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This paper proposes a theoretical framework for estimating a target-object shape, the location of which is not given. The framework uses mobile distance sensors and speed meters typically mounted on vehicles, the locations of which are also unknown. Each sensor continuously measures the distance from it to the target object. The proposed framework does not require any positioning function, anchor-location information, or additional mechanisms to obtain side information such as angle of arrival of signal. Under the assumption of a convex polygon target object, each edge length and vertex angle and their combinations are estimated and finally the shape of the target object is estimated. To the best of our knowledge, this is the first result in which a target-object shape was estimated using the data of mobile distance sensors without using their locations. [ABSTRACT FROM AUTHOR]

Sweep coverage plays an important role in many applications such as data gathering, sensing coverage and devices control. In this paper, we deal with the cooperative sweep coverage problem with multiple mobile sensors to periodically cover all positions of interest (PoIs) in the surveillance region. Different from traditional sweep coverage scenarios, the cooperative sweep coverage (CSC) problem allows the deployment of multiple sensors on the same trajectory to further reduce the sweep period or detection delay. We also consider the multi-sink sweep coverage (MSSC) problem where each mobile sensor must periodically transmit its collected data to a base station due to the limited storage capacity and power supply. Correspondingly, we propose two constant-factor approximations, namely ${{CoCycle}}$ C o C y c l e and ${{SinkCycle}}$ S i n k C y c l e , to minimize the maximum sweep period for these two problems. The approximation ratios of ${{CoCycle}}$ C o C y c l e and ${{SinkCycle}}$ S i n k C y c l e are proved to be 4 and 6 respectively. As far as we know, ${{SinkCycle}}$ S i n k C y c l e is the first approximation for the sweep coverage problem with multiple sinks. We also provide two optimal algorithms for the CSC problem in one dimensional case and a useful insight regarding the MSSC problem with only one available sink. Finally, we conduct various numerical experiments to validate the effectiveness and efficiency of our designs. [ABSTRACT FROM AUTHOR]

In this paper, a novel approach is proposed to obtain the optimal operating point of spectrum sensing in overlay spectrum sharing systems. The objective is to maximize the secondary service achievable capacity subject to the primary service collision probability as well as the other system and service constraints. In the related literature the miss detection probability, as the main reason of collision, is often considered to model the impact of spectrum sensing on the achievable ergodic capacity of the secondary service. In this paper, however, we directly consider the collision probability constraint in finding the optimal ergodic capacity instead of considering the miss detection probability. We then propose a framework in which other opportunities which lie in the wireless channel fluctuation and power allocation are also extracted in favor of achieved capacity. In addition to the conventional One-Shot (O-S) scheme, we also propose four novel approaches to solve the optimization problem: Modified-One-Shot (M-O-S) scheme, Multi-Shot (M-S) scheme, Conservative-Modified-One-Shot (C-O-S) scheme, and Restricted-Modified-One-Shot (R-O-S) scheme. Our studies show that the proposed formulation results in a higher secondary service capacity even when compared to the cases with very low miss detection probability. In the proposed schemes in this paper, the main decision parameter is the average (over fading) received interference at the secondary service receiver due to the primary service transmission, \(I\) , which can be simply measurable in the secondary transmitter. Extensive numerical studies are conducted to investigate various system aspects. Our studies further suggest that for very low, moderate, and very high values of \(I\) , the proper schemes are C-O-S, M-S, and M-O-S, respectively. [ABSTRACT FROM PUBLISHER]

Recently, people have been interested in sharing what they are watching on TV, allowing the development of Social TV Applications often based on mobile devices. In this context, this paper proposes IRTR (Improved Real-Time TV-channel Recognition): a new method aimed at recognizing in real time (live) what people are watching on TV without any active user interaction. IRTR uses the audio signal of the TV program recorded by smartphones and is performed through two steps: i) fingerprint extraction and ii) TV channel real-time identification. Step i) is based on the computation of the Audio Fingerprint (AF). The AF computation has been taken from the literature and has been improved in terms of power consumption and computation speed to make the smartphone implementation feasible by using an ad hoc cost function aimed at selecting the best set of AF parameters. Step ii) is aimed at deciding the TV channel the user is watching. This step is performed using a likelihood estimation algorithm proposed in this paper. The consumed power, computation and response time, and correct decision rate of IRTR, evaluated through experimental measures, show very satisfying results such as a correct decision rate of about 95%, about $2 s$ of computation time, and above 90% power saving with respect to the literature. [ABSTRACT FROM AUTHOR]

With the availability of various sensors in the smartphone, identifying a locomotion mode becomes convenient and effortless in recent years. Information about locomotion mode helps to improve journey planning, travel time estimation, and traffic management. Though there exists a significant amount of work towards locomotion mode recognition, the performance of these work is not pertinent and heavily depends on the labeled training instances. As it is impractical to gather a prior information (labeled instances) about all types of locomotion modes, the recognition model should be able to identify a new or unseen locomotion mode without having any corresponding training instance. This paper proposes a sensors based deep learning model to identify a locomotion mode by using labeled training instances. The approach also incorporates a concept of Zero-Shot learning to identify an unseen locomotion mode. The model obtains an attribute matrix based on the fusion of three semantic matrices. It also constructs a feature matrix by extracting the deep learning and hand-crafted features from the training instances. Later, the model builds a classifier by learning a mapping between attribute and feature matrices. Finally, this work evaluates the performance of the approach on collected and existing datasets using accuracy and F1 score. [ABSTRACT FROM AUTHOR]

The rising popularity of smartphones and vehicles equipped with onboard sensors sheds lights on building a city-scale sensing system for urban surveillance. This paper proposes a novel metric, urban resolution, to measure the quality of urban sensing. Urban resolution describes how sensitivity the urban sensing system could achieve for environment monitoring applications. Then, we study the relationship between resolution r and number of sensing nodes s , and reveal the linear growth relationship between \sqrtr and \sqrts. Furthermore, by employing a commonly used human/vehicle mobility model, SLAW, we find that the distribution model of urban sensing nodes is able to be described by a truncated Pareto distribution, and derive the complementary cumulative distribution function (CCDF) of urban resolution. The CCDF reveals the radio of the sub-regions which satisfy the required sensing quality to the whole region. Our findings provide valuable insights to infer the urban sensing quality according to the scale of urban sensing system or determine how many smartphone/vehicles needed for participating in urban sensing applications. Finally, based on five real datasets—three human/vehicle trajectory datasets and two environment monitoring datasets, we examine the metric of urban resolution and evaluate the main results in this paper. [ABSTRACT FROM PUBLISHER]

