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Satellites Over Seals (SOS), a project initiated in late 2016, is a crowdsourced method to determine factors behind the presence/absence patterns and to ultimately determine the global population of the Weddell seal (Leptonychotes weddellii). An iconic species, the Weddell seal is proposed to be part of the Antarctic Research and Monitoring Program required in the newly designated Ross Sea Region Marine Protected Area. This species is easy to detect via satellite imagery, due to its large size (3–4 m long, 1 m wide) and its dark color contrasting with the Antarctic coastal fast ice, where it aggregates on during breeding season. Using very high‐resolution satellite imagery (VHR; 0.31–0.60 m resolution) and the online platform Tomnod, we used VHR images from November 2010 and 2011 to cover the entirety of available fast ice around Antarctica. Before correcting for time of day or date, we searched for the presence/absence to identify a subset of where abundance estimates should be concentrated. More than 325 000 citizen scientists searched 790 VHR images, covering 268 611 km2 of fast ice, to determine the locations of seals. Algorithms ranked searchers to the degree their votes corresponded with others, a measure of searcher relative quality that we used to filter out unreliable searchers. Seal presence was detected on only 0.55% of available maps (total n = 1 116 058) within fast ice, revealing a sparse, irregular distribution. The rate of false‐negative detections was 1.7%, though false positives were high (67%), highlighting the importance of training for image interpretation to ensure differentiation between seals and landscape features (such as large rocks, ice chunks or depressions/holes in the ice). This approach not only allowed us to assess image resolution and quality, but also training, outreach and the effectiveness of this platform for introducing citizen scientists to the ecology of the Southern Ocean.

Citizen science programs can be effective at collecting information at large temporal and spatial scales. However, sampling bias is a concern in citizen science datasets and can lead to unreliable estimates. We address this issue with a novel approach in a first-of-its-kind citizen science survey of Weddell seals for the entire coast of Antarctica. Our citizen scientists inspected very high-resolution satellite images to tag any presumptive seals hauled out on the fast ice during the pupping period. To assess and reduce the error in counts in term of bias and other factors, we ranked surveyors on how well they agreed with each other in tagging a particular feature (presumptive seal), and then ranked these features based on the ranking of surveyors placing tags on them. We assumed that features with higher rankings, as determined by “the crowd wisdom,” were likely to be seals. By comparing citizen science feature ranks with an expert’s determination, we found that non-seal features were often highly ranked. Conversely, some seals were ranked low or not tagged at all. Ranking surveyors relative to their peers was not a reliable means to filter out erroneous or missed tags; therefore, we developed an effective correction factor for both sources of error by comparing surveyors’ tags to those by the expert. Furthermore, counts may underestimate true abundance due to seals not being present on the ice when the image was taken. Based on available on-the-ground haul-out location counts in Erebus Bay, the Ross Sea, we were able to correct for the proportion of seals not detected through satellite images after accounting for year, time-of-day, location (islet vs. mainland locations), and satellite sensor effects. We show that a prospective model performed well at estimating seal abundances at appropriate spatial scales, providing a suitable methodology for continent-wide Weddell Seal population estimates.

Accurate and topical spatial datasets representing human populations are foundational to solving humanitarian issues. This paper provides unique solutions to accurately map human settlements both at scale and across remote areas. A method of village boundary extraction from very high resolution optical satellite imagery is proposed. Furthermore, the supplement of a crowd-sourced validation process to filter the detections for higher accuracy and the automated mosaic techniques are detailed. To demonstrate the computational and informational scalability of the process, four distinct geographic locations in Nigeria, Somalia, Pakistan, and Afghanistan were analyzed for a total processed area of 592 000 km 2 , comprised of 1159 high-resolution DigitalGlobe images. The geographic variability of the locations and the scale of the projects required dynamic and automated solutions, respectively. The curated results exhibit high recall and precision of human settlement data in remote as well as urban areas. Crowdsourced validation allows complete control over the precision of the final village boundary layer, and given time, an effective 100% precision can be achieved. This highly scalable and precise system is perfectly adequate for processing regional and country-scale areas, with minimal human effort.

