A Lesson in Scaling 6LoWPAN -- Minimal Fragment Forwarding in Lossy Networks

This paper evaluates two forwarding strategies for fragmented datagrams in the IoT: hop-wise reassembly and a minimal approach to directly forward fragments. Minimal fragment forwarding is challenged by the lack of forwarding information at subsequent fragments in 6LoWPAN and thus requires additional data at nodes. We compared the two approaches in extensive experiments evaluating reliability, end-to-end latency, and memory consumption. In contrast to previous work and due to our alternate setup, we obtained different results and conclusions. Our findings indicate that direct fragment forwarding should be deployed only with care, since higher packet transmission rates on the link-layer can significantly reduce its reliability, which in turn can even further reduce end-to-end latency because of highly increased link-layer retransmissions.Comment: If you cite this paper, please use the LCN reference: M. S. Lenders, T. C. Schmidt, M. W\"ahlisch. "A Lesson in Scaling 6LoWPAN - Minimal Fragment Forwarding in Lossy Networks." in Proc. of IEEE LCN, 201

Internet of Things From Hype to Reality

The Internet of Things (IoT) has gained significant mindshare, let alone attention, in academia and the industry especially over the past few years. The reasons behind this interest are the potential capabilities that IoT promises to offer. On the personal level, it paints a picture of a future world where all the things in our ambient environment are connected to the Internet and seamlessly communicate with each other to operate intelligently. The ultimate goal is to enable objects around us to efficiently sense our surroundings, inexpensively communicate, and ultimately create a better environment for us: one where everyday objects act based on what we need and like without explicit instructions

A holistic architecture using peer to peer (P2P) protocols for the internet of things and wireless sensor networks

Wireless Sensor Networks (WSNs) interact with the physical world using sensing and/or actuation. The wireless capability of WSN nodes allows them to be deployed close to the sensed phenomenon. Cheaper processing power and the use of micro IP stacks allow nodes to form an “Internet of Things” (IoT) integrating the physical world with the Internet in a distributed system of devices and applications. Applications using the sensor data may be located across the Internet from the sensor network, allowing Cloud services and Big Data approaches to store and analyse this data in a scalable manner, supported by new approaches in the area of fog and edge computing. Furthermore, the use of protocols such as the Constrained Application Protocol (CoAP) and data models such as IPSO Smart Objects have supported the adoption of IoT in a range of scenarios. IoT has the potential to become a realisation of Mark Weiser’s vision of ubiquitous computing where tiny networked computers become woven into everyday life. This presents the challenge of being able to scale the technology down to resource-constrained devices and to scale it up to billions of devices. This will require seamless interoperability and abstractions that can support applications on Cloud services and also on node devices with constrained computing and memory capabilities, limited development environments and requirements on energy consumption. This thesis proposes a holistic architecture using concepts from tuple-spaces and overlay Peer-to-Peer (P2P) networks. This architecture is termed as holistic, because it considers the flow of the data from sensors through to services. The key contributions of this work are: development of a set of architectural abstractions to provide application layer interoperability, a novel cache algorithm supporting leases, a tuple-space based data store for local and remote data and a Peer to Peer (P2P) protocol with an innovative use of a DHT in building an overlay network. All these elements are designed for implementation on a resource constrained node and to be extensible to server environments, which is shown in a prototype implementation. This provides the basis for a new P2P holistic approach that will allow Wireless Sensor Networks and IoT to operate in a self-organising ad hoc manner in order to deliver the promise of IoT

Identifying and Mitigating Security Risks in Multi-Level Systems-of-Systems Environments

