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A. Chalak , S. Misra and M. S. Obaidat Corresponding Address:

A CLUSTER-HEAD SELECTION ALGORITHM FOR WIRELESS SENSOR NETWORKS. A. Chalak , S. Misra and M. S. Obaidat Corresponding Address: Mohammad S. Obaidat, Fellow of IEEE and SCS

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A. Chalak , S. Misra and M. S. Obaidat Corresponding Address:

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  1. A CLUSTER-HEAD SELECTION ALGORITHM FOR WIRELESS SENSOR NETWORKS A. Chalak, S. Misra and M. S. Obaidat Corresponding Address: Mohammad S. Obaidat, Fellow of IEEE and SCS President, Society for Modeling & Simulation International (SCS) Editor-in-Chief, International Journal of Communication Systems, Wiley Professor of Computer Science and Software Engineering, Monmouth University, W. Long Branch, NJ 07764, USA E-mail: Obaidat@monmouth.edu Website: www.monmouth.edu/mobaidat Professor Mohammad S. Obaidat

  2. Objectives • The aim of this paper is to present a new energy-efficient clustering approach for WSNs called Improved Minimum Separation Distance(IMSD). • In order to prolong the network lifetime, energy resources of each node in WSN need to be effectively managed. • With respect to energy efficiency, the best known protocols are hierarchical in nature. Energy consumption can be reduced efficiently by using clustering based hierarchical routing protocols. • IMSD extends the existing MSD clustering algorithm. The simulation results show that IMSD is more energy-efficient, implying that it is more effective in prolonging the network life time. Prof. Mohammad S. Obaidat

  3. Introduction • Sensors in WSN are small, inexpensive, low-power, intelligent and disposable. The sensor nodes are self-configuring and contain one or more sensors, integrated with wireless communication devices and data processing components and a limited energy source. • Due to the large number of nodes and the possibly hazardous environment in which these nodes are deployed, their batteries are often assumed to be nonreplaced. • The failure of a single node in the network could possibly cause network partition and dissect a part of the WSN off from the rest of the network. • Network lifetime is, therefore, dependent on the lifetime of individual nodes. This raises the issue of energy-efficient design of the network. This research effort focuses on the design of an energy-efficient cluster head selection algorithm for WSNs. Prof. Mohammad S. Obaidat

  4. Introduction • In a WSN, there are possibly very large number of sensing nodes, and a base station. • The sensing nodes have to route data about their environment to the base station. A sensing node is sometimes called a source and a base station is sometimes called a sink. • The sink node collects and interprets the data from all the source nodes in the network. The sink node may be connected to a wired network and may not have an energy limitation. The source nodes, on the other hand, are dependent on their limited batteries and become dead when their batteries are completely exhasuted. Prof. Mohammad S. Obaidat

  5. Introduction Wireless Sensor Network Area Prof. Mohammad S. Obaidat

  6. Introduction • Routing in Wireless Sensor Networks can be divided into flat-based, location-based, and hierarchical-based routing. • Flat-based routing: All nodes are typically assigned equal functionalities. • Location-based: Positions are exploited to route data in network. • Hierarchical-based: Nodes will play different roles in network. Prof. Mohammad S. Obaidat

  7. Introduction • WSN protocols are different from the traditional wireless protocols due to limited power supply, large network size, and inaccessible remote deployment environment. • A lot of energy is saved by using multi-hop communication than direct communication, as in short range communication due to the fact that consumption of energy is proportional to the square of the distance. • A routing protocol might be good for continuous data sensing while it may not perform well where it will have periodic monitoring. Prof. Mohammad S. Obaidat

  8. Introduction • Clustering approach is used for wireless communication in wireless sensor networks. In this case, nodes send their data to the nearest cluster head which then forwards it to the base station. In each round, the cluster head node sends out a beacon, and nodes that hear the beacon join the cluster. If there are too many nodes in the cluster, the cluster-head can reduce beacon signal strength so fewer nodes will hear it. • On the other hand, if the cluster is too small, the cluster head can increase its beacon signal strength to increase cluster membership. Thus, energy efficiency is increased by using clustering technique. • In this paper, we present the Improved Minimum Separation Distance (IMSD) cluster head selection algorithm, which is based on the total average energy available in the network. Prof. Mohammad S. Obaidat

  9. Terminology • LEACH Low Energy Adaptive Clustering Hierarchy • MSD Minimum Separation Distance • MST Minimum Spanning Tree • BS Base Station/Sink • CH Cluster Head • dec_MSD Decrease MSD by 10% • LCF Link Cost Factor • SEP Stable Election Protocol • gap decrease MSD by 10% Prof. Mohammad S. Obaidat

