Data Collection and Aggregation in Mobile Sensing

Nowadays, smartphones have become ubiquitous and are playing a critical role in key aspects of people\u27s daily life such as communication, entertainment and social activities. Most smartphones are equipped with multiple embedded sensors such as GPS (Global Positioning System), accelerometer, camera, etc, and have diverse sensing capacity. Moreover, the emergence of wearable devices also enhances the sensing capabilities of smartphones since most wearable devices can exchange sensory data with smartphones via network interfaces. Therefore, mobile sensing have led to numerous innovative applications in various fields including environmental monitoring, transportation, healthcare, safety and so on. While all these applications are based on two critical techniques in mobile sensing, which are data collection and data aggregation, respectively. Data collection is to collect all the sensory data in the network while data aggregation is any process in which information is gathered and expressed in a summary form such as SUM or AVERAGE. Obviously, the above two problems can be solved by simply collect all the sensory data in the whole network. But that will lead to huge communication cost. This dissertation is to reduce the huge communication cost in data collection and data aggregation in mobile sensing where the following two technical routes are applied. The first technical route is to use sampling techniques such as uniform sampling or Bernoulli sampling. In this way, an aggregation result with acceptable error can be can be calculate while only a small part of mobile phones need to submit their sensory data. The second technical rout is location-based sensing in which every mobile phone submits its geographical position and the mobile sensing platform will use the submitted positions to filter useless sensory data. The experiment results indicate the proposed methods have high performance

Modular Energy Efficient Protocols for Lower Layers of Wireless Sensor Networks

Wireless sensor networks (WSNs) emerged as one of the compelling research areas in recent years. It has produced promising solutions for several potential applications such as intrusion detection, target detection, industrial automation, environmental monitoring, surveillance and military systems, medical diagnosing systems, tactical systems, etc. WSNs consist of small size of sensor nodes that are disseminated in a targeted area to monitor the events for collecting the data of interest. Meanwhile, WSNs face many challenging problems such as high energy consumption, network scalability and mobility. These problems profoundly affect the lifetime of the network, limit the access to several WSN application areas, and the Quality of Service (QoS) provision parameters including throughput, latency, bandwidth, data buffering, resource constraints, data redundancy, and medium reliability. Although, there has been significant research conducted in WSNs over the last few years to maintain a high standard of communication, especially coverage, challenges of high power consumption, mobility and scalability to name a few. The major problem with WSNs at the low layers are the excessive energy consumption by the sensor’s transceiver. Other related challenges are mobility and scalability that limit the QoS provision. To handle these issues, novel modular energy efficient protocols are proposed for lower layers of WSNs. These modular based protocols improve the energy consumption, providing cross-layering support to handle mobility, scalability and data redundancy. In addition, there is a protocol that automates handling the idle listening process. Other protocols optimize data frame format for faster channel access, data frame transfer, managing acknowledgement time and retry transmission, check the capability of sensing the nature of environment to decide to use either active or passive mode that help save energy, determine shortest efficient path, packet generation rate, automatic active and sleep mode, smart queuing, data aggregation and dynamically selection of the cluster head node. All these features ensure the QoS provision and resolve many problems introduced by mobility and scalability for multiple application areas especially disaster recovery, hospital monitoring system, remotely handling the static and mobile objects and battlefield surveillance systems. Finally, modular energy efficient protocols are simulated, and results demonstrate the validity and compatibility of the proposed approaches for multiple WSNs application areas

Swarm intelligence techniques for optimization and management tasks insensor networks

