Federated learning coordinates distributed data sets to train models, which brings the significant impact of data selection on model performance. Personalized differential privacy, however, introduces heterogeneity into the vehicular data sets: the higher privacy protection may reduce the contribution of local models to model convergence. Therefore, the goal of this article is to dynamically optimize the combination of data sets to tackle the heterogeneity in differential private federated learning in Internet of Vehicles. This is extremely challenging without direct data access and a visible training process. Therefore, we propose an efficient hierarchical data selection method. First, the utility is evaluated using the convergence bound derived from the noise function and the cost function. Accordingly, a collection of high-value clients is selected to maximize the potential contribution of the combination to the global model. Then, we design an optimization function based on the unknown variables within the convergence bound and develop a low-complexity algorithm to approximate the sampling probability. Meanwhile, the aggregation weight of each model is adjusted to ensure unbiased estimation. Experimental results on two real-world trajectory data sets show that the scheme can reduce the meter error by 8.90% and 15.97%, respectively, and improve the convergence speed by 23.9% and 27.1%, respectively.

Due to the increasing number of electric vehicles and limited charging stations, charging service providers need to consider the minimum range of electric vehicles and the congestion level of charging stations to formulate pricing strategies and guide charging vehicles. However, this service faces the challenges of privacy protection and utility coupling between CSs and EVs. Besides, the minimum driving range is also affected by the time-varying data characteristics of vehicles. Therefore, this paper proposes an FL-assisted stable charging matching strategy considering congestion factors and range constraints. Specifically, we employ a federated learning scheme to predict the congestion factor of each charging station by learning the complex hidden characteristics of vehicles under the condition of privacy protection. Then, the pricing strategy and utility function are updated based on the congestion factor. Furthermore, under the driving range constraint, we transform the impact of the congestion factor on the expected utility into a regret value and use the Gale-Shapley algorithm based on regret minimization to achieve stable matching between vehicles and charging stations. Experimental results demonstrate the scheme’s effectiveness and that the charging success rate is improved by up to 11.7% compared with mainstream methods.

Physical layer cross-technology communication (PHY-CTC) opens new horizons for spectrum utilization and wireless cooperation in the crowded ISM band. Current PHY-CTC technologies can be divided into two categories: one aims for high communication reliability, but sacrifices compatibility with commodity devices and the other maintains compatibility but suffers dramatically limited reliability. The latter mainly leverages the WiFi OFDM signal to emulate the ZigBee signal, while the Cyclic Prefix (CP) in OFDM brings inevitable signal disturbance and errors. To address these issues, we present WibZig, the first WiFi-To-ZigBee PHY-CTC technology that achieves high reliability and is fully compatible with commodity devices. By carefully selecting a cluster of CCK codewords that exhibit similar phase characteristics to ZigBee chips, we can emulate any given ZigBee symbol with great accuracy. In addition, WibZig adaptively controls the first CCK codeword of each cluster to eliminate inter-cluster phase discontinuity when emulating a ZigBee packet with multiple clusters. WibZig requires no hardware modification and is compatible with most commodity devices. We implement WibZig on both USRP and commercial devices and conduct extensive evaluations under various settings, which demonstrate a 15x improvement in reliability and a 7x increase in range compared to the latest PHY-CTC work.

Partial label (PL) learning tackles the problem where each training instance is associated with a set of candidate labels, among which only one is the true label. In this paper, we propose a novel generative model PL-CGAN, which tackles the partial label learning problem with Conditional Generative Adversarial Nets (CGAN). Specially, PL-CGAN introduces a teacher model to refine a more reliable soft label vector of each training instance by iteratively ensembling the current learned prediction network with the formal one in an online manner. Besides, it adopts a MixUp data augmentation scheme to prevent the prediction network from overfitting to the noisy labels. In addition, it deploys a CGAN to generate training instances with its corresponding label vectors, which form a feature alignment based on consistency cost to enhance it’s label refinement capacity. Extensive experiments are conducted on synthesized and real-world partial label learning datasets, while the proposed approach demonstrates the state-of-the-art performance for partial label learning.