  10. RADIO COMMUNICATION MODEL • In a WSN, there are two types of energy costs; fixed cost in the electronics when transmitting or receiving a message, and variable energy costs which are proportional to the distance of transmission. • In our work, we consider a simple model where the radio dissipates 𝐸𝑒𝑙𝑒𝑐 = 50 nJ/bit to run the transmitter or receiver circuitry and ∈𝑎𝑚𝑝= 100 pJ/bit/𝑚2 for the amplifying signal. • These parameters are slightly better than the current state-of-the- art in radio design. We also assume d2 energy loss due to channel transmission. Prof. Mohammad S. Obaidat

  11. RADIO COMMUNICATION MODEL • Thus, to transmit a k bit message with distance d and considering free space model, the radio model as shown in Figure 1 expends: and to receive this message, the radio expends: Prof. Mohammad S. Obaidat

  12. RADIO COMMUNICATION MODEL • For these parameter values, receiving a message is not a low cost operation; the protocols should thus try to minimize not only the transmit distances, but also the number of transmit and receive operations for each message. • An inefficient use of the available energy leads to poor performance and short life cycle of the network. • Energy in these sensors is a scarce resource and must be managed in an efficient manner. The most significant factor in the development of WSN is typically power. It is necessary that the transmission distances of nodes is reduced in order to conserve energy. Prof. Mohammad S. Obaidat

  13. RADIO COMMUNICATION MODEL Prof. Mohammad S. Obaidat

  14. Background Black nodes represent cluster-heads • Clusters constructed by LEACH. Low Energy Adaptive Clustering Hierarchy (LEACH) uses a self-configuration approach and reduces much energy consumption. It does not consider the distribution of nodes. Cluster head nodes are distributed unequally. Data collected by cluster-head directly forwarded to base station. As cluster-heads are far away from the base station. Direct transmission is bad for energy conservation. Prof. Mohammad S. Obaidat

  15. Background (contd.) • Clusters constructed by LEACH MSD and MST. • Cluster constructing method has to be changed. • The communication between cluster-heads and base station also needs to be modified. • In this work, we present a modification of LEACH’s cluster-head selection algorithm to further reduce the total energy dissipation of sensors • Our cluster constructing method avoids the uneven member distribution for clusters. Prof. Mohammad S. Obaidat

  16. PROPOSED ALGORITHM: IMSD • Low Energy Adaptive Clustering Hierarchy (LEACH) is a hierarchical routing protocol. It is an energy-efficient protocol, as it group nodes into clusters. • It uses Cluster Head (CH) to fuse data before transmitting to the Base Station (BS). • But LEACH has some limitations: • First the geographical distribution of the CHs severely influences the overall energy consumption of the network. Spreading the CHs more evenly results in prolonging the lifetime of the network. • Second, in WSNs asymmetric communication is possible. That is, the base station can reach all the sensor nodes directly. While some sensor nodes cannot reach the base station directly, therefore these sensor nodes need help from other nodes to forward their data. Hence routing schemes are necessary. Prof. Mohammad S. Obaidat

  17. PROPOSED ALGORITHM: IMSD • Third, as the network size increases, the transmission distance within the cluster increases. This results in an increase in energy consumption. • In this paper, we present an energy-efficient cluster head selection algorithm to overcome the above three limitations. Prof. Mohammad S. Obaidat

  18. PROPOSED ALGORITHM: IMSD • The geographical distribution of the CHs severely influences the overall energy consumption of the network. • For prolonging the lifetime of the network, CHs should be spread evenly. Use of improved minimum separation distance between CHs improves the network lifetime. • The smallest distance allowed between CHs is minimum separation distance. The minimum separation distance can be smaller than the separation distance between CHs, but should not be larger. Prof. Mohammad S. Obaidat

  19. PROPOSED ALGORITHM: IMSD Prof. Mohammad S. Obaidat

  20. PROPOSED ALGORITHM: IMSD • An extension to LEACH is used in the proposed routing technique. The cluster formation carried out by the base station uses a centralized cluster formation algorithm. • The same steady-state protocol as LEACH is used in the proposed protocol. The current location & energy level of each node is informed to the base station during the set-up phase. • To determine the CH and the cluster for that round, centralized cluster formation algorithm is run at base station. • All the nodes in the network receive the information that is broadcast by base station after the creation of clusters. Before a node goes to sleep mode until it is time to transmit data to the CH, i.e., until the arrival of next slot, determination of its local TDMA is essential for each of the nodes, except the CH. Prof. Mohammad S. Obaidat

