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Opportunistic Spectrum Allocation for Interference Mitigation Amongst Coexisting Wireless Body Area Networks

Wireless Body Area Networks (WBANs) are seen as the enabling technology for developing new... (more)

Average Counting via Approximate Histograms

We propose a new algorithm for the classical averaging problem for distributed wireless sensors networks. This subject has been studied extensively and there are many clever algorithms in the literature. These algorithms are based on the idea of local exchange of information. They behave well in dense networks (e.g., in networks whose connections... (more)

Networking Wireless Energy in Embedded Networks

Wireless energy transfer has recently emerged as a promising alternative to realize the vision of perpetual embedded sensing. However, this technology transforms the notion of energy from merely a node’s local commodity to, similarly to data, a deployment-wide shareable resource. The challenges of managing a shareable energy resource are... (more)

Energy-Harvesting Wireless Sensor Networks (EH-WSNs): A Review

Wireless Sensor Networks (WSNs) are crucial in supporting continuous environmental monitoring, where sensor nodes are deployed and must remain... (more)

Harmonium: Ultra Wideband Pulse Generation with Bandstitched Recovery for Fast, Accurate, and Robust Indoor Localization

We introduce Harmonium, a novel ultra wideband (UWB) RF localization architecture that achieves decimeter-scale accuracy indoors. Harmonium strikes a balance between tag simplicity and processing complexity to provide fast and accurate indoor location estimates. Harmonium uses only commodity components and consists of a small, inexpensive,... (more)

Exploiting Electrical Grid for Accurate and Secure Clock Synchronization

Desynchronized clocks among network nodes in critical infrastructures can degrade system performance and even lead to safety incidents. Clock... (more)

Natural Timestamps in Powerline Electromagnetic Radiation

The continuous fluctuation of electric network frequency (ENF) presents a fingerprint indicative of time, which we call natural timestamp. This... (more)

Non-Bayesian Social Learning with Observation Reuse and Soft Switching

We propose a non-Bayesian social learning update rule for agents in a network, which minimizes the sum of the Kullback-Leibler divergence between the... (more)

Differentiating Clear Channel Assessment Using Transmit Power Variation

Clear Channel Assessment (CCA) is a core element of Wireless Sensor Network (WSN) Medium Access Control (MAC) protocols which is used on the... (more)

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About TOSN

TOSN publishes original research papers (approximately 30-40 printed pages each in ACM Transaction style) and tutorial and survey papers (approximately 30-50 printed pages each). We also accept short technical notes that focus on industrial technologies and practical experience.
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Forthcoming Articles
DISH: DIstributed SHuffling against selective jamming attack in IEEE 802.15.4e TSCH networks

The MAC standard amendment IEEE 802.15.4e is designed to meet the requirements of industrial and critical applications. In particular, the Time Slotted Channel Hopping (TSCH) mode divides time into periodic, equally-sized, slotframes composed of transmission timeslots. Then, it combines time slotted access with multi-channel and channel hopping capabilities, providing large network capacity, high reliability and predictable latency, while ensuring energy efficiency. Since every network node considers the same timeslots at each sloframe and selects physical channels according to a periodic function, TSCH produces a steady channel utilization pattern. This can be exploited by a selective jammer to entirely thwart communications of a victim node, in a way which is stealthy, effective and extremely energy efficient. This paper shows how a selective jamming attack can be successfully performed even though TSCH uses the IEEE 802.15.4e security services. Furthermore, we propose DISH, a countermeasure which randomly permutes the timeslot and channel utilization patterns at every slotframe in a consistent and completely distributed way, without requiring any additional message exchange. We have implemented DISH for the Contiki OS and tested its effectiveness on TelosB sensor nodes. Quantitative analysis for different network configurations shows that DISH effectively contrasts selective jamming with negligible performance penalty.

