This document summarizes a research paper about congestion control, routing, and scheduling in wireless networks with interference cancellation capabilities. It discusses using successive interference cancellation (SIC) to allow multiple concurrent transmissions and increase network capacity. The paper formulates the joint congestion control, routing, and scheduling problem and solves it in a distributed manner using dual decomposition. It develops a decentralized algorithm for link scheduling under the physical SINR interference model that coordinates local transmissions and achieves similar results to centralized greedy maximal scheduling. The paper evaluates the performance gains from SIC and shows that network flows can achieve up to twice their rates compared to networks without interference cancellation.
A New Paradigm for Load Balancing in WMNsCSCJournals
In this paper, we address the problem of load balancing in Wireless Mesh Networks. We consider a Cluster Based Wireless Mesh Architecture in which the WMN is divided into clusters that could minimize the updating overhead during topology change due to mobility of mesh nodes or congestion of load on a cluster. Each cluster contains a gateway that has complete knowledge about group memberships and link state information in the cluster. The gateway is often elected in the cluster formation process. We consider load of gateways and try to reduce it. As a matter of fact when a gateway undertakes to be an interface for connecting nodes of a wireless mesh network to other networks or internet, there would be some problems such as congestion and bottleneck, so we introduce a new paradigm for these problems. For solving bottleneck we use clustering to reduce load of gateways and after that by use of dividing cluster we prevent from bottleneck on gateways. We study how to detect congestion on a gateway and how can reduce loads of it that preventing from bottleneck on gateway and therefore increasing throughput of network to encountering many loads. So we propose an algorithm to detect bottleneck and remedies for load balancing in Wireless Mesh Networks. We also use Ns2-Emultion for implementing and testing the framework. Some qualitative results are provided to prove the correctness and the advantages of our framework.
Defeating jamming with the power of silence a gametheoretic analysisranjith kumar
Dear Student,
DREAMWEB TECHNO SOLUTIONS is one of the Hardware Training and Software Development centre available in
Trichy. Pioneer in corporate training, DREAMWEB TECHNO SOLUTIONS provides training in all software
development and IT-related courses, such as Embedded Systems, VLSI, MATLAB, JAVA, J2EE, CIVIL,
Power Electronics, and Power Systems. It’s certified and experienced faculty members have the
competence to train students, provide consultancy to organizations, and develop strategic
solutions for clients by integrating existing and emerging technologies.
ADD: No:73/5, 3rd Floor, Sri Kamatchi Complex, Opp City Hospital, Salai Road, Trichy-18
Contact @ 7200021403/04
phone: 0431-4050403
The novel applications of sensor networks impose some requirements in wireless sensor network design. With the energy efficiency and lifetime awareness, the throughput and network delayalso required to support emerging applications of sensor networks. In this paper, we propose
throughput and network delay aware intra-cluster routing protocol. We introduce the back-up links in the intra-cluster communication path. The link throughput, communication delay, packet loss ratio, interference, residual energy and node distance are the considered factors in finding efficient path of data communication among the sensor nodes within the cluster. The
simulation result shows the higher throughput and lower average packet delay rate for the proposed routing protocol than the existing benchmarks. The proposed routing protocol also shows energy efficiency and lifetime awareness with better connectivity rate.
A wireless network consists of a set of wireless nodes forming the network. The bandwidth allocation scheme used in wireless networks should automatically adapt to the network’s environments, where issues such as mobility are highly variable. This paper proposes a method to distribute the bandwidth for wireless network nodes depending on dynamic methodology;this methodology uses intelligent clustering techniques that depend on the student’s distribution at the university campus, rather than the classical allocation methods. We propose a clustering-based approach to solve the dynamic bandwidth allocation problem in wireless networks, enabling wireless nodes to adapt their bandwidth allocation according to the changing number of expected users over time. The proposed solution allows the optimal online bandwidth allocation based on the data extracted from the lectures timetable, and fed to the wireless network control nodes, allowing them to adapt to their environment. The environment data is processed and clustered using the KMeans clustering algorithm to identify potential peak times for every wireless node. The proposed solution feasibility is tested by applying the approach to a case study, at the Arab American University campus wireless network.
MULTI-CLUSTER MULTI-CHANNEL SCHEDULING (MMS) ALGORITHM FOR MAXIMUM DATA COLLE...IJCNCJournal
Interference during data transmission can cause performance degradation like packet collisions in Wireless Sensor Networks (WSNs). While multi-channels available in IEEE 802.15.4 protocol standard WSN technology can be exploited to reduce interference, allocating channel and channel switching
algorithms can have a major impact on the performance of multi-channel communication. This paper presents an improved Fuzzy Logic based Cluster Formation and Cluster Head (CH) Selection algorithm with enhanced network lifetime for multi-cluster topology. The Multi-Cluster Multi-Channel Scheduling
(MMS) algorithm proposed in this paper improves the data collection by minimizing the maximum interference and collision. The presented work has developed Cluster formation and cluster head (CH) selection algorithm and Interference-free data communication by proper channel scheduled. The extensive
simulation and experimental outcomes prove that the proposed algorithm not only provides an interference-free transmission but also provides delay minimization and longevity of the network lifetime, which makes the presented algorithm suitable for energy-constrained wireless sensor networks.
Wireless mesh networks offer high bandwidth Internet access for mobile users anywhere and at any time.
It is an emerging technology that uses wireless multi-hop networking to provide a cost-efficient way for
community or enterprise users to have broadband Internet access and share network resource. In this paper,
we have tried to give a comparative analysis of various Gateway Placement approaches which can be
helpful in understanding which approach will be useful in which situation.
A New Paradigm for Load Balancing in WMNsCSCJournals
In this paper, we address the problem of load balancing in Wireless Mesh Networks. We consider a Cluster Based Wireless Mesh Architecture in which the WMN is divided into clusters that could minimize the updating overhead during topology change due to mobility of mesh nodes or congestion of load on a cluster. Each cluster contains a gateway that has complete knowledge about group memberships and link state information in the cluster. The gateway is often elected in the cluster formation process. We consider load of gateways and try to reduce it. As a matter of fact when a gateway undertakes to be an interface for connecting nodes of a wireless mesh network to other networks or internet, there would be some problems such as congestion and bottleneck, so we introduce a new paradigm for these problems. For solving bottleneck we use clustering to reduce load of gateways and after that by use of dividing cluster we prevent from bottleneck on gateways. We study how to detect congestion on a gateway and how can reduce loads of it that preventing from bottleneck on gateway and therefore increasing throughput of network to encountering many loads. So we propose an algorithm to detect bottleneck and remedies for load balancing in Wireless Mesh Networks. We also use Ns2-Emultion for implementing and testing the framework. Some qualitative results are provided to prove the correctness and the advantages of our framework.
Defeating jamming with the power of silence a gametheoretic analysisranjith kumar
Dear Student,
DREAMWEB TECHNO SOLUTIONS is one of the Hardware Training and Software Development centre available in
Trichy. Pioneer in corporate training, DREAMWEB TECHNO SOLUTIONS provides training in all software
development and IT-related courses, such as Embedded Systems, VLSI, MATLAB, JAVA, J2EE, CIVIL,
Power Electronics, and Power Systems. It’s certified and experienced faculty members have the
competence to train students, provide consultancy to organizations, and develop strategic
solutions for clients by integrating existing and emerging technologies.
ADD: No:73/5, 3rd Floor, Sri Kamatchi Complex, Opp City Hospital, Salai Road, Trichy-18
Contact @ 7200021403/04
phone: 0431-4050403
The novel applications of sensor networks impose some requirements in wireless sensor network design. With the energy efficiency and lifetime awareness, the throughput and network delayalso required to support emerging applications of sensor networks. In this paper, we propose
throughput and network delay aware intra-cluster routing protocol. We introduce the back-up links in the intra-cluster communication path. The link throughput, communication delay, packet loss ratio, interference, residual energy and node distance are the considered factors in finding efficient path of data communication among the sensor nodes within the cluster. The
simulation result shows the higher throughput and lower average packet delay rate for the proposed routing protocol than the existing benchmarks. The proposed routing protocol also shows energy efficiency and lifetime awareness with better connectivity rate.
A wireless network consists of a set of wireless nodes forming the network. The bandwidth allocation scheme used in wireless networks should automatically adapt to the network’s environments, where issues such as mobility are highly variable. This paper proposes a method to distribute the bandwidth for wireless network nodes depending on dynamic methodology;this methodology uses intelligent clustering techniques that depend on the student’s distribution at the university campus, rather than the classical allocation methods. We propose a clustering-based approach to solve the dynamic bandwidth allocation problem in wireless networks, enabling wireless nodes to adapt their bandwidth allocation according to the changing number of expected users over time. The proposed solution allows the optimal online bandwidth allocation based on the data extracted from the lectures timetable, and fed to the wireless network control nodes, allowing them to adapt to their environment. The environment data is processed and clustered using the KMeans clustering algorithm to identify potential peak times for every wireless node. The proposed solution feasibility is tested by applying the approach to a case study, at the Arab American University campus wireless network.
MULTI-CLUSTER MULTI-CHANNEL SCHEDULING (MMS) ALGORITHM FOR MAXIMUM DATA COLLE...IJCNCJournal
Interference during data transmission can cause performance degradation like packet collisions in Wireless Sensor Networks (WSNs). While multi-channels available in IEEE 802.15.4 protocol standard WSN technology can be exploited to reduce interference, allocating channel and channel switching
algorithms can have a major impact on the performance of multi-channel communication. This paper presents an improved Fuzzy Logic based Cluster Formation and Cluster Head (CH) Selection algorithm with enhanced network lifetime for multi-cluster topology. The Multi-Cluster Multi-Channel Scheduling
(MMS) algorithm proposed in this paper improves the data collection by minimizing the maximum interference and collision. The presented work has developed Cluster formation and cluster head (CH) selection algorithm and Interference-free data communication by proper channel scheduled. The extensive
simulation and experimental outcomes prove that the proposed algorithm not only provides an interference-free transmission but also provides delay minimization and longevity of the network lifetime, which makes the presented algorithm suitable for energy-constrained wireless sensor networks.
Wireless mesh networks offer high bandwidth Internet access for mobile users anywhere and at any time.
It is an emerging technology that uses wireless multi-hop networking to provide a cost-efficient way for
community or enterprise users to have broadband Internet access and share network resource. In this paper,
we have tried to give a comparative analysis of various Gateway Placement approaches which can be
helpful in understanding which approach will be useful in which situation.
MULTI-HOP BANDWIDTH MANAGEMENT PROTOCOL FOR MOBILE AD HOC NETWORKSIJMIT JOURNAL
An admission control scheme should play the role of a coordinator for flows in a data communication network, to provide the guarantees as the medium is shared. The nodes of a wired network can monitor the medium to know the available bandwidth at any point of time. But, in wireless ad hoc networks, a node must consume the bandwidth of neighboring nodes, during a communication. Hence, the consumption of bandwidth by a flow and the availability of resources to any wireless node strictly depend upon the neighboring nodes within its transmission range. We present a scalable and efficient admission control scheme, Multi-hop Bandwidth Management Protocol (MBMP), to support the QoS requirements
in multi-hop ad hoc networks. We simulate several options to design MBMP and compare the performances of these options through mathematical analysis and simulation results, and compare its effectiveness with the existing admission control schemes through extensive simulations.
