Federated Learning (FL) can benefit applications using Multi-Hop Drone Networks (MDNs), but their distributed and dynamic connectivity challenges model training and aggregation. Traditional FL assumes stable topology and central server access, whereas MDNs rely on peer-to-peer exchanges without continuous connectivity, making it hard to ensure local model contributions are meaningful and non-redundant. To address this, we present a distributed participant selection algorithm in the context of MDNs. Specifically, at the beginning of each FL round, drones first identify their neighbors and measure the quality of communication links. With the identified neighbors, the drones select a subset of them to participate in the FL process based on their own and their neighbors’ contributions to the data needed for FL. This is done in a distributed manner, ensuring that only valuable data, which incorporates full sensor and area coverage, contributes to the FL model. For the subset of drones selected, we found the best path for these drones to transmit the local model via a distributed Steiner Tree problem formulation. Our simulation studies show that our distributed participant selection converges in 13 communication rounds for networks of up to 160 drones and achieves utility scores within 10% of a centralized baseline. Over 150 FL rounds with a real-world dataset, the trained global model using our approach yields an RMSE of 26.8% higher than the best centralized model. These results demonstrate the potential of our approach to enable resilient and scalable learning in dynamic drone networks, thereby advancing FL applications in distributed environments.

Teams of drones have been proposed for many monitoring and data collection applications, including forest fire monitoring, search and rescue, disaster response, and infrastructure inspection. However, robot systems can be stochastic, and uncertainty arises when operating environments are dynamic or hostile. This paper investigates the problem of assigning drones to tasks where the probability that a given group of drones can cooperatively complete a task follows a Poisson-Binomial distribution. We show how to determine if a solution exists and how to calculate an upper bound on the optimal solution. We present a variation of the branch-and-bound algorithm – termed Branch-and-Match – that is tailored to our problem and always finds an optimal solution at the cost of computation time. For a more tractable approach, we present a heuristics-based algorithm – termed M+ILS – that turns the problem into a balanced matching problem to find an initial solution then runs a variation of the Iterated Local Search (ILS) algorithm. Our M+ILS algorithm is applicable to distributed scenarios but finds suboptimal solutions. We evaluate these various algorithms in a simulated forest fire monitoring scenario based on the characteristics of a fleet of real drones. Our empirical results show that the M+ILS algorithm finds solutions with an average performance gap of 2.68% compared to the optimal solution found using the Branch-and-Match algorithm.

Federated Learning (FL) can benefit applications using Multi-Hop Drone Networks (MDNs), but their distributed and dynamic connectivity challenges model training and aggregation. Traditional FL assumes stable topology and central server access, whereas MDNs rely on peer-to-peer exchanges without continuous connectivity, making it hard to ensure local model contributions are meaningful and non-redundant. To address this, we present a distributed participant selection algorithm in the context of MDNs. Specifically, our participant selection scheme favors drones whose data most improves multi-modal and spatial coverage. Selected models are then aggregated via a communication-efficient Steiner-tree route. Our simulation studies show that our distributed participant selection converges in 4 communication rounds for networks of up to 18 drones and achieves utility scores within 5% of a centralized baseline. Over 150 FL rounds with a real-world dataset, the trained global model using our approach yields an RMSE of 26.8% higher than the best centralized model. These results demonstrate the potential of our approach to enable resilient and scalable learning in dynamic drone networks, thereby advancing FL applications in distributed environments.

Unmanned Aerial Vehicles (UAVs) have advanced significantly, with multi-UAV systems offering advantages such as redundancy, cost-effectiveness, and less dependency on infrastructure. These systems can benefit applications like remote surveillance, search and rescue, and environmental monitoring. Ideally, UAVs should behave distributively and make their own decisions. However, developing these distributed algorithms for multi-UAV systems presents challenges, particularly in real-world testing, where hardware constraints, environmental dependencies, and limited analytics can hinder the development progress. Simulations have emerged as a key solution, allowing for rapid iteration and detailed analytics. Yet, current simulators often lack the capability to accurately model both the physical dynamics of UAVs and the complexity of their communication networks. This paper introduces GazeboNS31, a Digital Twin system designed to bridge this gap. Specifically, we combine the Gazebo physics simulator, the NS-3 network simulator, the Ardupilot's flight controller, and our empirical measurements of energy consumption and computation time on various commonly used onboard computers. Experiment results show that GazeboNS3 enables seamless emulation of UAVs' behavior and communication in realistic environments.

In computer science and robotics research that focuses on algorithm designs, simulation is oftentimes the first step in validating the developed algorithms. However, simulation inputs need to be designed as close as possible to real-world scenarios so that a particular algorithm will perform equally well in simulation as in real-world testing. Designing credible simulation inputs is time-consuming and requires a fair amount of human labor, arguably due to the lack of an efficient way that streamlines the design process while having the capability to provide enough variations. In this study, we present the first-ever exploratory effort to use a Large Language Model (LLM) to facilitate generating simulation inputs. Specifically, we introduce two distributed Multi-Agent Path Finding (MAPF) algorithms and then utilize an LLM to generate variations of warehouse layouts to be used to validate our algorithms. We detail how to effectively prompt the LLM for simulated warehouse designs and compare algorithm performance on both human and LLM-created layouts. Our experimental results show that the LLM-generated layouts find the same algorithm performance trends as inputs designed by humans but require much less time to create, highlighting the LLM's potential to speed up simulation environment generation for algorithm testing.

In Mobile Augmented Reality (MAR) applications, rendering virtual objects accurately on the user’s screen relies on knowing the poses of the user and the virtual objects. Typically, Simultaneous Localization and Mapping (SLAM) is used for pose estimation, but SLAM consumes lots of space and time. Many MAR applications such as gaming and navigation do not need continuous mapping, hence Visual Odometry (VO) is developed that provides localization without map generation. This paper evaluates VO’s accuracy compared to SLAM by specifically considering feature points at varying depths which distinguishes indoor and outdoor environments. Our findings suggest VO performs better with distant feature points (i.e., commonly outdoors), while SLAM excels with closer feature points (i.e., typically indoors). These findings inspire us to design APPEAR, an adaptive approach that switches between VO and SLAM as needed. We implemented and evaluated APPEAR on a computer rather than a smartphone because the chosen VO method was not available on a phone. APPEAR demonstrates memory savings of up to 100 MB and a 1.67 x speed boost on medium-sized datasets without losing accuracy. Additionally, while Absolute Trajectory Error (ATE) has been the primary metric for evaluating pose estimation, we argue for more comprehensive quality metrics for MAR applications: accuracy, precision, and recall. We also define selective ATE where ATE is only calculated in the region where the virtual objects are visible.

