Given the significant amount of time that people spend indoors, public spaces are reinventing themselves to deliver immersive experiences. One approach is to provide interactive gaming, such as the spaces created in big international airports like Singapore. The problem is that these gaming areas rely on either (i) wearable devices, which can easily spread germs, particularly in highly transited areas, such as those in airports, or (ii) cameras, which are increasingly raising privacy concerns and are being forbidden in some public areas. To provide an immersive gaming experience that is device-free and privacy-aware, we propose a system based on mmWave radar. In particular, our work provides three contributions. (1) For the first time, we compare various tracking and human pose estimation (HPE) models in the SoA under a unified framework. This approach allows us to identify the best methods in terms of position accuracy, latency, and smoothness–which are critical gaming metrics. (2) We build an integrated system with tracking and HPE capabilities. Our system includes the design of four games with different levels of complexity and a dataset collected specifically to train models for gaming applications. (3) Our evaluation, which includes a subjective users’ survey and an objective comparison with a Kinect console, shows that today’s mmWave is suitable for games where only the user’s location is required or when coarse-grained gestures are needed. There are two key areas where mmWave research needs to improve to match camera-based consoles: sensors need a higher sampling rate, since a high frame rate is critical for seamless gaming, and HPE models need to master fine-grained gestures, in particular when arms end up in front of the body. All the code and datasets will be made available as a stepping stone for future work.

Machine learning models can now run on microcontrollers. Thanks to the advances in neural architectural search, we can automatically identify tiny machine learning (tinyML) models that satisfy stringent memory and energy requirements. However, existing methods often overlook the energy used during event detection and data gathering. This is critical for devices powered by renewable energy sources like solar power, where energy efficiency is paramount. To address it, we introduce SolarML, a solution designed specifically for solar-powered tinyML platforms, which optimizes the end-to-end system's inference accuracy and energy consumption, from data gathering and processing to model inference. Considering two applications of gesture recognition and keywords spotting, SolarML has the following contributions: 1) a hardware platform with an optimal event detection mechanism that reduces event detection costs by up to 10 x compared to state-of-the-art alternatives; 2) a joint optimization framework eNAS that reduces the energy consumption of the sensor and inference model by up to 2 x, compared to methods that only optimize the inference model. Jointly, they enable SolarML to run end-to-end gesture and audio inference on a battery-free tinyML platform by only harvesting solar energy for 30 and 57 seconds, respectively, in an office environment (500 lux). Source code is available at [1].

Over the past decade, visible light positioning has become increasingly important for precise localization systems, yet its widespread adoption is limited due to the necessity of modifying existing lighting systems. This article presents HueLoc, a novel method that bypasses this issue by using inherent features of light, such as the dominant colors in white light-emitting diode (LED) lights, and employs affordable, energy-efficient hue sensors for location services. We propose that by extracting the power at dominant wavelengths of LEDs, these can be uniquely identified using a specifically designed signature. The unique signatures can be used by mobile objects for spatial awareness and further localization using the proposed regression-based learning approach. Our experiments demonstrate that HueLoc attains a location-mapping accuracy of 100% and achieves decimeter-level localization precision with a moving object in uncontrolled lighting conditions. Moreover, these unique signatures can be combined with other RF-based technologies to enhance their localization accuracy. As an example, this article details the integration of Bluetooth features with light signatures using a three-stage incremental learning approach. The experimental results show that this fusion method significantly improves Bluetooth localization by over 75%, overcoming challenges associated with severe indoor multipath and achieving highly precise location accuracy within decimeters.

Cardiac patterns are being used to provide hard-to-forge biometric signatures in identification applications. However, this performance is obtained undercontrolled scenarioswhere cardiac signals maintain a relatively uniform pattern, facilitating the identification process. In this work, we analyze cardiac signals collected in morerealistic (uncontrolled) scenariosand show that their high signal variability makes them harder to obtain stable and distinct features. When faced with these irregular signals, the state-of-the-art (SOTA) reduces its performance significantly. To solve these problems, we propose the CardioID framework 1 with two novel properties. First, we design an adaptive method that achieves stable and distinct features by tailoring the filtering process according to each user’s heart rate. Second, we show that users can have multiple cardiac morphologies, offering us a bigger pool of cardiac signals compared to the SOTA. Considering threeuncontrolleddatasets, our evaluation shows two main insights. First, while using a PPG sensor with healthy individuals, the SOTA’s balanced accuracy (BAC) reduces from 90–95% to 75–80%, while our method maintains a BAC above 90%. Second, under more challenging conditions (using smartphone cameras or monitoring unhealthy individuals), the SOTA’s BAC reduces to values between 65–75%, and our method increases the BAC to values between 75–85%.