Power Aware Cluster to Minimize Load In Mobile Ad Hoc NetworksIJRES Journal
Mobile ad hoc networks (MANETs) are popularly known to their mobility and ease of usage. These networks are a set of identical nodes that move freely to communicate among networks and they are represented as a set of clusters. However, their wireless and active natures cause them to be more susceptible to various types of security attacks and transmission energy consumption so that they drop out of the network easily. Now-a-days the major challenge of MANETS is to endow with the assurance to the secure network services and also to provide a nearby balance of load for the cluster-heads. To meet this confront, certificate revocation with load balancing is an important central component to provide security and energy conservation in the network communications. In this paper, we focus on load balancing clustering to widen the lifetime of the cluster-head for a maximum time before allowing it to withdraw so as to distribute the responsibility to other legitimate nodes in the cluster to act as a cluster-head along with the issue of certificate revocation process. For quick, accurate, secure certificate revocation and to conserve energy, we propose the CCRV with Load Balancing Clustering scheme where we can reduce the burden of the cluster along with secure certificate revocation. In particular, to minimize the transmission energy consumption, we use the master slave model to operate the network with longer lifetime and we propose load balancing mechanism to enhance the lifetime of the cluster-head.
Distributed Three Hop Routing Protocol for Enhancing Routing Process in WSNpaperpublications3
Abstract: For Hybrid wireless network there is need of efficient data routing protocol for improvement of network capability and scalability. By using Distributed Three Hop routing protocol (DTR), we improve capacity of Hybrid network and Reduce overhead. In early Two- Hop Transmission protocol is used for eliminate route maintenance and limited number of hop in routing for Hybrid Wireless Network but it only considers the node transmission within a single cell while DTR can Also deals with Inter-cell communication in real word. DTR is Top-quality from Other routing Protocol to analyses the hybrid wireless networks with the parametric quantity such as throughput, QoS, packet loss, overhead (due to traffic), channel quality, load balancing, routing delivery, scalability and power consumption. DTR also has a congestion control algorithm to avoid load congestion in base station in the case of unbalanced traffic distributions in networks. DTR makes significantly lower overhead by eliminating route monitoring and maintenance. To enhance the efficiency of routing protocol in wireless network Weight Based Data Assignment technique is used for data allocation in distributed routing protocol using the technique of least delay detection to maintain less data congestion in the network . for that DTR divides a message data stream into segments and transmits the segments in a distributed manner.
Keywords: Data Fragmentation, Responses Delay, Hybrid Wireless Network, Three of Routing Protocol, Fuzzy Logic.
Title: Distributed Three Hop Routing Protocol for Enhancing Routing Process in WSN
Author: Amruta A. Mandhare, Kashmira J. Mayekar, Sayali L. Khanekar, Sarika V.Bodake, Sayali S. Bale.
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Congestion in Wireless Sensor Networks has negative impact on the Quality of Service.
Congestion effects the performance metrics, namely throughput and per-packet energy
consumption, network lifetime and packet delivery ratio. Reducing congestion allows better
utilization of the network resources and thus enhances the Quality of Service metrics of the
network. Traffic Aware Dynamic Routing to Alleviate Congestion in Wireless Sensor Networks
reduces congestion by considering one hop neighbor routing in the network. This paper
proposed an algorithm for Quality of Service Based Traffic-Aware Data forwarding for
congestion control in wireless sensor networks based on two hop neighbor information. On
detection of congestion, the algorithm forwards data packets around the congestion areas by
spreading the excessive packets through multiple paths. The path with light load or under
loaded nodes is efficiently utilized whenever congestion occurs. The main aspect of the
algorithm is to build path to the destination using two independent potential fields depth and
queue length. Queue length field solves the traffic-aware problem. Depth field creates a
backbone to forward packets to the sink. Both fields are combined to yield a hybrid potential
field to make dynamic decision for data forwarding. Network Simulator used for simulating the
algorithm is NS2. The proposed algorithm performs better.
Clustering effects on wireless mobile ad hoc networks performancesijcsit
A new era is dawning for wireless mobile ad hoc networks where communication will be done using a
group of mobile devices called cluster, hence clustered network. In a clustered network, protocols used by
these mobile devices are different from those used in a wired network; which helps to save computation
time and resources efficiently. This paper focuses on Cluster-Based Routing Protocol and Dynamic Source
Routing. The results presented in this paper illustrates the implementation of Ad-hoc On-Demand Distance
Vector routing protocol for enhancing mobile nodes performance and lifetime in a clustered network and to
demonstrate how this routing protocol results in time efficient and resource saving in wireless mobile ad
hoc networks.
Dear Student,
DREAMWEB TECHNO SOLUTIONS is one of the Hardware Training and Software Development centre available in
Trichy. Pioneer in corporate training, DREAMWEB TECHNO SOLUTIONS provides training in all software
development and IT-related courses, such as Embedded Systems, VLSI, MATLAB, JAVA, J2EE, CIVIL,
Power Electronics, and Power Systems. It’s certified and experienced faculty members have the
competence to train students, provide consultancy to organizations, and develop strategic
solutions for clients by integrating existing and emerging technologies.
ADD: No:73/5, 3rd Floor, Sri Kamatchi Complex, Opp City Hospital, Salai Road, Trichy-18
Contact @ 7200021403/04
phone: 0431-4050403
An Adaptive Cluster Head Election Algorithm for Heterogeneous Mobile Ad-hoc N...IJLT EMAS
Mobile ad-hoc network characterized as a homogenous and heterogeneous on the basis of node capabilities. Heterogeneity property may make issues for mobile ad-hoc network in context of coverage area, link stability, lifetime etc. To resolve these issues, require a mechanism to adapt different characteristics and make decision for smooth functioning. Heterogeneity also leads effective routing problem that occurs instability in route or path. Though to make effective routing in this situation, efficient clustering algorithm may be apply. In this paper, the effects of heterogeneity property are studied and analyzed. A cluster head algorithm is also suggested to deal with the effects of the property. Suggested algorithm is simulated in network simulation and performance is evaluated in context of computation cost, lifetime and number of clusters.
QoS controlled capacity offload optimization in heterogeneous networksjournalBEEI
An efficient resource allocation mechanism in the physical layer of wireless networks ensures that resources such as bandwidth and power are used with high efficiency in spite of low delay and high edge user data rate. Microcells in the network are typically set with bias settings to artificially increase the Signal-to-Interference-Plus-Noise Ratio, thus encouraging users to offload to the microcell. However, the artificial bias settings are tedious and often suboptimal. This work presents a low complexity algorithm for maximization of network capacity with load balancing in a heterogeneous network without the need for bias setting. The small cells were deployed in a grid topology at a selected distance from macrocell to enhance network capacity through coverage overlap. User association and minimum user throughput were incorporated as constraints to enable closer simulation to real word Quality of Service requirements. The results showed that the proposed algorithm was able to maintain less than 10% user drop rate. The proposed algorithm can increase user confidence as well as maintain load balancing, maintain the scalability, and reduce power consumption of the wireless network.
Dear Student,
DREAMWEB TECHNO SOLUTIONS is one of the Hardware Training and Software Development centre available in
Trichy. Pioneer in corporate training, DREAMWEB TECHNO SOLUTIONS provides training in all software
development and IT-related courses, such as Embedded Systems, VLSI, MATLAB, JAVA, J2EE, CIVIL,
Power Electronics, and Power Systems. It’s certified and experienced faculty members have the
competence to train students, provide consultancy to organizations, and develop strategic
solutions for clients by integrating existing and emerging technologies.
ADD: No:73/5, 3rd Floor, Sri Kamatchi Complex, Opp City Hospital, Salai Road, Trichy-18
Contact @ 7200021403/04
phone: 0431-4050403
Analysing Mobile Random Early Detection for Congestion Control in Mobile Ad-h...IJECEIAES
This research paper suggests and analyse a technique for congestion control in mobile ad hoc networks. The technique is based on a new hybrid approach that uses clustering and queuing techniques. In clustering, in general cluster head transfers the data, following a queuing method based on a RED (Random Early Detection), the mobile environment makes it Mobile RED (or MRED), It majorly depends upon mobility of nodes and mobile environments leads to unpredictable queue size. To simulate this technique, the Network Simulator 2 (or NS2) is used for various scenarios. The simulated results are compared with NRED (Neighbourhood Random Early Detection) queuing technique of congestion control. It has been observed that the results are improved using MRED comparatively.
IMPROVING PACKET DELIVERY RATIO WITH ENHANCED CONFIDENTIALITY IN MANETijcsa
In Mobile Ad Hoc Network (MANET), the collection of mobile nodes gets communicated without the need of any customary infrastructure. In MANET, repeated topology changes and intermittent link breakage
causes the failure of existing path. This leads to rediscovery of new route by broadcasting RREQ packet.The number of RREQ packet in the network gets added due to the increased amount of link failures. This result in increased routing overhead which degrades the packet delivery ratio in MANET. While designing
routing protocols for MANET, it is indispensable to reduce the overhead in route discovery. In our previous
work[17], routing protocol based on neighbour details and probabilistic knowledge is utilized, additionally
the symmetric cipher AES is used for securing the data packet. Through this protocol, packet delivery ratio
gets increased and confidentiality is ensured. But there is a problem in secure key exchange among the
source and destination while using AES. To resolve that problem, hybrid cryptographic system i.e.,
combination of AES and RSA is proposed in this paper. By using this hybrid cryptographic scheme and the
routing protocol based on probability and neighbour knowledge, enhanced secure packet delivery is
ensured in MANET
Algorithmic Construction of Optimal and Load Balanced Clusters in Wireless Se...M H
This paper proposes a clustering algorithm - Ba-lanced Minimum Radius Clustering (BMRC) - for use in large scale, distributed Wireless Sensor Networks (WSN). Cluster balancing is an intractable problem to solve in a distributed manner, and distribution is important, by reason of both avoiding specialised node vulnerability and minimising message overhead.The BMRC algorithm described here distributes several of the cluster balancing functions to the cluster-heads. In proposing this algorithm, several tentative claims have been made for it, namely that it is suitable for arbitrary number of cluster heads; that its pecifies a way to elect cluster heads and use them to create the local models; that it accomplishes optimal balanced clusters in distributed manner; that it is scalable and it uses the number-of-hops as a clustering parameter; that it is energy efficient. These claims were studied and verified by simulation.
Congestion is said to occur in the network when the resource demands exceed the capacity and packets are lost due to too much queuing in the network. During congestion, the network throughput may drop to zero and the path delay may become very high. A congestion control scheme helps the network to recover from the congestion state. In fact, security plays a vital role in Wireless Ad hoc network. This paper presents a systematic literature review to provide comprehensive and unbiased information about various current model Congestion Control conceptions, proposals, problems and solutions in Ad hoc for safety transportation. For this purpose, a total of 33 articles related to the security model in Congestion Control published between 2008 and 2013 were extracted from the most relevant scientific sources (IEEE Computer Society, ACM Digital Library, Springer Link and Science Direct). However, 18 articles were eventually analyzed due to several reasons such as relevancy and comprehensiveness of discussion presented in the articles. Using the systematic method of review, this paper succeeds to reveal the main security threats and Error control, challenges for security, security requirement in Congestion Control in Wireless Ad hoc network (CCWAN) and future research within this scope.
MULTI-HOP BANDWIDTH MANAGEMENT PROTOCOL FOR MOBILE AD HOC NETWORKSIJMIT JOURNAL
An admission control scheme should play the role of a coordinator for flows in a data communication network, to provide the guarantees as the medium is shared. The nodes of a wired network can monitor the medium to know the available bandwidth at any point of time. But, in wireless ad hoc networks, a node must consume the bandwidth of neighboring nodes, during a communication. Hence, the consumption of bandwidth by a flow and the availability of resources to any wireless node strictly depend upon the neighboring nodes within its transmission range. We present a scalable and efficient admission control scheme, Multi-hop Bandwidth Management Protocol (MBMP), to support the QoS requirements
in multi-hop ad hoc networks. We simulate several options to design MBMP and compare the performances of these options through mathematical analysis and simulation results, and compare its effectiveness with the existing admission control schemes through extensive simulations.