The combination of object recognition capability with Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs) benefits applications like remote surveillance, search and rescue, and infrastructural monitoring. Offloading deep learning-based object recognition models from UAVs to UGVs can address energy and computational constraints on UAVs. However, when multiple UAVs are in contact with one single UGV, how to provide timely offloading decisions considering various system status information such as dynamic network conditions and remaining energy levels on UAVs remains under-explored. This paper presents our work in this area. Our online offloading decision engine, running on the UAV's onboard computer, dynamically determines offloading decisions and encoding bitrates considering fluctuating network conditions. In addition, our task scheduler running on the UGV prioritizes tasks according to the system statuses of each UAV. We conducted extensive evaluations of our approach in both lab and field settings. Experiments show that our system reduces the end-to-end system latency by up to 88% compared to a previous study that reduces both offload data size and local computation.

Using robots to collect data is an effective way to obtain information from the environment and communicate it to a static base station. Furthermore, robots have the capability to communicate with one another, potentially decreasing the time for data to reach the base station. We present a Mixed Integer Linear Program that reasons about discrete routing choices, continuous robot paths, and their effect on the latency of the data collection task. We analyze our formulation, discuss optimization challenges inherent to the data collection problem, and propose a factored formulation that finds optimal answers more efficiently. Our work is able to find paths that reduce latency by up to 101% compared to treating all robots independently in our tested scenarios.

With the increasing prominence of smart mobile devices, an innovative distributed computing paradigm, namely Mobile Crowdsourcing (MCS), has emerged. By directly recruiting skilled workers, MCS exploits the power of the crowd to complete location-dependent tasks. Currently, based on online social networks, a new and complementary worker recruitment mode, i.e., socially aware MCS, has been proposed to effectively enlarge worker pool and enhance task execution quality, by harnessing underlying social relationships. In this paper, we propose and develop a novel worker recruitment game in socially aware MCS, i.e.,Acceptance-awareWorkerRecruitment (AWR). To accommodate MCS task invitation diffusion over social networks, we design a Random Diffusion model, where workers randomly propagate task invitations to social neighbors, and receivers independently make a decision whether to accept or not. Based on the diffusion model, we formulate the AWR game as a combinatorial optimization problem, which strives to search a subset of seed workers to maximize overall task acceptance under a pre-given incentive budget. We prove its NP hardness, and devise a meta-heuristic-based evolutionary approach namedMA-RAWRto balance exploration and exploitation during the search process. Comprehensive experiments using two real-world data sets clearly validate the effectiveness and efficiency of our proposed approach.

In recent years, various intelligent activity recognition systems have been developed based on radio frequency signals such as radar, Wi-Fi, and radio frequency identification (RFID). When only one target is present, these systems can often provide high accuracy in recognizing different activities. However, such activity identification systems often fail to work due to signal interference when multiple targets coexist. To address this problem, we propose a multitarget gesture recognition system, named SoDar, based on a commercial single-input multi-output (SIMO) dual-channel Doppler radar. First, we employ endpoint detection, low-pass filtering, and discrete wavelet transform for data preprocessing. Then, we design a multitarget signal separation algorithm by maximizing the signal-to-noise ratio, and further refine the obtained signal based on principle component analysis. Afterward, we put forward a two-stage feature extraction method to extract both static and dynamic features from each separated signal. Finally, a classification model is trained to recognize the gestures of multiple targets. To verify the performance of SoDar, we selected nine different combinations of six gestures for two targets and collected more than 8000 data samples. Experimental results showed that the accuracy of two-target gesture recognition is above 90%.

With the rise of crowdsourcing and mobile crowdsensing techniques, a large number of crowdsourcing applications or platforms ($\mathbb {CAP}$) have appeared. In the mean time,$\mathbb {CAP}$-related models and frameworks based on different research hypotheses are rapidly emerging, and they usually address specific issues from a certain perspective. Due to different settings and conditions, different models are not compatible with each other. However,$\mathbb {CAP}$urgently needs to combine these techniques to form a unified framework. In addition, these models needs to be learned and updated online with the extension of crowdsourced data and task types; thus, requiring a unified architecture that integrates lifelong learning concepts and breaks down the barriers between different modules. This paper draws on the idea of ubiquitous operating systems and proposes a novel OS (CrowdOS), which is an abstract software layer running between native OS and application layer. In particular, based on an in-depth analysis of the complex crowd environment and diverse characteristics of heterogeneous tasks, we construct the OS kernel and three core frameworks including task resolution and assignment framework (TRAF), integrated resource management (IRM), and task result quality optimization (TRO). In addition, we validate the usability of CrowdOS, module correctness and development efficiency. Our evaluation further revealsTRObrings enormous improvement in efficiency and a reduction in energy consumption.

With the proliferation of increasingly powerful mobile devices and wireless networks, mobile crowdsourcing has emerged as a novel service paradigm. It enables crowd workers to take over outsourced location-dependent tasks, and has attracted much attention from both research communities and industries. In this paper, we consider a mobile crowdsourcing scenario, where a mobile crowdsourcing task is too complex (e.g., post-earthquake recovery, citywide package delivery) but can be divided into a number of easier subtasks, which have interdependency between them. Under this scenario, we investigate an important problem, namelytask graph scheduling in mobile crowdsourcing(TGS-MC), which seeks to optimize a compact scheduling, such that the task completion time (i.e., makespan) and overall idle time are simultaneously minimized with the consideration of worker reliability. We analyze the complexity and NP-complete of the TGS-MC problem, and propose two heuristic approaches, including BFS-based dynamic priority schedulingBFSPriDalgorithm, and an evolutionary multitasking-basedEMTTSchalgorithm, to solve our problem from local and global optimization perspective, respectively. We conduct extensive evaluation using two real-world data sets, and demonstrate superiority of our proposed approaches.

