Massive garbage patches within all oceanic gyres have garnered global attention, underscoring microplastic pollution as an emerging concern. Various isolated approaches have been proposed for real-time monitoring of microplastics in environmental and industrial settings. However, these fragmented solutions may hinder distributed data and knowledge sharing across applications, limiting the potential for leveraging AI development to enhance early warning systems and decision-making in large-scale industrial operations. To address these challenges, this paper introduces a framework that utilizes Planetary Digital Twins (PDTs) and affordable modular flow cytometry tools. These innovations enable the real-time tracking and sharing of data on microplastic pollution, tracing their origins from polymer-based industrial processes to their presence in the environment. The proposed framework includes a distributed system architecture based on publisher/subscriber for robust and scalable data sharing. By integrating Industry 5.0 principles, which prioritize sustainability and resilient production processes, the digital twin technology enables a dynamic and interconnected monitoring network. The validation results suggest that the proposed system has the potential to expand and enhance environmental risk assessments on a global scale and support the development of mitigation strategies through improved data integration and sharing capabilities for microplastic monitoring.

We propose dataset capturing statistics of several large-scale real-world streaming events, delivering videos to different devices (TVs, desktops, mobiles, tablets, etc.), and over different networks (from 2.5G, 3G, and other early generation mobile networks to 5G and broadband). The data we capture include network-related statistics, playback statistics (session- and player-event-level), and characteristics of the encoded streams. Such data should enable a broad level of possible applications and uses in the research community: from analysis of the effectiveness of algorithms in streaming players to studies of QoE metrics, and end-to-end system optimizations. Examples of such possible studies based on the proposed datasets are also provided.

Reducing end-to-end streaming latency is critical to HTTP-based live video streaming. There are currently two technologies in this domain: Low-Latency HTTP Live Streaming (LL-HLS) and Low-Latency Dynamic Adaptive Streaming over HTTP (LL-DASH). Many players support LL-HLS and/or LL-DASH protocols, including Apple's AVPlayer, Shaka player, HLS.js Dash.js, and others. This paper is dedicated to the analysis of the performance of low-latency players and streaming protocols. The evaluation is based on a series of live streaming experiments, repeated using identical video content, encoders, encoding profiles, and network conditions, emulated by using traces of real-world networks. Several performance metrics, such as average stream bitrate, the amounts of downloaded media data, streaming latency, as well as buffering and stream switching statistics are captured and reported in our experiments. These results are subsequently used to describe the observed differences in the performance of LL-HLS and LL-DASH-based players.

The rapid growth of mobile devices combined with the vast increase in Internet traffic is posing a challenge to the underlying infrastructure, especially at the wireless edge. Device-to-device communication (D2D) has been recognized as a solution to alleviate the burden on the infrastructure by slowing down the deployment of new base stations. This paper proposes a step towards D2D communication over Information-Centric Networking (ICN) to facilitate content retrieval via a cross-layer assisted forwarding strategy. By choosing ICN, we can circumvent some of the limitations of current MANET routing protocols. We evaluate our proposed scheme on up to 150 nodes in an urban scenario composed of vehicles and pedestrians. Our simulation results showed that our strategy can download up to 13 times more packages than the baseline with lower latency when the cellular network is present.

Mobile traffic volume is increasing rapidly, pressuring the underlying infrastructure to quickly increase its capacity. New applications are further exacerbating this problem. Device-to-Device communication has been long recognized as a means to offload traffic from the infrastructure; however, the host-oriented model of the TCP/IP-based Internet poses challenges to this communication pattern. This paper addresses these issues by proposing a scheme that uses a data-centric model to fetch contents from nearby peers while increasing the resiliency of the network in cases of outages and disasters. We collected real data from social media to create a content request pattern and evaluate our approach through the simulation of realistic urban scenarios. Additionally, we analyze the scenario of large crowds in sports venues. Our simulation results show that we can offload traffic from the backhaul network by up to 51.7%, suggesting an advantageous path to support the surge in traffic while keeping complexity and cost for the network operator at manageable levels.

