Wi-Fi datasets play a crucial role in wireless networking research. They are often the result of extensive, demanding measurement campaigns. Unfortunately, researchers lack a clear assessment of where to place probes and when they have collected enough data and still obtain a representative view of the environment. Our goal is to make this process more efficient while preserving its rigor. We propose a framework that incorporates a calibration phase to evaluate the representativeness of a dataset. To this end, we use Earth Mover’s Distance (EMD) as a similarity metric to quantify data distribution differences and avoid redundant data captures. Through experimental campaigns in three distinct environments, we demonstrate that achieving a significant reduction in data collection effort is possible without compromising measurement reliability.

The passive capture of Wi-Fi traces using sniffers is a promising technique for characterizing the wireless activity of a target area without disturbing users with intrusive measurement tools. The main problem with this technique is that individual sniffers miss packets, which may lead to an inconsistent representation of the scenario. In this paper, we advocate that redundancy (i.e., collocating two or more individual sniffers) is necessary to achieve a reasonable picture of the wireless traffic at the time of the measurement. We formulate the notion of traffic completeness and investigate it experimentally by analyzing Wi-Fi traces obtained by up to 14 co-located sniffers (Raspberry Pi 4) in an office area. We make several observations, including the fact that all individual sniffers capture packets that none of the other sniffers capture. Our results confirm that, depending on the level of completeness that experimentation needs, redundancy is necessary. Moreover, we highlight the importance of characterization of the wireless environment. We define and analyze a few metrics for the characterization.

Payment-channel networks (PCN) enhance the impact of cryptocurrencies by providing a fast and consensus-free solution to the scalability problems of traditional blockchain protocols. However, PCNs often rely on powerful nodes with high availability, large storage capacity, and strong computational power, which hinders their adoption in mobile environments. In this paper, we consider a PCN architecture that extends the functionalities of traditional PCNs to wireless resource-constrained devices. We address the token theft problem, a vulnerability that is critical on wireless PCNs, and propose a countermeasure based on minimum time windows that lock tokens whenever a user disconnects. We evaluate our proposal with real data from Bitcoin’s Lightning Network and 3G/4G mobile broadband networks. The results show that the countermeasure is most effective when devices present high availability and that there is a security-efficiency trade-off when connectivity is low.

The passive capture of Wi-Fi traces using sniffers is a cost-efficient and disturbance-free technique to assess the wireless activity of a target area. However, because of the inherent characteristics of the wireless medium, a sniffer is likely to miss Wi-Fi packets leading to incomplete traces. In this paper, we formulate the notion of relative completeness and investigate it experimentally. We consider anonymized Wi-Fi traces from 10 co-located sniffers in residential and office areas (different intensities of Wi-Fi traffic). We observe that individual sniffers lead to low completeness. Consequently, it is necessary to increase redundancy by packing several sniffers together (which we call a super-sniffer) to gather more complete traces. We observe that the results do not depend on the hardware but rather on the environment and that the results improve by increasing the size of the super-sniffer irrespective of the scenario.

The enormous success of direct communication applications has shed light on the practical interest of Device-to-device (D2D) communications. However, to set up a direct link between two neighboring nodes, they have first to detect each other, which introduces a delay before they can start sending and receiving data. The link establishment delay can be particularly unfavorable in situations of strong mobility, as the availability of the direct communication link depends on how long the devices stay within communication range of each other. This paper reports on our experiments to evaluate the link establishment delay. We focus on Android devices and use the Nearby Connection Application Programming Interface (API), which supports Bluetooth Classic and Bluetooth Low Energy (BLE) to perform link connectivity. In a nutshell, we observe that the link establishment delay requires several seconds to complete in the case of Bluetooth Classic and even tens of seconds for BLE.

Sniffing is cost-efficient and trouble-less method for capturing Wi-Fi traces within an area of interest. However, a sniffer is likely to miss Wi-Fi packets because of inherent characteristics of the wireless medium, resulting in incomplete traces. In this paper, we investigate the completeness of Wi-Fi capture using sniffers and conduct experimental measurements to support our analyses. We collect Wi-Fi traces using ten co-located sniffers while moving the source node away in steps of ten meters. As expected, individual sniffers lead to low completeness. This motivates the study of completeness using redundant sniffers. Our experiments reveal that a certain gain in completeness is achieved even when the source node is far from the sniffers.

