WIRELESS COMMUNICATION HANDWRITTEN NOTES FOR IPDhruv Aggarwal
UNIT – I
Introduction To Wireless Communication Systems: Evolution of mobile radio communications; examples of wireless comm. systems; paging systems; Cordless telephone systems; overview of generations of cellular systems, comparison of various wireless systems.
Introduction to Personal Communication Services (PCS): PCS architecture, Mobility management, Networks signaling. A basic cellular system, multiple access techniques: FDMA, TDMA, CDMA.
Introduction to Wireless Channels and Diversity: Fast Fading Wireless Channel Modeling, Rayleigh/Ricean Fading Channels, BER Performance in Fading Channels, Introduction to Diversity modeling for Wireless Communications
UNIT - II
2G Networks: Second generation, digital, wireless systems: GSM, IS_136 (D-AMPS), IS-95 CDMA. Global system for Mobile Communication (GSM) system overview: GSM Architecture, Mobility Management, Network signaling, mobile management, voice signal processing and coding. Spread Spectrum Systems- Cellular code Division Access Systems-Principle, Power Control, effects of multipath propagation on code division multiple access.
UNIT - III 2.5G Mobile Data Networks: Introduction to Mobile Data Networks, General Packet Radio Services (GPRS): GPRS architecture, GPRS Network nodes, EDGE, Wireless LANs, (IEEE 802.11), Mobile IP.
Third Generation (3G) Mobile Services: Introduction to International Mobile Telecommunications 2000 (IMT 2000) vision, Wideband Code Division Multiple Access (W-CDMA), and CDMA 2000, Quality of services in 3G, Introduction to 4G.
UNIT – IV
Wireless Local Loop (WLL): Introduction to WLL architecture, WLL technologies. Wireless personal area networks (WPAN): Blue tooth, IEEE 802.15, architecture, protocol stack. Wi-Max, introduction to Mobile Adhoc Networks.
Global Mobile Satellite Systems, Case studies of IRIDIUM and GLOBALSTAR systems.
MicroStrip Antenna
Introduction .
Micro-Strip Antennas Types .
Micro-Strip Antennas Shapes .
Types of Substrates (Dielectric Media) .
Comparison of various types of flat profile printed antennas .
Advantages & DisAdvantages of MSAs .
Applications of MSAs .
Radiation patterns of MSAs .
How to Optimizing the Substrate Properties for Increased Bandwidth ?
Comparing the different feed techniques .
An overview of cognitive radio, comparison of cognitive radio vs. conventional radio, real-world applications for cognitive radio networks, how cognitive radios improve spectrum efficiency and address the wireless spectrum shortage.
WIRELESS COMMUNICATION HANDWRITTEN NOTES FOR IPDhruv Aggarwal
UNIT – I
Introduction To Wireless Communication Systems: Evolution of mobile radio communications; examples of wireless comm. systems; paging systems; Cordless telephone systems; overview of generations of cellular systems, comparison of various wireless systems.
Introduction to Personal Communication Services (PCS): PCS architecture, Mobility management, Networks signaling. A basic cellular system, multiple access techniques: FDMA, TDMA, CDMA.
Introduction to Wireless Channels and Diversity: Fast Fading Wireless Channel Modeling, Rayleigh/Ricean Fading Channels, BER Performance in Fading Channels, Introduction to Diversity modeling for Wireless Communications
UNIT - II
2G Networks: Second generation, digital, wireless systems: GSM, IS_136 (D-AMPS), IS-95 CDMA. Global system for Mobile Communication (GSM) system overview: GSM Architecture, Mobility Management, Network signaling, mobile management, voice signal processing and coding. Spread Spectrum Systems- Cellular code Division Access Systems-Principle, Power Control, effects of multipath propagation on code division multiple access.
UNIT - III 2.5G Mobile Data Networks: Introduction to Mobile Data Networks, General Packet Radio Services (GPRS): GPRS architecture, GPRS Network nodes, EDGE, Wireless LANs, (IEEE 802.11), Mobile IP.
Third Generation (3G) Mobile Services: Introduction to International Mobile Telecommunications 2000 (IMT 2000) vision, Wideband Code Division Multiple Access (W-CDMA), and CDMA 2000, Quality of services in 3G, Introduction to 4G.
UNIT – IV
Wireless Local Loop (WLL): Introduction to WLL architecture, WLL technologies. Wireless personal area networks (WPAN): Blue tooth, IEEE 802.15, architecture, protocol stack. Wi-Max, introduction to Mobile Adhoc Networks.
Global Mobile Satellite Systems, Case studies of IRIDIUM and GLOBALSTAR systems.
MicroStrip Antenna
Introduction .
Micro-Strip Antennas Types .
Micro-Strip Antennas Shapes .
Types of Substrates (Dielectric Media) .
Comparison of various types of flat profile printed antennas .
Advantages & DisAdvantages of MSAs .
Applications of MSAs .
Radiation patterns of MSAs .
How to Optimizing the Substrate Properties for Increased Bandwidth ?
Comparing the different feed techniques .
An overview of cognitive radio, comparison of cognitive radio vs. conventional radio, real-world applications for cognitive radio networks, how cognitive radios improve spectrum efficiency and address the wireless spectrum shortage.
Unit 4 -2 energy management in adhoc wireless networkdevika g
Unit 4 -2 energy management in adhoc wireless network unit 4-2 mobile computing ,classification of energy management transimmission power power management
CR : smart radio that has the ability to sense the external environment, learn from the history and make intelligent decisions to adjust its transmission parameters according
to the current state of the environment.
This ppt is about Smart Antenna which includes history, Introduction, Working of smart antenna and where this smart antennas can be used.This ppt also tells about the types of smart antenna and the main principle of working of smart antenna. Smart antennas mainly categorized as Adaptive and switched beam array.Among these two adaptive antenna is used for the efficient utilisation of frequency spectrum.
Unit 4 -2 energy management in adhoc wireless networkdevika g
Unit 4 -2 energy management in adhoc wireless network unit 4-2 mobile computing ,classification of energy management transimmission power power management
CR : smart radio that has the ability to sense the external environment, learn from the history and make intelligent decisions to adjust its transmission parameters according
to the current state of the environment.
This ppt is about Smart Antenna which includes history, Introduction, Working of smart antenna and where this smart antennas can be used.This ppt also tells about the types of smart antenna and the main principle of working of smart antenna. Smart antennas mainly categorized as Adaptive and switched beam array.Among these two adaptive antenna is used for the efficient utilisation of frequency spectrum.