Mechanisms and Policies for Controlling Distributed Solar Capacity

The rapid expansion of intermittent grid-tied solar capacity is making the job of balancing electricity's real-time supply and demand increasingly challenging. Recent work proposes mechanisms for actively controlling solar power in the grid at individual sites by enabling software to cap it as a fraction of its time-varying maximum output. However, while enforcing an equal fraction of each solar site's time-varying maximum output results in ``fair'' short-term contributions of solar power across all sites, it does not result in ``fair'' long-term contributions of solar energy. Enforcing fair long-term energy access is important when controlling distributed solar capacity, since limits on solar output impact the compensation users receive for net metering and the battery capacity required to store excess solar energy. This discrepancy arises from fundamental differences in enforcing ``fair'' access to the grid to contribute solar energy, compared to analogous fair-sharing in networks and processors. To address the problem, we first present both a centralized and distributed algorithm to enable control of distributed solar capacity that enforces fair grid energy access. We then present multiple policies that show how utilities can leverage this new distributed rate-limiting mechanism to reduce variations in grid demand from intermittent solar generation.

CapNet: Exploiting Wireless Sensor Networks for Data Center Power Capping

As the scale and density of data centers continue to grow, cost-effective data center management (DCM) is becoming a significant challenge for enterprises hosting large scale online and cloud services. The servers need to be monitored, and the scale of operations mandates an automated management with high reliability and real-time performance. Existing wired networking solutions for DCM come with high cost. In this paper, we propose a wireless sensor network as a cost-effective networking solution for DCM while satisfying the reliability and latency performance requirements of DCM. We have developed CapNet, a real-time wireless sensor network for power capping, a time-critical DCM function for power management in a cluster of servers. CapNet employs an efficient event-driven protocol that triggers data collection only upon the detection of a potential power capping event. We deploy and evaluate CapNet in a data center. Using server power traces, our experimental results on a cluster of 480 servers inside the data center show that CapNet can meet the real-time requirements of power capping. CapNet demonstrates the feasibility and efficacy of wireless sensor networks for time-critical DCM applications.

CloudNavi: Towards Ubiquitous Indoor Navigation Service with 3D Point Clouds

The rapid development of mobile computing has prompted indoor navigation to be one of the most attractive and promising applications. Conventional designs of indoor navigation systems depend on either infrastructures or indoor floor maps. This paper presents CloudNavi, a ubiquitous indoor navigation solution, which only relies on the point clouds acquired by 3D camera embedded in a mobile device. It pushes the design of indoor navigation to the extreme on five dimensions: accurate, easy-to-deploy, infrastructure-free, robust to environment, and universal. CloudNavi conducts a significant step towards realizing this vision by fully exploiting the advantages of point clouds. Particularly, CloudNavi first efficiently infers the walking trace of each user from captured point clouds. Many shared walking traces and associated point clouds are combined to generate the point cloud traces, which are then used to generate a 3D path-map. Accordingly, CloudNavi can accurately estimate the location of a user using a limited number of point clouds, and then guide the user to its destination from its current location. Extensive experiments are conducted on office building and shopping mall datasets. Experimental results indicate that CloudNavi exhibits outstanding navigation performance in both office building and shopping mall.

Maintenance of Smart Buildings using Fault Trees

Timely maintenance is an important means of increasing system dependability and life span. Fault Maintenance trees (FMTs) are an innovative framework incorporating both maintenance strategies and degradation models and serve as a good planning platform for balancing total costs (operational and maintenance) with dependability of a system. In this work, we apply the FMT formalism to a Smart Building application and propose a framework that efficiently encodes the FMT into Continuous Time Markov Chains. This allows us to obtain system dependability metrics such as system reliability and mean time to failure, as well as costs of maintenance and failures over time, for different maintenance policies. We illustrate the pertinence of our approach by evaluating various dependability metrics and maintenance strategies of a Heating, Ventilation and Air-Conditioning system.

Designing Green Communication Systems for Smart and Connected Communities via Dynamic Spectrum Access

In this paper we show that harnessing Dynamic Spectrum Access (DSA) in the context of Smart and Connected Communities (SCCs) can achieve notable benefits in terms of energy efficiency without sacrificing QoS. Specifically, this paper proposes a novel architecture for realizing SCCs using a small scale DSA enabled overlay network over legacy infrastructure, to improve the energy efficiency while guaranteeing QoS. Specifically, we propose a dynamic band selection approach that intelligently matches any message requirement to a suitable band type by exploiting distinct EM characteristics of various bands. We first formulate an optimization problem for determining the TTL constrained energy-efficient (TcE) path for any given message in the network. Then, we show that the TcE problem is NP-Hard and propose an exact pseudo-polynomial time dynamic programming (DP) algorithm to solve it. Then, we improve upon the time complexity by proposing a polynomial time greedy heuristic. Compared to the homogeneous band access approaches that opportunistically accesses free channels within a predetermined band, our simulation study shows that the proposed approach significantly improves the energy efficiency while preserving the QoS. Our study also reveals that the greedy heuristic achieves results that closely match those of the DP algorithm.