The Received Signal Strength Indicator (RSSI) is a preferred metric for localization and distance estimation among researchers. Although it is easy to use, as it is available for most operating systems, it is not entirely reliable when used alone. We assess the inconsistency of RSSI values in an experimental setup. We deploy multiple sniffers to investigate whether redundancy can reduce measurement errors and, if so, by how much. Among other measures, we consider the total number of packets that individual sniffers miss as well as bursts of consecutive misses. We analyze their implications for distance estimation and the detection of mobile targets. As a major outcome of our analysis, we estimate that the redundancy of three sniffers yields a good balance between accuracy and cost.

Mobile edge computing (MEC) allows for the execution of delay-sensitive and context-aware applications close to the end-users while alleviating backhaul utilization and computation at the core network. A recent trend is to extend the capabilities of the MEC using the available resources of end-user devices beyond the edge, such as vehicles. Because of the highly mobile nature of such devices, the beyond-the-edge computing resources available at the time the MEC offloads a task decays over time. In this paper, we propose and investigate the persistence of groups of computational nodes in a vehicular-augmented MEC scenario. The groups are formed according to the time of arrival of vehicles and their co-location within a cell. Through the analyses of two real-world vehicular scenarios in the cities of Rome and Rio de Janeiro. On the one hand, our results show that the persistence is highly heterogeneous, depending on the vehicular traffic density and the time of day. On the other hand, we are able to identify periods during which the persistence is short and periods that show stronger stability - it becomes then possible to schedule tasks to specific time slots depending on the requirements and the expected persistence.

Although D2D communications have been extensively investigated in the literature, relatively few works have focused on understanding the capacity of direct links in a real setup. In this paper, we propose an empirical characterization of the currently available high-speed D2D technologies in Android, namelyWi-Fi P2P andGoogle Nearby. To this end, we developed a custom Android application calledOcat which interacts with the available D2D APIs and measures the link's goodput. From the experimental campaign, we derive several useful observations. Concerning communication capacity, the goodput between Android devices ranges between 320~Mbits/s when nodes are within 20 meters of each other and 0.1~Mbits/s when the distance grows to 300 meters. Based on the experimental measurements, we propose a model of the upper-bound goodput as a function of the distance between two devices. Using the wireless signal strength as a link measurement, we combine it with the two-ray ground-reflection model to infer the goodput and obtain a good fit for the characterization of D2D links between Android devices. Our findings provide a reality check in regards to actual direct data-exchange capabilities of Android devices and can help assess system performance of D2D applications.

Assessing the performance of opportunistic networks requires a subtle understanding of both contact and intercontact patterns. While the analysis of intercontacts has attracted significant attention from the research community, surprisingly only a few works have focused on explaining what happens during a contact. In this paper, we perform an in-depth analysis of contacts using both empirical measurements and reference models. We make several observations that allow us to better capture the adaptive nature of device-to-device (D2D)links. In particular, in the case of Wi-Fi 5, we show that a slight modification of the nominal modulation scheme is enough to achieve an accurate characterization of opportunistic contacts for some categories of propagation models. As a consequence, we confirm previous observations that the evaluation of protocols and algorithms for D2D networks based on links of fixed rate may lead to inaccurate results. We finally propose a tool that extends mobility traces with plausible values of per-link capacity.

In order to match the energy demand of electric vehicles to the capacity of the power grid, it is fundamental to understand the occupancy of charging stations and to react accordingly. A Markov model of a fast charging station for lithium-ion (Li-ion) batteries, i.e., the most prevalent type today, is proposed. Li-Ion batteries present a two-step charging profile, making energy management particularly challenging. A wide range of situations is covered by considering three types of scenarios with and without waiting lines. The analytical results obtained from the steady-state solution of the Markov model reveal the behavior of multiple variables of interest: availability of the charging station to accept new customers (in terms of space and energy), number of customers, energy consumption, and power utilization. From the results, indicators for assessing the quality of service of the charging station are derived. Based on these indicators, customers may decide either to wait or to head toward another station. The owners of the stations, in turn, can predict the impact of investments in space and energy provisioning, when devising capacity planning strategies.

With over 300 billion vehicle trips made in the United States and 64 billion in France per year, network operators have the opportunity to utilize the existing road and highway network as an alternative data network to offload large amounts of delay-tolerant traffic. To enable the road network as a large-capacity transmission system, we exploit the existing mobility of vehicles equipped with wireless and storage capacities together with a collection of offloading spots. An offloading spot is a data storage equipment located where vehicles usually park. Data is transloaded from a conventional data network to the closest offloading spot and then shipped by vehicles along their line of travel. The subsequent offloading spots act as data relay boxes where vehicles can drop off data for later pick-up by other vehicles, depending on their direction of travel. The main challenges of this offloading system are how to compute the road path matching the performance requirements of a data transfer and how to configure the sequence of offloading spots involved in the transfer. We propose a scalable and adaptive centralized architecture built on software-defined networking that maximizes the utilization of the flow of vehicles connecting consecutive offloading spots. We simulate the performance of our system using real roads traffic counts for France. Results show that the centralized controlled offloading architecture can achieve an efficient and fair allocation of concurrent data transfers between major cities in France.

