This paper proposes an energy management framework for cellular heterogeneous networks (HetNets) supported by dynamic solar powered drones. A HetNet composed of a macrocell base station (BS), micro cell BSs, and drone small cell BSs are deployed to serve the networks' subscribers. The drones can land at pre-planned locations defined by the mobile operator and at the macrocell BS site where they can charge their batteries. The objective of the framework is to jointly determine the optimal trips of the drones and the MBSs that can be safely turned off in order to minimize the total energy consumption of the network. This is done while considering the cells' capacities and the minimum receiving power guaranteeing successful communications. To do so, an integer linear programming problem is formulated and optimally solved for three cases based on the knowledge level about future renewable energy statistics of the drones. A low complex relaxed solution is also developed. Its performances are shown to be close to those of the optimal solutions. However, the gap increases as the network becomes more congested. Numerical results investigate the performance of the proposed drone-based approach and show notable improvements in terms of energy saving and network capacity.

Most of the drone-based applications require a time-limited access to the spectrum to complete data transmission due to limited battery capacity of these flying units. This paper proposes an efficient spectrum and energy management solution by integrating the overlay cognitive radio technology. Therefore, we aim to use the drone as a secondary node and target to determine an optimized three-dimensional location and a resource control solution by which it can complete its data transfer and in parallel support the primary communication. To this end, a non-convex optimization problem is developed. The obtained solution minimizes the total energy consumption of the drone and, at the same time, maintains the required data rate level of the spectrum's owner. A resource allocation procedure and swarm intelligence-based positioning algorithm are jointly designed for this purpose. Numerical results show the efficiency of the proposed approach in terms of energy consumption savings and additional transmission opportunities as compared to other schemes.

In this paper, a generic scheduling framework to manage a fleet of micro unmanned aerial vehicles (UAVs) is proposed. The objective is to employ multiple UAVs in sequential and parallel ways to cover spatially and temporally distributed events in a geographical area of interest over a long period of time. The proactive scheduling framework considers several constraints and challenges including the limited battery capacities and technical specifications of the UAVs in addition to the necessity to regularly send back the UAVs to a docking station. A mixed integer linear programming (MILP) problem aiming at minimizing the total energy consumption is formulated after a series of linearization steps. Optimal UAV scheduling solutions are then obtained using off-the-shelf software. The proposed UAV scheduling framework is formulated in a generic manner and can be applied in multiple domains comprising short and/or long-term UAV missions while ensuring uninterrupted service. The obtained results can be used as convenient benchmarks for future heuristic UAV scheduling approaches.

Zone-level occupancy counting is a critical technology for smart buildings and can be used for applications, such as building energy management, surveillance, and public safety. Existing occupancy counting techniques typically require installation of large number of occupancy monitoring sensors inside a building and an established wireless network. In this paper, in order to achieve occupancy counting, we consider the use of Wi-Fi probe requests that are continuously transmitted from Wi-Fi enabled smart devices for discovering nearby access points. To this end, Wi-Fi Pineapple equipment are used for passively capturing ambient probe requests from Wi-Fi devices, such as smart phones and tablets, where no connectivity to a Wi-Fi network is required. This information is then used to localize users within coarsely defined occupancy zones, and subsequently to obtain occupancy count within each zone at different time scales. An interacting multimodel (IMM) Kalman filter technique is developed to improve occupancy counting accuracy. Our numerical results using Wi-Fi data collected at a university building show that the use of Wi-Fi probe requests in conjunction with IMM-based Kalman filters can be a viable solution for zone-level occupancy monitoring in smart buildings.

