To address increasing residential energy consumption and rising utility costs, modern homes can adopt intelligent systems capable of efficiently managing major appliances and utilizing renewable resources. Among residential loads, the Heating, Ventilation, and Air Conditioning (HVAC) system accounts for the largest share of energy usage in U.S. households, and its effective control offers significant potential for cost reduction. Additionally, homes today are increasingly equipped with photovoltaic (PV) systems and energy storage systems (ESS), which provide capabilities for on-site energy generation, storage, and grid interaction. Existing control approaches typically manage HVAC, PV, and ESS subsystems independently, limiting overall efficiency and economic benefit. In this paper, we propose a comprehensive Home Energy Management System (HEMS) that prioritizes efficient HVAC control along with economic operation of PV and ESS systems to minimize energy costs. We propose a Model Predictive Control (MPC) framework utilizing Gaussian Processes and Stochastic Long Short-Term Memory (StochLSTM) machine learning models to predict indoor temperature dynamics and HVAC energy consumption. An improved Adaptive Differential Evolution (ADE) is then used to determine the optimal HVAC control, subsequently guiding the economic operation of PV and ESS. We perform extensive evaluation of the proposed approach across various climate zones and under different operational scenarios using a high-fidelity and gold-standard energy simulator. Results versus state-of-the-art methods show that our framework achieves an average energy saving of 30.7% and cost reduction of 31.2%. Furthermore, we show our machine learning models approach improved efficiency with limited samples.

Precision agriculture technologies hold great poten-tial for improving crop monitoring and farming practices. These technologies heavily rely on collecting extensive data through environmental sensors, often scattered over large fields. However, the limited connectivity in rural areas, and the high cost of 4G/5G subscriptions, often hampers their deployment. Unmanned Aerial Vehicles (UAV s) are a valid alternative for sensor data collection across large agricultural fields. However, existing UAV-based solutions often rely on the unnecessary collection of complete information, making them inefficient and more costly. In this paper, we present an innovative framework for collecting agri-cultural sensor data using UAV s. The framework selects a set of hovering points to visit, from which the data of only a subset of sensors is collected. The data from the remaining sensors is inferred using machine learning. We introduce an optimization problem named Hovering Points Selection (HPS) to select the optimal set of hovering points, and we prove it to be NP-Hard. We then propose a polynomial ∊2 -greedy reinforcement learning heuristic, named DRONE (Determining hoveRing pOints with exploratioN and Exploitation), to solve HPS in polynomial time. To further expedite the inference component of DRONE, we also introduce Fast-DRONE, which relies on information theory for hovering point selection. We evaluate the performance of our proposed framework using both synthetic and real agricultural datasets. Results demonstrate up to three times performance improvements over a recent state-of-the-art approach in several scenarios and under different communication technologies.

Crop disease recognition is a fundamental keystone in enabling disease control, limiting disease spread, and mitigating farmers’ losses. Recently, advanced image processing techniques for crop disease detection, based on deep learning, have gained significant popularity. However, the practical deployment of these models in real farms remains challenging. This is mostly due to the lack of Internet connectivity which prevents the transmission of the acquired images to sufficiently powerful edge/cloud servers to execute such complex models. LoRa has emerged as a promising network solution for rural areas, thanks to its extensive communication range and cost-efficient deployment. However, the low data rate of this technology prevents its effective application for the transmission of large images for crop disease detection. In this paper, we propose a LoRa-based framework called iCrop. iCrop enables high disease classification accuracy while exploiting the cost-effectiveness of LoRa transmission technologies. Specifically, iCrop is based on a LoRa Node, which captures crop leaf images and preprocesses them through image segmentation. The node selects and transmits the most informative segments over LoRa to the LoRa Edge Server. The server, in turn, runs the disease classification using a Convolutional Nerual Network (CNN) deep learning model empowered with majority voting among segments. To prevent data losses, typical of LoRa transmission, we develop a reliable transmission protocol on top of LoRa, which takes care of retransmissions and efficient communication. Extensive experiments on a real LoRa testbed show the advantages over two comparison approaches with respect to several performance metrics.

Extreme outside temperatures resulting from heat waves, winter storms, and similar weather-related events trigger the Heating Ventilation and Air Conditioning (HVAC) systems, resulting in challenging, and potentially catastrophic, peak loads. As a consequence, such extreme outside temperatures put a strain on power grids and may thus lead to blackouts. To avoid the financial and personal repercussions of peak loads, demand response and power conservation represent promising solutions. Despite numerous efforts, it has been shown that the current state-of-the-art fails to consider (1) the complexity of human behavior when interacting with power conservation systems and (2) realistic home-level power dynamics. As a consequence, this leads to approaches that are (1) ineffective due to poor long-term user engagement and (2) too abstract to be used in real-world settings. In this article, we propose an auction theory-based power conservation framework for HVAC designed to address such individual human component through a three-fold approach: personalized preferences of power conservation, models of realistic user behavior , and realistic home-level power dynamics . In our framework, the System Operator sends Load Serving Entities (LSEs) the required power saving to tackle peak loads at the residential distribution feeder. Each LSE then prompts its users to provide bids , i.e., personalized preferences of thermostat temperature adjustments, along with corresponding financial compensations. We employ models of realistic user behavior by means of online surveys to gather user bids and evaluate user interaction with such system. Realistic home-level power dynamics are implemented by our machine learning-based Power Saving Predictions (PSP) algorithm, calculating the individual power savings in each user’s home resulting from such bids. A machine learning-based PSPs algorithm is executed by the users’ Smart Energy Management System (SEMS). PSP translates temperature adjustments into the corresponding power savings. Then, the SEMS sends bids back to the LSE, which selects the auction winners through an optimization problem called POwer Conservation Optimization (POCO). We prove that POCO is NP-hard, and thus provide two approaches to solve this problem. One approach is an optimal pseudo-polynomial algorithm called DYnamic programming Power Saving (DYPS), while the second is a heuristic polynomial time algorithm called Greedy Ranking AllocatioN (GRAN). EnergyPlus, the high-fidelity and gold-standard energy simulator funded by the U.S. Department of Energy, was used to validate our experiments, as well as to collect data to train PSP. We further evaluate the results of the auctions across several scenarios, showing that, as expected, DYPS finds the optimal solution, while GRAN outperforms recent state-of-the-art approaches.

Peer-to-peer (P2P) energy tradingis a decentralized energy market where local energyprosumersact as peers, trading energy among each other. Existing works in this area largely overlook the importance of user behavioral modeling and assume users’ sustained active participation and full compliance in the decision-making process. To overcome these unrealistic assumptions, and their deleterious consequences, in this article, we propose anautomatedP2P energy-trading framework that specifically considers the users’ perception by exploitingprospect theory. We formalize an optimization problem that maximizes the buyers’ perceived utility while matching energy production and demand. We prove that the problem is NP-hard and we propose a Differential Evolution-based Algorithm for Trading Energy (DEbATE) heuristic. Additionally, we propose two automated pricing solutions to improve the sellers’ profit based on reinforcement learning. The first solution, named Pricing mechanism with Q-learning and Risk-sensitivity (PQR), is based on Q-learning. Additionally, given the scalability issues ofPQR, we propose a Deep Q-Network-based algorithm calledProDQNthat exploits deep learning and a novel loss function rooted in prospect theory. Results based on real traces of energy consumption and production, as well as realistic prospect theory functions, show that our approaches achieve 26% higher perceived value for buyers and generate 7% more reward for sellers, compared to recent state-of-the-art approaches.