Traffic offloading using opportunistic device-to-device (D2D) communications is a new and exciting opportunity for cellular operators to cope with the unprecedented mobile data growth. A limitation of existing proposals is that they assume that all terminals are, by default, involved in the D2D forwarding process. In particular, they do not capture the need to reward seed users. For this reason, we include a rewarding cost in the design of the opportunistic offloading strategy. In our solution, we make the difference between nodes that receive content through the cellular channel only (leechers) and nodes that take part in the forwarding process (seeders). The key point for an operator is to design a global strategy to select which nodes act as seeders and which ones as leechers, in order to reduce the total dissemination cost. We formulate this question as a stochastic control problem that we solve using an application of Pontryagin's Maximum Principle. We provide a mathematical framework to devise the optimal strategy for opportunistic offloading under a generic cost model. First, we show that an optimal solution exists; then, from this policy, we extract some insights to develop heuristics. Finally, we discuss the advantages of the proposed model compared to the classic seeder-only model. We demonstrate that separating seeders/leechers leads to better incentive strategies in the most demanding cases of content with a large span of delivery delays.

Best-performing WLAN monitoring systems must capture as much wireless traffic as possible. To achieve this aim, several monitors are employed to capture wireless exchanges in a target area. Monitors potentially generate large traces that are all merged together to have a more complete, global view of the network behavior. Traces are often more equal than complementary, leading to the underutilization of monitors and to a higher system complexity. In this paper, we propose a methodology to make an efficient use of monitors in order to increase scalability. Such a methodology, based on trace similarity and community detection in graphs, ranks traces to reveal how many and which ones must be merged. Traces at the bottom of the rank, which belong to under-used monitors, are candidates to be relocated somewhere else to extend the target area. We evaluate the proposed methodology in two real-case scenarios. Results show that we can remove up to half of the monitors in our scenarios and still keep the same level of coverage.

Offloading is a promising technique for alleviating the ever-growing traffic load from infrastructure-based networks such as the Internet. Offloading consists of using alternative methods of transmission as a cost-effective solution for network operators to extend their transport capacity. In this paper, we advocate the use of conventional vehicles equipped with storage devices as data carriers whilst being driven for daily routine journeys. The road network can be turned into a large-capacity transmission system to offload bulk transfers of delay-tolerant data from the Internet. One of the challenges we address is assigning data to flows of vehicles while coping with the complexity of the road network. We propose an embedding algorithm that computes an offloading overlay where each logical link spans over multiple stretches of road from the underlying road infrastructure. We then formulate the data transfer assignment problem as a novel linear programming model we solve to determine the optimal logical paths matching the performance requirements of a data transfer. We evaluate our road traffic allocation scheme using actual road traffic counts in France. The numerical results show that 20% of vehicles in circulation in France equipped with only one Terabyte of storage can offload Petabyte transfers in a week.

Mobile data traffic is set to triple in three years from now according to Cisco. This trend is a real challenge for operators since wireless capacity is bounded.

Achieving efficient content dissemination in mobile opportunistic networks becomes a big challenge when content sizes are large and require more capacity than what contact opportunities between nodes may offer. Content fragmentation solves only part of the problem, as nodes still need to decide which fragment to send when a contact happens. To address this problem, we propose EPICS, a protocol designed to quickly exchange large contents in opportunistic networks. Using grey relational analysis, EPICS is able to balance the distribution of contents that have different sizes and creation times, providing fairer delay distribution and faster dissemination. We implemented and evaluated EPICS through real experimentation using Android devices. Results show that EPICS significantly reduces content dissemination delays when compared to classic approaches.

Cellular operators count on the potentials of offloading techniques to relieve their overloaded data channels. Beyond standard access point-based offloading strategies, a promising alternative is to exploit opportunistic direct communication links between mobile devices. Nevertheless, achieving efficient device-to-device offloading is challenging, as communication opportunities are, by nature, dependent on individual mobility patterns. We propose, design, and evaluate DROiD (Derivative Re-injection to Offload Data), an original method to finely control the distribution of popular contents throughout a mobile network. The idea is to use the infrastructure resources as seldom as possible. To this end, DROiD injects copies through the infrastructure only when needed: (i) at the beginning, in order to trigger the dissemination, (ii) if the evolution of the opportunistic dissemination is below some expected pace, and (iii) when the delivery delay is about to expire, in order to guarantee 100% diffusion. Our strategy is particularly effective in highly dynamic scenarios, where sudden creation and dissolution of clusters of mobile nodes prevent contents to diffuse properly. We assess the performance of DROiD by simulating a traffic information service on a realistic large-scale vehicular dataset composed of more than 10,000 nodes. DROiD substantially outperforms other offloading strategies, saving more than 50% of the infrastructure traffic even in the case of tight delivery delay constraints. DROiD allows terminal-to-terminal offloading of data with very short maximum reception delay, in the order of minutes, which is a realistic bound for cellular user acceptance.

