Smarter grid through collective intelligence: user awareness for enhanced performance

This paper examines the scenario of a university campus, and the impact on energy consumption of the awareness of building managers and users (lecturers, students and administrative staff).Peer ReviewedPostprint (published version

RoutesMobilityModel: easy realistic mobility simulation using external information services

Workshop on ns-3 (WNS '15). 13, May, 2015. Castelldefels, Spain.The current implementation of ns-3 provides only synthetic mobility models that disregard the map where the nodes are moving, however, the study of vehicular ad-hoc networks requires the usage of more realistic mobility models. The usage of mobility traces created by traffic simulators such as SUMO is feasible, however, these simulators possess a steep learning curve, which prevents their fruition for most researchers whose research focus and expertise are on the data communication layer. This paper presents a mobility model that generates realistic mobility traces that take into account the underlying maps, while maintaining the ease of usage that characterizes the synthetic mobility models. The module described herein is compared against SUMO and against the ns3::RandomWaypointMobilityModel of network simulator 3, to analyze the trade-off it implements in terms of realism and ease of usage

A module for Data Centric Storage in ns-3

Demo in Workshop on ns-3 (WNS3 2015). 13 to 14, May, 2015. Castelldefels, Spain.Management of data in large wireless sensor networks presents many hurdles, mainly caused by the limited energy available to the sensors, and by the limited knowledge of the sensors regarding the topology of the network. The first problem has been targeted by the introduction of in-network storage of sensed data, which can save much communication energy. The second issue found some relief with the introduction of geographical protocols that do not need knowledge regarding the network at large. Data Centric Storage systems such as QNiGHT [1][2] assume that each sensor knows its own geographical location, and they use geographical routing such as the Enhanced Greedy Perimeter Stateless Routing (EGPSR) protocol, sketched in Figure 1, to deliver packets to the sensor closest to a given point in the sensing area

AoI-based Multicast Routing over Voronoi Overlays with Minimal Overhead

The increasing pervasive and ubiquitous presence of devices at the edge of the Internet is creating new scenarios for the emergence of novel services and applications. This is particularly true for location- and context-aware services. These services call for new decentralized, self-organizing communication schemes that are able to face issues related to demanding resource consumption constraints, while ensuring efficient locality-based information dissemination and querying. Voronoi-based communication techniques are among the most widely used solutions in this field. However, when used for forwarding messages inside closed areas of the network (called Areas of Interest, AoIs), these solutions generally require a significant overhead in terms of redundant and/or unnecessary communications. This fact negatively impacts both the devices' resource consumption levels, as well as the network bandwidth usage. In order to eliminate all unnecessary communications, in this paper we present the MABRAVO (Multicast Algorithm for Broadcast and Routing over AoIs in Voronoi Overlays) protocol suite. MABRAVO allows to forward information within an AoI in a Voronoi network using only local information, reaching all the devices in the area, and using the lowest possible number of messages, i.e., just one message for each node included in the AoI. The paper presents the mathematical and algorithmic descriptions of MABRAVO, as well as experimental findings of its performance, showing its ability to reduce communication costs to the strictly minimum required.Comment: Submitted to: IEEE Access; CodeOcean: DOI:10.24433/CO.1722184.v1; code: https://github.com/michelealbano/mabrav

Reliable & Efficient Data Centric Storage for Data Management in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have become a mature technology aimed at performing environmental monitoring and data collection. Nonetheless, harnessing the power of a WSN presents a number of research challenges. WSN application developers have to deal both with the business logic of the application and with WSN's issues, such as those related to networking (routing), storage, and transport. A middleware can cope with this emerging complexity, and can provide the necessary abstractions for the definition, creation and maintenance of applications. The final goal of most WSN applications is to gather data from the environment, and to transport such data to the user applications, that usually resides outside the WSN. Techniques for data collection can be based on external storage, local storage and in-network storage. External storage sends data to the sink (a centralized data collector that provides data to the users through other networks) as soon as they are collected. This paradigm implies the continuous presence of a sink in the WSN, and data can hardly be pre-processed before sent to the sink. Moreover, these transport mechanisms create an hotspot on the sensors around the sink. Local storage stores data on a set of sensors that depends on the identity of the sensor collecting them, and implies that requests for data must be broadcast to all the sensors, since the sink can hardly know in advance the identity of the sensors that collected the data the sink is interested in. In-network storage and in particular Data Centric Storage (DCS) stores data on a set of sensors that depend on a meta-datum describing the data. DCS is a paradigm that is promising for Data Management in WSNs, since it addresses the problem of scalability (DCS employs unicast communications to manage WSNs), allows in-network data preprocessing and can mitigate hot-spots insurgence. This thesis studies the use of DCS for Data Management in middleware for WSNs. Since WSNs can feature different paradigms for data routing (geographical routing and more traditional tree routing), this thesis introduces two different DCS protocols for these two different kinds of WNSs. Q-NiGHT is based on geographical routing and it can manage the quantity of resources that are assigned to the storage of different meta-data, and implements a load balance for the data storage over the sensors in the WSN. Z-DaSt is built on top of ZigBee networks, and exploits the standard ZigBee mechanisms to harness the power of ZigBee routing protocol and network formation mechanisms. Dependability is another issue that was subject to research work. Most current approaches employ replication as the mean to ensure data availability. A possible enhancement is the use of erasure coding to improve the persistence of data while saving on memory usage on the sensors. Finally, erasure coding was applied also to gossiping algorithms, to realize an efficient data management. The technique is compared to the state-of-the-art to identify the benefits it can provide to data collection algorithms and to data availability techniques

