The structure of biological neural circuits—modular, hierarchical, and sparse—reflects an efficient trade-off between wiring cost, functional specialization, energy efficiency, and robustness. These principles offer valuable insights for artificial neural network (ANN) design, especially as networks grow in depth and scale. Sparsity, in particular, has been widely explored for reducing memory and computation, improving speed, and enhancing generalization. Motivated by systems neuroscience findings, we explore how patterns of functional connectivity in the mouse visual cortex—specifically, ensemble-to-ensemble communication—can inform ANN design. We introduce G2GNet, a novel architecture that imposes sparse, modular connectivity across feedforward layers. To our knowledge, this is the first architecture to incorporate biologically-observed functional-connectivity patterns as a structural bias in ANN design. We complement this static bias with a dynamic sparse training (DST) mechanism that prunes and regrows edges during training. We also propose a Hebbian-inspired rewiring rule based on activation correlations, drawing on principles of biological plasticity. Despite having significantly fewer parameters than fully-connected models, G2GNet achieves up to 75% sparsity while improving accuracy by up to 4.3% on benchmarks, including Fashion-MNIST, CIFAR-10, and CIFAR-100, outperforming dense baselines with far fewer computations.

Understanding how the structure of functional connectivity in the visual cortex changes over time and across brain states is crucial for elucidating the mechanisms by which neurons coordinate to process information and support behavior. Higherorder visual areas in mice are known to exhibit more distinct, segregated functional roles compared to the primary visual cortex (V1) [1], and they maintain stimulus representations over extended time scales [2]. However, the stability of the architecture of their functional connectivity across time and brain states remains less understood. In vivo mesoscopic two-photon calcium imaging was used to simultaneously record activity from thousands of neurons across V1 and the extra-striate anterolateral area (AL) in mice, during both visual stimulation (optical-flow/motion) and at resting state (i.e., absence of stimulus). We then applied the spike time-tiling (STTC) coefficient [3] to estimate the pairwise correlations of the neuronal firing and form the functional connectivity at the cell resolution. We then comparatively analyzed the functional connections within area AL and V1 under both stimulus-driven and resting-state conditions. The functional connectivity within AL remains consistently more robust over time than in area V1. Moreover, the structure of the functional connectivity in AL exhibits a smaller change between these two conditions compared to V1, indicating that functional connectivity derived from spontaneous activity more faithfully reflects the functional network architecture elicited by visual stimulation in this higherorder area. Finally, during the resting state, AL activity and functional connectivity are less dependent on pupil size than those of V1, indicating that arousal exerts a weaker modulatory effect on AL compared to V1.

Understanding how the primary visual cortex of mice represents the external sensory input separately from the internal states is a fundamental challenge in systems neuroscience. Our work contributes to the problem of decoupling the stimulusdriven and internally generated components of neural activity in the primary visual cortex. Internally generated (or intrinsic) activity refers to neural dynamics that are not directly driven by sensory stimuli, reflecting the brain's ongoing, endogenous processes. Neuronal activity encodes both external stimuli and internal cortical states. The internally modulated activity, though not directly observable, can be inferred from the shared structure in population responses, and thus, serves as a proxy for the internal cortical state. We developed a two-phase Partial Least Squares Regression (PLSR) framework that decomposes neural activity into two orthogonal low-dimensional subspaces: (1) a “population” subspace capturing global variability shared across neurons, and (2) a “stimulus” subspace containing dimensions that discriminate between stimulus conditions while being linearly uncorrelated with the population subspace. We focus on the granular (L4) and supragranular (L2/3) layers of awake mice exposed to visual stimuli consisting of optical flow directions, using mesoscopic two-photon calcium imaging. In both L4 and L2/3 layers, many components individually yield above-chance decoding accuracy, yet a small low-dimensional subspace preserves nearly the full decoding performance of the high-dimensional population. Stimulus-driven components exhibit strong cross-mouse correlations, indicating a conserved coding scheme present in both L4 and$\mathbf{L} \boldsymbol{2} \boldsymbol{/} \mathbf{3}$. These components are stable across the entire recording session, reflecting robustness of the underlying representation over time. Removing the global modulation did not abolish stimulus discriminability in either layer, suggesting that information about stimulus direction is not dependent on this global signal. Both L4 and L2/3 stimulus components exhibit comparable decoding performance as well as similar tuning representations, suggesting common encoding of stimulus direction across layers.

