A Real-time Energy-aware Routing Strategy for Wireless Sensor Networks

Zubair Khalid & Ghufran Ahmed Noor M Khan

GIK Institute of Engineering Sciences and Technology Topi-23640, District Sawabi, N.W.F.P., Pakistan

University of New South Wales Sydney, NSW 2052, Australia

{zubairkhalid, ghufran}@giki.edu.pk noor@unswalumni.com

Abstract- Recent advances in wireless technology have enabled the rapid development of Wireless Sensor Networks (WSN). It consists of ten to thousands of randomly deployed nodes collaborating to achieve a goal. WSN are used in a variety of applications [9]. In this paper, we are developing a real-time routing protocol for wireless sensor networks. Our goal is to provide "reliable" and "real-time" packet delivery services differentiating packets depending on their urgency and importance. The protocol can achieve the goal in a localized way based on geographic routing. Since it relies on only local decisions of individual nodes without global network information, it preserves nice properties such as scalability, self-healing, and adaptability while continuously guaranteeing real-time and reliability requirements of packets. Keywords: Wireless Sensor Networks (WSN)

I. INTRODUCTION

Recent technological improvements have made the deployment of small, inexpensive, low-power devices (nodes). These nodes are capable of local processing, and wireless communication. Such structure is called Wireless Sensor Networks (WSN) [1]. WSN are very much similar to ad-hoc networks, with little differences.

There are various applications of WSN. Habitat monitoring is one of the applications of WSN [2]. There are some other situations where we require data with aggressive deadline i.e. real time data. Example of such situations include WSN deployed in a battle field and soldiers are acting as base station or sink (a node responsible for collecting the data); and an emergency vehicle (a mobile sink) equipped with computing and communication devices in the context of a disaster management application. Since the WSN have limited resources, therefore supporting real-time communication is extremely challenging.

The three major tasks of a sensor node are sensing, computation & communication [5]. Sensing is what, sensor nodes are used for. Computation is done by every node in order to convert raw data into meaningful and processed data. This reduces the overall data size and less power is used in communication. The former can be achieved by data aggregation, which means that redundant data will not flow in the network. Communication is the major candidate of energy consumption [6]. According to a comparison of the cost of computation to communication in future platforms by Pottie and Kaiser [6], 3000 instructions can be executed for the same cost as the transmission of one bit over 100 m. Hence, energy

consumption is a measure issue and energy aware protocols are desirable.

Routing in WSN presents a particularly challenging problem [4, 3] due to the intrinsic nature of the medium used to route the message. WSN generally communicate via radio. The use of radio as a medium introduces various problems such as: wireless link quality, noise, collision, multi path, fading and restricted bandwidth. Hence the wireless channel for communication is unreliable. So the probability of packet loss is very high. This situation is further aggravated by the frequent node failure which changes the topology of the network and further increases the probability of packet loss in the network. Also as mentioned earlier, the real time data packets have aggressive deadlines to reach to the destination. Therefore, the routing protocol should be robust enough to handle such adverse conditions and ensure reliable delivery of packets while depending upon unreliable system components.

To address the above challenges, we propose a routing protocol which is energy aware and specially designed for real time data packets, in which time to reach destination is so critical. Hence, our real-time power-aware routing protocol dynamically finds he best trade-off between energy consumption and message latency: when routing real time packets with tight deadlines a higher transmission power is used to lower the latency and meet the deadline at the price of reduced network capacity and increased energy consumption; conversely, when routing non-real time packets with lax

Control Centre

Sensor Node Sensor Field

Sink (Gateway)

Satellite

Figure 1: Wireless Sensor Network with fixed sink

Event Area

Proceedings of Asia-Pacific Conference on Communications 2007

1-4244-1374-5/07/$25.00 ©2007 IEEE 381

deadlines a lower transmission power may be used to improve energy efficiency and network capacity [7].

The rest of the paper is organized as follows. In Section 2, we discuss our proposed approach and present the simulation & results in section 3. Finally, we conclude the paper in Section 4.

