Public bus services are widely deployed in cities around the world because they provide cost-effective and economic public transportation. However, from a passenger point of view urban bus systems can be complex and difficult to navigate, especially for disadvantaged users, i.e. tourists, novice users, older people, and people with impaired cognitive or physical abilities. We present Urban Bus Navigator (UBN), a reality-aware urban navigation system for bus passengers with the ability to recognize and track the physical public transport infrastructure such as buses. Unlike traditional location-aware mobile transport applications, UBN acts as a true navigation assistant for public transport users. Insights from a six-month long trial in Madrid indicate that UBN removes barriers for public transport usage and has a positive impact on how people feel about public transport journeys.@en

In this paper, we present TrainSense, a novel infrastructure to support the development and testing of mobile sensing applications. TrainSense merges a mote and a model train into a single mobile unit, and enhances the basic model train infrastructure with several important features required for the evaluation of mobile scenarios. First, we develop a real-time controller to send control packets to model trains and motes to manage the network topology. Second, we design and implement a positioning system with centimeter precision. Third, we use the power available on the tracks to provide unlimited energy to the motes. Fourth, we provide a way for the motes to dock into a custom USB port, for reprogramming and data download. We evaluate TrainSense in two ways: (i) we establish the correctness of the implementation and measure the performance of its components, and (ii) we demonstrate its practical use with two sample wireless sensor network application scenarios: self-deployment and data muling.@en

Handoff processes, the events where mobile nodes select the best access point available to transfer data, have been well studied in cellular and WiFi networks. However, wireless sensor networks (WSN) pose a new set of challenges due to their simple low-power radio transceivers and constrained resources. This paper proposes smart-HOP, a handoff mechanism tailored for mobile WSN applications. This work provides two important contributions. First, it demonstrates the intrinsic relationship between handoffs and the transitional region. The evaluation shows that handoffs perform the best when operating in the transitional region, as opposed to operating in the more reliable connected region. Second, the results reveal that a proper fine tuning of the parameters, in the transitional region, can reduce handoff delays by two orders of magnitude, from seconds to tens of milliseconds.@en

A key challenge for rapidly growing cities of today is to provide effective public transport services to satisfy the increasing demands for urban mobility. Toward this goal, the Internet of Things (IoT) has great potential to overcome existing deficiencies of public transport systems given its ability to embed smart technology into real-life urban contexts. In this paper, we show how this paradigm can be applied to the public transport domain and present the Urban Bus Navigator (UBN), an IoT enabled navigation system for urban bus riders. UBN provides two novel information services for bus users: 1) micro-navigation and 2) crowd-aware route recommendation. Micro-navigation refers to fine-grained contextual guidance of passengers along a bus journey by recognizing boarded bus vehicles and tracking the passenger's journey progress. Crowd-aware route recommendation collects and predicts crowd levels on bus journeys to suggest better and less crowded routes to bus riders. We present the technical system behind the UBN and report on results from an in-the-wild study in Madrid, Spain, that indicates removed barriers for public transport usage and a positive impact on how people feel about bus journeys.@en

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 off-the-shelf radio transceivers. 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 transceiver 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 and describe how multiple transceivers can be used to overcome the absorption effects of the human body. We validate our findings through an extensive set of measurements gathered in a home environment. Our tests indicate that by following a set of simple guidelines, we can increase the localization accuracy (the percentage of correct location estimations) by a factor of four (from 20% to 88%), and reduce the maximum localization error (from 7 to 4 m).@en

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 firstorder 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.@en

Cooperating Objects are, in the most general case, small computing devices equipped with wireless communication capabilities that are able to cooperate and organize themselves autonomously into networks of sensors, actuators and processing units to achieve a common task. Several areas could greatly benefit with the introduction of Cooperating Object technologies, ranging from automation (home, industrial, building) to healthcare and energy management. To exploit this potential the research community has to tackle various problems in various areas including hardware, algorithms and systems. In this paper we describe these research areas and their different challenges. Based on a survey, predominant work areas are selected that should receive the most attention.@en

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%.@en

Empirical studies on link blacklisting show that the delivery rate is sensitive to the calibration of the blacklisting threshold. If the calibration is too restrictive (the threshold is too high), all neighbors get blacklisted. On the other hand, if the calibration is too loose (the threshold is too low), unreliable links get selected. This paper investigates blacklisting analytically. We derive a model that accounts for the joint effect of the wireless channel (signal strength variance and coherence time) and the network (node density). The model, validated empirically with mote-class hardware, shows that blacklisting does not help if the wireless channel is stable or if the network is relatively sparse. In fact, blacklisting is most beneficial when the network is relatively dense and the channel is unstable with long coherence times.@en