Kostas Stamatiou

Designing intelligent energy harvesting communication systems

From being a scientific curiosity only a few years ago, energy harvesting (EH) is well on its way to becoming a game-changing technology in the field of autonomous wireless networked systems. The promise of long-term, uninterrupted and self-sustainable operation in a diverse array of applications has captured the interest of academia and industry alike. Yet the road to the ultimate network of perpetual communicating devices is plagued with potholes: ambient energy is intermittent and scarce, energy storage capacity is limited, and devices are constrained in size and complexity. In dealing with these challenges, this article will cover recent developments in the design of intelligent energy management policies for EH wireless devices and discuss pressing research questions in this rapidly growing field.

Superposition Coding Strategies: Design and Experimental Evaluation

We design and implement a software-radio system for Superposition Coding (SC), a multiuser transmission scheme that deliberately introduces interference among user signals at the transmitter, using a library of off-the-shelf point-to-point channel codes. We experimentally determine the set of rate-pairs achieved by this transmission scheme under a packet-error constraint. Our results suggest that SC can provide substantial gains in spectral efficiencies over those achieved by orthogonal schemes such as Time Division Multiplexing. Our findings also question the practical utility of the Gaussian approximation for the inter-user interference in Superposition-Coded systems.

On optimal transmission policies for energy harvesting devices

We consider an energy harvesting device whose state at a given time is determined by its energy level and an “importance” value, associated to the transmission of a data packet to the receiver at that particular time. We consider policies that, at each time, elect whether to transmit the data packet or not, based on the current energy level and data importance, so as to maximize the long-term average transmitted data importance. Under the assumption of i.i.d. Bernoulli energy arrivals, we show that the sensor should report only data with an importance value above a given threshold, which is a strictly decreasing function of the energy level, and we derive upper and lower bounds on the thresholds for any energy level. Leveraging on these findings, we construct a suboptimal policy that performs very close to the optimal one, at a fraction of the complexity. Finally, we demonstrate that a threshold policy, which on the average transmits with probability equal to the average energy arrival rate is asymptotically optimal as the energy storage capacity grows large.

Combining GeoEye-1 Satellite Remote Sensing, UAV Aerial Imaging, and Geophysical Surveys in Anomaly Detection Applied to Archaeology

This paper describes a method of combined ultra-high resolution satellite imaging, unmanned aerial vehicle (UAV) photography, and sub-surface geophysical investigation for anomaly detection, which was employed in a non-invasive survey of three archaeological sites in Northern Mongolia. The surveyed sites were a Bronze Age burial mound, a Turkish period tomb, and a steppe city fortification of unknown origin. For the satellite survey, 50 cm resolution pan-sharpened imagery was generated through a combination of multispectral and panchromatic data, collected from the GeoEye-1 earth-sensing satellite. The imagery was then used to identify the location of the aforementioned sites in an approximate area of 3000 km2 . Aerial photographs of the sites were obtained with two customized electric-powered UAVs: a fixed flying wing rear-propulsion plane and a multi-propeller “oktokopter” helicopter system. Finally, geophysical investigation was conducted with a GSM-19 Overhouser gradiometer, an EM38 electromagnetometer, and an IDS Detector Duo ground penetrating radar. The satellite imagery and aerial photographs were combined with the geophysical survey results and on-site surface observations to provide insight and contextual information about anomalies in multiple layers of data. The results highlight the effectiveness and robustness of the employed method for archaeological investigation in large, rugged and scarcely populated areas.

Random-Access Poisson Networks: Stability and Delay

We consider a Poisson network of sources, each with a destination at a given distance and a buffer of infinite capacity. Assuming independent Bernoulli arrivals, we characterize the stability region when one or two classes of users are present in the network. We then derive a fixed-point equation that determines the success probability of the typical source-destination link and evaluate the mean delay at each buffer.

