This paper proposes a two-tier heterogeneous network architecture for large machine-to-machine communication networks to statistically control quality of service. It analyzes the properties of connected machine networks, including degree distribution, diameter and average distance. It then models data transportation using queuing theory to obtain the average end-to-end delay and maximum throughput. The proposed architecture connects machines to data aggregators that form a small-world network to improve end-to-end performance and statistically guarantee tolerable delays without individual link feedback. Simulation results confirm this heterogeneous design effectively controls delays in a statistical manner.
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
Markovian Queueing Model for Throughput Maximization in D2D-Enabled Cellular ...IJECEIAES
Device-to-Device (D2D) communication has been considered a key enabling technol- ogy that can facilitate spectrum sharing in 4G and 5G cellular networks. In order to meet the high data rate demands of these new generation cellular networks, this paper considers the optimization of available spectrum resource through dynamic spectrum access. The utilization of continuous-time Markov chain (CTMC) model for efficient spectrum access in D2D-enabled cellular networks is investigated for the purpose of determining the impact of this model on the capacity improvement of cellular networks. The paper considers the use of CTMC model with both queueing and non-queueing cases called 13-Q CTMC and 6-NQ CTMC respectively with the aim of improving the overall capacity of the cellular network under a fairness constraint among all users. The proposed strategy consequently ensures that spectrum access for cellular and D2D users is optimally coordinated by designing optimal spectrum access probabilities. Numerical simulations are performed to observe the impact of the proposed Markovian queueing model on spectrum access and consequently on the capacity of D2D-enabled cellular networks. Results showed that the proposed 13-Q CTMC provide a more spectrumefficient sharing scheme, thereby enabling better network performances and larger capabilities to accommodate more users.
TOP 10 AD HOC NETWORKS PAPERS: RECOMMENDED READING – NETWORK RESEARCHIJCNCJournal
The high-level contribution of this paper is a detailed simulation based analysis about the impact of mobility models on the performance of node-disjoint and link-disjoint multi-path routing algorithms for mobile ad hoc networks (MANETs).
TOP 10 AD HOC NETWORKS PAPERS: RECOMMENDED READING – NETWORK RESEARCHIJCNCJournal
The high-level contribution of this paper is a detailed simulation based analysis about the impact of mobility models on the performance of node-disjoint and link-disjoint multipath routing algorithms for mobile ad hoc networks (MANETs).
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.
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
Markovian Queueing Model for Throughput Maximization in D2D-Enabled Cellular ...IJECEIAES
Device-to-Device (D2D) communication has been considered a key enabling technol- ogy that can facilitate spectrum sharing in 4G and 5G cellular networks. In order to meet the high data rate demands of these new generation cellular networks, this paper considers the optimization of available spectrum resource through dynamic spectrum access. The utilization of continuous-time Markov chain (CTMC) model for efficient spectrum access in D2D-enabled cellular networks is investigated for the purpose of determining the impact of this model on the capacity improvement of cellular networks. The paper considers the use of CTMC model with both queueing and non-queueing cases called 13-Q CTMC and 6-NQ CTMC respectively with the aim of improving the overall capacity of the cellular network under a fairness constraint among all users. The proposed strategy consequently ensures that spectrum access for cellular and D2D users is optimally coordinated by designing optimal spectrum access probabilities. Numerical simulations are performed to observe the impact of the proposed Markovian queueing model on spectrum access and consequently on the capacity of D2D-enabled cellular networks. Results showed that the proposed 13-Q CTMC provide a more spectrumefficient sharing scheme, thereby enabling better network performances and larger capabilities to accommodate more users.
TOP 10 AD HOC NETWORKS PAPERS: RECOMMENDED READING – NETWORK RESEARCHIJCNCJournal
The high-level contribution of this paper is a detailed simulation based analysis about the impact of mobility models on the performance of node-disjoint and link-disjoint multi-path routing algorithms for mobile ad hoc networks (MANETs).
TOP 10 AD HOC NETWORKS PAPERS: RECOMMENDED READING – NETWORK RESEARCHIJCNCJournal
The high-level contribution of this paper is a detailed simulation based analysis about the impact of mobility models on the performance of node-disjoint and link-disjoint multipath routing algorithms for mobile ad hoc networks (MANETs).
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.
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.
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 EFFICIENT ROUTING PROTOCOL FOR DELAY TOLERANT NETWORKS (DTNs)cscpconf
Delay-Tolerant Networks are those which lacks continuous communications among mobile
nodes . Distributed clustering scheme and cluster-based routing protocol are used for DelayTolerant
Mobile Networks (DTMNs). The basic idea is to distributive group mobile nodes with
similar mobility pattern into a cluster, which can then interchangeably share their resources for
overhead reduction and load balancing, aiming to achieve efficient and scalable routing in DTMN. Load balancing is carried out in two ways, Intra cluster load balancing and Inter cluster load balancing. The Convergence and stability become major challenges in distributed clustering in DTMN. An efficient routing protocol will be provided for the delay tolerant networks through which the stability of the network is maintained .Based on nodal contact probabilities, a set of functions including Sync(), Leave(), and Join() are devised for cluster formation and gateway selection. Finally, the gateway nodes exchange network information and perform routing
Developing QoS by Priority Routing for Real Time Data in Internet of Things (...IJCNCJournal
In networks, many application protocols such as CoAP, REST, XMPP ,AMQP have been proposed for IoT communication which includes p2p or S2S. In MANET Network convergence does the way for improvements in Internet of Things (IoT) communication with high potential for a wide range of applications. Each protocol focuses on some aspects of communication in the IoT. Hence, these application protocols have indicated of how IoT has integrated to enhanced and developed of a new service that require to guarantees
the wide range offered by the quality of services. In this paper, we will introduce a smart pathway that can be bridge the gap between IoT services with its real data traffic. Therefore, we enhanced the MANET routing protocol for computing two or more paths to pass the more that one high priority real traffic data via these paths to improve the gloomy picture of this protocol in the context of IoT. In particular, the good services
with high timely delivery of urgent data such as real time data environmental monitoring. After surveying the published and available protocol interoperability given for urban sensing. In this research, we have proposed a novel solution to integrate MANET overlays, and collaboratively formed over MANET, to boost urban data in IoT. Overlays are used to dynamic differentiate and fasten the delivery of high priority real application time data over low-latency MANET paths by integrating with the original specifications. Our experimental results showed the effectiveness on the network such as the overhead and network congestion. In addition, the initial
results of the light-weight improved the routing protocol over the baseline protocols in terms of the delay of reciveing the packets between nodes which lead to increase the throughput by reducing loss packets.
There are number of cluster based routing algorithms in mobile ad hoc networks. Since ad hoc networks are not accompanied by fixed access points, efficient routing is a must for such networks. Clustering approach is applied in mobile ad hoc network because clusters are more easily manageable and are more viable. It consists of segregating the given network into several reasonable clusters by using a clustering algorithm. By performing clustering we elect a worthy node from the cluster as the cluster head in such a way that we strive to reduce the management overheads and thus increasing the efficiency of routing. As for the fact that nodes in mobile ad hoc network have frequent host change and frequent topology change routing plays an important role for maintenance and backup mechanism to stabilize network performance. This paper aims to review the previous research papers and provide a survey on the various cluster based routing protocols in mobile ad hoc network. This paper presents analytical study of cluster based routing algorithms from literature. Index Terms— Ad- hoc networks, Cluster head, Clustering, Protocol, Route selection.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
QoS Oriented Coding For Mobility Constraint in Wireless Networksiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Information Extraction from Wireless Sensor Networks: System and ApproachesM H
Recent advances in wireless communication have made it possible to develop low-cost, and low power Wireless Sensor Networks (WSN). The WSN can be used for several application areas (e.g., habitat monitoring, forest fire detection, and health care). WSN Information Extraction (IE) techniques can be classified into four categories depending on the factors that drive data acquisition: event-driven, time-driven, query-based, and hybrid. This paper presents a survey of the state-of-the-art IE techniques in WSNs. The benefits and shortcomings of different IE approaches are presented as motivation for future work into automatic hybridization and adaptation of IE mechanisms.
The rapid need of wireless demands a great deal of security and reliable routing in order to keep all the data sources and equipments secure. In order to develop efficient and robust protocols, it is essential to understand the inherent characteristics of wireless networks such as connectivity, coverage and varying channel conditions. Wireless LAN introduces the concept that use can connect to any one at any place at anytime by using various mobile appliances that can be carried at any place. Now Communication is no longer limited to a one place by holding wired phones. This is the big boom to the I.T industry but it also brings a lot of opportunities and challenges for the Network Administrator who is looking after the Wireless LANs (WLAN). WLAN traffic travels over radio waves that cannot be constrained by the walls or any Simranjeet Kaur"Reliable and Efficient Routing in WLAN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd3585.pdf http://www.ijtsrd.com/computer-science/computer-network/3585/reliable-and-efficient-routing-in-wlan/simranjeet-kaur
Optimizing On Demand Weight -Based Clustering Using Trust Model for Mobile Ad...ijasuc
Mobile ad hoc networks are growing in popularity due to the explosive growth of modern
devices with wireless capability such as laptop, mobile phones, PDA, etc., makes the application more
challenging. The mobile nodes are vulnerable to security attacks. To protect the ad hoc network it is
essential to evaluate the trust worthiness. The proposed TWCA is similar to WCA in terms of cluster
formation and cluster head election. However, in WCA security features are not included. The proposed
TWCA is a cluster based trust evaluation, in which the mobile nodes are grouped into clusters with one
cluster head. It establishes trust relationship for the cluster based on the previous transaction result. The
simulation result confirms the efficiency of our scheme than the WCA and SEMC.
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.
Delay Tolerant Networks (DTNs) have high end-to-end latency, which is often faces disconnection, and unreliable wireless connections. It does not mean a delay service instead DTNs provides a service where network imposes disruption or delay. It operates in challenged networks with extremely limited resources such as memory size, CPU processing power etc. This paper presents an efficient trust managing mechanism for providing secure environment. The proposed dynamic trust management protocol uses a dynamic threshold updating which overcomes the problems with time changing dynamic characteristics by dynamically updating the criteria in response to changing network conditions. This reduces overheads and increases the efficient use of routing network even in conditions change. Also the dynamic threshold update reduces the false detection probability of the malicious nodes. To show the effectiveness of the proposed system, a detailed simulation in the presence of selfish and malicious nodes is performed with ONE simulator. Finally a comparative analysis of our proposed routing with previous routing protocols is also performed. The results demonstrate that presented algorithm deals effectively with selfish behavior with providing significant gain on effective delivery ratio in trade off with message overhead and delay
Heterogeneous Device-to-Device mobile networks
are characterised by frequent network disruption and unreliability
of peers delivering messages to destinations. Trust-based
protocols has been widely used to mitigate the security and
performance problems in D2D networks. Despite several efforts
made by previous researchers in the design of trust-based routing
for efficient collaborative networks, there are fewer related
studies that focus on the peers’ neighbourhood as a routing
metrics’ element for a secure and efficient trust-based protocol.
