We study the realism of Sionna v1.0.2 ray-tracing for outdoor cellular links in central Rome. We use a real measurement set of 1,664 user-equipments (UEs) and six nominal base-station (BS) sites. Using these fixed positions we systematically vary the main simulation parameters, including path depth, diffuse/specular/refraction flags, carrier frequency, as well as antenna's properties like its altitude, radiation pattern, and orientation. Simulator fidelity is scored for each base station via Spearman correlation between measured and simulated powers, and by a fingerprint-based k-nearest-neighbor localization algorithm using RSSI-based fingerprints. Across all experiments, solver hyper-parameters are having immaterial effect on the chosen metrics. On the contrary, antenna locations and orientations prove decisive. By simple greedy optimization we improve the Spearman correlation by 5% to 130% for various base stations, while kNN-based localization error using only simulated data as reference points is decreased by one-third on real-world samples, while staying twice higher than the error with purely real data. Precise geometry and credible antenna models are therefore necessary but not sufficient; faithfully capturing the residual urban noise remains an open challenge for transferable, high-fidelity outdoor RF simulation.

Accurate environment mapping is an important computing task for a wide range of smart city applications, including autonomous navigation, wireless network operations and extended reality environments. On the one hand, conventional smart city mapping techniques, such as satellite imagery, LiDAR scans, and manual annotations, often suffer from limitations related to cost, accessibility and accuracy. On the other hand, open-source mapping platforms, such as OpenStreetMap, have been widely utilized in artificial intelligence (AI) applications for environment mapping, serving as a source of ground truth. However, human errors and the evolving nature of real-world environments introduce biases that can negatively impact the performance of neural networks trained on such data. In this paper, we present a deep learning-based approach that integrates the DINOv2 architecture to improve building mapping by combining (possibly erroneous) maps from open-source platforms with pervasive radio frequency (RF) data collected from multiple wireless user equipments and base stations. Unlike prior methods, our approach leverages a vision transformer-based architecture to jointly process both RF and map modalities within a unified framework, effectively capturing spatial dependencies and structural priors for enhanced mapping accuracy. For the evaluation purposes, we employ a synthetic dataset co-produced by Huawei. To address the challenges associated with real-world data imperfections, we introduce controlled noise to its RF data so as to simulate real-world conditions. Additionally, we develop and train a model that leverages only aggregated path loss information to tackle the mapping problem. We measure the results according to three performance metrics: the Jaccard index (intersection over union, IoU), the Hausdorff distance, and the Chamfer distance. Our design achieves a macro IoU of 65.3%, significantly surpassing (i) the erroneous maps baseline, which yields 40.1%, (ii) an RF-only method from the literature, which yields 37.3%, and (iii) a non-AI fusion baseline that we designed which yields 42.2%. The comparative evaluation highlights the limitations of relying solely on RF data or on spatial data, as well as the effectiveness that AI can have on fusing data towards enhancing smart city mapping accuracy. We further validate our method on real-world data from the Oslo region, complementing the synthetic evaluation with a real deployment setting, where our best fusion model reaches 64.9% macro IoU. We additionally outline a strategy for deploying the model over larger areas by tiling the region with overlapping windows.

As the Metaverse evolves, robust authentication is essential to protect digital avatar privacy from identity threats such as theft, unauthorized access, and avatar spoofing. A user’s gait, serving as an intrinsic biometric signature of their avatar, offers a seamless and continuous authentication mechanism, enhancing security. Traditional authentication methods, including passwords, biometrics, and facial or fingerprint recognition, face challenges in virtual environments due to occlusions, spoofing risks, and hardware dependencies. To address these limitations, we introduce AutoGaitAnalyzer, a novel gait authentication framework that uses 16 gait features from a large-scale simulation of 5,000 users. Benchmarked against over 10 state-of-the-art models, AutoGaitAnalyzer outperforms all, establishing a new standard for avatar security in the Metaverse.