In the blink of an eye—less than half a second—dynamic neuronal networks in our brains process, maintain, and act upon an abundance of information. In my research I seek to construct an understanding, from a network perspective, of cognition built upon first principles of neurophysiology and computational models. To do this I incorporate a variety of methods and tools in my research, including intracranial electrophysiological recordings from humans, scalp electroencephalography from healthy younger and older adults, behavioral and neuroimaging studies involving patients with focal brain lesions, data-mining of large-scale databases, and brain-computer interfacing. Specifically my research program aims to answer three questions: 1) What role does the prefrontal cortex play in shaping and coordinating network activity during complex cognition and executive functioning? 2) Under what circumstances is this network altered or disrupted and what are the consequences of such disruption? And, 3) What are the principles that allow for network communication in noisy internal and external environments? My research addresses these questions across multiple scales ranging from basic neurophysiology to population-wide analyses of cognitive data collected from more than 400,000 participants. My goal is to take cognitive science outside of the laboratory and "into the wild" using distributed data collection and large-scale data analysis to help bridge psychology and basic physiology.
Cognitive Radio : Emerging Business Toward an Efficiently Smart Era of ICTNurmaya Widuri
Cognitive Radio is a hot issue in wireless technology. This is ultimately new wave of how radio technolgy communicate through spectrum effeciency. Furthermore, this new big thing bring a new wave of future ICT business lanscape toward efficiently smart era of ICT.
With the popularity of laptops, cell phones, PDAs, GPS devices, RFID, and intelligent electronics in the post-PC era, computing devices have become cheaper, more mobile, more distributed, and more pervasive in daily life. It is now possible to construct, from commercial off-the-shelf (COTS) components, a wallet size embedded system with the equivalent capability of a 90’s PC. Such embedded systems can be supported with scaled down Windows or Linux operating systems. From this perspective, the emergence of wireless sensor networks (WSNs) is essentially the latest trend of Moore’s Law toward the miniaturization and ubiquity of computing devices. Typically, a wireless sensor node (or simply sensor node) consists of sensing, computing, communication, actuation, and power components. These components are integrated on a single or multiple boards, and packaged in a few cubic inches. With state-of-the-art, low-power circuit and networking technologies, a sensor node powered by 2 AA batteries can last for up to three years with a 1% low duty cycle working mode. A WSN usually consists of tens to thousands of such nodes that communicate through wireless channels for information sharing and cooperative processing. WSNs can be deployed on a global scale for environmental monitoring and habitat study, over a battle field for military surveillance and reconnaissance, in emergent environments for search and rescue, in factories for condition based maintenance, in buildings for infrastructure health monitoring, in homes to realize smart homes, or even in bodies for patient monitoring [60; 76; 124; 142]. After the initial deployment (typically ad hoc), sensor nodes are responsible for self-organizing an appropriate network infrastructure, often with multi-hop connections between sensor nodes. The onboard sensors then start collecting acoustic, seismic, infrared or magnetic information about the environment, using either continuous or event driven working modes. Location and positioning information can also be obtained through the global positioning system (GPS) or local positioning algorithms. This information can be gathered from across the network and appropriately processed to construct a global view of the monitoring phenomena or objects. The basic philosophy behind WSNs is that, while the capability of each individual sensor node is limited, the aggregate power of the entire network is sufficient for the required mission. In a typical scenario, users can retrieve information of interest
from a WSN by injecting queries and gathering results from the so-called base stations (or sink nodes), which behave as an interface between users and the network. In this way, WSNs can be considered as a distributed database. It is also envisioned that sensor networks will ultimately be connected to the Internet, through which global information sharing becomes feasible. The era of WSNs is highly anticipated in the near future. In September 1999, WSNs w
Performance optimization of hybrid fusion cluster based cooperative spectrum ...Ayman El-Saleh
This presentation shows performance Optimization of Hybrid Fusion Cluster-based Cooperative Spectrum Sensing in Cognitive Radio Networks. For more details, send an email to ayman.elsaleh@gmail.com
Incremental clustering based object tracking in wireless sensor networksSachin MS
Emerging significance of moving object tracking has been actively pursued in the Wireless Sensor Network (WSN) community for the past decade. As a consequence, a number of methods from different angle of assessment have been developed while relatively satisfying performance. Amongst those, clustering based object tracking has shown significant results, which in term provides the network to be scalable and energy efficient for large-scale WSNs. As of now, static cluster based object tracking is the most common approach for large-scale WSN. However, as static clusters are restricted to share information globally, tracking can be lost at the boundary region of static clusters. In this paper, an Incremental Clustering Algorithm is proposed in conjunction with Static Clustering Technique to track an object consistently throughout the network solving boundary problem. The proposed research follows a Gaussian Adaptive Resonance Theory (GART) based Incremental Clustering that creates and updates clusters incrementally to incorporate incessant motion pattern without defiling the previously learned clusters. The objective of this research is to continue tracking at the boundary region in an energy-efficient way as well as to ensure robust and consistent object tracking throughout the network. The network lifetime performance metric has shown significant improvements for
Incremental Static Clustering at the boundary regions than that of existing clustering techniques.
Secure Spectrum Sensing In Cognitive Radio Sensor Networks: A Surveyijceronline
The rapid growth in wireless communications has contributed to a huge demand on the deployment of new wireless services in both the licensed and unlicensed frequency spectrum. Cognitive Radio Networks (CRNs) is a recently emerging paradigm that aim to opportunistically access the intermittent periods of unoccupied frequency bands and therefore increasing the spectral efficiency. Unlike conventional radios, cognitive radios can intelligently adjust their transmission/reception parameters based on the interaction with the environment and find the best available spectrum bands to use. CRNs rely on cooperation for much of their functionality to make network more efficient. However, due to the distributed nature of cooperative spectrum sensing, the network is vulnerable to new types of security threats. The current spectrum sensing methods do not provide security mechanism to mitigate against these attacks. Traditional security solutions for noncognitive wireless networks do not work well when they are confronted with these new attacks. Furthermore, the security mechanisms proposed for cognitive radio ad hoc networks are not applicable for resource constrained cognitive radio sensor networks. These present considerable obstacles to development of a security mechanism that can defend against such attacks. This paper investigates threats and defense mechanism applicable for cognitive radio sensor networks to use the proposed guidelines for future development of a security mechanism for cognitive radio sensor networks.