We advocate the use of a data shuttle service model to offload bulk transfers of delay-tolerant data from the Internet onto standard vehicles equipped with data storage capabilities. We first propose an embedding algorithm that computes an offloading overlay on top of the road infrastructure. The goal is to simplify the representation of the road infrastructure as raw maps are too complex to handle. In this overlay, each logical link maps multiple stretches of road from the underlying road infrastructure. We formulate then the data transfer assignment problem as a novel linear programming model that determines the most appropriate logical paths in the offloading overlay for a data transfer request. We evaluate our proposal using actual road traffic counts in France. Numerical results show that we can satisfy weekly aggregate requests in the petabyte range while achieving cumulative bandwidth above 10 Gbps with a market share of 20% and only one terabyte of storage per vehicle.

Most disruption-tolerant networking protocols focus on mere contact and intercontact characteristics to make forwarding decisions. We propose to relax such a simplistic approach and include multi-hop opportunities by annexing a node's vicinity to its network vision. We investigate how the vicinity of a node evolves through time and whether such knowledge is useful when routing data. By analyzing a modified version of the pure WAIT forwarding strategy, we observe a clear tradeoff between routing performance and cost for monitoring the neighborhood. By observing a vicinity-aware WAIT strategy, we emphasize how the pure WAIT misses interesting end-to-end transmission opportunities through nearby nodes. For the datasets we consider, our analyses also suggest that limiting a node's neighborhood view to four hops is enough to improve forwarding efficiency while keeping control overhead low.

In recent years, service-oriented architectures (SOA) have emerged as the main solution for the integration of legacy systems with new technologies in the enterprise world. A service is usually governed by a client service contract (CSC) that specifies, among other requirements, the rate at which a service should be accessed, and limits it to no more than a number of service requests during an observation period. Several approaches, using both static and dynamic credit-based strategies, have been developed to enforce the rate specified in the CSC. Existing approaches have problems related to starvation, approximations used in calculations, and rapid credit consumption under certain conditions. In this paper, we propose and validate DoWSS, a doubly weighted algorithm for service traffic shaping. We show via simulation that DoWSS possesses several advantages: It eliminates the approximation issues, prevents starvation, and contains the rapid credit consumption issue in existing credit-based approaches.

News articles are a captivating type of online content that capture a significant amount of Internet users' interest. They are particularly consumed by mobile users and extremely diffused through online social platforms. As a result, there is an increased interest in promptly identifying the articles that will receive a significant amount of user attention. This task falls under the broad scope of content popularity prediction and has direct implications in various contexts such as caching strategies or online advertisement policies. In this paper we address the problem of predicting the popularity of news articles based on user comments. We formulate the prediction task into a ranking problem where the goal is not to infer the precise attention that a content will receive but to accurately rank articles based on their predicted popularity. To this end, we analyze the ranking performance of three prediction models using a dataset of articles covering a four-year period and published by 20minutes.fr, an important French online news platform. Our results indicate that prediction methods improve the ranking performance and we observed that for our dataset a simple linear prediction method outperforms more dedicated prediction methods.

While a natural fit for modeling and understanding mobile networks, time-varying graphs remain poorly understood. Indeed, many of the usual concepts of static graphs have no obvious counterpart in time-varying ones. In this paper, we introduce the notion of temporal reachability graphs. A (tau,delta)-reachability graph is a time-varying directed graph derived from an existing connectivity graph. An edge exists from one node to another in the reachability graph at time t if there exists a journey (i.e., a spatiotemporal path) in the connectivity graph from the first node to the second, leaving after t, with a positive edge traversal time tau, and arriving within a maximum delay delta. We make three contributions. First, we develop the theoretical framework around temporal reachability graphs. Second, we harness our theoretical findings to propose an algorithm for their efficient computation. Finally, we demonstrate the analytic power of the temporal reachability graph concept by applying it to synthetic and real-life datasets. On top of defining clear upper bounds on communication capabilities, reachability graphs highlight asymmetric communication opportunities and offloading potential.

