In recent years, the number of wireless applications has increased significantly, resulting in the radio bands becoming expensive and prone to interference. There is a new research area aiming at mitigating these issues by creating communication links using ambient light. This area, called passive-VLC, not only exploits the visible light frequencies, but does so with low-power transmitters. All the previous work in passive-VLC, however, forget about individual wavelength bands of light, and do not exploit its wide spectrum, reducing the potential channel capacity. In this paper, we propose a novel method to transmit and decode data, using liquid crystal cells that modulate and consider the full spectrum, and put it to the test by prototyping a multi-symbol communication link. The main contribution of our work is to show that passive-VLC can move from spectrum-agnostic to spectrum-aware modulation. We explore this new domain by making use of a novel type of receiver (i.e., a spectrometer) and uncovering the advantages and caveats of this spectrum-aware approach.

To take advantage of Visible Light Communication (VLC) for low-power applications, such as IoT tags, researchers have been developing systems to modulate (backscatter) ambient light using LC shutters. Various approaches have been explored for single-pixel transmitters, but without following a principled approach. This has resulted in either relatively low data rates, short ranges, or the need for powerful artificial light sources. This paper takes a step back and proposes a more theoretical framework: ChromaLux. By considering the fundamental characteristics of liquid crystals (birefringence and thickness), we demonstrate that the design space is way larger than previously explored, allowing for much better systems. In particular, we uncover the existence of a transient state where the switching time can be reduced by an order of magnitude without lowering the contrast significantly, improving both range and data rate. Using a prototype, we demonstrate that our framework is applicable to different LCs. Our results show significant improvements over state-of-the-art single-pixel systems, achieving ranges of 50 meters at 1 kbps and with bit-error-rates below 1%.

Long Range Wide Area Network (LoRaWAN) covers the needs of energy-constrained IoT-devices for operational longevity and extended communication range in a best-effort fashion. However, Lo- RaWAN’s minimalist design cannot handle the traffic from dense deployments with more than a few hundred devices connected to a single gateway, since each LoRa-device transmits data-packets without any information regarding the availability of the medium. In this paper, we try to improve the scalability of LoRaWAN by manifolds, serving thousands of devices per gateway. We present a novel protocol called p persistent-Channel Activity Recognition Multiple Access (p-CARMA) that exploits LoRaWAN’s Channel Activity Detection (CAD) as a crude mechanism to assess if the channel is free. Due to CAD’s imperfections (it only scans for preambles, not for any channel activity) p-CARMA operates probabilistically with each device deciding on a p value based upon local estimation. At the beginning of operation, this estimate is derived from pure local information, that is without involvement of the gateway, and devices automatically adapt to changes in the environment. Then, the adaptation of p-value is assisted by critical information on the cumulative device-delays, multicasted by the gateway at regular, large timespans. To evaluate the performance of p-CARMA, we implemented it in ns-3 based upon a detailed characterization of LoRaWAN’s CAD mechanism involving an extensive set of real-world experiments. We compared p-CARMA to vanilla LoRaWAN as well as a variant using the theoretically optimal p = 1=N (N being the total number of devices). The simulation results show that p-CARMA achieves from three-fold, up to a twenty-fold higher Packet Reception Ratio than LoRaWAN while handling thousands of devices. Further, its adaptivity outperforms the fixed p-value by a factor of 5.25 when scaling up. Moreover, p-CARMA does so while consuming 37.31%-58.17% less energy on average per device compared to vanilla LoRaWAN.

The main obstacles to achieve truly ubiquitous sensing are (i) the limitations of battery technology - batteries are short-lived, hazardous, bulky, and costly - and (ii) the unpredictability of ambient power. The latter causes sensors to operate intermittently, violating the availability requirements of many real-world applications. In this paper, we present the Coalesced Intermittent Sensor (CIS), an intermittently-powered sensor that senses continuously! Although a single node will frequently be off charging, a group of nodes can -in principle- sense 24/7 provided that their awake times are spread apart. As communication is too expensive, we rely on inherent component variations that induce small differences in power cycles. This basic assumption has been verified through measurements of different nodes and power sources. However, desynchronizing nodes is not enough. An important finding is that a CIS designed for certain (minimal) energy conditions will become synchronized when the available energy exceeds the design point. Nodes employing a sleep mode (to extend their availability) do wake up collectively at some event, process it, and return to charging as the remaining energy is typically too low to handle another event. This results in multiple responses (bad) and missing subsequent events (worse) due to the synchronized charging. To counter this undesired behavior we designed an algorithm to estimate the number of active neighbors and respond proportionally to an event. We show that when intermittent nodes randomize their responses to events, in favorable energy conditions, the CIS reduces the duplicated captured events by 50% and increases the percentage of capturing entire bursts above 85%.

