Probabilistic Bisimulations for PCTL Model Checking of Interval MDPs

Verification of PCTL properties of MDPs with convex uncertainties has been investigated recently by Puggelli et al. However, model checking algorithms typically suffer from state space explosion. In this paper, we address probabilistic bisimulation to reduce the size of such an MDPs while preserving PCTL properties it satisfies. We discuss different interpretations of uncertainty in the models which are studied in the literature and that result in two different definitions of bisimulations. We give algorithms to compute the quotients of these bisimulations in time polynomial in the size of the model and exponential in the uncertain branching. Finally, we show by a case study that large models in practice can have small branching and that a substantial state space reduction can be achieved by our approach.Comment: In Proceedings SynCoP 2014, arXiv:1403.784

Energy-efficient Transitional Near-* Computing

Studies have shown that communication networks, devices accessing the Internet, and data centers account for 4.6% of the worldwide electricity consumption. Although data centers, core network equipment, and mobile devices are getting more energy-efficient, the amount of data that is being processed, transferred, and stored is vastly increasing. Recent computer paradigms, such as fog and edge computing, try to improve this situation by processing data near the user, the network, the devices, and the data itself. In this thesis, these trends are summarized under the new term near-* or near-everything computing. Furthermore, a novel paradigm designed to increase the energy efficiency of near-* computing is proposed: transitional computing. It transfers multi-mechanism transitions, a recently developed paradigm for a highly adaptable future Internet, from the field of communication systems to computing systems. Moreover, three types of novel transitions are introduced to achieve gains in energy efficiency in near-* environments, spanning from private Infrastructure-as-a-Service (IaaS) clouds, Software-defined Wireless Networks (SDWNs) at the edge of the network, Disruption-Tolerant Information-Centric Networks (DTN-ICNs) involving mobile devices, sensors, edge devices as well as programmable components on a mobile System-on-a-Chip (SoC). Finally, the novel idea of transitional near-* computing for emergency response applications is presented to assist rescuers and affected persons during an emergency event or a disaster, although connections to cloud services and social networks might be disturbed by network outages, and network bandwidth and battery power of mobile devices might be limited

Leveraging software-defined networking for modular management in wireless sensor networks

Thesis (PhD (Electronics))--University of Pretoria, 2019.Wireless sensor networks (WSNs) are becoming increasingly popular with the advent of the internet of things (IoT). Various real-world applications of WSNs such as in smart grids, smart farming, and smart health would require a potential deployment of thousands or maybe hundreds of thousands of sensor nodes/actuators. To ensure the proper working order and network efficiency of such a network of sensor nodes, an effective WSN management system has to be integrated. However, the inherent challenges of WSNs such as sensor/actuator heterogeneity, application dependency, and resource constraints have led to challenges in implementing effective traditional WSN management. This difficulty in management increases as the WSN becomes larger. Software-defined networking (SDN) provides a promising solution for flexible management of WSNs by allowing the separation of the control logic from the sensor nodes/actuators. The advantage with this SDN-based management in WSNs is that it enables centralized control of the entire WSN making it simpler to deploy network-wide management protocols and applications on demand. Therefore in a comprehensive literature review, this study highlights some of the recent work on traditional WSN management in brief and reviews SDN-based management techniques for WSNs in greater detail. All this while drawing attention towards the advantages that SDN brings to traditional WSN management. This study also investigates open research challenges in coming up with mechanisms for flexible and easier SDN-based WSN configuration and management. A profound research challenge uncovered in the literature review is the need for an SDN-based system that would provide an opportunity for rapid testing and implementation of management modules. Therefore, this study proposes SDNMM, a generic and modular WSN management system based on SDN. SDNMM introduces the concept of management modularity using a management service interface (MSI) that enables management entities to be added as modules. The system leverages the use of SDN in WSNs and by being modular it also allows for rapid development and implementation of IoT applications. The system has been built on an open-source platform to support its generic aspect and a sample resource management module implemented and evaluated to support the proposed modular management approach. Results showed how adding a resource management module via the MSI improved packet delivery, delay, control traffic and energy consumption over comparable frameworks. However, SDN-based implementation comes at a cost of control overhead traffic which is a performance bottleneck in WSNs due to the limited in-band traffic channel bandwidth associated with WSNs. This has driven the research community to look into methods of effectively reducing the overhead control traffic in a process known as control message quenching (CMQ). In this study, a state of the art overview of control traffic reduction techniques available and being implemented for SDN-based WSNs is also presented. It provides an insight on benefits, challenges and open research areas available in the field of control message quenching for SDN-based WSNs. This study opens the door to this widely unexplored research area in its current form. Additionally, this study introduces a neighbour discovery control message quenching (ND-CMQ) algorithm to aid the reduction of neighbour reports in an SDN-based 6LoWPAN framework. The algorithm produces a significant decrease in control traffic and as a result shows improvements in packet delivery rate, packet delay, and energy efficiency compared to not implementing any CMQ algorithm and also compared to an alternative FR-CMQ algorithm based on flow setup requests.Copperbelt University under the ministry of higher education in ZambiaCouncil for Scientific and Industrial Research (CSIR)Electrical, Electronic and Computer EngineeringPhD (Electronics)Unrestricte

