Abstract Let V be a set of n vertices, $${\mathcal M}$$ M a set of m labels, and let $${\textbf{R}}$$ R be an $$m \times n$$ m × n matrix ofs independent Bernoulli random variables with probability of success p; columns of $${\textbf{R}}$$ R are incidence vectors of label sets assigned to vertices. A random instance $$G(V, E, {\textbf{R}}^T {\textbf{R}})$$ G ( V , E , R T R ) of the weighted random intersection graph model is constructed by drawing an edge with weight equal to the number of common labels (namely $$[{\textbf{R}}^T {\textbf{R}}]_{v,u}$$ [ R T R ] v , u ) between any two vertices u, v for which this weight is strictly larger than 0. In this paper we study the average case analysis of Weighted Max Cut, assuming the input is a weighted random intersection graph, i.e. given $$G(V, E, {\textbf{R}}^T {\textbf{R}})$$ G ( V , E , R T R ) we wish to find a partition of V into two sets so that the total weight of the edges having exactly one endpoint in each set is maximized. In particular, we initially prove that the weight of a maximum cut of $$G(V, E, {\textbf{R}}^T {\textbf{R}})$$ G ( V , E , R T R ) is concentrated around its expected value, and then show that, when the number of labels is much smaller than the number of vertices (in particular, $$m=n^{\alpha }, \alpha <1$$ m = n α , α < 1 ), a random partition of the vertices achieves asymptotically optimal cut weight with high probability. Furthermore, in the case $$n=m$$ n = m and constant average degree (i.e. $$p = \frac{\Theta (1)}{n}$$ p = Θ ( 1 ) n ), we show that with high probability, a majority type randomized algorithm outputs a cut with weight that is larger than the weight of a random cut by a multiplicative constant strictly larger than 1. Then, we formally prove a connection between the computational problem of finding a (weighted) maximum cut in $$G(V, E, {\textbf{R}}^T {\textbf{R}})$$ G ( V , E , R T R ) and the problem of finding a 2-coloring that achieves minimum discrepancy for a set system $$\Sigma $$ Σ with incidence matrix $${\textbf{R}}$$ R (i.e. minimum imbalance over all sets in $$\Sigma $$ Σ ). We exploit this connection by proposing a (weak) bipartization algorithm for the case $$m=n, p = \frac{\Theta (1)}{n}$$ m

In the food and beverage industry many foods, beers and soft drinks usually need to get pasteurized, a process that holds a significant role in the quality and taste of the final product but is difficult to monitor due to the process nature. Soft sensing techniques, also called virtual sensing or surrogate sensing, can be leveraged to monitor the product quality, by using information available from other measurements and process parameters to calculate an estimation of the quantity of interest. In this paper, we develop a soft sensing methodology that is based on machine learning algorithms for continuous, end-to-end estimation of the temperature of products during the pasteurization process, with the vision to serve as an intermediate step towards monitoring live the final quality of the pasteurized products. This work studies a real beer pasteurization process in collaboration with Heineken’s plant in Patras, Greece and the results demonstrate notable performance in temperature prediction accuracy, with average root mean square error (RMSE) of 1.85°C in the test sets. Thus, we claim that it is possible to obtain measurements quite similar to the ones by the respective physical sensors with sufficient accuracy, and our methodology can be considered as a virtual low-cost solution for monitoring product quality in legacy pasteurizer operation.

Human Gesture Recognition (HGR) using smart wearable IoT devices has emerged as a new field in human-centered computing regarding various domains. Though there are many research works related to data processing methodologies and Neural Networks architectures in this field, a lack of research on how to efficiently identify and interpret the AI models’ exports into human gestures is observed. This paper proposes an innovative end-to-end approach of how to solve and evaluate effectively a major part of HGR problems in a real-world scenario, in real-time. This is achieved with the effective utilization of data processing methods, the adoption, and extension of a cutting-edge Deep Learning model architecture, as well as the introduction and implementation in practice of innovative methods, both for interpretation and evaluation, that increase the trustworthiness of the model’s predictions.As a case study, we deployed the introduced pipeline into a real-world scenario of gestures’ identification and classification regarding allergic symptoms. We adopted multidisciplinarity by collaborating with recognized allergists that validated the whole approach in real patients via two pilot phases. As a result, by delivering a real-world application of our approach, we achieved a superior performance concerning the reliability of the pipeline, being 91.6% in our laboratory pilot phase and 81.4% in patients’ pilot data. Lastly, it is worth mentioning here that our framework can be employed in most HGR problems with minor modifications in data processing and learning procedure configuration.

In the recent years, artificial intelligence, machine learning and IoT technologies have enabled a great number of industrial applications with profitable results. Predicting the remaining useful life (RUL) of turbofan engines constitutes a successful example of industrial AI, and it has received thorough attention from the researchers worldwide, with numerous novel and effective methods being proposed in the literature. Meanwhile, TinyML is a recent trend that has emerged in the AI field and demonstrates, amongst others, promising potential to break through the existing barriers of trusting and deploying real-time critical industrial AI solutions. In this context, this paper aims to further contribute to the literature and demonstrate the realization of RUL predictions in the extreme edge via TinyML, using the popular C-MAPSS dataset from NASA Ames Research Center, X-CUBE-AI tool and STMF767ZI microcontroller for the deployment of ML models. We benchmark different ML algorithms, with a special focus on deep learning algorithms (LSTMs and CNNs). The results indicate that there is potential for deploying machine learning models for RUL prediction in resource-scarce IoT devices, with acceptable accuracy loss, while taking advantage of the benefits TinyML has to offer over cloud-based AI inference.

Growing momentum in embedded systems and the wide use of sensors in everyday life, have motivated significantly, novel research in Internet of Things (IoT) systems and on-device Machine Learning (TinyML) processing. However, limitations in the energy stock and the computational capabilities of resource-scarce devices prevent the implementation of complex ML algorithms in IoT devices, which typically have limited computing power, small memory, and generate large amounts of data. This paper, aims to research and exploit the TinyML emerging technology for embedding intelligence in low-power devices, towards next generation IoT paradigm and smart sensing, in the context of SHM. In particular, the purpose is to provide integrated SHM functionality in plastic structures and thus make them "conscious" and self-explanatory (smart objects), by being able to localize any occurring impacts on the structure. We implement and benchmark Random Forest and Shallow Neural Network models on Arduino NANO 33 BLE, using an experimental dataset of piezoelectric sensor measurements concerning impact events in a thin plastic plate. The classification and model footprint results, 98.71% - 8KB and 95.35% - 12KB of accuracy and flash memory size for each model respectively, are very promising and constitute a solid baseline for motivating our concept.

