associated with the shared resource allocation approach. Then, we provide a differentiated QoS provisioning approach that accounts for the MS speed, its channel quality, as well as the loads at different BSs. Via extensive simulations in Qualnet, the benefits of the proposed class-based split handoff approach is demonstrated. The results also indicate traffic load balancing property of the proposed scheme in heavy traffic conditions.

Design of an efficient medium access control protocol is critical for proper functioning of a distributed cognitive radio network and better utilization of the available channels not being used by primary users. In this paper, we design a contention based distributed medium access control (MAC) protocol for the secondary users' channel access. The proposed MAC protocol allows collision-free access of the available channels and eventual utilization by secondary users, with spectrum sensing part being handled by exclusive sensing nodes. The effectiveness of the proposed MAC protocol is evaluated analytically and also through empirical simulations. We show how the protocol performs with respect to blocking probability, channel grabbing, and channel utilization1.

Batteries of field nodes in a wireless sensor network pose an upper limit on the network lifetime. Energy harvesting and harvesting aware medium access control protocols have the potential to provide uninterrupted network operation, as they aim to replenish the lost energy so that energy neutral operation of the energy harvesting nodes can be achieved. To further improve the energy harvesting process, there is a need for novel schemes so that maximum energy is harvested in a minimum possible time. Multi-hop radio frequency (RF) energy transfer is one such solution that addresses these needs. With the optimal placement of energy relay nodes, multi-hop RF energy transfer can save energy of the source as well as time for the harvesting process. In this work we experimentally demonstrate multi-hop RF energy transfer, wherein two-hop energy transfer is shown to achieve significant energy and time savings with respect to the single-hop case. It is also shown that the gain obtained can be translated to energy transfer range extension.

Multiple Description (MD) source coding is a technique that breaks a media stream into equally important sub-streams which can be sent over different paths for protection against channel errors in wireless networks. In this paper, we explore the possibility of sending the descriptions through different paths and intermediately merging them to exploit recovery of the corrupted descriptions from the uncorrupted ones before reaching the destination, thereby increasing the quality of received media at the destination. We first analytically model the end-to-end distortion of MD coded multipath transmission as a function of the path parameters and prove that intermediate recovery achieves an improvement of up to 9.2% in end-to-end distortion compared to traditional multipath transport. We then formulate the mesh route construction as a cross-layer optimization problem. Since the problem is highly complex, we propose an efficient heuristic routing strategy. We show that using this routing strategy, good paths (with up to 2 intermediate recovery nodes) can be constructed to achieve a lower distortion compared to the case without intermediate recovery.

Reservation multi-access protocol is a natural choice in wireless networks with high inter-nodal propagation delay and sporadic communications. There have been some proposals in delay-intensive networks, such as satellite networks as well as underwater networks, that address the variability of system state for tuning the access window. In this paper, we propose a new parametric dynamic reservation access scheme with an aim to improve the system utilization. We first analyze the system performance in many-to-one multi-access with long propagation delay data transfer scenario with the assumption of perfect propagation delay information and with fixed number of access slots. We then investigate the proposed system state aware approach to suitably adjust the number of access slots and investigate the optimum slotting strategy to maximize the system utilization. Via mathematical analysis, supported by discrete event simulations, we show that the system utilization and blocking probability with our proposed modified reservation protocol is consistently better compared to the competitive reservation protocols with fixed as well as dynamic access slots.

We present a new co-operative scheduling approach which provides not only more reliability but also higher effective capacity. Cooperation region is defined for active handoff users between two base stations (BSs) based on the user quality-of-service (QoS) requirements. A proper definition of co-operation or handoff region, where the user can be scheduled in a cooperative manner from more than one BS, is also given based on per-BS load and user QoS. Numerical results are presented showing reliability, user QoS performance, handoff region and capacity gain due to co-operative scheduling. Our analysis indicates that, when the QoS requirement is loose, co-operation provides relatively less gain and the handoff window size is also less. On the other hand, when the QoS requirement becomes more stringent, the gain in the effective capacity increases by using co-operation and the co-operation or handoff window size is also large, which can go up to 25% of the total area.

