Integrated Support for Handoff Management and Context-Awareness in Heterogeneous Wireless Networks

The overwhelming success of mobile devices and wireless communications is stressing the need for the development of mobility-aware services. Device mobility requires services adapting their behavior to sudden context changes and being aware of handoffs, which introduce unpredictable delays and intermittent discontinuities. Heterogeneity of wireless technologies (Wi-Fi, Bluetooth, 3G) complicates the situation, since a different treatment of context-awareness and handoffs is required for each solution. This paper presents a middleware architecture designed to ease mobility-aware service development. The architecture hides technology-specific mechanisms and offers a set of facilities for context awareness and handoff management. The architecture prototype works with Bluetooth and Wi-Fi, which today represent two of the most widespread wireless technologies. In addition, the paper discusses motivations and design details in the challenging context of mobile multimedia streaming applications

Structured Sparse Ternary Compression for Convolutional Layers in Federated Learning

In Cross-device Federated Learning, communication efficiency is of paramount importance. Sparse Ternary Compression (STC) is one of the most effective techniques for considerably reducing the per-round communication cost of Federated Learning (FL) without significantly degrading the accuracy of the global model, by using ternary quantization in series to topk sparsification. In this paper, we propose an original variant of STC that is specifically designed and implemented for convolutional layers. Our variant is originally based on the experimental evidence that a pattern exists in the distribution of client updates, namely, the difference between the received global model and the locally trained model. In particular, we have experimentally found that the largest (in absolute value) updates for convolutional layers tend to form clusters in a kernel-wise fashion. Therefore, our primary novel idea is to a-priori restrict the elements of STC updates to lay on such a structured pattern, thus allowing us to further reduce the STC communication cost. We have designed, implemented, and evaluated our novel technique, called Structured Sparse Ternary Compression (SSTC). Reported experimental results show that SSTC shrinks compressed updates by a factor of x3 with respect to traditional STC and with a reduction up to x104 with respect to uncompressed FedAvg, at the expense of negligible degradation of the global model accuracy

Multi-Stage Resource Allocation in Hybrid 25G-EPON and LTE-Advanced Pro FiWi Networks for 5G Systems

The 5G vision is not restricted solely to the wireless domain and its challenging requirements cannot be fulfilled with- out the efficient integration of cutting-edge technologies in all portions of the telecommunications infrastructure. The promoted architectures for next generation telecommunications systems involve high capacity network domains, which operate flexibly and seamlessly to offer full Quality of Experience to all types of subscribers. The proliferation of highly demanding multimedia services and the advanced features of modern communication devices necessitate the development of end-to-end schemes which can efficiently distribute large amount of network resources anywhere and whenever needed. The paper introduces a new resource allocation scheme for cutting-edge Fiber-Wireless networks is introduced that can be applied in the fronthaul portion of 5G-enabled architectures. The adopted technologies are the forthcoming 25G-EPON for the optical domain and the 5G-ready LTE-Advanced Pro for the wireless domain. The proposed scheme performs allocation decisions based on the outcome of an adjustable multi- stage optimization problem. The optimization factors are directly related to the major considerations in bandwidth distribution, namely priority-based traffic differentiation, power awareness, and fairness provision. The conducted evaluations prove that this approach is able to ensure high efficiency in network operations

TEMPOS: QoS Management Middleware for Edge Cloud Computing FaaS in the Internet of Things

Several classes of advanced Internet of Things (IoT) applications, e.g., in the industrial manufacturing domain, call for Quality of Service (QoS) management to guarantee/control performance indicators, even in presence of many sources of "stochastic noise" in real deployment environments, from scarcely available bandwidth in a time window to concurrent usage of virtualized processing resources. This paper proposes a novel IoT-oriented middleware that i) considers and coordinates together different aspects of QoS monitoring, control, and management for different kinds of virtualized resources (from networking to processing) in a holistic way, and ii) specifically targets deployment environments where edge cloud resources are employed to enable the Serverless paradigm in the cloud continuum. The reported experimental results show how it is possible to achieve the desired QoS differentiation by coordinating heterogeneous mechanisms and technologies already available in the market. This demonstrates the feasibility of effective QoS-aware management of virtualized resources in the cloud-to-things continuum when considering a Serverless provisioning scenario, which is completely original in the related literature to the best of our knowledge

Fog-Driven Context-Aware Architecture for Node Discovery and Energy Saving Strategy for Internet of Things Environments