Traditional wireless positioning methods exhibit limitations in the face of signal distortions prevalent in non-line-of-sight (NLOS) conditions, especially in the case of a single base station (BS). Moreover, the adoption of deep learning (DL) methodologies has lagged behind, largely due to the challenges associated with generating real-world datasets. In this paper, we present a comprehensive approach leveraging DL over large-scale synthetic wireless datasets (the recent WAIR-D in this case, which was co-produced by Huawei) to overcome such challenges and address the case of single-BS NLOS positioning. The aim of the paper is to practically explore the extent to which synthetic wireless datasets can help to achieve the positioning objectives. Towards this direction, we develop a map-based representation of a radio link, demonstrating its synergistic effect with feature-based representations in MLPs. Furthermore, we introduce a UNet-based neural model which incorporates input maps and radio link representations and generates as output a heatmap of potential device positions. This model achieves an 11.3-meter RMSE and 76.5% prediction accuracy on NLOS examples (1.5-meter, 99.9% for LOS) assuming perfect information, surpassing the MLP baseline by 47%. Finally, we provide further insights into the model’s ability to predict top device positions, the characteristics of predicted heatmaps as indicators of confidence, and the crucial role of map availability and radio path angles in model performance, thus revealing an unconventional perspective on incorrect predictions.

We present a computational modelling approach which targets capturing the specifics on how to virtually augment a Metaverse user’s available social time capacity via using an independent and autonomous version of her digital representation in the Metaverse. We motivate why this is a fundamental building block to model large-scale social networks in the Metaverse, and emerging properties herein. We envision a Metaverse-focused extension of the traditional avatar concept: An avatar can be as well programmed to operate independently when its user is not controlling it directly, thus turning it into an agent-based digital human representation. This way, we highlight how such an independent avatar could help its user to better navigate their social relationships and optimize their socializing time in the Metaverse by (partly) offloading some interactions to the avatar. We model the setting and identify the characteristic variables by using selected concepts from social sciences: ego networks, social presence, and social cues. Then, we formulate the problem of maximizing the user’s non-avatar-mediated spare time as a linear optimization. Finally, we analyze the feasible region of the problem and we present some initial insights on the spare time that can be achieved for different parameter values of the avatar-mediated interactions.

Conventional methods for outdoor environment reconstruction rely predominantly on vision-based techniques like photogrammetry and LiDAR, facing limitations such as constrained coverage, susceptibility to environmental conditions, and high computational and energy demands. These challenges are particularly pronounced in applications like augmented reality navigation, especially when integrated with wearable devices featuring constrained computational resources and energy budgets. In response, this paper proposes a novel approach harnessing ambient wireless signals for outdoor environment reconstruction. By analyzing radio frequency (RF) data, the paper aims to deduce the environmental characteristics and digitally reconstruct the outdoor surroundings. Investigating the efficacy of selected deep learning (DL) techniques on the synthetic RF dataset WAIR-D, the study endeavors to address the research gap in this domain. Two DL-driven approaches are evaluated (convolutional U-Net and CLIP+ based on vision transformers), with performance assessed using metrics like intersection-over-union (IoU), Hausdorff distance, and Chamfer distance. The results demonstrate promising performance of the RF-based reconstruction method, paving the way towards lightweight and scalable reconstruction solutions.

Apache Kafka is a widely-used event streaming platform for reliable high-volume real-time data exchange following a producer–consumer pattern. Despite its popularity, Apache Kafka requires expertise and attention to detail, and there are no default guidelines that can be applied to all use cases without careful consideration. In this paper, we propose a novel approach to optimise the number of partitions and brokers in Apache Kafka, which are two key configuration parameters, under the given characteristics and constraints of the target applications. In particular, we consider the distribution of data-intensive real-time flows exchanged between a set of producers and consumers, which is representative of fog computing environments for ML/AI analytics. We introduce a methodology for modelling the topic partitioning process in Apache Kafka and formulate an optimisation problem to determine the optimal number of partitions to satisfy the application requirements and constraints. We propose two efficient heuristics to solve the optimisation problem, considering the trade-off between resource utilisation and application performance. We evaluate the performance of our approach through numerical simulations, and we demonstrate its practicality by implementing a prototype on an Apache Kafka cluster and conducting experiments in three different scenarios focused on mass consumption vs. production and real-time data streaming. To carry out repeatable experiments in controlled conditions, we developed a reusable framework that fully automatises cluster setup and performance assessment, and we make it available to the community as open-source software.