During a disaster incident, existing network infrastructures might either be damaged or overloaded. However, disaster operations require real-time and low latency data communication, especially live video and audio streaming, as well as in integration with social networks. Thus, deployed tactical networks face several challenges to connect intervening teams. To date, there is still a lack of routing standards for such networks. Indeed, in such harsh environment, medical rescue teams, firefighters, military, police and even victims need to be steadily connected to a distant command center (CC) which conducts the rescue operations. In our recent works, a new routing protocol called ORACE-Net was proposed, evaluated and compared to the existing approaches. ORACE-Net aims to create a tactical Internet of Humans (IoH) emergency network. ORACE-Net is implemented on mobile devices and evaluated within realistic conditions. The complete proposed solution consists of the CROW2, an Internet of Humans-based platform for disaster relief and emergency communication. In this paper we describe the setup and the demonstration of the CROW2platform based on the our conducted experimentation.

Micro unmanned aerial vehicles (MUAVs) have attracted much interest as flexible communication means for multiple applications due to their versatility. Most of the MUAV-based applications require a time-limited access to the spectrum to complete data transmission due to limited battery capacity of the flying units. These characteristics are the origin of two main challenges faced by MUAV-based communication: 1) efficient-energy management, and 2) opportunistic spectrum access. This paper proposes an energy-efficient solution, considering the hover and communication energy, to address these issues by integrating cognitive radio (CR) technology with MUAVs. A non-convex optimization problem exploiting the mobility of MUAVs is developed for the underlay CR technique. The objective is to determine an optimized three-dimension (3D) location, for a secondary MUAV, at which it can complete its data transfer with minimum energy consumption and without harming the data rate requirement of the primary spectrum owner. Two algorithms are proposed to solve these optimization problems: a meta-heuristic particle swarm optimization algorithm (PSO) and a deterministic algorithm based on Weber formulation. Selected numerical results show the behavior of the MUAV versus various system parameters and that the proposed solutions achieve very close results in spite of the different conceptional constructions.

Unmanned Aerial Vehicles (UAV) have attracted a lot of attention in a variety of fields especially in intelligent transportation systems (ITS). They constitute an innovative mean to support existing technologies to control road traffic and monitor incidents. Due to their energy-limited capacity, UAVs are employed for temporary missions and, during idle periods, they are placed in stations where they can replenish their batteries. In this paper, the problem of UAV docking station placement for ITS is investigated. This constitutes the first step in managing UAV-assisted ITS. The objective is to determine the best locations for a given number of docking stations that the operator aims to install in a large geographical area. Based on average road network statistics, two essential conditions are imposed in making the placement decision: i) the UAV has to reach the incident location in a reasonable time, ii) there is no risk of UAV's battery failure during the mission. Two algorithms, namely a penalized weighted k-means algorithm and the particle swarm optimization algorithm, are proposed. Results show that both algorithms achieve close coverage efficiency in spite of their different conceptual constructions.

This paper proposes an energy management framework for cellular heterogeneous networks (HetNets) supported by dynamic drone small cells. A 3-tier HetNet is considered where macrocell, on#x002F;off switching micro cells, and solar-powered drone small cells are deployed to serve the networks' subscribers. In addition to energy harvesting, the drones can power their batteries via a charging station located at the macrocell site. Pre-planned locations are identified by the mobile operator for possible drones' placement. The objective of the framework is to optimally determine the positioning of the drones in addition to the micro cells status that can be turned off in order to minimize the daily energy consumption of the network. The framework takes also into account the cells' capacity and quality of service (QoS) metric defined by the minimum received power. An integer linear programming problem is formulated to optimally determine the network status during a time blocked period. The performance of this online scheme shows important advantages in terms of energy efficiency and connectivity compared to the traditional case without drones specially when the network is congested.

This paper proposes an optimized transmission scheme for Energy Harvesting (EH)-based two-way multiple-relay systems. All relays are considered as EH nodes that harvest energy from renewable and radio frequency (RF) sources, then use it to forward the information to the destinations. The power-splitting (PS) protocol, by which the EH node splits the input RF signal into two components for information transmission and energy harvesting, is adopted in the relay side. The objective is to optimize the PS ratios and the relays' transmit power levels in order to maximize the total sum-rate utility over multiple coherent time slots. An optimization approach based on geometric programming is proposed to solve the problem. Numerical results illustrate the behavior of the EH-based two-way multiple-relay system with respect to various parameters and compare the performance of the proposed approach with that of the dual problem-based approach.

