Resource allocation for transmit hybrid beamforming in decoupled millimeter wave multiuser-MIMO downlink

This paper presents a study on joint radio resource allocation and hybrid precoding in multicarrier massive multiple-input multiple-output communications for 5G cellular networks. In this paper, we present the resource allocation algorithm to maximize the proportional fairness (PF) spectral efficiency under the per subchannel power and the beamforming rank constraints. Two heuristic algorithms are designed. The proportional fairness hybrid beamforming algorithm provides the transmit precoder with a proportional fair spectral efficiency among users for the desired number of radio-frequency (RF) chains. Then, we transform the number of RF chains or rank constrained optimization problem into convex semidefinite programming (SDP) problem, which can be solved by standard techniques. Inspired by the formulated convex SDP problem, a low-complexity, two-step, PF-relaxed optimization algorithm has been provided for the formulated convex optimization problem. Simulation results show that the proposed suboptimal solution to the relaxed optimization problem is near-optimal for the signal-to-noise ratio SNR <= 10 dB and has a performance gap not greater than 2.33 b/s/Hz within the SNR range 0-25 dB. It also outperforms the maximum throughput and PF-based hybrid beamforming schemes for sum spectral efficiency, individual spectral efficiency, and fairness index

Geoinformatic and Hydrologic Analysis using Open Source Data for Floods Management in Pakistan

There is being observed high variability in the spatial and temporal rainfall patterns under changing climate, enhancing both the intensity and frequency of the natural disasters like floods. Pakistan, a country which is highly prone to climate change, is recently facing the challenges of both flooding and severe water shortage as the surface water storage capacity is too limited to cope with heavy flows during rainy months. Thus, an effective and timely predication and management of high flows is a dire need to address both flooding and long term water shortage issues. The work of this thesis was aimed at developing and evaluating different open source data based methodologies for floods detection and analysis in Pakistan. Specifically, the research work was conducted for developing and evaluating a hydrologic model being able to run in real time based on satellite rainfall data, as well as to perform flood hazard mapping by analyzing seasonality of flooded areas using MODIS classification approach. In the first phase, TRMM monthly rainfall data (TMPA 3B43) was evaluated for Pakistan by comparison with rain gauge data, as well as by further focusing on its analysis and evaluation for different time periods and climatic zones of Pakistan. In the next phase, TRMM rainfall data and other open source datasets like digital soil map and global land cover map were utilized to develop and evaluate an event-based hydrologic model using HEC-HMS, which may be able to be run in real time for predicting peak flows due to any extreme rainfall event. Finally, to broaden the study canvas from a river catchment to the whole country scale, MODIS automated water bodies classification approach with MODIS daily surface reflectance products was utilized to develop a historical archive of reference water bodies and perform seasonal analysis of flooded areas for Pakistan. The approach was found well capable for its application for floods detection in plain areas of Pakistan. The open source data based hydrologic modeling approach devised in this study can be helpful for conducting similar rainfall-runoff modeling studies for the other river catchments and predicting peak flows at a river catchment scale, particularly in mountainous topography. Similarly, the outcomes of MODIS classification analysis regarding reference and seasonal water and flood hazard maps may be helpful for planning any management interventions in the flood prone areas of Pakistan

Secure and Efficient Distributed Relay-Based Rekeying Algorithm for Group Communication in Mobile Multihop Relay Network

In mobile multihop relay (MMR) networks, Relay multicast rekeying algorithm (RMRA) is meant to ensure secure multicast communication and selective updating of keys in MMR networks. However, in RMRA, the rekeying is carried out after a specific interval of time, which cannot ensure the security for multicast communication on joining the member. Secondly, the rekeying scheme generates a huge communication overhead on the serving multihop relay base station (MR-BS) on frequent joining of members. Lastly, there is nothing about when a member left the group communication. Thus, the rekeying scheme of RMRA fails to provide forward and backward secrecy and also is not scalable. To solve this problem, an improved rekeying scheme based on broadcasting a new seed value on joining and leaving of a member for updating the ongoing key management is proposed. The proposed scheme solves the issue of forward and backward secrecy and the scalability in a very simplified way. The forward and backward secrecy of the proposed scheme has been extensively validated by formal method using rank theorem. Furthermore, mathematical derivation showed that the proposed scheme out-performed the RMRA in terms of communication cost and complexity