In the link layer frame retransmission strategies, the short-term variation of physical channel properties has not been fully exploited. To combat the negative impact of fading, channel memory can be used to make intelligent predictions about the upcoming channel state and frame retransmission timings can be adapted based on these predictions. In this study, two variants of simple yet efficient channel-adaptive stop-and-wait (SW) retransmission schemes are proposed for short-range wireless networks, wherein the transmitter node learns about the channel condition from the immediate past response of the receiver to adapt the next frame retransmission timing. Using Markov channel model, performance of the competitive SW protocols are captured. It is shown that, with a suitably chosen frame retransmission timing, the proposed schemes fair better than the existing SW retransmission schemes in terms of reduced energy consumption with a nominal data delivery rate trade-off. The performance gain is significant particularly in the harsh fading conditions.

The authors propose three variants of distributed and stateless forwarding strategies for wireless sensor networks, namely greedy minimum energy consumption forwarding protocol (GMFP), lifetime maximising GMFP (LM-GMFP) and variance minimising GMFP (VAR-GMFP), which aim at maximising the network lifetime while achieving a high forwarding success rate. GMFP selects a forwarding node that minimises per-packet energy consumption while maximising the forwarding progress. LM-GMFP extends the GMFP algorithm by also taking into account the remaining energy at the prospective one-hop forwarding nodes. In VAR-GMFP, on the other hand, the packet is forwarded to the next node that ensures a locally high mean and low variance of nodal remaining energy. Through simple probabilistic analysis the authors prove the intuition behind the optimum forwarding node selection for network lifetime maximisation. They then model the lifetime maximisation of a sensor network as an optimisation problem and compare the practical protocol-dependent network lifetime with the theoretical upper bound. Through extensive simulations the author demonstrate that the proposed protocols outperform the existing energy-aware protocols in terms of network lifetime and end-to-end delay.

This paper estimates the feasibility of operating field sensors using otherwise unwanted radio frequency (RF) energy in a wireless ad hoc sensor network. In the traditional concept, an exposed node is kept from any communication activities during a neighbor's transmission. To save energy, the node may go to sleep mode during its inactivity period. In contrast, it is proposed that whenever a node is not communicating it should attempt to collect energy from the ongoing transmissions in its vicinity. A two-tier network is considered with the field sensor nodes having rudimentary communication (transmission) functionality at the first tier and the routing-capable nodes at the second tier. Via geometric probabilistic analysis, aided by network simulations, the conditions on rectification efficiency and transmission duty cycle of network RF energy operated field nodes are derived for their uninterrupted processing and transmission activities.

Recent experimental results have shown that the minimum signal-to-interference ratio required at a receiver (CPth) depends on the order of arrival of the overlapping frames. For a given sender-receiver distance, this differential capture capability of a receiver leads to two distinct interference ranges (ri) around the receiver, and its value is much smaller when the sender's frame arrives earlier. This feature also suggests a possibility of increased spatial reuse by allowing the (secondary) nodes outside the primary receiver's rito communicate concurrently once the primary receiver starts its DATA reception. In this paper, we propose a liberal carrier sensing (LCS) scheme wherein some already available information at an otherwise 'exposed' receiver are exploited to help decide when it is safe to respond to a secondary transmission request. The proposed modification in the carrier sensing approach results in a significantly improved spatial reuse, thereby increasing overall system throughput. Our simulation studies show that, compared to the conventional carrier sensing scheme with differential capture capable receivers, the end-to-end TCP throughput with LCS can be improved by more than 20% in regular topologies and up to about 9% in random topologies.

In a wireless sensor network, as the sensor nodes have limited energy, it is important to minimize nodal energy consumption due to message communication to extend the network lifetime. Existing forwarding protocols either do not consider network performance and energy saving jointly, or they are not distributed. In this paper, we propose a hybrid approach, called minimum consumption maximum remaining energy (MIN-MAX-E) forwarding, which combines minimum energy consumption of transmitter-receiver pair along with maximum remaining energy of the receiver in making a relay node selection decision. Extensive simulation studies show that the proposed algorithm offers a significantly improved energy saving performance with respect to the existing energy-aware approaches.