In this paper, we propose and validate a trust-based protocol
that takes into account the similarity of peers’ neighbourhood
coefficients to improve routing performance in mobile HetNets
environments. The results of this study demonstrate that peers’
neighbourhood connectivity in the network is a characteristic
that can influence peers’ routing performance. Furthermore, our
analysis shows that our proposed protocol only forwards the
message to the companions with a higher probability of delivering
the packets, thus improving the delivery ratio and minimising
latency and mitigating the problem of malicious peers ( using
packet dropping strategy).
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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.
Studies towards heterogeneous Mobile Adhoc Network (MANET) as well as inter-domain routing is still in much infancy stage. After reviewing the existing literaturs, it was found that problems associated with scalability, interoperability, and security is not defined up to the mark as it should be part of pervasive computing in future networks. Moreover, it was found that existing studies do not consider the complexities associated with heterogeneous MANET to a large extent leading to narrowed research scope. Hence, this paper introduces a novel scheme called as Secure, Scalable and Interoperable (SSI )routing, where a joint algorithm is designed, developed, and implemented. The outcome exhibits the correctness of this scheme by simulation assisted by analysis for inter-domain routing.
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.
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 EFFICIENT ROUTING PROTOCOL FOR DELAY TOLERANT NETWORKS (DTNs)cscpconf
Delay-Tolerant Networks are those which lacks continuous communications among mobile
nodes . Distributed clustering scheme and cluster-based routing protocol are used for DelayTolerant
Mobile Networks (DTMNs). The basic idea is to distributive group mobile nodes with
similar mobility pattern into a cluster, which can then interchangeably share their resources for
overhead reduction and load balancing, aiming to achieve efficient and scalable routing in DTMN. Load balancing is carried out in two ways, Intra cluster load balancing and Inter cluster load balancing. The Convergence and stability become major challenges in distributed clustering in DTMN. An efficient routing protocol will be provided for the delay tolerant networks through which the stability of the network is maintained .Based on nodal contact probabilities, a set of functions including Sync(), Leave(), and Join() are devised for cluster formation and gateway selection. Finally, the gateway nodes exchange network information and perform routing
Developing QoS by Priority Routing for Real Time Data in Internet of Things (...IJCNCJournal
In networks, many application protocols such as CoAP, REST, XMPP ,AMQP have been proposed for IoT communication which includes p2p or S2S. In MANET Network convergence does the way for improvements in Internet of Things (IoT) communication with high potential for a wide range of applications. Each protocol focuses on some aspects of communication in the IoT. Hence, these application protocols have indicated of how IoT has integrated to enhanced and developed of a new service that require to guarantees
the wide range offered by the quality of services. In this paper, we will introduce a smart pathway that can be bridge the gap between IoT services with its real data traffic. Therefore, we enhanced the MANET routing protocol for computing two or more paths to pass the more that one high priority real traffic data via these paths to improve the gloomy picture of this protocol in the context of IoT. In particular, the good services
with high timely delivery of urgent data such as real time data environmental monitoring. After surveying the published and available protocol interoperability given for urban sensing. In this research, we have proposed a novel solution to integrate MANET overlays, and collaboratively formed over MANET, to boost urban data in IoT. Overlays are used to dynamic differentiate and fasten the delivery of high priority real application time data over low-latency MANET paths by integrating with the original specifications. Our experimental results showed the effectiveness on the network such as the overhead and network congestion. In addition, the initial
results of the light-weight improved the routing protocol over the baseline protocols in terms of the delay of reciveing the packets between nodes which lead to increase the throughput by reducing loss packets.
There are number of cluster based routing algorithms in mobile ad hoc networks. Since ad hoc networks are not accompanied by fixed access points, efficient routing is a must for such networks. Clustering approach is applied in mobile ad hoc network because clusters are more easily manageable and are more viable. It consists of segregating the given network into several reasonable clusters by using a clustering algorithm. By performing clustering we elect a worthy node from the cluster as the cluster head in such a way that we strive to reduce the management overheads and thus increasing the efficiency of routing. As for the fact that nodes in mobile ad hoc network have frequent host change and frequent topology change routing plays an important role for maintenance and backup mechanism to stabilize network performance. This paper aims to review the previous research papers and provide a survey on the various cluster based routing protocols in mobile ad hoc network. This paper presents analytical study of cluster based routing algorithms from literature. Index Terms— Ad- hoc networks, Cluster head, Clustering, Protocol, Route selection.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
QoS Oriented Coding For Mobility Constraint in Wireless Networksiosrjce
IOSR Journal of Electronics and Communication Engineering(IOSR-JECE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of electronics and communication engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in electronics and communication engineering. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
Information Extraction from Wireless Sensor Networks: System and ApproachesM H
Recent advances in wireless communication have made it possible to develop low-cost, and low power Wireless Sensor Networks (WSN). The WSN can be used for several application areas (e.g., habitat monitoring, forest fire detection, and health care). WSN Information Extraction (IE) techniques can be classified into four categories depending on the factors that drive data acquisition: event-driven, time-driven, query-based, and hybrid. This paper presents a survey of the state-of-the-art IE techniques in WSNs. The benefits and shortcomings of different IE approaches are presented as motivation for future work into automatic hybridization and adaptation of IE mechanisms.
The rapid need of wireless demands a great deal of security and reliable routing in order to keep all the data sources and equipments secure. In order to develop efficient and robust protocols, it is essential to understand the inherent characteristics of wireless networks such as connectivity, coverage and varying channel conditions. Wireless LAN introduces the concept that use can connect to any one at any place at anytime by using various mobile appliances that can be carried at any place. Now Communication is no longer limited to a one place by holding wired phones. This is the big boom to the I.T industry but it also brings a lot of opportunities and challenges for the Network Administrator who is looking after the Wireless LANs (WLAN). WLAN traffic travels over radio waves that cannot be constrained by the walls or any Simranjeet Kaur"Reliable and Efficient Routing in WLAN" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-6 , October 2017, URL: http://www.ijtsrd.com/papers/ijtsrd3585.pdf http://www.ijtsrd.com/computer-science/computer-network/3585/reliable-and-efficient-routing-in-wlan/simranjeet-kaur
Optimizing On Demand Weight -Based Clustering Using Trust Model for Mobile Ad...ijasuc
Mobile ad hoc networks are growing in popularity due to the explosive growth of modern
devices with wireless capability such as laptop, mobile phones, PDA, etc., makes the application more
challenging. The mobile nodes are vulnerable to security attacks. To protect the ad hoc network it is
essential to evaluate the trust worthiness. The proposed TWCA is similar to WCA in terms of cluster
formation and cluster head election. However, in WCA security features are not included. The proposed
TWCA is a cluster based trust evaluation, in which the mobile nodes are grouped into clusters with one
cluster head. It establishes trust relationship for the cluster based on the previous transaction result. The
simulation result confirms the efficiency of our scheme than the WCA and SEMC.
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.
Delay Tolerant Networks (DTNs) have high end-to-end latency, which is often faces disconnection, and unreliable wireless connections. It does not mean a delay service instead DTNs provides a service where network imposes disruption or delay. It operates in challenged networks with extremely limited resources such as memory size, CPU processing power etc. This paper presents an efficient trust managing mechanism for providing secure environment. The proposed dynamic trust management protocol uses a dynamic threshold updating which overcomes the problems with time changing dynamic characteristics by dynamically updating the criteria in response to changing network conditions. This reduces overheads and increases the efficient use of routing network even in conditions change. Also the dynamic threshold update reduces the false detection probability of the malicious nodes. To show the effectiveness of the proposed system, a detailed simulation in the presence of selfish and malicious nodes is performed with ONE simulator. Finally a comparative analysis of our proposed routing with previous routing protocols is also performed. The results demonstrate that presented algorithm deals effectively with selfish behavior with providing significant gain on effective delivery ratio in trade off with message overhead and delay
Heterogeneous Device-to-Device mobile networks
are characterised by frequent network disruption and unreliability
of peers delivering messages to destinations. Trust-based
protocols has been widely used to mitigate the security and
performance problems in D2D networks. Despite several efforts
made by previous researchers in the design of trust-based routing
for efficient collaborative networks, there are fewer related
studies that focus on the peers’ neighbourhood as a routing
metrics’ element for a secure and efficient trust-based protocol.
In this paper, we propose and validate a trust-based protocol
that takes into account the similarity of peers’ neighbourhood
coefficients to improve routing performance in mobile HetNets
environments. The results of this study demonstrate that peers’
neighbourhood connectivity in the network is a characteristic
that can influence peers’ routing performance. Furthermore, our
analysis shows that our proposed protocol only forwards the
message to the companions with a higher probability of delivering
the packets, thus improving the delivery ratio and minimising
latency and mitigating the problem of malicious peers ( using
packet dropping strategy).
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
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.
Studies towards heterogeneous Mobile Adhoc Network (MANET) as well as inter-domain routing is still in much infancy stage. After reviewing the existing literaturs, it was found that problems associated with scalability, interoperability, and security is not defined up to the mark as it should be part of pervasive computing in future networks. Moreover, it was found that existing studies do not consider the complexities associated with heterogeneous MANET to a large extent leading to narrowed research scope. Hence, this paper introduces a novel scheme called as Secure, Scalable and Interoperable (SSI )routing, where a joint algorithm is designed, developed, and implemented. The outcome exhibits the correctness of this scheme by simulation assisted by analysis for inter-domain routing.
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.
NEW TECHNOLOGY FOR MACHINE TO MACHINE COMMUNICATION IN SOFTNET TOWARDS 5Gijwmn
Machine to Machine communication or M2M, refers to a model of communication where devices communicate directly with each other using the available wired or wireless channels. M2M is a new concept proposed under 3GPP(3rd Generation Partnership Project); several research are working on providing solutions for M2M communication for the 5G networks. Challenges associated with M2M communication are the lack of standards, security, poor infrastructure, interoperability and diverse architecture. In this paper, we propose a new mechanism called TM2M5G (The Machine to Machine for 5G) based on SOFTNET platform which results in support of 5G heterogeneous network. In this paper, we
propose the architecture for M2M communication based on SOFTNET and provide new features support like security algorithms for data transmission among devices and scheduling algorithm for seamless transmission of data packets over the network. Finallysimulation results ofthis algorithm based on a system level simulator, considering two different approaches for analyzing the parameters such as delay, throughput and bandwidth are presented.
NEW TECHNOLOGY FOR MACHINE TO MACHINE COMMUNICATION IN SOFTNET TOWARDS 5Gijwmn
Machine to Machine communication or M2M, refers to a model of communication where devices
communicate directly with each other using the available wired or wireless channels. M2M is a new
concept proposed under 3GPP(3rd Generation Partnership Project); several research are working on
providing solutions for M2M communication for the 5G networks. Challenges associated with M2M
communication are the lack of standards, security, poor infrastructure, interoperability and diverse
architecture. In this paper, we propose a new mechanism called TM2M5G (The Machine to Machine for
5G) based on SOFTNET platform which results in support of 5G heterogeneous network. In this paper, we
propose the architecture for M2M communication based on SOFTNET and provide new features support
like security algorithms for data transmission among devices and scheduling algorithm for seamless
transmission of data packets over the network. Finallysimulation results ofthis algorithm based on a system
level simulator, considering two different approaches for analyzing the parameters such as delay,
throughput and bandwidth are presented.
