Accurate measurement of mobile network performance is crucial for optimizing user experience and ensuring regulatory compliance. Traditional methods like crowdsourcing approaches, though effective, depend heavily on user participation and extensive infrastructure. In this paper, we introduce adNPM, a novel technique for measuring download speed by embedded measurement code in ads displayed across web browsers and mobile apps, without requiring user participation. Through controlled lab tests and real-world deployments in 15 countries, we demonstrate that adNPM achieves accuracy comparable to well-established tools like Speedtest by Ookla and Opensignal while significantly reducing data consumption.

The literature has shown that combining a few non-Personal Identifiable Information (non-PII) is enough to make a user unique in a dataset including millions of users. This work demonstrates that a combination of a few non-PII items can be activated to nanotarget users. We demonstrate that the combination of the location and 5 rare (13 random) skills in a LinkedIn profile is enough to become unique in a user base of ∼ 970M users with a probability of 75%. The novelty is that these attributes are publicly accessible to anyone registered on LinkedIn and can be activated through advertising campaigns. We ran an experiment configuring ad campaigns using the location and skills of three of the paper’s authors, demonstrating how all the ads using ≥ 13 skills were delivered exclusively to the targeted user. We reported this vulnerability to LinkedIn, which initially ignored the problem, but fixed it as of November 2023.

Browser fingerprinting is an effective technique to track web users by building a fingerprint from their browser attributes. It is also stealthy because the tracker uses legitimate JavaScript API calls offered by the browser engine, which can be obfuscated before they are sent to a (third-party) server. Current browser fingerprinting methodologies employ coarse-grained collection and classification techniques, such as binary classification of fingerprinters based on the number of non-obfuscated exfiltrated attributes. As a result, they produce inconsistent findings. Meanwhile, the privacy of millions of web users is at risk daily. We address this gap by presenting FP-tracer, a novel methodology to detect and classify browser fingerprinters based on dynamic taint tracking and joint entropy classification. Our methodology enables detecting first- and third-party fingerprinters even when they use obfuscation by tainting attributes, propagating them, and logging when they are leaked (via 62 sources and 25 sinks). Moreover, it discriminates the invasiveness of fingerprinting activities, even from the same service, by measuring the joint entropy of the collected attributes and clustering them. We implement FP-tracer by extending Foxhound, a privacy-oriented Firefox fork with numeric type tainting, more taint tracking sources and sinks, support for multiple sources, and better logging capabilities. We embed our implementation in our automated crawling infrastructure, which is capable of testing websites in parallel using programmable and reproducible logic. We will open-source our implementation. We evaluate FP-tracer by performing a large-scale crawl over the Tranco Top 100K, and detect, amongst others, audio, canvas, and storage fingerprinting on the web. Among others, we find high fingerprinting activities in 8% of domains, with more moderate activity reaching 75%. Notably, fingerprinting is almost five times more likely to be performed by third-party scripts for high activity levels. In addition, we measure that the most severe category of fingerprinting obfuscates 46% of transmitted attributes, and 38% of fingerprinters involve two or more domains. Finally, we find that existing consent banners do not provide an effective defense against browser fingerprinting

Many Internet services obtain their revenue through the delivery of online advertisements based on the commercial exploitation of users’ profiles. The accuracy and size of these profiles have important implications in terms of advertisers’ campaign performance and users’ privacy. Despite the importance of auditing the profiling accuracy, very little effort has been devoted both in industry and academia. This paper presents the most comprehensive auditing effort to understand the profiling accuracy of four major online advertising platforms: Google, Facebook, Twitter, and LinkedIn. Our work unveils that less than 50% of the assigned interests are relevant. Moreover, platforms can distinguish what interests within the assigned ones are more relevant but hide this information from users and advertisers. Finally, we have proposed a very simple solution that only uses 25 general interests per user. This proposal outperforms all the analyzed platforms in terms of profile accuracy while improving users’ privacy.

