We consider the extension of network lifetime of battery driven wireless sensor networks by splitting the sensing area into uniform clusters and implementing heterogeneous modulation schemes at different members of the clusters. A cross-layer optimization has been proposed to reduce total energy expenditure of the network; at network layer, routing is done through uniform clusters; at MAC layer, each sensor node of the cluster is assigned fixed or variable time slots and at physical layer different member of the clusters is assigned different modulation techniques. MATLAB simulation proved substantial network lifetime gains.
In recent years wireless sensor networks (WSNs) are gaining momentum in almost every facet of life whether it is health care, home security, forest area supervision, monitoring earth movement or battle field surveillance. In particular, the core objective of WSN applications is to reliably detect the event and send the collective information to the base station or sink. But the main challenge to achieve this objective is posed by limited energy of sensor nodes which are generally battery driven. Till date a significant amount of research on energy efficient protocols has been come up but to the best of our belief none of the protocol is best suited for WSNs.
Wired networks follow traditional 7-layered Open System Interconnection (OSI) reference model with each layer responsible for explicit task like Physical layer is responsible for the transmission of raw bits, modulation schemes, data rate and transmission power of the nodes which effect the overall power consumption of the network. At data link layer again different sources of energy wastage like overhearing, idle listening, collision and transmission of control packets overhead are there. At network layer best-shortest route and routing functionalities consumes much of the energy of the network. With this way all the seven layers with different functionalities wastes considerable amount of network energy. In wired networks because of unlimited power supply, working with these protocols does not pose problems but for battery driven networks like WSNs such issues require serious re-consideration of already existing layered architecture and as such a cross-layer approach may prove prolific for WSNs.
The Cross-layer design may be defined as, “The breaking of OSI hierarchical layers in communication networks [
1) In [
2) A cross-layer solution based on duty cycling approach along with new kind of active wake-up circuit used in the communication [
3) A two-level node activity scheduling scheme for energy efficient communication has been proposed by [
4) A cross-layer solution for real time data delivery has been proposed by [
5) An adaptive modulation coding scheme (AMC) has been proposed in [
6) A cross-layer approach with an adaptive modulation & coding (AMC) at physical layer and truncated automatic repeat request (ARQ) at data link layer has been proposed in [
7) In [
8) An optimized data transmission method is proposed in [
9) In [
10) An energy aware cross-layer data gathering, RMC protocol for wireless sensor networks is presented in [
11) Receiver based routing has been proposed in [
12) A Cross-Layer Power Control Algorithm (CLPCA) and a Power Control Based Real-time Routing Protocol (PCBRRP) are suggested by [
13) Issues of channel allocation, routing and data rate control in layered architecture have been addressed in [
14) In [
The entire sensing area is divided into ordinary sensor nodes, cluster heads and border nodes. The ordinary sensor nodes monitor the environment and transmit its data to their respective cluster head nodes. The cluster head nodes in turn gather data from all ordinary sensor nodes of the cluster compress and aggregate the received data and then forward it to the border nodes. The border nodes in turn transmit the sensed data to Base station/sink. These border nodes are selected in such a way that these are supposed to be closer to Base station.
a) Routing information: The sensing area can be partitioned into any defined angle say at every 30, 45, 90 or 120 degrees and then the average distance of sensor nodes is taken into consideration to further divide them into uniform clusters. The main focus of this algorithm is to reduce hop distance among sensors and to reduce energy consumption during data transmission and reception.
b) Route set-up phase: The objective of this phase is to create uniform clusters, selecting cluster heads and border nodes. During the set-up phase, the base station collects the information of the position of all sensor nodes within the sensing area along with their unique identification number. To form uniform clusters and choosing cluster heads and border nodes, following steps are to be followed:
1) Initial energy of all sensor nodes is same say Einit = 2J(say).
2) The Base station is assumed to be located at a fixed position say (X, Y) m and have unlimited communication power with unlimited energy access.
3) Each node using its Global Positioning System (GPS) knows its location within the sensing area and sends its location along with its unique Identification Number (ID) to the Base station.
4) To calculate the “Centre Location” (C) with co-ordinates Cx & Cy of the sensing area, equation
5) To calculate the accurate distance (d) of each sensor node from the Centre location (C), equation
6) To form uniform clusters, sensing area can be split into any defined angle and accordingly different equations can be formed like:
Cluster 1st, for different values of x, y lies between: Cy ≤ y ≤ Cy + tanθ1(x − Cx).