The main contributions of this thesis are located in the domain of wireless sensor netorks. More in detail, we introduce energyaware algorithms and protocols in the context of the following topics: self-synchronized duty-cycling in networks with energy harvesting capabilities, distributed graph coloring and minimum energy broadcasting with realistic antennas. In the following, we review the research conducted in each case. We propose a self-synchronized duty-cycling mechanism for sensor networks. This mechanism is based on the working and resting phases of natural ant colonies, which show self-synchronized activity phases. The main goal of duty-cycling methods is to save energy by efficiently alternating between different states. In the case at hand, we considered two different states: the sleep state, where communications are not possible and energy consumption is low; and the active state, where communication result in a higher energy consumption. In order to test the model, we conducted an extensive experimentation with synchronous simulations on mobile networks and static networks, and also considering asynchronous networks. Later, we extended this work by assuming a broader point of view and including a comprehensive study of the parameters. In addition, thanks to a collaboration with the Technical University of Braunschweig, we were able to test our algorithm in the real sensor network simulator Shawn (http://shawn.sf.net). The second part of this thesis is devoted to the desynchronization of wireless sensor nodes and its application to the distributed graph coloring problem. In particular, our research is inspired by the calling behavior of Japanese tree frogs, whose males use their calls to attract females. Interestingly, as female frogs are only able to correctly localize the male frogs when their calls are not too close in time, groups of males that are located nearby each other desynchronize their calls. Based on a model of this behavior from the literature, we propose a novel algorithm with applications to the field of sensor networks. More in detail, we analyzed the ability of the algorithm to desynchronize neighboring nodes. Furthermore, we considered extensions of the original model, hereby improving its desynchronization capabilities.To illustrate the potential benefits of desynchronized networks, we then focused on distributed graph coloring. Later, we analyzed the algorithm more extensively and show its performance on a larger set of benchmark instances. The classical minimum energy broadcast (MEB) problem in wireless ad hoc networks, which is well-studied in the scientific literature, considers an antenna model that allows the adjustment of the transmission power to any desired real value from zero up to the maximum transmission power level. However, when specifically considering sensor networks, a look at the currently available hardware shows that this antenna model is not very realistic. In this work we re-formulate the MEB problem for an antenna model that is realistic for sensor networks. In this antenna model transmission power levels are chosen from a finite set of possible ones. A further contribution concerns the adaptation of an ant colony optimization algorithm --currently being the state of the art for the classical MEB problem-- to the more realistic problem version, the so-called minimum energy broadcast problem with realistic antennas (MEBRA). The obtained results show that the advantage of ant colony optimization over classical heuristics even grows when the number of possible transmission power levels decreases. Finally we build a distributed version of the algorithm, which also compares quite favorably against centralized heuristics from the literature.Las principles contribuciones de esta tesis se encuentran en el domino de las redes de sensores inalámbricas. Más en detalle, introducimos algoritmos y protocolos que intentan minimizar el consumo energético para los siguientes problemas: gestión autosincronizada de encendido y apagado de sensores con capacidad para obtener energía del ambiente, coloreado de grafos distribuido y broadcasting de consumo mínimo en entornos con antenas reales. En primer lugar, proponemos un sistema capaz de autosincronizar los ciclos de encendido y apagado de los nodos de una red de sensores. El mecanismo está basado en las fases de trabajo y reposo de las colonias de hormigas tal y como estas pueden observarse en la naturaleza, es decir, con fases de actividad autosincronizadas. El principal objectivo de este tipo de técnicas es ahorrar energía gracias a alternar estados de forma eficiente. En este caso en concreto, consideramos dos estados diferentes: el estado dormido, en el que los nodos no pueden comunicarse y el consumo energético es bajo; y el estado activo, en el que las comunicaciones propician un consumo energético elevado. Con el objetivo de probar el modelo, se ha llevado a cabo una extensa experimentación que incluye tanto simulaciones síncronas en redes móviles y estáticas, como simulaciones en redes asíncronas. Además, este trabajo se extendió asumiendo un punto de vista más amplio e incluyendo un detallado estudio de los parámetros del algoritmo. Finalmente, gracias a la colaboración con la Technical University of Braunschweig, tuvimos la oportunidad de probar el mecanismo en el simulador realista de redes de sensores, Shawn (http://shawn.sf.net). La segunda parte de esta tesis está dedicada a la desincronización de nodos en redes de sensores y a su aplicación al problema del coloreado de grafos de forma distribuida. En particular, nuestra investigación está inspirada por el canto de las ranas de árbol japonesas, cuyos machos utilizan su canto para atraer a las hembras. Resulta interesante que debido a que las hembras solo son capaces de localizar las ranas macho cuando sus cantos no están demasiado cerca en el tiempo, los grupos de machos que se hallan en una misma región desincronizan sus cantos. Basado en un modelo de este comportamiento que se encuentra en la literatura, proponemos un nuevo algoritmo con aplicaciones al campo de las redes de sensores. Más en detalle, analizamos la habilidad del algoritmo para desincronizar nodos vecinos. Además, consideramos extensiones del modelo original, mejorando su capacidad de desincronización. Para ilustrar los potenciales beneficios de las redes desincronizadas, nos centramos en el problema del coloreado de grafos distribuido que tiene relación con diferentes tareas habituales en redes de sensores. El clásico problema del broadcasting de consumo mínimo en redes ad hoc ha sido bien estudiado en la literatura. El problema considera un modelo de antena que permite transmitir a cualquier potencia elegida (hasta un máximo establecido por el dispositivo). Sin embargo, cuando se trabaja de forma específica con redes de sensores, un vistazo al hardware actualmente disponible muestra que este modelo de antena no es demasiado realista. En este trabajo reformulamos el problema para el modelo de antena más habitual en redes de sensores. En este modelo, los niveles de potencia de transmisión se eligen de un conjunto finito de posibilidades. La siguiente contribución consiste en en la adaptación de un algoritmo de optimización por colonias de hormigas a la versión más realista del problema, también conocida como broadcasting de consumo mínimo con antenas realistas. Los resultados obtenidos muestran que la ventaja de este método sobre heurísticas clásicas incluso crece cuando el número de posibles potencias de transmisión decrece. Además, se ha presentado una versión distribuida del algoritmo, que también se compara de forma bastante favorable contra las heurísticas centralizadas conocidas

