Method and Approach Mapping of Fair and Balanced Risk and Value-added Distribution in Supply Chains: A Review and Future Agenda

This paper proposes a fair and balanced risk and value-added distribution as a novel approach for collaborative supply chain. The objective of this article is to analyze the existing methods and approaches for risk management, value-adding, risk and revenue sharing to develop a new framework for balancing risk and value-adding in collaborative supply chains. The authors reviewed and synthesized 162 scientific articles which were published between 2001 and 2017 and. The reviewed articles were categorized into supply chain management and performance, risk management, value-added, fair risk and value-added distribution and supply chain negotiation. The potentials identified for future research were the importance of decision-making and sustainability for effectiveness of supply chain risk management. Most previous authors have applied an approach of revenue and risk-- sharing with both decentralized and centralized supply chains to achieve the fair risk and value-added distribution. The dominant methods we found in literature were game theory and complex mathematical formulation. Most literature focused on operation research techniques. We identified a lack of discussion of the intelligent system approach and a potential for future exploration. This paper guide future research and application agenda of fair risk and value-added distribution in supply chain collaboration. We developed a new framework for a fair and balanced risk and value-added distribution model. For a future agenda, we point towards the development of a systematic intelligent system applying soft-computing techniques and knowledge transfer for maintaining sustainable supply chains.Keywords Supply chain collaboration, Fair risk and value-added distribution, Revenue sharing, Risk management, Risk sharin

An Algorithm for Integrated Subsystem Embodiment and System Synthesis

Consider the statement,'A system has two coupled subsystems, one of which dominates the design process. Each subsystem consists of discrete and continuous variables, and is solved using sequential analysis and solution.' To address this type of statement in the design of complex systems, three steps are required, namely, the embodiment of the statement in terms of entities on a computer, the mathematical formulation of subsystem models, and the resulting solution and system synthesis. In complex system decomposition, the subsystems are not isolated, self-supporting entities. Information such as constraints, goals, and design variables may be shared between entities. But many times in engineering problems, full communication and cooperation does not exist, information is incomplete, or one subsystem may dominate the design. Additionally, these engineering problems give rise to mathematical models involving nonlinear functions of both discrete and continuous design variables. In this dissertation an algorithm is developed to handle these types of scenarios for the domain-independent integration of subsystem embodiment, coordination, and system synthesis using constructs from Decision-Based Design, Game Theory, and Multidisciplinary Design Optimization. Implementation of the concept in this dissertation involves testing of the hypotheses using example problems and a motivating case study involving the design of a subsonic passenger aircraft

Physical layer security for IoT applications

The increasing demands for Internet of things (IoT) applications and the tremendous increase in the volume of IoT generated data bring novel challenges for the fifth generation (5G) network. Verticals such as e-Health, vehicle to everything (V2X) and unmanned aerial vehicles (UAVs) require solutions that can guarantee low latency, energy efficiency,massive connectivity, and high reliability. In particular, finding strong security mechanisms that satisfy the above is of central importance for bringing the IoT to life. In this regards, employing physical layer security (PLS) methods could be greatly beneficial for IoT networks. While current security solutions rely on computational complexity, PLS is based on information theoretic proofs. By removing the need for computational power, PLS is ideally suited for resource constrained devices. In detail, PLS can ensure security using the inherit randomness already present in the physical channel. Promising schemes from the physical layer include physical unclonable functions (PUFs), which are seen as the hardware fingerprint of a device, and secret key generation (SKG) from wireless fading coefficients, which provide the wireless fingerprint of the communication channel between devices. The present thesis develops several PLS-based techniques that pave the way for a new breed of latency-aware, lightweight, security protocols. In particular, the work proposes: i) a fast multi-factor authentication solution with verified security properties based on PUFs, proximity detection and SKG; ii) an authenticated encryption SKG approach that interweaves data transmission and key generation; and, iii) a set of countermeasures to man-in-the-middle and jamming attacks. Overall, PLS solutions show promising performance, especially in the context of IoT applications, therefore, the advances in this thesis should be considered for beyond-5G networks

Optimal day-ahead scheduling frameworks for e-mobility ecosystem operation with drivers preferences under uncertainties