Impact of EU duty cycle and transmission power limitations for sub-GHz LPWAN SRDs : an overview and future challenges

Long-range sub-GHz technologies such as LoRaWAN, SigFox, IEEE 802.15.4, and DASH7 are increasingly popular for academic research and daily life applications. However, especially in the European Union (EU), the use of their corresponding frequency bands are tightly regulated, since they must confirm to the short-range device (SRD) regulations. Regulations and standards for SRDs exist on various levels, from global to national, but are often a source of confusion. Not only are multiple institutes responsible for drafting legislation and regulations, depending on the type of document can these rules be informational or mandatory. Regulations also vary from region to region; for example, regulations in the United States of America (USA) rely on electrical field strength and harmonic strength, while EU regulations are based on duty cycle and maximum transmission power. A common misconception is the presence of a common 1% duty cycle, while in fact the duty cycle is frequency band-specific and can be loosened under certain circumstances. This paper clarifies the various regulations for the European region, the parties involved in drafting and enforcing regulation, and the impact on recent technologies such as SigFox, LoRaWAN, and DASH7. Furthermore, an overview is given of potential mitigation approaches to cope with the duty cycle constraints, as well as future research directions

Efficient energy management for the internet of things in smart cities

The drastic increase in urbanization over the past few years requires sustainable, efficient, and smart solutions for transportation, governance, environment, quality of life, and so on. The Internet of Things offers many sophisticated and ubiquitous applications for smart cities. The energy demand of IoT applications is increased, while IoT devices continue to grow in both numbers and requirements. Therefore, smart city solutions must have the ability to efficiently utilize energy and handle the associated challenges. Energy management is considered as a key paradigm for the realization of complex energy systems in smart cities. In this article, we present a brief overview of energy management and challenges in smart cities. We then provide a unifying framework for energy-efficient optimization and scheduling of IoT-based smart cities. We also discuss the energy harvesting in smart cities, which is a promising solution for extending the lifetime of low-power devices and its related challenges. We detail two case studies. The first one targets energy-efficient scheduling in smart homes, and the second covers wireless power transfer for IoT devices in smart cities. Simulation results for the case studies demonstrate the tremendous impact of energy-efficient scheduling optimization and wireless power transfer on the performance of IoT in smart cities

Optimization of the overall success probability of the energy harvesting cognitive wireless sensor networks

Wireless energy harvesting can improve the performance of cognitive wireless sensor networks (WSNs). This paper considers radio frequency (RF) energy harvesting from transmissions in the primary spectrum for cognitive WSNs. The overall success probability of the energy harvesting cognitive WSN depends on the transmission success probability and energy success probability. Using the tools from stochastic geometry, we show that the overall success probability can be optimized with respect to: 1) transmit power of the sensors; 2) transmit power of the primary transmitters; and 3) spatial density of the primary transmitters. In this context, an optimization algorithm is proposed to maximize the overall success probability of the WSNs. Simulation results show that the overall success probability and the throughput of the WSN can be significantly improved by optimizing the aforementioned three parameters. As RF energy harvesting can also be performed indoors, hence, our solution can be directly applied to the cognitive WSNs that are installed in smart buildings