One-time login process in conventional authentication systems does not guarantee that the identified user is the actual user throughout the session. However, it is necessary to re-verify the user identity periodically throughout a login session, which is lacking in existing one-time login systems. Continuous authentication, which re-verifies the user identity without breaking the continuity of the session, can address this issue. However, existing methods for Continuous Authentication are either not reliable or not usable. In this paper, we introduce a usable and reliable Wearable-Assisted Continuous Authentication (WACA), which relies on the sensor-based keystroke dynamics and the authentication data is acquired through the built-in sensors of a wearable (e.g., smartwatch) while the user is typing. The acquired data is periodically and transparently compared with the registered profile of the initially logged-in user with one-way classifiers. With this, WACA continuously ensures that the current user is the user who logged-in initially. We implemented the WACA framework and evaluated its performance extensively on real devices with real users. The empirical evaluation of WACA reveals that WACA is feasible, and its error rate is as low as 1 percent with 30 seconds of processing time and 2-3 percent for 20 seconds. The computational overhead is minimal. Furthermore, WACA is capable of identifying insider threats with very high accuracy (99.2 percent) and also robust against powerful adversaries such as imitation and statistical attackers. We believe that this work has practical and far-reaching implications for the future of the usable authentication field. [ABSTRACT FROM AUTHOR]

In modern distributed cyber-physical systems (CPS), information fusion often plays a key role in automate and self-adaptive decision making process. However, given the heterogeneous and distributed nature of modern CPSs, it is a great challenge to operate CPSs with the compromised data integrity and unreliable communication links. In this paper, we study the distributed state estimation problem under the false data injection attack (FDIA) with probabilistic communication networks. We propose an integrated ”detection + fusion” solution, which is based on the Kullback-Leibler divergences (KLD) between local posteriors and therefore does not require the exchange of raw sensor data. For the FDIA detection step, the KLDs are used to cluster nodes in the probability space and to partition the space into secure and insecure subspaces. By approximating the distribution of the KLDs with a general χ2 distribution and calculating its tail probability, we provide an analysis of the detection error rate. For the information fusion step, we discuss the potential risk of double counting the shared prior information in the KLD-based consensus formulation method. We show that if the local posteriors are updated from the shared prior, the increased number of neighbouring nodes will lead to the diminished information gain. To overcome this problem, we propose a near-optimal distributed information fusion solution with properly weighted prior and data likelihood. Finally, we present simulation results for the integrated solution. We discuss the impact of network connectivity on the empirical detection error rate and the accuracy of state estimation. [ABSTRACT FROM AUTHOR]

Mobile advertising plays a vital role in the mobile app ecosystem. A major threat to the sustainability of this ecosystem is click fraud, i.e., ad clicks performed by malicious code or automatic bot problems. Existing click fraud detection approaches focus on analyzing the ad requests at the server side. However, such approaches may suffer from high false negatives since the detection can be easily circumvented, e.g., when the clicks are behind proxies or globally distributed. In this paper, we present AdSherlock, an efficient and deployable click fraud detection approach at the client side (inside the application) for mobile apps. AdSherlock splits the computation-intensive operations of click request identification into an offline procedure and an online procedure. In the offline procedure, AdSherlock generates both exact patterns and probabilistic patterns based on URL (Uniform Resource Locator) tokenization. These patterns are used in the online procedure for click request identification and further used for click fraud detection together with an ad request tree model. We implement a prototype of AdSherlock and evaluate its performance using real apps. The online detector is injected into the app executable archive through binary instrumentation. Results show that AdSherlock achieves higher click fraud detection accuracy compared with state of the art, with negligible runtime overhead. [ABSTRACT FROM AUTHOR]

Indoor navigation is a representative application of an indoor positioning system that uses a variety of equipment, including smartphones with various sensors. Many indoor navigation systems utilize Wi-Fi signals, as well as a variety of inertial sensors, such as a 3D accelerometer, digital compass, gyroscope, and barometer, to improve the accuracy of user location tracking. The inertial sensors are vulnerable to changes in the surrounding environments and sensitive to users behavior, but little research has been conducted on sensor fusion under these conditions. In this paper, we propose a dynamic sensor fusion framework (DSFF) that provides accurate user tracking results by dynamically calibrating inertial sensor readings in a sensor fusion process. The proposed method continually learns the errors and biases of each sensor due to the changes in user behavior patterns and surrounding environments. The learned patterns are then dynamically applied to the user tracking process to yield accurate results. The results of experiments conducted in both a single-story and a multi-story building confirm that DSFF provides accurate tracking results. The scalability of the DSFF will enable it to provide more accurate tracking results with various sensors, both existing and under development. [ABSTRACT FROM AUTHOR]

This paper presents a new framework for wireless video streaming services using the resource-constrained embedded camera sensors based on block compressive sensing. We propose adaptive encoding scheme that exploits high redundancy between successive video frames to reduce transmission cost while effectively handling occlusion effects and, at the same time, maintains the simple and energy conserving encoder design. The proposed methodology adapts the compression ratio for different blocks of the non-key frame depending on temporal correlation. We also propose compressed domain motion and measurements estimation techniques to exploit the high correlation between successive frames at the decoder. The proposed motion estimation technique makes use of restricted isometry property of the sensing matrix to seek the best matching measurement vector for motion estimation as opposed to block matching in conventional video coding. In the proposed measurement estimation technique, efficient utilization of bandwidth is achieved by skipping some measurements at the transmitter side. The skipped measurements are estimated at the receiver by exploiting the correlation between CS measurements of the non-key frame and corresponding motion predicted frame using multiple regression model. Extensive simulation results on a set of diverse video sequences are presented to demonstrate the effectiveness of the proposed video coding technique. [ABSTRACT FROM AUTHOR]

Wireless charging technology is considered as a promising solution to address the energy limitation problem for wireless sensor networks (WSNs). In scenarios where the deployed chargers are static, we generally require a number of chargers to work simultaneously. However, due to the radio interference among different wireless chargers, scheduling these chargers is generally necessary. This scheduling problem is challenging since each charger's charging utility cannot be calculated independently due to the nonlinear superposition charging effect caused by radio interference. In this paper, based on the concurrent charging model, we formulate the concurrent charging scheduling problem (CCSP) with the objective of quickly fully charging all the sensor nodes. After proving the NP-hardness of CCSP, we propose two efficient greedy algorithms, and give the approximation ratio of one of them. Both the two greedy algorithms’ performances are very close to that of a well-designed genetic algorithm (GA) which performs almost as well as a brute force algorithm at small network and charger scale. However, the running time of the two greedy algorithms is far lower than that of the GA. We conduct extensive simulations and specially implemented a testbed for wireless chargers. The results verified the good performance of the proposed algorithms. [ABSTRACT FROM AUTHOR]

To date, the majority of positioning systems have been designed to operate within environments that have a long-term stable macro-structure with potential small-scale dynamics. These assumptions allow the existing positioning systems to produce and utilize stable maps. However, in highly dynamic industrial settings these assumptions are no longer valid and the task of tracking people is more challenging due to the rapid large-scale changes in structure. In this paper, we propose a novel positioning system for tracking people in highly dynamic industrial environments, such as construction sites. The proposed system leverages the existing CCTV camera infrastructure found in many industrial settings along with radio and inertial sensors within each worker’s mobile phone to accurately track multiple people. This multi-target multi-sensor tracking framework also allows our system to use cross-modality training in order to deal with the environment dynamics. In particular, we show how our system uses cross-modality training in order to automatically keep track environmental changes (i.e., new walls) by utilizing occlusion maps. In addition, we show how these maps can be used in conjunction with social forces to accurately predict human motion and increase the tracking accuracy. We have conducted extensive real-world experiments in a construction site showing significant accuracy improvement via cross-modality training and the use of social forces. [ABSTRACT FROM AUTHOR]

