Abstract: Opportunistic Routing (OR) increases the
transmission reliability and network throughput. Traditional routing
protocols preselects one or more predetermined nodes before
transmission starts and uses a predetermined neighbor to forward a
packet in each hop. The opportunistic routing overcomes the
drawback of unreliable wireless transmission by broadcasting one
transmission can be overheard by manifold neighbors. The first
cooperation-optimal protocol for Multirate OR (COMO) used to
achieve social efficiency and prevent the selfish behavior of the
nodes. The novel link-correlation-aware OR improves the
performance by exploiting the miscellaneous low correlated forward
links. Context aware Adaptive OR (CAOR) uses active suppression
mechanism to reduce packet duplication. The Context-aware OR
(COR) can provide efficient routing in mobile networks. By using
Cooperative Opportunistic Routing in Mobile Ad hoc Networks
(CORMAN), the problem of opportunistic data transfer can be
tackled. While comparing to all the protocols, COMO is the best as it
achieves social efficiency and prevents the selfish behavior of the
nodes.
Abstract: This article proposes a hybrid algorithm for spectrum
allocation in cognitive radio networks based on the algorithms
Analytical Hierarchical Process (AHP) and Technique for Order of
Preference by Similarity to Ideal Solution (TOPSIS) to improve the
performance of the spectrum mobility of secondary users in cognitive
radio networks. To calculate the level of performance of the proposed algorithm a
comparative analysis between the proposed AHP-TOPSIS, Grey
Relational Analysis (GRA) and Multiplicative Exponent Weighting
(MEW) algorithm is performed. Four evaluation metrics are used.
These metrics are accumulative average of failed handoffs,
accumulative average of handoffs performed, accumulative average
of transmission bandwidth, and accumulative average of the
transmission delay. The results of the comparison show that AHP-TOPSIS Algorithm
provides 2.4 times better performance compared to a GRA Algorithm
and, 1.5 times better than the MEW Algorithm.
Abstract: The paper is focused to the evaluation railway tracks
in the Slovakia by using Multi-Criteria method. Evaluation of railway
tracks has important impacts for the assessment of investment in
technical equipment. Evaluation of railway tracks also has an
important impact for the allocation of marshalling yards. Marshalling
yards are in transport model as centers for the operation assigned
catchment area. This model is one of the effective ways to meet the
development strategy of the European Community's railways. By
applying this model in practice, a transport company can guarantee a
higher quality of service and then expect an increase in performance.
The model is also applicable to other rail networks. This model
supplements a theoretical problem of train formation problem of new
ways of looking at evaluation of factors affecting the organization of
wagon flows.
Abstract: Wireless Sensor Networks (WSNs), which sense
environmental data with battery-powered nodes, require multi-hop
communication. This power-demanding task adds an extra workload
that is unfairly distributed across the network. As a result, nodes run
out of battery at different times: this requires an impractical
individual node maintenance scheme. Therefore we investigate a new
Cooperative Sensing approach that extends the WSN operational life
and allows a more practical network maintenance scheme (where all
nodes deplete their batteries almost at the same time). We propose a
novel cooperative algorithm that derives a piecewise representation
of the sensed signal while controlling approximation accuracy.
Simulations show that our algorithm increases WSN operational life
and spreads communication workload evenly. Results convey a
counterintuitive conclusion: distributing workload fairly amongst
nodes may not decrease the network power consumption and yet
extend the WSN operational life. This is achieved as our cooperative
approach decreases the workload of the most burdened cluster in the
network.
Abstract: The paper is focused on the operational model for
transport the single wagon consignments on railway network by
using two different models of train formation. The paper gives an
overview of possibilities of improving the quality of transport
services. Paper deals with two models used in problematic of train
formatting - time continuously and time discrete. By applying these
models in practice, the transport company can guarantee a higher
quality of service and expect increasing of transport performance.
The models are also applicable into others transport networks. The
models supplement a theoretical problem of train formation by new
ways of looking to affecting the organization of wagon flows.
Abstract: In recent years, new techniques for solving complex
problems in engineering are proposed. One of these techniques is
JPSO algorithm. With innovative changes in the nature of the jump
algorithm JPSO, it is possible to construct a graph-based solution
with a new algorithm called G-JPSO. In this paper, a new algorithm
to solve the optimal control problem Fletcher-Powell and optimal
control of pumps in water distribution network was evaluated.