II. PROPOSED APPROACH

The proposed routing scheme is based on Logical Network Abridgement (LNA). The LNA procedure is capable of describing the intrinsic state of health of the overall network. Normally, users in the control centre wish to easily create well-designed networks, in terms of resiliency and performance. Furthermore, users need to identify, in an intuitive manner, the vulnerabilities that exist in a network in order to eliminate, or work around them. In addition, the consequences of actions taken to remedy failures or strengthen resiliency are often time consuming to understand in a large distributed system. The LNA procedure offers a quick and reliable algorithmic visual tool to achieve these [10].

Table 1 lists the parameters and their definitions that will be used throughout the paper.

TABLE I DESCRIPTION OF THE PARAMETERS

Term Definition Unit IE, i

Energy Impact Factor of ith node Unit less Bi

0 Starting Battery Level of ith node Joules Bi Current Battery Level of ith node Joules IV, i

Vulnerability Index of ith node Unit less n Total number of sensor nodes Unit less

IN, i Node Impact Factor of ith node Unit less

IP Path Impact Factor Unit less TP propagation time across a link Milliseconds TL Time left to meet the deadline Milliseconds Di, j Distance between ith and jth node Millimeters

2.1 Protocol Assumptions The proposed routing scheme considers the following basic assumptions (Most of the assumptions are the same as those made in [Mahaputra]): • Nodes are GPS-enabled and each node is aware of its

geographic location. Our protocol uses geographic information to make routing decisions.

• Node distribution is initially uniform and the node density is high enough to avoid network partition. Sensor nodes are deployed in large numbers; hence it is a valid assumption.

• Each node is assigned a unique ID to help us identify one node from other neighboring nodes.

• Presence of IEEE 802.11b MAC to facilitate reliable wireless communication.

• Radio range of all the nodes is assumed to be equal to R. Range R is not affected by change in the energy of the nodes as time progresses.

• All the sensor nodes start with the same energy before any traffic is routed through them.

2.2 Overview of the proposed approach The basic working of our new scheme is as follows: Each

node in the sensor field is defined by a set of attributes. An attribute is a data entity consisting of a type and a domain of values. An attribute value denotes the current value of an attribute. Attributes can be shared by neighbors and updates whenever there is a significant change. Now, we discuss each of these attributes separately with a little description:

2.2.1 Energy Impact Factor, IE

Sensor nodes can use up their limited supply of energy performing computations and transmitting information in a wireless environment, as such energy conserving forms of communication and computation are essential. Sensor node lifetime shows a strong dependence on the battery lifetime [4]. In a multi hop WSN, each node plays a dual role as data sender and data router. The malfunctioning of some sensor nodes due to power failure can cause significant topological changes and might require rerouting of packets and reorganization of the network. So there is a great need of avoiding low energy nodes in routing. This can be achieved by maintaining the following attribute for each node:

0,i

iiE B

BI = ; (See Table 1 for description of the terms) (1)

Therefore, from the above formula, we can conclude that we should avoid those paths which contain nodes having low value of IE.

2.2.2 Vulnerability Index, IV

The LNA [10] procedure can also be used to provide a quantitative view of vulnerability, as well as a qualitative one through the intuitive visualization of the abstracted network. Such a quantitative view can be established by calculating the vulnerability associated with each node in the network by considering how its removal impacts the depth of resiliency of the network and the number of nodes left in the largest connected cluster. This is captured in a vulnerability index for each Node as defined below:

{ } { }

, { } { }

11

i ibefore before

V i i iafter after

N LI

N L+

= ×+

(2)

Where, }{i

beforeN is the number of nodes before removing ith node }{i

afterN is the number of nodes after removing ith node }{i

beforeL is the number of levels before removing ith node }{i

afterL is the number of levels after removing ith node. Intuitively, a node that has high value of IV means the more

vulnerable it is. So failure of such node leads to the disconnection of the entire network. Thus, from the above formula, we can conclude that we should avoid those paths which contain nodes having higher vulnerability (IV) values, since it leads to the disconnection of the node from the rest of the network.