The literature has shown combining a few non-Personal Identifiable Information (non-PII) is enough to make a user unique in a dataset including millions of users. In this work, we demonstrate that the combination of the location and 6 rare (14 random) skills in a LinkedIn profile is enough to become unique in a user base of ~800M users with a probability of 75%. The novelty is these attributes are publicly accessible to anyone registered on LinkedIn and could be activated through advertising campaigns.

The quest for every time more personalized Internet experience relies on the enriched contextual information about each user. Online advertising also follows this approach. Among the context information that advertising stakeholders leverage, location information is certainly one of them. However, when this information is not directly available from the end users, advertising stakeholders infer it using geolocation databases, matching IP addresses to a position on earth. The accuracy of this approach has often been questioned in the past: however, the reality check on an advertising stakeholder shows that this technique accounts for a large fraction of the served advertisements. In this paper, we revisit the work in the field, that is mostly from almost one decade ago, through the lenses of big data. More specifically, we, i) benchmark two commercial Internet geolocation databases, evaluate the quality of their information using a ground-truth database of user positions containing over 2 billion samples, ii) analyze the internals of these databases, devising a theoretical upper bound for the quality of the Internet geolocation approach, and iii) we run an empirical study that unveils the monetary impact of this technology by considering the costs associated with a real-world ad impressions dataset.

Energy is today the most critical environmental challenge. The amount of carbon emissions contributing to climate change is significantly influenced by both the production and consumption of energy. Measuring and reducing the energy consumption of services is a crucial step toward reducing adverse environmental effects caused by carbon emissions. Millions of websites rely on online advertisements to generate revenue, with most websites earning most or all of their revenues from ads. As a result, hundreds of billions of online ads are delivered daily to internet users to be rendered in their browsers. Both the delivery and rendering of each ad consume energy. This study investigates how much energy online ads use in the rendering process and offers a way for predicting it as part of rendering the ad. To the best of the authors’ knowledge, this is the first study to calculate the energy usage of single advertisements in the rendering process. Our research further introduces different levels of consumption by which online ads can be classified based on energy efficiency. This classification will allow advertisers to add energy efficiency metrics and optimize campaigns towards consuming less possible.

The privacy of an individual is bounded by the ability of a third party to reveal their identity. Certain data items such as a passport ID or a mobile phone number may be used to uniquely identify a person. These are referred to as Personal Identifiable Information (PII) items. Previous literature has also reported that, in datasets including millions of users, a combination of several non-PII items (which alone are not enough to identify an individual) can uniquely identify an individual within the dataset. In this paper, we define a data-driven model to quantify the number of interests from a user that make them unique on Facebook. To the best of our knowledge, this represents the first study of individuals' uniqueness at the world population scale. Besides, users' interests are actionable non-PII items that can be used to define ad campaigns and deliver tailored ads to Facebook users. We run an experiment through 21 Facebook ad campaigns that target three of the authors of this paper to prove that, if an advertiser knows enough interests from a user, the Facebook Advertising Platform can be systematically exploited to deliver ads exclusively to a specific user. We refer to this practice as nanotargeting. Finally, we discuss the harmful risks associated with nanotargeting such as psychological persuasion, user manipulation, or blackmailing, and provide easily implementable countermeasures to preclude attacks based on nanotargeting campaigns on Facebook.

Viewability is one of the most important metrics used in ad-tech to measure the performance quality of ad campaigns. The viewability standard defines the visibility conditions an ad impression must meet to achieve a sufficient marketing effect to be considered viewed. The ad-tech industry offers opaque measures of viewability whose performance is questionable. To address this issue, we propose a novel methodology for measuring viewability in ad campaigns. The disclosure of the functional details of this technique makes it reproducible and auditable. Our solution has been deployed in production by a Demand Side Platform (DSP) to measure the viewability rate of the ad campaigns. Leveraging the infrastructure of this DSP, we compare the performance of our methodology with a commercial solution. Both techniques report a similar overall viewability rate of 50%. However, our solution measured the viewability in 93% of the ads served by the DSP, unlike to 74% of the ads measured by the commercial solution. A rough estimation indicates that this increase in the measured rate may lead to a revenue increase of $3.5 million per year for a mid-sized DSP serving 100M of ads per day.

