Owner-Intruder Contests with Information Asymmetry

Owner-Intruder Contests with Information Asymmetry Faheem Farooq, Depts. of Biology and Chemistry, Jay Bisen, Manaeil Hasan, and Akhil Patel, with Dr. Jan Rychtar, Dept. of Mathematics and Discrete Mathematics, and Dr. Dewey T. Taylor, Dept. of Mathematics and Discrete Mathematics We consider kleptoparasitic interactions between two individuals - Owner and Intruder - and model the situation as a sequential game in an extensive form. Owner is in a possession of a valuable resource when it spots Intruder. Owner has to decide whether to defend the resource; if the Owner defends, the Intruder has to decide whether to fight with the Owner. The individuals may value the resource differently and we distinguish three information cases: (a) both individuals know resource values to both of them, (b) individuals know only their own valuation, (c) individuals do not know the value at all. We solve the game in all three cases. We find that it is typically beneficial for the individuals to know as much information as possible. However, we identify several scenarios where knowing less seems better. We also show that an individual may or may not benefit from their opponent knowing less. Finally, we consider the same kind of interactions but with the reversed order of decisions. We find that typically the individual initiating the interaction has an advantage. However, when individuals know only their own valuation and not the valuations to their opponents, it is sometimes better when the opponent initiates.https://scholarscompass.vcu.edu/uresposters/1298/thumbnail.jp

Leveraging intelligence from network CDR data for interference aware energy consumption minimization

Cell densification is being perceived as the panacea for the imminent capacity crunch. However, high aggregated energy consumption and increased inter-cell interference (ICI) caused by densification, remain the two long-standing problems. We propose a novel network orchestration solution for simultaneously minimizing energy consumption and ICI in ultra-dense 5G networks. The proposed solution builds on a big data analysis of over 10 million CDRs from a real network that shows there exists strong spatio-temporal predictability in real network traffic patterns. Leveraging this we develop a novel scheme to pro-actively schedule radio resources and small cell sleep cycles yielding substantial energy savings and reduced ICI, without compromising the users QoS. This scheme is derived by formulating a joint Energy Consumption and ICI minimization problem and solving it through a combination of linear binary integer programming, and progressive analysis based heuristic algorithm. Evaluations using: 1) a HetNet deployment designed for Milan city where big data analytics are used on real CDRs data from the Telecom Italia network to model traffic patterns, 2) NS-3 based Monte-Carlo simulations with synthetic Poisson traffic show that, compared to full frequency reuse and always on approach, in best case, proposed scheme can reduce energy consumption in HetNets to 1/8th while providing same or better Qo

MULTIPLE METRICS AD-HOC ROUTING PROTOCOL FOR SMART METERING INFRASTRUCTURE

Smart grid is a modern version of power grid which utilizes integrated communication network for enhanced power generation, transmission, distribution and consumption. Smart metering infrastructure is one of the core components ofsmart grid paradigm in which smart meters in addition to their primary billing functions serve as distributed sensor nodes for enhanced grid's reliability. Smart metering communication network makes available the power usage related measurements of the customers to electricity companies in real time for greatly enhancing the planning, operation and outage response ofthe grid

Motion data analysis using accelerometer : for aircraft mobility studies and shipping industry

There are millions of shipments in transit every day. Some of the shipments are important enough that they are needed to be tracked and monitored during their journey. There are various methods to track the shipments, e.g., by scanning the bar-codes, tracking the courier, tracking the vehicle, and tracking the actual shipment. This thesis concerns an asset tracking device, which is attached to the shipment at the origin, then the shipment is tracked using this asset tracking device during transit, and finally, the device is detached from the shipment once it reaches its destination. The device is an IoT-based hardware equipped with multiple sensors (accelerometer, thermometer, barometer, and hygrometer), communication modules, a micro-controller, and a battery. It is sometimes required to attach the asset tracking device in a powered-on state to the shipment long before it leaves the origin warehouse. The device needs to consume as little power as possible in this idle state to have enough battery power left to track the journey when the shipment actually leaves the warehouse. The proposed solution in this thesis uses accelerometer data to detect any motion. This information is used to keep the device in a low-power mode as long as there is no motion. The device starts to operate in the normal mode once it detects movement; hence it leaves the origin warehouse with more battery capacity, which enables it to track the journey better. Secondly, shipments can be mishandled during transit and damaged upon arrival. This thesis proposes an algorithm to detect and report undesirable shocks that can potentially break the asset. Corrective actions can be taken beforehand if the mishandling is detected as soon as it occurs, reducing the time and the associated monetary costs incurred upon arrival of a broken shipment. Finally, to enable the use of air cargo, the asset tracking device needs to have an autonomous flight mode in which the cellular modem must be turned off to comply with aviation regulations. A method is proposed to automatically detect the plane take-off using acceleration and air pressure data which triggers the flight mode autonomously in the asset tracking device

