Due to the frequent topology changes in most wireless networks, low-power Neighbor Discovery (ND) is essential for many Wireless Sensor Networks (WSNs) and Internet of Things (IoT) applications. In this work, we present UWB-ND, a low-power ND protocol for ultra-wideband (UWB) radio networks that are becoming increasingly popular in IoT applications. To conserve energy, these IoT applications typically rely on other low-power radio technology such as Bluetooth Low Energy (BLE) for ND, requiring the integration of auxiliary radios in all nodes. Utilizing specific characteristics of UWB radios such as efficient Channel Activity Detection (CAD) and varying preamble modulation, UWB-ND introduces a low-power ND approach specific to UWB radios. Our evaluation shows that UWB-ND can reduce ND power consumption by 50%, compared to the state-of-the-art PI-based approach.

Positioning plays an important role in many IoT applications. Ultra-wideband (UWB)-based positioning is an alternative to GPS in indoor environments due to its multipath resilience and its accuracy and precision. In the presence of a large number of targets to localize, conventional UWB localization fails to provide a practical location update rate. UWB concurrency in conjunction with self-localization has been used in both time-based and phase-based localization making the targets to localize (tags), a relatively passive device with the sole role of receiving wireless packets and calculating its own location. More recently, phase-based concurrent angle estimation also offloaded the hardware complexity and cost to the anchors instead of the tags. In this work, our anchor-oriented approach combines inter-anchor and intra-anchor concurrency for phase-based localization but also allows time-based localization. Our experimental evaluation on a testbed consisting of Decawave platform shows that our technique is not only practical but also performant.

Ultra-wideband (UWB) is becoming a major localization technology enabler for the indoor environment. Traditional localization systems rely on time-of-arrival (ToA)-based methods such as two-way ranging (TWR) and time difference of arrival (TDoA). Such solutions cannot scale due to interference from multiple devices sharing the same part of the wireless spectrum. One solution is to use concurrent transmissions for a more efficient use of air time. Concurrency-based localization systems that utilize ToA cannot satisfy the accuracy requirements of many applications due to hardware time scheduling limitations. Angle of Arrival (AoA) is a promising solution that can provide scalability and accuracy when used in a concurrent transmission scheme. UWB radio platforms like Decawave DWM1002 with dual-UWB-chip design have made it possible to accurately measure AoA by calculating the phase difference of arrival (PDoA). State-of-the-art AoA estimation has then been extended to build self-localization systems with an unlimited number of tags and to handle multiple sources at the same time. These methods require tags with dual-chip design which adds cost and complexity. In this paper, we investigate the idea of estimating AoA on single-chip (single-antenna) tags receiving concurrent UWB signals from dual-chip anchors (intra-anchor concurrency). We call our system Single-Antenna AoA Estimation (SA-AoA). As opposed to inter-anchor concurrency, intra-anchor concurrency consists of receiving two concurrent packets from two different chips of the same anchor. By estimating AoA on single-chip tags, SA-AoA reduces the design complexity and the cost of tags by at least 50%. Our results suggest that the single-antenna AoA can achieve performance similar to dual-antenna AoA estimation.

Monitoring networks requires efficiently detecting abnormal events and summarizing connection information in big volumes of packet-level data. Some of these tasks can be accomplished with network and operating system utilities, but the questions should be relatively simple and each tool is designed to provide specific analysis. Another requirement is to be able to process data both in a centralized and decentralized manner, given the diversity in instrumentation and vantage points. On the other hand, database systems can answer complex questions phrased as queries, provided data is in the right format and is quickly loaded. Having such motivation in mind, we propose to monitor a network with queries, running on a traditional DBMS (i.e. not a custom-built system programmed in C or C++). Thus, queries can be processed in a central manner in a traditional database server or in a distributed fashion, with edge computing. Our experimental evaluation shows queries can indeed be used to monitor the network with low latency and reasonable delay on a low-resource device like the Raspberry Pi. We explain some interesting findings in a local network. In addition, we show queries can be efficiently evaluated in a small computing device capturing local traffic, showing promise for distributed monitoring.

Why do "classical" attacks such as phishing, IRS scams, etc., still succeed? How do attackers increase their chances of success? How do people reason about scams and frauds they face daily? More research is needed on these questions, which is the focus of this paper. We take a well-known attack, viz. company representative fraud, and study several parameters that bear on its effectiveness with a between-subjects study. We also study the effectiveness of a coherent language generation technique in producing phishing emails. We give ample room for the participants to demonstrate their reasoning and strategies.

Pose estimation is a building block for many location-based applications, such as safety applications in a construction site. Ultra-WideBand (UWB) Radios have been widely used for localization and can be used in pose (location and orientation angle of the object) estimation primarily because of the accuracy with which these radios can estimate the arrival time of radio signals. Current UWB pose estimation solutions do not perform adequately in Non-Line of Sight (NLoS) conditions. Some of these existing solutions in pose estimation rely on two or more types of sensors to tackle the NLoS challenge. These methods suffer from data fusion complexity, making the system not generalizable and limited to some specific simple environments, such as labs. In this paper, we propose ViPER, a UWB-based pose estimating system using only UWB radios. Our goal is to reduce the effects of the NLoS without the inclusion of any auxiliary sensors. ViPER uses low-pass filter, anchor and reference selection method to reduce the effect of NLoS in the measurements. It also estimates the pose of the entities using an optimization problem. We have evaluated ViPER in real- world highway construction and parking lot setting. We find that it improves the average packet reception ratio by 117% and decreases the error rate by 70% over the state of the art in Non-Line of Sight situation.