Incidents in online service systems could incur poor user experience and tremendous economic loss. To reduce the influence of incidents and guarantee service reliability, it is critical to identify root-cause metrics for engineers with clues to assist incident diagnosis. However, it is a challenging task due to the complicated dependencies and huge volume of various metrics in large-scale systems. Existing approaches are based on either anomaly detection or correlation analysis, performing not well in terms of accuracy or efficiency. To better understand the problem of root-cause metric identification, we conduct a preliminary study based on real-world data analysis and interactions with engineers. The key observation is that root-cause metrics should satisfy two requirements. One is that the metric is expected to behave abnormally during the incident; the other is that the anomaly pattern should meet physical meaning and engineers' demand. Motivated by the findings obtained from the study, we propose an effective approach named PatternMatcher to identifying root-cause metrics accurately. Specifically, PatternMatcher contains three steps, where coarse-grained anomaly detection aiming to filter out normal metrics, anomaly pattern classification aiming to filter out unimportant anomaly patterns, and root-cause metric ranking. An extensive study on four real-world datasets including 113 incident cases from a large commercial bank demonstrates that PatternMatcher outperforms all baseline approaches, achieving top-3 average accuracy of 0.91. Moreover, we have deployed PatternMatcher in practice and shared some successful cases from real deployment.

Much recent effort has been devoted to employing Unmanned Aerial Vehicles (UAVs) to implement airport-related tasks. However, a critical issue, collision avoidance, must be fully considered in this scenario. Herein, we study the efficient UAV navigation problem considering the safety issue near an airport. In detail, we first define the safe separation between the UAV and airplanes according to related aviation regulations. Thereafter, an effective tree-based scheme for navigating the UAV has been proposed to cope with the extra uncertainties induced by keeping the safe separation. An analytical derivation of the UAV's flying time is conducted to determine the optimal battery life. Extensive simulation is conducted to verify our proposed navigation scheme and the analytical derivation.

Visual-based Unmanned Aerial Vehicles (UAVs) (e.g. equipped with an optical camera) have become more popular in daily life, because of their flexibility and convenience in capturing images/videos. Existing works mainly focus on the image/video capturing efficiency, but overlook the privacy violations that may be caused by the misuse of UAVs. In this paper, we study the Privacy Preserving Navigation (PPN) problem of the path planning of a UAV in 3D space to cover a 2D Target Area (TA), so that TA is covered while the privacy of sub-areas within TA is violated. We prove the PPN is NP-hard and propose a heuristic solution to PPN. The real word data trace driven emulation results show our solution is effective.

Light-to-Camera Communications (LCC) have emerged as a new wireless communication technology with great potential to benefit a broad range of applications. However, the existing LCC systems either require cameras directly facing to the lights or can only communicate over a single link, resulting in low throughputs and being fragile to ambient illuminant interference. We present HYCACO, a novel LCC system, which enables multiple light emitting diodes (LEDs) with an unaltered camera to communicate via the non-line-of-sight (NLoS) links. Different from other NLoS LCC systems, the proposed scheme is resilient to the complex indoor luminous environment. HYCACO can decode the messages by exploring the mixed reflected optical signals transmitted from multiple LEDs. By further exploiting the rolling shutter mechanism, we present the optimal optical frequencies and camera exposure duration selection strategy to achieve the best performance. We built a hardware prototype to demonstrate the efficiency of the proposed scheme under different application scenarios. The experimental results show that the system throughput reaches 4.5 kbps on iPhone 6s with three transmitters. With the robustness, improved system throughput and ease of use, HYCACO has great potentials to be used in a wide range of applications such as advertising, tagging objects, and device certifications.

This paper identifies two energy saving opportunities of Wi-Fi interface emerged during smartphone's screen-off periods. Exploiting the opportunities, we propose a new power saving strategy, BackPSM, for screen-off Wi-Fi communications. BackPSM regulates client to send and receive packets in batches and coordinates multiple clients to communicate at different slots (i.e., beacon interval). The core problem in BackPSM is how to coordinate client without incurring extra traffic overheads. To handle the problem, we propose a novel paradigm, Out-of-Band Communication (OBC), for client-to-client direct communications. OBC exploits the Traffic Indication Map (TIM) field of Wi-Fi Beacon to create a free side-channel between clients. It is based upon the observation that a client may control 1 → 0 appearing on TIM bit by locally regulating packet receiving operations. We adopt this 1 → 0 as the basic signal, and leverage the time length in between two signals to encode information. We demonstrate that OBC can be used to convey coordination information with close to 100% accuracy. We have implemented and evaluated BackPSM on a testbed. The results show that BackPSM can decode the traffic pattern of peers reliably using OBC, and establish collision-free schedules fast to achieve out-ofband coordination of client communications. BackPSM reduces screen-off energy by up to 60% and outperforms the state-ofthe-art strategies by 16%-42%.

