Indoor localization has recently witnessed an increase in interest, due to the potential wide range of services it can provide by leveraging Internet of Things (IoT), and ubiquitous connectivity. ...Different techniques, wireless technologies and mechanisms have been proposed in the literature to provide indoor localization services in order to improve the services provided to the users. However, there is a lack of an up-to-date survey paper that incorporates some of the recently proposed accurate and reliable localization systems. In this paper, we aim to provide a detailed survey of different indoor localization techniques, such as angle of arrival (AoA), time of flight (ToF), return time of flight (RTOF), and received signal strength (RSS); based on technologies, such as WiFi, radio frequency identification device (RFID), ultra wideband (UWB), Bluetooth, and systems that have been proposed in the literature. This paper primarily discusses localization and positioning of human users and their devices. We highlight the strengths of the existing systems proposed in the literature. In contrast with the existing surveys, we also evaluate different systems from the perspective of energy efficiency, availability, cost, reception range, latency, scalability, and tracking accuracy. Rather than comparing the technologies or techniques, we compare the localization systems and summarize their working principle. We also discuss remaining challenges to accurate indoor localization.
Emerging technologies and applications including Internet of Things, social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built ...from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent-based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best tradeoff between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.
Edge machine learning involves the deployment of learning algorithms at the network edge to leverage massive distributed data and computation resources to train artificial intelligence (AI) models. ...Among others, the framework of federated edge learning (FEEL) is popular for its data-privacy preservation. FEEL coordinates global model training at an edge server and local model training at devices that are connected by wireless links. This work contributes to the energy-efficient implementation of FEEL in wireless networks by designing joint computation-and-communication resource management (<inline-formula> <tex-math notation="LaTeX">\mathrm {C}^{2} </tex-math></inline-formula>RM). The design targets the state-of-the-art heterogeneous mobile architecture where parallel computing using both CPU and GPU, called heterogeneous computing , can significantly improve both the performance and energy efficiency. To minimize the sum energy consumption of devices, we propose a novel <inline-formula> <tex-math notation="LaTeX">\mathrm {C}^{2} </tex-math></inline-formula>RM framework featuring multi-dimensional control including bandwidth allocation, CPU-GPU workload partitioning and speed scaling at each device, and <inline-formula> <tex-math notation="LaTeX">\mathrm {C}^{2} </tex-math></inline-formula> time division for each link. The key component of the framework is a set of equilibriums in energy rates with respect to different control variables that are proved to exist among devices or between processing units at each device. The results are applied to designing efficient algorithms for computing the optimal <inline-formula> <tex-math notation="LaTeX">\mathrm {C}^{2} </tex-math></inline-formula>RM policies faster than the standard optimization tools. Based on the equilibriums, we further design energy-efficient schemes for device scheduling and greedy spectrum sharing that scavenges "spectrum holes" resulting from heterogeneous <inline-formula> <tex-math notation="LaTeX">\mathrm {C}^{2} </tex-math></inline-formula> time divisions among devices. Using a real dataset, experiments are conducted to demonstrate the effectiveness of <inline-formula> <tex-math notation="LaTeX">\mathrm {C}^{2} </tex-math></inline-formula>RM on improving the energy efficiency of a FEEL system.
Mobile micro-clouds are promising for enabling performance-critical cloud applications. However, one challenge therein is the dynamics at the network edge. In this paper, we study how to place ...service instances to cope with these dynamics, where multiple users and service instances coexist in the system. Our goal is to find the optimal placement (configuration) of instances to minimize the average cost overtime, leveraging the ability of predicting future cost parameters with known accuracy. We first propose an offline algorithm that solves for the optimal configuration in a specific look-ahead time-window. Then, we propose an online approximation algorithm with polynomial time-complexity to find the placement in real-time whenever an instance arrives. We analytically show that the online algorithm is 0(1)-competitive for a broad family of cost functions. Afterwards, the impact of prediction errors is considered and a method for finding the optimal look-ahead window size is proposed, which minimizes an upper bound of the average actual cost. The effectiveness of the proposed approach is evaluated by simulations with both synthetic and real-world (San Francisco taxi) usermobility traces. The theoretical methodology used in this paper can potentially be applied to a larger class of dynamic resource allocation problems.