Nowadays, more and more datasets are published towards research and development of systems and models, enabling direct comparisons, continuous improvement of solutions, and researchers engagement with experimental, real life data. However, especially in the Structural Health Monitoring (SHM) domain, there are plenty of cases where new research projects have a unique combination of structure design and implementation, sensor selection and technological enablers that does not fit with the configuration of relevant individual studies in the literature. Thus, we share the data from our case study to the research community as we did not find any relevant repository available. More specifically, in this paper, we present a novel time-series dataset for impact detection and localization on a plastic thin-plate, towards Structural Health Monitoring applications, using ceramic piezoelectric transducers (PZTs) connected to an Internet of Things (IoT) device. The dataset was collected from an experimental procedure of low-velocity, low-energy impact events that includes at least 3 repetitions for each unique experiment, while the input measurements come from 4 PZT sensors placed at the corners of the plate. For each repetition and sensor, 5000 values are stored with 100 KHz sampling rate. The system is excited with a steel ball, and the height from which it is released varies from 10 cm to 20 cm. The dataset is available in GitHub (https://github.com/Smart-Objects/Impact-Events-Dataset).

Allergic diseases and, in particular, allergic rhinitis are among the most common chronic diseases, inducing disturbances in daily activities. They are caused primarily by the pollens of allergenic plants and symptoms can deteriorate due to various ambient conditions which work as irritants, such as humidity. In this paper, we present the development of an eHealth/mHealth holistic platform that utilizes the technologies of Internet of Things (IoT), Mobile Crowdsensing (MCS), Social Networking Services, Natural Language Processing (NLP), and Machine Learning (ML), in order to work as a sentinel and disease prevention tool for patients with allergic rhinitis symptoms. By efficiently combining human with machine intelligence, we provide a complementary sensing method for the comprehensive and large-scale monitoring of the disease in broad regions, and in real-time. Moreover, the users of our platform are encouraged to engage in the sensing process through a personalized health monitoring system in order to keep a constant awareness of their symptoms and, thus, deliver a successful adherence to their treatment. As an important use case, we adapted our platform to the USA region, but it can be easily extended to any other area with minor modifications. The design and complete implementation of our platform has been performed and validated in close cooperation with well-recognized academic medical doctors based in Greece who specialize in the control of allergic diseases (and rhinitis in particular) and provided valuable insights and detailed requirements analysis about the functionality and usability of the platform. To the best of our knowledge, this is the first study that examines allergic rhinitis monitoring in a complementary manner and on large scale, with the utilization of hybrid data sources.

The effective utilization of real-world data is an integral part of any IoT monitoring or AI-assisted system. Thus, data collection and annotation is an important step towards the successful development and realization of such systems. Nevertheless, in order to create reliable datasets, current data collection and annotation methodologies often require a controlled environment while also the presence of the volunteer contributing to the process, or any subject for that matter, and an expert, monitoring the procedure, is mandatory. These processes are heavily restrained by the recent COVID-19 pandemic outbreak.To address such issues, in this paper we propose a human-centered Web-based dataset creation and annotation tool that utilizes the Web Bluetooth API. The user can effectively collect gestures from a nearby device that supports the BLE protocol, assign tags to the collected data, and store them remotely, in real-time. The data storage, as well as its annotation, can also be performed remotely by an expert stakeholder. An off-the-shelf wearable sensorial device has been used indicatively for our tool demonstration purposes. To the best of our knowledge, this is the first attempt that exploits the Web Bluetooth API capabilities for the development of a Browser-based real-time data collection, storage, and annotation tool. Our tool can be also expanded to other applications that use the sensing device with only minor configuration changes and is also operable through any smart-device that supports a Web-Browser. Furthermore, our tool’s performance matches that of native applications’. Finally, the tool is successfully deployed and validated by integrating it into our ongoing ML platform that is related to allergic rhinitis gesture recognition.

In this paper, we propose a multidisciplinary Gesture Recognition case study using a Machine Learning approach for the detection and classification of allergic rhinitis-related gestures. Allergic diseases and especially allergic rhinitis are among the most common diseases in the world, mostly underappreciated, causing considerable impairment of daily activities, including job, and school productivity. For this reason, close monitoring and early recognition of symptoms worsening are considered essential. We hypothesize that recognizing allergic rhinitis to patients by such an approach may be a useful tool for such purpose.In our study, for the first time, the most common allergic rhinitis gestures are identified, based on patients' description and specialists' experience. Our data is retrieved by a large pool of active allergic rhinitis patients attending three specialized outpatient clinics in Greece. Gestures are recorded with the help of a wristband Bluetooth device incorporating a 3-axis accelerometer and a 3-axis gyroscope. Feature engineering and several signal processing methods are then applied to the raw sensor data (which are treated as 6-dimensional signals), and valuable features are extracted related to the time and frequency domains.To improve the performance of the Machine Learning models, we utilize Principal Component Analysis (PCA), and we also use functions such as Grid Search and Randomized Search, in order to achieve higher recognition accuracy by hyperparameter optimization.With these features and steps of processing, we built a classifier that can uniquely identify 15 allergic rhinitis gestures with an accuracy of 93% in a challenging variety of moves in the patient's head (nose, eye, ear). It is worth noting that allergic rhinitis gestures are more subtle, varied and spontaneous than other moves that have been considered in the literature so far. To the best of our knowledge, this is the first time that a Machine Learning approach is successfully applied in such a challenging field like respiratory diseases.

For the important problem of increasing levels of air pollution and noise in urban and rural areas, we propose a holistic, multi-dimensional approach to gather, monitor and analyze heterogeneous data sources of air pollutants and noise indicators, into an integrated, intelligent computational system. Although several interesting approaches have been developed for monitoring pollution and noise, however the challenge remains for even more detailed, precise, large scale monitoring.To overcome the limitations of current systems, we envision an integrated approach to human-centric environmental intelligence, bringing together modern IoT technology and the human factor. In particular, our approach emphasizes selected behavioural and health aspects, and the complementary use of sensing technology with citizen engagement and crowdsourcing methods. The proposed system will collect diverse data from heterogeneous sources, such as mobile and static wireless sensor networks, crowdsourcing, citizen questionnaires and social media analytics, to continuously combine objective estimations with subjective perception of air quality and noise. With the use of advanced AI and Deep Learning algorithms, our system will be able to estimate air pollutants concentration and noise levels in micro-scale with adequate precision over large urban-scale environments. Furthermore, tracking of behavioral and psychological users' input, as well as personal exposure to pollution, will allow studying the impact of air quality and noise on the users' daily habits and the interplay of ambient conditions with behavioural factors, towards an active engagement of citizens in a hybrid techno-social manner. A reference architecture for the realization of this human-centric environmental intelligence approach is presented. Also, a planned implementation at the city of Patras, Greece is discussed. To the best of our knowledge, this is one of the first holistic, multifaceted approaches to a surveillance system for air quality and noise in urban areas.

