Unmanned Aerial Vehicles (UAVs) or drones are increasingly used for urban applications like traffic monitoring and construction surveys. Autonomous navigation allows drones to visit waypoints and accomplish activities as part of their mission. A common activity is to hover and observe a location using on-board cameras. Advances in Deep Neural Networks (DNNs) allow such videos to be analyzed for automated decision making. UAVs also host edge computing capability for on-board inferencing by such DNNs. To this end, for a fleet of drones, we propose a novel Mission Scheduling Problem (MSP) that coschedules the flight routes to visit and record video at waypoints, and their subsequent on-board edge analytics. The proposed schedule maximizes the utility from the activities while meeting activity deadlines as well as energy and computing constraints. We first prove that MSP is NP-hard and then optimally solve it by formulating a mixed integer linear programming (MILP) problem. Next, we design two efficient heuristic algorithms, JSC and VRC, that provide fast sub-optimal solutions. Evaluation of these three schedulers using real drone traces demonstrate utility-runtime trade-offs under diverse workloads.

In this paper, we study the deployment of Unmanned Aerial Vehicles (UAVs) to collect data from IoT devices, by finding the data collection tour of each UAV. To ensure the 'freshness' of the collected data, a strict requirement is that the total time spent in the tour of each UAV, which consists of UAV flying time and data collection time, must be no greater than a given maximum data collection delay B, e.g., 20 minutes. In this paper, we consider a problem of using the minimum number of UAVs and finding their data collection tours, subject to the constraint that the total time spent in each tour is no greater than B. We study two variants of the problem, one is that a UAV needs to fly to the location of each IoT device to collect its data; the other variant is that a UAV is able to collect the data of the IoT device as long as their Euclidean distance is no greater than a given wireless transmission range. For the first variant of the problem, we propose a novel 4-approximation algorithm, which improves the best approximation ratio 32/7 so far. For the second variant, we design the first constant factor approximation algorithm. In addition, we evaluate the performance of the proposed algorithms via extensive experiments, and experimental results show that the average numbers of UAVs deployed by the proposed algorithms are from 11% to 19% less than those by existing algorithms.

Unmanned aerial vehicles (UAVs) have brought numerous potentials to establish flexible and reliable emergency networks in disaster areas when terrestrial communication infrastructures go down. Nevertheless, potential security threats may occur on UAVs during data transmissions due to the untrustful environment and open-access UAV networking. Moreover, UAVs typically have limited battery and computation capacity, making them unaffordable to execute heavy security provisioning operations when carrying out complicated rescue tasks. In this paper, we develop RescueChain, a secure and efficient information sharing scheme for UAV-aided disaster rescue. Specifically, we first implement a lightweight blockchain-based framework to safeguard data sharing under disasters and immutably trace misbehaving entities. A reputation-based consensus protocol is devised to adapt the weakly connected environment with improved consensus efficiency and promoted UAVs' honest behaviors. Furthermore, we introduce a novel vehicular fog computing based off-chain mechanism by leveraging ground vehicles as moving fog nodes to offload UAVs' heavy data processing and storage tasks. To optimally stimulate vehicles to share their idle computing resources, we also design a two-layer reinforcement learning-based incentive algorithm for UAVs and ground vehicles in the highly dynamic networks. Simulation results show that RescueChain can effectively accelerate consensus process, enhance user payoffs, and reduce delivery latency, compared with representative existing approaches.

Nowadays, aerial and ground robots, wearable and portable devices are becoming smaller, lighter, cheaper, and thus popular. It is now possible to utilize tens and thousands of them to form a swarm to complete complicated cooperative tasks, such as searching, rescuing, mapping, and battling. A swarm usually contains a large number of robots or devices, which are in short distance to each other and may move dynamically. So this paper studies the dynamic and dense swarms. The ultra-wideband (UWB) technology is proposed to serve as the fundamental technique for both networking and localization, because UWB is so time sensitive that an accurate distance can be calculated using timestamps of the transmit and receive data packets. A UWB swarm ranging protocol is designed in this paper, with key features: simple yet efficient, adaptive and robust, scalable and supportive. This swarm ranging protocol is introduced part by part to uncover its support for each of these features. It is implemented on Crazyflie 2.1 drones, STM32 microcontrollers powered aerial robots, with onboard UWB wireless transceiver chips DW1000. Extensive real world experiments are conducted to verify the proposed protocol with a total of 9 Crazyflie drones in a compact area.

