In this paper, we report evidence collected in the context of the Horizon 2020 NUDGE project about the effectiveness of digital tools such as smartphone apps and web portals to realize nudging interventions towards different energy efficiency goals: from the reduction of heating energy and electricity to the increase of self-consumption in energy prosumer households. We analyse recorded events from the interaction of participants with those tools in the context of three different pilot experiments. We first assess the level of end user engagement with the apps and the portal, counting the number of distinct days that they interact with them. We find it to be highly heterogeneous, with up to 25% of participants in the Greek pilot and 12% in the Portuguese pilot not using the mobile app at all, and the rest forming three distinct groups of low, medium and high engagement. The interaction with the apps almost always lasts fractions of a minute and involves accessing a few app screens. We next turn to the actual users’ exposure to the nudging features of the digital tools to find out that high percentages of users (up to 50%) exhibit zero or very occasional exposure to the app screens that implement nudges. The mobile app users, in particular, can be grouped into four clusters depending on the level of engagement with the app and their exposure to its nudging features. Disappointingly, more than half the pilot participants belong to the cluster combining low engagement with low exposure to nudging. Combining these data with self-statements of participants in post-intervention surveys, we find no significant correlation between the level of nudging exposure and the (self-stated) motivation/ intentions to save energy.and communication emerged as one of the most prominent factors affecting EERs' acceptance. The paper analysed the online information sources based on readability, credibility, and interactivity, focusing on accessibility and the ability to generate tailor-made suggestions. The analysis revealed that the online information sources are disorganised and dispersed. The online platforms rarely provide information on prior case studies and more on financial subsidies, guidelines, and EER benefits. Lastly, we discussed the main barriers and potential solutions for these challenges.

This work contributes towards optimizing edge analytics in Mobile Edge Computing (MEC) systems. We consider requests for computing tasks that are generated from users and can be satisfied either locally at their devices, or they can be offloaded to an edge server in their proximity for remote execution. We study a multi-user MEC system with limited energy autonomy for the mobile devices and with limitations on the computing capability of both mobile devices and at an edge server, where users can offload part of their computation load. We define a utility over 'resource residuals', that capture the difference between the resources assigned through our decisions, and those needed in practice, and we aim at the minimization of regret, i.e., of the difference between the utility obtained by an optimal offline benchmark that knows the system evolution in hindsight, and our online decision policy. We design an algorithm that jointly learns policies for offloading computations and scheduling them for execution at the shared MEC server. We prove that our algorithm is asymptotically optimal, i.e., it has no regret over the optimal static offline benchmark, and that its performance is independent of the number of devices in the system. From our numerical evaluation we conclude that our algorithm adapts to unpredictable demand changes, it learns to identify resource-limited devices, and it learns to share the server's resources.

Industrial Internet of things (IIoT), one enabler for Industry 4.0 Smart Factories, is a mission-critical and latency-sensitive application of 5G networks. Due to the stringent latency requirements in IIoT, coordinating the simultaneous transmissions of massive entities and knowing the interference they create to each other is not feasible. Additionally, due to the mobility feature of mobile robots and automated guided vehicles, the experienced channel fading may differ from the estimated one. Therefore, some uncertainties exist in IIoT networks while we decide the communication and control mechanisms. Within the context of IIoT, this paper discusses some resource allocation solutions from the perspective of Online Convex Optimization (OCO). OCO is a computationally lightweight and memory-efficient mathematical tool which tackles the optimization problems, given that the network environment is arbitrary and unknown. We first introduce the key performance indicators in IIoT networks and highlight the uncertain factors, which we may encounter while allocating the communication resources in IIoT. Then we provide an overview of main principles of OCO and present the comparison benchmarks and related metrics for performance evaluation. Moreover, we discuss the kind of resource allocation problems in IIoT that can be tackled by OCO. Finally, we summarize the advantages of applying OCO to IIoT networks.

We learn optimal user association policies for traffic from different locations to Access Points(APs), in the presence of unknown dynamic traffic demand. We aim at minimizing a broad family of α-fair cost functions that express various objectives in load assignment in the wireless downlink, such as total load or total delay minimization. Finding an optimal user association policy in dynamic environments is challenging because traffic demand fluctuations over time are non-stationary and difficult to characterize statistically, which obstructs the computation of cost-efficient associations. Assuming arbitrary traffic patterns over time, we formulate the problem of online learning of optimal user association policies using the Online Convex Optimization (OCO) framework. We introduce a periodic benchmark for OCO problems that generalizes state-of-the-art benchmarks. We exploit inherent properties of the online user association problem and propose PerOnE, a simple online learning scheme that dynamically adapts the association policy to arbitrary traffic demand variations. We compare PerOnE against our periodic benchmark and prove that it enjoys the no-regret property, with additional sublinear dependence of the network size. To the best of our knowledge, this is the first work that introduces a periodic benchmark for OCO problems and a no-regret algorithm for the online user association problem. Our theoretical findings are validated through results on a real-trace dataset.