With solar parks being established as one of the most important renewable energy systems, there is a strong need for more efficient use of the services they provide, as well as error detection and performance issues confrontation. Internet of Things (IoT) technology, aims to fill the gap, by offering low cost and sustainable solutions towards the efficient operation of these parks. In this paper, we present an in situ monitoring and alerting system, based on WSN technologies, regarding the early detection of Potential Induced Degradation (PID) and Hotspots failures, that can cause a significant drop in solar panels' performance. In order to do so, specific non-trivial attributes such as temperature, humidity, irradiance, current and voltage are continuously monitored at panel level, and processed in a cloud based platform to early identify these phenomena. In particular, sensor nodes send data to a centralized local sink module using a multi-hop WSN architecture, in order to establish a robust and large coverage area. Afterwards, the information is propagated to the cloud server, where deterministic diagnostic algorithms are applied. We present the reference architecture of our approach, alongside the corresponding hardware and software structural, individual components, as well as the integration process and the use case that runs over a real solar park.

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.

A lot of research has been contributed towards smart energy harvesting, efficient energy management and energy storage/supply capabilities, as they are considered a major bottleneck in Wireless Sensor Networks (WSNs). Similarly, there is an extreme interest to design new algorithms and protocols regarding energy harvesting prediction, load's energy consuming profiling, etc. Although those techniques improve energy efficiency, it still remains to solve the fundamental problem of energy provisioning in a more practical, real-life manner, as the majority of the hardware solutions choose to produce simple and robust implementations. In this paper, we present a smart energy management platform for low-power IoT systems that implements both energy harvesting and storage technologies but dynamically sets different power modes based on online monitoring measurements and energy harvesting prediction. With respect to power specifications our solution succeeds to both supply and inform the load system for future energy provisioning capability. Thus, our prototype can be characterised as a load agnostic device w.r.t. specification values, that can adjust to different conditions and use cases towards an effective system energy provisioning.

Temporal graphs abstractly model real-life inherently dynamic networks. Given a graph G, a temporal graph with G as the underlying graph is a sequence of subgraphs (snapshots) Gt of G, where t≥1. In this paper we study stochastic temporal graphs, i.e. stochastic processes G whose random variables are the snapshots of a temporal graph on G. A natural feature observed in various real-life scenarios is a memory effect in the appearance probabilities of particular edges; i.e. the probability an edge e∈E appears at time step t depends on its appearance (or absence) at the previous k steps. We study the hierarchy of models of memory-k, k≥0, in an edge-centric network evolution setting: every edge of G has its own independent probability distribution for its appearance over time. We thoroughly investigate the complexity of two naturally related, but fundamentally different, temporal path problems, called Minimum Arrival and Best Policy.

Bluetooth Low Energy is a fast growing protocol which has gained wide acceptance during last years. Key features for this growth are its high data rate and its ultra low energy consumption, making it the perfect candidate for piconets. However, the lack of expandability without serious impact on its energy consumption profile, prevents its adoption on more complex systems which depend on long network lifetime. Thus, a lot of academic research has been focused on the solution of BLE expandability problem and BLE mesh has been introduced on the latest Bluetooth version. In our point of view, most of the related work cannot be efficiently implemented in networks which are mostly comprised of constrained-resource nodes. Thus, we propose a new energy efficient tree algorithm for BLE static constrained-resources networks, which achieves a longer network lifetime by both reducing as much as possible the number of needed connection events and balancing the energy dissipation in the network.

Internet of Things (IoT) has become the backbone of current and future emerging applications both in the public and the private, industrial sector. The IoT paradigm, enhanced with intelligence and big data analytics, has found applications in a wide range of solutions such as smart home, smart city, industrial IoT etc. Even though IoT implies that cheap motes can conduct a specific task, thus a large number of them can be deployed, we aim to minimize the installed hardware while we still have a high level of quality of service. Machine Learning algorithms can support this challenge by generating virtual data via utilization of real data from a smaller subset of sensors. The generated data can replicate sensor behavior which would otherwise be difficult or impossible to track. It is also possible to use simulation models for data analysis model validation, by generating new data under varying conditions. In this paper, we propose a concept of an IoT testbed which allows virtual IoT resources to be immersed and tested in real life conditions, which are met in everyday life. Additionally, the features of the implemented testbed prototype are discussed while taking into account specific use cases, regarding luminosity scenarios in industrial environments.

The development of the emerging Smart Cities concept aims to enhance city infrastructures and services via the capabilities offered by information and communication technologies (ICT). One of the Smart City main pillars is Smart Mobility. Improvements to the existing physical transportation infrastructure are necessary, to accommodate safety and efficiency. This paper provides a technical overview regarding energy harvesting and packaging issues of the European Horizon 2020 funded project on Safe and green Sensor Technologies for self-explaining and forgiving Road Interactive aPplications (SAFE STRIP). In particular, it focuses on the integration of a number of on-road entities, named as On Road Units (ORU), into an intelligent road tapes/markers in order to improve road surface condition monitoring, traffic and safety. Even though ORU consists of multiple modules (ambient condition sensors, traffic sensors, communication, etc.), here we choose to discuss critical aspects regarding energy provisioning and harvesting, and packaging of this particular system. Particularly, besides primary and secondary batteries, we incorporate solar and piezoelectric harvesters as well as wireless charging receivers and antennas, while all of them co-exist under a hierarchical schema. Finally, we provide a design and implementation study, while results from a number of simulations are also outlined.