To scale the Internet of Things (IoT) beyond a single home or enterprise, we need an effective mechanism to manage the growth of data, facilitate resource discovery and name resolution, encourage data sharing, and foster cross-domain services. To address these needs, we propose a GlObaL Directory for Internet of Everything (GOLDIE). GOLDIE is a hierarchical location-based IoT directory architecture featuring diverse user-oriented modules and federated identity management. IoT-specific features include discoverability, aggregation and geospatial queries, and support for global access. We implement and evaluate the prototype on a Raspberry Pi and Intel mini servers. We show that a global implementation of GOLDIE could decrease service access latency by 87% compared to a centralized-server solution.

Programming a complete IoT application usually requires separated programming for device, edge and/or cloud sides, which slows down the development process and makes the project hardly portable. Existing solutions tackle this problem by proposing a single coherent language while leaving two issues unsolved: efficient migration among the three sides and the platform dependency of the binaries. We propose WIPROG, an integrated approach to IoT application programming based on WebAssembly. WIPROG proposes an edge-centric programming approach that enables developers to write the IoT application as if it runs on the edge. This is achieved by the peripheral-accessing SDKs and annotations specifying the computation placement. WIPROG automatically processes the program to insert auxiliary code and then compile it to WebAssembly. At runtime, WIPROG leverages dynamic code offloading with compact memory snapshotting to achieve efficient execution. WIPROG also provides interfaces for the customization of offloading policies. Results on real-world applications and computation benchmarks show that WIPROG achieves an average reduction by 18.7%~54.3% and 20.1%~57.6% in terms of energy consumption and execution time.

Current smart home IoT systems utilize trigger-action platforms, e.g., IFTTT, to manage devices from various vendors. These platforms deploy user-defined rules for automation among devices. However, these platforms may be abused by triggering malicious rule execution with forged IoT devices or events violating the execution integrity and the intentions of the users. To address this issue, we propose a ledger-based IoT platform called Ruledger, which ensures correct execution of rules by verifying the authenticity of the corresponding information. Ruledger utilizes smart contracts to enforce verifying the information associated with rule executions, e.g., the user and configuration information from users, device events, and triggers in the trigger-action platforms. In particular, we develop three algorithms to enable ledger-wallet based applications for Ruledger and guarantee that the records used for verification are stateful and correct. Thus, execution integrity of rules is ensured even if devices and platforms in the smart home systems are compromised. We prototype Ruledger in a real IoT platform, i.e., IFTTT, and evaluate the performance with various settings. The experimental results demonstrate Ruledger incurs an average of 12.53% delay, which is acceptable for smart home systems.

Ultra-low-power communication is critical for supporting the next generation of battery-operated or energy harvesting battery-less Internet of Things (IoT) devices. Duty cycling protocols and wake-up receiver (WuRx) technologies, and their combinations, have been investigated as energy-efficient mechanisms to support selective, event-driven activation of devices. In this paper, we go one step further and show how WuRx can be used for an efficient and multi-purpose low-power downlink (LPD) communication channel. We demonstrate how to (a) extend the wake-up signal to support low-power flexible and extensible unicast, multicast, and broadcast downlink communication and (b) utilize the WuRx-based LPD to also improve the energy efficiency of uplink data transfer. In addition, we show how the non-negligible energy overhead of conventional microcontroller based decoding of LPD communication can be substantially reduced by using the low-power universal asynchronous receiver/transmitter (LPUART) module of modern microcontrollers. Via experimental studies, involving both a functioning prototype and larger-scale simulations, we show that our proposed approach is compatible with conventional WLAN and offers a two-orders-of-magnitude improvement in uplink throughput and energy overheads over a competitive, IEEE 802.11 PSM-based baseline. This new LPD capability can also be used to improve the RF-based energy harvesting efficiency of battery-less IoT devices.