Good deployment of sensors empowers the network with effective monitoring ability. Different from omnidirectional sensors, the coverage region of a directional sensor is determined by not only the sensing radius (distance), but also its sensing orientation and spread angle. Heterogeneous sensing distances and spread angles are likely to exist among directional sensors, to which we refer as heterogeneous directional sensors. In this paper, we target on a bounded monitoring area and deal with heterogeneous directional sensors equipped with locomotion and rotation facilities to enable the sensors self-deployment. Two Enhanced Deployment A lgorithms, EDA-I and EDA-II, are proposed to achieve high sensing coverage ratio in the monitored field. EDA-I leverages the concept of virtual forces (for sensors movements) and virtual boundary torques (for sensors rotations), whereas EDA-II combines Voronoi diagram directed movements and boundary torques guided rotations. EDA-I computations can be centralized or distributed that differ in required energy and execution time, whereas EDA-II only allows centralized calculations. Our EDA-II outperforms EDA-I in centralized operations, while EDA-I can be adapted into a distributed deployment algorithm without requiring global information and still achieves comparably good coverage performance to its centralized version. To the best of our knowledge, this is perhaps the first work to employ movements followed by rotations for sensors self-deployment. Performance results demonstrate that our enhanced deployment mechanisms are capable of providing desirable surveillance level, while consuming moderate moving and rotating energy under reasonable execution time. [ABSTRACT FROM AUTHOR]

Existing work on placing additional relay nodes in wireless sensor networks to improve network connectivity typically assumes homogeneous wireless sensor nodes with an identical transmission radius. In contrast, this paper addresses the problem of deploying relay nodes to provide fault tolerance with higher network connectivity in heterogeneous wireless sensor networks, where sensor nodes possess different transmission radii. Depending on the level of desired fault tolerance, such problems can be categorized as: 1) full fault-tolerant relay node placement, which aims to deploy a minimum number of relay nodes to establish κ(κ≥1) vertex-disjoint paths between every pair of sensor and/or relay nodes and 2) partial fault-tolerant relay node placement, which aims to deploy a minimum number of relay nodes to establish κ(κ≥1) vertex-disjoint paths only between every pair of sensor nodes. Due to the different transmission radii of sensor nodes, these problems are further complicated by the existence of two different kinds of communication paths in heterogeneous wireless sensor networks, namely, two-way paths, along which wireless communications exist in both directions; and one-way paths, along which wireless communications exist in only one direction. Assuming that sensor nodes have different transmission radii, while relay nodes use the same transmission radius, this paper comprehensively analyzes the range of problems introduced by the different levels of fault tolerance (full or partial) coupled with the different types of path (one-way or two-way). Since each of these problems is NP-hard, we develop O(σκ2-approximation algorithms for both one-way and two-way partial fault-tolerant relay node placement, as well as O(σκ3-approximation algorithms for both one-way and two-way full fault-tolerant relay node placement (σ is the best performance ratio of existing approximation algorithms for finding a minimum κ-vertex connected spanning graph). To facilitate the applications in higher dimensions, we also extend these algorithms and derive their performance ratios in d-dimensional heterogeneous wireless sensor networks (d ≥3). Finally, heuristic implementations of these algorithms are evaluated via QualNet simulations. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a powerful hardware-based rogue access point (PrAP), which can relay back and forth traffic between a legitimate AP and a wireless station, and act as a man-in-the-middle attacker. Our PrAP is built of two dedicated wireless routers interconnected physically, and can relay traffic rapidly between a station and a legitimate AP. Through experiments, we demonstrate that the state-of-the-art time-based rogue AP (rAP) detectors cannot detect our PrAP, although perhaps effective against software-based rAP. In demonstrating that, we unveil new insight into fundamentals of time-based detectors for software-based rAPs and their operation: such techniques are only capable of detecting rAPs due to the speed of wireless AP bridging. To address the threat of such PrAPs, we propose a new tool for network administrators, a PrAP-Hunter based on intentional channel interference. Our PrAP-Hunter is highly accurate, even under heavy traffic scenarios. Using a high-performance (desktop) and low-performance (mobile phone) experimental setups of our PrAP-Hunter in various deployment scenarios, we demonstrate close to 100 percent of detection rate, compared to 60 percent detection rate by the state-of-the-art. We show that our PrAP-Hunter is fast (takes 5-10 seconds), does not require any prior knowledge, and can be deployed in the wild by real-world experiments at 10 coffee shops. [ABSTRACT FROM AUTHOR]

This paper presents a lightweight approach for the early detection of nocturnal epileptic seizures through analysis of inertial data and muscle contractions. Our approach uses an overlapping sliding window to derive the variance of data acquired by the MPU 9,250 motion tracking device and single channel surface ElectroMyoGram (sEMG). The Exponentially Weighted Moving Average (EWMA) is used to forecast the current value of the data variance. When the Kullback-Leibler divergence between the forecasted and measured variances deviates from past values, a signal is transmitted to the base station to set the current counter in an alarm window. If the filling ratio of the alarm window is greater than a predefined threshold, an alarm is triggered by the base station. The proposed approach is intended to improve the performance of existing detection systems based on data analysis from Accelerometer. The MPU 9,250 is 9-axis motion tracking and used to detect motor seizures, and it contains a 3-axis Accelerometer, Gyroscope, and Magnetometer. The sEMG is used to detect silent seizures without jerky movements. Our experimental results on a real dataset from an epileptic patient show that our proposed approach is able to increase detection accuracy and reduce the low false alarm rate. Comparison with a Probability Density Function (PDF) further demonstrates the detection efficiency of our approach. [ABSTRACT FROM AUTHOR]

Ad hoc localization of wireless sensor nodes is a fundamental problem in wireless sensor networks. Despite the recent proposals for the development of ad hoc localization algorithms, the fundamental behavior in systems using measurements has not been characterized. In this paper, we take a first step toward such a characterization by examining the behavior of error inducing parameters in multihop localization systems in an algorithm independent manner. We first derive the Cramé Rao Bound for Gaussian measurement error for multihop localization systems using distance and angular measurements. Later on, we use these bounds on a carefully controlled set of scenarios to study the trends in the error induced by the measurement technology accuracy, network density, beacon node concentration, and beacon uncertainty. By exposing these trends, the goal of this paper is to develop a fundamental understanding of the error behavior that can provide a set of guidelines to be considered during the design and deployment of multihop localization systems. [ABSTRACT FROM AUTHOR]

