4,190 research outputs found
Accelerated Voltage Regulation in Multi-Phase Distribution Networks Based on Hierarchical Distributed Algorithm
We propose a hierarchical distributed algorithm to solve optimal power flow
(OPF) problems that aim at dispatching controllable distributed energy
resources (DERs) for voltage regulation at minimum cost. The proposed algorithm
features unprecedented scalability to large multi-phase distribution networks
by jointly exploring the tree/subtrees structure of a large radial distribution
network and the structure of the linearized distribution power flow
(LinDistFlow) model to derive a hierarchical, distributed implementation of the
primal-dual gradient algorithm that solves OPF. The proposed implementation
significantly reduces the computation loads compared to the centrally
coordinated implementation of the same primal-dual algorithm without
compromising optimality. Numerical results on a 4,521-node test feeder show
that the designed algorithm achieves more than 10-fold acceleration in the
speed of convergence compared to the centrally coordinated primal-dual
algorithm through reducing and distributing computational loads.Comment: arXiv admin note: text overlap with arXiv:1809.0862
Understanding Security Requirements and Challenges in Internet of Things (IoTs): A Review
Internet of Things (IoT) is realized by the idea of free flow of information
amongst various low power embedded devices that use Internet to communicate
with one another. It is predicted that the IoT will be widely deployed and it
will find applicability in various domains of life. Demands of IoT have lately
attracted huge attention and organizations are excited about the business value
of the data that will be generated by the IoT paradigm. On the other hand, IoT
have various security and privacy concerns for the end users that limit its
proliferation. In this paper we have identified, categorized and discussed
various security challenges and state of the art efforts to resolve these
challenges
Learning Based Uplink Interference Management in 4G LTE Cellular Systems
LTEs uplink (UL) efficiency critically depends on how the interference across
different cells is controlled. The unique characteristics of LTEs modulation
and UL resource assignment poses considerable challenges in achieving this goal
because most LTE deployments have 1:1 frequency re-use, and the uplink
interference can vary considerably across successive time slots. In this work,
we propose LeAP, a measurement data driven machine learning paradigm for power
control to manage up-link interference in LTE. The data driven approach has the
inherent advantage that the solution adapts based on network traffic,
propagation and network topology, that is increasingly heterogeneous with
multiple cell-overlays. LeAP system design consists of the following
components: (i) design of user equipment (UE) measurement statistics that are
succinct, yet expressive enough to capture the network dynamics, and (ii)
design of two learning based algorithms that use the reported measurements to
set the power control parameters and optimize the network performance. LeAP is
standards compliant and can be implemented in centralized SON (self organized
networking) server resource (cloud). We perform extensive evaluations using
radio network plans from real LTE network operational in a major metro area in
United States. Our results show that, compared to existing approaches, LeAP
provides a 4.9x gain in the 20th percentile of user data rate, and 3.25x gain
in median data rate.Comment: Submitted to a journa
Self-Organization in Traffic Lights: Evolution of Signal Control with Advances in Sensors and Communications
Traffic signals are ubiquitous devices that first appeared in 1868. Recent
advances in information and communications technology (ICT) have led to
unprecedented improvements in such areas as mobile handheld devices (i.e.,
smartphones), the electric power industry (i.e., smart grids), transportation
infrastructure, and vehicle area networks. Given the trend towards
interconnectivity, it is only a matter of time before vehicles communicate with
one another and with infrastructure. In fact, several pilots of such
vehicle-to-vehicle and vehicle-to-infrastructure (e.g. traffic lights and
parking spaces) communication systems are already operational. This survey of
autonomous and self-organized traffic signaling control has been undertaken
with these potential developments in mind. Our research results indicate that,
while many sophisticated techniques have attempted to improve the scheduling of
traffic signal control, either real-time sensing of traffic patterns or a
priori knowledge of traffic flow is required to optimize traffic. Once this is
achieved, communication between traffic signals will serve to vastly improve
overall traffic efficiency
White Paper on Critical and Massive Machine Type Communication Towards 6G
The society as a whole, and many vertical sectors in particular, is becoming
increasingly digitalized. Machine Type Communication (MTC), encompassing its
massive and critical aspects, and ubiquitous wireless connectivity are among
the main enablers of such digitization at large. The recently introduced 5G New
Radio is natively designed to support both aspects of MTC to promote the
digital transformation of the society. However, it is evident that some of the
more demanding requirements cannot be fully supported by 5G networks.
Alongside, further development of the society towards 2030 will give rise to
new and more stringent requirements on wireless connectivity in general, and
MTC in particular. Driven by the societal trends towards 2030, the next
generation (6G) will be an agile and efficient convergent network serving a set
of diverse service classes and a wide range of key performance indicators
(KPI). This white paper explores the main drivers and requirements of an
MTC-optimized 6G network, and discusses the following six key research
questions:
- Will the main KPIs of 5G continue to be the dominant KPIs in 6G; or will
there emerge new key metrics?
