Energy Aware Load Balancing in Secure Heterogeneous Wireless Sensor Network

Transcription

1 Energy Aware Load Balancing in Secure Heterogeneous Wireless Sensor Network Chandrakant N Bangalore, India nadhachandra@gmail.com Abstract A Wireless Sensor Network(WSN) is a energy and security constraint ad-hoc network. This paper attempting to apply efficient techniques of load, energy and security such that network life can be increased with security. Energy effective load reconciliation in a WSN needs to spread workload across multiple sensor nodes based on its character of functionality such as temperature, light detection, guardianship in mind of security. Therefore, energy effective load reconciliation can be achieved and optimization of resource usage, maximize throughput, maximize network lifetime, minimize response time, and avoid overload by distributing work among identical type of sensor nodes with energy and security efficient routes. This will make use of multiple sensor nodes to reconciliation of load instead of a single sensor nodes hence this may increase reliability through redundancy. Network is divided logically, one is Physical group(pg) which represents a set of nodes which are physically neighbours to a node, another is Logical group(lg) which represents a set of nodes which grouped based on its type of functionality. Energy efficient routes can be evaluated in virtual groupings(pg,lg), route will chosen based on the cost of energy inspite of the route belongingness. Since each node in this network need not understand security techniques of other nodes, because, there could be different encryption techniques, packet size, protocol etc, hence header(cluster header) can have common security layer where security related things are evaluated. Keywords: Secure, WSN, Energy Efficient, Load Balance, Heterogeneous, Logical Group, Physical Group 1. Introduction A WSN is a collection of sensor nodes that communicate via wireless links, and these can not have a unique topology. These nodes cooperatively pass their data through the WSN to a main location. Load balancing in WSN involves the distribution of all computer and communication activities in two or more network nodes. This load balancing can help us to reduce the execution time of activities and to ensure that all resources in the system are used optimally. Ideally, the load balancing algorithm selects the node to process execution based on available information about all the resources in the network. Load balancing algorithms can be static, dynamic and adaptive. Static algorithms makes decisions by a priori knowledge of the network, therefore, overheads incurred in static algorithms is almost zero. In the case of dynamic algorithms, decisions are based on the information of system status ( loads on nodes ), therefore, incur overhead in the collection, storage and analysis of network status. Adaptive Algorithm is a kind of dynamic algorithms that adapt their activities to dynamically change the parameters of the algorithm to adapt to network conditions. WSNs require appropriate algorithm to make judicious use of finite energy resources of heterogeneous sensor nodes, therefore, we have to justify the cost calculation before running the load on it! 2. Literature Survey Load balancing in heterogeneous WSN nodes can be evaluated through cluster nodes, it is a novel idea, but few paper used as input to this concept. Paper [8] proposed a load-balanced clustering algorithm [14] for Wireless Sensor Networks on the basis of their distance and density distribution. In the cluster, nodes can join the cluster head by considering the cluster size and communication radius. Further, load balancing with energy efficiency[10][1] comprises of two parts [12]. The first part being Determining the number of cluster heads based on the distribution and communication radios of the nodes and the second is to select the cluster heads according to the residual energy, mobility, 694 Vol-2, Issue-05