Exploring robots may fail due to environmental hazards. Thus, robots need to account for the possibility of failure to plan the best exploration paths. Optimizing expected utility enables robots to find plans that balance achievable reward with the inherent risks of exploration. Moreover, when robots rendezvous and communicate to exchange observations, they increase the probability that at least one robot is able to return with the map. Optimal exploration is NP-hard, so we apply a constraint-based approach to enable highly-engineered solution techniques. We model exploration under the possibility of robot failure and communication constraints as an integer, linear program and a generalization of the Vehicle Routing Problem. Empirically, we show that for several scenarios, this formulation produces paths within 50% of a theoretical optimum and achieves twice as much reward as a baseline greedy approach.

During disasters when the communication and power infrastructures are unavailable, we can deploy low-power, low-cost, portable wireless nodes and use them to facilitate multi-hop voice communication between the survivors and the rescue team. Similarly, using multi-hop, multicast, we can facilitate communication between the rescue team and multiple survivors, with whom direct communication is not possible. This paper examines a multi-layer adaptive approach to the problem, the voice data captured from the sender are compressed based on the availability of the bandwidth and contention it might cause in the network. We also perform distributed admission control to ensure that the new streams entering the network do not affect the old ones. To evaluate, we implement the ideas on a testbed of 18 Raspberry Pi equipped with Xbee radios. Our experimental results show that voice data can be multicast to at least 6 destinations with acceptable voice quality in this setup.

Internet of Things (IoT) networks have become increasingly popular in recent years, and while they may be installed in certain environments with relative ease, the systems increase in size, cost, and complexity as they scale to smart buildings. Since these devices do not exist in flat, open areas, but rather exist in buildings where concrete walls and metal structures obstruct device communication ranges, many of the algorithms and systems that work in theory fall short in such real-world scenarios. This research develops a novel relay placement algorithm for IoT system coverage which takes into account the impact of various obstructions on the performance of wireless communication. In addition, this algorithm is incorporated into our IoT network deployment and management framework. We first evaluated our approach in simulation, then tested the system in a real-world scenario where its effectiveness is compared to previous systems and algorithms.

In this paper, we propose an improvement of the Hungarian method to optimally solve the task assignment problem for a multi-robot team. Our proposed method involves all robots collaboratively working together to disseminate cost information and then individually computing an assignment that optimizes a particular global goal. Through theoretical analysis, we show that our approach is able to produce a common optimal assignment, sending significantly fewer messages and resulting in faster convergence than other approaches based on the Hungarian method. Our experimental results back up this claim, demonstrating that, even in the worst case, our approach sends a fraction of the messages required by other assignment methods and as a result scales better as team size increases.

Smart cities are distributed heterogeneous systems of systems connected to each other via a variety of heterogeneous data streams involving multiple stakeholders and organizations. This complexity is reflected also in the data that have to be managed to provide a concrete and useful real time service to the citizens. The data ingestion phase is critical for the whole services, since it has to preserve the information, connect the new data with old data and establish right connections with city entities. This paper describes data ingestion and inspection in the Snap4City open source scalable Smart aNalytic APplication builder, with a specific focus on how heterogeneous data is represented, how its quality is inspected, and how to develop ingestion procedures in an efficient manner. The Snap4City ingestion processes are based on a semantic and unified data ingestion model, capable of aggregating different types of data. A performance comparison of different data ingestion modalities is presented.

Spatial crowdsourcing consists of location-specific tasks that require people to be physically at specific locations to complete them. In this paper we focus on worker selection for spatial crowdsourcing where each task requires multiple workers to accomplish. We mathematically formulate the problem and prove its APX-hardness. We develop efficient greedy algorithms with a good approximation ratio. Compared with state-of-the art approach, our proposed algorithm outperforms by 35%.

People may perform synchronized activities in a group setting. It is helpful to provide notifications to users and also the group leader whether people are in sync. This work aims to provide this support via analyzing motion data collected from wearable devices. We collected experimental data from smart watches worn by people, applied signal processing algorithms in both time and frequency domains for identification of the fine-grained group gesture status. We further developed a prototype system consisting of a smart watch, a smartphone, and a server. Our simulation results and actual system implementation demonstrate the feasibility of our approaches.

Mobile ad-hoc networks are becoming more common as robotic swarm technology becomes possible. One issue surrounding swarm technology is communication between robots. Communication costs time and energy, and can impact the performance of a swarm. In order to control the network, network state information must be acquired through network monitoring. We propose a novel REgression-based network Monitoring (REM) algorithm where robots in a swarm receive network state data only when necessary for the task at hand. This simple distributed algorithm will save time and energy in communication by creating predictive models using regressions, minimizing network monitoring overhead.

Underground environments present their unique challenges for wireless communication. This paper presents an empirical study of WiFi performance where aerial-ground robots are used to map, navigate, and search in an unknown underground environment. While wireless signal attenuates significantly around corners, WiFi's overall performance is encouraging.

Traditionally, vehicle tracking is accomplished using predeployed video camera networks, which relies on stationary cameras and searches for the target vehicle from videos. In this paper, we develop CrowdTracking, i.e., a crowd tracking system that people can collaboratively keep track of the moving vehicle by taking photographs, especially in areas where video cameras are deficient. In other words, the underlying support of CrowdTracking is mobile crowdsensing. Several novel ideas underpin CrowdTracking. First, the vehicle can be rapidly localized by using both photographing contexts (including the location and the shooting direction) of the photographer and the road network. Second, the moving speed of the vehicle can be estimated according to two localization results and the trajectory will be predicted. As a result, through continuously collecting photographs of the moving vehicle on different roads, the vehicle can be tracked and localized almost in real time. Through precisely localizing the specified vehicle, two optimization objectives are met: 1) maximizing the tracking coverage to the vehicle's actual trajectory and 2) minimizing the number of participants who are assigned vehicle-tracking tasks. We evaluate the localization method with a real dataset and report about 6 m error. We also evaluate the vehicle-tracking method of CrowdTracking using a synthetic data set and experimental results validate its effectiveness and efficiency.

