Reconfigurable intelligent surfaces (RIS) can enhance wireless power transfer efficiency in communication networks by steering electromagnetic waves from a transmitter (TX) toward zero-energy devices (ZEDs) for charging their batteries. In this letter, we use the Lyapunov optimization framework to develop an algorithm that dynamically adjusts the TX power and RIS phase configuration based on the data queue lengths at the ZEDs. This approach aims to maintain queue stability while minimizing the average TX power. Our simulation results demonstrate that the proposed method provides stability and reduces the average TX power, whereas the queue-agnostic benchmark fails to achieve that even with much higher TX power.

Communication networks can achieve self-sustainability with the help of reconfigurable intelligent surfaces (RIS) that direct electromagnetic waves toward zero-energy devices (ZEDs) for energy harvesting. The harvested energy charges the ZEDs' batteries, reduces their dependency on external energy sources and enables energy neutrality. In this letter, we propose a dynamic algorithm that adjusts the joint RIS and TX phases based on the battery levels to maximize a weighted-sum power metric favoring ZEDs with lower battery levels. The simulation results show that threshold-based weighting slightly underperforms compared to the weighting with optimal parameters while requiring less data exchange with the network entity manager.

The growth of Internet of Things (IoT) networks has made battery replacement in IoT devices increasingly challenging. This issue is particularly pronounced in scenarios with a large number of IoT devices, in locations where IoT devices are difficult to access, or when frequent replacement is necessary. The risk of losing or forgetting some IoT devices also exists, leading to a risk of hazardous chemical leakage and e-waste in nature. Radio Frequency(RF) wireless power transfer (WPT) offers an alternative solution for powering these devices. Moreover, it has been observed that the receivers absorb less than one-millionth of the transmitter energy while surrounding objects absorb the remainder. This situation opens up the possibility of leveraging existing wireless infrastructures, such as base stations (BSs), to charge IoT devices. In this thesis, we focus on analyzing the feasibility and limitations of battery-less operation of IoT devices using RF WPT technology, along with energy harvesting (EH) from existing wireless communication infrastructure. We explore both indoor and outdoor scenarios for powering IoT devices. Initially, we consider an outdoor environment where an IoT device periodically harvests energy from existing BSs and transmits a data packet related to sensor measurement. We analyze the coverage range of energy harvesting from a BS for powering IoT devices, which shows a tradeoff between the coverage range and the rate of sensor measurements. Additionally, we compare the operational domain in terms of the range and measurement rate for WPT and battery-powered technologies. Furthermore, we consider the coverage probability for a multi-site scenario, which is the likelihood that a randomly allocated IoT device harvests enough power to enable its operation. We derive an expression for this probability at a random location in terms of harvesting sufficient power for IoT device operation at a given measurement rate. Next, we consider the remote powering of IoT devices inside an aircraft. Wired sensors add weight and maintenance costs to the aircraft. Although replacing data cables with wireless communication reduces costs and simplifies deployment, providing power cables for the sensors remains challenging. We assume fixed locations for IoT devices inside an aircraft. The goal is to minimize the number of WPT transmitters for a given cabin geometry and IoT device duty cycles. We address WPT system design under channel uncertainties through robust optimization. Following this, we turn our attention to energy harvesting at a reconfigurable intelligent surface (RIS). The potential benefits of using RIS compared to traditional relays when it comes to improving wireless coverage have been debated in previous works, under the assumption that both technologies have a wired power supply. The comparison would be entirely different if the RIS can become self-sustaining, which is not possible for relays. Therefore, we explore energy harvesting for RIS, proposing an algorithm for phase adjustment to maximize energy harvesting from RF sources based on power measurements. Lastly, we explore the charging of zero-energy devices (ZEDs) via a RIS. Mitigating the path loss in WPT requires large antenna arrays, which leads to increased hardware complexity, as it demands an RF chain per antenna element. Alternatively, RIS offers high beamforming gain with simpler hardware. Therefore, we consider RIS-assisted RF charging of ZEDs. We develop dynamic algorithms for battery-aware and queue-aware scenarios, adjusting RIS phases and transmission power to meet the requirements.