Power Aware Cluster to Minimize Load In Mobile Ad Hoc NetworksIJRES Journal
Mobile ad hoc networks (MANETs) are popularly known to their mobility and ease of usage. These networks are a set of identical nodes that move freely to communicate among networks and they are represented as a set of clusters. However, their wireless and active natures cause them to be more susceptible to various types of security attacks and transmission energy consumption so that they drop out of the network easily. Now-a-days the major challenge of MANETS is to endow with the assurance to the secure network services and also to provide a nearby balance of load for the cluster-heads. To meet this confront, certificate revocation with load balancing is an important central component to provide security and energy conservation in the network communications. In this paper, we focus on load balancing clustering to widen the lifetime of the cluster-head for a maximum time before allowing it to withdraw so as to distribute the responsibility to other legitimate nodes in the cluster to act as a cluster-head along with the issue of certificate revocation process. For quick, accurate, secure certificate revocation and to conserve energy, we propose the CCRV with Load Balancing Clustering scheme where we can reduce the burden of the cluster along with secure certificate revocation. In particular, to minimize the transmission energy consumption, we use the master slave model to operate the network with longer lifetime and we propose load balancing mechanism to enhance the lifetime of the cluster-head.
Distributed Three Hop Routing Protocol for Enhancing Routing Process in WSNpaperpublications3
Abstract: For Hybrid wireless network there is need of efficient data routing protocol for improvement of network capability and scalability. By using Distributed Three Hop routing protocol (DTR), we improve capacity of Hybrid network and Reduce overhead. In early Two- Hop Transmission protocol is used for eliminate route maintenance and limited number of hop in routing for Hybrid Wireless Network but it only considers the node transmission within a single cell while DTR can Also deals with Inter-cell communication in real word. DTR is Top-quality from Other routing Protocol to analyses the hybrid wireless networks with the parametric quantity such as throughput, QoS, packet loss, overhead (due to traffic), channel quality, load balancing, routing delivery, scalability and power consumption. DTR also has a congestion control algorithm to avoid load congestion in base station in the case of unbalanced traffic distributions in networks. DTR makes significantly lower overhead by eliminating route monitoring and maintenance. To enhance the efficiency of routing protocol in wireless network Weight Based Data Assignment technique is used for data allocation in distributed routing protocol using the technique of least delay detection to maintain less data congestion in the network . for that DTR divides a message data stream into segments and transmits the segments in a distributed manner.
Keywords: Data Fragmentation, Responses Delay, Hybrid Wireless Network, Three of Routing Protocol, Fuzzy Logic.
Title: Distributed Three Hop Routing Protocol for Enhancing Routing Process in WSN
Author: Amruta A. Mandhare, Kashmira J. Mayekar, Sayali L. Khanekar, Sarika V.Bodake, Sayali S. Bale.
ISSN 2350-1022
International Journal of Recent Research in Mathematics Computer Science and Information Technology
Paper Publications
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Congestion in Wireless Sensor Networks has negative impact on the Quality of Service.
Congestion effects the performance metrics, namely throughput and per-packet energy
consumption, network lifetime and packet delivery ratio. Reducing congestion allows better
utilization of the network resources and thus enhances the Quality of Service metrics of the
network. Traffic Aware Dynamic Routing to Alleviate Congestion in Wireless Sensor Networks
reduces congestion by considering one hop neighbor routing in the network. This paper
proposed an algorithm for Quality of Service Based Traffic-Aware Data forwarding for
congestion control in wireless sensor networks based on two hop neighbor information. On
detection of congestion, the algorithm forwards data packets around the congestion areas by
spreading the excessive packets through multiple paths. The path with light load or under
loaded nodes is efficiently utilized whenever congestion occurs. The main aspect of the
algorithm is to build path to the destination using two independent potential fields depth and
queue length. Queue length field solves the traffic-aware problem. Depth field creates a
backbone to forward packets to the sink. Both fields are combined to yield a hybrid potential
field to make dynamic decision for data forwarding. Network Simulator used for simulating the
algorithm is NS2. The proposed algorithm performs better.
Clustering effects on wireless mobile ad hoc networks performancesijcsit
A new era is dawning for wireless mobile ad hoc networks where communication will be done using a
group of mobile devices called cluster, hence clustered network. In a clustered network, protocols used by
these mobile devices are different from those used in a wired network; which helps to save computation
time and resources efficiently. This paper focuses on Cluster-Based Routing Protocol and Dynamic Source
Routing. The results presented in this paper illustrates the implementation of Ad-hoc On-Demand Distance
Vector routing protocol for enhancing mobile nodes performance and lifetime in a clustered network and to
demonstrate how this routing protocol results in time efficient and resource saving in wireless mobile ad
hoc networks.
Dear Student,
DREAMWEB TECHNO SOLUTIONS is one of the Hardware Training and Software Development centre available in
Trichy. Pioneer in corporate training, DREAMWEB TECHNO SOLUTIONS provides training in all software
development and IT-related courses, such as Embedded Systems, VLSI, MATLAB, JAVA, J2EE, CIVIL,
Power Electronics, and Power Systems. It’s certified and experienced faculty members have the
competence to train students, provide consultancy to organizations, and develop strategic
solutions for clients by integrating existing and emerging technologies.
ADD: No:73/5, 3rd Floor, Sri Kamatchi Complex, Opp City Hospital, Salai Road, Trichy-18
Contact @ 7200021403/04
phone: 0431-4050403
An Adaptive Cluster Head Election Algorithm for Heterogeneous Mobile Ad-hoc N...IJLT EMAS
Mobile ad-hoc network characterized as a homogenous and heterogeneous on the basis of node capabilities. Heterogeneity property may make issues for mobile ad-hoc network in context of coverage area, link stability, lifetime etc. To resolve these issues, require a mechanism to adapt different characteristics and make decision for smooth functioning. Heterogeneity also leads effective routing problem that occurs instability in route or path. Though to make effective routing in this situation, efficient clustering algorithm may be apply. In this paper, the effects of heterogeneity property are studied and analyzed. A cluster head algorithm is also suggested to deal with the effects of the property. Suggested algorithm is simulated in network simulation and performance is evaluated in context of computation cost, lifetime and number of clusters.
QoS controlled capacity offload optimization in heterogeneous networksjournalBEEI
An efficient resource allocation mechanism in the physical layer of wireless networks ensures that resources such as bandwidth and power are used with high efficiency in spite of low delay and high edge user data rate. Microcells in the network are typically set with bias settings to artificially increase the Signal-to-Interference-Plus-Noise Ratio, thus encouraging users to offload to the microcell. However, the artificial bias settings are tedious and often suboptimal. This work presents a low complexity algorithm for maximization of network capacity with load balancing in a heterogeneous network without the need for bias setting. The small cells were deployed in a grid topology at a selected distance from macrocell to enhance network capacity through coverage overlap. User association and minimum user throughput were incorporated as constraints to enable closer simulation to real word Quality of Service requirements. The results showed that the proposed algorithm was able to maintain less than 10% user drop rate. The proposed algorithm can increase user confidence as well as maintain load balancing, maintain the scalability, and reduce power consumption of the wireless network.
Dear Student,
DREAMWEB TECHNO SOLUTIONS is one of the Hardware Training and Software Development centre available in
Trichy. Pioneer in corporate training, DREAMWEB TECHNO SOLUTIONS provides training in all software
development and IT-related courses, such as Embedded Systems, VLSI, MATLAB, JAVA, J2EE, CIVIL,
Power Electronics, and Power Systems. It’s certified and experienced faculty members have the
competence to train students, provide consultancy to organizations, and develop strategic
solutions for clients by integrating existing and emerging technologies.
ADD: No:73/5, 3rd Floor, Sri Kamatchi Complex, Opp City Hospital, Salai Road, Trichy-18
Contact @ 7200021403/04
phone: 0431-4050403
Analysing Mobile Random Early Detection for Congestion Control in Mobile Ad-h...IJECEIAES
This research paper suggests and analyse a technique for congestion control in mobile ad hoc networks. The technique is based on a new hybrid approach that uses clustering and queuing techniques. In clustering, in general cluster head transfers the data, following a queuing method based on a RED (Random Early Detection), the mobile environment makes it Mobile RED (or MRED), It majorly depends upon mobility of nodes and mobile environments leads to unpredictable queue size. To simulate this technique, the Network Simulator 2 (or NS2) is used for various scenarios. The simulated results are compared with NRED (Neighbourhood Random Early Detection) queuing technique of congestion control. It has been observed that the results are improved using MRED comparatively.
IMPROVING PACKET DELIVERY RATIO WITH ENHANCED CONFIDENTIALITY IN MANETijcsa
In Mobile Ad Hoc Network (MANET), the collection of mobile nodes gets communicated without the need of any customary infrastructure. In MANET, repeated topology changes and intermittent link breakage
causes the failure of existing path. This leads to rediscovery of new route by broadcasting RREQ packet.The number of RREQ packet in the network gets added due to the increased amount of link failures. This result in increased routing overhead which degrades the packet delivery ratio in MANET. While designing
routing protocols for MANET, it is indispensable to reduce the overhead in route discovery. In our previous
work[17], routing protocol based on neighbour details and probabilistic knowledge is utilized, additionally
the symmetric cipher AES is used for securing the data packet. Through this protocol, packet delivery ratio
gets increased and confidentiality is ensured. But there is a problem in secure key exchange among the
source and destination while using AES. To resolve that problem, hybrid cryptographic system i.e.,
combination of AES and RSA is proposed in this paper. By using this hybrid cryptographic scheme and the
routing protocol based on probability and neighbour knowledge, enhanced secure packet delivery is
ensured in MANET
Algorithmic Construction of Optimal and Load Balanced Clusters in Wireless Se...M H
This paper proposes a clustering algorithm - Ba-lanced Minimum Radius Clustering (BMRC) - for use in large scale, distributed Wireless Sensor Networks (WSN). Cluster balancing is an intractable problem to solve in a distributed manner, and distribution is important, by reason of both avoiding specialised node vulnerability and minimising message overhead.The BMRC algorithm described here distributes several of the cluster balancing functions to the cluster-heads. In proposing this algorithm, several tentative claims have been made for it, namely that it is suitable for arbitrary number of cluster heads; that its pecifies a way to elect cluster heads and use them to create the local models; that it accomplishes optimal balanced clusters in distributed manner; that it is scalable and it uses the number-of-hops as a clustering parameter; that it is energy efficient. These claims were studied and verified by simulation.
Congestion is said to occur in the network when the resource demands exceed the capacity and packets are lost due to too much queuing in the network. During congestion, the network throughput may drop to zero and the path delay may become very high. A congestion control scheme helps the network to recover from the congestion state. In fact, security plays a vital role in Wireless Ad hoc network. This paper presents a systematic literature review to provide comprehensive and unbiased information about various current model Congestion Control conceptions, proposals, problems and solutions in Ad hoc for safety transportation. For this purpose, a total of 33 articles related to the security model in Congestion Control published between 2008 and 2013 were extracted from the most relevant scientific sources (IEEE Computer Society, ACM Digital Library, Springer Link and Science Direct). However, 18 articles were eventually analyzed due to several reasons such as relevancy and comprehensiveness of discussion presented in the articles. Using the systematic method of review, this paper succeeds to reveal the main security threats and Error control, challenges for security, security requirement in Congestion Control in Wireless Ad hoc network (CCWAN) and future research within this scope.