In recent years, takeout ordering and delivery (TOD) has become an emerging service due to its convenience and efficiency. However, current online ordering platforms still suffer from some issues, such as limited delivery coverage and delayed delivery. To address these issues, we propose a spatial crowdsourcing (SC)-based system called crowd delivery network (CrowDNet) to have packages take hitchhiking rides with existing taxis. We first tackle passenger riding queries based on an evolutionary algorithm and then insert appropriate food delivery requests into a partial schedule with an improved insertion approach. Finally, we propose a ranking module based on the modern portfolio theory to recommend the delivery path, which can achieve a balance between the delivery cost and timely services. Evaluations based on three real-world datasets demonstrate that our proposed algorithms outperform baseline methods.

In this paper, we investigate the accelerometer and gyroscope motion sensor-based cross-site input inference attacks that may compromise the security of many mobile Web users, and quantify the extent to which they can be effective. We formulate our attacks as a typical multi-class classification problem and build an inference framework that trains a classifier in the training phase and predicts the user's new inputs in the attacking phase. To make our attacks effective and realistic, we design unique techniques and address major data quality and data segmentation challenges. We intensively evaluate the effectiveness of our attacks using 98 691 keystrokes collected from 20 participants. Overall, our attacks are effective, for example, they are about 10.8 times more effective than the random guessing attacks regarding inferring letters. We also perform experiments to evaluate the effect of using the data perturbation defense techniques on decreasing the accuracy of our input inference attacks. Our results demonstrate that researchers, smartphone vendors, and app developers should pay serious attention to the motion sensor-based cross-site input inference attacks that can be pervasively performed, and start to design and deploy effective defense techniques.

In recent years, bike-sharing systems have been widely deployed in many big cities, which provide an economical and healthy lifestyle. With the prevalence of bike-sharing systems, a lot of companies join the bike-sharing market, leading to increasingly fierce competition. To be competitive, bike-sharing companies and app developers need to make strategic decisions and predict the popularity of bike-sharing apps. However, existing works mostly focus on predicting the popularity of a single app, the popularity contest among different apps has not been explored yet. In this paper, we aim to forecast the popularity contest between Mobike and Ofo, two most popular bike-sharing apps in China. We develop CompetitiveBike, a system to predict the popularity contest among bike-sharing apps leveraging multi-source data. We extract two novel types of features: coarse-grained and fine-grained competitive features, and utilize Random Forest model to forecast the future competitiveness. In addition, we view mobile apps competition as a long-term event and generate the event storyline to enrich our competitive analysis. We collect data about two bike-sharing apps and two food ordering & delivery apps from 11 app stores and Sina Weibo, implement extensive experimental studies, and the results demonstrate the effectiveness and generality of our approach.

Human computer operations such as writing documents and playing games have become popular in our daily lives. These activities (especially if identified in a non-intrusive manner) can be used to facilitate context-aware services. In this paper, we propose to recognize human computer operations through keystroke sensing with a smartphone. Specifically, we first utilize the microphone embedded in a smartphone to sense the input audio from a computer keyboard. We then identify keystrokes using fingerprint identification techniques. The determined keystrokes are then corrected with a word recognition procedure, which utilizes the relations of adjacent letters in a word. Finally, by fusing both semantic and acoustic features, a classification model is constructed to recognize four typical human computer operations: 1) chatting; 2) coding; 3) writing documents; and 4) playing games. We recruited 15 volunteers to complete these operations, and evaluated the proposed approach from multiple aspects in realistic environments. Experimental results validated the effectiveness of our approach.

Navigation services using digital maps make people's travel much easier. However, these services often fail to provide specific routes to those destinations that lack micro data in digital maps, such as a small laundry store in a shopping area. In this paper, we propose CrowdNavi, a last mile navigation service in outdoor environments using crowdsourcing based on the guider-follower model. First, we collect trajectories of guiders and images of reference objects along trajectories. To guide followers by reference objects along the route, we design a Semantic Crowd Navigation model to generate fine-grained maps by integrating guiders' data. Second, we design two score functions to fulfill two main requirements and plan hints. Last, we provide context-aware navigation for followers based on the fine-grained map and detect deviation in real-time. Real world experiments conducted in three different areas show that our proposed system in combination with images of reference objects is efficient.

Monitoring group mobility and structure is crucial for understanding group activities and social relations. In this paper, we develop algorithms for fine-grained mobility classification and structure recognition of social groups utilizing mobile devices. First, we present a method that recognizes four levels of group mobility, including stationary, strolling, walking, and running. Second, using multiple types of mobile sensors, a novel relative position relationship estimation algorithm is developed to understand different moving group structures. We have conducted real-life experiments in which 12 volunteers moved in different small groups either in an office building or a shopping mall with various speeds and structures. Experimental results show that our approach achieves an accuracy of 99.5 percent in group mobility level classification and about 80 percent in group structure recognition.

With the rapid development of mobile networks and the proliferation of mobile devices, spatial crowdsourcing, which refers to recruiting mobile workers to perform location-based tasks, has gained emerging interest from both research communities and industries. In this paper, we consider a spatial crowdsourcing scenario: in addition to specific spatial constraints, each task has a valid duration, operation complexity, budget limitation, and the number of required workers. Each volunteer worker completes assigned tasks while conducting his/her routine tasks. The system has a desired task probability coverage and budget constraint. Under this scenario, we investigate an important problem, namely heterogeneous spatial crowdsourcing task allocation (HSC-TA), which strives to search a set of representative Pareto-optimal allocation solutions for the multi-objective optimization problem, such that the assigned task coverage is maximized and incentive cost is minimized simultaneously. To accommodate the multi-constraints in heterogeneous spatial crowdsourcing, we build a worker mobility behavior prediction model to align with allocation process. We prove that the HSC-TA problem is NP-hard. We propose effective heuristic methods, including multi-round linear weight optimization and enhanced multi-objective particle swarm optimization algorithms to achieve adequate Pareto-optimal allocation. Comprehensive experiments on both real-world and synthetic data sets clearly validate the effectiveness and efficiency of our proposed approaches.