SUMMARY Packet traces are important objects in networking, commonly used in a wide set of applications, including monitoring, troubleshooting, measurements, and validation, to cite a few. Many tools exist to produce and process such traces, but they are often too specific; using them as a basis for creating extended tools is then impractical. Some other tools are generic enough, but exhibit performance issues. This paper reports on our experience designing WiPal, a packet trace manipulation framework with a focus on IEEE 802.11. WiPal is designed for performance and re‐usability, while introducing several novel features compared to previous solutions. Besides presenting how WiPal's original design can benefit packet processing programs, we discuss a number of issues a program designer might encounter when writing packet trace processing software. An evaluation of WiPal shows that, albeit generic, it does not impact performance regarding execution speed. WiPal achieves performance levels observed only with specialized code and outperforms some well‐known packet processing programs. Copyright © 2011 John Wiley & Sons, Ltd.

In Enterprise Data Centers (EDC), service providers are usually governed by Client Service Contracts (CSC) that specify, among other requirements, the rate at which a service should be accessed. The contract limits the rate to no more than a number of service requests during a given observation period. In two-tier setups, a cluster of Service-Oriented Networking (SON) Appliances form a pre-processing tier that accesses services in the service tier. SON Appliances locally shape the flow of requests to enforce the global rate defined in the CSC. Off-the-shelf SON Appliances present architectural limitations that prevent them from being used to efficiently perform traffic shaping in the presence of multiple service hosts. In this paper, besides identifying these limitations, we provide two contributions in this field. First, we introduce a SON Appliance architecture fit for multi-service traffic shaping. Second, we propose and validate an algorithm for multipoint-to-multipoint service traffic shaping in two-tier EDCs. We show via simulation that our approach solves the multipoint-to-multipoint service traffic shaping problem while pushing the system to its maximum capacity.

So far, efforts attempting to characterize the spatiotemporal nature of disruption-tolerant networks (DTN) have relied on the dual notion of contacts and intercontacts. A contact happens when two nodes are within communication range of each other. An intercontact is simply defined as the dual of a contact, i.e., when two nodes are not in communication range of each other. We refer to this model as “binary”. Although the binary characterization allows understanding the main interaction properties of the network, it is not sufficient to capture a plethora of situations beyond the binary hypothesis. In this paper, we investigate the structural properties of the network when nodes are not in contact but do have a contemporaneous path connecting them. We first introduce the notion of n-ary intercontact and, to defend its adoption, we quantify the proportion of nodes bearing this new intercontact notion in well-known datasets available to the community. Surprisingly, we observe that most pairs of nodes are nearby (within a few hops) for significant amounts of time when not directly in contact. Finally, we compare the impact of our proposal with the classic intercontact definition and give incentives toward using the n-ary characterization to leverage new communication opportunities.

The future of Vehicular Ad-Hoc Networks (VANET) will rely mainly on the support of efficient information dissemination protocols, whether we talk about safety applications that warn the driver of an imminent collision, a simple update on traffic conditions, or road-side advertisements. One of the greatest challenges when designing such protocols is how to deliver packets efficiently in a highly mobile environments under intermittent connectivity. Surprisingly, this problem has been under-investigated in the literature. In this paper, we propose, design, and evaluate SERVUS1, a robust dissemination protocol that guarantees packet propagation with high delivery ratio and low overhead. SERVUS includes a new broadcast management mechanism that takes advantage of the inherent behavioral properties of the VANET environment. In particular, the proposed protocol can update isolated nodes (or clusters) with missing information while ensuring homogeneous information dispersal at low overhead. Through a number of analyses, we show that SERVUS is highly efficient with regard to the tradeoff between reliability and cost, and overcomes important issues like the broadcast storm problem and the topological temporal fragility.

Here, we first investigate data collection delegation strategies based on the relative importance of nodes in their social interactions. Second, by considering a prediction strategy that estimates the likelihood of two nodes meeting each other, we investigate how the delegation strategies perform on predicted traces. We evaluate the delegation strategies both in terms of coverage, and size of the delegation using real mobility data sets.

In this paper, we investigate the ability of using Delay Tolerant Networking (DTN) in Public Safety networks, where bandwidth and storage are constrained. We formalize the problem as a Wardrop equilibrium over a time-discretized graph. Driven by our findings, we propose RECOR, a centralized REsource-Constrained ORacle-based DTN routing mechanism, which spreads the demand across multiple store-carry forward paths to satisfy the node storage and link transport constraints observed in intervention situations. By applying the proposed mechanism to real Bluetooth-based DTN traces, we show that the transmission bottleneck can be compensated, but only up to a certain extent, by increasing storage capacity and delay. We also analyze the benefit of strategies that provide more resources to highly connected nodes (e.g. ambulances and firetrucks) which can then feed incentives and policies for DTN network engineering. Finally, the idea presented here is general and suggests the necessity of trace-driven simulation and specific modeling tools for appropriate design of future DTN resource management policies.

