Wireless Pers Commun DOI 10.1007/s11277-012-0664-9
Stable-Aware Evolutionary Routing Protocol for Wireless Sensor Networks Enan A. Khalil · Bara’a A. Attea
© Springer Science+Business Media, LLC. 2012
Abstract In real life scenario for wireless sensor networks (WSNs), energy heterogeneity among the sensor nodes due to uneven terrain, connectivity failure, and packet dropping is a crucial factor that triggered the race for developing robust and reliable routing protocols. Prolonging the time interval before the death of the first sensor node, viz. the stability period, is critical for many applications where the feedback from the WSN must be reliable. Although Low Energy Adaptive Clustering Hierarchy (LEACH) and LEACH-like protocols are fundamental and popular clustering protocols to manage the system’s energy and thus to prolong the lifespan of the network, they assume a near to a perfect energy homogeneous system where a node failure, drainage and re-energizing are typically not considered. More recent protocols like Stable Election Protocol (SEP) considers the reverse, i.e., energy heterogeneity, and properly utilizes the extra energy to guarantee a stable and reliable performance of the network system. While paradigms of computational intelligence such as evolutionary algorithms (EAs) have attracted significant attention in recent years to address various WSN’s challenges such as nodes deployment and localization, data fusion and aggregation, security and routing, they did not (to the best of our knowledge) explore the possibility of maintaining heterogeneous-aware energy consumption to guarantee a reliable and robust routing protocol design. By this, a new protocol named stable-aware evolutionary routing protocol (SAERP), is proposed in this paper to ensure maximum stability and minimum instability periods for both homogeneous/heterogeneous WSNs. SAERP introduces an evolutionary modeling, where the cluster head election probability becomes more efficient, to well maintain balanced energy consumption in both energy homogeneous and heterogeneous settings. The performance of SAERP over simulation for 90 WSNs is evaluated and compared to well known LEACH and SEP protocols. We found that SAERP is more robust and always ensures longer stability period and shorter instability period.
E. A. Khalil · B. A. Attea (B) Department of Computer Science, Baghdad University, Baghdad, Iraq e-mail:
[email protected] E. A. Khalil e-mail:
[email protected]
123
E. A. Khalil, B. A. Attea
Keywords Clustering · Evolutionary algorithm · Meta-heuristic · Stable-aware · Routing protocol · Wireless sensor networks
1 Introduction The rapid evolution of wireless technologies and the significant growth of wireless network services have made wireless communications an ubiquitous means for transporting information across many different domains. Within the framework of Wireless Sensor Networks (WSNs), there are many potential possibilities where a WSN can be deployed to support numerous applications. WSNs consist of sensors placed in remote areas to collect data and send it back to a base station (BS). Different attributes can characterize the sensor nodes including size, battery consumption, power level, lifetime of operation, movement characteristics (indicating whether the nodes are stationary or mobile), position characteristics (indicating whether the nodes are embedded into the system or independent of its surroundings), failure characteristics (indicating if the sensor has failed, or is degrading slowly). Figure 1 shows a general hardware platform description [1]. According to the radio energy dissipation model illustrated in Fig. 2 [2,3], in order to achieve an acceptable Signal-to-Noise Ratio (SNR) in transmitting a k-bit message over a distance d, the energy expended by the radio is given by: k × E elec + k × ε f s d 2 , if d ≤ d0 E T x (k, d) = (1) k × E elec + k × εmp d 4 , if d > d0 where E elec is the energy dissipated per bit to run the transmitter or the receiver circuit. There are two different radio model: the free space model (ε f s ) and the multi-path fading channel model (εmp ). When the distance, d, between the transmitter and receiver is less than or equal threshold value d0 , the algorithm adopts the free space model (d 2 power loss). Otherwise the algorithm adopts the multi-path fading channel model (d 4 power loss). By equating the two expressions at d = d0 , we have d0 = ε f s /εmp . To receive a k-bit message the radio expends E Rx (k) = k × E elec .
Fig. 1 Sensor hardware platform
123
Stable-Aware Evolutionary Routing Protocol
Fig. 2 Radio energy dissipation model
Fig. 3 Cluster based WSN architecture
Due to the fact that WSNs generally have battery operated sensor nodes, there is a lot of focus on finding the best energy efficient protocols for these networks. One of the best known approaches to minimize the energy consumption is to allow only some nodes in a cluster of sensor nodes, called cluster-heads (CHs), to communicate with the base station (see Fig. 3). Appropriate CH election can drastically reduce the energy consumption and enhance the lifetime of the network [4]. LEACH is one of the most famous clustering mechanisms that has formed the basis for many other approaches [5,6]. In LEACH, CHs are dynamically elected according to a priori optimal probability. CHs aggregate reports from their cluster members before forwarding them to the BS. By rotating the CH role uniformly among all nodes, each node tends to be a CH once every one epoch of rounds, and thus tend to expend the same energy over time. The analytical behavior of LEACH-type protocols is well suited for homogeneous WSNs, where all the sensor nodes are equipped with the same amount of energy. Homogeneous setting of energy typically occurs in near to perfect systems where a sensor’s energy is not likely to drain or fail due to uneven terrain of the region being sensed. However, in real life scenario for WSN, sensor nodes are prone to energy drainage and failure, and the network might be re-energized with new nodes deployment instead of replacing old
123
E. A. Khalil, B. A. Attea
batteries. Thus, wireless sensor networks are usually prone to energy imbalance and heterogeneity. Proper utilization of the extra energy can positively impact the performance of the network system and the reliability feedbacks from these systems. High reliability feedbacks can be achieved by prolonging the stability period (i.e., the time interval before the death of the first node), while shortening the instability region (i.e., time interval from the death of the first node until the death of the last sensor node). More recent protocols like Stable Election Protocol (SEP) consider the heterogeneous setting of energy in the system as one of the key factors in designing robust and stable routing protocols for WSNs. SEP extends LEACH’s epoch according to a two-tier energy heterogeneity setting. During the same epoch, the nodes with more energy (advanced nodes) take up the role of CHs more than the other nodes with less energy (normal nodes) [7]. In this paper, we are motivated by the fact that the heterogeneous-aware SEP protocol outperforms the heterogeneous-oblivious LEACH-type protocols in achieving longer stability periods, but, with the existence of extra energy in the network. To this end, we