The 2016 United States presidential election was marked by the abuse of targeted advertising on Facebook. Concerned with the risk of the same kind of abuse to happen in the 2018 Brazilian elections, we designed and deployed an independent auditing system to monitor political ads on Meta in Brazil. To do that we first adapted a browser plugin to gather ads from the timeline of volunteers using Facebook. We managed to convince more than 2,000 volunteers to help our project and install our tool. Then, we use a Convolution Neural Network (CNN) to detect political Meta ads using word embeddings. To evaluate our approach, we manually label a data collection of 10k ads as political or non-political and then we provide an in-depth evaluation of proposed approach for identifying political ads by comparing it with classic supervised machine learning methods. Finally, we deployed a real system that shows the ads identified as related to politics during the 2018 National Brazilian elections. We also investigated early electoral advertisement before the 2020 local Brazilian elections using our model on unsponsored content (regular posts in groups and pages). We noticed that not all political ads we detected were present in the Meta Ad Library for political ads on 2018. Additionally, we found possible early electoral advertisements in 2020, which is forbidden in Brazil. Our results emphasize the importance of enforcement mechanisms for declaring political ads and the need for independent auditing platforms.

We propose the Burst-Induced Poisson Process (BPoP), a model designed to analyze time series data such as feeds or search queries. BPoP can distinguish between the slowly-varying regular activity of a stable audience and the bursty activity of a curious audience, often seen in viral threads. Our model consists of two hidden, interacting processes: a self-feeding process (SFP) that generates bursty behavior related to viral threads, and a non-homogeneous Poisson process (NHPP) with step function intensity that is influenced by the bursts from the SFP. The NHPP models the normal background behavior, driven solely by the overall popularity of the topic among the stable audience. Through extensive empirical work, we have demonstrated that our model fits and characterizes a large number of real datasets more effectively than state-of-the-art models. Most importantly, BPoP can quantify the stable audience of media channels over time, serving as a valuable indicator of their popularity.

Karen Martins, Pedro O.S Vaz-de-Melo, Rodrygo Santos. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.

We show that the usual evaluation metrics used in machine learning are not appropriate to measure the performance of spatial disease cluster detection algorithms. We demonstrate that the usual recall and precision metrics give a distorted evaluation of the algorithms. To solve this problem, we propose new metrics based on probability predictive rules. We evaluate the performance of the main spatial disease cluster algorithms with these new metrics. Our analysis and experiments offer insights into when the usual metrics are not appropriate and also show that our proposal is very effective at eliminating the bias from the usual metrics.

The 2016 United States presidential election was marked by the abuse of targeted advertising on Facebook. Concerned with the risk of the same kind of abuse to happen in the 2018 Brazilian elections, we designed and deployed an independent auditing system to monitor political ads on Facebook in Brazil. To do that we first adapted a browser plugin to gather ads from the timeline of volunteers using Facebook. We managed to convince more than 2000 volunteers to help our project and install our tool. Then, we use a Convolution Neural Network (CNN) to detect political Facebook ads using word embeddings. To evaluate our approach, we manually label a data collection of 10k ads as political or non-political and then we provide an in-depth evaluation of proposed approach for identifying political ads by comparing it with classic supervised machine learning methods. Finally, we deployed a real system that shows the ads identified as related to politics. We noticed that not all political ads we detected were present in the Facebook Ad Library for political ads. Our results emphasize the importance of enforcement mechanisms for declaring political ads and the need for independent auditing platforms.

Measuring the affinity to a particular culture has been an active area of research. Countries and their residents can be characterized by many cultural aspects, such as clothing, music, art and food. As one of the central aspects, the cuisine of a country can reflect one of the dominant aspects of its culture. As such, the number of people interested in a typical national dish can be used to estimate the prevalence of that culture inside the host region. In this study, we measure the global spread of Brazilian culture across countries by exploring Facebook user’s preferences for typical Brazilian dishes through the Facebook Advertising Platform. To decide which dish will be considered typical from Brazil, we made use of spatial analysis to understand the distribution of interests around the world and to quantify how typical the dish is in Brazil and among Brazilian immigrants. This methodology can be generalized to other countries to infer cultural elements that emigrants usually take to and preserve in the countries they migrate to. Also, the interest in Brazilian typical dishes can be used to characterize countries in terms of Brazilian cultural exposition. While evaluating the cultural distance between Brazil and the countries with more Brazilian immigrants, we explore several measures of distance to compare these in the context of affinity to Brazilian cuisine. Our results revealed that these cultural distance measures can complement other metrics of distance applied to gravity-type models, for example, in order to explain flows of people between countries.