The energy problem in resource constrained wireless networks

Today Wireless Sensor Networks are part of a wider scenario involving several wireless and wired communication technology: the Internet Of Things (IoT). The IoT envisions billions of tiny embedded devices, called Smart Objects, connected in a Internet-like structure. Even if the integration of WSNs into the IoT scenario is nowadays a reality, the main bottleneck of this technology is the energy consumption of sensor nodes, which quickly deplete the limited amount of energy of available in batteries. This drawback, referred to as the energy problem, was addressed in a number of research papers proposing various energy optimization approaches to extend sensor nodes lifetime. However, energy problem is still an open issue that prevents the full exploitation of WSN technology. This thesis investigates the energy problem in WSNs and introduces original solutions trying to mitigate drawbacks related to this phenomenon. Starting from solutions proposed by the research community in WSNs, we deeply investigate critical and challenging factors concerning the energy problem and we came out with cutting-edge low-power hardware platforms, original software energy-aware protocols and novel energy-neutral hardware/software solutions overcoming the state-of-art. Concerning low-power hardware, we introduce the MagoNode, a new WSN mote equipped with a radio frequency (RF) front-end which enhances radio performance. We show that in real applicative contexts, the advantages introduced by the RF front-end keep packet re-trasmissions and forwards low. Furthermore, we present the ultra low-power Wake-Up Radio (WUR) system we designed and the experimental activity to validate its performance. In particular, our Wake-up Radio Receiver (WRx) features a sensitivity of -50 dBm, has a current consumption of 579nA in idle-listening and features a maximum radio range of about 19 meters. What clearly resulted from the experimental activity is that performance of the WRx is strongly affected by noise. To mitigate the impact of noise on WUR communication we implemented a Forward Error Correction (FEC) mechanism based on Hamming code. We performed several test to determine the effectiveness of the proposed solution. The outcome show that our WUR system can be employed in environment where the Bit Error Rate (BER) induced by noise is up to 10^2, vice versa, when the BER induced by noise is in the order of 10´3 or below, it is not worth to use any Forward Error Correction (FEC) mechanism since it does not introduce any advantages compared to uncoded data. In the context of energy-aware solutions, we present two protocols: REACTIVE and ALBA-WUR. REACTIVE is a low-power over-the-air programming (OAP) protocol we implemented to improve the energy efficiency and lower the image dissemination time of Deluge T2, a well-known OAP protocol implemented in TinyOS. To prove the effectiveness of REACTIVE we compared it to Deluge exploiting a testbed made of MagoNode motes. Results of our experiments show that the image dissemination time is 7 times smaller than Deluge, while the energy consumption drops 2.6 times. ALBA-WUR redesigns ALBA-R protocol, extending it to exploit advantages of WUR technology. We compared ALBA-R and ALBA-WUR in terms of current consumption and latency via simulations. Results show that ALBA-WUR estimated network lifetime is decades longer than that achievable by ALBA-R. Furthermore, end-to-end packet latency features by ALBA-WUR is comparable to that of ALBA-R. While the main goal of energy optimization approaches is motes lifetime maximization, in recent years a new research branch in WSN emerged: Energy Neutrality. In contrast to lifetime maximization approach, energy neutrality foresees the perennial operation of the network. This can be achieve only making motes use the harvested energy at an appropriate rate that guarantees an everlasting lifetime. In this thesis we stress that maximizing energy efficiency of a hardware platform dedicated to WSNs is the key to reach energy neutral operation (ENO), still providing reasonable data rates and delays. To support this conjecture, we designed a new hardware platform equipped with our wake-up radio (WUR) system able to support ENO, the MagoNode++. The MagoNode++ features a energy harvester to gather energy from solar and thermoelectric sources, a ultra low power battery and power management module and our WUR system to improve the energy efficiency of wireless communications. To prove the goodness in terms of current consumption of the MagoNode++ we ran a series of experiments aimed to assess its performance. Results show that the MagoNode++ consumes only 2.8 µA in Low Power Mode with its WRx module in listening mode. While carrying on our research work on solutions trying to mitigate the energy problem, we also faced a challenging application context where the employment of WSNs is considered efficient and effective: structural health monitoring (SHM). SHM deals with the early detection of damages to civil and industrial structures and is emerging as a fundamental tool to improve the safety of these critical infrastructures. In this thesis we present two real world WSNs deployment dedicated to SHM. The first concerned the monitoring of the Rome B1 Underground construction site. The goal was to monitor the structural health of a tunnel connecting two stops. The second deployment concerned the monitoring of the structural health of buildings in earthquake-stricken areas. From the experience gained during these real world deployments, we designed the Modular Monitoring System (MMS). The MMS is a new low-power platform dedicated to SHM based on the MagoNode. We validated the effectiveness of the MMS low-power design performing energy measurements during data acquisition from actual transducers