This papers investigates the effective control of electromagnetic radiation (EMR) in wireless adhoc communication networks. In particular, we focus on networks with wireless provision of energy, via the emerging technology of wireless power transfer (WPT). Our aim is to propose algorithmic methods towards optimizing the trade-off among the (potentially high) radiation levels in the network and the efficiency of power transfer. After formally defining EMR and relevant performance metrics, we critically discuss selected abstract radiation models (such as a well-studied scalar model) and identify their strengths and limitations. In particular, we highlight a recent vectorial representation of wireless power, which allows a very precise management of radiation, as well as a peer to peer model of wireless power exchange with negligible radiation levels. Under these models, we present selected algorithmic methods and heuristics for effective radiation control, such as adaptive schemes for charger configuration in highly mobile systems, the precise phase management of the wireless power waves and the evaluation and handling of overlaps in the wireless power transmission.

This paper addresses the optimization of power provisioning in systems of wireless energy transfer. In this context, a vectorial representation of wireless waves recently becomes a precious tool; being more reliable and precise than one-dimensional models, it enables an increased potential for power maximization and control that before seemed impossible. We study the deployment of nodes and chargers for power maximization, for the first time under the vector model. In particular, we present both offline and approximation protocols and provide an evaluation of their performance. The main idea of our approach is to take advantage of the high precision offered by the vector model of WPT waves, in order to fine-tune the exact positioning of wireless chargers. The results of the conducted simulations demonstrate the advantages of our approaches in terms of power maximization; interestingly our findings suggest that even some slight optimization in the exact placement of chargers can significantly improved received power.

Recent technological advances in the domain of Wireless Power Transfer (WPT) have enabled the employment of previously unrealistic methods for power management in wireless systems. At the same time, some of the classical scalar models have proved incapable of capturing the multi-dimensional aspects of WPT that are similar to the superposition of wave functions. In this work, we consider the vector model which is by now a widely accepted model for WPT and its validity has been confirmed experimentally in the literature. Under the vector model, we study the problem of power maximization in a wireless network consisting of wireless chargers. We take the state of the art one step further by assuming that chargers can use phase-shifting to adjust their output in order to improve the total power provided by the network of chargers at selected points in the network area. Even though the technology for phase-shifting already exists, researchers have only recently tried to study it from an algorithmic perspective and algorithmic solutions are nearly inexistent. In this paper, we provide a rigorous formulation for the problem of power maximization as a semi-definite program with rank constraints and we present efficient centralized and distributed solutions, and also heuristics where only local information is available.

Advances in Micro-Electro-Mechanical Systems (MEMS) and energy scavenging technologies are now able to provide viable solutions based on different application environments. In particular, rechargeable battery-operated Wireless Sensor Networks (WSN) can now be set upon a new basis that targets both ambient energy harvesting and wireless charging technologies in different forms and scales. Hence, the assumption of finite energy budget (that may also affect the system's performance) can be over-passed by potentially unlimited energy supply. The presented platform is able to effectively power a low power IoT system with processing, sensing and wireless communication capabilities. It embeds an advanced energy management IC that allows extremely high efficiency energy harvesting, suitable for low-power and miniaturized energy generators. Unlike other systems it not only supports a variety of power supply options, but also a hybrid energy storage scheme. This paper aims to provide a description of the system's design, the embedded intelligence and the performance in terms of energy autonomy.

In this paper, we focus on designing a holistic approach to gather, monitor and analyze heterogeneous sources on allergies and allergens into one system. The dimensions of mobile crowdsensing, the sensor-based measurements, and micro-blogging raw texts are combined to address all different situations and needs. We envision that our system can be used for public health monitoring in order to early predict allergy/allergens or allergy-related irritants outbreaks. To the best of our knowledge, our system is the first holistic approach of a surveillance system for allergy in the US region.

In this work, we investigate the interesting impact of mobility on the problem of efficient wireless power transfer in ad hoc networks. We consider a set of mobile agents (consuming energy to perform certain sensing and communication tasks), and a single static charger (with finite energy) which can recharge the agents when they get in its range. In particular, we focus on the problem of efficiently computing the appropriate range of the charger with the goal of prolonging the network lifetime. We first demonstrate (under the realistic assumption of fixed energy supplies) the limitations of any fixed charging range and, therefore, the need for (and power of) a dynamic selection of the charging range, by adapting to the behavior of the mobile agents which is revealed in an online manner. We investigate the complexity of optimizing the selection of such an adaptive charging range, by showing that two offline optimization problems (closely related to the online one) are NP-hard. To effectively address the involved performance trade-offs, we finally present a variety of adaptive heuristics, assuming different levels of agent information regarding their mobility and energy.

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.

We provide a cloud-based journey planner for public transport built upon an efficient core routing engine that updates efficiently timetable information in case of delays. We describe our mobile application along with a service that allows users to assess the suggested journeys offered by the application, built on top of an IoT/FIRE+ infrastructure. Our journey planner contributes to the establishment of a live community of travelers, equipped with an arsenal of inter-operable personalized renewable mobility services, for modern mobility in smart cities.

A Wireless Power Transfer (WPT) system consists of chargers which transmit power wirelessly and receivers which harvest the radio frequency energy from the chargers. WPT has evolved to a very active research subject, as well as a topic of rapid technological progress and emerging practical development and commercial applications. However, a solid foundational and algorithmic framework seems still necessary for WPT to achieve its full potential. In this respect, the talk aims to discuss different characteristic abstract WPT models (scalar, vector, peer to peer) and present key optimization problems (power maximization, coverage, placement, radiation control). Relevant algorithmic design and analysis methods and performance properties (and their trade-offs) are also provided, as well as interesting WPT notions and concepts (such as super-additive and cancellation phenomena in the received power, notions of electromagnetic radiation control in dense, strong WPT fields and energy-aware distributed network formation in large populations of very weak mobile nodes).

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.

In this paper, we study contention resolution protocols from a game-theoretic perspective. We focus on acknowledgment-based protocols, where a user gets feedback from the channel only when she attempts transmission. In this case she will learn whether her transmission was successful or not. Users that do not transmit will not receive any feedback. We are interested in equilibrium protocols, where no player has an incentive to deviate. The limited feedback makes the design of equilibrium protocols a hard task as best response policies usually have to be modeled as Partially Observable Markov Decision Processes, which are hard to analyze. Nevertheless, we show how to circumvent this for the case of two players and present an equilibrium protocol. For many players, we give impossibility results for a large class of acknowledgment-based protocols, namely age-based and backoff protocols with finite expected finishing time. Finally, we provide an age-based equilibrium protocol, which has infinite expected finishing time, but every player finishes in linear time with high probability.

