Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

A Review on Wireless Sensor Networks Optimization based on Swarm Intelligence Approaches

Swarm Intelligence Approaches applied to WSN clustering problems.

  • Login to see the comments

A Review on Wireless Sensor Networks Optimization based on Swarm Intelligence Approaches

  1. 1. A Review on Wireless Sensor Networks Optimization BASED ON SWARM INTELLIGENCE APPROACHES AREF SAFARI
  2. 2. Presentation Outlines • Introduction • Problem Definition • Main Paper Review • Presenter Suggestions • References
  3. 3. What is Wireless Sensor Network? A Wireless Sensor Network (WSN) is a collection of compact size low power computational nodes capable of detecting local environmental conditions, and forward such information to a central base station (sink) for appropriate processing. WSN Features:  Large Network size  Self-organized  All nodes acts as routers  No wired infrastructure
  4. 4. 4 The Difference between WSN and Ad-hoc • The number of nodes • The topology of a sensor network changes very frequently • WSN broadcast but ad hoc point-to point • Sensor node are limited in power computation capacities and memory • Sensor nodes may not have global identification
  5. 5. Problem Definition • Since sensor nodes are battery powered and may be used in dangerous or inaccessible environments, it is difficult to replace or recharge their power supplies. • Clustering is an effective approach to achieve energy efficiency in wireless sensor networks. Clustering allows aggregation and limits data transmissions.
  6. 6. Clustering Objectives • Energy Efficiency • Load Balancing • Fault-tolerance • Allows aggregation • Limits data transmission • CHs and gateway nodes can form a virtual backbone • Improve network lifetime • Reduce network traffic and the contention for the channel • Data aggregation and updates take place in CHs
  7. 7. 7 What is Clustering? • Nodes divided in virtual group according to some rules • Nodes belonging in a group can execute different functions from other nodes. Cluster member Clusterhead Gateway node Intra-Cluster link Cross-cluster link
  8. 8. 8 Clustering in WSN • Involves grouping nodes into clusters and electing a Cluster Head (CH). • Members of a cluster can communicate with their CH directly • CH can forward the aggregated data to the central base station through other CHs. • Clustering allows aggregation and limits data transmissions.
  9. 9. Clustering Approaches Clustering Approaches Energy Efficient Failure Recovery Balanced Clustering EEDC Yes N/A OK LEACH No Yes OK HEED Yes N/A Excellent DWEHC Yes N/A Excellent MOCA Yes N/A Good Attribute Clustering Yes Yes Excellent • EEDC-Dynamic clustering and energy efficient routing technique for WSN. • LEACH-Energy efficient communication protocol for WSN. • HEED: A hybrid energy efficient distributed clustering approach for ad- hoc sensor networks. • DWEHD: energy efficient dynamic clustering algorithm for ad-hoc sensor networks. • MOCA: Mobility resistant efficient clustering approach for ad-hoc sensor networks.
  10. 10. Swarm intelligence based fuzzy routing protocol for clustered wireless sensor networks. • The main drawback is that there is no control on the distribution of cluster heads over the network. • In addition to the problem of generating unbalanced clusters. • A Swarm Intelligence based Fuzzy routing protocol (named SIF) has been proposed in order to overcome the mentioned drawbacks.
  11. 11. Proposed Method Architecture Fuzzy C-Means (FCM) Mamdani FIS Rule Tuning (Firefly+SA) Generate Balanced Clusters determine the precise number of clusters Prolong the Network Life- time The fitness function can be defined according to the application specifications.
  12. 12. Proposed System Model • Network Model:  They consider a single-hop clustering- based WSN model.  All sensor nodes are randomly deployed, and once they are deployed, they become stationary.  They are equipped with GPS devices.  Each node can communicates directly with the sink and with the other nodes.  Time division multiple access (TDMA) scheduling algorithm is used for communication between CHs and member nodes
  13. 13. Energy Model:  In their paper, the first order radio model has been applied as energy model of the network: The dissipated energy in the transmitter node (ETX) and in the receiver node (ERX) with distance d for transmitting an L-bit data packet can be calculated as follows: where Eelect is the dissipated energy (per bit) in either transmitter or receiver circuit.
