The violation of information privacy in Smart Grids can be a significant barrier to customers' participation. Employing privacy protection models such as K-anonymity in a Smart Grid implementation adds desirable privacy guarantees. This work provides an approach to applying the Mondrian algorithm to ensure data within the system excludes Personally Identifiable Information. Results suggest that a dynamically generated generalization hierarchy minimizes information loss incurred by the anonymization process.

Visually impaired people face many daily encumbrances. Traditional visual enhancements do not suffice to navigate indoor environments. In this paper, we explore path finding algorithms such as Dijkstra and A* combined with graph coloring to find a safest and shortest path for visual impaired people to navigate indoors. Our mobile application is based on a database which stores the locations of several spots in the building and their corresponding label. Visual impaired people select the start and destination when they want to find their way, and our mobile application will show the appropriate path which guarantees their safety.

Low vision people face many daily encumbrances. Traditional visual enhancements do not suffice to navigate indoor environments, or recognize objects efficiently. In this paper, we explore how Augmented Reality (AR) can be leveraged to design mobile applications to improve visual experience and unburden low vision persons. Specifically, we propose a novel automated AR-based annotation tool for detecting and labeling salient objects for assisted indoor navigation applications like NearbyExplorer. NearbyExplorer, which issues audio descriptions of nearby objects to the users, relies on a database populated by large teams of volunteers and map-a-thons to manually annotate salient objects in the environment like desks, chairs, low overhead ceilings. This has limited widespread and rapid deployment. Our tool builds on advances in automated object detection, AR labeling and accurate indoor positioning to provide an automated way to upload object labels and user position to a database, requiring just one volunteer. Moreover, it enables low vision people to detect and notice surrounding objects quickly using smartphones in various indoor environments.

By June 3, 2021, the US experienced over 33 million total cases of Covid-19, surpassing 592,000 deaths. In response, the Centers for Disease Control and Prevention (CDC) advised masking, social distancing and avoiding mass gatherings. In this work, we seek to automatically identify physical mass gathering events including dates and locations from digital chatter, i.e., social media data. We also study spread and sentiment associated with such large gathering events, finding a moderate negative correlation between large public gatherings, overall sentiment, and reported Covid-19 case numbers post event.

Summary The growth of counterfeit goods has plagued the international community for decades. Nowadays, the battle against counterfeiting remains a significant challenge. Most of the current anti‐counterfeiting systems are centralized. Motivated by the evolution of blockchain technology, we propose (Block‐Supply), a decentralized anti‐counterfeiting supply chain that exploits NFC and blockchain technologies. This paper also proposes a new truly decentralized consensus protocol that, unlike most of the existing protocols, does not require PoW and randomly employs a different set of different size of validators each time a new block is proposed. Our protocol utilizes a game theoretical model to analyze the risk likelihood of the block's proposing nodes. This risk likelihood is used to determine the number of validators involved in the consensus process. Additionally, the game model enforces the honest consensus nodes' behavior by rewarding honest players and penalizing dishonest ones. Our protocol utilizes a novel, decentralized, dynamic mapping between the nodes that participate in the consensus process. This mapping ensures that the interaction between these nodes is executed anonymously and blindly. This way of mapping withstands many attacks that require knowing the identities of the participating nodes in advance, such as DDoS, Bribery, and Eclipse attacks.

Infrastructure-based Indoor Positioning Systems (IIPS) have emerged as critical components of wireless deployments for many enterprises so as to track mobile devices without additional device-side applications or computation. To provide transformative location-based services, it is important that IIPS compute accurate locations over a long period of time, scaling with a large number of tracked devices cost-effectively. In this paper, we present MotionScanner, which includes novel feature-based and end-to-end deep learning motion detection models to detect device motion solely from noisy, temporally sparse, and partial Wi-Fi measurements at access points. We further integrate MotionScanner into an IIPS so that the IIPS can exploit previously computed locations effectively to improve location accuracy, and skip unnecessary location computation. Building on observations of how location estimates of stationary devices scatter over time, we can monitor and enhance the performance of IIPS. We evaluate MotionScanner with data sets collected from real-world deployments of IIPS at two enterprises, and show that MotionScanner achieves 83% motion detection accuracy while saving 80% of computational resources.

