Blood pressure is a critical indicator of human health, making convenient and easy-to-use monitoring methods a significant research focus. Existing blood pressure monitoring techniques often rely on additional sensors or devices, imposing both usage and economic burdens on users. To tackle this problem, we propose MetaBP, a blood pressure monitoring system that leverages the camera on smart devices. The basic idea is to record a video when the fingertip covers the camera to capture the tiny skin color changes caused by the heartbeat and extract pulse signals from the video frames. Furthermore, MetaBP employs a meta-learning approach, enabling the model to quickly adapt to the blood pressure monitoring task with minimal data and develop personalized models. By leveraging meta-learning, the model can efficiently learn the unique features of an individual's physiological signals, providing accurate and personalized blood pressure estimation even with limited training samples. To obtain accurate measurements, we use a variational mode decomposition (VMD) method to reduce signal noise, ensuring that the pulse signals are both reliable and precise. To evaluate the robustness of MetaBP, we conduct experiments with 30 participants and implement MetaBP on commercial devices with camera parameters. The results demonstrate that MetaBP can accurately estimate systolic and diastolic blood pressure, with mean errors of 1.37 and 0.82 mmHg and standard deviations of 7.39 and 5.83 mmHg. Furthermore, our results demonstrate the feasibility of using widely available smart device cameras for reliable blood pressure monitoring, offering a practical solution for continuous health tracking.

serverless edge computing (SEC) seamlessly integrates edge computing with serverless computing, not only overcoming the limitations of resource-constrained edge nodes but also alleviating the high latency associated with cloud response. Due to the elastic scalability of serverless computing platforms, the cold start of latency-sensitive serverless functions (SFs) has become a significant challenge. Traditional strategies, such as resource reservation and prewarming, often suffer from low resource utilization. Meanwhile, offloading-based approaches simplify the problem by assuming a fixed high cold start delay cost, which is unsuitable for heterogeneous SEC scenarios. This article proposes a cold-start-aware offloading by double-dueling-DQN (CSODQN) model for SFs in a cloud-edge-device serverless computing system. The model creates an instance warming pool for SFs to enable reuse and allocates edge service node resources based on the priority of user and SFs, achieving multiobjective offloading optimization that considers cold starts. Our goal is to balance the frequency of cold start and resource utilization. To address the partially observable offloading optimization problem among agents, we employ a multiagent deep reinforcement learning approach. By introducing an priority of action-based sampling strategy, we accelerate the convergence of learning for each agent. Simulation results demonstrate that our method improves task success rates, reduces average task latency and cold start occurrences, and enhances resource utilization. Our approach alleviates the frequency of cold starts without excessively consuming system resources and costs, achieving long-term optimization of service quality, device energy consumption, and expenses.

As the serverless computing model is integrated into edge environments, it enhances the flexibility and scalability of IoT systems, significantly improving the Quality of Service (QoS) for Function as a Service (FaaS) users. However, serverless edge computing faces several challenges, with one primary issue being the latency caused by container cold starts. While container caching mitigates, determining optimal caching duration in resource-constrained edge environments requires balancing conflicting resource and latency costs. We formulate this fundamental trade-off through a ski-rental problem lens and propose the Prediction-Opinion based Caching Decision Algorithm (PCDA). Our solution introduces a Dual-phase caching with deterministic keep-alive windows and prediction-driven pre-warm windows, and a confidence metric λ that dynamically calibrates decision randomness against prediction errors. Extensive simulation experiments demonstrated that, compared to existing algorithms, PCDA performed best under various caching conditions, with an overall system cost reduction of 12.04% to 73.97%.

The current shortest path-based satellite routing algorithms do not consider the periodicity and persistence of links, resulting in high costs and unreliable maintenance of routing tables. In this regard, we propose a novel approach that leverages the periodicity and predictability of satellite constellation movements to rid repetitive route calculations and associated maintenance overheads across periods. Furthermore, within each period, we design a routing algorithm, named LMCRA (A Low Maintenance Cost and Reliable Routing Algorithm), which takes into account both the persistence and latency of inter-satellite links. Compared to traditional shortest path algorithms, LMCRA reduces the frequency of path switching caused by neighboring satellites moving out of communication range, thereby enhancing path stability, reducing maintenance costs, and maintaining low path latency. Additionally, a fast rerouting mechanism is considered to enhance the reliability of the routing algorithm. Simulation results demonstrate that, across various network configurations, time spans, and constellation densities, LMCRA significantly improves path stability compared to the conventional Dijkstra algorithm, while maintaining low latency costs.

