1. Introduction
Wireless Sensor Networks (WSNs) have drawn more attention from researchers recently because they find applications spanning over vast and varied areas such as fire detection, habitat sensing and target tracking etc., The sensor nodes deployed in WSNs usually consists of sensing, data processing and communicating components, which make them fully functional wireless node. The sensor nodes are tiny and typically powered by small batteries, for which replacement, even if possible, is highly expensive and intricate [1]. Hence, energy efficiency in WSN has become the practical challenge and design focus recently, besides the other requirements such as reliability and throughput.
The Multiple Input Multiple Output (MIMO) technique is a low power long distance communication method [2] which has the potential to support higher data rate and dramatically reduces transmit energy consumption over multipath fading channels. The MIMO technique deploys multiple antennas, both at the transmitter and receiver, and provides diversity gain [3]-[5]. The MIMO provides coding gain also, when it is used in conjunction with appropriate space time coding (e.g. space time trellis code). These gains can be utilized to conserve the energy of the sensor nodes. However, direct pertinence of MIMO technology in WSN is highly impractical due to the small physical size of a sensor node which may only be able to support single antenna. WSN nodes are usually designed to communicate using Single Input Single Output (SISO) technology through a single antenna.
As a solution to the problem, Cooperative Communication (CC) concept has been proposed in [6]. In CC, multiple nodes which have only single antenna, cooperate and coordinate among them, simultaneously transmit, receive, decode and retransmit the data. The CC establishes a Virtual MIMO (V-MIMO) system, so that energy efficient MIMO schemes can be utilized. In wireless environments, the received power falls off as the kth power of distance, with 2 ≤ k ≤ 6, where k is the channel path loss exponent. Hence, the transmit energy is conserved by using multi-hop routing [7] and smaller transmission distance (d ≤ 100 m) is chosen for each hop. The smaller transmission distance makes the transmit energy comparable with the circuit energy along the signal path. Hence, both transmit energy and circuit energy consumption needs to be considered in determining the optimal transmission distance.
In [8], the authors investigated the energy consumption of Multiple Input Single Output (MISO) in WSN. The authors assumed same bit error probability of the broadcast phase and the bit error probability of the cooperative phase. In addition, the authors used fixed distance d = 100 m. In [9], the authors investigated the energy efficiency of cooperative communication in WSN. The authors used optimal broadcast bit error probability and optimal number of cooperating nodes. In addition, the authors used fixed distance d = 200 m.
In the proposed work, an algorithm for energy efficient co-operative communication in WSN is proposed with optimal distance and optimal number of nodes.
The main contributions of the paper are
The rest of the paper is organized as follows. The related work is presented in section 2. Section 3 describes the system model. Simulation results are given in section 4. Finally, section 5 concludes the paper.
2. Related Work
Most of the pioneering research in the area of network lifetime prolongation has focused on transmission schemes to reduce the total energy required per bit. The total energy required per bit per hop includes both the circuit energy consumption and transmit energy consumption and is directly proportional to the transmission distance, reliability and the number of nodes participating in the CC. On the other hand, the total energy required per bit per node is directly proportional to the transmission distance, reliability and inversely proportional to the number of nodes participating in the CC.
In [6], the authors analyzed the energy consumption profiles of V-MIMO and SISO for single hop and showed that significant energy conservation can be obtained when the transmission distance exceeds 25 m. With the system parameters given in [6], when the distance between the transmitter and receiver is greater than 25 m called threshold distance, MIMO is energy efficient than SISO. The dependency of energy efficiency on propagation parameters and extra training overhead is investigated in [10].
In [11], the authors proposed an energy efficient algorithm to address the node selection problem in MIMO-based sensor networks. They investigated the effect of circuit power consumption on the performance of MIMO-based sensor networks. They demonstrated the energy efficiency of V-MIMO even for smaller distances, provided the circuit power consumption is small. The authors proposed a metric which is a function of channel conditions and remaining battery energy for selecting the cooperating nodes. They also analyzed the influence of number of cooperating nodes on network lifetime. They achieved additional energy saving and improved network lifetime by selecting the cooperating nodes.