Someone enjoys listening to playlists while commuting. He wants a different playlist of n songs each day, but always starting from Locked Out of Heaven , a Bruno Mars song. The list should progress in smooth transitions between successive and randomly selected songs until it ends up at Stairway to Heaven , a Led Zeppelin song. The challenge of automatically generating random and heterogeneous playlists is to find the appropriate balance among several conflicting goals. We propose two methods for solving this problem. One is called ROPE , and it depends on a representation of the songs in a Euclidean space. It generates a random path through a Brownian Bridge that connects any two songs selected by the user in this music space. The second is STRAW , which constructs a graph representation of the music space where the nodes are songs and edges connect similar songs. STRAW creates a playlist by traversing the graph through a steering random walk that starts on a selected song and is directed toward a target song also selected by the user. When compared with the state-of-the-art algorithms, our algorithms are the only ones that satisfy the following quality constraints: heterogeneity , smooth transitions , novelty , scalability , and usability . We demonstrate the usefulness of our proposed algorithms by applying them to a large collection of songs and make available a prototype.

Characterizing dynamic interactions is currently an important issue when analyzing complex social networks. In this paper, we reinforce the importance of social concepts as the strategic positioning of an actor in a social structure, thus bringing new insights to the analysis of complex networks. Specifically, we propose a new method to characterize relationships based on temporal node-attributes that captures how knowledge is transferred across the network. As a result, we unveil the differences of social relationships in different academic social networks and Q&A communities. We also validate our social definitions in terms of the importance of the edges as assessed by the betweenness centrality metric and compare our results with those of two existing methods. Finally, we apply our method to a ranking task in order to measure the academic importance of researchers.

How to classify the dynamic interactions in social networks? We address this task by exploring the behavior and the dynamics of the actors in a social network. Specifically, we reinforce the importance of social concepts to capture the social meaning of relationships. Then, we propose a new strategy to classify nodes and dynamic edges based on node-attribute relationships. We apply our proposal to two attribute scenarios: tokens associated with edges and social ties associated with communities. As a result, we unveil the differences of social relationships in different academic social network scenarios. Our method differs from an existing one by defining more elaborated classes in terms of social concepts and performing 7.7 times faster than it. Finally, we validate our social definitions in terms of the best positioned nodes according to network metrics, as well as investigate the robustness of our method .

There are many mobility models in the literature with diverse formats and origins. Besides the existence of studies that analyze and characterize these models, there is a need for a framework that can compare them in an easy way. MOCHA (Mobility framework for CHaracteristics Analysis) is a tool that characterizes and makes possible the comparison of mobility models without any hard work. We implemented 9 social, spatial and temporal characteristics, which were extracted from various (real and synthetic) distinct mobility traces. MOCHA has a classifying module that attributes each characteristic the statistic distribution that better describes it. As a validation process, all the traces were compared using the T-SNE method for data visualization, resulting in the approximation of similar traces. One of the advantages of using MOCHA is its ease of use, being able to read diverse traces formats and converting them to its standard format, allowing that different types of traces, such as check-in, GPS, contacts, and so on, to be compared. The metrics used in the tool can become a standard for trace analysis and comparison in the literature, allowing a better vision of where one trace belongs related to others. MOCHA is available for download at https://github.com/wisemap-ufmg/MOCHA.

We here focus on the task of learning Granger causality matrices for multivariate point processes. In order to accomplish this task, our work is the first to explore the use of Wold processes. By doing so, we are able to develop asymptotically fast MCMC learning algorithms. With $N$ being the total number of events and $K$ the number of processes, our learning algorithm has a $O(N(\,\log(N)\,+\,\log(K)))$ cost per iteration. This is much faster than the $O(N^3\,K^2)$ or $O(K^3)$ for the state of the art. Our approach, called GrangerBusca, is validated on nine datasets. This is an advance in relation to most prior efforts which focus mostly on subsets of the Memetracker data. Regarding accuracy, GrangerBusca is three times more accurate (in Precision@10) than the state of the art for the commonly explored subsets Memetracker. Due to GrangerBusca's much lower training complexity, our approach is the only one able to train models for larger, full, sets of data.