In recent years, organisations, governments, and cities have taken advantage of the many benefits and automated processes Information and Communication Technology (ICT) offers, evolving their existing systems and infrastructures into highly connected and complex Systems-of-Systems (SoS). These infrastructures endeavour to increase robustness and offer some resilience against single points of failure. The Internet, Wireless Sensor Networks, the Internet of Things, critical infrastructures, the human body, etc., can all be broadly categorised as SoS, as they encompass a wide range of differing systems that collaborate to fulfil objectives that the distinct systems could not fulfil on their own. ICT constructed SoS face the same dangers, limitations, and challenges as those of traditional cyber based networks, and while monitoring the security of small networks can be difficult, the dynamic nature, size, and complexity of SoS makes securing these infrastructures more taxing. Solutions that attempt to identify risks, vulnerabilities, and model the topologies of SoS have failed to evolve at the same pace as SoS adoption. This has resulted in attacks against these infrastructures gaining prevalence, as unidentified vulnerabilities and exploits provide unguarded opportunities for attackers to exploit. In addition, the new collaborative relations introduce new cyber interdependencies, unforeseen cascading failures, and increase complexity. This thesis presents an innovative approach to identifying, mitigating risks, and securing SoS environments. Our security framework incorporates a number of novel techniques, which allows us to calculate the security level of the entire SoS infrastructure using vulnerability analysis, node property aspects, topology data, and other factors, and to improve and mitigate risks without adding additional resources into the SoS infrastructure. Other risk factors we examine include risks associated with different properties, and the likelihood of violating access control requirements. Extending the principals of the framework, we also apply the approach to multi-level SoS, in order to improve both SoS security and the overall robustness of the network. In addition, the identified risks, vulnerabilities, and interdependent links are modelled by extending network modelling and attack graph generation methods. The proposed SeCurity Risk Analysis and Mitigation Framework and principal techniques have been researched, developed, implemented, and then evaluated via numerous experiments and case studies. The subsequent results accomplished ascertain that the framework can successfully observe SoS and produce an accurate security level for the entire SoS in all instances, visualising identified vulnerabilities, interdependencies, high risk nodes, data access violations, and security grades in a series of reports and undirected graphs. The framework’s evolutionary approach to mitigating risks and the robustness function which can determine the appropriateness of the SoS, revealed promising results, with the framework and principal techniques identifying SoS topologies, and quantifying their associated security levels. Distinguishing SoS that are either optimally structured (in terms of communication security), or cannot be evolved as the applied processes would negatively impede the security and robustness of the SoS. Likewise, the framework is capable via evolvement methods of identifying SoS communication configurations that improve communication security and assure data as it traverses across an unsecure and unencrypted SoS. Reporting enhanced SoS configurations that mitigate risks in a series of undirected graphs and reports that visualise and detail the SoS topology and its vulnerabilities. These reported candidates and optimal solutions improve the security and SoS robustness, and will support the maintenance of acceptable and tolerable low centrality factors, should these recommended configurations be applied to the evaluated SoS infrastructure

Bioinspired metaheuristic algorithms for global optimization

This paper presents concise comparison study of newly developed bioinspired algorithms for global optimization problems. Three different metaheuristic techniques, namely Accelerated Particle Swarm Optimization (APSO), Firefly Algorithm (FA), and Grey Wolf Optimizer (GWO) are investigated and implemented in Matlab environment. These methods are compared on four unimodal and multimodal nonlinear functions in order to find global optimum values. Computational results indicate that GWO outperforms other intelligent techniques, and that all aforementioned algorithms can be successfully used for optimization of continuous functions

Experimental Evaluation of Growing and Pruning Hyper Basis Function Neural Networks Trained with Extended Information Filter

In this paper we test Extended Information Filter (EIF) for sequential training of Hyper Basis Function Neural Networks with growing and pruning ability (HBF-GP). The HBF neuron allows different scaling of input dimensions to provide better generalization property when dealing with complex nonlinear problems in engineering practice. The main intuition behind HBF is in generalization of Gaussian type of neuron that applies Mahalanobis-like distance as a distance metrics between input training sample and prototype vector. We exploit concept of neuron’s significance and allow growing and pruning of HBF neurons during sequential learning process. From engineer’s perspective, EIF is attractive for training of neural networks because it allows a designer to have scarce initial knowledge of the system/problem. Extensive experimental study shows that HBF neural network trained with EIF achieves same prediction error and compactness of network topology when compared to EKF, but without the need to know initial state uncertainty, which is its main advantage over EKF