Bovine Respiratory Disease (BRD) is the second leading cause of death in young dairy calves, and is associated with less growth, and reduced long-term performance such as less milk production, which makes BRD a financial burden on a farm’s economy. Precision technologies, such as accelerometers, automatic feeders, and cameras have been extensively used to collect, summarize, and interpret changes in baseline dairy cattle behavior. While some efforts to evaluate the presence of statistical relationships between calves’ behavior and BRD status have been made, there is very little research in pairing such technologies with manual examinations to improve the accuracy and cost of BRD monitoring. In this paper, we propose a framework for diagnosis and early prediction of BRD in calves. This framework is composed by a machine learning model as well as by a cost-sensitive feature selection problem called Cost Optimization Worth (COW). COW maximizes prediction accuracy given a budget constraint. We show that COW is NP-Hard and propose an efficient heuristic with polynomial complexity. We validate our methodology on a real dataset of 46 automatic and manually collected features, representing 106 calves observed during the preweaning period of 50 days. Our results show that our machine learning model can correctly classify a sick cow with a 97% accuracy and up to 5 days prior to BRD diagnosis, outperforming a recent state-of-the-art approach. Furthermore, our feature selection results show that in a low-budget scenario, manually collected features are more valuable than automated features in detecting sick cows. Conversely, in a high-budget scenario, automated features report higher accuracy for the early prediction of BRD.

The widespread adoption of distributed energy resources, and the advent of smart grid technologies, have allowed traditionally passive power system users to become actively involved in energy trading. Recognizing the fact that the traditional centralized grid-driven energy markets offer minimal profitability to these users, recent research has shifted focus towards decentralized peer-to-peer (P2P) energy markets. In these markets, users trade energy with each other, with higher benefits than buying or selling to the grid. However, most researches in P2P energy trading largely overlook the user perception in the trading process, assuming constant availability, participation, and full compliance. As a result, these approaches may result in negative attitudes and reduced engagement over time. In this paper, we design an automated P2P energy market that takes user perception into account. We employ prospect theory to model the user perception and formulate an optimization framework to maximize the buyer's perception while matching demand and production. Given the non-linear and non-convex nature of the optimization problem, we propose Differential Evolution-based Algorithm for Trading Energy called DEbATE. Additionally, we introduce a risk-sensitive Q-learning algorithm, named Pricing mechanism with Q-learning and Risk-sensitivity (PQR), which learns the optimal price for sellers considering their perceived utility. Results based on real traces of energy consumption and production, as well as realistic prospect theory functions, show that our approach achieves a 26% higher perceived value for buyers and generates 7% more reward for sellers, compared to a recent state of the art approach.

Prolonged rotating outages and exorbitant energy bills, recently experienced in California and Texas, have exposed the limitations and need for modernizing electric power systems. The occurrence of such events is a consequence of peak loads, often due to extreme outside temperatures that simultaneously trigger Heating Ventilation Air Conditioning (HVAC) systems. Leveraging pervasive computing technologies, such as smart meters and smart thermostats, this paper introduces a comprehensive approach to perform residential HVAC power conservation and prevent these catastrophic events. Differently from previous solutions, our approach models realistic user behavior and HVAC dynamics of individual homes. Specifically, we formulate a novel reverse auction-based problem, called POwer Conservation Optimization (POCO). The goal is to perform power conservation by motivating users to temporarily adjust their HVAC thermostat settings in exchange for financial rewards. We prove that POCO ensures truthfulness and individual rationality of the auction mechanism, although it is an NP-hard problem. Therefore, we propose an efficient heuristic, called Greedy Ranking AllocatioN (GRAN), which we prove ensures the same formal properties, while incurring only a polynomial complexity. To predict power savings resulting from an HVAC thermostat adjustments, we propose a novel machine learning-based technique called Power Saving Prediction (PSP). In addition, we conduct an online survey to study the willingness to adopt the proposed system and to model realistic user behavior. Survey results show willingness of adoption above 79% and a highly heterogeneous and non-linear user behavior. We perform extensive experiments using high-fidelity simulator EnergyPlus. Results show that PSP outperforms a state-of-the-art solution obtaining 85% predictions within a 5% error margin. Furthermore, GRAN achieves near-optimal performance, outperforming a recent state-of-the-art approach obtaining results between 58% and 68% closer to the optimum.

The growth and widespread diffusion of Internet-of-Things devices and advanced metering infrastructure allows to closely monitor appliances in a user home. Although only few works have focused on the issue of individual home power consumption predictions, recent efforts have unveiled the complexity of this task. As opposed to building-level power predictions, the finer granularity of single home predictions is characterized by the high impact that individual user actions have on the power consumption. As a matter of fact, the current state of the art shows inadequate prediction performance. In this work, we investigate the issue of single home power prediction by analyzing a recent dataset of real power consumption data. We carry out a profound analysis of several processing parameters and environmental parameters that make this task so challenging, thus providing meaningful insights that can guide future research on individual home power consumption predictions. Results show an overall low daily error, and very accurate hourly predictions when less variable usage patterns occur.

With the acceleration of Information and Communication Technologies and the Internet-of-Things paradigm, smart residential environments , also known as smart homes , are becoming increasingly common. These environments have significant potential for the development of intelligent energy management systems and have therefore attracted significant attention from both academia and industry. An enabling building block for these systems is the ability of obtaining energy consumption at the appliance-level. This information is usually inferred from electric signals data (e.g., current) collected by a smart meter or a smart outlet, a problem known as appliance recognition . Several previous approaches for appliance recognition have proposed load disaggregation techniques for smart meter data. However, these approaches are often very inaccurate for low consumption and multi-state appliances. Recently, Machine Learning (ML) techniques have been proposed for appliance recognition. These approaches are mainly based on passive MLs, thus requiring pre-labeled data to be trained. This makes such approaches unable to rapidly adapt to the constantly changing availability and heterogeneity of appliances on the market. In a home setting scenario, it is natural to consider the involvement of users in the labeling process, as appliances’ electric signatures are collected. This type of learning falls into the category of Stream-based Active Learning (SAL). SAL has been mainly investigated assuming the presence of an expert , always available and willing to label the collected samples. Nevertheless, a home user may lack such availability, and in general present a more erratic and user-dependent behavior. In this article, we develop a SAL algorithm, called K -Active-Neighbors (KAN), for the problem of household appliance recognition. Differently from previous approaches, KAN jointly learns the user behavior and the appliance signatures. KAN dynamically adjusts the querying strategy to increase accuracy by considering the user availability as well as the quality of the collected signatures. Such quality is defined as a combination of informativeness , representativeness , and confidence score of the signature compared to the current knowledge. To test KAN versus state-of-the-art approaches, we use real appliance data collected by a low-cost Arduino-based smart outlet as well as the ECO smart home dataset. Furthermore, we use a real dataset to model user behavior. Results show that KAN is able to achieve high accuracy with minimal data, i.e., signatures of short length and collected at low frequency.