propose a stability-aware protocol that can achieve better stability performance, but in both homogenous and heterogeneous environments. Based on the principle of evolutionary algorithms (EAs) [8,9], an evolutionary based framework, the so called Stable-Aware Evolutionary Routing Protocol (SAERP), is proposed in this paper. EAs have been utilized extensively to address many problems in WSNs such as WSNs design and deployment, nodes localization, data aggregation and sensor fusion, energy-aware routing and clustering, nodes scheduling, WSNs defense and security mechanisms, and quality of service management. A recent interested survey on the different works of EAs in WSNs can be found in [10]. However, to the best of our knowledge, exploiting EAs in developing stability-aware routing in WSNs is yet an unexplored challenge. This paper presents the first attempt to utilize EAs in this direction. SAERP hypothesizes a possible energy-based heuristics for the individual solution’s initialization, fitness evaluation, and mutation to properly maintain longer stability and shorter instability periods. The remainder of this paper is structured as follows. Related work is discussed in Sect. 2. In Sect. 3, we introduce the proposed SAERP model. Simulation results, in both qualitative and quantitative forms, are derived in Sect. 4, followed by final conclusions in Sect. 5.
2 Related Works Heinzelman et al. [2,3] proposed Low Energy Adaptive Clustering Hierarchy (LEACH) protocol in 2000. It is one of the most popular hierarchical routing algorithms for sensor networks. The idea is to form clusters of the sensor nodes based on the received signal strength and use local cluster heads as routers to the sink. This will save energy since the transmissions will only be done by such cluster heads rather than all sensor nodes. Optimal number of cluster heads is estimated to be 5 % of the total number of nodes. All the data processing such as data fusion and aggregation are local to the cluster. Cluster heads change randomly over time in order to balance the energy dissipation of nodes. This decision is made by the node choosing a random number between 0 and 1. The node becomes a cluster head for the current round if the number is less than the following threshold: T (s) =
123
p 1− p∗ r mod 1p
if s ∈ G
0
otherwise
(2)
Stable-Aware Evolutionary Routing Protocol
where p is the desired percentage of CH nodes in the sensor population, r is the current round number, and G is the set of nodes that have not been CHs in the last 1/ p rounds (this set of round is called an epoch of the clustered sensor network). However in real life applications, an energy imbalance incident is usually occurs where the sensor nodes become more susceptible to failures caused by uneven terrain of the sensed region, and/or connectivity and packet dropping failures. Consequently, with the heterogeneity-oblivious protocols (e.g., LEACH-type protocols), if the same threshold is set for nodes with different energy settings, then there is no guarantee for properly utilizing the extra energy. The first attempt to overcome this weakness was in 2004, where Smaragdakis et al. [7] proposed Stable Election Protocol (SEP) protocol to scale well in heterogeneous setting and guarantee network’s reliability. SEP extends LEACH in assigning weighted election probabilities for the nodes to become CHs according to the initial energy of a node relative to that of other nodes in the network. SEP ensures that the CH election is randomly selected and distributed based on the fraction of energy of each node assuring a uniform use of the nodes energy. In this protocol, two types of nodes (i.e., two tier in-clustering) and two level hierarchies were considered. This prolongs the time interval before the death of the first node. If we consider that the fraction of advanced nodes (i.e., those with extra energy) is m and the additional energy factor between advanced and normal nodes is α, then the threshold in (2) is replaced by the threshold for normal sensors, T (snr m ), and the threshold for advanced nodes T (sadv ): pnrm if snr m ∈ G 1 1− p nrm r mod pnrm T (snr m ) = (3) 0 otherwise padv if sadv ∈ G 1 1− p r mod adv padv T (sadv ) = (4) 0 otherwise p (1+ where pnr m = p/(1+αm) is weighted probability for the normal nodes, padv = (1+α∗m) α) is the weighted probability for the advanced nodes, r is the current round, G is the set of normal nodes that have not become cluster heads within the last 1/ pnr m rounds of the epoch, and T (snr m )) is the threshold applied to a population of n ∗ (1 − m) normal nodes. This guarantees that each normal node will become a cluster head exactly once every (1/ p)∗(1+α∗m) rounds (this set of rounds is called heterogeneous epoch), and that the average number of cluster heads that are normal nodes per round per epoch is equal to n ∗ (1 − m) ∗ pnr m . Similarly, G is the set of advanced nodes that have not become cluster heads within the last 1/ padv rounds of the epoch, and T (sadv ) is the threshold applied to a population of n ∗ m advanced nodes. This guaranties that each advanced node will become a cluster head exactly once every (1/ p) ∗ (1 + α ∗ m)/(1 + α)) rounds per heterogeneous epoch (normally this set of round is called sub-epoch). Afterward, a number of protocols were proposed in the literature following the goal of SEP. Distributed Energy-Efficient Clustering (DEEC), Stochastic Distributed EnergyEfficient Clustering (SDEEC) [11], Stochastic and Balanced Distributed Energy-Efficient Clustering (SBDEEC) [12], Improved and Balanced LEACH (IB-LEACH) [13], Weighted Election Protocol (WEP) [14], and Energy Dissipation Forecast and Clustering Management (EDFCM) [15]. Readers can refer to the mentioned references for further reading, however, we present here one related protocol to SEP. An extension to the SEP protocol was considered by Aderohunmu’s thesis [16] by means of introducing three energy levels in two hierarchy settings. The proposed SEP-E optimizes the stable region of the network system by further increasing the epoch to accommodate the additional energy introduced to the system. An
123
E. A. Khalil, B. A. Attea
additional node called the ‘intermediate nodes’, with an intention to accommodate and cater for multi-nodes diversity is added in this protocol. This can be very important for some application specific settings such as the continuous re-energy of nodes throughout the data retrieval process, by deploying new nodes to replace dead ones. The intermediate nodes take an initial energy level between that of the advanced nodes and the normal nodes. Then, the total initial energy of the system is increased by the introduction of intermediate nodes: E total = n E 0 (1 − m − b) + nm E 0 (1 + α) + nbE 0 (1 + μ)
(5)
where n is the number of nodes, m is the proportion of advanced nodes to the total number of nodes n and b is the proportion of intermediate nodes. Then, the threshold in (2) is replaced by: pnrm if snr m ∈ G 1 1− p r mod nrm pnrm T (snr m ) = (6) 0 otherwise pint if sint ∈ G 1 T (sint ) = 1− pint r mod pint (7) 0 otherwise padv if sadv ∈ G 1 1− p adv r mod p T (sadv ) = (8) adv 0 otherwise p p (1 + μ), padv = (1+αm+μb) (1 + α) are where pnr m = p/(1 + αm + μb), pint = (1+αm+μb) the probabilities of becoming normal, intermediate and advanced nodes respectively.