With the general trend towards ubiquitous access to networks, more users will prefer to make voice calls through the Internet. Voice over IP (VoIP) as the application which facilitates voice calls through the Internet will increasingly occupy more traffic. The growth of delay sensitive traffic that requires special quality of service from the network will impose new constraints on network designers who should wisely allocate the limited resources to users based on their required quality of service. An efficient resource allocation depends upon gaining accurate information about the traffic profile of user applications. In this paper, we have studied the access level traffic profile of VoIP applications and proposed a realistic distribution model for VoIP traffic. Based on our model, we have introduced an algorithm for resource allocation in networks. It is shown that using our algorithm will enhance the delay and utilization performance of the network.

Receiver-side relay election has been recently proposed as an alternative to transmitter-side relay selection in wireless ad hoc networks. In this paper we study different prioritization schemes among potential relay nodes to achieve a better delay and contention resolution performance. We consider a priority criteria based on least remaining distance to the destination and propose a generalized mapping function to introduce relative priority among the eligible relay nodes. We show that, a suitable mapping can be found to achieve an optimum relay election performance, which also outperforms the random forwarding approach. Our intuition is guided by an analytic framework and verified by network simulation.

This work studies the impact of the network resource constraints on the performance of wireless sensor multi-hop relay networks. A differential space-time coding and the cooperative relay scheme for cross layer design is introduced in multi-hop wireless sensor networks. To analyze the network performance of such a system, the relay system from source to destination is simplified as a virtual bit pipe line, and a Markov chain model as well as an M/G/1 queueing system are used. The closed form expression for throughput and delay are derived in the infinite buffer case. A truncated Markov chain model and an M/G/1/K queueing system are used to model the finite buffer situation. Moreover, the system drop probability and the go through delay are studied based on the models.

In this paper we consider the problem of constructing a coordinate system in a sensor network where location information is not available. To this purpose we introduce the virtual coordinate assignment protocol (VCap) which defines a virtual coordinate system based on hop distances. As compared to other approaches, VCap is simple and have very little requirements in terms of communication and memory overheads. We compare by simulations the performances of greedy routing using our virtual coordinate system with the one using the physical coordinates. Results show that the virtual coordinate system can be used to efficiently support geographic routing.

In this paper, we characterize analytically the multiaccess interference (MAI) in wireless CDMA sensor networks with uniformly random distributed nodes and study the tradeoff between interference and connectivity. To provide a guideline for improving system behavior, three competitive deterministic topologies are evaluated along with the random topology in terms of link-level and network-level (routing) performance. The impact of the signature code length and the receiver design on network performance for different topologies is also studied.

We have introduced a novel concept called "managed mobility", and addressed the mobility of the relaying devices called mobile ad hoc relaying stations (MARSs) in the integrated cellular and ad hoc relaying (iCAR) system. We anticipate that the idea of managed mobility proposed in this paper for the iCAR system as well as the mobility management strategies may also be applied in other ad hoc networks, such as the self-reconfigurable sensor network, to reduce additional node deployment cost and increase fault tolerance.

In this paper we investigate the relative performance of two multipath routing schemes in relatively static and highly error-prone wireless networks (e-g., sensor networks), namely, selective preferential forwarding (SPF) (or primary/secondary routing) and recently proposed selective random forwarding (SRF), in terms of their packet throughput and traffic load distribution. For meshed multipath, aiming at achieving a good performance trade-off, we introduce a novel hybrid packet forwarding scheme that takes advantage of more uniform load distribution of SRF and a higher end-to-end throughput of SPF. Our approach is guided by analytic intuition and verified by simulations.