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.

We explore a new alternative for drones to gather information from sensors. Instead of using the traditional radio-frequency spectrum, whose broadcast nature makes it more difficult to poll specific objects, we utilize the light spectrum. In our system, the drone carries a light, and flies to an area that it is interested in polling. Only the sensor (tag) under the coverage of the light sends data back by backscattering the impinging light waves. Enabling this system poses two challenges. First, a reliable modulation method with light is required. The method must overcome noise dynamics introduced by the drone (mechanical oscillations), the object (backscattering effects) and the environment (interference from ambient light). Second, to facilitate the deployment of tags in pervasive applications, the design of the tag should be battery-less and have a small surface area. These requirements limit the amount of power available for reception, transmission and sensing, since the energy harvested by solar cells is proportional to their surface area. Regarding the first challenge, we show that the amplitude-based modulation methods used in state-of-the-art studies do not work in our scenario, and investigate instead a frequency-based approach. For the second challenge, we optimize the computation, reception and transmission of the tag to create a battery-less design that operates with frequency-modulated signals generated from light. We build a prototype for the drone and the tag, and test them under different lighting scenarios: dark, indoors, and outdoors with sunlight. The results show that, under standard indoor lighting, our system can attain a polling range of 1.1 m with a data rate of 120 bps, while the tag operates with small solar cells and consumes less than 1 mW.

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%.

Light-based positioning systems (LPS) are gaining significant attention as a means to provide localization with cm accuracy. Many of these systems estimate the object position based on the received light intensity, and work properly in `ideal' environments such as large open spaces without obstructions around the light-emitting diode (LED) and the receiver, where reflections are negligible. In more dynamic environments, such as indoor spaces with moving people and city roads with moving vehicles, materials cause a wide variety of reflections. This causes variations in the received light intensity and, as a consequence, gross localization errors in LPS. We propose a new multipath detection technique for improving LPS that does not require the knowledge of the channel impulse response and then, it is suited to be implemented in low-cost positioning receivers that use a single-pixel photodetector. To develop our technique, we (i) analyze the statistical properties of non-line-of-sight (NLOS) components, (ii) develop an automated testbed to study the reflections of different types of surfaces and materials, and (iii) design an algorithm to remove the NLOS components affecting the positioning estimate. Our experimental evaluation shows that, in complex environments, our methodology can reduce the localization error using LEDs up to 93%.

To compete with online shopping, retailers are constantly looking for ways to improve the display of their products and to track customers to obtain shopping patterns. We propose a general framework that exploits simple color sensors on ceilings to tackle the above-mentioned challenges. Our first contribution is a tunable lighting system that estimates accurately the true color of a product, and then, adjusts automatically the type of lighting to increase the product's appeal. Based on this accurate estimation of color, our second contribution is a system to track people anonymously. Relying solely on the reflections coming from people's clothes, hair and skin, we use color sensors to generate unique optical signatures for individuals. Our evaluation shows that, in spite of the limited information provided by color sensors, the optical signatures are precise enough to differentiate people with very similar appearance except for some minor differences in their clothing.

Positioning based on visible light is gaining significant attention. But most existing studies rely on a key requirement: The object of interest needs to carry an optical receiver (camera or photodiode). We remove this requirement and investigate the possibility of achieving accurate positioning in a passive manner—that is, without requiring objects to carry any optical receiver. To achieve this goal, we propose PassiveVLP, in which we exploit the reflective surfaces of objects and the unique propagation properties of LED luminaires. We present geometric models, a testbed implementation, and empirical evaluations to showcase the opportunities and challenges posed by this new type of passive positioning. Overall, we show that our PassiveVLP can track with high accuracy (a few centimeters) a subset of an object’s trajectory, and it can also identify passively the object’s ID.

