Abstract: Clustering is a crucial network design approach to enable large-scale wireless sensor networks (WSNs) deployments. A large variety of clustering approaches has been presented focusing on different performance metrics. Such protocols usually aim at minimizing communication overhead, evenly distributing roles among the participating nodes, as well as controlling the network topology. Simulations on such protocols are performed using theoretical models that are based on unrealistic assumptions like the unit disk graph communication model, ideal wireless communication channels and perfect energy consumption estimations. With these assumptions taken for granted, theoretical models claim various performance milestones that cannot be achieved in realistic conditions. In this paper, we design a new clustering protocol that adapts to the changes in the environment and the needs and goals of the user applications. We address the issues that hinder its performance due to the real environment conditions and provide a deployable protocol. The implementation, integration and experimentation of this new protocol and it's optimizations, were performed using the \textsf{WISEBED} framework. We apply our protocol in multiple indoors wireless sensor testbeds with multiple experimental scenarios to showcase scalability and trade-offs between network properties and configurable protocol parameters. By analysis of the real world experimental output, we present results that depict a more realistic view of the clustering problem, regarding adapting to environmental conditions and the quality of topology control. Our study clearly demonstrates the applicability of our approach and the benefits it offers to both research \& development communities.
Abstract: Smart cities are becoming a vibrant application domain for a number of science fields. As such, service providers and stakeholders are beginning to integrate co-creation aspects into current implementations to shape the future smart city solutions. In this context, holistic solutions are required to test such aspects in real city-scale IoT deployments, considering the complex city ecosystems. In this work, we discuss OrganiCity˘s implementation of an Experimentation-as-a-Service framework, presenting a toolset that allows developing, deploying and evaluating smart city solutions in a one-stop shop manner. This is the first time such an integrated toolset is offered in the context of a large-scale IoT infrastructure, which spans across multiple European cities. We discuss the design and implementation of the toolset, presenting our view on what Experimentation-as-a-Service should provide, and how it is implemented. We present initial feedback from 25 experimenter teams that have utilized this toolset in the OrganiCity project, along with a discussion on two detailed actual use cases to validate our approach. Learnings from all experiments are discussed as well as architectural considerations for platform scaling. Our feedback from experimenters indicates that Experimentation-as-a-Service is a viable and useful approach.
Abstract: Divisible load scenarios occur in modern media server applications since most multimedia applications typically require access to continuous and discrete data. A high performance Continuous Media (CM) server greatly depends on the ability of its disk IO subsystem to serve both types of workloads efficiently. Disk scheduling algorithms for mixed media workloads, although they play a central role in this task, have been overlooked by related research efforts. These algorithms must satisfy several stringent performance goals, such as achieving low response time and ensuring fairness, for the discrete-data workload, while at the same time guaranteeing the uninterrupted delivery of continuous data, for the continuous-data workload. The focus of this paper is on disk scheduling algorithms for mixed media workloads in a multimedia information server. We propose novel algorithms, present a taxonomy of relevant algorithms, and study their performance through experimentation. Our results show that our algorithms offer drastic improvements in discrete request average response times, are fair, serve continuous requests without interruptions, and that the disk technology trends are such that the expected performance benefits can be even greater in the future.
Abstract: In recent years, the evolution of urban environments, jointly with the progress of the Information and Communication sector, have enabled the rapid adoption of new solutions that contribute to the growth in popularity of Smart Cities. Currently, the majority of the world population
lives in cities encouraging different stakeholders within these innovative ecosystems to seek new solutions guaranteeing the sustainability and efficiency of such complex environments. In this work, it is discussed how the experimentation with IoT technologies and other data sources form the cities can be utilized to co-create in the OrganiCity project, where key actors like citizens, researchers and other stakeholders shape smart city services and applications in a collaborative fashion. Furthermore,
a novel architecture is proposed that enables this organic growth of the future cities, facilitating the experimentation that tailors the adoption of new technologies and services for a better quality of life, as well as agile and dynamic mechanisms for managing cities. In this work, the different components and enablers of the OrganiCity platform are presented and discussed in detail and include, among others, a portal to manage the experiment life cycle, an Urban Data Observatory to explore data assets,
and an annotations component to indicate quality of data, with a particular focus on the city-scale opportunistic data collection service operating as an alternative to traditional communications.