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.

In this paper we initiate the study of populations of agents with very limited capabilities that are globally able to compute order statistics of their arithmetic input values via pair-wise meetings. To this extent, we introduce the Arithmetic Population Protocol (APP) model, embarking from the well known Population Protocol (PP) model and inspired by two recent papers in which states are treated as integer numbers. In the APP model, every agent has a state from a set Q of states, as well as a fixed number of registers (independent of the size of the population), each of which can store an element from a totally ordered set S of samples. Whenever two agents interact with each other, they update their states and the values stored in their registers according to a joint transition function. This transition function is also restricted; it only allows (a) comparisons and (b) copy / paste operations for the sample values that are stored in the registers of the two interacting agents. Agents can only meet in pairs via a fair scheduler and are required to eventually converge to the same output value of the function that the protocol globally and stably computes. We present two different APPs for stably computing the median of the input values, initially stored on the agents of the population. Our first APP, in which every agent has 3 registers and no states, stably computes (with probability 1) the median under any fair scheduler in any strongly connected directed (or connected undirected) interaction graph. Under the probabilistic scheduler, we show that our protocol stably computes the median in O(n^6) number of interactions in a connected undirected interaction graph of n agents. Our second APP, in which every agent has 2 registers and O(n^2 log{n}) states, computes to the correct median of the input with high probability in O(n^3 log{n}) interactions, assuming the probabilistic scheduler and the complete interaction graph. Finally we present a third APP which, for any k, stably computes the k-th smallest element of the input of the population under any fair scheduler and in any strongly connected directed (or connected undirected) interaction graph. In this APP every agent has 2 registers and n states. Upon convergence every agent has a different state; all these states provide a total ordering of the agents with respect to their input values.

This paper addresses wirelessly networked populations of nodes (agents) that can both transmit and receive wireless power among each other, interacting locally in a peer to peer manner. In this setting, we study the important problem of network formation, in particular how the agents can distributively create a star structure. Extending the state of the art, we introduce energy considerations in network formation: in addition to the star construction, our goal is to achieve a certain target energy distribution among the agents. We assume a generalized, more realistic energy loss factor which may differ for each pairwise power exchange.

Summary Motivated by the problem of efficiently collecting data from wireless sensor networks via a mobile sink, we present an accelerated random walk on random geometric graphs (RGG). Random walks in wireless sensor networks can serve as fully local, lightweight strategies for sink motion that significantly reduce energy dissipation but introduce higher latency in the data collection process. In most cases, random walks are studied on graphs like Gn,p and grid. Instead, we here choose the RGG model, which abstracts more accurately spatial proximity in a wireless sensor network. We first evaluate an adaptive walk (the random walk with inertia) on the RGG model; its performance proved to be poor and led us to define and experimentally evaluate a novel random walk that we call γ‐stretched random walk. Its basic idea is to favour visiting distant neighbours of the current node towards reducing node overlap and accelerate the cover time. We also define a new performance metric called proximity cover time that, along with other metrics such as visit overlap statistics and proximity variation, we use to evaluate the performance properties and features of the various walks. Copyright © 2013 John Wiley & Sons, Ltd.

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.

This research attempts a first step towards investigating the aspect of radiation awareness in environments with abundant heterogeneous wireless networking. We call radiation at a point of a 3D wireless network the total amount of electromagnetic quantity the point is exposed to, our definition incorporates the effect of topology as well as the time domain, data traffic and environment aspects. Even if the impact of radiation to human health remains largely unexplored and controversial, we believe it is worth trying to understand and control. We first analyze radiation in well known topologies (random and grids), randomness is meant to capture not only node placement but also uncertainty of the wireless propagation model. This initial understanding of how radiation adds (over space and time) can be useful in network design, to reduce health risks. We then focus on the minimum radiation path problem of finding the lowest radiation trajectory of a person moving from a source to a destination point of the network region. We propose three heuristics which provide low radiation paths while keeping path length low, one heuristic gets in fact quite close to the offline solution we compute by a shortest path algorithm. Finally, we investigate the interesting impact on the heuristics' performance of diverse node mobility.

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.

Advancements in both on-board and wireless communication technologies provide the necessary backbone for the deployment of a network between vehicles. Most of the envisioned applications for these networks would be greatly favored by multimedia support provisioning. However, there are several issues to be handled in order to provide multimedia services with reasonable quality. In this work, we analyze a specific multimedia service that can be used by several interesting applications: video broadcasting. Particularly, we throughly discuss and evaluate the role of redundancy in improving this service. We have verified that although redundancy does increase the effectiveness in video broadcasting, coding techniques do not improve redundancy's efficiency.

We study the problem of energy-balanced data propagation in wireless sensor networks. The energy balance property is crucial for maximizing the time the network is functional, by avoiding early energy depletion of a large portion of sensors. We propose a distributed, adaptive data propagation algorithm that exploits limited, local network density information for achieving energy-balance while at the same time minimizing energy dissipation. We investigate both uniform and heterogeneous sensor placement distributions. By a detailed experimental evaluation and comparison with well-known energy-balanced protocols, we show that our density-based protocol improves energy efficiency significantly while also having better energy balance properties.

In this work, we overview some results concerning communication combinatorial properties in random intersection graphs and uniform random intersection graphs. These properties relate crucially to algorithmic design for important problems (like secure communication and frequency assignment) in distributed networks characterized by dense, local interactions and resource limitations, such as sensor networks. In particular, we present and discuss results concerning the existence of large independent sets of vertices whp in random instances of each of these models. As the main contribution of our paper, we introduce a new, general model, which we denote G(V, χ, f). In this model, V is a set of vertices and χ is a set of m vectors in ℝm. Furthermore, f is a probability distribution over the powerset 2χof subsets of χ. Every vertex selects a random subset of vectors according to the probability f and two vertices are connected according to a general intersection rule depending on their assigned set of vectors. Apparently, this new general model seems to be able to simulate other known random graph models, by carefully describing its intersection rule.

We study the problem of greedy, single path data propagation in wireless sensor networks, aiming mainly to minimize the energy dissipation. In particular, we first mathematically analyze and experimentally evaluate the energy efficiency and latency of three characteristic protocols, each one selecting the next hop node with respect to a different criterion (minimum projection, minimum angle and minimum distance to the destination). Our analytic and simulation findings suggest that any single criterion does not simultaneously satisfy both energy efficiency and low latency. Towards parameterized energy-latency trade-offs we provide as well hybrid combinations of the two criteria (direction and proximity to the sink). Our hybrid protocols achieve significant perfomance gains and allow fine-tuning of desired performance. Also, they have nice energy balance properties, and can prolong the network lifetime.