Invalid ad traffic is an inherent problem of programmatic advertising that has not been properly addressed so far. Traditionally, it has been considered that invalid ad traffic only harms the interests of advertisers, which pay for the cost of invalid ad impressions while other industry stakeholders earn revenue through commissions regardless of the quality of the impression. Our first contribution consists of providing evidence that shows how the Demand Side Platforms (DSPs), one of the most important intermediaries in the programmatic advertising supply chain, may be suffering from economic losses due to invalid ad traffic. Addressing the problem of invalid traffic at DSPs requires a highly scalable solution that can identify invalid traffic in real time at the individual bid request level. The second and main contribution is the design and implementation of a solution for the invalid traffic problem, a system that can be seamlessly integrated into the current programmatic ecosystem by the DSPs. Our system has been released under an open source license, becoming the first auditable solution for invalid ad traffic detection. The intrinsic transparency of our solution along with the good results obtained in industrial trials have led the World Federation of Advertisers to endorse it.

The OECD, the European Union and other public and private initiatives are claiming for the necessity of tools that create awareness among Internet users about the monetary value associated to the commercial exploitation of their online personal information. This paper presents the first tool addressing this challenge, the Facebook Data Valuation Tool (FDVT). The FDVT provides Facebook users with a personalized and real-time estimation of the revenue they generate for Facebook. Relying on the FDVT, we are able to shed light into several relevant HCI research questions that require a data valuation tool in place. The obtained results reveal that (i) there exists a deep lack of awareness among Internet users regarding the monetary value of personal information, (ii) data valuation tools such as the FDVT are useful means to reduce such knowledge gap, (iii) 1/3 of the users testing the FDVT show a substantial engagement with the tool.

For many years, the research community, practitioners, and regulators have used myriad methods and tools to understand the complex structure and behavior of ISPs from the edge of the network. Unfortunately, the nature of these techniques forces the researcher to find a balance between ISP-coverage, user scale, and accuracy. In this paper we present AdTag, a network measurement paradigm that leverages the opportunistic nature of online targeted advertising to measure the Internet from the edge of the network. We discuss and formalize AdTag's design space---including technical, ethical, deployability and economic factors---and its potential to analyze a wide spectrum of Internet connectivity aspects from the browser. We run several experiments to demonstrate that AdTag can be tailored towards geographic and device-based user groups, finding also several challenges to be faced in order to maximize the number of samples. In a 7-day campaign, AdTag could access more than 20K ISPs at a global scale (185 countries) using millions of edge nodes.

Nowadays Internet companies that offer valuable services "for free" are becoming ubiquitous. Users benefiting from these services have to expose their personal information through these services as they utilize them. On the other hand, personal information is becoming a merchandisable commodity, venues that sell personal information by auction are emerging. One of these markets is in the form of advertising systems. Despite being a lucrative business, the hoarding of user personal information by commercial companies is a growing issue primarily because of its non-transparent nature. In this paper we present a data valuation portal that shades light on what kinds of personal information is on market and the financial value of it.

The reported lack of transparency of the online advertising market may seriously affect the interests of advertisers. In this paper, we present a novel methodology that allows advertisers to independently assess the quality of display advertising campaigns. This methodology also serves to audit the accuracy and completeness of reports delivered by the vendor responsible for running a campaign. We have applied our methodology in 8 display ad campaigns configured in Google AdWords, which overall produced 160K ad impressions displayed in more than 7K publishers. Our results reveal that AdWords seems to provide incomplete information to advertisers. Specifically, we found that: (i) AdWords did not report 57% of publishers where ad impressions from our campaigns were delivered, (ii) AdWords reports a large fraction of contextually meaningful impressions based on (non-disclosed) criteria different from the publisher’s theme, (iii) higher CPM investment does not lead to get impressions delivered to more popular publishers, (iv) AdWords does not offer default control of frequency cap, (v) around 10% ad impressions in two of our campaigns were delivered to IP’s from Data Centers. The industry considers these IPs to be likely related to fraud. These findings should contribute to open a debate between advertisers and Ad Tech vendors to standardize the utilization of independent auditing methodologies as the one presented in this work.