The angle of arrival (AoA) estimation is one of the commonly used techniques for indoor localization. Ultrawideband (UWB) radios facilitate AoA estimation through the measurement of the phase difference of arrival (PDoA) at multiple receiver antennas. Concurrent transmissions in UWB radios aim to increase the efficiency of localization systems by exploiting wireless interference. This paper first investigates the feasibility of AoA estimation with UWB radios in a concurrent scheme. State-of-the-art UWB indoor localization solutions use time difference of arrival (TDoA) in a concurrent scheme. These solutions rely on accurate timestamping of the concurrently received packets. However, due to the scheduling uncertainty of the UWB transmitter platform used in this area, an unavoidable timing jitter of 8 ns causes up to 2.4 m of the localization error. Therefore, the accuracy of solutions based on concurrent TDoA relies on additional timestamp correction, which adds to the complexity of the system. Our results show that concurrent AoA estimation remains unaffected by the transmitter scheduling uncertainties. AoA-based localization techniques face two main challenges: (1) front-back ambiguity of AoA for antenna array of size two; and (2) AoA measurement device's unknown tilting. This paper then presents AnguLoc, an efficient and scalable indoor localization system that makes use of concurrent AoA estimation to reduce the number of required packet exchanges. AnguLoc uses an Angle Difference of Arrival (ADoA) technique, also generalizable to sequential AoA, to overcome the front-back angle measurement ambiguity problem, and to work with unknown tag tilting. We evaluate AnguLoc in an office environment on a recently introduced platform, Decawave PDoA node (DWM1002). Our results show that AnguLoc is 4 times faster than sequential AoA and improves the localization accuracy by up to 44.33% compared to state-of-the-art concurrency-based indoor localization solutions without relying on additional timestamp correction.

Clean Cooking is essential to maintain a healthy lifestyle. However, many people in developing economies do not have access to clean cooking. To promote clean cooking, first, we need to understand the cooking patterns in the household, and second, design interventions over those patterns. We also need to understand the grid and power supply readiness to support electricity-based clean cooking initiatives. In this paper, we provide an affordable and scalable energy monitoring system solution to instrument the cooking pattern in peri-urban Nepal. Our design consists of off-the-shelf power meters, minor changes in sockets/wiring at homes, data upload using cellular radio, and standard dashboard and analysis in the cloud. We deployed the system in 35 households in peri-urban Nepal and collected data from early August until the middle of October 2019. Our preliminary study indicates: 1) Cellular data access is a viable way to upload instrumentation data to the Internet in studies of this nature. 2) Data integrity and reliability are closely coupled with user behaviors and cellular reliability. 3) Deployment can be centralized instead of distributed, and cost can be affordable. 4) Continuous data collection from about three months shows poor power quality in the area.

Audio fingerprinting techniques were developed to index and retrieve audio samples by comparing a content-based compact signature of the audio instead of the entire audio sample, thereby reducing memory and computational expense. Different techniques have been applied to create audio fingerprints; however, with the introduction of deep learning, new data-driven unsupervised approaches are available. This article presents Sequence-to-Sequence Autoencoder Model for Audio Fingerprinting (SAMAF), which improved hash generation through a novel loss function composed of terms: Mean Square Error, minimizing the reconstruction error; Hash Loss, minimizing the distance between similar hashes and encouraging clustering; and Bitwise Entropy Loss, minimizing the variation inside the clusters. The performance of the model was assessed with a subset of VoxCeleb1 dataset, a“speech in-the-wild” dataset. Furthermore, the model was compared against three baselines: Dejavu, a Shazam-like algorithm; Robust Audio Fingerprinting System (RAFS), a Bit Error Rate (BER) methodology robust to time-frequency distortions and coding/decoding transformations; and Panako, a constellation-based algorithm adding time-frequency distortion resilience. Extensive empirical evidence showed that our approach outperformed all the baselines in the audio identification task and other classification tasks related to the attributes of the audio signal with an economical hash size of either 128 or 256 bits for one second of audio.

Cross-technology communication (CTC) is a technique that enables direct communication among different wireless technologies. Recent works in this area have made substantial progress, but CTC from ZigBee to WiFi remains an open problem. In this paper, we propose ZigFi, a novel CTC framework that enables communication from ZigBee to WiFi. ZigFi carefully overlaps ZigBee packets with WiFi packets. Through experiments we show that Channel State Information (CSI) of the overlapped packets can be used to convey data from ZigBee to WiFi. Based on this finding, we propose a receiver-initiated protocol and translate the decoding problem into a problem of CSI classification with Support Vector Machine. We further build a generic model through experiments, which describes the relationship between the Signal to Interference and Noise Ratio (SINR) and the symbol error rate (SER). Moreover, we extend ZigFi to multiple-to-one concurrent transmissions. We implement ZigFi on commercial-off-the-shelf WiFi and ZigBee devices. We evaluate the performance of ZigFi under different experimental settings. The results demonstrate that ZigFi achieves a throughput of 215.9bps, which is 18X faster than the state of the arts.

