Recent years, the rapid development of Internet of Things (IoT) technologies and applications have been witnessed. Three important features are characterized in modern IoT applications: 1) device heterogeneity; 2) long-range communication; and 3) cloud/edge-device integration. Difficulties are raised by the above features toward IoT application developers, e.g., predicting and evaluating the performance of the entire IoT application system. To deal with the above difficulties, we design and implement an IoT simulator, TinySim, which satisfies the requirements of high fidelity, high scalability, and seamless transplantation. TinySim takes advantage of the hardware-independent features of TinyLink programming language. Hence, a similar code can be used for both simulation and execution on real hardware platforms. Many virtual IoT devices can be simulated by TinySim at the PC end. These IoT devices can send or receive messages from the cloud or smartphones, making it possible for the developers to evaluate the entire system without the actual IoT hardware. We connect TinySim with Unity 3-D to provide high interactivity. To reduce the event synchronization overhead between TinySim and Unity 3-D, a dependence graph-based approach is proposed. We design an approximation-based approach to reduce the number of simulation events, greatly speeding up the simulation process. We carefully evaluate TinySim using benchmarks and two concrete case studies. TinySim can simulate representative IoT applications, such as smart flowerspot and shared bikes. We conduct extensive experiments to evaluate the performance of TinySim. Results show that TinySim can achieve high accuracy with an error ratio lower than 9.52% in terms of energy and latency. Further, TinySim can simulate 4000 devices within 11.2 physical-minutes for ten simulation-minutes, which is about$3\times $faster than the state-of-art approach.

Many IoT applications have the requirements of conducting complex IoT events processing (e.g., speech recognition) that are hardly supported by low-end IoT devices due to limited resources. Most existing approaches enable complex IoT event processing on low-end IoT devices by statically allocating tasks to the edge or the cloud. In this article, we present Queec, a QoE-aware edge computing system for complex IoT event processing under dynamic workloads. With Queec, the complex IoT event processing tasks that are relatively computation-intensive for low-end IoT devices can be transparently offloaded to nearby edge nodes at runtime. We formulate the problem of scheduling multi-user tasks to multiple edge nodes as an optimization problem, which minimizes the overall offloading latency of all tasks while avoiding the overloading problem. We implement Queec on low-end IoT devices, edge nodes, and the cloud. We conduct extensive evaluations, and the results show that Queec reduces 56.98% of the offloading latency on average compared with the state-of-the-art under dynamic workloads, while incurring acceptable overhead.

The recent years have witnessed the rapid growth of IoT (Internet of Things) applications. A typical IoT application usually consists of three essential parts: the device side, the cloud side, and the client side. The development of a complete IoT application is very difficult for non-expert developers because it involves drastically different technologies and complex interactions between different sides. Unlike traditional IoT development platforms which use separate approaches for these three sides, we present TinyLink 2.0, an integrated IoT development approach with a single coherent language. It achieves high expressiveness for diverse IoT applications by an enhanced IFTTT rule design and a virtual sensor mechanism which helps developers express application logic with machine learning. Moreover, TinyLink 2.0 optimizes the IoT application performance by using both static and dynamic optimizers, especially for resource-constrained IoT devices. We implement TinyLink 2.0 and evaluate it with eight case studies, a user study, and a detailed evaluation of the proposed programming language as well as the performance optimizers. Results show that TinyLink 2.0 can speed up IoT development significantly compared with existing approaches from both industry and academia, while still achieving high expressiveness.

Rapid development is essential for IoT (Internet of Things) application developers to obtain first-mover advantages and reduce the development cost. In this article, we present TinyLink, a holistic system for rapid development of IoT applications. The key idea of TinyLink is to use a top-down approach for designing both the hardware and the software of IoT applications. Developers write the application code in a C-like language to specify the key logic of their applications, without dealing with the details of the specific hardware components. Taking the application code as input, TinyLink automatically generates the hardware configuration as well as the binary program executable on the target hardware platform. TinyLink provides unified APIs for applications to interact with the underlying hardware components. We implement TinyLink and evaluate its performance using real-world IoT applications. Results show that (1) TinyLink achieves rapid development of IoT applications, reducing 52.58% of lines of code on average compared with traditional approaches; (2) TinyLink searches a much larger design space and thus can generate a superior solution for the hardware configuration, compared with the state-of-the-art approach; (3) TinyLink incurs acceptable overhead in terms of execution time and program memory.

