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A Service Approach for Architecting Application Independent Wireless Sensor Networks

The current sensor networks are assumed to be designed for specific applications, having data communication protocols strongly coupled to applications. The future sensor networks are envisioned as comprising heterogeneous devices assisting to a large range of applications. To achieve this goal, a new architecture approach is needed, having application specific features separated from the data communication protocol, while influencing its behavior. We propose a Web Services approach for the design of sensor network, in which sensor nodes are service providers and applications are clients of such services. Our main goal is to enable a flexible architecture in which sensor networks data can be accessed by users spread all over the world.

On the interplay of Internet of Things and Cloud Computing

The Internet of Things (IoT) is a novel paradigm relying on the interaction of smart objects (things) with each other and with physical and/or virtual resources through the Internet. Despite the recent advances that have made IoT a reality, there are several challenges to be addressed towards exploiting its full potential and promoting tangible benefits to society, environment, economy, and individual citizens. Recently, Cloud Computing has been advocated as a promising approach to tackle some of the existing challenges in IoT while leveraging its adoption and bringing new opportunities. With the combination of IoT and Cloud Computing, the cloud becomes an intermediate layer between smart objects and applications that make use of data and resources provided by these objects. On the one hand, IoT can benefit from the almost unlimited resources of Cloud Computing to implement management and composition of services related to smart objects and their provided data. On the other hand, the cloud can benefit from IoT by broadening its operation scope to deal with real-world objects. In spite of this synergy, the literature still lacks of a broad, comprehensive overview on what has been investigated on the integration of IoT and Cloud Computing and what are the open issues to be addressed in future research and development. The goal of this work is to fill this gap by systematically collecting and analyzing studies available in the literature aiming to: (i) obtain a comprehensive understanding on the integration of IoT and Cloud Computing paradigms; (ii) provide an overview of the current state of research on this topic; and (iii) identify important gaps in the existing approaches as well as promising research directions. To achieve this goal, a systematic mapping study was performed covering papers recently published in journals, conferences, and workshops, available at five relevant electronic databases. As a result, 35 studies were selected presenting strategies and solutions on how to integrate IoT and Cloud Computing as well as scenarios, research challenges, and opportunities in this context. Besides confirming the increasing interest on the integration of IoT and Cloud Computing, this paper reports the main outcomes of the performed systematic mapping by both presenting an overview of the state of the art on the investigated topic and shedding light on important challenges and potential directions to future research.

Efficient allocation of resources in multiple heterogeneous Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are useful for a wide range of applications, from different domains. Recently, new features and design trends have emerged in the WSN field, making those networks appealing not only to the scientific community but also to the industry. One such trend is the running different applications on heterogeneous sensor nodes deployed in multiple WSNs in order to better exploit the expensive physical network infrastructure. Another trend deals with the capability of accessing sensor generated data from the Web, fitting WSNs in novel paradigms of Internet of Things (IoT) and Web of Things (WoT). Using well-known and broadly accepted Web standards and protocols enables the interoperation of heterogeneous WSNs and the integration of their data with other Web resources, in order to provide the final user with value-added information and applications. Such emergent scenarios where multiple networks and applications interoperate to meet high level requirements of the user will pose several changes in the design and execution of WSN systems. One of these challenges regards the fact that applications will probably compete for the resources offered by the underlying sensor nodes through the Web. Thus, it is crucial to design mechanisms that effectively and dynamically coordinate the sharing of the available resources to optimize resource utilization while meeting application requirements. However, it is likely that Quality of Service (QoS) requirements of different applications cannot be simultaneously met, while efficiently sharing the scarce networks resources, thus bringing the need of managing an inherent tradeoff. In this paper, we argue that a middleware platform is required to manage heterogeneous WSNs and efficiently share their resources while satisfying user needs in the emergent scenarios of WoT. Such middleware should provide several services to control running application as well as to distribute and coordinate nodes in the execution of submitted sensing tasks in an energy-efficient and QoS-enabled way. As part of the middleware provided services we present the Resource Allocation in Heterogeneous WSNs (SACHSEN) algorithm. SACHSEN is a new resource allocation heuristic for systems composed of heterogeneous WSNs that effectively deals with the tradeoff between possibly conflicting QoS requirements and exploits heterogeneity of multiple WSNs. The proposed algorithm is designed to effectively exploit the heterogeneity of WSNs.The proposed algorithm is designed to enable running multiple applications simultaneously.SACHSEN is implemented as a functional component of an existent middleware.

Intrusion Detection System for Wireless Sensor Networks Using Danger Theory Immune-Inspired Techniques

An IDS framework inspired in the Human Immune System to be applied in the wireless sensor network context is proposed. It uses an improved decentralized and customized version of the Dendritic Cell Algorithm, which allows nodes to monitor their neighborhood and collaborate to identify an intruder. The work was implemented and tested both in simulation and in real sensor platform scenarios, comparing them to each other and was also compared to a Negative Selection Theory implementation in order to demonstrate its efficiency in detecting a denial-of-sleep attack and in energy consumption. Results demonstrated the success of the proposal.