  14. 14. Mamdani Inference System In the SIF, three variables are considered as fuzzy inputs, including residual energy, distance from the sink, and distance from the cluster centroid.
  15. 15. Input and Output Variables Input Output After fuzzification of the crisp input variables, the fuzzy inference engine processes the predetermined fuzzy rules optimized via FA-SA algorithm.
  16. 16. Proposed Hybrid SIF Model • Similar to the original LEACH protocol, the operation of SIF in the every round can be separated into two phases, • Setup phase and steady state phase: • At first clusters are formed and then CHs are selected. • During the setup phase, Fuzzy C-Means (FCM) algorithm is executed in the sink to form balanced clusters. • The sink needs to collect the information about the current state of nodes (e.g., residual energy, location, etc.). • After cluster forming, a priority factor (PF) is calculated via Mamdani fuzzy system for the each node within the each cluster. • In the every cluster, the node with the maximum PF is selected as the CH of that cluster.
  17. 17. Overall flowchart of SIF protocol for cluster head selection.
  18. 18. Time complexity analysis of the SIF protocol in each round
  19. 19. Motivation of Rules Tuning! • Appropriate selection of the fuzzy rules plays a key role in fuzzy systems. In the existing fuzzy-based protocols, the fuzzy rule base table is defined manually and remained fixed for all applications. • The Mamdani fuzzy rules of the SIF are tuned via the FA-SA algorithm. 1. In the proposed fuzzy system, five fuzzy membership functions are considered for the each input (Very Low, Low, Medium, High, and Very High). 2. So, the number of AND-based fuzzy if-then rules is 5 ×5 ×5 = 125, where each rule k has one parameter S k to be tuned. 3. Since the output has seven membership functions, the whole size of search space (SSP) is 7^125 . 4. Therefore, tuning of the fuzzy rule base table is an NP-hard problem.
  20. 20. The Mamdani fuzzy rule base table in SIF protocol.
  21. 21. Proposed Rule Optimization Approach (Rule Tuning) 1. In their approach, at first, Firefly (FA) is performed for the global searching among the search space. 2. Then, Simulated Annealing(SA) is applied to search locally in the vicinity of the best solution found via FA, in order to improve the best solution of FA. 3. In general, SA starts with a random initial solution. 4. However, in the proposed algorithm, the best solution of FA is used as the initial solution for SA.
  22. 22. During Optimization • A feasible solution for the optimization of fuzzy rule base table of SIF protocol: • In this example, the membership function “Medium” is determined for the first fuzzy rule, “Very Small” is considered for the second fuzzy rule, “Rather Small” is set for the third fuzzy rule, and so on.
  23. 23. SA Algorithm Characterizations • SA is a probabilistic technique for approximating the global optimum of a given function. Specifically, it is a metaheuristic to approximate global optimization in a large search space. • The name and inspiration of the algorithm demand an interesting feature related to the temperature variation. • This requires a Steady (Slow) reduction of the temperature as the simulation proceeds. • Optimization of a solution involves evaluating the neighbours of a state of the problem. • It is often used when the search space is discrete.
  24. 24. I. It starts from a state s0 and continues to either a maximum of kmax steps or until a state with an energy of emin or less is found. II. In the process, the call neighbour(s) should generate a randomly chosen neighbour of a given state s; the call random(0, 1) should pick and return a value in the range [0, 1], uniformly at random. III. The annealing schedule is defined by the call temperature(r), which should yield the temperature to use, given the fraction r of the time budget that has been expended so far. SA pseudocode:
  25. 25. SA Example 1. Simulated annealing searching for a maximum. 2. The objective here is to get to the highest point