  21. PROPOSED ALGORITHM: IMSD • From a list of eligible nodes, a CH is randomly chosen in the CH selection part. • In the network, the average energy of remaining nodes is calculated to determine which nodes are eligible. The nodes with energy level above the average are eligible, in order to spread the load evenly. • A new node has to be chosen to guarantee the minimum separation distance if a node that has been randomly chosen is too close, i.e., within the range of the minimum separation distance from all other chosen CHs. • If the desired number of CHs is not formed, then the minimum separation distance is decremented by 10% and the same algorithm is implemented until the desired number of CHs is attained. Prof. Mohammad S. Obaidat

  22. PROPOSED ALGORITHM: IMSD • Clusters are created the same way as in the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol, generally with at least the minimum separation distance when all CHs have been chosen and separated. • Asymmetric communication and routing in sensor network (AROS) architecture is used for simulation purpose [6] [7] [8]. • The data is transmitted by CH to base station when the CH gathers data from its cluster nodes. This is based on AROS. Prof. Mohammad S. Obaidat

  23. Performance Evaluation • We have implemented cluster head selection algorithms for WSN with different protocols LEACH, MSD and IMSD. The implementations have been carried out in MATLAB. • The following assumptions were made: • Sensors and the base station are all stationary after deployment; the BS is fixed at a distance from the sensor nodes. • All nodes are homogeneous and energy constrained with uniform energy. • Each sensor node initially has the ability to transmit data directly to base station. Prof. Mohammad S. Obaidat

  24. Performance Evaluation 4. Each node is assigned a unique identifier (ID). 5. Nodes can adjust their transmission power according to the distance from the receiver. 6. A node belongs to only one cluster, but may change its cluster during each round. 7. The nodes have initial uniform energy, of 0.5 Joules. 8. Each sensor node is location aware and the base station has strong computation power. 9. All sensors have enough energy to communicate with the sink directly at the initial stages. 10. Base station has location and energy info of all nodes. 11. The BS is fixed at a distance from the sensor nodes. Also, the Base station has "unlimited" power supply and high calculation capacity. Prof. Mohammad S. Obaidat

  25. Performance Evaluation • The node deployment used in the simulation study is shown in Figure 2. • The nodes are randomly distributed across the sensor field. The base station is situated inside the field. • Table I indicates the other parameters as set during simulation. Prof. Mohammad S. Obaidat

  26. Performance Evaluation Prof. Mohammad S. Obaidat

  27. Proposed Approach Minimum Separation Distance (MSD) Minimum Spanning Tree within Cluster Prof. Mohammad S. Obaidat

  28. Minimum Separation Distance (Algorithm) MSD = Minimum Separation Distance dc = Number of desired cluster heads, energy(n) = Remaining energy for node n avg = eligible = { n | energy(n) ≥ avg } CH = { } while ( |CH| < dc ) add(n,CH) remove(n,eligible) else add(n,CH) remove(n,eligible) endif endwhile Prof. Mohammad S. Obaidat

  29. Minimum Separation Distance (Modified Algorithm) while (( |CH| < dc ) && (gap>10)) while (( |CH| < dc ) && (n<count|eligible|)) do add(n, CH) endif endif n=n+1 endwhile gap = gap* 0.9 n=2 end_while Prof. Mohammad S. Obaidat

  30. Minimum Separation Distance (Modified Algorithm) 10 C 8 F A 5 4 B 4 E 9 D J G 7 6 H 6 I 8 avg_energy = 6.7 MSD = 5m CH = { } dc = 3 N = 10 eligible={C, F, J, D, I} Prof. Mohammad S. Obaidat

  31. Minimum Spanning Tree Direct communication in LEACH Replacement of direct communication to MSTs in intra-cluster is the main idea. For large network area using direct transmissions is not energy efficient. Average transmission distance of each node can be reduced by using MSTs instead of direct transmissions Thus the energy dissipation of transmitting data is reduced. Prof. Mohammad S. Obaidat

  32. Minimum Spanning Tree (Contd.) MST communication in LEACH All nodes including the CH are connected by a MST CH as the leader collects the data from the whole tree. CHs uses Relay nodes to forward the data to the BS. As network area is larger, the reduced transmission distance is greater. In the direct transmission, information of routing path is simple, and each node only needs to know its CH But in trees, each node must know the next node that it would send data to. Prof. Mohammad S. Obaidat