Optimal Discrete Net-Load Balancing in Smart Grids with High PV Penetration

Mitigating Supply-Demand mismatch is critical for smooth power grid operation. Traditionally, load curtailment techniques such as Demand Response have been used for this purpose. However, these cannot be the only component of a net-load balancing framework for Smart Grids with high PV penetration. These grids sometimes exhibit supply surplus causing over-voltages. Currently, these are mitigated using voltage manipulation techniques such as Volt-Var Optimizations which are computationally expensive, thereby increasing the complexity of grid operations. Taking advantage of recent technological developments that enable rapid selective connection of PV modules of an installation to the grid, we develop a unified net-load balancing framework which performs both load and solar curtailment. We show that when the available curtailment values are discrete, this problem is NP-hard and develop bounded approximation algorithms. Our algorithms produce fast solutions, given the tight timing constraints required for grid operation while ensuring that practical constraints such as fairness, network capacity limits, etc. are satisfied. We also develop an online algorithm which performs net-load balancing using only data available for the current interval. Using both theoretical analysis and practical evaluations, we show that our net-load balancing algorithms provide solutions which are close to optimal in a small amount of time.

A Scalable System for Apportionment and Tracking of Energy Footprints in Commercial Buildings

We propose a system that tracks each occupants personal share of energy use, or energy footprint, inside commercial building environments, and provides insights to occupants on the real-time energy impact of their actions. We propose a new space-centric policy for fair apportionment of energy in shared environments and demonstrate a method for automatically determining space-centric energy zones. We design and implement ePrints  a system for tracking personalized energy usage in real-time. ePrints supports different apportionment policies, with ¼s-level footprint computation time and graceful scaling with size of building, frequency of energy updates, and rate of occupant location changes. Finally, we present applications enabled by our system, such as mobile and wearable applications to provide users timely feedback on the energy impacts of their actions, as well as applications to provide energy saving suggestions and inform building-level policies.

SonicDoor: A Person Identification System Based on Modeling of Shape, Behavior and Walking Patterns

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 paper 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 9000 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.

Democratizing Authority in the Built Environment

Operating systems and applications in the built environment have relied upon central authorization and management mechanisms which restrict their scalability, especially with respect to administrative overhead. We propose a new set of primitives encompassing syndication, security, and service execution that unifies the management of applications and services across the built environment, while enabling participants to individually delegate privilege across multiple administrative domains with no loss of security or manageability. We show how to leverage a decentralized authorization syndication platform to extend the design of building operating systems beyond the single administrative domain of a building. The authorization system leveraged is based on blockchain smart contracts to permit decentralized and democratized delegation of authorization without central trust. Upon this, a publish/subscribe syndication tier and a containerized service execution environment are constructed. Combined, these mechanisms solve problems of delegation, federation, device protection and service execution that arise throughout the built environment. We leverage a high-fidelity city-scale emulation to verify the scalability of the authorization tier, and briefly describe a prototypical democratized operating system for the built environment using this foundation.

Smart Home Occupant Identification via Sensor Fusion Across On-Object Devices

Occupant identification proves crucial in many smart home applications such as automated home control and activity recognition. Previous solutions are limited in terms of deployment costs, identification accuracy, or usability. We propose SenseTribute, a novel occupant identification solution that makes use of existing and prevalent on-object sensors that are originally designed to monitor the status of objects they are attached to. SenseTribute extracts richer information content from such on-object sensor

AutoCalib: Automatic Traffic Camera Calibration at Scale

Emerging smart cities are typically equipped with thousands of outdoor cameras. However, these cameras are typically not calibrated, i.e., information such as their precise mounting height and orientation is not available. Calibrating these cameras allows measurement of real-world distances from the video, thereby, enabling a wide range of novel applications such as identifying speeding vehicles, city road planning, etc. Unfortunately, robust camera calibration is a manual process today and is not scalable. In this paper, we propose AutoCalib, a system for scalable, automatic calibration of traffic cameras. AutoCalib exploits deep learning to extract selected key-point features from car images in the video and uses a novel filtering and aggregation algorithm to automatically produce a robust estimate of the camera calibration parameters from just hundreds of samples.We have implemented AutoCalib as a service on Azure that takes in a video segment and outputs the camera calibration parameters. Using video from real-world traffic cameras, we show that AutoCalib is able to estimate real-world distances with an error of less than 12%