Passive indoor positioning systems (PIPS) enable non-invasive tracking of mobile devices (e.g., smartphones, Wi-Fi tags) using enterprise network infrastructure. This empowers operators of large airports and retail facilities to optimize cost-intensive resource allocation as well as to provide location-based services to their customers such as geo-fencing and proximity marketing. However, existing PIPS often generate noisy location estimates due to unpredictable interference and attenuation of wireless signals in indoor environments. To address this problem, we propose a novel map matching approach that uses a floor map to constrain location estimates to possible paths a mobile user can take.

Most current product anti-counterfeiting approaches can detect tag modification and tag cloning attacks, but cannot detect tag reapplication attacks, in which a counterfeiter removes a legitimate tag from a genuine product and reapplies it to a counterfeit or expired product. Reapplying a legitimate tag to a counterfeit product can result in major health and economic consequences. We propose a new Tag Reapplication Detection (TRD) approach to detect reapplication attacks, using low-cost Near Field Communication (NFC) tags and public key cryptography. The use of NFC makes TRD user- friendly since a large number of current cell phones are NFC-enabled. To detect reapplication attacks, TRD tracks how many times a tag has been read in the supply chain using an online authentication protocol. When tagged products are in the market, TRD allows customers to use their cell phones in authenticating products using an offline authentication protocol. We analyzed the security of TRD and found that it can detect reapplication attacks, in addition to modification and cloning attacks.

Crowd-sourced mobile embedded systems allow people to contribute sensor data, for critical applications, including transportation, emergency response and eHealth. Data integrity becomes imperative as malicious participants can launch software and Sybil attacks modifying the sensing platform and data. To address these attacks, we develop (1) a Trusted Sensing Peripheral (TSP) enabling collection of high-integrity raw or aggregated data, and participation in applications requiring additional modalities; and (2) a Secure Tasking and Aggregation Protocol (STAP) enabling aggregation of TSP trusted readings by untrusted intermediaries, while efficiently detecting fabricators. Evaluations demonstrate that TSP and STAP are practical and energy-efficient.

As sensor networking technologies continue to develop, the notion of adding large-scale mobility into sensor networks is becoming feasible by crowd-sourcing data collection to personal mobile devices. However, tasking such networks at fine granularity becomes problematic because the sensors are heterogeneous and owned by users instead of network operators. In this paper, we present Zoom, a multi-resolution tasking framework for crowdsourced geo-spatial sensor networks. Zoom allows users to define arbitrary sensor groupings over heterogeneous, unstructured and mobile networks and assign different sensing tasks to each group. The key idea is the separation of the task information ( what task a particular sensor should perform ) from the task implementation ( code ). Zoom consists of (i) a map, an overlay on top of a geographic region, to represent both the sensor groups and the task information, and (ii) adaptive encoding of the map at multiple resolutions and region-of-interest cropping for resource-constrained devices, allowing sensors to zoom in quickly to a specific region to determine their task. Simulation of a realistic traffic application over an area of 1 sq. km with a task map of size 1.5 KB shows that more than 90% of nodes are tasked correctly. Zoom also outperforms Logical Neighborhoods, the state-of-the-art tasking protocol in task information size for similar tasks. Its encoded map size is always less than 50% of Logical Neighborhood's predicate size.