Variability in single neuron responses presents a challenge in establishing reliable representations of visual stimuli essential for driving behavior. To enhance accuracy, integration of responses from multiple neurons is imperative. This study leverages simultaneous recordings from a large population (tens of hundreds) of neurons, achieved through in vivo mesoscopic 2-photon calcium imaging of the primary visual cortex (V1) in mice, under visual stimulus conditions as well as in resting state (absence of stimulus). The visual stimulus consisted of 16 distinct randomly shuffled directions of motion presented to the mice. We employed mutual information to identify neurons that contain the most significant information about the stimulus direction. As expected, neurons displaying high predictive power (HPP) in stimulus decoding exhibit elevated firing event rates during stimulus presentation. Furthermore, functional connectivity among HPP neurons during visual stimulation is denser and stronger compared to functional connectivity among other visually responsive neurons. Functional connections among HPP neurons appear to form independently of distance, suggesting a distributed yet highly coordinated network. In contrast, HPP neuron activity and functional connectivity differed significantly at resting state. Specifically, during the resting state, HPP neurons exhibited lower event rates and functional connectivity structure that was not significantly different from that of other visually responsive neurons. This suggests that HPP neurons are less susceptible to being driven simultaneously by internal brain states in the absence of a stimulus. Finally, the tuning properties of HPP neurons were unexpectedly diverse: while some were sharply tuned, others conveyed a similar amount of mutual information, despite exhibiting much weaker tuning. This study sheds light on the organization of neuronal ensembles important for decoding visual motion direction in mouse area V1, contributing to the understanding of information processing in mouse visual cortex.

Eye movements during text reading can provide insights about reading disorders. We developed the DysLexML, a screening tool for developmental dyslexia, based on various ML algorithms that analyze gaze points recorded via eye-tracking during silent reading of children. We comparatively evaluated its performance using measurements collected from two systematic field studies with 221 participants in total. This work presents DysLexML and its performance. It identifies the features with prominent predictive power and performs dimensionality reduction. Specifically, it achieves its best performance using linear SVM, with an accuracy of 97% and 84% respectively, using a small feature set. We show that DysLexML is also robust in the presence of noise. These encouraging results set the basis for developing screening tools in less controlled, larger-scale environments, with inexpensive eye-trackers, potentially reaching a larger population for early intervention. Unlike other related studies, DysLexML achieves the aforementioned performance by employing only a small number of selected features, that have been identified with prominent predictive power. Finally, we developed a new data augmentation/substitution technique based on GANs for generating synthetic data similar to the original distributions.

Absence epilepsy is a common childhood disorder featuring frequent cortical spike-wave seizures with a loss of awareness and behavior. Using the calcium indicator GCaMP6 with in vivo 2-photon cellular microscopy and simultaneous electrocorticography, we examined the collective activity profiles of individual neurons and surrounding neuropil patches in layer 2/3 (L2/3) of the visual cortex during spike-wave seizure activity over prolonged periods in 2 different stargazer mice. Our long-term objective is to predict in real-time a seizure. In this work, we focused on identifying the neuronal networks activated during epochs of interictal activity (i.e., between seizure activities) and seizure activity and analyzed their functional network connectivity. During interictal activity, most neurons are functionally connected with a large number of neighbors within the field of view, while in seizure epochs, the connectivity is reduced substantially. We also examined the discriminating power of groups of neurons in identifying seizure events. An SVM model based on the firing activity of neurons can reasonably accurately classify the interictal activity vs. seizure (e.g., 77.9% for the total accuracy with sensitivity equal to 85.3%, and specificity 73%).

Reliable detection of stimulus-driven states and their separation from internal state-driven spontaneous activity is an important step towards inferring temporal dynamics of neurons and its relation to the perception of external inputs. This is challenging, especially when no prior assumptions about the underlying model and data generating processes exist. To address this task, we applied efficient recurrence quantification analysis (RQA) based on global recurrence plots (RP) for accurate identification of the onset and offset of visual neuronal responses caused by distinct types of visual stimuli. In particular, these critical times are estimated by taking the first order difference of the line of synchronization extracted from the associated global RP. Our approach was evaluated using a real dataset of visually-driven neuronal responses and spontaneous activity (recorded by in vivo 2-photon calcium imaging). It accurately detects both the onset and offset time instants in the eventograms of pyramidal neurons in a completely model agnostic framework.

The following topics are dealt with: mobile computing; mobile radio; Internet of Things; protocols; wireless LAN; optimisation; wireless sensor networks; telecommunication traffic; telecommunication network routing.