Met behulp van de koppelingenbibliotheek in SCIA Engineer definëert men met één enkele muisklik de Layher steigerkoppelingen. Controles worden uitgevoerd conform EN12810 en EN12811
Why service (design)? - The Developer's ConferenceIsrael Lessak
Slides da palestra "Why service (design)? Como o design pode contribuir para a inovação em negócios", ministrada em 05/06/2016 na trilha de Análise de Negócios da The Developer's Conferece em São Paulo.
Dynamic Topology Re-Configuration in Multihop Cellular Networks Using Sequent...IJERA Editor
Cellular communications has experienced explosive growth in the past two decades. Today millions of people around the world use cellular phones. Cellular phones allow a person to make or receive a call from almost anywhere. Likewise, a person is allowed to continue the phone conversation while on the move. Cellular communications is supported by an infrastructure called a cellular network, which integrates cellular phones into the public switched telephone network. The cellular network has gone through three generations.The first generation of cellular networks is analog in nature. To accommodate more cellular phone subscribers, digital TDMA (time division multiple access) and CDMA (code division multiple access) technologies are used in the second generation (2G) to increase the network capacity. With digital technologies, digitized voice can be coded and encrypted. Therefore, the 2G cellular network is also more secure. The third generation (3G) integrates cellular phones into the Internet world by providing highspeed packet-switching data transmission in addition to circuit-switching voice transmission. The 3G cellular networks have been deployed in some parts of Asia, Europe, and the United States since 2002 and will be widely deployed in the coming years. The high increase in traffic and data rate for future generations of mobile communication systems, with simultaneous requirement for reduced power consumption, makes Multihop Cellular Networks (MCNs) an attractive technology. To exploit the potentials of MCNs a new network paradigm is proposed in this paper. In addition, a novel sequential genetic algorithm (SGA) is proposed as a heuristic approximation to reconfigure the optimum relaying topology as the network traffic changes. Network coding is used to combine the uplink and downlink transmissions, and incorporate it into the optimum bidirectional relaying with ICI awareness. Numerical results have shown that the algorithms suggested in this thesis provide significant improvement with respect to the existing results, and are expected to have significant impact in the analysis and design of future cellular networks.
TTACCA: TWO-HOP BASED TRAFFIC AWARE CONGESTION CONTROL ALGORITHM FOR WIRELESS...cscpconf
Congestion in Wireless Sensor Networks has negative impact on the Quality of Service.
Congestion effects the performance metrics, namely throughput and per-packet energy
consumption, network lifetime and packet delivery ratio. Reducing congestion allows better
utilization of the network resources and thus enhances the Quality of Service metrics of the
network. Traffic Aware Dynamic Routing to Alleviate Congestion in Wireless Sensor Networks
reduces congestion by considering one hop neighbor routing in the network. This paper
proposed an algorithm for Quality of Service Based Traffic-Aware Data forwarding for
congestion control in wireless sensor networks based on two hop neighbor information. On
detection of congestion, the algorithm forwards data packets around the congestion areas by
spreading the excessive packets through multiple paths. The path with light load or under
loaded nodes is efficiently utilized whenever congestion occurs. The main aspect of the
algorithm is to build path to the destination using two independent potential fields depth and
queue length. Queue length field solves the traffic-aware problem. Depth field creates a
backbone to forward packets to the sink. Both fields are combined to yield a hybrid potential
field to make dynamic decision for data forwarding. Network Simulator used for simulating the
algorithm is NS2. The proposed algorithm performs better.
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Optical network is an emerging technology for data communication
inworldwide. The information is transmitted from the source to destination
through the fiber optics. All optical network (AON) provides good
transmission transparency, good expandability, large bandwidth, lower bit
error rate (BER), and high processing speed. Link failure and node failure
haveconsistently occurred in the traditional methods. In order to overcome
the above mentioned issues, this paper proposes a robust software defined
switching enabled fault localization framework (SDSFLF) to monitor the
node and link failure in an AON. In this work, a novel faulty node
localization (FNL) algorithm is exploited to locate the faulty node. Then, the
software defined faulty link detection (SDFLD) algorithm that addresses the
problem of link failure. The failures are localized in multi traffic stream
(MTS) and multi agent system (MAS). Thus, the throughput is improved in
SDSFLF compared than other existing methods like traditional routing and
wavelength assignment (RWA), simulated annealing (SA) algorithm, attackaware RWA (A-RWA) convex, longest path first (LPF) ordering, and
biggest source-destination node degree (BND) ordering. The performance of
the proposed algorithm is evaluated in terms of network load, wavelength
utilization, packet loss rate, and burst loss rate. Hence, proposed SDSFLF
assures that high performance is achieved than other traditional techniques.
Mobile Agents based Energy Efficient Routing for Wireless Sensor NetworksEswar Publications
Energy Efficiency and prolonged network lifetime are few of the major concern areas. Energy consumption rated of sensor nodes can be reduced in various ways. Data aggregation, result sharing and filtration of aggregated data among sensor nodes deployed in the unattended regions have been few of the most researched areas in the field of wireless sensor networks. While data aggregation is concerned with minimizing the information transfer from source to sink to reduce network traffic and removing congestion in network, result sharing focuses on sharing of information among agents pertinent to the tasks at hand and filtration of aggregated data so as to remove redundant information. There exist various algorithms for data aggregation and filtration using different mobile agents. In this proposed work same mobile agent is used to perform both tasks data aggregation and data filtration. This approach advocates the sharing of resources and reducing the energy consumption level of sensor nodes.
CFMS: A CLUSTER-BASED CONVERGECAST FRAMEWORK FOR DENSE MULTI-SINK WIRELESS SE...ijwmn
Convergecast is one of the most challenging tasks in Wireless Sensor Networks (WSNs). Indeed, this data
collection process must be conducted while copying with packet collisions, nodes’ congestion or data
redundancy. These issues always result in energy waste which is detrimental to network efficiency and
lifetime. This paper is aimed to address these problems in large-scale multi-sink WSNs. Inspired by our
previous work MSCP, we designed a lightweight protocol stack that seamlessly combines clustering, pathvector routing, sinks’ duty cycling, data aggregation and transmission scheduling in order to minimise
message overhead and packet losses. Simulation results show that this solution can mitigate delay while
significantly increasing packet delivery and network lifetime.
CFMS: A Cluster-based Convergecast Framework for Dense Multi-Sink Wireless Se...ijwmn
Convergecast is one of the most challenging tasks in Wireless Sensor Networks (WSNs). Indeed, this data
collection process must be conducted while copying with packet collisions, nodes’ congestion or data
redundancy. These issues always result in energy waste which is detrimental to network efficiency and
lifetime. This paper is aimed to address these problems in large-scale multi-sink WSNs. Inspired by our
previous work MSCP, we designed a lightweight protocol stack that seamlessly combines clustering, pathvector routing, sinks’ duty cycling, data aggregation and transmission scheduling in order to minimise
message overhead and packet losses. Simulation results show that this solution can mitigate delay while
significantly increasing packet delivery and network lifetime.
CFMS: A CLUSTER-BASED CONVERGECAST FRAMEWORK FOR DENSE MULTI-SINK WIRELESS SE...ijwmn
Convergecast is one of the most challenging tasks in Wireless Sensor Networks (WSNs). Indeed, this data
collection process must be conducted while copying with packet collisions, nodes’ congestion or data
redundancy. These issues always result in energy waste which is detrimental to network efficiency and
lifetime. This paper is aimed to address these problems in large-scale multi-sink WSNs. Inspired by our
previous work MSCP, we designed a lightweight protocol stack that seamlessly combines clustering, pathvector routing, sinks’ duty cycling, data aggregation and transmission scheduling in order to minimise
message overhead and packet losses. Simulation results show that this solution can mitigate delay while
significantly increasing packet delivery and network lifetime.
Analysis of Neighbor Knowledge Based Bcast Protocol Performance For Multihop ...pijans
Reliable group communication is a challenging issue for most Mobile Ad-hoc Networks (MANETs) due to
dynamic nature of wireless mobile nodes, group key establishment and management, ensuring secure
information exchange and Quality of Service (QoS) in data transfer. Recently multicast and broadcast
routing protocols are emerging for supporting QoS aware group communication. In MANETs QoS
requirements can be quantified by a set of measurable pre-specified service attributes such as packet
delivery ratio, end-to-end delay, packet loss probability, network control overhead, throughput,
bandwidth, power consumption, service coverage area etc. In this paper, the performance of a neighbor
knowledge based broadcast protocol is analyzed using different QoS metrics (packet delivery ratio, end-toend delay, packet loss probability and network control overhead). BCAST is used as broadcast protocol.
The performance differentials are analyzed using NS-2 network simulator for varying number of data
senders (multicast group size) and data sending rate (offered traffic to the network) over QoS aware group
communication. Simulation results show that BCAST performs well in most cases and provides robust
performance even with high traffic environments.
Analysis of Neighbor Knowledge Based Bcast Protocol Performance For Multihop ...pijans
Reliable group communication is a challenging issue for most Mobile Ad-hoc Networks (MANETs) due to
dynamic nature of wireless mobile nodes, group key establishment and management, ensuring secure
information exchange and Quality of Service (QoS) in data transfer. Recently multicast and broadcast
routing protocols are emerging for supporting QoS aware group communication. In MANETs QoS
requirements can be quantified by a set of measurable pre-specified service attributes such as packet
delivery ratio, end-to-end delay, packet loss probability, network control overhead, throughput,
bandwidth, power consumption, service coverage area etc. In this paper, the performance of a neighbor
knowledge based broadcast protocol is analyzed using different QoS metrics (packet delivery ratio, end-toend delay, packet loss probability and network control overhead). BCAST is used as broadcast protocol.
The performance differentials are analyzed using NS-2 network simulator for varying number of data
senders (multicast group size) and data sending rate (offered traffic to the network) over QoS aware group
communication. Simulation results show that BCAST performs well in most cases and provides robust
performance even with high traffic environments.
INTERFERENCE-AWARE CHANNEL ASSIGNMENT FOR MAXIMIZING THROUGHPUT IN WMN pijans
Wireless Mesh network (WMN) is dynamically self-organizing and self-configured, with the nodes in the
network automatically establishing an ad-hoc network and maintaining the mesh connectivity. The ability
to use multiple-radios and multiple channels can be cashed to increase aggregate throughput of wireless
mesh network. Thus the efficient use of available interfaces and channels without interference becomes
the key factor. In this paper we propose interference aware clustered based channel assignment schemes
which minimizes the interference and increases throughput. In our proposed scheme we have given
priority to minimize interference from nearby mesh nodes in interference range than maximizing channel
diversity. We simulated our proposed work using NS-3 and results show that our scheme improves
network performance than BFSCA and Distributed Greedy CA.
Energy Proficient and Security Protocol for WSN: A Reviewtheijes
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
Theoretical work submitted to the Journal should be original in its motivation or modeling structure. Empirical analysis should be based on a theoretical framework and should be capable of replication. It is expected that all materials required for replication (including computer programs and data sets) should be available upon request to the authors.
