DISTRIBUTED DETECTION OF SAFE NODE CONFINE ATTACKS IN WIRELESS SENSOR NETWORKS

In hierarchical data aggregation a severe safety hazard is originated by node confine attacks, where a hacker achieves full control over a sensor node through direct physical access in wireless sensor networks and it makes a high risk of data privacy. For hierarchical data aggregation in wireless sensor networks a securing node capture attacks is proposed in this paper. Each cluster is headed by an aggregator and the aggregators are directly connected to sink as network is separated into number of clusters. To the selected set of nodes in first round of data aggregation, the aggregator by identifying the detecting nodes selects a set of nodes randomly and broadcast an exclusive value which contains their validation keys. To relocate the data when any node within the group needs it transfers portion of data to other nodes in that group this is encrypted by individual validation keys. Each receiving node decrypts, sums up the portions and transfers the encrypted data to the aggregator. The data with the shared secret key of the sink and forwards it to the sink as the aggregator aggregates and encrypts. In the second round of aggregation the set of nodes is reselected with new set of authentication keys. The proposed technique resolves the security threat of node capture attacks is demonstrated by simulation results

Securing Node Capture Attacks for Hierarchical Data Aggregation in Wireless Sensor Networks

Abstract -Serious security threat is originated by node capture attacks in hierarchical data aggregation where a hacker achieves full control over a sensor node through direct physical access in wireless sensor networks. It makes a high risk of data confidentiality. In this study, we propose a securing node capture attacks for hierarchical data aggregation in wireless sensor networks. Initially network is separated into number of clusters, each cluster is headed by an aggregator and the aggregators are directly connected to sink. The aggregator upon identifying the detecting nodes selects a set of nodes randomly and broadcast a unique value which contains their authentication keys, to the selected set of nodes in first round of data aggregation. When any node within the group needs to transfer the data, it transfers slices of data to other nodes in that group, encrypted by individual authentication keys. Each receiving node decrypts, sums up the slices and transfers the encrypted data to the aggregator. The aggregator aggregates and encrypts the data with the shared secret key of the sink and forwards it to the sink. The set of nodes is reselected with new set of authentication keys in the second round of aggregation. By simulation results, we demonstrate that the proposed technique resolves the security threat of node capture attacks

Active Topology Inference using Network Coding

Our goal is to infer the topology of a network when (i) we can send probes between sources and receivers at the edge of the network and (ii) intermediate nodes can perform simple network coding operations, i.e., additions. Our key intuition is that network coding introduces topology-dependent correlation in the observations at the receivers, which can be exploited to infer the topology. For undirected tree topologies, we design hierarchical clustering algorithms, building on our prior work. For directed acyclic graphs (DAGs), first we decompose the topology into a number of two-source, two-receiver (2-by-2) subnetwork components and then we merge these components to reconstruct the topology. Our approach for DAGs builds on prior work on tomography, and improves upon it by employing network coding to accurately distinguish among all different 2-by-2 components. We evaluate our algorithms through simulation of a number of realistic topologies and compare them to active tomographic techniques without network coding. We also make connections between our approach and alternatives, including passive inference, traceroute, and packet marking

Secure QoS-Aware Data Fusion to Prevent Node Misbehavior in Wireless Sensor Networks

Wireless Sensor Networks(WSNs) are composed of tiny devices with limited computation and energy capacities. Data fusion is an essential technique to achieve power efficiency in sensor nodes. Some nodes misbehave by increasing the defer time which obstruct the data fusion process. In this paper, an efficient Secured Quality of Service(QoS)-Aware Data Fusion(SQDF) for distributed Wireless Sensor Networks is proposed. The key feature of secure data fusion is to detect the misbehavior of a node… Expan

Wireless Network Coding: Opportunities and Challenges

Wireless networks suffer from a variety of unique problems such as low throughput, dead spots, and inadequate support for mobility. However, their characteristics such as the broadcast nature of the medium, spatial diversity, and significant data redundancy, provide opportunities for new design principles to address these problems. There has been recent interest in employing network coding in wireless networks. This paper explores the case for network coding as a unifying design paradigm for wireless networks, by describing how it addresses issues of throughput, reliability, mobility, and management. We also discuss the practical challenges facing the integration of such a design into the network stack

ROUTING TOPOLOGY RECOVERY FOR WIRELESS SENSOR NETWORKS

Liu, Rui Ph.D., Purdue University, December 2014. Routing Topology Recovery for Wireless Sensor Networks. Major Professor: Yao Liang

Active topology inference using network coding

Our goal, in this paper, is to infer the topology of a network when (i) we can send probes between sources and receivers at the edge of the network and (ii) intermediate nodes can perform simple network coding operations, i.e., additions. Our key intuition is that network coding introduces topology-dependent correlation in the observations at the receivers, which can be exploited to infer the topology. For undirected tree topologies, we design hierarchical clustering algorithms, building on our prior work in [24]. For directed acyclic graphs (DAGs), first we decompose the topology into a number of two source, two receiver (2-by-2) subnetwork components and then we merge these components to reconstruct the topology. Our approach for DAGs builds on prior work on tomography [36], and improves upon it by employing network coding to accurately distinguish among all different 2-by-2 components. We evaluate our algorithms through simulation of a number of realistic topologies and compare them to active tomographic techniques without network coding. We also make connections between our approach and other alternatives, including passive inference, traceroute, and packet marking