Visible Light Communication (VLC) based on LEDs has been a hot topic investigated for over a decade. However, most of the research efforts assume the intensity of LED light is constant. This hypothesis is not true when Smart Lighting is introduced to VLC, which requires LEDs to adapt their brightness based on the intensity of natural ambient light. Smart lighting saves power consumption and improves user comfort. However, intensity adaptation severely affects the throughput performance of data communication. In this paper, we propose SmartVLC, a system that can maximize the throughput (benefit communication) while still maintaining the LEDs' illumination function (benefit smart lighting). A novel Adaptive Multiple Pulse Position Modulation (AMPPM) scheme is proposed to support fine-grained dimming levels to avoid flickering while maximizing the throughput under each dimming level. SmartVLC is implemented on off-the-shelf commodity hardware. Several real-life challenges in both hardware and software are addressed to make it a robust real-time system. Comprehensive experiments are carried out to evaluate the system performance under multifaceted scenarios. Experimental results demonstrate that SmartVLC supports a communication distance up to 3.6m, and improves the throughput achieved with two state-of-the-art approaches by 40 and 12 percent on average, respectively, without bringing any flickering to users.

We analyze and optimize the performance of a new type of channel that exploits sunlight for wireless communication. Recent advances on visible light backscatter have shown that if mobile objects attach distinctive reflective patterns to their surfaces, simple photosensors deployed in our environments can decode the reflected light signals. Although the vision is promising, only initial feasibility studies have been performed so far. There is no analysis on how much information this channel can transmit or how reliable the links are. Achieving this vision is a complex endeavour because we have no control over (i) the sun or clouds, which determine the amount and direction of light intensity, and (ii) the mobile object, which determines the modulated reflection of sunlight. We investigate the impact of the surrounding light intensity and physical properties of the object (reflective materials, size and speed) to design a communication system that optimizes the encoding and decoding of information with sunlight. Our experimental evaluation, performed with a car moving on a regular street, shows that our analysis leads to significant improvements across many dimensions. Compared to the state of the art, we can encode seven times more information, and decode this information reliably from an object moving three times faster (53km/h) at a range that is four times longer (4m) and with three times lower light intensity (cloudy day).

Many research and industrial communities are betting on LoRa to provide reliable, long-range communication for the Internet of Things. This new radio technology, however, provides widely heterogeneous coverage; a LoRa link may span hundreds of meters or tens of kilometers, depending on the surrounding environment. This high variability is not captured by popular channel models for LoRa, and on-site measurements---a common alternative---are impractical due to the large geographical areas involved.

Transmitting information with visible light requires controlling the intensity of the light source. Many light sources in our environments, however, cannot be controlled (not only the sun but also plenty of light bulbs). These uncontrollable light sources provide an immense amount of ambient light that could be used for wireless communication, yet few studies are exploiting this opportunity. We provide a detailed analysis of a Hardware- and Physical-Layer to create safe and reliable wireless links relying solely on ambient light and simple photosensors. Motivated by recent studies, our platform builds upon liquid crystal displays (LCDs) to backscatter ambient light, but it provides a unique and novel feature: our platform utilizes frequency signals to modulate ambient light. Compared to the state-of-the-art, which rely on either pulse- or color-based modulation, our approach allows us to provide simultaneously: a simple and energy-efficient platform (no cameras), flicker-free communication (safe), and the ability to work reliably in spite of the interference and light fluctuations caused by uncontrollable light sources (reliable). We test our platform in indoor and outdoor environments, and show that an LCD surface of 6×8 cm can transmit 80 bps at ranges between 4 meters (indoors) and 60 meters (outdoors), consuming a fraction of the energy required by comparable systems using cameras.

Artificial lighting is a pervasive element in our daily lives. Researchers from different communities are investigating challenges and opportunities related to artificial lighting but from different angles: energy disaggregation, to monitor the status of light bulbs in buildings; and communication, to transmit information wirelessly. We argue that there is an unexplored synergy between these two communities. When a light bulb modulates its intensity for communication, it also affects the current it draws. This current signature is unique and could be used by energy disaggregation methods to identify the lights' status. These signatures however will be exposed to interference (collisions of signatures) and distortions due to power line effects. To overcome these problems, we build upon coding schemes to assign interference-resilient signatures, and we develop custom hardware to ameliorate distortions introduced by power lines. We validate our framework in a proof-of-concept testbed, perform simulations to test scalability, and use energy traces from real homes to evaluate the impact of other electric loads.

Localization based on visible light is gaining significant attention. But most existing studies rely on a key requirement: the object of interest needs to carry an optical receiver (camera or photodiode). We remove this requirement and investigate the possibility of achieving accurate localization in a passive manner, that is, without requiring objects to carry any optical receiver. To achieve this goal, we exploit the reflective surfaces of objects and the unique propagation properties of LED luminaires. We present geometric models, a testbed implementation, and empirical evaluations to showcase the opportunities and challenges posed by this new type of localization. Overall, we show that our method can track with high accuracy (few centimeters) a subset of an object's trajectory and it can also identify passively the object's ID.