Optimal control of pumps comprise of optimum timetable operation
(status on and off) for each of the pumps at the desired time interval.
Maximum number of status on and off for each pumps imposed to the
objective function as another constraint. To determine the optimal
operation of pumps, a model-based optimization-simulation
algorithm was developed based on G-JPSO and JPSO algorithms.
The proposed algorithm results were compared well with the ant
colony algorithm, genetic and JPSO results. This shows the
robustness of proposed algorithm in finding near optimum solutions
with reasonable computational cost.
Abstract: This paper proposes a novel heuristic algorithm that aims to determine the best size and location of distributed generators in unbalanced distribution networks. The proposed heuristic algorithm can deal with the planning cases where power loss is to be optimized without violating the system practical constraints. The distributed generation units in the proposed algorithm is modeled as voltage controlled node with the flexibility to be converted to constant power factor node in case of reactive power limit violation. The proposed algorithm is implemented in MATLAB and tested on the IEEE 37 -node feeder. The results obtained show the effectiveness of the proposed algorithm.
Abstract: The Gezi Park protests of 2013 have significantly changed the Turkish agenda and its effects have been felt historically. The protests, which rapidly spread throughout the country, were triggered by the proposal to recreate the Ottoman Army Barracks to function as a shopping mall on Gezi Park located in Istanbul’s Taksim neighbourhood despite the oppositions of several NGOs and when trees were cut in the park for this purpose. Once the news that the construction vehicles entered the park on May 27 spread on social media, activists moved into the park to stop the demolition, against whom the police used disproportioned force. With this police intervention and the then prime-minister Tayyip Erdoğan's insistent statements about the construction plans, the protests turned into anti- government demonstrations, which then spread to the rest of the country, mainly in big cities like Ankara and Izmir. According to the Ministry of Internal Affairs’ June 23rd reports, 2.5 million people joined the demonstrations in 79 provinces, that is all of them, except for the provinces of Bayburt and Bingöl, while even more people shared their opinions via social networks. As a result of these events, 8 civilians and 2 security personnel lost their lives, namely police chief Mustafa Sarı, police officer Ahmet Küçükdağ, citizens Mehmet Ayvalıtaş, Abdullah Cömert, Ethem Sarısülük, Ali İsmail Korkmaz, Ahmet Atakan, Berkin Elvan, Burak Can Karamanoğlu, Mehmet İstif, and Elif Çermik, and 8163 more were injured. Besides being a turning point in Turkish history, the Gezi Park protests also had broad repercussions in both in Turkish and in global media, which focused on Turkey throughout the events. Our study conducts content analysis of three Turkish reporting newspapers with varying ideological standpoints, Hürriyet, Cumhuriyet ve Yeni Şafak, in order to reveal their basic approach to news casting in context of the Gezi Park protests. Headlines, news segments, and news content relating to the Gezi protests were treated and analysed for this purpose. The aim of this study is to understand the social effects of the Gezi Park protests through media samples with varying political attitudes towards news casting.
Abstract: A Mobile Adhoc Network (MANET) is a collection of mobile nodes that communicate with each other with wireless links and without pre-existing communication infrastructure. Routing is an important issue which impacts network performance. As MANETs lack central administration and prior organization, their security concerns are different from those of conventional networks. Wireless links make MANETs susceptible to attacks. This study proposes a new trust mechanism to mitigate wormhole attack in MANETs. Different optimization techniques find available optimal path from source to destination. This study extends trust and reputation to an improved link quality and channel utilization based Adhoc Ondemand Multipath Distance Vector (AOMDV). Differential Evolution (DE) is used for optimization.
Abstract: Ecological systems are exposed and are influenced by
various natural and anthropogenic disturbances. They produce
various effects and states seeking response symmetry to a state of
global phase coherence or stability and balance of their food webs.
This research project addresses the development of a computational
methodology for modeling plankton food webs. The use of
algorithms to establish connections, the generation of representative
fuzzy multigraphs and application of technical analysis of complex
networks provide a set of tools for defining, analyzing and evaluating
community structure of coastal aquatic ecosystems, beyond the
estimate of possible external impacts to the networks. Thus, this
study aims to develop computational systems and data models to
assess how these ecological networks are structurally and
functionally organized, to analyze the types and degree of
compartmentalization and synchronization between oscillatory and
interconnected elements network and the influence of disturbances on
the overall pattern of rhythmicity of the system.