In [12], the authors proposed an energy efficient multi-hop cooperative MIMO scheme for limited number of available cooperating nodes in wireless sensor networks. They analyzed the energy consumption of SISO, multi-hop SISO and cooperative MIMO schemes. They also investigated the optimal number of transmitting and receiving cooperating nodes for a given transmission distance. The authors demonstrated the energy efficiency of proposed scheme and its demand for less network resource over conventional SISO, multi-hop SISO and cooperative MIMO schemes.
In [13], the authors analyzed the energy efficiency of cooperative schemes and showed that the cooperation is more energy efficient than non-cooperative single hop and multi-hop schemes. In [14] and [15], the authors proposed a space-time coded cooperation to reduce energy consumption. In [16], the authors proposed a scheme to exploit transmit diversity in multi-hop WSN and showed the improvement in energy saving as the number of hops increased when end-to-end outage probability is fixed.
In [17], the authors proposed an energy efficient chain based routing protocol for wireless sensor networks to minimize energy consumption, transmission delay and energy-delay cost. In [18], the authors proposed an Energy Balancing Cluster Head (EBCH) in WSN to maintain minimum inter cluster energy consumption of the network by balancing the intra cluster load among the cluster heads.
In [19], the authors proposed a scheme to find the optimal relay nodes and their corresponding radio interfaces that minimize energy consumption while satisfying the end-to end packet deadline requirement. In [20], the authors jointly optimized the hop distance and the number of cooperating nodes to improve the energy efficiency in wireless ad hoc networks. They showed that the Cooperative Multiple Input Single Output (C-MISO) transmission is energy efficient compared with SISO transmission for higher values of path loss exponent. However, the authors considered the end-to-end bit error probability as the error probability for both the broadcast phase and cooperative phase. The authors ignored the impact of channel gain on the energy consumption. Though the authors addressed the effect of bit error probability, they obtained a fixed number of cooperating nodes and optimal distance for a given bit error probability requirement and thus lack flexibility.
In [21], the authors proposed an energy-efficient network cooperation scheme to reduce the power consumption of secondary transmissions while maintaining the performance of primary transmissions. In [22], the authors proposed a protocol using cognitive relaying to provide continuous connections for target users and to minimize the outage probability of transmissions though optimal allocation of time slots.
In [23], the authors proposed efficient spectrum sensing strategies to reduce sensing overhead and to mitigate the interference to primary users in cognitive radio network. In [24], the authors proposed an energy efficient cooperative model for multicell multiantenna cooperative cellular networks. They analyzed the model under different cooperative transmission scenarios, channel conditions and interference levels. The authors achieved significant improvement in energy efficiency and outage probability performance.
In [25], the authors proposed an energy efficient cooperative algorithm to effectively take the relaying decision. They investigated the influence of transmission distance, number of receive nodes on the energy consumption of cooperative MIMO. The authors also demonstrated the influence of number of relay nodes on the energy consumption and saved energy about 10% than direct transmission.
In [26], the authors proposed a metric to select an appropriate next relay cluster for energy efficient CC in WSN. The authors have taken the circuit energy consumption into consideration for optimizing the energy consumption per unit transmission distance. However, the authors have neglected the influence of intra cluster error probability by assuming it as error free. The authors also failed to address the network life time issue by selecting a far away cluster. In [8], the authors investigated the energy consumption of various configurations such as SISO, Single Input Multiple Output (SIMO), Multiple Input Single Output (MISO) and MIMO in WSN. The authors configured a route which consumes minimum energy by selecting the best configuration for each hop. However, the authors assumed same bit error probability for intra cluster and inter cluster transmission to maintain the end to end reliability. Moreover, the authors fixed the inter hop distance irrespective of the cooperating nodes, leading to inefficient utilization of energy.