Distribution networks are envisaged to host significant number of electric vehicles (EVs) and potentially many charging stations (CSs) in the future to provide charging as well as vehicle-to-grid (V2G) services to the electric vehicle owners. A high number of electric EVs in the transportation sector necessitates an advanced scheduling framework for e-mobility ecosystem operation as a whole in order to overcome range anxiety and create a viable business model for CSs. Thus, the future e-mobility ecosystem will be a complex structure with different stakeholders seeking to optimize their operation and benefits. The main goal of this study is to develop a comprehensive day-ahead grid-to vehicle (G2V) and V2G scheduling framework to achieve an economically rewarding operation for the ecosystem of EVs, CSs and retailers using a comprehensive optimal charging/discharging strategy that accounts for the network constraints. To do so, a non-cooperative Stackelberg game, which is formed among the three layers, is proposed. The leader of the Stackelberg game is the retailer and the first and second followers are CSs and EVs, respectively. EV routing problem is solved based on a cost-benefit analysis rather than choosing the shortest route. The proposed method can be implemented as a cloud scheduling system that is operated by a non-profit entity, e.g., distribution system operators or distribution network service providers, whose role is to collect required information from all agents, perform the day-ahead scheduling, and ultimately communicate the results to relevant stakeholders. To facilitate V2G services and to avoid congestion at CSs, two types of trips, i.e., mandatory and optional trips, are defined and formulated. Also, EV drivers’ preferences are added to the model as cost/revenue threshold and extra driving distance to enhance the practical aspects of the scheduling framework. The stochastic nature of all stakeholders’ operation and their mutual interactions are modelled by proposing a three-layer joint distributionally robust chance-constrained (DRCC) framework. The proposed stochastic model does not rely on a specific probability distribution for stochastic parameters. To achieve computational tractability, the exact reformulation is implemented for double-sided and single-sided chance constraints (CCs). Furthermore, the impact of temporal correlation of uncertain PV generation on CSs operation is considered. To solve the problem, an iterative process is proposed to solve the non cooperative Stackelberg game and joint DRCC model by determining the optimal routes and CS for each EV, optimal operation of each CS and retailers, and optimal V2G and G2V prices. Extensive simulation studies are carried out for a e-mobility ecosystem of multiple retailers and CSs as well as numerous EVs based on real data from San Francisco, the USA. The simulation results shows the necessity and applicability of such a scheduling method for the e-mobility ecosystem

Video Caching, Analytics and Delivery at the Wireless Edge: A Survey and Future Directions

Future wireless networks will provide high bandwidth, low-latency, and ultra-reliable Internet connectivity to meet the requirements of different applications, ranging from mobile broadband to the Internet of Things. To this aim, mobile edge caching, computing, and communication (edge-C3) have emerged to bring network resources (i.e., bandwidth, storage, and computing) closer to end users. Edge-C3 allows improving the network resource utilization as well as the quality of experience (QoE) of end users. Recently, several video-oriented mobile applications (e.g., live content sharing, gaming, and augmented reality) have leveraged edge-C3 in diverse scenarios involving video streaming in both the downlink and the uplink. Hence, a large number of recent works have studied the implications of video analysis and streaming through edge-C3. This article presents an in-depth survey on video edge-C3 challenges and state-of-the-art solutions in next-generation wireless and mobile networks. Specifically, it includes: a tutorial on video streaming in mobile networks (e.g., video encoding and adaptive bitrate streaming); an overview of mobile network architectures, enabling technologies, and applications for video edge-C3; video edge computing and analytics in uplink scenarios (e.g., architectures, analytics, and applications); and video edge caching, computing and communication methods in downlink scenarios (e.g., collaborative, popularity-based, and context-aware). A new taxonomy for video edge-C3 is proposed and the major contributions of recent studies are first highlighted and then systematically compared. Finally, several open problems and key challenges for future research are outlined