Abstract Geographic routing scales well in sensor networks, mainly due to its stateless nature. Most of the algorithms in this area are concerned with guaranteeing a path toward the destination in the context of any network topology, while the optimality of the path is of little interest. In this paper, we are presenting a novel geographic routing algorithm with obstacle avoidance properties. It aims at finding the optimal path from a source to a destination when some areas of the network are unavailable for routing due to low local density or obstacle presence. It locally and gradually with time (but, as we show, quite fast) evaluates and updates the quality of the previously used paths and ignores non‐optimal paths for further routing. By means of extensive simulations, we are comparing its performance to existing state of the art protocols, showing that it performs much better in terms of path length and hop count thus minimizing latency, overall traffic and energy consumption. Copyright © 2008 John Wiley & Sons, Ltd.

We study the problem of fast and energy-efficient data collection of sensory data using a mobile sink, in wireless sensor networks in which both the sensors and the sink move. Motivated by relevant applications, we focus on dynamic sensory mobility and heterogeneous sensor placement. Our approach basically suggests to exploit the sensor motion to adaptively propagate information based on local conditions (such as high placement concentrations), so that the sink gradually "learns" the network and accordingly optimizes its motion. Compared to relevant solutions in the state of the art (such as the blind random walk, biased walks, and even optimized deterministic sink mobility), our method significantly reduces latency (the improvement ranges from 40% for uniform placements, to 800% for heterogeneous ones), while also improving the success rate and keeping the energy dissipation at very satisfactory levels.

Collecting sensory data using a mobile data sink has been shown to drastically reduce energy consumption at the cost of increasing delivery delay. Towards improved energy-latency trade-offs, we propose a biased, adaptive sink mobility scheme, that adjusts to local network conditions, such as the surrounding density, remaining energy and the number of past visits in each network region. The sink moves probabilistically, favoring less visited areas in order to cover the network area faster, while adaptively stopping more time in network regions that tend to produce more data. We implement and evaluate our mobility scheme via simulation in diverse network settings. Compared to known blind random, non-adaptive schemes, our method achieves significantly reduced latency, especially in networks with nonuniform sensor distribution, without compromising the energy efficiency and delivery success.

We investigate random intersection graphs, a combinatorial model that quite accurately abstracts distributed networks with local interactions between nodes blindly sharing critical resources from a limited globally available domain. We study important combinatorial properties (independence and hamiltonicity) of such graphs. These properties relate crucially to algorithmic design for important problems (like secure communication and frequency assignment) in distributed networks characterized by dense, local interactions and resource limitations, such as sensor networks. In particular, we prove that, interestingly, a small constant number of random, resource selections suffices to make the graph Hamiltonian and we provide tight evaluations of the independence number of these graphs.

We present the conceptual basis and the initial planning for an open source management architecture for wireless sensor networks (WSN). Although there is an abundance of open source tools serving the administrative needs of WSN deployments, there is a lack of tools or platforms for high level integrated WSN management. This is because of a variety of factors, including the lack of open source management tools, the immaturity of tools that offer manageability for WSNs, the limited high level management capabilities of sensor devices and architectures, and the lack of standardization. The current work is, to our knowledge, the first effort to conceptualize, formalize and design a remote, integrated management platform for the support of WSN research laboratories. The platform is based on the integration and extension of two innovative platforms: jWebDust, a WSN operation and management platform, and OpenRSM, an open source integrated remote systems and network management platform. The proposed system architecture can support several levels of integration (infrastructure management, functionality integration, firmware management), corresponding to different use-cases and application settings.

We study the important problem of tracking moving targets in wireless sensor networks. We try to overcome the limitations of standard state of the art tracking methods based on continuous location tracking, i.e. the high energy dissipation and communication overhead imposed by the active participation of sensors in the tracking process and the low scalability, especially in sparse networks. Instead, our approach uses sensors in a passive way: they only record and judiciously spread information about observed target presence in their vicinity; this information is then used by the (powerful) tracking agent to locate the target by just following the traces left at sensors. Our protocol is greedy, local, distributed, energy efficient and very successful, in the sense that (as shown by extensive simulations) the tracking agent manages to quickly locate and follow the target; also, we achieve good trade-offs between the energy dissipation and latency.

Motivated by emerging applications, we consider sensor net-works where the sensors themselves (not just the sinks) are mobile. Furthermore, we focus on mobility scenarios char-acterized by heterogeneous, highly changing mobility roles in the network. To capture these high dynamics of diverse sensory motion we propose a novel network parameter, the mobility level, which, although simple and local, quite accu-rately takes into account both the spatial and speed charac-teristics of motion. We then propose adaptive data dissem-ination protocols that use the mobility level estimation to optimize performance, by basically exploiting high mobility (redundant message ferrying) as a cost-effective replacement of flooding, e.g., the sensors tend to dynamically propagate less data in the presence of high mobility, while nodes of high mobility are favored for moving data around. These dissem-ination schemes are enhanced by a distance-sensitive proba-bilistic message flooding inhibition mechanism that further reduces communication cost, especially for fast nodes of high mobility level, and as distance to data destination decreases. Our simulation findings demonstrate significant performance gains of our protocols compared to non-adaptive protocols, i.e., adaptation increases the success rate and reduces la-tency (even by 15%) while at the same time significantly reducing energy dissipation (in most cases by even 40%). Also, our adaptive schemes achieve significantly higher mes-sage delivery ratio and satisfactory energy-latency trade-offs when compared to flooding when sensor nodes have limited message queues. Categories and Subject Descriptors: C.2.1 [Network

