Ontology-Based Context-Aware Service Discovery for Pervasive Environments

Existing service discovery protocols use a service matching process in order to offer services of interest to the clients. Potentially, the context information of the services and client can be used to improve the quality of service matching. To make use of context information in service matching, service discovery needs to address certain challenges. Firstly, it is required that the context information should have unambiguous representation. Secondly, the mobile devices should be able to disseminate context information seamlessly in the fixed network. And thirdly, dynamic nature of the context information should be taken into account. The proposed Context Aware Service Discovery (CASD) architecture deals with these challenges by means of an ontological representation and processing of context information, a concept of nomadic mobile context source and a mechanism of persistent service discovery respectively. This paper discusses proposed CASD architecture, its implementation and suggests further enhancements

On-site forest fire smoke detection by low-power autonomous vision sensor

Early detection plays a crucial role to prevent forest fires from spreading. Wireless vision sensor networks deployed throughout high-risk areas can perform fine-grained surveillance and thereby very early detection and precise location of forest fires. One of the fundamental requirements that need to be met at the network nodes is reliable low-power on-site image processing. It greatly simplifies the communication infrastructure of the network as only alarm signals instead of complete images are transmitted, anticipating thus a very competitive cost. As a first approximation to fulfill such a requirement, this paper reports the results achieved from field tests carried out in collaboration with the Andalusian Fire-Fighting Service (INFOCA). Two controlled burns of forest debris were realized (www.youtube.com/user/vmoteProject). Smoke was successfully detected on-site by the EyeRISTM v1.2, a general-purpose autonomous vision system, built by AnaFocus Ltd., in which a vision algorithm was programmed. No false alarm was triggered despite the significant motion other than smoke present in the scene. Finally, as a further step, we describe the preliminary laboratory results obtained from a prototype vision chip which implements, at very low energy cost, some image processing primitives oriented to environmental monitoring.Ministerio de Ciencia e Innovación 2006-TIC-2352, TEC2009-1181

A Data Annotation Architecture for Semantic Applications in Virtualized Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have become very popular and are being used in many application domains (e.g. smart cities, security, gaming and agriculture). Virtualized WSNs allow the same WSN to be shared by multiple applications. Semantic applications are situation-aware and can potentially play a critical role in virtualized WSNs. However, provisioning them in such settings remains a challenge. The key reason is that semantic applications provisioning mandates data annotation. Unfortunately it is no easy task to annotate data collected in virtualized WSNs. This paper proposes a data annotation architecture for semantic applications in virtualized heterogeneous WSNs. The architecture uses overlays as the cornerstone, and we have built a prototype in the cloud environment using Google App Engine. The early performance measurements are also presented.Comment: This paper has been accepted for presentation in main technical session of 14th IFIP/IEEE Symposium on Integrated Network and Service Management (IM 2015) to be held on 11-15 May, 2015, Ottawa, Canad

A neural network propagation model for LoRaWAN and critical analysis with real-world measurements

Among the many technologies competing for the Internet of Things (IoT), one of the most promising and fast-growing technologies in this landscape is the Low-Power Wide-Area Network (LPWAN). Coverage of LoRa, one of the main IoT LPWAN technologies, has previously been studied for outdoor environments. However, this article focuses on end-to-end propagation in an outdoor–indoor scenario. This article will investigate how the reported and documented outdoor metrics are interpreted for an indoor environment. Furthermore, to facilitate network planning and coverage prediction, a novel hybrid propagation estimation method has been developed and examined. This hybrid model is comprised of an artificial neural network (ANN) and an optimized Multi-Wall Model (MWM). Subsequently, real-world measurements were collected and compared against different propagation models. For benchmarking, log-distance and COST231 models were used due to their simplicity. It was observed and concluded that: (a) the propagation of the LoRa Wide-Area Network (LoRaWAN) is limited to a much shorter range in this investigated environment compared with outdoor reports; (b) log-distance and COST231 models do not yield an accurate estimate of propagation characteristics for outdoor–indoor scenarios; (c) this lack of accuracy can be addressed by adjusting the COST231 model, to account for the outdoor propagation; (d) a feedforward neural network combined with a COST231 model improves the accuracy of the predictions. This work demonstrates practical results and provides an insight into the LoRaWAN’s propagation in similar scenarios. This could facilitate network planning for outdoor–indoor environments

On the Relation Between Mobile Encounters and Web Traffic Patterns: A Data-driven Study