Localizing ground devices (GDs) is an important requirement for a wide variety of applications, such as infrastructure monitoring, precision agriculture, search and rescue operations, to name a few. To this end, unmanned aerial vehicles (UAVs) or drones offer a promising technology due to their flexibility. However, the distance measurements performed using a drone, an integral part of a localization procedure, incur several errors that affect the localization accuracy. In this paper, we provide analytical expressions for the impact of different kinds of measurement errors on the ground distance between the UAV and GDs. We review three range-based and three range-free localization algorithms, identify their source of errors, and analytically derive the error bounds resulting from aggregating multiple inaccurate measurements. We then extend the range-free algorithms for improved accuracy. We validate our theoretical analysis and compare the observed localization error of the algorithms after collecting data from a testbed using ten GDs and one drone, equipped with ultra wide band (UWB) antennas and operating in an open field. Results show that our analysis closely matches with experimental localization errors. Moreover, compared to their original counterparts, the extended range-free algorithms significantly improve the accuracy.

Spurious power consumption data reported from compromised meters controlled by organized adversaries in the Advanced Metering Infrastructure (AMI) may have drastic consequences on a smart grid’s operations. While existing research on data falsification in smart grids mostly defends against isolated electricity theft, we introduce a taxonomy of various data falsification attack types, when smart meters are compromised by organized or strategic rivals. To counter these attacks, we first propose a coarse-grained and a fine-grained anomaly-based security event detection technique that uses indicators such as deviation and directional change in the time series of the proposed anomaly detection metrics to indicate: (i) occurrence, (ii) type of attack, and (iii) attack strategy used, collectively known as attack context . Leveraging the attack context information, we propose three attack response metrics to the inferred attack context: (a) an unbiased mean indicating a robust location parameter; (b) a median absolute deviation indicating a robust scale parameter; and (c) an attack probability time ratio metric indicating the active time horizon of attacks. Subsequently, we propose a trust scoring model based on Kullback-Leibler (KL) divergence, that embeds the appropriate unbiased mean, the median absolute deviation, and the attack probability ratio metric at runtime to produce trust scores for each smart meter. These trust scores help classify compromised smart meters from the non-compromised ones. The embedding of the attack context, into the trust scoring model, facilitates accurate and rapid classification of compromised meters, even under large fractions of compromised meters, generalize across various attack strategies and margins of false data. Using real datasets collected from two different AMIs, experimental results show that our proposed framework has a high true positive detection rate, while the average false alarm and missed detection rates are much lesser than 10% for most attack combinations for two different real AMI micro-grid datasets. Finally, we also establish fundamental theoretical limits of the proposed method, which will help assess the applicability of our method to other domains.

Smart Connected Communities (SCCs) is a novel paradigm that brings together multiple disciplines, including social-sciences, computer science, and engineering. Large-scale surveys are a fundamental tool to understand the needs and impact of new technologies to human populations, necessary to realize the SCC paradigm. However, there is a growing debate regarding the reproducibility of survey results. As an example, it has been shown that surveys may easily provide contradictory results, even if the subject populations are statistically equivalent from a demographic perspective. In this paper, we take the initial steps towards addressing the problem of reproducibility of survey results by providing formal methods to quantitatively justify apparently inconsistent results. Specifically, we define a new dissimilarity metric between two populations based on the users answers to non-demographic questions. To this purpose, we propose two algorithms based on submodular optimization and information theory, respectively, to select the most representative questions in a survey. Results show that our method effectively identifies and quantifies differences that are not evident from a purely demographic point of view.

Renewable, heterogeneous and distributed energy resources are the future of power systems, as envisioned by the recent paradigm of Virtual Power Plants (VPPs). Residential electricity generation, e.g., through photovoltaic panels, plays a fundamental role in this paradigm, where users are able to participate in an energy sharing system and exchange energy resources among each other. In this work, we study energy sharing systems and, differently from previous approaches, we consider realistic user behaviors by taking into account the user preferences and level of engagement in the energy trades. We formulate the problem of matching energy resources while contemplating the user behavior as a Mixed Integer Linear Programming (MILP) problem, and show that the problem is NPHard. Since the solution of such problem requires the knowledge of the user behavioral model, we propose an heuristic based on reinforcement learning with bounded regret to learn such model while optimizing the system performance. Comparison with the state-of-the-art approaches using realistic simulations based on real traces shows that our method outperforms existing schemes in several efficiency metrics. Besides, the results reveal that increasing the amount of produced energy improves the learning ability of the system even in a short period. It gives practical insights for implementation of energy sharing systems.

Matching power demand during peak load hours is a well-known problem in power systems. In fact, the cost of producing electricity increases very rapidly when the demand is high, due to the need for starting backup generators and enhancing transmission system. Incentive-based Demand Response (DR) program is a new approach, enabled by recent advances in smart grid technologies, designed to deal with such problem. According to DR, the utility company can provide economical incentives to users in order to temporarily reduce their energy consumption during peak hours. It is, however, challenging to determine the procedure to distribute such incentives, as well as to ensure that users will be sufficiently engaged and satisfied to make the DR program effective. In this paper, we propose a reverse auction mechanism to enable an incentive-based DR program. We formulate the DR reverse auction as an integer linear programming (ILP) problem, which integrates a perceived-value utility, to model the user perception of electrical appliances, as well as the financial objectives of the utility company. We adopt a Vickrey-Clarke-Groves (VCG) based reverse auction mechanism to guarantee the truthfulness and individual rationality properties. Since the VCG auction requires to optimally solve the NP-Hard ILP problem, we propose a heuristic algorithm named Reverse Auction DemAnd Response (RADAR), and prove that RADAR preserves truthfulness. Extensive simulations using real power consumption data of several homes show that RADAR is effective in reducing demand peaks while outperforming previous solutions in terms of users' perceived utility.

Smart homes offer new possibilities for energy management. One key enabler of these systems is the ability to monitor energy consumption at the appliance level. Existing approaches rely mainly on data from aggregated smart meter readings, but lack sufficient accuracy to recognize several appliances. Conversely, smart outlets are a suitable alternative since they can provide accurate electrical readings on individual appliances. Previous approaches for appliance recognition based on smart outlets use passive machine learning, which are deficient in the flexibility and scalability to work with highly heterogeneous appliances in smart homes. In this paper, we propose a stream-based active learning approach, called K -Active-Neighbors (KAN), to address the problem of appliance recognition in smart homes. KAN is an interactive framework in which the user is asked to label signatures of recently used appliances. Differently from previous work, we consider the realistic case in which the user is not always available to participate in the labeling process. Therefore, the system simultaneously learns the signatures and also the user willingness to interact with the system, in order to optimize the learning process. We develop an Arduino-based smart outlet to test our approach. Results show that, compared to previous solutions, KAN achieves higher accuracy in up to 41% less time.

Abstract Recognizing users’ daily life activities without disrupting their lifestyle is a key functionality to enable a broad variety of advanced services for a Smart City, from energy-efficient management of urban spaces to mobility optimization. In this paper, we propose a novel method for human activity recognition from a collection of outdoor mobility traces acquired through wearable devices. Our method exploits the regularities naturally present in human mobility patterns to construct syntactic models in the form of finite state automata, thanks to an approach known asgrammatical inference. We also introduce a measure ofsimilaritythat accounts for the intrinsic hierarchical nature of such models, and allows to identify the common traits in the paths induced by different activities at various granularity levels. Our method has been validated on a dataset of real traces representing movements of users in a large metropolitan area. The experimental results show the effectiveness of our similarity measure to correctly identify a set of common coarse-grained activities, as well as their refinement at a finer level of granularity.