Abstract: This paper presents a novel algorithm for modeling
photovoltaic based distributed generators for the purpose of optimal
planning of distribution networks. The proposed algorithm utilizes
sequential Monte Carlo method in order to accurately consider the
stochastic nature of photovoltaic based distributed generators. The
proposed algorithm is implemented in MATLAB environment and
the results obtained are presented and discussed.
Abstract: Wireless Sensor Networks (WSNs) enable new
applications and need non-conventional paradigms for the protocol
because of energy and bandwidth constraints, In WSN, sensor node’s
life is a critical parameter. Research on life extension is based on
Low-Energy Adaptive Clustering Hierarchy (LEACH) scheme,
which rotates Cluster Head (CH) among sensor nodes to distribute
energy consumption over all network nodes. CH selection in WSN
affects network energy efficiency greatly. This study proposes an
improved CH selection for efficient data aggregation in sensor
networks. This new algorithm is based on Bacterial Foraging
Optimization (BFO) incorporated in LEACH.
Abstract: Feature selection has been used in many fields such as
classification, data mining and object recognition and proven to be
effective for removing irrelevant and redundant features from the
original dataset. In this paper, a new design of distributed intrusion
detection system using a combination feature selection model based
on bees and decision tree. Bees algorithm is used as the search
strategy to find the optimal subset of features, whereas decision tree
is used as a judgment for the selected features. Both the produced
features and the generated rules are used by Decision Making Mobile
Agent to decide whether there is an attack or not in the networks.
Decision Making Mobile Agent will migrate through the networks,
moving from node to another, if it found that there is an attack on one
of the nodes, it then alerts the user through User Interface Agent or
takes some action through Action Mobile Agent. The KDD Cup 99
dataset is used to test the effectiveness of the proposed system. The
results show that even if only four features are used, the proposed
system gives a better performance when it is compared with the
obtained results using all 41 features.
Abstract: In this paper, we provided a literature survey on the
artificial stock problem (ASM). The paper began by exploring the
complexity of the stock market and the needs for ASM. ASM
aims to investigate the link between individual behaviors (micro
level) and financial market dynamics (macro level). The variety of
patterns at the macro level is a function of the AFM complexity. The
financial market system is a complex system where the relationship
between the micro and macro level cannot be captured analytically.
Computational approaches, such as simulation, are expected to
comprehend this connection. Agent-based simulation is a simulation
technique commonly used to build AFMs. The paper proceeds by
discussing the components of the ASM. We consider the roles
of behavioral finance (BF) alongside the traditionally risk-averse
assumption in the construction of agent’s attributes. Also, the
influence of social networks in the developing of agents interactions is
addressed. Network topologies such as a small world, distance-based,
and scale-free networks may be utilized to outline economic
collaborations. In addition, the primary methods for developing
agents learning and adaptive abilities have been summarized.
These incorporated approach such as Genetic Algorithm, Genetic
Programming, Artificial neural network and Reinforcement Learning.
In addition, the most common statistical properties (the stylized facts)
of stock that are used for calibration and validation of ASM are
discussed. Besides, we have reviewed the major related previous
studies and categorize the utilized approaches as a part of these
studies. Finally, research directions and potential research questions
are argued. The research directions of ASM may focus on the macro
level by analyzing the market dynamic or on the micro level by
investigating the wealth distributions of the agents.
Abstract: Most of the existing video streaming protocols
provide video services without considering security aspects in
decentralized mobile ad-hoc networks. The security policies adapted
to the currently existing non-streaming protocols, do not comply with
the live video streaming protocols resulting in considerable
vulnerability, high bandwidth consumption and unreliability which
cause severe security threats, low bandwidth and error prone
transmission respectively in video streaming applications. Therefore
a synergized methodology is required to reduce vulnerability and
bandwidth consumption, and enhance reliability in the video
streaming applications in MANET. To ensure the security measures
with reduced bandwidth consumption and improve reliability of the
video streaming applications, a Secure Low-bandwidth Video
Streaming through Reliable Multipath Propagation (SLVRMP)
protocol architecture has been proposed by incorporating the two
algorithms namely Secure Low-bandwidth Video Streaming
Algorithm and Reliable Secure Multipath Propagation Algorithm
using Layered Video Coding in non-overlapping zone routing
network topology. The performances of the proposed system are
compared to those of the other existing secure multipath protocols
Sec-MR, SPREAD using NS 2.34 and the simulation results show
that the performances of the proposed system get considerably
improved.