In the era when Facebook and Twitter dominate the market for social media, Google has introduced Google+ (G+) and reported a significant growth in its size while others called it a ghost town. This begs the question of whether G+ can really attract a significant number of connected and active users despite the dominance of Facebook and Twitter. This paper presents a detailed longitudinal characterization of G+ based on large-scale measurements. We identify the main components of G+ structure and characterize the key feature of their users and their evolution over time. We then conduct detailed analysis on the evolution of connectivity and activity among users in the largest connected component (LCC) of G+ structure, and compare their characteristics to other major online social networks (OSNs). We show that despite the dramatic growth in the size of G+, the relative size of the LCC has been decreasing and its connectivity has become less clustered. While the aggregate user activity has gradually increased, only a very small fraction of users exhibit any type of activity, and an even smaller fraction of these users attracts any reaction. The identity of users with most followers and reactions reveal that most of them are related to high-tech industry. To our knowledge, this study offers the most comprehensive characterization of G+ based on the largest collected datasets.

Online Social Networks (OSNs) are being intensively used by professional users (e.g., companies, politician, athletes, celebrities, etc) in order to interact with a huge amount of regular OSN users with different purposes (marketing campaigns, customer feedback, public reputation, etc). Hence, due to the large catalog of existing OSNs, professional users usually count with OSN accounts in different systems. In this context an interesting question is whether professional users publish the same information across their OSN accounts, or actually they use different OSNs in a different manner. We define as cross-posting activity the action of publishing the same information in two or more OSNs. In this paper we aim at characterizing the cross-posting activity of professional OSN users across three major OSNs, Facebook, Twitter and Google+. To achieve this goal we perform a large-scale measurement-based analysis across more than 2M posts collected from 616 professional users with active accounts in the three referred OSNs.

This paper presents a comprehensive empirical study on the correlations between users' interest similarity and various social features across three interest domains (i.e., movie, music and TV). This study relies on a large dataset, containing 479, 048 users and 5, 263, 351 user-generated interests, captured from Facebook. We identify the social features from three types of the users' information - demographic information (e.g., age, gender, location), social relations (i.e., friendship), and users' interests. The results reveal that the interest similarity follows the homophily principle. Particularly, the results show that two users are more likely to be alike in their interests 1) if they exhibit more similarity in their demographic characteristics (e.g., similar age, same gender, or close to each other geographically), or 2) if they are more intimate in their friendship, or 3) if they present a higher average interest individuality (i.e., a measurement for estimating the personalized characteristics of a user's interests). The empirical observations could be exploited to infer how two users are alike in their interests according to the social features, which could be further harnessed by various practical applications and services, such as recommendation system and advertisement service.

Content discovery is a critical issue in unstructured Peer-to-Peer (P2P) networks as nodes maintain only local network information. However, similarly without global information about human networks, one still can find specific persons via his/her friends by using social information. Therefore, in this paper, we investigate the problem of how social information (i.e., friends and background information) could benefit content discovery in P2P networks. We collect social information of 384,494 user profiles from Facebook, and build a social P2P network model based on the empirical analysis. In this model, we enrich nodes in P2P networks with social information and link nodes via their friendships. Each node extracts two types of social features–Knowledge and Similarity–and assigns more weight to the friends that have higher similarity and more knowledge. Furthermore, we present a novel content discovery algorithm which can explore the latent relationships among a node’s friends. A node computes stable scores for all its friends regarding their weight and the latent relationships. It then selects the top friends with higher scores to query content. Extensive experiments validate performance of the proposed mechanism. In particular, for personal interests searching, the proposed mechanism can achieve 100% of Search Success Rate by selecting the top 20 friends within two-hop. It also achieves 6.5 Hits on average, which improves 8x the performance of the compared methods.