  26. 26. Firefly Algorithm  The primary purpose for a firefly's flash is to act as a signal system to attract other fireflies. Formulation of the firefly algorithm by assuming: I. Any individual firefly will can be attracted to all other fireflies. (hermaphrodite) II. Attractiveness is proportional to their brightness, and for any two fireflies, the less bright one will be attracted by the brighter one; III. If there are no fireflies brighter than a given firefly, it will move randomly. IV. The brightness should be associated with the objective function.
  27. 27. Criticism on Firefly Algorithm • Nature-inspired metaheuristics in general have attracted criticism in the research community for hiding their lack of novelty behind an elaborate metaphor. • The firefly algorithm has been criticized as differing from the well- established particle swarm optimization only in a negligible way.
  28. 28. Simulation Results Comparison of the number of alive sensor nodes versus rounds.
  29. 29. Comparison of the maximum intra-cluster distance versus the WSN number
  30. 30. Comparison of the network lifetime. Simulation results shows that the proposed protocol could better reduce energy consumption in average scenarios and prolong lifetime of the wireless sensor network with respect to the parameters: FND (First Node Dies), HND (Half Node Dies) and LND (Last Node Dies).
  31. 31. So what we have in this article? • Q1: Mamdani Inference System for …………..…? • Q2: Firefly Algorithm for ……………….? • Q3: Simulated Annealing for …………………? • A1: Handling Uncertainty, Network Balancing • A2: Global search via FA (Rule Tuning) • A3: Local Search via SA (neighbour Search Feature!) (Rule Tuning) • So: The proposed Hybrid system guarantees to generate balanced clusters over the network, but also has the ability to determine the precise number of clusters
  32. 32. Summarize • In the setup phase (also referred as clustering phase), the FCM algorithm is performed in the sink to generate balanced clusters, and then, appropriate CHs are selected via the Mamdani fuzzy system. • Each CH creates a TDMA scheduling and broadcasts it to the non- CH nodes telling them when they can transmit their data packets. • Once the clusters are created and the TDMA schedule is fixed, data transmission can begin (steady state phase). • CHs send the gathered data to the sink. • After a given time spent on the steady phase, the current round is finished, and the network would be ready to enter another round.
  33. 33. Other Related Works 1- Energy-Efficient Hierarchical Routing for Wireless Sensor Networks: A Swarm Intelligence Approach: • Their proposed protocol consists of three phases: 1. Set-up phase: Bee-Cluster 2. Route discovery phase: Bee-Search 3. Data transmission phase: Bee-Carrier Integration of three phases for clustering, data routing and transmission, is the key aspect of our proposed protocol, which ultimately contributes to its robustness.
  34. 34. Route discovery in randomly distributed WSNs
  35. 35. 2- FUCA: Fuzzy‐based unequal clustering algorithm to prolong the lifetime of wireless sensor networks • This protocol forms unequal clusters. This is to balance the energy consumption. Cluster head selection is done through fuzzy logic approach. • Input variables are the distance to base station, residual energy, and density. • Competition radius and rank are the two output fuzzy variables. • Mamdani method is employed for fuzzy inference.
  36. 36. Fuzzy set for input variable “density” Fuzzy set for output variable “rank”
  37. 37. Rules of Model
  38. 38. Simulation Results Number of dead nodes versus number of rounds Number of alive nodes versus number of rounds
  39. 39. Presenter Suggestions Ishibuchi Inference System (replacing Mamdani) ACO algorithm is more capable to model the Graph system such as WSN in compare with Bee and Firefly algorithm. Differential Equation (DE) can be more efficient (probably) in compare of SA algorithm (because of vector based system and WSNs worked with Distance metric) K- Neighbour-hood Clustering instead of FCM. Type-2 Fuzzy System (replacing type-1)=Handling More uncertainties
  40. 40. References • Haiso Ishibuchi, Hybrid Evolutionary Algorithms, Springer Berlin Heidelberg, (2007). • A. Abbasi and M. Younis, A Survey on Clustering Algorithms for Wireless Sensor Networks, (2007). • M. Younis, M. Krunz, and S. Ramasubramanian, Node Clustering in Wireless Sensor Networks: Recent Developments and Deployment Challenges, (2006). • Jeffrey D. Rupp, AMPL: Active Multi-power Level Cluster Formation, (2006). • T. Anker, D. Bickson, D. Dolev, and B. Hod, Efficient Clustering for Improving Network Performance in Wireless Sensor Networks, (2008).