1,721,071 research outputs found
A novel approach for distributed simulation of wireless mobile systems
No full textThis position paper introduces the motivation and preliminary implementation issues of a distributed simulation middleware designed to increase the performance and speed-up in the distributed simulation of wireless systems characterized by mobile hosts. Topology changes due to simulated hosts' mobility map on dynamic causality effects in the "areas of influence" of each mobile device. We analyze the preliminary definition of a new dynamic mechanism for the runtime management and distributed allocation of model-components executed over a cluster of Physical Execution Units (PEUs). A migration mechanism dynamically adapts the topology changes in the wireless network to a reallocation of model components over the PEUs. The aim is the reduction of communication overheads, between the PEUs, required to distribute the event-messages between model components. The distributed simulation framework is based on HLA-compliant runtime infrastructure and preliminary, adaptive load-balancing and migration heuristics. © IFIP International Federation for Information Processing 2003
Intelligence at the IoT Edge: Activity Recognition with Low-Power Microcontrollers and Convolutional Neural Networks
No full textRecently, Deep Learning (DL) techniques have shown their effectiveness for Human Activity Recognition (HAR) tasks. However, due to the storage and computational requirements, most of the existing HAR solutions assume that the training and inference phases are offloaded to the cloud or an external server, with a harmful impact on the network load of the mobile/wearable device as well as on the user’s privacy. A promising solution is represented by the emerging Edge Artificial Intelligence (AI) techniques that aim at moving the data analytics closer to the sensing units or directly on them. In this paper, we present our preliminary results about the offloading of HAR inference tasks on low-power microcontroller units. We consider the problem of detecting critical movements (e.g. falling, running) of workers within an industrial environment for safety purposes. The full pipeline of the HAR system is presented by using an Arduino BLE 33 Sense as a wearable unit: for the detection task, a DL model based on a Convolutional Neural Networks (CNN) is trained on the inertial sensor data. A dynamic range quantization technique is used to reduce the size of the model which is then loaded on the firmware. Preliminary results show that the accuracy of the CNN model is 97% and overcomes baseline, non-DL techniques, while the quantization technique ensures a reduction of 53% of the model size
A distributed mechanism for power saving in IEEE 802.11 wireless LANs
No full textThe finite battery power of mobile computers represents one of the greatest limitations to the utility of portable computers. Furthermore, portable computers often need to perform power consuming activities, such as transmitting and receiving data by means of a random-access, wireless channel. The amount of power consumed to transfer the data on the wireless channel is negatively affected by the channel congestion level, and significantly depends on the MAC protocol adopted. This paper illustrates the design and the performance evaluation of a new mechanism that, by controlling the accesses to the shared transmission channel of a wireless LAN, leads each station to an optimal Power Consumption level. Specifically, we considered the Standard IEEE 802.11 Distributed Coordination Function (DCF) access scheme for WLANs. For this protocol we analytically derived the optimal average Power Consumption levels required for a frame transmission. By exploiting these analytical results, we define a Power Save, Distributed Contention Control (PS-DCC) mechanism that can be adopted to enhance the performance of the Standard IEEE 802.11 DCF protocol from a power saving standpoint. The performance of an IEEE 802.11 network enhanced with the PS-DCC mechanism has been investigated by simulation. Results show that the enhanced protocol closely approximates the optimal power consumption level, and provides a channel utilization close to the theoretical upper bound for the IEEE 802.11 protocol capacity. In addition, even in low load situations, the enhanced protocol does not introduce additional overheads with respect to the standard protocol
Distribuzione a pagamento di musica sulla rete radiomobile via Internet: Aspetti tecnici ed economici
No full textMusica Digitale: significato, formati e trend di mercat
HLA-based adaptive distributed simulation of wireless mobile systems
No full textWireless networks' models differ from wired ones at least in the innovative dynamic effects of host-mobility and open-broadcast nature of the wireless medium. Topology changes due to simulated hosts' mobility map on causality effects in the "areas of influence" of each mobile device. The analysis of wireless networks of interest today may include a potentially high number of simulated hosts, resulting in performance and scalability problems for discrete-event sequential simulation tools and methods, on a single physical execution unit (PEU). In a distributed simulation, the main bottleneck becomes the communication and synchronization required to maintain the causality constrains between distributed model components. We propose an HLA-based, dynamic mechanism for the runtime management and allocation of model entities in a distributed simulation of wireless networks models, over a cluster of PEUs. By adopting a runtime evaluation of causal bindings between model entities we map the causal effects of virtual topology changes to dynamic migration of data structures. Preliminary results demonstrate that the prototype heuristics lead to a reduction in the percentage of external communication between the PEUs, limited overheads and performance enhancements for a worst-case scenario
