Energy Aware Load Balancing in Secure Heterogeneous Wireless Sensor
Network
Chandrakant N
Bangalore, India
[email protected]Abstract
A Wireless Sensor Network(WSN) is a energy and se-curity constraint ad-hoc network. This paper attempt-ing to apply efficient techniques of load, energy and curity such that network life can be increased with se-curity. Energy effective load reconciliation in a WSN needs to spread workload across multiple sensor nodes based on its character of functionality such as temper-ature, light detection, guardianship in mind of secu-rity. Therefore, energy effective load reconciliation can be achieved and optimization of resource usage, max-imize throughput, maxmax-imize network lifetime, minmax-imize 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 multi-ple sensor nodes to reconciliation of load instead of a single sensor nodes hence this may increase reliabil-ity through redundancy. Network is divided logically, one is Physical group(PG) which represents a set of nodes which are physically neighbours to a node, an-other is Logical group(LG) which represents a set of nodes which grouped based on its type of functional-ity. 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 tech-niques 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 Bal-ance, Heterogeneous, Logical Group, Physical Group
1. Introduction
A WSN is a collection of sensor nodes that com-municate 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 balanc-ing 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 ex-ecution time of activities and to ensure that all resources in the system are used optimally. Ideally, the load bal-ancing 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 deci-sions 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 appro-priate 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 algo-rithm [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 clus-ter size and communication radius. Further, load bal-ancing 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 communi-cation radios of the nodes and the second is to select the cluster heads according to the residual energy, mobility,
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 ap-plications can be developed through a flexible ser-vice 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 mul-tiple 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 ex-pander routing method, the method of exex-pander rout-ing performance significantly better in terms of delay packet transmission, while achieving efficient load bal-ancing energy in directed networks.
In Paper [13], proposed protocol Secure Load Bal-ancing (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 clus-ters.
In Paper [6], authors have studied the potential pa-rameters of energy conservation and maximum perfor-mance 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 send-ing useless data, hence the author has proposed a multi-Sink Directed diffusion (MSDD) to address this prob-lem 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
Implementa-tion
In this work we are using techniques of paper [2] with load balancing algorithms . Initially we are con-sidering load balancing factor and the generation of the simulated results . The network is logical (Logi-cal Group- LG ) and physi(Logi-cally (Physi(Logi-cal 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 neigh-bours 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)=Eidle(u) +
X v∈V X p∈P (v) w(p) ∗ A(v) ∗ E(u, p) (1)
Algorithm 1 Load Balancing in WSN
Require: Initialize N Nodes with L, P G, LG, etc
Require: L <= Number of Work Load
Pro-cesses(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 be-longs to PG.
7: end if
8: end while 9: end while
Here, Eidle(u) is the average amount of energy
con-sumed by node u per unit of time during its idle state. The lifetime of sensor node u is calculated by,
T(u)=Einit/E(u) (2)
Here, Einit is the initial amount of energy provided to
each sensor node.
Generally, the load balance for a given graph G rep-resenting 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,
¯ wi= n X j=1 wj/n (3)
Let f be the fraction of the total network area covered by a mobile node [8] , then
f =πR
2
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 pa-rameter.
The relationship between the local density, the cover assembly and forwarding Was condensed-through prob-ability equation (6). Assuming that, g be the number of adjacent neighbours of node n1 and gb be the number
of nodes of n1 that are covered by the broadcast and the forwarding probability at the node n1 is as follows,
Pn1= g−gb ¯ g ; if g ≤ ¯g (6) g−gb g ; if g > ¯g
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 respec-tively which is specified in the below equation (7) and (8). N= K X i=0 Ri (7) N= L X i=0 Si (8)
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 wi, here work load
is distributed across the edges/nodes so that finally the weight of each node is (approximately) equal to ¯wi, i.e.,
¯ wi= n X j=1 wj/n (9)
We have assumed that WSN is a set of heterogeneous nodes called h1, h2...hn as specified in equation (10),
where H is a set of h1, h2, ... hn.