Modeling wireless access and spectrum markets is challenging due to a plethora of technological and economic aspects that affect their performance. This work develops a modeling framework for analysing such markets using network economics, game theory, and queueing networks. The framework models the service selection of users as well as the competition and coalition among providers. It also develops tools and algorithms to analytically compute the Nash equilibriums (NEs) under the presence of discontinuities in the derivatives of the utility functions of providers. The analysis of different market scenarios reveals various interesting trends in the offered prices, market share, and revenue of providers depending on the user utility function, traffic demand, and mobility pattern. It also demonstrates the role of the quality of service (QoS) in the user utility function in reducing the intensity of competition and allowing for higher prices and revenue. However, the analysis of large-scale markets exhibits a high computational complexity. To improve the computational efficiency, we developed a network aggregation methodology based on the theorem of Norton. This aggregation allows the construction of equivalent networks for a specific region of interest, omitting the details of the entire networks. We demonstrate the aggregation algorithm in the context of capacity planning.

Assessing the impact of different network conditions on user experience is important for improving the telecommunication services. We have developed a modular framework that includes monitoring and data collection tools and algorithms for user-centric analysis and prediction of the QoE in video streaming. The MLQoE employs several machine learning (ML) algorithms and tunes their hyper-parameters. It dynamically selects the ML algorithm that exhibits the best performance and its parameters automatically based on the input (e.g., network and systems metrics). We applied the MLQoE for predicting the QoE of the video streaming service in the context of two field studies, one performed in the production environment of a large telecom operator and the other at our Institute. The analysis indicated the parameters with the dominant impact on the perceived QoE and revealed that the QoE vary across users. This motivates the use of customized adaptation mechanisms in video streaming under network performance degradation. The MLQoE results in fairly accurate predictions e.g., a median error in predicting the QoE of 0.0991 and 0.5517 in the first (second) field study, respectively, on the MOS scale.

The rapid advances in networking, mobile computing, and virtualization, lead to a dramatic increase in the traffic demand. A cost-effective solution for serving it, while maintaining a good quality of service (QoS), would be to offload a part of the traffic originally targeted for cellular base stations (BSs) to a WiFi infrastructure. Related work on the WiFi offloading often considers markets with a single provider and omits parameters, such as the effect of the offloading on the perceived QoS by users, the capacity of the WiFi infrastructure, and competition of providers. In contrast to these approaches, this paper develops a detailed modeling framework for analysing the WiFi offloading using network economics, game theory, and queueing networks. It also proposes a novel network aggregation technique to reduce the computational complexity of the analysis. Using this framework, the performance of WiFi offloading was evaluated under various scenarios with respect to the bandwidth of BSs and APs, coverage of WiFi, and user preferences. Our results highlight that it is not always profitable for providers to invest in a large WiFi infrastructure. The limited capacity of the WiFi APs restricts the benefits of the offloading.

The impact of the network performance on the quality of experience (QoE) for various services is not well-understood. Assessing the impact of different network and channel conditions on the user experience is important for improving the telecommunication services. The QoE for various wireless services including VoIP, video streaming, and web browsing, has been in the epicenter of recent networking activities. The majority of such efforts aim to characterize the user experience, analyzing various types of measurements often in an aggregate manner. This paper proposes the MLQoE, a modular algorithm for user-centric QoE prediction. The MLQoE employs multiple machine learning (ML) algorithms, namely, Artificial Neural Networks, Support Vector Regression machines, Decision Trees, and Gaussian Naive Bayes classifiers, and tunes their hyper-parameters. It uses the Nested Cross Validation (nested CV) protocol for selecting the best classifier and the corresponding best hyper-parameter values and estimates the performance of the final model. The MLQoE is conservative in the performance estimation despite multiple induction of models. The MLQoE is modular, in that, it can be easily extended to include other ML algorithms. The MLQoE selects the ML algorithm that exhibits the best performance and its parameters automatically given the dataset used as input. It uses empirical measurements based on network metrics (e.g., packet loss, delay, and packet interarrival) and subjective opinion scores reported by actual users. This paper extensively evaluates the MLQoE using three unidirectional datasets containing VoIP calls over wireless networks under various network conditions and feedback from subjects (collected in field studies). Moreover, it performs a preliminary analysis to assess the generality of our methodology using bidirectional VoIP and video traces. The MLQoE outperforms several state-of-the-art algorithms, resulting in fairly accurate predictions.

