Mobile Edge Computing Empowers Internet of Things

In this paper, we propose a Mobile Edge Internet of Things (MEIoT) architecture by leveraging the fiber-wireless access technology, the cloudlet concept, and the software defined networking framework. The MEIoT architecture brings computing and storage resources close to Internet of Things (IoT) devices in order to speed up IoT data sharing and analytics. Specifically, the IoT devices (belonging to the same user) are associated to a specific proxy Virtual Machine (VM) in the nearby cloudlet. The proxy VM stores and analyzes the IoT data (generated by its IoT devices) in real-time. Moreover, we introduce the semantic and social IoT technology in the context of MEIoT to solve the interoperability and inefficient access control problem in the IoT system. In addition, we propose two dynamic proxy VM migration methods to minimize the end-to-end delay between proxy VMs and their IoT devices and to minimize the total on-grid energy consumption of the cloudlets, respectively. Performance of the proposed methods are validated via extensive simulations

City Data Fusion: Sensor Data Fusion in the Internet of Things

Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. We then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. Our main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed Systems and Technologies (IJDST), 201

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

The Fog Makes Sense: Enabling Social Sensing Services With Limited Internet Connectivity

Social sensing services use humans as sensor carriers, sensor operators and sensors themselves in order to provide situation-awareness to applications. This promises to provide a multitude of benefits to the users, for example in the management of natural disasters or in community empowerment. However, current social sensing services depend on Internet connectivity since the services are deployed on central Cloud platforms. In many circumstances, Internet connectivity is constrained, for instance when a natural disaster causes Internet outages or when people do not have Internet access due to economical reasons. In this paper, we propose the emerging Fog Computing infrastructure to become a key-enabler of social sensing services in situations of constrained Internet connectivity. To this end, we develop a generic architecture and API of Fog-enabled social sensing services. We exemplify the usage of the proposed social sensing architecture on a number of concrete use cases from two different scenarios.Comment: Ruben Mayer, Harshit Gupta, Enrique Saurez, and Umakishore Ramachandran. 2017. The Fog Makes Sense: Enabling Social Sensing Services With Limited Internet Connectivity. In Proceedings of The 2nd International Workshop on Social Sensing, Pittsburgh, PA, USA, April 21 2017 (SocialSens'17), 6 page

Ensuring Cyber-Security in Smart Railway Surveillance with SHIELD

Modern railways feature increasingly complex embedded computing systems for surveillance, that are moving towards fully wireless smart-sensors. Those systems are aimed at monitoring system status from a physical-security viewpoint, in order to detect intrusions and other environmental anomalies. However, the same systems used for physical-security surveillance are vulnerable to cyber-security threats, since they feature distributed hardware and software architectures often interconnected by ‘open networks’, like wireless channels and the Internet. In this paper, we show how the integrated approach to Security, Privacy and Dependability (SPD) in embedded systems provided by the SHIELD framework (developed within the EU funded pSHIELD and nSHIELD research projects) can be applied to railway surveillance systems in order to measure and improve their SPD level. SHIELD implements a layered architecture (node, network, middleware and overlay) and orchestrates SPD mechanisms based on ontology models, appropriate metrics and composability. The results of prototypical application to a real-world demonstrator show the effectiveness of SHIELD and justify its practical applicability in industrial settings

Context Aware Computing for The Internet of Things: A Survey

As we are moving towards the Internet of Things (IoT), the number of sensors deployed around the world is growing at a rapid pace. Market research has shown a significant growth of sensor deployments over the past decade and has predicted a significant increment of the growth rate in the future. These sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data we need to understand it. Collection, modelling, reasoning, and distribution of context in relation to sensor data plays critical role in this challenge. Context-aware computing has proven to be successful in understanding sensor data. In this paper, we survey context awareness from an IoT perspective. We present the necessary background by introducing the IoT paradigm and context-aware fundamentals at the beginning. Then we provide an in-depth analysis of context life cycle. We evaluate a subset of projects (50) which represent the majority of research and commercial solutions proposed in the field of context-aware computing conducted over the last decade (2001-2011) based on our own taxonomy. Finally, based on our evaluation, we highlight the lessons to be learnt from the past and some possible directions for future research. The survey addresses a broad range of techniques, methods, models, functionalities, systems, applications, and middleware solutions related to context awareness and IoT. Our goal is not only to analyse, compare and consolidate past research work but also to appreciate their findings and discuss their applicability towards the IoT.Comment: IEEE Communications Surveys & Tutorials Journal, 201

Semantic In-Network Complex Event Processing for an Energy Efficient Wireless Sensor Network

Wireless Sensor Networks (WSNs) consist of spatially distributed sensor nodes that perform monitoring tasks in a region and the gateway nodes that provide the acquired sensor data to the end user. With advances in the WSN technology, it has now become possible to have different types of sensor nodes within a region to monitor the environment. This provides the flexibility to monitor the environment in a more extensive manner than before. Sensor nodes are severely constrained devices with very limited battery sources and their resource scarcity remains a challenge. In traditional WSNs, the sensor nodes are used only for capturing data that is analysed later in more powerful gateway nodes. This continuous communication of data between sensor nodes and gateway nodes wastes energy at the sensor nodes, and consequently, the overall network lifetime is greatly reduced. Existing approaches to reduce energy consumption by processing at the sensor node level only work for homogeneous networks. This thesis presents a sensor node architecture for heterogeneous WSNs, called SEPSen, where data is processed locally at the sensor node level to reduce energy consumption. We use ontology fragments at the sensor nodes to enable data exchange between heterogeneous sensor nodes within the WSN. We employ a rule engine based on a pattern matching algorithm for filtering events at the sensor node level. The event routing towards the gateway nodes is performed using a context-aware routing scheme that takes both the energy consumption and the heterogeneity of the sensor nodes into account. As a proof of concept, we present a prototypical implementation of the SEPSen design in a simulation environment. By providing semantic support, in-network data processing capabilities and context-aware routing in SEPSen, the sensor nodes (1) communicate with each other despite their different sensor types, (2) filter events at the their own level to conserve the limited sensor node energy resources and (3) share the nodes' knowledge bases for collaboration between the sensor nodes using node-centric context-awareness in changing conditions. The SEPSen prototype has been evaluated based on a test case for water quality management. The results from the experiments show that the energy saved in SEPSen reaches almost 50% by processing events at the sensor node level and the overall network lifetime is increased by at least a factor of two against the shortest-path-first (Min-Hop) routing approach