Major wireless operators are nowadays facing network capacity issues in striving to meet the growing demands of mobile users. At the same time, 3G-enabled devices increasingly benefit from ad hoc radio connectivity (e.g., Wi-Fi). In this context of hybrid connectivity, we propose Push-and-track, a content dissemination framework that harnesses ad hoc communication opportunities to minimize the load on the wireless infrastructure while guaranteeing tight delivery delays. It achieves this through a control loop that collects user-sent acknowledgements to determine if new copies need to be reinjected into the network through the 3G interface. Push-and-Track includes multiple strategies to determine how many copies of the content should be injected, when, and to whom. The short delay-tolerance of common content, such as news or road traffic updates, make them suitable for such a system. Based on a realistic large-scale vehicular dataset from the city of Bologna composed of more than 10,000 vehicles, we demonstrate that Push-and-Track consistently meets its delivery objectives while reducing the use of the 3G network by over 90%.

Connectivity patterns in intermittently-connected mobile networks (ICMN) can be modeled as edge-Markovian dynamic graphs. We propose a new model for epidemic propagation on such graphs and calculate a closed-form expression that links the best achievable delivery ratio to common ICMN parameters such as message size, maximum tolerated delay, and link lifetime. These theoretical results are compared to those obtained by replaying a real-life contact trace.

We analyze the dynamics of a mobility data set collected in a pipelined disruption-tolerant network (DTN), a particular class of intermittently-connected wireless networks characterized by a 1-D topology. First, we collected and investigated traces of contact times among thousands of participants of a rollerblading tour in Paris. The data set shows extreme dynamics in the mobility pattern of a large number of nodes. Most strikingly, fluctuations in the motion of the rollerbladers cause a typical accordion phenomenon—the topology expands and shrinks with time, thus influencing connection times and opportunities between participants. Second, we show through an analytical model that the accordion phenomenon, through the variation of the average node degree, has a major impact on the performance of epidemic dissemination. Finally, we test epidemic dissemination and other existing forwarding schemes on our traces, and conclude that routing should adapt to the varying, though predictable, nature of the network. To this end, we propose DA-SW (Density-Aware Spray-and-Wait), a measurement-oriented variant of the spray-and-wait algorithm that tunes, in a dynamic fashion, the number of a message copies to be disseminated in the network. The particularity of DA-SW is that it relies on a set of abaci that represents the three phases of the accordion phenomenon: aggregation, expansion, and stabilization. We show that DA-SW leads to performance results that are close to the best case (obtained with an oracle).

Some current and future pervasive data driven applications must operate in “extreme” environments where end-to-end connectivity cannot be guaranteed at all times. In fact, it is likely that in these environments partitions are, rather than exceptions, part of the normal network operation. In this paper, we introduce Cover, a suite of adaptive strategies to control the trajectory of “infrastructure” nodes, which are deployed to bridge network partitions and thus play a critical role in data delivery. In particular, we focus on applications where end (or target) nodes are mobile and their mobility is unknown. Our goal is then to deploy and manage infrastructure nodes so that application-level requirements such as reliable data delivery and latency are met while still limiting deployment cost and balancing the load among infrastructure nodes. Cover achieves these goals using a localized and adaptive approach to infrastructure management based on the observed mobility of target nodes. To this end, Cover takes advantage of contact opportunities between infrastructure nodes to exchange information about their covered zones, and thus, help monitor targets in a more efficient fashion. Through extensive simulations, we show how Cover's adaptive features yield a fair distribution of targets per infrastructure node based only on limited network knowledge.

Is the temporal dimension alone sufficient to characterize contacts in opportunistic networks? Several studies analyze the temporal aspect of contacts with significant results concerning contact and inter-contact distributions. Nevertheless, only the temporal dimension does not give a complete overview of contact characterization. In this paper, we propose the surround indicator as a metric to exhibit the contact's surrounding environment in opportunistic networks. We evaluate the surround indicator on two existing datasets and show that contacts have too heterogeneous and too unstable surrounds to be considered only in terms of duration. Besides a large variability of the surrounding environment within the duration of a single contact, it is frequent to observe contacts of identical duration that exhibit differences in their surrounds of more than a hundred times.