Concurrent ranging exploits features of the channel impulse response (CIR) of received packets to allow an ultra-wideband (UWB) initiator node to concurrently measure the distance from multiple UWB responder nodes. Concurrent ranging enables indoor localization systems to reduce the required number of ranging packet exchanges, leading to less air utilization and energy consumption, and faster location update rate. Despite the research in this area, it is still challenging to build a practical UWB concurrent ranging system for real-world environments. Existing concurrent ranging solutions are not scalable because (1) they require strong assumptions about either the responders or the environment and (2) they fail to maintain the ranging accuracy in longer distances due to errors caused by clock drift. In this work, we present R3, a Reflection Resilient Ranging solution, to address these critical scalability issues in UWB concurrent ranging. R3 makes use of the difference in the time deviation of ranging signals to detect concurrent responders and it is equipped with a clock skew correction method that enables accurate concurrent ranging in long distances. We evaluate R3 using Decawave DW1000 UWB chip by deploying the radio nodes in an office environment. Our results show that R3 effectively detects concurrent ranging peaks in the presence of strong multipath. When we equip R3 with a clock skew correction method, it reduces the concurrent ranging error induced by clock drift by at least 54 cm in long distances (>50 m) and by more than 97% in average when the ranging response delay is arbitrarily large.

Ultra-wideband signals have been used for accurate ranging and localization application during the last few years. State of the art UWB ranging applications can estimate the distances with less than a 5 cm error. Existing localization solutions create their own ranging traffic. In this paper, we investigate the possibility of piggybacking the information required by ranging application over existing network traffic. In addition, we study the feasibility of piggybacking of sensing information over ranging traffic and finally, we propose our technique for Simultaneous Ranging and Communication (SRAC) in UWB networks which adaptively changes the ranging mode from active to passive by using either ranging traffic or sensing traffic to accomplish the ranging and sensing goals while reducing the network traffic to minimum possible. We integrated our proposed solution to RIOT operating system and evaluated its performance over a mesh of UWB-enabled nodes. Our results indicate almost 40% reduction in network traffic.

The use of two radios per node increases the energy efficiency of wireless sensor networks. Given that data collection is one of the most important functions in wireless sensor networks, this paper presents and compares two new data collection protocols for wireless sensor networks with two radios, DCTP-A and DCTP-I. DCTP-A builds the collection tree alternating the radio band each node while DCTP-I builds two independent collection trees. The protocols were implemented in TinyOS and evaluated experimentally in a testbed in the physical world using the 900MHz and 2.4GHz radio bands, compared to the state of the art (CTP and CTP-Multi) and to each other, considering the metrics delivery rate, latency, throughput in a saturated network scenario, total number of messages and the cost of maintaining routes. The results show the gain of the protocols for wireless sensor networks with two radios. DCTP-A achieved almost 100% of delivery rate, while DCTP-I achieved up to 90% delivery rate with less number of beacons messages.

Cross-technology communication (CTC) is a technique that enables direct communication among different wireless technologies. Recent works in this area have made positive progress, but high-throughput CTC from ZigBee to WiFi remains an open problem. In this paper, we propose ZigFi, a novel CTC framework that enables direct communication from ZigBee to WiFi. Without impacting the ongoing WiFi transmissions, ZigFi carefully overlaps ZigBee packets with WiFi packets. Through experiments we show that Channel State Information (CSI) of the overlapped packets can be used to convey data from ZigBee to WiFi. Based on this finding, we propose a receiver-initiated protocol and translate the decoding problem into a problem of CSI classification with Support Vector Machine. We further build a generic model through experiments, which describes the relationship between the Signal to Interference and Noise Ratio (SINR) and the symbol error rate (SER). We implement ZigFi on commercial-off-the-shelf WiFi and ZigBee devices. We evaluate the performance of ZigFi under different experimental settings. The results demonstrate that ZigFi achieves a throughput of 215.9bps, which is 18X faster than the state-of-the-art.

We examine the ability of networks formed by mobile sensor nodes to diffuse information in the case when communication is only possible during opportunistic encounters. Our setting assumes that mobile nodes are continuously sensing the world and acquiring new information. We form an abstract model of this situation and show by theoretical analysis, simulation, and real mobility data that the diffusion of information in this setting cannot be as efficient as when we allow arbitrary contact patterns between the nodes with the same overall contact statistics. This establishes a fundamental asymptotic limitation on the information diffusion capacity of such opportunistic mobile sensor networks - the encounter patterns arising out of physical motions in a geometric space are not ideal for information diffusion.

Non-intrusive occupant identification enables numerous applications in Smart Buildings such as personalization of climate and lighting. Current techniques do not scale beyond 20 people, whereas commercial buildings have 100 or more people. This article proposes a new method to identify occupants by sensing their body shape, movement, and walking patterns as they walk through a SonicDoor, a door instrumented with three ultrasonic sensors. The proposed method infers contextual information, such as paths and historical walks through different doors of the building. Each SonicDoor is instrumented with ultrasonic ping sensors, one on top sensing height and two on the sides of the door sensing width of the person walking through the door. SonicDoor detects a walking event and analyzes it to infer whether the Walker is using a phone, holding a handbag, or wearing a backpack. It extracts a set of features from the walking event and corrects them using a set of transformation functions to mitigate the bias. We deployed five SonicDoors in a real building for two months and collected data consisting of over 9,000 walking events spanning over 170 people. The proposed method identifies 100 occupants with an accuracy of 90.2%, which makes it suitable for commercial buildings.