LEDs in our buildings, vehicles, and consumer products are rapidly gaining visible light communication capabilities. LED links however are notorious for being unreliable: shadowing, blockage, mobility, external light, all of these issues can disrupt the connectivity easily. Therefore, unless a reliable and cost-efficient data link layer is designed, VLC will be confined to niche applications. In this paper, we reveal a reason for unreliable VLC: a single type of photodetector at the receiver cannot establish a reliable link. We show that the photodetectors with complementary properties, in terms of optical spectral response and field-of-view, are necessary to handle the wide dynamic range of optical noise (such as the sun and other unwanted light sources) and mobility of users. Motivated by our experimental observations, we design a reliable and adaptive receiver for VLC (REAL-VLC) for low-end communication systems, an inexpensive receiver that senses light with complementary photodetectors and configures itself (physical and data link layers) dynamically to maintain the communication link. We implement the hardware and the software of REAL-VLC in low-end platforms, and experimentally validate it in representative test scenarios and a proof-of-concept application that consists of mobile nodes maintaining a VLC link under various lighting and path conditions.

Visible light is gaining significant attention as a medium to achieve accurate relative localization. Most of the studies in the area focus on indoor positioning and rely on two important assumptions: (i) lights are static, and (ii) the receiver has line-of-sight with multiple lights. These requirements limit the application of localization methods in scenarios where nodes have a single light and are mobile, such as motorbikes or swarms of robots. In general, this particular type of scenarios (single lights moving on a plane) leads to under-determined localization systems where no unique solution can be found. We follow a holistic approach that includes theory, simulations, and experiments to overcome some of the limitations present in such type of scenarios. Our theoretical and simulation results show that if nodes are enhanced with sensors providing relative directions (such as compasses), we can derive dependencies in the system to obtain unique solutions. Our proof-of-concept implementation validates our model by showing that single lights can provide relative localization with high accuracy: an average error below 5 cm.

Visible Light Communication (VLC) based on LEDs has been a hot topic investigated for over a decade. However, most of the research efforts in this area assume the intensity of the light emitted from LEDs is constant. This is not true any more when Smart Lighting is introduced to VLC in recent years, which requires the LEDs to adapt their brightness according to the intensity of the natural ambient light. Smart lighting saves power consumption and improves user comfort. However, intensity adaptation severely affects the throughput performance of the data communication. In this paper, we propose SmartVLC, a system that can maximize the throughput (benefit communication) while still maintaining the LEDs' illumination function (benefit smart lighting). A new adaptive multiple pulse position modulation scheme is proposed to support fine-grained dimming levels to avoid flickering and at the same time, maximize the throughput under each dimming level. SmartVLC is implemented on low-cost commodity hardware and several real-life challenges in both hardware and software are addressed to make SmartVLC a robust realtime system. Comprehensive experiments are carried out to evaluate the performance of SmartVLC under multifaceted scenarios. The results demonstrate that SmartVLC supports a communication distance up to 3.6m, and improves the throughput achieved with two state-of-the-art approaches by 40% and 12% on average, respectively, without bringing any flickering to users.

In this work, we propose a new communication system for illuminated areas, indoors and outdoors. Light sources in our environments --such as light bulbs or even the sun-- are our signal emitters, but we do not modulate data at the light source. We instead propose that the environment itself modulates the {ambient} light signals: if mobile elements `wear' patterns consisting of distinctive reflecting surfaces, single photodiode could decode the disturbed light signals to read passive information. Achieving this vision requires a deep understanding of a new type of communication channel. Many parameters can affect the performance of passive communication based on visible light: the size of reflective surfaces, the surrounding light intensity, the speed of mobile objects, the field-of-view of the receiver, to name a few. In this paper, we present our vision for a passive communication channel with visible light, the design challenges and the evaluation of an outdoor application where our receiver decodes information from a car moving at 18 km/h.

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.