Therapeutic advances in treating patients with multiple myeloma (MM), including novel immunotherapies, have improved the disease control, but it remains incurable. Although traditional immune check point inhibitors have shown limited clinical benefit, targeting alternative immune-inhibitory pathways may offer a novel way to address relapsed disease. Blockade of the immune regulator TIGIT was shown to enhance anti-tumor immunity in preclinical MM models. Beyond TIGIT, the DNAM-1 axis includes the novel inhibitory receptor PVRIG. In this study we evaluated the expression of DNAM-1 axis receptors and the function of PVRIG in bone marrow of individuals with MM, specifically highlighting PVRIG blockade as a potential therapeutic opportunity in combination with bi-specific T-cell engager (BiTE).

In recent years, with the widening applications of the Internet of Things (IoT), more and more perception services (e.g. air quality indicator services, road traffic congestion monitoring services, etc) with different arguments (e.g. data type, source location, creator, etc) will be deployed by dedicated IT infrastructure service providers for constructing customized IoT systems with low cost by subscription. So it is an indispensable step to check whether the required perception services with specified arguments have been available for the constructing IoT through discovery method to reduce the redundancy of service deployment. However, it is a challenging problem to design efficient (i.e. achieving high accuracy and low response delay with low overhead), highly robust, and trustworthy mechanisms for discovering perception services on resource-constrained IoT devices. To solve this problem, we proposed a distributed service discovery method, named VSA-SD, based on the Vector Symbolic Architecture (VSA). This method employs hyperdimensional vectors to describe services in a distributed manner, and measures the degree of service matching by calculating the Hamming distance, thereby achieving service discovery. We implemented VSA-SD in NBUFlow, which is an IoT task construction and offloading test platform, and evaluated its performance through comprehensive experiments. Results show that VSA-SD outperforms the centralized, hybrid, and other distributed service discovery mechanisms in terms of accuracy, response delay, overhead, robustness, trustability, interoperability, and mobility.

In satellite networks, existing congestion resolution methods do not consider the predictability and stability of paths, leading to frequent path switches and high maintenance costs. In this regard, we propose a novel congestion resolution approach, named MOLM, which introduces a continuous neighbor set during path updates. This set includes nodes capable of establishing sustainable connections with the predecessors and successors of congested nodes. Combined with a multi-objective simulated annealing framework, MOLM iteratively derives an optimal selection from this set to replace congested nodes. Additionally, we employ a Fast Reroute mechanism based on backup paths (FRR-BP) to address node failures. The simulation results indicate that the optimal node endows the new path with optimal path stability and path latency.

Unmanned Surface Vessel (USV) has been widely used in various fields due to its autonomous advantages, and path planning is a crucial technology for autonomy. However, using global path planning alone cannot avoid moving obstacles, while using local path planning alone may lead to falling into local minima and fail to reach the target. Therefore, this paper proposed the Dynamic Target Artificial Potential Field (DTAPF) algorithm which use a dynamic point that follows the global path generated by the A* algorithm as the target point of the Artificial Potential Field (APF). In addition, in order to improve the safety of USV navigation and response time of the traditional centralized path planning methods, we proposed an edge computing architecture for global path planning and an Offset Guidance method to avoid moving obstacles while confirming to the Collision Regulation (CORLEGs) for navigation safety. The experimental results show that, using the method proposed in this paper, USV can reach the target in an environment with moving obstacles with high probability (about 99.4%), and compared to traditional APF algorithm, our method can reduce collision probability by 71% with almost no increase in average path length and average navigation time. Besides, our architecture has much lower computing delay than local computing, and also lower than cloud computing.

The integration of biometric-based user authentication into wearable devices has become increasingly important for protecting users' private information and property. In this paper, we propose a two-factor authentication mechanism, PressHeart, which utilizes widely-used Photoplethysmography (PPG) sensors embedded in wearable devices. Our observations reveal that PPG sensors can implicitly measure excitation press signals when the users press the skin or the device, which implies individual wearing and behavioral habits that can serve as reliable factors for user authentication. For better separating the press signals from PPG signals and extracting sufficient signals for user authentication, we introduce two adaptive segmentation methods and a specific feature set for feature extraction in PressHeart. To validate the performance of Press Heart, we develop a prototype with a PPG sensor and conduct experiments involving 14 participants. The experiment results demonstrate that PressHeart can achieve an average of 94.9 % accuracy with high authentication efficiency and security.

