<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article  PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article"><front><journal-meta><journal-id journal-id-type="publisher-id">WET</journal-id><journal-title-group><journal-title>Wireless Engineering and Technology</journal-title></journal-title-group><issn pub-type="epub">2152-2294</issn><publisher><publisher-name>Scientific Research Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.4236/wet.2016.71003</article-id><article-id pub-id-type="publisher-id">WET-62960</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Computer Science&amp;Communications</subject></subj-group></article-categories><title-group><article-title>
 
 
  Issues and Challenges in Node Connectivity in Mobile Ad Hoc Networks: A Holistic Review
 
</article-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>ohit</surname><given-names>Jain</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Satish</surname><given-names>Chand</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib></contrib-group><aff id="aff1"><addr-line>Division of Computer Engineering, Netaji Subhas Institute of Technology (NSIT), University of Delhi, New Delhi, India</addr-line></aff><pub-date pub-type="epub"><day>26</day><month>11</month><year>2015</year></pub-date><volume>07</volume><issue>01</issue><fpage>24</fpage><lpage>35</lpage><history><date date-type="received"><day>10</day>	<month>September</month>	<year>2015</year></date><date date-type="rev-recd"><day>accepted</day>	<month>19</month>	<year>January</year>	</date><date date-type="accepted"><day>22</day>	<month>January</month>	<year>2016</year></date></history><permissions><copyright-statement>&#169; Copyright  2014 by authors and Scientific Research Publishing Inc. </copyright-statement><copyright-year>2014</copyright-year><license><license-p>This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/</license-p></license></permissions><abstract><p>
 
 
  One of the fundamental properties of an ad hoc network is its connectivity. Maintaining connectivity in wireless networks is extremely difficult due to dynamic changing topology of MANETs. There are several techniques to understand the connectivity level for a given network topology. In this paper, we examine the existing methods and discuss the issues and challenges that are still insurmountable in order to enhance the connectivity properties of wireless multi hop networks.
 
</p></abstract><kwd-group><kwd>Ad Hoc Networks</kwd><kwd> Connectivity</kwd><kwd> Topology Control</kwd><kwd> Critical Transmitting Range</kwd><kwd> Node Density</kwd><kwd> Energy Consumpution Routing</kwd><kwd> Critical Points</kwd><kwd> k-Connectivity</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>1. Introduction</title><p>Amobilead hoc network (MANET) is formed by a group of wireless autonomous mobile nodes to create a typical environment for dynamic communication with no fixed infrastructure or administration. They operate in a decentralized and self organizing manner [<xref ref-type="bibr" rid="scirp.62960-ref1">1</xref>] [<xref ref-type="bibr" rid="scirp.62960-ref2">2</xref>] . This type of network has been existing since 1970s [<xref ref-type="bibr" rid="scirp.62960-ref3">3</xref>] [<xref ref-type="bibr" rid="scirp.62960-ref4">4</xref>] , but only last couple of years has witnessed a tremendous research in this area [<xref ref-type="bibr" rid="scirp.62960-ref5">5</xref>] - [<xref ref-type="bibr" rid="scirp.62960-ref7">7</xref>] . There are several issues related to MANETs that include route construction with minimum overhead [<xref ref-type="bibr" rid="scirp.62960-ref8">8</xref>] - [<xref ref-type="bibr" rid="scirp.62960-ref12">12</xref>] , less bandwidth consumption, energy issue, security attacks [<xref ref-type="bibr" rid="scirp.62960-ref13">13</xref>] - [<xref ref-type="bibr" rid="scirp.62960-ref15">15</xref>] , node misbehavior [<xref ref-type="bibr" rid="scirp.62960-ref16">16</xref>] - [<xref ref-type="bibr" rid="scirp.62960-ref18">18</xref>] , etc. Some of the important routing protocols, broadcast protocols, and clustering protocols designed especially for ad hoc networks have been discussed in [<xref ref-type="bibr" rid="scirp.62960-ref19">19</xref>] - [<xref ref-type="bibr" rid="scirp.62960-ref27">27</xref>] . Some of them are designed for energy-efficient operation in an existing network topology, some deal with the effects of mobility, and some consider both the aspects. There are various studies in literature, but none discusses the latest developments related tothe ad hoc network connectivity. To the best of authors’ knowledge, this is the first such study that reviews the importance of node connectivity unlike other properties like stable route construction, bandwidth, energy consumption, etc. in multihop wireless networks.</p><p>The advantage of ad hoc networks is that the mobile devices communicate with each other in a peer-to-peer fashion, establish a self organizing network without the need of any access point or any pre-existing infrastructure. In short, they can be formed in a spontaneous way, that’s why they are known as ad hoc networks. In these networks, each node plays two vital roles: as mobile node and as router to transfer the packets of other nodes. To achieve a fully connected ad hoc network, there must be a multihop path from a node to every other node depending on two topology attributes: radio transmission range and node density. Their combination plays a vital role in node connectivity.