The International Journal of Engineering & Science would take much care in making your article published without much delay with your kind cooperation
Fast Data Collection with Interference and Life Time in Tree Based Wireless S...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
IMPLEMENTING PACKET BROADCASTING ALGORITHM OF MIMO BASED MOBILE AD-HOC NETWOR...IJNSA Journal
With the rapid growth of wireless communication infras,,tructure over the recent few years, new challenges has been posed on the system and analysis on wireless adhoc networking. Implementation of MIMO communication in such type of network is enhancing the packet transmission capabilities. There are different techniques for cooperative transmission and broadcasting packet in MIMO equipped Mobile Adhoc Network. We have employed a model network in the OPNET environment and propose a new scheduling algorithm based on investigating the different broadcasting algorithm. The new broadcasting algorithm improves the packet transmission rate of the network based on energy performance of the network and minimizes the BER for different transmission mode which is illustrated in this paper. The simulations are done in MATLAB and OPNET environment and the simulated result for the packet transmission rate are collected and shown in the tabular form. Also simulate the network for generating a comparative statement for each mobile node. And performance analysis is also done for the model network. The main focus is to minimize BER and improve information efficiency of the network.
k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
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Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
COLLEGE BUS MANAGEMENT SYSTEM PROJECT REPORT.pdfKamal Acharya
The College Bus Management system is completely developed by Visual Basic .NET Version. The application is connect with most secured database language MS SQL Server. The application is develop by using best combination of front-end and back-end languages. The application is totally design like flat user interface. This flat user interface is more attractive user interface in 2017. The application is gives more important to the system functionality. The application is to manage the student’s details, driver’s details, bus details, bus route details, bus fees details and more. The application has only one unit for admin. The admin can manage the entire application. The admin can login into the application by using username and password of the admin. The application is develop for big and small colleges. It is more user friendly for non-computer person. Even they can easily learn how to manage the application within hours. The application is more secure by the admin. The system will give an effective output for the VB.Net and SQL Server given as input to the system. The compiled java program given as input to the system, after scanning the program will generate different reports. The application generates the report for users. The admin can view and download the report of the data. The application deliver the excel format reports. Because, excel formatted reports is very easy to understand the income and expense of the college bus. This application is mainly develop for windows operating system users. In 2017, 73% of people enterprises are using windows operating system. So the application will easily install for all the windows operating system users. The application-developed size is very low. The application consumes very low space in disk. Therefore, the user can allocate very minimum local disk space for this application.
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Vaccine management system project report documentation..pdfKamal Acharya
The Division of Vaccine and Immunization is facing increasing difficulty monitoring vaccines and other commodities distribution once they have been distributed from the national stores. With the introduction of new vaccines, more challenges have been anticipated with this additions posing serious threat to the already over strained vaccine supply chain system in Kenya.
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
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Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
2. QU et al.: WIRELESS NETWORKS WITH INTERFERENCE CANCELATION CAPABILITIES 3109
Clearly, efficient link scheduling together with SIC help in
promoting better spatial reuse as well as transmission con-
currence, resulting in increased throughput performance. In
this paper, we consider a wireless network where nodes are
endowed with SIC capabilities, and we study the problem of
link scheduling, under the SINR interference model, in the
context of a cross-layer network design.
We consider a network utility maximization (NUM) problem
(similar to [25] and [26]) in a multihop wireless network and
decouple the cross-layer optimization into congestion control
and routing/scheduling subproblems. The congestion control
subproblem can easily be solved at the source node of each flow
by only using local information, and following a back pressure
framework, the routing/scheduling subproblem is converted
into a weight scheduling problem where the weight information
can be illustrated as some scale of the queue length at each
node. However, as mentioned earlier, previous work has shown
that the link scheduling under a binary interference model is
an NP-hard problem [2] and under the SINR model as NP-
complete [5], both with and without SIC, and polynomial-time
approximation algorithms are presented (e.g., [10] and [24]).
Scheduling methods such as maximum weight scheduling [11]
and greedy maximal scheduling (GMS) [12] have shown to
achieve 100% throughput in most practical wireless networks
with the second method being more amenable to distributed
implementation. In this paper, we will consider the GMS ap-
proach for solving the link scheduling problem under the SIC
constraints and the physical interference model; now, given the
complexity of the scheduling problem in centralized settings,
we develop a decentralized method for solving it. Our main
contributions are as follows. First, we revise the interference
localization method in [19] and show that it can be used to
maintain the interference constraints in a network with SIC
capabilities. Second, we present a search-based method for
determining the minimum interference neighborhood of each
link. Our design reveals that the network throughput perfor-
mance is mainly dependent on how much local information
can be coordinated at each communication link. We show that
our decentralized algorithm yields the same maximal schedule
obtained by the centralized GMS method.
The rest of this paper is organized as follows. In Section II,
we briefly survey the work related to cross-layer optimization
with and without SIC. Our system model, the interference
localization technique, and problem formulation are presented
in Section III. Section IV presents the dual decomposition for
decoupling the cross-layer design problem as well as the design
of our decentralized scheduling method. Finally, numerical
results are presented in Section V, and conclusions are drawn
in Section VI.
II. RELATED WORK
Recently, there have been growing interests to exploit inter-
ference among adjacent concurrent transmissions to increase
the network throughput. Mitran et al. in [13] formulated a
cross-layer design optimization to solve the joint problem of
routing and scheduling in a multihop wireless network with
advanced physical-layer techniques for interference cancela-
tion, such as SIC, superposition coding, and dirty-paper coding.
The authors formulated the problem of routing and scheduling
under the physical interference model as a max–min optimiza-
tion problem and developed a column generation method for
solving it efficiently. The authors have shown that SIC signifi-
cantly improves the network performance and, in particular, the
max–min per node throughput. Jiang et al. in [18] noted that
SIC is a very promising interference exploitation technique for
increasing the network throughput due to its ability in enabling
multiple concurrent transmissions. Upon developing a cross-
layer optimization framework for the routing and scheduling
problem, the authors studied the optimal interaction between
interference exploitation, through SIC, and interference avoid-
ance, through link scheduling. The authors have shown that
substantial performance gains can be achieved when both tech-
niques are combined.
Now, the asymptotic transmission capacity of ad hoc net-
works with SIC is studied in [15] and [16]; the former con-
sidered that all signals within one hop from transmitters can be
successfully decoded, and the latter supposed a more realistic
SIC model in their analysis. Sen et al. in [17] studied the extent
of throughput gains that is possible under SIC from a MAC-
layer perspective. They argued that only little gains could be
achieved in restricted scenarios (mainly for upload traffic in
wireless local area networks). Furthermore, when transmitters
choose their bit rates independently, not much gain can be
achieved. However, the authors showed (in a two-transmitter
scenario) that one way to maximize the gain is through transmit
power level selection such that the feasible bit rate is equal for
both transmissions. Lv et al. in [22] proposed a layered protocol
model and a layered physical model (to model the interference)
and studied the problem of link scheduling to characterize the
advantages of SIC. The authors analyzed the capacity of a net-
work with SIC and demonstrated the importance of designing
SIC-aware scheduling. It was shown that significant through-
put gains (20%–100%) can be obtained in chain/cell network
topologies. The problem of link scheduling with interference
cancelation using the SINR interference model is studied in
[23] where Yuan et al. assumed multiuser decoding receivers.
The authors showed that the optimal scheduling problem with
(successive or parallel) interference cancelation is NP-hard
and developed compact linear programming (LP) methods for
obtaining exact solutions. The authors showed that in the lower
SINR regime, interference cancelation yields significant im-
provements. Similarly, in [24], Goussevskaia and Wattenhofer
studied the same problem but developed approximation algo-
rithms for solving the scheduling problem in polynomial time.
SIC has shown to achieve up to 20% performance gains over
networks that do not have interference cancelation capabilities.
It should be noted that all of the aforementioned methods
solve the link scheduling problem in a centralized manner;
given the complexity of the problem, decentralized methods are
more practical. In [25], a distributed method that jointly adapts
decisions made by different layers is proposed. Chen et al.
presented then a dual driven decomposition approach for the
original problem, which is further decomposed into two sub-
problems (one for congestion control and another for routing/
scheduling), and the three are correlated through Lagrangian
3. 3110 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 64, NO. 7, JULY 2015
multipliers. A fully distributed algorithm following the
decomposition is then presented. In [31] and [32], Joo and
Shroff and Joo et al., respectively, revised the distributed
scheduling methods in wireless networks and classified them
into different categories. For the graph-based interference
model, the authors mainly focused on the problem of schedul-
ing and proposed a revision of the GMS algorithm to satisfy the
distributed design. For the more practical interference model,
Le et al., in [19], investigated the link scheduling problem by
considering GMS; to simplify the complex relationship govern-
ing the interference, the authors presented a method to localize
the interference around each link, thereby each link coordinates
its scheduling within a local neighborhood while maintain-
ing scheduling feasibility. The authors subsequently devel-
oped a decentralized scheduling method, which was shown to
yield maximal link scheduling similar to the centralized GMS
method. These decentralized methods did not however consider
network with interference cancelation capabilities.
Note that our work is similar to [25] and [26] in that we
try to design an efficient distributed cross-layer method to
improve the performance of wireless multihop networks. Our
work, however, differs from previous work in that we consider
networks with SIC capabilities in our distributed cross-layer
design with the SINR interference model. This makes the
design of a decentralized method more complicated because of
the particularity of the SIC constraints.
III. SYSTEM MODEL
A. Network Model
We consider a network of N nodes and L links; we assume
stationary nodes, each equipped with SIC capability. Let dn,n
be the Euclidean distance between two nodes n and n , and let
Gnn be the channel gain from n to n such that Gnn = d−pl
n,n ,
where pl is the path-loss index. The transmission power at each
node is assumed to be fixed and equal to Pw. Let F be the set of
flows in the network, where each flow f (f ∈ F) is identified by
a source s(f) and a destination d(f) and a transmission rate yf .
B. Interference Model With SIC
In the physical interference model (also known as the ad-
ditive interference model), in the presence of concurrent trans-
missions on neighboring links, one transmission (e.g., on link i)
is successful if the SINR at the intended receiver is above a
certain threshold β. Then, the physical interference model can
be formulated as
SINRt(i),r(i) =
PwGt(i),r(i)
η + ∀ n∈NA−t(i) PwGn,r(i)
≥ β (1)
where η is the background noise power. NA is the set of all
active nodes in the network, and t(i) and r(i) are the transmitter
and the receiver of link i. β is the minimum SINR threshold
that must be maintained to support a successful transmission
on link i while guaranteeing a tolerable bit error rate. If the
SINR requirement is not met, then the received packet cannot
be correctly extracted from the received signal. In this paper,
we assume β ≥ 1.
Now, SIC allows a signal to be correctly decoded in the
presence of other concurrent transmissions. Here, the receiver
starts decoding the strongest signal from the combined received
signal; then, the decoded signal is subtracted, and the process
is repeated on the residual signal until the signal of interest is
either decoded or no more decoding is possible. This technique
therefore allows a signal to be correctly received given that
other stronger signals are decoded first. Next, we illustrate the
SINR constraints in the presence of SIC. Consider two links
i and i adjacent to each other. Denote by P1
r(i)(P2
r(i)) the
strength of the signal received at destination r(i) from t(i)
(respectively, from t(i )) and suppose P2
r(i) > P1
r(i). Here, r(i)
will attempt to decode the signal received from t(i ) first; this
signal can be decoded if
SINR2
r(i) =
P2
r(i)
η + P1
r(i)
≥ β. (2)
If the signal of t(i ) is successfully decoded at r(i), then r(i)
will subtract it from the combined signal and will attempt to
decode the signal arriving from t(i), i.e.,
SINR1
r(i) =
P1
r(i)
η
≥ β. (3)
The given procedure can be generalized in a straightforward
manner to any number of transmissions.