This paper first defines a novel spatial-temporal coverage metric, $k$ -depth coverage, for mobile crowdsensing (MCS) problems. This metric considers both the fraction of subareas covered by sensor readings and the number of sensor readings collected in each covered subarea. Then iCrowd, a generic MCS task allocation framework operating with the energy-efficient Piggyback Crowdsensing task model, is proposed to optimize the MCS task allocation with different incentives and $k$ -depth coverage objectives/constraints. iCrowd first predicts the call and mobility of mobile users based on their historical records, then it selects a set of users in each sensing cycle for sensing task participation, so that the resulting solution achieves two dual optimal MCS data collection goals—i.e., Goal. 1 near-maximal $k$ -depth coverage without exceeding a given incentive budget or Goal. 2 near-minimal incentive payment while meeting a predefined $k$ -depth coverage goal. We evaluated iCrowd extensively using a large-scale real-world dataset for these two data collection goals. The results show that: for Goal.1, iCrowd significantly outperformed three baseline approaches by achieving 3-60 percent higher $k$ -depth coverage; for Goal.2, iCrowd required 10.0-73.5 percent less incentives compared to three baselines under the same $k$ -depth coverage constraint. [ABSTRACT FROM AUTHOR]

We consider the problem of performing distributed visual analysis for a video sequence in a visual sensor network that contains sensor nodes dedicated to processing. Visual analysis requires the detection and extraction of visual features from the images, and thus the time to complete the analysis depends on the number and on the spatial distribution of the features, both of which are unknown before performing the detection. In this paper, we formulate the minimization of the time needed to complete the distributed visual analysis for a video sequence subject to a mean average precision requirement as a stochastic optimization problem. We propose a solution based on two composite predictors that reconstruct randomly missing data, on quantile-based linear approximation of feature distribution and on time series analysis methods. The composite predictors allow us to compute an approximate optimal solution through linear programming. We use two surveillance video traces to evaluate the proposed algorithms, and show that prediction is essential for minimizing the completion time, even if the wireless channel conditions vary and introduce significant randomness. The results show that the last value predictor together with regular quantile-based distribution approximation provide a low complexity solution with very good performance. [ABSTRACT FROM PUBLISHER]

Interference-aware spectrum sensing extends conventional interference-agnostic sensing by exploring the fact that missed detection does not necessarily result in outage or intolerable interference on the primary user. While related work has motivated and demonstrated the benefits of interference-aware spectrum sensing for spectrum hole discovery, the inherent conflict of interests among the set of cooperative users with interference-aware spectrum detectors has not been properly formulated and addressed. In this paper, we aim to bridge the gap by considering a coalition-based model, where the set of secondary users is to be partitioned into multiple coalitions for cooperative spectrum sensing and access. The goal is to maximize the utility sum of all secondary users while observing the protection requirement of the primary user. Since the user with the largest interference probability in a coalition critically limits the detection performance of the coalition, there is a trade-off in determining the optimal coalition structure in the network. To proceed, we first formulate a joint threshold detection and coalition formation problem under the target cooperative model, and then explore important properties of the target problem. Our proposed algorithms based on polyblock approximation and search space partition can effectively solve the formulated problem with significant performance gains and complexity reduction against baseline approaches proposed in related work. [ABSTRACT FROM PUBLISHER]

Wireless sensor networks are vulnerable to energy holes, where sensors close to a static sink are fast drained of their energy. Using a mobile sink (MS) can conquer this predicament and extend sensor lifetime. How to schedule a traveling path for the MS to efficiently gather data from sensors is critical in performance. Some studies select a subset of sensors as rendezvous points (RPs). Non-RP sensors send data to the nearest RPs and the MS visits RPs to retrieve data. However, these studies assume that sensors produce data with the same speed and have no limitation on buffer size. When the two assumptions are invalid, they may encounter serious packet loss due to buffer overflow at RPs. In the paper, we show that the path planning problem is NP-complete and propose an efficient path planning for reliable data gathering (EARTH) algorithm by relaxing these impractical assumptions. It forms a spanning tree to connect all sensors and then selects each RP based on hop count and distance in the tree and the amount of forwarding data from other sensors. An enhanced EARTH (eEARTH) algorithm is also developed to further reduce path length. Both EARTH and eEARTH incur less computational overhead and can flexibly recompute new paths when sensors change sensing rates. Simulation results verify that they can find short traveling paths for the MS to collect sensing data without packet loss, as compared with existing methods. [ABSTRACT FROM AUTHOR]

Positioning with visual sensors in indoor environments has many advantages: it doesn’t require infrastructure or accurate maps, and is more robust and accurate than other modalities such as WiFi. However, one of the biggest hurdles that prevents its practical application on mobile devices is the time-consuming visual processing pipeline. To overcome this problem, this paper proposes a novel lifelong learning approach to enable efficient and real-time visual positioning. We explore the fact that when following a previous visual experience for multiple times, one could gradually discover clues on how to traverse it with much less effort, e.g., which parts of the scene are more informative, and what kind of visual elements we should expect. Such second-order information is recorded as parameters, which provide key insights of the context and empower our system to dynamically optimise itself to stay localised with minimum cost. We implement the proposed approach on an array of mobile and wearable devices, and evaluate its performance in two indoor settings. Experimental results show our approach can reduce the visual processing time up to two orders of magnitude, while achieving sub-metre positioning accuracy. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) bring many benefits and conveniences to road safety and drive comfort in future transportation systems. However, VANETs suffer from almost all security issues as same as wireless networks. Sybil attack is one of the most risky threats since it violates the fundamental assumption of VANETs-based applications that all received information are correct and trusted. Sybil attacker can generate multiple fake identities to disseminate false messages. In this paper, we propose a novel Sybil attack detection method based on Received Signal Strength Indicator (RSSI), Voiceprint, to conduct a widely applicable, lightweight and full-distributed detection for VANETs. Unlike most of previous RSSI-based methods that compute the absolute position or relative distance according to RSSI values, or make statistic testing based on RSSI distributions, Voiceprint adopts RSSI time series as the vehicular speech and compares the similarity among all received series. Voiceprint does not rely on any predefined radio propagation model, and conducts independent detection without support of the centralized node. Moreover, we improve Voiceprint by allowing it to conduct detection on Service Channel (SCH) to shorten observation time. Furthermore, we extend Voiceprint with change-points detection to identify those illegitimate nodes performing power control. Extensive simulations and real-world experiments demonstrate that Voiceprint is an effective method considering the cost, complexity, and performance. [ABSTRACT FROM AUTHOR]

Dynamic spectrum access (DSA) is the key to solving worldwide spectrum shortage. The open wireless medium subjects DSA systems to unauthorized spectrum use by illegitimate users. Secondary-user authentication is thus critical to ensure the proper operations of DSA systems. This paper presents SpecGuard, the first crowdsourced spectrum misuse detection framework for DSA systems. In SpecGuard, a transmitter is required to embed a spectrum permit into its physical-layer signals, which can be decoded and verified by ubiquitous mobile users. We propose three novel schemes for embedding and detecting a spectrum permit at the physical layer. The first scheme relies on a higher transmission power to embed the spectrum permit. To alleviate the assumptions on the additional transmission power, the second scheme is proposed with a limited negative impact on the normal data transmission. The third scheme takes a different approach by adopting a novel constellation design and exploiting the trust between the transmitter and the receiver. Crowdsourced spectrum misuse detection eliminates the need for the deployment of dedicated sensors and thus greatly reduces the deployment and maintenance cost. Detailed theoretical analyses, MATLAB simulations, and USRP experiments confirm that our schemes can achieve correct, low-intrusive, and fast spectrum misuse detection. [ABSTRACT FROM AUTHOR]

