Energy Efficient Cross-Layer Routing Protocol in Wireless Sensor Networks Based on Fuzzy Logic Toleen Jaradat, Driss Benhaddou, Manikanden Balakrishnan*, Ala Al-Fuqaha** Engineering Technology, University of Houston *ECE Department, Mexico State University **CEAS, CS Department, Western Michigan University
[email protected]
network lifetime. In recent research literature, several approaches have been proposed to extend the lifetime of WSNs, namely: power-aware protocols, low power hardware designs, power-saving sleep mode, and transmission range optimization [1, 2].
Abstract— Resources are scarce in Wireless Sensor Networks (WSN) and one of the challenges is to design a lightweight communication protocol to support efficient and uniform power consumption among nodes. In this paper, we propose an energy aware routing scheme based on a cross-layer approach for WSNs with the objective to minimize the overall consumed energy; thus, maximizing the network lifetime. The remaining battery reserve capacity, link quality and transmission power for nodes within the local communication range are taken into consideration to determine the next hop relay node to reach the network sink. Parameters from different stack layers (i.e., physical, MAC, and network) are presented to a fuzzy logic system controller which makes a next hop routing decision. The performance of the proposed cross-layer algorithm is evaluated using discrete event simulation (OMNET++ Modeler).
Cross-layer design of network protocols is a promising approach that can benefit WSN applications. Due to limited resources, knowledge from different OSI layers can be used to jointly optimize the overall performance of the network. The main goal of this study is to develop a routing protocol based on cross-layer approach to disseminate network state information effectively with the intention of minimizing the consumed energy; thus, maximizing the network lifetime. The main idea is to have a routing protocol select the next hop based on the energy level of the surrounding nodes. The next hop will dynamically change based on the energy state of the whole network. This work proposes a cross-layer routing algorithm that uses the information from different layers to help the routing protocol make a decision about the next hop. Given that surrounding nodes’ energy will change over time, the proposed algorithm uses a fuzzy logic based approach to make a selection of the next hop. The surrounding nodes’ energy changes depending on multiple factors and will have a statistical significance from neighboring node’s perspective. To avoid a frequent change of the routing decision, the algorithm uses a fuzzy logic controller with multiple input parameters. The efficacies of the proposed protocol are compared to Ad Hoc on Demand Distance Vector routing (AODV) as the algorithm was implemented on modified AODV protocol. Testing and analysis is carried out using software-based simulation techniques, i.e., discrete event simulation using OMNET++.
Keywords-Wireless Sensor Networks, Energy Efficiency, Cross Layer Design, Link Quality, Routing Protocol, Fuzzy logic system
I.
INTRODUCTION
With technological advances in microelectronics, telecommunications and mobile computing fields, Wireless Sensor Networks (WSNs) are becoming ubiquitous in different applications (medical, smart building, smart grid, disaster recovery, etc.). Even though WSNs are specific to the application they are designed for, the fundamental goal of typical WSN is to collect and aggregate meaningful information from raw local sensor nodes (called motes) and forward it towards a special type of nodes called “sink” to produce useful aggregated data (e.g. temperature, humidity, sound, vibration, pressure, motion, and power). Routing plays an integral role in forwarding the information from source to sink nodes. The resource constraints in terms of limited battery power, low computational capacity, short wireless transmission range and hostile environments create challenging requirements that should be carefully addressed to make optimal routing decisions.
The remainder of the paper is organized as follows: section II presents a literature review of the energy-aware and crosslayer routing protocols proposed in the current research literature. Section III provides detailed description of the proposed energy efficient cross layer routing protocol. Performance evaluation of the proposed protocol is presented in section IV. Finally section V summarizes our conclusions and discusses future research directions.
Using minimum hops as the metric for choosing a route in WSNs might not always be the best choice as selection of the next hop does not take in consideration energy. As a consequence, the protocol will choose that hop until it is dead and then select another one when the “dead” node fails. This will create holes in the network and may cause a fragmented network. The long network lifetime requirement of applications demand for unique protocol design that aims to drain energy uniformly among all nodes; thus, leading to increasing the
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II.