The recent exponential growth of the Internet of Things (IoT) and its mobile devices asks for the advancement of mobile networks technology. In such direction, current and next generations of cellular and vehicular networks foresees adhoc communication. In such paradigm, as entities are constantly moving, connections between them are intermittent and of little, if none, reliability. Recent studies have explored entities connections and how to design efficient ad-hoc communication algorithms for them. In this paper, using real mobility data, we study how and to what extent the entities mobility affects the network (global) and the entities (local) connectivity. We propose and leverage existing mobility metrics to capture both the depth and the spread of entities trajectories. Furthermore, we show how global connectivity is related to the depth of the trajectories, while local connectivity is related to the spread of the trajectories. Finally, as we use both human and vehicular mobility data, we discuss how their mobility nature can affect their mobility characteristics and connectivity.

Predicting the outcome of sports events is a hard task. We quantify this difficulty with a coefficient that measures the distance between the observed final results of sports leagues and idealized perfectly balanced competitions in terms of skill. This indicates the relative presence of luck and skill. We collected and analyzed all games from 198 sports leagues comprising 1503 seasons from 84 countries of 4 different sports: basketball, soccer, volleyball and handball. We measured the competitiveness by countries and sports. We also identify in each season which teams, if removed from its league, result in a completely random tournament. Surprisingly, not many of them are needed. As another contribution of this paper, we propose a probabilistic graphical model to learn about the teams' skills and to decompose the relative weights of luck and skill in each game. We break down the skill component into factors associated with the teams' characteristics. The model also allows to estimate as 0.36 the probability that an underdog team wins in the NBA league, with a home advantage adding 0.09 to this probability. As shown in the first part of the paper, luck is substantially present even in the most competitive championships, which partially explains why sophisticated and complex feature-based models hardly beat simple models in the task of forecasting sports' outcomes.

In this work we propose, implement, and evaluate Group Regularity Model (GRM), a novel mobility model that accounts for the role of group meetings regularity in human mobility. We show that existing mobility models for humans do not capture the regularity of human group meetings present in real mobility traces. We characterize the statistical properties of such group meetings in real mobility traces and design GRM accordingly. We show that GRM maintains the typical pairwise contact properties of real traces, such as contact duration and inter-contact time distributions. In addition, GRM accounts for the role of group mobility, presenting group meetings regularity and social communities' structure. Finally, we evaluate state-of-art social-aware protocols for opportunistic routing and show that their performance in synthetic traces generated by GRM is similar to their performance in real-world traces.

In co-authorship social networks, nodes are authors linked by co-authorship interactions. As time is a relevant aspect of such interactions, concepts and metrics designed to static networks have to be adapted to temporal networks. Tie strength is one of those concepts. Here, we verify if current tie strength definitions are valid for temporal networks by analyzing the strength of ties dynamism over temporal co-authorship networks. Surprisingly, our results show that most ties, even the strong ones, tend to perish over time. Thus, most co-authorships are symbiotic without positive concerns. Also, real co-authorship social networks from different research areas have more weak and random ties than strong and bridge ties.

In recent years, there is a growing research interest in smart city applications based on opportunistic communications. The opportunistic networks in these scenarios are composed of disconnections, network partitions, high delay and strong influence of human mobility. To cope with these challenges, social-inspired approaches have been proposed considering the structure of networks and personal user features, however, limited studies have explored temporal variations of features and influence of exogenous variables, disregarding adaptive forwarding policies recommended for dynamic scenarios. In this paper, we address these challenges while investigating both, the temperature and the season calendar, as environmental features able to model the behavior of users' mobility and peer contacts. The results showed distinct social and spatiotemporal features characterized by thermal conditions able to affect the network performance. We also identified critical points of temperature able to provide early signals about the network changes. Finally, our results indicate that environmental data are crucial information towards the design of the next generation opportunistic mobile networks.

