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基于DAG方法的一个新的查询应答驱动路由协议与移动网络可达性分析(IJCNIS-V5-N12-7)

I. J. Computer Network and Information Security, 2013, 12, 53-59

Published Online October 2013 in MECS (https://www.wendangku.net/doc/ae2644929.html,/)

DOI: 10.5815/ijcnis.2013.12.07

A New QUERY-REPLY Driven Routing Protocol with Reachability Analysis for Mobile Networks: DAG based Approach

Paulami Dey, Parag Kumar Guha Thakurta

National Institute of Technology, Durgapur, Durgapur-713209, West Bengal, India

paulamidey77@https://www.wendangku.net/doc/ae2644929.html,, parag.nitdgp@https://www.wendangku.net/doc/ae2644929.html,

Abstract — An efficient Query-Reply based routing protocol for mobile networks is proposed in this paper. The alternative paths have been generated between source and destination nodes in the network. A Directed Acyclic Graph (DAG) is developed on the basis of selected right path among the alternatives. The reachability relationship is established on DAG and subsequently it introduces a proactive routing approach. As a result, the time complexity for the proposed routing method is reduced to a desired extent. The simulation studies confirm the improvements of the proposed model over the others. Index Terms — Directed Acyclic Graph, Reachability relation, QUERY-REPLY, Routing

I.I NTRODUCTION

In recent time, rapid escalation of cellular telephony is combined with a need for improved and efficient routing strategies. A powerful messaging pattern with a two way conversation over a channel is also required at the same time. During congestion in the network, one of such strategies known as Call Admission Control (CAC) is used to restrict the amount of users utilizing the network. The rest of the users are not permitted to any slot during this period [1]. Subsequently, Quality of Service (QoS) can be ascertained for the admitted users. Hence, it establishes two near contradictory prerequisites – propagating packets as well as ensuring Quality of Service (QoS) when all users are sending packet at the same time [2].

There are various routing strategies used in mobile networks till date. Some of those have used request-reply approach to obtain routing paths. The Directed Acyclic Graph (DAG) [3] approach is used to predict the nature of routing in advance. One of DAG based approach in [4] provides a reliable routing path in terms of multiple segments between source and destination nodes. With effect, a group of routing protocols for a network of processes is presented in [5] to route data to each member of the family. A request-reply based method in [6] develops a DAG to present a framework for loop-free on demand routing. Another routing method [7] is operated by controlling the request and response zones of network nodes during the route search process in order to reduce the routing overhead and network load. Again, a DAG based routing method [8] was introduced to support various path direction among the nodes in the network. A different routing model proposed in [9] results improved communication delay with packet delivery. The independent DAGs were developed [10] to compute independency on links and corresponding nodes. In this context, a research in [11] establishes a reachability relation which subsequently reduces the network traffic with high attainability. To improve sustainability of the network against failure, a proactive routing method has been discussed in [12].

The model proposed in this paper provides a new routing protocol for mobile networks. The work begins with representing a network to its equivalent graph GG. The alternative routing paths have been generated between source and destination nodes in GG through QUERY-REPLY packets. The right path among the alternatives has been selected following certain criteria defined on the model. Hence a Directed Acyclic Graph (DAG) GG′ is developed from the right path. Further it establishes a reachability relationship between these two nodes. Thus a proactive routing approach has been introduced to reduce the time complexity for communication to the desired extent. In addition, an improved scalability is obtained with the proposed work for larger number of the nodes in the network. The improvements of the proposed model over the other approaches are discussed through the experimental results. The rest of the paper is organized as follows. The model proposed here is described in section II. The experimental results for the proposed model are discussed in section III. The advantage of the work is concluded with its future scope in section IV.

II.P ROPOSED M ODEL

A. System Model and assumptions

The standard cellular structure [13] for mobile networks in Fig. 1 (a) is considered as the layout of the proposed system. Each cell in the layout is represented as CC pppp, where pp denotes the radial distance (rr) from mobile switching centre (MT) and the sequence number

54 A New QUERY-REPLY Driven Routing Protocol with Reachability Analysis for

Mobile Networks: DAG based Approach

of a cell in a specific radial distance (rr)is denoted by pp. This structure is alternatively represented here as a graph GG=(nn,ee), where nn is the set of nodes denoting base stations (BSs) and mobile switching centre (MT)

participating in communication, and ee denotes the set of edges representing the connectivity among the nodes. The equivalent GG for rr=3 as a prototype is shown in Fig. 1(b).