Abstract: Evaluating target tracking protocols for wireless sensor networks that can localize multiple mobile devices, can be a very challenging task. Such protocols usually aim at minimizing communication overhead, data processing for the participating nodes, as well as delivering adequate tracking information of the mobile targets in a timely manner. Simulations on such protocols are performed using theoretical models that are based on unrealistic assumptions like the unit disk graph communication model, ideal network localization and perfect distance estimations. With these assumptions taken for granted, theoretical models claim various performance milestones that cannot be achieved in realistic conditions. In this paper we design a new localization protocol, where mobile assets can be tracked passively via software agents. We address the issues that hinder its performance due to the real environment conditions and provide a deployable protocol. The implementation, integration and experimentation of this new protocol and it's optimizations, were performed using the WISEBED framework. We apply our protocol in multiple indoors wireless sensor testbeds with multiple experimental scenarios to showcase scalability and trade-offs between network properties and configurable protocol parameters. By analysis of the real world experimental output, we present results that depict a more realistic view of the target tracking problem, regarding power consumption and the quality of tracking information. Finally we also conduct some very focused simulations to assess the scalability of our protocol in very large networks and multiple mobile assets.
Abstract: In this paper, we discuss key findings, technological challenges and socioeconomic opportunities in Smart City era. Most of the conclusions were gathered during SmartSantander project, an EU project that is developing a city-scale testbed for IoT and Future Internet experimentation, providing an integrated framework for implementing Smart City services.
Abstract: We address the issue of measuring distribution fairness in Internet-scale networks. This problem has several interesting instances encountered in different applications, ranging from assessing the distribution of load between network nodes for load balancing purposes, to measuring node utilization for optimal resource exploitation, and to guiding autonomous decisions of nodes in networks built with market-based economic principles. Although some metrics have been proposed, particularly for assessing load balancing algorithms, they fall short. We first study the appropriateness of various known and previously proposed statistical metrics for measuring distribution fairness. We put forward a number of required characteristics for appropriate metrics. We propose and comparatively study the appropriateness of the Gini coefficient (G) for this task. Our study reveals as most appropriate the metrics of G, the fairness index (FI), and the coefficient of variation (CV) in this order. Second, we develop six distributed sampling algorithms to estimate metrics online efficiently, accurately, and scalably. One of these algorithms (2-PRWS) is based on two effective optimizations of a basic algorithm, and the other two (the sequential sampling algorithm, LBS-HL, and the clustered sampling one, EBSS) are novel, developed especially to estimate G. Third, we show how these metrics, and especially G, can be readily utilized online by higher-level algorithms, which can now know when to best intervene to correct unfair distributions (in particular, load imbalances). We conclude with a comprehensive experimentation which comparatively evaluates both the various proposed estimation algorithms and the three most appropriate metrics (G, CV, andFI). Specifically, the evaluation quantifies the efficiency (in terms of number of the messages and a latency indicator), precision, and accuracy achieved by the proposed algorithms when estimating the competing fairness metrics. The central conclusion is that the proposed metric, G, can be estimated with a small number of messages and latency, regardless of the skew of the underlying distribution.
Abstract: We discuss key findings and technological challenges related to SmartSantander, an EU project that is developing a city-scale experimental facility for Internet of Things and Future Internet experimentation. The main goal of the project is to design and construct a city scale lab for experimentation and provide an integrated framework for implementing Smart City services.
Abstract: A number of infrastructures are being deployed for Future Internet experimentation purposes, providing access to large-scale IoT resources to researchers and industry. SmartSantander is among the largest ones, deployed at the city center of Santander. We discuss SmartSantander˘s augmentation using smartphones provided by volunteers, in order to increase sensing resources and ubiquity. Our system allows developers to write code for Android and automatically deploy their experiments to Android devices, alongside the SmartSantander platform. Initial results produced by experiments with a small number of volunteers show that the system provides meaningful extensions to the existing platform.
Abstract: A number of Future Internet testbeds are being deployed around the world for research experimentation and development. SmartSantander
is an infrastructure of massive scale deployed inside a city centre. We argue that utilising the concept of participatory sensing can augment the functionality and potential use-cases of such a system and be beneficiary in a number of scenarios. We discuss
the concept of extending SmartSantander with participatory sensing through the use of volunteers˘ smartphones. We report on our design and implementation, which allows for developers to write
their code for Android devices and then deploy and execute on the devices automatically through our system. We have tested our implementation in a number of scenarios in two cities with the help
of volunteers with promising results; the data collected enhance the ones by fixed infrastructure both quantitatively and qualitatively across the cities, while also engaging citizens more directly.
Abstract: Although we have reached new levels in smart city installations and systems, efforts so far have focused on providing diverse sources of data to smart city services consumers while neglecting to provide ways to simplify making good use of them. In this context, one first step that will bring added value to smart cities is knowledge creation in smart cities through anomaly detection and data annotation, supported in both an automated and a crowdsourced manner. We present here LearningCity, our solution that has been validated over an existing smart city deployment in Santander, and the OrganiCity experimentation-as-a-service ecosystem. We discuss key challenges along with characteristic use cases, and report on our design and implementation, together with some preliminary results derived from combining large smart city datasets with machine learning.