IoT Transmission Technologies for Distributed Measurement Systems in Critical Environments

Distributed measurement systems are spread in the most diverse application scenarios, and Internet of Things (IoT) transmission equipment is usually the enabling technologies for such measurement systems that need to feature wireless connectivity to ensure pervasiveness. Because wireless measurement systems have been deployed for the last years even in critical environments, assessing transmission technologies performances in such contexts is fundamental. Indeed, they are the most challenging ones for wireless data transmission due to their intrinsic attenuation capabilities. Several scenarios in which measurement systems can be deployed are analysed. Firstly, marine contexts are treated by considering above-the-sea wireless links. Such setting can be experienced in whichever application requiring remote monitoring of facilities and assets that are offshore installed. Some instances are offshore sea farming plants, or remote video monitoring systems installed on seamark buoys. Secondly, wireless communications taking place from the underground to the aboveground are covered. This scenario is typical of precision agriculture applications, where the accurate measurement of underground physical parameters is needed to be remotely sent to optimise crops reducing the wastefulness of fundamental resources (e.g., irrigation water). Thirdly, wireless communications occurring from the underwater to the abovewater are addressed. Such situation is inevitable for all those infrastructures monitoring conservation status of underwater species like algae, seaweeds and reef. Then, wireless links happening traversing metal surfaces and structures are tackled. Such context is commonly encountered in asset tracking and monitoring (e.g., containers), or in smart metering applications (e.g., utility meters). Lastly, sundry harsh environments that are typical of industrial monitoring (e.g., vibrating machineries, harsh temperature and humidity rooms, corrosive atmospheres) are tested to validate pervasive measurement infrastructures even in such contexts that are usually experienced in Industrial Internet of Things (IIoT) applications. The performances of wireless measurement systems in such scenarios are tested by sorting out ad-hoc measurement campaigns. Finally, IoT measurement infrastructures respectively deployed in above-the-sea and underground-to-aboveground settings are described to provide real applications in which such facilities can be effectively installed. Nonetheless, the aforementioned application scenarios are only some amid their sundry variety. Indeed, nowadays distributed pervasive measurement systems have to be thought in a broad way, resulting in countless instances: predictive maintenance, smart healthcare, smart cities, industrial monitoring, or smart agriculture, etc. This Thesis aims at showing distributed measurement systems in critical environments to set up pervasive monitoring infrastructures that are enabled by IoT transmission technologies. At first, they are presented, and then the harsh environments are introduced, along with the relative theoretical analysis modelling path loss in such conditions. It must be underlined that this Thesis aims neither at finding better path loss models with respect to the existing ones, nor at improving them. Indeed, path loss models are exploited as they are, in order to derive estimates of losses to understand the effectiveness of the deployed infrastructure. In fact, some transmission tests in those contexts are described, along with providing examples of these types of applications in the field, showing the measurement infrastructures and the relative critical environments serving as deployment sites. The scientific relevance of this Thesis is evident since, at the moment, the literature lacks a comparative study like this, showing both transmission performances in critical environments, and the deployment of real IoT distributed wireless measurement systems in such contexts