LITTERATURE REVIEW
In general, communication between nodes in WSNs consumes more energy than local sensing or processing operations [5]. This fact leads communication protocol
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toward the destination region. It follows the query-response model. This routing protocol assumes that each node knows its location, energy level, and its neighbors’ locations and energy levels. Since GEAR is a location-based routing, each sensor node requires localization hardware, such as Global Positioning System (GPS). Each node has to keep an estimated cost and a learning cost of reaching the destination through its neighbors. The estimated cost is a combination of the residual energy and the physical distance to the destination. The learned cost is a refinement of the estimated cost that accounts for routing around holes in the networks. A hole occurs when a node does not have any closer neighbor to the target region than itself. If there are no holes, the estimated cost equals the learned cost. The learned cost is propagated one hop back every time a packet reaches the destination so that route setup for the next packet is adjusted.
designers to take a particular interest in energy efficient routing in WSNs focusing on minimizing the total energy consumption and maximizing life time [6]. Routing protocols in WSNs can be classified, in terms of network structure, into three main categories [3]: data-centric flat routing, hierarchical routing and location based routing. Data centric flat routing protocols are query-based and they use an attribute-based naming mechanism to specify the desired data, which helps to eliminate many redundant transmissions. Hierarchical protocols use cluster heads to aggregate the data towards the base station and consequently reduce the number of packets in the network in order to save energy. Locationbased protocols make use of the position information to relay the data to the preferred regions within the network rather than overwhelming the whole network with traffic. A. Data Centric Flat Routing Protocols In general, data-centric flat routing protocols perform innetwork aggregation of data to achieve energy efficient dissemination. Data aggregation allows the fusion of data coming from different sources in order to eliminate redundancy, minimize the number of transmissions and thus save energy. In this routing approach, the sink node sends queries with attribute-based naming to specific regions and waits for desired types of data from the sensors located in the selected regions. Sensor Protocols for Information via Negotiation (SPIN) is considered as one of the first data centric flat routing protocols [3]. SPIN nodes assign high-level names to their data, called meta-data. In order to save energy metadata is used to allow nodes to negotiate with each other before transmitting the actual data to avoid transmitting redundant data in the network.
D. Cross Layer Routing Approach Cross layer design is another promising paradigm for energy and lifetime optimization in wireless systems [6]. The central idea of cross-layer design is to optimize the control and exchange of information over two or more layers to achieve major performance improvements by exploiting the interactions between various protocol layers. Fig. 1 illustrates the cross-layer information exchange process between the different layers in WSNs.
B. Hierarchical protocols The basic idea of hierarchical routing (or cluster based routing) is to organize the sensor nodes into clusters. Clusterheads perform local data fusion and aggregation functions to reduce the number of the packets and energy consumption in the network. This approach enables better scalability of the network by allowing multi-hop communication within the clusters. The quintessential protocol in this category is the Low Energy Adaptive Clustering Hierarchy protocol or LEACH [4]. LEACH is a cluster-based protocol with distributed cluster formation based on the received signal strength. The algorithm randomly selects cluster heads and rotates this role to different node in order to distribute the consumption of energy uniformly.
Figure 1. Cross layer information exchange Cross Layer Routing Protocol for Wireless Sensor Networks (XLRP) is designed to support different transmitting power levels based on the volume of the data [7]. XLRP relies on the information from the application and physical layers and it supports transmission at varying power levels based on the volume of the data being transmitted. This protocol saves energy by switching OFF unintended receivers based on the power of the received radio signal. An Experimental Implementation of a Cross-Layer Network Protocol Stack for Wireless Sensor Networks (X-Layer) introduces the pairwise interactions between non-neighboring layers such as the interaction between the transport and the physical layers [8]. These bonus interactions represent opportunities for the cross-layer approach to improve performance over the traditional independently layered network stack where data structures and buffers can be shared across many layers; thus, reducing memory requirements. The objective of this algorithm is to reduce the transmission power and thus reduce the energy consumption while still maintaining good link quality.
