Power Aware Ant Colony Routing Algorithm for Mobile Ad ... - SERSC

International Journal of Software Engineering and Its Applications Vol. 9, No. 12 (2015), pp. 197-212 http://dx.doi.org/10.14257/ijseia.2015.9.12.18

Power Aware Ant Colony Routing Algorithm for Mobile Ad-hoc Networks Alaa E. Abdallah1, Emad E. Abdallah1, Feras Hanandeh1, Ashraf Aljammal1 and Essam Al-Daoud2 1

Faculty of Prince Al-Hussein Bin Abdallah II For Information Technology, Hashemite University, P.O. Box 150459, Zarqa 13115, Jordan 2 Department of Computer Science, Zarqa University Zarqa- Jordan 1 aabdallah,bsoul,emad,[email protected] 2 aabdallah, emad, feras, [email protected], [email protected] Abstract

Due to the limited lifetime of nodes in ad hoc and sensor network, energy efficiency needs to be an important design consideration in any routing algorithm. Most of the existing Ant colony based routing algorithms grantee the packet delivery. However, they suffer from the high power consumption due to the huge number of control messages to establish and maintain a route from a source to a destination. This paper introduces two new power-aware ant colony routing algorithms for mobile ad hoc network under three main concurrent constraints. (1) Localized algorithms where only information about neighbors' nodes is needed. (2) Maximize the algorithms delivery rate. (3) Minimize the energy consumption. Our new algorithms are based on the idea of extracting a sub-graph of the original network topology and combine it with the advantage of ant based routing algorithms. Extensive experiments are conducted to prove that the new algorithms have significant improvement on the network lifetime (up to twice) without affecting the high delivery rate. Keywords: Ad hoc network, Local algorithm, Energy efficient, Ant colony routing, Geometric sub-graph

1. Introduction A Mobile ad hoc network (MANET) is a network formed of a set of wireless nodes which communicate with other nodes without any supporting network infrastructure. In MANET, a node can directly communicate with another node if and only if the two nodes are located within the transmission range of each other. However, if a node wishes to communicate with another node from outside the transmission range, it uses the help from other nodes which play as a router to receive and forward packets. Mobile nodes in ad hoc networks can move, join and leave the network freely. Thus, routing in such networks is a crucial problem. However for many networks, a more important problem is providing an energy efficient routing protocol because of the limited battery life of the wireless nodes. Routing algorithms in mobile ad hoc network can be classified into two basic types [1] (1) Proactive [2-3], where each node in the network keeps routing information about every other node up-to-date all the time. In this kind of protocol the nodes periodically broadcast their routing tables to their neighbors. Proactive routing has a short response time but it consume huge overhead exchanging routing information especially when the mobility rate is big. (2) Reactive [4-6], where a node maintains only the routes that are currently in use. In this strategy, if the route is not available, the current node holding the packet issues a destination search request which is performed by flooding a short control

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International Journal of Software Engineering and Its Applications Vol. 9, No. 12 (2015)

message. Obviously, there will be no routing table broadcast, thus less overhead but longer time to find a route. Another family of routing algorithms called position-based routing [1, 7-11]. Positionbased routing algorithms assume that a node is aware of its position using cheap instruments like GPS (Global Positioning System), the position of its neighbors by using periodic hello messages, and the position of the destination using location service algorithms [12-13]. Position-based routing is known to have much less overhead than proactive and reactive protocols but they may fail to find a route or they may find a nonoptimum route in some situations. Over the last few years, researchers proposed a new set of routing algorithms that are inspired by real ants’ behavior and their way to find the shortest path between food and their nest [14-18]. The protocols works as follows [19-20], while searching for food, ants deposit on the ground a substance called pheromone to give them a way back to the nest. By time the shorter paths would have more ants going through them, thus more pheromone consecration. Other ants can smell pheromone, and when searching for a path, they tend to choose, with high probability, paths marked by strong pheromone concentrations. Most of ant-colony routing algorithms suffer from the high power consumption due to the huge number of control messages which are needed to establish a route from the source to the destination. Many routing strategies use a sub-graph of the unit disk graph such that only the edges in the sub-graph are used for routing. Therefore, much research effort has gone into the development of algorithms for ad hoc network topology control (see [21- 23] for surveys). In this paper, we propose a new set of routing algorithms for mobile ad hoc network which utilize the network nodes positions to extract a sub-graph of the original Unit Disk Graph (UDG). Then we apply ANTHOCNET [24] routing over the extracted sub-graph. Our simulations show that the new algorithms have significant improvement on the network lifetime. The rest of this paper is organized as follows. Section 2 is devoted to background material. In Section 3, we briefly review some related position-based and ant-based routing algorithms. In Section 4 we give detailed descriptions of the new proposed routing algorithms. In Section 5, we present experimental results to demonstrate the much improved performance of the proposed methods in comparison with existing techniques. Finally, we conclude and point out some future directions in Section 6.