Figure 1: Representation of mobile networks (a) Cellular layout,

(b) Equivalent graph structure.

The flexibility in the structure of GG is maintained in such a way that the cccccccc (ii) ?ii∈nn due to handoff and cabling cost (cccccccc cccc (ii,jj)?ii,jj∈nn) can be minimized. The detailed outline for such minimization is described as follows.

A location updates strategy [14] which includes mobility and call arrival pattern is applied when mobile users cross a position area (PA) boundary. Then two factors related to such updating are considered as– (i) the pprrccpp(PPPP(ii)) is defined as the probability of the user being located in PPPP(ii) and (ii) the ccaaaaCCcccccc(ii) is expressed as the average cost in PPPP(ii) with assuming call arrival following Poisson distribution. Thus the minimization of cost function (cccccccc (ii) ?ii∈nn) is presented as follows. ccppjj ee cccc ii aa ee: mmii nn ii mm ii mm ee?pprrccpp?PPPP(ii)?.ccaaaaCCcccccc(ii)

nn

ii=1

(1) ccssppjj ee cccc cc cc pp ii=?1, ffccrr ssppuucccc ee ii nn PPPP(ii)

0, cccc?eerreeii cc ee(2) In (2), the update per user is represented by a binary string {pp nn ,.....,pp1}?ii∈nn.

Generally, the perpendicular bisectors for each edge in a symmetric hexagonal cell intersect at a fixed point. The placement of a BS as a node (nn) in GG at that fixed point reduces the cabling cost [15] (cccccccc cccc(ii,jj)?ii,jj∈nn) rather than placing it in any other position within the cell. Thus the minimization of the factors including cccccccc(ii)?ii∈nn and (cccccccc cccc(ii,jj)?ii,jj∈nn) are dependent on placing a node (nn) in a cell.

B. Problem Statement

The problem addressed in this work can be stated as – given a GG=(nn,ee), find a Directed Acyclic Graph (DAG) (GG′=(nn′,ee′)) obtained from right path pp (ii,jj), among multiple valid routing paths (PP:?pp (ii,jj)∈PP) generated by applying a procedure on GG, such that GG′ presents a reachability relation between ii ccnnuu jj,ee?eerree ii,jj∈nn.

For instance, the reachability relation between ii and jj provides knowledge of a legitimate connection guaranteeing confirmation of data flow which subsequently reduces the time complexity of routing. However, the main hypothesis considered over the problem is presented next.

C. Main Hypothesis

By using GG, with its corresponding ′ffccrr ccaa aa (ii,jj)∈nn, it is possible to minimize the routing time with the help of rechability relation between ii and jj.

Before describing the detailed framework of the proposed model, some useful terminologies with respect to the model are defined as follows.

D. Terminologies used

(a)QUERY and REPLY Packets: Two packets named as QUERY and REPLY are used by the nodes to send data as well as receiving acknowledgement respectively.

(b)Node’s unique ID: Each node in the network is identified with its unique ID to identify specific source and destination respectively.

(c)Hop-metric: Two Hop metrics known as H_Query and H_Reply are associated with each node in the network to find the valid routing paths between specific source and destination respectively.

E. Steps of the Proposed Work

(i) Generation of Routing Paths:

The set of routing paths (PP) have been generated as a result of execution of the Routing Path Generation algorithm. Initially, the source node (nn SSSS) broadcasts the QUERY packet with the unique ID (II II nn IIDD)

of the

destination node(nn IIDD). Here, the H_Query values are initialized with the following instances.