Challenges and Solutions for Location-based Routing in Wireless Sensor Networks with Complex Network Topology

Complex Network Topologies (CNTs)–network holes and cuts–often occur in practical WSN deployments. Many researchers have acknowledged that CNTs adversely affect the performance of location-based routing and proposed various CNT- aware location-based routing protocols. However, although they aim to address practical issues caused by CNTs, many proposed protocols are either based on idealistic assumptions, require too much resources, or have poor performance. Additionally, proposed protocols are designed only for a single routing primitive–either unicast, multicast, or convergecast. However, as recent WSN applications require diverse traffic patterns, the need for an unified routing framework has ever increased. In this dissertation, we address these main weaknesses in the research on location- based routing. We first propose efficient algorithms for detecting and abstracting CNTs in the network. Using these algorithms, we present our CNT-aware location- based unicast routing protocol that achieves the guaranteed small path stretch with significantly reduced communication overhead. We then present our location-based multicast routing protocol that finds near optimal routing paths from a source node to multicast member nodes, with efficient mechanisms for controllable packet header size and energy-efficient recovery from packet losses. Our CNT-aware convergecast routing protocol improves the network lifetime by identifying network regions with concentrated network traffic and distributing the traffic by using the novel concept of virtual boundaries. Finally, we present the design and implementation details of our unified routing framework that seamlessly integrates proposed unicast, multicast, and convergecast routing protocols. Specifically, we discuss the issues regarding the implementation of our routing protocols on real hardware, and the design of the framework that significantly reduces the code and memory size to fit in a resource constrained sensor mote. We conclude with a proactive solution designed to cope with CNTs, where mobile nodes are used for “patching” CNTs to restore the network connectivity and to optimize the network performance

Influence Analysis towards Big Social Data

Large scale social data from online social networks, instant messaging applications, and wearable devices have seen an exponential growth in a number of users and activities recently. The rapid proliferation of social data provides rich information and infinite possibilities for us to understand and analyze the complex inherent mechanism which governs the evolution of the new technology age. Influence, as a natural product of information diffusion (or propagation), which represents the change in an individual’s thoughts, attitudes, and behaviors resulting from interaction with others, is one of the fundamental processes in social worlds. Therefore, influence analysis occupies a very prominent place in social related data analysis, theory, model, and algorithms. In this dissertation, we study the influence analysis under the scenario of big social data. Firstly, we investigate the uncertainty of influence relationship among the social network. A novel sampling scheme is proposed which enables the development of an efficient algorithm to measure uncertainty. Considering the practicality of neighborhood relationship in real social data, a framework is introduced to transform the uncertain networks into deterministic weight networks where the weight on edges can be measured as Jaccard-like index. Secondly, focusing on the dynamic of social data, a practical framework is proposed by only probing partial communities to explore the real changes of a social network data. Our probing framework minimizes the possible difference between the observed topology and the actual network through several representative communities. We also propose an algorithm that takes full advantage of our divide-and-conquer strategy which reduces the computational overhead. Thirdly, if let the number of users who are influenced be the depth of propagation and the area covered by influenced users be the breadth, most of the research results are only focused on the influence depth instead of the influence breadth. Timeliness, acceptance ratio, and breadth are three important factors that significantly affect the result of influence maximization in reality, but they are neglected by researchers in most of time. To fill the gap, a novel algorithm that incorporates time delay for timeliness, opportunistic selection for acceptance ratio, and broad diffusion for influence breadth has been investigated. In our model, the breadth of influence is measured by the number of covered communities, and the tradeoff between depth and breadth of influence could be balanced by a specific parameter. Furthermore, the problem of privacy preserved influence maximization in both physical location network and online social network was addressed. We merge both the sensed location information collected from cyber-physical world and relationship information gathered from online social network into a unified framework with a comprehensive model. Then we propose the resolution for influence maximization problem with an efficient algorithm. At the same time, a privacy-preserving mechanism are proposed to protect the cyber physical location and link information from the application aspect. Last but not least, to address the challenge of large-scale data, we take the lead in designing an efficient influence maximization framework based on two new models which incorporate the dynamism of networks with consideration of time constraint during the influence spreading process in practice. All proposed problems and models of influence analysis have been empirically studied and verified by different, large-scale, real-world social data in this dissertation