Adaptive Coded Modulation Classification and Spectrum Sensing for Cognitive Radio Systems. Adaptive Coded Modulation Techniques for Cognitive Radio Using Kalman Filter and Interacting Multiple Model Methods

The current and future trends of modern wireless communication systems place heavy demands on fast data transmissions in order to satisfy end users’ requirements anytime, anywhere. Such demands are obvious in recent applications such as smart phones, long term evolution (LTE), 4 & 5 Generations (4G & 5G), and worldwide interoperability for microwave access (WiMAX) platforms, where robust coding and modulations are essential especially in streaming on-line video material, social media and gaming. This eventually resulted in extreme exhaustion imposed on the frequency spectrum as a rare natural resource due to stagnation in current spectrum management policies. Since its advent in the late 1990s, cognitive radio (CR) has been conceived as an enabling technology aiming at the efficient utilisation of frequency spectrum that can lead to potential direct spectrum access (DSA) management. This is mainly attributed to its internal capabilities inherited from the concept of software defined radio (SDR) to sniff its surroundings, learn and adapt its operational parameters accordingly. CR systems (CRs) may commonly comprise one or all of the following core engines that characterise their architectures; namely, adaptive coded modulation (ACM), automatic modulation classification (AMC) and spectrum sensing (SS). Motivated by the above challenges, this programme of research is primarily aimed at the design and development of new paradigms to help improve the adaptability of CRs and thereby achieve the desirable signal processing tasks at the physical layer of the above core engines. Approximate modelling of Rayleigh and finite state Markov channels (FSMC) with a new concept borrowed from econometric studies have been approached. Then insightful channel estimation by using Kalman filter (KF) augmented with interacting multiple model (IMM) has been examined for the purpose of robust adaptability, which is applied for the first time in wireless communication systems. Such new IMM-KF combination has been facilitated in the feedback channel between wireless transmitter and receiver to adjust the transmitted power, by using a water-filling (WF) technique, and constellation pattern and rate in the ACM algorithm. The AMC has also benefited from such IMM-KF integration to boost the performance against conventional parametric estimation methods such as maximum likelihood estimate (MLE) for channel interrogation and the estimated parameters of both inserted into the ML classification algorithm. Expectation-maximisation (EM) has been applied to examine unknown transmitted modulation sequences and channel parameters in tandem. Finally, the non-parametric multitaper method (MTM) has been thoroughly examined for spectrum estimation (SE) and SS, by relying on Neyman-Pearson (NP) detection principle for hypothesis test, to allow licensed primary users (PUs) to coexist with opportunistic unlicensed secondary users (SUs) in the same frequency bands of interest without harmful effects. The performance of the above newly suggested paradigms have been simulated and assessed under various transmission settings and revealed substantial improvements