Residential energy consumption has been rising rapidly during the last few decades. Several research efforts have been made to reduce residential energy consumption, including demand response and smart residential environments. However, recent research has shown that these approaches may actually cause an increase in the overall consumption, due to the complex psychological processes that occur when human users interact with these energy management systems. In this article, using an interdisciplinary approach, we introduce a perceived-value driven framework for energy management in smart residential environments that considers how users perceive values of different appliances and how the use of some appliances are contingent on the use of others. We define a perceived-value user utility used as an Integer Linear Programming (ILP) problem. We show that the problem is NP-Hard and provide a heuristic method called COndensed DependencY (CODY). We validate our results using synthetic and real datasets, large-scale online experiments, and a real-field experiment at the Missouri University of Science and Technology Solar Village. Simulation results show that our approach achieves near optimal performance and significantly outperforms previously proposed solutions. Results from our online and real-field experiments also show that users largely prefer our solution compared to a previous approach.

According to the Smart City Council, an adequate telecommunications infrastructure is vital for the success of businesses, industries as well as residents of Smart cities. However, currently available standard and cellular technologies, such as 3/4G, GSM (Global System for Mobile Communications) and LTE (Long-Term Evolution), are rapidly reaching their limit mainly due to increased traffic demand. Such limitations are only going to worsen in the next years, due to the advent of Internet of Things technologies that are expected to interconnect billions of devices to the Internet. In this paper, we propose a novel network architecture that supports several delay-tolerant (non-real-time) Smart city applications and services (e.g., gathering air pollution information), and therefore, a promising approach to address the burdening of increased traffic demand to Smart city's legacy standard and cellular communication infrastructure. The proposed architecture is based on an innovative diverse band-aware Dynamic Spectrum Access (d-DSA) paradigm, that allows a certain wireless device to opportunistically access idle channels in multiple licensed/unlicensed spectrum bands. d-DSA radio devices are mounted on Smart city's urban vehicles (e.g., taxis) that act as mobile routers to gather, carry, and forward various types of data traffic. This results in a time-varying and unpredictable delay-tolerant network (DTN) where each node can access whitespace channels and transmit in multiple spectrum bands. Given lack of research in efficient routing schemes for such d-DSA DTN networks, we propose a distributed and lightweight d-DSA aware Geographical Routing (dDSA-GR) protocol, that utilizes a weighted linear metric for selecting a suitable spectrum band, and classic georouting principle for choosing next hop node in the path route between any node pair in d-DSA DTNs. Results on realistic traces based on the map of Lexington, KY, USA, show that our dDSA-GR routing protocol outperforms baseline approaches in terms of network delay, message delivery ratio, and energy efficiency, under all considered scenarios.

This paper presents a novel network architecture, termed neXt-generation CHAllenged NeTwork (X-CHANT), for improving connectivity in rural environments. The underlying idea is to deploy diverse Dynamic Spectrum Access (d-DSA) radio devices on the public transportation vehicles, such as buses. This results in a d-DSA enabled delay-tolerant network in which the devices can operate in various (un)licensed bands (e.g., TV, LTE, ISM, CBRS), if available. Given the lack of research in efficient routing for such time-varying d-DSA enabled networks, we propose a novel diverse DSA aware routing (dDSAaR) protocol that jointly exploits various (un)licensed bands besides the time-varying yet sufficiently predictable mobility of public transportation vehicles. We compare X-CHANT, utilizing dDSAaR, to the conventional non-DSA/DSA architectures, utilizing a standard (single band) routing protocol (e.g., Epidemic). We use real bus mobility traces collected at the University of Massachusetts, Amherst campus. Results show that X-CHANT achieves better message delivery, negligible message overhead, and better energy expenditure, at the expense of a slight increase in delay. Never-theless, the delay improves with higher predictable mobility.

In the aftermath of large-scale disasters, such as earthquakes or hurricanes, existing communication infrastructures are often critically impaired, preventing timely information exchange between the survivors, responders, and the coordination center. Smart devices, movable base stations, easily deployable WiFi routers, and unimpaired communication towers can be used to set up temporary networks, called disaster response networks (DRNs). However, such networks are challenged by rapid energy depletion of smart devices as well as component failures. To address these issues, in this paper we propose a novel energy-efficient yet robust DRN topology, termed Bio-DRN, that mimics the inherent robustness of a biological network of living organisms, called gene regulatory network (GRN). Specifically, the Bio-DRN is a subgraph of the DRN topology generated by one-to-one mapping between the structurally similar genes and DRN components, i.e., survivors, points of interest like shelter points, and the coordination center. We first formulate the construction of Bio-DRN topology as an integer linear programming optimization problem, and show that it is NP-hard. Then, we present a sub-optimal heuristic that constructs the Bio-DRN topology as a common subgraph of both GRN and DRN topologies. Our experimental study on a real disaster prone region in Bhaktapur, Nepal, shows that Bio-DRN preserves the topological properties of GRN, such as low graph density and motif abundance, and achieves both energy efficiency and network robustness, while ensuring timely message delivery.

Activity recognition has been introduced as a means of detecting an action from a series of observations. Although in the literature, the terms "activity recognition" and "human activity recognition" are mostly used interchangeably, there exist several interesting applications for non-human subjects. In this work, we study animal activity recognition with special focus on horse gaits recognition. The on-body and unobtrusive system developed in this paper has several potential applications which can raise attention towards financial, emotional and veterinary health issues related to animals. Leveraging the pervasive use of smartwatches for activity tracking, we develop a smartwatch application to collect accelerometer data. The application is based on novel outlier detection and feature extraction techniques, in conjunction with state-of-the-art machine learning approaches. We perform real life experiments with two horses to evaluate the performances of our proposed system. To this aim, we place the monitoring device both on the horse saddle and the rider's wrist. The results show a high accuracy in both scenarios, which allows a seamless and unobtrusive use of our wearable device application by the rider. In addition, we study the effects of sliding window size and sampling frequency, providing useful insights for future research in horse gaits recognition.

Mobile crowdsensing harnesses the sensing power of modern smartphones to collect and analyze data beyond the scale of what was previously possible with traditional sensor networks. Given the participatory nature of mobile crowdsensing, it is imperative to incentivize mobile users to provide sensing services in a timely and reliable manner. Most importantly, given sensed information is often valid for a limited period of time, the capability of smartphone users to execute sensing tasks largely depends on their mobility pattern, which is often uncertain. For this reason, in this paper, we propose IncentMe, a framework that solves this core issue by leveraging game-theoretical reverse auction mechanism design. After demonstrating that the proposed problem is NP-hard, we derive two mechanisms that are parallelizable and achieve higher approximation ratio than existing work. IncentMe has been extensively evaluated on a road traffic monitoring application implemented using mobility traces of taxi cabs in San Francisco, Rome, and Beijing. Results demonstrate that the mechanisms in IncentMe outperform the state of the art work by improving the efficiency in recruiting participants by 30 percent.