Node Deployment in Homogeneous and Heterogeneous Wireless Sensor NetworkIJMTST Journal
Optimal sensor deployment is necessary condition in homogeneous and heterogeneous wireless sensor
network. Effective deployment of sensor nodes is a major point of concern as performance and lifetime of any
WSN. Proposed sensor deployment in WSN explore every sensor node sends its data to the nearest sink node
of the WSN. In addition to that system proposes a hexagonal cell based sensor deployment which leads to
optimal sensor deployment for both homogeneous and heterogeneous sensor deployment. Wireless sensor
networks are receiving significant concentration due to their potential applications ranging from surveillance
to tracking domains. In limited communication range, a WSN is divided into several disconnected sub-graphs
under certain conditions. We deploy sensor nodes at random locations so that it improves performance of the
network.This paper aims to study, discuss and analyze various node deployment strategies and coverage
problems for Homogeneous and Heterogeneous WSN.
Wireless Sensor Networks UNIT-1
You can watch my lectures at:
Digital electronics playlist in my youtube channel:
https://www.youtube.com/channel/UC_fItK7wBO6zdWHVPIYV8dQ?view_as=subscriber
My Website : https://easyninspire.blogspot.com/
Comprehensive Review on Base Energy Efficient Routing ProtocolIJRES Journal
With the faster growing in electronics industry, small inexpensive battery powered wireless sensors have made an impact on the communications with the physical world. The Wireless Sensor Networks (WSN) consists of hundreds of sensor nodes which are resource constrained. WSN nodes monitor various physical and environmental conditions very cooperatively. WSN uses various nodes for the communication. WSN has become one of the interested areas in the field of research from last few years. To enhance the lifetime of the whole networks energy reduction is the necessary consideration for design and analyse of the clustering and routing protocols. This paper describes the study of various energy efficient routing protocols in WSNs which are important for their designing purpose so as to meet the various resource constraints.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
Concepts and evolution of research in the field of wireless sensor networksIJCNCJournal
The field of Wireless Sensor Networks (WSNs) is experiencing a resurgence of interest and a continuous evolution in the scientific and industrial community. The use of this particular type of ad hoc network is becoming increasingly important in many contexts, regardless of geographical position and so, according to a set of possible application. WSNs offer interesting low cost and easily deployable solutions to perform a remote real time monitoring, target tracking and recognition of physical phenomenon. The uses of these sensors organized into a network continue to reveal a set of research questions according to particularities target applications. Despite difficulties introduced by sensor resources constraints, research contributions in this field are growing day by day. In this paper, we present a comprehensive review of most recent literature of WSNs and outline open research issues in this field.
A Top-down Hierarchical Multi-hop Secure Routing Protocol for Wireless Sensor...ijasuc
This paper proposes a new top-down hierarchical, multi-hop, secure routing protocol for the wireless
sensor network, which is resilient to report fabrication attack. The report fabrication attack tries to
generate bogus reports by compromising the sensor nodes to mislead the environment monitoring
application executed by randomly deployed wireless sensor nodes. The proposed protocol relies on
symmetric key mechanism which is appropriate for random deployment of wireless sensor nodes. In the
proposed protocol, base station initiates the synthesis of secure hierarchical topology using top down
approach. The enquiry phase of the protocol provides assurance for the participation of all the cluster
heads in secure hierarchical topology formation. Further, this methodology takes care of failure of head
node or member node of a cluster. This protocol ensures confidentiality, integrity, and authenticity of the
final report of the monitoring application. The simulation results demonstrate the scalability of the
proposed protocol.
A wireless sensor network has important applications such as remote environmental monitoring and target tracking, particularly in recent years with the help of sensors that are smaller, cheaper, and intelligent. Sensors are equipped with wireless interfaces with which they can communicate with one another to form a network. A WSN consists of a number of sensor nodes (few tens to thousands) working together to monitor a region to obtain data about the environment. The design of a WSN depends significantly on the application, and it must consider factors such as the environment, the applications design objectives, cost, hardware, and system constraints.
Current Activities in WSN: Developing test bed for target tracking Using Passive Infrared and Ultrasonic Sensors Improving the delivery rate in low power wireless networks .Guided Navigation of Friendly Vehicle towards tracked Object. Design and development of smart mines and explosive ordinance for intelligent activation and deactivation and safe recovery based on secure WSN. Design of a data mule for data collection from remotely placed sensor nodes
The course gives the thorough concepts of the wireless sensor networks, applications examples. It also gives detailed study of sensor node architecture and various protocols used in wireless sensor networks. It also covers issues related to topology, clustering ,synchronization and operating execution environment used for wireless sensor networks.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
DATA TRANSMISSION IN WIRELESS SENSOR NETWORKS FOR EFFECTIVE AND SECURE COMMUN...IJEEE
Data transmission occurs from transmitting node to sink node, which communicate each other via large number of intermediate nodes or directly to an external base station. A network consists of numbers of nodes with one as a source and one or more as a destination node.
The development of the wireless sensor networks (WSNs) in various applications like Defense, Health,
Environment monitoring, Industry etc. always attract many researchers in this field. WSN is the network
which consists of collection of tiny devices called sensor nodes. Sensor node typically combines wireless
radio transmitter-receiver and limited energy, restricted computational processing capacity and
communication band width. These sensor node sense some physical phenomenon using different
transduces. The current improvement in sensor technology has made possible WSNs that have wide and
varied applications. While selecting the right sensor for application a number of characteristics are
important. This paper provides the basics of WSNs including the node characteristics. It also throws light
on the different routing protocols.
A sensor node, also known as a mote (chiefly in North America), is a node in a sensor network that is capable of performing some processing, gathering sensory information and communicating with other connected nodes in the network. A mote is a node but a node is not always a mote.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Wireless Sensor networks are dense networks, which consist of small low cost
sensors having severely constrained computational and energy resources, which operate in
an adhoc environment. Sensor network combines the aspects of distributed sensing,
computing and communication. Despite the numerous applications of sensor networks in
various fields there are various issues which need to be explored and resolved such as
resource constraints, routing, coverage, security, information collection and gathering etc.
In this paper we aim to provide the detailed overview of the wireless sensor technologies and
issues related to them, such as advancement of sensor technology, architecture, applications,
issues and the work done in the field of routing, coverage and security.