Wireless power transfer (WPT) techniques are emerging as a fundamental component of next-generation energy management in mobile networks. In this context, the use of UAVs opens many possibilities, either using them as mobile energy storage devices to recharge IoT nodes, or to prolong their operation time via smart charging themselves at ground stations. This paper surveys the recent literature on WPT as it applies to UAVs and identifies several open research challenges for the future. As a first step, we tessellate the related research corpus in four fundamental categories (architectures, power and communications enabling technologies, optimization with respect to spatial concepts, optimization of operational aspects). Second, for each category, we provide a critical review of the recent WPT UAV approaches with respect to the way they specialize the general concept of WPT and the extent of their applicability. The survey presents the latest advances in WPT UAV methodologies and related energy-centric services, spanning all the way from the communications aspects deep in the small- and large-scale deployments, up to the operational and applications aspects. Finally, motivated by the rich conclusions of this critical analysis, we identify open challenges for future research. Our approach is horizontal, as the selected publications were drawn from across all vertical areas of research on UAVs. This paper can help the readers to deeply understand how WPT is currently applied to UAVs, and select interesting open research opportunities to pursue.

The recent advances in wireless energy transfer (WET) provide an alternate and reliable option for replenishing the battery of pervasive and portable devices, such as smart-phones. The peer-to-peer (P2P) mode of WET brings improved flexibility to the charging process among the devices as they can maintain their mobility while replenishing their battery. Few existing works in P2P-WET unrealistically assume the nodes to be exchanging energy at every opportunity with any other node. Also, energy exchange between the nodes is not bounded by the energy transfer limit in that inter-node meeting duration. In this regard, the parametric heterogeneity (in terms of device's battery capacity and WET hardware) among the nodes also affects the energy transfer bound in each P2P interaction, and thus, may lead to unbalanced network energy distributions. This inherent heterogeneity aspect has not been adequately covered in the P2P-WET literature so far, especially from the point of view of maintaining a balanced energy distribution in the networked population. In this work, we present a Heterogeneity-aware Wireless Energy Transfer (HetWET) method. In contrast to the existing literature, we devise a fine-grained model of wireless energy transfer while considering the parametric heterogeneity of the participating devices. Thereafter, we enable the nodes to explore and dynamically decide the peers for energy exchange. The performance of HetWET is evaluated using extensive simulations with varying heterogeneity settings. The evaluation results demonstrate that HetWET can maintain lower energy losses and achieve more balanced energy variation distance compared to three different state-of-the-art methods.

Battery aging is one of the major concerns for the pervasive devices such as smartphones, wearables and laptops. Current battery aging mitigation approaches only partially leverage the available options to prolong battery lifetime. In this regard, we claim that wireless crowd charging via network-wide smart charging protocols can provide a useful setting for applying battery aging mitigation. In this paper, for the first time in the state-of-the-art, we couple the two concepts and we design a fine-grained battery aging model in the context of wireless crowd charging, and two network-wide protocols to mitigate battery aging. Our approach directly challenges the related contemporary research paradigms by (i) taking into account important characteristic phenomena in the algorithmic modeling process related to fine-grained battery aging properties, (ii) deploying ubiquitous computing and network-wide protocols for battery aging mitigation, and (iii) fulfilling the user QoE expectations with respect to the enjoyment of a longer battery lifetime. Simulation-based results indicate that the proposed protocols are able to mitigate battery aging quickly in terms of nearly 46.74-60.87 % less reduction of battery capacity among the crowd, and partially outperform state-of-the-art protocols in terms of energy balance quality.