Facebook, the most popular Online social network is a virtual environment where users share information and are in contact with friends. Apart from many useful aspects, there is a large amount of personal and sensitive information publicly available that is accessible to external entities/users. In this paper we study the public exposure of Facebook profile attributes to understand what type of attributes are considered more sensitive by Facebook users in terms of privacy, and thus are rarely disclosed, and which attributes are available in most Facebook profiles. Furthermore, we also analyze the public exposure of Facebook users by accounting the number of attributes that users make publicly available on average. To complete our analysis we have crawled the profile information of 479K randomly selected Facebook users. Finally, in order to demonstrate the utility of the publicly available information in Facebook profiles we show in this paper three case studies. The first one carries out a gender-based analysis to understand whether men or women share more or less information. The second case study depicts the age distribution of Facebook users. The last case study uses data inferred from Facebook profiles to map the distribution of worldwide population across cities according to its size.

Location-Based services with social networks improve users' experience and enrich people's social live. However, location information is often inadequate due to privacy and security concerns. We seek to infer users' ‘Current City’ on Facebook for coarse location based applications. We first extract users' multiple explicit and implicit location attributes, and analyze correlations of these attributes from two perspective: user-centric and user-friends. We observe that both user-centric and user-friends location attributes tightly correlate to a user's Current City (e.g., 60% of users stay in their hometown, 60% of users live in the same city as 50% of their friends). Based on extensive analysis and observations on location attributes correlations, we have constructed a Current City Prediction model (CCP) using artificial neural network (ANN) learning frameworks. The experimental results indicate that we achieve accuracy levels of 84% for city-level prediction and 98% for country-level which are increases of 9% and 18%, respectively than what is possible with Tweecalization.

During recent years, a few countries have put in place online antipiracy laws and there has been some major enforcement actions against violators. This raises the question that to what extent antipiracy actions have been effective in deterring online piracy? This is a challenging issue to explore because of the difficulty to capture user behavior, and to identify the subtle effect of various underlying (and potentially opposing) causes. In this paper, we tackle this question by examining the impact of two major antipiracy actions, the closure of Megaupload and the implementation of the French antipiracy law, on publishers in the largest BitTorrent portal who are major providers of copyrighted content online. We capture snapshots of BitTorrent publishers at proper times relative to the targeted antipiracy event and use the trends in the number and the level of activity of these publishers to assess their reaction to these events. Our investigation illustrates the importance of examining the impact of antipiracy events on different groups of publishers and provides valuable insights on the effect of selected major antipiracy actions on publishers' behavior.

In the era when Facebook and Twitter dominate the market for social media, Google has introduced Google+ (G+) and reported a significant growth in its size while others called it a ghost town. This begs the question that "whether G+ can really attract a significant number of connected and active users despite the dominance of Facebook and Twitter?".

BitTorrent is the most popular peer-to-peer (P2P) content delivery application where individual users share various types of content with tens of thousands of other users. The growing popularity of BitTorrent is primarily due to the availability of valuable content without any cost for the consumers. However, apart from the required resources, publishing valuable (and often copyrighted) content has serious legal implications for the users who publish the material. This raises the question that whether (at least major) content publishers behave in an altruistic fashion or have other motives such as financial incentives. In this paper, we identify the content publishers of more than 55 K torrents in two major BitTorrent portals and examine their characteristics. We discover that around 100 publishers are responsible for publishing 67% of the content, which corresponds to 75% of the downloads. Our investigation reveals several key insights about major publishers. First, antipiracy agencies and malicious users publish “fake” files to protect copyrighted content and spread malware, respectively. Second, excluding the fake publishers, content publishing in major BitTorrent portals appears to be largely driven by companies that try to attract consumers to their own Web sites for financial gain. Finally, we demonstrate that profit-driven publishers attract more loyal consumers than altruistic top publishers, whereas the latter have a larger fraction of loyal consumers with a higher degree of loyalty than the former.