A novel routing technique for mobile ad hoc networks (manet)ijngnjournal
Actual network size depends on the application and the protocols developed for the routing for this kind of
networks should be scalable and efficient. Each routing protocol should support small as well as large
scale networks very efficiently. As the number of node increase, it increases the management functionality
of the network. Graph theoretic approach traditionally was applied to networks where nodes are static or
fixed. In this paper, we have applied the graph theoretic routing to MANET where nodes are mobile. Here,
we designed all identical nodes in the cluster except the cluster head and this criterion reduces the
management burden on the network. Each cluster supports a few nodes with a cluster head. The intracluster
connectivity amongst the nodes within the cluster is supported by multi-hop connectivity to ensure
handling mobility in such a way that no service disruption can occur. The inter-cluster connectivity is also
achieved by multi-hop connectivity. However, for inter-cluster communications, only cluster heads are
connected. This paper demonstrates graph theoretic approach produces an optimum multi-hop connectivity
path based on cumulative minimum degree that minimizes the contention and scheduling delay end-toend.
It is applied to both intra-cluster communications as well as inter-cluster communications. The
performance shows that having a multi-hop connectivity for intra-cluster communications is more power
efficient compared to broadcast of information with maximum power coverage. We also showed the total
number of required intermediate nodes in the transmission from source to destination. However, dynamic
behavior of the nodes requires greater understanding of the node degree and mobility at each instance of
time in order to maintain end-to-end QoS for multi-service provisioning. Our simulation results show that
the proposed graph theoretic routing approach will reduce the overall delay and improves the physical
layer data frame transmission.
Topology Management for Mobile Ad Hoc Networks ScenarioIJERA Editor
Cooperative communication is the main accessing point in present days. These results can be accessed through proactive protocol like route request packet sending and route request packet receiving. The main issue is how communication will be done in MANETS. Mobile Ad-hoc networks are self-configurable networks; each node behaves like server and client in MANET. COCO (Capacity Optimized Cooperative Communication) model was developed for accessing these types of resources in MANETs. This model can’t provide sufficient communication or overall network performance. This model provides sufficient capability improvement in mobile ad-hoc networks, but this model will be taking more power resources for doing this work. exploitation simulation examples, we have a tendency to show that physical layer cooperative communications have important impacts on the performance of topology control and network capability, and also the proposed topology management scheme will considerably improve the network capability in MANETs with cooperative communications
Efficient P2P data dissemination in integrated optical and wireless networks ...TELKOMNIKA JOURNAL
The Quality of Service (QoS) resource consumption is always the tricky problem and also
the on-going issue in the access network of mobile wireless part because of its dynamic nature of network
wireless transmissions. It is very critical for the infrastructure-less wireless mobile ad hoc network that is
distributed while interconnects in a peer-to-peer manner. Toward resolve the problem, Taguchi method
optimization of mobile ad hoc routing (AODVUU) is applied in integrated optical and wireless networks
called the adLMMHOWAN. Practically, this technique was carry out using OMNeT++ software by building
a simulation based optimization through design of experiment. Its QoS network performance is examined
based on packet delivery ratio (PDR) metric and packet loss probabilities (PLP) metric that consider
the scenario of variation number of nodes. During the performing stage with random mobile connectivity
based on improvement in optimized front-end wireless domain of AODVUU routing, the result is performing
better when compared with previous study called the oRia scheme with the improvement of 14.1% PDR
and 43.3% PLP in this convergence of heterogeneous optical wireless network.
International Journal of Engineering Research and DevelopmentIJERD Editor
Electrical, Electronics and Computer Engineering,
Information Engineering and Technology,
Mechanical, Industrial and Manufacturing Engineering,
Automation and Mechatronics Engineering,
Material and Chemical Engineering,
Civil and Architecture Engineering,
Biotechnology and Bio Engineering,
Environmental Engineering,
Petroleum and Mining Engineering,
Marine and Agriculture engineering,
Aerospace Engineering.
MOVEMENT ASSISTED COMPONENT BASED SCALABLE FRAMEWORK FOR DISTRIBUTED WIRELESS...ijcsa
Intelligent networks are becoming more enveloping and dwelling a new generation of applications are
deployed over the peer-to-peer networks. Intelligent networks are very attractive because of their role in
improving the scalability and enhancing performance by enabling direct and real-time communication
among the participating network stations. A suitable solution for resource management in distributed wireless systems is required which should support fault-tolerant operations, requested resources (at shortest path), minimize overhead generation during network management, balancing the load distribution between the participating stations and high probability of lookup success and many more. This article
presents a Movement Assisted Component Based Scalable Framework (MAC-SF) for the distributed
network which manages the distributed wireless resources and applications; monitors the behavior of the
distributed wireless applications transparently and attains accurate resource projections, manages the
connections between the participating network stations and distributes the active objects in response to the
user requests and changing processing and network conditions. This system is also compared with some
exiting systems. Results shows that MAC-SF is a better system and can be used in any wireless network.
BIO-INSPIRED SEAMLESS VERTICAL HANDOVER ALGORITHM FOR VEHICULAR AD HOC NETWORKSijwmn
One of the most important factors to implement VANET is by considering the variety of wireless networks available around the city as well as the vehicles traffic scenarios. However, by providing a diverse range of wireless access technologies, it is necessary to provide continuous network connectivity as well as selecting the most suitable network technology and performance. Many researchers have worked on building algorithms for selecting the best network to improve the handover process. However, with high-speed vehicles mobility, the vertical handover process became the most challenging task in order to achieve realtime network selection. This paper proposes a bio-inspired network selection algorithm influenced by insect's behaviour which combines Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). The proposed algorithm is applied to process multi-criteria parameters to evaluate the best available network and then execute the handover process seamlessly. The results demonstrate the benefits of the proposed Multi-Criteria ABC-PSO method by reducing the handover decision delays by 25%. It gives the optimum performance in terms of network selections and reduces the handover latency by 14.5%. The proposed algorithm also reduces the number of unnecessary handovers by 48% for three different mobility scenarios based on traffic environments (highway, urban and traffic jam).
Resource optimization-based network selection model for heterogeneous wireles...IAESIJAI
The internet of things (IoT) environment prerequisite seamless connectivity for meeting real-time application requirements; thus, required efficient resource management techniques. Heterogeneous wireless networks (HWNs) have been emphasized for providing seamless connectivity with high quality of service (QoS) performance to provision IoT applications. However, the existing resource allocation scheme suffers from interference and fails to provide a quality experience for low-priority users. As a result, induce bandwidth wastage and increase handover failure. In addressing the research issues this paper presented the resource-optimized network selection (RONS) method for HWNs. The RONS method employs better load balancing to reduce handover failure and maximizes resource utilization through dynamic slot optimization. The RONS method assures tradeoffs between high performance to high priority users and quality of experience (QoE) for low priority users. The experiment outcome shows the RONS achieves very good performance in terms of throughput, packet loss, and handover failures in comparison with existing resource selection methods.
Multicast routing strategy for SDN-cluster based MANET IJECEIAES
The energy limitation and frequent movement of the mobile Ad hoc network (MANET) nodes (i.e., devices) make the routing process very difficult. The multicast routing problem is one of the NP-complete problems. Therefore, the need for a new power-aware approach to select an optimum multicast path with minimum power consumption that can enhance the performance and increase the lifetime of MANET has become urgent. Software defined network (SDN) is a new technique that can solve many problems of the traditional networks by dividing the architecture into data part and control part. This paper presents three power-aware multicast routing strategies for MANET. First one called a Reactive Multicast routing strategy for Cluster based MANET by using SDN (RMCMS), second one called Proactive Multicast routing strategy for Cluster based MANET by using SDN (PMCMS) and third one represents Modification of PMCMS called M-PMCMS. Moreover, it produces a new mathematical model to build a multicast tree with minimum power consumption and takes into account the remaining power in each node. All proposed multicast strategies operate based on this mathematical model and aim to maximize the MANET lifetime by exploiting the advantages of SDN and clustering concepts. They consider the multicast tree with minimum power consumption as an optimal one. The simulation results illustrated that RMCMS is better than PMCMS, M-PMCMS, and MAODV in terms of power consumption and network overhead while M-PMCMS is the best one in terms of dropped packets ratio (DPR) and average end to end (E2E) delay.
Novel Optimization to Reduce Power Drainage in Mobile Devices for Multicarrie...IJECEIAES
With increasing adoption of multicarrier-based communications e.g. 3G and 4G, the users are significantly benefited with impressive data rate but at the cost of battery life of their mobile devices. We reviewed the existing techniques to find an open research gap in this regard. This paper presents a novel framework where an optimization is carried out with the objective function to maintain higher level of equilibrium between maximized data delivery and minimized transmit power. An analytical model considering multiple radio antennae in the mobile device is presented with constraint formulations of data quality and threshold power factor. The model outcome is evaluated with respect to amount of power being conserved as performance factor. The study was found to offer maximum energy conservation and the framework also suits well with existing communication system of mobile networks.
Novel Optimization to Reduce Power Drainage in Mobile Devices for Multicarrie...IJECEIAES
With increasing adoption of multicarrier-based communications e.g. 3G and 4G, the users are significantly benefited with impressive data rate but at the cost of battery life of their mobile devices. We reviewed the existing techniques to find an open research gap in this regard. This paper presents a novel framework where an optimization is carried out with the objective function to maintain higher level of equilibrium between maximized data delivery and minimized transmit power. An analytical model considering multiple radio antennae in the mobile device is presented with constraint formulations of data quality and threshold power factor. The model outcome is evaluated with respect to amount of power being conserved as performance factor. The study was found to offer maximum energy conservation and the framework also suits well with existing communication system of mobile networks.
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2. 1898 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 14, NO. 4, APRIL 2015
a thorough examination of average message delivery time for
small-world networks in the continuum limit. Via random net-
work analysis, [16] studies the properties of giant component
in wireless multi-hop networks, while [17] provides a heteroge-
neous structure for such networks and conducts the throughput
and delay analysis. Furthermore, the concepts of rumor and
gossip routing algorithms are also widely employed in sensor
networks [18] and ad hoc networks [19]. As for disconnected
delay-tolerant MANETs and generalized complex networks,
[20] and [21] respectively provide the social network analysis
for information flow and epidemic information dissemination.