2. Preliminaries 2.1. The Network Model We assume that a set of n wireless hosts is represented as a point set V in the 2D space. All nodes have the same communication range of r, which represented as a circle of radius r. Let 𝑑𝑖𝑠𝑡(𝑢, 𝑣) be the Euclidean distance between the nodes u and v:𝑑𝑖𝑠𝑡(𝑢, 𝑣) = √(𝑢𝑥 − 𝑣𝑥 )2 + (𝑢𝑦 − 𝑣𝑦 )2 . Two nodes are connected by an edge if the Euclidean distance between them is at most r. The resulting graph is called a unit disk graph UDG(V, r). For node u, we denote the set of its neighbors by N(u). A path from s to d is a sequence of nodes s = v1, v2, ….., vk = d, such that (vi ,vi+1) are neighbors. 2.2. The Routing Problem Given a unit disk graph UDG(V) corresponding to a set of points V , and a pair (s,d) where 𝑠, 𝑑 ∈ 𝑉, the problem of routing is to discover a path in UDG(V) from s to d. It’s not an easy task to design a power efficient routing algorithm for many reasons (1) the wireless links in the network are not known to any node in the network because nodes may not have fixed locations. (2) Number of neighbors keeps changing because of the

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interference and noise which affect the transmission range. (3) Nodes in the network can only see neighbors and does not have a global look to the whole network. We are interested in the following performance measures for routing algorithms: the delivery rate [11] which is the percentage of times that the algorithm succeeds in delivering its packet, the average packet delay, the overall overhead and the power consumption [25-28], it’s the amount of energy consumed by a message sent in the network. If the transmission range is fixed for all the nodes, then the number of nodes in the route path and the number of control messages are the main source of the energy required for the routing task. 2.3. Related UDG sub-graphs A distributed algorithm is called local if each node of the network only uses information obtained uniquely from the nodes located no more than a constant (independent of the size of the network) number of hops from it. Thus, during the algorithm, no node is ever aware of the existence of other nodes in the network further away than this constant number of hops. In the following we present some local distributed algorithm to extract a sub-graph from UDG.  Gabriel Graph (GG(G)) [29-30]: given any two adjacent nodes p and q in UDG, the edge (p, q) belongs to GG if, and only if, the closed disc of which line segment pq is a diameter contains no other nodes of V, see Figure 1. It is known that if UDG is 2dimensional then the Gabriel sub-graph is planar, where the edges intersect only at their common end-vertices, and if the UDG is connected, then its Gabriel sub-graph is also connected [31].

Figure 1. Edge x is Counted in GG(G) Graph if the Disc of Diameter x Contains no Other Nodes  YAO_K(G) Graph [32-33]: with an integer parameter k ≥ 6 is defined as follows. Each node p divides the unit disk around it to k equal cones, see Figure 2. In each cone, only the edge (p,q) to the nearest neighbor q, if any, is part of the directed YAO_K(G). Ties are broken arbitrarily. The undirected graph obtained if the direction of each edge is ignored. If the UDG is connected, then its YAO_K(G) Graph is also connected [34-35].  Half Space Proximal Graph HSP(G) is defined as follows [36]. Each node p in UDG performs the following procedure: p draws an edge (p, q) to the nearest neighbor q, and draws a perpendicular line intersecting the edge (p, q) at its midway point, all nodes in the half plane contains q are discarded. The procedure then continues with the next non discarded node. See Figure 3. It has been proved in [36] that HSP(G) is connected, has an out-degree of at most six, and contains the minimum weight spanning tree as its sub_graph.