Reflective middleware for wireless sensor networks

Wireless Sensor Networks (WSNs) are distributed systems whose main goal is to collect and deliver data to applications. This paper proposes a reflective, service-oriented middleware for WSN. The middleware provides an abstraction layer between applications and the underlying network infrastructure and it also keeps the balance between application QoS requirements and the network lifetime. It monitors both network and application execution states, performing a network adaptation whenever it is needed. Simulation results show that the network residual energy can be increased in more than 100% when adopting an adaptation strategy, while the application QoS requirement is respected.

An efficient heuristic for selecting active nodes in wireless sensor networks

Energy saving is a paramount concern in wireless sensor networks (WSNs). A strategy for energy saving is to cleverly manage the duty cycle of sensors, by dynamically activating different sets of sensors while non-active nodes are kept in a power save mode. We propose a simple and efficient approach for selecting active nodes in WSNs. Our primary goal is to maximize residual energy and application relevance of selected nodes to extend the network lifetime while meeting application-specific QoS requirements. We formalize the problem of node selection as a knapsack problem and adopt a greedy heuristic for solving it. An environmental monitoring application is chosen to derive some specific requirements. Analyses and simulations were performed and the impact of various parameters on the process of node selection was investigated. Results show that our approach outperforms a naive scheme for node selection, achieving large energy savings while preserving QoS requirements.

A flexible middleware system for wireless sensor networks

The current wireless sensor networks (WSN) are assumed to be designed for specific applications, having data communication protocols strongly coupled to applications. The future WSNs are envisioned as comprising of heterogeneous devices assisting to a large range of applications. To achieve this goal, a flexible middleware layer is needed, separating application specific features from the data communication protocol, while allowing applications to influence the WSN behavior for energy efficiency. We propose a service-based middleware system for WSNs. In our proposal, sensor nodes are service providers and applications are clients of such services. Our main goal is to enable an interoperability layer among applications and sensor networks, among different sensors in a WSN and eventually among different WSN spread all over the world.

WSNs clustering based on semantic neighborhood relationships

We propose a semantic clustering model based on a fuzzy inference system to find out the semantic neighborhood relationships in wireless sensor networks in order to both reduce energy consumption and improve the data accuracy. As a case study we describe a structural health monitoring application which was used to illustrate and assess the proposed model. We conduct experiments in order to evaluate the proposal in two different scenarios of damage with different data aggregation methods. We also compared our proposal, using the same data set, with a deterministic clustering method and with the LEACH algorithm. The results indicate that our approach is an energy-efficient clustering method for WSNs, outperforming both the deterministic clustering and LEACH algorithms in about 70% and 47% of energy savings respectively. The energy saving comes from the fact that we have a more efficient in-network data aggregation process since by exploiting the semantic relation between sensor nodes we can potentially aggregate more similar data and consequently, decrease the data redundancy (thus minimizing transmissions). Nodes that are semantically unrelated can operate in low-duty cycle, further reducing the energy consumption. Moreover, our proposal has the potential to improve the data accuracy provided for the application where accuracy is a QoS requirement in typical WSN applications.

SUTIL - Network selection based on utility function and integer linear programming

This work presents SUTIL, a mechanism for network selection in the context of next generation networks (NGN). SUTIL selection mechanism prioritizes networks with higher relevance to the application and lower energy consumption and it enables full and seamless connectivity to mobile user devices and applications. Consequently, SUTIL contributes to realize the vision of ubiquitous computing, in which services, devices, and sensor-enriched environments interact anytime, anywhere to accomplish human designed tasks. The provided solution is based on utility function and integer linear programming and it aims at: (i) maximizing the user satisfaction while meeting application QoS and (ii) minimizing the energy consumption of devices when connecting to a target network. The solution is global since it considers for a given base station all devices that are simultaneously candidate for handoff. Simulation results showed the benefits of SUTIL usage in NGN environments.

A semantic middleware for autonomic wireless sensor networks

In this paper we present a proposal that combines the benefits of autonomic and semantic sensor networks to build a semantic middleware for autonomic wireless sensor networks. The key feature of the proposed middleware is a rule-based reasoning engine based on ontology and fuzzy logic. We also propose a semantic-aware topology control based on computing semantic neighborhoods relationships. The middleware was tailored to provide support for Structural Health Monitoring applications. However, it has a flexible architecture and it can be extensible to several other application domains such as ambient intelligence, habitat monitoring and fire detection. We use the oil platform structural health monitoring domain as a case study. The paper presents the middleware architecture and the proposed ontologies.