JamLab: Augmenting Sensornet Testbeds with Realistic and Controlled Interference Generation

Radio interference drastically affects the performance of sensor-net communications, leading to packet loss and reduced energy-efficiency. As an increasing number of wireless devices operates on the same ISM frequencies, there is a strong need for understanding and debugging the performance of existing sensornet protocols under interference. Doing so requires a low-cost flexible testbed infrastructure that allows the repeatable generation of a wide range of interference patterns. Unfortunately, to date, existing sensornet testbeds lack such capabilities, and do not permit to study easily the coexistence problems between devices sharing the same frequencies. This paper addresses the current lack of such an infrastructure by using off-the-shelf sensor motes to record and playback interference patterns as well as to generate customizable and repeat-able interference in real-time. We propose and develop JamLab: a low-cost infrastructure to augment existing sensornet testbeds with accurate interference generation while limiting the overhead to a simple upload of the appropriate software. We explain how we tackle the hardware limitations and get an accurate measurement and regeneration of interference, and we experimentally evaluate the accuracy of JamLab with respect to time, space, and intensity. We further use JamLab to characterize the impact of interference on sensornet MAC protocols

SensLAB Very Large Scale Open Wireless Sensor Network Testbed

International audienceThis paper presents a precise description of SensLAB: Very Large Scale Open Wireless Sensor Network Testbed that has been developed and deployed in order to allow the evaluation of scalable wireless sensor network protocols and applications. SensLAB's main and most important goal is to o er an accurate open access multi-users scienti c tool to support the design, development, tuning, and experimentation of real large-scale sensor network applications. The SensLAB testbed is composed of 1024 nodes and it is distributed among 4 sites. Two sites o er access to mobile nodes. Every sensor node is also able to be con gured as a sink node and can exchange data with any other sink node of the whole SensLAB testbed (locally or remotely) or any computer on the Internet. The hardware designed on purpose and software architectures that allow to reserve, con gure, deploy embedded software, boot wireless sensor nodes and gather experimental data and monitoring information are described in details. We also present short demonstration examples to illustrate the use of the SensLAB testbed

Using SensLAB as a First Class Scienti c Tool for Large Scale Wireless Sensor Network Experiments

International audienceThis paper presents a description of SensLAB(Very Large Scale Open Wireless Sensor Network Testbed) that has been developed and deployed in order to allow the evaluation through experimentations of scalable wireless sensor network protocols and applications. SensLAB's main and most important goal is to o er an accurate open access multiusers scienti c tool to support the design, the development tuning, and the experimentation of real large-scale sensor network applications. The SensLAB testbed is composed of 1024 nodes over 4 sites. Each site hosts 256 sensor nodes with speci c characteristics in order to o er a wide spectrum of possibilities and heterogeneity. Within a given site, each one of the 256 nodes is able both to communicate via its radio interface to its neighbors and to be con gured as a sink node to exchange data with any other "sink node". The hardware and software architectures that allow to reserve, con gure, deploy rmwares and gather experimental data and monitoring information are described. We also present demonstration examples to illustrate the use of the SensLAB testbed and encourage researchers to test and benchmark their applications/protocols on a large scale WSN testbed

Indoor Localization Based on Wireless Sensor Networks

Indoor localization techniques based on wireless sensor networks (WSNs) have been increasingly used in various applications such as factory automation, intelligent building, facility management, security, and health care. However, existing localization techniques cannot meet the accuracy requirement of many applications. Meanwhile, some localization algorithms are affected by environmental conditions and cannot be directly used in an indoor environment. Cost is another limitation of the existing localization algorithms. This thesis is to address those issues of indoor localization through a new Sensing Displacement (SD) approach. It consists of four major parts: platform design, SD algorithm development, SD algorithm improvement, and evaluation. Platform design includes hardware design and software design. Hardware design is the foundation for the system, which consists of the motion sensors embedded on mobile nodes and WSN design. Motion sensors are used to collect motion information for the localizing objects. A WSN is designed according to the characteristics of an indoor scenario. A Cloud Computing based system architecture is developed to support the software design of the proposed system. In order to address the special issues in an indoor environment, a new Sensing Displacement algorithm is developed, which estimates displacement of a node based on the motion information from the sensors embedded on the node. The sensor assembly consists of acceleration sensors and gyroscope sensors, separately sensing the acceleration and angular velocity of the localizing object. The first SD algorithm is designed in a way to be used in a 2-D localization demo to validate the proposal. A detailed analysis of the results of 2-D SD algorithm reveals that there are two critical issues (sensor’s noise and cumulative error) affecting the measurement results. Therefore a low-pass filter and a modified Kalman filter are introduced to solve the issue of sensor’s noises. An inertia tensor factor is introduced to address the cumulative error in a 3-D SD algorithm. Finally, the proposed SD algorithm is evaluated against the commercial AeroScout (WiFi-RFID) system and the ZigBee based Fingerprint algorithm