Worker selection is a key issue in mobile crowd sensing (MCS). While the previous worker selection approaches mainly focus on selecting a proper subset of workers for a single MCS task, a multitask-oriented worker selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes ActiveCrowd, a worker selection framework for multitask MCS environments. We study the problem of multitask worker selection under two situations: worker selection based on workers' intentional movement for time-sensitive tasks and unintentional movement for delay-tolerant tasks. For time-sensitive tasks, workers are required to move to the task venue intentionally and the goal is to minimize the total distance moved. For delay-tolerant tasks, we select workers whose route is predicted to pass by the task venues and the goal is to minimize the total number of workers. Two greedy-enhanced genetic algorithms are proposed to solve them. Experiments verify that the proposed algorithms outperform baseline methods under different experiment settings (scale of task sets, available workers, varied task distributions, etc.).

Visual crowdsensing is successfully applied in numerous application areas, yet little work has been done on measuring and improving the quality of worker contributed visual data. Rather than evaluating the visual quality based on traditional metrics such as resolution, we focus on data diversity, which is crucial for a broad stream of visual crowdsensing tasks. Two representative diversity-oriented task types are studied, namely static object imagery and evolving event photography. The former aims to collect multi-facet/ aspect yet low redundant data about a stationary object, while the latter wants to detect and collect details of key scenes throughout an event. We link these quality needs with data utility and propose a unified visual crowdsensing framework called UtiPay. Data utility is characterized by the macro and micro diversity needs: at the macro level, the pyramid-tree approach is proposed for multi-attribute-based data grouping; at the micro level, we use several strategies for intra-group data selection and worker contribution measurement. To study the impact of our proposed utility measurement approaches, we propose two utility-enhanced payment schemes as incentive mechanisms: Uti and Uti-Bid. Experiments over several user studies with a total of 43 subjects validate the performance of UtiPay for measuring and enhancing the data quality of visual crowdsensing tasks.

Nowadays industrial control devices are crucial for infrastructure-critical systems such as factories, power plants, and water treatment facilities. Devices with IP addresses are visible on the Internet and they connect cyber space and physical world. The first step in protecting devices from attackers is a deep understanding of the devices' characteristics in the cyber space. In this paper, we take a first step in this direction by investigating physical devices running one of the two specific protocols that are widely adopted in industrial control systems. In order to detect these devices in real-time, we propose a two-stage discovery mechanism: first filtering out unqualified hosts from 4 billion remote hosts and then identifying physical devices from qualified candidates. We have conducted a real-world experiment to verify the mechanism and identified dozens of thousands of physical devices from the entire Internet. Results show that our method discovers all devices in 20 hours with 89.5% precision and 79.3% recall.

Nowadays, more and more physical devices embed computing and networking capabilities and are visible on the Internet. These devices include webcams, net-printers, and industrial control equipments, etc. Collecting information about these devices is crucial to preserve cyber-security and facilitate security auditing for system administrators. In this paper, we propose a scalable framework for physical device profiling. It leverages banner grabbing to identify device types and running services, and uses clock skew to determine a device ID. Our framework scales well. We implement a prototype system and use it to profile Webcams and industrial control device. The results show that our system can effectively profile and identify Webcams in real time. We deploy it on the cloud server and use it to detect 4 billion IP addresses to profile 1.2 million Webcams and more than 60 thousand industrial control devices in 20 hours.

Mobile social media enables people to record ongoing physical events they witness and share them instantaneously online. However, since these event pictures are often individually provided, they are typically fragmented and possess high redundancy. Though there have been studies about visual event summarization, they pay little attention to collaborative sensing, subevent detection, and event summary. In this paper, we present several building blocks for a cooperative visual sensing and sharing system. We create a virtual opportunistic community associated with an event, where members collaborate to cover different aspects of the event. More specifically, a crowd-powered approach is first used to localize the event. We then propose three subevent segmentation methods based on crowd-event interaction patterns. Based on the segmentation results, we summarize the event at two levels: multi-facet subevent summary and crowd-behavior-based highlights. Experiments over 21 online datasets and two real world datasets demonstrate the effectiveness of our approaches.

Monitoring group mobility and structure is crucial for public safety management and emergency evacuation. In this paper, we propose a fine-grained mobility classification and structure recognition approach for social groups based on hybrid sensing using mobile devices. First, we present a method which classifies group mobility into four levels, including stationary, strolling, walking and running. Second, by combining mobile sensing and Wi-Fi signals, a novel relative position relationship estimation algorithm is developed to understand moving group structures of different shapes. We have conducted real-life experiments in which eight volunteers form two to three small groups moving in a teaching building with different speed and structures. Experimental results show that our approach achieves an accuracy of 99.5% in mobility classification and about 80% in group structure recognition.

Nowadays people spend a substantial amount of time waiting in different places such as supermarkets and amusement parks. Detecting the status of queuing may benefit both users and business. In this paper, we present QueueSense, a queuing recognition system to assist in a queue management system. QueueSense consists of clients on smartphones that provide automatic, energy-efficient, and accurate queuing recognition, and a server in the cloud that collects data, identifies multi-queue lines, and provides waiting time estimation. In order to be useful, QueueSense should be able to recognize queuing behavior in various queuing scenarios without greatly decreasing the battery life of mobile phones. We present features of queuing and build the classifier on smartphones to automatically recognize queue classifier without human input. We investigate the complicated nature of energy consumption for queue recognition on phones and design an effective algorithm to maximize energy savings while guaranteeing accuracy of queue recognition. We evaluate QueueSense performance using the data set from real world queuing scenarios collected over a three-month period. Empirical results show that QueueSense is adaptive to various queuing scenarios with both high recognition accuracy and energy efficiency. We further implemented a prototype of QueueSense, the first queue detection system using smartphones. We conducted real-world experiments in a dining hall and a supermarket near a university campus. Through implementation and evaluation, we demonstrate that QueueSense is capable of detecting waiting lines that occur in our daily lives.

