The development of Wireless Sensor Networks (WSNs) is enabled by the recent advances in wireless communication and sensing technologies. WSN have a wide range of scientific and commercial applications. In many applications the sensed data is useless if the location of the event is not associated with the data. Thus localization plays a substantial role in WSNs. Increased dependence on devices and sensed data presses for more efficient and accurate localization schemes. In many Internet of Things (IoT) deployments the area covered is large making it impossible to localize all devices and Sensor Networks (SNs) using single-hop localization techniques. A solution to this problem is to use a multi-hop localization technique to estimate devices' positions. In small areas SNs require at least three anchor nodes within their transmission range to estimate their location. Despite numerous existing localization techniques, the fundamental behavior of multi-hop localization is, as yet, not fully examined. Thus, we study the main characteristics of multi-hop localization and propose new solutions to enhance the performance of multi-hop localization techniques. We examine the assumptions in existing simulation models to build a more realistic simulation model, while studying and investigating the behavior of multi-hop localization techniques in large scale deployments before the actual deployment. We find that the introduced error follows the Gaussian distribution, but the estimated distance follows the Rayleigh distribution. We use the new simulation model to characterize the effect of hops on localization in both dense and sparse multi-hop deployments. We show that, contrary to common beliefs, in sparse deployments it is better to use long hops, while in dense deployments it is better to use short hops. Using short hops in dense deployments generates a large amount of traffic. Thus we propose a new solution which decreases and manages the overhead generated during the localization process. The proposed solution decreased the number of messages exchanged by almost 70% for DV-Distance and 55% for DV-Hop. Finally, we utilize mobile anchors instead of fixed anchors and propose a solution for the collinearity problem associated with the mobile anchor and use Kalman Filter (KF) to enhance the overall localization accuracy. Through simulation studies, we show that the scheme using a Kalman Filter decreases the estimation errors than using single direction by 31% and better than using weighted averages by 16% . As well, our new scheme overcomes the collinearity problem that appears from using mobile anchor nodes. / Thesis (Ph.D, Computing) -- Queen's University, 2014-04-30 01:53:55.817

The use of electronics connected to local networks and the Internet is growingall the time. Nowadays you can control your electronics in your house even when away from home, which opens up for potential security threats. The purpose of this report is to point out the potential risks with connecting home electronics to the Internet and to shed light on what security mechanisms that are needed in these kinds of systems. This report contains a theoretical part in which relevant material has been summarized. This material includes the smart home solution Tellstick Net and the wireless technologies ZigBee and Z-Wave, which are commonly used in home automation. The Tellstick Net system was mapped out and a risk analysis with attack trees was performed. After the analysis of the system, the implementation of two potential security threats were attempted. The two attempted attacks were replay attack and cross-site request forgery. The replay attack was unsuccessful due to the way the system communicates and keeps connections alive. However, the cross-site request forgery was discovered to be successful in some cases. It depended on if the browser of the target supported cross-origin resource sharing, as that property protects against cross-site request forgery. Finally, the report discusses what impact the found security deficiencies have, what they entail and how they reflect on the need for security in smart technologies for the home.

Denna rapport syftar till att beskriva arbetet kring att kunna detektera närvaro i ett rum medhjälp av så enkla sensorer som möjligt, kopplade till en Arduino. Samtidigt som detta skerså används också systemet till att med samma sensorer visa klimatet i rummet. Läsaren fåren inblick i problematiken med att detektera människor samt inom funktionen av de valdasensorerna. Utöver detta studeras energiförbrukningen i systemet. Rapportenmynnar ut i enslutsats där en procentuell chans för närvaro presenteras via en internetuppkoppling medhjälp av en omfattande testning av sensorernas beteende.

Smart Homes (SHs), as subsets of the Internet of Things (IoT), make use of Machine Learning and Arti cial Intelligence tools to provide technology-enabled solutions which assist their occupants and users with their Activities of Daily Living (ADL). Some SH provide always-present, health management support and care services. Having these services provided at home enables SH occupants such as the elderly and disabled to continue to live in their own homes and localities thus aiding Ageing In Place goals and eliminating the need for them to be relocated in order to be able to continue receiving the same support and services. Introducing and interconnecting smart, autonomous systems in homes to enable these service provisions and Assistance Technologies (AT) requires that certain interfaces in, and connections to, SH are exposed to the Internet, among other public-facing networks. This introduces the potential for cyber-physical attacks to be perpetrated through, from and against SH. Apart from the actual threats posed by these attacks to SH occupants and their homes, the potential that these attacks might occur can adversely a ect the adoption or uptake of SH solutions. This thesis identi es key attributes of the di erent elements (things or nodes and rooms or zones) in SHs and the relationships that exist between these elements. These relationships can be used to build SH security baselines for SHs such that any deviations from this baseline is described as anomalous. The thesis demonstrates the application of these relationships to Anomaly Detection (AD) through the analysis of several hypothetical scenarios and the decisions reached about whether they are normal or anomalous. This thesis also proposes an Internet of Things Digital Forensics Framework (IDFF), a Forensics Edge Management System (FEMS), a FEMS Decision-Making Algorithm (FDMA) and an IoT Incident Response plan. These tools can be combined to provide proactive (autonomous and human-led) Digital Forensics services within cyber-physical environments like the Smart Home.