C. Link Scheduling With Interference Localization
We consider a time-division-multiple-access-based MAC
layer where time is divided into slots of equal length, and each
time slot has two parts: a schedule and a transmission. The
schedule part has several intervals, and each interval is further
divided into minislots. We define the set of links that can be
concurrently active in the same time slot (without violating the
SINR requirements) as a (feasible) configuration (or conf for
short). Then, our objective is to generate a new configuration
during the “schedule” period under SIC constraints and trans-
mits data during the “transmission” period (each active link will
transmit one packet during the “transmission” period). Note
that, in a wireless network without SIC capabilities, in [19],
Le et al. noted that only those concurrent transmissions within
a neighborhood of a particular link may create significant
cumulative interference at the receiver of this link. Accordingly,
they presented an “interference localization” technique that
allowed them to decentralize the link scheduling problem. The
authors presented a method to determine for each link a neigh-
borhood such that interference from active links outside this
neighborhood will have negligible impact on received signal
at the receiver of this link. Namely, for link l, the maximum
interference that can be tolerated is
Imax
l =
PwGt(l),r(l)
β
− η. (4)
Let INl and nINl denote the interference neighborhood
and noninterference neighborhood of link l. INl is a circle
4. QU et al.: WIRELESS NETWORKS WITH INTERFERENCE CANCELATION CAPABILITIES 3111
centered around the receiver of l and whose radius will be
determined later. All links whose transmitters are inside INl
will be able to exchange information (therefore coordinate)
with the transmitter of link l for scheduling purposes. nINl
(complement of INl) contains the set of links whose cumula-
tive interference is assumed to be negligible at rl. Le et al. in
[19] have shown that given a constant (0 < < 1), for link l
to be feasible, the upper bound on the interference coming from
active links located in INl should not exceed (1 − )Imax
l , and
the total interference coming from links located in nINl cannot
exceed Imax
l . Therefore, when all active links in some set are
feasible, we obtain a feasible configuration.
Now, to account for the SIC property of the nodes in the
network, we first revise the interference localization technique
presented earlier as follows. For a particular link l, we divide the
network into three regions: the strongest signal area, the inter-
ference area, and the noninterference area. The strongest signal
area (Al) refers to a circular area with radius dt(l),r(l) centered
around the receiver of link l. The interference area refers to the
ring-like area (INl − Al) with radius from dt(l),r(l) to a certain
length λ(l) (λ(l) ≥ dt(l),r(l)) centered around the receiver of
link l. The noninterference area denotes the region outside the
interference area (nINl). Next, we introduce the definition of
vector
−→
λ .
Definition 1: For any feasible link l, λ(l) denotes the lower
bound of the radius of the interference neighborhood (INl)
such that the total interference coming from links located in
the noninterference area does not exceed Imax
under SIC
constraints.
We assume that each link l (l ∈ L) is able to communicate
with any link whose transmitter is located in INl, or any link l
such that t(l) ∈ INl , to exchange link weight information and
coordinate the link scheduling. Definition 1 implies that there is
a relationship between and
−→
λ (we will present a procedure to
compute
−→
λ based on in the following section). Furthermore,
can be used to control the potential scheduling overload, and
we will verify in Section V that will have a significant effect
on the achievable network performance.
Note that, in any feasible configuration, we assume that all
“active” links l (such that t(l ) ∈ Al) have stronger received
signals at r(l) than the signal arriving from t(l), and therefore,
using SIC, r(l) is capable of successively decoding those
signals prior to decoding the signal arriving from t(l).
Definition 2: Given a certain
−→
λ , Θ(
−→
λ ) is a class of schedul-
ing algorithms such that a particular scheduling method will
belong to Θ(
−→
λ ) if it yields an active schedule satisfying
the following constraints: 1) For any active link l ∈ L, the
total interference coming from active transmitters located in
INl − Al does not exceed (1 − )Imax
(l); and 2) let k de-
note the link from any active transmitter located in Al to
r(l); then, the cumulative interference at r(k) = r(l) coming
from active transmitters located in INk − Ak does not exceed
(1 − )Imax
(k).
The second constraint in the given definition implies that all
active transmitters within the neighborhood of r(l) (i.e., in Al)
should have their signals decoded at r(l) prior to decoding the
signal arriving from t(l). Here, r(l) will attempt to success-
fully decode each of these arriving signals (using SIC); this is
possible because for each signal, we assume that the cumulative
interference from active transmitters located in INk − Ak does
not exceed (1 − )Imax
(k), which is required for successful
decoding of the signal of t(k) at r(l).
Combining Definitions 1 and 2, it can be shown that for any
(0 < < 1), there exists a vector
−→
λ and a class of scheduling
methods Θ(
−→
λ ) such that any scheduling algorithm belonging
to Θ(
−→
λ ) will result in a feasible schedule satisfying the SIC
constraints. This is summarized in the following theorem.
Theorem 1: Given any 0 < < 1, there exists a vector λ
that satisfies Definition 1, and the result of schedule Θ(λ) that
satisfies Definition 2 is feasible under SIC constraints.
D. Problem Formulation
Following the given discussion, we assume that all active
links in one conf can simultaneously transmit. Denote by E
the set of all possible configurations/schedules, where each
conf is indexed by e. Each conf (e) is represented by a
|L|-dimensional rate vector −→r e
, where for each link l, re
l can
be defined as
re
l =
c, if l ∈ e
0, otherwise
(5)
where c is a constant link transmission rate. We define the
feasible rate region Γ as the convex hull of these rate vectors.
We assume through time sharing that all interior points of Γ
are attainable. We define a link-flow matrix v to describe the
routing of flows in the network, where each element vlf ∈
v(∀ l ∈ L, f ∈ F) corresponds to the fraction of flow f deliv-
ered over link l. We assume a utility function U(yf ) to be twice
differentiable, increasing, and strictly concave [25]. Our design
target can be summarized in the following utility maximization
problem:
[OBJECTIVE]
max
yf ≥0,∀ f∈F
f∈F
U(yf ). (6)
Similar to [26], a feasible routing must satisfy two constraints:
the interference and link capacity constraints (7) and the flow
balance constraints (8).
[CONSTRAINTS]
f∈F
vlf ∈ Γ ∀ l ∈ L (7)
yif + Υ−
if ≤ Υ+
if ∀ i ∈ N, f ∈ F (8)
where yif (i ∈ N, f ∈ F) denotes the node-flow variable such
that yif = yf when i = s(f), and otherwise yif = 0. Υ−
if =
l∈L:r(l)=i vlf and denotes the fraction of flow f incoming
into node i, and Υ+
if = l ∈L:t(l )=i vl f denotes the fraction
of flow f outgoing of node i.
5. 3112 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 64, NO. 7, JULY 2015
IV. DISTRIBUTED CROSS-LAYER DESIGN WITH
SUCESSIVE INTERFERENCE CANCELLATION
A. Dual Decomposition
Similar to [25] and [26], we resort to the dual driven
Lagrangian decomposition approach to get a distributed so-
lution. For completeness, we briefly describe the process of
decomposition, and the detailed demonstration can be found in
[25]. Consider the dual problem of primal problem (6), i.e.,
min
uif ≥0,∀ i∈N,f∈F
D(u) (9)
with partial dual function
D(u) = max
yf ≥0,v∈Γ
×
⎧
⎨
⎩
f∈F
U(yf ) −
f∈F i∈N
uif yif + Υ−
if − Υ+
if
⎫
⎬
⎭
(10)
where uif is a Lagrangian multiplier, and u = [uif ]i∈N,f∈F .
Then, (10) can be further decomposed into the following two
subproblems:
D1(u) = max
yif ≥0
f∈F i∈N
(U(yif ) − yif uif ) (11)
D2(u) = max
v∈Γ
f∈F i∈N
uif Υ−
if − Υ+
if . (12)
Here, D1(u) can be solved by (13), which is the standard rate
control problem at the source node of each flow, i.e.,
yf = U −1
(uf ) (13)
where uf = uif if node i = s(f). For the routing/scheduling
problem D2(u), we have the following identity:
D2(u) = max
v∈Γ
l∈L
vlf∗ max
f∈F
ut(l)f − ur(l)f (14)
where f∗
= arg maxf∈F {ut(l)f − ur(l)f }, l ∈ L.
Based on (14), the routing/scheduling problem can be solved
by the following two-step process.
Step 1) For each link l, we can use local informa-
tion u to find a flow f∗
that satisfies f∗
=
arg maxf∈F {ut(l)f − ur(l)f }. Let wl = ut(l)f∗ −
ur(l)f∗ be the weight of link l. Here, wl can also
be interpreted as the scaled queue length at link l
with flow f∗
. In each time slot, the links in one
conf can be active to send data to the receivers (we
assume one packet transmission per active link per
time slot). The given algorithm can be interpreted as
a back pressure process to solve the routing problem.
Step 2) We convert (14) into its reduced form as follows:
D2(u) = max
v∈Γ
l∈L
vlf∗ wl. (15)
The formulation of (15) can be seen as an ordinary link
scheduling problem where each link is associated with its
weight wl. However, it is difficult to solve the scheduling prob-
lem because the interference relationship under the physical
interference model (SINR) is nonconvex and combinatorial. In
the sequel, we present a simplified distributed link scheduling
method taking into account the SIC constraints and using the
interference localization technique presented earlier. We first
propose a method to calculate the vector
−→
λ under a certain ,
and then, a distributed scheduling method is proposed under
SIC constraints.
B. Identifying the Interference Neighborhood
Here, we present a binary-search-based method to determine
vector
−→
λ under a certain value of . Recall that, according
to Definition 1, λ(l) is the lower bound of the radius of the
interference neighborhood INl, which guarantees that, under a
feasible scheduling method (see Definition 2), the total interfer-
ence coming from links in the noninterference region (nINl)
does not exceed Imax
. For a link l, denote dmax
(l) as the
distance from the farthest node in the network to the receiver of
link l. The search procedure for link l starts from dmax
(l), and
we use a bisection search method to reduce the gap between
the current search radius and the optimal λ(l). At every level
of current radius, we have to determine the maximum inter-
ference coming from the active links whose transmitters are
located outside the current radius. One simple way to decide the
maximum interference is to sum up the received signals from
all transmitters outside the current radius; it should be noted
that since some of these transmitters may not be active in our
configuration, this calculated maximum interference represents
an upper bound. An alternative approach is to solve a simple
SIC-based scheduling problem on the links whose transmitters
are located outside the current radius. We repeat this procedure
(of updating the radius of interference neighborhood) for link
l until a tolerable performance is attained. This procedure is
illustrated in Algorithm 1. Note that Algorithm 1 is a central-
ized procedure that needs to be performed only once for a static
wireless network.
All links in the area outside the current radius are initially
stored in a link set Ψ. For current link l, we associate weight
attribute wal(l ) = PwGt(l ),r(l), ∀ l ∈ Ψ and 0 otherwise. We
also define a binary variable pi(i ∈ L), which is equal to 1 when
link i is active, and otherwise, it is zero. Then, we define our
optimization objective as
Maximize :
i∈L
wal(i)pi. (16)
Similarly, we define another binary variable qt(j)(j ∈ L),
which is equal to 1 when node t(j) is an active transmitter,
and otherwise, it is zero. Let L+
n be the set of links whose
transmitter is node n and L−
n be the set of links whose receiver
is node n. Therefore, we have
qt(j) =
i∈L+
t(j)
pi (17)
i∈L+
n
pi ≤ 1. (18)
6. QU et al.: WIRELESS NETWORKS WITH INTERFERENCE CANCELATION CAPABILITIES 3113
In this paper, we only consider the half-duplex mode at each
node. Then, our half-duplex constraints can be written as
pi + pj ≤ 1 ∀ n ∈ N : i ∈ L−
n, j ∈ L+
n . (19)
Similar to [18], we use the concept of residual-SINR or r-SINR
for short. Due to its interference cancelation capability, a
receiver node can sequentially cancel all interfering signals,
which are stronger than the one of interest; therefore, one only
needs to consider the interference from senders whose signals
are weaker than that of the intended one. The r-SINR can be
defined as
r_SINRt(i),t(i) =
PwGt(i),r(i)
Gt(k),r(i)≤Gt(i),r(i)
k=i,t(i)=t(k) PwGt(k),r(i)qk + η
.