Content caching has been experiencing revived interest within the context of current and next generation wireless networks. It brings content closer to users, decreasing the aggregate delivery costs and service delays for network providers. Recommender systems have become integral components of content provision sites. They offer personalized recommendations, increasing the individual user satisfaction and engagement with the content provider’s platform. Traditionally, caching and recommendation decisions are taken separately. However, there is a recent persistent trend where both network and content providers tend to deploy their own content delivery solutions. In light of this, we explore how the phenomenally conflicting objectives of content caching and recommendation can be jointly addressed. In this chapter we approach recommender systems as network traffic engineering tools that actively shape content demand to serve both user- and network-centric performance objectives. We introduce a model that captures the coupling between caching decisions and issued recommendations. Based on experimental evidence, we describe the impact of recommendations on user content requests and present a systematic way of engineering the user recommendations. Our viewpoint to recommender systems raises some concerns, not least ethical ones. Hence, we introduce a measure called preference distortion tolerance to quantify how much the engineered recommendations distort the original user content preferences. We describe the “recommendation window” as a way to controllably bound the distortion that recommendations will undergo and discuss specific properties that the recommender system should have in order to ensure that the “Quality of Recommendations (QoR)" is kept high by the issued recommendations. We formulate a joint optimization problem for the content to cache and recommend to each user, aiming to maximize the cache hit ratio. The preference distortion tolerance is embedded as a constraint to this problem formulation and marks an equally important dimension for assessing possible solutions. We prove that the problem lacks those properties that would guarantee its approximability, and devise a low-complexity practical algorithm that solves it efficiently. The algorithm is essentially a form of lightweight control over user recommendations, so that the recommended content is both appealing to the end-user and more friendly to the caching system and the network resources. We thoroughly evaluate it, by establishing its main properties and analytical performance bounds. We conduct an extensive sensitivity analysis over various system parameters, both analytically and through simulations with real and synthetic dataset. Next we extend our model and approach for a system in which users can access content from multiple caches, and analyse the intermediate joint caching and user association problem. We then discuss open directions and provide a detailed taxonomy of the related work. Due to space limitations, we do not provide proofs for our results. However, the interested reader may refer to [1-3] for more propositions and their detailed proofs.

The ever-growing trend of mobile users to consume high quality videos in their devices has pushed the backhaul network to its limits. Caching at Small-cell Base Stations (SBSs) has been established as an effective mechanism for alleviating this burden. Next generation mobile networks promise high Access Network density, hence multiple SBS association options per mobile user may exist. In this paper, we study the joint problem of mobile user association to SBSs and content placement to SBS-collocated caches, aiming to further optimize the utilization of backhaul network. The problem is solved periodically, considering time intervals where the users' location to the system is assumed to be fixed. First, we decompose the joint problem into two sub-problems that are solve sequentially, namely the content preference similarity-driven user association and the demand aware content placement sub-problems. We then propose a heuristic that consists of multiple phases. In particular, at a preparation phase, it performs clustering of users based on the similarity of their content preferences, accounting also for geographical constraints. The resulting clusters are then utilized for the demand-aware association of users to the SBS, while the placement of content is driven by the resulting local demand in each SBS, and takes place at the end. We demonstrate the effectiveness of our heuristic by evaluating its performance against multiple schemes that either lack a preparation phase or do not account for geographical constraints. As it is evident through the numerical results, the user clustering that takes place during the preparation phase can increase the overall cache hit ratio up to 20%.

Caching at the edge of the radio network is increasingly viewed as a promising countermeasure to the staggering demand for mobile video content. The persistent orientation of newer generations of mobile communication systems towards lower latency and faster radio access speeds only strengthens the arguments in its favor. When content caching is coordinated with other radio resource management functions, in particular, the benefits for the end users and the network operator are significant. In this paper, we investigate these benefits in cache-enabled small cell networks that jointly control (i) the Small-Cell Base Stations (SBSs) that serve as network access points for the mobile users; and (ii) the content that is stored at the SBS co-located caches. Our main contribution is a fast-converging computationally simple heuristic algorithm that iterates between assigning users to small cells and content to SBS caches, to maximize the overall cache hit ratio. The algorithm solutions compete with the optimal assignments at small problem instances and outperform alternative solutions for larger instances, especially when the content demand exhibits spatial locality. Combining good performance with non-prohibitive complexity, the algorithm could become a valuable tool for small cell network operators seeking to optimize the use of radio network resources.

In this paper, we investigate the performance gains that are achievable when jointly controlling (i) in which Small-cell Base Stations (SBSs) mobile users are associated to, (ii) which content items are stored at SBS co-located caches and (iii) which content items are recommended to the mobile users who are associated to different SBSs. We first establish a framework for the joint user association, content caching and recommendations problem, by specifying a set of necessary conditions for all three component functions of the system. Then, we provide a concrete formulation of the joint problem when the objective is to maximize the total hit ratio over all caches. We analyze the problems that emerge as special cases of the joint problem, when one of the three functions is carried out independently, and use them to characterize its complexity. Finally, we propose a heuristic that tackles the joint problem. Proof-of-concept simulations demonstrate that even this simple heuristic outperforms an optimal algorithm that takes only caching and recommendation decisions into account and provide evidence of the achievable performance gains when decisions over all three functions are jointly optimized.

Caching decisions typically seek to cache content that satisfies the maximum possible demand aggregated over all users. Recommendation systems, on the contrary, focus on individual users and recommend to them appealing content in order to elicit further content consumption. In our paper, we explore how these, phenomenally conflicting, objectives can be jointly addressed. First, we formulate an optimization problem for the joint caching and recommendation decisions, aiming to maximize the cache hit ratio under minimal controllable distortion of the inherent user content preferences by the issued recommendations. Then, we prove that the problem is NP-complete and that its objective function lacks those monotonicity and submodularity properties that would guarantee its approximability. Hence, we proceed to introduce a simpler heuristic algorithm that essentially serves as a form of lightweight control over recommendations so that they are both appealing to end-users and friendly to network resources. Finally, we draw on both analysis and simulations with real and synthetic datasets to evaluate the performance of the algorithm. We point out its fundamental properties, provide bounds for the achieved cache hit ratio, and study its sensitivity to its own as well as system-level parameters.