The efficient exploitation of renewable energy sources is crucial for addressing the global energy crisis and increase in CO2 emissions. Energy management system aggregators, functioning as nonprofit cooperatives within energy communities, manage renewable energy resources and incentivize residents towards self-consumption through dynamic, cost-attractive pricing schemes, receiving subsidies and long-term contracts as reward. This interaction between aggregator and residents is typically modeled using bi-level optimization frameworks, however, research studies often ignore the role of aggregators as self-consumption catalysts and the conflicting nature of the residents’ objectives. Moreover, lack of cooperation between the decision levels results in inflexible decision making when prioritizing between the respective objectives. This paper defines and formulates a bi-level multi-objective optimization problem for optimizing self-consumption in energy communities, while considering the residents’ welfare by maximizing satisfaction of their appliance-scheduling preferences and minimizing energy costs. We introduce the Bi-level Multi-Objective Energy Management System II (BiMO-EMS-II), composed of an Adaptive Population Transfer strategy, a Uniform Partially Mapped Crossover and a Decision Making heuristic with Cooperation. Our experimental evaluation has shown that BiMO-EMS-II simultaneously offers near-optimal self-consumption at the aggregator level and a high-quality trade-off between the conflicting objectives at the resident level, subject to different objective prioritization and decision-making assumptions.

The European Green Deal has outlined ambitious near-term objectives to reduce CO2emissions and attain climate neutrality. Yet, effectively exploiting self-generated renewable energy within energy communities remains insufficiently explored. Energy management system aggregators often provide demand-response and ancillary services through demand-side management programs that incentivise residents using reduced energy costs or monetary compensation. However, their primary objectives of profit maximization and flexibility request satis-faction do not address the practical intricacies associated with energy self-consumption. This paper defines and formulates a bi-level multi-objective optimization problem for maximizing self-consumption within an energy community, while considering the residents' welfare by maximizing satisfaction of their appliance-scheduling preferences and minimizing energy costs. We propose a Bi-level Multi-Objective Energy Management System (BiMO-EMS) based on evolutionary computation for aggregator participation in the day-ahead energy market. Through experimental evaluation, we demonstrate that BiMO-EMS achieves a 98% approximation of the optimal solution with regards to self-consumption, while establishing a pricing scheme that offers a high-quality trade-off between the welfare-oriented objectives in terms of performance and diversity.

This demonstration paper introduces the Smart Out-of-Home Advertising Platform (SOAP), which leverages Geographic Information Systems (GIS) data and state-of-the-art Artificial Intelligence (AI) approaches to provide: (i) a documented, data-informed pricing model for billboards, which can be used to justify billboard prices to advertisers; and (ii) a set of non-dominated solutions (each corresponding to a different allocation of billboards to a given campaign) that explores the trade-offs between multiple conflicting objectives (e.g., cost and coverage). To the best of our knowledge, SOAP is the first to tackle such challenges in the context of Multi Objective Optimization (MOO).

This demo paper presents EnterCY, an integrated Virtual and Augmented Reality Tourism platform for Cyprus. The platform's web-based, spatio-temporal virtual exploration component allows potential visitors to explore the rich cultural heritage, variety of activities, and wealth of sightseeing locations in Cyprus before their visit. EnterCY also enhances tourists' experiences during their visit through its mobile component, which offers on-site visual and audio guidance, personalized recommendations, as well as entertaining and learning features (e.g., story-telling), based on mobile-friendly Augmented Reality, location-awareness and Machine Learning technologies. Through Immersive Reality technologies, the platform provides for an after visit experience by creating personalized 360 video mementos of tourists' tours and supports integrated features that allow for experience sharing in popular social media platforms.

The Internet of Things (IoT) extends connectivity beyond traditional computing devices to different types of smart objects, equipped with various sensors and actuators. These objects range from smart lightbulbs and thermostats to smart watches and fitness trackers, or even heavy machinery used in various industrial sectors. Due to device heterogeneity, the complexity of developing applications that require the collection and sharing of data across multiple IoT devices is high, as developers need to be familiar with a diverse set of supported services and APIs. While existing approaches have proposed solutions to this challenge, they rely on the use of resource-intensive cloud-based components, they do not offer the degree of extensibility desired by developers, and they often trade off some of the richness of real-time data for ease of use. Cuttlefish is a flexible and lightweight middleware that offers a unified API to help with the development of applications that utilize multiple heterogeneous IoT devices. It abstracts away much of the complexity involved with orchestrating different devices at runtime. At the same time, it avoids the aforementioned caveats of existing approaches through a simple and efficient design, yet one that offers a rich set of capabilities to developers.

Hyper-responsive mobile applications, such as augmented reality and online games, require ultra-low latency access to back-end services and data at runtime. While fog computing tries to meet such latency requirements by placing corresponding back-end services and data closer to clients, for e.g., within an access network, assuming a fixed back-end server throughout execution is problematic due to user mobility. A more flexible approach is thus required to allow for adapting to changes in network conditions when users roam, by relocating back-end services and data to closer available infrastructure. Support for real-time migration of software services exists, however, migrating associated disk state remains a bottleneck. This paper presents FOGFS, a fog file system that employs intelligent snapshotting, migration and synchronization mechanisms to speed up the migration of an application`s disk state between different edge locations at runtime. The experimental evaluation of our prototype implementation reveals that the attainable speed-up is as much as 3. 3 × compared to conventional migration approaches.

The uptake of wearable technology suggests that the time is ripe to explore new opportunities for improving mobile experiences. Apps, however, are not keeping up with the pace of technological advancement because wearables are treated as standalone devices, although their individual capabilities better classify them as peripherals with complementary roles. We foresee that the next generation of apps will orchestrate multiple wearable devices to enhance mobile user experiences. However, currently there is limited support for combining heterogeneous devices. This paper introduces Multi-Wear, a platform to scaffold the development of apps that span multiple wearables. It demonstrates experimentally how MULTI-WEAR can help bring changes to mobile apps that go beyond conventional practices.