Thanks to the collective action of participating smartphone users, mobile crowdsensing allows data collection at a scale and pace that was once impossible. The biggest challenge to overcome in mobile crowdsensing is that participants may exhibit malicious or unreliable behavior, thus compromising the accuracy of the data collection process. Therefore, it becomes imperative to design algorithms to accurately classify between reliable and unreliable sensing reports. To address this crucial issue, we propose a novel Framework for optimizing Information Reliability in Smartphone-based participaTory sensing (FIRST) that leverages mobile trusted participants (MTPs) to securely assess the reliability of sensing reports. FIRST models and solves the challenging problem of determining before deployment the minimum number of MTPs to be used to achieve desired classification accuracy. After a rigorous mathematical study of its performance, we extensively evaluate FIRST through an implementation in iOS and Android of a room occupancy monitoring system and through simulations with real-world mobility traces. Experimental results demonstrate that FIRST reduces significantly the impact of three security attacks (i.e., corruption, on/off, and collusion) by achieving a classification accuracy of almost 80% in the considered scenarios. Finally, we discuss our ongoing research efforts to test the performance of FIRST as part of the National Map Corps project.

Sensor networks are commonly adopted to collect a variety of measurements in indoor and outdoor settings. However, collecting such measurements from every node in the network, although providing high accuracy and resolution of the phenomena of interest, may easily cause sensors’ battery depletion. In this article, we show that measurement correlation can be successfully exploited to reduce the amount of data collected in the network without significantly sacrificing the monitoring accuracy. In particular, we propose an online adaptive measurement technique with which a subset of nodes are dynamically chosen as monitors while the measurements of the remaining nodes are estimated using the computed correlations. We propose an estimation framework based on jointly Gaussian distributed random variables, and we formulate an optimization problem to select the monitors under a total cost constraint. We show that the problem is NP-Hard and propose three efficient heuristics. We also develop statistical approaches that automatically switch between learning and estimation phases to take into account the variability occurring in real networks. Simulations carried out on real-world traces show that our approach outperforms previous solutions based on compressed sensing, and it can be successfully applied to the real application of solar irradiance prediction of photovoltaics systems.

Residential energy consumption is skyrocketing, as residential customers in the U.S. alone used 1.4 trillion kilowatt-hours in 2014 and the consumption is expected to increase in the next years. Previous efforts to limit such consumption have included demand response and smart residential environments. However, recent research has shown that such approaches can actually increase the overall energy consumption due to the numerous complex human psychological processes that take place when interacting with electrical appliances. In this paper we propose a social-behavioral aware framework for energy management in smart residential environments. We envision a smart home where appliances are interconnected using the paradigm of the Internet of Things and where users have a maximum energy budget, for example to reduce their energy bills. Using an experimental and interdisciplinary approach, we define social behavioral models to understand how users perceive different appliances, and how the use of some appliances are contingent on the use of others. We make use of large scale online surveys involving 1500 users to gather data and quantify such models. Based on these models we define a social behavioral aware user utility that is adopted as the objective function of a Mixed Integer Linear Programming problem. The problem looks for a set of appliances that maximizes the user utility while ensuring that the energy budget constraint is met. We show that the problem is NP-Hard and provide a heuristic method to efficiently find a solution. Results on synthetic and real data show that our approach outperforms previously proposed solutions that do not consider the social-behavioral implications, and it requires few iterations to converge towards a final solution.

Unmanned Aerial Vehicles (UAVs) have enormous potentials for several important applications, such as search and rescue and structural health monitoring. An important requirement for these applications is the ability to accurately localize objects, such as sensors or ``smart-things'', equipped with wireless communication capability. However, most previous works in this area neglect the unavoidable errors that are involved in the localization process, thus resulting in poor performance in practice. In this paper, for the first time, we express the measurement error on the ground as a function of the rolling, altitude, and instrumental precision provided by the hardware on the drone. We takeaway two lessons from this analysis: to limit the ground error (i) all the waypoints used to measure the same node must be at a sufficiently large ground distance from the node itself, and (ii) they must not be collinear among themselves nor with the node. We validate the error expressions derived analytically through real experiments using the 3DR Solo Drone.

Traffic monitoring is a key enabler for several planning and management activities of a Smart City. However, traditional techniques are often not cost efficient, flexible, and scalable. This paper proposes an approach to traffic monitoring that does not rely on probe vehicles, nor requires vehicle localization through GPS. Conversely, it exploits just a limited number of cameras placed at road intersections to measure car end-to-end traveling times. We model the problem within the theoretical framework of network tomography, in order to infer the traveling times of all individual road segments in the road network. We specifically deal with the potential presence of noisy measurements, and the unpredictability of vehicles paths. Moreover, we address the issue of optimally placing the monitoring cameras in order to maximize coverage, while minimizing the inference error, and the overall cost. We provide extensive experimental assessment on the topology of downtown San Francisco, CA, USA, using real measurements obtained through the Google Maps APIs, and on realistic synthetic networks. Our approach provides a very low error in estimating the traveling times over 95% of all roads even when as few as 20% of road intersections are equipped with cameras.

Smart and connected communities (SCCs) are emerging as a novel paradigm that allows the community residents to be connected with surrounding environments through smart technologies. However, there remain important challenges to fully exploit the potential of SCCs in improving societal well-being and prosperity. In particular, there is a need for designing green communication systems that are also capable of providing high quality of service (QoS) to distribute and collect information to and from SCCs. However, simultaneously satisfying both of these criteria is difficult due to varying demands posed by heterogeneous sensing modalities, lack of dedicated infrastructure in rural/sub-urban areas, and certain sustainability constraints. While low-power short-range technologies often fail to achieve high QoS, using 3G or 4G technologies (LTE, LTE-A, GSM) for SCCs will eventually face spectrum scarcity and cross technology interference. In recent times, Dynamic spectrum access (DSA) has been proposed as a solution to overcome policy constraints and improve spectrum scarcity by spectrum sharing. In this article, we show that harnessing DSA in the context of SCCs can also achieve notable benefits in terms of energy efficiency and sustainability. Specifically, we propose a novel architecture for designing sustainable SCCs using a small-scale DSA-enabled overlay network that improves end-to-end energy efficiency of the network while guaranteeing QoS. We also propose a dynamic spectrum band selection approach that intelligently matches any message requirement to a suitable band type by exploiting distinct electro-magnetic characteristics of various bands. Since data generated in SCCs are typically valuable only when delivered within a certain hard (or soft ) deadline, we formulate a linear optimization problem for determining the most energy-efficient path that ensures a delivery time within the hard deadline. After proving that such a problem is NP-Hard, we propose an exact pseudo-polynomial time dynamic programming algorithm to solve it followed by a polynomial time greedy heuristic. Additionally, we formulate a non-linear optimization problem to find the optimal path when the message delivery time is defined as a soft deadline and extend our greedy heuristic to handle soft deadlines. Compared to the homogeneous band access approaches that opportunistically access free channels within a given spectrum band, our extensive simulation study shows that the proposed dynamic multi-band selection approach significantly improves the achievable energy efficiency while meeting various hard and soft deadlines.

