A Query Driven Routing Protocol for Wireless Sensor Nodes in Subsurface

Download (0)

Full text


A Query Driven Routing Protocol for

Wireless Sensor Nodes in Subsurface


Professor, MM Institute of Computer Technology & Business Management, MM University, Mullana (Ambala), Haryana, India

email: dimplejunejagupta@gmail.com *2

Research Scholar, MM Institute of Computer Technology & Business Management, MM University, Mullana (Ambala), Haryana, India

email: atul44885@gmail.com 3

Professor, YMCA Institute of Engineering, Faridabad, Haryana, India email:ashokkale2@rediffmail.com

Abstract - This work proposes a query driven routing strategy that aims to in-correlate diverse sensor input generated beneath the earth. The proposed communication strategy behaves as a multistage data collection system that clubs the data from various sensors and the same can be used to detect and track comprehensive events that go unnoticed using conventional drilling and mining tools. The strategy would be able to describe dynamic behavior of complicated subsurface and its exploration, allowing us to detect real time events and differentiate among various events happening simultaneously, that need to be addressed on priority basis. The strategy is a unique contribution to subsurface exploration technology as none has proposed the deployment and hence communication strategy for this field, in particular.

Keywords: Wireless Sensor Networks, Routing Strategies, Subsurface Exploration, Data Collection System.


Subsurface exploration [1] is a complex system and it comprises of a sequence of activities directed to provide oil and other hydrocarbons. The exploration is performed in multiple stages wherein the procedure is distributed across various domains occurring at a predefined order. The exploration is an expensive procedure which is currently performed solely on the basis of a system that provides inaccurate and incomplete information as a consequence of which 70% of oil goes unnoticed.

Conventional mining tools fail to provide, the scientists with a complete and central repository of available resources and correlated view of contents and other real time events such as crack in pipeline, available contents etc. happening underneath. In order to address such critical and complex system having the potential to improve economy and meet the needs of present scenario, we propose the exploitation of WSN. This work is an extension of our previous work [2] which, was motivated from the needs mentioned above. Although the previous work proposed a novel and an improved deployment of sensors under the earth crust, however it ignored the communication scheme, to be adopted by sensors. Although there exists many communication and routing protocols for sensor networks but, it is obvious that existing routing protocol are not suitable to be employed in subsurface.

Today, WSN makes use of novel ad hoc networking technology to facilitate inter-sensor communication [3, 4]. The flexibility of installing and configuring a sensor network has been greatly enhanced. Lot of research have been carried out in the field of sensor networks, including design issues related to the physical and media access layers [5, 6, 7] and routing protocols [8, 9, 10]. But least has been done towards devising an efficient routing algorithm for sensors in subsurfaces.



Traditionally in WSN network application, communications within nodes have seen a lot of development and changes. Generally WSNs communication depends on two major factors viz data to be collected and energy available. We have seen many different routing protocols for conventional applications of WSNs, which have been developed or wished-for to solve the challenges posed by these networks. The existing protocols are based on diverse assumptions as regards the application background of the concerned network as well as operational manners. Routing mechanisms have been defined for traditional applications as well as for underwater operations too. But none have been developed or proposed for subsurface exploration at the time of listing this paper. Routing in WSNs, even for traditional applications is intrinsically challenging owing to its distinctiveness, which separates it from other wireless networks like ad-hoc mobile networks (MANET), cellular network or simple Wi-Fi systems. It has been discussed and debated that for a large number of nodes to be deployed, it is not feasible to build an IP based global addressing scheme, since it would lead to a mammoth ID overhead maintenance. This being the reason that traditional IP based routing cannot be used for any of the WSNs applications. In addition to this, unlike our traditional communication networks, application of sensors within subsurface requires to flow accumulated data based on various parameters to the base station. This could be done in single hop (as in Direct Reporting) or via multiple hops (as in Directed Diffusion). The other apparent factor to be considered is the resource constraints within wireless sensor nodes. These nodes have limited energy, processing and storage capabilities. Keeping in mind the resource constraints, we have proposed Query Driven data reporting model, since it requires transmission only on “as and when required” basis.

A. Design Challenges of Routing Protocols

The first and foremost goal of any WSN is to prolong the lifetime of the network, while maintaining the communication quality. The design of routing protocols is dependent upon various issues which designers consider while zeroing upon an efficient communication technique. Some of the major issues to be considered are:

Maintaining trait between precision and energy consumption: The energy source for sensors is always limited. The available energy is used both for communication as well as in-node data processing. Hence energy management must always be taken care of while deciding upon a protocol.