Ökningen av antalet Internet of Things (IoT)-nätverk har gjort batteri-byte i IoT-enheter alltmer utmanande. Detta problem är särskilt besvärligt i scenarier med ett stort antal IoT-enheter, på platser där IoT-enheter är svåra att komma åt, eller när frekventa batteribyten är nödvändiga. Risken att förlora eller glömma bort vissa IoT-enheter finns också, vilket leder till en risk för farligt kemiskt läckage och elektronikskrot i naturen. Trådlös energiöverföring(eng. wireless power transfer, WPT) via radiofrekvens-signaler erbjuder en alternativ lösning för att strömförsörja dessa enheter. När en trådlösa sändare skickar signaler når mindre än en miljondel av energin till de tilltänkta mottagarna,medan resten absorberas av omgivningen. Denna situation skapar möjligheten att utnyttja befintlig trådlös infrastruktur, såsom basstationer,för att ladda IoT-enheter med WPT-teknik. I denna avhandling fokuserar vi på att analysera genomförbarheten och begränsningarna av batterilös drift av IoT-enheter med radiofrekvens-WPT-teknologi, tillsammans med energi utvinningfrån befintlig trådlös kommunikationsinfrastruktur. Vi utforskar energiförsörjning av IoT-enheter både i inomhus- och utomhusscenarier.

Först utforskar vi en utomhusmiljö där en IoT-enhet periodiskt skördar energi från befintliga basstationers signaler och använder den till att sända datapaket relaterade till sensormätningar. Vi analyserar täckningsområdet runten basstation för energiutvinning, vilket visar en avvägning mellan räckvidd och sensormätningens frekvens. Vi jämför WPT-lösningen med batteridrivna teknologier. Därefter analyserar vi täckningssannolikheten i ett fall medmånga basstationer. Detta är sannolikheten att en slumpmässigt placeradIoT-enhet kan skörda tillräckligt med kraft för sin drift. Vi härleder ett uttryck för denna sannolikhet för en given mätningfrekvens.

Därefter analyserar vi energiförsörjning till IoT-enheter inuti ett flygplan.Kabelanslutna sensorer adderar vikt och underhållskostnader till flygplan.Även om kostnaderna minskar ifall datakablar ersätts med trådlös kommunikation så krävs fortfarande strömkablar, men dessa kan eventuellt ersättasmed WPT. Vi antar att IoT-enheterna har förutbestämda placeringar inuti ett flygplan. Målet är att minimera antalet WPT-sändare med hänsyn till kabinens geometri och IoT-enheternas driftcykler. Vi adresserar WPT systemdesign under kanalosäkerheter genom robust optimering.

Efter detta vänder vi vår uppmärksamhet mot energiutvinning för en omkonfigurerbar intelligent yta (eng. reconfigurable intelligent surface, RIS). De potentiella fördelarna med RIS jämfört med traditionella reläer när det gäller att förbättra trådlös täckning har debatterats i tidigare arbeten, under antagandet att båda teknikerna är kabelanslutna till en strömkälla. Jämförelsen blir en annan ifall vi kan göra RIS självförsörjande, eftersom detta inte är möjligt för reläer. Därför utforskar vi trådlös energiutvinning för RIS och föreslår en algoritm för fasjustering för att maximera energiutvinning från radiofrekvens-signaler baserat på energimätningar.

Slutligen utforskar vi laddning av nollenergienheter (eng. zero-energy devices,ZED) med stöd av en RIS som reflekterar signaler mot enheterna. Föratt motverka utbredningsförlusterna i WPT krävs traditionellt stora gruppantennerpå basstationen, vilket leder till ökad hårdvarukomplexitet, eftersomivdet kräver en radio per antennelement. Alternativt erbjuder RIS hög lobformningsvinstmed enklare hårdvara. Därför analyserar vi RIS-assisterad WPTladdningav ZED. Vi utvecklar dynamiska algoritmer för batterimedvetna ochkömedvetna scenarier där vi justerar RIS-faser och sändningseffekten för attuppfylla kraven.

