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Chapter 24 Using Ant Colony Agents for Designing Energy-Efficient Protocols for Wireless Ad Hoc and Sensor Networks Isaac Woungang (Department of Computer.

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Presentation on theme: "Chapter 24 Using Ant Colony Agents for Designing Energy-Efficient Protocols for Wireless Ad Hoc and Sensor Networks Isaac Woungang (Department of Computer."— Presentation transcript:

1 Chapter 24 Using Ant Colony Agents for Designing Energy-Efficient Protocols for Wireless Ad Hoc and Sensor Networks Isaac Woungang (Department of Computer Science ,Ryerson University, Toronto, Ontario, Canada) Sanjay K. Dhurandher (Division of Information Technology, Netaji Subhas Institute of Technology (NSIT),University of Delhi, India) Mohammad S. Obaidat (Department of Computer Science and Software Engineering Monmouth University, NJ, USA)

2 Summary Literature Survey Proposed Protocol (ACO-CMMBCR)
Study of Existing Protocols (MTPR,MMBCR,CMMBCR, EAAR etc.) Finding their Limitations Proposed Protocol (ACO-CMMBCR) Design Goals Algorithm Comparison with Benchmark Protocols Proving the correctness of protocol through results Graphical Plots

3 Introduction Mobile Ad-hoc Networks (MANETs)
Special type of wireless network Nodes form a temporary network without any fixed infrastructure Nodes are mobile Each node can act as a source, destination or just an intermediate node.

4 Uses of MANETs Military services Rescue Operations    

5 Literature Survey

6 MMBCR Protocol Description
It aims towards efficient usage of residual battery of nodes Even a single dead node in the network results into poor performance of the network, hence its very important to have least dead nodes in the network Uses a variable MBR (Minimum Residual Battery) of a path which is equal to the minimum battery left of a node along that path To select a path from Source ‘S’ to Destination ‘D’ ,the protocol finds the MBR of all possible paths The path with highest MBR is selected for Routing

7 EAAR Protocol Description
Provides efficient energy usage in the network Based on the Foraging Behavior in Ant Swarms Implements the Ant Colony Optimization(ACO) scheme on the MMBCR protocol. Multipath transmission properties of ant swarms increases packet delivery ratio. Defines a pheromone variable for each path Ti(n,d) = MBR / H Where MBR=Minimum Residual Batter Energy, H=Hops The above formula is used in the selection of a path

8 EAAR Protocol Features and Discussion
Emphasizes more on maximizing Residual battery of nodes Doesn’t minimizes the total energy consumption It’s a good approach for a network where lot of weak nodes are present This is not a good approach when there is not a big issue with residual battery of nodes. Let’s Say if each node in the network has a high battery capacity left then the next step should be to minimize total transmission energy usage rather than focusing more on saving residual battery of a node.

9 MTPR Protocol Description Minimum Transmission Power Routing
Minimizes the Total Transmission energy in the network For each path a cost variable is calculated. Cost is directly proportional to the Energy consumption along the path Higher the cost poorer is the path Cost = ∑ Energy(i,j) Where Energy(i,j) is the energy required to transmit data from node I to node j Energy(I,j) is directly proportional to the square of sidtance between node I and node j

10 MTPR Protocol Limitations Doesn’t bother about
residual Battery of a node Dead Node Large number of Dead nodes

11 CMMBCR Protocol (Conditional Max-Min Battery Capacity Routing)
Description Selects between MTPR and MMBCR scheme The selection is done on the basis of a variable gamma The main reason of doing a selection over here is that each protocol has its own advantage in a particular circumstance So basically CMMBCR tries to mix up the advantages of both MTPR and MMBCR in one protocol Defines a variable gamma which is used for selection Value of gamma determines which protocol should be used

12 CMMBCR Protocol Algorithm
Step1:Find the MBR of each path from source to destination Step2:Compare MBR of each path with gamma. If a path has MBR > gamma then put it in Set(A) Step3: Check If Set(A) is not empty MTPR MMBCR

13 ACO-based algorithm by Camilo et al.[5]
This algorithm maximizes the network lifetime of wireless sensor networks. The lengths of the routing paths, the node's energy level, and the amount of pheromone trail available on the connections between the nodes, are considered as design parameters to construct a routing tree that has optimized energy branches. The potential energy saving that this scheme may have benefited if the node's status was investigated or if multiple sink nodes were integrated, was not investigated.

