An Event-based Local Action Paradigm to Improve Energy Efficiency in Queriable Wireless Sensor Actuator Networks

Wireless sensor networks (WSN) are deployed in a multitude of applications both in industrial and academic fields. In recent years, due to the emerge of Internet of Things (IoT) technologies and Vehicle2X communication scenarios, novel challenges for wireless sensor network platforms - regarding hardware and software - arose. Thus, challenges known from big data processing have reached the WSN scope and consequently approaches and methods have been devised to handle these. One such approach is queriable wireless sensor networks which enable their users the specification of sensing tasks in a declarative way without the need to re-program nodes in case the application requirements change. As many current WSN applications feature active parts with which nodes can directly influence their environment, the term wireless sensor actuator networks (WSAN) has been coined, setting such networks apart from solely passively measuring networks.In this article, we will present a short introduction to big data processing in wireless sensor networks which motivates the usage of queriable networks. We will show that in order to enable a WSAN to carry out actions energy-efficiently and in a timely manner, an event-based action model is favorable. Additionally, we will demonstrate how such an event system can be used to improve sub query performance in WSNs. We conclude with an evaluation regarding the benefit of combining this approach with wake-up receiver technologies based on a qualitative energy efficiency definition for WSN

METADATA CHALLENGE FOR QUERY PROCESSING OVER HETEROGENEOUS WIRELESS SENSOR NETWORK

ABSTRACT Wireless sensor networks become integral part of our life. These networks can be used for monitoring the data in various domain due to their flexibility and functionality. Query processing and optimization in the WSN is a very challenging task because of their energy and memory constraint. In this paper, first our focus is to review the different approaches that have significant impacts on the development o

Dynamic Detection and Tracking of Composite Events in Wireless Sensor Networks

In questa tesi si presenta un sistema (MaD-WiSe) per la gestione efficiente di dati in reti di sensori senza fili (WSN) in scenari statici, e si forniscono diverse tecniche di ottimizzazione validate da risultati sperimentali su una rete di sensori reale. Si presenta inoltre un nuovo linguaggio dichiarativo (EQL) per esprimere eventi compositi da rilevare e tracciare in modo dinamico e autonomo e si fornisce uno schema di implementazione e un simulatore per la valutazione delle performance

Reingegnerizzazione e Routing Multi-Hop nel sistema per la gestione di dati in reti di sensori Mad-WiSe

Questo lavoro nasce dall’esigenza dei responsabili del progetto Mad-WiSe (Management of Data in Wireless Sensor networks), che vede collaborare il laboratorio “Wireless Networks and Multimedia Networked Information Systems” dell’ istituto ISTI del CNR di Pisa con il Dipartimento di Informatica dell’Università di Pisa, di dare continuità al proprio progetto in seguito al rilascio da parte dell’ Università della California di Berkeley della versione 2.x di TinyOs, il sistema operativo utilizzato dai nodi delle reti wireless di sensori. Il lavoro di tesi svolto tratta il porting da TinyOs 1.x a TinyOs 2.x e l’implementazione della funzionalità di Multi-hop Routing su Mad-WiSe, un sistema di trattamento di interrogazioni complesse per reti di sensori, finalizzato a rendere l’utilizzo di questa tecnologia accessibile a tutti, attraverso l’utilizzo di un linguaggio di query SQL-like. La tesi si articola in sette capitoli: il primo è dedicato a una breve esposizione delle tecnologie e dei lavori correlati al progetto. Nel secondo capitolo è esposta la struttura di Mad-WiSe, del suo stack protocollare e delle funzionalità offerte da ogni livello. Nel terzo capitolo si affronta la questione della reingegnerizzazione di Mad-WiSe al TinyOs 2.x soffermandosi sulle differenze concettuali e sulle funzionalità introdotte e modificate rispetto al TinyOs 1.x. Il quarto capitolo spiega come è stato implementato il Multi-hop Routing adattando l’algoritmo AODV al livello di rete già esistente. Gli ultimi tre capitoli, il quinto, il sesto e il settimo consistono rispettivamente nelle conclusioni, l’elenco delle figure e in una serie di note bibliografiche

Un linguaggio di interrogazione e un ottimizzatore per interagire con una rete di sensori.

Le reti di sensori senza fili, composte da nodi autonomi ciascuno provvisto di batteria, sensori, antenna radio, processore, memoria, timer, stanno trovando innumerevoli applicazioni in molti campi. In questa tesi e' stato definito un linguaggio per interrogare una rete di sensori senza fili similmente a come si farebbe per un database tradizionale. Nella tesi e' stato realizzato il parser delle query e la generazione del piano di esecuzione distribuito fra vari nodi, secondo un'algebra opportunatamente definita. E' stato progettato e realizzato l'ottimizzatore delle query. La particolarita' della strategia di ottimizzazione e' dovuta al fatto che si cerca di minimizzare il consumo di energia nella rete di sensori

Data centric storage framework for an intelligent wireless sensor network

In the last decade research into Wireless Sensor Networks (WSN) has triggered extensive growth in flexible and previously difficult to achieve scientific activities carried out in the most demanding and often remote areas of the world. This success has provoked research into new WSN related challenges including finding techniques for data management, analysis, and how to gather information from large, diverse, distributed and heterogeneous data sets. The shift in focus to research into a scalable, accessible and sustainable intelligent sensor networks reflects the ongoing improvements made in the design, development, deployment and operation of WSNs. However, one of the key and prime pre-requisites of an intelligent network is to have the ability of in-network data storage and processing which is referred to as Data Centric Storage (DCS). This research project has successfully proposed, developed and implemented a comprehensive DCS framework for WSN. Range query mechanism, similarity search, load balancing, multi-dimensional data search, as well as limited and constrained resources have driven the research focus. The architecture of the deployed network, referred to as Disk Based Data Centric Storage (DBDCS), was inspired by the magnetic disk storage platter consisting of tracks and sectors. The core contributions made in this research can be summarized as: a) An optimally synchronized routing algorithm, referred to Sector Based Distance (SBD) routing for the DBDCS architecture; b) DCS Metric based Similarity Searching (DCSMSS) with the realization of three exemplar queries – Range query, K-nearest neighbor query (KNN) and Skyline query; and c) A Decentralized Distributed Erasure Coding (DDEC) algorithm that achieves a similar level of reliability with less redundancy. SBD achieves high power efficiency whilst reducing updates and query traffic, end-to-end delay, and collisions. In order to guarantee reliability and minimizing end-to-end latency, a simple Grid Coloring Algorithm (GCA) is used to derive the time division multiple access (TDMA) schedules. The GCA uses a slot reuse concept to minimize the TDMA frame length. A performance evaluation was conducted with simulation results showing that SBD achieves a throughput enhancement by a factor of two, extension of network life time by 30%, and reduced end-to-end latency. DCSMSS takes advantage of a vector distance index, called iDistance, transforming the issue of similarity searching into the problem of an interval search in one dimension. DCSMSS balances the load across the network and provides efficient similarity searching in terms of three types of queries – range query, k-query and skyline query. Extensive simulation results reveal that DCSMSS is highly efficient and significantly outperforms previous approaches in processing similarity search queries. DDEC encoded the acquired information into n fragments and disseminated across n nodes inside a sector so that the original source packets can be recovered from any k surviving nodes. A lost fragment can also be regenerated from any d helper nodes. DDEC was evaluated against 3-Way Replication using different performance matrices. The results have highlighted that the use of erasure encoding in network storage can provide the desired level of data availability at a smaller memory overhead when compared to replication