In this paper, inspired by small-world phenomenon, we con-
nect data aggregators (DAs) to machine swarm and propose a
promising two-tier heterogeneous architecture with DA’s small-
world network for statistical traffic control in large M2M
communication networks. To address efficient dissemination
control for routing and QoS such as surveillance applications,
we first analytically supply the condition to establish connected
M2M networks and explore some essential geometric proper-
ties (i.e., degree distribution, network diameter, and average
distance) for the networks. Analytic bounds of average distance
characterize the average number of hops that machines’ packets
need to traverse over the swarm, thus dominating the QoS
guarantee capability for reliable communications. Furthermore,
through G/G/1 (i.e., both inter-arrival time and service time dis-
tributions of a traffic queue are arbitrary distributions) queuing
network model for traffic modeling, the practical data trans-
portation takes place in connected M2M networks. Both the
average end-to-end delay and maximum achievable throughput
per machine from information dissemination in machine swarm
multi-hop networking are examined. However, to control QoS
over such networks still serves great difficulty due to the
large network size. Aiming at statistical performance in large
M2M networks, we propose a statistical control mechanism
for the networks by establishing the heterogeneous network
architecture and exploiting statistical QoS guarantee for end-to-
end transmissions without the need of feedback control at each
link. By forming DA’s network with small-world property and
linking machines to DAs, this novel heterogeneous architecture
significantly improves the performance of end-to-end traffic
for tolerable delay and makes dependable communications
possible from guaranteing traffic QoS, with extremely simple
network operation for each machine.
The contributions of this paper are summarized as follows.
1) To understand geometric properties of large M2M net-
works and thus benchmark performance, we first analyti-
cally examine network connectivity, degree, distribution,
network diameter, and average distance under Poisson
Point Process (PPP) machine distribution.
2) Introducing queuing network theory into such network
analysis for practical data transportation, the average de-
layandachievablethroughput for messagedeliveryincon-
nected M2M networks are analytically obtained as well as
the QoS guaranteed throughput in real applications.
3) Standing on hereby established analysis, statistical dis-
semination control is proposed that incorporates DA’s
network with machine swarm (or sensor swarm) for fa-
vorable heterogeneous network architecture.
4) Due to infeasible end-to-end information exchange and
subsequent precise control, we exploit statistical QoS
guarantees over two-tier heterogeneous network archi-
tecture to exhibit remarkable enhancement of system
performance, and to facilitate the merits of small-world
phenomenon into engineering reality.
Simulation results show that our proposed control yields the
significant throughput gain for delay guarantee performance.
Such system performance asymptotically relieves Gupta and
Kumar’s dilemma [23] in scaling perspective for general wire-
less networks. Note that this paper is based on our prelimi-
nary research in [24] and [25]. However, different from [25]
which only considers the average number of hops, this paper
deals with the actual packet delay including transmission and
queuing latency, which necessitates a new queuing network
analysis. Furthermore, while the work in [24] provides the
upper bound performance of end-to-end delay dedicated for the
proposed routing algorithm, this paper studies the asymptotic
performance of several statistical QoS requirements, such as
end-to-end delay and maximum throughput as well as the
throughput under guaranteed delay, for a general forwarding
scheme in M2M network. What is more important, our previous
work focuses on obtaining the traffic performance under a
specific scenario setting, which can simplify the analysis, while
failing to maintain the same level of transmission qualities
when the scenario changes, e.g., the network topology or traffic
pattern becomes different. In this paper, we solve this chal-
lenge through statistical dissemination control by leveraging
the heterogeneous network architecture. In particular, the upper
layer of DAs’ network enables shortcut transmissions to reduce
the excess end-to-end delay from the long route transmissions
in the lower layer of machine swarm. A comprehensive per-
formance analysis upon such a heterogeneous architecture is
also included in this paper. With these accomplishments, we
provide an original and significant paradigm to facilitate M2M
communications, practically realizing information dissemina-
tion control to meet the need of time sensitive applications in
next-generation wireless standards.
The rest of this paper is organized as follows. Section II
presents related work and system model. Section III and
Section IV provide M2M network topology analysis and queu-
ing network model for large M2M networks, respectively.
Statistical dissemination control with heterogeneous architec-
ture is proposed in Section V with performance evaluation in
Section VI. Section VII gives the conclusion and ends the paper.
II. BACKGROUND AND SYSTEM MODEL
M2M communication network consists of tremendous self-
organized machines/sensors and enables autonomous connec-
tions among different applications for ubiquitous communica-
tions upon such large swarm system. To facilitate this scenario
into practice, providing the connectivity accompanied with
reliable transportation is a must for such large network. In the
following, we highlight the relevant research and introduce the
M2M network model using geometric random graph (GRG)
as its topology and local clustering property are suitable for
benchmarking large wireless ad hoc sensor networks.
For More Details Contact G.Venkat Rao
PVR TECHNOLOGIES 8143271457
3. LIN et al.: STATISTICAL DISSEMINATION CONTROL IN LARGE M2M COMMUNICATION NETWORKS 1899
A. Background
Scanning the literature, [26]–[35] explore QoS issues for
traffic control over next-generation wireless systems (i.e., 3GPP
LTE/LTE-A based cellular systems). [26] offers a reallocation-
based assignment that maximizes the spectral efficiency with
QoS guarantees in multi-service wireless systems, achieving
a good tradeoff between performance and computational com-
plexity. To deal with scheduling policies over multi-hop wire-
less networks, [36] provides an analytical traffic delay analysis
and [37] proposes a low-complexity congestion control with
respect to per-flow delay. Aiming to minimize the energy
consumption of overall heterogeneous network and preserving
the QoS for users, [27] examines the optimal control for wake
up mechanisms of femtocells. Studying the delay-throughput
tradeoff, [28] utilizes an efficient power allocation scheme with
minimized delay and high throughput for real-time services in
distributed wireless networks. To reduce latencies and increase
fairness in terms of transmitted frames, [38] further provides
a distributed and online fair resource management in video
surveillance sensor networks. With regard to power efficiency
in vehicle-to-roadside infrastructure communication networks,
[29] proposes a joint power and sub-carrier assignment policy
under delay-aware QoS requirements. Reference [30] studies
a tight integration of device-to-device communications into
an LTE-A network, desirably exploiting spectrum of the ex-
isting radio networks. Reference [31] provides a systematic
framework for the power and energy optimal system design in
cellular-based M2M communications. Reference [32] proposes
an efficient overlay to provide GSM connectivity within an
LTE carrier for low data rate M2M customers. Aiming at a
great number of applications in M2M communications, [33]
proposes a massive access management on the air interface
and [34] designs the data collectors to efficiently serve many
uplink transmissions based on a random access scheme. To
alleviate interference via cognitive radio technology, [39] pur-
suits capacity maximization under the SINR model in multi-
hop cognitive radio networks. Moreover, [35] applies cognitive
M2M communications in the smart grid and integrates reliabil-
ity and timeliness in the QoS study. However, above excellent
efforts do not thoroughly explore network characterizations and
thus need end-to-end information regarding network topology,
while end-to-end information requires non-scalable complexity
of overhead. In this paper, instead of acquiring such information
upon large networks with catastrophic complexity, we propose
a totally different philosophy to proceed statistical dissemina-
tion control based on network topology analysis and traffic
queuing model in large M2M communication networks.
B. Network Model
Suggested by [1], [2], [4], a cloud based M2M networks
consists of two-tier network architectures. As in Fig. 1(a), the
first tier contains a swarm of machines, which equip short-
range wireless networking capability. The second tier involves
wireless infrastructure and service cloud. The cloud’s gateways
are smart devices that collect and process data from machines
and manage their operation. The service cloud further pro-
vides the accesses to M2M service, linking physical world
Fig. 1. Network topology of heterogeneous network architecture for statistical
control in cloud M2M communication networks. (a) Cloud based scenario for
M2M communication networks. (b) Heterogeneous network architecture for
proposed statistical control.
to cyber world. Rather than guided by central controller(s)
as in conventional cellular networks, nodes (i.e., machines)
in M2M networks distributedly communicate with each other.
There is no identity difference among nodes. Furthermore,
due to wireless propagation and outage, each node can only
communicate with other nodes within certain distance. It is
assumed that there are n nodes in a M2M network, while each
pair of nodes within distance r can communicate. Meanwhile,
instead of exploiting classical Erdös-Rényi random graph [40],
the geometric topology and local clustering property of GRG
make itself preferable as a mathematical graphical model for
wireless ad hoc networks [41]. The GRG model is based on
a homogeneous PPP that randomly distributes nodes on the
unit area to characterize the spatial distribution of nodes. In
particular, GRG model with parameter n and r (i.e., GRG(n,r))
defines a graph with n nodes following homogeneous PPP and
edges that are established when pairs of nodes are closer than
radius r. With this understanding, GRG model is well suited
for modeling M2M networks, especially for the first tier of
machine swarm. Thus we adopt GRG(n,r) for M2M network
model, where nodes follow PPP and randomly distribute on the
[0, 1] × [0, 1] flat square.
C. Connectivity of M2M Networks and Information
Dissemination Control
To transmit data packets across large machine swarm, con-
structing a connected M2M network is the necessity to ensure
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every packet could be sent to its corresponding destination
from the source. Note that, in the rest of paper, we claim that
a network has certain properties with high probability (i.e.,
almost surely) as n → ∞ and r(n) → 0. The connectivity of
M2M network will be provided via network topology analysis
later in Section III; however, we summarize the idea as follows.
While each machine equips with short-range communication
capability, multi-hop networking is necessary for end-to-end
data transportation [4]. That is, for a single source-destination
pair, there exist a source machine, a destination machine, and
several relay machines that forward traffic from the source
to the destination. Without loss of generality, it is assumed
that sequences of packets follow the general arrival process
and the general service time, and each transmission link is
modeled as a G/G/1/∞/FCFS (or G/G/1 for the conventional
abbreviation) queue [42]. In particular, such a queue represents
a queuing system with a single server, infinite buffer size,
and the scheduling discipline of first-come-first-serve (FCFS),
where packet interarrival times have a general (meaning arbi-
trary) distribution and service times have a (different) general
distribution. By connecting each link of G/G/1 queue, the en-
tire G/G/1 queuing network is established for M2M network.
Thus, upon this queuing network model, the analysis of network
connectivity and information dissemination (i.e., end-to-end de-
lay and maximum system throughput) are ready to be exploited.
In addition, as some real-time applications require bounded
packet delay for end-to-end transmissions, the statistical QoS
guarantee is considered. In particular, given the required sta-
tistical delay bounds, the QoS guaranteed throughput that sat-
isfies the delay requirement is derived via Markov inequality.
Meanwhile, considering source’s excessive traffic loads for
prodigious incoming data or poor forwarding capability from
long multi-hop transportation, we further develop an effective
statistical dissemination control with heterogeneous network
architecture. This architecture significantly improves the end-
to-end traffic performance under tremendous amounts of ma-
chines in large M2M network. The idea is as follows. As shown
in Fig. 1(b), we aim to establish an “information highway” of
ultra-fast forwarding potential for machine swarm by adding
few DAs to form a small-world network [14], [22], [43] and
connecting them with infrastructure networks. Once source’s
packets access to this “highway” after link transmissions to DA,
these packets only traverse few steps in DA’s network to arrive
at the area near the destination from small-world phenomenon.
The comprehensive examination for such promising architec-
ture with corresponding transmission improvement is presented
later in Section V-B. Standing on top of these accomplishments,
the proposed statistical control successfully facilitates reliable
information disseminations over large M2M communications
of tremendous number of machines.
III. M2M NETWORK TOPOLOGY ANALYSIS
To achieve information dissemination among all nodes in an
M2M network, we first investigate the network connectivity by
social network analysis [7], [14] for a connected M2M network.