Graph filters are a recent and powerful tool to process information in graphs. Yet despite their advantages, graph filters are limited. The limitation is exposed in a filtering task that is common, but not fully solved in sensor networks: the identification of a signal's peaks and pits. Choosing the correct filter necessitates a-priori information about the signal and the network topology. Furthermore, in sparse and irregular networks graph filters introduce distortion, effectively rendering identification inaccurate, even when signal-specific information is available. Motivated by the need for a multi-scale approach, this paper extends classical results on scale-space analysis to graphs. We derive the family of scale-space kernels (or filters) that are suitable for graphs and show how these can be used to observe a signal at all possible scales: from fine to coarse. The gathered information is then used to distributedly identify the signal's peaks and pits. Our graph scale-space approach diminishes the need for a-priori knowledge, and reduces the effects caused by noise, sparse and irregular topologies, exhibiting: (i) superior resilience to noise than the state-of-the-art, and (ii) at least 20% higher precision than the best graph filter, when evaluated on our testbed.

Visible light communication (VLC), a novel technology that enables standard Light-Emitting-Diodes (LEDs) to transmit data, is gaining significant attention. In the near future, this technology could enable devices containing LEDs-such as car lights, city lights, screens and home appliances-to form their own networks. VLC, however, is currently limited to point-to-point communication. To unleash VLC's full potential, we need to provide it with more sophisticated networking capabilities. In this paper, we present the design and implementation of a novel platform aimed at distributed multi-hop visible light communication. Compared to the state-of-the-art, our platform provides similar data rates and coverage, but adds two unique characteristics: (i) 360° coverage, which is necessary to investigate an important property of LED communication: directionality, and (ii) a flexible design, which allows our platform to be connected to many experimental boards such as Arduino, Beagle bone, Raspberry Pi and sensor nodes. To quantify the communication capabilities of our board, we evaluate three key components: link quality, neighbor discovery and packet forwarding. Overall, we hope that our work will lower the entry barrier for members of the pervasive and networking communities to investigate and exploit future LED-based networks.

The decentralized detection of event regions is a fundamental building block for monitoring and reasoning about spatial phenomena. However, so far the problem has been studied almost exclusively for static networks. This study proposes a theoretical framework with which we can analyze event detection algorithms suitable for large-scale mobile networks. Our analysis builds on the following insight: the inherent trends of spatial events are well captured by the spectral domain of the network graph. Using this framework, we propose novel local algorithms that are location-free; that work with mobile nodes and dynamic events; that operate on 3D topologies; and that are simple to implement. We are not aware of event detection algorithms possessing all these traits. Simulations based on complex oil spill traces showcase the resilience and robustness of our methods. Additionally, we demonstrate their validity for practical scenarios by evaluating them on a 105 node testbed.

Wi-Fi based localization has proven to be a compelling alternative to GPS for mobile devices. But Wi-Fi scanning consumes a large amount of energy on smartphones because they perform full scans, i.e. all the channels in their band(s) are visited. This inefficient behavior greatly reduces battery life, raising the threshold for user acceptance. We propose a novel, incremental approach that reduces the energy consumption of Wi-Fi localization by scanning just a few selected channels. We evaluate our incremental scanning approach on eight Android devices using traces from five test subjects. Our results show that, compared to full scans, incremental scanning can reduce the energy consumption between 20.64% and 57.79%. The modern smartphones included in our study all show an energy reduction of at least 40%.

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.

Hand-off (or hand-over), the process where mobile nodes select the best access point available to transfer data, has been well studied in wireless networks. The performance of a hand-off process depends on the specific characteristics of the wireless links. In the case of low-power wireless networks, hand-off decisions must be carefully taken by considering the unique properties of inexpensive low-power radios. This paper addresses the design, implementation and evaluation of smart-HOP, a hand-off mechanism tailored for low-power wireless networks. This work has three main contributions. First, it formulates the hard hand-off process for low-power networks (such as typical wireless sensor networks - WSNs) with a probabilistic model, to investigate the impact of the most relevant channel parameters through an analytical approach. Second, it confirms the probabilistic model through simulation and further elaborates on the impact of several hand-off parameters. Third, it fine-tunes the most relevant hand-off parameters via an extended set of experiments, in a realistic experimental scenario. The evaluation shows that smart-HOP performs well in the transitional region while achieving more than 98 percent relative delivery ratio and hand-off delays in the order of a few tens of a milliseconds.