Wireless sensor networks using network coding for structural health monitoring

Wireless Sensor Networks (WSNs) have been deployed for the purpose of structural health monitoring (SHM) of civil engineering structures, e.g. bridges. SHM applications can potentially produce a high volume of sensing data, which consumes much transmission power and thus decreases the lifetime of the battery-run networks. We employ the network coding technique to improve the network efficiency and prolong its lifetime. By increasing the transmission power, we change the node connectivity and control the number of nodes that can overhear transmitted messages so as to hopefully realize the capacity gain by use of network coding. In Chapter 1, we present the background, to enable the reader to understand the need for SHM, advantages and drawbacks of WSNs and potential the application of network coding techniques has. In Chapter 2 we provide a review of related research explaining how it relates to our work, and why it is not fully applicable in our case. In Chapter 3, we propose to control transmission power as a means to adjust the number of nodes that can overhear a message transmission by a neighbouring node. However, too much of the overhearing by high power transmission consumes aggressively limited battery energy. We investigate the interplay between transmission power and network coding operations in Chapter 4. We show that our solution reduces the overall volume of data transfer, thus leading to significant energy savings and prolonged network lifetime. We present the mathematical analysis of our proposed algorithm. By simulation, we also study the trade-offs between overhearing and power consumption for the network coding scheme. In Chapter 5, we propose a methodology for the optimal placement of sensor nodes in linear network topologies (e.g., along the length of a bridge), that aims to minimise the link connectivity problems and maximise the lifetime of the network. Both simple packet relay and network coding are considered for the routing of the collected data packets towards two sink nodes positioned at both ends of the bridge. Our mathematical analysis, verified by simulation results, shows that the proposed methodology can lead to significant energy saving and prolong the lifetime of the underlying wireless sensor network. Chapter 6 is dedicated to the delay analysis. We analytically calculate the gains in terms of packet delay obtained by the use of network coding in linear multi-hop wireless sensor network topologies. Moreover, we calculate the exact packet delay (from the packet generation time to the time it is delivered to the sink nodes) as a function of the location of the source sensor node within the linear network. The derived packet delay distribution formulas have been verified by simulations and can provide a benchmark for the delay performance of linear sensor networks. In the Chapter 7, we propose an adaptive version of network coding based algorithm. In the case of packet loss, nodes do not necessary retransmit messages as they are able to internally decide how to cope with the situation. The goal of this algorithm is to reduce the power consumption, and decrease delays whenever it can. This algorithm achieves the delay similar to that of three-hop direct-connectivity version of the deterministic algorithm, and consumes power almost like one-hop direct-connectivity version of deterministic algorithm. In very poor channel conditions, this protocol outperforms the deterministic algorithm both in terms of delay and power consumption. In Chapter 8, we explain the direction of our future work. Particularly, we are interested in the application of combined TDMA/FDMA technique to our algorithm.Open Acces

Energy adaptive buildings:From sensor data to being aware of users

Energie besparen is fundamenteel voor het realiseren van een duurzame energievoorziening. Het besparen van energie draagt bij aan milieudoelstellingen, verbetert de zakelijke positie van landen, en levert werkgelegenheid. Er zijn tal van mogelijkheden voor het behalen van aanzienlijke energiebesparingen in gebouwen gezien individuen en bedrijven gebaat zijn bij energiebesparingen en daardoor zelf de verantwoordelijkheid nemen. Het is bewezen dat het gedrag van gebouwgebruikers een grote impact heeft op de verwarming en ventilatie van ruimtes, en op het energieverbruik van verlichting en huishoudelijke apparaten. Huidige gebouwautomatiseringssystemen kunnen niet overweg met veranderingen in het gedrag van gebruikers en zijn daardoor niet in staat om het energieverbruik terug te dringen met behoud van gebruikerscomfort. Mijn promotieonderzoek wordt gedreven door het doel om een dergelijk energy adaptive building te realiseren dat intelligent systemen aanstuurt en zich aanpast aan de gebruiker en gebruikersactiviteiten door deze te leren, terwijl energieverspilling wordt teruggedrongen. Mijn focus ligt op het ontwikkelen van een framework, beginnende bij de hardware infrastructuur voor sensoren en actuatoren, het verwerken en analyseren van de sensordata, en de nodige informatie over de omgeving en gebruikersactiviteiten verkrijgen zodat het gebouw aangestuurd kan worden. Onze oplossing kan 35% besparen op het totale energieverbruik van een gebouw. Als een succesverhaal, besparen de software systemen zelfs 80% op het energieverbruik van de verlichting in het restaurant van de Bernoulliborg. Wij commercialiseren de resultaten verkregen in ons onderzoek door het oprichten van de start-up SustainableBuildings, een spin-off bedrijf van onze universiteit, om onze oplossing aan te bieden aan kantoorgebouwen.Saving energy is the foundation for achieving a sustainable energy supply. Saving energy contributes to environmental objectives, improves the competitiveness of a country’s businesses, and boosts employment. There are numerous opportunities for achieving significant energy savings in buildings since individuals and businesses have an interest themselves in saving energy and will shoulder the responsibility for doing so.Occupant behaviour has shown to have large impact on space heating and cooling demand, energy consumption of lighting and appliances. Current building automation systems are unable to cope with changes caused by occupants’ behaviour and interaction with the environment, therefore they fail to reduce unnecessary energy consumption while preserving user comfort.My PhD research is driven by the aim of realising such energy adaptive buildings that facilitate intelligent control, that learn and adapt to the building users and their activities, while reducing energy waste. My particular focus is on a framework, going from the hardware infrastructure for sensing and actuating, to processing and analysing sensor data, providing necessary information about the environment and occupants’ activities for the system to produce adaptive control strategies, regulating the environment accordingly.Our solution can save 35% of energy for a single building. As a success story, the software system saves 80 percent on energy spent for lighting in the restaurant of the Bernoulliborg.We are commercialising the results of our research by creating the SustainableBuildings start-up, a spin-off from our university, to offer the solutions to non-residential buildings, first in the Netherlands, and later extending wider