The advancements in peer-to-peer wireless power transfer (P2P-WPT) have empowered the portable and mobile devices to wirelessly replenish their battery by directly interacting with other nearby devices. The existing works unrealistically assume the users to exchange energy with any of the users and at every such opportunity. However, due to the users' mobility, the inter-node meetings in such opportunistic mobile networks vary, and P2P energy exchange in such scenarios remains uncertain. Additionally, the social interests and interactions of the users influence their mobility as well as the energy exchange between them. The existing P2P-WPT methods did not consider the joint problem for energy exchange due to user's inevitable mobility, and the influence of sociality on the latter. As a result of computing with imprecise information, the energy balance achieved by these works at a slower rate as well as impaired by energy loss for the crowd. Motivated by this problem scenario, in this work, we present a wireless crowd charging method, namely MoSaBa, which leverages mobility prediction and social information for improved energy balancing. MoSaBa incorporates two dimensions of social information, namely social context and social relationships, as additional features for predicting contact opportunities. In this method, we explore the different pairs of peers such that the energy balancing is achieved at a faster rate as well as the energy balance quality improves in terms of maintaining low energy loss for the crowd. We justify the peer selection method in MoSaBa by detailed performance evaluation. Compared to the existing state-of-the-art, the proposed method achieves better performance trade-offs between energy-efficiency, energy balance quality and convergence time.

The COVID-19 crisis resulted in a sudden and dramatic change in how manufacturing environments operate. Safe distancing among workers plays a pivotal role in preventing the spread of viral diseases such as COVID-19. Although general purpose commercial products already help prevention, enforcing ad hoc distancing without manufacturing production optimization can significantly decrease the production performance throughput. In this paper, we highlight the intrinsic trade-off of two concepts: worker health preservation versus factory productivity. We first motivate the importance of safe distancing in manufacturing shop-floors by analyzing a worker mobility dataset in a manual assembly scenario, given the safe distancing public health recommendations. Then, we suggest the quantification of the relation of the two concepts through exploiting pervasive computing technologies. Furthermore, we provide an insightful recommendation on the need of a holistic methodological framework, specifically tailored for addressing the Industry 4.0 requirements, as well as the worker necessities.

Peer-to-peer wireless power transfer (P2P-WPT) enables portable devices to mutually exchange energy. In opportunistic mobile networks, P2P-WPT can be uncertain due to the varying user inter-meeting duration. Existing P2P-WPT methods (unrealistically) assume the users to be exchanging energy at each opportunity, to be able to interact with all users, or the inter-node meeting duration to be unaffected by users' mobility. In this paper, in contrast to the state-of-the-art, not only we constitute more fine-grained, realistic assumptions for P2P-WPT, but also we design MobiWeb, a mobility-aware energy balancing method, which employs (for the first time) a predictor for estimating the mobility information of users. MobiWEB selects the different pairs of peers for energy exchange, such that the network energy distribution is balanced while minimizing the loss and energy difference between the peers. MobiWEB, when compared to the state-of-the-art, achieves different performance trade-offs between energy balance quality, convergence time, and energy-efficiency.

As data awareness in manufacturing companies increases with the deployment of sensors and Internet of Things (IoT) devices, data-driven maintenance and prediction have become quite popular in the Industry 4.0 paradigm. Machine Learning (ML) has been recognised as a promising, efficient and reliable tool for fault detection use cases, as it allows to export important knowledge from monitored assets. Scientists deal with issues such as the small amount of data that indicate potential problems, or the imbalance which exists between the standard process data and the data inadequacy of the systems to make a high precision forecast. Currently, in this context, even large industries are not able to effectively predict abnormal behaviors in their tools, processes and equipment, when adopting strategies to anticipate crucial events. In this paper, we propose a methodology to enable prediction of a packing machine's stoppages in manufacturing process of a large industry, by using forecasting techniques based on univariate time series data. There are more than 100 reasons that cause the machine to stop, in a quite big production line length. However, we use a single signal, concerning the machines operational status to make our prediction, without considering other fault or warning signals, hence its characterization as "agnostic". A workflow is presented for cleaning and preprocessing the data, and for training and evaluating a predictive model. Two predictive models, namely ARIMA and Prophet, are applied and evaluated on real data from an advanced machining process used for packing. Training and evaluation tests indicate that the results of the applied methods perform well on a daily basis. Our work can be further extended and act as reference for future research activities that could lead to more robust and accurate prediction frameworks.