Wireless energy transfer technology based on magnetic resonant coupling has emerged as a promising technology for wireless sensor networks, by providing controllable yet continual energy to sensors. In this paper, we study the use of a mobile charger to wirelessly charge sensors in a rechargeable sensor network so that the sum of sensor lifetimes is maximized while the travel distance of the mobile charger is minimized. Unlike existing studies that assumed a mobile charger must charge a sensor to its full energy capacity before moving to charge the next sensor, we here assume that each sensor can be partially charged so that more sensors can be charged before their energy depletions. Under this new energy charging model, we first formulate two novel optimization problems of scheduling a mobile charger to charge a set of sensors, with the objectives to maximize the sum of sensor lifetimes and to minimize the travel distance of the mobile charger while achieving the maximum sum of sensor lifetimes, respectively. We then propose efficient algorithms for the problems. We finally evaluate the performance of the proposed algorithms through experimental simulations. Simulation results demonstrate that the proposed algorithms are very promising. Especially, the average energy expiration duration per sensor by the proposed algorithm for maximizing the sum of sensor lifetimes is only 9 percent of that by the state-of-the-art algorithm while the travel distance of the mobile charger by the second proposed algorithm is only about from 1 to 15 percent longer than that by the state-of-the-art benchmark. [ABSTRACT FROM AUTHOR]

The unique characteristics of underwater channels, such as the mobility of sensor nodes, require robust and efficient security mechanisms for secure and efficient data collection in mobile underwater sensor networks (UWSNs). In this paper, we propose a novel three-tier framework for securing data collections in mobile UWSNs, called SenseVault. First, to support both the authentication of intra-cluster nodes and the authentication of inter-cluster nodes, we propose a cubic cluster formation approach to adapt to dynamic environments with flexible split and merge operations, and use cryptographic hash functions to derive secret keys for the authentication of nodes moving among the clusters. Second, we design a lightweight node revocation and authentication key update mechanism based on higher-order polynomial. To provide strong resilience against misbehavior of the compromised cluster head, we further introduce a Tripartite Cooperation Update (TCU) scheme by leveraging the cooperation among surface stations, safe neighboring cluster heads, and safe intra-cluster members. Third, we propose a deterministic quantization approach, called Virtual Phase Shift (VPS) for the secure generation of secret keys at the physical layer. Extensive experimental results demonstrate the effectiveness of the proposed framework under different scenarios. [ABSTRACT FROM AUTHOR]

Smartphone theft is a non-negligible problem that causes serious concerns on personal property and privacy. The existing solutions to this problem either provide only functions like retrieving a phone, or require dedicated hardware to detect thefts. How to protect smartphones from being stolen at all times is still an open problem. In this paper, we propose iGuard, a real-time anti-theft system for smartphones. iGuard utilizes only the inertial sensing data from the smartphone. The basic idea behind iGuard is to distinguish different people holding a smartphone, by identifying the order of the motions during the ‘take-out’ behavior and how each motion is performed. For this purpose, we design a motion segmentation algorithm to detect the transition between two motions from the noisy sensing data. We then leverage the distinct feature contained in each sub-segment of a motion to estimate the probability that the motion is performed by the smartphone owner himself/herself. Based on such pre-processed data, we propose a Markov based model to track the behavior of a smartphone user. According to this model, iGuard instantly alarms once the tracked data deviate from the smartphone owner's usual habit. We implement iGuard on Android and evaluate its performance in real environments. The experimental results show that iGuard is accurate and robust in various scenarios. [ABSTRACT FROM AUTHOR]

Mobile crowdsourcing platforms often want to incentivize workers to finish tasks with high quality and truthfully report their solutions by providing proper rewards. Most existing incentive mechanisms reward workers based on the comparison among workers’ reported solutions. However, these mechanisms are vulnerable to worker collusion, i.e., workers coordinate to misreport their solutions. We address such an issue by proposing a novel rewarding mechanism based on a ${truth detection}$ t r u t h d e t e c t i o n technology, which relies on the independent verification of the correctness of each worker’s response to some question with an imperfect accuracy. We model the interactions between the platform and workers as a two-stage Stackelberg game. In Stage I, the platform optimizes the reward mechanism parameters associated with truth detection to maximize its payoff. In Stage II, the workers decide their effort levels and reporting strategies to maximize their payoffs (which depend on the output of the truth detector). We analyze the game’s equilibrium and show that our proposed mechanism can effectively mitigate worker collusion. We also propose a novel rule, named filtered majority, for the platform to more effectively aggregate the workers’ solutions. Our proposed aggregation rule utilizes truth detection and outperforms the conventional simple majority rule. We further characterize the impact of the truth detection accuracy on the platform’s decisions. Surprisingly, under the simple majority rule, we show that as the truth detection accuracy improves, the platform should always incentivize more workers to exert effort and truthfully report. However, under our proposed filtered majority rule, we show that as the truth detection accuracy improves, in some cases, the platform should incentivize fewer workers and save costs. We further examine the impact of the workers’ imperfect estimation of the truth detection accuracy on the platform’s decisions. [ABSTRACT FROM AUTHOR]

In cognitive radio networks, the main objective of spectrum sensing is to exploit spectrum holes left by the primary users (PUs). Different PUs’ traffic patterns might provide different opportunities for second user (SU) spectrum access. In this paper, we identify the PUs’ traffic patterns and then maximize SU transmission accordingly. First a theoretical framework is developed to cluster PU traffic patterns based on a Bayesian nonparametric inference model, in which the number of traffic types is unknown. Second, in order to exploit the spectrum holes, we study a sensing-transmission structure to optimize the SU transmission strategy. Specifically, we exploit the short and long transmission opportunities based on the PU traffic pattern and channel idle time distribution. Finally, we propose a threshold-based sensing-transmission method that optimizes the SU utility, while protecting PU transmissions. Both sensing and transmission errors are considered for perfect sensing with/without acknowledgement-based transmission and imperfect sensing, respectively. From the simulation results, we show that the proposed technique outperforms the nonparametric mean shift clustering algorithm. Furthermore, we utilize these clustering results to optimize the SU’s transmission strategy with perfect and imperfect sensing. We compare our proposed technique with the probabilistic sensing-transmission structure and show the performance gain in terms of throughput. [ABSTRACT FROM PUBLISHER]

The distributed nature of the CSMA/CA-based wireless protocols, for example, the IEEE 802.11 distributed coordinated function (DCF), allows malicious nodes to deliberately manipulate their backoff parameters and, thus, unfairly gain a large share of the network throughput. In this paper, we first design a real-time backoff misbehavior detector, termed as the fair share detector (FS detector), which exploits the nonparametric cumulative sum (CUSUM) test to quickly find a selfish malicious node without any a priori knowledge of the statistics of the selfish misbehavior. While most of the existing schemes for selfish misbehavior detection depend on heuristic parameter configuration and experimental performance evaluation, we develop a Markov chain-based analytical model to systematically study the performance of the FS detector in real-time backoff misbehavior detection. Based on the analytical model, we can quantitatively compute the system configuration parameters for guaranteed performance in terms of average false positive rate, average detection delay, and missed detection ratio under a detection delay constraint. We present thorough simulation results to confirm the accuracy of our theoretical analysis as well as demonstrate the performance of the developed FS detector. [ABSTRACT FROM AUTHOR]

