A Component-based Approach for Service Distribution in Sensor Networks

International audienceThe increasing number of distributed applications over Wireless Sensor Networks (WSNs) in ubiquitous environments raises the need for high-level mechanisms to distribute sensor services and integrate them in modern IT systems. Existing work in this area mostly focuses on low-level networking issues, and fails to provide high-level and off-the-shelf programming abstractions for this purpose. In this paper, we therefore consider WSN programming models and service distribution as two interrelated factors and we present a new component-based abstraction for integrating WSNs within existing IT systems. Our approach emphasizes on reifying distribution strategies at the software architecture level, thus allowing remote invocation of component services, and facilitating interoperability of sensor services with the Internet through Web service-enabled components. The latter is efficiently provided by incorporating the REST architectural style—emphasizing on abstraction of high-level services as resources—to our component-based framework. The preliminary evaluation results show that the proposed framework has an acceptable memory overhead on a TelosB sensor platform

Deep Learning for Network Traffic Monitoring and Analysis (NTMA): A Survey

Modern communication systems and networks, e.g., Internet of Things (IoT) and cellular networks, generate a massive and heterogeneous amount of traffic data. In such networks, the traditional network management techniques for monitoring and data analytics face some challenges and issues, e.g., accuracy, and effective processing of big data in a real-time fashion. Moreover, the pattern of network traffic, especially in cellular networks, shows very complex behavior because of various factors, such as device mobility and network heterogeneity. Deep learning has been efficiently employed to facilitate analytics and knowledge discovery in big data systems to recognize hidden and complex patterns. Motivated by these successes, researchers in the field of networking apply deep learning models for Network Traffic Monitoring and Analysis (NTMA) applications, e.g., traffic classification and prediction. This paper provides a comprehensive review on applications of deep learning in NTMA. We first provide fundamental background relevant to our review. Then, we give an insight into the confluence of deep learning and NTMA, and review deep learning techniques proposed for NTMA applications. Finally, we discuss key challenges, open issues, and future research directions for using deep learning in NTMA applications.publishedVersio

Clustering objectives in wireless sensor networks: A survey and research direction analysis

Wireless Sensor Networks (WSNs) typically include thousands of resource-constrained sensors to monitor their surroundings, collect data, and transfer it to remote servers for further processing. Although WSNs are considered highly flexible ad-hoc networks, network management has been a fundamental challenge in these types of net- works given the deployment size and the associated quality concerns such as resource management, scalability, and reliability. Topology management is considered a viable technique to address these concerns. Clustering is the most well-known topology management method in WSNs, grouping nodes to manage them and/or executing various tasks in a distributed manner, such as resource management. Although clustering techniques are mainly known to improve energy consumption, there are various quality-driven objectives that can be realized through clustering. In this paper, we review comprehensively existing WSN clustering techniques, their objectives and the network properties supported by those techniques. After refining more than 500 clustering techniques, we extract about 215 of them as the most important ones, which we further review, catergorize and classify based on clustering objectives and also the network properties such as mobility and heterogeneity. In addition, statistics are provided based on the chosen metrics, providing highly useful insights into the design of clustering techniques in WSNs.publishedVersio

RESTful Service Development for Resource-constrained Environments

International audienceThe use of resource-constrained devices, such as smartphones, PDAs, Tablet PCs, and Wireless Sensor Networks (WSNs) is spreading rapidly in the business community and our daily life. Accessing services from such devices is very common in ubiquitous environments, but mechanisms to describe, implement and distribute these services remain a major challenge. Web services have been characterized as an efficient and widely-adopted approach to overcome heterogeneity, while this technology is still heavyweight for resource-constrained devices. The emergence of REST architectural style as a lightweight and simple interaction model has encouraged researchers to study the feasibility of exploiting REST principles to design and integrate services hosted on devices with limited capabilities. In this chapter, we discuss the state-of-the-art in applying REST concepts to develop Web services for WSNs and smartphones as two representative resource-constrained platforms, and then we provide a comprehensive survey of existing solutions in this area. In this context, we report on the DIGIHOME platform, a home monitoring middleware solution, which enables efficient service integration in ubiquitous environments using REST architectural style. In particular, we target our reference platforms for homemonitoring systems, namelyWSNs and smartphones, and report our experiments in applying the concept of Component-Based Software Engineering (CBSE) in order to provide resource-efficient RESTful distribution of Web services for those platforms

Optimizing Sensor Network Reprogramming via In-situ Reconfigurable Components

International audienceWireless reprogramming of sensor nodes is a critical requirement in long-lived Wireless Sensor Networks (WSNs) for several concerns, such as fixing bugs, upgrading the operating system and applications, and adapting applications behavior according to the physical environment. In such resource-poor platforms, the ability to efficiently delimit and reconfigure the necessary portion of sensor software--instead of updating the full binary image--is of vital importance. However, most of existing approaches in this field have not been widely adopted to date due to the extensive use of WSN resources or lack of generality. In this article, we therefore consider WSN programming models and run-time reconfiguration models as two interrelated factors and we present an integrated approach for addressing efficient reprogramming in WSNs. The middleware solution we propose, RemoWare, is characterized by mitigating the cost of post-deployment software updates on sensor nodes via the notion of in-situ reconfigurability and providing a component-based programming abstraction to facilitate the development of dynamic WSN applications. Our evaluation results show that RemoWare imposes a very low energy overhead in code distribution and component reconfiguration, and consumes approximately 6% of the total code memory on a TelosB sensor platform