</p><p>In a cellular network, also known as infrastructure network, the mobile devices remain connected even if there is a wireless link to at least one of the base stations within its communication range. In ad hoc networks, there are no fixed routers; all nodes are capable of movement that can be dynamically connected in an arbitrary manner. Each node acts as router to maintain the connectivity of the entire network. In case the communication gets broken, the connectivity between other nodes also gets destroyed. If the substantiality or spatial density of nodes is too low, the multihop system for communication will not perform well. In this paper, we study and analyze various techniques related to network connectivity along with their impact on the network topology. The organization of the remaining paper is as follows. Section II discusses the importance of connectivity in ad hoc networks. Section III discusses the research works done in the area. Section IV points out the major issues and challenges in ad hoc network connectivity. Finally, the paper is concluded in section V.</p></sec><sec id="s2"><title>2. Why Node Connectivity Is Important?</title><p>Connectivity in ad hoc networks varies because of the continuous movement of the nodes due to their dynamic nature. It is possible that the movement of one or more nodes from one point to another causes the network partitioning. Maintaining connectivity is a challenge due to unstructured nature of the network topology and the frequent occurring of links and nodes failures due to interference, mobility, radio channel effects, and battery limitations [<xref ref-type="bibr" rid="scirp.62960-ref27">27</xref>] . This makes connectivity as one of the main problems in an ad hoc network. Many researchers have discussed methods to improve the network connectivity, which will be discussed in next section. The factors leading to network failure that partitions it into two or more components and can annihilate the end-to-end connectivity as well may be divided into the following four categories:</p><p> Node or Device Failure</p><p> Critical Points or Weak Points</p><p> Link or Edge Failure</p><p> Power or Battery Failure</p><p>a) Node Failure: The node failure occurs when an intermediate node or device acting as router is not available due to hardware/software failure or the node moves out of the communication range or the network. The situation has been described in <xref ref-type="fig" rid="fig1">Figure 1</xref>. Here node A and Dare communicating through node C. If node C fails due to battery failure or it moves out of the communication range, then an alternate route must be discovered and communication should be restored by rerouting the traffic through some other node B. The amount of time that two mobile nodes will stay connected is calculated by expression:</p><disp-formula id="scirp.62960-formula344"><graphic  xlink:href="http://html.scirp.org/file/3-6801265x6.png"  xlink:type="simple"/></disp-formula><fig-group id="fig1"><label><xref ref-type="fig" rid="fig1">Figure 1</xref></label><caption><title> Illustration of Node Failure or Power Failure of Node C.</title></caption><fig id ="fig1_1"><label></label><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/3-6801265x7.png"/></fig></fig-group><p>where</p><disp-formula id="scirp.62960-formula345"><graphic  xlink:href="http://html.scirp.org/file/3-6801265x8.png"  xlink:type="simple"/></disp-formula><disp-formula id="scirp.62960-formula346"><graphic  xlink:href="http://html.scirp.org/file/3-6801265x9.png"  xlink:type="simple"/></disp-formula><disp-formula id="scirp.62960-formula347"><graphic  xlink:href="http://html.scirp.org/file/3-6801265x10.png"  xlink:type="simple"/></disp-formula><p>In the above equations<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x11.png" xlink:type="simple"/></inline-formula>, <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x12.png" xlink:type="simple"/></inline-formula>are the speeds and <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x13.png" xlink:type="simple"/></inline-formula> ,<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x14.png" xlink:type="simple"/></inline-formula> are the moving directions of two nodes I and j respectively.</p><p>After calculating the link expiration time (LET) in all</p><p>Links within the network, the highest amount of LET is determined:</p><p>The probability of the proper operation of link between node I and node j is obtained by:</p><disp-formula id="scirp.62960-formula348"><graphic  xlink:href="http://html.scirp.org/file/3-6801265x15.png"  xlink:type="simple"/></disp-formula><p>where <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x16.png" xlink:type="simple"/></inline-formula> is the biggest time period which the two nodes are connected in the network.</p><p>b) Power Failure: Power failure occurs when the battery of the node is too low, making it unable to serve as router. The situation has been explained in <xref ref-type="fig" rid="fig1">Figure 1</xref>.</p><p>c) Link Failure: Link failure can occur due to various factors: link obstacle between communication nodes, fading, node mobility and excessive interference. <xref ref-type="fig" rid="fig2">Figure 2</xref> illustrates the link fault when node Y moves to different position, resulting in communication link between X and Y, degrading because of an obstacle. Obviously these failures break the local connectivity between nodes and can disrupt the end-to-end connectivity as well. The path stability can be expressed by the following mathematical expression:</p><disp-formula id="scirp.62960-formula349"><graphic  xlink:href="http://html.scirp.org/file/3-6801265x17.png"  xlink:type="simple"/></disp-formula><p><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x18.png" xlink:type="simple"/></inline-formula>, where <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x19.png" xlink:type="simple"/></inline-formula> is the expiration time of the path joining S &amp; D, <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x20.png" xlink:type="simple"/></inline-formula>is the expiration time of the link connecting node <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x20.