H= { h1, h2,...hn} (10)
where each node is having different security techniques i.e.,h1 is having a security technique called s1 and so
on, therefore equation (11) shows, S is a set of security techniques of all heterogeneous nodes.
Figure 2. Number of processes in 100 nodes v/s time in load-balancing .
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.
Figure 5. Total Energy of 200 Nodes v/s Number of Processes in the Network.
S= { s1,s2...sn} (11)
here H and S are having one to one relationships. Here also s1,s2...sn can have sub security techniques called
a1,a2...an. 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 cen-tral/header node of WSN. The Encryption functionality can include a set of security functions to encrypt, de-crypt and sign applicative data,ensuring confidentiality and integrity.
There is a maximum N(N-1) bidirectional com-munication link can happen between nodes with us-ing a header node (Mk) who is placed with a com-mon security protocol software called M. Integration modulation(i1..im) is the process of linking together
different secured nodes (n1,n2...nm) and software
ap-plications functionally to act as a coordinated whole. Therefore, M in Mk is set of integration modules is given in equation (12),
M= { i1+i2...im} (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 s1and s2is given in equation (13),
P(s1ands2)=0 (disjoint) (13)
As all nodes are heterogeneous in nature, hence we have assumed that s16= s26= s36= ...sn.
Say node n1wants to send a request R1to the node
n2, equation (14) shows the process, here every node
has to send their request or response through Mk.
Algorithm 2 Energy Efficient Load Balancing in WSN Require: Initialize N Nodes with L, P G, LG, etc
Require: L <= Number of Work Load
Pro-cesses(l1,l2,l3...ln=L)
Require: N <= Number of Nodes in the net-work(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 con-sumes 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 con-sumes min. energy e do
9: Allocate this process l to Node i. 10: Allocate load to free node which can
be-longs to PG. 11: end while 12: end if 13: end while 14: end while n1−→ n2= n1→ Mk + Mk→ n2 (14)
and node n2sends a response R2back to the node n1,
equation (15) shows the reverse process of equation 14). n2−→ n1= n2→ Mk + Mk→ n1
(15)
Here Mk is an central node with M which does integra-tion between all nodes in the network. Basically it does mappings and conversions across the network.
This kind of network may suffer from communi-cation 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 re-quest/response from nodes.
Considering communication failure due to mali-cious(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 R1to node n1,
equa-tion (16) shows the process of sending a request to Mk,
L−→ n1= L → Mk + Mk → n1 (16)
Mk does not forward R1 to n1, instead Mk will
vali-date the request whether this node is registered in Mk’s registry or not. The integration should support this re-quest / response but technically this is not the case in this scenario. If it is a registered node then forwards the request to node n1 asks for validity of node L for the
first time. Then again n1will 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 tradition-ally be assigning processes to the sensor nodes. Apply-ing the algorithm concepts PG LG takes less time com-pared to the concepts of PG. Figure [9] and [8] shows the simulation results of 100 and 500 nodes, respec-tively. Therefore, the proposed algorithm can be useful in the case of heterogeneous nodes.
With the load balancing algorithm, we also imple-mented 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 En-ergy 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 iden-tical 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 attempt-ing to apply efficient techniques of load, energy and curity such that network life can be increased with se-curity. Energy effective load reconciliation in a WSN needs to spread workload across multiple sensor nodes based on its character of functionality such as temper-ature, 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 re-sponse time, and avoid overload by distributing work among identical type of sensor nodes with energy and security efficient routes. This will make use of mul-tiple sensor nodes to reconciliation of load instead of
a single sensor nodes hence this may increase reliabil-ity through redundancy. Network is divided logically, one is Physical group(PG) which represents a set of nodes which are physically neighbours to a node, an-other is Logical group(LG) which represents a set of nodes which grouped based on its type of functional-ity. 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 tech-niques 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 sim-ulation result is encouraging and it is worth of using proposed algorithm for energy efficient load balancing secure heterogeneous WSN.
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