From statistical- to machine learning-based network traffic prediction

Nowadays, due to the exponential and continuous expansion of new paradigms such as Internet of Things (IoT), Internet of Vehicles (IoV) and 6G, the world is witnessing a tremendous and sharp increase of network traffic. In such large-scale, heterogeneous, and complex networks, the volume of transferred data, as big data, is considered a challenge causing different networking inefficiencies. To overcome these challenges, various techniques are introduced to monitor the performance of networks, called Network Traffic Monitoring and Analysis (NTMA). Network Traffic Prediction (NTP) is a significant subfield of NTMA which is mainly focused on predicting the future of network load and its behavior. NTP techniques can generally be realized in two ways, that is, statistical- and Machine Learning (ML)-based. In this paper, we provide a study on existing NTP techniques through reviewing, investigating, and classifying the recent relevant works conducted in this field. Additionally, we discuss the challenges and future directions of NTP showing that how ML and statistical techniques can be used to solve challenges of NTP.publishedVersio

A Generic Component-based Approach for Programming, Composing and Tuning Sensor Software

International audienceWireless sensor networks (WSNs) are being extensively deployed today in various monitoring and control applications by enabling rapid deployments at low cost and with high flexibility. However, high-level software development is still one of the major challenges to wide-spread WSN adoption. The success of high-level programming approaches in WSNs is heavily dependent on factors such as ease of programming, code well-structuring, degree of code reusability, required software development effort and the ability to tune the sensor software for a particular application. Component-based programming has been recognized as an effective approach to satisfy such requirements. However, most of the componentization efforts in WSNs were ineffective due to various reasons, such as high resource demand or limited scope of use. In this article, we present Remora, a novel component-based approach to overcome the hurdles of WSN software implementation and configuration. Remora offers a well-structured programming paradigm that fits very well with resource limitations of embedded systems, including WSNs. Furthermore, the special attention to event handling in Remora makes our proposal more practical for embedded applications, which are inherently event-driven. More importantly, the mutualism between Remora and underlying system software promises a new direction towards separation of concerns in WSNs. This feature also offers a practical way to develop sensor middleware services which should be generic and developed close to the operating system. Additionally, it allows the customization of sensor software--deploying only application-required system-level services on nodes, instead of installing a fixed large system software image for any application. Our evaluation results show that the deployed Remora applications have an acceptable memory overhead and a negligible CPU cost compared with the state-of-the-art development models

Programming Sensor Networks Using REMORA Component Model

International audienceThe success of high-level programming models in Wireless Sensor Networks (WSNs) is heavily dependent on factors such as ease of programming, code well-structuring, degree of code reusability, and required software development effort. Component-based programming has been recognized as an effective approach to meet such requirements. Most of componentization efforts inWSNs were ineffective due to various reasons, such as high resource demand or limited scope of use. In this paper, we present REMORA, a new approach to practical and efficient component-based programming in WSNs. REMORA offers a well-structured programming paradigm that fits very well with resource limitations of embedded systems, including WSNs. Furthermore, the special attention to event handling in REMORA makes our proposal more practical for WSN applications, which are inherently event-driven. More importantly, the mutualism between REMORA and underlying system software promises a new direction towards separation of concerns in WSNs. Our evaluation results show that a well-configured REMORA application has an acceptable memory overhead and a negligible CPU cost

WiSeKit: A Distributed Middleware to Support Application-level Adaptation in Sensor Network

International audienceApplications for Wireless Sensor Networks (WSNs) are being spread to areas in which the contextual parameters modeling the environment are changing over the application lifespan. Whereas software adaptation has been identified as an effective approach for addressing context-aware applications, the existing work on WSNs fails to support context-awareness and mostly focuses on developing techniques to re-program the whole sensor node rather than reconfiguring a particular portion of the sensor application software. Therefore, enabling adaptivity in the higher layers of a WSN architecture such as the middleware and application layers, beside the consideration in the lower layers, becomes of high importance. In this paper, we propose a distributed component-based middleware approach, named WiSeKit, to enable adaptation and reconfiguration of WSN applications. In particular, this proposal aims at providing an abstraction to facilitate development of adaptive WSN applications. As resource availability is the main concern of WSNs, the preliminary evaluation shows that our middleware approach promises a lightweight, fine-grained and communication-efficient model of application adaptation with a very limited memory and energy overhead

Programming Sensor Networks Using REMORA Component Model

International audienceThe success of high-level programming models in Wireless Sensor Networks (WSNs) is heavily dependent on factors such as ease of programming, code well-structuring, degree of code reusability, and required software development effort. Component-based programming has been recognized as an effective approach to meet such requirements. Most of componentization efforts inWSNs were ineffective due to various reasons, such as high resource demand or limited scope of use. In this paper, we present REMORA, a new approach to practical and efficient component-based programming in WSNs. REMORA offers a well-structured programming paradigm that fits very well with resource limitations of embedded systems, including WSNs. Furthermore, the special attention to event handling in REMORA makes our proposal more practical for WSN applications, which are inherently event-driven. More importantly, the mutualism between REMORA and underlying system software promises a new direction towards separation of concerns in WSNs. Our evaluation results show that a well-configured REMORA application has an acceptable memory overhead and a negligible CPU cost