Rapid development is essential for IoT (Internet of Things) application developers to obtain first-mover advantages and reduce the development cost. In this paper, we present TinyLink, a holistic system for rapid development of IoT applications. The key idea of TinyLink is to use a top-down approach for designing both the hardware and the software of IoT applications. Developers write the application code in a C-like language to specify the key logic of their applications, without dealing with the details of the specific hardware components. Taking the application code as input, TinyLink automatically generates the hardware configuration as well as the binary program executable on the target hardware platform. TinyLink provides unified APIs for applications to interact with the underlying hardware components. We implement TinyLink and evaluate its performance using real-world IoT applications. Results show that: (1) TinyLink achieves rapid development of IoT applications, reducing 52.58% of lines of code in average compared with traditional approaches; (2) TinyLink searches a much larger design space and thus can generate a superior solution for the hardware configuration, compared with the state-of-the-art approach; (3) TinyLink incurs acceptable overhead in terms of execution time and program memory.

Rapid prototyping of IoT platforms is essential for developers to obtain first-mover advantage, validate feasibility of innovative ideas and the key technologies. In this paper, we present TinyLink, an approach for rapid and cost-effective prototyping of IoT platforms. With TinyLink, developers can specify the key platform functionalities and let TinyLink deal with the details of hardware components. Then TinyLink creates user constraints from the functionalities, as well as the inherent hardware constraints. The high level goal is to automatically select the hardware components so that they can satisfy the user requirements with the lowest cost. TinyLink solves the optimization problem and outputs a list of hardware components. We implement TinyLink and evaluate it using real-world IoT platform requirements. Results show that TinyLink achieves a lower cost compared with an existing IoT application, without affecting the functionalities of the platform.

We introduce Mosaic, a mobile sensor network system for city scale sensing. Currently, we focus on accurate PM2.5 measurements. We design and implement low-cost smart sensors for this purpose. Those sensors are specifically designed for moving vehicles such as buses and taxies. The mobile sensing capability of our system enables a wide coverage of a city with a modest number of sensors. We incorporate a three-layer architecture in Mosaic. Mosaic faces many practical challenges which need to be properly addressed, e.g., reliable node hardware and software design, and accurate PM2.5 measurements. We have built three versions of Mosaic nodes. We present a novel data calibration approach combining traditional Artificial Neural Network (ANN) and Support Vector Machines (SVMs). We launch real-world deployments of Mosaic in two main cities in China -- Hangzhou and Ningbo. Our initial efforts show that Mosaic is a feasible approach for city scale sensing and our system can produce highly accurate PM2.5 measurements.

Wireless link correlation can greatly affect the performance of wireless protocols such as flooding, and opportunistic routing. Researchers have proposed a variety of approaches to optimize existing protocols exploiting link correlation. Most existing works directly measure link correlation using packet-level transmissions and receptions. Measurement alone is insufficient because it lacks predictive power and scalability. In this paper, we present CorModel, a model for predicting link correlation in low-power wireless networks. Based on the underlying causes of link correlation, we explore four easily measurable parameters for our modeling. Besides PHY-layer parameters that previous studies have explored, we find that network-layer parameters can also have significant impact on link correlation. We validate our model and illustrate its usefulness by integrating it into existing protocols for more accurate correlation estimation. Experimental results show that our model can significantly increase the accuracy of wireless link estimation, resulting in better protocol performance.

In wireless sensor networks, sensor nodes are usually self-organized, delivering data to a central sink in a multi-hop manner. Reconstructing the per-packet routing path enables fine-grained diagnostic analysis and performance optimizations of the network. The performances of existing path reconstruction approaches, however, degrade rapidly in large scale networks with lossy links. In this paper, we propose Pathfinder, a robust path reconstruction method against packet losses as well as routing dynamics. At the node side, Pathfinder exploits temporal correlation between a set of packet paths and efficiently compresses the path information using path difference. At the PC side, Pathfinder infers packet paths from the compressed information and employs intelligent path speculation to reconstruct the packet paths with high reconstruction ratio. We evaluate several variations of Pathfinder as well as two most related approaches using traces from a large scale deployment and extensive simulations. Results show that Pathfinder outperforms existing approaches, achieving both high reconstruction ratio and low transmission overhead.