The effect of group mobility on the efficacy of routing in next generation mobile networks

© 2016 IEEE.A key challenge in next generation mobile networks is ensuring effective routing that efficiently adapts to the special characteristics of the various mobility schemes. The purpose of this paper is to study and illustrate how group mobility affects the network performance of a wireless ad hoc network depending on the type of movement, in a space with or without obstacles. In the scope of this paper, we created a simulator of a MANET that uses AODV routing protocol, while the entities of the network move according to the chosen group mobility model. Despite the fact that the routing protocol supports mobility in general, the results greatly vary depending on the specific mobility scenario. The strong connection between mobility properties and network performance is revealed

Connectivity and coverage in machine-type communications

Machine-type communication (MTC) provides a potential playground for deploying machine-to-machine (M2M), IP-enabled 'things' and wireless sensor networks (WSNs) that support modern, added-value services and applications. 4G/5G technology can facilitate the connectivity and the coverage of the MTC entities and elements by providing M2M-enabled gateways and base stations for carrying traffic streams to/from the backbone network. For example, the latest releases of long-term evolution (LTE) such as LTE-Advanced (LTE-A) are being transformed to support the migration of M2M devices. MTC-oriented technical definitions and requirements are defined to support the emerging M2M proliferation. ETSI describes three types of MTC access methods, namely a) the direct access, b) the gateway access and c) the coordinator access. This work is focused on studying coverage aspects when a gateway access takes place. A deployment planar field is considered where a number of M2M devices are randomly deployed, e.g., a hospital where body sensor networks form a M2M infrastructure. An analytical framework is devised that computes the average number of connected M2M devices when a M2C gateway is randomly placed for supporting connectivity access to the M2M devices. The introduced analytical framework is verified by simulation and numerical results

Connectivity and coverage in machine-type communications

Machine-type communication (MTC) provides a potential playground for deploying machine-to-machine (M2M), IP-enabled 'things' and wireless sensor networks (WSNs) that support modern, added-value services and applications. 4G/5G technology can facilitate the connectivity and the coverage of the MTC entities and elements by providing M2M-enabled gateways and base stations for carrying traffic streams to/from the backbone network. For example, the latest releases of long-term evolution (LTE) such as LTE-Advanced (LTE-A) are being transformed to support the migration of M2M devices. MTC-oriented technical definitions and requirements are defined to support the emerging M2M proliferation. ETSI describes three types of MTC access methods, namely a) the direct access, b) the gateway access and c) the coordinator access. This work is focused on studying coverage aspects when a gateway access takes place. A deployment planar field is considered where a number of M2M devices are randomly deployed, e.g., a hospital where body sensor networks form a M2M infrastructure. An analytical framework is devised that computes the average number of connected M2M devices when a M2C gateway is randomly placed for supporting connectivity access to the M2M devices. The introduced analytical framework is verified by simulation and numerical results

The impact of mobility patterns on the efficiency of data forwarding in MANETs

One of the most challenging requirements in cutting-edge Mobile Ad hoc Networks is the need for adaptive and efficient routing. Networks capable of adapting their behavior based on current conditions are often characterized as self-organizing networks, which are lately considered very promising for future applications. This work examines the impact of the different mobility properties on the performance of self-organizing networks. For that purpose, a simulator was developed to model different mobility patterns and study the way they affect the effectiveness of the well-known AODV routing protocol. Particularly, this paper focuses on the effect of the different mobility schemes on network topology and consequently to the overall network performance. The results reveal the tight correlations between node mobility characteristics and network metrics

Testing hypotheses about the harm that capitalism causes to the mind and brain: a theoretical framework for neuroscience research

In this paper, we will attempt to outline the key ideas of a theoretical framework for neuroscience research that reflects critically on the neoliberal capitalist context. We argue that neuroscience can and should illuminate the effects of neoliberal capitalism on the brains and minds of the population living under such socioeconomic systems. Firstly, we review the available empirical research indicating that the socio-economic environment is harmful to minds and brains. We, then, describe the effects of the capitalist context on neuroscience itself by presenting how it has been influenced historically. In order to set out a theoretical framework that can generate neuroscientific hypotheses with regard to the effects of the capitalist context on brains and minds, we suggest a categorization of the effects, namely deprivation, isolation and intersectional effects. We also argue in favor of a neurodiversity perspective [as opposed to the dominant model of conceptualizing neural (mal-)functioning] and for a perspective that takes into account brain plasticity and potential for change and adaptation. Lastly, we discuss the specific needs for future research as well as a frame for post-capitalist research