In particular, we study some useful geometric properties that
facilitate our analysis of information flow later in Section IV.
A. Connected M2M Networks
As mentioned in Sections II-B and II-C, given a GRG(n,r)
model for M2M network, we obtain the following lemmas for
network connectivity.
Lemma 1: For n nodes following PPP on a unit area (i.e., [0,
1] × [0, 1] flat square), the partition of flat square into smaller
square grids with area logn/n is applied. Then, there is at least
one node in each square grid almost surely, when n → ∞.
Proof: Firstly, under mentioned partition, there are
n/logn square grids in flat square. With PPP, the number of
nodes in each square grid follows a Poisson random variable
with mean logn and the probability that a specific square
grid has no node is exp(−logn). Since the probability of an
intersection of events is no larger than any individual among
them, the probability that there is a square with no node is
nexp(−logn)/logn, which approaches to zero for n → ∞. It is
noted that Lemma 1 holds for any partition that gives square
grids with area larger than logn/n, especially the one gives
square grids with area (r/
√
5)2 and r ≥ 5logn/n. Thus, we
have Corollary 1 for the connectivity of M2M networks.
Corollary 1: For a M2M network with GRG(n,r), if r ≥
5logn/n, the network in connected almost surely.
Proof: Followed from Lemma 1, each node in a square
grid is able to connect to its four neighborhood square grids
almost surely and it becomes the lattice structure. Furthermore,
since lattice structures are connected, the given GRG model and
therefore the M2M network is connected almost surely.
B. Geometric Properties
In the following, aiming at connected M2M networks, we
explore geometric properties of networks that are essential for
determining the dissemination performance in large networks.
1) Degree Distribution: We model a M2M network (i.e.,
GRG(n,r)) by assuming that all nodes follow PPP and edges
are established when pairs of nodes are closer than certain
distance r, its degree distribution can be obtained from ER
model. Considering a specific vertex in GRG(n,r), an edge
connecting to its neighbor is present with the probability πr2
for total n−1 possible neighbors. The degree distribution then
follows a binomial distribution. Since we are interested in large
M2M networks (i.e., n → ∞), the degree distribution thus can
be expressed by a Poisson distribution as
pk = Pr{K = k} =
(nπr2)
k
exp{−nπr2}
k!
. (1)
The degree distribution of networks characterizes the number
of nodes that potentially connects to other node in networks,
representing the incoming traffic load that contributes to a sin-
gle link for multi-hop communications in M2M networks. This
is a crucial factor for reliable information dissemination, since
traffic overload might bring deadlocks in link transmissions and
deteriorate the QoS of end-to-end traffic.
2) Network Diameter: The network diameter for connected
M2M networks can be directly obtained from Lemma 1 as
shown in Theorem 1.
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Theorem 1: While r ≥ 5logn/n for a connected M2M
network with GRG(n,r) model, the network diameter R(n) in
terms of hop-count has the upper bound 2
√
5/r and the lower
bound
√
2/r almost surely.
Proof: Please see Appendix A.
3) Average Distance: To obtain the average distance for
pairs of nodes in M2M networks, we examine the Euclidean
distance over all pairs of vertexes in GRG(n,r) model.
Lemma 2: For Euclidian distance d, let dx and dy denote
for the projective length of d in unit area for x and y axes,
respectively. Then, in connected GRG(n,r), d in terms of hop-
count has the upper bound
√
5(dx +dy)/r and the lower bound
d/r, given r ≥ 5logn/n.
Proof: Please see Appendix B.
While the bounds are
√
5dx/r +
√
5dy/r and d/r , we
assume
√
5(dx + dy)/r and d/r to be integers in Lemma 2.
However, when r → 0, the difference can be neglected. Before
exploring the average distance of M2M networks, we first have
the following proposition for uniform random variable that
arises from machines’ random positions.
Proposition 1: Let U(S) denote the uniform distribution on
S, where S is a connected subinterval of R k, and | · | and
· denote absolute value for one dimension and Euclidean
distance. For a unit square, if two random variables X, Y ∼
U([0,1]), E[|X − Y|] = 1/3. And if these X, Y ∼ U([0,1] ×
[0,1]), E[ X −Y ] ≥
√
2/3.
The equality of Proposition 1 comes from the derivation
in calculus and the inequality comes from
√
a2 +b2 ≥ (|a| +
|b|)/
√
2. Finally, we conclude with Theorem 2.
Theorem 2: While r ≥ 5logn/n for a connected M2M
network with GRG(n,r) model, the average distance d(n) in
terms of hop-count is bounded almost surely as
√
2
3r
< d(n) <
2
√
5
3r
. (2)
Proof: Please see Appendix C.
For connected M2M networks, especially for r ∼ logn/n,
Theorems 1 and 2 suggest that R(n) and d(n) follow the order
of 1/r. The asymptotic notations [44] of R(n) and d(n) are both
Θ(1/r) = Θ( n/logn). Furthermore, while d(n) provides av-
erage hop number for traversed packets, it highly relates to
average delay for end-to-end transmissions as we will see in the
following section and thus plays a critical role for information
dissemination in M2M networks.
IV. QUEUING NETWORK MODEL
Without the need of end-to-end information to escape catas-
trophic complexity, information dissemination becomes the
only way in machine swarm. As suggested by Section II-C, we
exploit an open G/G/1 queuing network model for delay and
throughput analysis of M2M networks. Furthermore, the diffu-
sion approximation is used to analyze the queuing network. Our
analytical methodology to deal with wireless networks have
general inter-arrival and service time distributions by providing
closed form expressions of end-to-end delay and maximum
achievable throughput per node. In the following, to fully
understand practical data transportation, we present the traffic
model and an equivalent queuing network model in connected
M2M networks.
A. The Traffic Model
For an GRG(n,r) of connected M2M network with r ≥
5logn/n, there are n nodes uniformly distributed over an
unit area, numbered from 1 to n, and each capable of trans-
mitting at W bits per second. The set of neighbors of node
i is denoted by N(i) and each node can be a source or a
destination of packets. The external arrival of jobs (i.e., new
packets arrive in the network) is a renewal process with rate λe.
The squared coefficient of variance (SCV) of inter-arrival time
of new packets equals to c2
A. We assume that packets of size L
bits are generated by each node according to an independent
identical distribution (i.i.d.) Poisson process with rate λ. The
mean and SCV of the service time at node i are denoted by E[Si]
and c2
Bi = (E[S2
i ] − E[Si]2)/E[Si]2, respectively. As a packet is
generated by a node, it transverses over the network by multi-
hop relaying until it reaches the destination. The probability
that a packet received by its destination is p(n) and is referred
as “absorption probability”. Alternatively, the probability that a
packet received by a node is forwarded to a neighboring node
is (1 − p(n)). If a packet is not absorbed by a node, then all
the neighboring nodes are equally likely to be the next hop
of the packet. p(n) characterizes the degree of locality of the
traffic as: The traffic is highly localized for large p(n), while
small p(n) implies unlocalized traffic. Therefore, it is easy
to quantify the dependence of delay and capacity on average
distance d(n) of the network as presented in Section IV-B. In
addition, each node is assumed to have infinite buffers and thus
no packets are dropped in the network. The packets are served
by nodes in FCFS manner. The queuing network model for
connected M2M network is shown in Fig. 2(a). The stations
of queuing network represent nodes of M2M network and the
forwarding probability pi j equals to the probability that a packet
is transmitted from the queue at node i to the queue at node j.
Moreover, the queue at a node as a station in queuing network
is provided in Fig. 2(b).
B. End-to-End Delay Analysis
The end-to-end delay in a connected M2M network is defined
as the sum of the queuing and transmission delays at all
intermediate relaying nodes. To evaluate the delay, we first
prove Lemma 3 that relates absorption probability with average
distance of the network. After that, by deriving expressions for
some parameters of the queuing network, we analytically obtain
the end-to-end delay.
Lemma 3: In a M2M network, the number of hops traversed
by a packet d(n) equals to 1/p(n).
Proof: Please see Appendix D.
d(n) in Lemma 3 is obtained from (2) and provides p(n) for
connected M2M networks.
Lemma 4: Given the number of neighbors of node i as Ki,
the probability that a packet is forwarded from node i’s queue
to node j’s queue, denoted by pi j, is (1− p(n))/Ki if j ∈ N(i);
otherwise, it is zero.
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Fig. 2. Queuing network model for a connected M2M network. (a) A con-
nected M2M network as queuing network. (b) A machine in a connected M2M
network represented as a station of the queuing network.
While pi j(n) equals to the product of Pr{i transmits the
packet to j | packet not absorbed} and Pr{packet is not
absorbed by i}, Pr{i transmits the packet to j | packet not
absorbed} equals to 1/Ki if j ∈ N(i); otherwise, it equals to
0. Pr{packet is not absorbed by i} = 1− p(n).
To illustrate how the number of neighbors of a node in a
M2M network (i.e., Ki for node i) affects the visit ratio of that
node (i.e., the average number of times a packet will visit such
node as the packet enters the network) in Lemma 5, we start
from the following proposition.
Proposition 2: For node i, let Ki denotes the number of
its neighbors and K
(2)
i denotes the number of its two-hop
neighbors. Given a connected M2M network with GRG(n,r)
model (i.e., r ≥ 5logn/n), we have 1/δ = E[∑
K
(2)
i
j=1 1/Kj] >
1−exp{−nπr2}.
From Section III-B1, Ki is a Poisson random variable fol-
lowing the distribution in (1). Via branching process, K
(2)
i is
the second generation of node i, whose distribution is qk−1 =
Pr{K(2) = k−1} = (kpk)/χ with χ = ∑k kpk for k ≥ 1. K
(2)
i be-
comes a Poisson random variable with mean nπr2 and we have
1
δ = E E ∑k
j=1
1
Kj
|K
(2)
i = k = nπr2E 1
K1
and E 1
K1
=
∑∞
k=0 exp{−nπr2}(nπr2)
k
k!×k > ∑∞
k=0 exp{−nπr2} (nπr2)
k
k!×(k+1) = 1
nπr2 −
exp{−nπr2}
nπr2 .
Lemma 5: With parameter δ obtained for given connected
M2M network by Proposition 2, the visit ratio of a node i,
denoted by ei, equals to ei = δ/{n[δ−(1− p(n))]}.
Proof: Please see Appendix E.
Lemma 6: The effective packet arrival rate at a node i,
denoted by λi, equals to λδ/(δ−1+ p(n)).
Since the packet generation process at each node is an i.i.d.
Poisson process with rate λ, new packets arrive in the network
at rate λe = nλ. Furthermore, there are two sources of packet
arrivals at a node: The packets that are generated at the node
and the packets that are forwarded to the node by other nodes.
The utilization factor of node i, denoted by ρi, is given by
ρi = λiE[Si]. Furthermore, regarding the spatial concurrency
constraints in link transmissions, nodes close to a receiver must
be idle to avoid collisions which results in the loss of packets.
With nπr2 nodes surrounding node i, the event for successful
link transmissions to i follows a Bernoulli process with suc-
cess probability 1/(nπr2). Thus, we have E[Si] = nπr2L/W.