The use of smartphones and wearables as sensing devices has created innumerable context inference apps including a class of workout tracking apps. Workout data generated by mobile tracking apps can assist both users and physicians in achieving better health care, rehabilitation, and self-motivation. Previous approaches impose extra burdens on users by requiring users to select types of exercises or to start/stop sessions. In this paper, we propose MiLift, a practical end-to-end workout tracking system that performs automatic segmentation to remove user burdens. MiLift uses commercial off-the-shelf smartwatches to accurately and efficiently track both cardio and weightlifting workouts without manual inputs from users. For weightlifting tracking, MiLift supports both machine-based and free weight exercises, and proposes a lightweight repetition detection algorithm to ensure efficiency. A research study of 22 users shows that MiLift can achieve above 90 percent average precision and recall for cardio workout classification, weightlifting session detection, and weightlifting type classification. MiLift can also count repetitions of weightlifting exercises with an average error of 1.12 reps (out of an average of 9.65). Our empirical app study on a Moto 360 watch suggests that MiLift can extend watch battery lives by up to [ABSTRACT FROM AUTHOR]

Studies have shown that using smartphones while walking—called distracted walking —significantly increases the risk of pedestrians colliding with dangerous objects. In this paper, we explore how to mitigate this problem by exploiting the phone's built-in sensors only and developing an application called BumpAlert. BumpAlert provides a generic solution without requiring any prior knowledge of the user's surroundings by estimating distances to nearby objects using the phone's speakers and microphones. This process is enhanced further by using the images acquired from the phone's rear camera, when necessary. We have evaluated BumpAlert under a variety of settings ranging from aisle to outdoor environments with walls, pillars, signboards, dustbins, and people, etc., that are common in our daily surroundings. Our evaluation has shown an average accuracy of BumpAlert to be higher than 95 percent with a less than 2 percent false-positive rate to detect frontal objects 2–4m away, which suffices for the user to react and avoid collision. Even though BumpAlert is unable to detect all dangerous situations, most participants of our user study feel safer when they walk with BumpAlert enabled. Integrating our current design of BumpAlert with other safety systems can provide a practical solution for protecting distracted pedestrians. [ABSTRACT FROM AUTHOR]

Mobile crowdsensing is a new paradigm in which a group of mobile users exploit their smart devices to cooperatively perform a large-scale sensing job. One of the users’ main concerns is the cost of data uploading, which affects their willingness to participate in a crowdsensing task. In this paper, we propose an efficient Prediction-based User Recruitment for mobile crowdsEnsing (PURE), which separates the users into two groups corresponding to different price plans: Pay as you go (PAYG) and Pay monthly (PAYM). By regarding the PAYM users as destinations, the minimizing cost problem goes to recruiting the users that have the largest contact probability with a destination. We first propose a semi-Markov model to determine the probability distribution of user arrival time at points of interest (PoIs) and then get the inter-user contact probability. Next, an efficient prediction-based user-recruitment strategy for mobile crowdsensing is proposed to minimize the data uploading cost. We then propose PURE-DF by extending PURE to a case in which we address the tradeoff between the delivery ratio of sensing data and the recruiter number according to Delegation Forwarding. We conduct extensive simulations based on three widely-used real-world traces: roma/taxi, epfl, and geolife. The results show that, compared with other recruitment strategies, PURE achieves a lower recruitment payment and PURE-DF achieves the highest delivery efficiency. [ABSTRACT FROM PUBLISHER]

The work reported in this paper investigates the performance of the Particle Filter (PF) algorithm for tracking a moving object using a wireless sensor network (WSN). It is well known that the PF is particularly well suited for use in target tracking applications. However, a comprehensive analysis on the effect of various design and calibration parameters on the accuracy of the PF has been overlooked. This paper outlines the results from such a study. In particular, we evaluate the effect of various design parameters (such as the number of deployed nodes, number of generated particles, and sampling interval) and calibration parameters (such as the gain, path loss factor, noise variations, and nonlinearity constant) on the tracking accuracy and computation time of the particle-filter-based tracking system. Based on our analysis, we present recommendations on suitable values for these parameters, which provide a reasonable trade-off between accuracy and complexity. We also analyze the theoretical Cramér-Rao Bound as the benchmark for the best possible tracking performance and demonstrate that the results from our simulations closely match the theoretical bound. In this paper, we also propose a novel technique for calibrating off-the-shelf sensor devices. We implement the tracking system on a real sensor network and demonstrate its accuracy in detecting and tracking a moving object in a variety of scenarios. To the best of our knowledge, this is the first time that empirical results from a PF-based tracking system with off-the-shelf WSN devices have been reported. Finally, we also present simple albeit important building blocks that are essential for field deployment of such a system. [ABSTRACT FROM AUTHOR]

This paper proposes a framework to maximize the lifetime of the wireless sensor networks (WSNs) by using a mobile sink when the underlying applications tolerate delayed information delivery to the sink. Within a prescribed delay tolerance level, each node does not need to send the data immediately as it becomes available. Instead, the node can store the data temporarily and transmit it when the mobile sink is at the most favorable location for achieving the longest WSN lifetime. To find the best solution within the proposed framework, we formulate optimization problems that maximize the lifetime of the WSN subject to the delay bound constraints, node energy constraints, and flow conservation constraints. We conduct extensive computational experiments on the optimization problems and find that the lifetime can be increased significantly as compared to not only the stationary sink model but also more traditional mobile sink models. We also show that the delay tolerance level does not affect the maximum lifetime of the WSN. [ABSTRACT FROM AUTHOR]

The integration of Global Positioning System (GPS) receivers and sensors into mobile devices has enabled collaborative sensing applications, which monitor the dynamics of environments through opportunistic collection of data from many users' devices. One example that motivates this paper is a probe-vehicle-based automotive traffic monitoring system, which estimates traffic congestion from GPS velocity measurements reported from many drivers. This paper considers the problem of achieving guaranteed anonymity in a locational data set that includes location traces from many users, while maintaining high data accuracy. We consider two methods to reidentify anonymous location traces, target tracking, and home identification, and observe that known privacy algorithms cannot achieve high application accuracy requirements or fail to provide privacy guarantees for drivers in low-density areas. To overcome these challenges, we derive a novel time-to-confusion criterion to characterize privacy in a locational data set and propose a disclosure control algorithm (called uncertainty-aware path cloaking algorithm) that selectively reveals GPS samples to limit the maximum time-to-confusion for all vehicles. Through trace-driven simulations using real GPS traces from 312 vehicles, we demonstrate that this algorithm effectively limits tracking risks, in particular, by eliminating tracking outliers. It also achieves significant data accuracy improvements compared to known algorithms. We then present two enhancements to the algorithm. First, it also addresses the home identification risk by reducing location information revealed at the start and end of trips. Second, it also considers heading information reported by users in the tracking model. This version can thus protect users who are moving in dense areas but in a different direction from the majority. [ABSTRACT FROM AUTHOR]