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x21.png" xlink:type="simple"/></inline-formula> and the node<inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x20.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x21.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x22.png" xlink:type="simple"/></inline-formula>, k is an integer where <inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x20.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x21.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x22.png" xlink:type="simple"/></inline-formula><inline-formula><inline-graphic xlink:href="http://html.scirp.org/file/3-6801265x23.png" xlink:type="simple"/></inline-formula></p><p>d) Critical Points: The weak or critical points of a topology are those links and nodes whose failure results in partioning the network into two or more components. For example, the critical node or articulation node is defined as a node that partitions the network due to its failure. The node C in <xref ref-type="fig" rid="fig3">Figure 3</xref> is a critical node because the network is partitioned if this node fails. Similarly the link CD in <xref ref-type="fig" rid="fig4">Figure 4</xref> is a critical link since failure of the link CD partitions the network into two components. Such links are also known as bridge links.</p><fig id="fig2"  position="float"><label><xref ref-type="fig" rid="fig2">Figure 2</xref></label><caption><title> Illustration of Link between X and Y breaks due to some obstacle</title></caption><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/3-6801265x24.png"/></fig><fig id="fig3"  position="float"><label><xref ref-type="fig" rid="fig3">Figure 3</xref></label><caption><title> Illustration of critical node C</title></caption><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/3-6801265x25.png"/></fig><fig id="fig4"  position="float"><label><xref ref-type="fig" rid="fig4">Figure 4</xref></label><caption><title> Illustration of critical link CD</title></caption><graphic mimetype="image"   position="float"  xlink:type="simple"  xlink:href="http://html.scirp.org/file/3-6801265x26.png"/></fig></sec><sec id="s3"><title>3. Literature Overview</title><p>The problem of node connectivity was very first time discussed by Cheng and Robertazziin 1989 [<xref ref-type="bibr" rid="scirp.62960-ref28">28</xref>] . They investigated the influence of node density and transmission range of a node‘s broadcast in a multihop radio network modeled by spatial Poisson process. They suggested that for optimizing the transmission range, its value should be lower bounded to maintain desired network connectivity. However, they could not implement it in real scenario. The paper [<xref ref-type="bibr" rid="scirp.62960-ref29">29</xref>] , an extension of [<xref ref-type="bibr" rid="scirp.62960-ref28">28</xref>] , discusses the disconnectedness of Poisson distributed nodes. It provides some insights on critical coverage range vs. critical transmission range of the nodes placed in a square area according to Poisson fixed density. This problem is further discussed in [<xref ref-type="bibr" rid="scirp.62960-ref30">30</xref>] for one dimensional line segment that determines the critical transmitting range for Poisson distributed nodes in square area. However, these works [<xref ref-type="bibr" rid="scirp.62960-ref29">29</xref>] [<xref ref-type="bibr" rid="scirp.62960-ref30">30</xref>] are difficult to apply in real scenarios because in a Poisson process the actual number of deployed nodes is a random variable itself whose only average value can be found. The paper [<xref ref-type="bibr" rid="scirp.62960-ref31">31</xref>] discusses the critical power of nodes in a network for transmission to ensure network connectivity by using the percolation theory [<xref ref-type="bibr" rid="scirp.62960-ref32">32</xref>] . The probabilistic lower and upper bounds for isolated and connected nodes fail to explain about nodes not placed independently in a disc. The paper [<xref ref-type="bibr" rid="scirp.62960-ref33">33</xref>] investigates the connectivity of hybrid ad hoc networks consisting the Poisson distributed nodes using the percolation theory. It reports that for populated regions one dim sparse network is well suited and the pure ad hoc network is useful relatively low density areas. For density critical areas, the cellular network can provide the acceptable connectivity. The paper [<xref ref-type="bibr" rid="scirp.62960-ref34">34</xref>] estimates the nearest neighbors when network becomes disconnected. In this work, the same number of nearest neighbors is maintained for each node. If each node is connected to less than 0.074 logn nearest neighbors the network is asymptotically disconnected. If each node is connected to more than 5.1774 logn nearest neighbors, then it is asymptotically connected. The paper [<xref ref-type="bibr" rid="scirp.62960-ref35">35</xref>] discusses the connectivity augmentation problem and determines a set of edges of minimum weight to be inserted in order to make the resulting graph λ-vertex edge connected. It is reported that the problem is NP hard for λ &gt; 1. In [<xref ref-type="bibr" rid="scirp.62960-ref36">36</xref>] , the same work has been discussed by using the concept of minimum geometric disk cover (MGDC) problem, commonly used in wireless networking applications or facility location problems. The MGDC problem is “for a given set of points P in Euclidean plane and a rational number r &gt; 0, it intends to find a set of centers C with minimum cardinality such that every point P is covered by a r radius disk centered at one of the points in C.” This problem is however NP?hard problem. The paper [<xref ref-type="bibr" rid="scirp.62960-ref27">27</xref>] uses