Passive radio-frequency identification (RFID) systems carry critical importance for Internet of Things (IoT) applications due to their energy harvesting capabilities. RFIDbased position estimation, in particular, is expected to facilitate a wide array of location-based services for IoT applications with low-power requirements. In this paper, considering monostatic and bistatic configurations and 3-D antenna radiation pattern, we investigate the accuracy of received signal strength-based wireless localization using passive ultra high frequency RFID systems. The Cramer-Rao lower bound (CRLB) for the localization accuracy is derived, and is compared with the accuracy of maximum likelihood estimators for various RFID antenna configurations. Numerical results show that due to RFID tag/antenna sensitivity, and the directional antenna pattern, the localization accuracy can degrade at blind locations that remain outside of the RFID reader antennas' main beam patterns. In such cases optimizing elevation angle of antennas are shown to improve localization coverage, while using bistatic configuration improves localization accuracy significantly.

Cellular operators are increasingly turning toward renewable energy (RE) as an alternative to using traditional electricity in order to reduce operational expenditure and carbon footprint. Due to the randomness in both RE generation and mobile traffic at each base station (BS), a surplus or shortfall of energy may occur at any given time. To increase energy self-reliance and minimize the network’s energy cost, the operator needs to efficiently exploit the RE generated across all BSs. In this paper, a hybrid energy sharing framework for cellular network is proposed, where a combination of physical power lines and energy trading with other BSs using smart grid is used. Algorithms for physical power lines deployment between BSs, based on average and complete statistics of the net RE available, are developed. Afterward, an energy management framework is formulated to optimally determine the quantities of electricity and RE to be procured and exchanged among BSs, respectively, while considering battery capacities and real-time energy pricing. Three cases are investigated, where RE generation is unknown, perfectly known, and partially known ahead of time. Results investigate the time varying energy management of BSs and demonstrate considerable reduction in average energy cost thanks to the hybrid energy sharing scheme.

Cellular base stations (BSs) are increasingly becoming equipped with renewable energy generators to reduce operational expenditures and carbon footprint of wireless communications. Moreover, advancements in the traditional electricity grid allow two-way power flow and metering that enable the integration of distributed renewable energy generators at BS sites into a microgrid. In this paper, we develop an optimized energy management framework for microgrid-connected cellular BSs that are equipped with renewable energy generators and finite battery storage to minimize energy cost. The BSs share excess renewable energy with others to reduce the dependency on the conventional electricity grid. Three cases are investigated where the renewable energy generation is unknown, perfectly known, and partially known ahead of time. For the partially known case where only the statistics of renewable energy generation are available, stochastic programming is used to achieve a conservative solution. Results show the time varying energy management behaviour of the BSs and the effect of energy sharing between them.

Renewable energy (RE) is an emerging solution for reducing carbon dioxide (CO2) emissions from cellular networks. One of the challenges of using RE sources is to handle its inherent uncertainty. In this paper, a RE powered cellular network is investigated. For a one-day operation cycle, the cellular network aims to reduce energy procurement costs from the smart grid by optimizing the amounts of energy procured from their locally deployed RE sources as well as from the smart grid. In addition to that, it aims to determine the extra amount of energy to be sold to the electrical grid at each time period. Chance constrained optimization is first proposed to deal with the randomness in the RE generation. Then, to make the optimization problem tractable, two well- know convex approximation methods, namely; Chernoff and Chebyshev based-approaches, are analyzed in details. Numerical results investigate the optimized energy procurement for various daily scenarios and compare between the performances of the employed convex approximation approaches.

In this paper, a multiple relay selection scheme for Energy Harvesting (EH)-based two-way relaying is investigated. All the relays are considered as EH nodes that harvest energy from renewable and radio frequency sources, then use it to forward the information to the sources. The time-switching protocol (TS), in which the receiver switches between transmitted information and harvested energy, is adopted in the relay side. The goal is to find the optimal TS ratios associated with the selected relays that maximize a rate-based utility function over multiple coherent time slots. Two metrics reflecting the degrees of fairness in the optimization are investigated. A joint-optimization solution based on binary particle swarm optimization is proposed to solve the problem. Numerical results illustrate the behavior of the TWR network according to the considered utility functions, the generated amount renewable energy, in addition to other system parameters.