We focus on email-based attacks, a rich field with well-publicized consequences. We show how current Natural Language Generation (NLG) technology allows an attacker to generate masquerade attacks on scale, and study their effectiveness with a within-subjects study. We also gather insights on what parts of an email do users focus on and how users identify attacks in this realm, by planting signals and also by asking them for their reasoning. We find that: (i) 17% of participants could not identify any of the signals that were inserted in emails, and (ii) Participants were unable to perform better than random guessing on these attacks. The insights gathered and the tools and techniques employed could help defenders in: (i) implementing new, customized anti-phishing solutions for Internet users including training next-generation email filters that go beyond vanilla spam filters and capable of addressing masquerade, (ii) more effectively training and upgrading the skills of email users, and (iii) understanding the dynamics of this novel attack and its ability of tricking humans.

People counting has many applications in smart buildings. For example, adjusting HVAC systems based on the number of occupants in each room can save a significant amount of energy. In addition, security and safety of the building can be managed by determining the number and location of occupants. Different technologies and sensing platforms have proposed for accurate and efficient people counting. However, these solutions are expensive, hard to deploy, or privacy invasive. We investigate the possibility of placing an 8×8 IR array sensor at the doorways and counting the number of people inside rooms. Our solution is real-time, inexpensive, privacy preserving with much less deployment constraints compared to its competitors. The proposed solution deals with realistic and dynamic changes in the sensing environment by leveraging a combination of Otsus thresholding and modeling thermal noise distribution. We evaluated our solution via several controlled and uncontrolled real-world environments. The results show an average of 93% accuracy in estimating the number of occupants in rooms.

Increase in size and complexity of web pages has challenged the efficiency of HTTP. Recent developments to speed up the web have resulted in two promising protocols, HTTP/2 (RFC 7540) at the application layer and QUIC (multiplexed stream transport over UDP). Google servers are using HTTP/2 and QUIC whereas other major sites like Facebook and Twitter have begun using HTTP/2. In this paper, we compare the performance of HTTP/2 vs QUIC+SPDY 3.1 by studying the Web page load times. In the first set of experiments, we serve synthetic pages (only static objects) over both protocols in emulated controlled network conditions and then extend it to real network, both wired and cellular (2G/3G in India and 3G/4GLTE in US). Further, we conduct experiments on a set of web pages on the most popular sites from the Internet (Alexa Rankings) in controlled conditions. We find QUIC to perform better overall under poor network conditions (low bandwidth, high latency and high loss), for e.g. more than 90% of synthetic pages loaded faster with QUIC in 2G compared to 60% in 4GLTE. This is due to the lower connection establishment latency and improved congestion control mechanism in QUIC. However, QUIC does not offer significant advantage when a webpage consists of many small-sized objects.

The growth of the Internet of Things and the trends to deploy more sensors everywhere has led to search for cost effective ways to connect the devices to the Internet. In the most common network architecture for home IoT, the devices use low-power wireless or WiFi to connect to an AP and access the Internet backend. We present a study on the feasibility and efficiency of an alternate network architecture for home IoT. In the proposed architecture, sensors and devices are directly attached to WiFi access points and utilize the computing resources of WiFi APs. We design and implement several sensing applications based on the proposed architecture and report on the performance and limitations of the approach. We find that the proposed architecture allows WiFi APs to become the computational, networking, and storage host for sensing applications without degrading the AP's primary function of providing Internet access to the home users.

The presence of ambient light is a key challenge for reliable and robust low cost embedded visible light communication system. The photodetector used by these systems can perform poorly when subjected to bright ambient light or fluctuating ambient light. To solve this problem, we present an ambient light cancellation mechanism for low cost embedded LED to photodiode communication systems that utilizes a digital potentiometer to adaptively nullify the ambient light to provide an always ZERO output no matter what the ambient light intensity is. The proposed technique allows the receiver to correctly receive the light transmitted by the transmitter without any interference from the ambient light. We provide a detailed description of the modulation and demodulation schemes as well as ambient light cancellation mechanism, and their evaluations. The results show our proposed system can provide a reliable and robust visible light communication with extremely low symbol error rate (almost 0) and an acceptable data rate up to 3kbps given an operating distance of 50 centimeters.

In this paper, we consider the problem of distributing patrol officers inside a building to maximize the probability of catching multiple intruders while minimizing the distance the patrol officers travel to reach the locations of the intruders. In our problem setting, the patrol officers are assisted by the information collected by a network of binary proximity sensors installed in the building. We claim that learning even common movement sub-patterns that originate due to the constrained physical environment helps to find likely locations of intruders where each major location is instrumented using a sensor node. We use a series of binary detection events to infer likely future trajectories in a real-world building. For a given set of detectable nodes on the inferred future trajectories, we aim to find the optimal patrol dispatch node location with high exposure to intruders' future appearance using patrol officers in limited numbers, ideally fewer than the intruders. In order to prevent possible crime and perform responsive defense against potential intruders, our algorithm also tries to reduce the travel distance from patrols current positions to their dispatched positions at the same time. We validate our proposed scheme in terms of detection accuracy by varying the number of intruders, robustness against missing events, and responsiveness compared to a practical baseline counterpart through real-world system experiments.