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Figure 2. The Shortest Edge in Each Cone is Counted in YAO_ K(G)

Figure 3. HSP(G) Graph, the Shortest Edge is Chosen, All Other Nodes behind the Perpendicular Line are Discarded

3. Related Routing Algorithms Routing in Mobile ad hoc network depends on many factors including network topology, the type of information available during routing, and the specific underlying network characteristics that could be used to define a heuristic to find a path quickly and efficiently. The following reviews some representative ant-based and position-based routing algorithms that are closely related to our proposed approach. We briefly discuss the algorithmic methodologies as well as their limitations.  Greedy routing [30]: a position-based routing, where the current node c forwards the packet to the neighbor node p that minimizes the remaining distance to the destination node d. the same procedure is repeated until the destination node is reached or no such node p exists. This routing method suffers from the so called local minimum phenomenon, in which a packet may get stuck at a node that does not have a neighbor that makes a progress to the destination, even though the source and the destination are connected by a path in the network.  DREAM [37]: in this algorithm, the current node c forwards the packet to all neighbors in the direction of the destination d. A node is considered to be in the direction of d if it is located in a 2D cone that starts at current node and ends with a circle centered at d, the circle radius equal to vmax * (t1 − t0) where t1 is the current time, t0 is the time stamp of the position information that c has about d and vmax is the maximum speed of the node in the network.  ANTHOCNET [24] is a reactive ant colony optimization algorithm. To find a route from a source to a destination, two types of control packet are generated, forward and backward ants. Over a specific time interval, the source node generates periodic forward ants (control packet) for the destination. The ants flood the network through all possible ways Figure 4. Each ant updates the weight of every visited link by a fixed number (deposit on the ground a substance called pheromone). Obviously ants that pick

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the shortest path from the source to the destination will arrive first to the destination. At this point the destination will generate backward ants to go through the exact same shortest path which will change the status of the links by increasing the pheromone concentration on the links along the path. The source node knows now the shortest path from the source to the destination. To prevent infinite loops in the route discovery process, each visited node u keeps the following about the forward ants: the identity of the forward ants and the arrival time to u. When an ant visits node u, it checks the ants arrival time and its identity. If the time are within a certain limit of those produced by another ant of the same generation then the ant is forwarded. Otherwise, the ant is discarded. The drawback of ANTHOCNET is the number of forward and backward control messages that needs to be sent in the network for establishing routes to the destination and the time needed before paths between the nodes of the network is established.

Figure 4. ANTHOCNET Route Discovery Process, to find a Route from S to D, S Generates Periodic Forward Ants (Control Packet) for the Destination D. The Ants Flood the Network through all Possible Ways  ANTNET [38]: considered to be a proactive ant colony routing algorithm for mobile switch networks. The algorithm starts by lunching a forward ant at regular intervals from the source node. Depend on the information stored at the current node routing table, the next node is selected as follows: suppose that a forward ant is currently residing in node n and this node has k neighbors h1,h2, ...,hk, and Φi is the amount of pheromone assigned to nhi. The forward ant will select hi as the next node with a probability pi which is calculated using Equation 1. The probability of selecting the next node is proportional to the pheromone concentration. As in ANTHOCNET, when the forward ant reaches the destination, it generates a backward ant that goes through the opposite direction of the path chosen by the corresponding forward ant. The backward ant updates pheromone values as it moves on its way to the source node. Because ANTNET route discover process select one neighbor at each intermediate node, the end to end delay is higher than many other ant colony routing algorithms. In General most of the ant colony routing algorithms including ANTHOCNET have a very high delivery rate and find routes whose lengths are very close to the length of the shortest path [39-38].

4. Proposed Routing Algorithms In this section we explain our new proposed algorithms, ANTSUB(HSP(G)) and ANTSUB(YAO_ 6(G)). They are able to find nearly optimum routes and save up to 50% of the energy consumption compared with the famous ANTHOCNET routing. As stated before, ANTSUB(HSP(G)) and ANTSUB(YAO_6(G)) are reactive algorithms, thus a route is searched only when a node wants to send data to another node outside its transmission range. The route discovery is done only through control ant messages. Data