Node Deployment: The traditional deployment of sensor nodes can be either deterministic or non-deterministic. In case of deterministic deployment, data flow along a predetermined route. While in the case of non-deterministic deployment, the nodes are not placed uniformly, hence the communication is generally multi-hop. In our previous work we have laid down the deployment strategy for mobile nodes within subsurface, which led to a fixed location pattern of mobile nodes. These nodes have had an optimal vertical spacing; hence directed diffusion becomes the choice for data flow within them.

Data Aggregation: Since in WSNs a huge amount of redundant data is being generated, several techniques like minima, maxima, average, duplicate suppression can be used for Data Aggregation. While choosing any routing protocol data aggregation always remain one of the most important factors, since communicating redundant data will lead to wastage of scarce and valuable energy sources.

Quality of Service: In certain of the WSN applications, delivery of sensed date is highly time critical, since otherwise it is rendered useless. Examples of such applications are detection of hurricane or certain other calamities. While in other applications, time criticality is not involved, and the data may be transmitted as per eases. In such cases special care can be taken to maximize the lifetime of network as well as available energy resources. Example of such applications can be habitat monitoring.

Fault Tolerance: In any of the application some nodes might fail due to unavoidable factors like energy, damage or environmental interference. In case of such a failure the working of overall network should not be hampered, and it should continue with its normal tasks. The routing protocol should be so robust as to create new links.

Scalability: The routing protocol should be scalable enough to adjust any number of nodes within network. There can be just hundreds of nodes at time, while at other times there can be thousands of nodes to cater to.

Data Reporting Model: Data reporting model of WSNs fluctuate significantly depending on the application and on the time criticality factor of the data. Data reporting can be time-driven, event driven, query-driven or even a hybrid model of these. The routing protocol is predominantly dependent of the choice of data reporting model.

Connectivity: Sensor nodes are never secluded from one another and are anticipated to be highly connected.


B. Review of relevant work

Sensor nodes are always limited in resources; let it be computation, storage or power. While last decade has seen a lot of research work in the area of routing for WSN, it has been found that conservative ad-hoc routing techniques are not viable for large and intense sensor networks. In recent years, sensor networks have been the focal point of a increasing research effort and many routing mechanisms for sensor networks have been projected and developed.

As per Jiang and Manivannan in their work [11], the issues involved in designing efficient routing protocols, identifying several important desired features of a routing protocol and have compared and contrasted the existing routing protocols with respect to these features. The authors have given a very simple classification of the routing protocols based on various criteria’s. viz: (1) Depending on how the sender of a message gains a route to the receiver (like Proactive protocols, Reactive protocols and Hybrid protocols), (2) According to nodes’ participating style (like Direct communication, Flat communication and Clustering protocols), and (3) Depending on whether a routing protocol is location aware or not (like Location aware and Location-less protocols).

Bandyopadhyay and Coyle in their work [12] proposed Energy Efficient Hierarchical Clustering (EEHC), which is a randomized and distributed clustering algorithm. It is used for organizing sensors within network in a hierarchy of clusters. In the clustered setup, there exists a processing center which can be one of the normal sensors or one with extra resources available within it. The end level sensors responsible for collection of data, within the network, are connected to data processing center through a hierarchy of cluster heads. The algorithm works in a bottom-up fashion, which makes a calculation of the communication cost involved. The cost involved between a transmitting sensor and the final receiving processing unit, consists of energy consumed for transmission from sensor node to level 1 cluster head, then from level 1 to level 2 cluster head, and so on, finally from highest level cluster head to the processing unit itself. The energy consumed as calculated in this protocol depends on the parameters p (probability that a called volunteer cluster head becomes actual cluster head) and k (used for cluster head election on each level).

Heinzelman and Chandrakasan in their work [10] presented Low-Energy Adaptive Clustering Hierarchy (LEACH). LEACH is a cluster-based routing protocol which aims at minimizing global energy consumption in sensor network by randomized rotation of local cluster heads, such as to evenly distribute the energy load among the sensors. In each round of process, clusters are organized in a set-up phase, followed by a steady-state phase. After a definite time, which is determined in advance, the next round begins. During the process of clusters creation, every node decides in favor or against becoming cluster head for the current round. Each node chooses values, which lies between possible probability values i.e. 0 and 1. This value is then compared with T(n), which is based on the recommended percentage of cluster heads for the network and the number of times the node has become cluster head in past.

While [12] & [10] had been example of cluster-based routing, having two different types of nodes (cluster heads and normal sensing nodes), researchers have proposed and implemented flat routing too [13] & [8], in which all nodes are essentially designated the same responsibilities. Yu, Govindan and Estrin in [13] proposed Geographical and Energy Aware Routing (GEAR). This is an energy proficient algorithm which makes use of energy aware neighbor selection to route a packet towards the target region without flooding.