In the era of mobile computing, understanding human mobility patterns is crucial in order to better design protocols and applications. Many studies focus on different aspects of human mobility such as people's points of interests, routes, traffic, individual mobility patterns, among others. In this work, we propose to look at human mobility through a social perspective, i.e., analyze the impact of social groups in mobility patterns. We use the MIT Reality Mining proximity trace to detect, track and investigate group's evolution throughout time. Our results show that group meetings happen in a periodical fashion and present daily and weekly periodicity. We analyze how groups' dynamics change over day hours and find that group meetings lasting longer are those with less changes in members composition and with members having stronger social bonds with each other. Our findings can be used to propose meeting prediction algorithms, opportunistic routing and information diffusion protocols, taking advantage of those revealed properties.

The problem to accurately and parsimoniously characterize random series of events (RSEs) seen in the Web, such as Yelp reviews or Twitter hashtags, is not trivial. Reports found in the literature reveal two apparent conflicting visions of how RSEs should be modeled. From one side, the Poissonian processes, of which consecutive events follow each other at a relatively regular time and should not be correlated. On the other side, the self-exciting processes, which are able to generate bursts of correlated events. The existence of many and sometimes conflicting approaches to model RSEs is a consequence of the unpredictability of the aggregated dynamics of our individual and routine activities, which sometimes show simple patterns, but sometimes results in irregular rising and falling trends. In this paper we propose a parsimonious way to characterize general RSEs, namely the Burstiness Scale (BuSca) model. BuSca views each RSE as a mix of two independent process: a Poissonian and a self-exciting one. Here we describe a fast method to extract the two parameters of BuSca that, together, gives the burstiness scale ψ, which represents how much of the RSE is due to bursty and viral effects. We validated our method in eight diverse and large datasets containing real random series of events seen in Twitter, Yelp, e-mail conversations, Digg, and online forums. Results showed that, even using only two parameters, BuSca is able to accurately describe RSEs seen in these diverse systems, what can leverage many applications.

MOTIVATION: The 1000 Genomes Project (1KGP) and thousands of Genome-Wide Association Studies (GWAS) performed during the last years have generated an enormous amount of information that needs to be integrated to better understand the genetic architecture of complex diseases in different populations. This integration is important in areas such as genetics, epidemiology, anthropology, as well as admixture mapping design and GWAS-replications. Network-based approaches that explore the genetic bases of human diseases and traits have not yet incorporated information on genetic diversity among human populations. RESULTS: We propose Disease-ANCEstry networks (DANCE), a graph-based web tool that allows to integrate and visualize information on human complex phenotypes and their GWAS-hits, as well as their risk allele frequencies in different populations. DANCE provides an interactive way to explore the human SNP-Disease Network and its projection, a Disease-Disease Network. With these functionalities, DANCE fills a gap in our ability to handle and understand the knowledge generated by GWAS and 1KGP. We provide a number of case studies that show how DANCE can be used to explore the relationships between human complex diseases, their genetic bases and variability in different human populations. AVAILABILITY AND IMPLEMENTATION: DANCE is freely available at http://ldgh.com.br/dance/ We recommend using DANCE with Mozilla Firefox, Safari, Chrome or Internet Explorer (v9 or v10). CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

With the advancement of information systems, means of communications are becoming cheaper, faster, and more available. Today, millions of people carrying smartphones or tablets are able to communicate practically any time and anywhere they want. They can access their e-mails, comment on weblogs, watch and post videos and photos (as well as comment on them), and make phone calls or text messages almost ubiquitously. Given this scenario, in this article, we tackle a fundamental aspect of this new era of communication: How the time intervals between communication events behave for different technologies and means of communications. Are there universal patterns for the Inter-Event Time Distribution (IED)? How do inter-event times behave differently among particular technologies? To answer these questions, we analyzed eight different datasets from real and modern communication data and found four well-defined patterns seen in all the eight datasets. Moreover, we propose the use of the Self-Feeding Process (SFP) to generate inter-event times between communications. The SFP is an extremely parsimonious point process that requires at most two parameters and is able to generate inter-event times with all the universal properties we observed in the data. We also show three potential applications of the SFP: as a framework to generate a synthetic dataset containing realistic communication events of any one of the analyzed means of communications, as a technique to detect anomalies, and as a building block for more specific models that aim to encompass the particularities seen in each of the analyzed systems.