In this paper, we investigate the Energy Harvesting (EH)-based two-way relaying system using Amplify-and-Forward (AF) and Decode-and-Forward (DF) strategies. The relay is considered as an EH node that harvests the received Radio Frequency (RF) signal and uses this harvested energy to forward the information. Two relaying protocols based on Time Switching (TS) and Power Splitting (PS) receiver architectures are proposed to enable EH and information processing at the relay. Analytical throughput expressions are derived and optimized for both protocols. The goal is to find the optimal TS and PS ratios that maximize the total throughput Numerical results illustrate the performance of TS and PS protocols for different strategies, and show that at high signal-to-noise ratio, PS is superior to TS, and AF is superior to DF in terms of achievable sum-rate.

In this paper, the production decisions across multiple energy suppliers in smart grid, powering cellular networks are investigated. The suppliers are characterized by different offered prices and pollutant emissions levels. The challenge is to decide the amount of energy provided by each supplier to each of the operators such that their profitability is maximized while respecting the maximum tolerated level of CO2 emissions. The cellular operators are characterized by their offered quality of service (QoS) to the subscribers and the number of users that determines their energy requirements. Stochastic geometry is used to determine the average power needed to achieve the target probability of coverage for each operator. The total average power requirements of all networks are fed to an optimization framework to find the optimal amount of energy to be provided from each supplier to the operators. The generalized alpha-fair utility function is used to avoid production bias among the suppliers based on profitability of generation. Results illustrate the production behavior of the energy suppliers versus QoS level, cost of energy, capacity of generation, and level of fairness.

Performance of passive ultrahigh frequency radio-frequency identification (UHF RFID) systems depends on the energy level of the backscattered signal from the RF tag to the RFID reader. In the indoor environment, the transmitted signals are corrupted by the multi-path channels fading that reduce the energy of the backscatter signal and modify the waveform of the continuous wave (CW) that result a short read range. Chirp spread spectrum (CSS) signals and channel shortening (CS) techniques are used separately to combat the effect of the multi-path channel fading in different contexts. In this paper, we propose two methods that use jointly CSS and CS in the multiple-input single-output (MISO) passive UHF RFID system that employs multiple antennas at the reader and one antenna at the tag First method increases significantly the received energy on the tag. The harvest energy ensures the backscatter communication from tag to reader. Second method offers a good trade-off between maintaining of high received energy and preserving the main characteristics of the waveform of the CSS, on the tag. This method is required to ensure a secured communication between reader and tag.

Providing efficient routing service over tactical multi-hop ad-hoc networks is a crucial building block in wireless communication networks especially during a disaster relief. To date, there is still a lack of routing standards for such networks. Indeed, in such harsh environment, medical rescue teams, firefighters, military, police and even victims need to be steadily connected to a distant command center (CC) which conducts the rescue operations. In this paper, we propose a new multi-hop routing approach called ORACE-Net. The proposed protocol uses advertisement packets to establish routes from deployed nodes towards the CC (i.e. Direct Route Establishment). Then, it utilizes the data packets to establish reverse routes (from the CC to all nodes in the network). We implemented and evaluated our approach in realistic scenario using tactical and on-body mobile nodes. Our experiments include also an Internet of Thing (IoT) platform and a real-time dynamic topology website which are used for analyzing the behavior of the protocol. The experimental results show that our protocol increases the mobile nodes connectivity and packet delivery rate. Also, it reduces the average round trip time delay for the on-body nodes compared to the tactical deployed base stations.