Many IoT applications consist of two types of actions: interaction with the device, which can be sensors or actuators, and interaction with the data, for example, to reveal insights. In this poster, we introduce a software stack that provides these functionalities in a scalable manner. The API for device interaction is designed with generality in mind so that widest possible array of devices are supported and in large numbers. The analytics framework, called Composer, is designed to allow user code to be easily integrated into data analytics. We present the design, describe the implementation and deployment, and present some evaluation results. We share the performance data from a live deployment with tens of thousands of active users to demonstrate the scalability of the design.

Significant energy wasted in private homes each year. Most of the times, the residents do not know the cause of energy waste in their homes. We designed several activities to encourage the homeowners to learn about how energy is used at their homes and start thinking about eliminating those waste. The program consists of twelve weekly activities in which the homeowners participate. We analyzed monthly electricity bills for all the program's participants and found that energy savings achieved by the participants has close relationship to level of their engagement in the program. People who were highly involved in program saved much more energy (5%) compared to participants who were involved in less than 25% of activities.

Experimental research in low-power wireless networking lacks a reference benchmark. While other communities such as databases or machine learning have standardized benchmarks, our community still uses ad-hoc setups for its experiments and struggles to provide a fair comparison between communication protocols. Reasons for this include the diversity of network scenarios and the stochastic nature of wireless experiments. Leveraging on the excellent testbeds and tools that have been built to support experimental validation, we make the case for a reference benchmark to promote a fair comparison and reproducibility of results. This abstract describes early design elements and a benchmarking methodology with the goal to gather feedback from the community rather than propose a definite solution.

In many wireless sensing and control application deployments, there is often a gateway device to bridge between the low power IEEE 802.15.4 network and the Internet. The bridge has at least two interfaces. One interface communicates with the 802.15.4 wireless. The other interface either communicates with WiFi or wired network. When a user wants to send a command to the wireless controller, lets say at a smart home, the user may use a smartphone and send command over WiFi to the gateway, often through a cloud service provider. Then gateway shuttles the message from the wired or WiFi chip to the 802.15.4 chip. Then the gateway transmits the messages over the 802.15.4 chip into the 802.15.4 network. In this work, we design a novel modulation technique that runs on the WiFi devices (e.g., Smartphone) and demodulation technique that runs on 802.15.4 devices (e.g., A wireless controller in a smart home) to enable WiFi devices to directly communicate with 802.15.4 devices without any gateway. The key idea is to utilize crosstalk between 802.11 and 802.15.4 channels as the medium for communication. We implemented the proposed technique on multiple platforms and are able to successfully achieve a data rate of 2 bytes per second with less than 10% bit error rate in uncontrolled environments.

Built around a cost-effective embedded Linux platform, OpenVLC is an open source project (www.openvlc.org) for research in Visible Light Communication (VLC) Networks. In this work, we introduce and demonstrate the OpenVLC1.0 platform, a flexible, software-defined, and low-cost research platform. OpenVLC1.0 consists of a simple electronic design, and a new driver of the Linux operating system that implements the MAC, part of the PHY layers and it offers an interface to Internet protocols. The electronics of OpenVLC implement a flexible optical front-end consisting of commodity low- and high-power Light Emitting Diodes (LEDs), photodiodes (PDs), and ancillary electronic circuitry. In order to quickly start playing with VLC Networks, we have designed and developed a printed circuit board (OpenVLC1.0 cape). The cape can be plugged into the main embedded Beaglebone board. Researchers can then swiftly build PHY and MAC protocols using the software implementation (OpenVLC1.0 driver), and prototype innovative solutions in realistic network setups. In this demo, we show that OpenVLC1.0 can switch between different MAC protocols, it can choose different optical channel for data transmission and reception, and it can be employed jointly with standard TCP/IP diagnostic tools.

RPL is an IPv6 routing protocol for low-power and lossy networks (LLNs) designed to meet the requirements of a wide range of LLN applications including smart grid AMIs, home and building automation, industrial and environmental monitoring, health care, wireless sensor networks, and the Internet of Things (IoT) in general with thousands and millions of nodes interconnected through multihop mesh networks. RPL constructs tree-like routing topology rooted at an LLN border router (LBR) and supports bidirectional IPv6 communication to and from the mesh devices by providing both upward and downward routing over the routing tree. In this article, we focus on the interoperability of downward routing and supporting its two modes of operations (MOPs) defined in the RPL standard (RFC 6550). Specifically, we show that there exists a serious connectivity problem in RPL protocol when two MOPs are mixed within a single network, even for standard-compliant implementations, which may result in network partitions. To address this problem, this article proposes DualMOP-RPL , an enhanced version of RPL, which supports nodes with different MOPs for downward routing to communicate gracefully in a single RPL network while preserving the high bidirectional data delivery performance. DualMOP-RPL allows multiple overlapping RPL networks in the same geographical regions to cooperate as a single densely connected network even if those networks are using different MOPs. This will not only improve the link qualities and routing performances of the networks but also allow for network migrations and alternate routing in the case of LBR failures. We evaluate DualMOP-RPL through extensive simulations and testbed experiments and show that our proposal eliminates all the problems we have identified.