We here investigate the potential of participatory sensor networks derived from location sharing systems, such as Foursquare, to understand the human dynamics of cities. We propose the City Image visualization technique, which builds a transition graph mapping people's movements between location categories, and demonstrate its use to identify similarities and differences of human dynamics across cities by clustering cities according to their citizens' routines. We also analyze centrality metrics of the transition graphs built for different cities, considering transitions between specific venues. We show that these metrics complement the City Image technique, contributing to a deeper understanding of city dynamics.

Participatory sensing systems (PSSs) have the potential to become fundamental tools to support the study, in large scale, of urban social behavior and city dynamics. To that end, this work characterizes the photo sharing system Instagram, considered one of the currently most popular PSS on the Internet. Based on a dataset of approximately 2.3 million shared photos, we characterize user's behavior in the system showing that there are several advantages and opportunities for large scale sensing, such as a global coverage at low cost, but also challenges, such as a very unequal photo sharing frequency, both spatially and temporally. We also observe that the temporal photo sharing pattern is a good indicator about cultural behaviors, and also says a lot about certain classes of places. Moreover, we present an application to identify regions of interest in a city based on data obtained from Instagram, which illustrates the promising potential of PSSs for the study of city dynamics.

An important issue in the design of a wireless sensor network (WSN) is to devise techniques to make efficient use of its energy, and thus, extend its lifetime. When two or more WSNs are deployed in the same place and their sensors cooperate with the other networks, they may improve their operability, by extending its lifetime by trading routing favors or increasing the data entropy by a common data aggregation. Despite being obvious and simple, this idea brings with it many implications that hinder cooperation between the networks. Whereas a WSN has a rational and selfish character, it will only cooperate with another WSN if this provides services that justify the cooperation. The goal of this work is to present the Virtual Cooperation Bond (VCB) protocol, that is a distributed protocol that makes different WSNs to cooperate, enabling cooperation if, and only if, all the different WSNs benefit with the cooperation. In the simulation results, we consider WSNs with different configurations and we show that the proposed protocol enables cooperation solely when the cooperation is beneficial to both networks, and in this case, it saves their energies and extends their lifetimes.

Cities are not identical and evolve over time, and sensing in large scale can be used to capture these differences. Research in Wireless Sensor Networks has provided several tools, techniques and algorithms to solve the problem of sensing in restricted scenarios (e.g., factory). However, sensing large scale areas, such as big cities, brings many challenges and incurs high costs related to system building and management. Thus, sensing those areas becomes more feasible when people collaborate among themselves using their portable devices, and building what has been named participatory sensing systems. This work analyzes an emerging type of network derived from this type of system, the Participatory Sensor Network (PSN), where nodes are autonomous mobile entities and the sensing depends on whether they want to participate in the sensing process. Based on four datasets of participatory sensing systems (27 million of records), we show that this type of network brings many challenges related to structural problems, e.g. instant coverage very limited, and also because of big data issues, which may restrict the use of this emerging type of network. However, it presents also, as shown here, many advantages and open opportunities, mainly related to large scale study of cities dynamics.

In this paper, we argue that the ability to accurately spot random and social relationships in dynamic networks is essential to network applications that rely on human routines, such as, e.g., opportunistic routing. We thus propose a strategy to analyze users' interactions in mobile networks where users act according to their interests and activity dynamics. Our strategy, named Random rElationship ClASsifier sTrategy (RECAST), allows classifying users' wireless interactions, separating random interactions from different kinds of social ties. To that end, RECAST observes how the real system differs from an equivalent one where entities' decisions are completely random. We evaluate the effectiveness of the RECAST classification on real-world user contact datasets collected in diverse networking contexts. Our analysis unveils significant differences among the dynamics of users' wireless interactions in the datasets, which we leverage to unveil the impact of social ties on opportunistic routing.

How often do individuals perform a given communication activity in the Web, such as posting comments on blogs or news? Could we have a generative model to create communication events with realistic inter-event time distributions (IEDs)? Which properties should we strive to match? Current literature has seemingly contradictory results for IED: some studies claim good fits with power laws; others with non-homogeneous Poisson processes. Given these two approaches, we ask: which is the correct one? Can we reconcile them all? We show here that, surprisingly, both approaches are correct, being corner cases of the proposed Self-Feeding Process (SFP). We show that the SFP (a) exhibits a unifying power, which generates power law tails (including the so-called "top-concavity" that real data exhibits), as well as short-term Poisson behavior; (b) avoids the "i.i.d. fallacy", which none of the prevailing models have studied before; and (c) is extremely parsimonious, requiring usually only one, and in general, at most two parameters. Experiments conducted on eight large, diverse real datasets (e.g., Youtube and blog comments, e-mails, SMSs, etc) reveal that the SFP mimics their properties very well.