Similar to Cognitive radio wireless sensor networks applications, challenges and research trends (20)
OWL stands for Web Ontology Language
OWL is built on top of RDF
OWL is for processing information on the web
OWL was designed to be interpreted by computers
OWL was not designed for being read by people
OWL is written in XML
OWL has three sublanguages
- OWL Lite , OWL DL , OWL Full
OWL is a W3C standard
Security of software defined networking (sdn) and cognitive radio network (crn)Ameer Sameer
Security of Software Defined Networking (SDN)
Overview
Definition Software Defined Networking (SDN)
SDN security & Security Challenges
SDN Attack Surface & Attacks Examples
SDN Threat Model
Open Research issues SDN
Future Research Directions
Simulator for Software Defined Networking
Security of Cognitive Radio Network (CRN)
Overview
Definition Cognitive Network
Security of Cognitive Radios & Threats
Security issues in cognitive radio
Attacks and the proposed defense mechanisms
Open Research issues in Cognitive Radio
Evaluation Methodologies for Cognitive Networking
Future Research Directions
Simulator for Cognitive Radio
The Open Network Operating System (ONOS) is the first open source SDN network operating system targeted specifically at the Service Provider and mission critical networks. ONOS is purpose built to provide the high availability (HA), scale-out, and performance these networks demand.
The Internet of Things (IoT) is the network of physical objects or "things" embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data. .The Internet of Things allows objects to be sensed and controlled remotely across existing network infrastructure .
Generating a custom Ruby SDK for your web service or Rails API using Smithyg2nightmarescribd
Have you ever wanted a Ruby client API to communicate with your web service? Smithy is a protocol-agnostic language for defining services and SDKs. Smithy Ruby is an implementation of Smithy that generates a Ruby SDK using a Smithy model. In this talk, we will explore Smithy and Smithy Ruby to learn how to generate custom feature-rich SDKs that can communicate with any web service, such as a Rails JSON API.
Builder.ai Founder Sachin Dev Duggal's Strategic Approach to Create an Innova...Ramesh Iyer
In today's fast-changing business world, Companies that adapt and embrace new ideas often need help to keep up with the competition. However, fostering a culture of innovation takes much work. It takes vision, leadership and willingness to take risks in the right proportion. Sachin Dev Duggal, co-founder of Builder.ai, has perfected the art of this balance, creating a company culture where creativity and growth are nurtured at each stage.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
Key Trends Shaping the Future of Infrastructure.pdfCheryl Hung
Keynote at DIGIT West Expo, Glasgow on 29 May 2024.
Cheryl Hung, ochery.com
Sr Director, Infrastructure Ecosystem, Arm.
The key trends across hardware, cloud and open-source; exploring how these areas are likely to mature and develop over the short and long-term, and then considering how organisations can position themselves to adapt and thrive.
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
The Art of the Pitch: WordPress Relationships and SalesLaura Byrne
Clients don’t know what they don’t know. What web solutions are right for them? How does WordPress come into the picture? How do you make sure you understand scope and timeline? What do you do if sometime changes?
All these questions and more will be explored as we talk about matching clients’ needs with what your agency offers without pulling teeth or pulling your hair out. Practical tips, and strategies for successful relationship building that leads to closing the deal.
JMeter webinar - integration with InfluxDB and GrafanaRTTS
Watch this recorded webinar about real-time monitoring of application performance. See how to integrate Apache JMeter, the open-source leader in performance testing, with InfluxDB, the open-source time-series database, and Grafana, the open-source analytics and visualization application.
In this webinar, we will review the benefits of leveraging InfluxDB and Grafana when executing load tests and demonstrate how these tools are used to visualize performance metrics.
Length: 30 minutes
Session Overview
-------------------------------------------
During this webinar, we will cover the following topics while demonstrating the integrations of JMeter, InfluxDB and Grafana:
- What out-of-the-box solutions are available for real-time monitoring JMeter tests?
- What are the benefits of integrating InfluxDB and Grafana into the load testing stack?
- Which features are provided by Grafana?
- Demonstration of InfluxDB and Grafana using a practice web application
To view the webinar recording, go to:
https://www.rttsweb.com/jmeter-integration-webinar
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024
Cognitive radio wireless sensor networks applications, challenges and research trends
1. Cognitive Radio Wireless Sensor Networks:
Applications, Challenges and Research Trends
Prepared by: Ameer Sameer Hamood
University of Babylon - Iraq
Information Technology - Information Networks
2. Cognitive Radio Wireless Sensor Networks
Overview
Definition cognitive radio wireless sensor
network
Advantages of Using CR in WSNs
Application of CR-WSNs
Challenges
Research Trends
3. A cognitive radio wireless sensor network is one of the
candidate areas where cognitive techniques can be
used for opportunistic spectrum access.
Communications in wireless sensor networks (WSNs)
consist of hundreds of WS nodes deployed throughout
the sensor field and the distance between two
neighboring WS nodes is generally limited to few
meters.
Definition cognitive radio wireless sensor network
4. A sink node or base station is responsible for collecting the data
from the WS nodes in single or multiple-hop manner. The sink
node then sends the collected data to the users via a gateway,
often using the internet or any other communication channel.
Figure 1 shows the scenario of conventional WSNs.
Definition cognitive radio wireless sensor network (cont..)
Figure 1
5. A typical cognitive radio sensor network (CRSN) architecture.
cognitive radio sensor network (CRSN) architecture
Figure 2
6. CRSN deployment in licensed spectrum environment
Example of CRSN deployment in licensed spectrum environment
Figure 3
7. CR-WSN is a new paradigm in a WS network arena that utilizes
the spectrum resource efficiently for bursty traffic. The system has
the capability of packet loss reduction, power waste reduction,
high degree of buffer management, and has better communication
quality. See fig 2:
Advantages of using CR in WSNs
Figure 4
8. 1- Efficient Spectrum Utilization and Spaces for New
Technologies .
2- Multiple Channels Utilization .
3- Energy Efficiency .
4- Global Operability .
5- Application Specific Spectrum Band Utilization .
6- Financial Advantages to the Incumbents by Renting or
Leasing .
7- Avoiding Attacks .
Advantages of Using CR in WSNs
9. CR-WSNs may have a wide range of application domains.
Indeed, CR-WSN can be deployed anywhere in place of WSNs.
Some examples of prospective areas where CR-WSNs can be
deployed are as follows: facility management, machine
surveillance and preventive maintenance, precision agriculture,
medicine and health, logistics, object tracking, telemetries,
intelligent roadside, security, actuation and maintenance of
complex systems, monitoring of indoor and outdoor environments.