Wireless power transfer technologies lead the way towards new paradigms for pervasive networking and have already penetrated the mobile and portable user device research and market. Their use is not only limited to charging devices like smartphones wirelessly by using a central wireless charger, but it is also extended to peer-to-peer (P2P) wireless crowd charging, when a user device shares energy directly with another user device. This paper surveys the literature over the period 20142020 on both P2P and central wireless crowd charging from the point of view of algorithmic applications as it applies to ubiquitously networked user devices and identifies some open research challenges for the future.

Wireless edge networks in smart industrial environments increasingly operate using advanced sensors and autonomous machines interacting with each other and generating huge amounts of data. Those huge amounts of data are bound to make data management (e.g., for processing, storing, computing) a big challenge. Current data management approaches, relying primarily on centralized data storage, might not be able to cope with the scalability and real time requirements of Industry 4.0 environments, while distributed solutions are increasingly being explored. In this paper, we introduce the problem of distributed data access in multi-hop wireless industrial edge deployments, whereby a set of consumer nodes needs to access data stored in a set of data cache nodes, satisfying the industrial data access delay requirements and at the same time maximizing the network lifetime. We prove that the introduced problem is computationally intractable and, after formulating the objective function, we design a two-step algorithm in order to address it. We use an open testbed with real devices for conducting an experimental investigation on the performance of the algorithm. Then, we provide two online improvements, so that the data distribution can dynamically change before the first node in the network runs out of energy. We compare the performance of the methods via simulations for different numbers of network nodes and data consumers, and we show significant lifetime prolongation and increased energy efficiency when employing the method which is using only decentralized low-power wireless communication instead of the method which is using also centralized local area wireless communication.

Quick energy depletion is an everyday problem in the lives of billions of smartphone users worldwide. Among the various methods for energy replenishment of battery powered devices like smartphones, the recent paradigm of wireless crowd charging has been gaining more and more attention. Having even limited knowledge on the crowd network properties can be crucial for the crowd energy conservation protocol design. A key characteristic of such crowds is the active presence and involvement of the users in online social networks. In this paper, we suggest (for the first time in the state of the art) the exploitation of online social information in order to tune the wireless crowd charging process. We examine a dataset of encounter records and corresponding self reported online social network data of a group of people. Based on the online social graph structure, we design a wireless crowd charging protocol which targets at balancing the available energy among the mobile users in the crowd while maintaining low energy losses. Based on the reported daily encounters of the users, we simulate wireless crowd charging for seven consecutive days and we compare the performance of our protocol with the performance of another state of the art protocol which does not use online social information. Interestingly enough, we demonstrate that online social network information can indeed influence the wireless crowd charging process.

The vast amounts of data generated in wireless industrial networked deployments introduce significant challenges on the data distribution process to consumer nodes within the timeframes imposed by the requirements of the Industry 4.0 paradigm. Using technological and methodological enablers, we can compose centralized or decentralized data distribution methods, which are able to help meeting the data requirements of the industrial applications. In this paper, using the technological enablers of WirelessHART, RPL and the methodological enabler of proxy selection as building blocks, we compose the protocol stacks of four different methods (both centralized and decentralized) for data distribution in wireless industrial networks over the IEEE 802.15.4 physical layer. Although there have been several comparisons of relevant methods in the recent literature, we identify that most of those comparisons are either theoretical, or based on abstract simulation tools, unable to uncover the specific, detailed impacts of the methods to the underlying networking infrastructure. We implement the presented methods in OMNeT++ and we evaluate their performance via a detailed simulation analysis. Interestingly enough, we demonstrate that the careful selection of a limited set of proxies for data caching in the network can lead to increased data delivery success rate and low data access latency.