The multi-million sports-betting market is based on the fact that the task of predicting the outcome of a sports event is very hard. Even with the aid of an uncountable number of descriptive statistics and background information, only a few can correctly guess the outcome of a game or a league. In this work, our approach is to move away from the traditional way of predicting sports events, and instead to model sports leagues as networks of players and teams where the only information available is the work relationships among them. We propose two network-based models to predict the behavior of teams in sports leagues. These models are parameter-free, that is, they do not have a single parameter, and moreover are sport-agnostic: they can be applied directly to any team sports league. First, we view a sports league as a network in evolution, and we infer the implicit feedback behind network changes and properties over the years. Then, we use this knowledge to construct the network-based prediction models, which can, with a significantly high probability, indicate how well a team will perform over a season. We compare our proposed models with other prediction models in two of the most popular sports leagues: the National Basketball Association (NBA) and the Major League Baseball (MLB). Our model shows consistently good results in comparison with the other models and, relying upon the network properties of the teams, we achieved a ≈ 14% rank prediction accuracy improvement over our best competitor.

How often humans communicate with each other? What are the mechanisms that explain how human actions are distributed over time? Here we answer these questions by studying the time interval between calls and SMS messages in an anonymized, large mobile network, with 3.1 million users, over 200 million phone calls and 300 million SMS messages, spanning 70 GigaBytes. Our first contribution is the Truncated Autocatalytic Process (TAP) model, that explains the time between communication events (ie., times between phone-initiations) for a single individual. The novelty is that the model is ‘autocatalytic’, in the sense that the parameters of the model change, depending on the latest inter-event time: long periods of inactivity in the past result in long periods of inactivity in the future, and vice-versa. We show that the TAP model mimics the inter-event times of the users of our dataset extremely well, despite its parsimony and simplicity. Our second contribution is the TAP-classifier, a classification method based on the inter-event times and in addition to other features. We showed that the inferred sleep intervals and the reciprocity between outgoing and incoming calls are good features to classify users. Finally, analyze the network effects of each class of users and we found surprising results. Moreover, all of our methods are fast, and scale linearly with the number of customers.

The United States National Basketball Association (NBA) is one of the most popular sports league in the world and is well known for moving a millionary betting market that uses the countless statistical data generated after each game to feed the wagers. This leads to the existence of a rich historical database that motivates us to discover implicit knowledge in it. In this paper, we use complex network statistics to analyze the NBA database in order to create models to represent the behavior of teams in the NBA. Results of complex network-based models are compared with box score statistics, such as points, rebounds and assists per game. We show the box score statistics play a significant role for only a small fraction of the players in the league. We then propose new models for predicting a team success based on complex network metrics, such as clustering coefficient and node degree. Complex network-based models present good results when compared to box score statistics, which underscore the importance of capturing network relationships in a community such as the NBA.

An important issue in the design of a wireless sensor network (WSN) is to devise techniques to make efficient use of its energy, and thus, extend its lifetime. When two WSNs are deployed at the same place and their sensors cooperate with the other networks forwarding their packets, the distance of the transmissions decreases and, therefore, the power consumption as well. The goal of this work is to examine the extent to which different WSNs can cooperate and save their energy. Simulation results reveal that different densities and data collecting rates among WSNs, the routing algorithm and the path loss exponent have major impact in the establishment of cooperation.

A gossip protocol randomly decides the set of nodes that will forward a packet it received. Gossiping was proposed to be used in dynamic topology networks such as Wireless Sensor Networks (WSNs). This work proposes Gossiping using the Energy Map (GEM), a new gossiping-based protocol to perform energy-aware broadcasting in WSNs. The key idea is to change the random selection of neighbors in a way that the selection process uses the energy map. In our protocol, the routing flow is directed to the nodes with the greatest energy reserves, balancing the network energy and preserving nodes localized inside low energy regions to perform sensing tasks.