Vehicular Route Identification Using Mobile Devices Integrated Sensors
No full textLocation based services are commonly used by several mobile applications and services, to provide content related to the area in which the user is located. This enables services such as navigation, particularly useful for vehicular applications, though possibly exposing private information about the user, which has to explicitly grant the location permission. However, smartphone have also many other sensors off the shelf, which currently do not require any permission to be used, and may be leveraged to track the users movements, hence the location, thus raising potentially serious privacy issues. In this paper we present a study which shows that by analyzing data obtained through the accelerometer and the magnetometer, it is possible to achieve less than 50 meters of localization accuracy even for long journeys, and 95% of accuracy on the road identification
Performance evaluation of hybrid crowdsensing systems with stateful CrowdSenSim 2.0 simulator
No full textMobile crowdsensing (MCS) has become a popular paradigm for data collection in urban environments. In MCS systems, a crowd supplies sensing information for monitoring phenomena through mobile devices. Depending on the degree of involvement of users, MCS systems can be participatory, opportunistic or hybrid, which combines strengths of above approaches. Typically, a large number of participants is required to make a sensing campaign successful which makes impractical to build and deploy large testbeds to assess the performance of MCS phases like data collection, user recruitment, and evaluating the quality of information. Simulations offer a valid alternative. In this paper, we focus on hybrid MCS and extend CrowdSenSim 2.0 in order to support such systems. Specifically, we propose an algorithm for efficient re-route users that would offer opportunistic contribution towards the location of sensitive MCS tasks that require participatory-type of sensing contribution. We implement such design in CrowdSenSim 2.0, which by itself extends the original CrowdSenSim by featuring a stateful approach to support algorithms where the chronological order of events matters, extensions of the architectural modules, including an additional system to model urban environments, code refactoring, and parallel execution of algorithms
Bluetooth Mesh Technology for the Joint Monitoring of Indoor Environments and Mobile Device Localization: A Performance Study
Full text linkBluetooth Mesh is a recent SIG standard enabling the deployment of multi-hop Wireless Sensor Networks (WSNs) over Bluetooth Low Energy (BLE) communication links. The standard introduces many novel and interesting features in the Internet of Things (IoT) domain, such as the seamless integration among sensors and mobile and wearable devices, and the support for a wide range of different IoT application profiles. At the same time, fine-grained assessments of the performance are still needed to understand the potential of the technology. In this paper, we investigate the usage of Bluetooth Mesh solutions for the joint monitoring of indoor spaces and humans. Through the deployment of a test-bed, we evaluate the performance of Bluetooth Mesh WSNs under varying traffic loads and network sizes. In addition, by exploiting the short-range, multi-hop communications, we propose a procedure for the indoor localization of mobile devices and evaluate its accuracy. The results demonstrate that the technology supports reasonable delivery ratio under high traffic loads, however the network and localization performance sharply decreases when increasing the number of hops between the source and destination nodes
An MCS Navigation System Based on Road Surface Quality for Bicycle Riders
No full textRoad surface quality is a major concern for bicycle riders and plays an important role in the mobility infrastructure. In the era where smarter cities aim to increase the well-being of citizens and the efficiency of infrastructures, navigation systems relying on Mobile Crowdsensing (MCS) are mostly designed for car drivers, and account for road traffic conditions. To cover the gap, in this paper, we propose a full architectural pipeline of an MCS-based navigation system for bicycle riders that accounts for the road surface quality. The MCS paradigm leverages the sensor data produced by the personal devices of participating citizens to describe phenomena of common interest. Our system classifies road segments using inertial sensor data gathered by users, using a combination of supervised and unsupervised methods, as human labeling in this context is impractical and too subjective. We prove the efficacy of our method in a controlled environment, and then we implement and deploy the full system in a real city, finally reporting on its results
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