Braginsky and Estrin proposed Rumor Routing [8], which is a well-organized technique to distribute queries to nodes that observed events of interest in the network. This method proves significant power cost reductions due to logical compromise between flooding queries and flooding event notifications within network. The idea behind Rumor Routing is to create paths leading to each event. The algorithm makes use of a set of long-lived agents that create paths directed towards the events they encounter.

In the current body of research done in the area of wireless sensor networks, attention has not been given to propose a communication technique for sensor nodes in subsurface. Most current routing protocols have been laid down for traditional applications on earth surface or even underwater. Therefore there exists a need for networks geared towards subsurface to have an efficient routing protocol, custom defined for them. We also believe that sensor networks in subsurface should have the ability to communicate data on Query Reporting Model, which will lead to maximizing their life time and energy sources. So, the current work focuses on developing a communication protocol which is able to achieve the stated objectives.


A. Directed Diffusion Routing, and its implementation in case of Subsurface Exploration


Fig 1: Node placement within subsurface

The interconnecting zig-zag nodes (hollow ones) are placed near the base station or the oil well head, since otherwise the communication path would be too long for the nodes. Fig 2 depicts the directed diffusion used for this particular case.

In Directed Diffusion Communication Technique, each node reports to its nearest neighbor. This message is relayed by other nodes until it reaches the base station. This communication technique is superior to that of Direct Reporting since it does not create any bottle necks, and even those nodes which are far away from the Base Station can pass on their message to the Base Station in a multi hop fashion.

The biggest limitation of this technique is that nodes near base station usually have a short life span. To overcome this problem, data aggregation techniques can be used. Directed Diffusion is a popular data aggregation paradigm for WSN routing. This protocol, as proposed by C. Intanagonwiwat et al. [14], is a data-centric and application-aware protocol in the sense that all data generated by the sensor nodes is represented by attribute-value pairs. The protocol aggregates data coming from different sources enroute, while eliminating redundancy, reducing the number of transmissions and hence saving on scarce network resources like energy and processing capabilities. Contrary to conventional end-to-end routing, Directed Diffusion has the capability of discovering routes from multiple sources to a single destination, allowing in-network aggregation of superfluous data. In this protocol, sensors evaluate events and create gradients of information in their individual vicinity. The base station seeks data by sending interests.

This work proposes to exploit Query Driven Data Reporting Models (QDRRM), so that the nodes do not wear off too soon. The interest generated by the routing protocol is used to describe a sensing task required to be done by the network. This interest diffuses through the network hop-by-hop and each node broadcasts it to its neighbors. As this interest passes hop by hop through the sensor nodes, it on way creates gradients, which in turn collects the data to satisfy the query as generated by the base station.

Fig 2: Directed Communication within nodes in Subsurface Base Station


Base Station


B. Incorporating Mobility into Directed Diffusion

As already illustrated Directed Diffusion can be used in case of subsurface exploration. However in order to enhance the efficiency of routing strategy, we propose to exploit mobility characteristics of sensors used while deployment. The deployment strategy [2] could form r-strips along with connecting nodes known as GCS (Global Connectivity Sensors). These GCS’s has the potential to move parallel to r-strips in order to collect data from sensors. Now since for subsurfaces, data reporting is not event driven, hence it can be collected by GCS as per convenience, aggregated and forwarded to next r-strip or base station.

It is assumed that during the course of communication, nodes would remain static. The deployed GCS act as collectors responsible for not only data collection but also to take intelligent routing decision. The proposed mechanism works in two phases: Forward data collection phase and backward data collection phase. The two phases and the working algorithm are given in the following subsections.

Forward Data Collection

There are ‘a’ sensors numbered 0….a−1, within a single r-strip, such that the collector follows the cycle of 0→1→. . .→a-1→a-2→….→1→0. When the GCS arrives at an r-strip sensor, it stays there long enough to collect the data accumulated since last visit of the collector, emptying the sensor’s buffer. Let αi be sensor data accumulation rate at individual sensor node i, where i [0, n−1]. Let βi be the data collection rate of the mobile collector when its visits the ith node. We assume that βi > αi; otherwise, the sensed data will eventually be lost. The time to collect data from each head is divided into two parts: the time for travel to the head and the time for transferring the data. Suppose di is the time for collector to travel from ith sensor to the (i+1)th sensor (modulo n).