In large-scale resource-constrained systems, such as wireless sensor networks, global objectives should be ideally achieved through inexpensive local interactions. A technique satisfying these requirements is information potentials, in which distributed functions disseminate information about the process monitored by the network. Information potentials are usually computed through local aggregation or gossiping. These methods however, do not consider the topological properties of the network, such as node density, which could be exploited to enhance the performance of the system. This paper proposes a novel aggregation method with which a potential becomes sensitive to the network topology. Our method introduces the notion of affinity spaces, which allow us to uncover the deep connections between the aggregation scope (the radius of the extended neighborhood whose information is aggregated) and the network's Laplacian (which captures the topology of the connectivity graph). Our study provides two additional contributions: (i) It characterizes the convergence of information potentials for static and dynamic networks. Our analysis captures the impact of key parameters, such as node density, time-varying information, as well as of the addition (or removal) of links and nodes. (ii) It shows that information potentials are decomposed into wave-like eigenfunctions that depend on the aggregation scope. This result has important implications, for example it prevents greedy routing techniques from getting stuck by eliminating local-maxima. Simulations and experimental evaluation show that our main findings hold under realistic conditions, with unstable links and message loss.

Fair rate allocation deals with the fundamental problem of sharing the channel efficiently and fairly. In wireless networks, several notable works have proposed optimal solutions to this problem. These approaches work well for static networks, but rely on an assumption that renders them sub-optimal when nodes are mobile: at each computation step, nodes must collect the state of all their neighbors (1-hop knowledge assumption). In large-scale mobile networks, nodes need to continuously adapt to changing network conditions. Under these circumstances, it is hard to gather complete 1-hop information accurately and promptly. The key to any efficient solution in mobile networks is fast convergence with limited information. In this paper, we propose a simple decentralized algorithm for fair rate allocation that works well even with partial 1-hop information. The algorithm converges linearly and can be tuned to approach a wide range of trade-offs (from proportional to harmonic fairness). Our evaluation, using real-world mobility traces from 400 taxi cabs, shows that even in the challenging case of highly dynamic and dense networks, the algorithm assigns rate efficiently (mean error of 2.5% from the optimum), while using on average 37% of the 1-hop information.

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.

Indoor localization based on signal strength fingerprinting has received significant attention from the community. This method is attractive because it does not require complex hardware beyond a simple radio transmitter. However, its main limitation is the inaccuracy caused by the variability of the signal strength. When applied to the localization of people, the signal variability can be attributed to three main sources: environmental dynamics (movement of people or objects), movement of transceiver (changes in the position and/or orientation of the transceivers) and body effects (distortion of the wireless signal due to body absorption). Our work focuses on the impact of the last two sources and provides two important contributions. First, we present an analysis to quantify the effects of antenna disorientation and transmitter misplacement. For the RFID system used in our work, these effects can decrease the localization accuracy by up to 50%. Motivated by these results, we identify parts of the human body where tags are less affected by unintentional movements. Second, we describe how multiple transmitters can be used to overcome the absorption effects of the human body. Our results indicate that four transmitters provide a reasonable trade-off between accuracy and hardware cost. We validate our findings through an extensive set of measurements gathered in a home environment. Our tests indicate that by following the guidelines proposed in this paper, the localization accuracy can improve from around 20% up to 88%.

Wireless low-power transceivers used in sensor networks typically operate in unlicensed frequency bands that are subject to external radio interference caused by devices transmitting at much higher power. Communication protocols should therefore be designed to be robust against such interference. A critical building block of many protocols at all layers is agreement on a piece of information among a set of nodes. At the MAC layer, nodes may need to agree on a new time slot or frequency channel, at the application layer nodes may need to agree on handing over a leader role from one node to another. Message loss caused by interference may break agreement in two different ways: none of the nodes uses the new information (time slot, channel, leader) and sticks with the previous assignment, or - even worse - some nodes use the new information and some do not. This may lead to reduced performance or failures. In this paper, we investigate the problem of agreement under external radio interference and point out the limitations of traditional message-based approaches. We propose JAG, a novel protocol that uses jamming instead of message transmissions to make sure that two neighbouring nodes agree, and show that it outperforms message-based approaches in terms of agreement probability, energy consumption, and time-to-completion. We further show that JAG can be used to obtain performance guarantees and meet the requirements of applications with real-time constraints.

