An Energy-Efficient Routing Protocol for Wireless Sensor Networks through NonLinear Optimization W. Farjow1, A. Chehri2
H. T, Mouftah2, X. Fernando1
Dept. of Electrical and Computer Eng. Ryerson University 350 Victoria Street, Toronto, ON M5B 2K3, Canada
[email protected]
School of Information Technology and Engineering University of Ottawa 800 King Edward Avenue, ON, K1N 6N5, Canada achehri,
[email protected]
Abstract— Wireless sensor networks (WSN) are an im-
portant new technology for remote monitoring. However, an important feature that distinguishes the WSNs from traditional monitoring systems is their energy constraints. Unlike traditional remote monitoring systems, the energy management policy of sensor node needs to take into consideration. Nodes in a multi-hop WSN need to transmit their own information and to relay each other’s information to a gateway, and there are usually multiple available paths between a node and the gateway. In this paper we proposed an algorithms that minimizing the energy per bit, or equivalently, by maximizing the amount of information delivered to the BS under certain energy constraints. The simulations are adopted for mining applications scenario. The results show that the proposed algorithm can reduce the consumed energy of the sensor nodes. Keywords-component; Energy efficiency, wireless sensor networks, undeground mines.
1.
INTRODUCTION
A wireless sensor network (WSN) is composed of a large number of sensor nodes that are deployed for environmental sensing, monitoring, and maintenance. Traditionally, a sensor node is mainly powered by a nonrechargeable battery, which has a limited energy storage capacity. As a result, a WSN can only function for a limited amount of time. A lot of research efforts have been dedicated to prolong the lifetime of a WSN by improving its energy efficiency [1]. The use of wireless technologies for monitoring in mining community seems very interesting because of the many advantages of wireless solutions. Compared to the wired systems, WSNs provide a huge advantage since they do not require expensive wiring cable to connect sensors to a base station [6]. The advantages of such system include low installation cost. This allows for easy sensor installation even in hard locations, easy expansion. The WSNs have also great flexibility. In addition, the system is generally easy to manage and maintain, since many of these networks are self-configuring. Most of these energy aware techniques are developed from the perspectives of link adaptation, transmission sche-
duling or information routing, and many of them are developed heuristically. They do not provide an answer to one of the most fundamental questions for an energy constrained network: what is the most efficient way to utilize the limited energy such that the energy required to transmit one bit is minimized? The answer to the above question lies in two aspects. First, a node not only needs to transmit its own information, but also to relay the information for other nodes. Therefore it is critical to determine how to divide the limited energy between the transmission of the selfinformation and the relay-information. Second, there are usually more than one multi-hop between a source and its destination. Which path(s) to choose and how to divide the information flow and limited energy among the multiple paths will have a significant impact on the energy efficiency of the entire network [2]-[9]. In this paper we analyze a joint power and adaptive modulation scheme with energy routing protocol. In addition, we assumed that the network used a suitable power saving MAC protocol with a sleep mode mechanism. The simulations were performed in an underground mining topology. The remainder of this paper is organized as follows. In Section II, the system model and the assumptions are addressed.. Section III provides numerical evaluations using random WSN topology as well as realistic node deployment. Finally, the Section V concludes the paper. 2.
SYSTEM MODEL AND ASSUMPTIONS
We consider a wireless sensor multi-hop network that composes of large number of sensor nodes. Each node that equips with single antenna knows its own location by distributed localization method. The wireless links are modeled as Rayleigh fading channels. During the routing, the node that correctly decode the packet form forwarding cluster according to a certain rule and transmit the packet cooperatively to the next forwarding gateway. This process continues until the packet is sent to the destination. 2.1 Radio Link Model The Rayleigh flat-fading with path-loss is assumed to modeling the wireless channel model between the sensors.