Designs for the Quality of Service Support in Low-Energy Wireless Sensor Network Protocols

A Wireless Sensor Network (WSN) consists of small, low cost, and low energy sensor nodes that cooperatively monitor physical quantities, control actuators, and perform data processing tasks. A network may consist of thousands of randomly deployed self-configurable nodes that operate autonomously to form a multihop topology. This Thesis focuses on Quality of Service (QoS) in low-energy WSNs that aim at several years operation time with small batteries. As a WSN may include both critical and non-critical control and monitoring applications, QoS is needed to make intelligent, content specific trade-offs between energy and network performance. The main research problem is defining and implementing QoS with constrained energy budget, processing power, communication bandwidth, and data and program memories. The problem is approached via protocol designs and algorithms. These are verified with simulations and with measurements in practical deployments. This Thesis defines QoS for WSNs with quantifiable metrics to allow measuring and managing the network performance. The definition is used as a basis for QoS routing protocol and Medium Access Control (MAC) schemes, comprising dynamic capacity allocation algorithm and QoS support layer. Dynamic capacity allocation is targeted at reservation based MACs, whereas the QoS support layer operates on contention based MACs. Instead of optimizing the protocols for a certain use case, the protocols allow configurable QoS based on application specific requirements. Finally, this Thesis designs sensor self-diagnostics and diagnostics analysis tool for verifying network performance. Compared to the related proposals on in-network sensor diagnostics, the diagnostics also detects performance problems and identifies reasons for the issues thus allowing the correction of problems. The results show that the developed protocols allow a clear trade-off between energy, latency, throughput, and reliability aspects of QoS while incurring a minimal overhead. The feasibility of results for extremely resource constrained WSNs is verified with the practical implementation with a prototype hardware platform having only few Million Instructions Per Second (MIPS) of processing power and less than a hundred kBs data and program memories. The results of this Thesis can be used in the WSN research, development, and implementation in general. The developed QoS definition, protocols, and diagnostics tools can be used separately or adapted to other applications and protocols