A PARADIGM SHIFTING APPROACH IN SON FOR FUTURE CELLULAR NETWORKS

The race to next generation cellular networks is on with a general consensus in academia and industry that massive densification orchestrated by self-organizing networks (SONs) is the cost-effective solution to the impending mobile capacity crunch. While the research on SON commenced a decade ago and is still ongoing, the current form (i.e., the reactive mode of operation, conflict-prone design, limited degree of freedom and lack of intelligence) hinders the current SON paradigm from meeting the requirements of 5G. The ambitious quality of experience (QoE) requirements and the emerging multifarious vision of 5G, along with the associated scale of complexity and cost, demand a significantly different, if not totally new, approach to SONs in order to make 5G technically as well as financially feasible. This dissertation addresses these limitations of state-of-the-art SONs. It first presents a generic low-complexity optimization framework to allow for the agile, on-line, multi-objective optimization of future mobile cellular networks (MCNs) through only top-level policy input that prioritizes otherwise conflicting key performance indicators (KPIs) such as capacity, QoE, and power consumption. The hybrid, semi-analytical approach can be used for a wide range of cellular optimization scenarios with low complexity. The dissertation then presents two novel, user-mobility, prediction-based, proactive self-optimization frameworks (AURORA and OPERA) to transform mobility from a challenge into an advantage. The proposed frameworks leverage mobility to overcome the inherent reactiveness of state-of-the-art self-optimization schemes to meet the extremely low latency and high QoE expected from future cellular networks vis-à-vis 5G and beyond. The proactiveness stems from the proposed frameworks’ novel capability of utilizing past hand-over (HO) traces to determine future cell loads instead of observing changes in cell loads passively and then reacting to them. A semi-Markov renewal process is leveraged to build a model that can predict the cell of the next HO and the time of the HO for the users. A low-complexity algorithm has been developed to transform the predicted mobility attributes to a user-coordinate level resolution. The learned knowledge base is used to predict the user distribution among cells. This prediction is then used to formulate a novel (i) proactive energy saving (ES) optimization problem (AURORA) that proactively schedules cell sleep cycles and (ii) proactive load balancing (LB) optimization problem (OPERA). The proposed frameworks also incorporate the effect of cell individual offset (CIO) for balancing the load among cells, and they thus exploit an additional ultra-dense network (UDN)-specific mechanism to ensure QoE while maximizing ES and/or LB. The frameworks also incorporates capacity and coverage constraints and a load-aware association strategy for ensuring the conflict-free operation of ES, LB, and coverage and capacity optimization (CCO) SON functions. Although the resulting optimization problems are combinatorial and NP-hard, proactive prediction of cell loads instead of reactive measurement allows ample time for combination of heuristics such as genetic programming and pattern search to find solutions with high ES and LB yields compared to the state of the art. To address the challenge of significantly higher cell outage rates in anticipated in 5G and beyond due to higher operational complexity and cell density than legacy networks, the dissertation’s fourth key contribution is a stochastic analytical model to analyze the effects of the arrival of faults on the reliability behavior of a cellular network. Assuming exponential distributions for failures and recovery, a reliability model is developed using the continuous-time Markov chains (CTMC) process. Unlike previous studies on network reliability, the proposed model is not limited to structural aspects of base stations (BSs), and it takes into account diverse potential fault scenarios; it is also capable of predicting the expected time of the first occurrence of the fault and the long-term reliability behavior of the BS. The contributions of this dissertation mark a paradigm shift from the reactive, semi-manual, sub-optimal SON towards a conflict-free, agile, proactive SON. By paving the way for future MCN’s commercial and technical viability, the new SON paradigm presented in this dissertation can act as a key enabler for next-generation MCNs