Design and Analysis of a Query Processor for Brick

Brick is a recently proposed metadata schema and ontology for describing building components and the relationships between them. It represents buildings as directed labeled graphs using the RDF data model. Using the SPARQL query language, building-agnostic applications query a Brick graph to discover the set of resources and relationships they require to operate. Latency-sensitive applications, such as user interfaces, demand response and model-predictive control, require fast queries --- conventionally less than 100ms. We benchmark a set of popular open-source and commercial SPARQL databases against three real Brick models using seven application queries and find that none of them meet this performance target. This lack of performance can be attributed to design decisions that optimize for queries over large graphs consisting of billions of triples, but give poor spatial locality and join performance on the small dense graphs typical of Brick. We present the design and evaluation of HodDB, a RDF/SPARQL database for Brick built over a node-based index structure. HodDB performs Brick queries 3-700x faster than leading SPARQL databases and consistently meets the 100ms threshold, enabling the portability of important latency-sensitive building applications. This paper is an extension of a previously published work (same title, published BuildSys 2017).

A Framework for Privacy-Preserving Data Publishing with Enhanced Utility in Cyber-Physical Systems

Cyber-physical systems have enabled the collection of massive amounts of data in an unprecedented level of spatial and temporal granularity. Publishing these data can prosper big data research which will in turn help improve overall system efficiency and resiliency. The main challenge in publishing these datasets is to ensure the usefulness of published data while providing necessary privacy protection. In our previous work we presented PAD - a privacy-preserving data publishing framework that can guarantee k-anonymity for data record owners while achieving better data utility than traditional anonymization techniques. PAD learns the features of interest to data users from their interactions with the data publishing system and then customizes data publishing processes to the intended use of data. However, our previous work can only produce better data utility when the interested feature is linear in the original data record. In this paper, we extend PAD to nonlinear features. In addition, we study a method to improve learning efficiency, which is particularly useful when the number of interactions between a data user and the data publishing system is limited. Our experiments demonstrate that for various data-driven applications PAD can achieve enhanced utility while remaining highly resilient to privacy threats.

Privacy-Preserving Truth Discovery in Crowd Sensing Systems

The recent proliferation of human-carried mobile devices has given rise to the crowd sensing systems. However, the sensory data provided by individual participants are usually not reliable. To identify truthful values from the crowd sensing data, the topic of truth discovery, whose goal is to estimate user quality and infer truths through quality-aware data aggregation, has drawn significant attention. Though able to improve aggregation accuracy, existing truth discovery approaches fail to address the privacy concerns of individual users. In this paper, we propose a novel privacy-preserving truth discovery (PPTD) framework for crowd sensing systems, which can achieve the protection of not only users' sensory data but also their reliability scores derived by the truth discovery approaches. The key idea of the proposed framework is to perform weighted aggregation on users' encrypted data using homomorphic cryptosystem. In order to deal with large-scale data, we also propose to parallelize PPTD with MapReduce framework. Additionally, we design an incremental PPTD scheme for the scenarios where the crowd sensing data are collected in a streaming manner. Through extensive experiments on not only synthetic data but also real-world crowd sensing systems, we justify the guarantee of strong privacy and high accuracy of our proposed framework.

W3W: Energy Management of Hybrid Energy Supplied Sensors for Internet of Things

The usage of hybrid energy supplied sensors in the Internet of Things has enabled longer lifetime of sensors and expanded scope of applications. These sensors can combine advantages of environmental energy harvesting techniques and wireless energy harvesting techniques. However, how to coordinate them is still a challenge and has not been studied extensively. In this paper, we present a system based on mobile crowd wireless charging to manage energy of hybrid energy supplied sensors. When environmental energy is insufficient, the system will utilize smart devices carried by mobile users as chargers to provide wireless energy. We construct and study a W3W problem in the system: \underline{w}hen to leverage mobile crowd wireless charging to support rechargeable sensors, \underline{w}here to perform wireless energy transfer, and \underline{w}hom to allocate and incentivize as chargers to maximize useful energy value over all sensors subject to a budget. In order to control the actual quality of wireless energy charging, we propose a design principle named task completion trustfulness. We consider offline and online conditions and design corresponding algorithms with incentive allocations. Extensive simulations are conducted to demonstrate the effectiveness of our algorithms, which also validates our theoretical results.