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packets to be sent after the route discover process is finished and the route is established. In order to decrease the energy consumption at each node in the network, we have to keep the number of control messages as small as possible. ANTSUB(HSP(G)): consider a source node, s, has a set of data packets and wants to send them to a destination node, say d. Since s does not know how to reach d, it starts a route discover process as follows: using HSP(G) algorithm s finds the set of edges around it that belongs to HSP(G), then s sends several forward ants with unique sequence numbers through each edge at regular time intervals, see Figure 5. Each neighbor receives a forward ants run the HSP(G). Based on the values of pheromone trails the current node selects the next hop as follows: consider the forward ant is currently at node n and this node has k neighbors h1,h2, ...,hk, and Φi is the amount of pheromone assigned to nhi. The forward ant will select hi as the next node with a probability pi which is calculated using Equation 1. As on any ant colony routing algorithm, when an ant visits a node through an edge, it deposits a specific amount of pheromone on that edge. The information about the values of pheromone on the neighbors edges is stored on a table at each node (which is going to be used while sending the data packets). 𝛷𝑖

𝑝𝑖 = ∑𝑘

𝑗=1 𝛷𝑗

(1)

In addition to the pheromone trial table, each node keeps another table which we call back routing table. The table saves the id of each ant enters the node, the ant source node, the ant destination node and the ant time stamp. This is used to discard duplicate ants. With the help of the same table, the backward ant traverses in the reverse direction. If a node does not hear any packet going to a given destination for a specific amount of time (order of seconds), it deletes the back routing table. The pheromone trails for that destination will be deleted from the pheromone trail table as well. Algorithm 1 shows in details the route discovery process. We have tried to use YAO_6(G) instead HSP(G) graph during the routing process of the Algorithm 1 to a get a new routing algorithm ANTSUB(YAO_6(G)). It did not show better power saving than ANTSUB (HSP(G)) but still it’s better than other ant colony routing algorithms.

Figure 5. ANTSUB (HSP(G)) Route Discovery Process, to find a route from S to D. Each Node Receives a Forward Ant Extracts the Edges Around C that Belongs to HSP(G) and Broadcast the Ant to all Neighbors in HSP(G). This Process is done in a Periodic Basses

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5. Simulation Results In this section we describe our simulation environment, evaluate the performance of our new algorithms under several simulation scenarios and compare them with other related routing algorithms. 5.1. Simulation Environment The event-driven simulator can be described as follows: 1. We define 300mX300m square 2. A set V of n points (where n ∈ 100, 150, 200, 250, 300) is randomly positioned in the square, this would lead to different node densities. 3. Each node has a transmission range equal to 15m. 4. If the network graph is not fully connected, all connected components in the graph are calculated. 5. The largest connected component (LCC) among all the connected components is selected to perform the routing algorithms. 6. For each network, one source-destination pair is selected randomly from the LCC. 7. It is suggested in [24] to consider simulations with node density per unit disk of around 5 in 2D environment. The proposed simulation environment satisfies this density. 8. To estimate the performance, the algorithms are executed on 300 networks and report the average results. 9. The algorithms are evaluated in terms of delivery rate, the average packet delay, the overall overhead, and the power consumption of the whole network. 10. The delivery rate is defined as the ratio of the number of packets received by the destination node to the number of packets sent by the source node.


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11. To calculate the average power consumption, the model proposed by Heinzelman et al. [40] is utilized, which assumes the radio dissipates Eelec=50nJ/bit power to run the transmitter. Thus both transmitter and receiver nodes consume Eelec to transmit one bit receiver circuitry. 12. The radio dissipates Eamp=100pJ/bit/m2 to run the transmit amplifier, assuming d2 energy loss due to channel transmission, where d is the distance between nodes. This implies the sender consumes (Eamp * d2) power to transmit one bit. 13. According to the above wireless model, transmitting k−bits message at distance d the transmitter expends ETx(k, d) = Eelec * k + Eamp * k * d2. To receive the k−bits, the receiver node expends ERx(k) = Eelec * k. 5.2. Observed Result In Figure 6, we studied the effect of the running time on the average delivery rate of the new proposed algorithms and compared them with other well-known routing algorithms. Clearly, all studied routing algorithms reach 100% delivery rate by time, except of greedy algorithm due to the local minimum problem. Our algorithms ANTSUB(HSP(G)) and ANTSUB(YAO_6(G)) reaches 100% delivery rate way faster than ANTNET. The reason is that the new algorithms generate discovery ants though all possible ways at the source node and employ a connected sub-set of the original set of edges used by ANTNET, which means less chance to go through wrong paths, thus the shortest and the accurate path will be reached faster. ANTNET forwards ants to only one neighbor depend on the information stored at the node itself, which may lead to a longer path. Hence, more time to reach the destination.

