Releasing network isolation problem in group-based industrial wireless sensor networks

In this paper, we propose a cross-layer optimization scheme named Adjusting the Transmission Radius (ATR), which is based on the Energy Consumed uniformly Connected K-Neighborhood (EC-CKN) sleep scheduling algorithm in wireless sensor networks (WSNs). In particular, we discovered two important problems, namely, the death acceleration problem and the network isolation problem, in EC-CKN-based WSNs. Furthermore, we solve these two problems in ATR, which creates sleeping opportunities for the nodes that cannot get a chance to sleep in the EC-CKN algorithm. Simulation and experimental results show that the network lifetime of ATR-Connected-K-Neighborhood-based WSNs increases by 19%, on average, and the maximum increment is 41%. In addition, four important insights were discovered through this research work and presented in this paper

Solutions and Tools for Secure Communication in Wireless Sensor Networks

Secure communication is considered a vital requirement in Wireless Sensor Network (WSN) applications. Such a requirement embraces different aspects, including confidentiality, integrity and authenticity of exchanged information, proper management of security material, and effective prevention and reaction against security threats and attacks. However, WSNs are mainly composed of resource-constrained devices. That is, network nodes feature reduced capabilities, especially in terms of memory storage, computing power, transmission rate, and energy availability. As a consequence, assuring secure communication in WSNs results to be more difficult than in other kinds of network. In fact, trading effectiveness of adopted solutions with their efficiency becomes far more important. In addition, specific device classes or technologies may require to design ad hoc security solutions. Also, it is necessary to efficiently manage security material, and dynamically cope with changes of security requirements. Finally, security threats and countermeasures have to be carefully considered since from the network design phase. This Ph.D. dissertion considers secure communication in WSNs, and provides the following contributions. First, we provide a performance evaluation of IEEE 802.15.4 security services. Then, we focus on the ZigBee technology and its security services, and propose possible solutions to some deficiencies and inefficiencies. Second, we present HISS, a highly scalable and efficient key management scheme, able to contrast collusion attacks while displaying a graceful degradation of performance. Third, we present STaR, a software component for WSNs that secures multiple traffic flows at the same time. It is transparent to the application, and provides runtime reconfigurability, thus coping with dynamic changes of security requirements. Finally, we describe ASF, our attack simulation framework for WSNs. Such a tool helps network designers to quantitatively evaluate effects of security attacks, produce an attack ranking based on their severity, and thus select the most appropriate countermeasures

Is Fragmentation a Threat to the Success of the Internet of Things?

The current revolution in collaborating distributed things is seen as the first phase of IoT to develop various services. Such collaboration is threatened by the fragmentation found in the industry nowadays as it brings challenges stemming from the difficulty to integrate diverse technologies in system. Diverse networking technologies induce interoperability issues, hence, limiting the possibility of reusing the data to develop new services. Different aspects of handling data collection must be available to provide interoperability to the diverse objects interacting; however, such approaches are challenged as they bring substantial performance impairments in settings with the increasing number of collaborating devices/technologies.Comment: 16 pages, 2 figures, Internet of Things Journal (http://ieee-iotj.org

A Comprehensive Review on Time Sensitive Networks with a Special Focus on Its Applicability to Industrial Smart and Distributed Measurement Systems

The groundbreaking transformations triggered by the Industry 4.0 paradigm have dramati-cally reshaped the requirements for control and communication systems within the factory systems of the future. The aforementioned technological revolution strongly affects industrial smart and distributed measurement systems as well, pointing to ever more integrated and intelligent equipment devoted to derive accurate measurements. Moreover, as factory automation uses ever wider and complex smart distributed measurement systems, the well-known Internet of Things (IoT) paradigm finds its viability also in the industrial context, namely Industrial IoT (IIoT). In this context, communication networks and protocols play a key role, directly impacting on the measurement accuracy, causality, reliability and safety. The requirements coming both from Industry 4.0 and the IIoT, such as the coexistence of time-sensitive and best effort traffic, the need for enhanced horizontal and vertical integration, and interoperability between Information Technology (IT) and Operational Technology (OT), fostered the development of enhanced communication subsystems. Indeed, established tech-nologies, such as Ethernet and Wi-Fi, widespread in the consumer and office fields, are intrinsically non-deterministic and unable to support critical traffic. In the last years, the IEEE 802.1 Working Group defined an extensive set of standards, comprehensively known as Time Sensitive Networking (TSN), aiming at reshaping the Ethernet standard to support for time-, mission-and safety-critical traffic. In this paper, a comprehensive overview of the TSN Working Group standardization activity is provided, while contextualizing TSN within the complex existing industrial technological panorama, particularly focusing on industrial distributed measurement systems. In particular, this paper has to be considered a technical review of the most important features of TSN, while underlining its applicability to the measurement field. Furthermore, the adoption of TSN within the Wi-Fi technology is addressed in the last part of the survey, since wireless communication represents an appealing opportunity in the industrial measurement context. In this respect, a test case is presented, to point out the need for wirelessly connected sensors networks. In particular, by reviewing some literature contributions it has been possible to show how wireless technologies offer the flexibility necessary to support advanced mobile IIoT applications

