Amorphous Placement and Informed Diffusion for Timely Monitoring by Autonomous, Resource-Constrained, Mobile Sensors

Personal communication devices are increasingly equipped with sensors for passive monitoring of encounters and surroundings. We envision the emergence of services that enable a community of mobile users carrying such resource-limited devices to query such information at remote locations in the field in which they collectively roam. One approach to implement such a service is directed placement and retrieval (DPR), whereby readings/queries about a specific location are routed to a node responsible for that location. In a mobile, potentially sparse setting, where end-to-end paths are unavailable, DPR is not an attractive solution as it would require the use of delay-tolerant (flooding-based store-carry-forward) routing of both readings and queries, which is inappropriate for applications with data freshness constraints, and which is incompatible with stringent device power/memory constraints. Alternatively, we propose the use of amorphous placement and retrieval (APR), in which routing and field monitoring are integrated through the use of a cache management scheme coupled with an informed exchange of cached samples to diffuse sensory data throughout the network, in such a way that a query answer is likely to be found close to the query origin. We argue that knowledge of the distribution of query targets could be used effectively by an informed cache management policy to maximize the utility of collective storage of all devices. Using a simple analytical model, we show that the use of informed cache management is particularly important when the mobility model results in a non-uniform distribution of users over the field. We present results from extensive simulations which show that in sparsely-connected networks, APR is more cost-effective than DPR, that it provides extra resilience to node failure and packet losses, and that its use of informed cache management yields superior performance

Amorphous Placement and Retrieval of Sensory Data in Sparse Mobile Ad-Hoc Networks

Abstract—Personal communication devices are increasingly being equipped with sensors that are able to passively collect information from their surroundings – information that could be stored in fairly small local caches. We envision a system in which users of such devices use their collective sensing, storage, and communication resources to query the state of (possibly remote) neighborhoods. The goal of such a system is to achieve the highest query success ratio using the least communication overhead (power). We show that the use of Data Centric Storage (DCS), or directed placement, is a viable approach for achieving this goal, but only when the underlying network is well connected. Alternatively, we propose, amorphous placement, in which sensory samples are cached locally and informed exchanges of cached samples is used to diffuse the sensory data throughout the whole network. In handling queries, the local cache is searched first for potential answers. If unsuccessful, the query is forwarded to one or more direct neighbors for answers. This technique leverages node mobility and caching capabilities to avoid the multi-hop communication overhead of directed placement. Using a simplified mobility model, we provide analytical lower and upper bounds on the ability of amorphous placement to achieve uniform field coverage in one and two dimensions. We show that combining informed shuffling of cached samples upon an encounter between two nodes, with the querying of direct neighbors could lead to significant performance improvements. For instance, under realistic mobility models, our simulation experiments show that amorphous placement achieves 10% to 40% better query answering ratio at a 25% to 35% savings in consumed power over directed placement.National Science Foundation (CNS Cybertrust 0524477, CNS NeTS 0520166, CNS ITR 0205294, EIA RI 0202067

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

A survey on cost-effective context-aware distribution of social data streams over energy-efficient data centres

Social media have emerged in the last decade as a viable and ubiquitous means of communication. The ease of user content generation within these platforms, e.g. check-in information, multimedia data, etc., along with the proliferation of Global Positioning System (GPS)-enabled, always-connected capture devices lead to data streams of unprecedented amount and a radical change in information sharing. Social data streams raise a variety of practical challenges, including derivation of real-time meaningful insights from effectively gathered social information, as well as a paradigm shift for content distribution with the leverage of contextual data associated with user preferences, geographical characteristics and devices in general. In this article we present a comprehensive survey that outlines the state-of-the-art situation and organizes challenges concerning social media streams and the infrastructure of the data centres supporting the efficient access to data streams in terms of content distribution, data diffusion, data replication, energy efficiency and network infrastructure. We systematize the existing literature and proceed to identify and analyse the main research points and industrial efforts in the area as far as modelling, simulation and performance evaluation are concerned

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Seamless connectivity:investigating implementation challenges of multibroker MQTT platform for smart environmental monitoring