Asynchronous low-power listening techniques reduce the energy footprint of radio communication by enforcing link layer duty cycling. At the same time, these techniques make broadcast traffic significantly more expensive than unicast traffic. Because broadcast is a key network primitive and is widely used in various protocols, recently several techniques have been proposed to reduce the amount of broadcast activity by merging broadcasts from different protocols. In this paper we focus on collection protocols and investigate the more extreme approach of eliminating broadcast completely. To this end, we design, implement and, evaluate a Broadcast-Free Collection Protocol, BFC. We derive first-order models to quantify the costs of broadcasts, and evaluate the performance of BFC on a public testbed. Compared to the Collection Tree Protocol, the de facto standard for data collection, BFC achieves double-digit percentage improvements on the duty cycles. The specific benefits to individual nodes depend on the relative cost of unicast activity; we show that the nodes that benefit the most are the sink's neighbors, which are crucial for network lifetime extension. Eliminating broadcast also brings several other advantages, including extra flexibility with link layer calibrations and energy savings in the presence of poor connectivity.

Radio link quality estimation in Wireless Sensor Networks (WSNs) has a fundamental impact on the network performance and also affects the design of higher-layer protocols. Therefore, for about a decade, it has been attracting a vast array of research works. Reported works on link quality estimation are typically based on different assumptions, consider different scenarios, and provide radically different (and sometimes contradictory) results. This article provides a comprehensive survey on related literature, covering the characteristics of low-power links, the fundamental concepts of link quality estimation in WSNs, a taxonomy of existing link quality estimators, and their performance analysis. To the best of our knowledge, this is the first survey tackling in detail link quality estimation in WSNs. We believe our efforts will serve as a reference to orient researchers and system designers in this area.

For high-density networks, several studies have proposed field-based routing paradigms to uniformly distribute the traffic load throughout the network. However, as network density decreases, we observe major shortcomings of the current state-of-the-art: (i) fewer number of neighbors reduce the number of available paths and leads to path merging and (ii) the paths directed towards the border of the network merge into a single path. These path merging effects decrease significantly the energy balance, and as consequence, the lifetime of the network. In this paper, we propose a novel mechanism to enable a better load balancing for single-source and multiple-source scenarios. Our evaluations demonstrate that by using the proposed methodology, the network lifetime can be prolonged between 30% and 40%.

Link quality estimation is a thorny problem in wireless sensor networks, because its accuracy affects the design and the efficiency of networking protocols and applications. Especially in the context of low-power wireless, estimating the link quality poses a sort of catch-22 dilemma, whereby a large number of packet samples are required to accurately estimate a channel, but only a few samples should be used due to limited energy resources. This paradox becomes even more severe in mobile wireless sensor networks, since the high variability of the medium imposes even stricter constraints on the timing in which the estimation has to be carried out. In this paper we propose the Triangle Metric, a metric that combines geometrically the information of PRR, LQI, and SNR into a robust estimator that guarantees a fast and reliable assessment of the link quality. Our evaluation shows that the triangle metric can identify the quality of links using as few as 10 packet samples, making it an eligible solution for highly mobile sensor networks.

We address the problem of generating customized, controlled interference for experimental and testing purposes in wireless sensor networks. The known coexistence problems between electronic devices sharing the same ISM radio band drive the design of new solutions to mitigate interference. The validation of these techniques and the assessment of protocols under external interference require the creation of reproducible and well-controlled interference patterns on real nodes, a nontrivial and time-consuming task. In this paper, we study methods to generate a precisely adjustable level of interference on a specific channel, with lowcost equipment and rapid calibration. We focus our work on the platforms carrying the CC2420 radio chip. We show that, by setting the CC2420 in special mode, we can easily generate repeatable and precise patterns of interference. We show how this method is extremely useful for researchers to quickly investigate the behaviour of sensor network protocols and applications under different patterns of interference. We further evaluate the performance of our proposed method.

Link quality estimation (LQE) in wireless sensor networks (WSNs) is a fundamental building block for an efficient and cross-layer design of higher layer network protocols. Several link quality estimators have been reported in the literature; however, none has been thoroughly evaluated. There is thus a need for a comparative study of these estimators as well as the assessment of their impact on higher layer protocols. In this paper, we perform an extensive comparative simulation study of some well-known link quality estimators using TOSSIM. We first analyze the statistical properties of the link quality estimators independently of higher-layer protocols, then we investigate their impact on the Collection Tree Routing Protocol (CTP). This work is a fundamental step to understand the statistical behavior of LQE techniques, helping system designers choose the most appropriate for their network protocol architectures.