Unmanned Surface Vessel (USV) is widely used in various fields, and obstacle avoidance is a key technology for USV. Accurately detecting the position and type of obstacles is crucial for obstacle avoidance. Active sensors can accurately detect the position of obstacles but cannot distinguish their types, necessitating the use of vision sensors. Therefore, this paper employs stereo vision and proposes a stereo obstacle detection and localization method, based on SGBM and YOLOv5. The SGBM algorithm generates depth information, while the YOLOv5 algorithm detects the position of obstacles in the image. To address the issue of errors in measuring the continuous position of obstacles, this paper uses a Kalman filter to smooth the trajectory. Experimental results show that the proposed method improves obstacle localization accuracy by approximately 38% compared to raw measurements. Besides, considering the energy constraints and computing capabilities of lightweight USV, which cannot meet the demands of processing vision tasks, this paper introduces an edge-device collaboration architecture, by offloading the USV computing tasks to edge nodes, thereby extending the computing capacity and energy efficiency of the USV. Experimental results indicate that the proposed architecture meets the real-time obstacle avoidance requirements of USV.

Mobility-aware devices are crucial components of Industrial Internet of Things (IIoT). However, they face limitations in terms of battery capacity and computation power, which restrict their ability to provide services requiring broad bandwidth and strong computation power for computation-intensive tasks. While offloading can strengthen device computation power, ineffective offloading decisions result from device mobility and limited adaptability to changes in environmental resources, or are not applicable to the current mobile edge computing (MEC) environment. In this paper, we address these challenges by proposing a mobility-aware computation offloading and task migration approach (MCOTM) based on trajectory and resource prediction to address this issue of mobility offloading, which minimizes task turnaround time and system energy consumption. Simultaneously, our approach enhances the decision agent continuously to decrease task migration rates. MCOTM uses Lagrange interpolation equations to determine the trajectory of mobile devices, and Long Short-Term Memory (LSTM) to track the time-varying resources characteristics in IIoT. These prediction results will be used to assist Deep Deterministic Policy Gradient (DDPG) for making online computation offloading, task migration and resource allocation decisions. Experimental results show that the proposed MCOTM effectively reduces task turnaround time by at least 42% and system energy consumption by 10% while maintaining a low task migration rate of around 50%, even with an increasing number of tasks.

Unmanned surface vessel (USV) has been widely used in various fields due to its autonomous advantages, and path planning is a crucial technology for autonomy. However, using global path planning alone cannot avoid moving obstacles, while using local path planning alone may lead to falling into local minima and fail to reach the target. Therefore, this article proposed the dynamic target artificial potential field (DTAPF) method which use a dynamic point that follows the global path generated by the A* algorithm as the target point of the artificial potential field (APF). In addition, in order to improve response time and safety of unmanned surface vessel (USV) navigation of the traditional centralized path planning methods, we proposed an edge computing architecture for global path planning and an offset guidance method to avoid moving obstacles while confirming to the collision regulations (CORLEGs). The experimental results show that, using the method proposed in this article, USV can reach the target in an environment with moving obstacles with high probability (about 99.4%), and compared to the traditional APF algorithm, our method can reduce collision probability by 71% with almost no increase in average path length and average navigation time. Besides, our architecture has much lower computing delay than local computing, and also lower than cloud computing.

With the popularity of wearable devices, verifying the user identity of wearable devices is critical for system security, especially when performing sensitive operations such as financial payments. In this article, we propose M-PPG, which uses photoplethysmography (PPG) measurements with motor vibrations to authenticate user identities. Specifically, our observations reveal a strong correlation between PPG measurements and users, such as the user's heartbeat characteristics and wearing habits reflected by PPG measurements are implicitly associated with users' identities, and the motor vibration provides stable excitation signals for better security performance. M-PPG requires zero user effort with a built-in sensor and actuator in most wearable devices, i.e., PPG sensor and motor. In addition, we reveal that indoor lighting strobe noise is an unavoidable noise in PPG-based authentication methods, and a noise reduction mechanism is designed to mitigate such phenomenon. Moreover, a multiple authentication scheme and an adaptive update scheme are proposed to keep a long-term authentication in M-PPG. The experimental results among 20 participants demonstrate that M-PPG achieves an average accuracy of 94.5% in daily situations. With a well-designed authentication mechanism, the pass rate of legitimate users can reach 99.9%, while the pass rate of attackers is only 0.6%.

TRAF6dn FITC-labeled peptide cultured with MM cell lines for 72 hours and monitored FITC positive MM cells using immunofluorescent microscopy (20X).

Evaluation of AKT signaling pathway in primary MM cells treated with a TRAF6dn inhibitory peptide.