Monopolies in access networks present challenging problems for network users: providers have no economic incentives to improve service quality or reduce cost. Virtualization of the access infrastructure can enable competition between providers without the need for deploying different physical access networks. For example, municipal broadband networks are owned by local governments and can be sliced to host multiple service providers. In this work, we evaluate the economic incentives in virtualized access networks and compare them to current access networks with limited (or no) provider competition. We also consider fine-grained competition among transit providers to reflect emerging ideas on offering network connectivity and services dynamically through marketplaces. We use an agent-based simulator in a research testbed to obtain results on quality and cost trends as well as on the ability of providers to thrive in the marketplace. Our findings show that having a shared infrastructure and multiple Internet providers is beneficial for subscribers while maintaining a sustainable ecosystem. Moreover, because the risk associated with deploying infrastructure is shared, Internet providers have more incentive to innovate. Thus, our results highlight the importance of virtualizing access networks to leverage economic drivers in this environment.

This paper studies information dissemination in wireless ad hoc networks, using standard routing protocols, such as OLSR, as well as Information-Centric Networking. We performed simulations using NS-3 and ndnSIM with different node counts and transport protocols. Our simulations show that TCP performs better in lower hop count scenarios, while NDN performs better in higher hop count scenarios.

Pricing of network services is a key aspect of Internet operation. Most current providers offer end-users only simple and coarse pricing choices. With technological developments for fine-grained service control and automation of dynamic contracts, it can be expected that users and applications can choose among available network service choices at fast timescales. A key question in the context of these economic dynamics is how provider competition and user choices affect price and quality of network services. In this paper, we develop an agent-based simulation system to assess these impacts on economic dynamics. We simulate a monopoly, a duopoly, and an oligopoly, where providers compete and innovate. We derive expected results for simple competition, but also observe that innovation improves a provider's competitiveness in the marketplace. Our results not only show general trends, but provide quantitative results that can help providers to explore and develop pricing strategies to maximize their profits while providing end-users with the choices they need.

New attacks are emerging that target the Internet infrastructure. Modern routers use programmable network processors that may be exploited by merely sending suitably crafted data packets into a network. Hardware monitors that are co-located with processor cores can detect attacks that change processor behavior with high probability. In this paper, we present a solution to the problem of secure, dynamic installation of hardware monitoring graphs on these devices. We also address the problem of how to overcome the homogeneity of a network with many identical devices, where a successful attack, albeit possible only with small probability, may have devastating effects.

We present a method to identify and localize people by leveraging existing CCTV camera infrastructure along with inertial sensors (accelerometer and magnetometer) within each person's mobile phones. Since a person's motion path, as observed by the camera, must match the local motion measurements from their phone, we are able to uniquely identify people with the phones' IDs by detecting the statistical dependence between the phone and camera measurements. For this, we express the problem as consisting of a two-measurement HMM for each person, with one camera measurement and one phone measurement. Then we use a maximum a posteriori formulation to find the most likely ID assignments. Through sensor fusion, our method largely bypasses the motion correspondence problem from computer vision and is able to track people across large spatial or temporal gaps in sensing. We evaluate the system through simulations and experiments in a real camera network testbed.

This work describes a new approach for localizing people by cooperative sensor fusion of lightweight camera and wearable accelerometer measurements. We present the algorithm to identify people moving around as they are detected by cameras deployed in the infrastructure. The algorithm uses a correlation metric to develop an ID matching algorithm that can associate people in the scene to their global ID emitted from a wireless accelerometer sensor node worn on their belts. First we conduct a set of preliminary experiments to verify that the quantities of interest easily measurable by off-the-shelf components. We validate our metric and the performance of the proposed ID matching algorithm using simulations on testbed data that also includes a crowded scenario.

We discuss the performance of Petrobras production Kirchhoff prestack seismic migration on a cluster of 64 GPUs and 256 CPU cores. Porting and optimization of the application hot spot (98.2% of a single CPU core execution time) to a single GPU reduces total execution time by a factor of 36 on a control run. We then argue against the usual practice of porting the next hot spot (1.5% of single CPU core execution time) to the GPU. Instead, we show that cooperation of CPU and GPU reduces total execution time by a factor of 59 on the same control run. Remaining GPU idle cycles are eliminated by overloading the GPU with multiple requests originated from distinct CPU cores. However, increasing the number of CPU cores in the computation reduces the gain due to the combination of enhanced parallelism in the runs without GPUs and GPU saturation on runs with GPUs. We proceed by obtaining close to perfect speed-up on the full cluster over homogeneous load obtained by replicating control run data. To cope with the heterogeneous load of real world data we show a dynamic load balancing scheme that reduces total execution time by a factor of 20 on runs that use all GPUs and half of the cluster CPU cores with respect to runs that use all CPU cores but no GPU.