In [9], the authors investigated the energy efficiency of cooperative communication in WSN. The authors used optimal broadcast bit error probability and optimal number of cooperating nodes to minimize the energy consumption. However, the authors fixed the inter hop distance irrespective of the cooperating nodes. Hence, a strategy which determines the optimal transmission distance which minimizes the total energy consumption by taking the bit error probability of broadcast and cooperative phase and circuit energy consumption into account while satisfying the end to end reliability and data rate requirements is yet to be proposed.
3. System Model
3.1 Multihop C-MISO Scheme
A densely populated WSN which consists of thousands of sensor nodes is considered. The nodes are stationary and each node has single omni directional antenna with communication range of radius d. The nodes are randomly located according to a uniform distribution with a node density ρ. It is further assumed that the transmission between WSN nodes are perfectly synchronized [14] and the channel is a flat Rayleigh fading channel. The data are modulated by 4-QAM and transmitted to a remote sink through multi-hop cooperative transmission.
The Fig. 1 shows the system model and describes a typical dual hop C-MISO communication. The transmission in each hop can be divided into two phases: broadcast phase (i.e local communication) and cooperative phase (i.e. long haul communication). During broadcast phase, the Source Node (SN) broadcasts its data to the neighbouring nodes. The nodes which are within the broadcast distance db (db ≤ 8 m [8]) are regarded as neighbours and those neighbour nodes which are willing to participate in the cooperative transmission are regarded as Cooperating Nodes (CNs). During cooperative phase, the CNs encode the received data using distributed STBC and simultaneously transmit to the relay node (i.e. inter hop node).
Fig. 1.System Model
The Inter hop Node (IN) decodes the information and broadcasts to the neighbor nodes that are willing to take part in the cooperative transmission. The Inter hop nodes are selected, such that the hop distance d is equal to 100 m in the existing work. However, in the proposed work the Inter hop nodes are selected based on the derived optimal distance equation. Out of the many sensor nodes available in the cooperation range, the cooperative nodes are selected randomly. The CNs of the IN encode the received data using distributed STBC and simultaneously transmit to the next relay node. This process is repeated until the Destination Node (DN) is reached. Nodes CN1, CN2 and CN3 are the CNs of SN and CN4, CN5 and CN6 are the CNs of IN.
3.2 Minimization of Energy Consumption
In this paper, the energy model proposed in [6] and followed in [8] is used. Based on [6], the power consumption of the power amplifier is given by
where Pampl is the power consumption of all the power amplifiers, Ptx is the power required for transmission and α is the modulation and power amplifier dependent parameter which is given by
where η is the drain efficiency of the power amplifier and ξ is the modulation dependent peak to average ratio which is given by
where M = 2b is the size of the constellation and b is the number of bits used to represent a symbol. The transmission power Ptx of equation (1) is given by
where is the energy required per bit at the receiver for a given bit error probability requirement, Rb is the bit rate, d is the transmission distance, Gt is the transmitter antenna gain, Gr is the receiver antenna gain, λ is the carrier wavelength, Ml is the link margin and Nf is the receiver noise figure. The total power consumption by all other circuit blocks except power amplifier is given by
where Nt is the number of transmitting nodes and Nr is the number of receiving nodes. Table 1 lists the system parameters as in [6].
Table 1.System Parameters
Equation (5) is represented in terms of energy consumption of the transmitter circuit blocks ECktTX and energy consumption of the receiver circuit blocks ECktRX as
where B is the transmission bandwidth and
The value of Nt is considered as 1 for broadcast phase and as N for cooperative phase. The value of Nr is considered as N for broadcast phase and as 1 for cooperative phase. The bit error probability for M-ary QAM PMQAM with transmit and receive diversity is given by equation (9) as in [8]
where ║ · ║2 is the Frobenius norm, HNr×Nt is the channel gain matrix and N0 is the noise power. By substituting equation (4) in equation (9) and after making necessary rearrangements, the energy consumption per bit Ebt is given by equation (10) as in [8]
In C-MISO system, during local transmission, the source node or inter hop node transmits its data to the cooperating nodes with the transmit energy consumption ELocTX, which is computed using equation (10) by considering d = db, PMQAM = Pb, ELocTX = Ebt and Nt = 1, and is given by
where db is the broadcast distance, Pb is the bit error probability of the broadcast phase, ║Hb,Nr×Nt║2 is the channel gain of the broadcast phase with Nt = 1 and Nr = 1. When the number of sensor nodes participating in the cooperation is N, then the total energy consumption for local communication is given by
where ECktTX and ECktRX are given by equations (7) and (8).