Cost-effectiveness of traffic enforcement: case study from Uganda

BACKGROUND: In October 2004, the Ugandan Police department deployed enhanced traffic safety patrols on the four major roads to the capital Kampala. OBJECTIVE: To assess the costs and potential effectiveness of increasing traffic enforcement in Uganda. METHODS: Record review and key informant interviews were conducted at 10 police stations along the highways that were patrolled. Monthly data on traffic citations and casualties were reviewed for January 2001 to December 2005; time series (ARIMA) regression was used to assess for a statistically significant change in traffic deaths. Costs were computed from the perspective of the police department in $US 2005. Cost offsets from savings to the health sector were not included. RESULTS: The annual cost of deploying the four squads of traffic patrols (20 officers, four vehicles, equipment, administration) is estimated at$72,000. Since deployment, the number of citations has increased substantially with a value of $327 311 annually. Monthly crash data pre- and post-intervention show a statistically significant 17$603 per death averted or $27 per life year saved discounted at 3$300 GDP per capita). CONCLUSION: The costs of traffic safety enforcement are low in comparison to the potential number of lives saved and revenue generated. Increasing enforcement of existing traffic safety norms can prove to be an extremely cost-effective public health intervention in low-income countries, even from a government perspective

Nation Branding: Ghana

Akwaaba – welcome to Ghana, also referred to as "island of peace" in one of the most chaotic regions on earth. Ghana is usually known as “Gold Coast” due to large gold deposits in the southern parts of the country. The Ghana as a relatively new nation has not developed extensive symbols. If successful the branding of Ghana would allow the country to flourish at an incredible rate. The nation branding of Ghana can be done from its Coat of arms which represents the most distinctive emblems originated from nationalist movement. The key elements considered while branding Ghana are Gold, Cocoa, Oil and Volta Lake. Ghana is rich mineral resources such as gold, diamonds, manganese, limestone, bauxite, iron ore as well as various clays and granite deposits. Despite of all the advantages of Ghana, the branding is not so successful because Ghana is deprived of any international brands, which provide a success guide framework to emerging brands. The main impediment is the fact that Ghana cannot be separated from the negative image of 'brand' Africa. Keywords: Nation Branding, Ghana, Africa, Business, Gold, Cocoa, Oil, Volta Lak

Dairy Industry of Pakistan

The livestock sector alone contributes 11% of Pakistan’s GDP, with an estimated 42 billion litres of milk produced per annum. Economic Survey of Pakistan 2009 assertions that Pakistan has a herd size of around 63 million animals – the 3rd largest in the world. After witnessing all the issues regarding the dairy industry of Pakistan, it can be concluded that the dairy industry possesses potential of growth and is very important from economic perspective. The major problem with dairy farming in Pakistan is the low milk yields of Pakistani cattle and buffaloes. This low production potential of Pakistani animals is mainly attributable to a few clearly identifiable issues such as lack of a systematic national breed improvement program, lack of availability of good quality fodder and nutrients and poor farm management practices. On average a dairy animal in Pakistan yields 6-8 times less milk than a dairy animal of the developed world. So Pakistan needs to have a coordinated and integrated strategy/approach beginning from enhancing per animal productivity, going straight to milk procedures/procurement and minimize the wastage. Keywords: Dairy Industry, Pakistan, Milk, Productivity, per animal productivity, livestock, farm management practice

Deep reinforcement learning for automatic run-time adaptation of UWB PHY radio settings

Ultra-wideband technology has become increasingly popular for indoor localization and location-based services. This has led recent advances to be focused on reducing the ranging errors, whilst research focusing on enabling more reliable and energy efficient communication has been largely unexplored. The IEEE 802.15.4 UWB physical layer allows for several settings to be selected that influence the energy consumption, range, and reliability. Combined with the available link state diagnostics reported by UWB devices, there is an opportunity to dynamically select PHY settings based on the environment. To address this, we propose a deep Q-learning approach for enabling reliable UWB communication, maximizing packet reception rate (PRR) and minimizing energy consumption. Deep Q-learning is a good fit for this problem, as it is an inherently adaptive algorithm that responds to the environment. Validation in a realistic office environment showed that the algorithm outperforms traditional Q-learning, linear search and using a fixed PHY layer. We found that deep Q-learning achieves a higher average PRR and reduces the ranging error while using only 14% of the energy compared to a fixed PHY setting in a dynamic office environment.Comment: 13 pages, 9 figures, 9 tables and submitted to IEEE Transactions on Cognitive Communications and Networkin