Advancing SDN from OpenFlow to P4: a survey

Software-defined Networking (SDN) marked the beginning of a new era in the field of networking by decoupling the control and forwarding processes through the OpenFlow protocol. The Next Generation SDN is defined by Open Interfaces and full programmability of the data plane. P4 is a domain-specific language that fulfills these requirements and has known wide adoption over recent years from Academia and Industry. This work is an extensive survey of the P4 language covering domains of application, a detailed overview of the language, and future directions

Testing hypotheses about the harm that capitalism causes to the mind and brain: a theoretical framework for neuroscience research

In this paper, we will attempt to outline the key ideas of a theoretical framework for neuroscience research that reflects critically on the neoliberal capitalist context. We argue that neuroscience can and should illuminate the effects of neoliberal capitalism on the brains and minds of the population living under such socioeconomic systems. Firstly, we review the available empirical research indicating that the socio-economic environment is harmful to minds and brains. We, then, describe the effects of the capitalist context on neuroscience itself by presenting how it has been influenced historically. In order to set out a theoretical framework that can generate neuroscientific hypotheses with regards to the effects of the capitalist context on brains and minds, we suggest a categorization of the effects, namely deprivation, isolation and intersectional effects. We also argue in favor of a neurodiversity perspective [as opposed to the dominant model of conceptualizing neural (mal-)functioning] and for a perspective that takes into account brain plasticity and potential for change and adaptation. Lastly, we discuss the specific needs for future research as well as a frame for post-capitalist research

End-to-end deep graph convolutional neural network approach for intentional islanding in power systems considering load-generation balance

Intentional islanding is a corrective procedure that aims to protect the stability of the power system during an emergency, by dividing the grid into several partitions and isolating the elements that would cause cascading failures. This paper proposes a deep learning method to solve the problem of intentional islanding in an end-to-end manner. Two types of loss functions are examined for the graph partitioning task, and a loss function is added on the deep learning model, aiming to minimise the load-generation imbalance in the formed islands. In addition, the proposed solution incorporates a technique for merging the independent buses to their nearest neighbour in case there are isolated buses after the clusterisation, improving the final result in cases of large and complex systems. Several experiments demonstrate that the introduced deep learning method provides effective clustering results for intentional islanding, managing to keep the power imbalance low and creating stable islands. Finally, the proposed method is dynamic, relying on real-time system conditions to calculate the result

Digital product passports as enablers of digital circular economy: a framework based on technological perspective

Taking into consideration the existing Industry 4.0 infrastructures and the rise of Industry 5.0 (I5.0), more and more solutions are being developed, aiming towards increased environmental consciousness through advanced technologies, and human centricity. However, there are ongoing requirements on data traceability, and access to the related actors, to ensure the establishment of sustainable solutions, within the context of a digital circular economy (DCE) environment. Digital product passports (DPPs) constitute such novel technological solution that can enable the transition toward DCE and sustainable I4.0 and I5.0, as digital identities that are assigned to physical products, capable of tracing their lifecycles through data such as their technical specifications, usage instructions, and repair and maintenance information. Although the respective research community has started providing a thorough analysis of DPPs potential to constitute a CE enabler, their technical requirements are still unclear. As part of our contribution to this issue, we propose a fundamental CE framework with integrated DPP characteristics, with the potential of being adapted in different sector stages for the generation and distribution of DPPs both for stakeholders and consumers. The corresponding solution is further supported through a systematic literature review that follows a technological approach to the DPPs implementation

Joint wireless resource and computation offloading optimization for energy efficient internet of vehicles

The Internet of Vehicles (IoV) is an emerging paradigm, which is expected to be an integral component of beyond-fifth-generation and sixth-generation mobile networks. However, the processing requirements and strict delay constraints of IoV applications pose a challenge to vehicle processing units. To this end, multi-access edge computing (MEC) can leverage the availability of computing resources at the edge of the network to meet the intensive computation demands. Nevertheless, the optimal allocation of computing resources is challenging due to the various parameters, such as the number of vehicles, the available resources, and the particular requirements of each task. In this work, we consider a network consisting of multiple vehicles connected to MEC-enabled roadside units (RSUs) and propose an approach that minimizes the total energy consumption of the system by jointly optimizing the task offloading decision, the allocation of power and bandwidth, and the assignment of tasks to MEC-enabled RSUs. Due to the original problem complexity, we decouple it into subproblems and we leverage the block coordinate descent method to iteratively optimize them. Finally, the numerical results demonstrate that the proposed solution can effectively minimize total energy consumption for various numbers of vehicles and MEC nodes while maintaining a low outage probability