Federated learning (FL) allows model training from local data collected by edge/mobile devices while preserving data privacy, which has wide applicability to image and vision applications. A ...challenge is that client devices in FL usually have much more limited computation and communication resources compared to servers in a data center. To overcome this challenge, we propose PruneFL -a novel FL approach with adaptive and distributed parameter pruning, which adapts the model size during FL to reduce both communication and computation overhead and minimize the overall training time, while maintaining a similar accuracy as the original model. PruneFL includes initial pruning at a selected client and further pruning as part of the FL process. The model size is adapted during this process, which includes maximizing the approximate empirical risk reduction divided by the time of one FL round. Our experiments with various datasets on edge devices (e.g., Raspberry Pi) show that: 1) we significantly reduce the training time compared to conventional FL and various other pruning-based methods and 2) the pruned model with automatically determined size converges to an accuracy that is very similar to the original model, and it is also a lottery ticket of the original model.
Mobile edge computing is a new cloud computing paradigm, which makes use of small-sized edge clouds to provide real-time services to users. These mobile edge-clouds (MECs) are located in close ...proximity to users, thus enabling users to seamlessly access applications running on MECs. Due to the co-existence of the core (centralized) cloud, users, and one or multiple layers of MECs, an important problem is to decide where (on which computational entity) to place different components of an application. This problem, known as the application or workload placement problem, is notoriously hard, and therefore, heuristic algorithms without performance guarantees are generally employed in common practice, which may unknowingly suffer from poor performance as compared with the optimal solution. In this paper, we address the application placement problem and focus on developing algorithms with provable performance bounds. We model the user application as an application graph and the physical computing system as a physical graph, with resource demands/availabilities annotated on these graphs. We first consider the placement of a linear application graph and propose an algorithm for finding its optimal solution. Using this result, we then generalize the formulation and obtain online approximation algorithms with polynomial-logarithmic (poly-log) competitive ratio for tree application graph placement. We jointly consider node and link assignment, and incorporate multiple types of computational resources at nodes.
Live Service Migration in Mobile Edge Clouds Machen, Andrew; Shiqiang Wang; Leung, Kin K. ...
IEEE wireless communications,
2018-February, 2018-2-00, Letnik:
25, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Mobile edge clouds (MECs) bring the benefits of the cloud closer to the user, by installing small cloud infrastructures at the network edge. This enables a new breed of real-time applications, such ...as instantaneous object recognition and safety assistance in intelligent transportation systems, that require very low latency. One key issue that comes with proximity is how to ensure that users always receive good performance as they move across different locations. Migrating services between MECs is seen as the means to achieve this. This article presents a layered framework for migrating active service applications that are encapsulated either in virtual machines (VMs) or containers. This layering approach allows a substantial reduction in service downtime. The framework is easy to implement using readily available technologies, and one of its key advantages is that it supports containers, which is a promising emerging technology that offers tangible benefits over VMs. The migration performance of various real applications is evaluated by experiments under the presented framework. Insights drawn from the experimentation results are discussed.
Federated learning (FL) is an emerging technique for training machine learning models using geographically dispersed data collected by local entities. It includes local computation and ...synchronization steps. To reduce the communication overhead and improve the overall efficiency of FL, gradient sparsification (GS) can be applied, where instead of the full gradient, only a small subset of important elements of the gradient is communicated. Existing work on GS uses a fixed degree of gradient sparsity for i.i.d.-distributed data within a datacenter. In this paper, we consider adaptive degree of sparsity and non-i.i.d. local datasets. We first present a fairness-aware GS method which ensures that different clients provide a similar amount of updates. Then, with the goal of minimizing the overall training time, we propose a novel online learning formulation and algorithm for automatically determining the near-optimal communication and computation trade-off that is controlled by the degree of gradient sparsity. The online learning algorithm uses an estimated sign of the derivative of the objective function, which gives a regret bound that is asymptotically equal to the case where exact derivative is available. Experiments with real datasets confirm the benefits of our proposed approaches, showing up to 40% improvement in model accuracy for a finite training time.
