Generic Adaptation Support for Wireless Sensor Networks

Wireless Sensor Networks are used in various and expanding application scenarios and are also considered to be important elements of the Internet of Things. They monitor and deliver data, which is not only used for research but to an increasing degree also in business environments. With the increasing complexity of these scenarios and the increasing dependency on the availability of the sensor network data, the requirements to a Wireless Sensor Network increase at the same pace. Since Wireless Sensor Networks are typically implemented using resource-constrained platforms, sensor network algorithms are typically optimised for specific operating conditions such as static or mobile networks, high or low traffic etc. However, due to scenario complexity and dynamic real-world conditions a static configuration of a Wireless Sensor Network software cannot always meet the requirements. Moreover, these requirements of the sensor network's user can change over time, for example concerning accuracy. Therefore, the sensor network software has to adapt itself to cope with dynamic system conditions and user requirements. This thesis presents the TinyAdapt and TinySwitch frameworks to solve the aforementioned problems. TinyAdapt, our generic adaptation framework for Wireless Sensor Networks, allows for the autonomous adaptation of arbitrary sensor network algorithms based on explicit and intuitively defined user preferences and on automatically monitored network conditions. Due to a two-phase approach, run-time adaptation is executed completely and efficiently on standard sensor node hardware and does not need support from, e.g., the base station. The creation of adaptive applications is guided by a complete workflow, which is presented as well. When changing parameters of an algorithm is not enough to achieve the desired adaptation results, the algorithm has to be exchanged completely. However, several limitations of TinyOS and the sensor node hardware limit the use of simple code exchange by node reprogramming for efficient adaptation. TinySwitch, our generic switching framework, allows to switch between alternative algorithms that are already installed in parallel. TinySwitch analyses these algorithms, determines their dependencies and creates all code to enable one of the algorithms while isolating all others. Due to its minimal overhead, TinySwitch is perfectly suited for run-time adaptation in TinyAdapt

Implementation of a herd management system with wireless sensor networks

This paper investigates an adaptation of Wireless Sensor Networks (WSNs) to cattle monitoring applications. The proposed solution facilitates the requirement for continuously assessing the condition of individual animals, aggregating and reporting this data to the farm manager. There are several existing approaches to achieving animal monitoring, ranging from using a store and forward mechanism to employing GSM-based techniques; these approaches only provide sporadic information and introduce a considerable cost in staffing and physical hardware. The core of this study is to overcome the aforementioned drawbacks by using alternative cheap, low power consumption sensor nodes capable of providing real-time communication at a reasonable hardware cost. In this paper, both the hardware and software has been designed to provide a solution which can obtain real-time data from dairy cattle whilst conforming to the limitations associated with WSNs implementations

Heterogeneous component interactions: Sensors integration into multimedia applications

Resource-constrained embedded and mobile devices are becoming increasingly common. Since few years, some mobile and ubiquitous devices such as wireless sensor, able to be aware of their physical environment, appeared. Such devices enable proposing applications which adapt to user's need according the context evolution. It implies the collaboration of sensors and software components which differ on their nature and their communication mechanisms. This paper proposes a unified component model in order to easily design applications based on software components and sensors without taking care of their nature. Then it presents a state of the art of communication problems linked to heterogeneous components and proposes an interaction mechanism which ensures information exchanges between wireless sensors and software components

Unified radio and network control across heterogeneous hardware platforms

Experimentation is an important step in the investigation of techniques for handling spectrum scarcity or the development of new waveforms in future wireless networks. However, it is impractical and not cost effective to construct custom platforms for each future network scenario to be investigated. This problem is addressed by defining Unified Programming Interfaces that allow common access to several platforms for experimentation-based prototyping, research, and development purposes. The design of these interfaces is driven by a diverse set of scenarios that capture the functionality relevant to future network implementations while trying to keep them as generic as possible. Herein, the definition of this set of scenarios is presented as well as the architecture for supporting experimentation-based wireless research over multiple hardware platforms. The proposed architecture for experimentation incorporates both local and global unified interfaces to control any aspect of a wireless system while being completely agnostic to the actual technology incorporated. Control is feasible from the low-level features of individual radios to the entire network stack, including hierarchical control combinations. A testbed to enable the use of the above architecture is utilized that uses a backbone network in order to be able to extract measurements and observe the overall behaviour of the system under test without imposing further communication overhead to the actual experiment. Based on the aforementioned architecture, a system is proposed that is able to support the advancement of intelligent techniques for future networks through experimentation while decoupling promising algorithms and techniques from the capabilities of a specific hardware platform

AMISEC: Leveraging Redundancy and Adaptability to Secure AmI Applications

Security in Ambient Intelligence (AmI) poses too many challenges due to the inherently insecure nature of wireless sensor nodes. However, there are two characteristics of these environments that can be used effectively to prevent, detect, and confine attacks: redundancy and continuous adaptation. In this article we propose a global strategy and a system architecture to cope with security issues in AmI applications at different levels. Unlike in previous approaches, we assume an individual wireless node is vulnerable. We present an agent-based architecture with supporting services that is proven to be adequate to detect and confine common attacks. Decisions at different levels are supported by a trust-based framework with good and bad reputation feedback while maintaining resistance to bad-mouthing attacks. We also propose a set of services that can be used to handle identification, authentication, and authorization in intelligent ambients. The resulting approach takes into account practical issues, such as resource limitation, bandwidth optimization, and scalability

Design and analysis of adaptive hierarchical low-power long-range networks

A new phase of evolution of Machine-to-Machine (M2M) communication has started where vertical Internet of Things (IoT) deployments dedicated to a single application domain gradually change to multi-purpose IoT infrastructures that service different applications across multiple industries. New networking technologies are being deployed operating over sub-GHz frequency bands that enable multi-tenant connectivity over long distances and increase network capacity by enforcing low transmission rates to increase network capacity. Such networking technologies allow cloud-based platforms to be connected with large numbers of IoT devices deployed several kilometres from the edges of the network. Despite the rapid uptake of Long-power Wide-area Networks (LPWANs), it remains unclear how to organize the wireless sensor network in a scaleable and adaptive way. This paper introduces a hierarchical communication scheme that utilizes the new capabilities of Long-Range Wireless Sensor Networking technologies by combining them with broadly used 802.11.4-based low-range low-power technologies. The design of the hierarchical scheme is presented in detail along with the technical details on the implementation in real-world hardware platforms. A platform-agnostic software firmware is produced that is evaluated in real-world large-scale testbeds. The performance of the networking scheme is evaluated through a series of experimental scenarios that generate environments with varying channel quality, failing nodes, and mobile nodes. The performance is evaluated in terms of the overall time required to organize the network and setup a hierarchy, the energy consumption and the overall lifetime of the network, as well as the ability to adapt to channel failures. The experimental analysis indicate that the combination of long-range and short-range networking technologies can lead to scalable solutions that can service concurrently multiple applications