Service providers within an enterprise network are often governed by client service contracts (CSC) that specify, among other constraints, the rate at which a particular service instance may be accessed. The service can be accessed via multiple points (typically middleware appliances) in a proxy tier configuration. The CSC and thus the rate specified have to be collectively respected by all the middleware appliances. The appliances locally shape the service requests to respect the global contract. We investigate the case where the CSC limits the rate to a service to X requests with an enforcement/observation interval of T seconds across all the middleware appliances. In this paper, we extend, implement, and investigate the Credit-based Algorithm for Service Traffic Shaping (CASTS) in a production level enterprise network setting. CASTS is a decentralized algorithm for service traffic shaping in middleware appliances. We show that CASTS respects the CSC and improves the responsiveness of the system to the variations of the input rate and leads to larger service capacity when compared to the traditional static allocation approach.

An efficient way to bound the size of clusters in large-scale self-organizing wireless networks is to rely on a budget-based strategy. The side effect of conventional budget- based clustering approaches is that they generate a potentially large number of small, even single-node, clusters. The consequence is that while clusters are bounded, their average size may be far from the expected value (the budget), which negatively impacts the performance of the communication systems running on top of it. In contrast, we propose Fair and Flexible Budget- Based Clustering (FFBC) to form size-controlled clusters in large-scale self-organizing networks. For a given target cluster size, our approach outperforms previous budget-based algorithms by creating clusters of average size closer to the requested value and avoiding isolated nodes.

We analyze the dynamics of a mobility dataset collected in a pipelined disruption-tolerant network (DTN), a particular class of intermittently-connected wireless networks characterized by a one-dimensional topology. First, we collected and investigated traces of contact times among a thousand participants of a rollerblading tour in Paris. The dataset shows extreme dynamics in the mobility pattern of a large number of nodes. Most strikingly, fluctuations in the motion of the rollerbladers cause a typical accordion phenomenon - the topology expands and shrinks with time, thus influencing connection times and opportunities between participants. Second, we show through an analytical model that the accordion phenomenon, through the variation of the average node degree, has a major impact on the performance of epidemic dissemination. Finally, we test epidemic dissemination and other existing forwarding schemes on our traces, and argue that routing should adapt to the varying, though predictable, nature of the network. To this end, we propose DA-SW (density-aware spray-and-wait), a measurement-oriented variant of the spray-and-wait algorithm that tunes, in a dynamic fashion, the number of a message copies disseminated in the network. We show that DA-SW leads to performance results that are close to the best case (obtained with an oracle).

The proliferation of mobile computing devices and local wireless networks has promoted a growing interest in location-aware systems. The problem with existing positioning techniques is that they are designed to position dimensionless objects. Such an assumption may lead to practical inconsistencies, as objects might overlap in the resulting coordinate system. Moreover, it is usual to neglect the effects of an object volume and its physical characteristics on signal propagation. In the scenario considered throughout this paper (positioning containers in a harbor), such characteristics can be finely estimated. We propose VAPS, a volume-aware positioning system that takes advantage of the waveguide effect generated by containers. Although VAPS is specific to the harbor scenario, its principles can be extended and adapted to other situations. VAPS maps discrete RSSI levels into hop-counts and relies on realistic propagation models to obtain near-perfect positioning at a very low control overhead. Our results demonstrate that, in scenarios where the assumptions made by traditional approaches fail, the new considerations of VAPS do make a difference.

An interesting approach for the location service problem in wireless mesh networks is the one that relies on location agendas to model node displacements on an absolute time basis. To the best of our knowledge, the literature still lacks the design and evaluation of such an approach. In this paper, we introduce and analyze an agenda-based location service that reduces the overhead due to location updates. We evaluate the performance of our scheme with real mobility traces and observe that mobile nodes spend in average 80% of their time at a maximum distance of one hop from the expected locator. Furthermore, 81% of the location update messages are constrained within this radius. This drastically reduces the propagation areas when compared for instance with DHT-based approaches.

We propose Otiy, a node-centric location service that limits the impact of location updates generate by mobile nodes in IEEE 802.11-based wireless mesh networks. Existing location services use node identifiers to determine the locator (aka anchor) that is responsible for keeping track of a node's location. Such a strategy can be inefficient because: (i) identifiers give no clue on the node's mobility and (ii) locators can be far from the destination thus increasing location updates path length and bandwidth consumption. To solve these issues, Otiy introduces a new strategy that identifies nodes to play the role of locators based on the likelihood of a destination to be close to these nodes -- i.e., locators are identified depending on the mobility pattern of nodes. Otiy relies on the cyclic mobility patterns of nodes and creates a slotted agenda composed of a set of predicted locations, defined according to the past and present patterns of mobility. Correspondent nodes fetch this agenda and use it as a reference for identifying which locator is currenty in charge for the node. Over a period of about one year, the weekly proportion of nodes having at least 50% of exact location predictions is in average about 75%. This proportion increases by 10% when nodes also consider their closeness to the locator from only what they know about the network. In this paper we focus on the analysis of the concepts behind Otiy leaving more quantitative and comparative results for future work.