Reconfigurable Intelligent Surfaces (RISs) have gained immense popularity in recent years because of their ability to improve wireless coverage and their flexibility to adapt to the changes in a wireless environment. These advantages are due to RISs' ability to control and manipulate radio frequency (RF) wave propagation. RISs may be deployed in inaccessible locations where it is difficult or expensive to connect to the power grid. Energy harvesting (EH) can enable the RIS to self-sustain its operations without relying on external power sources. In this paper, we consider the problem of energy harvesting for RISs in the absence of coordination with the ambient RF source. We consider both direct and indirect EH scenarios and show that the same mathematical model applies to them. We propose a sequential phase-alignment algorithm that maximizes the received power based on only power measurements. We prove the convergence of the proposed algorithm to the optimal value under specific circumstances. Our simulation results show that the proposed algorithm converges to the optimal solution in a few iterations and outperforms the random phase update method in terms of the number of required measurements.

Energy harvesting can enable a reconfigurable intelligent surface (RIS) to self-sustain its operations without relying on external power sources. In this paper, we consider the problem of energy harvesting for RISs in the absence of coordination with the ambient RF source. We propose a series of sequential phase-alignment algorithms that maximize the received power based on only power measurements. We prove the convergence of the proposed algorithm to the optimal value for the noiseless scenario. However, for the noisy scenario, we propose a linear least squares estimator. We prove that within the class of linear estimators, the optimal set of measurement phases are equally-spaced phases. To evaluate the performance of the proposed method, we introduce a random phase update algorithm as a benchmark. Our simulation results show that the proposed algorithms outperform the random phase update method in terms of achieved power after convergence while requiring fewer measurements per phase update. Using simulations, we show that in a noiseless scenario with a discrete set of possible phase shifts for the RIS elements, the proposed method is sub-optimal, achieving a higher value than the random algorithm but not exactly the maximum feasible value that we obtained by exhaustive search.

Conventional Internet of things (IoT) devices are powered by batteries. However, batteries pose a risk of e-waste and chemical leakage to the environment. An alternative way to power remote IoT devices is to harvest ambient RF energy. It is especially beneficial when battery replacement is costly and could enable large-scale IoT deployments at net-zero energy cost. In this paper, we assume that the IoT devices periodically harvest energy from multiple surrounding base stations (BSs) and use the harvested energy to take sensor measurements and transmit related data packets. We propose an approximate method to analyze the feasibility of this approach in terms of which measurement rates can be supported. To this end, we assume a Poisson point process for the locations of the BSs. We derive mathematical expressions for the coverage probability (i.e., the probability that an IoT device harvests enough energy to operate at a given measurement rate) and the required BS site density in the presence of channel uncertainties, blockage, and harvesting nonlinearities. We derive the parameters of a Gamma distribution to approximate the distribution of the harvested power. For a coverage probability of 0.5, we derive a simplified approximate expression for the required site density that closely describes the one obtained empirically.

Wireless power transfer (WPT) is an alternative technology to conventional batteries for powering Internet of things (IoT) devices. WPT is especially beneficial in situations when battery replacement is infeasible or expensive. It can also reduce battery-related e-waste. In this paper, we analyze the limits of adopting WPT technology for remote powering of IoT devices. We assume that an IoT device periodically harvests energy from a base station (BS) and transmits a data packet related to the sensor measurement under shadow fading channel conditions. Our goal is to characterize the epsilon-coverage range, where epsilon is the probability of the coverage. Our analysis shows a tradeoff between the coverage range and the rate of sensor measurements, where the maximal epsilon-coverage range is achieved as the sensor measurement rate approaches zero. We demonstrate that the weighted sum of the sleep power consumption and the harvesting sensitivity power of an IoT device limits the maximal e-coverage range. Beyond that range, the IoT device cannot harvest enough energy to operate. The desired rate of the sensor measurements also significantly impacts the epsilon-coverage range. Our results suggest that for an IoT device designed using current technology, the maximal 0.95-coverage range is in the order of 120 m. When high measurement rates are required, the coverage range drops to 50-100 m. Compared to battery-powered IoT devices, WPT is well-suited for medium-range applications plus when battery replacement is costly.