This paper investigates the trade-off between security and usability in recognition-based graphical authentication mechanisms. Through a user study (N=103) based on a real usage scenario, it draws insights about the security strength and memorability of a chosen password with respect to the amount of images presented to users during sign-up. In particular, it reveals the users' predisposition in following predictable patterns when selecting graphical passwords, and its effect on practical security strength. It also demonstrates that a "sweet-spot" exists between security and usability in graphical authentication approaches on the basis of adjusting accordingly the image grid size presented to users when creating passwords. The results of the study can be leveraged by researchers and practitioners engaged in designing intelligent graphical authentication user interfaces for striking an appropriate balance between security and usability.

Poor Internet performance currently undermines the efficiency of hyper-responsive mobile apps such as augmented reality clients and online games, which require low-latency access to real-time backend services. While edge-assisted execution, i.e. moving entire services to the edge of an access network, helps eliminate part of the communication overhead involved, this does not scale to the number of users that share an edge infrastructure. This is due to a mismatch between the scarce availability of resources in access networks and the aggregate demand for computational power from client applications. Instead, this paper proposes a hybrid edge-assisted deployment model in which only part of a service executes on LTE edge servers. We provide insights about the conditions that must hold for such a model to be effective by investigating in simulation different deployment and application scenarios. In particular, we show that using LTE edge servers with modest capabilities, performance can improve significantly as long as at most 50% of client requests are processed at the edge. Moreover, we argue that edge servers should be installed at the core of a mobile network, rather than the mobile base station: the difference in performance is negligible, whereas the latter choice entails high deployment costs. Finally, we verify that, for the proposed model, the impact of user mobility on TCP performance is low.

The proliferation of wearable and smartphone devices with embedded sensors has enabled researchers and engineers to study and understand user behavior at an extremely high fidelity, particularly for use in industries such as entertainment, health, and retail. However, identified user patterns are yet to be integrated into modern systems with immersive capabilities, such as VR systems, which still remain constrained by limited application interaction models exposed to developers. In this paper, we present Smart VR, a platform that allows developers to seamlessly incorporate user behavior into VR apps. We present the high-level architecture of Smart VR, and show how it facilitates communication, data acquisition, and context recognition between smart wearable devices and mediator systems (e.g., smartphones, tablets, PCs). We demonstrate Smart VR in the context of a VR app for retail stores to show how it can be used to substitute the requirement of cumbersome input devices (e.g., mouse, keyboard) with more natural means of user-app interaction (e.g., user gestures such as swiping and tapping) to improve user experience.

Mobile applications are constrained by the available memory of mobile devices. We present C-RAM, a system that uses cloud-based memory to extend the memory of mobile devices. Itsplits application stateand its associated computation between a mobile device and a cloud node to allow applications to consume more memory, while minimizing the performance impact. C-RAM thus enables developers to realize new applications or port legacy desktop applications with a large memory footprint to mobile platforms without explicitly designing them to account for memory limitations. To handle network failures with partitioned application state, C-RAM uses a new snapshot-based fault tolerance mechanism in which changes to remote memory objects are periodically backed up to the device. After failure, or when network usage exceeds a given limit, the device rolls back execution to continue from the last snapshot. C-RAM supports local execution with an application state that exceeds the available device memory through a user-level virtual memory: objects are loaded on-demand from snapshots in flash memory. Our C-RAM prototype supports Objective-C applications on the unmodified iOS platform. With C-RAM, applications can consume 10$\times$more memory than the device capacity, with a negligible impact on application performance. In some cases, C-RAM even achieves a significant speed-up in execution time (up to 9.7$\times$).

To improve the performance of Massively Multiplayer Online Games (MMOGs) in mobile networks, we explore the potential benefits of an edge-assisted deployment model: part of the MMOG backend service executes closer to the end user at the edge of the LTE network. We investigate the impact on game late

No abstract available.

Continuous queries in wireless sensor networks (WSNs) are founded on the premise of query routing tree structures (denoted as T), which provide sensors with a path to the querying node. Predominant data acquisition systems for WSNs construct such structures in an ad-hoc manner and therefore there is no guarantee that a given query workload will be distributed equally among all sensors. That leads to data collisions which represent a major source of energy waste. In this paper we present the energy-driven tree construction (ETC) algorithm, which balances the workload among nodes and minimizes data collisions, thus reducing energy consumption, during data acquisition in WSNs. We show through real micro-benchmarks on the CC2420 radio chip and trace-driven experimentation with real datasets from Intel Research and UC-Berkeley that ETC can provide significant energy reductions under a variety of conditions prolonging the longevity of a wireless sensor network.

This paper considers the requirements for a scalable, easily manageable, fault-tolerant, and efficient data center network fabric. Trends in multi-core processors, end-host virtualization, and commodities of scale are pointing to future single-site data centers with millions of virtual end points. Existing layer 2 and layer 3 network protocols face some combination of limitations in such a setting: lack of scalability, difficult management, inflexible communication, or limited support for virtual machine migration. To some extent, these limitations may be inherent for Ethernet/IP style protocols when trying to support arbitrary topologies. We observe that data center networks are often managed as a single logical network fabric with a known baseline topology and growth model. We leverage this observation in the design and implementation of PortLand, a scalable, fault tolerant layer 2 routing and forwarding protocol for data center environments. Through our implementation and evaluation, we show that PortLand holds promise for supporting a ``plug-and-play" large-scale, data center network.