Current sensor networks have limited use after deployment due to inability to dynamically update sensing tasks in the networks. Assigning multiple tasks to multiple sensor groups in a sensor network deployed over a large geographic region at fine granularity is problematic because the sensors are heterogeneous, potentially mobile, and owned by users instead of network operators. We demonstrate Zoom [1], a multi-resolution tasking framework for crowdsourced geo-spatial sensor networks. The key idea in Zoom is decoupling the task specification from the task implementation using a spatial 2-dimensional representation of the tasking region (e.g., maps). Zoom allows users to define sensor groups over heterogeneous, unstructured and mobile networks and assign different sensing tasks to each group. To the best of our knowledge, this is the first work to propose a map based approach for mobile sensing systems.

Current feature tracking frameworks in sensor networks exploit advantages of either mobility, where mobile sensors can provide micro scale information of a small sensing area or numerical models that can provide macro scale information about the environment but not both. With the continual development of sensor networks, mobility becomes an important feature to integrate next generation sensing systems. In addition, recent advances in environmental modeling also allow us to better understand basic behavior of the environment. In order to further improve existing sensing systems, we need a new framework that can take advantages of existing fixed sensor networks, mobile sensors and numerical models. We develop CoTrack, a Collaborative Tracking framework, that allows mobile sensors to cooperate with fixed sensors and numerical models to accurately track dynamic features in an environment. The key innovation in CoTrack is the incorporation of numerical models at different scales and sensor measurements to guide mobile sensors for tracking. The framework includes three components: a macro model for large-scale estimation, a micro model for locale estimation of specific features based on sensor measurements, and an adaptive sampling scheme that guides mobile sensors to accurately track dynamic features. We apply our framework to track salinity intrusion in the Columbia River estuary in Oregon, United States. Our framework is fast and can reduce tracking error by more than half compared to existing data assimilation and state-of-the-art numerical models.

A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps.

For many applications in mobile wireless ad hoc networks (MANETs), forming an end-to-end data path is not always necessary; instead, the primary routing goal is often data collection or dissemination where only a data source is known. Routing algorithms must be carefully chosen in order to suit the needs of applications employing them. Our focus is on data collection applications in MANETs where limited mobility information is required to route data in a scalable manner. To address this goal, we employ the concepts of delay-tolerant networking (DTN) in which data makes progress toward a destination with high latency expectations and little knowledge of routing topology. Specifically, we present time-to-network (TTN) forwarding, a method of forwarding data generated by mobile nodes to a network endpoint in such a way that delivery latency is lowered without high networking cost. By segmenting mobility patterns into trips, we are able to apply TTN to a vehicular network using only an estimated destination arrival time for each vehicle. We evaluate TTN using mobility data from the TRANSIMS simulator for a real road network. Results show that our algorithm produces collection-to-network latencies similar to more generic algorithms but at a lower cost and with higher efficiency. Furthermore, we establish a lower bound for delivery latency in our experiments and compare it to TTN. This also helps normalize the interpretation of results specific to our mobility model.

For distributed smart camera networks to perform vision-based tasks such as subject recognition and tracking, every camera's position and orientation relative to a single 3-D coordinate frame must be accurately determined. In this paper, we present a new camera network localization solution that requires successively showing a 3-D feature point-rich target to all cameras, then using the known geometry of a 3-D target, cameras estimate and decompose projection matrices to compute their position and orientation relative to the coordinatization of the 3-D target's feature points. As each 3-D target position establishes a distinct coordinate frame, cameras that view more than one 3-D target position compute translations and rotations relating different positions' coordinate frames and share the transform data with neighbors to facilitate realignment of all cameras to a single coordinate frame. Compared to other localization solutions that use opportunistically found visual data, our solution is more suitable to battery-powered, processing-constrained camera networks because it requires communication only to determine simultaneous target viewings and for passing transform data. Additionally, our solution requires only pairwise view overlaps of sufficient size to see the 3-D target and detect its feature points, while also giving camera positions in meaningful units. We evaluate our algorithm in both real and simulated smart camera networks. In the real network, position error is less than 1 ('') when the 3-D target's feature points fill only 2.9% of the frame area.