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.

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.

This paper considers a wireless sensor powered by an energy harvesting device, which reports data of varying importance to its receiver. Modeling the ambient energy supply by a two-state Markov chain ("GOOD" and "BAD"), assuming a finite battery capacity constraint, and associating data transmission with a given energy cost, we propose low-complexity transmission policies, that achieve near-optimal performance in terms of the average long-term importance of the reported data. In particular, we derive the performance of the Balanced Policy (BP), which adapts the transmission probability to the harvesting state, such that energy harvesting and consumption are balanced. Our analysis demonstrates that the performance of the BP largely depends on the power-to-depletion, defined as the power that a fully charged battery can supply on average over a BAD period. Numerical results show that the optimal BP achieves near-optimal performance and that a BP which avoids energy overflow further reduces the gap with respect to the globally optimal policy. A heuristic BP, based on the analysis of a system with a deterministic and periodic energy supply, is also proposed, and the parallels between the deterministic system and its stochastic counterpart are discussed.

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.

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 e is a constraint on the packet error probability, the transmission capacity of the network is proportional to e1/L, when e → 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.

We consider a cellular network where base stations are randomly deployed according to a Poisson point process and new jobs arrive in time and space according to a Poisson space-time point process, and are assigned to base stations according to different assignment schemes with/without base station cooperation. We derive “macroscopic” spatial averages of local base station performance metrics, such as traffic load, utilization factor, and delay, under different models on the service time distribution which include dependence on distance. Moreover, we determine and derive properties of the traffic capacity of the network, defined as the maximum spatio-temporal job density under a given constraint on the percentage of unstable base stations. The proposed model provides a baseline for the study of irregular cellular networks with bursty spatio-temporal data traffic.

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.

As Energy Harvesting Devices (EHD) become more widely deployed in sensor network platforms, the need arises for "smart" operation policies which can ensure long-term, autonomous and reliable operation. Existing research has relied on the implicit assumption of perfect knowledge of the energy available in the EHD. However, estimating the energy level of the batteries or super-capacitors employed in real-world EHDs, commonly known as State-Of-Charge (SOC), is a non-trivial task. In this paper, we design operation policies that maximize the long-term reward under imperfect knowledge of the SOC. Through an array of simulation results, we quantify the performance degradation due to imperfect SOC knowledge, and show that it increases with decreasing storage capacity and increasing variance in the energy arrival process. In the particular case of a two-state controller, i.e., a controller which knows only if the SOC is HIGH or LOW, we prove that, for a linear reward function, there is no performance loss, while, for a logarithmic reward function, simulations show that the loss is typically less than 5%.

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.

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.

Sensors powered by energy harvesting devices (EHD) are increasingly being deployed in practice, due to the demonstrated advantage of long-term, autonomous operation, without the need for battery replacement. This paper is concerned with the following fundamental problem: how should the harvested energy be managed to ensure optimal performance, if the statistical properties of the ambient energy supply are known? To formulate the problem mathematically, we consider an EHD-powered sensor which senses data of varying importance and model the availability of ambient energy by a two-state Markov chain (“GOOD” and “BAD”). Assuming that data transmission incurs an energy cost, our objective is to identify low-complexity transmission policies, which achieve good performance in terms of average long-term importance of the transmitted data. We derive the performance of a Balanced Policy (BP), which adapts the transmission probability to the ambient energy supply, so as to balance energy harvesting and consumption, and demonstrate that it performs within 5% of the globally optimal policy. Moreover, a BP which avoids energy overflow by always transmitting when the sensor battery is fully charged is shown to perform within 4% of the optimum. Finally, we identify a key performance parameter of the system, the relative battery capacity, defined as the ratio of the battery capacity to the expected duration of the BAD harvesting period.

In this paper, we derive fundamental performance limits of underwater (UW) networks via an analysis of the average behavior of random deployments. In particular, we consider an UW network that consists of a Poisson point process of transmitters, each with a receiver at a given distance. The link channel model accounts for path-loss, frequency-dependent absorption, and Rayleigh fading. We evaluate the probability of a successful transmission, defined as the probability that the signal-to-interference-and-noise ratio at the typical receiver is greater than a predetermined threshold, over different network and channel realizations. We then determine the network throughput density and the transmission capacity, defined as the maximum throughput density such that a constraint on the success probability is satisfied. The dependence of these metrics on the operating frequency and other system parameters is quantified through the proposed framework.