3 The Proposed SAERP Although the protocols mentioned in the previous section have been devoted to designing stability-aware heuristic protocols, but to the best of our knowledge no attempt is made to investigate the chance of meta-heuristic approaches, such as EAs, for tackling this routing problem. While the field of EAs has been used extensively in tackling several WSNs challenges (such as nodes deployment and power assignment [17], nodes localization [18,19], energy aware routing protocols [20–25], and Scheduling [26–28]), the developing of an EAbased stability aware routing in WSNs is yet unexplored. This has triggered us to develop a new evolutionary based routing protocol that can be considered as an improvement to both the heterogeneous oblivious protocols such as LEACH and the heterogeneous aware protocols such as SEP. The proposed protocol, the so-called Stability-Aware Evolutionary Routing Protocol (SAERP), aims to prolong the stability period of the network and to shorten the instability period. The key idea with SAERP is to inject energy-aware heuristics for both population initialization and mutation operator while constructing an appropriate fitness function for approaching robust performance. Meta-heuristic algorithms, such as population-based Evolutionary Algorithms (EAs), are problem-independent search strategies that orchestrate an interaction between local improvement (or problem-dependent) procedures and higher level (or problem-independent) strategies to create a process capable of escaping from local optima and performing a robust search of a solution space through utilizing one or more neighborhood structures as a means of defining admissible moves to transition from one solution to another, or to build or destroy solutions in constructive and destructive processes [8,9]. In common underlying idea behind all EAs is the same: given a population of individual solutions, each constitute a distinct
123
Stable-Aware Evolutionary Routing Protocol
survival capability, they are evolved through processes such as selection, survival-of-the-fittest, recombination, mutation, and competition until either a fit enough solution is found or a previously set computational limit is reached. This section presents the characteristics of the proposed SAERP in both informal and formal ways. Let I = (I1 , . . . , I N ) denote the encoding of a clustered WSN with N sensors, where Ii ∈ {0, 1, −1}. Inactive, non-CH, and CH sensors are denoted by codes −1, 0, and 1 respectively. Then, to initialize a population of n individual solutions: ∀i ∈ {1, . . . , N } and ∀ j ∈ {1, . . . , n} ⎧ ⎨ 1 if E(sensori ) ≥ E avg (r ) ∧ rand ≤ p j Ii = 0 if E(sensori ) < E avg (r ) ∨ rand > p ⎩ −1 if E(sensori ) = 0
(9)
where p is the desired percentage of the CHs defined in Eq. 2, rand is a uniform random number, E avg is the average energy of the sensors in the current round r , and E(sensori ) is the residual energy of sensor i. This representation implicitly facilitates the formation of a dynamic number of CHs during the single and throughout the whole rounds of the routing protocol. Associated with each individual is a fitness (objective) value measured by a fitness function, ϕ, which numerically quantifies how good that individual is a solution to the routing optimization problem. For SAERP, the proposed objective function is defined as the minimization of the total dissipated energy in the network, measured as the sum of the total energy dissipated from the non-CHs to send data signals to their CHs, and the total energy spent by CH nodes to aggregate the data signals and send the aggregated signals to the base station. Formally speaking:
nc nc ϕSAERP (I ) = E T X s,C Hi + E R X + E D A + E T X C Hi ,B S (10) i=1 s∈ci
i=1
where nc is the total number of CHs, s is a non-CHs associated to the i th CH node, B S is the base-station, E T X node1,node2 is the energy dissipated for transmitting data from node1 to node2, and E R X and E D A are the energy dissipated for receiving and aggregating data. The next component of the proposed EA is the selection operator. It selects partners using binary tournament selection from the current population and transfers them to the mating pool for reproduction. To produce a mating pool of n parents, the binary tournament selects the best individual from two randomly selected individuals of the population set, and repeats this process n times. A formal definition of this selection operator, S : I 2 → I , is as follows: I i,r 1 , I i,r 2 , ∀i ∈ {1, . . . , n} and r 1, r 2 ∼ U {1, . . . , n} are two uniformly distributed random numbers from the set {1, . . . , n}, then:
i,r 1 if ϕ I i,r 1 ≤ ϕ(I i,r 2 ) I Ii = (11) I i,r 2 otherwise Recombination and mutation are the perturbation operators, which can alter the routing solutions found in the population. A proportion pc of pairs of parents in the population are chosen for recombination. For each pair of parents, two cut points, r 1, r 2, are randomly selected from the range {1, . . . , N − 1}, and the participating parent individuals, I 1 , I 2 are then swapped at alleles between these two points as follows:
I 1 = I11 , . . . , Ir11 , Ir21+1 , . . . , Ir22 , Ir12+1 , . . . , I N1
(12) I 2 = I12 , . . . , Ir21 , Ir11+1 , . . . , Ir12 , Ir22+1 , . . . , I N2
123
E. A. Khalil, B. A. Attea
Each active allele in the new individuals is then mutated with the probability pm . Here the heuristic goes when only sensors with above average residual energy are considered under mutation. Once an allele is chosen for mutation, its value is inverted from 0 to 1 and vice versa: ∀i ∈ {1, . . . , N } and ∀ j ∈ {1, . . . , n} j 1 − Ii if , rand ≤ pm and E(sensori ) ≥ E avg (r ) j Ii = j otherwise Ii
(13)
In each round of the routing protocol, the cluster formation phase generates an initial population of solutions, the fitness of which is then evaluated and based on the fitness values, the parents are selected to generate a new population via recombination and mutation operators. This process is repeated until the termination condition (usually after a number of generations) of the evolutionary algorithm occurs.