A General Framework for Spectrum Sensing Using Dedicated Spectrum Sensor Networks

Efficient spectrum sensing is essential for the success of Dynamic Spectrum Assignment (DSA) in Cognitive Radio Networks (CRNs). In conventional spectrum sensing schemes, secondary users (SUs) have to intelligently schedule their sensing and accessing cycles so that the spectrum opportunities are optimally exploited while the primary users are not harmed. In this paper, we propose a new sensing service model in which a dedicated spectrum sensor network (SSN) is adopted for the spectrum sensing tasks. We will describe the general framework for this SSN-enabled CRN and present the major challenges in such architecture. We will also study one of these challenges and formulate it as a boundary detection problem with notable and unknown erroneous inputs. A novel cooperative boundary detection scheme is designed which explores the recent advances in machine learning and computational geometry. We prove that our cooperative boundary detection can asymptotically approach the optimal solution. Real test-bed as well as simulation experiments show that compared with the traditional schemes, cooperative boundary detection can significantly reduce the spectrum sensing overhead and improve the effectiveness of DSA.

A Framework for the Inference of Sensing Measurements based on Correlation

Network monitoring is an essential component of network operation and it is usually responsible for a significant overhead in large scale networks such as sensor and data center networks. In this paper, we show that measurement correlation can be successfully exploited to reduce the network monitoring overhead. In particular, we propose an online adaptive measurement technique with which a subset of nodes are dynamically chosen as monitors while the measurements of the remaining nodes are estimated using the computed correlations. We propose an estimation framework based on jointly Gaussian distributed random variables, and formulate an optimization problem to select the monitors under a total cost constraint. We show that the problem is NP-Hard and propose three efficient heuristics. We also develop statistical approaches that automatically switch between learning and estimation phases to take into account the variability occurring in real networks. Simulations carried out on real-world traces show that our approach outperforms previous solutions based on compressed sensing and it is able to reduce the monitoring overhead by 50% while incurring a low estimation error. Additionally, we show the efficacy of our approach for the specific application of sensor network based solar irradiance prediction of photovoltaics systems.

Inverted HVAC: Greenifying Older Buildings, One Room at a Time

Emerging countries predominantly rely on room-level air conditioning units (window ACs, space heaters, ceiling fans) for thermal comfort. These distributed units have manual, decentralized control leading to sub-optimal energy usage for two reasons: excessive setpoints by individuals, and inability to interleave different conditioning units for maximal energy savings. We propose a novel inverted HVAC approach: cheaply retrofitting these distributed units with on-off control and providing centralized control augmented with room and environmental sensors. Our binary control approach exploits an understanding of device consumption characteristics at on/off and factors this into the control algorithms to minimize consumption. We implement this approach as HAWADAAR in a prototype 180 square-feet room to evaluate its efficacy over a 7-month period experiencing both hot and cold climates. Through a post analysis, we show that our on-off algorithms are not far from a theoretically-optimal approach based on a priori information that precisely knows the optimal control points to minimize consumption. We collect enough evidence to plausibly scale our empirical evaluation, demonstrating countrywide benefits: with just 20% market penetration, Hawadaar can save up to 6% of electricity per capita in residential and commercial sectors --- resulting in a substantial countrywide impact

A Scalable Room Occupancy Prediction with Transferable Time Series Decomposition of CO2 Sensor Data

Human occupancy counting is crucial for both space utilisation and building energy optimisation. In the current article, we present a semi-supervised domain adaptation method for carbon dioxide - Human Occupancy Counter Double Plus (DA-HOC++), a robust way to estimate the number of people within in one room by using data from a carbon dioxide sensor. In our previous work, the proposed Seasonal Decomposition for Human Occupancy Counting (SD-HOC) model can accurately predict the number of individuals when the training and labelled data are adequately available. DA-HOC++ is able to predict the number of occupancy with minimal training data, as little as one-day data. DA-HOC++ accurately predicts indoor human occupancy for five different rooms across different countries using a model trained from a small room and adapted to the other rooms. We evaluate DA-HOC++ with two baseline methods - support vector regression technique and SD-HOC model. The results demonstrate that DA-HOC++'s performance on average is better by 10.87% in comparison to SVR and 8.65% in comparison to SD-HOC.