- How to deliver different E2E service mandates with different KPI
requirements considering joint-optimization at the physical up to the
application layer?
- What are the key enablers towards designing ultra-low power receivers and
highly efficient sleep modes?
- How to tackle a disruptive rather than incremental joint design of a
massively scalable waveform and medium access policy for global MTC
connectivity?
- How to support new service classes characterizing mission-critical and
dependable MTC in 6G?
- What are the potential enablers of long term, lightweight and flexible
privacy and security schemes considering MTC device requirements?Comment: White paper by http://www.6GFlagship.co
A Staged Approach to Evolving Real-world UAV Controllers
A testbed has recently been introduced that evolves controllers for arbitrary
hover-capable UAVs, with evaluations occurring directly on the robot. To
prepare the testbed for real-world deployment, we investigate the effects of
state-space limitations brought about by physical tethering (which prevents
damage to the UAV during stochastic tuning), on the generality of the evolved
controllers. We identify generalisation issues in some controllers, and propose
an improved method that comprises two stages: in the first stage, controllers
are evolved as normal using standard tethers, but experiments are terminated
when the population displays basic flight competency. Optimisation then
continues on a much less restrictive tether, effectively free-flying, and is
allowed to explore a larger state-space envelope. We compare the two methods on
a hover task using a real UAV, and show that more general solutions are
generated in fewer generations using the two-stage approach. A secondary
experiment undertakes a sensitivity analysis of the evolved controllers.Comment: Evolutionary Intelligence preprin
Amateur Drone Monitoring: State-of-the-Art Architectures, Key Enabling Technologies, and Future Research Directions
The unmanned air-vehicle (UAV) or mini-drones equipped with sensors are
becoming increasingly popular for various commercial, industrial, and
public-safety applications. However, drones with uncontrolled deployment poses
challenges for highly security-sensitive areas such as President house, nuclear
plants, and commercial areas because they can be used unlawfully. In this
article, to cope with security-sensitive challenges, we propose point-to-point
and flying ad-hoc network (FANET) architectures to assist the efficient
deployment of monitoring drones (MDr). To capture amateur drone (ADr), MDr must
have the capability to efficiently and timely detect, track, jam, and hunt the
ADr. We discuss the capabilities of the existing detection, tracking,
localization, and routing schemes and also present the limitations in these
schemes as further research challenges. Moreover, the future challenges related
to co-channel interference, channel model design, and cooperative schemes are
discussed. Our findings indicate that MDr deployment is necessary for caring of
ADr, and intensive research and development is required to fill the gaps in the
existing technologies.Comment: arXiv admin note: text overlap with arXiv:1510.07390 by other author
Smart Routing: Towards Proactive Fault-Handling in Software-Defined Networks
Software-defined networking offers numerous benefits against the legacy
networking systems through simplifying the process of network management and
reducing the cost of network configuration. Currently, the management of
failures in the data plane is limited to two mechanisms: proactive and
reactive. Such failure recovery techniques are activated after occurrences of
failures. Therefore, packet loss is highly likely to occur as a result of
service disruption and unavailability. This issue is not only related to the
slow speed of recovery mechanisms, but also the delay caused by the failure
detection process. In this paper, we define a new approach to the management of
fault tolerance in software-defined networks where the goal is to eliminate the
convergence process altogether, rather than speed up failure detection and
recovery. We propose a new framework, called Smart Routing, which works based
on the forewarning signs on failures in order to compute alternative paths and
isolate the risky links from the routing tables of the data plane devices. We
validate our framework through a set of experiments that demonstrate how the
underlying model runs
Decentralized Traffic Management Strategies for Sensor-Enabled Cars
Traffic Congestions and accidents are major concerns in today's
transportation systems. This thesis investigates how to optimize traffic flow
on highways, in particular for merging situations such as intersections where a
ramp leads onto the highway. In our work, cars are equipped with sensors that
can detect distance to neighboring cars, and communicate their velocity and
acceleration readings with one another. Sensor-enabled cars can locally
exchange sensed information about the traffic and adapt their behavior much
earlier than regular cars.
We propose proactive algorithms for merging different streams of
sensor-enabled cars into a single stream. A proactive merging algorithm
decouples the decision point from the actual merging point. Sensor-enabled cars
allow us to decide where and when a car merges before it arrives at the actual
merging point. This leads to a significant improvement in traffic flow as
velocities can be adjusted appropriately. We compare proactive merging
algorithms against the conventional priority-based merging algorithm in a
controlled simulation environment. Experiment results show that proactive
merging algorithms outperform the priority-based merging algorithm in terms of
flow and delay
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