Analyzing agent-based models is a complex task. Agent-based models typically contain complex non-linear interactions between agents and generate emergent properties that cannot easily be explained. They are most commonly analyzed using sensitivity analysis techniques. While these techniques help understanding agent-based models better, they are not a one-size-fits-all solution. This paper explores the novel use of causal discovery algorithms from the field of causality as an additional means to analyze agent-based models. We propose the AbACaD methodology: Agent-based model Analysis using Causal Discovery. In this methodology, emergence in agent-based models is analyzed using causal discovery in combination with both machine learning and sensitivity analysis techniques. AbACaD combines different causal discovery algorithms, using a novel causal graph merging algorithm, to generate a causal graph based on agent-based simulation outcomes. This graph represents the causal relationships between the model parameters and the output variables of the model, and is then exploited to improve the understanding of emergent properties in the model. To demonstrate the effectiveness of AbACaD, it is applied to two models: the El Farol bar model, and an airport security and efficiency model. New emergent properties, such as the moment agents change their strategy in the El Farol bar model were identified. Furthermore, we found queue length to be an important factor in the number of casualties in an improvised explosive device (IED) attack. These emergent properties were well identified using AbACaD, but are hard to identify with traditional analysis techniques alone.

AATOM, the Agent-based Airport Terminal Operations Model simulator is open-source, agent-based at its core, and contains several calibrated presets and templates of basic airport terminal components that can readily be used. Agents in this simulator follow the AATOM architecture, an activity-based architecture for human airport agents. This allows analysis based on agent activities, such as shopping and check-in, which is of vital interest for airports. The combination of agent-based modeling and the presence of basic airport terminal components makes AATOM a unique simulator, allowing the modeler to only focus on implementation of important features of their model. The usefulness of AATOM is demonstrated by presenting case studies in the areas of airport security, gate assignment and resilience.

With the advent of low-cost, embedded sensor-actuator devices, the applications of cyber-physical systems have spread multi-fold in domains like infrastructure, manufacturing, automation, etc. Wireless sensor-actuator networks (WSANs) act as the backbone for applications in these domains. Typical WSAN deployments focus on energy-efficiency (in-turn lifetime) as replacing batteries is labor intensive and expensive. However, many CPS applications require highly-reliable data delivery with strict time bounds. Unfortunately, the classical approach of scheduling/prioritizing flows for bounded time communication is hard to implement with energy-constrained embedded devices. In this work, we present FLEET, a communication primitive that guarantees timely data delivery with 1) low latency by scheduling a maximum number of end-to-end flows within a short time span; 2) highly energy-efficient networking; and 3) reliable data delivery. Using a smart parallelization technique, FLEET achieves simultaneous transmissions while guaranteeing data delivery. This reduces the average duty-cycle of the nodes and makes it more energy-efficient than many state-of-the-art protocols. By combining multiple routing strategies, FLEET not only simplifies the schedulability problem but also accommodates more flows within a time span reducing delay considerably. Overall, with respect to the state of the art, FLEET offers a delay and duty cycling reduction by 2.2 and 2.8 times, respectively.

AATOM, the Agent-based Airport Terminal Operations Model simulator is open-source, agent-based at its core, and contains several calibrated presets and templates of basic airport terminal components that can readily be used. Agents in this simulator follow the AATOM architecture, an activity-based architecture for human airport agents. This allows analysis based on agent activities, such as shopping and check-in, which is of vital interest for airports. The combination of agent-based modeling and the presence of basic airport terminal components makes AATOM a unique simulator, allowing the modeler to only focus on implementation of important features of their model. The usefulness of AATOM is demonstrated by presenting case studies in the areas of airport security, gate assignment and resilience.

Along with exciting visions for 5G communications and the Tactile Internet, the networking requirement of attaining extremely low end-to-end latency has appeared. While network devices are typically equipped with buffers to counteract packet loss caused by short-lived traffic bursts, the more those buffers get filled, the more delay is added to every packet passing through.

In this paper, we develop congestion avoidance methods that harness the power of fully programmable data-planes. The corresponding programmable switches, through languages such as P4, can be programmed to gather and react to important packet meta-data, such as queue load, while the data packets are being processed. In particular, we enable P4 switches to (1) track processing and queuing delays of latency-critical flows and (2) react immediately in the data-plane to congestion by rerouting the affected flows. Through a proof-of-concept implementation in emulation and on real hardware, we demonstrate that a data-plane approach reduces average and maximum delay, as well as jitter, when compared to non-programmable approaches.