2 the cluster number single node to cluster and distances of their heads and member nodes server cluster heads. Paper [7] proposed to architecture where new applications can be developed through a flexible service Rapidly composition. This architecture congestion Helps to Control Which load-balancing and adaptively adjust the work load over multipaths. In this algorithm, an evaluation metric and path vacant ratio is used to evaluate-and find a set of link-disjoint paths from all available paths. On top of this algorithm, a threshold is applied sharing algorithm to split the packets into multiple segments will be delivered via That multipaths to the destination path vacant depending on the ratio. In paper [11], author has investigated the effect of load balancing routing in WSNs stochastic undirected and directed with randomly placed nodes. It concluded that the stochastic routing does not necessarily achieve efficient load balancing energy in undirected networks. They have analyzed the performance of the algorithm stochastic routing distributed and decentralized, ie expander routing method, the method of expander routing performance significantly better in terms of delay packet transmission, while achieving efficient load balancing energy in directed networks. In Paper [13], proposed protocol Secure Load Balancing (SLB), which employs the pseudo-sinks that are a small number of nodes inviolable special sensors with more computing resources, storage, and energy. This algorithm reduces accuracy problem safely transmitting data clusters near congested free or pseudo-sinks clusters. In Paper [6], authors have studied the potential parameters of energy conservation and maximum performance of the network by balancing traffic across the WSN. They have shown that the distribution of traffic generated by each sensor node through multiple paths instead of using a single path allows significant energy savings, therefore, proposed a new analytical model for load-balanced systems. Paper [4] talks about energy efficient routing in WSN Which is not easy despite having Directed Diffusion routing protocol data centric. The data is forwarded through all the sink nodes impose the overhead of sending useless data, hence the author has proposed a multi- Sink Directed diffusion (MSDD) to address this problem by transmitting data towards the nearest sink. This protocol implements a load balancing by selecting the sink to next hand after the energy level of the nodes in the original path falls below a threshold value uncertain. Figure 1. WSN Group Formation 3. Proposed Techniques and Implementation In this work we are using techniques of paper [2] with load balancing algorithms. Initially we are considering load balancing factor and the generation of the simulated results. The network is logical (Logical Group- LG ) and physically (Physical Group -PG ) separated on the basis of functionality and physical, respectively existence. In this network, each node is having a LG (Logical Group) ID that is different from the cluster group where LG Id is unique and logically grouped based on the identical functionality of sensor nodes, but a node can have identifications more than one group to indicate that you can participate in more than one feature extraction for example, the detection of earthquakes and landslides observation. Whenever a sensor node sends a packet with LG Id your neighbours, if the neighbours are logically and physically available within the coverage area then those nodes can receive this package or any immediate neighbour can pass this package to his neighbour and so on, until it reaches LG appropriate nodes. This group ID is only logical group ID representing the set of nodes functionally similar, therefore, communication between different groups is based on the easy group ID. In fig. 1, node 1 and 5 are in PG ( Physics Group ) and LG X node, respectively, however the node 2 is included in both groups ( LG and PG). In each of LG, each node may receive packets that are listed for the entire group, but PG nodes are neighbours there interested node can receive these packets. Algorithm 1 describes the process of assigning load to nodes in the network. The average amount of energy consumed by node u per unit of time due to the different transmissions within the WSN is denoted by E(u) [5], E(u) =E idle (u) + v V p P (v) (1) w(p) A(v) E(u, p) 695 Vol-2, Issue-05

3 Algorithm 1 Load Balancing in WSN Require: Initialize N Nodes with L, P G, LG, etc Require: L <= Number of Work Load Processes(l1,l2,l3...ln=L) 1: while l <= L do 2: while i <= N do 3: if Type of node i belongs to a LG == Process Type of l then 4: Allocate this process l to Node i. 5: else 6: Allocate load to free node which can belongs to PG. 7: end if 8: end while 9: end while Here, E idle (u) is the average amount of energy consumed by node u per unit of time during its idle state. The lifetime of sensor node u is calculated by, T(u) =E init /E(u) (2) Here, E init is the initial amount of energy provided to each sensor node. Generally, the load balance for a given graph G representing the network with n nodes where each node contains work load wi, the goal is to distribute load across the edges so that finally the weight of each node is (approximately) equal to, w i = n w j /n (3) j=1 Let f be the fraction of the total network area covered by a mobile node [8], then f = πr2 A (4) The average number of neighbours n of the network can be obtained by using the following equation, n = (N 1)kf (5) where k is a constant, referred to as a connectivity parameter. The relationship between the local density, the cover assembly and forwarding Was condensed-through probability equation (6). Assuming that, g be the number of adjacent neighbours of node n1 and g b be the number of nodes of n1 that are covered by the broadcast and the forwarding probability at the node n1 is as follows, P n1 = g g b ḡ ; if g ḡ g g b g ; if g > ḡ (6) Adding all the nodes of physical or logical groups are equivalent number of nodes in the network. Say, K,L be the total number of groups of PG and LG respectively and R, S be the size of each group of PG and LG respectively which is specified in the below equation (7) and (8). K N= R i (7) i=0 L N= S i (8) i=0 Group Relations can be defined as follows, let there is a set of 2 groups like M and W and wanted to express which node of M is communicating with which node of W. Here, one way to do that is by listing the set of pairs (m,w) and recognizing the nodes. The accessing relation can be represented by a subset of the Cartesian Product M W. In general, a relation R from a set A to a set B will be understood as a subset of the Cartesian Product A B, i.e., R A B. If an element a A is related to an element b B, we often write arb instead of (a, b) R. The set {a A arb for some b B} is called the domain of R. The set {b B arb for some a A} is called the range of R. The load balancing [9] in the given a graph G(summation of PG and/or LG) contains N nodes where each node contains work load w i, here work load is distributed across the edges/nodes so that finally the weight of each node is (approximately) equal to w i, i.e., n w i = w j /n (9) j=1 We have assumed that WSN is a set of heterogeneous nodes called h 1, h 2...h n as specified in equation (10), where H is a set of h 1, h 2,... h n. H = { h 1, h 2,...h n } (10) where each node is having different security techniques i.e.,h 1 is having a security technique called s 1 and so on, therefore equation (11) shows, S is a set of security techniques of all heterogeneous nodes. 696 Vol-2, Issue-05