14 ACO-based algorithm by Wen et al.[6]
This algorithm is designed for minimizing the time delay in wireless sensor networks when transferring the data, while accounting for the energy level of a node as constraint. In their scheme, ant agents routing-tables of each node are built based on partial pheromones and heuristic values. These values are then updated by a back round ant that holds the network load and delay information. A reinforcement learning technique is employed to address the tradeoff between delay and energy level at each node. It results to an energy efficient scheme compared to the AntNet scheme [24], in terms of energy consumed by each packet during transmission. However, this scheme did not address the situation when the traffic load at a node might turn out to be heavy.

15 A two-steps algorithm by Salehpour et al.[25]
This algorithm combines a Clustering technique with an ACO-based heuristic to design an energy-efficient routing scheme for wireless sensor networks. In the first step, the Low-Energy Adaptive Clustering Hierarchy algorithm (LEACH) [5] is used to achieve clustering and message transmission in the network, resulting to evenly distributed energy consumption among all the nodes in the network. In the second step, an ACO-based heuristic (the AntNet scheme [24]) is invoked by the cluster heads (which are inherited from the first step) to send the aggregated data packets to the base station, and this process repeats iteratively until convergence is reached.

16 A two-steps algorithm by Salehpour et al.[25] (Contd.)
In the latter, backward and forward ant agents are used in collaboration to explore the routing possibilities of the data packets throughout the network. These are based on the information gathered by each node regarding the amount of pheromone on the paths to its neighbors and the decision made by ants based on the energy level of the neighbor nodes. One major concern with this scheme is that the heuristic value associated with each node is dependent on the energy level of that node. No method was disclosed to estimate this value, and the impact of this parameter on the obtained optimized solutions was not investigated.

17 ACO-based algorithm by Wang et al.[7]
This algorithm uses quality-of-service (QoS) provisioning and balanced energy consumption as target to achieve energy efficiency. In this scheme, service differentiation between Real time (RT) and Best effort (BE) traffic is made through designing a specific pheromone model where artificial ants are extended. This yields ants that are endowed with the capability of emitting two types of pheromone: (1) RT pheromone scheme - used to achieve the above-mentioned balance energy consumption for BE traffic considering the path hop count and minimum residual energy along the path as constraint parameters. (2) BE heuristic scheme - which focuses on ensuring the necessary QoS provisioning on the selected routing path between a sensor and a sink.

18 ACO-based algorithm by Wang et al.[7] (Contd.)
The routing tables at each node are updated according to these BE and RT pheromone matrices. Although this scheme was proved to balance the energy consumption in the whole network in real world situations. The authors neglected to compare their scheme against similar state-state-of-the-art well-known schemes, in terms of efficiency, or energy related performance metrics.

19 Protocol by Dhurandher et al. [8]
The authors proposed an ant swarm-based algorithm that integrates both the power consumption at each node when routing a data packet and multi-path transmission features of artificial ants. In their proposed scheme, the energy usage is minimized by means of : The path discovery process, inspired from the features of AntHocNet [9]. And designed based on parameters such as route hop count and minimum battery energy remaining from the weakest node of the route.

20 Protocol by Dhurandher et al. [8] (Contd.)
On the other hand, multi-path transmission is used to divert the packet flow in case of link failure (assumed to occur one at a time), leading to less number of dead nodes compared to the AntHocNet [9] scheme. The merit of this protocol is that energy-awareness is used as a factor to increase the time that the protocol takes to judge the best possible route to be used for the data packets transmission. As pointed out by the authors, their proposed protocol was not tested in a real test-bed environment using in real-life scenario applications.