Cluster 2nd, for different values of x, y lies between: Cy + tan(θ2 − θ1)(x − Cx) ≤ y ≤ Cy + tan(θ2 − θ1)(x − Cx).
Cluster 3rd, for different values of y, x lies between: Cx ≤ x ≤ Cx + tan(θ3 − θ2)(y − Cy) and so on
For Cluster n, for different values of x and y lies between: Cy + tan(θn − θn−1)(x − Cx) ≤ y ≤ Cy.
7) After cluster formation, for selection of cluster heads, mean distance of cluster sensor nodes from the Centre location (C) can be calculated as follows:
where
8) Node whose distance from Centre Location (C) is highest among all within the cluster can be chosen as a Border node, as it ensures that this particular node (Border node) is farthest from Centre Location (C) but will be near to the Base station/sink.
The proposed UCLEAH algorithm provides a centralized network topology and as the circuitry design of sensor nodes is simple with limited battery and processing capabilities, the channelization can be done by the base station only.
The 802.11 Physical layer frame structure consists of 4 bytes preamble, 1 byte PHY Header, 1 byte delimiter and up to 127 bytes of payload data. The preamble is composed of 10 repeats of “short training sequence” and 2 repeats of “long training sequence” generally for channel estimation as well as frequency and time synchronization with the receivers whereas the Header gives packet configuration like its format, length and data rates. The payload data contains actual data to be transmitted. It also contains different types of frames like management, data and control frames. Each frame further consists of MAC header, payload information and frame check sequence.
Optimizing data packet length in wireless networks to minimize energy consumption has found considerable attention in literature [
1) Energy model: The total energy consumption model for transmitting single data packet is given in [
where α is the amplifier co-efficient, Erx is the energy consumption during the reception of per data packet, d is hop-distance and Efixed is the fixed energy required by transmitter/receiver to transmit or receive one data packet. Time taken to transmit a data packet of r bits is given by:
where r1 is for actual data bits and r0 are overhead (delimiter/header and pattern sequence) bits and B is the signal bandwidth. Total energy consumed during transmission/reception per data packet in terms of bits can be given as:
The above equations clearly depicts that the total energy consumption during transmission and reception of a data packet is directly proportional to the number of bits contained in data packet and the hop distance. Thereby the proposed packet structure and UCLEAH algorithm both helps to achieve lesser total energy consumption of the network.
2) Allotment of TDMA slots and capacity enhancement through frequency re-use: Using UCLEAH, sensor nodes which lie in the diameter of 30 or 35 meters from cluster heads may be provided fixed TDMA slots but sensor nodes which are little far i.e. above 35 meters of diameter from their respective cluster heads may be provided variable TDMA slots. Secondly capacity enhancement through frequency re-use of the sensing network can be done using the following equation:
where k is the enhancement due to multiplexing using TDMA slots in a cluster and u is the number of adjacent clusters in the sensing area. The base station or sink allots time slots and assigns frequency channels using above equations to all the sensors of the network.
3) Proposed modulation schemes: As is evident from Equations (1) to (4) that energy consumption is directly proportional to hop distance. The sensor nodes might be at different distances from their respective cluster heads, so employing single modulation scheme on all members of the sensing network surely won’t be a good idea. Using energy consumption and symbol error formulas given by [
Scenario I:
・ Each modulation scheme (Homogenous modulation) applied on all the members of the cluster.
・ Constant Bit Error Rate (BER) along with the ratio of energy per bit to the noise variance (Eb/No) for different modulation schemes maintained.
・ Each modulation scheme applied in White Gaussian Noise Channel (AWGN) environment.
In this scenario, when BPSK, 8-PSK modulation techniques (being very simple to generate and demodulate) are applied on all the members of the cluster, these performed good in terms of energy efficiency but proved fairly in-effective in terms of channel throughput and data rates whereas when 16PSK and 4QAM modulation techniques (being complex to design and demodulate) are applied on all cluster members, these techniques provided high channel throughput but consumed good chunk of energy of the network.
Scenario II:
・ Different modulation schemes (Heterogeneous modulation) applied on different members of the cluster.
・ Constant Bit Error Rate (BER) along with the ratio of energy per bit to the noise variance (Eb/No) for different modulation schemes maintained.
・ Each modulation scheme applied in White Gaussian Noise Channel (AWGN) environment.