the random graph theory and theory of Kolmogorov complexity to establish the network connectivity via building local cluster head connections between nearby cluster heads without considering global network topology. In [<xref ref-type="bibr" rid="scirp.62960-ref37">37</xref>] , the radio transmission range problem is analyzed and the probabilistic bounds for isolated nodes and connected nodes with uniform nodes on 1 - 2- and 3-dimension are calculated. It reports that the transmitting range of nodes can be reduced substantially from the deterministic requirements if there is high probability of connectedness. The paper [<xref ref-type="bibr" rid="scirp.62960-ref38">38</xref>] extends the work [<xref ref-type="bibr" rid="scirp.62960-ref37">37</xref>] and discusses the asymptotic minimum node degree of a graph on uniform points in d dimension .Some more studies on radio transmission range problem are discussed in [<xref ref-type="bibr" rid="scirp.62960-ref39">39</xref>] - [<xref ref-type="bibr" rid="scirp.62960-ref44">44</xref>] . The works [<xref ref-type="bibr" rid="scirp.62960-ref39">39</xref>] - [<xref ref-type="bibr" rid="scirp.62960-ref42">42</xref>] however do not consider inhomogeneous nodes. The issue of k-connectivity with respect to different transmission ranges has been discussed in [<xref ref-type="bibr" rid="scirp.62960-ref40">40</xref>] [<xref ref-type="bibr" rid="scirp.62960-ref41">41</xref>] . The same has been analyzed using the stochastic connectivity properties of the wireless multihop networks in [<xref ref-type="bibr" rid="scirp.62960-ref43">43</xref>] . The paper [<xref ref-type="bibr" rid="scirp.62960-ref44">44</xref>] discusses the connectivity for inhomogeneous node distributions with random waypoint (RWP) nodes. The paper [<xref ref-type="bibr" rid="scirp.62960-ref45">45</xref>] extends the Bettsetter’s work [<xref ref-type="bibr" rid="scirp.62960-ref44">44</xref>] by incorporating the deployment border effects on the range to provide k-connectivity. In [<xref ref-type="bibr" rid="scirp.62960-ref46">46</xref>] , the critical transmission power based on Bettsetter [<xref ref-type="bibr" rid="scirp.62960-ref44">44</xref>] is discussed to maintain k-connectivity, ensuring k-neighbors of a node is a necessary condition but not the sufficient condition for k-connectivity. It is because the network graph may have critical points which can cause the network failure and destroys the end-to-end network connectivity. In [<xref ref-type="bibr" rid="scirp.62960-ref47">47</xref>] [<xref ref-type="bibr" rid="scirp.62960-ref48">48</xref>] , the critical transmitting range for connectivity in both stationary and mobile ad hoc networks has been analyzed. The paper [<xref ref-type="bibr" rid="scirp.62960-ref47">47</xref>] also discusses the probability for establishing a multihop path between two Poisson distributed nodes on an infinite line with a given distance. The paper [<xref ref-type="bibr" rid="scirp.62960-ref49">49</xref>] discusses about the node that keeps a multihop path to a fixed base station with the nodes moving in a straight line away from the base station. The k-connectivity concept has been further extended in [<xref ref-type="bibr" rid="scirp.62960-ref50">50</xref>] that studies the critical number of neighbors needed for k-connectivity. There are critical or weak points that play a major role in destroying the network connectivity. The works [<xref ref-type="bibr" rid="scirp.62960-ref40">40</xref>] [<xref ref-type="bibr" rid="scirp.62960-ref41">41</xref>] [<xref ref-type="bibr" rid="scirp.62960-ref43">43</xref>] [<xref ref-type="bibr" rid="scirp.62960-ref45">45</xref>] [<xref ref-type="bibr" rid="scirp.62960-ref50">50</xref>] have not discussed critical points. The paper [<xref ref-type="bibr" rid="scirp.62960-ref51">51</xref>] characterizes the critical transmitting range by using the asymptotic distribution of the longest minimum spanning tree [<xref ref-type="bibr" rid="scirp.62960-ref52">52</xref>] [<xref ref-type="bibr" rid="scirp.62960-ref53">53</xref>] . In [<xref ref-type="bibr" rid="scirp.62960-ref54">54</xref>] , the problem of minimizing the maximum of node transmitting ranges while achieving connectedness is discussed. The basic assumption here is that the relative distance of all nodes is considered as input to the centralized topology control algorithm. In [<xref ref-type="bibr" rid="scirp.62960-ref55">55</xref>] , a distributed topology control protocol is discussed to minimize the energy required to communicate with a given master node. In this work, every node is equipped with a GPS receiver to provide position information. Initially every node iteratively broadcasts its position to different search regions. When the node is able to calculate a set of nodes, called as its enclosure, based on the position information obtained from neighbors, this process stops. Its major drawback is that the number of iterations to determine the enclosure depends on the definition of initial search region, which affects the energy consumption of the protocol. The same problem has been analyzed in [<xref ref-type="bibr" rid="scirp.62960-ref56">56</xref>] using directional information obtained by using multi-directional antenna. But such setup is not possible in sensor networks because the nodes are very simple and have no centralized communication facility.</p><p>In order to prevent failures from partitioning the network and to maintain end-to-end connectivity, researchers recommend the network topology to be K-connected. The K-connectivity refers that the network should have K-disjoint routes between each node pair, which may be edge disjoint or noded is joint. Till now, the main focus has been on to determine the combination of node density and transmission range in order to provide k-node cconnectivity in a specific deployment scenario in homogeneous nodes or non-homogenous nodes in MANETs. However, very few papers [<xref ref-type="bibr" rid="scirp.62960-ref57">57</xref>] - [<xref ref-type="bibr" rid="scirp.62960-ref59">59</xref>] have defined the critical points or weak points in MANETs which results in the network partition in two or more components.