This model is a practicable particularly for the static WSNs [9]. We denote the fading channel coefficient correspond to where the amplitude | | is transmitted symbol as Rayleigh distributed with probability density function given according to: | |
exp
Ω
,
Ω
0
(1)
| |
,
, ηM
G
,
(3)
where is the separation distance, G is the gain factor 1 m, which is calculated by: 10 log
G
(4)
G G
η is the path loss exponent and M the link margin, which are all assumed to be the same for all hops. The corresponding average SNR (Signal-to-noise Ratio) ( ): Ω ηM
G
(5)
.
is the where , is the energy of the transmitted signal, the power spectral density of the system bandwidth, the receiver noise figure. AWGN on the channel, and The measured SNR indicate the quality of a single wireless link. 2.2 Modulation Scheme Actually the two schemes are playing different and complementary roles: while the -QAM (M-ary Quadrature Amplitude Modulation) provides better spectral efficiency, the -FSK (M-ary Frequency Shift Keying) assures power efficiency. The non-coherent M-FSK (NCMFSK) with small order of constellation size has been considered the most energy-efficient modulation in WSNs over Rayleigh and Rician fading channels [9]. It is shown that the BER (Bit Error Rate) tioned upon of a M-ary FSK is obtained as: =
∞
2
1
, condi-
1
(6) Where I x is is the zeroth order modified Bessel function. The average SER of NC-MFSK is given by: P=
∞
P γ fγ dγ
(7)
It is shown that P is upper bounded by P
1
1
1
1
M γ
(8)
P
M
2
(9)
Combining (7) and (11) yield the expression for the required transmission power using modulation order M over a link, 1
(2)
When transmitting over a link with transmission the received signal power , is given by power
at
γ
1
Where: Ω
Since, it aims to obtain the maximum energy consumption; we approximate the above upper bound as equality:
3.
P
M
2 .
G
M σ B.N
(10)
Ω
SIMULATIONS RESULTS
In order to evaluate the performance of the transmission metrics we adopt an optimization algorithm. The simulations were performed in an underground mining topology. Figure 1 shows an example of sensor deployment in an underground mine gallery. However, an extension to other topologies is trivial. Nevertheless, a particular characteristic in route investigation in a mine gallery is that they are easy to compute. The linear topology of a mine gallery implies (in most cases) that the route form a node to sink is already predetermined. A fixed number of nodes are randomly deployed in a virtual underground mine topology. Each node can control its power, its modulation order and over which multihop path the data are relayed. For example, the Chipcon CC1000 radio can vary its transmission power between -20 dbm and 10 dbm. First, we present some numerical results to evaluate the energy efficiency for a fixed delay constraint ( 500 ). The system described has been simulated taking as a reference IEEE 802.15.4 standard in order to make a realistic choice of the simulation parameter values. For this purpose, we assume that all modulation schemes operate in B = 65.5 KHz and the = 2.4 GHz Industrial Scientist and Medical (ISM) band has been utilized for WSNs. According to the Federal Communications Commission (FCC 15.247 RSS-210 standard) for United States / Canada, the maximum allowed antenna gain is 6 dBi [6]. In this work, we assume that Gt = Gr = 5 dBi. In addition, we assume the end-to-end bit error probability 10 . The channel specific low values of the route is P path loss model for underground mining environment is applied [11] (path loss exponent η equal to 1.96). The major parameters for the evaluation of the simulations are summarized in Table I Table (I) summarizes the system parameters for simulation. Parameter
0.35
Value 100 mW
Ω
1
30 dB
3 mW
1.96
10 mW
40 dB
3 mW
7 mW
10 dB
9 mW
62.5 Khz
3 mW
1024*8 bit
-3
3
2.5
5
We evaluate the performance of the algorithm for dense WSN network. Therefore, we deployed 30 nodes in a virtual underground mine Topology. In order to represent a realistic multi-hop WSN network, an exponential distribution of node’s remaining energy is used. Compared to the far-away nodes, the nodes close to the sink use often their energies to relay the data. With the time, these nodes drain overall energy. An exponential distribution of energy consummation of a multi-hop path can represent perfectly this scenario. To alleviate this responsibility, the others nodes should help to balance the expenses of the energy.