This paper reports our experience with crowd monitoring technologies in the challenging real-world conditions of a modern, open-space museum. We seized the opportunity to use the NEMO science center as a testbed, and studied the effectiveness of neighborhood discovery and density estimation algorithms in a network formed by visitors wearing bracelets emitting RF beacons. The diverse set of conditions (flash crowds in open spaces vs. single person booths) revealed three interesting findings: (i) state-of-the-art density estimation fails in 80% of the cases, (ii) RSS-based classifiers fail too, because their underlying assumptions do not hold in many scenarios, and (iii) neighborhood discovery can obtain exact information in an energy-efficient way, provided that static and mobile nodes are differentiated to filter out “passers by” clobbering the true popularity of an exhibit. The overall lesson from the experiment is that today’s algorithms are quite far from the ideal of monitoring popularity in a privacy-preserving and energy-efficient way with minimal infrastructure across the set of heterogeneous conditions encountered in practice.

Typical Wireless Sensor Networks (WSN) deployments use more nodes than needed to accurately sense the phenomena of interest. This redundancy can be leveraged by switching-on only a subset of nodes at any time instant (node-scheduling) and putting the remaining nodes sleep. This effectively extends the network lifetime. In addition to sensing coverage, node-scheduling schemes must also ensure that (i) the network stays connected, and (ii) the time needed to wake-up the complete protocol stack after sleeping is minimized. We present Sleeping Beauty, a highly-efficient data collection protocol that aids node-scheduling schemes in both aspects. Sleeping Beauty uses a slotted and tightly synchronized communication primitive, where a node keeps its radio off for most of the time, except in the slots when it needs to participate for successful communication. Further, an efficient neighbor-discovery mechanism is included that provides partial, but sufficient topology information (potential parents) to avoid network partitions. Furthermore, Sleeping Beauty employs a novel, yet simple clock-offset estimation technique that maintains highly-accurate time synchronization over long radio-off periods (i.e., less than 500 us deviation even after 45, min of sleeping). This minimizes time wasted in resynchronizing the network in between data collection rounds. Through experiments on two different testbeds, we verified that Sleeping Beauty decreases the duty cycle up to a factor of 3 compared to state-of-the-art techniques, while achieving similar delivery ratios.

Opportunistic routing protocols tackle the problem of efficient data collection in dynamic wireless sensor networks, where the radio is duty-cycled to save energy and the topology changes unpredictably due to node mobility and/or link dynamics. Unlike protocols that maintain a routing structure, in opportunistic protocols nodes forward packets to any neighbor that wakes up first, reducing latency and energy costs and increasing the resilience to network dynamics.
We claim the performance of existing opportunistic routing protocols can be improved while retaining their resilience by harnessing the synergy between duty cycling and opportunistic forwarding. To prove this claim, we present Staffetta, the first practical duty-cycle adaptation scheme for opportunistic low-power wireless protocols. Staffetta dynamically adapts each node's wake-up frequency to its current forwarding cost, so nodes closer to the sink become more active than nodes farther away. In this way, Staffetta biases the forwarding choices toward the sink as the neighbor waking up first is also likely to offer high routing progress. Experiments on two testbeds with four different opportunistic routing mechanisms demonstrate that Staffetta achieves severalfold performance improvements compared with a fixed wake-up frequency. As a case a point, Staffetta enables ORW, the state-of-the-art opportunistic routing protocol, to reduce end-to-end packet latency by 79-452 × and energy consumption by 2.75-9× while increasing packet delivery ratio compared with ORW's default link-layer settings.

Cooperation is the foundation of wireless ad hoc networks with nodes forwarding their neighbors' packets for the common good. However, energy and bandwidth constraints combined with selfish behaviour lead to collapsed networks where all nodes defect. Researchers have tried to incentivize or enforce the nodes for cooperation in various ways. However, these techniques do not consider the heterogeneous networks in which a diverse set of nodes with different cognitive capabilities exist. Furthermore, in ad hoc networks identity is a fuzzy concept. It is easy to forge multiple identities and hide defective behaviour. Moreover, the nature of the wireless medium is always ambiguous due to collisions, interference and asymmetric links. In all this uncertainty, having complete information about the intentions of the nodes and acting on it is not straightforward. Backed by evolutionary game theory and multi-agent systems research, we adapt and modify two meta strategies to embrace this uncertainty. These modified meta strategies, Win Stay Loose Shift and Stochastic Imitate Best Strategy, do not require strict identity information and only depend on nodes' own capabilities. Nodes monitor the traffic in their neighbourhood by using a two-hop overhearing method, and decide whether they should be cooperative or defective. We show that nodes are able to discover and use the best strategy in their locality and protect themselves against the exploitation by free riders who devise Sybil attacks by changing their identities.