Abstract: The adoption of technologies like the IoT in urban environments, together with the intensive use of smartphones, is driving transformation towards smart cities. Under this perspective, Experimentation-as-a-Service within OrganiCity aims to create an experimental facility with technologies, services, and applications that simplify innovation within urban ecosystems. We discuss here tools that facilitate experimentation, implementing ways to organize, execute, and administer experimentation campaigns in a smart city context. We discuss the benefits of our framework, presenting some preliminary results. This is the first time such tools are paired with large-scale smart city infrastructures, enabling both city-scale experimentation and cross-site experimentation.
Abstract: We briefly present the design and architecture of a system that aims to simplify the process of organizing, executing and administering crowdsensing campaigns in a smart city context over smartphones volunteered by citizens. We built our system on top of an Android app substrate on the end-user level, which enables us to utilize smartphone resources. Our system allows researchers and other developers to manage and distribute their “mini” smart city applications, gather data and publish their results through the Organicity smart city platform. We believe this is the first time such a tool is paired with a large scale IoT
infrastructure, to enable truly city-scale IoT and smart city experimentation.
Abstract: We present the NanoPeers architecture paradigm, a
peer-to-peer network of lightweight devices, lacking all or
most of the capabilities of their computer-world counterparts.
We identify the problems arising when we apply current
routing and searching methods to this nano-world, and
present some initial solutions, using a case study of a sensor
network instance; Smart Dust. Furthermore, we propose
the P2P Worlds framework as a hybrid P2P architecture
paradigm, consisting of cooperating layers of P2P
networks, populated by computing entities with escalating
capabilities. Our position is that (i) experience gained
through research and experimentation in the field of P2P
computing, can be indispensable when moving down the
stair of computing capabilities, and that (ii) the proposed
framework can be the basis of numerous real-world applications,
opening up several challenging research problems.
Abstract: We consider the conflicting problems of ensuring data-access load balancing and efficiently processing range queries on peer-to-peer data networks maintained over Distributed Hash Tables (DHTs). Placing consecutive data values in neighboring peers is frequently used in DHTs since it accelerates range query processing. However, such a placement is highly susceptible to load imbalances, which are preferably handled by replicating data (since replication also introduces fault tolerance benefits). In this paper, we present HotRoD, a DHT-based architecture that deals effectively with this combined problem through the use of a novel locality-preserving hash function, and a tunable data replication mechanism which allows trading off replication costs for fair load distribution. Our detailed experimentation study shows strong gains in both range query processing efficiency and data-access load balancing, with low replication overhead. To our knowledge, this is the first work that concurrently addresses the two conflicting problems using data replication.
Abstract: We consider the conflicting problems of ensuring data-access load balancing and efficiently processing range queries on peer-to-peer data networks maintained over Distributed Hash Tables (DHTs). Placing consecutive data values in neighboring peers is frequently used in DHTs since it accelerates range query processing. However, such a placement is highly susceptible to load imbalances, which are preferably handled by replicating data (since replication also introduces fault tolerance benefits). In this paper, we present HotRoD, a DHT-based architecture that deals effectively with this combined problem through the use of a novel locality-preserving hash function, and a tunable data replication mechanism which allows trading off replication costs for fair load distribution. Our detailed experimentation study shows strong gains in both range query processing efficiency and data-access load balancing, with low replication overhead. To our knowledge, this is the first work that concurrently addresses the two conflicting problems using data replication.
Abstract: Recent activity in the field of Internet-of-Things experimentation has focused on the federation of discrete testbeds, thus placing less effort in the integration of other related technologies, such as smartphones; also, while it is gradually moving to more application-oriented paths, such as urban settings, it has not dealt in large with applications having social networking features. We argue here that current IoT infrastructure, testbeds and related software technologies should be used in such a context, capturing real-world human mobility and social networking interactions, for use in evaluating and fine-tuning realistic mobility models and designing human-centric applications. We discuss a system for producing traces for a new generation of human-centric applications, utilizing technologies such as Bluetooth and focusing on human interactions. We describe the architecture for this system and the respective implementation details presenting two distinct deployments; one in an office environment and another in an exhibition/conference event (FET'11, The European Future Technologies Conference and Exhibition) with 103 active participants combined, thus covering two popular scenarios for human centric applications. Our system provides online, almost real-time, feedback and statistics and its implementation allows for rapid and robust deployment, utilizing mainstream technologies and components.