Architecture to Support Quality of Service in Arrowhead Systems

Presented at INForum - Simpósio de Informática (INFORUM 2015). 7 to 8, Sep, 2015. Covilhã, Portugal.The Arrowhead project [1] considers to normalize all interactions involving embedded systems by mediating them through services. The Service Oriented Architecture (SOA) paradigm is applied to both the interactions that provide the service requested by the user, and other support actions such as the authentication and registration of the devices, and the services they provide, the look-up of devices and service provided, and orchestration of services for creation of more complex services. To this purpose, services are divided into Core Services, which are present in every environment supporting Arrowhead applications, and user services that implement the applications. The Core Services set comprises, at least, Authentication Service, Registration Service and Orchestration Service

Ranking coherence in Topic Models using Statistically Validated Networks

Probabilistic topic models have become one of the most widespread machine learning techniques in textual analysis. Topic discovering is an unsupervised process that does not guarantee the interpretability of its output. Hence, the automatic evaluation of topic coherence has attracted the interest of many researchers over the last decade, and it is an open research area. The present article offers a new quality evaluation method based on Statistically Validated Networks (SVNs). The proposed probabilistic approach consists of representing each topic as a weighted network of its most probable words. The presence of a link between each pair of words is assessed by statistically validating their co-occurrence in sentences against the null hypothesis of random co-occurrence. The proposed method allows one to distinguish between high-quality and low-quality topics, by making use of a battery of statistical tests. The statistically significant pairwise associations of words represented by the links in the SVN might reasonably be expected to be strictly related to the semantic coherence and interpretability of a topic. Therefore, the more connected the network, the more coherent the topic in question. We demonstrate the effectiveness of the method through an analysis of a real text corpus, which shows that the proposed measure is more correlated with human judgement than the state-of-the-art coherence measures

Smarter grid through collective intelligence: user awareness for enhanced performance

Purpose – This paper examines the scenario of a university campus, and the impact on energy consumption of the awareness of building managers and users (lecturers, students and administrative staff). Design/methodology/approach – This study draws a comparison between direct fruition of the information by both skilled (building managers) and unskilled (users) recipients, and the effect of peer pressure and beneficial competition between users in applying the good practices. In fact, the usage of edutainment, implemented by the automatic publication on the Twitter platform of energy consumption data from different users, can promote general users’ awareness on best practices and their effect on energy consumption. In addition, the use of a social network platform allows the interaction between users, sharing experiences and increasing the collective intelligence in the energy efficiency field. Findings – Tests revealed that enhanced awareness helped managers to identify strategies that, if implemented in the whole building, could reduce energy consumption by about 6%. The tests on university users’ awareness hint that the expected energy savings can reach 9%, in addition to the previous 6%. In fact, the measures were implemented in one of the three common rooms, and at building level the total energy consumption decreased by 3.42%, proving that a large deal of energy can be saved by capillary actions targeting society at large. The emerging collective intelligence of the final users ends up having a stronger effect on energy saving than the actions of more educated professionals. Practical implications – The approach used in this paper moved the burden of evolving the energy saving strategies to new scenarios onto the collective intelligence of the users, by connecting the users – and their experiences in new scenarios – using a social network to provide guidelines to other users involved in the same decision processes. Originality/Value – The authors of the paper use social technologies (Twitter, graphical interfaces) for a social goal (promote user awareness on energy usage) for the benefit of the society (energy savings)