The SCV of inter-arrival time at node i, denoted by c2
Ai, is
approximated using c2
Ai = 1 + ∑n
j=0(c2
Bj − 1)(pi j(n))2eje−1
i =
1+(c2
Bi −1)(1− p(n))2ψ and ψ > [1−exp{−nπr2}]/(nπr2)−
exp{−nπr2} where c2
B0 = c2
A and ψ is obtained from the same
manner as for ei. According to the diffusion approximation,
the approximate expression for the probability that the number
of packets at node i equals to t, denoted by πi(t), is 1 − ρi
if t = 0; otherwise, it is ρi(1 − ˆρi) ˆρi
t−1
as t > 0. where ˆρi =
exp{−2(1−ρi)/(c2
Aiρi +c2
Bi)}. The mean number of packets at
node i, denoted by Li, is therefore Li = ρi/(1− ˆρi). With above
results, we present the end-to-end delay in the following.
Theorem 3: For a connected M2M network with parameter
δ described in Proposition 2, the average end-to-end delay,
denoted by D(n), is
D(n) =
ρ[(δ−1)d(n)+1]
λδ(1− ˆρ)
. (3)
Proof: Please see Appendix F.
C. Maximum Achievable Throughput
For a connected M2M network, we derive the expression for
maximum achievable throughput λmax in the following. λmax is
the maximum value of the packet arrival rate λ at the nodes for
which the average end-to-end delay remains finite.
Theorem 4: For a connected M2M network with parameter
δ described in Proposition 2, the maximum achievable through-
put is
λmax =
W [(δ−1)d(n)+1]
δd(n)nπr2L
. (4)
Also from (4), λmax = Θ(1/[d(n)nr2]).
To have finite delay, λiE[Si] = λinπr2L/W < 1. Furthermore,
as n → ∞, we have δ → 1 from Proposition 2.
The result of Theorem 4 coincides with Gupta and Kumar’s
classic research [23] for the asymptotic case where n → ∞. It
is obvious that λmax increases with decreasing in d(n). From
Theorem 2 in Section III-B3, for a connected M2M network,
r ≥ 5logn/n and the number of hops between arbitrary
source and destination pair would be Θ( n/logn). Thus, we
get λmax = Θ(1/[d(n)nr2]) = Θ(1/
√
nlogn), which confirms
with the asymptotic capacity of multi-hop wireless ad hoc
networks as from [23].
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V. STATISTICAL DISSEMINATION CONTROL
As real applications require dependable networking per-
formance across the swarm, QoS along with large network
diameter creates a new challenge. Aiming at statistical per-
formance in large M2M networks [24], [25], we propose a
statistical control mechanism for the networks by establishing
the heterogeneous network architecture. By constructing the
“small-world shortcut” scheme [14] among DAs, the effective
traffic control is obtained with great system throughput under
desired QoS constraint (i.e., delay bound). In particular, any
machine can identify a shortcut through a corresponding DA,
such that end-to-end delay can be significantly reduced under
such heterogeneous network architecture.
A. Statistical QoS Guarantee
The real-time services generally care the end-to-end delay
and demand bounded delays. Rather than providing determin-
istic QoS guarantees (i.e., the probability of delay requirement
violation is zero), a more practical and reasonable solution is
to provide statistical guarantees [45] (i.e., the probability that
the packet violates its delay constraint is bounded) for QoS as
Pr{Delay ≥ Dmax} ≤ τ, where Dmax is the delay requirement
and τ is used to characterize the degree of statistical QoS
guarantee. By different inequalities or bounds, Pr{Delay ≥
Dmax} ≤ f(Delay,Dmax). Formulating f function by packet
delay analysis, we thus get the end-to-end throughput with
statistical delay guarantees as the maximum available load from
the source that satisfies the constraint: f(Delay,Dmax) ≤ τ.
1) QoS Guaranteed Throughput in M2M Networks: For
reliable communications in large connected M2M networks,
we obtain the end-to-end throughput with QoS guarantee via
Markov inequality as follows.
Proposition 3: For a connected M2M network with
parameter δ described in Proposition 2, the QoS guaranteed
throughput is obtained as λQ
max =
[(δ−1)d(n)+1](c2
Bi lnT+2)
δd(n)E[Si](2−c2
Ai lnT)
=
c2
Bi lnT+2
2−c2
Ai lnT
λmax, where T = 1 − {d(n)E[Si]}/(τDmax) =
1 − [d(n)nπr2L]/(WτDmax) and λQ
max = Θ(1/[d(n)nr2]).
Via Markov inequality, the statistical delay guarantee is
provided as Pr{Delay ≥ Dmax} ≤ {ρ[(δ − 1)d(n) + 1]}/
[λδ(1 − ˆρ)Dmax] and λQ
max is the maximum arrival rate holding
such inequality. Proposition 3 shows that even providing
statistical QoS guarantee in end user’s traffic, we still can
maintain the system throughput that closes to the maximum
achievable throughput for the asymptotic case (i.e., λQ
max
and λmax follow the same order as n → ∞). Note that there
exists a tradeoff between the delay requirement and the
attainable throughput (i.e., both maximum achievable and QoS
guaranteed throughput). In particular, from the formulation of
λQ
max and λmax, they both include the delay factor (i.e., d(n))
in the denominator and thus increase (decrease) when the
corresponding delay decreases (increases).
B. Small-World Shortcut
To leverage small-world feature into machine swarm, we first
create a promising two-tier heterogeneous network architecture
by adding some DAs to help machines’ transmissions. After
that, we confirm shorter average distance for pairs of nodes
via shortcut in network. We finally present the end-to-end
delay reduction with improved system throughput and therefore
confirm our proper control for information dissemination in
large M2M communication networks.
1) Heterogeneous Network Architecture: The heteroge-
neous network architecture for connected M2M network, as
shown in Fig. 1(b), consists of two layers. The lower layer is
the machine swarm modeled by a GRG model with n nodes
and radius r, while the upper layer is a 2D-lattice network
with m2 DAs. The size of DA’s lattice network is much
smaller as compared machine’s wireless ad hoc network (i.e.,
m n). Such DAs partition the bottom [0, 1] × [0, 1] unit
area into equal m × m grids with average n/m2 nodes per
grid and each DA can communicate with z random selected
nodes in the correspondent grid. To prevent traffic jam and
thus deadlock in DA’s network, we assume each DA’s service
rate, denoted by 1/E[SD
i ], is the product of machine’s service
rate and the number of serving machines. That is, E[SD
i ] =
(m2/n)×L/W. In the following, through network property and
system performance, we exhibit that with few DAs (i.e., m2 and
m n) installed in machine swarm, smaller number of hops
and thus significant performance improvement are feasible to
robust and reliable information dissemination in large M2M
networks.
Note that we consider the uniformly deployment of homo-
geneous DAs here, as machines are uniformly distributed in
a given area from the GRG model of M2M network. When
the machine’s deployment becomes uneven due to issues like
mobility, our model can be easily adaptive to the heterogeneous
scenario by adjusting two related parameters (i.e., the total
number of DAs m2 and the amount of machines z that each
DA can simultaneously communicate with). For example, for
the dense machine area, we can increase the number of DAs
(i.e., with larger m) or/and enhances the serving capability of
DA (i.e., with larger z). Similar concern can be applied to
sparse machine area. However, to avoid an unclear delivery of
our main contribution, we focus on the homogeneous DAs for
heterogeneous network architecture in the rest of paper.
2) Shorter Distance via Shortcut: To obtain the average hop
number for node pairs under heterogeneous architecture, we
first examine the average hops from nodes (machines) to DAs.
Lemma 7: In connected GRG(n,r) with m2 DAs forming
upper lattice network and each DA serves z nodes, the heteroge-
neous architecture has the average distance from nodes to DAs
dL(n) in terms of hop-count almost surely when n, m → ∞ and
mr → 0 as π/(120zmr) < dL(n) < 2
√
5/(3zmr).
Proof: Please see Appendix G.
With the aid of Lemma 7, we provide the average distance
via shortcut (i.e., DA’s lattice network as an illustration) for
heterogeneous architecture in the following.
Theorem 5: While r ≥ 5logn/n for a connected M2M net-
work with GRG(n,r) model and m2 DAs form lattice network
as each DA serves z nodes concurrently, the heterogeneous
architecture has the average distance dS(n) in terms of hop-
count almost surely when n, m → ∞ and mr → 0 as π
60zmr + 2m
3 <
dS(n) < 4
√
5
3zmr + 2m
3 .
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TABLE I
SIMULATION PARAMETERS AND VALUE SETTINGS FOR PERFORMANCE EVALUATIONS
Proof: Please see Appendix H.
With Theorem 5 and m = Θ(1/
√
r), the shortcut is ex-
hibited from the average distance as dS(n) = Θ(1/
√
r) =
Θ( 4
n/logn), which is much smaller than d(n) = Θ(1/r) for
plain machine swarm.
Delay Reduction for Improved Throughput: In this section,
aiming at heterogeneous architecture of large M2M networks,
we derive average end-to-end delay, maximum achievable
throughput, and the achievable throughput under QoS con-
straint to bring the merits of heterogeneous framework. Since
DA’s lattice network partitions the machine swarm into m × m
grids, there are approximate n/m2 nodes in each grid. Similar to
Theorem 5, DS(n) is obtained from the aggregation of the delay
from a node to DA, denoted by DL(n), and the delay in upper
DA’s lattice, denoted by DU (n).
Theorem 6: While r ≥ 5logn/n for a connected M2M
network with GRG(n,r) model and m2 DAs form lattice
network as each DA serves z nodes concurrently, the average
end-to-end delay DS(n) for the heterogeneous architecture
is DS(n) = 2DL(n) + DU (n) = 2ρL[(δ−1)dL(n)+1]
λδ(1− ˆρL)
+ ρU m2
nλ(1− ˆρU )
,
where ρL and ˆρL are obtained as usual by p(n) = 1/dL(n),
ρU = {2mρL[(δ − 1)dL(n) + 1]}/(3nπr2δdL(n)), and ˆρU =
exp −2(1−ρU )/ 1+m(c2
Bi−1) 1− 3
2m
2
ρU +c2
Bi .
Proof: Please see Appendix I.
When n → ∞, DL(n) dominates end-to-end delay as m =
Θ(1/
√
r). Furthermore, since the utilization factor proportions
to the average distance (e.g., ρL ∝ dL(n)), DS(n) = Θ(1/
√
r) =
Θ( 4
n/logn) and D(n) = Θ(1/r) = Θ( n/logn). Therefore,
through the assistance from shortcut of DA’s network, the
heterogeneous architecture for M2M networks enjoys signif-
icant delay reduction. In the following, we further provide
maximum achievable and QoS guaranteed throughput for this
architecture.