21 ACO-based solution by Okdem and Karaboga [10]
By considering the energy conservation at each node, their routing scheme is designed in such a way as: (1) To deal with failure in communication node - this is addressed by sustaining multiple paths alive in the routing task; (2) To deal with the energy level at each node and the length of the paths - these are handled by implementing a mechanism that chooses the nodes with more energy when performing the routing process; (3) To incorporate the ACO-based approach - where artificial ants contribute in designing effective multi-path data transmission from source to sink based on the information gathered at each node about the amount of pheromone on the available paths. In order to validate their approach, the authors introduced a real-time test environment made of a router chip, implemented in the form of a small sized hardware component. However, the case of multiple sink nodes was not investigated.

22 ACO-based multipath routing algorithm by Xia and Wu [11]
This algorithm uses the energy consumption of each path and the available power of nodes as criteria for selecting the optimal routing path (among multiple available paths) for the delivery of packets from source nodes to the sink node. It improves the simple ACO (SACO) scheme [26], in the sense that an optimized state transition and global pheromone update rules are introduced to increase the possibility of ants to find a new path: To avoid local optimization. To maintain the multi-paths possibility when transferring the data packets from the source nodes to the sink respectively. However, the mobility of sensor nodes was not taken into consideration.

23 ACO-based energy-efficient routing protocol by Misra et al.[12]
This Protocol combines the effect of power consumption when transmitting a packet, the residual battery capacity of a node, and the multi-path transmission properties of artificial ant swarms. In their scheme, the path discovery phase is inspired from AntHocNet [9], but with distinct functionally. The routes are maintained through new pheromone reinforcement and evaporation techniques, leading to the use of multi-path transmission through the "good routes" only rather than all the possible paths. Even though this scheme showed good promises, the effectiveness of the proposed scheme was not tested in real test-bed using practical scenarios.

24 A self-governed ACO-inspired routing scheme by Mahadevan and Chiang [13]
The authors proposed a self-governed ACO-inspired routing scheme to solve the packet routing problem with minimal energy consumption for each hop communication, leading to maximum lifetime of the network. Their scheme is inspired from the Max-Min ant system (MMAS) [14] to produce optimized routing paths to transfer the data from source nodes to the sink, while considering energy efficiency and self-healing as design criteria. However, their proposed scheme was not compared against few other state-of-the-art benchmarks, nor implemented in a real tested in order to judge its efficiency when dealing with practical scenarios.

25 ACO-based routing protocol by Hui et al. [15]
This Protocol considers the node energy, the frequency of a node acting as a router to achieve the routing, and the path delay, as design criteria. Their scheme is based on the idea that using the lowest energy path does not necessary mean obtaining the long-term network lifetime due to the fact that the optimal path may quickly get energy depleted. The authors have followed the basic ACO principle for selecting the optimal path to transmit the data form source nodes to sink. The originality of their scheme stands in the fact that in its route discovery and maintenance phase, the routing tables at each node were updated according to the pheromone and energy levels at that node. However, node mobility was not considered.

26 ACO-Energy Saving Routing (A-ESR) algorithm by Kim et al. [16]
The energy saving problem was formulated as an energy-consumption minimized network (EMN) optimization problem. It is based on the concept of traffic centrality of a node, defined as a measure involving the traffic volume (in bytes) on a link and the density of traffic carried on that link; then solved using the ACO method where only a single artificial ant is considered. The optimized energy efficiency level produced by the proposed by the algorithm is dependent on a controlling factor that was used to weight the traffic centrality. However, the authors neglected to indicate how the value of this factor can be allocated in a dynamic manner.

27 ACO-based energy-aware Routing(ABEAR) by Ren et al. [17]
Their proposed scheme introduces a congestion matric and uses it along with a combination of reactive route setup procedure and proactive neighbor maintenance procedure in its routing phase to find suitable paths for transferring the data from source to destination. In this process, the link quality, remaining energy at each node, and pheromone values are integrated as design variables in the ACO approach when performing the routing computation, with the goal to reduce the network lifetime.