The book includes a set of research and survey articles featuring the recent advances in theory and applications of wireless mesh networking technology which will be a significant component in the ...next generation (e.g., fourth generation) mobile communication networks. Wireless mesh networks consist of mesh clients and mesh routers, where the mesh routers form a wireless infrastructure/backbone and interwork with the wired networks to provide multihop wireless Internet connectivity to the mesh clients. Wireless mesh networking has emerged as one of the most promising concept for self-organizing and auto-configurable wireless networking to provide adaptive and flexible wireless Internet connectivity to mobile users. This concept can be used for different wireless access technologies such as IEEE 802.11, 802.15, 802.16-based WLAN, WPAN, and WMAN technologies. Potential application scenarios for wireless mesh networks include access and backhaul support for cellular networks, home networks, enterprise networks, community networks, and intelligent transport system networks. Development of wireless mesh networking technology has to deal with challenging architecture and protocol design issues, and there is an increasing interest on this technology among the researchers in both academia and industry. There are many on-going research projects in different universities and industrial research labs. Also, many startup companies are building mesh networking platforms based on off-the-shelf wireless access technologies and developing demanding applications and services. This book provides a unified view of the state-of-the-art achievements in the area of wireless mesh networking technology. The contributed articles from the leading experts in this field cover both the theoretical concepts (e.g., information-theoretic analysis) and system-level implementation issues. The topics include information-theoretic analysis of wireless mesh networks, challenges and issues in designing architectures and protocols for wireless mesh networks, medium access control and routing protocols for wireless mesh networks, cross-layer performance analysis and optimization in wireless mesh networks, multimedia over wireless mesh, trust and security in wireless mesh networks, cooperation and incentive techniques, reconfigurable and cognitive radio for wireless mesh networks, MIMO wireless mesh networks, wireless mesh test-beds and hardware prototypes, and applications and services. The book starts with the essential background on the basic concepts and architectures of wireless mesh networking (through one/two survey articles), and then it presents advanced level materials in a step-by-step fashion so that the readers can follow the book easily. The rich set of references in each of the articles are invaluable to the researchers. The book is useful for both researchers and practitioners in this area. Also, it can be adopted as a graduate-level textbook for an advanced course on wireless communication networks.
Although accumulating evidence suggests that arousal pathways in the brain play important roles in emergence from general anesthesia, the roles of monoaminergic arousal circuits are unclear. In this ...study, the authors tested the hypothesis that methylphenidate (an inhibitor of dopamine and norepinephrine transporters) induces emergence from isoflurane general anesthesia.
Using adult rats, the authors tested the effect of intravenous methylphenidate on time to emergence from isoflurane general anesthesia. They then performed experiments to test separately for methylphenidate-induced changes in arousal and changes in minute ventilation. A dose-response study was performed to test for methylphenidate-induced restoration of righting during continuous isoflurane general anesthesia. Surface electroencephalogram recordings were performed to observe neurophysiological changes. Plethysmography recordings and arterial blood gas analysis were performed to assess methylphenidate-induced changes in respiratory function. Intravenous droperidol was administered to test for inhibition of methylphenidate's actions.
Methylphenidate decreased median time to emergence from 280 to 91 s. The median difference in time to emergence without methylphenidate compared with administration of methylphenidate was 200 155-331 s (median, 95% CI). During continuous inhalation of isoflurane, methylphenidate induced return of righting in a dose-dependent manner, induced a shift in electroencephalogram power from delta (less than 4 Hz) to theta (4-8 Hz), and induced an increase in minute ventilation. Administration of intravenous droperidol (0.5 mg/kg) before intravenous methylphenidate (5 mg/kg) largely inhibited methylphenidate-induced emergence behavior, electroencephalogram changes, and changes in minute ventilation.
Methylphenidate actively induces emergence from isoflurane general anesthesia by increasing arousal and respiratory drive, possibly through activation of dopaminergic and adrenergic arousal circuits. The authors' findings suggest that methylphenidate may be useful clinically as an agent to reverse general anesthetic-induced unconsciousness and respiratory depression at the end of surgery.
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