This article presents a novel framework for data-centric storage (DCS) in a wireless sensor and actor network (WSAN) that employs a randomly selected set of data replication nodes, which also change over time. This enables reductions in the average network traffic and energy consumption by adapting the number of replicas to applications' traffic, while balancing energy burdens by varying their locations. To that end, we propose and validate a simple model to determine the optimal number of replicas, in terms of minimizing average traffic/energy consumption, based on measurements of applications' production and consumption traffic. Simple mechanisms are proposed to decide when the current set of replication nodes should be changed, to enable new applications and nodes to efficiently bootstrap into a working WSAN, to recover from failing nodes, and to adapt to changing conditions. Extensive simulations demonstrate that our approach can extend a WSAN's lifetime by at least 60%, and up to a factor of 10× depending on the lifetime criterion being considered. The feasibility of the proposed framework has been validated in a prototype with 20 resource-constrained motes, and the results obtained via simulation for large WSANs have been also corroborated in that prototype.

Content management appears as an essential requirement in order to deploy enhanced P2P networks. In P2P networks, the content that is requested by the query node could be located at different locations/nodes; therefore an efficient search mechanism is required. The proposed search algorithm in this paper, called socP2P, relies on peers' social relationships (friendships, shared interests, shared background and experiences) to improve the content discovery compared to similar solutions. With socP2P nodes can improve searches by using knowledge gained by `overheard information' during their stay in the network. In addition, our algorithm exploits peers' common interests, friendships, and capability of memorizing experiences (received and routed queries) by them. Simulation results show that socP2P is able to achieve a high success rate, low delay and low overhead. We have verified that our algorithm is not only useful in finding popular contents in the network but also good enough to locate rare files. The obtained results reflect that exploiting social information in P2P networks leads to a more efficient content search mechanism.

Nowadays Passive Optical Network (PON) requires that Optical Network Units (ONUs) wake up periodically to check if the Optical Line Terminal (OLT) has any message directed to them. This implies that ONUs change from sleeping mode in which they just consume 1 W to active mode in which the consumption goes up to 10 W. In many cases, the OLT does not have any packets for the ONU and it goes to sleep again, what supposes a waste of energy. In this paper, we propose a novel Hybrid ONU that relies on a low-cost and low-energy technology, IEEE 802.15.4, to wake up those ONUs that are going to receive a packet. Our first estimations demonstrates that our solution would save around 25000$ per year and OLT.

This paper studies data-centric storage (DCS) as a suitable system to perform data aggregation on wireless sensor and actor networks (WSANs), in which sensor and actor nodes collaborate together in a fully distributed way without any central base station that manages the network or provides connectivity to the outside world. The authors compare different multi-replication DCS proposals and choose the best one to be applied when studying data aggregation. In addition, the authors provide mathematical models for the production, consumption and overall network traffic for different application profiles. Those application profiles are based on the ability of a particular application to perform data aggregation and on what type of traffic is dominant, either the consumption or the production one. Furthermore, the authors provide closed formulas for each application profile that defines the optimal number of replicas that minimise the overall network traffic. Finally, the authors validate the proposed models via simulation.