This paper investigates the interactions between multiple heterogeneous cellular networks and energy retailers existing in the smart grid. The energy procurement decision of cellular networks is optimized while considering dynamic energy pricing and pollution levels of energy sources. The objective of the optimization problem is to maximize a utility metric based on mobile operators' profits while limiting the amount of CO2 emitted by all networks. In this study, we derive closed-form expressions of the optimal energy to procure from each retailer in order to power the marcocell and small cell base stations. The derived solutions depend on the retailer pricing function and the optimized metrics. Three metrics reflecting the degree of fairness among mobile operators are considered. Selected simulations investigate the behavior of different actors in the energy procurement decision. Results show a significant CO2 emissions reduction can be achieved thanks to the optimized demand side management from smart grid.

Green operation of a specific category of femtocell networks, where a central controller can control the network within the premises of a campus or building, is investigated. An energy efficient approach for switching off redundant femtocell access points (FAPs) is proposed. It consists of selecting the best FAP to switch off, then moving the femto user equipments (FUEs) to other active FAPs without compromising their quality of service (QoS). Simulation results show that significant energy savings are obtained with the proposed approach, while satisfying QoS requirements.

Radio-Frequency IDentification (RFID) systems carry critical importance for reaping the benefits of a diverse set of applications that will be enabled by the emerging Internet of Things (IoT). In particular, RFID based position estimation and tracking will facilitate a wide array of location based services. Considering a monostatic RFID configuration with a three dimensional antenna radiation pattern, this paper investigates the limits of localization accuracy for received signal strength (RSS) based wireless localization using passive UHF RFID systems. The Cramer-Rao Lower Bound (CRLB) for the localization accuracy are derived, and are compared with the localization accuracy of maximum likelihood estimators for various RFID antenna topologies. Numerical results show that due to the directional antenna pattern used in RFID systems, the localization accuracy can degrade at blind locations that remain outside of the RFID readers' main beam patterns.Cramer-Rao Lower Bound (CRLB) for the localization accuracy are derived, and are compared with the localization accuracy of maximum likelihood estimators for various RFID antenna topologies. Numerical results show that due to the directional antenna

In this paper, time-varying continuous wave (CW) signals are proposed for passive ultrahigh frequency radio frequency identification (UHF RFID) communication in multipath fading channels. The energy of the CW signals transmitted by a RFID reader is used to power up the internal circuitry of the RF tags and to backscatter their information to the reader. In this paper, three different candidates of time-varying CW signals, namely linear FM, quadratic FM, and sinusoidal FM signals, are proposed to reduce the effects of the multipath fading on the energy of the impinged signal on the RF tag. This will result in stronger backscatter signal to the reader and larger RFID system reading range. The results show that the performance of passive UHF RFID communication is considerably enhanced when the proposed signals are used.

In this paper, the performance of monostatic and bistatic passive ultra high frequency radio frequency identification (UHF RFID) systems under the effects of cascaded fading channels and interference is studied. The performance metric used is tag detection probability defined as the probability that the instantaneous received power is higher than the reader's sensitivity. A closed-form expression of the detection probability is derived using cascaded forward and backscatter fading channels and the reader antennas orientation relative to the tag. Furthermore, the performance of passive UHF RFID systems under reader-to-tag interference caused by both the desired RFID signal and multiple RFID interferers is analyzed, and the effect of constructive and destructive interferences is examined. In addition, the maximum reading range in ideal, multipath fading, and interfering environments is presented. To the best of our knowledge, this is the first work that provides a 3-D performance analysis of the passive UHF RFID systems under cascaded fading channels. The obtained results are very useful for the design and optimization of passive UHF RFID systems from an RF physical channel point of view.

Radio Frequency Identification (RFID) system uses the principle of radiative power transfer between the reader and the tag antenna. Usually, it operates in a forward-link limited regime where the read range is limited by the ability to deliver power to the tag. Most of current researches consider the reader interrogation zone to be determined only by its read range assuming omni-directional antennas. However, the reliable interrogation zone also depends on interference effect, channel fading and the relative reader-tag antennas orientations. In this paper, we analyze the performance of the passive RFID systems taking into consideration reader antennas orientation and forward link fading for both desired signal and multiple RFID interferers. Using our model, we examine the effect of constructive and destructive (i.e. tag jamming) interferences in passive RFID system from the RF point of view. We also analyze the tag detection rate at different fading environments modeled by Rician distribution.