4 Simulation Results In this section, we investigate the performance of SAERP against the well known heuristic protocol LEACH, and the heterogeneity-aware SEP protocol. A centralized single-hop clustering version of these protocols is implemented where the BS has the duty to optimize the CHs election for cluster formation and to distribute the result to all sensors. The evaluation of the protocols performance is given in terms of the length of the stability vs. instability period, throughput, and the computational time. MATLAB is used to implement the simulation, assuming playgrounds of 100 m × 100 m sensor fields, each with 100 sensors deployed randomly (much of the WSN literature assumes that the sensors will be randomly deployed). 1 + C 2 + · · · + C 100 = 2100 − 1 alternative For 100 sensors, we have a search space of C100 100 100 clustering solutions, which is far too large, and the existing solutions in literature to this problem, like LEACH and SEP, are based on heuristic approaches. According to the heterogeneity of the sensors, the simulations were performed on three types of WSNs. The first case assumes homogeneous sensor networks, while the second and the third experiments assume heterogeneous sensor networks with advanced nodes of 10 and 20 %, respectively. Moreover, half of the simulations assumes a center-located BS (i.e., the maximum distance of any node from the BS is about 70 m), while the second half of the simulations assumes a corner-located BS (i.e., the maximum distance of any node from the sink is about 141.42 m). Then, the test-bed will consist of a total of 90 WSNs divided into 6 groups: 15 WSNs with homogeneous sensor nodes and centered-located BS (WSNs#1), 15 WSNs with homogeneous sensor nodes and corner-located BS (WSNs#2), 15 WSNs with heterogeneous sensor nodes (10 % advanced nodes) and centered-located BS (WSNs#3), 15 WSNs with heterogeneous sensor nodes (10 % advanced nodes) and corner-located BS (WSNs#4), 15 WSNs with heterogeneous sensor nodes (20 % advanced nodes) and cornerlocated BS (WSNs#5), and finally, and 15 WSNs with heterogeneous sensor nodes (20 % advanced nodes) and corner-located BS (WSNs#6). Moreover, the average results of each 15 simulations are provided both qualitatively and quantitatively. Furthermore, to be fair in comparison, the characteristics of the networks and communication models used for the competent protocol simulations are identical to the radio model presented in most of the related literature including, e.g., [2,3,7], and [16]. In this model, a radio consumes E elec = 50 nJ/bit to run the transmitter or receiver circuitry. The initial energy of a normal node is set to E 0 = 0.5 J, the free space ε f s = 10 pJ/bit/m2 , the multi-
123
Stable-Aware Evolutionary Routing Protocol
path fading channel εmp = 0.0013 pJ/bit/m4 , and E D A = 5nJ/bit/r epor t. The size of the message that nodes send to their cluster heads as well as the size of the (aggregate) message that a cluster head sends to the BS is set to 4,000 bits. Other parameter settings that complete the characteristics of SAERP are binary tournament selection, two-point crossover with pc = 0.6, mutation with pm = 0.03, and population size of 20 individuals allowed to evolve for 20 generations. Figures 4, 5, 6, 7, 8 and 9 depict the behaviors of the protocols as number of alive nodes per round for the 6 groups of WSNs. Figures 10, 11, 12, 13, 14 and 15 show the stability versus instability performance of LEACH, SEP, and SAERP (for each pair of bars, the left depicts the stability period while the right depicts the instability period). Table 1 summarizes Figs. 10, 11, 12, 13, 14 and 15 as the total stability gain/instability reduction as measured by the percentage of added/reduced rounds of SAERP over both LEACH and SEP. Moreover, Tables 2, 3, 4, 5, 6 and 7 present the protocols throughput for the 6 groups of WSNs with the center-located BS and corner-located BS. Throughput is measured by the total number of
100 LEACH SEP SAERP
90 80
Alive Nodes
70 60 50 40 30 20 10 0
0
500
1000
1500
Round Number Fig. 4 Average number of alive nodes per round for WSNs#1 100 LEACH SEP SAERP
90 80
Alive Nodes
70 60 50 40 30 20 10 0
0
500
1000
1500
2000
2500
Round Number Fig. 5 Average number of alive nodes per round for WSNs#2
123
E. A. Khalil, B. A. Attea 100 LEACH SEP SAERP
90 80
Alive Nodes
70 60 50 40 30 20 10 0
0
500
1000
1500
2000
2500
3000
Round Number Fig. 6 Average number of alive nodes per round for WSNs#3 100 LEACH SEP SAERP
90 80
Alive Nodes
70 60 50 40 30 20 10 0
0
200
400
600
800
1000 1200 1400 1600 1800
Round Number Fig. 7 Average number of alive nodes per round for WSNs#4