4 Figure 2. Number of processes in 100 nodes v/s time in load-balancing. Figure 5. Total Energy of 200 Nodes v/s Number of Processes in the Network. Figure 3. Number of processes in 500 nodes v/s time in load-balancing. Figure 4. Total Energy of 50 Nodes v/s Number of Processes in the Network. S = { s 1,s 2...s n } (11) here H and S are having one to one relationships. Here also s 1,s 2...s n can have sub security techniques called a 1,a 2...a n. One important note is [3], in this network each node need not understand security techniques of other nodes because of their own encryption techniques etc, hence this network is fully dependent on central/header node of WSN. The Encryption functionality can include a set of security functions to encrypt, decrypt and sign applicative data,ensuring confidentiality and integrity. There is a maximum N(N-1) bidirectional communication link can happen between nodes with using a header node (Mk) who is placed with a common security protocol software called M. Integration modulation(i 1..i m ) is the process of linking together different secured nodes (n 1,n 2...n m ) and software applications functionally to act as a coordinated whole. Therefore, M in Mk is set of integration modules is given in equation (12), M = { i 1 +i 2...i m } (12) Each node s request or response has to reach Mk, then Mk will process it and delivered to the intendant node(s). As described in earlier paragraph, say security technique s 1 and s 2 is given in equation (13), P( s 1 and s 2 ) =0 (disjoint) (13) As all nodes are heterogeneous in nature, hence we have assumed that s 1 s 2 s 3...s n. Say node n 1 wants to send a request R 1 to the node n 2, equation (14) shows the process, here every node has to send their request or response through Mk. 697 Vol-2, Issue-05

5 Algorithm 2 Energy Efficient Load Balancing in WSN Require: Initialize N Nodes with L, P G, LG, etc Require: L <= Number of Work Load Processes(l1,l2,l3...ln=L) Require: N <= Number of Nodes in the network(n1,n2,n3...nm=n) Require: E <= Energy Level of each node in N (e1,e2,e3...en=e), here e1 for n1, e2 for n2...en for nm 1: while l <= L do 2: while i <= N do 3: if Type of node i belongs to a LG == Process Type of l then 4: while Until find a node from i which consumes min. energy e do 5: Allocate this process l to Node i. 6: end while 7: else 8: while Until find a node from i which consumes min. energy e do 9: Allocate this process l to Node i. 10: Allocate load to free node which can belongs to PG. 11: end while 12: end if 13: end while 14: end while n 1 n 2 = n 1 Mk + Mk n 2 (14) and node n 2 sends a response R 2 back to the node n 1, equation (15) shows the reverse process of equation 14). n 2 n 1 = n 2 Mk + Mk n 1 (15) Here Mk is an central node with M which does integration between all nodes in the network. Basically it does mappings and conversions across the network. This kind of network may suffer from communication failures such as, central mode failure or entry of malicious nodes or network energy lost or natural disaster etc. The header node failure can occur due to non supportive security technology or invalid request/response from nodes. Considering communication failure due to malicious(l) node(s) entry into the network. However L cannot communicate with other nodes directly, hence this node has to contact Mk for request and response. Say node L wants to send a request R 1 to node n 1, equation (16) shows the process of sending a request to Mk, L n 1 = L Mk + Mk n 1 (16) Mk does not forward R 1 to n 1, instead Mk will validate the request whether this node is registered in Mk s registry or not. The integration should support this request / response but technically this is not the case in this scenario. If it is a registered node then forwards the request to node n 1 asks for validity of node L for the first time. Then again n 1 will check the genuinity of the same and reply back to Mk if it is genuine node or sends acknowledgement(ack L) packet to Mk if it is malicious node. Simulated results for the load balancing is shown in Figure 2 and 3, PG and PG have considered scenarios with LG. Load balancing through PG is a normal spread the load among sensor nodes algorithm may traditionally be assigning processes to the sensor nodes. Applying the algorithm concepts PG LG takes less time compared to the concepts of PG. Figure [9] and [8] shows the simulation results of 100 and 500 nodes, respectively. Therefore, the proposed algorithm can be useful in the case of heterogeneous nodes. With the load balancing algorithm, we also implemented an efficient energy strategy, so that the web of life can be improved / increased with load balancing. A brief algorithm is given in Algorithm 2, which describes the process of assigning workload to nodes with energy efficient. In this network, L is Number of Work Load Processes called l1,l2,l3...ln and N is the Number of Nodes in the network called n1,n2,n3...nm and E is Energy Level of each node in N called e1,e2,e3...en, here e1 corresponds to n1, similarly e2 corresponds n2 and so on. Simulated results for efficient load balancing of the energy is shown in Figure 4 and 5 50 and 200 nodes, respectively, both graphs of simulation results are identical and show that we can increase the lifetime of the network, while the load allocation in the nodes. 4. Conclusions A Wireless Sensor Network(WSN) is a energy and security constraint ad-hoc network. This paper attempting to apply efficient techniques of load, energy and security such that network life can be increased with security. Energy effective load reconciliation in a WSN needs to spread workload across multiple sensor nodes based on its character of functionality such as temperature, light detection, guardianship in mind of security. Therefore, energy effective load reconciliation can be achieved and optimization of resource usage, maximize throughput, maximize network lifetime, minimize response time, and avoid overload by distributing work among identical type of sensor nodes with energy and security efficient routes. This will make use of multiple sensor nodes to reconciliation of load instead of 698 Vol-2, Issue-05