Figure 6. The Average Delivery Rate of (HSP(G)), ANTSUB(YAO_6(G)), and other routing algorithms at different time clock. The number of nodes equal to 150


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Figure 7. The Average Delivery Rate of (HSP(G)), ANTSUB(YAO_6(G)), and Other Routing Algorithms at Different Network Density. The Results are Taken after 80ms In Figure 7, we studied the effect of the node density on the delivery rate. The running time is fixed in this study (80ms). The new proposed algorithms ANTSUB(HSP(G)) ANTSUB(YAO_6(G)) share highest delivery rate with ANTHOCNET. By increasing the node density, greedy algorithm delivery rate increases. This can be explained by the number of links, more links means more paths to the destination and less chances to face a local minimum. The delivery rate of all examined ant colony routing algorithms decreases by increasing the number of nodes because they all need more time to find an accurate route to the destination. Since, ANTSUB(HSP(G)) uses less number of edges than ANTSUB(YAO_6(G)) it has higher delivery rate.

Figure 8. The Average Packet Delay of (HSP(G)), ANTSUB(YAO_6(G)), and Other Routing Algorithms at Different Time Clock. The Number of Nodes Equal to 150

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Figure 9. The Average Packet Delay of (HSP(G)), ANTSUB(YAO_6(G)), and Other Routing Algorithms at Different Network Density. The Results are Taken after 80ms In Figure 8 and Figure 9, we studied the delay of our proposed routing algorithms and compare them with ANTNET and ANTHOCNET. In both figures we calculated end to end delay based on the number of hops visited by each packet. Figure 8 shows the average packet delay vs. time, in the beginning, the average packet delay of ANTHOCNET is smaller than all other routing techniques. The average delay of ANTSUB(HSP(G)) and ANTSUB(YAO_6(G)) reach to their minimum faster than the ANTNET. As time passes on, all algorithms converge to the paths with almost the same lengths, where packets experience almost the same delay. Figure 9 studied the effect of increasing number of nodes against the delay; as expected increasing number of nodes will increase the delay for all studied algorithms because the length of the path to the destination will increase.

Figure 10. The Average Power Consumption of (HSP(G)), ANTSUB(YAO_6(G)), and Other Routing Algorithms at Different Time Clock. The Number of Nodes Equal to 150


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Figure 11. The Average Power Consumption of (HSP(G)), ANTSUB(YAO_6(G)), and Other Routing Algorithms at Different Network Density. The Results are Taken after 80ms

The main contribution of the paper can be found in Figure 10, where we show the average power consumption from the whole network after running the algorithm for a specific amount of time; clearly the ANTHOCNET has consumed huge power to discover and maintain routes. It’s almost double the power consumed by our proposed algorithms. ANTNET has the lowest power consumptions compared with other studied algorithms; this due to the nature of the algorithm, each node receives an ant, instead of re- broadcast it, the ant is forwarded to one of the neighbors. ANTSUB(HSP(G)) and ANTSUB(YAO_6(G)) send ants to all source neighbors in the sub-graph and then uses similar forwarding schema to ANTNET. The power consumption is close to ANTNET since they use a sub-graph of UDG which lead to reduce the number of ants, because the pheromone concentration will increase on the right path much faster. After the millisecond 160, in all algorithms the power consumption stops increasing dramatically because most of the algorithms usually finds route by this time, thus they stop generating discovery ants. Figure 11, depict how the number of nodes affect the overall power consumption of all algorithms. As predicted, ANTSUB(HSP(G)), ANTSUB(YAO_6(G)), and ANTNET have much less power consumption than ANTHOCNET. It is essential to keep the overhead of any ad-hoc routing algorithm as small as possible. Large overhead makes a routing algorithm not scalable and sometimes useless. One of the necessary parameters that should be kept small is the amount of control traffic exchanged in the network when a routing protocol is running. Figure 12 displays the number of generated ants by the new proposed algorithms along with the other studied algorithms in different clock times. Clearly, the number of generated ant in ANTHOCNET grows very quickly specially before the millisecond 160. For ANNET the number of ants grows in a linear function of time, because ANTNET generates an ant at every time clock. The total number of ants generated in ANTSUB(HSP(G)) and ANTSUB(YAO_6(G)) is relatively higher than ANTNET but it still raises linearly, because each node has a bounded degree after extracting the sub-graph.