H_Query=?0, ffccrr nn IIDD

NNNNNNNN, cccc?eerreeii cc ee (3)

The destination node (nn IIDD) having H_Query value as

0 would receive the QUERY packet. After that, nn IIDD sets

its H_Reply value to its H_Query value. Further

nn IIDD sends a REPLY packet as an acknowledgment along

with its own H_Reply value and ID (II II nn SSSS) of nn SSSS. Each

intermediate node (nn RRDD) in the path receiving and passing

the REPLY sets its H_Reply value with incrementing

H_Reply from the previous nn RRDD by one. This process is

continued until II II nn SSSS matches with II II nn RRDD. The procedure

is described by the following algorithm 1.

Algorithm 1: Routing path Generation

Input: A graph GG=(nn,ee)

Output: A set of routing paths(PP).

Functions Used:

Broadcast_packet (Packet, II II nn IIDD): nn SSSS broadcasts

packet along with II II nn IIDD.

Receive_packet (Packet,nn IIDD): Node nn IIDD receives

the packet.

Send_packet (Packet, II II nn SSSS, (H_Reply) nn IIDD): Node

nn IIDD sends out the Packet along with its H_Reply value.

Method:

(ii) Selection of Right Path:

The right path pp (ii,jj)?ii,jj∈nn is selected among the

set PP by following rules.

Rule 1: If there are different (H_Reply) nn SSSS values for various routing paths(PP), then the right path pp (ii,jj)?ii,jj∈nn is with minimum (H_Reply) nn SSSS value. Rule 2: If there are more than one routing paths (P) obtained with same minimum (H_Reply)nn SSSS values, then the concept of logical clock [16] is used to evaluate the right one.

Thus the selection of the right path based on that concept is discussed as- a logical clock CC ii(cc) at a nnccuuee ii,( ii∈nn) in the GG is assigned as the timestamp of a specific REPLY sending event (cc). Again the REPLY passes through a node jj and subsequently this event (pp) is assigned a timestamp CC jj(pp). When ii sends out a REPLY through a path as an acknowledgement for the source node nn SSSS, then cc RRDDPPNNRR is assigned to the value of CC ii(cc). This REPLY passes through jj as said before. Hence the value of CC jj would become as follows.

CC jj=max?CC jj(pp),cc RRDDPPNNRR+uu?,uu>0 (4)

Where, uu is the communication delay between sending and receiving the packet.

In this way, there is a timestamp value CC SSSS at nn SSSS for every routing path. Subsequently these values are compared and the corresponding routing path with the minimum value among all is considered as the right path pp (ii,jj)?ii,jj∈nn.

(iii) DAG Representation:

A DAG is used in the proposed work to find the rechability relation between nn SSSS and nn IIDD- which subsequently reduces the time complexity for communication. To obtain a DAG from the selected right path, initially, nn SSSS with a min (H_Reply) nn SSSS value is considered as an initial condition. Then the nodes nn SSSS and nn IIDD are linked through a series of intermediate nodes (nn II NN) on the basis of sequential decrement of H_Reply values. This process is continued until H_Reply value becomes zero. The procedure is described by the algorithm 2 as follows.

Algorithm 2: DAG Representation

Input: A right path pp(ii,jj)?ii,jj∈nn. Output: A graph GG′=(nn′,ee′).Method:

Remark:If the entire network having a total number of nodes N is divided into m layers with nn aa nodes in each of them i.e.,∑nn aa mm aa=1=NN, then the number of possible DAGs over that network is expressed as follows:

II(NN)

=???….??(2[aa]mm?1aa=1

NN?mm+1?∑nn kk mm?1kk=1

nn mm=1

NN?mm+1?nn1

nn2=1

NN?mm+1

nn1=1

NN

mm=1

?2[aa?1]) (5)

(iv)Introduction of Reachability relation:

A GG′provides a directed path between nn SSSS and nn IIDD. Moreover, a transitive closure is developed as a result for such computation. Hence it introduces a reachability relation between nn SSSS and nn IIDD. The reachability relation obtained from GG’ represents a strict partial order among the nodes. Generally, the transitive reduction is an inverse process of transitive closure with maintaining the connectivity. In this context, the transitive reduction is unique for each GG′ in the entire network. Naturally the reachability relation represents the transitive closure of its edge set.