28 Energy-Aware ACO Routing Algorithm (EAACA) by Cheng et al. [18]
In their scheme, the residual energy of the one-hop neighbor of each node, and the distance from source to sink are used as design criteria in the selection of the paths to route the data packets. In the route discovery phase, the information gathered by each node regarding the amount of pheromone on the paths and the decision made by ants based on the residual energy of its one-hop neighbor are used to establish all valid paths between the sensor nodes and the destination node before the source node starts releasing the data packets. In the route maintenance phase, probe packets are sent to the destination node periodically to monitor the quality of the chosen transmission paths. Although this scheme was shown to balance the energy consumption at each node, the case of mobile sensor nodes was neglected.

29 Adaptable and balanced ACO-based routing algorithm by Dominiquez-Medina and Cruz-Cortes [19]
This algorithm considers memory and power supply as criteria to minimize the energy consumption and latency in data transmission. The ACO design of the proposed scheme is a combination of : The ACO-based Location Aware Routing for WSNs (ACLR) [20] - which attempts to establish an equilibrium between the sensor nodes lifetime and the delay of the transmissions. and the Energy Efficient Ant Based Routing Algorithm (EEABR) [5] - which considers the energy efficiency in order to maximize the network lifetime. However, the proposed scheme was not implemented in a real tested in order to judge its efficiency when dealing with practical scenarios.

30 ACO-CMMBCR Consists of 2 main parts : 1) Implementing the ACO scheme
Defining pheromone for each path Greater the pheromone means better the path 2) Dynamic Protocol Selection Does an intelligent selection Selection between (MTPR+ACO) and (MMBCR+ACO) depending upon the value of gamma Selection is done to minimize energy usage

31 Implementing ACO on CMMBCR
Defining the pheromones A-CMMBCR considers the combination of two routing schemes , hence it uses two pheromones Pheromone(mm) for MMBCR and pheromone(mt) for MTPR. Pheromone(mt)=1/(Total Transmission energy of path * Number of Hops) Pheromone(mm)= MBR/(Number of Hops) where, MBR=Minimum battery of a node in the path. Total transmission power is the sum of transmission power to send data to next hop for each node in the path.

32 Implementing ACO on CMMBCR
Using the pheromones The two pheromones are used for deciding the path to be chosen for routing. Routing table is organized in such a way that paths from source ‘S’ to destination ‘D’ are stored. For each path two pheromones are stored and MBR of that path is also stored. The purpose of storing these values for a path is that these are used when the selection is done.

33 A-CMMBCR Algorithm If a source node 'S' wants to send data to a destination node 'D' then following steps must take place: Step1: The node S checks its routing table to find whether a path to D exists or not. If a path exists, it sends the data to the next Hop; else Step 2 is performed. Step2: The node S broadcasts route request packet (RREQ). Then Step 3 is performed. Step3: If any neighbor node’s routing table has a path to D exists it replies back to node S through Route Reply packet (RREP) else it broadcasts the RREQ. Step3 is followed for each intermediate node thus receiving the RREQ. If no path for D is available, the intermediate node relays the RREQ packet.

34 A-CMMBCR Algorithm Step 4:
As the RREQ packet is broadcast in the network, it can eventually reach the destination node D. At the destination node, Route Reply packet (RREP) is generated and reply is sent back to S. RREP is passed to node S through the intermediate nodes along the path from which RREQ was received. Now as each node receives the RREP packet, it updates its routing table

35 Performance Evaluation of the A-CMMBCR Protocol
In this work, we have used the GLOMOSIM simulator [24] to compare: The ETB-MDSR scheme against the CMMBCR [12], The Minimum Transmission Power Routing (MTPR) [27]), and the Energy-Aware Routing protocol (EAAR) [8] schemes. On the basis of the performance metrics: The network energy usage. The load distribution (in terms of number of packets per node).

36 Simulation Parameters
Number of nodes 40 Simulation time 500 (s) Initial energy of nodes All Nodes were initiated with an equal energy value Terrain dimension 2000 (m) x 2000 (m) Traffic Type CBR, with the following scenarios: CBR S 0S 250S CBR S 250S 400S CBR S 400S 500S MAC protocol IEEE Mobility model Random waypoint (when applicable) or none.