People in social environments often appear in groups to have face-to-face interactions, automatically recognizing these groups will facilitate many applications while being unobtrusive. In this work, we focus on recognizing multiple concurrent intentional groups of people in proximity using a real-time distributed approach running on commodity mobile devices. Specifically, we study Bluetooth signal strength probability distribution for proximity estimation and model the group probability distribution under Bluetooth signal strength. Further, we develop a real-time distributed group determination algorithm. We implement a prototype system on iOS platforms which includes both the group recognition service and a mobile social application using the service. Both our simulation and prototype results show that the proposed approach can recognize proximity-based intentional groups with high accuracy.

No abstract available.

Nowadays people spend a substantial amount of time waiting in different places such as supermarkets and amusement parks. Detecting the status of queuing may benefit both users and business. In this paper, we present QueueSense, a queuing recognition system on mobile phones to assist in a queue management system. QueueSense extracts features of queuing behavior and classifies queueing via collaboration among people waiting in line. It measures the disparity of people in different lines using relative position changing rate and partitions different queues using a hierarchical clustering approach. We implement a prototype of QueueSense on Android platforms using widely available multi-modal sensors and it is the first queue detection system on mobile phones. We conduct real-world experiments at a dining hall and a supermarket near a university campus. Through implementation and evaluation, we demonstrate that QueueSense is capable of detecting waiting lines that occur in our daily lives with high accuracy.

User intent is an integral part of mobile phone applications as it delivers events to applications, notifies applications of relevant events, or triggers applications. Current smartphone applications either require users to manually start them or they run as background jobs. In this work, we propose UserIntent, a new paradigm for automatically selecting the right smartphone application based on user intent captured. UserIntent consist of two parts: user intent detection and mechanism for triggering a smartphone app. Action cues act as user intent and a context-aware selection algorithm chooses a suitable smartphone application. In order to demonstrate how UserIntent works, we also develop a concrete application that recognizes speaker and talk content based on gestures captured.

Sensor-equipped mobile devices have allowed users to participate in various social networking services while they are on the go. We focus on proximity-based mobile social networking environments where users share information obtained from different places via their mobile devices when they are in proximity. Since people are more likely to share information if they can benefit from the sharing or if they think the information is of interest to others, there might exist community structures where users who share information more often are grouped together. Communities in proximity-based mobile social networks represent social groups where connections are built when people are in proximity. We consider information influence (i.e., specify who shares information with whom) as the connection, and the space and time related to the shared information as the social contexts. To model the potential information influences, we construct an influence graph by integrating the social contexts into the contacts of mobile users. Further, we propose a twophase strategy to detect and track context-aware communities based on the influence graph and show how the context-aware community structure improves the performance of two types of mobile social applications.

Nowadays smartphone users often enjoy the availability of multi-networks by switching between the networks for better network performance, energy efficiency of smartphones, and more data offloading to less expensive networks. However, network switching inevitably brings about network disruptions leading to user experience degradation. In this paper, we propose an application context model that is used in conjunction with a heuristic network selection mechanism, which selects a network using three metrics (i.e., network performance, energy consumption, and cost). A Bayes classifier is used to provide a probability for the network selection given applications running during network disruptions. We construct the classifier via crowd-sourced data by considering smartphone users profile and the operating environments. We implement a prototype context-aware handoff on the Android platform and conducted an experiment in a real world scenario through one case study, switching between cellular and WiFi networks. The evaluation results suggest that context aware handoff achieves 25% energy cost, nearly one-third data offloading, and more than twice throughput with only one third of the network switchings.

With the advances in sensing, communication, and computation, there is an increasing need to track mobile events such as air pollutant diffusion, toxic gas leakage, or wildfire spreading using mobile sensors such as robots. Lots of existing work use control theory to plan the path of mobile sensors by assuming that the event evolution is known in advance. This assumption has severely limited the applicability of existing approaches. In this work, we aim to design a detection and tracking algorithm that is capable of identifying multiple events with dynamic event signatures and providing event evolution history that may include event merge, split, create and destroy. Simulation results show that our approach can identify events with low event count difference, high event membership similarity, and accurate event evolution decisions, while using a reasonable number of tracking robots.

Wireless sensor networks may be deployed in many applications to detect and track events of interest. Events can be either point events with an exact location and constant shape, or region events which cover a large area and have dynamic shapes. While both types of events have received attention, no event detection and tracking protocol in existing wireless sensor network research is able to identify and track region events with dynamic identities, which arise when events are created or destroyed through splitting and merging. In this paper, we propose DRAGON, an event detection and tracking protocol which is able to handle all types of events including region events with dynamic identities. DRAGON employs two physics metaphors: event center of mass, to give an approximate location to the event; and node momentum, to guide the detection of event merges and splits. Both detailed theoretical analysis and extensive performance studies of DRAGON's properties demonstrate that DRAGON's execution is distributed among the sensor nodes, has low latency, is energy efficient, is able to run on a wide array of physical deployments, and has performance which scales well with event size, speed, and count.

Low-power Wireless Networks (LWNs) have become increasingly available for mission-critical applications such as security surveillance and disaster response. In particular, emerging low-power wireless audio platforms provide an economical solution for ad hoc voice communication in emergency scenarios. In this paper, we develop a system called Adaptive Stream Multicast (ASM) for voice communication over multihop LWNs. ASM is composed of several novel components specially designed to deliver robust voice quality for multiple sinks in dynamic environments: 1) an empirical model to automatically evaluate the voice quality perceived at sinks based on current network condition; 2) a feedback-based Forward Error Correction (FEC) scheme where the source can adapt its coding redundancy ratio dynamically in response to the voice quality variation at sinks; 3) a Tree-based Opportunistic Routing (TOR) protocol that fully exploits the broadcast opportunities on a tree based on novel forwarder selection and coordination rules; and 4) a distributed admission control algorithm that ensures the voice quality guarantees when admitting new voice streams. ASM has been implemented on a low-power hardware platform and extensively evaluated through experiments on a test bed of 18 nodes. The experiment results show that ASM can achieve satisfactory multicast voice quality in dynamic environments while incurring low-communication overhead.