BitTorrent is the most popular P2P content delivery application where individual users share various type of content with tens of thousands of other users. The growing popularity of BitTorrent is primarily due to the availability of valuable content without any cost for the consumers. However, apart from required resources, publishing (sharing) valuable (and often copyrighted) content has serious legal implications for users who publish the material (or publishers). This raises a question that whether (at least major) content publishers behave in an altruistic fashion or have other incentives such as financial. In this study, we identify the content publishers of more than 55K torrents in two major BitTorrent portals and examine their behavior. We demonstrate that a small fraction of publishers is responsible for 67 % of the published content and 75 % of the downloads. Our investigations reveal that these major publishers respond to two different profiles. On the one hand, antipiracy agencies and malicious publishers publish a large amount of fake files to protect copyrighted content and spread malware respectively. On the other hand, content publishing in BitTorrent is largely driven by companies with financial incentives. Therefore, if these companies lose their interest or are unable to publish content, BitTorrent traffic/portals may disappear or at least their associated traffic will be significantly reduced.

This paper presents a novel framework for Data Centric Storage in a wireless sensor and actor network that enables the use of a randomly-selected set of data replication nodes which also change over the time. This allows reducing the average network traffic and energy consumption by adapting the number of replicas to applications' traffic, while balancing energy burdens by varying their location. To that end we propose and validate a simple model to determine the optimal number of replicas, in terms of minimizing average traffic/energy consumption, from the measured applications' production/consumption traffic. Simple protocols/mechanisms are proposed to decide when the current set of replication nodes should be changed, to enable new applications and sensor nodes to efficiently bootstrap into a working sensor network, to recover from failing nodes, and to adapt to changing conditions. Extensive simulations demonstrate that our approach can extend a sensor network's lifetime by at least a 60%, and up to a factor of 10x depending on the lifetime criterion being considered.

Wireless technologies are rapidly evolving and the users are demanding the possibility of changing its point of attachment to the Internet (i.e. default router) without breaking the IP communications. This can be achieved by using Mobile IP or NEMO, however mobile clients must forward its data packets through its Home Agent (HA) in order to communicate with its peers. This sub-optimal route (lack of route optimization) reduces considerably the communications performance, increases the delay and the infrastructure load. Additionally, since the HA must forward all the mobile clients' data packets, it can become the bottleneck of such networks. In this paper we present the fP2P-HN architecture, a P2P-based solution that allows deploying several HAes throughout the Internet. With this architecture a mobile client can select a closer HA to its topological position in order to reduce the delay of the paths towards its peers. Furthermore it incorporates flexible HAes that, as we will see, reduce the load at these entities. The main challenge of our solution is signaling the location of the HAes in Internet. We provide an analytical model that evaluates the costs and the benefits of the fP2P-HN architecture. The model shows that the signaling grows logarithmically with the number of HAes and that the reduction is, at least, 20% (lower bound).

Access to Wireless Sensor Networks (WSNs) from external applications will become a key aspect in order to successfully deploy this technology. This paper presents the Light-Weight Exterior Sensornet Protocol (LWESP), to enable the communication between applications and WSNs. This protocol is a combination of the Sensor Communication Language (SENCOMLNG) and the eXtensible Binary Enconding (XBE32). SENCOMLNG is an XML-based language. XBE32 provides an efficient encoding of the SENCOMLNG in order to reduce the bandwidth utilization and the processing cost in the WSN gateways, that in many scenarios could also be resource-restricted devices. Finally, we present a test-bed based on JAVA as well as a comparison between our proposal and other solutions showing that LWESP outperforms all the previous proposals in terms of bandwidth utilization.

Mobility in packet networks has become a critical issue in the last years. Mobile IP and the network mobility basic support protocol are the IETF proposals to provide mobility. However, both of them introduce performance limitations, due to the presence of an entity (home agent) in the communication path. Those problems have been tried to be solved in different ways. A family of solutions has been proposed in order to mitigate those problems by allowing mobile devices to use several geographically distributed home agents (thus making shorter the communication path). These techniques require a method to discover a close home agent, among those geographically distributed, to the mobile device. This paper proposes a peer-to-peer based solution, called peer-to-peer home agent network, in order to discover a close home agent. The proposed solution is simple, fully global, dynamic and it can be developed in IPv4 and IPv6.