In order to reduce the amount of radio communications, wireless sensor networks may perform data aggregation, where relaying nodes perform in-network processing. Guaranteeing security in aggregation schemes is particularly challenging because node compromises in such a scenario are doubly problematic, both in terms data confidentiality (eavesdropping) and availability (denial of service). Indeed, by compromising an aggregator node the attacker would endanger all of the readings that are part of the aggregate the node is in charge of.

In age-based routing protocols, nodes forward data toward a position which is an estimation of the destination's coordinates. The fresher the estimation, the higher the probability to reach the destination. In such a class of solutions, it is fundamental that accurate estimations of nodes' positions be disseminated throughout the network in order for the forwarding algorithm to converge in a reasonable time and with as few hops as possible. In general, dissemination is done following uniform distributions. In this paper, we argue that coordinate dissemination should rely on measured parameters (thus adaptive to the network conditions), such as the mobility of a node. We investigate the impact of node locality on the performance of dissemination algorithms and propose a new algorithm that adjusts the dissemination level of a node's coordinates based on the displacements of the node. Through extensive simulations, we show that our proposal leads to both faster discovery and significant reduction of the overhead when compared to flat solutions.

As the size of mobile self-organizing networks increases, the efficiency of location services must increase as well so that addressing/routing scalability does not become an issue. In this paper, we propose a novel architecture, called Twins, tailored for self-organizing networks. Twins architecture involves addressing and locating nodes in large networks, forwarding packets between them, and managing in the presence of mobility/topology changes. Twins defines a logical multidimensional space for addressing and forwarding, while location service and management operations make use of a one-dimensional space. To improve scalability and performance, forwarding is hop-by-hop with greedy next-hop choice and the location service uses a rendezvous paradigm to distribute information among nodes. In this paper, we describe the Twins architecture and present a performance evaluation to assess scalability, fairness in the overhead distribution among nodes, and routing robustness.

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The need for efficient location mechanisms is an important issue in scalable self-organizing networks. Existing solutions are inherently dependent on the spatial distribution of nodes in the topology. This leads to limitations that go against the principles of self-organization. In this paper, we propose Twins, an easily-managed location service for self-organizing networks. Twins defines a logical multidimensional space that is a strict mathematical representation of the network geographic space. This representation is obtained through Hilbert space-filling curves. The geographic space is used for addressing and routing, while localization is based on the curve. Control messages are routed based on the logical structure while data packets are routed in a hop-by-hop basis with greedy next-hop choice. In this paper, we evaluate the Twins management operations in terms of fairness of space sharing and logical/geographic distances between nodes and their location servers. Our results show that Twins assures a fair distribution of control overhead and scales well with the number of nodes.

This paper proposes the Tribe protocol, an indirect routing strategy for wireless self-organizing networks. The protocol is intended to be applied in environments with large number of users, where mobility is taken into account, and the correct operation of the system does not require the support of a fixed (wired or wireless) infrastructure. In Tribe, nodes build a network infrastructure which describes the node's relative location according to the current node's neighborhood. Furthermore, routing is unique and completely independent of any global connectivity ensured by a network-level routing protocol. The architecture is generic, self-organizing, and independent of IP-like addressing limitations.

Measuring the quality of multicast multi-layered applications based only on the amount of data that arrives at the receivers is insufficient in many circumstances. This paper proposes a three-metric approach that improves the global quality/fairness of the multicast sessions. We define a simple function that takes into account the density of satisfied users, the amount of allocated bandwidth in the multicast tree, and the degradation at the receivers. We analyze the proposed multicriteria algorithm in an environment with other competing flows and show the improvement of the global quality of multicast multi-layered applications.

Through a detailed analysis of theoretical and real MPEG-compressed video streams, this paper addresses the task of extracting deterministic parameters from such sources in order to provide deterministic service guarantees. We propose and analyze the single-input single-output (SISO) algorithm with quality adjustment to compute the coherent parameters for the XGOP-B deterministic traffic model. Contrary to other proposals, the SISO algorithm leads to a single set of parameters for each input traffic and does not ask for external decisions to perform this stage. Moreover, the accuracy/complexity can be adjusted by a special variable. Our results show that the proposed approach leads to improved accuracy and that the complexity can be decreased by three orders of magnitude.