We consider a wireless multi-hop network with sources that are Poisson distributed and relays which are placed on the source-destination line. Given a combined TDMA/ALOHA MAC protocol, we explore the following question of optimal spatial reuse: Increasing the number of nodes that are simultaneously scheduled to transmit in a route allows nodes to transmit more often. At the same time, it results in an increase of intra-route and inter-route interference, which has a negative impact on the end-to-end delay and throughput. In a regime of large source-destination distances $R$, we find that it is delay-optimal for either only one node, or a number of nodes that increases linearly in $R$, to be scheduled in each slot, depending on the ALOHA probability. If the transmission probability is also optimized, we find that maximum spatial reuse is delay-optimal. Scaling laws for the end-to-end delay and throughput are derived in all cases.

We consider a network where each route comprises a backlogged source, a number of relays and a destination at a finite distance. The locations of the sources and the relays are realizations of independent Poisson point processes. Given that the nodes observe a TDMA/ALOHA MAC protocol, our objective is to determine the number of relays and their placement such that the mean end-to-end delay in a typical route of the network is minimized.We first study an idealistic network model where all routes have the same number of hops, the same distance per hop and their own dedicated relays. Combining tools from queueing theory and stochastic geometry, we provide a precise characterization of the mean end-to-end delay. We find that the delay is minimized if the first hop is much longer than the remaining hops and that the optimal number of hops scales sublinearly with the source-destination distance. Simulating the original network scenario reveals that the analytical results are accurate, provided that the density of the relay process is sufficiently large. We conclude that, given the considered MAC protocol, our analysis provides a delay-minimizing routing strategy for random, multihop networks involving a small number of hops.

We consider a network of transmitters, each with a receiver at a fixed distance, and locations drawn independently according to a homogeneous Poisson Point Process (PPP). The transmitters and the receivers are equipped with multiple antennas. Under a channel model that includes Rayleigh fading and path-loss, and an outage model for packet successes, we examine the performance of various spatial multiplexing techniques, namely zero-forcing (ZF), ZF with successive interference cancellation (ZFSIC or VBLAST) and DBLAST. In each case, we determine the number of streams that maximizes the transmission capacity, defined as the maximum network throughput per unit area such that a constraint on the outage probability is satisfied. Numerical results showcase the benefit of DBLAST over ZF and VBLAST in terms of the transmission capacity. In all cases, the transmission capacity scales linearly in the number of antennas.

We consider an ad hoc network where frequency hopping (FH), convolutional coding and multiple antennas are employed in order to combat fading and multiple access (MA) interference. The transmitters are distributed in space according to the Poisson distribution and the corresponding receivers are situated at a fixed distance. Under a channel model that accounts for Rayleigh fading and path-loss, we derive an approximation to the frame-error-probability (FEP). This is in turn used to obtain simple expressions for network metrics of interest, such as the network throughput and the information efficiency, previously introduced in the literature. A number of interesting observations is made with respect to how the aforementioned metrics are affected by the code diversity order, the choice of multiple- input multiple-output (MIMO) technique and the propagation exponent.

We consider an ad hoc network where frequency hopping (FH) and coding are employed in order to mitigate multiple-access interference. In contrast to previous work, in our model hopping takes place at the symbol level. As a result, different interference levels are encountered across a codeword/packet, creating interference diversity which is exploited through coding. Moreover, the nodes are equipped with multiple antennas which can be used for further error protection or increase of the transmission rate through space-time block coding or spatial multiplexing, respectively. Assuming an infinite network of terminals, modelled as a Poisson random field, the performance is evaluated in terms of the product (spectral efficiency) times (distance), often referred to as information efficiency. With a cross-layer design perspective in mind, results are presented for various combinations of routing strategies, MIMO techniques and code diversity orders.