x 10
Conventional Scheme Apadtative Scheme
Total Energy Consumed (mJ)
B
-174 dBm/Hz
2
1.5
1
0.5
0
0
10
20
30 Distance (m)
40
50
60
Fig. 2. Energy E for single hop transmission as function of distance length. We analyze the impact of route length on the performance of the each optimization scheme by varying the rough lengths form 1 to 8 hops. Averaging the results over a large number of experiments gives a better picture of the system’s performance. The sensor node needs to decide the amount of energy it should allocate for sensing and transmission in each time slot by taking into account the battery energy level. We use a Monte Carlo simulation (the simulation was evaluated for 1000 times). Fig. 3 shows the averages bit consumed power vs route length per bit. The simulation results show that the adaptive schemes can give huge energy savings compared to conventional scheme. 0.016 Conventional Scheme Apadtative Scheme
Fig. 1: An example of sensor deployment in an underground mine gallery. Figure 2 shows the consumed energy over single hop communication, in terms of energy per bit, as function of modulation order and hop length, for hop lengths 1 - 60 m, We can find that when the radio range R is smaller than a critical distance, the non-adaptive scheme outperforms the conventional scheme because the circuit energy consumption is dominant in total energy consumed by all nodes. However, when the transmitting distance exceeds this critical distance, the energy consumption of adaptive scheme is much smaller than that conventional scheme. From this figure, we can see that when the transmission range increase the required energy to transmit a bit increase, this seem evident. However, if we increase the modulation order this would waste valuable energy for transmission over shorter distances, where a higher modulation is highly energy consumption. This is an important point that will be useful by simulations of multihop routes is used.
Total Energy Consumed (mJ)
0.014 0.012 0.01 0.008 0.006 0.004 0.002 0
1
2
3
4
5
6
7
8
Hop
Fig. 3. Simulation results for multihop communication averaged over 1000 runs. 4.
CONCLUSION
In this work, we investigate on the minimizing the energy per bit from energy management perspective. Our goal is to adapt all routes in the networks to transmit the data from the node to the sink with the best energy performance. This is calculated according to certain quality of service
(QoS) in under to provide an energy-efficiency routing strategy. The simulation shows that we can reduce the consumed energy of the node selects the adaptive transmission power and modulation order. Other results are currently being analyzed to evaluate the performance of the proposed algorithm. REFERENCES [1] F. Weiwei, L. Feng, Y. Liu, Y. Fangnan, S. Lei, S. Nishio, “Energy-efficient cooperative communication for data transmission in wireless sensor networks”, IEEE Trans. on Consumer Electronics, vol. 56, no. 4, pp. 2185 – 2192, Nov., 2010. [2] A. Chehri, H. Mouftah, “Energy-Aware Multi-Hop Transmission for Sensor Networks based on Adaptive Modulation”, IEEE 6th International Conference on Wireless and Mobile Computing. Networking and Communications. 2010
[3] C. Schurgers, O. Aberborne, M. Srivastava, “Modulation scaling for energy-aware communication systems”, in ISLPED, pp. 96-99, 2001.
[4] Y. Yu, V. K. Prasanna, “Energy-Efficient Multi-Hop Packet Transmission using Modulation Scaling in Wireless Sensor Networks”, Globecom, San Fransisco, 1 - 5 December 2003.
[5] S. Cui, A. J. Goldsmith, A. Bahai, “Energy-Constrained modulation optimization”, IEEE Trans. on Wireless Communication, Vol.4, no. 5, pp. 2349 - 2360, Sept., 2005.
[6] J. Abouei, K. Plataniotis and S. Pasupathy, “Green modulation in dense wireless sensor networks”, IEEE International Conference on Acooustics, Speech and Signal Processing (ICASSP’10), Dallas, Texas, USA, March 2010.
[7] H.C. Yang, L. Yang, and K.Wu. “Minimum-Energy Route Configuration for Wireless Ad Hoc Networks”, 25th IEEE International Performance, Computing, and Communications Conference (IPCCC). Apr. 2006, Phoenix, USA
[8] C.V. Phan, Y. Park, H. H. Choi, J. Cho, J. G Kim, “An energy-efficient transmission strategy for wireless sensor networks”, IEEE Trans. on Consumer Electronics, vol. 56 , no. 2, pp. 597 – 605, May 2010.
[9] J. Abouei, J. D. Brown, K. N. Plataniotis, S. Pasupathy: On the Energy Efficiency of LT Codes in Proactive Wireless Sensor Networks. IEEE Transactions on Signal Processing 59(3): 1116-1127 (2011).
[10] X. Liang, W. Li, T. A. Gulliver, “Energy efficient modulation design for wireless sensor networks”, in Proc. IEEE Pacific Rim Conference on Communication, Computers and Signal Processing (PACRIM’07), pp. 98- 101, Aug. 2007.
[11] A. Chehri, H. T. Mouftah, P. Fortier, H. Aniss: Experimental Testing of IEEE801.15.4/ZigBee Sensor Networks in Confined Area. CNSR 2010: 244-247.
