Reconfigurable middleware architectures for large scale sensor networks

Wireless sensor networks, in an effort to be energy efficient, typically lack the high-level abstractions of advanced programming languages. Though strong, the dichotomy between these two paradigms can be overcome. The SENSIX software framework, described in this dissertation, uniquely integrates constraint-dominated wireless sensor networks with the flexibility of object-oriented programming models, without violating the principles of either. Though these two computing paradigms are contradictory in many ways, SENSIX bridges them to yield a dynamic middleware abstraction unifying low-level resource-aware task reconfiguration and high-level object recomposition. Through the layered approach of SENSIX, the software developer creates a domain-specific sensing architecture by defining a customized task specification and utilizing object inheritance. In addition, SENSIX performs better at large scales (on the order of 1000 nodes or more) than other sensor network middleware which do not include such unified facilities for vertical integration

Application collaboration in ubiquitous computing environments

With the emergence of mobile and ubiquitous computing environments, there is a requirement to enable collaborative applications between components of these environments. As many of these applications (e.g. MP3 players) have been designed to operate in isolation, making them work together is often complicated by two, different aspects: firstly, a lack of protocols to enable the systems to bind to each other for interaction and, secondly, semantic and ontological differences in the meta-data describing the data to be shared. An abstraction termed a Self-Managed Cell has previously been proposed as an architectural pattern for building autonomous systems, that can represent entities ranging from individual devices to entire environments, and have described mechanisms that enable such cells to establish peer-to-peer bindings facilitating interaction at the system and management level. Semantic and ontological differences in the meta-data describing information to be shared between peers and application level aspects of interaction still exist, and prevent successful, autonomous application collaboration. Typical approaches to application collaboration, particularly in the database world, require the presence of a third-party administrator to manage ontological differences; such an approach is incompatible with interactive, autonomous systems. This dissertation presents a novel approach to automatic collection mapping suitable for deployment in autonomous, interacting systems. The approach facilitates the collaboration of SMC application-level data collections by identifying areas of conflict and using meta-data values associated with those collections to establish commonality. Music sharing and traditional “book” library catalogue matching applications, exploiting this mapping mechanism, have been developed to facilitate the sharing of data between peers. Protocols and abstractions are used to establish commonality and collaboration between the systems, and the mapping mechanism is used to enhance interoperability at the application level

Resource Management in a Peer to Peer Cloud Network for IoT

Software-Defined Internet of Things (SDIoT) is defined as merging heterogeneous objects in a form of interaction among physical and virtual entities. Large scale of data centers, heterogeneity issues and their interconnections have made the resource management a hard problem specially when there are different actors in cloud system with different needs. Resource management is a vital requirement to achieve robust networks specially with facing continuously increasing amount of heterogeneous resources and devices to the network. The goal of this paper is reviews to address IoT resource management issues in cloud computing services. We discuss the bottlenecks of cloud networks for IoT services such as mobility. We review Fog computing in IoT services to solve some of these issues. It provides a comprehensive literature review of around one hundred studies on resource management in Peer to Peer Cloud Networks and IoT. It is very important to find a robust design to efficiently manage and provision requests and available resources. We also reviewed different search methodologies to help clients find proper resources to answer their needs

Logic-centred architecture for ubiquitous health monitoring

One of the key points to maintain and boost research and development in the area of smart wearable systems (SWS) is the development of integrated architectures for intelligent services, as well as wearable systems and devices for health and wellness management. This paper presents such a generic architecture for\ud multiparametric, intelligent and ubiquitous wireless sensing platforms. It is a transparent, smartphone-based sensing framework\ud with customizable wireless interfaces and plug‘n’play capability to easily interconnect third party sensor devices. It caters to wireless\ud body, personal, and near-me area networks. A pivotal part of the platform is the integrated inference engine/runtime environment\ud that allows the mobile device to serve as a user-adaptable personal health assistant. The novelty of this system lays in a rapid visual\ud development and remote deployment model. The complementary visual InferenceEngineEditor that comes with the package enables\ud artificial intelligence specialists, alongside with medical experts, to build data processing models by assembling different components\ud and instantly deploying them (remotely) on patient mobile devices. In this paper, the new logic-centered software architecture for ubiquitous health monitoring applications is described, followed by a\ud discussion as to how it helps to shift focus from software and hardware development, to medical and health process-centered design of new SWS applications

Empirical investigation of decision tree ensembles for monitoring cardiac complications of diabetes

Cardiac complications of diabetes require continuous monitoring since they may lead to increased morbidity or sudden death of patients. In order to monitor clinical complications of diabetes using wearable sensors, a small set of features have to be identified and effective algorithms for their processing need to be investigated. This article focuses on detecting and monitoring cardiac autonomic neuropathy (CAN) in diabetes patients. The authors investigate and compare the effectiveness of classifiers based on the following decision trees: ADTree, J48, NBTree, RandomTree, REPTree, and SimpleCart. The authors perform a thorough study comparing these decision trees as well as several decision tree ensembles created by applying the following ensemble methods: AdaBoost, Bagging, Dagging, Decorate, Grading, MultiBoost, Stacking, and two multi-level combinations of AdaBoost and MultiBoost with Bagging for the processing of data from diabetes patients for pervasive health monitoring of CAN. This paper concentrates on the particular task of applying decision tree ensembles for the detection and monitoring of cardiac autonomic neuropathy using these features. Experimental outcomes presented here show that the authors' application of the decision tree ensembles for the detection and monitoring of CAN in diabetes patients achieved better performance parameters compared with the results obtained previously in the literature

Internet of Things Strategic Research Roadmap

Internet of Things (IoT) is an integrated part of Future Internet including existing and evolving Internet and network developments and could be conceptually defined as a dynamic global network infrastructure with self configuring capabilities based on standard and interoperable communication protocols where physical and virtual “things” have identities, physical attributes, and virtual personalities, use intelligent interfaces, and are seamlessly integrated into the information network