Flux: a platform for dynamically reconfigurable mobile crowd-sensing

Flux is a platform for dynamically reconfigurable crowd-sensing using mobile devices, programmed under a notion of region-based sensing. Each region is defined by a set of physical constraints that determine the sensing scope, e.g., based on device position or other environmental variables, plus a set of periodic tasks that perform the actual sensing. The resulting behavior is inherently dynamic: as a device's state changes, e.g. moves in space, it enters and/or leaves different regions, thereby changing the set of active tasks; moreover, regions can be added, deleted, and reprogrammed on-the-fly. Flux makes use of a domain-specific language for sensing tasks that is compiled into abstract bytecode, later executed by a low-footprint virtual machine within a device, guaranteeing runtime safety by construction. For region dissemination, Flux employs a broker which holds a centralised (but changeable) region configuration plus gateways that mirror the configuration throughout different network access points to which devices connect. Sensing data is streamed by devices to gateways and then back to the broker. Live or archived data streams are in turn fed by the broker to data processing clients, that interface with the broker using a publish/subscribe API. Two case-study experiments illustrate \flux{} at work.

sTube+: An IoT Communication Sharing Architecture for Smart After-sales Maintenance in Buildings

Nowadays, manufacturers want to send the data of products to the cloud, so that they can conduct analysis and improve their operation and services. Manufacturers are looking for a self-contained solution. It is neither feasible to negotiate with each building to use the buildings network (e.g., WiFi) nor practical to establish its own network infrastructure since products are deployed in different buildings. The vendor can rent a dedicated channel from an ISP to act as a thing-to-cloud communication (TCC) link for each of IoT devices. Yet the choices for TCC links will be small as compared to thousands of requirements on costs and data rates from IoT applications. We address this issue by proposing a communication sharing architecture sTube+, sharing tube. The objective of sTube+ is to organize a greater number of IoT devices, with heterogeneous requirements to efficiently share fewer choices of TCC links, and transmit their data to the cloud. We architect a layered architecture for data delivery, develop algorithms to optimize the overall monetary cost, and prototype a fully functioning system. We evaluate sTube+ by experiments and simulations. We develop a case study on smart maintenance of chillers and pumps, using sTube+ as the underlying network architecture.

Dynamic Enhanced Field Division: an Advanced Localizing and Tracking Middleware

Tracking moving objects is always a critical challenge in cyber-physical systems. Researchers have proposed many tracking algorithms. However, most of the proposed algorithms cannot be used for on-demand deployment because of the unavailable preset fingerprints(prior landmark or context information) in their assumption. Another issue is that the algorithms with models built in an interference-free environment cannot work in interference- rich environments. To address those issues, we propose a localizing and tracking algorithm called Enhanced Field Division (EFD), which dynamically divides the field into areas with unique signatures and tracks the target without any fingerprints. We also implemented a proof-of-concept localization platform to demonstrate the tracking accuracy and the algorithm performance in practical, interference-rich environment.

FIRST: A Framework for Optimizing Information Quality in Mobile Crowdsensing Systems

Mobile crowdsensing allows data collection at a scale and rate that was once impossible. One of the biggest challenges in mobile crowdsensing is to accurately classify between reliable and unreliable sensing reports. To this end, we propose a novel Framework for optimizing Information Reliability in Smartphone-based participaTory sensing (FIRST), that leverages the collective action of mobile trusted participants (MTPs) to securely assess the reliability of sensing reports. FIRST models and solves the challenging problem of determining before deployment the minimum number of MTPs to be used in order to achieve desired classification accuracy. We extensively validate FIRST with real-world mobility traces and through an implementation in iOS and Android of a system leveraging human participants. Experimental results demonstrate that FIRST is effective in optimizing information reliability by reducing the impact of three security attacks (i.e., corruption, on/off, and collusion), while outperforming prior work by achieving on the average a classification accuracy of almost 80% in the considered scenarios. We conclude the paper by discussing our ongoing research efforts in cooperation with the U.S. Department of Geological Survey (USGS) to develop a smartphone-based system to test the performance of FIRST as part of the USGS National Map Corps project.

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