aggregated packets received at base-station from the cluster-heads until a specified percentage of the total network energy is dissipated. It is worthy to note from the results in Figs. 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, and 15 that SAERP clearly outperforms both LEACH and SEP by enlarging the stability period with additional hundreds of rounds and furthermore significantly reducing the instability period down to no more than 46–125 rounds rather than about 390–1,575, and 390–1,300 instability rounds for, respectively, LEACH, and SEP. A closer examination of the individual runs for the simulated protocols demonstrates that the superiority of SAERP over both LEACH and SEP comes from the fact that both LEACH and SEP lack enough assumption about the residual energy of the sensor nodes. For LEACH and during all its rounds, CHs are elected, according to the desired fixed percentage p, from both normal and advanced nodes with no discrimination between them resulting in a faster death of normal nodes. The plateau parts of the red-curves in Figs. 5, 6, 8 and 9 also say that LEACH exhausts the extra energy of the 10–20 advanced nodes of the heterogeneous WSNs groups at the later rounds. For SEP, we
123
Stable-Aware Evolutionary Routing Protocol 100 LEACH SEP SAERP
90 80
Alive Nodes
70 60 50 40 30 20 10 0
0
500
1000
1500
2000
2500
3000
Round Number Fig. 8 Average number of alive nodes per round for WSNs#5
100 90 80
LEACH SEP SAERP
Alive Nodes
70 60 50 40 30 20 10 0 0
500
1000
1500
2000
2500
3000
3500
Round Number Fig. 9 Average number of alive nodes per round for WSNs#6
1400 1200
1224.7 979.86
982.6
1000 800 600
393.14
394.6
400 200
46
0 LEACH
SEP
SAERP
Fig. 10 Stability versus instability periods (as measured by number of rounds) for WSNs#1
123
E. A. Khalil, B. A. Attea 1600
1351.84
1400 1200
1305 1053.3
978.86
991.7
1000 800 600 400 200 0
46.1
LEACH
SEP
SAERP
Fig. 11 Stability versus instability periods (as measured by number of rounds) for WSNs#2 1600
1413.57
1419.3
1400 1200
1107.9 1130 983.73
1000 800 600 400 200 0
46.4
LEACH
SEP
SAERP
Fig. 12 Stability versus instability periods (as measured by number of rounds) for WSNs#3 1130.1
1200 1000
921
915.6
800
550.8
536
600 400
110
200 0
LEACH
SEP
SAERP
Fig. 13 Stability versus instability periods (as measured by number of rounds) for WSNs#4 1600
1384.17
1400
1219.8
1200 1000
924.93
986.6 847.6
800 600 400
123.6
200 0
LEACH
SEP
SAERP
Fig. 14 Stability versus instability periods (as measured by number of rounds) for WSNs#5
123
Stable-Aware Evolutionary Routing Protocol 1800
1575.84
1600 1200 1000
1327.7
1298.5
1400
1032.9
918.46
800 600 400
123.7
200 0
LEACH
SEP
SAERP
Fig. 15 Stability versus instability periods (as measured by number of rounds) for WSNs#6 Table 1 Gain/reduction (in terms of number of rounds) of SAERP against LEACH and SEP SAERP
LEACH
SEP
% Stability gain
% Instability reduction
% Stability gain
% Instability reduction
WSNs#1
+24.98
+88.30
+24.63
+88.34
WSNs#2
+33.31
+96.58
+23.89
+95.35
WSNs#3
+44.27
+96.71
+28.10
+95.89
WSNs#4
+22.70
+79.47
+23.42
+80.02
WSNs#5
+31.88
+91.07
+23.63
+85.41
WSNs#6
+44.55
+91.57
+26.14
+89.78
Table 2 Throughput (total number of aggregated packets received at BS) over dissipated energy for WSNs#1
% Dissipated energy
LEACH
SEP
SAERP
10
1183.4
1193.6
4301.5
20
2366.6
2373.4
7374.1
30
3543.4
3554.1
10070.0
40
4719.9
4733.1
12650.0
50
5900.0
5922.3
15164.0
60
7084.5
7103.3
17703.0
70
8261.9
8285.9
20264.0
80
9446.7
9465.9
22799.0
90
10625.0↑
10643.0↑
23011.0
100
11815.0
11825.0
23283.0↑
see that the only heuristic added over LEACH is that the awareness of nodes heterogeneity such that even CHs are elected from both the normal and advanced nodes but the probability of electing the advanced nodes is more than that of the normal ones. This distinction between the desired percentage of normal nodes to be CHs, i.e., pnr m , and the desired percentage of advanced nodes to be CHs, i.e., padv leading to a longer stability period (as compared with LEACH), until the extinction of advanced nodes energy. On the other hand, we found that SAERP utilizes a more adaptive heuristics for CHs election. For the initial rounds of the homogeneous WSNs and as all or most of the nodes have
123
E. A. Khalil, B. A. Attea Table 3 Throughput (total number of aggregated packets received at BS) over dissipated energy for WSNs#2
Table 4 Throughput (total number of aggregated packets received at BS) over dissipated energy for WSNs#3
Table 5 Throughput (total number of aggregated packets received at BS) over dissipated energy for WSNs#4
123
% Dissipated energy
LEACH
SEP
SAERP
10
1301.7
1359.8
1284.9
20
2601.3
2717.3
2179.5
30
3902.0
4071.0
3536.3
40
5195.4
5427.4
5355.9
50
6494.5
6789.1
7558.1
60
7794.6
8143.2
10017.0
70
9095.2
9490.1
12531.0
80
10393.0↑
10845.0↓
15083.0
90
11651.0
12208.0
17634.0
100
13368.0
14220.0