(20)
Indeed, when scheduling variable vp
i = 1 (link i is active), this
implies that all other stronger received signals from adjacent
senders at r(i) have been correctly decoded, and the decoding
of the signal of interest at link i is also successful. Namely, if
vp
i = 1, then the following two constraints should be satisfied:
r_SINRt(i),r(i) ≥ β, (pi = 1, i ∈ L) (21)
r_SINRt(j),r(i) ≥ β, j =i, t(j)=t(i), Gt(j),t(i) ≥Gt(i),r(i),
pj = 1, j, i ∈ L) . (22)
To convert the SIC constraints into an LP format, we define a bi-
nary variable ρi,t(j) to describe the relationship of pi and qt(j).
Let ρi,t(j) = 1 if and only if pi = 1 and qt(j) = 1; otherwise, it
is zero. Then, the relationship can be written as follows:
pi ≥ ρi,t(j) (23)
qt(j) ≥ ρi,t(j) (24)
ρi,t(j) ≥ pi + qt(j) − 1. (25)
Now, we can use mathematical programming to describe
constraints (21) and (22) as
PwGt(j),r(i) −
Gt(j),r(i)≥Gt(k),r(i)
t(k)=t(j)
βPwGt(k),r(i)qt(k) − βη
≥ 1 − ρi,t(j) Mi,t(j)
Mi,t(j) = PwGt(j),r(i)
−
Gt(j),r(i)≥Gt(k),r(i)
t(k)=t(j)
βPwGt(k),r(i) − βη (26)
PwGt(i),r(i) −
Gt(i),r(i)≥Gt(k),r(i)
t(k)=t(i)
βPwGt(k),r(i)qk − βη
≥ (1 − pi)Hi
Hi =PwGt(i),r(i) −
Gt(i),r(i)≥Gt(k),r(i)
t(k)=t(i)
βPwGt(k),r(i)−βη. (27)
Algorithm 1: Determination of Interference Neighbourhood
1 Initialize (0 < < 1);
2 Initialize itrCut;
3 for l : l ∈ L do
4 Initialize curr.decision = 1;
5 Initialize curr.radius = dmax
(l);
6 Initialize success.radius = dmax
(l);
7 Initialize curr.decision = 1;
8 for i = 1 to itrCut do
9 curr.interval = (dmax
(l) − dt(l),r(l))/2i
;
10 curr.radius = curr.radius + (1 − curr.
decision) ∗ curr.interval − curr.decision ∗
curr.radius;
11 Generate wal based on curr.radius;
12 Solve optimal objective (16) under constraints
(17)–(19), (23)–(27);
13 if (16) > Imax
then
14 curr.decision = 0;
15 else
16 success.radius = curr.radius;
17 curr.decision = 1;
18 end
19 λ(l) = success.radius;
20 end
21 end
C. Distributed Scheduling Algorithm With SIC
Based on Algorithm 1, we can calculate
−→
λ under a certain
value of . Let Δ1
l be the set of all links k such that t(l) is
located in Ak, Δ2
l be the set of all links k such that t(l) is
located in INk − Ak, and Δl = Δ1
l Δ2
l . At the beginning
of each scheduling period, each link l broadcasts its weight
information (wl) to links in Δl and ΔIN
l [the set of links whose
transmitters are located in the interference neighborhood of link
l (i.e., in INl)]. We further divide ΔIN
l into two link sets:
ΔIN1
l and ΔIN2
l . The former denotes the set of links k such
that t(k) is located in Al, and the latter denotes the set of links
k such that t(k ) is located in INl − Al. The weight of link
l is computed as illustrated in Section IV-A. We assume that
each link l(l ∈ L) maintains two local link sets, i.e., the current
active link set (currsl for short) and a candidate link set (cansl
for short). The former contains links that have been added
into a feasible configuration conf in a particular scheduling
period, and the latter contains links that are candidate links
to be added into conf. At the beginning of each scheduling
period, we initialize currsl = ∅ and cansl = {l, Δl ΔIN
l }.
Each scheduling period consists of several intervals, and in each
interval, each link l makes a decision as to whether it should
be added into currsl or removed from cansl. Therefore, the
purpose of our scheduling method is to generate a new con-
figuration that satisfies the SIC constraints under the physical
interference model such that the sum of the weights of the
links in this configuration is the largest possible. Our scheduling
method follows the classical GMS method but is implemented
in a distributed manner.
7. 3114 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 64, NO. 7, JULY 2015
To make sure that the new configuration, which has been
generated, satisfies the SIC constraints under the physical in-
terference model, each link will execute two main procedures
as follows. At the beginning of each scheduling interval, each
link l (l ∈ cansl) compares its weight to the weights of links
in cansl. If link l has the largest weight among all links in
cansl, it will run Algorithm 2 to try to add itself into currsl.
The detailed process is illustrated next. Link l first broadcasts a
REQ message to links in Δl. Any link in Δl will add link l into
a local auxiliary link set (auxs for short); now, any link l ∈
{currsl auxsl} will determine whether it remains feasible
under SIC constraints upon adding link l to the current schedule
as follows. For each link l ∈ {currsl auxsl}, we define the
SIC link set (sicsl for short) as the set of links whose trans-
mitters are in ΔIN1
l {currsl auxsl } and receivers are
r(l ). According to Theorem 1, any link l ∈ {currsl auxsl}
satisfies SIC constraints if 1) the total interference (I1) coming
from ΔIN2
l is ≤ (1 − )Imax
(l ) and 2) all links k ∈ sicsl
are feasible (i.e., total interference (I2) coming from ΔIN2
k is
≤ (1 − )Imax
(k)).
According to the given procedure, if any link l ∈
{currsl auxsl} does not satisfy the SIC constraints, then
link l will send an ERROR message to link l indicating that link
l cannot be added to the configuration (that is, link l is causing
strong interference making the current schedule not feasible).
If link l ∈ auxsl does not receive any ERROR message from
its neighbors, it adds itself into currsl, removes itself from
cansl, auxsl, and broadcasts a SUCCESS message to all its
neighbors to update their local link sets (cans, currs, and
auxs). Otherwise, it removes itself from cansl, auxsl and
broadcasts a REMOVE message to all its neighbors to update
their local link sets (cans, auxs). The given process enforces
that when adding a new link to a feasible configuration, the
current schedule remains feasible under SIC constraints.
The main purpose of our second procedure is to remove links
in cans (e.g., link l ∈ cansl), which have no chance of being
added into currs. After the new conf is generated at each
interval, all links l (l ∈ cansl) need to make a decision as to
whether they satisfy SIC constraints as follows. For each link
l ∈ cansl, we define another SIC link set (sicsl for short) as
the set of links whose transmitters are in ΔIN1
l currsl and
receiver is r(l). Similar to the process in the first procedure,
any link l ∈ cansl does not satisfy SIC constraints if 1) the
total interference (I1) coming from ΔIN2
l is > (1 − )Imax
(l)
or 2) for any link k ∈ sicsl is infeasible (i.e., total interference
(I2) coming from ΔIN2
k is > (1 − )Imax
(k )). After the given
process, if there is a link l in cansl that does not satisfy
SIC constraints, then link l will remove itself from cansl and
broadcast a REMOVE message to all its neighbors to update
their local link sets (cans, currs). The given process makes
sure that each link in cans satisfies SIC constraints with the
current schedule.
In our distributed implementation, we set the number of inter-
vals per scheduling period to a fixed value. In each scheduling
interval, each link will run Algorithms 2 and 3 to generate new
feasible schedule/configuration during the scheduling period.
Once a schedule is obtained, links that have been selected will
transmit in the transmission period one packet each.
Algorithm 2: Distributed Scheduling Method With SIC
(Link l)
1 Link l broadcast REQ message to all links in Δl;
2 for link l in Δl {currsl auxsl} do
3 if link l received REQ message from link l then
4 Link l adds link l into auxsk;
5 Link l calculates cumulative interference I1;
6 if I1 > (1 − )Imax
(l )) then
7 Link l broadcasts ERROR message to link l;
8 else
9 Generate sicsl ;
10 for k : k ∈ sicsl do
11 Link k temporarily calculates cumulative
interference I2;
12 if I2 > (1 − )Imax
(k) then
13 Link l broadcasts ERROR message to link l;
14 end
15 end
16 end
17 end
18 end
19 if Link l receives no ERROR messages then
20 currsl = currsl l;
21 cansl = cansl − l;
22 auxsl = auxsl − l;
23 link l broadcasts a SUCCESS message to all its neigh-
bors to update their local link sets(cans, currs, auxs);
24 Goto Algorithm 3;
25 else
26 cansl = cansl − l;
27 link l broadcasts a REMOVE message to all its neigh-
bors to update their local link sets(cans, auxs);
28 end
Algorithm 3: Distributed Scheduling Method With SIC
(Part II)
1 for link k in Δl cansl do
2 if link k received a SUCCESS message from link l then
3 Link k calculates cumulative interference I1;
4 if I1 > (1 − )Imax
(k)) then
5 Link k broadcasts a REMOVE message to its neigh-
bors to update their local link sets(cans);
6 else
7 Generate sicsk;
8 for i : i ∈ sicsk do
9 Link i calculates cumulative interference I2;
10 if I2 > (1 − )Imax
(i) then
11 Link k broadcasts a REMOVE message to its
neighbors to update their local link sets(cans);
12 end
13 end
14 end
15 end
16 end
8. QU et al.: WIRELESS NETWORKS WITH INTERFERENCE CANCELATION CAPABILITIES 3115
D. Joint Transport, Routing, and Scheduling With SIC
Consider the dual problem (9) and suppose that the function
D(u) is not necessarily differentiable. Therefore, (9) can be
solved using the subgradient method. Now, it is easy to verify
that
v+
if (u) − yif (u) + v−
if (u) (28)
is a subgradient of D(u) at point u. Thus, based on the subgradi-
ent method, the update algorithm can be formulated as follows:
uif (t+1)= uif (t)+α v+
if (u(t))− yif (u(t))+v−
if (u(t))
+
(29)
where α is a positive step size. Equation (29) achieves optimal-
ity when α is set to a sufficiently small value [25]. The given
dual algorithm (presented in Section IV-A) solves the cross-
layer problem through a distributed manner where at the trans-
port layer, nodes in the network individually update their prices
according to (29) and the source of each flow f individually
adjusts its rate (y) according to the local congestion price (u);
for solving the routing/scheduling subproblem, we generate a
new conf at each time slot by using Algorithms 2 and 3, which
work in a distributed manner. We summarize our joint conges-
tion control, routing, and scheduling with SIC in Algorithm 4.