We elaborate on two fundamental models for the emerging technology of Wireless Power Transfer in ad hoc communication networks. The first model is scalar, basically assuming that the received power by multiple transmitters is additive. The second model is vectorial, highlighting the detailed interference between RF waves of different power sources, thus, it is more precise (especially in the far field regions of dense charging systems) and allows addressing interesting superadditive (constructive) and cancellation (destructive) phenomena on the received power. Under these models, we present selected state of the art algorithms for key problems, such as how to deploy and configure the wireless chargers and how to achieve good trade-offs between efficient charging and electromagnetic radiation. We conclude with some future trends and directions in this fascinating topic.

Industrial IoT networks are typically used for monitoring systems and supporting control loops, as well as for movement detection systems, process control and factory automation. To this end, data generated by monitoring IoT devices are collected, elaborated and sent to controllers and actuators. The routing of data from IoT sensors to actuators is an integral part of any large-scale industrial network for maintaining critical delay requirements. Centralised schemes are typically used, whereby data are transferred to a central network controller, from where they are accessed by any other node requiring them. This may result in significant overheads and suboptimal resource consumption. In this paper, we propose a distributed, cooperative Data Management Layer (DML), whereby nodes cooperate to store data within the network. The DML is decoupled yet interacts with the underlying Network Plane. Specifically, given a set of data, the sets of nodes generating and requesting them, and a maximum access delay that requesting nodes can tolerate, the DML efficiently identifies a limited set of proxies in the network where data are stored. Given the mentioned constraints, we investigate the (computationally difficult) problem of finding which network nodes to select as proxies and we propose a simple method to address it. We demonstrate that the proposed method (i) guarantees that access delay stays below the given threshold, and (ii) significantly outperforms centralised and even distributed approaches, both in terms of access latency and in terms of maximum latency guarantees.

We study how to efficiently transfer energy wirelessly in ad hoc networks of battery-limited devices, towards prolonging their lifetime. We assume a weak population of distributed devices which are exchanging energy in a "peer-topeer", manner with each other. We address a quite general case of diverse energy levels and priorities in the network and study the problem of how the system can efficiently reach a weighted energy balance state distributively. We present three protocols that achieve different performance trade-offs between energy balance quality, convergence time and energy efficiency.

Wireless energy transfer is an emerging technology that is used in networks of battery-powered devices in order to deliver energy and keep the network functional. Existing state-of-the-art studies have mainly focused on applying this technology on networks of relatively strong computational and communicational capabilities (wireless sensor networks, ad-hoc networks), also they assume one-directional energy transfer from special chargers to the network nodes. Different from these works, we here study (for the first time in the state-of-theart) interactive, "peer-to-peer" wireless charging in populations of much more resource-limited, mobile agents that abstract distributed portable devices. In this new model for interactive wireless charging, we assume that the agents are capable of achieving bi-directional wireless energy transfer acting both as energy transmitters and harvesters. We consider the cases of both loss-less and lossy energy transfer and provide an upper bound on the time needed to reach a balanced energy distribution in the population. We investigate the delicate impact of the diversity of energy levels on eventual energy balance achieved and highlight some key elements of the charging procedure. In the light of the above, we design and evaluate three interaction protocols that achieve different tradeoffs between energy balance, time and energy efficiency.

We study how to efficiently transfer energy wirelessly in ad hoc networks of battery-limited devices, towards prolonging their lifetime. In contrast to the state-of-the-art, we assume a much weaker population of distributed devices which are exchanging energy in a "peer to peer" manner with each other, without any special charger nodes. We address a quite general case of diverse energy levels and priorities in the network and study the problem of how the system can efficiently reach a weighted energy balance state distributively, under both loss-less and lossy power transfer assumptions. Three protocols are designed, analyzed and evaluated, achieving different performance trade-offs between energy balance quality, convergence time and energy efficiency.