Let us assume GG′nn SSSS nn IIDD

kk=1, if and only if nn IIDD is reachable from nn SSSS through kk number of intermediate nodes. Here, if any path with GG′ss aa kk=1 is previously selected, then there is no need to check again that path as a candidate solution for further analysis. Thus it reduces time complexity for routing in advance. In such a way it enhances Quality of Service (QoS) for the network. A

salient feature of introducing such reachability relation is concluded with the following lemma. Lemma: Every reachability relation GG′ss aa kk has a unique transitive skeleton (σσ?GG′ss aa kk?) with polynomial time reduction.

Proof:There is an algorithm PP for constructing GG′which can be considered as an event XX. Henceforth reachability relation obtained on the basis of the transitive closure property from GG′ is considered as an event RR. Some properties are considered regarding these events as follows.

(a)Given an instance II xx of XX, PP produces an

instance II yy of RR.

(b)PP executes in polynomial time i.e., |II xx|.

(c)Answer to II yy yes iff answer to II xx is yes. Obviously, with satisfying these properties, it is concluded that RR is polynomial time reducible from XX

(XX≤pp RR).

F. Example of the Proposed Model

The performance for the proposed model has been analyzed with an example on the basis of Fig. 1(b). Here, n00and n34are considered as the source node and the destination node respectively. In between these two nodes, there are several possible alternative paths for communication. The five paths among these alternatives have been shown in Fig. 2 along with (H_Query, H_Reply) values respectively.

Figure 2: Alternative routing paths for n00 and n34 as source and

destination nodes

The path 3 shown in Fig. 2 is selected as the right path according to the criteria with least value of H_Reply, that is 3. Hence the DAG is shown in Fig. 3(a) and 3(b) based on path 3.

Figure 3: DAG representation: (a) QUERY and REPLY shown

separately, (b) Final GG′ for path 3

After the establishment of GG′ for path 3, the reachability relation has been introduced for nodes n00 and n34. In addition, the reachability relationship for the entire network (rr=3) is shown in Fig. 4 for the source as n00.

Figure 4: The reachability relations for n00 in rr=3

III.E XPERIMENTAL R ESULTS

This work is analyzed with MATLAB version 7.6.0.324 (R2008a) on processor Intel(R) Core(TM) i5-2410M CPU@ 2.30 GHz and RAM 4.00 GB. The following four performance metrics have been introduced here to evaluate the effectiveness for the proposed model.

A. Speedup Performance

The dedicated path has been established as a result of our proposed model. So, it is not required any searching delay to finding path for further analysis. In effect, the speed up is increased for the proposed model accordingly rather than traditional [17] one.

Speed up here is

calculated in terms of transmitting packet through a dedicated path. The behavioral representation between speed up performance and the number of nodes in the network is shown in Fig. 5.

Figure 5: Characteristic curve on speed up Vs. and the number

of nodes in the network

B. Quality of Service (QoS):

The delay is considered as the QoS metric for the proposed model. Generally, the delay is increased due to the lack of connectivity in end-to-end paths. In our work, the DAG provides the right path for communication in advance. Therefore, the QoS of the network is improved in the aspect of minimized delay. For example, the time cc required to traverse nn number of hops with a factor 1pp (nn )?, where pp (nn ) denotes QoS metric of the

network, is presented as pp (nn )= ?nn log nn . Here, the delay II is proportional to cc and it is expressed as a

function of (

1

?nn log nn

). Therefore, the improved QoS for the proposed model is obtained than the traditional approach [18] as shown in Fig. 6.

Figure 6: Improved QoS of the proposed model

C. Dropped Packet Rate (DPR)

It is proportional to the total number of packets dropped in the network. As the dedicated path established in previous, the possibility of dropped packet is less. Therefore, the reduced DPR results for increased transmission as shown in Fig.7.

Figure 7: DPR Vs transmission

D. Routing Overhead

Routing overhead is dependent on the total number of packets sent during the transmission. Due to the follow up of the proactive routing approach by the proposed model, the reduced routing overhead is obtained for the number of nodes in the network than the traditional approach [19] as shown in Fig. 8.

Figure 8: Reduced Routing overhead obtained for the proposed

model

VI.