Internet of Things är en utveckling som inte endast sträcker sig till de industriella områdena idag. Det når nu mera även ut till välfärdsteknologin. Både användare och företag påverkas idag av detta faktum. Företag har ett behov, och en plikt att värna om sina kunder, och kunder har ett behov av att göra sin röst hörd. Detta arbete har som syfte att utforska acceptansen hos de äldre till denna smarta välfärdsteknologi, samt vad som krävs för att öka acceptansen. Arbetet tillnärmar sig frågan genom litteratur som bas för enkäter och intervjuer, utförda i Sverige och Norge.Det visar sig att acceptansen beror på många olika faktorer, som alla uppfattas olika från person till person. Men att det i många fall kan koka ned till behovet. De flesta människorna vill helst bo hemma så länge som möjligt, i alla fall i Sverige och Norge. Men innan det har gått så långt så kan det vara oklart hur öppna folk är till teknologi som har möjlighet till att uppfattas som integritetskränkande. Nyckeln ligger i att göra teknologin så anpassningsbar som möjligt, vad gäller både funktion och utseende, samt andra faktorer som säkerhet, förståelse, användbarhet och kostnad.

Background. With the rise of Internet of Things and Internet connected devices many things become convenient and efficient but these products also carry risks. Even though a lot of people own devices like this not so many think of the consequences if these devices aren't secure. Objectives. Given this our thesis aims to discover the implications of vulnerable devices and also at what rate there are insecure, unpatched devices compared to the patched, secure counterpart. Methods. The approach implemented uses Shodan to find these devices on the internet and also to find version information about each device. After the devices are found the objective is to calculate a CVSS score on the vulnerabilities and the exploit that can abuse the vulnerability, if there exists any. Results. What we found was that 71.85% of a smart home server brand was running an insecure version. As to the consequences of having an insecure device, it can be severe.Conclusions. We found that, for instance, an attacker can without much difficulty shut off alarms in your smart home and then proceed to break into your house. Keywords: Vulnerability; Shodan; Internet of Things (IoT); Patching

The Internet-of-Things (IoT) is a network of interconnected devices with sensing, monitoring and processing functionalities that work in a cooperative way to offer services. Smart buildings, self-driving cars, house monitoring and management, city electricity and pollution monitoring are some examples where IoT systems have been already deployed. Amongst different kinds of devices in IoT, cameras play a vital role, since they can capture rich and resourceful content. However, since multiple IoT devices share the same gateway, the data that is produced from high definition cameras congest the network and deplete the available computational resources resulting in Quality-of-Service degradation corresponding to the visual content. In this thesis, we present an edge-based resource management framework for serving video processing applications in an Internet-of-Things (IoT) environment. In order to support the computational demands of latency-sensitive video applications and utilize effectively the available network resources, we employ edge-based resource management policy. We evaluate our proposed framework with a face recognition use case.

The Liang dynasty poet Xiao Gang’s “poems on things” (yongwu shi) have traditionally been read as shallow, overwrought descriptions of palace life devoid of the allegorical dimensions that were thought to ennoble the genre. This thesis argues that the figurative dimensions of these supposedly non-figurative poems must be understood in the context of the profound influence Buddhism and Buddhist thought had on Xiao Gang’s conceptions of literary practice. Through close readings of six “poems on things,” I demonstrate that Xiao Gang’s use of descriptive language doubles as an exploration of Buddhist concepts of sensuous reality, emptiness, and dependent co-arising. By exploring Xiao Gang’s thematization of abstract Buddhist philosophical concepts in the traditionally Confucian genre of the yongwu shi, I suggest that the impact of Buddhist ideas on Chinese figurative modes of poetic meaning was more profound than scholars of this period have previously suggested.

The Internet of Things (IoT) technology is undergoing expansion into different aspects of our life, changing the way businesses operate and bringing in efficiency and reliability of digital controls on various levels. Processing large amount of data from connected sensor networks becomes a challenging task. Specific part of it related to fleet management requires processing of the data on boards of vehicles equipped with multiple electronic devices and sensors for maintenance and operation of such vehicles. Herewith the efficiency of various configurations of employing Kalman filter algorithm for on-the-fly pre-processing of the sensory network originated data streams in IoT systems is investigated. Contextual grouping of the data streams for pre-processing by specialized Kalman filter units is found to be able to satisfy the logistics of IoT system operations. It is demonstrated that interconnection of the elementary Kalman filters into an organized network, the compositional Kalman filter, allows to take advantage of the redundancy of data streams to accomplish IoT pre-processing of the raw data. This includes intermittent data imputation, missing data replacement, lost data recovery, as well as error events detection and correction. Architectures are proposed and tested for the interaction of elementary Kalman filters in detection of GPS outage events and their compensation via data replacement procedure, as well as GPS offset occurrence detection and its compensation via data correction routine. Demonstrated is the efficiency of the suggested compositional designs of elementary Kalman filter networks for the purpose of data pre-processing in IoT systems.

One application of the Internet of Things that is currently developed is the collection of data from district heating systems. This development leads to the problem of how the collected data should be used to operate the systems in a manner that reduces the dependency on fossil fuels. This thesis proposes a characterization of this problem into visualization tasks and requirements, as well as a visualization application, based on the tool Power BI, for gaining insight into how to operate a district heating system. The tasks and requirements were elicited during collaborative meetings and prototyping together with district heating experts. The perceived effectiveness of the visualization application was validated with a usability evaluation, including the usability metric for user experience questionnaire, with four district heating experts. The perceived efficiency was, however, found to be lacking compared to the other usability aspects. A redesign of one of the Power BI components, which according to the evaluation should improve the application, is also suggested.