37 Simulation Scenarios Scenarios Data size Node speed Mobility 1
100 times Control Packet Size - None 2 125 times Control Packet Size 3 150 times Control Packet Size 4 10 m/s Random waypoint 5 6

38 Results Benchmark Protocols used for comparison are :
MTPR,EAAR and CMMBCR Graph1. Energy usage vs. number of node

39 Results Analysis Graph1 Analysis
It is observed that in terms of energy consumed per packet, the A-CMMBCR scheme outperforms all other schemes. This is attributed to the features of the ACO scheme used in A-CMMBCR as it generates multi-paths from one node to another, thus provides an alternative for path overloading. In addition, the path with highest pheromone on it consumes less energy than selected using the EAAR scheme.

40 Graph2. Network Energy usage vs. number of node.

41 Results Analysis Graph2 Analysis
It can be observed that compared to the other schemes, the A-CMMBCR scheme uses least energy as the number of nodes increases. This can be justified by the fact that the ACO framework used in A-CMMBCR optimizes the energy usage. Indeed, as the number of nodes increases, the density increases, which requires an efficient usage of energy. In the A-CMMBCR scheme, ACO helps in serving this purpose by equally distributing the packets to the nodes, thereby boosting the residual battery of nodes, and hence saving the energy at each node.

42 Graph3. Average traffic distribution vs. number of node
It can be observed that in the case of the A-CMMBCR scheme, the traffic distribution is even. This can be justified by the fact that thanks to the design features of its ACO framework, the A-CMMBCR systematically distributes the packets to the paths that are less condensed.

43 Conclusion In this Presentation, we overviewed recent proposals on the use of ACO-based algorithms for designing energy-efficient routing protocols for ad hoc wireless and sensor networks. It was reported that the studied family of ACO heuristics yielded a much better solution to the energy consumption problem compared to conventional approaches. We also introduced an enhancement to a recently proposed ACO-based routing protocol (called A-CMMBCR), which belongs to the aforementioned family of protocols.

44 Conclusion (Contd.) We have showed through simulations that A-CMMBCR outperformed the CMMBCR, EAAR and MTPR schemes, in terms of energy consumed per packet, energy usage, average traffic distribution, used as performance metrics. We believe that the ACO paradigm will continue to be used as a powerful algorithmic framework that can contribute in solving various types of optimization problems, including energy-related problems that may arise in next generation networks, including green networks.

45 References [1] C.-K. Toh, “Maximum Battery Life Routing to Support Ubiquitous Mobile Computing in Wireless Ad Hoc Networks”, IEEE Communications Magazine, Vol. 39, No. 6, June 2001, pp [2] Sanjay K. Dhurandher , Sudip Mishra and Mohammad S. Obaidat , “ An Energy-Aware Routing Protocol for Ad-Hoc Networks Based on Foraging Behavior in Ant Swarms,” IEEE ICC 2009, pp. 1-5. [3] K. Scott and N. Bambos, “Routing and Channel Assignment for Low Power Transmission in PCS”, Proc. Intl. Conf. Universal Personal Communications (ICUPC‟96), Cambridge, MA, 1996, pp [4] C. E. Perkins, E. M. Belding-Royer, and S. Das, “Ad Hoc On-demand Distance Vector (AODV) Routing”, IETF Internet Draft, 2001. [5] T. Camilo, C. Carreto, J. Silva, F. Boavida, "An Energy-Efficient Ant Base Routing Algorithm for Wireless Sensor Networks", In ANTS 2006, 5th International Workshop on Ant Colony Optimization and Swarm Intelligence, 4150, pp , 2006.