Multitasking on Wireless Sensor Networks

A Wireless Sensor Network (WSN) is a loose interconnection among distributed embedded devices called motes. Motes have constrained sensing, computing, and communicating resources and operate for a long period of time on a small energy supply. Although envisioned as a platform for facilitating and inspiring a new spectrum of applications, after a decade of research the WSN is limited to collecting data and sporadically updating system parameters. Programming other applications, including those that have real-time constraints, or designing WSNs operating with multiple applications require enhanced system architectures, new abstractions, and design methodologies. This dissertation introduces a system design methodology for multitasking on WSNs. It allows programmers to create an abstraction of a single, integrated system running with multiple tasks. Every task has a dedicated protocol stack. Thus, different tasks can have different computation logics and operate with different communication protocols. This facilitates the execution of heterogeneous applications on the same WSN and allows programmers to implement a variety of system services. The services that have been implemented provide energy-monitoring, tasks scheduling, and communication between the tasks. The experimental section evaluates implementations of the WSN software designed with the presented methodology. A new set of tools for testbed deployments is introduced and used to test examples of WSNs running with applications interacting with the physical world. Using remote testbeds with over 100 motes, the results show the feasibility of the proposed methodology in constructing a robust and scalable WSN system abstraction, which can improve the run-time performance of applications, such as data collection and point-to-point streaming

Detecting Impersonation Attacks in a Static WSN

The current state of security found in the IoT domain is highly flawed, a major problem being that the cryptographic keys used for authentication can be easily extracted and thus enable a myriad of impersonation attacks. In this MSc thesis a study is done of an authentication mechanism called device fingerprinting. It is a mechanism which can derive the identity of a device without relying on device identity credentials and thus detect credential-based impersonation attacks. A proof of concept has been produced to showcase how a fingerprinting system can be designed to function in a resource constrained IoT environment. A novel approach has been taken where several fingerprinting techniques have been combined through machine learning to improve the system’s ability to deduce the identity of a device. The proof of concept yields high performant results, indicating that fingerprinting techniques are a viable approach to achieve security in an IoT system

IRIS: Efficient Visualization, Data Analysis and Experiment Management for Wireless Sensor Networks

The design of ubiquitous computing environments is challenging, mainly due to the unforeseeable impact of real-world environments on the system performance. A crucial step to validate the behavior of these systems is to perform in-field experiments under various conditions. We introduce IRIS, an experiment management and data processing tool allowing the definition of arbitrary complex data analysis applications. While focusing on Wireless Sensor Networks, IRIS supports the seamless integration of heterogeneous data gathering technologies. The resulting flexibility and extensibility enable the definition of various services, from experiment management and performance evaluation to user-specific applications and visualization. IRIS demonstrated its effectiveness in three real-life use cases, offering a valuable support for in-field experimentation and development of customized applications for interfacing the end user with the system

Privacy in characterizing and recruiting patients for IoHT-aided digital clinical trials