The advent of pervasive wireless communication technologies and the deployment of heterogeneous communication systems and architectures, from cellular communication systems to sensors and ad hoc networks, have fostered the research community to deal with the limitations and new assumptions of the wireless and mobile communication systems. The main limitations typically involve the resources like battery energy, computation and bandwidth. The new assumptions include the unprecedented dynamics caused by mobility, heterogeneity, variable channel conditions, the traffic characterization of new emerging applications and the open, distributed management architecture of wireless communication systems like the ad hoc and wireless sensor networks. This special issue brings together eight contributions on proposals for the resources management and mobility management in wireless networks, from different viewpoints, including QoS support, dynamic channel allocation, fault-tolerance, power saving and power efficiency, clustering and security issues. The papers have been selected from a total of more than 40 submitted papers, due to their originality and contribution to the field of wireless communications and mobile computing, with a particular emphasis on resources and mobility management in wireless networks – the theme of this special issue. The paper ‘An efficient distributed fault-tolerant protocol for dynamic channel allocation’ by Azzedine Boukerche, Tingxue Huang and Kaouther Abrougui, provides a proposal for a dynamic and distributed channel allocation scheme which incorporates the advantages of fault-tolerance and mobility support, and its performance evaluation. The paper ‘Mitigating the impact of node mobility on ad hoc clustering’ by Rituparna Ghosh and Stefano Basagni, evaluates the impact of different node mobility assumptions and provides some guidelines for improving the effectiveness and overhead reduction of clustering solutions in wireless ad hoc network scenarios. The paper ‘Transmission power and duration-aware playout control for packetized media streaming over wireless links’ by Yan Li and Nickolas Bambos illustrates and analyzes resource-saving solutions for transmission power control and playout buffer control to support QoS for media streaming over wireless links. The paper ‘Energy and connectivity performance of routing groups in multi-radio multi-hop networks’ by Michele Rossi, Leonardo Badia, Paolo Giacon and Michele Zorzi, provides a modelling and analysis of the in-network aggregation of wireless devices into groups, exploiting the connectivity potential of multiple radios to simplify the routing management in multi-hop wireless networks. The paper ‘MUSAQ: a multimedia session-aware QoS provisioning scheme for cellular networks’ by Mona El-Kadi Rizvi and Stephan Olariu, provides a QoS management scheme that reduces the self-contention of multiple streams composing a multimedia communication session, by exploiting the prioritization made possible by the knowledge of the relationships between competing flows in a multimedia session. The paper ‘A modified hopfield network for mobility management’ by Javid Taheri and Albert Y. Zomaya, proposes a novel and efficient approach based on heuristics and neural network techniques for optimizing the location management solutions of mobile devices. The paper ‘An application-driven approach to designing secure wireless sensor networks’ by Eric Sabbah, Kyoung-Don Kang, Nael Abu-Ghazaleh, Adnan Majeed and Ke Liu, introduces some concepts and guidelines for realizing a dynamically reconfigurable application-driven and architecture-dependent security framework coping with dynamics and resource limitations in wireless sensor networks. The paper ‘Should I send now or send later? A decision-theoretic approach to transmission scheduling in sensor networks with mobile sinks’ by Ladislau Boloni and Damla Turgut, proposes a decision theoretic approach aiming to reduce the energy and bandwidth resources used in wireless sensor networks with mobile sinks, under different assumptions regarding the knowledge of mobility parameters. We wish to thank all the authors for their great work and for considering the Wiley's WCMC journal for submitting their papers. Special thanks go to the anonymous reviewers for their help and dedication in reviewing the papers and providing useful comments to authors for paper improvements. Thanks to the EiC, Prof. Mohsen Guizani for allowing us to host this special issue in the prestigious Wiley's WCMC journal. We hope that this special issue will represent a timely and significant reference for future researches.

In this paper, we survey the current state-of-the-art in middleware and systems for Wireless Sensor Networks (WSN). We provide a discussion on the definition ofWSN middleware, design issues associated with it, and the taxonomies commonly used to categorize it. We also present a categorization of a number of such middleware platforms, using middleware functionalities and challenges which we think will play a crucial role in developing software for WSN in the near future. Finally, we provide a short discussion on WSN middleware trends.

Recent rapid technological developments have led to the development of tiny, low-power, low-cost sensors. Such devices integrate sensing, limited data processing and communication capabilities. The effective distributed collaboration of large numbers of such devices can lead to the efficient accomplishment of large sensing tasks. This talk focuses on several aspects of energy efficiency. Two protocols for data propagation are studied: the first creates probabilistically optimized redundant data transmissions to combine energy efficiency with fault tolerance, while the second guarantees (in a probabilistic way) the same per sensor energy dissipation, towards balancing the energy load and prolong the lifetime of the network. A third protocol (in fact a power saving scheme) is also presented, that directly and adaptively affects power dissipation at each sensor. This "lower level" scheme can be combined with data propagation protocols to further improve energy efficiency

In ad-hoc mobile networks (MANET), the mobility of the nodes is a complicating factor that significantly affects the effectiveness and performance of the routing protocols. Our work builds upon the recent results on the effect of node mobility on the performance of available routing strategies (i.e. path based, using support) and proposes a protocol framework that exploits the usually different mobility rates of the nodes by adopting the routing strategy during execution. We introduce a metric for the relative mobility of the nodes, according to which the nodes are classified into mobility classes. These mobility classes determine, for any pair of origin and destination, the routing technique that best corresponds to their mobility properties. Moreover, special care is taken for nodes remaining almost stationary or moving with high (relative) speeds. Our key design goal is to limit the necessery implementation changes required to incorporate existing routing protocols in our framework. We provide extensive evaluation of the proposed framework, using a well-known simulator (NS2). Our first findings demonstrate that the proposed framework improves, in certain cases, the performance of the existing routing protocols.

We introduce a new modelling assumption in wireless sensor networks, that of node redeployment (addition of sensor devices during the protocol evolution) and we extend the modelling assumption of heterogeneity (having sensor devices of various types). These two features further increase the highly dynamic nature of such networks and adaptation becomes a powerful technique for protocol design. Under this model, we design, implement and evaluate a power conservation scheme for efficient data propagation. Our protocol is adaptive: it locally monitors the network conditions (density, energy) and accordingly adjusts the sleep-awake schedules of the nodes towards best operation choices. Our protocol operates does not require exchange of control messages between nodes to coordinate.Implementing our protocol we combine it with two well-known data propagation protocols and evaluate the achieved performance through a detailed simulation study using our extended version of Ns2. We focus in highly dynamic scenarios with respect to network density, traffic conditions and sensor node resources. We propose a new general and parameterized metric capturing the trade-off between delivery rate, energy efficiency and latency. The simulation findings demonstrate significant gains (such as more than doubling the success rate of the well-known Directed Diffusion propagation paradigm) and good trade-offs. Furthermore, redeployment of sensors during network evolution and/or heterogeneous deployment of sensors drastically improve (when compared to equal total "power" simultaneous deployment of identical sensors at the start) the protocol performance (the success rate increases up to four times while reducing energy dissipation and, interestingly, keeping latency low).