Determinant of Inflation in Pakistan: An Econometrics Analysis, Using Johansen Co Integration Approach

The main object behind the study is to explore the long run and short run dynamics of inflation in case of Pakistan. For this purpose study used annual data 1971 to 2012. Johansen co integration approach is used to check long run equilibrium while ECM (Error Correction Model) is used to check short run dynamics. The result highlighted GDP, M2, energy crises, import and current government expenditure, output gap and adaptive expectation create inflation while development expenditure negatively corrected with inflation. The study concluded that in Pakistan demand side and supply side inflation persist. Key Word: Inflation, Long run and Short ru

Neuromorphic AI Empowered Root Cause Analysis of Faults in Emerging Networks

Mobile cellular network operators spend nearly a quarter of their revenue on network maintenance and management. A significant portion of that budget is spent on resolving faults diagnosed in the system that disrupt or degrade cellular services. Historically, the operations to detect, diagnose and resolve issues were carried out by human experts. However, with diversifying cell types, increased complexity and growing cell density, this methodology is becoming less viable, both technically and financially. To cope with this problem, in recent years, research on self-healing solutions has gained significant momentum. One of the most desirable features of the self-healing paradigm is automated fault diagnosis. While several fault detection and diagnosis machine learning models have been proposed recently, these schemes have one common tenancy of relying on human expert contribution for fault diagnosis and prediction in one way or another. In this paper, we propose an AI-based fault diagnosis solution that offers a key step towards a completely automated self-healing system without requiring human expert input. The proposed solution leverages Random Forests classifier, Convolutional Neural Network and neuromorphic based deep learning model which uses RSRP map images of faults generated. We compare the performance of the proposed solution against state-of-the-art solution in literature that mostly use Naive Bayes models, while considering seven different fault types. Results show that neuromorphic computing model achieves high classification accuracy as compared to the other models even with relatively small training dat

Can Temperature be Used as a Predictor of Data Traffic? A Real Network Big Data Analysis

The proliferation of mobile devices and big data has made it possible to understand the human movements and forecasts of precise and intelligent short and long-term data consumption of services like call, sms, or internet data which has interesting and promising applications in modern cellular networks. Human nature and moods are known to be synonymous with the physical attributes of mother nature such as temperature. The change in those physical features affects the human routines and activities such as cellular data consumptions. The future of telecommunication lies in the exploration of heap of information and data available to companies and inferring the valuable results through extensive analysis. In this paper, we analyze three main traits of cellular activity: sms, call, and internet. This paper investigates whether the relationship between the temperature and the cellular data consumption exits or not. This work introduces a novel approach to identify the strength of relationship between the temperature and cellular activity (sms, call, internet) and discuss the methods to quantify the relationship using correlation method. The real network CDR big data set - Milano Grid data set is used to analyze the behavior of the cellular activity with respect to temperature

Mitigating Financial Burden of Elderly through Social Protection Schemes: Issues and Challenges for Pakistan

The study on social protection programs for elderly in Pakistan indicates an absence of concrete and clear frameworks developed by government. Most social security and cash assistance programs for the elderly are ad hoc arrangements which are made in response to circumstantial demands or advocated by international donor organizations which usually have their own agenda and priority. Ageing is the reality of every individual’s life therefore it is important to save the social status of elderly people & ensure a progressive life. It is also observed that there are programs and frameworks but contains duplication and overlapping that create hurdles in designing a comprehensive and purposeful social protection strategy for elderly in Pakistan. The lack of such initiatives can cause difficulty for elderly people in planning their future. This paper presents a review of available programs offered for elderly in Pakistan in order to reduce the financial burden and what challenges are faced by them in accessing information about these programs. The paper also looks at identifying gaps and suggests a “way forward” for future contribution towards this important yet ignored issue. The paper recommends that social protection have been categorized in policies process, design and delivery & financing and they must be planned, monitored& evaluated effectively to improve the quality of life of elderly people in Pakistan