Cooperative mobility maintenance techniques for information extraction from mobile wireless sensor networks

Recent advances in the development of microprocessors, microsensors, ad-hoc wireless networking and information fusion algorithms led to increasingly capable Wireless Sensor Networks (WSNs). Besides severe resource constraints, sensor nodes mobility is considered a fundamental characteristic of WSNs. Information Extraction (IE) is a key research area within WSNs that has been characterised in a variety of ways, ranging from a description of its purposes to reasonably abstract models of its processes and components. The problem of IE is a challenging task in mobile WSNs for several reasons including: the topology changes rapidly; calculation of trajectories and velocities is not a trivial task; increased data loss and data delivery delays; and other context and application specific challenges. These challenges offer fundamentally new research problems. There is a wide body of literature about IE from static WSNs. These approaches are proved to be effective and efficient. However, there are few attempts to address the problem of IE from mobile WSNs. These attempts dealt with mobility as the need arises and do not deal with the fundamental challenges and variations introduced by mobility on the WSNs. The aim of this thesis is to develop a solution for IE from mobile WSNs. This aim is achieved through the development of a middle-layer solution, which enables IE approaches that were designed for the static WSNs to operate in the presence of multiple mobile nodes. This thesis contributes toward the design of a new self-stabilisation algorithm that provides autonomous adaptability against nodes mobility in a transparent manner to both upper network layers and user applications. In addition, this thesis proposes a dynamic network partitioning protocol to achieve high quality of information, scalability and load balancing. The proposed solution is flexible, may be applied to different application domains, and less complex than many existing approaches. The simplicity of the solutions neither demands great computational efforts nor large amounts of energy conservation. Intensive simulation experiments with real-life parameters provide evidence of the efficiency of the proposed solution. Performance experimentations demonstrate that the integrated DNP/SS protocol outperforms its rival in the literature in terms of timeliness (by up to 22%), packet delivery ratio (by up to 13%), network scalability (by up to 25%), network lifetime (by up to 40.6%), and energy consumption (by up to 39.5%). Furthermore, it proves that DNP/SS successfully allows the deployment of static-oriented IE approaches in hybrid networks without any modifications or adaptations

A methodology for real-time data sustainability in smart city: Towards inferencing and analytics for big-data

International audienc

Wireless Sensor Network Simulation: The Current State and Simulation Tools

This paper presents a survey of simulation tools and methods for wireless sensor networks. Wireless sensor network modelling and simulation methodologies are presented alongside judgments concerning their capabilities, relative ease of use and accuracy. Finally, we propose mixed-mode simulation methodology that integrates a simulated environment with real wireless sensor network testbed hardware in order to improve both the accuracy and scalability of results when evaluating different prototype designs and systems

Integration of federated machine learning and blockchain for the provision of secure big data analytics for Internet of Things

Big data enables the optimization of complex supply chains through Machine Learning (ML)-based data analytics. However, data analytics comes with challenges such as the loss of control and privacy leading to increased risk of data breaches. Federated Learning (FL) is an approach in the ML arena that promises privacy-preserving and distributed model training. However, recent attacks on FL algorithms have raised concerns about the security of this approach. In this article, we advocate using Blockchain to mitigate attacks on FL algorithms operating in Internet of Things (IoT) systems. Integrating Blockchain and FL allows securing the trained models’ integrity, thus preventing model poisoning attacks. This research presents a practical approach for the integration of Blockchain with FL to provide privacy-preserving and secure big data analytics services. To protect the security of user data and the trained models, we propose utilizing fuzzy hashing to detect variations and anomalies in FL-trained models against poisoning attacks. The proposed solution is evaluated via simulating attack modes in a quasi-simulated environment

Secure and Reliable IoT Networks Using Fog Computing with Software-Defined Networking and Blockchain

Designing Internet of Things (IoT) applications faces many challenges including security, massive traffic, high availability, high reliability and energy constraints. Recent distributed computing paradigms, such as Fog and multi-access edge computing (MEC), software-defined networking (SDN), network virtualization and blockchain can be exploited in IoT networks, either combined or individually, to overcome the aforementioned challenges while maintaining system performance. In this paper, we present a framework for IoT that employs an edge computing layer of Fog nodes controlled and managed by an SDN network to achieve high reliability and availability for latency-sensitive IoT applications. The SDN network is equipped with distributed controllers and distributed resource constrained OpenFlow switches. Blockchain is used to ensure decentralization in a trustful manner. Additionally, a data offloading algorithm is developed to allocate various processing and computing tasks to the OpenFlow switches based on their current workload. Moreover, a traffic model is proposed to model and analyze the traffic indifferent parts of the network. The proposed algorithm is evaluated in simulation and in a testbed. Experimental results show that the proposed framework achieves higher efficiency in terms of latency and resource utilization

Chaotic salp swarm algorithm for SDN multi-controller networks

Software-defined networking (SDN) is a novel network paradigm that enables flexible management for networks. However, with the increase in network capacity, a single controller of SDN has many limitations on both performance and scalability. Distributed multi-controller deployment is a promising method to satisfy fault tolerant and scalability. There are still open research issues related to controllers placement, and the optimal number of deployed controllers. In this paper, a dynamic optimization algorithm that is based on the Salp Swarm Optimization Algorithm (SSOA) is developed with the introduction of chaotic maps for enhancing the optimizer’s performance. The algorithm dynamically evaluates the optimum number of controllers and the optimal connections between switches and controllers in large scale SDN networks. In order to evaluate the proposed algorithm, several experiments were conducted and implemented in various scenarios. Moreover, the algorithm was compared to the linear and meta-heuristic algorithms. Simulation results show that the proposed algorithm outperforms meta-heuristic algorithms and a game theory based algorithm in terms of execution time and reliability.publishedVersionPeer reviewe

Model Mediation to Overcome Light Limitations—Toward a Secure Tactile Internet System

Enabling haptic communication as well as voice and data over the future 5G cellular system has become a demand. Tactile Internet is one of the main use cases of the 5G system that will allow the transfer of haptic communications in real time. Latency, availability, reliability, and security are the main design challenges of the tactile Internet system and haptic based bilateral teleoperation systems. An end-to-end latency of 1 ms remains the main challenge toward tactile Internet system realization, not only for the processing and coding delays but mainly for the limitations of light. In this work, we analyze the key solutions to overcome the light limitations and enable the tactile Internet over any distances with the required latency. Building a virtual model or model mediated for the remote environment at the edge cloud unit near to the end user is the main solution. By means of AI, the virtual model can predict the behavior of the remote environment and thus, the end user can interact with the virtual environment with a high system experience. This literature review covers the existing work of the model mediated bilateral teleoperated systems and discusses its availability for the tactile Internet system. Furthermore, the security issues of tactile Internet system and the effect of model mediated system on the required security level of tactile Internet applications are discussed. Finally, a structure for the tactile Internet system, with the deployment of model mediation, is suggested