17970.0↑
% Dissipated energy
LEACH
SEP
SAERP
10
1422.4
1433.7
2424.4
20
2841.4
2853.3
4606.3
30
4254.6
4273.7
5993.9
40
5675.9
5696.4
7021.9
50
7086.1
7112.4
8317.5
60
8502.5
8531.7
10028.0
70
9923.3
9955.6
12194.0
80
11326.0↑
11370.0
14666.0
90
12674.0
12829.0↑
17293.0
100
14266.0
15084.0
19835.0↑
% Dissipated energy
LEACH
SEP
SAERP
10
1110.4
1110.2
2447.7
20
2220.3
2221.6
4077.4
30
3321.3
3330.3
5494.3
40
4433.2
4441.5
6818.7
50
5540.2
5553.6
8107.1
60
6640.5
6664.3
9366.5
70
7753.6
7772.8
10594.0
80
8862.7
8884.2
11818.0
90
9968.7↑
9988.5↑
12030.0
100
11286.0
11296.0
12264.0↑
Stable-Aware Evolutionary Routing Protocol Table 6 Throughput (total number of aggregated packets received at BS) over dissipated energy for WSNs#5
Table 7 Throughput (total number of aggregated packets received at BS) over dissipated energy for WSNs#6
% Dissipated energy
LEACH
SEP
SAERP
10
1222.1
1273.0
926.73
20
2442.9
2540.0
1509.3
30
3663.3
3817.0
2387.1
40
4883.7
5084.2
3557.6
50
6101.1
6349.1
4804.8
60
7324.4
7622.5
6028.3
70
8532.2
8886.9
7258.4
80
9756.3↑
10151.0↓
8449.0
90
10976.0
11446.0
9602.1
100
12698.0
13201.0
9995.1↑
% Dissipated energy
LEACH
SEP
SAERP
10
1331.7
1340.2
1593.2
20
2664.6
2662.1
3005.3
30
3982.2
3993.3
3842.1
40
5312.3
5335.5
4506.7
50
6644.5
6651.7
5419.7
60
7975.5↓
7983.3
6485.7
70
9300.4
9311.8
7667.9
80
10636.0
10645.0↓
8865.7
90
11926.0
12035.0
10035.0
100
13705.0
14429.0
11413.0↑
residual energy above the average energy, SAERP elects a large number of CHs. Later on, the number of CHs will decrease as the number of nodes with below average energy increase. This adaptation to the residual energy of the nodes and the average network energy will eventually prolong the time until the first node (i.e., normal) dies. For heterogeneous WSNs, SAERP acts as SEP-like protocol in the initial rounds when the advanced nodes have above average energy. Then, while rounds proceed and the energy of some advanced nodes become below average, SAERP turns CH role also to the normal nodes with energy above average and excluding those advanced nodes with below average energy. In these rounds (those demonstrated in Figs. 5, 6, 8 and 9 by the additional periods between the first advanced dead node of SEP and the first normal/advanced dead node of SAERP), the collaboration between the normal and advanced nodes to be CHs will empower SAERP to prolong the time until the first node dies. In contrary, we found that SEP, in these rounds, continues to elect advance nodes more often than normal nodes as CHs. Shortly speaking, due to the additional heuristics of SAERP, we found it exploits the energy imbalance between the normal and the advanced nodes more effectively than SEP’s exploitation. Again, the apparent discrepancy between the stability and instability periods of SAERP in Figs. 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 and 15, as compared with those of LEACH and SEP, expresses additional favorite behavior of SAERP. A more uniform distribution of CHs acted
123
E. A. Khalil, B. A. Attea
by SAERP leads it to perform a more balanced energy consumption than both LEACH and SEP and this will eventually cause that all the remaining 99 nodes die, on average, within a very small number of instable rounds. This can easily be demonstrated by the near vertical black-curves in Figs. 4, 5, 6, 7, 8, and 9. From the results presented in Tables 2, 3, 4, 5, 6 and 7, one can see that during the stability period the throughput of the proposed protocol significantly outperforms LEACH and SEP in the first four groups of homogeneous and heterogeneous WSNs (i.e., Tables 2, 3, 4 and 5). The italicized results in the tables indicate the network’s throughput within the corresponding protocols’ stability periods, whilst others are for the instability periods. Also, the up-arrow/down-arrow notations indicate that the corresponding protocol’s stability period ends before/after the corresponding percentage of the dissipated energy. As can be seen from the tables, more than 90 % of the energy is utilized by SAERP during the stability period. On the other hand, LEACH, in the stability period, consumes only 60 % to less than 90 %, while all remaining energy is dissipated during the instability period. For SEP, the energy dissipated during the stability period ranges from 80 % to less than 90 %. Another conclusion can be drawn from the results of Tables 2, 3, 4 and 5. SAERP provides more throughput than both LEACH and SEP while dissipating the same amount of the energy. For example, while the protocols consume 50 % of the network energy, one can see that SAERP provides, depending on the network heterogeneity, from 4804.8 to 15164 packets, LEACH provides from 5540.2 to 7086.1 packets, and SEP provides from 5553.6 to 7112.4 