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Figure 12. The Average Number of Generated Ants of (HSP(G)), ANTSUB(YAO_6(G)), and other Routing Algorithms at Different Time Clock. The number of nodes equal to 150

6. Conclusions In this paper, we propose two new ant colony routing algorithms for mobile ad hoc network (ANTSUB(YAO_6(G)), and ANTSUB(HSP(G))). We utilize the network nodes positions to extract a sub-graph of the original UDG graph. The route discovery process of the new algorithms is done only over the extracted sub-graph. Simulation results demonstrate that the new proposed algorithms have improved the network lifetime dramatically, up to twice the lifetime of other ant colony routing algorithms, without affecting the high delivery rate advantage. For future work, we plan to analyze the relationship between number of nodes, size of sub-graph, transmission range, delivery rate, and power consumption for further improvement.

References [1] [2]

[3] [4] [5]

[6]

[7]

[8]

M. Mauve, J. Widmer and H. Hartenstein, “A survey of position-based routing in mobile ad-hoc networks”, IEEE Network Magazine, vol. 15, no. 6, (2001), pp. 30-39. C. Perkins and P. Bhagwat, “Highly dynamic destination sequenced distance- vector routing (DSDV) for mobile computers”, In Proc. of the SIGCOMM, Conference on Communications Architectures, Protocols and Applications, London, (1994) September, pp.234–244. T. Clausen and P. Jacquet, “Optimized link state routing protocol (OLSR)”, In RFC 3626, IETF Network Working Group, (2003) October. D. Johnson and D. Malts, “Dynamic source routing in ad hoc wireless networks”, In Mobile Computing (ed. T. Imielinski and H. Korth), Kluwer Academic Publishers, chapter 5, (1996), pages 153–181. C. Perkins and E. Royer, “Ad hoc on-demand distance vector routing”, In Proc. of the 2nd IEEE Workshop on Mobile Computing System and Application(WMCSA), San Juan, (1999) February, pp. 90–100. S. Basagni, I. Chlamtac and V. Syrotiuk, “Dynamic source routing for ad hoc networks using the global positioning system”, In Proc. of the IEEE Wireless Communications and Networking Conference(WCNC’99), New Orleans LA, (1999) September, pp 21–24. A.E. Abdallah, T. Fevens, J. Opatrny and I. Stojmenovic, “Power-aware 3d position-based routing algorithms using adjustable transmission ranges for ad hoc and sensor networks”, Ad Hoc Networks, vol. 8, no. 1, (2010), pp. 15–29. A.E. Abdallah, T. Fevens and J. Opatrny, “High delivery rate routing algorithms for 3d ad hoc network”, Computer Communications, vol. 31, no. 4, (2008), pp. 807– 817.


209


[9]

[10] [11]

[12] [13]

[14] [15]

[16] [17] [18] [19] [20]

[21] [22]

[23] [24]

[25]

[26] [27] [28] [29] [30]

[31]

[32] [33] [34] [35]