Over the last years significant efforts have been made to prevent and/or minimize exposure to a wide range of environmental risks (e.g. air pollution and nutrition) that adverse health effects especially among vulnerable populations including pregnant women. Towards this direction, mHealth approaches can provide the means for remotely capturing the environmental factors that affect maternal health, and replace traditional, tedious ways of logging, e.g. face-to-face interviews. This work presents mMamee, a mHealth platform for monitoring and assessing maternal environmental exposure. mMamee employs a client-server architecture and addresses the integration of sensing data and descriptive input on maternal daily habits. The future application of this platform to monitor environmental exposure during pregnancy is outlined. The conclusions derived highlight the feasibility of mMamee for the realization of long-term epidemiological studies.

Advances in networking and regulatory changes on access and competition rules enable new network architectures, service paradigms, and partnerships, opening new opportunities for business cases. Unlike traditional cellular-based markets, new spectrum and wireless access markets are formed that have larger sizes, are more diverse, and can offer an improved set of services. The analysis of such markets is challenging due to a plethora of phenomena that manifest in different spatio-temporal scales. The main objective of this work is the development of a modular multi-layer modeling framework and simulation platform for analyzing wireless access markets. This framework employs game theory and queueing-theoretical models to instantiate a market at multiple spatio-temporal scales. At a microscopic layer, it models each entity of the market in a fine level of detail. By applying various aggregations, it also models the average behavior of certain clusters of entities. In that way, it can analyze a certain phenomenon at the appropriate level of detail, addressing the tradeoff between the loss of accuracy and computational complexity. The analysis then focuses on the flex service, a novel paradigm which allows users to select their provider dynamically. The proposed framework is used to model and analyze the performance of markets that offer the flex service. It employs various metrics, such as blocking probability, percentage of disconnected users, social welfare, and profit, to assess whether this service is beneficial to users, regulators, and providers, respectively. Furthermore, it highlights various challenges in modeling such markets and demonstrates the advantages in using the proposed multi-layer framework.

The Forthroid is a location-based system that “augments” physical objects with multimedia information and enables users to receive information about physical objects or request services related to physical objects. It employs computer-vision techniques and Quick Response codes (QR-codes). We have implemented a prototype on Android platforms and evaluated its performance with systems metrics and subjective tests. We discuss our findings and challenges in prototyping on Android OS. The analysis indicates that the network and the server are the main sources of delay, while the CPU load may vary depending on the specific Forthroid operation. The preliminary subjective test results suggest that users tolerate these delays and the offered services can be particularly useful.

This paper focuses on a comparative statistical analysis of the performance of VoIP calls under various situations, namely, during a handover and under different background traffic conditions at a wireless access point (AP). Using empirical-based measurements, it demonstrates that these network conditions exhibit distinct statistical behaviour, in terms of SNR, packet losses and end-to-end delays, and thus, impact the VoIP user quality in a different manner. The analysis shows that both network conditions and codec type, as well as their interaction, have a significant effect on the PESQ MOS values. Moreover, it indicates statistically highly significant differences between the estimations of the PESQ and E-model. Finally, it highlights the benefits of the packet loss concealment of the AMR 12.2kb/s under these network conditions.

This paper proposes a novel localization technique based on a multivariate Gaussian modeling of the signal strength measurements collected from several access points (APs) at different locations. It considers a discretized grid-like form of the environment and computes a signature at each cell of the grid. At run time the system compares the signature at the unknown position with the signature of each cell using the Kullback-Leibler Divergence estimation (KLD) between their corresponding probability densities. The paper evaluates the performance of the proposed technique and compares it with other statistical fingerprint-based localization systems. The performance analysis studies were conducted at the premises of a research laboratory and an aquarium under various conditions. Furthermore, the paper evaluates the impact of the number of APs and the size of the measurement datasets.

Models of traffic demand are fundamental inputs to the design and engineering of data networks. In this paper we address this requirement in the context of large-scale wireless infrastructures using real measurement data from the University of North Carolina (UNC) wireless campus network. Our modeling effort focuses on capturing the demand variation in both the spatial and temporal domain in a way that scales well with the size of the wireless network. The network traffic dynamics are studied over two different week-long monitoring periods at various levels of spatial aggregation, from individual buildings to the whole network. We model traffic workload in terms of wireless sessions and network flows and find several modeling elements that are reusable in both temporal and spatial dimensions. The same set of parametric distributions for the session-and flow-related traffic variables capture the network traffic demand in both monitoring periods. Even more interestingly, these same distributions can characterize traffic dynamics at finer spatial scales, such as a single building or a group of buildings. We use our models to generate synthetic traffic and compare with trace data. The comparison clearly illustrates the trade-off between model scalability and reusability, on the one hand, and accuracy in capturing local-scale traffic dynamics on the other. Our main contribution is a novel behavioral approach for traffic demand modeling in large wireless networks that features high flexibility in the exploitation of the spatial and temporal resolution available in data traces.