Noise map can provide useful information to control noise pollution. We propose a people-centric noise collection system called the Ear-Phone. Due to the voluntary participation of people, the number and location of samples cannot be guaranteed. We propose and study two methods, based on compressive sensing, to reconstruct the missing samples.

Wireless sensor networks are revolutionizing the instrumentation of the physical world, across scientific, industrial and military applications. In this paper, we describe our efforts developing and classroom-testing hands-on materials for use in undergraduate-accessible courses on sensor networks. In Winter 2008 at Portland State University, we introduced an in-class laboratory component to a sensor networks course that had previously been entirely lecture-based. For the laboratory exercises, we utilized Sun's Java-programmable Sun SPOT [7] sensor network technology. We found the Sun SPOT based laboratory activities to be quite powerful as a teaching and excitement-fostering tool.

This article investigates a wireless acoustic sensor network application—monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our goal is to use automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the cane toad. This is a challenging application because it requires high frequency acoustic sampling, complex signal processing, wide area sensing coverage and long-lived unattended operation. We set up two prototypes of wireless sensor networks that recognize vocalizations of up to ninth frog species found in northern Australia. Our first prototype consists of only resource-rich Stargate devices. Our second prototype is more complex and consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica2 devices operating in concert. In the hybrid system, the Mica2s are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks such as fast Fourier transforms (FFTs) and machine learning. The hybrid system incorporates four algorithms designed to account for the sampling, processing, energy, and communication bottlenecks of the Mica2s (1) high frequency sampling, (2) thresholding and noise reduction, to reduce data transmission by up to 90%, (3) sampling scheduling, which exploits the sensor network redundancy to increase the effective sample processing rate, and (4) harvesting-aware energy management, which exploits sensor energy harvesting capabilities to extend the system lifetime. Our evaluation shows the performance of our systems over a range of scenarios, and demonstrate that the feasibility and benefits of a hybrid systems approach justify the additional system complexity.

Two of the fundamental challenges associated with data gathering in sensor networks are data classification and data aggregation. This paper provides a solution to classify and aggregate sensor readings. We leverage our previous experience[7] and use Competitive Learning Neural Network (CLNN) as the data classification mechanism. We then propose and evaluate Graph Theory Based Aggregation (GTBA) which combines outputs of CLNN across the network. We have evaluated two main interpretations of GTBA on real data sets produced by the WSN and on a testbed consisting of MicaZ motes. We demonstrate its ability to deduce an accurate representation of the data and distinguish the noise free data with a high probability.

In 2003, the MIT Technology review magazine listed wireless sensor networks as one of "Ten Emerging Technologies That Will Change the World" [2]. Five years later, are wireless sensor networks ready for the undergraduate classroom? Our goal is to develop lab exercises that are suitable for activity-driven teaching of undergraduate students. We here describe our lab exercises and our experience class-testing them in the Winter quarter of 2008 at Portland State University.

The Columbia River (CoRie) Observation Network includes an extensive array of fixed stations monitoring the Columbia River estuary and nearby coastal ocean. At each station, variable combinations of in-situ sensors measure one or more physical properties of water or atmosphere. Using a multi-scale data assimilation model, the CORIE modeling system integrates models and field controls to produce a simulation of 3D circulation, in a region centered in the estuary and plume. The CORIE data assimilation framework [1] combines observational data with numerical data models to produce an estimated system state for the physical process. To augment the fixed observational network, additional data is collected during periodical cruises of a mobile sensor station. Because these cruises are expensive and rare, an important goal for scientists is to sample data at points that most reduce the uncertainty of the data assimilation model. This is challenging, since the estuary environment is very dynamic, and therefore the optimal cruise path cannot be determined in advance. The goal of our system is to move the mobile station as the data assimilation proceeds in order to maximally reduce the uncertainty in the data assimilation process.