During the long haul communication, all the N CNs transmit their data to the next inter hop node or destination node with the transmit energy consumption ELngTX, which is computed using equation (10) by considering d = dc, PMQAM = Pc, ELngTX = Ebt and Nt = N and is given by
where dc is the long haul transmission distance, ║Hc,Nr×Nt║2 is the channel gain of the cooperative phase with Nt = N and Nr = 1, and Pc is the bit error probability of the cooperative phase. Thus, the total energy consumption for long haul communication is given by
The total energy consumption for a single hop (ECChop) is the sum of the energy consumption for local communication and long haul communication. Hence, ECChop is obtained by adding equations (12) and (14) as
where C1 = ECktRX + ECktTX.
By substituting equations (11) and (13) in equation (15) and rearranging, we get the total energy consmption for a single hop ECChop as
where
Based on [9], the end to end bit error probability Pe is represented in terms of Pb and Pc as
By substituting Pc = Pe - Pb in equation (16), the total energy consumption for a single hop ECChop is given by
The total energy consumption ECChop is minimized by taking partial derivation of equation (20) with respect to Pb and equating it to zero
By substituting the values of C2 and C3 in equation (22) and cancelling the like term yields,
The number of nodes within a circle of radius d with a WSN of node density ρ is given by
On substituting equation (24) in equation (23), with N = Nt and k = 2, since the broadcast distance is small, the equation (23) becomes
From equation (25), the optimal broadcast bit error probability Pb is given by
From equation (25), the optimal long haul distance dPbopt is given by
The dPbopt is the long haul distance dc with optimal broadcast bit error probability and minimum total energy consumption. The dPbopt also depends on the channel path loss exponent, the node density and the number of cooperating nodes which determines the size of the channel gain matrix.
The Fig. 2 shows effect of number of nodes N on the total transmit energy, total circuit energy and total energy consumption per bit for fixed distance case with dc = 100 m.
Fig. 2.Energy consumption per bit over number of cooperating nodes for dc = 100 m
From the figure, it is clear that as the number of nodes N increases, the total circuit energy consumption increases linearly with N, the total transmit energy consumption decreases exponentially with N. The total energy consumption decreases and reaches a minimum value at N = 5, and then increases. At N = 5, the total transmit energy consumption is approximately equal to the total circuit energy. From Fig. 2, it is clear that cooperative communication is energy efficient if total transmit energy consumption is approximately equal to the total circuit energy.
3.3 Algorithm for Energy Minimization
The algorithm can be initiated by the source node or any other controller node of the network. For the given Pe, the algorithm selects a Pb which is lower than Pe and roughly in the range - from 1/10th of Pe to 1/100th of Pe. Choosing a value for Pb above 1/10th of Pe results in more number of CNs (greater than 10) and choosing a value below 1/100th of Pe results in CNs less than 2. The Pc is determined using equation (18). The algorithm for energy minimization in the form of pseudocode is given below.
AlgorithmPseudocode for energy minimization.
Initially, by considering the value of N as 2, the algorithm calculates the total circuit energy, local transmit energy, long haul transmission distance and long haul transmit energy. It then compares total transmit energy with the total circuit energy. If the total transmit energy is approximately equal to the total circuit energy, the algorithm stops. The value of N is NPbopt and the corresponding dc is the dPbopt. Otherwise, the value of N is incremented by one and the whole process of energy computation and comparison is repeated.
4. Simulation Results
The CMISO system is simulated using the simulation parameters listed in Table 2. The nodes are randomly deployed according to uniform probability distribution and the source and destination are randomly selected.
