The Ubiquitous Operating System(UOS) is a new type of operating system in response to the new patterns and scenarios of future human-cyber-physical ternary ubiquitous computing. Compared with traditional operating systems, one of the fundamental requirements of UOS is to adaptively manage numerous heterogeneous sensors according to dynamic environments and diverse tasks. However, traditional management focuses on the sensors’ parameters and interfaces without highlighting the perception effect that is users’ concern and dynamic changing. It also lacks a unified management approach for heterogeneous sensors. To overcome the limitations, we propose a novel heterogeneous sensors dynamic management model UbiCap, i.e., Ubiquitous Capability, which is based on the capability abstraction and adaptive run-time capability management mechanism. The capability provides a unified abstract for heterogeneous sensors. The adaptive run-time capability management mechanism transfers the management object from low-level hardware sensors to high-level sensing capability. The capability required and the available capability are matched to support run-time adaptive sensors selection. We implement a software prototype iS2ROS(intelligent Sensor Selection Robot Operating System) based on the UbiCap model. We then simulate a forest fire spot monitoring scenario where iS2ROS selects the optimal image sensor during the identification task execution while light or weather condition changes. Experiment results show that the iS2ROS achieves comparative sensing effectiveness through UbiCap with 50% power consumption lower compared to the traditional both-sensors approach.

Signa1 quality assessment (SQA) is an important topic in the field of wearable electrocardiogram (ECG) monitoring. Existing ECG SQA models focus on the intra-patient paradigm where the training and testing data are from same individuals. However, due to the individual differences in ECG morphology, features extracted from the the training patients may not be applicable to the new patient. Therefore, these models may suffer severe performance degradation in the inter-patient paradigm which is closer to the reality. In this paper, we propose a novel adaptive ECG SQA model called AdapSQA for the inter-patient paradigm using unsupervised domain adaptation in order to enhance its feature extraction adaptability to the new patient. To realize our AdapSQA, a lightweight baseline model for ECG SQA is first built for better feature extraction in wearable systems. Then, a domain adaptation layer is introduced to align the feature distribution of the training patients and the the new patient by minimizing the distance between the two domains. In this way, a baseline model can be adaptive to a new patient without extra annotation. To evaluate the proposed model, a patient-specific ECG Noise Dataset was generated based on the public datasets since there is no public open source of interest. Experimental results demonstrate that our proposed AdapSQA outperforms state-of-the-art approaches in term of the average inter-patient accuracy to 93.67% with a smaller standard deviation of 4.41%, and is able to achieve lightweight deployment for wearable systems.

Early detection of melanocytes can save lives from melanoma. Most individuals can’t be professionally diagnosed since it’s time-consuming, costly, and inconvenient. Smartphonebased early skin cancer diagnosis has emerged as a new approach. The existing computer-aided skin cancer diagnosis methods and mobile deep learning technology have been studied, and it is found that the existing smartphone-based skin cancer detection and identification methods rely on the support of background cloud services. Accuracy, reaction time, and patient data confidentiality are issues. A novel early detection and recognition model of melanoma skin cancer based on mobile deep learning, Melanlysis, is proposed. The model uses the EfficientNetLite-0 deep learning model to have low latency and considers the imbalance of the existing open-source skin image dataset. The proposed classification model is implemented and evaluated. Experimental results show that compared with the existing EfficientNetLite-0, MobileNet V2, and ResNet-50 models, the accuracy of correctly identifying malignant or non-melanoma is over 94%. At the same time, an Android application based on this mobile deep learning model was developed to diagnose potential malignant melanoma. Users can quickly obtain the classification results of melanoma through the application.

New generation airborne embedded system has deployed Graphical Processing Units (GPUs) to raise processing capability to meet growing computational demands. Comparing with the cloud system, the airborne embedded system usually has a fixed application set, but strict real-time constraints. Unfortunately, the inherent GPU scheduler does not consider the application priority, which cannot provide the sufficient real-time capability to the airborne embedded system. To meet timeliness requirements, it is necessary to predict timing behaviors of those applications and design a real-time scheduling policy based on priority and deadline. We therefore propose WAMP2S, a workload-aware GPU performance model based pseudo-preemptive real-time scheduling algorithm for the airborne embedded system. The workload-aware GPU performance model can accurately predict the execution time of an application, which is running concurrently with other applications on GPU. The pseudo-preemptive real-time scheduling algorithm can provide the approximate preemption by dynamically adjusting GPU computing resources for active applications. Unlike previous work on GPU performance model and GPU real-time scheduling, WAMP2S considers the impact of co-executing workload on the execution time estimation and provides a software-only approach for preemption support. In addition, WAMP2S implements a prototype GPU scheduler without any source code analysis. We evaluate the proposed GPU performance model and real-time scheduling algorithm in both simulated and realistic application sets. Experimental results illustrate that WAMP2S can achieve low prediction error and high scheduling success ratio.

Energy-efficient Wi-Fi tethering has received sustained attention. However, most existing Wi-Fi tethering schemes use maximum power to transmit data regardless of the distance between a mobile access point (MAP) on a smartphone and other associated devices. This problem is becoming increasingly important with the popularity of MIMO deployment because they offer more offload traffic and a higher data rate. In this paper, we design a Distance-aware Adaptive Transmission Power Control (called DATPC) scheme. Hence, DATPC can set the appropriate transmission power at the right moment. We have prototyped DATPC on commercial 802.11n WiFi devices and evaluate its performance in various indoor and outdoor scenarios. Experimental results show that within 3m distance, DATPC reduces the energy consumption of a MAP smartphone by up to 60% while ensuring the same transmission quality as the default maximum transmission power when sending data packets.