With the proliferation of WiFi-enabled devices, people expect to be able to use them everywhere, be it at work, while commuting, or when visiting friends. In the latter case, home owners are confronted with the burden of controlling the access to their WiFi router, and usually resort to simply sharing the password. Although convenient, this solution breaches basic security principles, and puts the burden on the friends who have to enter the password in each and every of their devices. The use of social networks, specifying the trust relations between people and devices, provides for a more secure and more friendly authentication mechanism. In this paper, we progress the state-of-the-art by abandoning the centralized solution to embed social networks in WiFi authentication; we introduce EAP-SocTLS, a decentralized approach for authentication and authorization of WiFi access points and other devices, exploiting the embedded trust relations. In particular, we address the (quadratic) search complexity when indirect trust relations, like the smartphone of a friend's kid, are involved. We show that the simple heuristic of limiting the search to friends and devices in physical proximity makes for a scalable solution. Our prototype implementation, which is based on WebID and EAP-TLS, uses WiFi probe requests to determine the pool of neighboring devices and was shown to reduce the search time from 1 minute for the naive policy down to 11 seconds in the case of granting access over an indirect friend.

In data gathering wireless sensor network applications, data correlation among the sensor nodes have been utilized to extend network lifetimes. It has been shown that the data correlation also exists between nodes that are far away, contrary to the assumption that correlation decreases as a function of distance. Therefore, it is possible to group the nodes based on the correlation among their data regardless of their location. Given that data from one active node per group is sufficient to reconstruct the sensed data for the remaining sensor nodes, most of the nodes can be kept in low-power sleep mode. However, only few active nodes will usually create a disconnected network, and hence failing the purpose of the deployment. In this paper we formalize this problem, referred to as Sleep-route, of selecting the minimum number of connected active nodes that are sufficient to predict the sensed data for remaining sleeping nodes with high accuracy. We prove that the problem is NP-hard. Thus, we develop a greedy algorithm, Sleep-route heuristic that provides near-optimal solutions. Using Contiki-based simulations, we show that our scheme can extend network lifetime up to 42% as compared to the state-of-the-art solutions.

Analyzing the power consumption of smartphones is difficult because of the complex interplay between soft- and hardware. Currently, researchers rely on mainly two options: external measurement tools, which are precise but constrain the mobility of the device and require the annotation of power traces; or modelling methods, which allow mobility and consider explicitly the state of events, but have less accuracy and lower sampling rates than external tools.

We address the challenges of mobile power analysis with a novel power metering toolkit, called NEAT, which comprises a coin-sized power measurement board that fits inside a typical smartphone, and analysis software that automatically fuses the event logs taken from the phone with the obtained power trace. The combination of high-fidelity power measurements and detailed information about the state of the phone's hardware and software components allows for fine-grained analysis of complex and short-lived energy patterns.

We equipped smartphones with NEAT and conducted various experiments to highlight (i) its accuracy with respect to model-based approaches, showing errors upwards of 20%; (ii) its ability to gather accurate and well annotated user-data "in the wild", which would be hard to do with current external meters; and (iii) the importance of having fine-granular and expressive traces by resolving kernel energy bugs.

We address the problem of estimating the neighborhood cardinality of nodes in dynamic wireless networks. Different from previous studies, we consider networks with high densities (a hundred neighbors per node) and where all nodes estimate cardinality concurrently. Performing concurrent estimations on dense mobile networks is hard; we need estimators that are not only accurate, but also fast, asynchronous (due to mobility) and lightweight (due to concurrency and high density). To cope with these requirements, we propose Estreme, a neighborhood cardinality estimator with extremely low overhead that leverages the rendezvous time of low-power medium access control (MAC) protocols. We implemented Estreme on the Contiki OS and show a significant improvement over the state-of-the-art. With Estreme, 100 nodes can concurrently estimate their neighborhood cardinality with an error of ≈10%. State-of-the-art solutions provide a similar accuracy, but on networks consisting of a few tens of nodes and where only a fraction of nodes estimate the cardinality concurrently.

In spite of using unreliable resource-constrained devices, sensor networks can nowadays deliver 99.9% of their data with duty cycles well below 1%. This remarkable performance is, however, dependent on one or more of the following assumptions: low traffic rates, medium size densities and static nodes.