6 a single sensor nodes hence this may increase reliability through redundancy. Network is divided logically, one is Physical group(pg) which represents a set of nodes which are physically neighbours to a node, another is Logical group(lg) which represents a set of nodes which grouped based on its type of functionality. Energy efficient routes can be evaluated in virtual groupings(pg,lg), route will chosen based on the cost of energy inspite of the route belongingness. Since each node in this network need not understand security techniques of other nodes, because, there could be different encryption techniques, packet size, protocol etc, hence header(cluster header) can have common security layer where security related things are evaluated. The simulation result is encouraging and it is worth of using proposed algorithm for energy efficient load balancing secure heterogeneous WSN. References [1] F. Bouabdallah, N. Bouabdallah, and R. Boutaba. Loadbalanced routing scheme for energy-efficient wireless sensor networks. In Global Telecommunications Conference, IEEE GLOBECOM IEEE, pages 1 6, [2] N. Chandrakant, P. D. Shenoy, K. R. Venugopal, and L. M. Patnaik. Restricting the admission of selfish or malicious nodes into the network by using efficient security services in middleware for manets. In Proceedings of the 2011 International Conference on Communication, Computing & Security, ICCCS 11, pages , New York, NY, USA, ACM. [3] Chandrakant N, Deepa Shenoy P, Venugopal K R and L M Patnaik. Middleware services for security in scalable and non-scalable heterogeneous nodes of manets,issn: x, issue vol.4 no.2. In International Journal of Future Generation Communication and Networking(IJFGCN), pages 1 12, [4] A. Eghbali, N. Javan, A. Dareshoorzadeh, and M. Dehghan. An energy efficient load-balanced multi-sink routing protocol for wireless sensor networks. In Telecommunications, ConTEL th International Conference on, pages , [5] N. B. Fatma Bouabdallah and R. Boutaba. On balancing energy consumption in wireless sensor networks. pages 1 16, march [6] Fatma Othman, Nizar Bouabdallah and Raouf Boutaba. Load-balanced routing scheme for energy-efficient wireless sensor networks. [7] S. Li, S. Zhao, X. Wang, K. Zhang, and L. Li. Adaptive and secure load-balancing routing protocol for serviceoriented wireless sensor networks, [8] Y. Liao, H. Qi, and W. Li. Load-balanced clustering algorithm with distributed self-organization for wireless sensor networks. Sensors Journal, IEEE, 13(5): , [9] Robert Elssser and Burkhard Monien and Stefan Schamberger. Load balancing in dynamic networks. [10] A. Tarachand, V. Kumar, A. Raj, A. Kumar, and P. Jana. An energy efficient load balancing algorithm for clusterbased wireless sensor networks. In India Conference (INDICON), 2012 Annual IEEE, pages , [11] U. Wijetunge, A. Pollok, and S. Perreau. Load balancing effect of stochastic routing in wireless sensor networks. In Telecommunication Networks and Applications Conference (ATNAC), 2012 Australasian, pages 1 6, [12] F. Xia, X. Zhao, H. Liu, J. Li, and X. Kong. An energyefficient and load-balanced dynamic clustering protocol for ad-hoc sensor networks. In Cyber Technology in Automation, Control, and Intelligent Systems (CY- BER), 2012 IEEE International Conference on, pages , [13] S. zdemir. Secure load balancing for wireless sensor networks via inter cluster relaying. In Kithab Proceedings, pages , [14] R. Zhang, Z. Jia, and L. Wang. A maximum-votes and load-balance clustering algorithm for wireless sensor networks. In Wireless Communications, Networking and Mobile Computing, WiCOM 08. 4th International Conference on, pages 1 4, Vol-2, Issue-05