Delay characterization of multihop transmission in a Poisson field of interference

We evaluate the end-to-end delay of a multihop transmission scheme that includes a source, a number of relays, and a destination, in the presence of interferers located according to a Poisson point process. The medium access control (MAC) protocol considered is a combination of TDMA and ALOHA, according to which nodes located a certain number of hops apart are allowed to transmit with a certain probability. Based on an independent transmissions assumption, which decouples the queue evolutions, our analysis provides explicit expressions for the mean end-to-end delay and throughput, as well as scaling laws when the interferer density grows to infinity. If the source always has packets to transmit, we find that full spatial reuse, i.e., ALOHA, is asymptotically delay-optimal, but requires more hops than a TDMA-ALOHA protocol. The results of our analysis have applications in delay-minimizing joint MAC/routing algorithms for networks with randomly located nodes. We simulate a network where sources and relays form a Poisson point process, and each source assembles a route to its destination by selecting the relays closest to the optimal locations. We assess both theoretically and via simulation the sensitivity of the end-to-end delay with respect to imperfect relay placements and route crossings.

Channel Diversity in Random Wireless Networks

The goal of this paper is to explore the benefits of channel diversity in wireless ad hoc networks. Our model is that of a Poisson point process of transmitters, each with a receiver at a given distance. A packet is divided in blocks which are transmitted over different subbands determined by random frequency hopping. At the receiver, a maximum-likelihood decoder is employed to estimate the transmitted packet/codeword. We show that, if L is the Hamming distance of the error correction code and ε is a constraint on the packet error probability, the transmission capacity of the network is proportional to ε1/L, when ε → 0. The proportionality constant depends on the code selection, the packet length, the geometry of the symbol constellation and the number of receive antennas. This result implies that, at the cost of a moderate decoding complexity, large gains can be achieved by a simple interference randomization scheme during packet transmission. We also address practical issues such as channel estimation and power control. We find that reliable channel information can be obtained at the receiver without significant rate loss and demonstrate that channel-inversion power control can increase the transmission capacity.

Correlated energy generation and imperfect State-of-Charge knowledge in energy harvesting devices

Nowadays, many devices in wireless sensor networks are provided with energy harvesting capability to allow for their continuous operation over long periods of time. In principle, the energy level within each sensor should be managed optimally to ensure the best performance. Network engineers, however, often consider optimality under the idealized assumption of perfect knowledge about the State-of-Charge (SOC) of the device. This information is not always realistic or accurate. In our previous work [1], we showed that optimal policies for sensing, transmission, and battery usage should rather consider uncertainty on the SOC of the device. In this paper, we extend that investigation, therein performed in the idealized scenario of i.i.d. energy arrivals, by considering a correlated energy generation process. We show that the knowledge of the SOC and that of the energy generation process are useful in a complementary manner, that is they can be traded for each other. Moreover, the knowledge on the state of the energy generation process can obviate the need for acquiring accurate SOC information. This investigation paves the road for a new line of research in wireless sensor networks, allowing a tighter interaction between the designers of energy harvesting and battery storage mechanisms on the one hand, and the engineers of network operation and control policies on the other.

The delay-optimal number of hops in Poisson multi-hop networks

We study the delay and throughput in a wireless multihop network with sources that form a Poisson point process and relays which are placed equidistantly on the sourcedestination line. A combined TDMA/ALOHA MAC protocol with intra-route TDMA and inter-route ALOHA is employed. We give bounds on the delay-optimal number of hops and derive the asymptotic delay-throughput tradeoff as the source-destination distance R gets large. The delay includes both the service times and waiting times in the buffers of the typical route. One main finding is that when the transmission probability and number of hops are jointly optimized for minimum delay, the number of hops scales as R⅔ while the delay scales as R4over3.

The Throughput of Underwater Networks: Analysis and Validation using a Ray Tracing Simulator

We propose a theoretical framework to evaluate the expected throughput of underwater networks over an ensemble of node topologies and propagation environments. The analysis is based on the assumptions that the transmitters are spatially distributed according to a Poisson point process, and that the channel follows a Rayleigh fading distribution, with a mean that is determined by spreading loss and frequency-dependent absorption. We evaluate the probability of a successful transmission, i.e., the probability that the signal-to-interference-and-noise ratio at the typical receiver is greater than a given threshold, and determine the maximum network throughput density over the transmitter density and the operating frequency. The theoretical results are validated using a realistic underwater channel simulator based on ray tracing. It is demonstrated that, for a number of practical scenarios, the theoretical and simulated throughput match provided that the spreading-loss exponent is appropriately fitted to the simulation scenario. Overall, the proposed framework provides easy-to-obtain network throughput results, which can be used as a complement or an alternative to time-costly, deployment-dependent network simulations.