</p><p>The work of different authors have been analyzed with their viewpoints on the ad hoc network connectivity along with techniques used and defects in more pristine manner in <xref ref-type="table" rid="table1">Table 1</xref>. Besides such rigorous works, there are still some major issues and challenges left that need be looked uponin order to have better node connectivity in MANETs.</p></sec><sec id="s4"><title>4. Issues and Challenges</title><p>a) Mobility Prediction: Mobility prediction of a node is the estimation of its future locations. The definition of location depends on the type of wireless networks. In infrastructure networks location refers to the access points to which mobile terminal is connected. In infrastructureless networks the location refers to geographical coordinates. Its main advantage is to predict the link expiration time to improve the node connectivity and routing performance. Many location prediction methods are discussed in literature [<xref ref-type="bibr" rid="scirp.62960-ref60">60</xref>] [<xref ref-type="bibr" rid="scirp.62960-ref61">61</xref>] . However, no studies discuss about the prediction of future locations considering realistic scenarios. This is one of the biggest challenges in MANETs.</p><p>b) Connectivity Efficiency and Cost Objective: Since in order to deploy static nodes in place of critical nodes in which lack of nodes are felt, deployment cost of static nodes must be considered. While placing such nodes attention must be paid on to maximize the connectivity and to have minimum cost. But to achieve both connectivity efficiency and cost objective while deploying the critical points is a NP-complete problem. Since few authors have attempt this part [<xref ref-type="bibr" rid="scirp.62960-ref62">62</xref>] but no work has done to find the critical links and critical nodes considering the both factor cost objective and connectivity efficiency. Since it is NP-complete problem, more optimized technique need to be followed in order to achieve this goal</p><p>c) Energy Consumption issues: One of the key challenges is to save the limited energy and use it to prolong the network lifetime considering the network connectivity constraints. Since the energy is the most valuable resources in MANETs, its status should continuously be monitored after network deployment. The paper [<xref ref-type="bibr" rid="scirp.62960-ref53">53</xref>] - [<xref ref-type="bibr" rid="scirp.62960-ref56">56</xref>] [<xref ref-type="bibr" rid="scirp.62960-ref63">63</xref>] discusses this issue. Researchers have considered the random waypoint mobility model, which is unrealistic model. There is a need to look this aspect in real scenarios.</p><table-wrap-group id="1"><label><xref ref-type="table" rid="table1">Table 1</xref></label><caption><title> Analysis of the existing work related to network connectivity in ad hoc networks</title></caption><table-wrap id="1_1"><table><tbody><thead><tr><th align="center" valign="middle" >Methods</th><th align="center" valign="middle" >Assumptions</th><th align="center" valign="middle" >Findings</th><th align="center" valign="middle" >Drawbacks</th></tr></thead><tr><td align="center" valign="middle" >Random Network Model [<xref ref-type="bibr" rid="scirp.62960-ref27">27</xref>]</td><td align="center" valign="middle" >Given Cluster head that form a dominant independent set in the network graph [<xref ref-type="bibr" rid="scirp.62960-ref64">64</xref>] [<xref ref-type="bibr" rid="scirp.62960-ref65">65</xref>] .</td><td align="center" valign="middle" >Provides Network connectivity with very high probability [<xref ref-type="bibr" rid="scirp.62960-ref66">66</xref>] .</td><td align="center" valign="middle" >Results are of theoretical interest and cannot be apply to real scenarios</td></tr><tr><td align="center" valign="middle" >Spatial Poisson Process and Percolation theory [<xref ref-type="bibr" rid="scirp.62960-ref28">28</xref>]</td><td align="center" valign="middle" >Considers mobile stations that are confirmed to a single dimensional line. At a particular instant in time it is assumed that stations spatial positions can be modeled as a one dimensional Poisson process.</td><td align="center" valign="middle" >Node’s broadcast message percolate, if the nodes are randomly distributed according to homogeneous Poisson point process on an infinitely large area</td><td align="center" valign="middle" >Work did not consider the actual distance between nodes.</td></tr><tr><td align="center" valign="middle" >Packet radio network model [<xref ref-type="bibr" rid="scirp.62960-ref29">29</xref>]</td><td align="center" valign="middle" > Nodes in the network lie in bounded area.  The homogeneous Poisson assumption.  Each node is able to communicate with any other node that is at most R unit distance from it.  All nodes generate Poisson streams of traffic at an identical rate.</td><td align="center" valign="middle" >No optimal number of nearest neighbor or magic number can exist.</td><td align="center" valign="middle" >Difficult to apply in real scenario. Also in disk based models, fading, interference and attenuation does not considerer.</td></tr><tr><td align="center" valign="middle" >Connectivity of radio networks [<xref ref-type="bibr" rid="scirp.62960-ref30">30</xref>]</td><td align="center" valign="middle" > Transmitters are distributed according to one dimensional Poisson process of density d.  