Abstract: DNA Barcode provides good sources of needed
information to classify living species. The classification problem has
to be supported with reliable methods and algorithms. To analyze
species regions or entire genomes, it becomes necessary to use the
similarity sequence methods. A large set of sequences can be
simultaneously compared using Multiple Sequence Alignment which
is known to be NP-complete. However, all the used methods are still
computationally very expensive and require significant computational
infrastructure. Our goal is to build predictive models that are highly
accurate and interpretable. In fact, our method permits to avoid the
complex problem of form and structure in different classes of
organisms. The empirical data and their classification performances
are compared with other methods. Evenly, in this study, we present
our system which is consisted of three phases. The first one, is called
transformation, is composed of three sub steps; Electron-Ion
Interaction Pseudopotential (EIIP) for the codification of DNA
Barcodes, Fourier Transform and Power Spectrum Signal Processing.
Moreover, the second phase step is an approximation; it is
empowered by the use of Multi Library Wavelet Neural Networks
(MLWNN). Finally, the third one, is called the classification of DNA
Barcodes, is realized by applying the algorithm of hierarchical
classification.
Abstract: Cloud computing has emerged as a promising
direction for cost efficient and reliable service delivery across data
communication networks. The dynamic location of service facilities
and the virtualization of hardware and software elements are stressing
the communication networks and protocols, especially when data
centres are interconnected through the internet. Although the
computing aspects of cloud technologies have been largely
investigated, lower attention has been devoted to the networking
services without involving IT operating overhead. Cloud computing
has enabled elastic and transparent access to infrastructure services
without involving IT operating overhead. Virtualization has been a
key enabler for cloud computing. While resource virtualization and
service abstraction have been widely investigated, networking in
cloud remains a difficult puzzle. Even though network has significant
role in facilitating hybrid cloud scenarios, it hasn't received much
attention in research community until recently. We propose Network
as a Service (NaaS), which forms the basis of unifying public and
private clouds. In this paper, we identify various challenges in
adoption of hybrid cloud. We discuss the design and implementation
of a cloud platform.
Abstract: This paper proposes a novel heuristic algorithm that aims to determine the best size and location of distributed generators in unbalanced distribution networks. The proposed heuristic algorithm can deal with the planning cases where power loss is to be optimized without violating the system practical constraints. The distributed generation units in the proposed algorithm is modeled as voltage controlled node with the flexibility to be converted to constant power factor node in case of reactive power limit violation. The proposed algorithm is implemented in MATLAB and tested on the IEEE 37 -node feeder. The results obtained show the effectiveness of the proposed algorithm.
Abstract: Security can be defined as the degree of resistance to, or protection from harm. It applies to any vulnerable and valuable assets, such as persons, dwellings, communities, nations or organizations. Cybercrime is any crime committed or facilitated via the Internet. It is any criminal activity involving computers and networks. It can range from fraud to unsolicited emails (spam). It includes the distant theft of government or corporate secrets through criminal trespass into remote systems around the globe. Nigeria like any other nations of the world is currently having her own share of the menace that has been used even as tools by terrorists. This paper is an attempt at presenting cyber security as an issue that requires a coordinated national response. It also acknowledges and advocates the key roles to be played by stakeholders and the importance of forging strong partnerships to prevent and tackle cybercrime in Nigeria.
Abstract: Cooperative spectrum sensing is a crucial challenge in
cognitive radio networks. Cooperative sensing can increase the
reliability of spectrum hole detection, optimize sensing time and
reduce delay in cooperative networks. In this paper, an efficient
central capacity optimization algorithm is proposed to minimize
cooperative sensing time in a homogenous sensor network using OR
decision rule subject to the detection and false alarm probabilities
constraints. The evaluation results reveal significant improvement in
the sensing time and normalized capacity of the cognitive sensors.