We present an IoT testbed architecture for Smart Buildings that enables the seamless and scalable integration of crowd-sourced resources such as smartphones and tablets. The purpose of this integration is dual. First, the embedded sensory capabilities of the resources provided by the crowd are combined with the sensing capabilities of the building for efficient smart actuations. Second, the system is able to interact with its users in a direct, personal way both for incentivising them to provide sensory data from their devices and to receive feed-back on their preferences and experienced comfort. The above are exposed to the experimenter as a set of services thus providing great agility on developing and evaluating a broad range of use case scenarios. We demonstrate this flexibility by deploying a testbed in the premises of a building and by evaluating several crowd incentive policies in the context of a smart luminance scenario. The scenario is based on Participatory Sensing principles to create live luminance maps, aggregate user preferences and accordingly adjust the luminance units.

Experimentally driven research is considered to be a key factor for growing the Internet industry. There is a large number of existing experimentation facilities which can be adapted to a seamless federation into a unified platform. Through this common federation, innovative experiments become possible and are able to break the boundaries of testbeds interoperability barriers. This way, infrastructure developers can utilize common tools of the federation, allowing them to not only focus on their core testbed functionalities, activities and services but also collaboratively combine them. In this work realized in the frame of the IoT Lab European research project, we provide a federation roadmap which paves the way towards a holistic integration of three testbeds, different both in terms of location and in terms of provided services and functionalities. The core element of each testbed is a set of networked constraint devices that can perform sensing and actuation tasks. This set is further enhanced with mobile user devices (smartphones), which can significantly improve the federation's capabilities by introducing crowdsensing functionalities. This enhancement actively employs users and therefore inserts the human factor in the data collection and storage process. For this reason, we also ensure full compliance with personal data and privacy protection rules, by implementing an anonymity, privacy and security preserving platform, on top of which the users may share their data.

Rapid technological advances in the domain of Wireless Energy Transfer (WET) pave the way for novel methods for energy management in Wireless Distributed Systems and recent research efforts have already started considering network models that take into account these new technologies. In this paper, we follow a new approach in studying the problem of efficiently charging a set of rechargeable nodes using a set of wireless energy chargers, under safety constraints on the electromagnetic radiation incurred. In particular, we define a new charging model that greatly differs from existing models in that it takes into account real technology restrictions of the chargers and nodes of the system, mainly regarding energy limitations. Our model also introduces non-linear constraints (in the time domain), that radically change the nature of the computational problems we consider. In this charging model, we present and study the Low Radiation Efficient Charging Problem (LREC), in which we wish to optimize the amount of "useful" energy transferred from chargers to nodes (under constraints on the maximum level of imposed radiation). We present several fundamental properties of this problem and provide indications of its hardness. Finally, we propose an iterative local improvement heuristic for LREC, which runs in polynomial time and we evaluate its performance via simulation. Our algorithm decouples the computation of the objective function from the computation of the maximum radiation and also does not depend on the exact formula used for the computation of the electromagnetic radiation in each point of the network, achieving good trade-offs between charging efficiency and radiation control, it also exhibits good energy balance properties. We provide extensive simulation results supporting our claims and theoretical results.

Wireless power transfer is used to fundamentally address energy management problems in Wireless Rechargeable Sensor Networks. In such networks mobile entities traverse the network and wirelessly replenish the energy of sensor nodes. In recent research on collaborative wireless charging, the mobile entities are also allowed to charge each other. In this work, we enhance the collaborative feature by forming a hierarchical charging structure. We distinguish the chargers into two groups, the hierarchically lower Mobile Chargers (MCs) which charge sensor nodes and the hierarchically higher Special Chargers (SCs) which charge MCs. We propose and implement four new collaborative charging protocols, in order to achieve efficient charging and improve important network properties. Our protocols are either centralized or distributed, and assume different levels of network knowledge. Extensive simulation findings demonstrate significant performance gains, with respect to non-collaborative state of the art charging methods. In particular, our protocols improve several network properties and metrics, such as the network lifetime, routing robustness, coverage and connectivity. A useful feature of our methods is that they can be suitably added on top of non-collaborative protocols to further enhance their performance.