Two transmitters are connected by an edge apart at most i distance of their communication range.</td><td align="center" valign="middle" >Solve s the problem of [<xref ref-type="bibr" rid="scirp.62960-ref29">29</xref>] in 1-dim. Studies uniform distributed nodes on 1-dim line segment and estimates critical transmitting range for Poisson distributed nodes</td><td align="center" valign="middle" >Difficult to be apply in real scenario, only the expected number of deployed nodes can be controlled</td></tr><tr><td align="center" valign="middle" >Critical power for asymptotic connectivity in wireless networks [<xref ref-type="bibr" rid="scirp.62960-ref31">31</xref>]</td><td align="center" valign="middle" >Nodes placed randomly in disc of unit area.</td><td align="center" valign="middle" >Estimates the critical power of a node, needed to transmit to ensure that the network connectivity.</td><td align="center" valign="middle" >Not applicable for real applications.</td></tr><tr><td align="center" valign="middle" >Connectivity in ad hoc and hybrid networks [<xref ref-type="bibr" rid="scirp.62960-ref33">33</xref>]</td><td align="center" valign="middle" > Assumes a large scale wireless network with low density of nodes per unit area.  Power constraints are modeled by a maximum distance above which two nodes are not directly connected.</td><td align="center" valign="middle" >Base stations significantly help in increasing the network connectivity only for large density</td><td align="center" valign="middle" >Findings restricted only to one dimensional case.</td></tr><tr><td align="center" valign="middle" >Number of neighbors needed for connectivity of wireless networks [<xref ref-type="bibr" rid="scirp.62960-ref34">34</xref>]</td><td align="center" valign="middle" > In a network with n randomly placed nodes each node should be connected to Ө (log n) neighbors.  N nodes are placed uniformly and independently in unit square region.  Critical constant appear to be close to 1.</td><td align="center" valign="middle" > Increasing number of nodes should not effect the number of nearest neighbor otherwise once obtained a disconnected network  Asymptotic connectivity results when every node is connected to its nearest 5.1774logn neighbors, while asymptotic disconnectivity results when each node is connected to less then 0.074logn nearest neighbors.</td><td align="center" valign="middle" >Result is of theoretical interest and cannot be applicable to real scenario</td></tr><tr><td align="center" valign="middle" >Approximation algorithms and Connectivity Augmentation Problem [<xref ref-type="bibr" rid="scirp.62960-ref35">35</xref>]</td><td align="center" valign="middle" >Network is treated as undirected graph [<xref ref-type="bibr" rid="scirp.62960-ref67">67</xref>] and each possible link is either feasible or infeasible.</td><td align="center" valign="middle" >Determines a set of edges of minimum weight to be inserted so that so that the resulting graph is λ-vertex (edge) connected. The problem is NP hard for λ &gt; 1</td><td align="center" valign="middle" >Does not consider the possibility of adding new vertices into the graph</td></tr><tr><td align="center" valign="middle" >A probabilistic analysis for the radio range assignment problem in ad hoc networks [<xref ref-type="bibr" rid="scirp.62960-ref37">37</xref>]</td><td align="center" valign="middle" >N nodes having same transmission range are distributed in d-dimensional region.</td><td align="center" valign="middle" >Estimates bounds for isolated nodes and connected nodes on a one dimensional line segment.</td><td align="center" valign="middle" >It is only for static networks. It does not incorporate the possibility of transient or permanent node failures in their model. That will lead to requirements for desirable topological characteristic to be harsher compared to theoretical prediction</td></tr></tbody></table></table-wrap><table-wrap id="1_2"><table><tbody><thead><tr><th align="center" valign="middle" >Phase transition [<xref ref-type="bibr" rid="scirp.62960-ref39">39</xref>]</th><th align="center" valign="middle" >n nodes located randomly in some service area, each assumed to transmit with a fixed radio power in an idealized environment where it can be heard by other nodes within same radius.</th><th align="center" valign="middle" >Discusses the theory of Bernoulli random graphs [<xref ref-type="bibr" rid="scirp.62960-ref65">65</xref>] that can help in proving when the phase transition in ad hoc network occurs.</th><th align="center" valign="middle" >Classical theory of random graph models is not suitable to model the ad hoc network problems.</th></tr></thead><tr><td align="center" valign="middle" >Minimum node degree and connectivity of wireless multi hop network [<xref ref-type="bibr" rid="scirp.62960-ref40">40</xref>]</td><td align="center" valign="middle" >1. Random uniform distribution of nodes and a simple link model 2. All nodes are free to move in the system area according to a certain mobility model 3. Radio link model is assumed</td><td align="center" valign="middle" >Finds the uniform distribution of homogeneous nodes in a rectangular deployment area and establishes a relationship between the minimum transmission range and the probabilistic behavior of minimum node degree</td><td align="center" valign="middle" >Results are of theoretical interest and require a very high node density to ensure k-connectivity which would lead to interference and low throughput in real networks</td></tr><tr><td align="center" valign="middle" >Minimum node degree and k-connectivity of a wireless multi hop network in bounded area [<xref ref-type="bibr" rid="scirp.62960-ref45">45</xref>]</td><td align="center" valign="middle" >Circumvent the border effect which assumes boundless network deployment area.