Abstract. This thesis explores the performance and efficiency of MQTT-based infrastructure Internet of Things (IoT) sensor networks for smart environment. The study focuses on the impact of network latency and broker switching in distributed multi-broker MQTT platforms. The research involves three case studies: a cloud-based multi-broker deployment, a Local Area Network (LAN)-based multi-broker deployment, and a multi-layer LAN network-based multi-broker deployment. The research is guided by three objectives: quantifying and analyzing the latency of multi-broker MQTT platforms; investigating the benefits of distributed brokers for edge users; and assessing the impact of switching latency at applications. This thesis ultimately seeks to answer three key questions related to network and switching latency, the merits of distributed brokers, and the influence of switching latency on the reliability of end-user applications

Réseaux ad hoc : système d'adressage et méthodes d'accessibilité aux données

RÉSUMÉ Au cours de la dernière décennie, un nouveau type de réseaux sans fil a suscité un grand intérêt dans la communauté scientifique: ce sont les réseaux ad hoc. Ils existent sous la variante des réseaux mobiles ad hoc (MANET), et des réseaux de capteurs sans fil (RCSF). Les réseaux mobiles ad hoc sont constitués de noeuds mobiles qui communiquent les uns avec les autres sans l‘aide d‘une d'infrastructure centralisée. Les noeuds se déplacent librement et sont soumis à des déconnexions fréquentes en raison de l'instabilité des liens. Cela a pour conséquence de diminuer l'accessibilité aux données, et de modifier la façon dont les données sont partagées dans le réseau. Comparable aux réseaux MANET, un RCSF est composé d'un ensemble d'unités de traitements embarquées, appelées capteurs, communiquant via des liens sans fil et dont la fonction principale est la collecte de paramètres relatifs à l'environnement qui les entoure, telles que la température, la pression, ou la présence d'objets. Les RCSF diffèrent des MANET de par le déploiement à grande échelle des noeuds, et trouvent leur application dans diverses activités de la société, tels les processus industriels, les applications militaires de surveillance, l'observation et le suivi d'habitat, etc. Lorsqu‘un grand nombre de capteurs sont déployés avec des dispositifs d'actionnement appelés acteurs, le RCSF devient un réseau de capteurs et d‘acteurs sans fil (RCASF). Dans une telle situation, les capteurs collaborent pour la détection des phénomènes physiques et rapportent les données afférentes aux acteurs qui les traitent et initient les actions appropriées. De nombreux travaux dans les RCSF supposent l'existence d'adresses et d'infrastructures de routage pour valider leurs propositions. Cependant, l‘allocation d‘adresses et le routage des données liées aux événements détectés dans ces réseaux restent des défis entiers, en particulier à cause du nombre élevé de capteurs et des ressources limitées dont ils disposent. Dans cette thèse, nous abordons le problème de l'accessibilité aux données dans les MANET, et les mécanismes d‘adressage et de routage dans les RCSF de grande taille.----------ABSTRACT During the last decade, a new type of wireless networks has stirred up great interest within the scientific community: there are ad hoc networks. They exist as mobile ad hoc networks (MANET), and wireless sensor (WSN). The mobile ad hoc networks consist of mobile nodes that communicate with each other without using a centralized infrastructure. The nodes move freely and are subject to frequent disconnections due to links instability. This has the effect of reducing data accessibility, and change the way data are shared across the network. Similar MANET networks, a WSN consists of a set of embedded processing units called sensors that communicate with each other via wireless links. Their main function is the collection of parameters relating to the environment around them, such as temperature, pressure, motion, video, etc. WSNs differ from the MANETs due to the large scale deployment of nodes, and are expected to have many applications in various fields, such as industrial processes, military surveillance, observation and monitoring of habitat, etc. When a large number of sensors which are resource-impoverished nodes are deployed with powerful actuation devices, the WSN becomes a Wireless Sensor and Actor Network (WSAN). In such a situation, the collaborative operation of sensors enables the distributed sensing of a physical phenomenon, while actors collect and process sensor data to perform appropriate action. Numerous works in WSN assumes the existence of addresses and routing infrastructure to validate their proposals. However, assigning addresses and delivering detected events remains highly challenging, specifically due to the sheer number of nodes. In this thesis, we address the problem of data accessibility in MANET, and that of addressing and routing in large scale WSN. This involves techniques such as data caching and replication to prevent the deterioration of data accessibility. The addressing system in WSN includes a distributed address allocation scheme and a routing infrastructure for both actors and sensors. Moreover, with the birth of the multimedia sensors, the traffic may be mixed with time sensitive packets and reliability-demanding packets. For that purpose, we also address the problem of providing quality of service (QoS) in the routing infrastructure for WSN