C

ONCLUSIONS

The procedure of establishing the reach ability relation through DAG in mobile networks is described in this

work. This protocol reduces time complexity for the routing method. The importance of the transitive reduction is discussed to make it more significant in finding the right path. It increases the flexibility of dynamic call routing. Further study on extending this work to construct the routing table is in progress.

.

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Paulami Dey,passed B.Tech from WBUT, India in 2011. She completed M.TECH in Computer Science and Engineering department at National Institute of Technology, Durgapur, India in 2013. Currently she is working in IBM India Pvt. Ltd. as an Associate System Engineer.

Parag Kumar Guha Thakurta, Assistant Professor in National Institute of Technology Durgapur from India. His main research interests include mobile computing.

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移动魔百和常见问题处理

移动通信有限公司魔百和故障处理指引中国移动通信集团公司分公司 2015年12月 目录

第1章故障总体描述 故障主要分为以下几类: 1.开通注册类,此类故障出现在用户刚开通魔百和业务时。如:手机是否欠费停机;机顶盒串号和手机号的订购关系出现问题。 2.机顶盒类,此类故障一般是软、硬件故障所致。 3.播放使用类故障,此类故障一般是用户使用不当,用户侧网络故障,或牌照方CDN 出现故障造成。

第2章故障分类 在各地市魔百和业务推广过程中,故障投诉较多,为有效指导后续售后工作,本文档对各类问题进行了归类汇总,分析原因、总结处理方法。 2.1 业务开通类 2.1.1 报错类 故障现象:“登陆失败报90056错误”、“开机认证失败”、“登陆失败”、“访问服务器失败”、“开通失败”、“网络异常” 产生问题原因: 1、机顶盒之前已经正常激活并且获取到账号密码,但是当前账号密码已经失效; 2、由于网络问题,导致首次开机没有获取到正确的账号密码。 解决方法: 1、排查网络是否正常。 2、确定机顶盒在AAA系统中无订购关系(拨打50客服查询) 3、机顶盒恢复出厂设置,重新获取一下用户名密码。 按机顶盒遥控器“设置”——“设备信息”——“启用诊断” 在第一步就会出现红色报错信息,则说明网络异常或机顶盒网络配置不正确,需先解决网络或网络配置问题; 在第二步出现开机认证失败的红色报错信息,则可以确定是开通问题,订购关系异常; 2.1.2 开机提示“机顶盒不支持互联网开通” 故障现象:开机提示“您的机顶盒不支持互联网开通,请到营业厅开通” 产生问题原因及解决方法 1、新装用户查询工单是否竣工,至少走到“施工回访”阶段。 2、机顶盒无订购关系,查询录单信息(盒子串码、手机号)录入是否有误,盒 子是否发放错误,有误则重新办理订购。 3、机顶盒恢复出厂设置,重新效验。 恢复出厂设置:按遥控器“设置”——“恢复出厂设置”——输入密码10086,点击“重置机顶盒”即自动重启,重启后需要对网络重新进行配置。

移动性浊音阳性提示腹水量

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3.内分泌障碍 肝硬化或肝功能不全时,肝降解功能减退。一方面抗利尿激素与醛固酮等灭活功能降低致钠、水潴留;另一方面血液循环中一些扩血管性血管活性物质浓度增高,这些物质引起外周及内脏小动脉阻力减低,心排血量增加,内脏处于高动力循环状态。由于内脏血管扩张,内脏淤血,造成有效循环血容量相对不足及低血压,机体代偿性释放出血管紧张素II及去甲肾上腺素,以维持血压。这样因反射性地兴奋交感神经系统释放出一些缩血管物质,使肾血流量减低肾小球滤过率下降,加之抗利尿激素释放,引起肾小管钠、水回吸收增加,导致钠水潴留并形成腹水。