Network traffic load in an IEEE802.11 infrastructure arises from the superposition of traffic accessed by wireless clients associated with access points (APs). An accurate characterization of these data can be beneficial in modelling network traffic and addressing a variety of problems including coverage planning, resource reservation and network monitoring for anomaly detection. This study focuses on the statistical analysis of the traffic load measured in a campus-wide IEEE802.11 infrastructure at each AP.

With the increasing deployment of wireless networks, network management and configuration of wireless Access Points (APs) has become one of the main concerns of network operators. While statistics and measurements regarding the overall usage of individual APs are readily available, the limited knowledge of the wireless traffic demand in terms of the type of application hinders efficient network provisioning. This paper provides an extensive application-based characterization of a large-scale wireless network, going beyond the port-number limitation, across three levels, namely, network, clients, and APs. We found that the most popular application types, in terms of the number of flows, bytes, and clients, are web and peer-to-peer; and while the majority of APs is dominated by them, APs of the same building type have large differences in their traffic mix. File transfer flows, such as FTP and P2P, are heavier in wired than in wireless networks. Finally, an interesting dichotomy among APs, in terms of their dominant application type and downloading and uploading behavior was observed.

Campus wireless LANs (WLANs) are complex systems with hundreds of access points (APs) and thousands of users. To analyze the performance of wireless networking protocols, researchers need to construct simulations and testbed experiments that reproduce the characteristics of these networks. However, the generation of realistic models and benchmarks is challenging and there is only a limited set of models of roaming and access based on real measurement data. We employed graph theory, modeled the roaming activity as a graph and measured its degree of connectivity. The negative binomial distribution models well the degree of connectivity. Furthermore, we analyzed the evolution of the roaming activity in the spatial and temporal domain and its impact on the degree of connectivity of the graph.

We compared the traffic from hosts connected to the network via a wired or wireless interface, emphasizing the impact of 802.11 on packet delay and loss. Our study uses only passive monitoring techniques, namely, inference from TCP header traces. This enabled us to study a population of several thousand hosts in a real production environment, in which more than 31 million TCP connections were made. Our first contribution is methodological. Passive methods always have some degree of uncertainty, and we overcome this limitation by mostly relying on relative differences between wired and wireless traffic. Our analysis revealed that wireless clients experienced substantially higher packet delay variability than wired clients but their loss rates are surprisingly similar. We found that both the number of unnecessary TCP retransmissions and, even more substantially, the number of interrupted connections are higher for the wireless LAN than for the wired LAN. To the best of our knowledge, this is the first research effort to directly contrast wired and wireless traffic of a large production network

Our goal is to model the arrival of wireless clients at the access points (APs) in a production 802.11 infrastructure. Such models are critical for benchmarks, simulation studies, design of capacity planning and resource allocation, and the administration and support of wireless infrastructures. Our contributions include a novel methodology for modeling the arrival processes of clients at APs and the use of a powerful visualization tool for finding detailed interior features and quantile plots with simulation envelope for goodness-of-fit test. Time-varying Poisson processes can model well the arrival processes of clients at APs. We validate these results by modeling the visit arrivals at different time intervals and APs. Furthermore, we propose a clustering of the APs based on their visit arrival and functionality of the area in which these APs are located.

Our goal is to perform a system-wide characterization of the workload of wireless access points (APs) in a production 802.11 infrastructure. The key issues of this study are the characterization of the traffic at each access point (AP), its modeling, and a comparison among APs of different wireless campus-wide infrastructures. Unlike most other studies, we compare two networks using similar data acquisition techniques and analysis methods. This makes the results more generally applicable. We analyzed the aggregate traffic load of APs and found that the log normality is prevalent. The distributions of the wireless received and sent traffic load for these infrastructures are similar. Furthermore, we discovered a dichotomy of APs: there are APs with the majority of clients that are uploaders and APs in which the majority of their clients are downloaders. Also, the number of non-unicast wireless packets and the percentage of roaming events are large. Finally, there is a correlation between the number of associations and traffic load in the log-log scale

Our goal is to characterize the traffic load in an IEEE802.11 infrastructure. This can be beneficial in many domains, including coverage planning, resource reservation, network monitoring for anomaly detection, and producing more accurate simulation models. The key issue that drives this study is traffic forecasting at each wireless access point (AP) in an hourly timescale. We conducted an extensive measurement study of wireless users on a major university campus using the IEEE802.11 wireless infrastructure. We propose several traffic models that take into account the periodicity and recent traffic history for each AP and present a time-series forecasting methodology. Finally, we build and evaluate these forecasting algorithms and discuss our findings.