Wireless sensor networks are comprised of a vast number of ultra-small fully autonomous computing, communication and sensing devices, with very restricted energy and computing capabilities, which co-operate to accomplish a large sensing task. Such networks can be very useful in practice in applications that require fine-grain monitoring of physical environment subjected to critical conditions (such as inaccessible terrains or disaster places). Features including the huge number of sensor devices involved, the severe power, computational and memory limitations, their dense deployment and frequent failures, pose new design and implementation aspects. The efficient and robust realization of such large, highly-dynamic, complex, non-conventional, environments is a challenging algorithmic task.

Summary form only given. We study the problem of energy-balanced data propagation in wireless sensor networks. The energy balance property guarantees that the average per sensor energy dissipation is the same for all sensors, during the entire execution of the protocol. This property is important since it prolongs the network's lifetime by avoiding early energy depletion of sensors. We propose a new algorithm that in each step decides whether to propagate data one-hop towards the final destination (the sink), or to send data directly to the sink. This randomized choice balances the (cheap) one-hop transmissions with the direct transmissions to the sink, which are more expensive but "bypass" the sensors lying close to the sink. Note that, in most protocols, these close to the sink sensors tend to be overused and die out early. By a detailed analysis we precisely estimate the probabilities for each propagation choice in order to guarantee energy balance. The needed estimation can easily be performed by current sensors using simple to obtain information. Under some assumptions, we also derive a closed form for these probabilities. The fact (shown by our analysis) that direct (expensive) transmissions to the sink are needed only rarely, shows that our protocol, besides energy-balanced, is also energy efficient.

Abstract In this paper, we demonstrate the significant impact of (a) the mobility rate and (b) the user density on the performance of routing protocols in ad‐hoc mobile networks. In particular, we study the effect of these parameters on two different approaches for designing routing protocols: (a) the route creation and maintenance approach and (b) the ‘support’ approach that forces few hosts to move, acting as ‘helpers’ for message delivery. We study one representative protocol for each approach, i.e. AODV for the first approach and RUNNERS for the second. We have implemented the two protocols and performed a large scale and detailed simulation study of their performance. The main findings are: the AODV protocol behaves well in networks of high user density and low mobility rate, while its performance drops for sparse networks of highly mobile users. On the other hand, the RUNNERS protocol seems to tolerate well (and in fact benefit from) high mobility rates and low densities. Copyright © 2004 John Wiley & Sons, Ltd.

We present the NanoPeers architecture paradigm, a peer-to-peer network of lightweight devices, lacking all or most of the capabilities of their computer-world counterparts. We identify the problems arising when we apply current routing and searching methods to this nanoworld, and present some initial solutions, using a case study of a sensor network instance; Smart Dust. Furthermore, we propose the P2P Worlds framework as a hybrid P2P architecture paradigm, consisting of cooperating layers of P2P networks, populated by computing entities with escalating capabilities. Our position is that: (i) experience gained through research and experimentation in the field of P2P computing, can be indispensable when moving down the stair of computing capabilities, and that (ii) the proposed framework can be the basis of numerous real-world applications, opening up several challenging research problems.

We introduce a new model of ad-hoc mobile networks, which we call hierarchical, that are comprised of dense subnetworks of mobile users (corresponding to highly populated geographical areas, such as cities), interconnected across access ports by sparse but frequently used connections (such as highways). For such networks, we present an efficient routing protocol which extends the idea (introduced in [4]) of exploiting the co-ordinated motion of a small part of an ad-hoc mobile network (the "support") to achieve very fast communication between any two mobile users of the network. The basic idea of the new protocol presented here is, instead of using a unique (large) support for the whole network, to employ a hierarchy of (small) supports (one for each city) and also take advantage of the regular traffic of mobile users across the interconnection highways to communicate between cities. We combine here theoretical analysis (average case estimations based on random walk properties) and experimental implementations (carried out using the LEDA platform) to claim and validate results showing that such a hierarchical routing approach is, for this class of ad-hoc mobile networks, significantly more efficient than a simple extension of the basic "support" idea presented in [4].

Article An efficient communication strategy for ad-hoc mobile networks Share on Authors: Ioannis Chatzigiannakis Computer Technology Institute, Patras, Greece Computer Technology Institute, Patras, GreeceView Profile , Sotiris Nikoletseas Computer Technology Institute, Patras, Greece Computer Technology Institute, Patras, GreeceView Profile , Paul Spirakis Computer Technology Institute, Patras, Greece Computer Technology Institute, Patras, GreeceView Profile Authors Info & Claims PODC '01: Proceedings of the twentieth annual ACM symposium on Principles of distributed computingAugust 2001 Pages 320–322https://doi.org/10.1145/383962.384053Published:01 August 2001 34citation438DownloadsMetricsTotal Citations34Total Downloads438Last 12 Months1Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

In this paper, we analyze the stability properties of the FIFO protocol in the Adversarial Queueing model for packet routing. We show a graph for which FIFO is stable for any adversary with injection rate r ≰ 0.1428. We generalize this results to show upper bound for stability of any network under FIFO protocol, answering partially an open question raised by Andrews et al. in [2]. We also design a network and an adversary for which FIFO is non-stable for any r ≱ 0.8357, improving the previous known bounds of [2].

A new model for intrusion and its propagation through various attack schemes in networks is considered. The model is characterized by the number of network nodes, and two parameters f and g. Parameter f represents the probability of failure of an attack to a node and is a gross measure of the level of security of the attacked system and perhaps of the in truder's skills;g represents a limit on the number of attacks that the intrusion software can ever try, when it issues them from a particular (broken) network node,due to the danger to be discovered. The success of the attack scheme is characterized by two factors: the number of nodes captured (the spread factor) and the number of virtual links that a defense mechanism has to trace from any node where the attack is active to the origin of the intrusion (the traceability factor). The goal of an intruder is to maximize both factors. In our model, we present four different ways (attack schemes) by which an intruder can organize his attacks. Using analytic and experimental methods, we first show that for any O < f < 1, there exists a constant g for which any of our attack schemes can achieve a Θ (n) spread and traceability factor with high probability, given sufficient propagation time. We also show for three of our attack schemes that the spread and the traceability factors are, with high probability, linearly related during the whole duration of the attack propagation. This implies that it will not be easy for a detection mechanism to trace the origin of the intrusion, since it will have to trace a number of links proportional to the nodes captured.