Network emulation allows us to evaluate network protocol implementations, typically in higher fidelity than simulations. This advantage comes at a cost. Emulation often requires much larger IO or computational resources than simulations. As a result, it is common to see some research projects doing simulations with up to hundred thousand nodes while emulations typically scale up to a few hundred nodes. In this paper, we present CloudNet, a network protocol emulation platform that leverages the commodity cloud computing service to scale emulations to thousands of nodes. CloudNet uses a light-weight virtualization technique called LXC containers to emulate a single node. The network protocol code and the protocol state for each node is maintained in its respective container. CloudNet then uses properties of the network topology to determine where to place these containers among many physical machines researchers might rent on the cloud service. CloudNet's careful mapping of nodes to the containers makes network performance more predictable and suitable for emulation even on a shared commodity cloud, which were previously thought to be unsuitable for serious network emulation. Through extensive experiments, we establish that CloudNet is scalable to thousand-node networks while providing accurate emulation results.

Wireless sensor network platforms share the wireless communication channels with Wi-Fi and Bluetooth based networks, resulting in heavy use of these bands. As a consequence, platforms in wireless sensor networks need to carefully consider external interference to achieve reliable communication. In this paper, we present an experimental analysis of wireless channels for wireless sensor network operating on dual frequency bands. Specifically, we designed a set of detailed experiments aiming to find out correlation patterns in 900 MHz and 2.4 GHz ISM bands. We conducted our experiments on two testbeds and investigated the band correlation between two distinctive radio transceivers in two different office-space environments. From our data samples, we quantified frequency channel and band correlations in parallel experiments that eliminate artifacts stemming from different external activity on the test site. We found that network formed in 900 MHz band has 15% more connectivity than network formed in 2.4 GHz band, even on radio channels that minimize overlap with Wi-Fi networks.

Researchers have proposed many multi-channel and dual-band communication systems to address the limitations of single-channel hardware and software. The most common dual-band communication for sensor network applications use 900 MHz and 2.4 GHz radios. There are now some testbeds such as Flocklab and Twonet that allow experimentation with dual-radio systems. However, there is no widely accepted efficient and comprehensive mechanism to survey all the channels of the dual band systems during networking experiments. In this work, we evaluate two mechanisms that can survey the RF environment in multiple channels in both the bands concurrently. We implement these two mechanisms on Twonet and evaluate them. We find that the channel scanning methodology is generally sound but sometimes static channel assignment may be attractive due to its simplicity.

In this poster, we present a novel approach to study and reveal network protocol information from radio activities instrumentation in wireless sensor network. Recent studies have analyzed radio activities; however, most of these studies focus on estimating energy consumption, since radio chip usually dominates the energy consumption of nodes. In our work, we analyze radio activities with a different purpose, which aims to reveal network protocols and application workloads by an analysis of fine-grained low level radio activities on the nodes. We design a feature called Radio Awake Length Counter and use it to classify and reveal network activity. Results from experiments on a real world testbed indicate that our approach can achieve up to 97% accuracy to identify the routing protocols, average 85% accuracy to distinguish application workloads.

Time synchronization is an essential service in many sensor network applications. Harsh environment which causes nodes to fail, go offline, or reboot can challenge many time synchronization protocols. In this work, we first characterize this challenge and use a real time clock in one of the nodes in the network to improve robustness of time synchronization. Our experiments show that our approach improves the robustness of state-of-the-art offline time synchronization protocols.

Recent work has shown that network protocols which rely on precisely-timed concurrent transmissions can achieve reliable, energy-efficient, and conceptually simple network flooding. While these multi-transmitter schemes work well, they require all nodes in the network to forward every data packet, which has inherent inefficiencies for non-flooding traffic patterns (where not all nodes need to receive the data). In this work, we formalize the concept of the “useful” forwarder set for point-to-point transmissions in low power multi-transmitter networks, those nodes which help forward data to the destination. We present a mechanism for approximating membership in this set based on simple heuristics. Incorporating forwarder selection on our 66-node testbed reduced radio duty cycle by 30% and increased throughput by 49% relative to concurrent flooding while preserving a 99.4% end-to-end packet reception ratio under the collection traffic pattern. We envision forwarder selection as a fundamental task in an efficient multi-transmitter networking stack. This work demonstrates that adding forwarder selection can improve energy efficiency and throughput while providing similar end-to-end packet delivery rates to flooding.

Making wireless sensor node platforms energy efficient is one of the major research thrusts in the sensor network community. Energy metering lies at the foundation of this research, either by providing direct measurements for profiling, or by serving as the base for the formulation and fitting of energy usage models. Most of the literature and tools, however, make their measurements on a very small subset of the node population, and usually at a single point in time, before deployment. In this paper we set out to evaluate the cost, in loss of precision, of not having constant and ubiquitous measurement. Through experiments on a 240-node sensor-network testbed, we find that the variations in energy consumption due to temperature change are small, and we establish a model between environmental temperature changes and power consumption of Quanto testbed motes. We also find that different nodes of the same kind can have up to 15% variation in power draw, suggesting a need to deploy instrumentation on a subset of nodes. We quantify the energy estimation error of different metering techniques and characterize the conditions in which the errors disappear. Overall, we find that a small number of measurements in time and across nodes is adequate for accurate estimation of network-wide energy use.