C. Location-Based Protocols In location-based protocols, nodes are addressed by their location where the distance to the neighboring nodes can be estimated by signal strength or by GPS receivers. In most cases, location information is needed in order to calculate the distance between two particular nodes and is used to estimate energy in an efficient manner. Geographical and Energy-Aware Routing (GEAR) employs the use of geographic information while disseminating queries to approximate regions since data queries often include geographic attributes [3]. It uses energy-aware and geographically informed neighbor selection to route packets
The proposed Cross-Layer Information-Sharing Architecture for Wireless Sensor Networks (X-LISA) design in [9] is an information-sharing architecture that facilitates vertical as well
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as horizontal cross-layer optimizations in wireless sensor networks through a cross-layer optimization interface (CLOI). The information addressed by CLOI is stored and maintained in the local data structure of nodes. All layers have access to this information to ensure cross-layer optimizations.
transmission level to enable the routing algorithm to make a decision about what node to select as the next hop to reach the base station. Nodes exchange energy information by piggybacking their current levels along with interest and data messages. Each node maintains a linked list of neighbors’ residual energy levels. Parameters collected by each node in a localized manner are used as inputs to the fuzzy logic controller; the output of this step is a probability of choosing a given neighbor as a router node. The fuzzy logic controller basically helps in the decision making process by weighing the tradeoffs between significance and precision [10]. Fuzzy logic systems are well known as model-free where the membership functions are not based on statistical distributions. Over time the system exhibits self-organization characteristics where the path changes to avoid over-burdening router nodes by selecting paths that can extend the lifetime of the system.
Although different researchers show many advantages with cross layer design, previous work has mostly focused on joint design of two or three layers such as the PHY, MAC and routing layers. Our work extends the previous work by introducing the energy level of the neighboring nodes in the equation. This information piggy backs in the control packets communicated between nodes. Our protocol uses the freely available byte in ZigBee protocol to avoid transmission of more information as the communication consumes more energy. III.
THE PROPOSED FUZZY-LOGIC BASED TECHNIQUE
WSNs are required to be highly scalable and the fuzzy-based routing scheme can naturally lend itself towards minimizing the consumed energy through dynamic “statistical” reselection of the next hop. The proposed routing technique makes use of a self-adaptive scheme based on a fuzzy control algorithm that adapts according to varying measureable parameters. The proposed technique takes into consideration scalability (in terms of the number of nodes), self-learning (i.e., adapt to changes in the ambient environment), and focuses on the entire network longevity (i.e., extend the lifetime of the network as a whole). The algorithm uses two main components, a fuzzy controller and a cross-layer module that collects parameters from other layers. These components are implemented in all nodes as part of the routing algorithms. Each node has two roles, namely: sensing and data forwarding. In general a node would have multiple neighbors where data can be forwarded. In WSNs, a node does not have a global view of the network (i.e., a node keeps track of neighbors connectivity only). Thus, a routing protocol design should depend on local data to achieve network-wide goals. The next hop selection is determined by the proposed algorithm based on the local view of the node and will have a positive global impact on the network.
Figure 2. Node communication in WSNs The four basic elements of a typical fuzzy logic controller are shown in Fig. 3. These elements are: the fuzzifier, the inference engine, the Fuzzy Rule Base (FRB) and the defuzzifier. The roles that these four elements serve in the preposed algorithm are as follows:
A. Sensor node roles and best neighbor election To illustrate our point, let’s take a look at the simplified WSN displayed in Fig. 2. Node A does not have a global view of the network; however it knows the characteristics of the links to nodes within its transmission range (i.e., nodes B , C ,andD in this case). All other links are unknown to node A view. The problem develops if node A keeps choosing node “D as its router node each time it needs to send data to the sink without taking in consideration other alternates. In this case, node D would deplete its battery reserve and die. To resolve this issue, the proposed algorithm changes the “router node” dynamically using a fuzzy logic controller as the energy of the neighboring nodes changes. Thus, extending the lifetime of each sensor node and maintaining similar energy consumption across all nodes in the network.