LEDBAT++ is a novel less-than-best-effort congestion control algorithm. However, it still shows aggressiveness when competing with CUBIC in shallow buffer networks or with BBRv2 in low latency networks. In order to maintain its low-priority performance over diversified network environments, we propose PeaceKeeper, which combines reinforcement learning algorithm to dynamically adjust the target based on the network state. Extensive simulations show that, compared to LEDBAT++, the throughput of the primary flow competing with PeaceKeeper improved by 30.76% to 173.63%. Additionally, compared to heuristics adjusting the target, PeaceKeeper increases the link bandwidth utilization by 50.91%.

In recent years, wireless networks and applications have grown rapidly and converged across a wide variety of scenarios. More and more applications require wireless networks for high bandwidth and low latency. However, due to the attenuated propagation of wireless signals, bandwidth changes rapidly in a short period. TCP fails to work properly in such an environment and suffers from low network link utilization and high latency. To solve above problems, this paper proposes a receiver-driven congestion control framework, named NUiVa. NUiVa decouples the congestion avoidance phase of sender side congestion control and implements it on the receiver side. In addition, NUiVa uses one-way delay to detect network congestion and controls the sending rate of senders via the receiving window field in the packet header. We confirm that the throughput degradation caused by network flips can be mitigated by NUiVa. And the throughput of data transmission can be further improved by the design of the receiver’s algorithm. The evaluation results show that NUiVa improves the throughput of TCP stream by 10 to 23 percent in most cases and reduces the queuing delay by an average of 29 percent.

With the emergence of 4G/5G cellular networks, mobile Internet is becoming increasingly popular and numerous mobile applications have emerged, which puts higher demands on the accuracy of predicting available bandwidth. However, compared to WiFi and wired networks, the links of 4G/5G cellular networks are highly dynamic, and the unstable nature of their network environments makes the bandwidth trajectories vary. Traditional bandwidth prediction models rarely consider the bandwidth characteristics of various network scenarios, and a single prediction model is difficult to be applied to all scenarios, which makes it challenging to achieve high accuracy of available bandwidth prediction. To solve the above problems, we propose a real-time bandwidth prediction method called CapRadar. The method classifies bandwidth into scenarios and matches the optimal prediction model for each type of scenario, i.e., switches the prediction model in real time according to the changes of the scenario. Specifically, we first extract the statistical characteristics of the bandwidth using statistical method, and based on the extracted characteristics, a SVM classifier is used to detect different network scenarios. After that, the best prediction model is matched for them in the algorithm library based on the scenario classification results. The experimental results show that CapRadar can reduce the root mean square error (RMSE) by about 18.9% and the mean error (MAE) by about 21.5%. For practical applications, we use a pre-trained SVM model for real-time scenario detection, and then we can dynamically switch the prediction model.

The popularization and development of Wi-Fi and 5G networks have introduced new applications requiring high data rates and low latency. However, the vast random packet loss caused by mobility and channel conditions in wireless networks can worsen the performance of traditional TCP congestion control algorithms. Therein, BBR [1] was proposed in 2016, and claims to operate at the optimum point. BBR strives to match the congestion window to the bandwidth-delay product, calculated from the measured bottleneck bandwidth and round trip times. Through simulations, we found that BBR suffers from different degrees of throughput degradation for different loss rates. The improved BBR, Yinker, is proposed to address this issue. Precisely, we dynamically adjust BBR's pacing_gain based on the network conditions, including loss rate and congestion degree. We have evaluated Yinker in both real-world environments and trace-based emulations and compared its performance with different BBR variants and state-of-the-art schemes, including Cubic, Verus, and Copa. On average, TCP D*, BBR v2, Copa, and BBR have 4.24×, 2.34×, 2.01×, and 1.48× lower throughput compared to Yinker, respectively. This outstanding delay performance comes at little cost in latency. For instance, compared to TCP D* (which achieves the lowest latency), Yinker's latency is only about 3% more.

Background Multiple myeloma (MM) patients have variable responses to mRNA vaccination to COVID-19. Little is known regarding their vaccine-induced antibody levels over time. Methods We monitored spike IgG antibody levels over 24 weeks among a subset of 18 MM patients who showed a full response after two mRNA vaccinations. MM patients had a more rapid decline in antibody levels as compared to 8 healthy controls, with power law half-lives of 72 days (versus 107 days) and exponential half-lives of 37 days (versus 51 days). Results The patients with longer SARS-CoV-2 antibody half-lives were more likely to have undetectable monoclonal protein than those with shorter half-lives, suggesting better disease control may correlate with longer duration of vaccine-induced antibodies. Regardless, by 16 weeks post-second dose of mRNA vaccination, the majority of patients had antibody levels below 250 binding arbitrary units per milliliter, which would be unlikely to contribute significantly to preventing COVID-19. Conclusions Thus, even MM patients who respond adequately to vaccination are likely to require more frequent booster doses than the general population.