We use ti to represent the time to collect data from the ith head (ti >0). The total time ‘T’ is the sum of time

duration taken by GCS collector to visit all sensor nodes once, along with time taken for collecting the data.

T= 2 (∑ )

The proposed routing algorithm assumes that mobile GCS collectors have intelligent routing decision capabilities. While moving parallel to r-strips, they collect data from sensors and also decide the appropriate sensor where this data is to be dropped.




By exploiting the mobility characteristics of GCS, we can change interconnection between the nodes and thus obtain different network topologies. We model the routing protocol as a multiple hop transmission taking number of hops as our cost metric. This problem can be solved by any shortest-path routing algorithm. The shortest path is to be decided and stored as meta-information within all GCS.

These r-strips are positioned such that the x and y co-ordinates of each node are given by: xij =

yij = (j-1)√ 5 / ,where i = 0, 1 ,2,…. and j refers to the r-strip number = 1, 2 ,3….

As stated, GCS will move parallel to r-strips and on way collect information from r-strip sensor. With the help of meta information available within GCS, shortest route will be decided. Here in Fig 5, data from node (4, 3) will be collected by GCS (3,2) and passed on to node(3,2). Node (3, 2) will buffer the data till GCS (2, 1) arrives in its proximity, upon which the data is transferred to it. GCS (2, 1) will communicate the data to node (3, 1), and from here it travels through the r-strip and finally to base station.

In generic this data movement (→) may be represented as

node (a, n) → GCS(n, n-1) → node(a-1, n-1) if n is odd node (a, n) → GCS (n, n-1) → node (a, n) if n is even

node (4,3)

node (3,2)

node (3,1)

Fig 4: Initial placement of node after deployment has been completed

Fig 5: Relocation of GCS as per proposed routing algorithm Path of data movement Path of GCS movement node (3, 3)

node (2, 2)

node (2, 1)

GCS (3, 2) GCS (2, 1)

node (0, 1) (x01, y01)

(0,0) node (1, 1)

(x11, y11) (r,0) node (3, 1)

(x31, y31) (3r,0) node (4, 1)

(x41, y41) (4r,0) node (5, 1)

(x51, y51) (4r,0)

node (0, 2) (x02, y02) (-r/2,√15r/2) node (1, 2)

(x12, y12) (r/2,√15r/2) node (3, 2)

(x32, y32) (5r/2,√15r/2) node (3, 2)

(x32, y32) (57/2,√15r/2)

node (0, 3) (x03, y03) (0, √15r) node (1, 3)

(x13, y13) (r, √15r) node (2, 3)

(x23, y23) (2r, √15r) node (4, 3)

(x43, y43) (4r, √15r) node (5, 3)


Since the data movement and distance traveled is maximum for nodes at lowest level i.e where n is of higher values, hence the movement of last GCS is initiated first.

Backward Data Collection

Once GCS travels to the r-strip node nearest to base station, reverse travel needs to be initiated. Here the GCS in place of coming back in the same direction parallel to r-strip would exploit the mobility aspect of r-strip nodes. Fig 6 below represents a subsection of the complete deployed network, as depicted in Fig 5. Here GCS (3, 2), which had started its journey at time t=0, has reached near node (0, 3) at time t=k5. Now at this point of time GCS in place of traveling back, is proposed to replace node(0,3), which replaces node(1,3), and so on, until node(5,3) occupies the position adorned by GCS(3,2) at time t=0. Hence we then will have a new node acting as a GCS and the forward data collection phase executes recursively. The main significance of using this backward phase is that it reduces the activity performed by individual sensor acting as GCS, thereby increasing the lifetime of network. Moreover, it reduces the delay and forwarding cost.

Since all nodes are assumed to be equivalent, this goal-swapping makes no functional difference to the network. This resolution strategy is applied recursively, so that

node(j-1, n) ~replaces~ node(j, n) , , … …

here, n is the r-strip number, while a is the offset of farthest node from base station in any given r-strip

The proposed mechanism executes in an iterative replacing successive nodes within given r-strip, and finally enabling the farthest node to occupy the initial position of GCS.

C. Working Algorithm for proposed routing protocol:

Input required for the algorithm:

Value of n, which is the number of r-strips deployed in subsurface

Value of a, which is offset of farthest node from base station in any given r-strip.

Time ki = di + ti is the time for collector to travel from ith sensor to the (i+1)th sensor (modulo n plus time required collect data from the ith head (ti >0).