Application of CR-WSNs
10. 1- Military and Public Security Applications
2- Health Care
3- Home Appliances and Indoor Applications
4- Bandwidth-Intensive Applications
5- Real-Time Surveillance Applications
6- Transportation and Vehicular Networks
7- Diverse Purpose Sensing
Application of CR-WSNs
11. 1- Detection, False Alarm, and Miss-Detection Probability
2- Hardware
3- Topology Changes
4- Fault Tolerance
5- Manufacturing Costs
6- Clustering
7- Channel Selection
8- Scalability
9- Power Consumption
10 Quality of Service (QoS)
11- Security
12- Sensing Techniques
Challenges
12. No clear standard exists and there have been several unclear
proposals. Many areas need to be explored, such as low
computational and energy efficient spectrum sensing, spectrum
management, clustering, energy consumption, spectrum handoff,
channel allocation, channel access, geo-location information
sharing, self-topology generation, cross-layer optimization of
protocol stacks, etc. In addition, many issues remain to be
resolved, such as coexistence with other CR systems, legal
issues to access incumbent channels, limit of interference with
PUs, transmission power control etc.
Research Trends
13. Number of research papers related to WSCRNs published in IEEE
journals.
Research Trends (cont ..)
Figure 5
14. A CR wireless sensor network is a type of wireless sensor network that
comprises spatially-distributed autonomous CR equipped wireless
sensors to monitor the physical or environmental conditions
cooperatively.
The following are the potential challenges for the success of CR-WSNs
Development of a wireless sensor with the required cognitive
capabilities,
Development of extremely low power consumable CR wireless sensor
with energy harvesting facilities,
Capability of operating at high volumetric densities,
Producing low cost CR wireless sensors,
Development of autonomous and unattended operable algorithms and
protocols,
Highly intelligent and adaptive to the environment
Should be robust on security for attacks and should work in an
untrustworthy environment,
Development of globally operable CR wireless sensor etc.
Conclusions
15. 1- Gyanendra Prasad Joshi, Seung Yeob Nam, and Sung Won Kim
IEEE Recommended Practice for Information Technology
2- Yang D., Xu Y., Gidlund M. Wireless coexistence between IEEE 802.11 and
IEEE 802.15.4-based networks: A survey. Int. J. Distr. Sens. Netw. 2011;2011:1–
17.
3- Cognitive Radio Sensor Networks
Ozgur B. Akan Osman B. Karli Ozgur Ergul
Reference
power waste reduction
A Game Theory Based Strategy for Reducing Energy Consumption in Cognitive WSN
Energy consumption and spectrum availability are two of the most severe constraints of WSNs due to their intrinsic nature. The introduction of cognitive capabilities into these networks has arisen to face the issue of spectrum scarcity but could be used to face energy challenges too due to their new range of communication possibilities. game theory for cognitive WSNs presented strategy improves energy consumption by taking advantage of the new change-communication-channel capability. Based on game theory, the strategy decides when to change the transmission channel depending on the behavior of the rest of the network nodes. The strategy presented is lightweight but still has higher energy saving rates as compared to noncognitive networks and even to other strategies based on scheduled spectrum sensing. Simulations are presented for several scenarios that demonstrate energy saving rates of around 65% as compared to WSNs without cognitive techniques.
The system has the capability of packet loss reduction, power waste reduction, high degree of buffer management, and has better communication quality. This section discusses the advantages of using cognitive radio in WSNs.
Efficient Spectrum Utilization and Spaces for New Technologies
The electromagnetic spectrum is a precious gift of Nature. The amount of available useable spectrum bands cannot be increased but they can be used more efficiently. With the exception of industrial, scientific and medical (ISM) radio bands, one requires a license from the government of the respective country to utilize the radio bands. Owing to the high cost associated with spectrum licensing, many researchers and hardware manufacturers have focused on developing devices for ISM bands. Therefore, ISM bands are overcrowded limiting the development of new technologies
On the other hand, many licensed spectrum bands are either underutilized or unutilized
Cognitive radio wireless sensors can use the unutilized spectrum, called white spaces, without disturbing the license holders. Unlicensed users can use those bands with little or no cost, so that more technologies can be developed for these bands.
Multiple Channels Utilization
Most traditional WSNs use a single channel for communication
In WSNs, upon the detection of an event, sensor nodes generate the traffic of packet bursts. At the same time, in densely deployed WSNs, a large number of wireless sensor nodes within the event area attempt to acquire the same channel at the same time. This increases the probability of collisions, and decreases the overall communication reliability due to packet losses, leading to excessive power consumption and packet delay. CR-WSNs access multiple channels opportunistically to alleviate this potential challenge.
Energy Efficiency
In WSNs, there is a large amount of power waste for packet retransmission due to packet losses. CR wireless sensors may be able to change their operating parameters to adapt to channel conditions. Therefore, energy consumption due to a packet collision and retransmission can be mitigated.
Global Operability
Each country has its own spectrum regulation rules. A certain band available in one country might not be available in another. Traditional wireless sensors with a preset working frequency might not work in cases where the manufactured wireless sensors are deployed in different regions. On the other hand, if nodes are equipped with cognitive radio capability, they can overcome the spectrum incompatibility problem by changing their communication frequency band. Therefore, CR wireless sensors have the potential to be operated almost anywhere in the world.
Application Specific Spectrum Band Utilization
Currently, the number of wireless sensors deployed for different applications has increased. In WSN, data traffic is usually correlated both temporally and spatially. When any event occurs, WSNs generate packet bursts and they remain silent when there is no event. These temporal and spatial correlations introduce to the design challenge of the communication protocols for WSN. With the intelligent communication protocols in CR-WSN, it is possible that the wireless sensors deployed for the same purpose use the spectrum of different incumbents in spatially overlapping regions. This is possible with cooperative communication among SUs, which obviously mitigates interference issues.
Financial Advantages to the Incumbents by Renting or Leasing
Whenever and wherever some licensed spectrum bands are not required, license holders can lease their spectrum to the SUs at low cost. This can be done while retaining the access of the incumbents on the spectrum bands whenever necessary. This is very good opportunity for those who cannot obtain a direct license for a certain spectrum due to legal or financial issues. This is a win-win approach to the incumbents and SUs.
Avoiding Attacks
Unlike CRWS, most off-the-shelf wireless sensors work only on particular frequency bands. Taking advantage of the wide range of spectrum usability, SUs in CR-WSNs can avoid several types of attacks.
CR-WSNs may have a wide range of application domains. Indeed, CR-WSN can be deployed anywhere in place of WSNs. Some examples of prospective areas where CR-WSNs can be deployed are as follows: facility management, machine surveillance and preventive maintenance, precision agriculture, medicine and health, logistics, object tracking, telemetries, intelligent roadside, security, actuation and maintenance of complex systems, monitoring of
indoor and outdoor environments. This section discusses some of the potential areas where CR-WSNs can be deployed with examples.