Ad hoc wireless sensor networks derive much of their promise from their potential for autonomously monitoring remote or physically inaccessible locations. As we begin to deploy sensor networks in real world applications, concerns are being raised about the fidelity and integrity of the sensor network data. In this paper, we motivate and propose an online algorithm that leverages competitive learning neural network for characterization of a dynamic, unknown environment. Based on the proposed characterization sensor networks can autonomously construct multimodal views of their environments and derive the conditions for verifying data integrity over time

This demonstration shows a powerful high-level, heterogeneous sensor networking framework, Cascades. We intend to demonstrate how, with this framework, application designers have great control over implementation designs without the requirement of in-depth development. Several key components and example applications will be demonstrated, along with some important concepts used in this Python-based framework. The emphasis will be on video sensing and application control in heterogeneous sensor networks consisting of Crossbow Stargate (PC-class) and MicaZ (mote-class) devices.

This paper investigates a wireless, acoustic sensor network application-monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our goal is to use automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the cane toad. This is a challenging application because it requires high frequency acoustic sampling, complex signal processing and wide area sensing coverage. We set up two prototypes of wireless sensor networks that recognize vocalizations of up to 9 frog species found in northern Australia. Our first prototype is simple and consists of only resource-rich Stargate devices. Our second prototype is more complex and consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica2 devices operating in concert. In the hybrid system, the Mica2s are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks such as fast Fourier transforms (FFTs) and machine learning. The hybrid system incorporates three algorithms designed to account for the sampling, processing and communication bottlenecks of the Mica2s (i) high frequency sampling, (ii) compression and noise reduction, to reduce data transmission by up to 90%, and (iii) sampling scheduling, which exploits the sensor network redundancy to increase the effective sample processing rate. We evaluate the performance of both systems over a range of scenarios, and demonstrate that the feasibility and benefits of a hybrid systems approach justify the additional system complexity.

Complex heterogeneous sensor networks that include both traditional scalar and richer video data are becoming possible. With such technologies comes the task of making them useful to the applications that rely on them. Unfortunately, such applications are extremely diverse in the interaction requirements of the system components. This paper argues that the system software for such sensor systems needs to balance optimization for performance and providing a flexible interface that can be tailored to the requirements of the user application. We propose Cascades, a cascading filter mechanism that can allow users to quickly compose and re-task diverse sensor systems (which include both scalar and video-based sensors) with highly optimized application-specific algorithms.

This demonstration shows a wireless, acoustic sensor network application--- monitoring amphibian populations in the monsoonal woodlands of northern Australia. Our system uses automatic recognition of animal vocalizations to census the populations of native frogs and the invasive introduced species, the Cane Toad (see Fig. 1). This is a challenging application because it requires high frequency acoustic sampling, complex signal processing and wide area sensing coverage [2]. Our prototype consists of a hybrid mixture of Stargates and inexpensive, resource-poor Mica motes operating in concert. The Mica motes are used to collect acoustic samples, and expand the sensor network coverage. The Stargates are used for resource-intensive tasks.

We present a novel scheme for node localization in a delay-tolerant sensor network (DTN). In a DTN, sensor devices are often organized in network clusters that may be mutually disconnected. Some mobile robots may be used to collect data from the network clusters. The key idea in our scheme is to use this robot to perform location estimation for the sensor nodes it passes based on the signal strength of the radio messages received from them. Thus, we eliminate the processing constraints of static sensor nodes and the need for static reference beacons. Our mathematical contribution is the use of a robust extended Kalman filter (REKF)-based state estimator to solve the localization. Compared to the standard extended Kalman filter, REKF is computationally efficient and also more robust. Finally, we have implemented our localization scheme on a hybrid sensor network test bed and show that it can achieve node localization accuracy within 1 m in a large indoor setting.