  33. Minimum Spanning Tree (Contd.) Forming Minimum Spanning Tree Every node in network broadcasts HELLO message with a certain frequency. Nodes receiving HELLO message respond back with RESPONSE message with network-id, energy-remaining, neighbor-id’s, distance between neighbors. With this information every node creates a spanning tree for its cluster. And informs neighbors the next node that it would send data to. Prof. Mohammad S. Obaidat

  34. Proposed Scheme d = distance between two node MST d < MSD d ≥ MSD Prof. Mohammad S. Obaidat

  35. Radio Energy Model k bit packet Transmit Electronics Transmit Amplifier Transmitter d k bit packet Receive Electronics Receiver Radio Energy Model Prof. Mohammad S. Obaidat

  36. Radio Energy Model Radio Characteristics • While estimating the transmit energy, we assumes d2 energy loss in channel transmission. In our work, we assume a simple model where the radio dissipates Prof. Mohammad S. Obaidat

  37. Radio Energy Model Thus, to transmit a k-bit message a distance d using our radio model, the radio expends: (1) and to receive this message, the radio expends: (2) Prof. Mohammad S. Obaidat

  38. Radio Energy Model For these parameter values, receiving a message is not a low cost operation; the protocols should thus try to minimize not only the transmit distances, but also the number of transmit and receive operations for each message. Prof. Mohammad S. Obaidat

  39. Performance Evaluation Prof. Mohammad S. Obaidat

  40. Performance Evaluation • Simulation area: Defines the dimensions of the sensor field. We considered a field measuring 100 x 100 m2. • Number of nodes: The model of sensor network that was considered consisted of 20/50/100/200/350/500 nodes randomly deployed into the field for 6 different experiments. • Radio propagation model: Used to predict the distance from the signal power of each node. Free space model and multi path fading model are used in this simulation. Prof. Mohammad S. Obaidat

  41. Performance Evaluation • Energy model - The energy model, which is a node attribute, represents the amount of energy in a node. Energy consumption takes place for each and every packet transmitted (tx-power) or received (rx-power). • Communication model - The communication between the nodes is considered one way. A cluster head advertises itself to the common nodes which in turn join and transmit data to the CH. Efficient utilization of the energy resources of the sensor nodes increases the lifetime of the network. In the ideal network, all sensor nodes would live exactly the same period of time. Prof. Mohammad S. Obaidat

  42. Assumptions • Sensors and the base station are all stationary after deployment. • All nodes are homogeneous and have the same capabilities. • Each sensor node initially has the ability to transmit data directly to base station. • Each node is assigned a unique identifier (ID). • A node belongs to only one cluster, but may change its cluster during each round. • Each sensor node is location aware and the base station has strong computation power. • All sensors have enough energy to communicate with the sink directly at the initial stages. Prof. Mohammad S. Obaidat

  43. Simulation tool • In this paper, we have analyzed the cluster head selection algorithms for WSN with different protocols LEACH, MSD and IMSD. • We used MATLABfor the analysis which is good for analyzing at the Physical layer. • Matlabis a computing platform that enables various simulation projects. It has been particularly used for PHY layer study of wireless communications. Prof. Mohammad S. Obaidat

  44. Simulation tool (cont…) • What does wireless-matlab provide? • Radio propagation: free space, two-ray, and lognormal shadowing • Mobility: random waypoint model • PHY: broadcast, dynamic transmission rate and power • MAC: IEEE 802.11 (CSMA/CA and RTS-CTS- DATA-ACK) • NET: ad hoc routing. Prof. Mohammad S. Obaidat

  45. Simulation Parameters Prof. Mohammad S. Obaidat

  46. Results and Analysis • We have performed the simulation in order to determine how much we can lower the energy consumption in the sensor network • The obtained results of IMSD through simulation are compared with the simulated results of LEACH, SEP and MSD. • The metrics used are: 1. No. of rounds of operations; a one round is specific time slice (or period) after which the algorithm selects new cluster heads for next round/period. So, we calculate the total number of rounds the network sustains with energy dissipation at each round. • Average energy consumed, 3. No. of nodes alive, etc. Prof. Mohammad S. Obaidat

  47. Performance Evaluation Results • In Figures 3 and 4, we see that a more efficient utilization of power makes the sensor network stay alive a longer period of time. • We can also see that as soon as the sensor nodes in the network start to die, the whole network dies shortly. • Figure 5, shows the average energy consumed for every 200 rounds of simulation. It also shows how IMSD average energy sustains for longest duration. Prof. Mohammad S. Obaidat

  48. Performance Evaluation Results Prof. Mohammad S. Obaidat

  49. Performance Evaluation Results Prof. Mohammad S. Obaidat

  50. Performance Evaluation Results Prof. Mohammad S. Obaidat

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