Compromised smart meters reporting false power consumption data in Advanced Metering Infrastructure (AMI) may have drastic consequences on a smart grid's operations. Most existing works only deal with electricity theft from customers. However, several other types of data falsification attacks are possible, when meters are compromised by organized rivals. In this paper, we first propose a taxonomy of possible data falsification strategies such as additive, deductive, camouflage and conflict, in AMI micro-grids. Then, we devise a statistical anomaly detection technique to identify the incidence of proposed attack types, by studying their impact on the observed data. Subsequently, a trust model based on Kullback-Leibler divergence is proposed to identify compromised smart meters for additive and deductive attacks. The resultant detection rates and false alarms are minimized through a robust aggregate measure that is calculated based on the detected attack type and successfully discriminating legitimate changes from malicious ones. For conflict and camouflage attacks, a generalized linear model and Weibull function based kernel trick is used over the trust score to facilitate more accurate classification. Using real data sets collected from AMI, we investigate several trade-offs that occur between attacker's revenue and costs, as well as the margin of false data and fraction of compromised nodes. Experimental results show that our model has a high true positive detection rate, while the average false alarm rate is just 8%, for most practical attack strategies, without depending on the expensive hardware based monitoring.

Large scale disasters require prompt rescue and relief operations to restrict further casualties. To carry out such operations, it is essential to have a communication infrastructure between survivors and responders, which is often impaired due to the disaster. Off-the-shelf wireless devices such as smartphones, PDAs and Laptops offer an effective solution towards the establishment of makeshift communication infrastructure. However, in the absence of bonafide power sources, it becomes imperative to judiciously utilize energy (battery power) of such devices such that the network is functional until primary infrastructure is restored. This paper proposes a novel approach, called Cluster based Topological Routing (CTR) that prolongs the longevity of the network by exploiting the natural gathering of survivors in shelter points. In particular, the clustering algorithm identifies such survivor groups combined with a data forwarding approach, to minimize the number of data transmissions yet guaranteeing the required packet delivery and network latency. Our extensive simulation study shows that CTR yields twice the network lifetime than existing routing approaches in disaster response networks, while ensuring comparable packet delivery and network latency.

Unmanned Aerial Vehicles (UAVs) are increasingly being adopted for military and civilian applications. UAVs available on the market are well known to be resource constrained, especially in terms of available energy. As a result, it is very challenging to predict the critical performance characteristics of a UAV, such as flight time or the ability of a UAV to complete a mission, given the system parameters. Nevertheless, such predictions would have several benefits, such as improving the effectiveness of mission planners and optimization algorithms in general, as well as enabling researchers to perform more realistic simulations. The goal of this paper is to gain understanding in how physical, mechanical, or electrical hardware aspects of a UAV affect the UAV performance and ultimately its capability to accomplish a mission. We propose two models for UAV performance. The first model considers basic UAV operations, while the second model considers the UAV physical characteristics as well as the mission specifications to predict the UAV flight time and the number of waypoints it can safely traverse. We validate our models thorough experiments using a real test-bed based on 3DR Solo UAVs. The results show that our approach is able to reliably predict performance to within less than 5\% margin of error.

In this article, we address the problem of autonomously deploying mobile sensors in an unknown complex environment. In such a scenario, mobile sensors may encounter obstacles or environmental sources of noise, so that movement and sensing capabilities can be significantly altered and become anisotropic. Any reduction of device capabilities cannot be known prior to their actual deployment, nor can it be predicted. We propose a new algorithm for autonomous sensor movements and positioning, called DOMINO (DeplOyment of MobIle Networks with Obstacles). Unlike traditional approaches, DOMINO explicitly addresses these issues by realizing a grid-based deployment throughout the Area of Interest (AoI) and subsequently refining it to cover the target area more precisely in the regions where devices experience reduced sensing. We demonstrate the capability of DOMINO to entirely cover the AoI in a finite time. We also give bounds on the number of sensors necessary to cover an AoI with asperities. Simulations show that DOMINO provides a fast deployment with precise movements and no oscillations, with moderate energy consumption. Furthermore, DOMINO provides better performance than previous solutions in all the operative settings.

This paper addresses the problem of efficiently restoring sufficient resources in a communications network to support the demand of mission critical services after a large-scale disruption. We give a formulation of the problem as a mixed integer linear programming and show that it is NP-hard. We propose a polynomial time heuristic, called iterative split and prune (ISP) that decomposes the original problem recursively into smaller problems, until it determines the set of network components to be restored. ISP's decisions are guided by the use of a new notion of demand-based centrality of nodes. We performed extensive simulations by varying the topologies, the demand intensity, the number of critical services, and the disruption model. Compared with several greedy approaches, ISP performs better in terms of total cost of repaired components, and does not result in any demand loss. It performs very close to the optimal when the demand is low with respect to the supply network capacities, thanks to the ability of the algorithm to maximize sharing of repaired resources.

This paper addresses the problem of efficiently restoring sufficient resources in a communications network to support the demand of mission critical services after a large scale disruption. We give a formulation of the problem as an MILP and show that it is NP-hard. We propose a polynomial time heuristic, called Iterative Split and Prune (ISP) that decomposes the original problem recursively into smaller problems, until it determines the set of network components to be restored. We performed extensive simulations by varying the topologies, the demand intensity, the number of critical services, and the disruption model. Compared to several greedy approaches ISP performs better in terms of number of repaired components, and does not result in any demand loss. It performs very close to the optimal when the demand is low with respect to the supply network capacities, thanks to the ability of the algorithm to maximize sharing of repaired resources.

Mobile sensor networks are the most promising solution to cover an Area of Interest (AoI) in safety critical scenarios. Mobile devices can coordinate with each other according to a distributed deployment algorithm, without resorting to human supervision for device positioning and network configuration. In this paper, we focus on the vulnerabilities of the deployment algorithms based on Voronoi diagrams to coordinate mobile sensors and guide their movements. We give a geometric characterization of possible attack configurations, proving that a simple attack consisting of a barrier of few compromised sensors can severely reduce network coverage. On the basis of the above characterization, we propose two new secure deployment algorithms, named SecureVor and Secure Swap Deployment (SSD). These algorithms allow a sensor to detect compromised nodes by analyzing their movements, under different and complementary operative settings. We show that the proposed algorithms are effective in defeating a barrier attack, and both have guaranteed termination. We perform extensive simulations to study the performance of the two algorithms and compare them with the original approach. Results show that SecureVor and SSD have better robustness and flexibility and excellent coverage capabilities and deployment time, even in the presence of an attack.

Over-provisioned femtocell networks can be used to serve indoor locations that see high peak loads, such as airports or train stations. However, networks designed for high peak loads are mostly under-utilized, which is wasteful from an energy-use perspective. This paper introduces a femtocell selective activation problem. We motivate the use of selective activation in femtocell networks using real femtocell power measurements. We formally define the selective activation problem, and introduce GreenFemto, a distributed femtocell selective activation algorithm. We prove that GreenFemto converges to a locally Pareto optimal solution. Detailed simulations of an LTE wireless system are used to demonstrate the performance of GreenFemto. We find that GreenFemto uses up to 55% fewer femtocells to serve a given load, relative to an existing femtocell power-saving technique. Furthermore, we show that GreenFemto comes within 15% of a globally optimal solution. We conclude that selective activation can be successfully applied to femtocell networks to both reduce power consumption, and reduce outage probabilities.