Figure 3. Fuzzy Logic Controller components 1) Fuzzification of the input variables Our algorithm uses the remaining battery level, received signal strength and a transmission level energy of a node as the input parameters. The first step is to take the crisp inputs and map them to the appropriate fuzzy sets. The modified input variables are: (1) The Relative Energy Level (REL) of a node, defined as the residual energy of a node with respect to the neighborhood. This factor is calculated as:
=
B. Fuzzy Controller Design The proposed scheme uses three parameters: battery level of neighboring node, received signal strength, and node
(1)
Where Emax, Emin are the maximum and minimum energy levels in the neighborhood. Enode is the Node’s residual energy
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level. Based on this definition, the higher the REL, the lesser is the criticality of a node. (2) The Received Signal Strength (RSSI), this value is measured directly using the radio chip (CC 2420). To calculate RSSI, we used the theoretical power relations between an idealized transmitting antenna and a receiving sensor node based on Friis’ transmission equation:
=
Figure 4. Battery Level Membership Function The Degree of Membership (DOM) represents the magnitude of participation of a node’s energy level, RSSI or TX level in a fuzzy set. Even through REL is already between 0 and 1, DOM is still used as it is part of the fuzzy process of Mamdani’s process used here. Fuzzy Rules or rule evaluation occurs when the system takes the fuzzified inputs, and applies them to the antecedents of the fuzzy rules. It is then applied to the consequent membership functions. Using the rule-based structure of fuzzy logic, a series of IF X AND Y THEN Z rules are defined for the output response given the input conditions. The proposed fuzzy rules as shown in Table 3.
(2)
Where PRx is the received power, PTx is the transmission power, λ is the signal wavelength and d is distance between the sender and the receiver, Gt and Gr are the antenna gain of the transmitter and the receiver. The proposed system supports seven transmission power levels as shown in Table 1 [12]. Switching from one level to another based on the battery level of a node at the MAC layer could help extend the lifetime of the node.
Table 3. Proposed Fuzzy Rules 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
Table 1. Transmission levels and their power consumptions Output power level
Current Consumption [mA]
Power (mW)
Equivalence distance (m)
level 7
17.4
31.32
up to 100m
level 6
16.5
29.7
up to 97m
Level 5
15.2
27.36
up to 93m
Level 4
13.9
25.02
up to 89m
Level 3
12.5
22.5
up to 85m
Level 2
11.2
20.16
up to 81m
Level 1
9.9
17.86
up to 77m
The linguistic variables for the fuzzy parameters and their values are shown in Table 2. Table 2. The parameters and their possible values Parameters
Values
Remaining battery
Low, Moderate, High
Distance
Low, Moderate, High
RSSI
Low, Moderate, High
Probability
Low, Moderate, High
REL Low Low Low Low Low Low Low Low Low Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate Moderate High High High High High High High High High
TX Low Low Low Moderate Moderate Moderate High High High Low Low Low Moderate Moderate Moderate High High High Low Low Low Moderate Moderate Moderate High High High
RSSI Low Moderate High Low Moderate High Low Moderate High Low Moderate High Low Moderate High Low Moderate High Low Moderate High Low Moderate High Low Moderate High
Decision 0 0 0 0 P P 0 P 1 0 P P P 1 1 P 1 1 0 P 1 P 1 1 1 1 1
3) Fuzzy Inference Engine The fuzzy inference engine aggregates the rule outputs. The unification process is done based on the calculated rules’ weights and the Max-Min interference method.
2) Membership functions The design uses triangular membership functions (Fig. 4) for each input and output as it is commonly used and simple to implement. The selection of member function is based on the author experience as complex or simple function does not add any prevision to the fuzzy controller.
4) Defuzzification and Fuzzy Control The input for the defuzzification process is the aggregate output fuzzy set and the output is a single crisp number, where the fuzzy response Pf is computed using the following centroid method:
=
∑ %() "#$% &×$% ∑ %() " #$% &
(3)
Where n is the number of rules activated. ki is the output consequent value activated. ∑-,./ *#+, & is the corresponding Rule Degree of Membership Value. The adaptive learning of nodes is achieved by in-network processing (i.e. nodes making their own decision as messages are sent/received) as nodes are in promiscuous modes. It is
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A node is considered dead when it has no more energy. The simulation model has one base station considered as the destination node for all data packets. The control messages are 64 bytes long, and the size of a data packet is set 512 bytes. Table 4 lists the configuration parameters used in our study.
important to point out also that looping is avoided in the network by dropping redundant messages received by intermediate nodes. A sequence number is assigned for each data packet by the message originator.
Table 4: Simulation Parameters
Figure 5. Binary representation of system parameters A pre-defined array of 16 bits that represent the status of a node based on fuzzy input parameters is shown in Fig. 5. The significance of this array of bits is as follows (from right to left): 1.