Table 2.Simulation Parameters
For a given broadcast error probability Pb = 0.73×10-4 and end to end bit error probability Pe = 10-3, the number of CNs N is varied from 2 to 10 and total circuit energy and total transmit energy per bit is computed and shown in Fig. 3. NPbopt is the minimum value of N for which total transmit energy is approximately equal to the total circuit energy.
Fig. 3.Energy consumption per bit over number of cooperating nodes for Pe = 10-3 and Pb = 0.73×10-4 for a hop.
From Fig. 3, the optimum number of co-operating nodes NPbopt is equal to 4. For NPbopt = 4, the optimal distance dPbopt is computed and is given by dPbopt = 75 m. For dPbopt = 75 m, the total energy consumption per bit for both SISO & C-MISO is calculated and tabulated in Table 3.
Table 3.Comparison of per bit total energy consumption between SISO & C-MISO for dPbopt = 75 m
From the Table 3, it is evident that C-MISO is energy efficient than SISO.
For a given NPbopt = 2 , the end to end error probability Pe is varied and the corresponding dPbopt and long haul transmit energy ELngTX is computed. The long haul transmit energy, ELngTX signed as (ELngTX (×10-5) J) in Fig. 4.
Fig. 4.Optimal long haul transmission distance over end to end bit error probability Pe under different number of optimal cooperating nodes.
The simulation is repeated for NPbopt = 4, 6 and 8. From Fig. 4, it is evident that for a given NPbopt, the long haul transmit energy ELngTX remains constant even if we decrease the end to end bit error probability Pe.
For a fixed NPbopt = 2, the Pe is varied and the long haul transmit energy consumption is calculated for a bit through single hop using equation (13) and is shown in Fig. 5. For a given Pe, Pb is selected such that NPbopt = 2. The experiment is repeated for NPbopt = 4 and 6. It is observed that the energy required for transmission remains almost constant with decrease in Pe, since the transmission distance is decreased inorder to compensate for increase in transmit energy due to decrease in Pe. It is also observed that for a given Pe, the transmit energy increases with NPbopt. The reason is that the transmission distance increases as per equation (27) with increase in NPbopt for a given Pe.
Fig. 5.Long haul transmit energy consumption over end to end bit error probability Pe under different number of optimal cooperating nodes.
The variation in optimal broadcast error probability Pb with respect to Pe for a given NPbopt is shown in Fig. 6. For a fixed NPbopt = 2, the Pe is varied and corresponding optimal broadcast error probability Pb is selected such that NPbopt = 2. The experiment is repeated for NPbopt = 4 and 6. It is clear from the figure that the Pb decreases with decrease in Pe for a given NPbopt. It is also clear from the figure that for a given Pe, decrease in Pb cause the NPbopt to decrease.
Fig. 6.Optimal broadcast bit error probability Pb over end to end bit error probability Pe under different number of optimal cooperating nodes.
The Table 4 gives the simulation results for the total energy consmption for two different cases by varying the end to end bit error probability Pe. For both the cases 20,000 bits are transmitted from source to destination with dSD = 300 m.
Table 4.Comparison of total energy consumption between SISO and C-MISO under different end to end bit error probability Pe for end to end transmission of Ni = 20,000 bits through multiple hops with NPbopt = 4 and dSD = 300 m.
Traditional fixed distance case :
The total energy consumption is calculated for SISO and CMISO (4×1) with fixed hop distance d = 100 m [8] (d > dPbopt). The simulation is repeated by varying the end to end bit error probability Pe.
Proposed optimal distance case :
For each Pe, the optimal distance dPbopt is computed (i.e. d = 75 m, 64 m and 56 m for Pe =10-3, 10-5 and 10-7 respectively). The total energy consumption is calculated for SISO and CMISO (4×1) with dPbopt. Fig. 7 shows the energy consumption for these two cases.
Fig. 7.Total energy consumption comparison between SISO and C-MISO under proposed and fixed distance cases for end to end transmission through multiple hops over different end to end bit error probability Pe for NPbopt = 4 and Ni = 20,000 bits.