The heterogeneous and dynamic nature of tactical coalition networks poses several challenges to common network management tasks, due to the lack of complete and accurate network information. In this paper, we consider the problem of redeploying services in mobile tactical networks. We propose M-iTop, an algorithm for inferring the network topology when only partial information is available. M-iTop initially constructs a virtual topology that overestimates the number of network components, and then repeatedly merges links in this topology to resolve it towards the structure of the true network. We perform extensive simulations and show that M-iTop enables an efficient redeployment of services over the network despite the limitation of partial information.

Network monitoring is an essential component of network operation and, as the network size increases, it usually generates a significant overhead in large scale networks such as sensor and data center networks. In this paper, we show that measurement correlation often exhibited in real networks can be successfully exploited to reduce the network monitoring overhead. In particular, we propose an online adaptive measurement technique with which a subset of nodes are dynamically chosen as monitors while the measurements of the remaining nodes are estimated using the computed correlations. We propose an estimation framework based on jointly Gaussian distributed random variables, and formulate an optimization problem to select the monitors which minimize the estimation error under a total cost constraint. We show that the problem is NP-Hard and propose three efficient heuristics. In order to apply our framework to real-world networks, in which measurement distribution and correlation may significantly change over time, we also develop a learning based approach that automatically switches between learning and estimation phases using a change detection algorithm. Simulations carried out on two real traces from sensor networks and data centers show that our algorithms outperforms previous solutions based on compressed sensing and it is able to reduce the monitoring overhead by 50% while incurring a low estimation error. The results further demonstrate that applying the change detection algorithm reduces the estimation error up to two orders of magnitude.

Provisioning the capacity of wireless networks is difficult when peak load is significantly higher than average load, for example, in public spaces like airports or train stations. Service providers can use femtocells and small cells to increase local capacity, but deploying enough femtocells to serve peak loads requires a large number of femtocells that will remain idle most of the time, which wastes a significant amount of power. To reduce the energy consumption of over-provisioned femtocell networks, we formulate a femtocell selective activation problem, which we formalize as an integer nonlinear optimization problem. Then we introduce Green Femto, a distributed femtocell selective activation algorithm that deactivates idle femtocells to save power and activates them on-the-fly as the number of users increases. We prove that Green Femto converges to a locally Pareto optimal solution and demonstrate its performance using extensive simulations of an LTE wireless system. Overall, we find that Green Femto requires up to 55% fewer femtocells to serve a given user load, relative to an existing femtocell power-saving procedure, and comes within 15% of a globally optimal solution.

Full knowledge of the routing topology of the Internet is useful for a multitude of network management tasks. However, the full topology is often not known and is instead estimated using topology inference algorithms. Many of these algorithms use Traceroute to probe paths and then use the collected information to infer the topology. We perform real experiments and show that, in practice, routers may severely disrupt the operation of Traceroute and cause it to only provide partial information. We propose iTop, an algorithm for inferring the network topology when only partial information is available. iTop constructs a virtual topology, which overestimates the number of network components, and then repeatedly merges links in this topology to resolve it toward the structure of the true network. We perform extensive simulations to compare iTop to state-of-the-art inference algorithms. Results show that iTop significantly outperforms previous approaches and its inferred topologies are within 5% of the original networks for all considered metrics. Additionally, we show that the topologies inferred by iTop significantly improve the performance of fault localization algorithms when compared with other approaches.

Mobile sensor networks enable the monitoring of remote and hostile environments without requiring human supervision. Several approaches have been proposed in the literature to let mobile sensors self-deploy over a region of interest. In this paper we study, for the first time, the vulnerabilities of one of the most referenced approaches to mobile sensor deployment, namely the Voronoi-based approach. We show that, by compromising a small number of sensors, an attacker can influence the sensor deployment causing a significant reduction of the monitoring capability of the network. We propose a secure deployment algorithm called SecureVOR. We formally prove that SecureVOR has guaranteed termination and that it allows legitimate sensors to detect the malicious behavior of compromised nodes. We also show by extensive simulations that SecureVOR is able to fulfill the network monitoring goals even in presence of an attack, at the expense of a small performance overhead.

In this paper, we study the problem of selecting paths to improve the performance of network tomography applications in the presence of network element failures. We model the robustness of paths in network tomography by a metric called expected rank. We formulate an optimization problem to cover two complementary performance metrics: robustness and probing cost. The problem aims at maximizing the expected rank under a budget constraint on the probing cost. We prove that the problem is NP-Hard. Under the assumption that the failure distribution is known, we propose an algorithm called RoMe with guaranteed approximation ratio. Moreover, since evaluating the expected rank is generally hard, we provide a bound which can be evaluated efficiently. We also consider the case in which the failure distribution is not known, and propose a reinforcement learning algorithm to solve our optimization problem, using RoMe as a subroutine. We run a wide range of simulations under realistic network topologies and link failure models to evaluate our solution against a state-of-the-art path selection algorithm. Results show that our approaches provide significant improvements in the performance of network tomography applications under failures.

The virtual force approach is at the basis of many solutions proposed for deploying mobile sensors. In this paper we study the vulnerabilities of this approach. We show that by compromising a few mobile sensors, an attacker can influence the movement of other sensors and prevent the achievement of the network coverage goals. We introduce an attack, called opportunistic movement, and give an analytical study of its efficacy. We show that in a typical scenario this attack can reduce coverage by more than 50 percent, by only compromising a 7 percent of the nodes. We propose two algorithms to counteract the above mentioned attack, DRM and SecureVF. DRM is a light-weight algorithm which randomly repositions sensors from overcrowded areas. SecureVF requires a more complex coordination among sensors but, unlike DRM, it enables detection and identification of malicious sensors. We investigate the performance of DRM and SecureVF through simulations. We show that DRM can significantly reduce the effects of the attack, at the expense of an increase in the energy consumption due to additional movements. By contrast, SecureVF completely neutralizes the attack and allows the achievement of the coverage goals of the network even in the presence of localization inaccuracies.

In this paper we point out the vulnerabilities of the virtual force approach to mobile sensor deployment, which is at the basis of many deployment algorithms. For the first time in the literature, we show that some attacks significantly hinder the capability of these algorithms to guarantee a satisfactory coverage. An attacker can compromise a few mobile sensors and force them to pursue a malicious purpose by influencing the movement of other legitimate sensors. We make an example of a simple and effective attack, called Opportunistic Movement, and give an analytical study of its efficacy. We also show through simulations that, in a typical scenario, this attack can reduce coverage by more than 50% by compromising a number of nodes as low as the 7%. We propose SecureVF, a virtual force deployment algorithm able to neutralize the above mentioned attack. We show that under SecureVF malicious sensors are detected and then ignored whenever their movement is not compliant with the moving strategy provided by SecureVF. We also investigate the performance of SecureVF through simulations, and compare it to one of the most acknowledged algorithms based on virtual forces. We show that SecureVF enables a remarkably improved coverage of the area of interest, at the expense of a low additional energy consumption.