As larger numbers of Wireless Sensor Network (WSN) applications get deployed in our homes and offices, it is desirable to use the same network to run different applications. We present and analyze the problem of scheduling and supporting the execution of multiple heterogeneous applications on top of the same WSN. First, we establish that using the same MAC or network protocol is not sufficient to obtain acceptable performance across a set of applications that require different types of communication services from the protocol stack (e.g., low-rate reliable many-toone collection vs point-to-point low-latency bulk-data streaming). Hence, we propose a framework to dynamically reconfigure the WSN and adapt its power consumption, transmission reliability, and data throughput to the different requirements of the applications. The framework makes it possible to specify, at design time, distinct network, MAC and radio protocols for each application as well as the events and policies triggering the WSN reconfigurations. At run-time, the WSN automatically reconfigures itself in response to these events and according to these policies. Through experiments on a 119-node testbed, we show that the proposed approach can reconfigure the whole network in few hundreds of milliseconds while incurring little memory and control overhead.

We present Twonet, a large-scale sensor network testbed with dual-radio nodes. Twonet has 100 Opal nodes with low-power 32-bit ARM CPU and 2.4 GHz and 900 MHz radios. These nodes are managed by a network of 20 Raspberry Pi nodes at tier 2 and a PC server at tier 1. These nodes together provide a robust testbed for public access. Twonet represents a major addition to the collection of wireless sensor network testbeds that are publicly available. We hope Twonet's availability will foster sensor network research based on a modern 32-bit sensor node architecture and multi-channel wireless networking.

We describe CTP, a collection routing protocol for wireless sensor networks. CTP uses three techniques to provide efficient, robust, and reliable routing in highly dynamic network conditions. CTP's link estimator accurately estimates link qualities by using feedback from both the data and control planes, using information from multiple layers through narrow, platform-independent interfaces. Second, CTP uses the Trickle algorithm to time the control traffic, sending few beacons in stable topologies yet quickly adapting to changes. Finally, CTP actively probes the topology with data traffic, quickly discovering and fixing routing failures. Through experiments on 13 different testbeds, encompassing seven platforms, six link layers, and multiple densities and frequencies, and detailed observations of a long-running sensor network application that uses CTP, we study how these three techniques contribute to CTP's overall performance.

We consider the problem of distributing time-sensitive information from a collection of sources to mobile users traversing a wireless mesh network. Our strategy is to distributively select a set of well-placed nodes (warehouses) to act as intermediaries between the information sources and clusters of users. Warehouses are selected via the distributed construction of Hierarchical Well-Separated Trees (HSTs), which are sparse structures that induce a natural spatial clustering of the network. Unlike many traditional multicast protocols, our approach is not data driven. Rather, it is agnostic to the number and position of sources as well as to the mobility patterns of users. Whereas source-rooted tree multicast algorithms construct a separate routing infrastructure to support each source, our sparse and flexible infrastructure is precomputed and efficiently reused by sources and users, its cost amortized over time. Moreover, the route acquisition delay inherent in on-demand wireless ad hoc network protocols is avoided by exploiting the HST addressing scheme. Our algorithm ensures with high probability a guaranteed stretch bound for the information delivery path, and is robust to lossy links and node failure by providing alternative HST-induced routes. Nearby users are clustered and their requests aggregated, further reducing communication overhead.

Understanding human mobility in an environment can be approached in many forms, one of which is to recover the underlying structure of user movement. In our work, we show that we can use a network of binary proximity sensors to detect paths between nodes and also extract highly popular trajectories users take. We show that with sufficient amount of these binary data, even with no prior knowledge of the location of these sensors, we can capture a correlation between the detection timestamps in the case where a physical path exists between any two nodes. Our algorithm also generates characteristics of the path, such as the distribution of transition times and volume. We further show that with sampling techniques we can estimate the underlying trajectories that generated the time stamps. We have tested our algorithm on a simulator and two sensor network deployments. We found that, despite the lack of position information about the sensor nodes, with timestamps alone our algorithm can accurately detect the trajectories and is robust enough to use in a real-world office building.

In this paper, we present a routing scheme that exploits knowledge about the behavior of mobile sinks within a network of data sources to minimize energy consumption and network congestion. For delay-tolerant network applications, we propose to route data not to the sink directly, but to send it instead to a relay node along an announced or predicted path of the mobile node that is close to the data source. The relay node will stash the information until the mobile node passes by and picks up the data. We use linear programming to find optimal relay nodes that minimize the number of necessary transmissions while guaranteeing robustness against link and node failures, as well as trajectory uncertainty.