Both energy efficiency and high data relevancy are crucial for wireless sensor network applications. Network clustering and event-driven protocols are two main approaches typically used to fulfill those requirements. Existing techniques are either focused only on performance of clusterheads or too restricted to specific applications; however, few of previous work took advantage of the combination of both approaches. In this paper, we formalize the combined problem of network clustering in an event-driven manner, show its NP-Completeness and present an innovative distributed heuristic solution. We hereby propose AWARE, an event-driven and energy-aware network clustering technique. AWARE groups highly active nodes together and thus make clusterheads report more efficiently with only relevant data. This approach guarantees the benefit of both reduced energy consumption and aggregated highly relevant data. Furthermore, AWARE is not only able to recover from node failures for in-network communication, but also provides a communication structure for high level services. We theoretically prove that AWARE indeed provides the solution to the problem, as it completes in constant time, forms connected clusters and can recover from node failures. Finally, our extensive performance studies also validate the effectiveness and efficiency of AWARE.

Wireless sensor networks may be deployed in many applications to detect and track events of interest. Events can be either point events with an exact location and constant shape, or region events which cover a large area and have dynamic shapes. While both types of events have received attention, no event detection and tracking protocol in existing wireless sensor network research is able to identify and track region events with dynamic shapes(NH-change shapes to identities), which arise when events are created or destroyed through splitting and merging. In this paper, we propose DRAGON, an event detection and tracking protocol which is able to handle all types of events including region events with dynamic identities. DRAGON employs two physics metaphors: event center of mass, to give an approximate location to the event; and node momentum, to guide the detection of event merges and splits. Both detailed theoretical analysis and extensive performance studies of DRAGON's properties demonstrate that DRAGON's execution is distributed among the sensor nodes, has low latency, is energy efficient, is able to run on a wide array of physical deployments, and has performance which scales well with event size, speed, and count.

With the advances in sensing, communication, and computation, there is an increasing need to track mobile events such as air pollutant diffusion, toxic gas leakage, or wildfire spreading using mobile sensors such as robots. Lots of existing work use control theory to plan the path of mobile sensors by assuming that the event evolution is known in advance. This assumption has severely limited the applicability of existing approaches. In this work, we aim to design a detection and tracking approach that is capable of identifying multiple events with dynamic event signatures and providing event evolution history that may include event merge, split, create and destroy. Simulation results show that our approach can identify events with low event count difference, high event membership similarity, and accurate event evolution decisions, while using a reasonable number of tracking robots.

We present PIM-WSN, a protocol independent multicast (PIM) protocol tailored for IPv6 wireless sensor networks (WSNs). Existing solutions for multicast in WSNs are limited because they either support multicast only from a single source node (usually the root node) or they limit the multicast group size to constrain memory usage. Our design allows any node to be a mulitcast source with an unlimited number of subscribers. We constrain the memory usage by approximating multicast group membership using a fixed sized Bloom filter. The efficiency of the protocol degrades as the false positive rate of the Bloom filter increases; however, correct operation is always maintained. Using detailed simulations we show that PIM-WSN achieves 1) high packet delivery rate (over 97%), 2) low latency per hop (less than 5 ms), and 3) lower radio utilization than all other comparable protocols (by more than 50%). Using a ten-hop testbed of TelosB motes we have verified our implementation of PIM-WSN for TinyOS 2.× with the Blip IPv6 networking stack which uses only 5,978 bytes of ROM and 235 bytes of RAM.

Low-power Wireless Networks (LWNs) have become increasingly available for mission-critical applications such as security surveillance and disaster response. In particular, emerging low-power wireless audio platforms provide an economical solution for ad hoc voice communication in emergency scenarios. In this paper, we develop a system called Adaptive Stream Multicast (ASM) for voice communication over multi-hop LWNs. ASM is composed of several novel components specially designed to deliver robust voice quality for multiple sinks in dynamic environments: 1) an empirical model to automatically evaluate the voice quality perceived at sinks based on current network condition, 2) a feedback-based Forward Error Correction scheme where the source can adapt its coding redundancy ratio dynamically in response to the voice quality variation at sinks, 3) a Tree-based Opportunistic Routing (TOR) protocol that fully exploits the broadcast opportunities on a tree based on novel forwarder selection and coordination rules, and 4) a distributed admission control algorithm that ensures the voice quality guarantees when admitting new voice streams. ASM has been implemented on a low-power hardware platform and extensively evaluated through experiments on a testbed of 18 nodes.

Random routing protocols in sensor networks forward packets to randomly selected neighbors. These packets are agents carrying information about events, or queries seeking such information. We derive the probability of a packet visiting a given node in a given step as well as the rendezvous probability of agents and queries within a specific number of hops at a given node(s) in a 2-D grid-based sensor network. The utility of the model is demonstrated by determining the protocol parameters to optimize performance of rumor routing protocol under different constraints, e.g., to evaluate the number of queries and agents to maximize the probability of rendezvous for a given amount of energy. Monte Carlo simulations are used to validate the model. The closed form exact solution presented, unlike existing models relying on asymptotic behavior, is applicable to small and medium-scale networks as well. An upper bound is provided for the case where the packet is not sent back to its immediate forwarding node. Simulation results indicate that the model is a good approximation even for sparse arrays with 75 % of the nodes. The model can be used to set parameters and optimize performance of several classes of random routing protocols.

Increased interest in Wireless Sensor Networks (WSNs) by scientists and engineers is forcing WSN research to focus on application requirements. Data is available as never before in many fields of study; practitioners are now burdened with the challenge of doing data-rich research rather than being data-starved. In-situ sensors can be prone to errors, links between nodes are often unreliable, and nodes may become unresponsive in harsh environments, leaving to researchers the onerous task of deciphering often anomalous data. Presented here is the REDFLAG fault detection service for WSN applications, a Run-timE, Distributed, Flexible, detector of faults, that is also Lightweight And Generic. REDFLAG addresses the two most worrisome issues in data-driven wireless sensor applications: abnormal data and missing data. REDFLAG exposes faults as they occur by using distributed algorithms in order to conserve energy. Simulation results show that REDFLAG is lightweight both in terms of footprint and required power resources while ensuring satisfactory detection and diagnosis accuracy. Because REDFLAG is unrestrictive, it is generically available to a myriad of applications and scenarios.