Unmanned Aerial Vehicles (UAVs) have wide applications in many fields, e.g. multiple Unmanned Aerial Vehicles (UAVs) cooperatively tracking multiple targets. This paper studies the multiple UAVs cooperatively tracking multiple targets by vision surveillance system, where the images/videos of targets have direction requirements. One target is covered by a UAV if and only if its position is within the Field Of View (UAV) as well as the UAV is within a requested angle of the target's face direction. The objective is to maximize the total covered targets number by the UAVs, which can not be solved by existing models. A simple effective distributed, online cooperation algorithm for this problem is designed in this paper. The theoretical analysis shows our algorithm achieves constant factor to the optimal.

This paper identifies two energy saving opportunities of Wi-Fi interface emerged during smartphone's screen-off periods. Exploiting the opportunities, we propose a new power saving strategy, BackPSM, for screen-off Wi-Fi communications. BackPSM regulates client to send and receive packets in batches and coordinates multiple clients to communicate at different slots (i.e., beacon interval). The core problem in BackPSM is how to coordinate client without incurring extra traffic overheads. To handle the problem, we propose a novel paradigm, Out-of-Band Communication (OBC), for client-to-client direct communications. OBC exploits the TIM (Traffic Indication Map) field of Wi-Fi Beacon to create a free side-channel between clients. It is based upon the observation that a client may control 1 → 0 appearing on TIM bit by locally regulating packet receiving operations. We adopt this 1 → 0 as the basic signal, and leverage the time length in between two signals to encode information. We demonstrate that OBC can be used to convey coordination information with close to 100% accuracy. We have implemented and evaluated BackPSM on a testbed. The results show that BackPSM reduces screen-off energy by up to 60%, and outperforms state-of-the-art strategies by 16%-42%.

Multi-hop over-the-air reprogramming is essential for remote installation of software patches and upgrades in wireless sensor networks (WSNs). Several recent coding-based reprogramming protocols have been proposed to enable efficient code dissemination in high packet loss environments. An accurate and formal analysis of the performance of these protocols, however, has not been studied sufficiently in the literature. In this paper, we present a novel high-fidelity analytical model based on the shortest path algorithm to measure the completion time by incorporating overhearing and packet coding. This model can be applied to any coding-based reprogramming protocol by substituting the coding part with protocol specific operations. We conduct extensive testbed experiments to evaluate the performance of our proposed model. Based on the analytical and numerical experiments, we find that 1) overhearing causes significant reduction of the completion time in dense wireless sensor networks, particularly, it reduces 50-70 percent of the total completion time when the packet reception rate is 0.896; 2) coding delay plays a key role in the total completion time compared to the communication delay when the packet coding parameters are selected appropriately, for example, the communication delay is about 65 percent of the coding delay when the number of packets per page is 16 for the finite field size 28; 3) the total completion time can be minimized when the number of packets per page is close to 24 and the finite field size is close to 24.

This paper presents ScaPSM (i.e., Scalable Power-Saving Mode Scheduler), a design that enables scalable competing background traffic scheduling in crowd event 802.11 deployments with Power-Saving Mode (PSM) radio operation. ScaPSM prevents the packet delay proliferation of previous study, if applied in the crowd events scenario, by introducing a new strategy of adequate competition among multiple PSM clients to optimize overall energy saving without degrading packet delay performance. The key novelty behind ScaPSM is that it exploits delay-aware load balance to control judiciously the qualification and the number of competing PSM clients before every beacon frame’s transmission, which helps to mitigate congestion at the peak period with increasing the number of PSM clients. With ScaPSM, the average packet delay is bounded and fairness among PSM clients is simultaneously achieved. ScaPSM is incrementally deployable due to only AP-side changes and does not require any modification to the 802.11 protocol or the clients. We theoretically analyze the performance of ScaPSM. Our experimental results show that the proposed design is practical, effective, and featuring with significantly improved scalability for crowd events.

Energy balance is a critical issue in wireless sensor networks. Several mixed data transmission (MDT) schemes have been proposed to achieve energy balance. However, most existing works are lack of theoretical study, especially understanding the relationship between network-wide energy balancing and lifetime optimization. In this paper, we conduct comprehensive theoretical analysis to the two-level based MDT scheme when applying to network-wide energy balancing, and eventually to maximize the network lifetime. We propose a novel network model, named energy balance area (EBA), and formally analyze its characteristics under the two-level based MDT scheme. To maximize the network lifetime, we convert the transmission probability allocation problem in the MDT scheme into an EBA partitioning (EBA-PT) problem, which is shown to be NP-hard. We then propose a heuristic approximation algorithm to determine the optimal configuration of EBAs, which is proven in this paper to be the key for maximizing the network lifetime. In this way, we obtain a near-optimal result. Our experimental studies show that network lifetime can be further improved as compared the hop-by-hop and the two-level based MDT schemes.