In this paper, an actual deployment of a wireless sensor network is described. The purpose of the sensor network is to monitor and analyze air quality in Doha. Small scale wireless sensor stations communicate with a backend server to relay their measurements in real-time. Data stored on the server is subjected to intelligent processing and analysis in order to present it in different formats for different categories of end users. This paper describes a user friendly computation of an air quality index to disseminate the data to the general public. In addition, it describes data presentation for environmental experts using dedicated software tools, e.g. the R software system and its OpenAir package. Analysis and assessment of real measurement data is also performed in the paper.

The performance of IEEE 802.15.4-based wireless sensors for applications in electromagnetically and physically harsh environments, such as those of typical industrial floors, is addressed in this paper. The results are based on a series of experiments carried out in indoor and industrial environments in the presence of different types of electromagnetic interference (EMI) on the 2.4 GHz ISM band. The performance of the wireless sensors is assessed using the packet loss and packet received rates. The results show that IEEE 802.15.4-based wireless sensors operating on low transmission power levels perform well in the presence of high levels of EMI. However, performance is sensitive to the nature and the peak power of the interference. The results reported herein provide a useful performance guidance when deploying ad hoc wireless sensor networks in industrial floors.

This paper addresses non-coherent detection and performance of weak M-ary chirp spread-spectrum (MCSS) signals in non-Gaussian impulsive noise. The structure of the locally optimum Bayes detector (LOBD) is derived and consists of a zero-memory nonlinearity (ZMNL), a function of first-order noise density, followed by the non-coherent Gaussian envelope detector for MCSS signals. The performance of this receiver has been evaluated and closed-form expressions for symbol error rate (SER) and bit error rate (BER) have been derived. It has been shown that the SER and BER performances are functions of the signal-to-noise ratio (SNR), the sample size per symbol interval, the input data size, and the modulation parameters of MCSS signals. A comparison between the coherent and non-coherent detection for weak MCSS signals is also given.

In this paper, performance of a wireless communication system based on wideband signals has been experimentally evaluated in a point-to-point topology for potential deployment in industrial environments. A class of wideband signals based on Chirp Spread Spectrum (CSS) modulation has been chosen in this work. Two experimental scenarios have been considered: 1) coexistence with other wireless systems, and 2) subjection to time-varying electromagnetic interferences. The results indicate that wireless systems based on wideband CSS signals perform well in terms of bit and packet error rates as well as throughput when coexisting with other wireless systems and when disturbed by interference in the same frequency band. The results reported are of particular interest to those who develop low power and high reliability wireless systems for industrial applications, particularly in situations that already have other wireless systems in use.

In this paper, an application of wireless sensor networks for leakage detection in underground water pipes is considered. A mobile sensor immersed in the water pipe is assumed to communicate with relay nodes on the ground surface, relaying its measured information to a base station for processing. A communication protocol between the relay nodes and the base station is presented. The proposed protocol is based on probabilistic reservation of transmission slots based on channel knowledge. The proposed protocol can be considered as an OFDMA extension of the reservation Aloha protocol. Simulation results show the superiority of the proposed communication approach.

Detection and error rate performance of weak M-ary chirp spread-spectrum (MCSS) signals in Middleton's class-A noise are addressed. The structure of the optimal weak-signal receiver is derived and consists of a zero-memory nonlinearity (ZMNL), a function of first-order noise density, followed by Gaussian MCSS correlator receiver. Bit error rate (BER) bounds on the performance of this receiver are derived and illustrated as a function of signal-to-noise ratio (SNR), modulation and noise environment parameters, and detection sample size for MCSS modulations. Optimal 2-, 4-, 8-, and 16-ary CSS modulations are determined and their performances are examined. The results show that considerable improvement in performance can be achieved when the optimal weak-signal receiver is used for detection of MCSS signals in class-A noise.