Algorithm 4: Distributed Cross-Layer Design With SIC
1 Initialize max iteration count as itrmax;
2 Initialize y, v, u;
3 for i = 1 to itrmax do
4 for n ∈ N do
5 node n updates u by calculating (29);
6 end
7 for f ∈ F do
8 Source node of flow f updates y by calculating (13);
9 end
10 for l ∈ L do
11 link l updates wl;
12 end
13 Generate a feasible conf by Algorithms 2 and 3;
14 Data Transmission based on currently obtained
schedule;
15 end
E. Complexity Analysis
The whole procedure includes a centralized process for
identifying the interference neighborhood (see Algorithm 1)
and a link scheduling process under SIC, which is done in
a distributed manner at each iteration (see Algorithms 2 and
3). To analyze the complexity of the first part, we convert the
inverse LP (ILP) problem into a complete binary tree for the
worst case of solving the optimal objective (16). A route that
starts from the root of the tree to the leaf is a feasible solution
(or schedule). Based on backtracking, it is clear that the time
Fig. 1. Eight-node network topology.
complexity of a tree traversal search is O(2n
), where n is the
number of links in wal. However, in most cases, there is no need
for a complete traversal search, owing to the SINR constraints
between links. In practice, we can prune some invalid branches
(i.e., the branch-and-cut method [34]) to improve the efficiency
of the search.
For the run time complexity of the distributed scheduling
with SIC, we omit the communication overload during the
scheduling interval and only focus on the worst case compu-
tation analysis. As shown in Algorithms 2 and 3, it is easy to
verify that the time complexity of links in cansl is O(n) +
O(n2
) = O(n2
) (i.e., the weight comparison and the order of
SIC decoding), where n is the number of feasible neighbors
(i.e., active links) for link l. Given that there is no need to com-
pare the weight information in links in currsl auxsl sicsl,
their run time complexity is O(n2
). For each link that is not in
cansl currsl auxsl sicsl, it keeps silent and therefore
no calculation. Hence, the worst-case run time at each link is
O(n2
), which is polynomial.
V. NUMERICAL RESULTS
Here, we present numerical results to study the performance
of the cross-layer design method for solving the problem of
joint transport, routing, and scheduling (JTRS) in wireless
networks with interference cancelation. We are particularly in-
terested in studying the efficiency of the distributed scheduling
(JTRDS) method with SIC, and we present comparisons with
centralized scheduling methods [JTRCS; both Pick & Com-
pare (P&C) and GMS]. We also present comparisons of our
cross-layer design method with and without SIC to assess the
benefits of interference cancelation (JTRDS-SIC and JTRDS,
respectively). We use a CPLEX solver to solve the ILP problem
in Algorithm 1 to determine the radius of the interference
neighborhood for each link. For our evaluation, we consider two
random networks (Network 1 and Network 2), with eight nodes
(48 links) and 15 nodes (124 links), each randomly distributed
in a square region of 100 m × 100 m. The topologies of
the networks are shown in Figs. 1 and 2. Under the physical
interference model, the transmission power of each node is set
to Pw = 0.001 W. We assume the path-loss index pl = 4; the
background noise η is set to 10−10
W; the SINR threshold for
a successful transmission is β = 1, = 0.05 (unless otherwise
specified); and the transmission capacity of each link is c = 1
(packet/time slot). We assume that there are two flows and four
flows in Network 1 and Network 2, respectively.
9. 3116 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 64, NO. 7, JULY 2015
Fig. 2. Fifteen-node network topology.
Fig. 3. Utility achieved by JTRDS-SIC ( = 0.05) and JTRCS-SIC (P&C).
We start by evaluating the performance benefits of JTRDS-
SIC in Network 1. There are two flows in the network (Flow 1:
node 1 → node 8; Flow 2: node 3 → node 7). We compare our
distributed method with P&C, which is a centralized scheduling
method and is shown to achieve 100% throughput [19], [20]. In
the P&C scheduling method, we randomly generate a maximal
schedule under SIC constraints at each time slot (by randomly
selecting links to be included in the schedule as long as they
satisfy the interference constraints) and compare the current
schedule with the schedule generated in the previous time slot;
the schedule with larger weight (sum of the link weights) is
always selected for data transmission at each time slot. In our
distributed method, we set = 0.05. Figs. 3 and 4 show the
utility and the congestion price of both methods. Clearly, the
figures show that our distributed method quickly converges
to the optimal solution and oscillates around it; however, the
centralized P&C has slower convergence time, which is due to
the random selection of transmission links to be included in the
schedule. To better understand the obtained results, we look at
how these two methods route the two flows and the achievable
individual flow rate; the results are shown in Tables I and II. We
observe that both methods select different routes for the flows
and that flow 1 achieves an optimal rate of 0.8519 (using the
centralized P&C scheduling method), whereas flow 1 achieves
Fig. 4. Congestion prices of JTRDS-SIC ( = 0.05) and JTRCS-SIC (P&C).
TABLE I
AVERAGE RATES OF FLOWS THROUGH DIFFERENT
LINKS WITH JTRCS-SIC (P&C)
TABLE II
AVERAGE RATES OF FLOWS THROUGH DIFFERENT
LINKS WITH JTRDS-SIC
TABLE III
SOURCE NODE, DESTINATION NODE OF EACH FLOW IN THE NETWORK
a flow rate of 0.8332 using the proposed distributed scheduling
method and a gap of 2% between the two methods. Flow 2,
however, achieves the same flow rate of 1 in both methods. It
is to be noted that GMS achieves exactly similar results to our
method (results are omitted in the figures).
Next, we consider the larger network (Network 2) with four
flows (Flow1–Flow4: Table III). We start by studying the effect
of the parameter on the achievable flow rate. We numerically
solve our JTRDS-SIC in this 15-node network, and the obtained
results are shown in Fig. 5. We observe that when is smaller,
10. QU et al.: WIRELESS NETWORKS WITH INTERFERENCE CANCELATION CAPABILITIES 3117
Fig. 5. Flow rates (under JTRDS-SIC) versus .
Fig. 6. Average number of active links per schedule versus .
the achievable flow rates are higher, and as increases, the rate
decreases. Clearly, when is small, the interference neighbor-
hood of a link gets larger (see Fig. 7); therefore, a link l will be
able to coordinate its scheduling/transmission with more links
in its interference neighborhood, i.e., INl. This implies that,
ultimately, each selected schedule may contain, on average,
more active links (see Fig. 6), which, in turn, implies better
spectrum spatial reuse in the network. However, it should be
noted that a larger interference neighborhood may result in
higher scheduling overhead to coordinate the selection of the
schedule. Now, conversely, a larger implies a smaller inter-
ference neighborhood, and as a result, most of the links in the
network will be located outside the neighborhood of a particular
link, preventing any effective coordination in the selection of
the schedule and resulting in lower attainable flow rates.
The flow rate continues to decrease until it reaches a mini-
mum value (at = 0.7) beyond which it starts to increase. This
can be explained as follows. When = 0.7, as we mentioned
earlier, the radius of the interference neighborhood is small (see
Fig. 7), and thus, fewer links may exist within the (INl − Al)
area. Fig. 8 indicates that almost 0 links within that area may
be active. However, according to the protocol, the value of
the tolerable interference assigned to links within that area
Fig. 7. Average radius of neighborhood versus .
Fig. 8. Average number of active links in different areas versus .
is set to (1 − )Imax
; given that almost no links are active
within that area (see Fig. 8), this tolerable interference value
is wasted and would have been better off allocated to links
outside this interference neighborhood (i.e., nINl), where
the other transmission links are located. As increases, the
value of (1 − )Imax
decreases, and more tolerable interference
(i.e., Imax
) is allocated to those links outside the interference
neighborhood of link l, and such links will attempt to schedule
their transmissions concurrently with link l. Fig. 8 shows that
as increases, more links outside the neighborhood are active
in the schedule and none of the links within the interference
neighborhood are. This explains the behavior of the traffic flow
rates shown in Fig. 5 where beyond = 0.7, the rates start
to increase. Fig. 6 shows the average number of active links
per schedule, and as previously explained, smaller indicates
better coordination to construct the schedule and, therefore,
more active links per schedule, hence better spatial reuse, and
larger yields lower spatial reuse.
Finally, we study the benefits of SIC by comparing the
performance of JTRDS-SIC with JTRDS, where in the latter,
nodes do not have any SIC capabilities. The results (individual
flow rates) are shown in Fig. 9, and we use a value of = 0.3.
The selection of = 0.3 is motivated by Fig. 7 where we show
11. 3118 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 64, NO. 7, JULY 2015
Fig. 9. Achievable flow rates (JTRDS-SIC versus JTRS ( = 0.3)).
that both methods have close average radius for the interference
neighborhood. Fig. 9 shows that flows achieve much higher
rates in a network with SIC capabilities (almost twice the rate
is achieved by most of the flows). To better understand, we
observe from Fig. 6 that the JTRDS-SIC method (when =
0.3) has a much better schedule length than that of the JTRDS
method; indeed, the schedule length (i.e., number of active
links per selected schedule) of JTRDS-SIC is almost twice that
of JTRDS. This shows that SIC is effectively managing the
interference in the network and promoting transmission con-
currence, leading to better achievable flow rates in the network.
VI. CONCLUSION
In this paper, we have studied the benefits of SIC in improv-
ing the performance of wireless networks. We considered solv-
ing an NUM problem in the context of cross-layer optimization
of the joint congestion control, routing, and scheduling problem
under the SINR interference model. Through dual decompo-
sition, we divided our design problem into a congestion con-
trol subproblem and a routing/scheduling subproblem. Given
the complexity of the scheduling subproblem, we presented
a decentralized method for solving the link scheduling prob-
lem. Our decentralized method benefits from the interference
localization concept to help neighboring links coordinate their
transmissions, taking into account SIC constraints, and without
causing sufficient interference that may corrupt ongoing sched-
uled transmissions. Our approach to solving the joint design
problem is completely decentralized. We have numerically
solved the cross-layer optimization, and we have shown that
our distributed resource allocation method achieves very close
results to centralized methods (e.g., our achieved results are
below 2% from the centralized P&C scheduling method, which
achieves 100% throughput performance, and we obtain similar
results to the centralized GMS). We also studied the benefits
of SIC, and we have shown that the flows in the network may
achieve up to twice the achievable rates in a network with-
out SIC. We have shown that networks with SIC capabilities
promote better transmission concurrence and, therefore, better
spectrum reuse.
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Long Qu received the B.S. degree in communica-
tion engineering from Zhengzhou University, Henan,
China, in 2010. He is currently working toward
the Ph.D. degree in communication and information
systems with Ningbo University, Zhejiang, China.
From December 2012 to December 2013, he was
a visiting Ph.D. student with Concordia University,
Montreal, QC, Canada. His current research interests
include cross-layer design in wireless communica-
tion systems and wireless network optimization.
Jiaming He received the M.S. and Ph.D. degrees
from Zhejiang University, Hangzhou, China, in 1993
and 1996, respectively.
He is currently a Professor with Ningbo Univer-
sity, Zhejiang, China. His research interests include
broadband wireless communication systems.
Chadi Assi (SM’08) received the B.Eng. degree
from the Lebanese University, Beirut, Lebanon, in
1997 and the Ph.D. degree from the City University
of New York (CUNY), New York, NY, USA, in
2003.
He is currently a Full Professor with the Concor-
dia Institute for Information Systems Engineering,
Concordia University, Montreal, QC, Canada. Be-
fore joining Concordia University, he was a Visiting
Researcher with Nokia Research Center, Boston,
MA, USA, where he worked on quality of service in
passive optical access networks. His main research interests include networks
and network design and optimization. His current research interests include
network design and optimization, network modeling, and network reliability.
Dr. Assi is on the Editorial Board of the IEEE COMMUNICATIONS SUR-
VEYS AND TUTORIALS, IEEE TRANSACTIONS ON COMMUNICATIONS, and
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY. He was a recipient of
the prestigious Mina Rees Dissertation Award from CUNY in August 2002 for
his research on wavelength-division multiplexing optical networks.