Wireless sensor networks have been used to gather data and information in many diverse application settings. The capacity of such networks remains a fundamental obstacle toward the adaptation of sensor network systems for advanced applications that require higher data rates and throughput. In this paper, we explore potential benefits of integrating directional antennas into wireless sensor networks. While the usage of directional antennas has been investigated in the past for ad hoc networks, their usage in sensor networks bring both opportunities as well as challenges. In this paper, Sectored-Antenna Medium Access Control (SAMAC), an integrated cross-layer protocol that provides the communication mechanisms for sensor network to fully utilize sectored antennas, is introduced. Simulation studies show that SAMAC delivers high energy efficiency and predictable delay performance with graceful degradation in performance with increased load. [ABSTRACT FROM AUTHOR]

Existing localization algorithms for mobile sensor networks are usually based on the Sequential Monte Carlo (SMC) method. They either suffer from low sampling efficiency or require high beacon density to achieve high localization accuracy. Although papers can be found for solving the above problems separately, there is no solution which addresses both issues. In this paper, we propose an energy efficient algorithm, called WMCL, which can achieve both high sampling efficiency and high localization accuracy in various scenarios. In existing algorithms, a technique called bounding-box is used to improve the sampling efficiency by reducing the scope from which the candidate samples are selected. WMCL can further reduce the size of a sensor node's bounding-box by a factor of up to 87 percent and, consequently, improve the sampling efficiency by a factor of up to 95 percent. The improvement in sampling efficiency dramatically reduces the computational cost. Our algorithm uses the estimated position information of sensor nodes to improve localization accuracy. Compared with algorithms adopting similar methods, WMCL can achieve similar localization accuracy with less communication cost and computational cost. Our work has additional advantages. First, most existing SMC-based localization algorithms cannot be used in static sensor networks but WMCL can work well, even without the need of experimentally tuning parameters as required in existing algorithms like MSL*. Second, existing algorithms have low localization accuracy when nodes move very fast. We propose a new algorithm in which WMCL is iteratively executed with different assumptions on nodes' speed. The new algorithm dramatically improves localization accuracy when nodes move very fast. We have evaluated the performance of our algorithm both theoretically and through extensive simulations. We have also validated the performance results of our algorithm by implementing it in real deployed static sensor networks. To the best of our knowledge, we are the first to implement SMC-based localization algorithms for wireless sensor networks in real environment. [ABSTRACT FROM AUTHOR]

RFID and Wireless Sensor Networks exemplify computationally constrained environments, where the compact nature of the components cannot support complex computations or high communication overhead. On the other hand, such components should support security applications such as message integrity, authentication, and time stamping. The latter are efficiently implemented by Hash Message Authentication Codes (HMAC). As clearly stated in the literature, current approved implementations of HMAC require resources that cannot be supported in constrained components. An approach to implement a compact HMAC by the use of stream ciphering is presented in this paper. [ABSTRACT FROM AUTHOR]

Recently, wireless sensor networks featuring direct sink access have been studied as an efficient architecture to gather and process data for numerous applications. In this paper, we focus on the joint effect of clustering and data correlation on the performance of such networks. More specifically, we propose a novel cluster-based data collection scheme for sensor networks with direct sink access (CDC-DSA) and provide an analytical framework to evaluate its performance in terms of energy consumption, latency, and robustness. In our scheme, cluster heads use a low-overhead and simple medium access control (MAC) conceptually similar to ALOHA to contend for the reachback channel to the data sink. Since, in our model, data is collected periodically, packet arrival is not modeled by a continuous random process, and therefore, we base our framework on a transient analysis rather than a steady-state analysis. Using random geometry tools, we study how the optimal average cluster size and energy savings, under the proposed MAC, vary according to the level of data correlation in the network. Extensive simulations for various protocol parameters show that our analysis is fairly accurate for a wide range of parameters. Our results suggest that, despite the trade-off between energy consumption and latency, both can be substantially reduced by a proper clustering design. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient MAC protocol: the throughput maximized MAC protocol (TM-MAC), inspired by the availability that a number of ultrawideband (UWB) transmission parameters can be tuned to better match the requirements of data flow. In TM-MAC, we implement a concurrent multiuser access scheme instead of a mutual exclusion method such as TDMA and random access. For multiuser interference, we establish a model to adaptively adjust the data transmission rate to generate the expected signal to interference noise ratio (SINR) at the receiver side for reliable communications, We also analyze the relationship among the theoretical maximum channel capacity, achievable maximum channel capacity, and data transmission rate. According to network topology, TM-MAC redivides each piconet into several subsets in which communication pairs can make communication simultaneously and achieve the maximum throughput using the highest data rate. In subset formation, we propose a general analytical framework that captures the unique characteristics of shared wireless channel and throughput variance, as well as allows the modeling of a large class of systemwide throughput maximization via the specification of the per-link utilization function. For algorithm essential parameters design, we consider the influence of traffic type on the system performance. Heavy tailed distribution, compared to Poisson distribution for most existing work, is exploited to accurately model the real traffic to achieve the adaptation of our algorithm. Simulation results show that our algorithm can maximize throughput to achieve short latency. [ABSTRACT FROM AUTHOR]

In multihop wireless sensor networks that are often characterized by many-to-one (convergecast) traffic patterns, problems related to energy imbalance among sensors often appear. Sensors closer to a data sink are usually required to forward a large amount of traffic for sensors farther from the data sink. Therefore, these sensors tend to die early, leaving areas of the network completely unmonitored and reducing the functional network lifetime. In our study, we explore possible sensor network deployment strategies that maximize sensor network lifetime by mitigating the problem of the hot spot around the data sink. Strategies such as variable-range transmission power control with optimal traffic distribution, mobile-data-sink deployment, multiple-data-sink deployment, nonuniform initial energy assignment, and intelligent sensor/relay deployment are investigated. We suggest a general model to analyze and evaluate these strategies. In this model, we not only discover how to maximize the network lifetime given certain network constraints but also consider the factor of extra costs involved in more complex deployment strategies. This paper presents a comprehensive analysis on the maximum achievable sensor network lifetime for different deployment strategies, and it also provides practical cost-efficient sensor network deployment guidelines. [ABSTRACT FROM AUTHOR]

Wireless sensor networks are becoming increasingly important in recent years due to their ability to detect and convey real-time, in-situ information for many civilian and military applications. A fundamental challenge for such networks lies in energy constraint, which poses a performance limit on the achievable network lifetime. We consider a two-tier wireless sensor network and address the network lifetime problem for upper-tier aggregation and forwarding nodes (AFNs). Existing flow routing solutions proposed for maximizing network lifetime require AFNs to split flows to different paths during transmission, which we call multisession flow routing solutions. If an AFN is equipped with a single transmitter/receiver pair, a multisession flow routing solution requires a packet-level power control at the AFN so as to conserve energy, which calls for considerable overhead in synchronization among the AFNs. In this paper, we show that it is possible to achieve the same optimal network lifetime by power control on a much larger timescale with the so-called single-session flow routing solutions, under which the packet-level power control and, thus, strict requirement on synchronization are not necessary. We also show how to perform optimal single-session flow routing when the bit-rate of composite flows generated by AFNs is time-varying, as long as the average bit-rate can be estimated. [ABSTRACT FROM AUTHOR]