</td><td align="center" valign="middle" >Eliminate the border effects in order to provide an improved estimation of probabilistic characteristic, including the upper bound and lower bound of minimum node degree and k-connectivity.</td><td align="center" valign="middle" >High node density would lead to interference and low throughput in real networks</td></tr><tr><td align="center" valign="middle" >Torus Convention [<xref ref-type="bibr" rid="scirp.62960-ref46">46</xref>]</td><td align="center" valign="middle" >Nodes are distributed on a unit square according to a homogeneous Poisson point process with density λ.</td><td align="center" valign="middle" >Eliminate the need to consider boundary effects that may affect the critical transmission range for k-connectivity</td><td align="center" valign="middle" >It is of theoretical interest, requiring a very high node density to ensure k-connectivity which would lead to interference and low throughput in real networks.</td></tr><tr><td align="center" valign="middle" >Critical transmitting range for connectivity in sparse wireless ad hoc networks [<xref ref-type="bibr" rid="scirp.62960-ref47">47</xref>]</td><td align="center" valign="middle" > Nnodes, each capable of communicating with nodes within a radius of r, randomly and uniformly distributed in d?dimensional region of side of length l.  Consider both stationary and mobile nodes</td><td align="center" valign="middle" >Estimates tolerating parseness i.e. Requiring 90% of nodes to be in the same connected component results in the significant reduction in the required transmission ranges of nodes.</td><td align="center" valign="middle" >If physical node degree is upper bounded by a constant, then the resulting communication graph is disconnected</td></tr><tr><td align="center" valign="middle" >Connectivity in finite ad hoc networks [<xref ref-type="bibr" rid="scirp.62960-ref48">48</xref>]</td><td align="center" valign="middle" >Uniform distribution of nodes in [0,z] where z &gt; 0 for one dimensional network</td><td align="center" valign="middle" >Finds the probability of connectivity of one dimensional finite ad hoc network formed by uniform distribution of nodes</td><td align="center" valign="middle" >Probability of network was not correct and has been corrected by [<xref ref-type="bibr" rid="scirp.62960-ref68">68</xref>]</td></tr><tr><td align="center" valign="middle" >Cell extension and Mobility patterns [<xref ref-type="bibr" rid="scirp.62960-ref49">49</xref>]</td><td align="center" valign="middle" > All relay nodes are randomly distributed and fixed.  Mobile nodes and relay nodes move right at same velocity.  Relay nodes are classified into two groups. In first group both relay nodes never move and in other group relay nodes move left at velocity</td><td align="center" valign="middle" >Finds that multi-hop networking with two kind of mobile relay nodes degrades cell extension performance compared with fixed relay nodes.</td><td align="center" valign="middle" >It is of theoretical interest and cannot be apply to real scenarios. Also, Distributions of mobile nodes are considered only on street not on plane.</td></tr><tr><td align="center" valign="middle" >Asymptotic critical transmission radius and critical neighbor number for k-connectivity [<xref ref-type="bibr" rid="scirp.62960-ref50">50</xref>]</td><td align="center" valign="middle" >Uniform n point process over a unit area disk or square.</td><td align="center" valign="middle" >Obtained the improved asymptotic almost sure upper bound on the critical neighbor number for k-connectivity.</td><td align="center" valign="middle" >It does not consider interference, though dense networks produce strong interference. Decrease the network throughput.</td></tr></tbody></table></table-wrap><table-wrap id="1_3"><table><tbody><thead><tr><th align="center" valign="middle" >Geometric random graph theory [<xref ref-type="bibr" rid="scirp.62960-ref51">51</xref>]</th><th align="center" valign="middle" > Number of nodes placed in uniformly distributed area.  Homogeneous topology control</th><th align="center" valign="middle" >Asymptotic distribution to analyze the critical transmission range</th><th align="center" valign="middle" >It can only be used to model dense ad hoc networks, not for sparse ad hoc networks [<xref ref-type="bibr" rid="scirp.62960-ref69">69</xref>]</th></tr></thead><tr><td align="center" valign="middle" >Centralized topology control algorithm [<xref ref-type="bibr" rid="scirp.62960-ref54">54</xref>]</td><td align="center" valign="middle" >Relative distance of all nodes are given as input to a centralized topology control algorithm</td><td align="center" valign="middle" >Minimize the maximum transmitting range of nodes. Also, reduce the energy consumption and improve the network throughput</td><td align="center" valign="middle" >No information has been given to select the appropriate number of neighbors for LINT heuristic (Local Information No Topology). Danger of Network partition in LINT.</td></tr><tr><td align="center" valign="middle" >Distributed position based network protocol [<xref ref-type="bibr" rid="scirp.62960-ref55">55</xref>]</td><td align="center" valign="middle" >Every node is equipped with a GPS receiver. Also, The number of iteration needed to determine the enclosure is based on the definition of the initial search engine</td><td align="center" valign="middle" >Minimizes the energy needed to communicate with a single master node.</td><td align="center" valign="middle" >The search engine is a critical aspect which affects the energy consumption of the protocol.</td></tr><tr><td align="center" valign="middle" >Cone-based distributed topology control algorithm [<xref ref-type="bibr" rid="scirp.62960-ref56">56</xref>]</td><td align="center" valign="middle" >Each node has some power function which transmits the minimum power needed to establish a communication link to a node far away from that node.