In this paper, we characterize analytically the multiaccess interference in wireless code-division multiple-access sensor networks with uniformly random distributed nodes and study the tradeoff between interference and connectivity. To provide a guideline for improving system behavior, three competitive deterministic topologies are evaluated along with the random topology in terms of link-level and network-level (routing) performance. The impact of signature code length and receiver design on the network performance for different topologies is also studied.

For reliability of communication and simplicity, often times packet are replicated along predetermined routes to the destination. Alternatively, for traffic load balancing, data traffic is distributed along disjoint or meshed multiple routes to the destination - called selective forwarding. In this paper, we study and quantify the resource usage in these schemes, namely, packet replication and selective forwarding approaches. Our evaluation shows that for successfully routing a message using forward error correction coding technique, packet replication wastes much higher network resource, such as channel bandwidth and battery power.

The integrated cellular ad hoc relaying (iCAR) system is a representative heterogeneous wireless system, proposed to address the congestion problem in the wireless networks. In this paper, we present an analytic model based on Markov chains for the queuing delay performance of iCAR. Our results show that the new call requests in iCAR have a significantly lower queuing delay than that of the conventional cellular system. The analytic model developed in this paper may serve as the guideline for the delay performance evaluation of the next generation heterogeneous wireless systems.

Due to limited functionalities and potentially large number of sensors, conventional routing strategies proposed for distributed control applications (such as mobile ad hoc networks) are not directly applicable in wireless sensor networks. In this paper, we propose a novel mesh multipath routing (M-MPR) with selective forwarding of packets. Our evaluation shows that M-MPR achieves much improved throughput performance over conventional disjoint multipath routing, with comparable power consumption and receiver complexity. We also show that for comparable throughput, M-MPR achieves better load distribution and requires lesser route maintenance overhead with respect to packet forwarding along a preferred route.

Abstract The performance of existing QoS routing protocols is often constrained with high control traffic and database maintenance overhead. We observe that by proper coupling of nodal mobility and location information, better QoS support can be achieved with reduced control traffic and database requirements. In this paper, we investigate the performance of a location‐aware QoS routing protocol, calledtrigger‐based distributed routing(TDR), for mobile ad hoc networks. In this protocol, the nodal database size is reduced by maintaining only local neighborhood information, and route maintenance control overhead is kept low by maintaining only one route at a time for a session. Distributed rerouting control and directed alternate route discovery help reduce the rerouting control overhead and perform quicker route repair. Moreover, rerouting based on signal degradation history makes it possible to minimize the in‐session route failure. Our evaluation shows that the TDR protocol has significantly better QoS support and reduced overhead requirements compared to the existing QoS routing protocols in ad hoc networks. Copyright © 2003 John Wiley & Sons, Ltd.

We propose a dynamic multipath routing (DMPR) scheme to improve resource utilization of a network for a given quality of service (QoS) in carrying delay sensitive traffic. The proposed scheme takes advantage of available alternate routes which can be used beneficially in communication networks or switches, without increasing the network latency. Through a simulation study of circuit-switched, virtual circuit-switched (ATM) and packet-switched (Internet) networks we demonstrate the benefit of using DMPR over a single-path routing (SPR) scheme. We also discuss the implementation aspects of the DMPR scheme in data networks and the overhead associated with it.

Integrated cellular and ad hoc relaying systems (iCAR) is a new wireless system architecture based on the integration of cellular and modern ad hoc relaying technologies. It addresses the congestion problem due to unbalanced traffic in a cellular system and provides interoperability for heterogeneous networks. The iCAR system can efficiently balance traffic loads between cells by using ad hoc relaying stations (ARS) to relay traffic from one cell to another dynamically. This not only increases the system's capacity cost effectively, but also reduces the transmission power for mobile hosts and extends system coverage. We compare the performance of the iCAR system with conventional cellular systems in terms of the call blocking/dropping probability, throughput, and signaling overhead via analysis and simulation. Our results show that with a limited number of ARSs and some increase in the signaling overhead (as well as hardware complexity), the call blocking/dropping probability in a congested cell and the overall system can be reduced.