Current data collection protocols for wireless sensor networks are mostly based on quasi-static minimum-cost routing trees. We consider an alternative, highly-agile approach called backpressure routing, in which routing and forwarding decisions are made on a per-packet basis. Although there is a considerable theoretical literature on backpressure routing, it has not been implemented on practical systems to date due to concerns about packet looping, the effect of link losses, large packet delays, and scalability. Addressing these concerns, we present the Backpressure Collection Protocol (BCP) for sensor networks, the first ever implementation of dynamic backpressure routing in wireless networks. In particular, we demonstrate for the first time that replacing the traditional FIFO queue service in backpressure routing with LIFO queues reduces the average end-to-end packet delays for delivered packets drastically (75% under high load, 98% under low load). Further, we improve backpressure scalability by introducing a new concept of floating queues into the backpressure framework. Under static network settings, BCP shows a more than 60% improvement in max-min rate over the state of the art Collection Tree Protocol (CTP). We also empirically demonstrate the superior delivery performance of BCP in highly dynamic network settings, including conditions of extreme external interference and highly mobile sinks.

Powertron is a web-based application that visualizes wireless sensor network deployment data. In this particular demo, we use Powertron to show application-level power data collected from more than 250 sensor nodes. In addition, we expose the routing layer of the deployment by providing real-time interactive visual representation of links, routes, and CTP-related statistics. We hope that the community will have feedback on how such a tool can be extended and generalized to fit a variety of wireless sensor network applications.

The performance of sensor network protocols is greatly affected by the network topology: the network layout, the link dynamics, and the sink placement. We propose the Expected Network Delivery (END), a protocol-independent collection metric that captures the impact of the topology on protocol performance. Our preliminary results with two collection protocols on various topologies on the MoteLab testbed show that the END metric enables a more systematic and topology-aware evaluation of protocol performance.

Most sensor network research and software design has been guided by an architectural principle that permits multinode data fusion on small-form-factor, resource-poor nodes, or motes . While we were among the earliest promoters of this approach, through experience we found that this principle leads to fragile and unmanageable systems and explore an alternative. The Tenet architecture is motivated by the observation that future large-scale sensor network deployments will be tiered , consisting of motes in the lower tier and masters , relatively unconstrained 32-bit platform nodes, in the upper tier. Tenet constrains multinode fusion to the master tier while allowing motes to process locally-generated sensor data. This simplifies application development and allows mote-tier software to be reused. Applications running on masters task motes by composing task descriptions from a novel tasklet library. Our Tenet implementation also contains a robust and scalable networking subsystem for disseminating tasks and reliably delivering responses. We show that a Tenet pursuit-evasion application exhibits performance comparable to a mote-native implementation while being considerably more compact. We also present two real-world deployments of Tenet system: a structural vibration monitoring application at Vincent Thomas Bridge and an imaging-based habitat monitoring application at James Reserve, and show that tiered architecture scales network capacity and allows reliable delivery of high rate data. 1

This paper presents and evaluates two principles for wireless routing protocols. The first is datapath validation: data traffic quickly discovers and fixes routing inconsistencies. The second is adaptive beaconing: extending the Trickle algorithm to routing control traffic reduces route repair latency and sends fewer beacons.

We describe Neutron, a version of the TinyOS operating system that efficiently recovers from memory safety bugs. Where existing schemes reboot an entire node on an error, Neutron's compiler and runtime extensions divide programs into recovery units and reboot only the faulting unit. The TinyOS kernel itself is a recovery unit: a kernel safety violation appears to applications as the processor being unavailable for 10-20 milliseconds.

Most sensor network research and software design has been guided by an architectural principle that permits multi-node data fusion on small-form-factor, resource-poor nodes, or motes. We argue that this principle leads to fragile and unmanageable systems and explore an alternative. The Tenet architecture is motivated by the observation that future large-scale sensor network deployments will be tiered, consisting of motes in the lower tier and masters, relatively unconstrained 32-bit platform nodes, in the upper tier. Masters provide increased network capacity. Tenet constrains multi-node fusion to the master tier while allowing motes to process locally-generated sensor data. This simplifies application development and allows mote-tier software to be reused. Applications running on masters task motes by composing task descriptions from a novel tasklet library. Our Tenet implementation also contains a robust and scalable networking subsystem for disseminating tasks and reliably delivering responses. We show that a Tenet pursuit-evasion application exhibits performance comparable to a mote-native implementation while being considerably more compact.

Unpredictable and heterogeneous links in a wireless sensor network require techniques to avoid low delivery rate and high delivery cost. Three commonly used techniques to help discover high quality paths include (1) link-layer retransmission, (2) blacklisting bad links, and (3) end-to-end routing metrics. Using simulation and testbed experiments, we present the first systematic exploration of the tradeoffs of combinations of these approaches, quantifying the effects of each of these three techniques. We identify several key results: one is that per-hop retransmissions (ARQ) is a necessary addition to any other mechanism if reliable data delivery is a goal. Additional interactions between the services are more subtle. First, in a multihop network, either blacklisting or reliability metrics like ETX can provide consistent high-reliability paths when added to ARQ. Second, at higher deployment densities, blacklisting has a lower routing overhead than CTX. But at lower densities, blacklisting becomes less stable as the network partitions. These results are consistent across both simulation and testbed experiments. We conclude that ETX with retransmissions is the best choice in general, but that blacklisting may be worth considering at higher densities, either with or without ETX.