</td><td align="center" valign="middle" >Reduce the power consumption and discuss the modification to deal with mobility.</td><td align="center" valign="middle" >Initial range assignment for nodes and its step increase has not been discussed.</td></tr><tr><td align="center" valign="middle" >Depth first search algorithm [<xref ref-type="bibr" rid="scirp.62960-ref57">57</xref>]</td><td align="center" valign="middle" >Nodes know their location and periodically update their neighbors with their current locations.</td><td align="center" valign="middle" >Determine the critical links whose failure cause partitioning of the network and then supporting these links either by modifying the trajectory of the nodes involved in the critical links or bringing an outside node to reinforce them.</td><td align="center" valign="middle" >Increase delivery rates due to Prolonged network connectivity. Communication overhead due to running DFS (Depth First Search) for detecting critical links was not measured.</td></tr><tr><td align="center" valign="middle" >Localized algorithm for testing k-connectivity [<xref ref-type="bibr" rid="scirp.62960-ref59">59</xref>]</td><td align="center" valign="middle" > Each node makes its own decision based on the information available in its local neighborhood.  Each node verifies whether or not itself and each of its p-hop neighbors of a given node is k-connected.  All nodes declare themselves locally 1-connected.</td><td align="center" valign="middle" >Find the critical nodes and links using local topology and location information</td><td align="center" valign="middle" >Detected critical points may not be global critical points due to existence of alternate routes</td></tr><tr><td align="center" valign="middle" >Genetic Algorithm and Fish Swarm Algorithm [<xref ref-type="bibr" rid="scirp.62960-ref62">62</xref>]</td><td align="center" valign="middle" >Realistic Mobility Model</td><td align="center" valign="middle" >Improve the node connectivity issue by adding static nodes with consideration to deployment cost</td><td align="center" valign="middle" >Energy loss while deploying static nodes is not considered</td></tr><tr><td align="center" valign="middle" >Linear Programming and Neural Networks [<xref ref-type="bibr" rid="scirp.62960-ref63">63</xref>]</td><td align="center" valign="middle" >Consider a network of homogeneous and energy constrained sensor nodes that are randomly deployed in a sensor field.</td><td align="center" valign="middle" >scheme is quite effective to deliver 95% of packets to their destination with increase in network coverage</td><td align="center" valign="middle" >It cannot guarantee the full sensing coverage of the network.</td></tr><tr><td align="center" valign="middle" >Fractal Propagation Model [<xref ref-type="bibr" rid="scirp.62960-ref70">70</xref>]</td><td align="center" valign="middle" >For every two nodes within the transmission range will be connected with a probability as function of their geometrical distance.</td><td align="center" valign="middle" >Giant component size can be characterized by a single parameter i.e. average node degree.</td><td align="center" valign="middle" >Giant component size has been estimated empirically rather than analytically</td></tr><tr><td align="center" valign="middle" >Undirected Geometric Random Graph [<xref ref-type="bibr" rid="scirp.62960-ref71">71</xref>]</td><td align="center" valign="middle" >n nodes are randomly and uniformly distributed on a square and link exist between two nodes if the power received at one node from the other nodes is greater then a given threshold.</td><td align="center" valign="middle" >It shows an empirical formula relating the giant component size and the average node degree in random geometric graphs.</td><td align="center" valign="middle" >Giant component size of network has been estimated empirically rather then analytically.</td></tr></tbody></table></table-wrap></table-wrap-group></sec><sec id="s5"><title>5. Conclusion</title><p>In this paper, we have reviewed the existing literature with respect to network connectivity. The network connectivity is an important property of ad hoc network because the mobile nodes change the network topology very frequently. If the network becomes disconnected, the data may not be sent to the desired destination. Most of the existing works have considered unrealistic scenarios and very less works discuss the major factors contributing to network disconnectivity in MANETs. We have highlighted important parameters that affect the network connectivity, which include battery/power failure, link failure, critical points, and node failure. The main issues in network connectivity are mobility prediction, energy consumption and connectivity efficiency along with connectivity cost and these issues need to be explored further to understand network connectivity.</p></sec><sec id="s6"><title>Cite this paper</title><p>MohitJain,SatishChand, (2016) Issues and Challenges in Node Connectivity in Mobile Ad Hoc Networks: A Holistic Review. Wireless Engineering and Technology,07,24-35. doi: 10.4236/wet.2016.71003</p></sec></body><back><ref-list><title>References</title><ref id="scirp.62960-ref1"><label>1</label><mixed-citation publication-type="other" xlink:type="simple">Basu, S.S. and Chaudhari, A. (2003) Self Adaptive Topology Management for Mobile Ad Hoc Network. Journal of the Institution of Engineers (India), 84, 7-13.</mixed-citation></ref><ref id="scirp.62960-ref2"><label>2</label><mixed-citation publication-type="other" xlink:type="simple">Johnson, D.B. and Maltz, D.A. Dynamic Source Routing in Ad Hoc Wireless Networks. 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