This article presents two lifetime models that describe two of the most common modes of operation of sensor nodes today, trigger-driven and duty-cycle driven. The models use a set of hardware parameters such as power consumption per task, state transition overheads, and communication cost to compute a node's average lifetime for a given event arrival rate. Through comparison of the two models and a case study from a real camera sensor node design we show how the models can be applied to drive architectural decisions, compute energy budgets and duty-cycles, and to preform side-by-side comparison of different platforms. Based on our models we present a MATLAB Wireless Sensor Node Platform Lifetime Prediction and Simulation Package (MATSNL). This demonstrates the use of the models using sample applications drawn from existing sensor node measurements.

In this demonstration we present the BehaviorScope, a system for interpreting human activity patterns using a sensor network and its application to elder monitoring in assisted living. The BehaviorScope provides a runtime, user-programmable framework that processes streams of timestamped sensor data along with prior context information to infer activities and generate appropriate notifications. Human activities are described in high-level scripts that are directly mapped to a hierarchy of probabilistic grammars that parse low-level sensor measurements into high-level distinguishable activities. Activities of interest are pre-programmed into a specification that is used by the system to interpret the incoming sensor data stream. The system interprets the activities to generate summaries and other triggered notifications that are propagated to stakeholders via email and cell-phone text messages.

This paper presents BScope, a new system for interpreting human activity patterns using a sensor network. BScope provides a runtime, user-programmable framework that processes streams of timestamped sensor data along with prior context information to infer activities and generate appropriate notifications. The users of the system are able to describe human activities with high-level scripts that are directly mapped to hierarchical probabilistic grammars used to parse low-level sensor measurements into high-level distinguishable activities. Our approach is presented, though not limited, in the context of an assisted living application in which a small, privacy-preserving camera sensor network of five nodes is used to monitor activity in the entire house over a period of 25 days. Privacy is preserved by the fact that camera sensors only provide discrete high-level features, such as motion information in the form of image locations, and not actual images. In this deployment, our primary sensing modality is a distributed array of image sensors with wide-angle lenses that observe people's locations in the house during the course of the day. We demonstrate that our system can successfully generate summaries of everyday activities and trigger notifications at runtime by using more than 1.3 million location measurements acquired through our real home deployment.

The identification of replicas in a database is fundamental to improve the quality of the information. Deduplication is the task of identifying replicas in a database that refer to the same real world entity. This process is not always trivial, because data may be corrupted during their gathering, storing or even manipulation. Problems such as misspelled names, data truncation, data input in a wrong format, lack of conventions (like how to abbreviate a name), missing data or even fraud may lead to the insertion of replicas in a database. The deduplication process may be very hard, if not impossible, to be performed manually, since actual databases may have hundreds of millions of records. In this paper, we present our parallel deduplication algorithm, called FER- APARDA. By using probabilistic record linkage, we were able to successfully detect replicas in synthetic datasets with more than 1 million records in about 7 minutes using a 20- computer cluster, achieving an almost linear speedup. We believe that our results do not have similar in the literature when it comes to the size of the data set and the processing time.

Although imaging is an information-rich sensing modality, the use of cameras in sensor networks is very often prohibited by factors such as power, computation cost, storage, communication bandwidth and privacy. In this paper we consider information selective and privacy-preserving address-event imagers for sensor networks. Instead of providing full images with a high degree of redundancy, our efforts in the design of these imagers specialize on selecting a handful of features from a scene and outputting these features in address-event representation. In this paper we present our initial results in modeling and evaluating address-event sensors in the context of sensor networks. Using three different platforms that we have developed, we illustrate how to model address-event cameras and how to build an emulator using these models. We also present a lightweight classification scheme to illustrate the computational advantages of address-event sensors. The paper concludes with an evaluation of the classification algorithm and a feasibility study of using COTS components to emulate address-event inside a sensor network.

We discuss an imaging architecture for sensor network applications, that employs a 32 times 32 address-event representation (AER) imager. At the sensor level, pixels convert light intensity into a pulse density modulated stream of address events. Two different types of COTS wireless radio nodes are used, along with two separate approaches to wireless data transmission - one as a train of AER addresses, and the other as a histogram of the active pixels. Information transmitted in the limited-bandwidth network yields effective means for detection and partial recognition of the object even at very low bit rates, yielding a maximum experimental frame-rate of close to 6fps