Mobility and network traffic have been traditionally studied separately. Their interaction is vital for generations of future mobile services and effective caching, but has not been studied in depth with real-world big data. In this paper, we characterize mobility encounters and study the correlation between encounters and web traffic profiles using large-scale datasets (30TB in size) of WiFi and NetFlow traces. The analysis quantifies these correlations for the first time, across spatio-temporal dimensions, for device types grouped into on-the-go Flutes and sit-to-use Cellos. The results consistently show a clear relation between mobility encounters and traffic across different buildings over multiple days, with encountered pairs showing higher traffic similarity than non-encountered pairs, and long encounters being associated with the highest similarity. We also investigate the feasibility of learning encounters through web traffic profiles, with implications for dissemination protocols, and contact tracing. This provides a compelling case to integrate both mobility and web traffic dimensions in future models, not only at an individual level, but also at pairwise and collective levels. We have released samples of code and data used in this study on GitHub, to support reproducibility and encourage further research (https://github.com/BabakAp/encounter-traffic).Comment: Technical report with details for conference paper at MSWiM 2018, v3 adds GitHub lin

Analysis of packet scheduling for UMTS EUL - design decisions and performance evaluation

The UMTS Enhanced Uplink (EUL) provides higher capacity, increased data rates and smaller latency on the communication link from users towards the network. In this paper we present a performance comparison of three distinct EUL scheduling schemes (one-by-one, partial parallel and full parallel) taking into account both the packet level characteristics and the flow level dynamics due to the (random) user behaviour.\ud Using a very efficient hybrid analytical and simulation approach we analyse the three schemes with respect to performance measures such as mean file transfer time and fairness. In UMTS, a significant part of the system capacity will be used to support non-elastic voice traffic. Hence, part of our investigation is dedicated to the effects that the volume of voice traffic has on the performance of the elastic traffic supported by the EUL. Finally, we evaluate the impact that implementation specifics of a full parallel scheduler has on these measures.\ud \ud Our main conclusion is that our partial parallel scheduler, which is a hybrid between the one-by-one and full parallel, outperforms the other two schedulers in terms of mean flow transfer time, and is less sensitive to volume and nature of voice traffic. However, under certain circumstances, the partial parallel scheduler exhibits a somewhat lower fairness than the alternatives

Sustainability and Community Networks

Community networks are IP-based computer networks that are operated by a community as a common good. In Europe, the most well-known community networks are Guifi in Catalonia, Freifunk in Berlin, Ninux in Italy, Funkfeuer in Vienna and the Athens Wireless Metropolitan Network in Greece. This paper deals with community networks as alternative forms of Internet access and alternative infrastructures and asks: What does sustainability and unsustainability mean in the context of community networks? What advantages do such networks have over conventional forms of Internet access and infrastructure provided by large telecommunications corporations? In addition what disadvantages do they face at the same time? This article provides a framework for thinking dialectically about the un/sustainability of community networks. It provides a framework of practical questions that can be asked when assessing power structures in the context of Internet infrastructures and access. It presents an overview of environmental, economic, political and cultural contradictions that community networks may face as well as a typology of questions that can be asked in order to identify such contradictions

A vision-based monitoring system for very early automatic detection of forest fires

Trabajo presentado a la "I International Conference on Modelling, Monitoring and Management of Forest Fires" celebrada en Toledo del 17 al 19 de Septiembre de 2008.International Conference on Modelling, Monitoring and Management of Forest Fires I This paper describes a system capable of detecting smoke at the very beginning of a forest fire with a precise spatial resolution. The system is based on a wireless vision sensor network. Each sensor monitors a small area of vegetation by running on-site a tailored vision algorithm to detect the presence of smoke. This algorithm examines chromaticity changes and spatio-temporal patterns in the scene that are characteristic of the smoke dynamics at early stages of propagation. Processing takes place at the sensor nodes and, if that is the case, an alarm signal is transmitted through the network along with a reference to the location of the triggered zone - without requiring complex GIS systems. This method improves the spatial resolution on the surveilled area and reduces the rate of false alarms. An energy efficient implementation of the sensor/processor devices is crucial as it determines the autonomy of the network nodes. At this point, we have developed an ad hoc vision algorithm, adapted to the nature of the problem, to be integrated into a single-chip sensor/processor. As a first step to validate the feasibility of the system, we applied the algorithm to smoke sequences recorded with commercial cameras at real-world scenarios that simulate the working conditions of the network nodes. The results obtained point to a very high reliability and robustness in the detection process.This work is funded by Junta de Andalucía (CICE) through project 2006-TIC-2352.Peer Reviewe