Middleware for wireless sensor network virtualization

Sensor and network virtualization technology are used in smart home, smart grid, smart city and many other applications of Internet of Things (IoT) that deploy Wireless Sensor Network (WSN) to facilitate multiple sensor data transmission over multiple networks. Existing WSNs are designed for a specific application running on low data rate network. The challenge is how to ensure multiple sensor data for multiple applications be transmitted over multiple heterogeneous networks having different transmission rates while ensuring Quality-of-Service (QoS). The research has developed a middleware that provides sensor and network virtualization with guaranteed QoS. The middleware was designed comprising of two layers: Application Dependent Layer Middleware (ADLM) and Network Dependent Layer Middleware (NDLM). The ADLM combined multiple sensor data to form services based of Service Oriented Application (SOA). It is comprised of service handling manager that combines various sensor data and form services, QoS manager that assigns priority and service scheduling manager that forwards the service frames. The NDLM facilitated seamless transmissions of various service data over multiple heterogeneous networks. It consists of hypervisor which is composed of flowvisor and the powervisor. The flowvisor is madeup of transmit and routing managers responsible for routing and transmitting service packets. The powervisor consists of a resource manager that determines and selects the node with the highest battery power. The middleware was implemented and evaluated on a real experimental testbed. The experimental results showed that the middleware increased throughput by 8.7% and reduced the numbers of packets transmissions from the node by 68.7% compared to proxy middleware using SOA. In addition, end-to-end transmission delay was reduced by 85.2% when compared to SenShare using SOA. The flowvisor at the gateway decreased the waiting time of packets in the queue by 59.8%, when the flowvisor raised the output rate up to 2.5 times the maximum arrival rate of WSN packets. The powervisor increased the node’s life time by 17.6% when compared to VITRO by limiting the transmission power to the existing battery voltage level. In brief, the middleware has provided guaranteed QoS by increasing throughput, reducing end-to-end delay and minimizing energy consumption. The middleware is highly recommended for IoT applications such as smart city and smart grid

Eco: A Hardware-Software Co-Design for In Situ Power Measurement on Low-end IoT Systems

Energy-constrained sensor nodes can adaptively optimize their energy consumption if a continuous measurement exists. This is of particular importance in scenarios of high dynamics such as energy harvesting or adaptive task scheduling. However, self-measuring of power consumption at reasonable cost and complexity is unavailable as a generic system service. In this paper, we present Eco, a hardware-software co-design enabling generic energy management on IoT nodes. Eco is tailored to devices with limited resources and thus targets most of the upcoming IoT scenarios. The proposed measurement module combines commodity components with a common system interfaces to achieve easy, flexible integration with various hardware platforms and the RIOT IoT operating system. We thoroughly evaluate and compare accuracy and overhead. Our findings indicate that our commodity design competes well with highly optimized solutions, while being significantly more versatile. We employ Eco for energy management on RIOT and validate its readiness for deployment in a five-week field trial integrated with energy harvesting

Detecting Software Attacks on Embedded IoT Devices

Internet of Things (IoT) applications are being rapidly deployed in the context of smart homes, automotive vehicles, smart factories, and many more. In these applications, embedded devices are widely used as sensors, actuators, or edge nodes. The embedded devices operate distinctively on a task or interact with each other to collectively perform certain tasks. In general, increase in Internet-connected things has made embedded devices an attractive target for various cyber attacks, where an attacker gains access and control remote devices for malicious activities. These IoT devices could be exploited by an attacker to compromise the security of victim’s platform without requiring any physical hardware access. In order to detect such software attacks and ensure a reliable and trustworthy IoT application, it is crucial to verify that a device is not compromised by malicious software, and also assert correct execution of the program. In the literature, solutions based on remote attestation, anomaly detection, control-flow and data-flow integrity have been proposed to detect software attacks. However, these solutions have limited applicability in terms of target deployments and attack detection, which we inspect thoroughly. In this dissertation, we propose three solutions to detect software attacks on embedded IoT devices. In particular, we first propose SWARNA, which uses remote attestation to verify a large network of embedded devices and ensure that the application software on the device is not tampered. Verifying the integrity of a software preserves the static properties of a device. To secure the devices from various software attacks, it is imperative to also ensure that the runtime execution of a program is as expected. Therefore, we focus extensively on detecting memory corruption attacks that may occur during the program execution. Furthermore, we propose, SPADE and OPADE, secure program anomaly detection that runs on embedded IoT devices and use deep learning, and machine learning algorithms respectively to detect various runtime software attacks. We evaluate and analyse all the proposed solutions on real embedded hardware and IoT testbeds. We also perform a thorough security analysis to show how the proposed solutions can detect various software attacks