Low power CMOS IC, biosensor and wireless power transfer techniques for wireless sensor network application

The emerging field of wireless sensor network (WSN) is receiving great attention due to the interest in healthcare. Traditional battery-powered devices suffer from large size, weight and secondary replacement surgery after the battery life-time which is often not desired, especially for an implantable application. Thus an energy harvesting method needs to be investigated. In addition to energy harvesting, the sensor network needs to be low power to extend the wireless power transfer distance and meet the regulation on RF power exposed to human tissue (specific absorption ratio). Also, miniature sensor integration is another challenge since most of the commercial sensors have rigid form or have a bulky size. The objective of this thesis is to provide solutions to the aforementioned challenges

Security Analysis and Evaluation of Smart Toys

During the last years, interconnectivity and merging the physical and digital technological dimensions have become a topic attracting the interest of the modern world. Internet of Things (IoT) is rapidly evolving as it manages to transform physical devices into communicating agents which can consecutively create complete interconnected systems. A sub-category of the IoT technology is smart toys, which are devices with networking capabilities, created for and used in play. Smart toys’ targeting group is usually children and they attempt to provide a higher level of entertainment and education by offering an enhanced and more interactive experience. Due to the nature and technical limitations of IoT devices, security experts have expressed concerns over the effectiveness and security level of smart devices. The importance of securing IoT devices has an increased weight when it pertains to smart toys, since sensitive information of children and teenagers can potentially be compromised. Furthermore, various security analyses on smart toys have discovered a worryingly high number of important security flaws. The master thesis focuses on the topic of smart toys’ security by first presenting and analyzing the necessary literature background. Furthermore, it presents a case study where a smart toy is selected and analyzed statically and dynamically utilizing a Raspberry Pi. The aim of this thesis is to examine and apply methods of analysis used in the relevant literature, in order to identify security flaws in the examined smart toy. The smart toy is a fitness band whose target consumers involve children and teenagers. The fitness band is communicating through Bluetooth with a mobile device and is accompanied by a mobile application. The mobile application has been installed and tested on an Android device. Finally, the analyses as well as their emerged results are presented and described in detail. Several security risks have been identified indicating that developers must increase their efforts in ensuring the optimal level of security in smart toys. Furthermore, several solutions that could minimize security risks and are related to our findings are suggested, along with potentially interesting topics for future work and further research

Multi-tenant slicing for spectrum management on the road to 5G

©2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The explosive data traffic demand in the context of the 5G revolution has stressed the need for network capacity increase. As the network densification has almost reached its limits, mobile network operators are motivated to share their network infrastructure and the available resources through dynamic spectrum management. Although some initial efforts have been made in this direction by concluding sharing agreements at a coarse granularity (i.e., months or years), the 5G developments require fine timescale agreements, mainly enabled by network slicing. In this article, taking into account the radical changes foreseen for next generation networks, we provide a thorough discussion of the challenges that network slicing brings in the different network parts, while introducing a new entity capable of managing the end-to-end slicing in a coherent manner. In addition, according to the paradigm shift of operators sharing their resources in a common centralized pool, we design a cooperative game to study the potential cooperation aspects among the participants. The experimental results highlight the performance and financial gains achievable by operators through multi-tenant slicing, providing them with the necessary incentives for network upgrade toward 5G.Peer ReviewedPostprint (author's final draft

Application of reinforcement learning with Q-learning for the routing in industrial wireless sensors networks