Battery level: It indicates the amount of energy left in the node’s batteries.
3.
Transmission level: to support the seven power transmission levels described in TelosB datasheet. IV.
36, scattered uniformly in the field
Topology
450 m by 450 m
Radio range of nodes
250 m
Channel bandwidth
1.6 Mb/s
Simulation run time
1000 sec
Initial energy of nodes
300 Joules
Transmission power consumption
Varied from .2916352 to .5131468 mW for data messages, From .036454 to .0641433 mW for control message.
Mode: The mode indicates whether a sensor can function as a router (value 1) or not (value 0).
2.
Number of nodes
Receive: power consumed
35.46 mW
Number of sinks
1
Event/data message size
512 bytes
Control message size
64 bytes
Data generation rate
1 per 3 sec
Propagation model
Free space propagation model
PERFORMANCE EVALUATION
OMNET++ was used to simulate the algorithm. OMNET++ (Objective Modular Network Testbed) is an object oriented discrete event simulator that uses C++ as the programming language. It is mainly used to simulate communication networks and other distributed systems [13]. This work utilizes the AdHocSim package developed in OMNeT++ to model ad hoc networks, in which AODV protocol and several mobility models are implemented. AdHocSim was developed by Nicola Concer as part of his thesis at the Department of Computer Science of the University of Bologna. AdHocSim provides a simple platform on which the simulation model of our energy efficient protocol is developed.
Sink nodes generate interests (specifying the monitoring rate and duration) and broadcasted it throughout the network. Source nodes in the simulation periodically (every 3 sec in our case) generate events (monitor information) and communicate them to sink nodes throughout the task duration. We modified the messages to include the node’s current energy level and all nodes maintain an energy linked-list to keep the collected neighborhood energy information. Two performance metrics have been used; namely, the residual energy in individual nodes and the overall residual energy in the network. In addition, the simulation keeps track of the evolution of the residual energy over time until the first node dies.
To test, analyze and easily compare the performance of the proposed algorithm, the AdhocSim AODV algorithm was modified to implement the proposed energy efficient routing algorithm. The modification added the power module that measures the energy used by a node’s radio each time a node sends or receives a packet. The mobility module was also eliminated from AdHocSim to make the network topology fixed where nodes have static positions. Two simulations were run one for AODV without the proposed algorithm and the other for AODV with the proposed algorithm. Both simulations have the exact same node distribution.
Fig. 7 shows the energy consumption of the first-dying node in the network, nodes start with 300 Joules at the beginning of the simulation and data is recorded every 20 seconds. The first dying node represents the weakest node in the network at the simulation instance. It is important for the network to keep the nodes alive as long as possible, so avoiding the weakest link is significant for prolonging network lifetime and connectivity. As the results show, AODV has almost linear energy drain even for the weakest node. This confirms that there is no energy adaptiveness at the node level while the cross layer fuzzy based routing performs differently over the simulation period. Using our proposed approach, nodes become more conservative when the battery parameter REL becomes LOW, this affects the fuzzy decision and consequently helps in choosing another node for forwarding the packets.
In order to add the power module to the network topology the “mobilehost.ned” file was edited so it contains the new input connection from the MAC layer. The simulation model used in our study has 36 nodes forming a random topology. Initially, each node has the same energy level as specified in the energy model (i.e. 300 Joules).
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12000
AODV
Total Network Energy Remaining (J)
Residual Energy (J)
350 300 250
Cross Layer Fuzzy Based Routing
200 150 100 50
10000
AODV
8000 6000
Cross Layer Fuzzy Based Routing
4000 2000 0
0
0 100 200 300 400 500 600 700 800 900 1000 1100 1200 1300 1400 1500 1600 1700
0 40 80 120 160 200 240 280 320 360 400
Simulation Time (S) Simulation Time (S) Figure 8. Residual Network Energy
Figure 7. First dying node’s energy profile
REFERENCES
Network energy was extracted from all nodes every 100 sec during the simulation for the purpose of comparing the evolution of the total residual energy over time for the routing protocols under study. The results are presented in Fig. 8. It is clear from the histogram that the network lifetime is prolonged when using the proposed cross-layer fuzzy based routing approach. In this specific simulation scenario, the network lifetime was extended by 1000 simulation seconds. The performance improvement might be much higher with optimized fuzzy and configuration parameters. Adapting these parameters is a subject of further research. I.