Military and Public Security Applications
Conventional WSNs are used in many military and public security applications, such as: (a) chemical biological radiological and nuclear (CBRN) attack detection and investigation; (b) command control; (c) gather the information of battle damage evaluation; (d) battlefield surveillance; (e) intelligence assistant (f) targeting, etc. In the battlefield or in disputed regions, an adversary may send jamming signals to disturb radio communication channels
In such situations, because CR-WSs can handoff frequencies over a wide range, CR-WSNs can use different frequency bands, thereby avoiding the frequency band with a jamming signal. In addition, some military applications require a large bandwidth, minimum channel access and communication delays. For such applications, CR-WSNs can be a better option.
Health Care
In a health care system, such as telemedicine, wearable body sensors are being used increasingly. Numerous wireless sensor nodes are placed on patients and acquire critical data for remote monitoring by health care providers. In 2011, the IEEE 802.15 Task Group 6 (BAN)
approved a draft of a standard for body area network (BAN) technology. Wireless BAN-assisted health care systems have already been in practice in some remote areas of developing countries, such as in Nepal and India
Wireless BAN for healthcare systems is suitable for areas, where the number of health specialists is relatively low.
Medical data is critical, delay and error sensitive. Therefore, the limitation of traditional WSN, as discussed in the previous section confines the potentiality of telemedicine. The QoS may not be achieved at a satisfactory level if the operating spectrum band is crowded in convenient ‘telemedicine with BAN’. The use of ‘CR wearable body wireless sensors’ can mitigate these problems due to bandwidth, jamming and global operability, hence improve reliability.
Home Appliances and Indoor Applications
Many potential and emerging indoor applications require a dense WSNs environment to achieve an adequate QoS. Conventional WSNs experience significant challenges in achieving reliable communication because ISM bands in indoor areas are extremely crowded
Some examples of the indoor applications of WSNs are intelligent buildings, home monitoring systems, factory automation, personal entertainment, etc. CR-WSNs can mitigate the challenges faced by conventional indoor WSNs applications.
Bandwidth-Intensive Applications
Multimedia applications, such as on-demand or live video streaming, audio, and still images over resource constrained WSNs, are extremely challenging because of their huge bandwidth requirements. Other WSN applications, such as WSNs in a hospital environment, vehicular WSNs, tracking, surveillance, etc., have vast spatial and temporal variations in data density correlated with the node density. These applications are bandwidth-hungry, delay intolerable and bursty in nature. Because in CR-WSN, SUs can access multiple channels whenever available and necessary, CR-WSN is very suitable for these types of bandwidth-hungry applications. Rehmani et al. reported channel bonding in CR-WSNs for such bandwidth intensive applications.
Real-Time Surveillance Applications
Real-time surveillance applications, such as traffic monitoring, biodiversity mapping, habitat monitoring, environmental monitoring, environmental conditions monitoring that affect crops and livestock, irrigation, underwater WSNs, vehicle tracking, inventory tracking, disaster relief operations, bridges or tunnel monitoring, require minimum channel access and communication delay. Some real-time surveillance applications are highly delay-sensitive and require high reliability. A delay due to a link failure can also occur in multihop WSNs if the channel condition is not good. On the other hand, WS nodes hop to another channel if they find another idle channel with a better condition in CR-WSNs. Channel aggregation and the use of multiple channels concurrently are possible in CR-WSNs to increase the channel bandwidth
Transportation and Vehicular Networks
The IEEE 1609.4 standard proposes multi-channel operations in wireless access for vehicular environments (WAVE). The WAVE system operates on the 75 MHz spectrum in the 5.9 GHz band with one control channel and six service channels. All vehicular users will have to contend for channel access and use it to transmit the information in the 5.9 GHz band. However, it still suffers from spectrum insufficiency problems. This spectrum scarcity issue and the requirements of cognitive radio in WAVE have been studied .
Some preliminary works in CR-enabled vehicular communications have already been done . Vehicular wireless sensor networks are emerging as a new network paradigm for proactively gathering monitoring information in urban environments. CR-WSNs are likely to be more relevant in this field. Although this area still needs to be examined, some protocols for highway safety using CR-WSNs have been proposed .
Diverse Purpose Sensing
Increasingly, the use of wireless sensors in the same area for different objectives coexists. In a conventional WSN, those wireless sensors attempt to access the channel in non-cooperative manners. With the help of an efficient medium access control (MAC) protocol, CR-WSN might select different channels for different applications considering load balancing and fairness.
CR-WSNs differ from conventional WSNs in many aspects. Because protecting the right of PUs is the main concern in CR-WSN, it has many new challenges including the challenges in the conventional WSNs.
1. Detection, False Alarm, and Miss-Detection Probability
The detection probability is a metric used for correct detection by CRWS regarding the absence of PUs on the channel. The miss-detection probability is a metric for CRWS failing to detect the presence of the primary signal on the channel, and the false-alarm probability is a metric for the CRWS failing to detect the absence of the primary signal.
In CR-WSNs, a false alarm and miss detection can violate the right of the incumbents on the channel, which is the violation of the main principle of CRNs. The right to access a network by the incumbents should be respected in any type of CRN. A false alarm can cause spectrum under-utilization and a missed detection might cause interference with the PUs. In addition, most application areas of CRWS discussed in this paper are very critical in terms of the delay and correctness of data. In CR-WSNs, a false alarm and miss-detection causes a long waiting delay, frequent channel switching and significant degradation in throughput. The issues of the false alarm and miss-detection probability for CR ad hoc networks and IEEE 802.22 WRAN have been well studied. However, this area still needs to be explored for CR-WSNs. This area needs to be examined further to meet the research challenges of CR-WSNs.
2. Hardware
CR wireless sensors have hardware constraints in terms of computational power, storage and energy. Unlike conventional wireless sensors, they have a responsibility to sense channels, analyze, decide, and act. CR wireless sensors should be capable of changing the parameter or transmitters based on an interaction with its environment
, a CRWS consists of six basic units: (i) a sensing unit; (ii) a processing and storage unit; (iii) a CR unit; (iv) a transceiver unit; (v) a power unit; and (vi) a miscellaneous unit.