packets. This can also be traced back to the positive collaboration between the proposed fitness function and the maintained heuristics. While the goal of the fitness function is to minimize the overall network’s energy consumption, the heuristics insure larger stability period. As a result, SAERP gets less energy consumption, more throughput, and larger stability period. However, this collaboration has side effect in two groups of WSNs (as shown in the stability periods of Tables 6 and 7 where LEACH and SEP have higher throughput than SAERP). Due to the added heuristics, we found in these two cases that the number of CHs selected by SAERP is less than that selected by LEACH and SEP. This in turn will effect on the degradation of the protocol’s throughput. Apart from the tournament selection, and the 2-point crossover operators that construct SAERP, it is reasonable again to ask whether the proposed fitness function and the two designed heuristic operators have together any collaboration to maintain the design goal of SAERP and to provide robust stability period. To answer this question, Table 8 captures the essential computational collaboration between the fitness function and the heuristic operators and exploring their effect on the final protocol’s effectiveness by examining SAERP with
Table 8 Gain/reduction (in terms of number of rounds) of SAERP with heuristics against SAERP without heuristics # Rounds of SAERP without heuristics
# Rounds of SAERP with heuristics
Stable
Instable
Stable
% Stability gain
% Instability reduction
Instable
WSNs#1
1077.9
1037.4
1224.7
46
+13.53
+95.57
WSNs#2
1084.9
2806.4
1305
46.1
+20.29
+98.36
WSNs#3
1076.5
WSNs#4
696.7
3009.3
1419.3
1342
1130.1
+31.60
+98.46
110
+62.21
+91.80
46.4
WSNs#5
699.6
2493.5
1219.8
123.6
+74.36
+95.04
WSNs#6
709.8
3053.9
1327.7
123.7
+87.05
+95.95
123
Stable-Aware Evolutionary Routing Protocol Table 9 Computational time as measured in seconds
Number of alive nodes
LEACH
SEP
SAERP
10
0.0459
0.0477
0.0880
20
0.0518
0.0541
0.1572
30
0.0619
0.0592
0.2424
40
0.0707
0.0738
0.3744
50
0.0807
0.0842
0.4662
60
0.0964
0.0991
0.6104
70
0.1042
0.1069
0.7464 0.9148
80
0.1175
0.1182
90
0.1278
0.1280
1.1756
100
0.1413
0.1366
1.2915
and without heuristic operators. The table presents the number of rounds within the stability and instability periods of the protocols together with the resulted percentage of stability gain/instability reduction. The presented results clearly indicate the necessity of the heuristic operators to work together with the fitness function to transcend the limit of the fitness function and get SAERP larger stability and smaller instability periods. Moreover, one can see that the positive impact gets greater for heterogeneous WSNs which again confirm the usefulness of the heterogeneous-aware heuristic operators provided by SAERP. When using Intel Core i5 CPU 2.27 GHz, the EA-based protocol takes additional time (albeit small) to run each round (see Table 9). This result comes naturally because SAERP handles more than one solution (as the case in LEACH and SEP) in each round. For 20 different individuals to be evolved in 20 generations, SAERP needs to process 400 alternative solutions at each round. To run each round, the average time needed for either LEACH or SEP ranges from about 0.04–0.14 s for a network containing 10–100 nodes. However, SAERP takes from 0.08 to 1.2 s to run each round for a network containing 10–100 nodes.
5 Conclusions Energy heterogeneity is one of the key issues in the design of WSNs protocols that aim to prolong the stability period of the network until the first node dies. Several hierarchically clustering protocols have worked on extending the stability time of WSNs, but there still exists the need for a more robust protocol design that is heterogeneity and residual energy aware. The LEACH’s heterogeneous-oblivious clustering scheme and the SEP’s only heterogeneous-awareness clustering heuristic have some shortage. While LEACH assumes only homogeneous environments, SEP considers only energy heterogeneous systems. This paper developed “SAERP”, an EA-based clustering algorithm that can adaptively cluster a WSN in two-level hierarchy for both homogeneous and heterogeneous energy settings. By designing a suitable collaboration between the heuristic operators and the problem-independent EA components one can get a positive impact on the final protocol performance and the stability of the system network. In this regard, the heuristic designed for SAERP to cope well with the energy distribution and heterogeneous setting in WSN leads to beneficial consequence where SAERP outperforms both LEACH and SEP in providing maximum stability and minimum instability periods for both homogeneous/heterogeneous WSNs.