Corl8: A System for Analyzing Diagnostic Measures in Wireless Sensor Networks

Due to an increasing demand to monitor the physical world, researchers are deploying wireless sensor networks more than ever before. These networks comprise a large number of sensors integrated with small, low-power wireless transceivers used to transmit data to a central processing and storage location. These devices are often deployed in harsh, volatile locations, which increases their failure rate and decreases the rate at which packets can be successfully transmitted. Existing sensor debugging tools, such as Sympathy and EmStar, rely on add-in network protocols to report status information, and to collectively diagnose network problems. Some protocols rely on a central node to initiate the diagnosis sequence. These methods can congest network channels and consume scarce resources, including battery power. In this thesis, we present Corl8, a system for analyzing diagnostic traces in wireless sensor networks. Our method relies on diagnostic data that is periodically transmitted to a network sink as a part of the standard sensor payload to enable fault diagnosis. Corl8 does not require any specific data to be present in the system, making it flexible. Our system provides an interactive environment for exploring correlated changes across different diagnostic measures within an individual node. It also supports processing on a batch level to automatically flag interesting correlations. The system\u27s flexibility makes it applicable for use in any wireless sensor network that transmits diagnostic measures. The analysis methods are user-configurable, but we suggest settings and analyze their performance. For our evaluation, we use data from five real-world deployments from the Intelligent River(R) project consisting of 36 sensor nodes

On-device Security and Privacy Mechanisms for Resource-limited Devices: A Bottom-up Approach

This doctoral dissertation introduces novel mechanisms to provide on-device security and privacy for resource-limited smart devices and their applications. These mechanisms aim to cover five fundamental contributions in the emerging Cyber-Physical Systems (CPS), Internet of Things (IoT), and Industrial IoT (IIoT) fields. First, we present a host-based fingerprinting solution for device identification that is complementary to other security services like device authentication and access control. Then, we design a kernel- and user-level detection framework that aims to discover compromised resource-limited devices based on behavioral analysis. Further we apply dynamic analysis of smart devices’ applications to uncover security and privacy risks in real-time. Then, we describe a solution to enable digital forensics analysis on data extracted from interconnected resource-limited devices that form a smart environment. Finally, we offer to researchers from industry and academia a collection of benchmark solutions for the evaluation of the discussed security mechanisms on different smart domains. For each contribution, this dissertation comprises specific novel tools and techniques that can be applied either independently or combined to enable a broader security services for the CPS, IoT, and IIoT domains

Activity-Aware Sensor Networks for Smart Environments

The efficient designs of Wireless Sensor Network protocols and intelligent Machine Learning algorithms, together have led to the advancements of various systems and applications for Smart Environments. By definition, Smart Environments are the typical physical worlds used in human daily life, those are seamlessly embedded with smart tiny devices equipped with sensors, actuators and computational elements. Since human user is a key component in Smart Environments, human motion activity patterns have key importance in building sensor network systems and applications for Smart Environments. Motivated by this, in this thesis my work is focused on human motion activity-aware sensor networks for Smart Environments. The main contributions of this thesis are in two important aspects: (i) Designing event activity context-aware sensor networks for efficient performance optimization as well as resource usage; and (ii) Using binary motion sensing sensor networks\u27 collective data for device-free real-time tracking of multiple users. Firstly, I describe the design of our proposed event activity context-aware sensor network protocols and system design for Smart Environments. The main motivation behind this work is as follows. A sensor network, unlike a traditional communication network, provides high degree of visibility into the environmental physical processes. Therefore its operation is driven by the activities in the environment. In long-term operations, these activities usually show certain patterns which can be learned and effectively utilized to optimize network design. In this thesis I have designed several novel protocols: (i) ActSee for activity-aware radio duty-cycling, (ii) EAR for activity-aware and energy balanced routing, and (iii) ActiSen complete working system with protocol suites for activity-aware sensing/ duty-cycling/ routing. Secondly, I have proposed and designed FindingHuMo (Finding Human Motion), a Machine Learning based real-time user tracking algorithm for Smart Environments using Sensor Networks. This work has been motivated by increasing adoption of sensor network enabled Ubiquitous Computing in key Smart Environment applications, like Smart Healthcare. Our proposed FindingHuMo protocol and system can perform device-free tracking of multiple (unknown and variable number of) users in the hallway environments, just from non-invasive and anonymous binary motion sensor data

XR-RF Imaging Enabled by Software-Defined Metasurfaces and Machine Learning: Foundational Vision, Technologies and Challenges

We present a new approach to Extended Reality (XR), denoted as iCOPYWAVES, which seeks to offer naturally low-latency operation and cost-effectiveness, overcoming the critical scalability issues faced by existing solutions. iCOPYWAVES is enabled by emerging PWEs, a recently proposed technology in wireless communications. Empowered by intelligent (meta)surfaces, PWEs transform the wave propagation phenomenon into a software-defined process. We leverage PWEs to i) create, and then ii) selectively copy the scattered RF wavefront of an object from one location in space to another, where a machine learning module, accelerated by FPGAs, translates it to visual input for an XR headset using PWEdriven, RF imaging principles (XR-RF). This makes for an XR system whose operation is bounded in the physical layer and, hence, has the prospects for minimal end-to-end latency. Over large distances, RF-to-fiber/fiber-to-RF is employed to provide intermediate connectivity. The paper provides a tutorial on the iCOPYWAVES system architecture and workflow. A proof-of-concept implementation via simulations is provided, demonstrating the reconstruction of challenging objects in iCOPYWAVES produced computer graphics