Theorem 7: While r ≥ 5logn/n for a connected M2M
network with GRG(n,r) model and m2 DAs form lattice net-
work as each DA serves z nodes concurrently, the maximum
achievable throughput λS
max for the heterogeneous architec-
ture is
λS
max = min
W (δ−1)dL(n)+1
δdL(n)nπr2L
,
3W
2mL
. (5)
Also from (5), λS
max = Θ(1/[dL(n)nr2]). Furthermore, since
ˆρL and ˆρU are functions of λ (i.e., arrival rate per machine),
the QoS guaranteed throughput, denoted by λSQ
max, satisfies νL/
(1− ˆρL)+νU /(1− ˆρU )=τDmax/(2E[Si]) where νL = dL(n) and
νU = m3/3n2πr2 and λSQ
max = Θ(1/[dL(n)nr2]), too.
Proof: Please see Appendix J.
Theorem 7 shows that λS
max increases with decreasing dL(n),
and DL(n) dominates DS(n) for the asymptotic case (i.e.,
n → ∞). Both λS
max and λSQ
max follow Θ(1/ 4
n(logn)3). Con-
sequently, we successfully provide a heterogeneous archi-
tecture that embraces significant throughput gain, as bench-
mark to Gupta and Kumar’s results [23], for large M2M
communications.
VI. PERFORMANCE EVALUATION
We compare the performance of the proposed heterogeneous
network architecture with plain machine swarm. Simulation
results confirm that heterogeneous architecture achieves re-
markable delay reduction as well as high throughput gain with
only few DAs installed, favored by practical implementation
in large M2M networks. All simulation parameters and value
settings are listed in Table I. In particular, to ensure every
packet could be sent to its corresponding destination from
the source,a connected M2M network is first established via
the proposed analysis (i.e., selecting the appropriate machine
communication range r with respect to the total machine num-
ber n). When a source machine generates a packet, it routes
the packet to a specific destination, uniformly selected among
other machines. Moreover, for plain machine swarm, source
simply hops forward based on the sensing and relaying; for
heterogeneous architecture, it employs dissemination without
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selecting a particular DA. In the following, we first evaluate
average distance to DAs and end-to-end distance for plain
machine swarm and heterogeneous architecture. Next, end-to-
end packet delay, maximum system throughput, and through-
put under guaranteed delay are thoroughly examined for such
different architecture and compared with Gupta and Kumar’s
milestone results [23]. Finally, the simulation validation in the
Metropolis is established to facilitate our design into an even
more practical stage.
A. Average Distance for Plain Machine Swarm and
Heterogeneous Architecture
To examine the improvement of average distance under
heterogeneous architecture, we first study the average distance
from machines to DAs. Regarding 2000 machines in swarm and
averaging over 100 samples, Fig. 3(a) shows that such distance
decreases with respect to increased amounts of DAs m and their
connection links to nodes z. m and z jointly decide the required
number of hops for machines’ data to leave for DAs’ small-
world shortcut, which equivalently provides an ultra-fast in-
formation “highway” among machines. Based on these results,
Fig. 3(b) and (c) show the asymptotic behaviors of average end-
to-end distance for information dissemination within various
settings of heterogeneous architecture as compared within plain
machine swarm. As z = 2, Fig. 3(b) shows that such distance
reductions become conspicuous with respect to increasing size
of machine swarm in scaling perspective. Furthermore, estab-
lishing DAs’ network with m = 3, Fig. 3(c) demonstrates such
distance improvement regarding DAs’ connection links. Both
figures demonstrate consistency with average distance scales
with the order of Θ( n/logn) for plain machine swarm and
with the order of Θ( 4
n/logn) for heterogeneous architecture
as suggested in Theorem 2 and Theorem 5. As a summary, all
these results confirm asymptotically greater performance for
information dissemination (i.e., less end-to-end delay and more
system throughput) in our proposed network architecture over
plain machine swarm.
B. End-to-End Delay Reduction via Small-World Shortcut
Fig. 4 provides the asymptotic behaviors of average end-to-
end delay for M2M networks under plain machine swarm and
proposed heterogeneous architecture. While Fig. 4(a) shows the
delay reduction via small-world shortcut of proposed schemes
regarding z = 3 (i.e., each DA links to three machines)and
various m (i.e., there are m2 DAs in heterogeneous architec-
ture), Fig. 4(b) shows the similar reduction regarding m = 3
and various z. Such improvement on delay comes from the
innovative design of DAs’ structure to proceed the small-world
phenomenon (i.e., the upper lattice network). Moreover, above
results also serve as the benchmark for the theoretical lower
bound of achievable end-to-end delay and indicate the practi-
cability of heterogeneous architecture. To sum up, by adding
few DAs and tailoring their network structure, heterogeneous
architecture brings much less delay and thus facilitates reliable
disseminations in large next-generation networks such as M2M
networks.
Fig. 3. Average distance in scaling perspective for M2M networks under
plain machine swarm (Plain) and proposed heterogeneous architecture (Het.),
where m2 is the amount of DAs and z is the number of machines that each
DA communicates with. (a) Average distance to DA over 2000 machines for
various heterogeneous network architectures with respect to various m and z.
(b) Asymptotic behaviors of average end-to-end distance for plain ma-
chine swarm and various heterogeneous network architectures with z = 2.
(c) Asymptotic behaviors of average end-to-end distance for plain machine
swarm and various heterogeneous network architectures with m = 3.
C. Maximum Throughput and Throughput Under
Guaranteed Delay
%While the performance over plain machine swarm is pre-
dicted by Gupta and Kumar’s analysis [23], Fig. 5(a) and
(b) depict the maximum achievable system throughput for
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Fig. 4. Asymptotic behaviors of average end-to-end delay for M2M networks
under plain machine swarm (Plain) and proposed heterogeneous architecture
(Het.), where m2 is the amount of DAs and z is the number of machines
that each DA can communicate with. (a) Average end-to-end delay for plain
machine swarm and various heterogeneous network architectures with z = 3.
(b) Average end-to-end delay for plain machine swarm and various heteroge-
neous network architectures with m = 3.
different heterogeneous architectures. While the results of [23]
accurately predict the networking performance of machine
swarm, especially for great amounts of machines, proposed
controls provide remarkable throughput improvements due to
tolerable end-to-end delay. Such enhancement becomes signif-
icant as the small-world phenomenon of DAs’ network takes
charge of end-to-end data transportation (i.e., more DAs as
m increased and better accessibility to DAs as z increased).
Specifically, by decreasing the time duration in machines’
ad hoc network or increasing the time sojourned in DAs’
network, machines’ packets experience much less delay and
thus bring greater system throughput for M2M communication
networks.
Furthermore, the corresponding circumstance also happens
for QoS guaranteed throughput as shown by Fig. 6(a) and
(b). Given the delay bound Dmax = 1000 ms and the violation
probability τ = 0.02, Fig. 6(a) and (b) provides the throughput
under QoS guarantees with regard to maximum throughput
in Fig. 5(a) and (b). While both maximum and QoS guaran-
teed throughput of plain machine swarm asymptotically follow
Θ(1/
√
nlogn) as expected from Theorem 4, more throughput
is obtained for proposed schemes due to heterogeneous archi-
Fig. 5. Asymptotic behaviors of maximum system throughput with regard to
QoS guarantee for M2M networks, where m2 is the amount of DAs and z is the
number of machines that each DA can communicate with. (a) Maximum system
throughput for Gupta’s results [23] (i.e., Θ(1/
√
nlogn)), plain machine swarm
(Plain), and various heterogeneous network architectures (Het.) with z = 4.
(b) Maximum system throughput for Gupta’s results [23] (i.e., Θ(1/
√
nlogn)),
plain machine swarm (Plain), and various heterogeneous network architectures
(Het.) with m = 6.
tecture as it asymptotically follows Θ(1/ 4
n(logn)3) shown in
Theorem 7.
In addition, with respect to different QoS requirements,
Fig. 7 exhibits the effectiveness of heterogeneous network
architecture for greater achievable throughput as compared with
plain swarm under the same QoS constraint. Loose τ gives more
throughput for both plain and heterogeneous schemes as sug-
gested by the previous discussion of existing tradeoff, but het-
erogeneous schemes are able to provide promising guaranteed
throughput even under strict QoS demand for tight τ. Moreover,
Fig. 8 further provides the exhaustive throughput comparison
among different scenarios to complete our evaluation. While
QoS guaranteed throughput is upper bounded by maximum
achievable throughput, the great throughput improvement is
provided by heterogeneous architecture as compared with plain
machine swarm. This suggests that even when there are tremen-
dous amounts of machines as in the next-generation networks,
our methodology still accommodates desire QoS guarantees
and system throughput by establishing ultra-fast (in terms of
routing) “highway” through heterogeneous architecture.
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Fig. 6. Asymptotic behaviors of guaranteed system throughput with regard to
QoS guarantee for M2M networks. (a) Statistical QoS guaranteed throughput
with Dmax = 1000 ms and τ = 0.02 for plain machine swarm (Plain) and various
heterogeneous network architectures (Het.) with z = 4. (b) Statistical QoS
guaranteed throughput with Dmax = 1000 ms and τ = 0.02 for plain machine
swarm (Plain) and various heterogeneous network architectures (Het.) with
m = 6.
Fig. 7. QoS guaranteed throughput with respect to τ, Dmax = 1000 ms and
5000 machines for plain machine swarm (Plain) and heterogeneous network
architectures (Het.) with z = 2, where m2 is the amount of DAs and z is the
number of machines that each DA communicates with.
Fig. 8. Comprehensive throughput comparisons for Gupta’s results [23], plain
machine swarm (Plain), and heterogeneous network architecture (Het.).
TABLE II
METROPOLIS SIMULATION PARAMETERS AND VALUES
D. Simulation Validation in the Metropolis
A metropolis is an extremely large city normally within
the area of hundreds of km2
that sets up with several blocks
for varies purposes, e.g., business, industries, and residence.
Each block consists of millions machines to support human’s
daily life. To simulate upon this real-world scenario with the
proposed heterogeneous architecture in Fig. 1(b), each grid in
the figure can serve as a block in Metropolis and each end-to-
end data transportation includes three types of communications.
In particular, those are the M2M communication with low data
rate and energy cost, the machine-to-DA communication with
medium data rate, and the DA-to-DA communication with high
data rate. We adopt the related values from [3] as shown in
Table II and set up the experiment as follows. The 1 Mb data
is sent from the source machine to the destination machine
in both plain machine swarm and heterogeneous architecture
separately. Moreover, DAs’ communication capabilities are
characterized as the number of machines z that can be served
simultaneously by each single DA.
Fig. 9 shows the optimal required number of DAs for hetero-
geneous architecture with respect to the number of machines.
As the DA’s capability linearly increases, the required number
of DAs drops exponentially. It suggests that few powerful
DAs are preferable than bunch of DAs with limited capability.
Furthermore, Fig. 10 shows the average end-to-end delay with
respect to different area sizes of Metropolis. As the area size
increases (so does the number of machines in each block),
the heterogeneous architecture supports much less traffic delay
than the plain machine swarm. For example, with the area
size 60 km2
and 108 machines, the delay from heterogeneous
architecture is 115 s as compared to 2,500 s from the swarm.
Moreover, the linear curves in the log scale of Fig. 10(b)
confirms our asymptotic results, and suggest that the hetero-
geneous architecture outperforms the plain machine swarm
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Fig. 9. The optimal number of DAs with respect to the number of machines
and DAs’ communication capabilities z.