Agrégation de données dans les réseaux de capteurs sans fil

Wireless Sensor Networks (WSNs) have been regarded as an emerging and promis- ing field in both academia and industry. Currently, such networks are deployed due to their unique properties, such as self-organization and ease of deployment. How- ever, there are still some technical challenges needed to be addressed, such as energy and network capacity constraints. Data aggregation, as a fundamental solution, pro- cesses information at sensor level as a useful digest, and only transmits the digest to the sink. The energy and capacity consumptions are reduced due to less data packets transmission. As a key category of data aggregation, aggregation function, solving how to aggregate information at sensor level, is investigated in this thesis.We make four main contributions: firstly, we propose two new networking-oriented metrics to evaluate the performance of aggregation function: aggregation ratio and packet size coefficient. Aggregation ratio is used to measure the energy saving by data aggregation, and packet size coefficient allows to evaluate the network capac- ity change due to data aggregation. Using these metrics, we confirm that data ag- gregation saves energy and capacity whatever the routing or MAC protocol is used. Secondly, to reduce the impact of sensitive raw data, we propose a data-independent aggregation method which benefits from similar data evolution and achieves better re- covered fidelity. Thirdly, a property-independent aggregation function is proposed to adapt the dynamic data variations. Comparing to other functions, our proposal can fit the latest raw data better and achieve real adaptability without assumption about the application and the network topology. Finally, considering a given application, a tar- get accuracy, we classify the forecasting aggregation functions by their performances. The networking-oriented metrics are used to measure the function performance, and a Markov Decision Process is used to compute them. Dataset characterization and classification framework are also presented to guide researcher and engineer to select an appropriate functions under specific requirements.Depuis plusieurs années, les réseaux de capteurs sans fil sont considérés comme un domaine émergent et prometteur tant dans le milieu universitaire que dans l’industrie. De tels réseaux ont déjà été largement déployés en raison de leurs propriétés clés, telles que l’auto-organisation et leur autonomie en énergie. Cependant, il reste de nombreux défis scientifiques telles que la réduction de la consommation d’énergie sur des capteurs de plus en plus petits et la capacité du réseau tenant compte de liens à bande passante réduite. Selon nous, l’agrégation de données apparaît comme une so- lution pour ces deux défis, car au lieu d’envoyer une donnée, l’agrégation va traiter les informations collectées au niveau du capteur et produire une donnée agrégée qui sera effectivement transmise au puits. L’énergie et la capacité du réseau seront donc économisées car il y aura moins de transmissions de données. Le travail de cette thèse s’intéresse principalement aux fonctions d’agrégationNous faisons quatre contributions principales. Tout d’abord, nous proposons deux nouvelles métriques pour évaluer les performances des fonctions d’agrégations vue au niveau réseau : le taux d’agrégation et le facteur d’accroissement de la taille des paquets. Le taux d’agrégation est utilisé pour mesurer le gain de paquets non trans- mis grâce à l’agrégation tandis que le facteur d’accroissement de la taille des pa- quets permet d’évaluer la variation de la taille des paquets en fonction des politiques d’agrégation. Ces métriques permettent de quantifier l’apport de l’agrégation dans l’économie d’énergie et de la capacité utilisée en fonction du protocole de routage con- sidéré et de la couche MAC retenue. Deuxièmement, pour réduire l’impact des don- nées brutes collectées par les capteurs, nous proposons une méthode d’agrégation de données indépendante de la mesure physique et basée sur les tendances d’évolution des données. Nous montrons que cette méthode permet de faire une agrégation spa- tiale efficace tout en améliorant la fidélité des données agrégées. En troisième lieu, et parce que dans la plupart des travaux de la littérature, une hypothèse sur le com- portement de l’application et/ou la topologie du réseau est toujours sous-entendue, nous proposons une nouvelle fonction d’agrégation agnostique de l’application et des données devant être collectées. Cette fonction est capable de s’adapter aux données mesurées et à leurs évolutions dynamiques. Enfin, nous nous intéressons aux outilspour proposer une classification des fonctions d’agrégation. Autrement dit, consid- érant une application donnée et une précision cible, comment choisir les meilleures fonctions d’agrégations en termes de performances. Les métriques, que nous avons proposé, sont utilisées pour mesurer la performance de la fonction, et un processus de décision markovien est utilisé pour les mesurer. Comment caractériser un ensem- ble de données est également discuté. Une classification est proposée dans un cadre précis

A Fog Computing Architecture for Disaster Response Networks

In the aftermath of a disaster, the impacted communication infrastructure is unable to provide first responders with a reliable medium of communication. Delay tolerant networks that leverage mobility in the area have been proposed as a scalable solution that can be deployed quickly. Such disaster response networks (DRNs) typically have limited capacity due to frequent disconnections in the network, and under-perform when saturated with data. On the other hand, there is a large amount of data being produced and consumed due to the recent popularity of smartphones and the cloud computing paradigm. Fog Computing brings the cloud computing paradigm to the complex environments that DRNs operate in. The proposed architecture addresses the key challenges of ensuring high situational awareness and energy efficiency when such DRNs are saturated with large amounts of data. Situational awareness is increased by providing data reliably, and at a high temporal and spatial resolution. A waypoint placement algorithm places hardware in the disaster struck area such that the aggregate good-put is maximized. The Raven routing framework allows for risk-averse data delivery by allowing the user to control the variance of the packet delivery delay. The Pareto frontier between performance and energy consumption is discovered, and the DRN is made to operate at these Pareto optimal points. The FuzLoc distributed protocol enables mobile self-localization in indoor environments. The architecture has been evaluated in realistic scenarios involving deployments of multiple vehicles and devices