Resource and power management in next generation networks

The limits of today’s cellular communication systems are constantly being tested by the exponential increase in mobile data traffic, a trend which is poised to continue well into the next decade. Densification of cellular networks, by overlaying smaller cells, i.e., micro, pico and femtocells, over the traditional macrocell, is seen as an inevitable step in enabling future networks to support the expected increases in data rate demand. Next generation networks will most certainly be more heterogeneous as services will be offered via various types of points of access (PoAs). Indeed, besides the traditional macro base station, it is expected that users will also be able to access the network through a wide range of other PoAs: WiFi access points, remote radio-heads (RRHs), small cell (i.e., micro, pico and femto) base stations or even other users, when device-to-device (D2D) communications are supported, creating thus a multi-tiered network architecture. This approach is expected to enhance the capacity of current cellular networks, while patching up potential coverage gaps. However, since available radio resources will be fully shared, the inter-cell interference as well as the interference between the different tiers will pose a significant challenge. To avoid severe degradation of network performance, properly managing the interference is essential. In particular, techniques that mitigate interference such Inter Cell Interference Coordination (ICIC) and enhanced ICIC (eICIC) have been proposed in the literature to address the issue. In this thesis, we argue that interference may be also addressed during radio resource scheduling tasks, by enabling the network to make interference-aware resource allocation decisions. Carrier aggregation technology, which allows the simultaneous use of several component carriers, on the other hand, targets the lack of sufficiently large portions of frequency spectrum; a problem that severely limits the capacity of wireless networks. The aggregated carriers may, in general, belong to different frequency bands, and have different bandwidths, thus they also may have very different signal propagation characteristics. Integration of carrier aggregation in the network introduces additional tasks and further complicates interference management, but also opens up a range of possibilities for improving spectrum efficiency in addition to enhancing capacity, which we aim to exploit. In this thesis, we first look at the resource allocation in problem in dense multitiered networks with support for advanced features such as carrier aggregation and device-to-device communications. For two-tiered networks with D2D support, we propose a centralised, near optimal algorithm, based on dynamic programming principles, that allows a central scheduler to make interference and traffic-aware scheduling decisions, while taking into consideration the short-lived nature of D2D links. As the complexity of the central scheduler increases exponentially with the number of component carriers, we further propose a distributed heuristic algorithm to tackle the resource allocation problem in carrier aggregation enabled dense networks. We show that the solutions we propose perform significantly better than standard solutions adopted in cellular networks such as eICIC coupled with Proportional Fair scheduling, in several key metrics such as user throughput, timely delivery of content and spectrum and energy efficiency, while ensuring fairness for backward compatible devices. Next, we investigate the potentiality to enhance network performance by enabling the different nodes of the network to reduce and dynamically adjust the transmit power of the different carriers to mitigate interference. Considering that the different carriers may have different coverage areas, we propose to leverage this diversity, to obtain high-performing network configurations. Thus, we model the problem of carrier downlink transmit power setting, as a competitive game between teams of PoAs, which enables us to derive distributed dynamic power setting algorithms. Using these algorithms we reach stable configurations in the network, known as Nash equilibria, which we show perform significantly better than fixed power strategies coupled with eICIC

Multi-Level Multi-Objective Programming and Optimization for Integrated Air Defense System Disruption

The U.S. military\u27s ability to project military force is being challenged. This research develops and demonstrates the application of three respective sensor location, relocation, and network intrusion models to provide the mathematical basis for the strategic engagement of emerging technologically advanced, highly-mobile, Integrated Air Defense Systems. First, we propose a bilevel mathematical programming model for locating a heterogeneous set of sensors to maximize the minimum exposure of an intruder\u27s penetration path through a defended region. Next, we formulate a multi-objective, bilevel optimization model to relocate surviving sensors to maximize an intruder\u27s minimal expected exposure to traverse a defended border region, minimize the maximum sensor relocation time, and minimize the total number of sensors requiring relocation. Lastly, we present a trilevel, attacker-defender-attacker formulation for the heterogeneous sensor network intrusion problem to optimally incapacitate a subset of the defender\u27s sensors and degrade a subset of the defender\u27s network to ultimately determine the attacker\u27s optimal penetration path through a defended network

\u3ci\u3eThe Conference Proceedings of the 1998 Air Transport Research Group (ATRG) of the WCTR Society, Volume 4 \u3c/i\u3e

UNOAI Report 98-9https://digitalcommons.unomaha.edu/facultybooks/1152/thumbnail.jp