Nowadays there is a tremendous amount of smart and connected devices that produce data. The so-called IoT is so pervasive that its devices (in particular the ones that we take with us during all the day - wearables, smartphones...) often provide some insights on our lives to third parties. People habitually exchange some of their private data in order to obtain services, discounts and advantages. Sharing personal data is commonly accepted in contexts like social networks but individuals suddenly become more than concerned if a third party is interested in accessing personal health data. The healthcare systems worldwide, however, begun to take advantage of the data produced by eHealth solutions. It is clear that while on one hand the technology proved to be a great ally in the modern medicine and can lead to notable benefits, on the other hand these processes pose serious threats to our privacy. The process of testing, validating and putting on the market a new drug or medical treatment is called clinical trial. These trials are deeply impacted by the technological advancements and greatly benefit from the use of eHealth solutions. The clinical research institutes are the entities in charge of leading the trials and need to access as much health data of the patients as possible. However, at any phase of a clinical trial, the personal information of the participants should be preserved and maintained private as long as possible. During this thesis, we will introduce an architecture that protects the privacy of personal data during the first phases of digital clinical trials (namely the characterization phase and the recruiting phase), allowing potential participants to freely join trials without disclosing their personal health information without a proper reward and/or prior agreement. We will illustrate what is the trusted environment that is the most used approach in eHealth and, later, we will dig into the untrusted environment where the concept of privacy is more challenging to protect while maintaining usability of data. Our architecture maintains the individuals in full control over the flow of their personal health data. Moreover, the architecture allows the clinical research institutes to characterize the population of potentiant users without direct access to their personal data. We validated our architecture with a proof of concept that includes all the involved entities from the low level hardware up to the end application. We designed and realized the hardware capable of sensing, processing and transmitting personal health data in a privacy preserving fashion that requires little to none maintenance

Promocijas darbs

Elektroniskā versija nesatur pielikumusPromocijas darba mērķis ir sniegt skaidru priekšstatu par bezvadu sensoru tīklu testgultnes platformu izmantošanu un funkcionalitāti, analizēt, kādi testgultnes platformu uzlabojumi ir nepieciešami lai atbalstītu bezvadu sensoru tīklu pētniecību un izstrādi līdz 7. tehnoloģiju gatavības līmenim, kā arī izstrādāt un novērtēt identificētos uzlabojumus. Uzlabojumi ir izstrādāti EDI TestBed testgultnes platformai un novērtēti pamatojoties uz pieciem pabeigtiem un diviem vēl notiekošiem reāliem izmantošanas gadījumiem, diapazonā no 3. līdz 7. tehnoloģiju gatavības līmenim. Katram uzlabojumam tiek sniegtas vadlīnijas un prasības, lai to varētu iekļaut jebkurā saderīgā testgultnes platformā. Promocijas darbā tiek definēts termins "testgultnes platforma" un pamatota šī termina nepieciešamība. Atslēgvārdi: BST; IoT; testgultne; testgultnes platforma; pārskats; sistemātiskais pārskats; bezvadu sensoru tīkli; lietu internetsThe aim of this thesis is to provide a clear view of the usage and functionality of testbed facilities for wireless sensor networks, to analyze what improvements to testbed facilities are needed to support research and development of wireless sensor networks up to Technology Readiness Level 7 and to develop and evaluate the identified improvements. The improvements have been developed for the EDI TestBed facility and evaluated on the basis of five completed and two ongoing real use cases, ranging from Technology Readiness Level 3 to Technology Readiness Level 7. Guidelines and requirements are provided for each improvement so that it can be incorporated into any compatible testbed facility. The thesis defines the term "testbed facility" and justifies the need for this term. Keywords: WSN; IoT; testbed; testbed facility; review; systematic review; wireless sensor networks; internet of things

A Survey on Virtualization of Wireless Sensor Networks

Wireless Sensor Networks (WSNs) are gaining tremendous importance thanks to their broad range of commercial applications such as in smart home automation, health-care and industrial automation. In these applications multi-vendor and heterogeneous sensor nodes are deployed. Due to strict administrative control over the specific WSN domains, communication barriers, conflicting goals and the economic interests of different WSN sensor node vendors, it is difficult to introduce a large scale federated WSN. By allowing heterogeneous sensor nodes in WSNs to coexist on a shared physical sensor substrate, virtualization in sensor network may provide flexibility, cost effective solutions, promote diversity, ensure security and increase manageability. This paper surveys the novel approach of using the large scale federated WSN resources in a sensor virtualization environment. Our focus in this paper is to introduce a few design goals, the challenges and opportunities of research in the field of sensor network virtualization as well as to illustrate a current status of research in this field. This paper also presents a wide array of state-of-the art projects related to sensor network virtualization