In this work, we develop an IPv6 enabled smart building test-bed facility, by combining sensing and communication devices and functionalities. We address the Internet of Things paradigm by using diverse heterogeneous devices such as smartphones, sensor motes, NFC technology and traditional electrical devices, each one serving a specific role in the test-bed facility. Also, we extend a basic actuation component by making it self-aware, in terms of supported resources. Those enhancements allow us to enrich the test-bed's capabilities in terms of M2M communication, portability and decentralization of the actuation process. Finally, we provide a simple smart room scenario for a tunable combination of energy efficiency and comfort, which automatically adjusts the room's light level based on ambient conditions and user preferences and demonstrate the feasibility of our system.

We study the problem of efficient wireless energy recharging in Wireless Rechargeable Sensor Networks (WRSN). In such networks a special mobile entity (called the Mobile Charger) traverses the network and wirelessly replenishes the energy of sensor nodes. In contrast to most current approaches, we envision methods that are distributed and adaptive and use limited network information. Also, our methods can be used together with any underlying routing protocol (since they implicitly adapt to it). We propose alternative strategies for efficient recharging, addressing key issues of wireless recharging which we identify, most notably (i) to what extent each sensor should be recharged (ii) what are good trajectories the MC should follow. A simulation evaluation indicates significant performance gains in the network lifetime. Actually, the partial knowledge charging protocol's performance gets quite close to the performance of the centralized, global knowledge method and perform particularly well in heterogeneous networks.

This work addresses networked embedded systems enabling the seamless interconnection of smart building automations to the Internet and their abstractions as web services. In our approach, such abstractions are used to primarily create a flexible, holistic and scalable system and allow external end-users to compose and run their own smart/green building automation application services on top of this system. Towards this direction, in this paper we present a smart building test-bed consisting of several sensor motes and spanning across seven rooms. Our test-bed's design and implementation simultaneously addresses several corresponding system layers; from hardware interfaces, embedded IPv6 networking and energy balancing routing algorithms to a RESTful architecture and over the web development of sophisticated, smart, green scenarios. In fact, we showcase how IPv6 embedded networking combined with RESTful architectures make the creation of building automation applications as easy as creating any other Internet Web Service.

We investigate the problem of efficient wireless energy recharging in Wireless Rechargeable Sensor Networks (WRSNs). In such networks special mobile entities (called the Mobile Chargers) traverse the network and wirelessly replenish the energy of sensor nodes. In contrast to most current approaches, we envision methods that are distributed and use limited network information. We propose four new protocols for efficient recharging, addressing key issues which we identify, most notably (i) what are good coordination procedures for the Mobile Chargers and (ii) what are good trajectories for the Mobile Chargers. Two of our protocols (DC, DCLK) perform distributed, limited network knowledge coordination and charging, while two others (CC, CCGK) perform centralized, global network knowledge coordination and charging. As detailed simulations demonstrate, one of our distributed protocols outperforms a known state of the art method, while its performance gets quite close to the performance of the powerful centralized global knowledge method.

Through recent technology advances in the field of wireless energy transmission, Wireless Rechargeable Sensor Networks (WRSN) have emerged. In this new paradigm for WSNs a mobile entity called Mobile Charger (MC) traverses the network and replenishes the dissipated energy of sensors. In this work we first provide a formal definition of the charging dispatch decision problem and prove its computational hardness. We then investigate how to optimize the trade-offs of several critical aspects of the charging process such as a) the trajectory of the charger, b) the different charging policies and c) the impact of the ratio of the energy the MC may deliver to the sensors over the total available energy in the network. In the light of these optimizations, we then study the impact of the charging process to the network lifetime for three characteristic underlying routing protocols; a greedy protocol, a clustering protocol and an energy balancing protocol. Finally, we propose a Mobile Charging Protocol that locally adapts the circular trajectory of the MC to the energy dissipation rate of each sub-region of the network. We compare this protocol against several MC trajectories for all three routing families by a detailed experimental evaluation. The derived findings demonstrate significant performance gains, both with respect to the no charger case as well as the different charging alternatives; in particular, the performance improvements include the network lifetime, as well as connectivity, coverage and energy balance properties.