210

S. Giordano, I. Stojmenovic and L. Blazevic, “Postion based routing algorithms for ad hoc networks for ad hoc networks: A taxonomy”, In Ad hoc wireless Networking (ed. X. Cheng, X. Huang, and D.Z. Du), Kluwer, (2003) July, pp. 103–136. P. Bose, P. Morin, I. Stojmenovic and J. Urrutia, “Routing with guaranteed delivery in ad hoc wireless networks”, Wireless Networks journal, vol. 7, no. 6, (2001), pp. 609– 616. T. Fevens, A.E. Abdallah and B. Bennani, “Randomized ab-face-ab routing algorithms in mobile ad hoc network”, In 4th International Conference on AD-HOC Networks and Wireless, volume 3738 of LNCS, Mexico, (2005) October, pp. 43–56. J. Hightower and G. Borriello, “Location systems for ubiquitous computing, “Computer, vol. 34, no. 8, (2001), pp. 57–66. J. Li, J. Jannotti, D. De Couto, D. Karger and R. Morris, “A scalable location service for geographic adhoc routing”, In Proc. of the 6th ACM In- ternational Conference on Mobile Computing and Networking (MobiCom), Boston, (2000) August, pp. 120–130. S. Kamali and J. Opatrny, “A position based ant colony routing algorithm for mobile ad-hoc networks”, Jornal of Networks, vol. 3, no. 4, (2008), pp. 31–41. Caro Gianni Gambardella Ducatelle, Frederick and L. Maria, “Ant Agents for Hybrid Multipath Routing in Mobile Ad Hoc Networks”, In Proceedings of the conference on Wireless On-demand Network Systems and Services, Swizerland, (2005) January, pp. 44–53. P. Thulasiraman, J. Wang, E. Osagie and R. Thulasiram, “Hopnet: A hybrid ant colony optimization routing algorithm for mobile ad hoc network”, Ad Hoc Networks, vol. 7, no. 4, (2009), pp. 690 – 705. G. Caro, M. Saleem and M. Farooq, “Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions”, Information Sciences, vol. 181, no. 20, (2011), pp. 4597– 4624. S. Xu, M. Liu and S. Sun, “An agent-assisted qos-based routing algorithm for wireless sensor networks”, Journal of Network and Computer Applications, vol. 35, no. 1, (2012), pp. 29 – 36. D. Caro Gianni Gambardella Dorigo, Marco and L. Maria, “Ant algorithms for discrete optimization”, Artif. Life, (1999). L. Ang, A. Zungeru and K. Seng, “Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison”, Journal of Network and Computer Applications, vol. 35, no. 5, (2012), pp. 1508 – 1536. Y. Zhao, “Motion vector routing protocol: A position based routing protocol for mobile ad hoc networks”, In PhD. thesis, University Of Arizona, USA, (2005) April. X-Y. Li, “Topology control in wireless ad hoc networks”, Book Chapter of Ad Hoc Networking, IEEE Press, edited by Stefano Basagni, Marco Conti, Silvia Giordano and Ivan Stojmenovic, (2003), pp. 175– 203. R. Rajaraman, “Topology control and routing in ad hoc networks: a survey”, ACM SIGACT News, vol. 33, no. 2, (2002), pp. 60–73. F. Ducatelle, G. Caro and L. Gambardella, “Anthocnet: An adaptive nature- inspired algorithm for routing in mobile ad hoc networks”, Self-Organisation in Mobile Networking, vol. 16, no. 5, (2005), pp. 443–455. G. Karmakar Kh. Alam, J. Kamruzzaman and M. Murshed, “Dynamic adjustment of sensing range for event coverage in wireless sensor networks”, Journal of Network and Computer Applications, vol. 46, no. 0, (2014), pp. 139–153. I. Stojmenovic and X. Lin, “Power aware localized routing in ad hoc networks”, IEEE Trans. on Parallel and Distributed Systems, vol. 12, no. 11, (2001), pp. 1122– 1133. C. Yu, B. Lee and H. Youn, “Energy efficient routing protocols for mobile ad hoc networks”, Wireless Communications and Mobile Computing Journal, vol. 3, no.11, (2003), pp. 959–973. B. Zhang and H. Mouftah, “Energy-aware on-demand routing protocols for wireless ad hoc networks”, Wireless Networks, vol. 12, no. 4, (2006), pp. 481–494. K. Gabriel and R. Sokal, “A new statistical approach to geographic variation analysis”, Systematic Zoology, vol. 18, no. 3, (1969), pp. 259–278. G. Kao, T. Fevens and J. Opatrny, “Position-based routing on 3D geometric graphs in mobile ad hoc networks”, In Proc. of the 17th Canadian Conference on Computational Geometry (CCCG’05), Windsor, (2005) August, pp. 88–91. P. Bose, L. Devroye, W. Evans and D. Kirkpatrick, “On the spanning ratio of gabriel graphs and betaskeletons”, In Proc. of the Latin American Theoretical Infocomatics (LATIN), Mexico, (2002) April, pp. 479–493. P. Bose, J. Gudmundsson and P. Morin, “Ordered theta graphs”, Computa- tional Geometry: Theory and Applications, vol. 28, no. 1, (2004), pp. 11–18. A.C. Yao, “On constructing minimum spanning trees in k-dimensional spaces and related problems”, SIAM Journal on Computing, vol. 11, no. 4, (1982), pp. 721–736. J. Keil and C. Gutwin, “Classes of graphs which approximate the complete euclidean graph”, Discrete Computational Geometry, vol. 7, no. 1, (1992), pp. 13–28. X. Li, P. Wan and Y. Wang, “Power efficient and sparse spanner for wireless ad hoc networks”, In Proc. of the IEEE International Conference on Computer Communications and Networks (ICCCN01), Arizona, (2001) October, pp. 564–567.