123
E. A. Khalil, B. A. Attea
References 1. Gupta, I. (2005). Cluster-head election using fuzzy logic for wireless sensor networks. In Communication networks and services research conference, pp. 255–260. 2. Heinzelman, W. B. (2000). Application-specific protocol architectures for wireless networks. Ph.D. thesis, Massachusetts Institute of Technology. 3. Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (Oct. 2002). An application-specific protocol architecture for wireless microsensor networks. In IEEE Transactions on Wireless Communications, pp. 660–670. 4. Awwad, S. A. B., Ng, C. K., Noordin, N. K., & Rasid, M. F. A. (Nov. 2011). Cluster based routing protocol for mobile nodes in wireless sensor networks. Wireless Personal Communications, 61(2), 251–281. 5. Handy, M. J., Haase, M., & Timmermann, D. (Sept. 2002). Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In Proceedings of the 4th international workshop on mobile and wireless communications network, pp. 368–372. 6. Voigt, T., Dunkels, A., Alonso, J., Ritter, H., & Schiller, J. (Jun. 2004). Solar-aware clustering in wireless sensor networks. In proceedings of the ninth international symposium on computers and communications, pp. 238–243. 7. Smaragdakis, G., Matta, I., & Bestavros, A. (Aug. 2004). SEP: A stable election protocol for clustered heterogeneous wireless sensor networks. In Second international workshop on sensor and actor network protocols and applications (SANPA 2004), Boston, MA. 8. Eiben, A. E., & Smith, J. E. (2003). Introduction to evolutionary computing, Natural Computing Series. Berlin: Springer. 9. Yu, X., & Gen, M. (2010). Introduction to evolutionary algorithms. Berlin: Springer. 10. Kulkarni, R. V., Förster, A., & Venayagamoorthy, G. K. (2011). Computational intelligence in wireless sensor networks: A survey. IEEE Communication Survey & Tutorials, 13(1), 68–96. 11. Elbhiri, B., Saadane, R., & Aboutajdine, D. (2009). Stochastic distributed energy-efficient clustering (SDEEC) for heterogeneous wireless sensor networks. ICGST International Journal on Computer Network and Internet Research CNIR, 9(II), 11–17. 12. Brahim, E., Rachid, S., Pagès Zamora, A., & Aboutajdine, D. (2009). Stochastic and balanced distributed energy-efficient clustering SBDEEC for heterogeneous wireless sensor networks. INFOCOMP Journal of Computer Science, 8, 11–20. 13. Said, B., abdellah, E., Hssane, A. B., & Hasnaoui, M. L. (2010). Improved and balanced LEACH for heterogeneous wireless sensor networks. International Journal on Computer Science and Engineering (IJCSE), 02(08), 2633–2640. 14. Rashed, M. G., & Kabir, M. H. (2010). Weighted election protocol for clustered heterogeneous wireless sensor networks. Journal of Mobile Communication, 4(2), 38–42. 15. Zhou, H., Wu, Y., Hu, Yanqi, & Xie, G. (2010). A novel stable selection and reliable transmission protocol for clustered heterogeneous wireless sensor networks. Computer Communications, 33(15), 1843–1849. 16. Aderohunmu, F. A. (2010). Energy management techniques in wireless sensor networks: Protocol design and evaluation. M.Sc. thesis, University of Otago. 17. Konstantinidis, A. (2009). Multiobjective deployment and power assignment in wireless sensor networks using metaheuristics. Ph.D. Dissertation, University of Essex. 18. Nan, G.-F., Li, M.-Q., & Li, J. (2007). Estimation of node localization with a real-coded genetic algorithm in WSNs. In Proceedings of the international conference on machine learning and cybernetics (Vol. 2, pp. 873–878). 19. Marks, M., & Niewiadomska-Szynkiewicz, E. (2007). Two-phase stochastic optimization to sensor network localization. In Proceedings of the international conference on sensor technologies and applications sensor communications, pp.134–139. 20. Islam, M., Thulasiraman, P., & Thulasiram, R. (2003). A parallel ant colony optimization algorithm for all-pair routing in MANETs. In P. Thulasiraman (Ed.), Proceedings of the international parallel and distributed processing symposium. 21. Xue, F., Sanderson, A., & Graves, R. (2006). Multi-objective routing in wireless sensor networks with a differential evolution algorithm. In A. Sanderson (Ed.), Proceedings of the IEEE international conference on networking, sensing and control ICNSC (pp. 880–885). 22. Wazed, S., Bari, A., Jaekel, A., & Bandyopadhyay, S. (2007). Genetic algorithm based approach for extending the lifetime of two-tiered sensor networks. In A. Bari (Ed.), Proceedings of the 2nd international symposium on wireless pervasive computing ISWPC.
123
Stable-Aware Evolutionary Routing Protocol 23. Hussain, S., Matin, A. W., & Islam, O. (2007). Genetic algorithm for energy efficient clusters in wireless sensor networks. In A. W. Matin (Ed.), Proceedings of the 4th international conference on information technology ITNG (pp. 147–154). 24. Attea, B. A., & Khalil, E. A. (2011). A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks. Applied Soft Computing. doi:10.1016/j.asoc.2011.04.007. 25. Khalil, E. A., & Attea, B. A. (2011). Energy-aware evolutionary routing protocol for dynamic clustering of wireless sensor networks. Swarm and Evolutionary Computation. doi:10.1016/j.swevo.2011.06.004. 26. Tang, Q., Tummala, N., Gupta, S., & Schwiebert, L. (2005). Communication scheduling to minimize thermal effects of implanted biosensor networks in homogeneous tissue. IEEE Transactions on Biomedical Engineering, 52(7), 1285–1294. 27. Shi, Z., Zhe, Z., Qian-nan, L., & Jian, C. (Sep. 2006). Dynamic alliance based on genetic algorithms in wireless sensor networks. In Proceedings of the international conference on wireless communications, networking and mobile computing WiCOM, 22–24, pp. 1–4. 28. Jin, M. H., Liu, W. Z., Hsu, D., & Kao, C. Y. (Dec. 2005). Compact genetic algorithm for performance improvement in hierarchical sensor networks management. In Proceedings 8th international symposium on parallel architectures, algorithms and networks ISPAN, pp. 7–9.
Author Biographies Enan A. Khalil received the B.S., and M.S. in computer science from University of Baghdad, Baghdad, Iraq in 2008 and 2011, respectively. His main interests are computational intelligence, evolutionary algorithms, wireless sensor networks, and security issues in WSNs. He is currently preparing for Ph.D. graduate in Turkey.
Bara’a A. Attea (a.k.a. Baraa A. Atiyah) received the B.S. and M.S. degrees in computer science from University of Baghdad, Baghdad, Iraq in 1993 and 1996, respectively, and the Ph.D. degree in computer science from University of Technology, Baghdad in 2002. She is now an assistant professor in the Department of Computer Science at the University of Baghdad. Her current research interests are evolutionary algorithms, data mining, and applications of bio-inspired algorithms in wireless sensor networks.
123