The consolidation of the Fog Computing paradigm and the ever-increasing diffusion of Internet of Things (IoT) and smart objects are paving the way toward new integrated solutions to efficiently provide services via short-mid range wireless connectivity. Being the most of the nodes mobile, the node discovery process assumes a crucial role for service seekers and providers, especially in IoT-fog environments where most of the devices run on battery. This paper proposes an original model and a fog-driven architecture for efficient node discovery in IoT environments. Our novel architecture exploits the location awareness provided by the fog paradigm to significantly reduce the power drain of the default baseline IoT discovery process. To this purpose, we propose a deterministic and competitive adaptive strategy to dynamically adjust our energy-saving techniques by deciding when to switch BLE interfaces ON/OFF based on the expected frequency of node approaching. Finally, the paper presents a thorough performance assessment that confirms the applicability of the proposed solution in several different applications scenarios. This evaluation aims also to highlight the impact of the nodes' dynamic arrival on discovery process performance

Context-aware and automatic configuration of mobile devices in cloud-enabled ubiquitous computing

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/s00779-013-0698-3. Copyright @ Springer-Verlag London 2013.Context-sensitive (or aware) applications have, in recent years, moved from the realm of possibilities to that of ubiquity. One exciting research area that is still very much in the realm of possibilities is that of cloud computing, and in this paper, we present our work, which explores the overlap of these two research areas. Accordingly, this paper explores the notion of cross-source integration of cloud-based, context-aware information in ubiquitous computing through a developed prototypical solution. Moreover, the described solution incorporates remote and automatic configuration of Android smartphones and advances the research area of context-aware information by harvesting information from several sources to build a rich foundation on which algorithms for context-aware computation can be based. Evaluation results show the viability of integrating and tailoring contextual information to provide users with timely, relevant and adapted application behaviour and content

Qos‐aware approximate query processing for smart cities spatial data streams

Large amounts of georeferenced data streams arrive daily to stream processing systems. This is attributable to the overabundance of affordable IoT devices. In addition, interested practitioners desire to exploit Internet of Things (IoT) data streams for strategic decision‐making purposes. However, mobility data are highly skewed and their arrival rates fluctuate. This nature poses an extra challenge on data stream processing systems, which are required in order to achieve prespecified latency and accuracy goals. In this paper, we propose ApproxSSPS, which is a system for approximate processing of geo‐referenced mobility data, at scale with quality of service guarantees. We focus on stateful aggregations (e.g., means, counts) and top‐N queries. ApproxSSPS features a controller that interactively learns the latency statistics and calculates proper sampling rates to meet latency or/and accuracy targets. An overarching trait of ApproxSSPS is its ability to strike a plausible balance between latency and accuracy targets. We evaluate ApproxSSPS on Apache Spark Structured Streaming with real mobility data. We also compared ApproxSSPS against a state‐of‐the‐art online adaptive processing system. Our extensive experiments prove that ApproxSSPS can fulfill latency and accuracy targets with varying sets of parameter configurations and load intensities (i.e., transient peaks in data loads versus slow arriving streams). Moreover, our results show that ApproxSSPS outperforms the baseline counterpart by significant magnitudes. In short, ApproxSSPS is a novel spatial data stream processing system that can deliver real accurate results in a timely manner, by dynamically specifying the limits on data samples

A Framework for QoS- Enabled Semantic Routing in Industrial Networks: Overall Architecture and Primary Protocols

The manufacturing sector represents a notable use case of the Industry 4.0 revolution, heavily stressing the capability of plants to ensure the desired QoS. Currently, manufacturing plants are characterized by an increasing amount of non-mission-critical traffic, in addition to traditional mission-critical safety-related traffic, which is negligible in comparison. Since computing and networking capabilities are no longer as abundant as in the past, there is the need to properly manage available resources. To ensure challenging QoS requirements, we propose a novel protocol suite specifically designed for our QoS-enabled semantic routing framework. Such a framework adopts an architecture that fits the characteristics of modern manufacturing environments and exploits an overlay networking solution providing a semantic routing substrate that operates both at the application and network layers

Evaluating Filtering Strategies for Decentralized Handover Prediction in the Wireless Internet

The rapid diffusion of heterogeneous forms of wireless connectivity is pushing the tremendous growth of the commercial interest in mobile services, i.e., distributed applications to portable wireless terminals that roam during service provisioning. In the case of both location-dependent mobile services and mobile services with session continuity requirements, there is a growing need for decentralized and lightweight solutions to predict cell handovers, in order to enable proactive service management operations that anticipate actual terminal reconnections at their newly visited cells. The paper discusses how to predict client handovers between IEEE 802.11 cells in a portable and completely decentralized way, only by exploiting RSSI monitoring and with no need of external global positioning systems. In particular, the paper focuses on proposing and comparing different filtering techniques for mitigating Received Signal Strength Indication abrupt fluctuations. Experimental results point out that i) filtering techniques can relevantly improve the efficiency and effectiveness of handover prediction, and ii) the choice of the most appropriate filtering solution to adopt should be made at provisioning time depending on specific service/system requirements, e.g., privileging minimum overhead vs. greater prediction proactivity