Location management refers to the problem of updating and searching the current locations of mobile nodes in a wireless network. To make it efficient, the sum of update/lookup costs of a location database must be minimized. Previous work relying on fixed location databases is unable to fully exploit the knowledge of user mobility patients in the system so as to achieve this minimization. We present a novel location management architecture that can adapt dynamically to user pattern changes. The key idea is to use a hierarchy of agents that act as mobile location databases that rapidly replicate to other base-stations in response to a change in user patterns. We provide an analytical cost model for location management and through simulations demonstrate our algorithm's ability to handle location updates and call finding.

This paper proposes CSMAC (CDMA Sensor MAC), a novel self-organizing, location-aware media-access control (MAC) protocol for DS-CDMA sensor networks to support applications that feature high bandwidth traffic and stringent latency requirements. Previously proposed MAC protocols for sensor networks such as SMAC (W. Ye et al, IEEE Proc. Infocom, pp. 1567-1576, 2002) primarily prioritize energy efficiency over latency. Our protocol design balances the considerations of energy efficiency, latency, accuracy, and fault-tolerance in sensor networks. CSMAC uses a combination of DS-CDMA and frequency diversity in channel allocation to reduce channel interference and consequently message latency in the network. It features two algorithms that exploit the location awareness of sensor nodes to enable intelligent and efficient network formation for collaborative sensing tasks: turn off redundant node (TORN) and select minimum neighbor (SMN).

The paper investigates an anycast communication paradigm for a hybrid sensor/actuator network, consisting of both resource-rich and resource-impoverished devices. The key idea is to exploit the capabilities of resource-rich devices (called micro-servers) to reduce the communication burden on smaller sensor nodes which are energy, bandwidth and memory constrained. The goal is to deliver sensor data to the nearest micro-server, which can (i) store it, (ii) forward it to other micro-servers using out-of-band communication, or (iii) perform the desired actuation. Our approach is to construct an anycast tree rooted at each potential event source, which micro-servers can dynamically join and leave. Our anycast mechanism is self-organizing, distributed, robust, scalable, and incurs very little overhead. Simulations using ns-2 show that our anycast mechanism can reduce network energy consumption by more than 50%, both the mean end-to-end latency of the transmission and the mean number of transmissions by more than 50%, and achieves 99% data delivery rate for low and moderate micro-server mobility rate.

Localization of small wireless sensor devices, with the deployment of the minimal infrastructure or hardware, has been the topic of significant research over the past few years. We have developed the Robust Extended Kalman Filter (REKF) localization system [1], which enables a mobile, data gathering robot to localize static sensor devices, by combining the RSSI data received from the motes, with estimates of its trajectory. The REKF localization system is particularly well suited to delay-tolerant sensor networks, where node positions need not be known in real time. We have observed accuracies ranging from approximately 30cm to 1m in practice.

Embedded networked sensors promise to revolutionize the way we interact with our physical environment and require scalable, ad hoc deployable and energy-efficient node localization/positioning.This paper describes the motivation, design, implementation, and experimental evaluation (on sharply resource-constrained devices) of a self-configuring localization system using radio beacons. We identify beacon density as an important parameter in determining localization quality, which saturates at a transition density. We develop algorithms to improve localization quality by (i) automating placement of new beacons at low densities (HEAP) and (ii) rotating functionality among redundant beacons while increasing system lifetime at high densities (STROBE).

Beacon placement strongly affects the quality of spatial localization, a critical service for context-aware applications in wireless sensor networks; yet this aspect of localization has received little attention. Fixed beacon placement approaches such as uniform and very dense placement are not always viable and will be inadequate in very noisy environments in which sensor networks may be expected to operate (with high terrain and propagation uncertainties). We motivate the need for empirically adaptive beacon placement and outline a general approach based on exploration and instrumentation of the terrain conditions by a mobile human or robot agent. We design, evaluate and analyze three novel adaptive beacon placement algorithms using this approach for localization based on RF-proximity. In our evaluation, we find that beacon density rather than noise level has a more significant impact on beacon placement algorithms. Our beacon placement algorithms are applicable to a low (beacon) density regime of operation. Noise makes moderate density regimes more improvable.