Industrial Wireless Sensor Networks (IWSN) usually have a centralized management approach, where a device known as Network Manager is responsible for the overall configuration, definition of routes, and allocation of communication resources. The routing algorithms need to ensure path redundancy while reducing latency, power consumption, and resource usage. Graph routing algorithms are used to address these requirements. The dynamicity of wireless networks has been a challenge for tuning and developing routing algorithms, and Machine Learning models such as Reinforcement Learning have been applied in a promising way in Wireless Sensor Networks to select, adapt and optimize routes. The basic concept of Reinforcement Learning is the existence of a learning agent that acts and changes the state of the environment, and receives rewards. However, the existing approaches do not meet some of the requirements of the IWSN standards. In this context, this thesis proposes the Q-Learning Reliable Routing approach, where the Q-Learning model is used to build graph routes. Two approaches are presented: QLRR-WA and QLRR-MA. QLRR-WA uses a learning agent that adjusts the weights of the cost equation of a state-of-the-art routing algorithm to reduce the latency and increase the network lifetime. QLRR-MA uses several learning agents so nodes can choose connections in the graph trying to reduce the latency. Other contributions of this thesis are the performance comparison of the state-of-the-art graph-routing algorithms and the evaluation methodology proposed. The QLRR algorithms were evaluated in a WirelessHART simulator, considering industrial monitoring applications with random topologies. The performance was analyzed considering the average network latency, network lifetime, packet delivery ratio and the reliability of the graphs. The results showed that, when compared to the state of the art, QLRR-WA reduced the average network latency and improved the lifetime while keeping high reliability, while QLRR-MA reduced latency and increased packet delivery ratio with a reduction in the network lifetime. These results indicate that Reinforcement Learning may be helpful to optimize and improve network performance.As Redes Industriais de Sensores Sem Fio (IWSN) geralmente têm uma abordagem de gerenciamento centralizado, onde um dispositivo conhecido como Gerenciador de Rede é responsável pela configuração geral, definição de rotas e alocação de recursos de comunicação. Os algoritmos de roteamento precisam garantir a redundância de caminhos para as mensagens, e também reduzir a latência, o consumo de energia e o uso de recursos. O roteamento por grafos é usado para alcançar estes requisitos. A dinamicidade das redes sem fio tem sido um desafio para o ajuste e o desenvolvimento de algoritmos de roteamento, e modelos de Aprendizado de Máquina como o Aprendizado por Reforço têm sido aplicados de maneira promissora nas Redes de Sensores Sem Fio para selecionar, adaptar e otimizar rotas. O conceito básico do Aprendizado por Reforço envolve a existência de um agente de aprendizado que atua em um ambiente, altera o estado do ambiente e recebe recompensas. No entanto, as abordagens existentes não atendem a alguns dos requisitos dos padrões das IWSN. Nesse contexto, esta tese propõe a abordagem Q-Learning Reliable Routing, onde o modelo Q-Learning é usado para construir os grafos de roteamento. Duas abordagens são propostas: QLRR-WA e QLRR-MA. A abordagem QLRR-WA utiliza um agente de aprendizado que ajusta os pesos da equação de custo de um algoritmo de roteamento de estado da arte, com o objetivo de reduzir a latência e aumentar a vida útil da rede. A abordagem QLRR-MA utiliza diversos agente de aprendizado de forma que cada dispositivo na rede pode escolher suas conexões tentando reduzir a latência. Outras contribuições desta tese são a comparação de desempenho das abordagens com os algoritmos de roteamento de estado da arte e a metodologia de avaliação proposta. As abordagens do QLRR foram avaliadas com um simulador WirelessHART, considerando aplicações de monitoramento industrial com diversas topologias. O desempenho foi analisado considerando a latência média da rede, o tempo de vida esperado da rede, a taxa de entrega de pacotes e a confiabilidade dos grafos. Os resultados mostraram que, quando comparado com o estado da arte, o QLRR-WA reduziu a latência média da rede e melhorou o tempo de vida esperado, mantendo alta confiabilidade, enquanto o QLRR-MA reduziu a latência e aumentou a taxa de entrega de pacotes, ao custo de uma redução no tempo de vida esperado da rede. Esses resultados indicam que o Aprendizado por Reforço pode ser útil para otimizar e melhorar o desempenho destas redes