With the emergence of the Internet of things (IoT) networks, the replacement of batteries for IoT devices became challenging. In particular, the battery replacement is more expensive and cumbersome for scenarios where there are many IoT devices; or where the IoT devices are in unreachable locations; or when they have to be replaced often. Some IoT devices might be lost or forgotten, and there is a risk of hazardous chemicals leakage and e-waste in large scale in nature. Radio frequency (RF) wireless power transfer (WPT) is an alternative technology for powering those devices. It has been shown that only less than one millionth of the transmitted energy is absorbed by the receivers, the rest is absorbed by the objects in the environment. We can utilize the existing infrastructure for wireless communications such as base stations (BS) to charge IoT devices.

The present work is devoted to analyze the feasibility and limitations of the battery-less operation of IoT devices with RF WPT technology and energy harvesting from existing infrastructure for wireless communications. We study the indoor and outdoor scenarios for powering of IoT devices.

In the first scenario, we consider an outdoor environment where an IoT device periodically harvests energy from an existing BS and transmits a data packet related to the sensor measurement under shadow fading channel conditions. We analyze the limits (e.g., coverage range) of energy harvesting from a BS for powering IoT devices. We characterize the "epsilon-coverage range, where" is the probability of the coverage. Our analysis shows a tradeoff between the coverage range and the rate of sensor measurements, where the maximal "epsilon-coverage range is achieved as the sensor measurement rate approaches zero. We demonstrate that the summation of the sleep power consumption and the harvesting sensitivity power of an IoT device limits the maximal "epsilon-coverage range. Beyond that range, the IoT device cannot harvest enough energy to operate. The desired rate of the sensor measurements also significantly impacts the "epsilon-coverage range. We also compare the operational domain in terms of the range and measurement rate for the WPT and battery-powered technologies.

In the second scenario, we consider the remote powering of IoT devices inside an aircraft. Sensors currently deployed on board have wired connectivity, which increases weight and maintenance costs for aircraft. Removing cables for wireless communications of sensors on board alleviates the cost, however, the powering of sensors becomes a challenge inside aircraft. We assume that the IoT devices have fixed and known locations inside an aircraft. The design problem is to minimize the number of WPT transmitters given constraints based on the cabin geometry and duty cycle of the IoT devices. We formulate a robust optimization problem to address the WPT system design under channel uncertainties. We also derive an equivalent integer linear programming and solve that for an optimal deployment to satisfy the duty cycle requirements of the cabin sensors.

Sensors currently deployed on board have wired connectivity, which increases weight and maintenance costs for aircraft. Removing cables for wireless communications of sensors on board alleviates the cost, however, the powering of sensors becomes a challenge inside aircraft. Wireless power transfer (WPT) via radio-frequency (RF) signals is an emerging solution to remotely power sensors for battery-less operation with long-lived capacitors. In this article, we design a WPT system for aircraft IoT-type applications, including low data rate inside (LI) sensors by determining the number, location, and tilt angles of WPT transmitters given constraints based on the cabin geometry and duty cycle of the sensors. We formulate a robust optimization problem to address the WPT system design under channel uncertainties. We also derive an equivalent integer linear programming and solve that for an optimal deployment to satisfy the duty cycle requirements of LI sensors. We perform experiments inside the cabin to validate the wireless avionics intracommunications channel model. Our simulations demonstrate the feasibility of 90% robust design with 14 WPT transmitters for duty cycles less than 0.1% while keeping the human radiation exposure below the recommended reference value of 4.57 W/m(2).