无线传感器网络路由协议研究【开题报告】

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基于无线传感网络的大型结构健康监测系统_尚盈

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各大运营商 网络接入点设置

手机上网一点通各大运营商接入点设置 很多朋友在购买了新的手机后不知道如何才能顺利上网。不管您用的是Android,iOS,还是Symbian系统的手机,都需要进行简单的接入点配置。随着3G网的用户越来越多,很多人已经加入了这个新群体。但是合理的使用2G网络还是很有必要的,会为您节省不少流量。针对这一问题,笔者将详细的为您介绍各个运营商的网络接入点设置方法,让您的手机冲浪更加便捷、愉快。 首先为您介绍的是中国移动接入点的设置: 一、中国移动NET、WAP及彩信设置 (一)中国移动NET设置 Android用户依次点击手机菜单、设置、无线和网络、移动网络设置、接入点名称、新建接入点。 名称:中国移动NET设置;接入点名称:cmnet;代理:空白;端口:空白;用户名:空白;密码:空白;服务器:空白;MMSC:空白;彩信代理:空白;彩信端口:空白;彩信协议:wap 2.0;MCC:460,MNC:00,;身份验证类型:空白;接入点名称类型:default。 进入Android设置及无线和网络

(二)中国移动WAP设置 名称:中国移动WAP设置;接入点名称:cmwap;代理:10.0.0.172;端口:80;用户名:空白;密码:空白;服务器:空白;MMSC:空白;彩信代理:空白;彩信端口:;彩信协议:wap 2.0;MCC:460,MNC:00,;身份验证类型:空白;接入点名称类型:default。 移动网络设置及接入点名称 (三)中国移动彩信设置(收发彩信用) 名称:中国移动彩信设置;接入点名称:cmwap;代理:10.0.0.172;端口:80;用户名:空白;密码:空白;服务器:空白;MMSC:https://www.wendangku.net/doc/ae2644929.html,;彩信代理:10.0.0.172;彩信端口:80;彩信协议:wap 2.0;MCC:460,MNC:00,;身份验证类型:空白;接入点名称类型:mms 。

无线传感网路由协议的分析比较

无线传感网路由协议的分析比较 无线传感网技术是对当今经济和社会进步发挥重要作用的技术,对于现代军事、信息技术、制造业等多个重要的领域产生着巨大的影响。而无线路由协议则是无线传感网研究中的热点问题。文章对于几个典型的平面路由协议和分层路由协议进行了介绍,分析了它们各自的利弊,并对它们进行了比较。 标签:无线传感网;路由协议;传感器节点 1 无线传感网概述 无线网络即使用无线传输介质的网络。目前有两种无线网络,基础设施网络和对等网络。基础设施网络的无线终端需要配置无线网卡,并通过接入点(AP)连接入网。对等网络即Ad hoc网络,不需要AP的支持,终端设备之间可以直接通信。无线Ad hoc网络又可分为两类,移动Ad hoc网络和无线传感器网络。前者的终端是快速移动的,后者的结点是静止的或者移动很慢。 无线传感网由大量的静止或移动的传感器组成,它们以自组织和多跳的方式构成无线网络,相互协作以探测、处理和传输网络覆盖区域内感知对象的监测信息,并报告给用户。无线传感器网络技术在军事应用、智能家居、环境监测、建筑物质量监控、医疗护理等各个方面都有广泛应用[1]。 无线传感网的系统结构包括监测区域(Sensor Field)、传感器节点(Sensor Node)和汇聚节点(Sink Node)[2]。监测区域中包含了各种需要采集数据的观察对象;传感器节点用于采集观察对象的相关数据,并将处理后数据传给汇聚节点;汇聚节点用于收集由传感器节点传递来数据,并将数据传送到远程中心进行集中处理。 2 无线路由协议 无线路由协议是无线传感网研究中的热点问题。无线传感网的路由协议负责在源节点和目的节点之间可靠地传输数据,包括路由选择和数据转发两个功能。根据网络的拓扑结构是否有层次,可以将路由无线路由协议分为平面路由协议和分层路由协议[3]。 2.1 平面路由协议 平面路由协议适用于具有平面结构的网络,所有节点之间地位平等,协议相对简单。源节点和目的节点之间一般存在多条路径,可共同承担网络负荷,通常不存在瓶颈,网络具有较强的健壮性。然而,节点的组织、路由的建立、控制与维持所产生的开销需要占用较大的带宽,从而影响网络数据的传输速率。另外,当网络规模较大时需要损耗很大的能量,并且网络的可扩展性较差。因此,平面路由协议只适用于规模较小的网络。

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(完整版)网络广告创意的原则与方法

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