Neighbor discovery is a fundamental service for initialization and managing network dynamics in wireless sensor networks and mobile sensing applications. In this paper, we present a novel design principle named Talk More Listen Less (TMLL) to reduce idle-listening in neighbor discovery protocols by learning the fact that more beacons lead to fewer wakeups. We propose an extended neighbor discovery model for analyzing wakeup schedules in which beacons are not necessarily placed in the wakeup slots. Furthermore, we are the first to consider channel occupancy rate in discovery protocols by introducing a new metric to trade off among duty-cycle, latency and channel occupancy rate. Guided by the TMLL principle, we have designed Nihao, a family of energy-efficient asynchronous neighbor discovery protocols for symmetric and asymmetric cases. We compared Nihao with existing state of the art protocols via analysis and real-world testbed experiments. The result shows that Nihao significantly outperforms the others both in theory and practice.

This paper presents ToneSense, a new paradigm for communication between devices using different wireless technologies. ToneSense encodes information into the transmission power levels of regular frames. Receivers sense the power strength and decode as meaningful information bits. Evaluations show that ToneSense achieves ≤ 4% symbol error rate. Our work sheds light on wireless coexistence problems and cross-technology communications.

Over-the-air reprogramming is a key service in wireless sensor networks. The service can disseminate a new code image to every sensor node in the network. For security reasons, every code image must be authenticated to prevent an attacker from installing its code to the network. In this paper, an authentication method named Hierarchical Hash Tree (HHT) is proposed to reduce the overheads of Sreluge, which is a reprogramming protocol based on rateless codes. HHT is a composed structure including two layers of Merkle Tree. The pages from code image are used to construct small hash trees in bottom. For reducing communication overhead, the roots of bottom trees are aggregated into root fingerprints, which are used to build top tree. Then, the security is analysed for proposed method and mathematical analysis are provided for the HHT overheads. Furthermore, we implement pages authentication using HHT in Sreluge. Experimental results show that our method can cut pages authentication overhead by at least half that of Sreluge for more than 3-KByte code image. And dissemination completion time of our method is about 60% that of Sreluge.

Multi-hop over-the-air reprogramming is essential for the remote installation of software patches and upgrades in wireless sensor networks (WSNs). Recently, coding-based reprogramming protocols are proposed to address efficient code dissemination in environments with high packet loss rate. The problem of analyzing the performance of these protocols, however, has not been explored in the literature. In this paper, we present a high-fidelity analytical model based on Dijkstra's shortest path algorithm to measure the completion time of coding-based reprogramming protocols. Our model takes into account not only page pipelining and negotiation, but also coding computation. Results from extensive simulations of a representative coding-based reprogramming protocol called Rateless Deluge are in good agreement with the performance predicted by our model, thus validating our approach. Our analytical results show both the number of packets per page and the finite field size have significant impact on completion time. Most notably, the time overhead of coding computation exceeds that of communication when the number of packets per page is 24 and the finite field size is at least 24.

Data fusion or In-network processing methods were often adopted in Wireless Sensor Networks (WSNs) to reduce data communication and prolong network lifetime, which made WSNs application can be described as a set of tasks (sensing, processing) and dependencies among them. Task assignment has become a

The event-driven nature of wireless sensor networks (WSNs) leads to unpredictable network load. As a result, congestion may occur at sensors that receive more data than they can forward, which causes energy waste, throughput reduction and packet loss. In this paper, we propose a rate-based fairness-aware congestion control protocol (FACC), which controls congestion and achieves approximately fair bandwidth allocation for different flows. In FACC, we categorize intermediate relaying sensor nodes into near-source nodes and near-sink nodes. Near-source nodes maintain per-flow state, and allocate an approximately fair rate to each passing flow. On the other hand, near-sink nodes do not need to maintain per-flow state and use a light-weight probabilistic dropping algorithm based on queue occupancy and hit frequency. Our simulation results and analysis show that FACC provides better performance than previous approaches in terms of throughput, packet loss, and fairness.