Our goal is to explore characteristics of the environment that provide opportunities for caching, prefetching, coverage planning, and resource reservation. We conduct a one-month measurement study of locality phenomena among wireless Web users and their association patterns on a major university campus using the IEEE 802.11 wireless infrastructure. We evaluate the performance of different caching paradigms, such as single user cache, cache attached to an access point (AP), and peer-to-peer caching. In several settings such caching mechanisms could be beneficial. Unlike other measurement studies in wired networks in which 25% to 40% of documents draw 70% of Web access, our traces indicate that 13% of unique URLS draws this number of Web accesses. In addition, the overall ideal hit ratio of the user cache, cache attached to an access point, and peer-to-peer caching paradigms (where peers are coresident within an AP) are 51%, 55%, and 23%, respectively. We distinguish wireless clients based on their inter-building mobility, their visits to APs, their continuous walks in the wireless infrastructure, and their wireless information access during these periods. We model the associations as a Markov chain using as state information the most recent AP visits. We can predict with high probability (86%) the next AP with which a wireless client will associate. Also, there are APs with a high percentage of user revisits. Such measurements can benefit protocols and algorithms that aim to improve the performance of the wireless infrastructures by load balancing, admission control, and resource reservation across APs.

We present the cooperative location-sensing system (CLS), an adaptive location-sensing system that enables devices to estimate their position in a self-organizing manner without the need for an extensive infrastructure or training. Hosts cooperate and share positioning information. CLS uses a grid representation that allows an easy incorporation of external information to improve the accuracy of the position estimation. We evaluated the performance of CLS via simulation and investigated the impact of the density of landmarks, degree of connectivity, range error, and grid resolution on the accuracy. We found that the average error is less than 2% of the transmission range, when used in a terrain with 20% of the hosts to be landmarks, average network connectivity above 7, and distance estimation error equal to 5% of the transmission range.

The bulk of proposed pervasive computing devices such as PDAs and cellular telephones operate as thin clients within a larger infrastructure. To access services within their local environment, these devices participate in a service discovery protocol which involves a master directory that registers all services available in the local environment. These directories typically are isolated from each other. Devices that move across service discovery domains have no access to information outside their current local domain. In this paper we propose an application-level protocol called VIA that enables data sharing among discovery domains. Each directory maintains a table of active links to other directories that share related information. A set of linked directories forms a data cluster that can be queried by devices for information. The data cluster is distributed, self-organizing, responsive to data mobility, and robust to failures. Using application-defined data schemas, clusters organize themselves into a hierarchy for efficient querying and network resource usage. Through analysis and simulation we describe the behavior of VIA under different workloads and show that the protocol overhead for both maintaining a cluster and handling failures grows slowly with the number of gateways.

This paper presents 7DS, a novel peer-to-peer data sharing system. 7DS is an architecture, a set of protocols and an implementation enabling the exchange of data among peers that are not necessarily connected to the Internet. Peers can be either mobile or stationary. It anticipates the information needs of users and fulfills them by searching from information among peers. We evaluate via extensive simulations the effectiveness of our system for data dissemination among mobile devices with a large number of user mobility scenarios. We model several general data dissemination approaches and investigate the effect of the wireless converage range, 7DS, host density, query interval and cooperation strategy among the mobile hosts. Using theory from random walks, random environments and diffusion of controlled processes, we model one of these data dissemination schemes and show that the analysis confirms the simulation results for scheme

We present an architecture that enables the sharing of information among mobile, wireless, collaborating hosts that are intermittently connected to the Internet. Participants in the system obtain data objects from Internet-connected servers, cache them and exchange them with others who are interested in them. The system exploits the fact that there is a high locality of information access within a geographic area. It aims to increase the data availability to participants with lost connectivity to the Internet. We investigate how user mobility and query patterns affect data dissemination in such an environment. We discuss the main components of the system and possible applications. Finally, we present simulation results that show that the ad hoc networks can be very effective in distributing popular information.