A closed-loop contaminant plume monitoring system is being developed that integrates wireless sensor network based monitoring with numerical models for subsurface plumes. The system is based on a novel virtual sensor network architecture that supports the formation, usage, adaptation, and maintenance of dynamic subsets of nodes. This automated monitoring system is intended to capture transient plumes to assess the source, track plumes in real-time, and predict future plumes behavior using numerical models that can be continuously re-calibrated by sensor data. This paper presents recent progress made in (1) developing a proof-of-concept study using a porous media test bed with sensors deployed; and (2) developing distributed algorithms for virtual sensor networking.

In this paper we present a protocol for sensor applications that require periodic collection of raw data reports from the entire network in a timely manner. We formulate the problem as an NP-hard graph coloring problem. We then present TIGRA - a distributed heuristic for graph coloring that takes into account application semantics and special characteristics of sensor networks. TIGRA ensures that no interference occurs and spatial channel reuse is maximized by assigning a specific time slot for each node to transmit. Although the end-to-end delay incurred by sensor data collection largely depends on specific topology, platform, and application, TIGRA provides a transmission schedule that guarantees near-optimal delay on sensor data collection.

In the future, we are likely to see a tremendous need for context-aware applications which adapt to available context information such as physical surroundings, network, or system conditions. We aim to provide a fundamental support for these applications - a real-time context information collection service. This service delivers the right context information to the right user at the right time. The complexity of providing the real-time context information service arises from 1) the dynamically changing status of information sources, 2) the diverse user requirements in terms of data accuracy and service latency, and 3) constantly changing system conditions. In this paper, we take into consideration these dynamics and focus on addressing the trade-offs between timeliness, accuracy, and cost for information collection in distributed real-time environments. We propose a middleware-based approach to enable a judicious composition of services for accuracy-aware scheduling and cost-aware database maintenance. Specifically, we characterize the problem in terms of quality-of-service satisfaction (QoSSat), quality-of-data satisfaction (QoDSat), and cost. We propose a middleware framework for the real-time information collection process, where the information mediator coordinates and facilitates communication between information sources and consumers. We design a family of algorithms for real-time request scheduling, request servicing, and directory service maintenance to be implemented at the mediator to support QoSSat and QoDSat. Our studies indicate that the composition of our proposed scheduling algorithm and directory service maintenance policy can improve the overall efficiency of the system. We also observe that the proposed policies perform very well as the system scales in the number of information sources and consumer requests

Efficient resource provisioning that allows for cost-effective enforcement of application QoS relies on accurate system state information. However, maintaining accurate information about available system resources is complex and expensive, especially in mobile environments where system conditions are highly dynamic. Resource provisioning mechanisms for such dynamic environments must therefore be able to tolerate imprecision in system state while ensuring adequate QoS to the end-user. In this paper, we address the information collection problem for QoS-based services in mobile environments. Specifically, we propose a family of information collection policies that vary in the granularity at which system state information is represented and maintained. We empirically evaluate the impact of these policies on the performance of diverse resource provisioning strategies. We generally observe that resource provisioning benefits significantly from the customized information collection mechanisms that take advantage of user mobility information. Furthermore, our performance results indicate that effective utilization of coarse-grained user mobility information renders better system performance than using fine-grained user mobility information. Using results from our empirical studies, we derive a set of rules that supports seamless integration of information collection and resource provisioning mechanisms for mobile environments. These results have been incorporated into an integrated middleware framework AutoSeC (Automatic Service Composition) to provide support for dynamic service brokering that ensures effective utilization of system resources over wireless networks.

Sensors are typically deployed to gather data about the physical world and its artifacts for a variety of purposes that range from environment monitoring, control, to data analysis. Since sensors are resource constrained, often sensor data is collected into a sensor database that resides at (more powerful) servers. A natural tradeoff exists between the sensor resources (bandwidth, energy) consumed and the quality of data collected at the server. Blindly transmitting sensor updates at a fixed periodicity to the server results in a suboptimal solution due to the differences in stability of sensor values and due to the varying application needs that impose different quality requirements across sensors. We propose adaptive data collection mechanisms for sensor environments that adjusts to these variations while at the same time optimizing the energy consumption of sensors. Our experimental results show significant energy savings compared to the naive approach to data collection.

Summary form only given. Many distributed applications in the real world now require real time services in which aggregate queries need to be computed over a set of values. These applications can often tolerate varying degrees of inaccuracy in the results. System designers, on the other hand, would like to provide services with low inaccuracy and minimal management overhead. We focus on addressing the tradeoffs between timeliness, accuracy and cost for data aggregation in distributed environments. Specifically, we address the problem of time-sensitive computation of aggregate queries (count, sum and min) over a set of values represented by intervals with lower and upper bounds. These intervals are approximations based on most recent values about distributed sources. In order to meet the precision constraints from users, a subset of sources needs to be probed for exact values. We first propose algorithms for batch selection of the probing set, where selection is done before probing without the knowledge of the actual values. In addition, we propose an iterative selection approach where the selection of the next probing source depends on the previous returned value.

In this paper, we focus on addressing the tradeoffs between timeliness, accuracy and cost for applications requiring real-time information collection in distributed real-time environments. In this scenario, information consumers require data from information sources at varying levels of accuracy and timeliness. To accommodate the diverse characteristics of information sources and varying requirements from information consumers, we use an information mediator to coordinate and facilitate communication between information sources and consumers. We develop algorithms for real-time request scheduling and directory service maintenance and compare our techniques with several other proposed strategies. Our studies indicate that the judicious composition of our proposed intelligent policies can improve the overall efficiency of the system. Furthermore, the proposed policies perform very well as the system scales in the number of information sources and consumer requests.