382

2.2.3 Node Impact Factor, IN (Local Cost Function) This attribute can be derived from the previous two

attributes, that is:

,,

,

V iN i

E i

II

I= ; (See table 1 for a description of terms) (3)

Intuitively, a node that has high value of IN means the more critical it is. So failure of such node leads to the failure of the data delivery. Hence, from the above formula, we can conclude that we should avoid those paths which contain nodes having higher value of IN. 2.2.4 Path Impact Factor, IP (Global Cost Function)

The problem of routing can be considered as designing a policy at each node, so that the overall path from source ‘S’ to destination ‘D’ is optimal, i.e. min ∑IN,i

2, where ∑IN,i is the global cost function of a path. In our scheme, it is represented by IP (Impact factor of path), i.e.

∑=

=k

iiNP II

1

2, ; (See table 1 for a description of terms) (4)

Where, k is the number of nodes in a particular path. Intuitively, a path that has high value of IP means that the path contains critical nodes. Hence, the route having low IP value produces the best energy efficient routing. 2.3 Packet forwarding

Whenever a source node wants to send data to a destination node, a routing path is established. This path is based on many factors (like IP etc.). Since the source may have real-time data packets with aggressive deadline, therefore it follows the procedure below to choose the best path: 2.3.1 Calculation of link and node costs

We consider the factors for the two cost functions on each particular link and node separately. We define the following two cost functions, cost1 (for time awareness) and cost2 (for energy awareness) for a link and a node respectively:

1{ }

1cos

km

ijm

tI D−

==∑ (5)

Where, Dij is the cost of mth link between node i & j and (k-1) is the number of links in a particular path

2,

1cos

k

P N ii

tII I I=

= =∑ [From equation (4)] (6)

Where, k is the number of nodes in a particular path.

Finally the source will receive these two cost functions of each path to destination, from each of its neighbor. Now, any packet originating from the source will be characterized by a packet ID, source ID, destination ID and the time left (TL) to deliver the packet [8]. Each neighboring node of source computes the TP by using the formula:

τnctI

T ijiP +=

cos, (7)

Where, TP,i is the propagation time if sent through ith node, c is the speed of light, n is the number of hops and τ is the processing time of a single node.

The source will forward the packet only if certain conditions

are met: If },...,1{ ni ∈∀ , (TL<TP,i), packet is dropped. This is because TP represents the lower bound on the packet

delivery time. Sending or forwarding the data packet is useful only when we are sure that it’ll reach the destination by the deadline. Otherwise, we discard it. A check for this condition before forwarding ensures that no data packets will be unnecessarily forwarded if the time left to deliver the packet (TL) is less than this lower bound; it is no use forwarding the packet any further, as it will not be able to reach the destination before its deadline. Hence, a check for this condition before forwarding ensures that no data packets will be unnecessarily forwarded. This leads to save energy and low congestion at intermediate hops [8].

In contrast, if (TL>TP,i), then the protocol give high priority to ith node and forwards the packet through ith node. If the same condition satisfied for more than one node, then the protocol selects and gives high priority to that node having minimum of costII function i.e. minimum IP.

In case of non-real time packets, it only considers the costII function (neglecting the costI function, and hence TP value), since now delivery time is not an issue and we have no time constraint. So the source selects that node having minimum costII function.

The same procedure will be run at each node, i.e. each node checks both the cost functions before forwarding the packets to its neighbor nodes.

III. SIMULATION & RESULTS

3.1 Simulation Setup To investigate the performance and the scalability of the

proposed protocol, we generate sensor networks with 50 nodes and carry out extensive simulations. We consider two different performance metrics, costI & costII, which are crucial to improve the quality of service and prolonging the lifetime of WSN. Figure 3 shows simulation setup. We perform a set of experiment shown in Fig. 4a & 4b. In the first experiment (fig. 4a), we draw a plot between IP and number of packets.