The adoption of multi-sensor data fusion techniques is essential to effectively merge and analyze heterogeneous data collected by multiple sensors, pervasively deployed in a smart environment. Existing literature leverages contextual information in the fusion process, to increase the accuracy of inference and hence decision making in a dynamically changing environment. In this paper, we propose a context-aware, self-optimizing, adaptive system for sensor data fusion, based on a three-tier architecture. Heterogeneous data collected by sensors at the lowest tier are combined by a dynamic Bayesian network at the intermediate tier, which also integrates contextual information to refine the inference process. At the highest tier, a self-optimization process dynamically reconfigures the sensory infrastructure, by sampling a subset of sensors in order to minimize energy consumption and maximize inference accuracy. A Bayesian approach allows to deal with the imprecision of sensory measurements, due to environmental noise and possible hardware malfunctions. The effectiveness of our approach is demonstrated with the application scenario of the user activity recognition in an Ambient Intelligence system managing a smart home environment. Experimental results show that the proposed solution outperforms static approaches for context-aware multi-sensor fusion, achieving substantial energy savings whilst maintaining a high degree of inference accuracy. [ABSTRACT FROM AUTHOR]

Dynamic spectrum access (DSA) enables secondary networks to find and efficiently exploit spectrum opportunities. A key factor to design a DSA network is the spectrum sensing algorithms for multiple channels with multiple users. Multi-user cooperative channel sensing reduces the sensing time, and thus it increases transmission throughput. However, in a multi-channel system, the problem becomes more complex since the benefits of assigning users to sense channels in parallel must also be considered. A sensing schedule, indicating to each user the channel that it should sense at different sensing moments, must be thus created to optimize system performance. In this paper, we formulate the general sensing scheduling optimization problem and then propose several sensing strategies to schedule the users according to network parameters with homogeneous sensors. Later on, we extend the results to heterogeneous sensors and propose a robust scheduling design when we have traffic and channel uncertainty. We propose three sensing strategies, and, within each one of them, several solutions, striking a balance between throughput performance and computational complexity, are proposed. In addition, we show that a sequential channel sensing strategy is the one to be preferred when the sensing time is small, the number of channels is large, and the number of users is small. For all the other cases, a parallel channel sensing strategy is recommended in terms of throughput performance. We also show that a proposed hybrid sequential-parallel channel sensing strategy achieves the best performance in all scenarios at the cost of extra memory and computation complexity. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are one of the most important ingredients in the Internet of Things. Thus it is vital to design a good power saving protocol, which operates at the medium access control (MAC) layer, for a WSN since sensors are generally battery-powered. On the other hand, organizing a WSN into coronas centered at the sink is a simple effective technique to achieve low-overhead routing where every sensor needs neither to broadcast beacons nor to maintain routing/neighbor tables. Hence in this paper, we propose an optimal and maximized configurable power saving protocol, named Green-MAC, for a corona-based WSN, which has the following attractive features. (i) By using the generalized Chinese remainder theorem, Green-MAC guarantees that any two sensors in the neighboring coronas can simultaneously wake up in bounded time regardless of their schedule offset as well as their respective cycle lengths. (ii) Given the cycle length, the ATF-ratio (i.e. the fraction of awake time frames in a cycle) of each sensor reaches the theoretical minimum. (iii) Under the minimum ATF-ratio constraints, the number of configurable ATF-ratios of each sensor reaches the theoretical maximum. (iv) An ATF-ratio configuration scheme is proposed for Green-MAC such that the power consumption of a WSN can be minimized while the worst event-to-sink delay requirement can be fulfilled with high probability. Both theoretical analysis and simulation results demonstrate that Green-MAC greatly outperforms existing power saving protocols for corona-based WSNs, including Q-MAC and Queen-MAC, in terms of ATF-ratio, configurability, network lifetime, delay violation ratio, and event-to-sink throughput. [ABSTRACT FROM PUBLISHER]

Effective coordination can dramatically reduce radio interference and avoid packet collisions for multistation wireless local area networks (WLANs). Coordination itself needs consume communication resource and thus competes with data transmission for the limited wireless radio resources. In traditional approaches, control frames and data packets are transmitted in an alternate manner, which brings a great deal of coordination overhead. In this paper, we propose a new communication model where the control frames can be "attachedâ to the data transmission. Thus, control messages and data traffic can be transmitted simultaneously and consequently the channel utilization can be improved significantly. We implement the idea in OFDM-based WLANs called $(h)$Jam, which fully explores the physical layer features of the OFDM modulation method and allows one data packet and a number of control messages to be transmitted together. $(h)$Jam is implemented on the GNU Radio testbed consisting of eight USRP2 nodes. We also conduct comprehensive simulations and the experimental results show that $(h)$Jam can improve the WLANs efficiency by up to 200 percent compared with the existing 802.11 family protocols. [ABSTRACT FROM AUTHOR]

In this paper, we present Esense, a new paradigm of communication between devices that have fundamentally different physical layers. Esense is based on sensing and interpreting energy profiles. While our ideas are generic enough to be applicable in a variety of contexts, we illustrate the usefulness of our ideas by presenting novel solutions to existing problems in three distinct research domains. As part of these solutions, we demonstrate the ability to communicate between devices that follow two different standards: IEEE 802.11 and 802.15.4. We consider two scenarios here: 1) where there is no background traffic and 2) where there is background 802.11 traffic. In each case, we build an "alphabet setâ: a set of signature packet sizes that can be used for Esense communication. Specifically for the second case, we take a measurement-based alphabet set construction by considering WiFi traces from actual deployments. Based on practical observations and experiments, we theoretically quantify the maximum achievable transmission rate when using Esense. With background traffic, we could potentially construct an alphabet of size as high as 100. Such a large alphabet size promises efficient Esense communication. We show via a prototype implementation that effective communication is indeed feasible even when both sides use different physical layers. [ABSTRACT FROM AUTHOR]

Spectrum sensing is key to the realization of dynamic spectrum access. To protect primary users' communications from the interference caused by secondary users, spectrum sensing must meet the strict detectability requirements set by regulatory bodies, such as the FCC. Such strict detection requirements, however, can hardly be achieved using PHY-layer sensing techniques alone with one-time sensing by only a single sensor. In this paper, we jointly exploit two MAC-layer sensing methods—cooperative sensing and sensing scheduling— to improve spectrum sensing performance, while incurring minimum sensing overhead. While these sensing methods have been studied individually, little has been done on their combinations and the resulting benefits. Specifically, we propose to construct a profile of the primary signal's RSSs and design a simple, yet near-optimal, incumbent detection rule. Based on this constructed RSS profile, we develop an algorithm to find 1) an optimal set of sensors; 2) an optimal point at which to stop scheduling additional sensing; and 3) an optimal sensing duration for one-time sensing, so as to make a tradeoff between detection performance and sensing overhead. Our evaluation results show that the proposed sensing algorithms reduce the sensing overhead by up to 65 percent, while meeting the requirements of both false-alarm and misdetection probabilities of less than 0.01. [ABSTRACT FROM PUBLISHER]