Sensing units contain sensors and analog to digital converters (ADCs). The analog signal observed by the sensor is converted to a digital signal and sent to the processing unit. The CRWS should have cognition capability using a state-of-the art artificial intelligence technology. This capability is accommodated in the CR unit. The CR unit needs to adapt the communication parameters dynamically, such as carrier frequency, transmission power, and modulation. The unit needs to select the best available channel, share the spectrum with other users, and manage the spectrum mobility, i.e., vacate the currently using channel in the case the PU wants to use that channel. A transceiver unit is responsible for receiving and sending data.
Because the energy harvesting techniques in wireless sensor nodes have developed rapidly, the energy harvesting or recharging units are optional and sensor specific. A miscellaneous unit is an application- specific additional unit, such as a location-finding unit, energy harvesting unit, and mobilizing unit, etc. Akan et al. proposed a similar hardware structure of a CR wireless sensor.
Designing intelligent hardware for CR-WSNs is a very challenging issue. Many artificial intelligence techniques have been proposed to fulfill the basic principle of CR, i.e., observation, reconfiguration and cognition. Some examples include artificial neural networks (ANNs), metaheuristic algorithms, hidden Markov models (HMMs), rule-based systems, ontology-based systems (OBSs), and case-based systems (CBSs). The factors that affect the choice of AI techniques, such as responsiveness, complexity, security, robustness, and stability, are discussed in reference. Nevertheless, it is unclear how much intelligent hardware for WS-CRNs is intelligent enough and no threshold has been defined for it.
Topology Changes
Topology directly affects the network lifetime in WSNs. Depending on the application, CR wireless sensors may be deployed statically or dynamically. In any type of WSN, hardware failure is common due to hardware malfunctioning and energy depletion. The topologies for CR-WSNs may be the same as conventional WSNs, but they are prone to change more frequently than ad hoc CRNs. Akan et al. reported that CR-WSNs have the following topologies: (i) Ad Hoc CR-WSNs; (ii) Clustered CR-WSNs; (iii) Heterogeneous and Hierarchical CR-WSNs; and (iv) Mobile CR-WSNs.
Basically, the minimum output power required to transmit a signal over a distance δ is proportional to δn, where 2 ≤ n < 4. The exponent n is closer to four for low-lying antennae and near-ground channels, as is typical in wireless sensor network communication. Therefore, routes that have more hops with shorter hop distances can be more power efficient than those with fewer hops but longer hop distances.
Nevertheless, it is not always possible to find such a route in static sensor networks topology. Therefore, an adaptive self-configuration topology mechanism is important for CR-WSNs for obtaining scalability, reducing energy consumption and achieving better network performance. An adaptive self-configuration topology mechanism performs better than static topology, even though it is a challenging issue to design and implement. This area has not received much research attention.
Fault Tolerance
CR-WSNs should have self-forming, self-configuration and self-healing properties. In other words, whenever some nodes or links fail, an alternative path that avoids the faulty node or link must be derived. In CR-WSNs, faults can occur for a variety of reasons, such as hardware or software malfunctioning, or natural calamities, e.g., fire, floods, earthquakes, volcanic eruptions, or tsunamis etc. A CR-WSN should always be prepared to deal with such situations. There are several types of faults, such as node fault, network fault, and sink fault, etc. Souza et al. surveyed the well fault tolerance in WSNs. Hoblos et al. modeled the fault tolerance or reliability Rk(t) of a wireless sensor node using the Poisson distribution within the time interval (0,t) as follows:
[Math Processing Error]
where λk is the failure rate of wireless sensor node k and t is the time period.
The fault tolerance is one of the challenging issues in CR-WSNs. The protocols designed for CR-WSNs should have a level of fault tolerance capability so that the overall function of the WSNs should not be interrupted.
Manufacturing Costs
Generally, CR-wireless sensors have been deployed in large numbers. Therefore, the cost should be significantly low. In contrast to conventional WSNs, which require less memory and computation capability, CR-WSNs require moderate memory and computational capabilities. To reduce the hardware cost, an algorithm that requires less computational power and memory should be developed. Designing such an algorithm is a challenging issue. Furthermore, CR wireless sensors should contain intelligent radio, application specific positioning systems (e.g., GPS), energy harvesting unit etc. which obviously increase the production cost.
Clustering
Logically grouping and organizing similar CR wireless sensors in their proximity has several advantages. Grouping sensor nodes into clusters has been pursued in WSNs to achieve the network scalability, reduce energy consumption and reduce the communication overhead. Several types of clustering exist, such as static, dynamic, single hop and multihop, homogeneous and heterogeneous. Very little work has been done in this area, which is also one of the research challenges.
Channel Selection
Because there is no dedicated channel to send data, sensors need to negotiate with the neighbors and select a channel for data communication in CR-WSNs. This is a very challenging issue, because there is no cooperation between the PUs and SUs. PUs may arrive on the channel any time. If the PU claims the channel, the SUs have to leave the channel immediately. Therefore, data channels should be selected intelligently considering the PU's behavior on the channel and using some AI algorithms.
Scalability
For some applications, CR wireless sensors should be deployed in huge numbers. Unlike conventional WS nodes, CR wireless sensors require cooperation among nodes for spectrum information sharing. This is very difficult to coordinate in a heterogeneous CR wireless sensor environment. Algorithms and protocols developed for CR-WSNs should be capable of solving these issues due to the growing size of the network.
Power Consumption
CR wireless sensors are power constraint devices with a limited energy source. In addition to the energy needed for spectrum sensing, channel negotiation, route discovery, transmission and reception of data packets, backoff, and data processing, CR wireless sensors also require energy for frequent spectrum handoff. A CR wireless sensor needs to sense the PUs' activities on the channel. Many applications require multiple antennas to monitor the PUs' activities, hence more energy is consumed.
Although there are several proposals for energy harvesting, these techniques have their own limitations. In some application scenarios, energy harvesting or the replenishment of power resources is not possible.
In ad hoc CRNs, power consumption is an important design factor, but not the primary consideration. However, in CR-WSNs, it is one of the main performance metrics that directly affect the network lifetime.
Quality of Service (QoS)
In conventional WSNs, the QoS is generally characterized by four parameters: bandwidth, delay, jitter and reliability. To avoid hazardous consequences in critical applications, WSNs need to maintain an adequate level of QoS. QoS support is a challenging issue due to resource constraints, such as processing power, memory, and power sources in wireless sensor nodes. This is more challenging in CR-WSNs because in addition to the challenges in WSNs, it has one more challenge to protect the rights of PUs to access the incumbent spectra. PU's communication should be interference free with the SUs. This is more challenging because it is difficult to predict the PU's arrival on the channel. A miss-detection of the primary signal and a false alarm can cause additional challenges.