Source

Dn1,n2

Dn1,2+Dn2,4

n1

min(X, Y)+Dn5,n6

n2

n5

n4

X=Dn1,n2+Dn2,n4 Y=Dn1,n3+Dn3,n4 n6

Dn1,n2+Dn2,n7

n7

Dn1,n2+Dn2,n7+Dn7,n6

Figure 2: Calculating CostI Function

Destination

Dn1,n3

Dn1,n3+Dn3,n4

n3

383

Simulation shows that the selection of routes for forwarding data packets from source to sink is depend upon two factors: TP,i and IP. For real-time packets, it first takes the decision on TP,i value, then after that, consider IP value; i.e. the protocol follows that path having minimum TP value, and if two paths have same TP values, then it follows that path having minimum IP value to prolong the network life time. In fig. 4b, we have compared the packet delivery percentage with the deadlines. When the deadline is long enough, the schemes achieve very high packet delivery percentage. As we make the deadlines more aggressive, we observe that the delivery percentage reduces drastically for the proposed routing scheme, but still it has higher delivery ratio than other schemes for packets with aggressive deadlines.

Figure 3: WSN Setup

Figure 4a: Path Impact Factor of WSN

Figure 4b: Packet Delivery in WSN

IV. CONCLUSIONS & FUTURE WORK

We have presented in this paper a new protocol for routing in WSN, which is design especially for real-time packets. It is a dynamic LNA based routing protocol, a type of a routing strategy for the constraint-based routing. We have shown in our experiments that our scheme provides automatic adaptation to different routes when network condition changes. Also, it is robust for unpredictable link failures. We have considered two cost functions in the protocol: costI is for time awareness and costII is for energy awareness. The parameters in the costII function, such as impact factor of energy (IE), impact factor of node (IN) and impact factor of path (IP) can be tuned to make the routing best for a particular application. Lots of research still needs to be done on the selection of parameter values and understanding the relationship between different parameters. Our experiments not only focus on the shortest path, but also on energy awareness and vulnerability factor of nodes. .

Our future plan includes extending the routing protocol to allow gateway mobility. Introducing sink mobility in the protocol while ensuring robustness in an energy efficient manner will increase the capability of WSN.

REFERENCES [1] Madalin Cosma, Dan Pescaru, Bogdan Ciubotaru, Doru Todinca. “Routing and Topology Extraction Protocol for a Wireless Sensor Network using Video Information.”, Proc. Of 3rd Romanian-Hungarian Joint Symposium on Applied Computational Intelligence (SACI), May 25-26, 2006. Timisoara, Romania. [2] R. Szewczyk2004, E. Osterwil, J. Polastre, M. Hamilton. “A. Mainwaring: Habitat Monitoring with Sensor Networks,” Communications of the ACM, Vol. 47, No. 6, June 2004, pp. 34-40 [3] C. Intanagonwiwat et al., "Directed diffusion: A scalable and robust communication paradigm for sensor networks," in the Proceedings of MobiCOM '00, MA, 2000. [4] W. Heinzelman et al., "Energy-efficient communication protocol for wireless sensor networks," in the Proceedings of Hawaii Intl. Conf. System Sciences, Hawaii, 2000. [5] Frank Schewetzer and Banno Tilch. “Self-Assembling of network in an agent based model”, Physical Review E, volume 66, article 026113, 2002. [6] G. Pottie and W. Kaiser, “Wireless intergrated network sensors,” Communications of the ACM, vol. 43, pp. 51–58, May 2000. [7] Octav Chipara, Zhimin He, Guoliang Xing, Qin Chen, Xiaorui Wang, Chenyang Lu, John Stankovic and Tarek Abdelzaher, "Real-time Power Aware Routing in Wireless Sensor Networks", the 14th IEEE International Workshop on Quality of Service (IWQoS 2006), New Haven, CT, June 2006. [8] A. Mahapatra, K. Anand, and D. P. Agrawal, “QoS and energy aware routing for real-time traffic in wireless sensor networks,” Computer Commun., vol. 29, no. 4, pp. 437–445, February 2006 [9] Ankit M, Arpit M, Deepak T J, R Venkateswarlu and D.Janakiram; "TinyLAP: A Scalable Learning Automata-Based Energy Aware Routing Protocol for Sensor Networks", Paper submitted as technical report at Department of Computer Science and Engineering, Indian Institute of Technology, Madras [10] T. N. Arvanitis, C. C. Constantinou, A. S. Stepanenko, Y. Sun, B. Liu, and K. Baughan, “Network visualisation and analysis tool based on logical network abridgment,” in Proc. Military Commun. Conf. (MilCom’05), vol. 1, October 2005, pp. 106–112.

384