[1] M. Sujeethnanda, P. Nayak, and G. Ramamurthy, “A Novel Approach to an Energy Aware Routing Protocol for Mobile WSN: QoS Provision,” in International Conference on Advanaces in Computing and Communications (ICACC), pp. 38 – 4, 2012. [2] L. Tran-Thanh and J. Levendovszky, “A Novel Reliability Based Routing Protocols for Power Aware Communications in Wireless Sensor Networks,” in Wireless Communications and Networking Conference (WCNC), pp. 1-6, 2009. [3] K. Akkaya and M. Younis, “A Survey on Routing Protocols for Wireless Sensor Networks,”Ad Hoc Networks, Vol. 3, pp. 325-349, May 2005. [4] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “EnergyEfficient Communications Protocols for Wireless Microsensor Networks (LEACH),” Proc. of the 33rd Hawaii International Conference on Systems Science, Volume 8, pp. 3005-3014, 2000. [5] B. Kechar, A. Louzani, L. Sekhri, and M. Khelfi, “Energy Efficient Cross-Layer MAC Protocol for Wireless Networks,” in Proc. of the 2nd International Workshop on Verification and Evaluation of Computer and Communication Systems, July 2008. [6] T. Melodia, M. C. Vuran, and D. Pompili, “The State of the Art in Cross-Layer Design for Wireless Sensor Network,” in Wireless systems and network architectures in next generation internet: Second International Workshop of the EURO-NGI Network of Excellence, pp. 78-92, Italy 2005. [7] N. Zhao and L. Sun, “Research on Cross-Layer Frameworks Design in Wireless Sensor Networks,” in Proc. of the Third International Conference on Wireless and Mobile Communications, France, 2007. [8] R. Gunasekaran and H. Qi, “XLRP: Cross Layer Routing Protocol for Wireless Sensor Networks,” in Wireless Communications and Networking Conference, (WCNC 2008), pp. 2135 – 2140, 2008. [9] I. F. Akyildiz, M. C. Vuran, and O. B.Akan, “A Cross-Layer Protocol for Wireless Sensor Networks,” in Proc. of the 40th Annual Conference on Information Sciences and Systems, Princeton, NJ, pp. pages 1102 – 1107, 2006. [10] M. Balakrishnan and E. E. Johnson, “Fuzzy Diffusion for Distributed Sensor Networks,” in Military Communications Conference, (MILCOM 2005), Atlantic City, NJ, pp. 1-6, 2005. [11] G. Goebel (2003). An Introduction to Fuzzy Control Systems [online]. Available: http://www.faqs.org/docs/fuzzy/. [12] J. Polastre, R. Szewczyk and D. Culler, “Telos: Enabling Ultra-Low Power Wireless Research,” in Proc. of the 4th international symposium on Information processing in sensor networks, Los Angeles, CA, April 2005, pp. 364-369. [13] OMNET Technologies, OMNET Modeler 8.0.[online]. Available: http://www.omnet.com/
CONCLUSIONS AND FURTHER WORK
WSN is a multi-hop constraint-based network where energy is a limited resource. In this work, we proposed a fuzzy logic based system to prolong the sensor nodes’ lifetime. A routing decision is made by each node based on the output of fuzzy logic system. The used parameters include: the neighboring node transmission level, neighboring node battery reserve level and link quality. Simulation results shows that the proposed cross layer fuzzy based algorithm outperforms AODV in terms of energy consumption. It’s important to mention here that the parameters of the proposed algorithm are not optimized but we plan to investigate this in future studies. We also plan to compare this algorithm with other proposed energy aware algorithms. The results from this work can be used to better understand the impact of link quality on the overall routing performance. Some directions for future work include the investigation of more parameters that could be included in the fuzzy decision controller (e.g., mobility and noise levels) and compare with other energy-aware protocols such as LEACH, GEAR, and XLRP. We also plan to implement the proposed protocol in a real network using Telos motes and compare the proposed protocol with other energy-aware sensor routing protocols.
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