[36] E. Chaves, S. Dobrev, E. Kranakis, J. Opatrny, L. Stacho, H. Tejeda and J. Urrutia, “Half-space proximal: A new local test for extractinga bounded dialation spanner of a unit disk graph”, In Proc. of the 9th International Conference on Principles of Distributed Systems (OPIDIS2005), Italy, (2006) December, pp. 235–245. [37] S. Basagni, I. Chlamtac, V. Syrotiuk and B. Woodward, “A distance routing effect algorithm for mobility (DREAM)”, In Proc. of the 4th annual ACM/IEEE international conference on Mobile computing and networking (MOBICOM), Dallas, (1998), pp. 76–84. [38] U. Sorges, M. Gunes and I. Bouazizi, “Ara - the ant-colony based routing algorithm for manets”, In Proceedings of the International Conference on Parallel Processing Workshops, Vancouver, (2002) August, pp. 79–89. [39] G. Caro and M. Dorigo, Ant colonies for adaptive routing in packet- switched communications networks, In Proceedings of the 5th ACM In- ternational Conference on Parallel Problem Solving from Nature, (1998), pp. 673–682. [40] W. Heinzelman, A. Chandrakasan and H. Balakrishnan, “Energy-efficient communication protocols for wireless microsensor networks”, In Proc. of the International conference on System Sciences, Hawaii, (2000) January, pp. 3005–3014.

Authors A.E. Abdallah, is currently an Assistant Professor in the Department of Computer Science at the Hashemite University (HU), Jordan. He received his PhD in Computer Science from Concordia University in 2008, where he worked on routing algorithms for mobile ad hoc networks. He received his BS from Yarmouk University, Jordan, and MS in from the University of Jordan in 2000 and 2004, respectively. Prior to joining HU, he was a network researcher at consulting private company in Montreal (2008 - 2011). His current research interests include Routing Protocols for Ad Hoc Networks, Parallel and Distributed Systems, and Multimedia Security. E.E. Abdallah, is currently an Associate Professor in the Department of Computer Information Systems at the Hashemite University (HU), Jordan. He received his PhD in Computer Science from Concordia University in 2008, where he worked on multimedia security, pattern recognition and 3D object recognition. He received his BS in Computer Science from Yarmouk University, Jordan, and MS in Computer Science from the University of Jordan in 2000 and 2004, respectively. Prior to joining HU, he was a Software Developer at SAP Labs Montreal. Feras Hanandeh, is currently an Associate professor at the Prince Al Hussein Bin Abdullah II Faculty of Information Technology, Hashemite University, Jordan. He obtained his PhD in computer science from University Putra Malaysia, Malaysia in 2006. His current research interests include distributed databases, parallel databases focusing on issues related to integrity maintenance, transaction processing, query processing and optimization; grid computing, artificial intelligence and geographical information systems.


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Ashraf Aljammal, received the BSc degree in computer science from Albalqa' Applied University, Al-Salt, Jordan, in 2006, the master's degree from Science University of Malaysia, USM, Malaysia, in 2007, and the PhD degree from Science University of Malaysia, USM, Malaysian, in 2011. He is currently an assistant professor in the department of Computer Science of the Hashemite University, Zarqa, Jordan, since 2012. His research interests include network monitoring, network security, cloud computing, parallel and distributed system security.

Essam Al-Daoud, is currently a Professor in the Department of Computer Science at Zarka University. He received his BSc from Mu’tah University, MSc from Al al-Bayt University and his PhD in Computer Science from University Putra Malaysia in 2002. His research interests include machine learning, soft-computing, cryptography, bioinformatics and quantum cryptography.

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