A Max-Min Task Offloading Algorithm for Mobile Edge Computing Using Non-Orthogonal Multiple Access

To mitigate computational power gap between the network core and edges, mobile edge computing (MEC) is poised to play a fundamental role in future generations of wireless networks. In this letter, we consider a non-orthogonal multiple access (NOMA) transmission model to maximize the worst task to be offloaded among all users to the network edge server. A provably convergent and efficient algorithm is developed to solve the considered non-convex optimization problem for maximizing the minimum number of offloaded bits in a multi-user NOMAMEC system. Compared to the approach of optimized orthogonal multiple access (OMA), for given MEC delay, power and energy limits, the NOMA-based system considerably outperforms its OMA-based counterpart in MEC settings. Numerical results demonstrate that the proposed algorithm for NOMA-based MEC is particularly useful for delay sensitive applications.Comment: 5 pages, 5 figure

Transmedia intertextuality - Does it work in performance? Follow the Sun as a proof of concept project

Follow the Sun is a contemporary music project that explores transmedia intertextuality in performance. It is a proof of concept exercise that is designed to transform the landscape for future generations of cultural consumers and change the culture of expectation in hard to reach communities. It combines creative content into a multi-platform and interactive global performance project where each community devises and controls the content. It will inhabit the contemporary version of our physical realm – and trigger responses on an individual and societal level by mapping onto the socio-cultural and environmental history of all who devise and experience it. Unrestricted and equal access to/participation for all, irrespective of level, ability or environment. The project is accessible via one/many/all available platforms

Circular Economy Snapshot: BMW Drivenow

BMW's views of the future of car ownership influenced its decision to enter the car-sharing business. DriveNow estimates that an average car is used only about 4% of the time and with half the global population predicted to live in cities by 2050 and parking becoming ever more difficult, urban residents are increasingly looking for alternatives to ownership. Observing that in cities that have embraced car-sharing a single such vehicle has the potential to replace dozens of cars, the company determined it needed to be in the car-sharing sector. It also allows BMW to access customers it normally has trouble reaching, as the average age of the company's buyer is in their mid-40s but the average age of a car sharing user is 32. Younger generations are not as attached to car ownership and continue to make multi-modal choices in transportation.While in the past BMW Group was in the business of selling cars, by the year 2020 it has a vision to be the world's leading provider of premium vehicles and premium services for individual mobility – where cars are provided as a mobility service. The company is equally seeking to make mobility climate-friendly and easy on resources, and has been increasingly combining its car-sharing offers with electric drivetrain solutions which generate zero emissions.Their newest electric vehicle model (i3) incorporates recycled and eco-friendly materials and is being introduced to DriveNow customers

Toward Open Integrated Access and Backhaul with O-RAN

Millimeter wave (mmWave) communications has been recently standardized for use in the fifth generation (5G) of cellular networks, fulfilling the promise of multi-gigabit mobile throughput of current and future mobile radio network generations. In this context, the network densification required to overcome the difficult mmWave propagation will result in increased deployment costs. Integrated Access and Backhaul (IAB) has been proposed as an effective mean of reducing densification costs by deploying a wireless mesh network of base stations, where backhaul and access transmissions share the same radio technology. However, IAB requires sophisticated control mechanisms to operate efficiently and address the increased complexity. The Open Radio Access Network (RAN) paradigm represents the ideal enabler of RAN intelligent control, but its current specifications are not compatible with IAB. In this work, we discuss the challenges of integrating IAB into the Open RAN ecosystem, detailing the required architectural extensions that will enable dynamic control of 5G IAB networks. We implement the proposed integrated architecture into the first publiclyavailable Open-RAN-enabled experimental framework, which allows prototyping and testing Open-RAN-based solutions over end-to-end 5G IAB networks. Finally, we validate the framework with both ideal and realistic deployment scenarios exploiting the large-scale testing capabilities of publicly available experimental platforms

Multi Agent DeepRL based Joint Power and Subchannel Allocation in IAB networks

Integrated Access and Backhauling (IAB) is a viable approach for meeting the unprecedented need for higher data rates of future generations, acting as a cost-effective alternative to dense fiber-wired links. The design of such networks with constraints usually results in an optimization problem of non-convex and combinatorial nature. Under those situations, it is challenging to obtain an optimal strategy for the joint Subchannel Allocation and Power Allocation (SAPA) problem. In this paper, we develop a multi-agent Deep Reinforcement Learning (DeepRL) based framework for joint optimization of power and subchannel allocation in an IAB network to maximize the downlink data rate. SAPA using DDQN (Double Deep Q-Learning Network) can handle computationally expensive problems with huge action spaces associated with multiple users and nodes. Unlike the conventional methods such as game theory, fractional programming, and convex optimization, which in practice demand more and more accurate network information, the multi-agent DeepRL approach requires less environment network information. Simulation results show the proposed scheme's promising performance when compared with baseline (Deep Q-Learning Network and Random) schemes.Comment: 7 pages, 6 figures, Accepted at the European Conference on Communication Systems (ECCS) 202

A resilient key predistribution scheme for multiphase wireless sensor networks

In wireless sensor networks, sensor nodes eventually die due to battery depletion. Wireless sensor networks (WSNs) in which new nodes are periodically redeployed with certain intervals, called generations, to replace the dead nodes are called multi-phase wireless sensor networks. In the literature, there are several key predistribution schemes proposed for secure operation of WSNs. However, these schemes are designed for single phase networks which are not resilient against continuous node capture attacks; even under temporary attacks on the network, the harm caused by the attacker does not heal in time. However, the periodic deployments in multi-phase sensor networks could be utilized to improve the resiliency of the WSNs by deploying nodes with fresh keys. In the literature, there is limited work done in this area. In this paper, we propose a key predistribution scheme for multi-phase wireless sensor networks which is highly resilient under node capture attacks. In our scheme, called RGM (random generation material) key predistribution scheme, each generation of deployment has its own random keying material and pairwise keys are established between node pairs of particular generations. These keys are specific to these generations. Therefore, a captured node cannot be abused to obtain keys of other generations. We compare the performance of our RGM scheme with a well-known multi-phase key predistribution scheme and showed that RGM achieves up to three-fold more resiliency. Even under heavy attacks, our scheme's resiliency performance is 50% better in steady state

The impact of generational differences on the workplace

Purpose – The aim of this paper is to explore workplace implications of the changing workforce demographic. Design/methodology/approach – The author identifies the different generations in today's workforce. The workplace expectations of the different generations are explored. Findings – Corporate real estate (CRE) managers need to establish the different needs of the different generations. In addition, the CRE manager needs to create an environment that allows all generations to coexist in the same workplace. Practical implications – CRE managers can use the information to assist in alignment of their workplace to the different generational expectations of the workforce. Originality/value – The paper fills a void by evaluating office occupiers' workplace preferences based on age.</p