Fig. 10. Average end-to-end delay of plain machine swarm (Plain) and various
heterogeneous architecture (Het.) with optimal number of DAs and different
communication capabilities z.
with about 95% delay reduction for 10 billion machines. To
conclude, by efficiently connecting few DAs to construct small-
world shortcuts, proposed statistical control accompanied with
heterogeneous architecture resolves the undependable end-to-
end transmissions via asymptotically Θ( 4
n/logn) delay and
Θ(1/ 4
n(logn)3) throughput, thus fulfilling the reliable infor-
mation dissemination in large M2M communication networks.
VII. CONCLUSION
In this paper, we resolve the most critical challenge on
providing statistical control for reliable information dissemi-
nation over large M2M communication networks. Examining
network topology of M2M networks, the geometric proper-
ties of such large networks are well studied to analytically
characterize message delivery over connected M2M networks.
Moreover, by leveraging queuing network model, the practical
data transportation is employed and both the average end-to-
end delay and maximum achievable throughput for these con-
nected networks are accessible. Based on above explorations,
the promising statistical control with sophisticated small-world
network of data aggregators and thus the heterogeneous archi-
tecture are proposed to establish shortcut paths among machine
communications. Performance evaluation verifies that instead
of exploiting long concatenation of multi-hop transmissions in
the machine swarm, our heterogeneous network architecture
enables machines to communicate through overlaid ultra-fast
“highway”, like shortcut in small-world networks, with de-
sired throughput. It is particularly crucial for next-generation
networks of tremendous amounts of machines. Therefore, we
successfully achieve reliable communications via our proposed
methodology and facilitate novel traffic control in M2M com-
munication networks.
APPENDIX A
THE PROOF OF THEOREM 1
Partitioning the unit area into square grids with area (r/
√
5)2
as from Lemma 1 and Corollary 1, each node in a square
grid is connected to its neighbor nodes within four adja-
cent square grids. The center of bottom left square grid is
at (r/2
√
5,r/2
√
5) and the center of top right is at (1 −
r/2
√
5,1 − r/2
√
5). Hence, the Euclidean distance has lower
bound
√
2(1 − 2r/
√
5). With maximum step length r for each
hop, the lower bound of R(n) is given as
√
2/r − 2
√
2/
√
5 ≈√
2/r. On the other hand, the upper bound of R(n) can be
obtained as follows. While each node in the square grid is able
to connect with nodes in its four adjacent square grids and there
is at least one node in each square grid, it exists a path from
bottom left square grid, passing bottom right square grid, to top
right square grid. As the side of square grids r/
√
5, this path
length is 2
√
5/r and gives the upper bound.
APPENDIX B
THE PROOF OF LEMMA 2
Partitioning the unit area into square grids with area (r/
√
5)2
as usual and substituting each square grid with a single nodes,
these nodes are connected and form a lattice structure from
Corollary 1. For an arbitrary path with length d in the lattice
graph, there must be a corresponding path, satisfying the upper
bound
√
5(dx + dy)/r, in GRG(n,r). On the other hand, while
the maximum step length is r for each hop, d follows the lower
bound and therefore we end the proof.
APPENDIX C
THE PROOF OF THEOREM 2
For GRG(n,r), let y be a random chosen node with position
Xn and x1,x2,...,xn−1 be the rest of nodes with positions
X1,X2,...,Xn−1, we have X1,X2,...,Xn ∼ U([0,1] × [0,1]).
We further let the projections of these n nodes on x axis
be Y1,Y2,...,Yn ∼ U([0,1]). For d(n) of M2M network, we
have the lower bound ( Xn − X1 + Xn − X2 + ... + Xn −
Xn−1 )/[r(n − 1)] and the upper bound [2
√
5(|Yn −Y1| + |Yn −
Y2| + ... + |Yn − Yn−1|)]/[r(n − 1)] from Lemma 2. By law
of large number, ( Xn − X1 + ... + Xn − Xn−1 )/(n − 1) →
E[ Xn − X1 ] and (|Yn − Y1| + ... + |Yn − Yn−1|)/(n − 1) →
E[|Yn −Y1|]. From Proposition 1, two bounds are obtained to
end the proof.
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APPENDIX D
THE PROOF OF LEMMA 3
Let s denote the number relay nodes that forward a packet
before reaching the destination. Pr{s = l} = (1−p(n))l−1 p(n)
for l ≥1 and d(n)=E[s]=∑∞
l=1 l(1−p(n))l−1 p(n)=1/p(n).
APPENDIX E
THE PROOF OF LEMMA 5
From diffusion approximation method [42], the visit ratio
of a node in a queuing network is defined as the average
number of times a packet is forwarded by (i.e. visit) the node.
For node i, ei = p0i(n) + ∑n
j=1 pji(n)ej where p0i denotes the
probability that a packet enters the queuing network from node
i. While the packets arrive at each node according i.i.d. Poisson
process, p0i(n) (i.e. also the probability that node i generates
the new packet) equals to 1/n. Applying this and pji(n) from
Lemma 4, we have ei = 1/n + ∑
K
(2)
i
j=1 ej(1 − p(n))/Ki = 1/n +
ei(1 − p(n))∑
K
(2)
i
j=1 1/Ki where the last equality comes from the
assumption of symmetry [46] (i.e. ei = ej, ∀1 ≤ i; j ≤ n).
Furthermore, with the aid of Proposition 2, we conclude with
ei = 1/n+[ei(1− p(n))]/δ.
APPENDIX F
THE PROOF OF THEOREM 3
Let Di denote the average packet delay at node i. According
to Little’s Formula, Di =Li/λi =[ρi(δ−1+p(n))]/[λδ(1− ˆρi)].
By symmetry, the average packet delay at all nodes is the
same. Thus, the average end-to-end delay equals to Di times
the average distance (in terms of hop-count) between the source
and destination nodes, i.e. D(n) = d(n)Di, which leads to (3).
APPENDIX G
THE PROOF OF LEMMA 7
The upper bound is a direct result from re-scaling
Theorem 2. For the lower bound, we assume the link to
the lattice is at the boundary of each grid, while z = 1. The
average Euclidean distance to that link then has the lower
bound as m4 1/m
0
v
0
u
0
2π
0 r2dθdrdudv = π/{120m} ≤
A/B where A =
1/m
0
1/m
0
1/m
0
1/m
0 min{u,v,1/m −
u,1/m − v, (x−u)2 +(y−v)2}dudvdxdy and B =
1/m
0
1/m
0
1/m
0
1/m
0 dudvdxdy. With another re-scaling
again, the lower bound is obtained for general z (i.e. z ≥ 1) and
ends the proof.
APPENDIX H
THE PROOF OF THEOREM 5
As usual, we examine z = 1 case and then obtain results for
general z from a re-scaling. Considering the lower bound of
dS(n), we first prove that the minimal distance of almost all
pairs of nodes comes from the assistance of upper DA’s lattice
as follows. While the maximum distance of node pairs through
upper lattice is 4
√
5/mr + 2m from a similar approach as in
Lemma 7, the coverage area with such distance for machine
swarm is π(2mr + 4
√
5/m)2 and tends to zero when m → ∞
and mr → 0. Thus, for (1 − ε)n(n − 1)/2 node pairs with any
small ε > 0, the distance of these pairs can be provided by
the minimal distance coming from the assistance of upper DA’s
lattice. From Lemma 7, both bounds of dS(n) are thus obtained
from the summation of the average distance to DA (from the
closet uplink for lower bound) and the average distance in DA’s
lattice network.
APPENDIX I
THE PROOF OF THEOREM 6
Firstly, DL(n) is directly obtained from Theorem 3 as ab-
sorption probability p(n) equals to 1/dL(n). On the other
hand, let DU
i denote the average packet delay at DA i. From
Little’s Formula, DU
i = 3ρU
i m/[2nλ(1− ˆρU
i )]. By symmetry, the
average packet delay at all DAs is the same. DU (n) equals to DU
i
times the average distance in DA’s network (i.e. 2m/3) and thus
ends the proof.
APPENDIX J
THE PROOF OF THEOREM 7
Similar to Theorem 4 and Proposition 3, λS
max is obtained
from restricting finite delay in nodes and DAs, while λSQ
max is
acquired by employing Markov inequality with DS(n).
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s10255-011-0091-9
Shih-Chun Lin (S’06) received the B.S. degree
in electrical engineering and the M.S. degree in
communication engineering from National Taiwan
University in 2008 and 2010, respectively. He is
currently with the School of Electrical and Com-
puter Engineering, Georgia Institute of Technology,
Atlanta, GA, USA. His research interests include
large machine-to-machine communication, wireless
underground sensor networks, software-defined net-
working, and statistical scheduling in wireless sys-
tems.
Lei Gu (M’13) received the B.S. degree in mathe-
matics and the Ph.D. degree in applied mathematics
both from Shanghai JiaoTong University, Shanghai,
China, in 2006 and 2011, respectively. From 2011 to
2012, he was a Postdoctoral Research Associate with
Intel-NTU Connected Context Computing Center,
Taipei, Taiwan, ROC. From 2011 to 2013, he was
a Postdoctoral Research Associate with Fudan Uni-
verysity, Shanghai, China. Since 2013, he has been
with China Telecom Cooperation Shanghai Research
Institute, Shanghai, China, as a Senior Technical
Staff Member. His research interests include analysis of complex networks,
networking and wireless communications, machine-to-machine communica-
tion and smart cities.
Kwang-Cheng Chen (M’89–SM’94–F’07) received
the B.S. degree from the National Taiwan University
in 1983, and the M.S. and Ph.D degrees from the
University of Maryland, College Park, MD, USA, in
1987 and 1989, all in electrical engineering. From
1987 to 1998, he worked with SSE, COMSAT, IBM
Thomas J. Watson Research Center, and National
Tsing Hua University, in mobile communications
and networks. Since 1998, he has been with National
Taiwan University, Taipei, Taiwan, ROC, and is the
Distinguished Professor and Associate Dean for aca-
demic affairs at the College of Electrical Engineering and Computer Science,
National Taiwan University. His recent research interests include wireless com-
munications, network science, and data analytics. He has been actively involved
in the organization of various IEEE conferences as General/TPC chair/co-chair,
and has served in editorships with a few IEEE journals. He also actively partic-
ipates in and has contributed essential technology to various IEEE 802, Blue-
tooth, and LTE and LTE-A wireless standards. He has authored and co-authored
over 200 IEEE papers and near 30 granted US patents. He co-edited (with
R. DeMarca) the book Mobile WiMAX (Wiley, 2008) and authored the book
Principles of Communications (River, 2009), and co-authored (with R. Prasad)
another book Cognitive Radio Networks (Wiley, 2009). He is an IEEE Fellow
and has received a number of awards including the 2011 IEEE COMSOC WTC
Recognition Award, and has co-authored a few award-winning papers published
in IEEE journals and conferences, including the 2014 IEEE Jack Neubauer
Memorial Award and the 2014 IEEE COMSOC AP Outstanding Paper Award.
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