This work presents a distributed routing protocol to help mobile users locate items in passive wireless environments. In these environments, a mobile user can detect items that are in its proximity, but items can not communicate directly with each other. For example, an area where passive RFID tags are embedded in the environment and mobile users are provided with RFID readers. The localization protocol is based on the following idea: while searching for a specific node, mobile users populate the memory of the nodes they encounter with information about the nodes they have already seen; later, this information is used to guide other users. The contribution of our work is to demonstrate the feasibility of the proposed protocol, in particular, we provide: (a) a lower bound for the required memory space (in the nodes) to store routing information, (b) a proof that disseminate-while-search routing is loop-free and (c) an study on the extent of user mobility required to disseminate the routing information. A proof-of-concept of the proposed protocol was implemented in a small test-bed of MicaZ motes resembling a passive environment.

Radio interference plays a central role for the performance of Wireless Sensor Networks (WSN). Interference not only leads to packet loss, but it also affects the function of MAC and routing protocols. Hitherto, testing the impact of interference on WSN experimentally has been difficult because of the unavailability of low-cost tools to create reproducible and well-controlled interference patterns.In this demo we present a simple and inexpensive method to generate controllable and repeatable interference patterns for 802.15.4 devices. The demo is presented as a game, where a user is required to achieve a given interference level.

Recent experimental studies have shown that wireless links in real sensor networks can be extremely unreliable, deviating to a large extent from the idealized perfect-reception-within-range models used in common network simulation tools. Previously proposed geographic routing protocols commonly employ a maximum-distance greedy forwarding technique that works well in ideal conditions. However, such a forwarding technique performs poorly in realistic conditions as it tends to forward packets on lossy links. Based on a recently developed link loss model, we study the performance of a wide array of forwarding strategies, via analysis, extensive simulations and a set of experiments on motes. We find that the product of the packet reception rate and the distance improvement towards destination ( PRR × d ) is a highly suitable metric for geographic forwarding in realistic environments.

Experimental studies have demonstrated that the behavior of real links in low-power wireless networks (such as wireless sensor networks) deviates to a large extent from the ideal binary model used in several simulation studies. In particular, there is a large transitional region in wireless link quality that is characterized by significant levels of unreliability and asymmetry, significantly impacting the performance of higher-layer protocols. We provide a comprehensive analysis of the root causes of unreliability and asymmetry. In particular, we derive expressions for the distribution, expectation, and variance of the packet reception rate as a function of distance, as well as for the location and extent of the transitional region. These expressions incorporate important environmental and radio parameters such as the path loss exponent and shadowing variance of the channel, and the modulation, encoding, and hardware variance of the radios.

The wireless sensor networks community, has now an increased understanding of the need for realistic link layer models. Recent experimental studies have shown that real deployments have a "transitional region" with highly unreliable links, and that therefore the idealized perfect-reception-within-range models used in common network simulation tools can be very misleading. In this paper, we use mathematical techniques from communication theory to model and analyze the low power wireless links. The primary contribution of this work is the identification of the causes of the transitional region, and a quantification of their influence. Specifically, we derive expressions for the packet reception rate as a function of distance, and for the width of the transitional region. These expressions incorporate important channel and radio parameters such as the path loss exponent and shadowing variance of the channel; and the modulation and encoding of the radio. A key finding is that for radios using narrow-band modulation, the transitional region is not an artifact of the radio non-ideality, as it would exist even with perfect-threshold receivers because of multi-path fading. However, we hypothesize that radios with mechanisms to combat multi-path effects, such as spread-spectrum and diversity techniques, can reduce the transitional region.

Recent experimental studies have shown that wireless links in real sensor networks can be extremely unreliable, deviating to a large extent from the idealized perfect-reception-within-range models used in common network simulation tools. Previously proposed geographic routing protocols commonly employ a maximum-distance greedy forwarding technique that works well in ideal conditions. However, such a forwarding technique performs poorly in realistic conditions as it tends to forward packets on lossy links. We identify and illustrate this weak-link problem and the related distance-hop trade-off, whereby energy efficient geographic forwarding must strike a balance between shorter, high-quality links, and longer lossy links. The study is done for scenarios with and without automatic repeat request (ARQ).

In designing, analyzing and monitoring wireless sensor networks (WSN), engineers are interested in global performance metrics such as energy consumption, delivery rate, and delay. Such metrics are often variable and stochastic in nature. In traditional broadband networks the variability is primarily due to traffic variations and bandwidth constraints; in WSN there are additional sources of variability including energy, computation constraints, and most significantly, due to the inherently unreliable nature of wireless links. The goal of our study is to understand how link-layer unreliability impacts global performance metrics.