1: INITIALISE 2: set q=a and w=n 3: set time=0

4: Method: transfer (q, w) {start}

5: GCS (w, w-1) enquires data from node (q, w) 6: if data {node (q, w)} = true

7: buffer data {node (q, w)} to GCS (w, w-1)

8: if [w mod 2] = 1

9: transfer data {node (q, w)} from GCS (w, w-1) to node (q-1, w-1) Fig 6: Sub-section of deployed network to show reverse

node (3, 3) GCS (3, 2)

node (0, 3) node (1, 3)

node (2, 3) node (4, 3)

node (5, 3)

time t=0 time t=k1 time t=k2 time t=k3 time t=k4


10: else

11: transfer data {node (q, w)} from GCS (w, w-1) to node (q, w-1) 12: Method: transfer (q, w) {end}

13: find z = time (modulo ki) 14: if z != 0

15: start a new thread { threadz }

16: call transfer (a, n-z)

17: initialize movement of GCS (n, n-z) 18: GCS (n, n-1) arrives at node (a-1, n) 19: call transfer (a-1, n)

20: stop


The work presented in this paper lays the foundation for devising an efficient query driven routing protocol for subsurface exploration, using the deployment strategy suggested in our previous work. Also, it has been able to exploit the mobility of sensors to great extent. The current work show how sensor mobility characteristics can be used to displace normal sensors as well as GCS to formulate a new network layout, for data aggregation as well as dissemination. GCS’s which travel parallel to r-strips accumulate data on their way, and pass them over to next level r-strip, which definitely leads to a better route for the packets headed towards the base station. The proposed work is still in its initial phase of implementation and we expect that in future, we would able to respond to queries generated from the source.


[1] Dimple Juneja, et al. “On the role of Wireless Sensor Networks in Subsurface Exploration: An Overview”. IEEE International Advance Computing Conference' 2009 (IACC), ISBN 978-981-08-2465-5, pp.3159-3162

[2] Dimple Juneja, et al. “A Novel and Efficient Algorithm for Deploying Mobile Sensors in Subsurface”. Computer and Information Science. ISSN 1913-8989 (Print), ISSN 1913-8997 (Online), Vol 3, No 2 (2010).

[3] G. J. Pottie and W. J. Kaiser. “Wireless integrated network sensors”. ACM Commun., Vol. 43, No. 5, 2000, pp. 51–58.

[4] K. Sohrabi, J. Gao, V. Ailawadhi, and G. J. Pottie. “Protocols for self-organization of a wireless sensor network”. IEEE Personal Commun., Vol. 7, No. 5, 2000, pp. 16–27.

[5] E. Shih, S.-H. Cho, N. Ickes, R. Min, A. Sinha, A. Wang, and A. Chandrakasan. “Physical layer driven protocol and algorithm design for energy-efficient wireless sensor networks”. ACM International Conf. on Mobile Computing and Networking (MobiCom), 2001, pp. 272–287.

[6] A. Woo and D. E. Culler. “A transmission control scheme for media access in sensor networks”. ACM International Conf. on Mobile Computing and Networking (MobiCom), 2001, pp. 221–235.

[7] W. Ye, J. Heidemann, and D. Estrin. “An energy-efficient MAC protocol for wireless sensor networks”. IEEE INFOCOM, 2002, pp. 1567–1576.

[8] D. Braginsky and D. Estrin, “Rumor Routing Algorithm for Sensor Networks”, Proceedings of the First Workshop on Sensor Networks and Applications (WSNA), Atlanta, GA October 2002, pp. 22-31..

[9] D. Ganesan, R. Govindan, S. Shenker, and D. Estrin. “Highly resilient, energy efficient multipath routing in wireless sensor networks.” ACM Mobile Comput. and Commun. Review, Vol. 5 No. 4, pp. 11–25, Oct. 2001.

[10] H. Balakrishnan, A. Chandrakasan, and W. R. Heinzelman, ”Energy-Efficient Communication Protocol for Wireless Microsensor Networks”, In proceedings of the Hawaii International Conference on System Sciences (HICS), pp 1-10, January 2000.

[11] Qiangfeng Jiang and D. Manivannan. Routing Protocols for Sensor Networks. First IEEE Conference on Consumer Communications and Networking, CCNC 2004., pp.:93-98, 5-8 Jan 2004.

[12] S. Bandyopadhyay, and E. J. Coyle, “An energy efficient hierarchical clustering algorithm for wireless sensor networks,” in Proc. IEEE INFOCOM 2003, vol. 3, pp. 1713 - 1723, March 2003.

[13] Yu, Y., R. Govindan and D. Estrin. “Geographical and Energy Aware Routing: A Recursive Data Dissemination Protocol for Wireless Sensor Networks”. UCLA Computer Science Department Technical Report UCLA/CSD-TR-01-0023, 2001