Fig. 8 compares the number of times a relay node can participate in the transmission of 20000 bits of data for the traditional fixed hop distance and the proposed case with optimal hop distance. It is clear from the Fig. 8 that the number of participations for the proposed case is higher than traditional case.
Fig. 8.Comparison of number of participations by a relay node in the transmission of 20000 bits between proposed and fixed distance cases over different end to end bit error probability Pe for NPbopt = 4.
Finally, the performance of the proposed case is compared with the traditional fixed distance case using energy delay product as the metric in Fig 9. The energy delay product is the product of the total energy consumed in the process of transmitting the data from source to the destination and the number of hops required to reach the destination from source.
Fig. 9.Comparison of energy delay product between proposed and fixed distance cases over different end to end bit error probability Pe for NPbopt = 4 and Ni = 20,000 bits.
The delay incurred is dependent on the number of hops. More number of hops results in more amount of delay. The delay cost associated with the local information exchange also increases. Hence, for the cooperative transmission to be energy efficient and delay efficient, it requires lower EDP. The figure shows the EDP for two cases. The case (i) is the traditional fixed distance with d > dPbopt (i.e. d = 100 m[8] irrespective of Pe) and case (ii) is the proposed work with d = dPbopt (i.e. d = 75 m, 64 m and 56 m for Pe = 10-3, 10-5 and 10-7 respectively). For a fixed NPbopt = 4, the Pe is varied and corresponding optimal broadcast error probability Pb is selected such that NPbopt = 4. To transmit the data through the source to destination distance dSD of 300 m, traditional case requires 3 hops. Whereas the number of hops required for the proposed case is 4 for Pe = 10-3 and 5 for Pe = 10-4 and Pe = 10-5. Though the traditional case requires less number of hops, it consumes more energy than the proposed case and results in higher EDP. From the figure, it is clear that the proposed case results in lower EDP and hence it is energy and delay efficient.
Fig. 10 shows the energy consumption of CMISO for the proposed and traditional fixed distance [[8], Jong-Moon Chung et al.] case. As in [8], the node density is varied from ρ = 3×10-3 to 7×10-3. The source to destination distance is fixed as dSD = 500 m. From source to destination, 20000 bits are transmitted with four cooperating nodes NPbopt = 4. The bit error probability is fixed as Pe = 10-3 for both broadcast and cooperative transmission. From the figure, it is clear that for all the node densities, the total energy consumption of the proposed case is significantly less when compared to the traditional fixed distance case [8] and hence proves to be energy efficient.
Fig. 10.Comparison of energy consumption between traditional fixed distance [[8], Jong-Moon Chung] and proposed case through multiple hops for NPbopt = 4 and Ni = 20,000 bits.
The proposed case achieves a marginal reduction in the energy consumption with increase in node density and the variation is significant between the node density ρ = 6×10-3 and 7×10-3, where as the energy consumption remains constant for the traditional fixed distance case. The increase in node density increases the possibility of availability of nodes in best locations and decreases the average distance between the nodes. The amount of reduction in the average distance value between the nodes is insignificant when compared with the larger hop distance of the traditional case. Whereas, the amount of reduction in the average distance value between the nodes is marginally significant when compared with the comparatively smaller hop distance of the proposed case. In WSN, usually the nodes are deployed in larger numbers leading to higher node densities. Therefore, it is clear that the proposed case is energy efficient than the traditional case at all the node density values considered. It is also clear that the proposed case is more energy efficient at higher node densities when compared to lower node densities.
5. Conclusion
In this paper, an algorithm for energy efficient cooperative communication in WSN is proposed. The equations for optimal broadcast bit error probability and hence the optimal long haul distance that maximizes the energy efficiency is derived. The performance of the proposed scheme is evaluated in terms of energy and delay. The proposed scheme is compared with the traditional case. The simulation results show that the proposed scheme is both energy and delay efficient compared with traditional case. Since the proposed scheme is energy and delay efficient, it is suitable for real time applications.
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