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47 References [11] S. Xia, S. Wu, "Ant Colony-based Energy-Aware Multipath Routing Algorithm for Wireless Sensor Networks", In Proc. of 2nd International Symposium on Knowledge Acquisition and Modeling (KAM'09), Nov. 30-Dec 1, Wuhan, China, pp , [12] S. Misra, S. K. Dhurandherb, M. S. Obaidatc, P. Guptab, K. Verma, P. Narula, "An ant swarm-inspired energy-aware routing protocol for wireless ad-hoc networks", The Journal of Systems and Software 83 (2010) 2188–2199. [13] V. Mahadevan and F. Chiang, "iACO: A Bio-inspired Power Efficient Routing Scheme for Sensor Networks", Intl. Journal of Computer Theory and Engineering, vol.2, No.6, pp , Dec, [14] T. Stutzle and H. Hoos, "MAX-MIN Ant system and local search for combinatorial optimization problems", In S. Voß, S. Martello, I.H. Osman, and C. Roucairol, editors, Meta-Heuristics: Advances and Trends in Local Search Paradigms for Optimization, Kluwer Academics, Boston, MA, USA, pp. 313–329, 1999.

48 References [15] X. Hui, Z. Zhi-gang, N. Feng, "A Novel Routing Protocol in Wireless Sensor Networks based on Ant Colony Optimization", Intl. Journal of Intelligent Information Technology Application, vol. 3, No. 1, pp. 1-5, [16] Y-M. Kim E-J. Lee H-S. Park, "Ant Colony Optimization Based Energy Saving Routing for Energy-Efficient Networks", IEEE Communications Letters, vol. 15, Issue 7, pp , July, [17] J. Ren, Y. Tu, M. Zhang, and Y. Jiang, "An Ant-Based Energy-Aware Routing Protocol for Ad hoc Networks", In Proc. of Intl. Conference on Computer Science and Service System (CSSS), Nanjing, China, pp , June 27-29, [18] D. Cheng, Y. Xun, T. Zhou, W. Li, "An Energy Aware Ant Colony Algorithm for the Routing of Wireless Sensor Networks", Journal on Communications in Computer and Information Science, Springer, USA, vol. 134, , [19] C. Dominquez-Medina, N. Cruz-Cortes, "Energy-efficient and location-aware ant colony based routing algorithms for wireless sensor networks", In Proc. of the 13th ACM Annual Conference on Genetic and Evolutionary Computation (GECCO'11), New York, NY, USA, ISBN: , 2011.

49 References [20] X. Wang, Q. Li, N. Xiong, and Y. Pan, "Ant Colony Optimization-Based Location-Aware Routing for Wireless Sensor Networks", Springer-Verlag, Lecture Notes in Computer Science, 5258:109–120, [21] S. K. Dhurandher, M. S. Obaidat, and M. Gupta, "Application of Ant Colony Optimization to Develop Energy Efficient Protocol in Mobile Ad-Hoc Networks", Journal of Network and Computer Applications 34(5): , [22] K. Scott and N. Bambos, “Routing and Channel Assignment for Low Power Transmission in PCS”, In Proc. of the International Conference Universal Personal Communications (ICUPC’96), Cambridge, MA, USA, pp , 1996 [23] S. Singh, M. Woo, and C. S. Raghavendra, “Power-Aware Routing in Mobile Ad Hoc Networks”, In Proc. of the 4th Annual ACM/IEEE International Conference on Mobile Computing and Networking, Dallas, TX, USA, pp , [24] G. A. Di Caro and M. Dorigo, "AntNet: A mobile agents approach to adaptive routing", Technical report IRIDIA/97-12, IRIDIA, Université Libre de Bruxelles, 1997.

50 References [25] A.-A. Salehpour, B. Mirmobin, A. Afzali-Kusha, S. Mohammadi, "An energy efficient routing protocol for cluster-based wireless sensor networks using ant colony optimization", In Proc. of the International Conference on Innovations in Information Technology (IIT), Al Ain, United Arab Emirates, Dec , pp , [26] A. P. Engelbrecht, "Computational Intelligence: An Introduction", 2nd Edition, University of South Africa, Pretoria, John Wiley & Sons Ltd., ISBN , [27] L. M. Gambardella and M. Dorigo, "Solving Symmetric and Asymmetric TSPs by Ant Colonies", In Proc. of IEEE International Conference on Evolutionary Computation, Nagoya , Japan, pp. 622–627, May 20-22, 1996.

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