In this scenario, it is observed that when the information transmitted with in a distance of up to 35 - 40 meters all the modulation schemes performed almost in a similar manner whereas when the distance increases say above 40 meters BPSK, 8PSK performed poorly in terms of channel throughput on the other hand 16PSK or 4QAM modulation schemes provided high channel throughput but are little expensive in terms of consumption of energy.
Modulation Scheme | Energy consumption along with symbol error estimation formulas |
---|---|
BPSK | |
QPSK | |
M-PSK | |
M-QAM |
The focus of this paper is to extend the network lifetime to maximum and also to balance the entire network in terms of channel throughput and data rates. It is therefore, recommended that sensor nodes within the diameter of 35 to 40 meters be run on BPSK or 8PSK modulation schemes and when the distance between sensor nodes and their respective cluster head and cluster head and border node is above 40 meters 16PSK or 4QAM modulation techniques should be implemented. With this way we cannot only successfully sense the area for much greater time but also with effective data rates and channel efficiencies.
In this section, the base station controlled uniform clustering of the sensing area is done in accordance with the proposed UCLEAH algorithm and clusters have been formed at θ = 30˚ and 45˚ respectively.
All the optimizations are based on numerical equations and are performed in MATLAB. Default parameters used during simulation are given as
In simulation, for a fixed distance and constant bit error rate (BER) of 10−5 a range of noise values from No to 128 No has been chosen for the above mentioned modulation schemes.
It is observed that for any modulation scheme, the probability of receiving a data packet decreases with the increase in packet size. Therefore, small packet sizes may help to increase energy efficiency. However it is true only if no packet overhead is considered.
The trade off in packet size with or without overhead can be seen in the above figure. When packet overhead is zero or minimum, energy tends to decrease but with the rise in packet overheads, the packet size also increases.
Description | Value |
---|---|
Radio cost (Efixed) | 2.80 mcJ per symbol |
Packet size (r) | 350 bits |
Overhead bits (r0) | 60 bits |
Carrier frequency (f) | 2.4 GHz |
Amplifier co-efficient (α) | 0.02 |
Signal bandwidth (B) | 100 kHz |
Equations (1) to (4) clearly shows that distance and transmission time are directly proportional to the energy consumed.
By implementing above mentioned two scenarios through simulation (see
Due to this trade off, it is highly recommended through this paper that to balance the entire network, a heterogeneous modulation technique may be implemented on the sensing area i.e. sensor nodes which falls into a diameter of up to 40 meters from their respective cluster heads may transmit data using BPSK or 8PSK modulation techniques and all other transmission which is above 40 meters (i.e. from cluster heads to border nodes and border nodes to base station) may use 16PSK and 4QAM modulation schemes. This way not only network lifetime can be extended but higher data rates can also be achieved.
To achieve desired results optimization of above mentioned parameters can be done according to the applicability and resources of WSNs.
In this paper we explored physical, MAC and routing layer parameters to enhance the network lifetime of wireless sensor networks. Through simulation
results it is shown that once the uniform clustering at routing layer is done, optimum packet size as well as modulation scheme can be applied on each member of the sensing area to conserve energy of every node of the network. The results presented in this paper may help network designers in a greater way. The main contributions of this paper are as follows:
ü Cross-layer information exchange especially among bottom three layers of OSI model is the best possible way to conserve energy of WSNs.
ü Optimal hop distance (by using uniform clustering) and packet size (without overheads) are two crucial parameters for achieving energy efficiency in WSNs.
ü The entire network operates at the optimum hop distance due to which communication energy becomes independent of channel noise and thereby helps in enhancing Quality of Services (QoS) of the network.
ü To use energy of WSNs in a best possible way, inter layer information exchange among bottom three layers is required. Each node’s hop distance in its cluster helps to optimize the data packet as well as its modulation scheme at physical layer.
Future work: This research work can be further extended by exchanging information between transport and application layers. Other than this our future work may include testing of this analysis on hardware (motes) and evaluate the results.
We are thankful to Dr. Baljeet Singh, Associate Professor (Mathematics) for his suggestions and valuable insights.
Babber, K. and Randhawa, R. (2017) A Cross-Layer Optimization Framework for Energy Efficiency in Wireless Sensor Networks. Wireless Sensor Network, 9, 189-203. https://doi.org/10.4236/wsn.2017.96011