In order to prolong the lifetime of a wireless sensor network (WSN) devoted to monitoring an area of interest, a useful means is to exploit network redundancy, activating only the sensors that are strictly necessary for coverage and making them work with the minimum necessary sensing radius. In this article, we introduce the first algorithm that reduces sensor coverage redundancy through joint Sensor Activation and sensing Radius Adaptation (SARA) in general application scenarios comprising two classes of devices: sensors with variable sensing radius and sensors with fixed sensing radius. This device heterogeneity is explicitly addressed by modeling the coverage problem through Voronoi-Laguerre diagrams that, differently from Voronoi diagrams, allow for correctly identifying each sensor coverage region depending on the sensor current radius and the radii of its neighboring nodes. SARA executes quickly with guaranteed termination and, given the currently available nodes, it always guarantees maximum coverage. By means of extensive simulations, we show that SARA obtains remarkable improvements with respect to previous solutions, ensuring, in networks with heterogeneous nodes, longer network lifetime and wider coverage.

Critical homeland security applications such as monitoring zones contaminated by chemical or biological attacks and monitoring the spread of forest fires, require the timely creation of barrier of sensors along the border to be monitored. The strict time requirements and the hazardous nature of these contexts impede manual sensor positioning. Mobile Wireless Sensor Networks have the potential to meet the desired coverage requirements, by exploiting the device locomotion capabilities. In this paper we propose MobiBar, a distributed and asynchronous algorithm for k-barrier coverage with mobile sensors. We formally prove that MobiBar terminates in a finite time and that the final deployment provides the maximum level of barrier coverage with the available sensors. We compare MobiBar to a recent virtual force-based approach by means of simulations, which show the superiority of our solution. Furthermore, we show the self-healing capability of MobiBar to quickly recover from sudden sensor faults.

In this paper, we address the problem of deploying heterogeneous mobile sensors over a target area. Traditional approaches to mobile sensor deployment are specifically designed for homogeneous networks. Nevertheless, network and device homogeneity is an unrealistic assumption in most practical scenarios, and previous approaches fail when adopted in heterogeneous operative settings. For this reason, we introduce VorLag, a generalization of the Voronoi-based approach which exploits the Laguerre geometry. We theoretically prove the appropriateness of our proposal to the management of heterogeneous networks. In addition, we demonstrate that VorLag can be extended to deal with dynamically generated events or uneven energy depletion due to communications. Finally, by means of simulations, we show that VorLag provides a very stable sensor behavior, with fast and guaranteed termination and moderate energy consumption. We also show that VorLag performs better than its traditional counterpart and other methods based on virtual forces.

In this paper we propose GREASE, a distributed algorithm to deploy mobile sensors in an unknown environment with obstacles and field asperities that may cause sensing anisotropies and non uniform device capabilities. These aspects are not taken into account by traditional approaches to the problem of mobile sensor self-deployment. GREASE works by realizing a grid-shaped deployment throughout the Area of Interest (AoI) and adaptively refining the grid to find new sensor positions to cover the target area more precisely in the zones where devices experience reduced movement, sensing and communication capabilities. We give bounds on the number of sensors necessary to cover an AoI with obstacles and noisy zones. Simulations show that GREASE provides a fast deployment with precise movements and no oscillations, with moderate energy consumption.

GENESI develops structural health monitoring systems for critical infrastructures such as tunnels, bridges, dams, private and public buildings, providing cutting edge green wireless sensor networks technology. The main goal of the project is that of overcoming once and for the barriers that make current wireless sensor network-based monitoring systems unfit for many applications. GENESI will provide solutions for sensor network technology enabling virtually infinite network lifetime. The resulting Green sEnsor NEtworks for Structural monitoring will be truly pervasive, robust and will be able to automatically adapt to application requirements and end-users demands. This poster paper provides an agile synopsis of the structure, aims and objectives of GENESI, providing also its vision for structural health monitoring and expected outcomes.

In this paper we address the problem of deploying heterogeneous mobile sensors over a target area. We show how traditional approaches designed for homogeneous networks fail when adopted in the heterogeneous operative setting.

The use of mobile sensors is of great relevance for a number of strategic applications devoted to monitoring critical areas where sensors can not be deployed manually. In these networks, each sensor adapts its position on the basis of a local evaluation of the coverage efficiency, thus permitting an autonomous deployment. Several algorithms have been proposed to deploy mobile sensors over the area of interest. The applicability of these approaches largely depends on a proper formalization of rigorous rules to coordinate sensor movements, solve local conflicts and manage possible failures of communications and devices. In this paper we introduce P&P, a communication protocol that permits a correct and efficient coordination of sensor movements in agreement with the PUSH&PULL algorithm. We deeply investigate and solve the problems that may occur when coordinating asynchronous local decisions in the presence of an unreliable transmission medium and possibly faulty devices such as in the typical working scenario of mobile sensor networks. Simulation results show the performance of our protocol under a range of operative settings, including conflict situations, irregularly shaped target areas, and node failures.

Unexpected increases in demand and most of all flash crowds are considered the bane of every Web application as they may cause intolerable delays or even service unavailability. Proper quality of service policies must guarantee rapid reactivity and responsiveness even in such critical situations. Previous solutions fail to meet common performance requirements when the system has to face sudden and unpredictable surges of traffic. Indeed they often rely on a proper setting of key parameters which requires laborious manual tuning, preventing a fast adaptation of the control policies. We contribute an original self-overload control (SOC) policy. This allows the system to self-configure a dynamic constraint on the rate of admitted sessions in order to respect service level agreements and maximize the resource utilization at the same time. Our policy does not require any prior information on the incoming traffic or manual configuration of key parameters. We ran extensive simulations under a wide range of operating conditions, showing that SOC rapidly adapts to time varying traffic and self-optimizes the resource utilization. It admits as many new sessions as possible in observance of the agreements, even under intense workload variations. We compared our algorithm to previously proposed approaches highlighting a more stable behavior and a better performance.

This paper addresses the problems of admission control and server selection in a system consisting of several geographically replicated web servers and several access points. We propose a fully distributed solution in which every access point continuously monitors the availability of all server side resources, using a mixture of active and passive measurements. Based on those measures, each access point autonomously applies its decisions to the requests it receives. Admission control is performed prioritizing requests belonging to already admitted sessions, in order to maximize the chance of successfully terminating ongoing sessions. Furthermore, session information is taken into account when performing a probabilistic request redirection and server choice, in order to improve load balancing and mitigate flash crowd effects. Extensive simulations, performed in compliance with industry standards, show that our method exhibits a stable behavior during overloads and improves service quality in terms of both reduced response time and higher successful session termination.

This paper tackles the problem of autonomic admission control for web clusters. The main contribution of this work is the proposal of a new session admission algorithm that self-configures a dynamic constraint on the rate of incoming new sessions to guarantee the respect of Service Level Agreements (SLA). Unlike other approaches, our policy does not need any prior information on the incoming traffic, nor any assumption on the probability distribution of request inter-arrival or service time. Furthermore, it does not require any manual configuration or parameter tuning. We performed extensive simulations under a range of operating conditions and compared our algorithm to other previously proposed approaches. The simulations show that our policy rapidly adapts to the given traffic profile and improves service throughput while respecting the response time constraints imposed by the SLAs. It also improves service quality by reducing the oscillations of response time and number of active clients common to other policies.