New generation airborne embedded system has deployed Graphical Processing Units (GPUs) to raise processing capability to meet growing computational demands. Applications in the airborne embedded system have strict real-time constraints. Therefore, it is necessary to accurately predict timing behaviors of those applications. Many previous work propose GPU performance models to estimate the execution time of applications. However, most of those models do not consider the impact of co-execution on the GPU performance. In this paper, we propose a workload-aware GPU performance model to predict the execution time of applications executed concurrently on a single GPU. Experimental results illustrate that the proposed model can achieve a 5.1%-11.6% prediction error in a real airborne embedded hardware platform.

Personal mobile sensing is fast permeating our daily lives to enable activity monitoring, healthcare and rehabilitation. Combined with deep learning, these applications have achieved significant success in recent years. Different from conventional cloud-based paradigms, running deep learning on devices offers several advantages including data privacy preservation and low-latency response for both model inference and update. Since data collection is costly in reality, Google’s Federated Learning offers not only complete data privacy but also better model robustness based on multiple user data. However, personal mobile sensing applications are mostly user-specific and highly affected by environment. As a result, continuous local changes may seriously affect the performance of a global model generated by Federated Learning. In addition, deploying Federated Learning on a local server, e.g., edge server, may quickly reach the bottleneck due to resource constraint and serious failure by attacks. Towards pushing deep learning on devices, we present MDLdroid, a novel decentralized mobile deep learning framework to enable resource-aware on-device collaborative learning for personal mobile sensing applications. To address resource limitation, we propose a ChainSGD-reduce approach which includes a novel chain-directed Synchronous Stochastic Gradient Descent algorithm to effectively reduce overhead among multiple devices. We also design an agent-based multi-goal reinforcement learning mechanism to balance resources in a fair and efficient manner. Our evaluations show that our model training on off-the-shelf mobile devices achieves 2x to 3.5x faster than single-device training, and 1.5x faster than the master-slave approach.

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%.

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.

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.

Cyber-Physical Systems (CPS) tightly integrate cyber and physical components and transcend discrete and continuous domains. It is greatly desired that the physical components being controlled and the software implementation of control algorithms can be verified together. We present an efficient approach to reachability analysis of Hybrid Automata Pushdown System (HAPS) models for cyber/physical co-verification of CPS. We have realized this approach and applied it to real-world control systems. The evaluation has shown that HAPS is an effective model for co-verification of CPS and our approach has major potential in verifying system-level properties of CPS, therefore improving the reliability of CPS.

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.

Network reprogramming is a crucial service in wireless sensor networks (WSNs) that relies on epidemic strategy for spreading software updates by just having a local view of the networks. Securing the process of network reprogramming is essential in some certain WSNs applications, state-of-the-art secure network reprogramming protocols for WSNs aim for the efficient source authentication and integrity verification of code image, however, due to the resource constrains of WSNs, existing secure network reprogramming protocols are vulnerable to Denial of Service (DoS) attacks when sensor nodes can be compromised (insider DoS attacks). In this paper, we identify different types of DoS attacks exploiting the epidemic propagation strategies used by Deluge and propose corresponding analysis models to attempt to quantify the cost of these attacks damage. Simulation further shows the impact of insider DoS attacks on network reprogramming in WSNs.

Future many-core processors will require high-performance yet energy-efficient on-chip networks to provide a communication substrate for the increasing number of cores. Recent advances in silicon nanophotonics create new opportunities for on-chip networks. To efficiently exploit the benefits of nanophotonics, we propose Firefly - a hybrid, hierarchical network architecture. Firefly consists of clusters of nodes that are connected using conventional, electrical signaling while the inter-cluster communication is done using nanophotonics - exploiting the benefits of electrical signaling for short, local communication while nanophotonics is used only for global communication to realize an efficient on-chip network. Crossbar architecture is used for inter-cluster communication. However, to avoid global arbitration, the crossbar is partitioned into multiple, logical crossbars and their arbitration is localized. Our evaluations show that Firefly improves the performance by up to 57% compared to an all-electrical concentrated mesh (CMESH) topology on adversarial traffic patterns and up to 54% compared to an all-optical crossbar (OP XBAR) on traffic patterns with locality. If the energy-delay-product is compared, Firefly improves the efficiency of the on-chip network by up to 51% and 38% compared to CMESH and OP XBAR, respectively.

It is known that the execution of programs exhibits repetitive phases; in other words, the execution of programs can be partitioned into segments of execution, during which the application exhibits unique architectural properties. This property has been used for various optimization goals. In addition, phase information is utilized to reduce the run time of the architectural simulation. Conventionally, an application is examined in an architecture-independent manner (such as the number of times a basic block is executed) to extract information about the phases and then only the representative execution intervals are executed to analyze architectural choices. We claim that such approaches are becoming inadequate in the many-core era as application execution is not dominated by the instructions only, but instead the communication structure of the application is becoming as important as the instruction behavior. Hence, we propose to utilize communication behavior to determine the phases of an application. Our results reveal that the inclusion of the communication information can increase the accuracy of the phase detection significantly. Specifically, for SPLASH2 and Mine-Bench applications, the average (geometric mean) CPI error rate with the instruction-based phase detection is 11.01%, while our phase detection scheme has an average error rate of 3.41% when compared to the simulations that run the applications to completion.