Security
Wireless sensors are normally deployed in an unattended environment, and are prone to security and privacy issues. CR wireless sensors can be attacked physically, and the data can be stolen. CR-WSNs are more vulnerable to security threats than the conventional WSNs, because there is no strict cooperation between PUs and SUs communication. The data collected by CR wireless sensors can be sniffed, destroyed or altered by unauthorized entities. In addition, attackers can interfere with PUs transmission or prevent the use of the channel by SUs through spectrum sensing data falsification (SSDF). CR-WSNs should have some acceptable level of security robustness against these potential threats and attacks. In addition to the security issues in conventional WSNs, additional security challenges are there for CR-WSNs. Some of the security issues in CR-WSNs can be as follows: (a) unacceptable interference to licensed users; (b) prohibiting the use of idle channels for SUs; (c) prohibiting the use of common control channels by virtually creating a bottleneck problem; (d) access to private data; (e) modification of data; and (f) injection of false data. Some possible attacks in CR-WSNs are discussed below.
11.1. Physical Layer Attack
A jamming attack is one type of attack on a physical layer (PHY). In this type of attack, the adversary transmits radio signals on the wireless channels to interfere with CR wireless sensors normal operations. These adversaries may have powerful and sophisticated hardware and software. If the adversary blocks the entire network, it constitutes DoS attack at PHY. Another type of attack on PHY is a tampering attack. In this, the attacker may damage the CR wireless sensors, replace the entire nodes, or part of its hardware.
11.2. MAC Layer Attack
In a MAC layer attack, the attackers violate the rules defined in the protocols and disturb the normal operation, e.g., sending undesirable packets repeatedly to disturb the normal operation and exhaust battery power, using channels selfishly, and show uncooperative behavior on the priority of a cooperative MAC-layer. This may lead to DoS attacks at the MAC layer.
11.3. Routing Layer Attack
In a routing layer, the attackers alter the information on routing packets and misguide the packet forwarding sensors on the networks. The same things can happen to data packets as well. The following routing layer attacks are possible in CR-WSNs
(a)
Wormhole attack: In a wormhole attack, an attacker builds bogus route information and tunnels the packet to another location. This creates routing loops and wastes energy.
(b)
Sinkhole attack: In a sinkhole attack, the adversary provides false information to the wireless sensor nodes in the networks, such as it has the shortest route or efficient route etc.
(c)
Sybil attack: In a Sybil attack, a single node may present multiple identities to the other nodes in a network. This may mislead the geographic routing protocols as the adversary appears to be in multiple locations at the same time.
These issues are entirely open research issues, and need to be explored further to meet its research challenges.
12. Sensing Techniques
One of the main objectives of imbedding a CR in a wireless sensor is to utilize the unused licensed spectrum opportunistically. Here, opportunistically means the SUs should protect the accessing right of the PUs whenever necessary. The interference of SUs to PU depends on the sensing accuracy of SUs. If SUs can sense the channels with high accuracy, interference with the PU decreases. Depending on the sensing technique, there is a tradeoff between the sensing delay and sensing accuracy. The technique that takes a long sensing time has more accuracy with the cost of delays and vice versa. Basically, there are two types of sensing techniques: (a) signal processing techniques; and (b) cooperative sensing techniques. signal processing sensing techniques for CR-WSNs can be divided further into matched filter detection, energy detection, cyclostationary feature detection and some other techniques. Similarly, cooperative sensing can be divided further into centralized spectrum sensing, decentralized spectrum sensing and hybrid spectrum-sensing techniques.
12.1. Signal Processing Techniques
(a)
Matched filter detection: The matched filter detection technique requires a demodulation of the PU's information signal at PHY and MAC layers, such as the modulation type and order, pulse shaping, packet format, operating frequency, bandwidth, etc. CR wireless sensors receive that information from the PU's pilots, preambles, synchronization words or spreading codes etc. The advantage of the matched filter method is that it takes a short time and requires fewer samples of the received signal. This decreases the received signal SNR and number of signal samples required. However, matched filter detection techniques consume considerable power and require high computational complexity and perfect knowledge of the target users.
(b)
Energy detection: Energy detection detects the signal based on the sensed energy. This does not require prior knowledge of the PU's signals. This is a very popular technique because of its simplicity. The main disadvantage of this technique is its lower accuracy. Energy detection cannot discriminate the PUs' signal from the SUs' signal. This technique cannot be used to detect spread spectrum signals and has poor performance under a low SNR.
(c)
Cyclostationary feature detection: In cyclostationary feature-detection techniques, modulated signals are coupled with sine wave carriers, pulse trains, repeated spreading, hopping sequences, or cyclic prefixes. The cyclostationary feature detection technique provides better performance, even in low SNR regions. This has good signal classification ability. However, it is more complex than energy detection and high speed sensing cannot be achieved. This cannot work if the target signal's characteristics are unknown.
12.2. Cooperative Sensing
CR wireless sensors may encounter incorrect judgments because radio-wave propagation through the wireless channels has adverse factors, such as multi-path fading, shadowing, and building penetration. In addition, CR wireless sensors are hardware constraints and cannot sense multiple channels simultaneously. Therefore, CR wireless sensors cooperate and share their sensing information with each other to improve the sensing performance and accuracy. Three types of cooperative sensing exist: (a) centralized cooperative spectrum sensing; (b) decentralized cooperative spectrum sensing; and (c) hybrid cooperative spectrum sensing.
In centralized spectrum sensing techniques, each CR wireless sensor performs its own local spectrum sensing independently and makes a decision as to whether the PU's signal is present or absent on a particular channel. The CR wireless sensors then forward their decisions to a central cooperator, such as a cluster head, collector, or server. The central cooperator fuses the received decision of the CR wireless sensors and makes a final decision to infer the absence or presence of the PU. Whenever a CR wireless sensor wants to send data, it request channel information to the central cooperator.
In decentralized cooperation, the CR wireless sensors share intra-cluster information among clusters. CR wireless sensors in a cluster perform local spectrum sensing and inform the other CR wireless sensors within the clusters of their spectrum decision. There is no central cooperator; therefore it works in a decentralized manner. This scheme requires a periodic update on the spectrum information table, hence requires more storage and computation.
In hybrid cooperation, CR wireless sensors share information in a decentralized manner. However, the central cooperator may request a cluster head to send channel information whenever necessary. Although, cooperative sensing has advantages over non-cooperative sensing, it requires additional computation and resources, which is a challenge in hardware constraint CR wireless sensors.