COSEN: A Chain Oriented Sensor Network for ... - Semantic Scholar

COSEN: A Chain Oriented Sensor Network for Efficient Data Collection Nahdia Tabassum, Quazi Ehsanul Kabir Mamun, Yoshiyori Urano GITS, Waseda University 1011 Okuboyama, Nishi-Tomida, Honjo-shi, Saitama, Japan 367-0035

Abstract Periodical information collection from unreachable remote terrain and then transmit information to a remote base station is one of the targeted applications of sensor networks. But the energy restriction of battery operated sensor nodes certainly makes this task difficult and complicated because once deployed in the target field, it is not feasible to change the battery periodically. Therefore, in order to keep the networks operating for long time, efficient utilization of energy is considered with highest priority. In this paper we propose COSEN – a chain oriented sensor network for collecting information efficiently. COSEN is efficient in the ways that it ensures maximal utilization of network energy, it makes the lifetime of the network longer, as well as it takes much lower time to complete a round. Simulation results show that COSEN demonstrates around 20% better performance than that of PEGASIS in respect of number of rounds before the first sensor dies. It also saves about 260% time on average in comparison to PEGASIS. Comparative analysis and simulation show that COSEN noticeably gives a good compromise between energy efficiency and latency.

Keywords - Sensor network, data collection, data transmission, energy efficiency and latency.

1. Introduction In general for most of the applications in wireless communication we focus on bandwidth efficiency and higher throughput. Energy efficiency comes as secondary concern because devices are connected to the mains or come with a sufficiently capable or/and rechargeable power source. On the contrary, wireless sensor networks (WSN) are the class of wireless communication where data throughputs are very low

but they have a tiny irreplaceable power unit or rechargeable source. These tiny sensors gain popularity with the advances of MEMS (MicroElectro-Mechanical Systems) based sensor technology, low-power electronics, and low-power radio design [1, 2, 3]. There are numerous areas of applications for them e.g. environment and agriculture monitoring, industrial control and monitoring, home automation, security monitoring and defense also. Among the various scopes one of the major applications of sensor network is to collect information periodically from a remote terrain where each node continually senses the environment and sends back this data to the base station (BS) which is usually located at considerably far from the target field [4]. The battery operated tiny sensors with processing and communication unit are deployed once over the target area either manually or randomly, they are self-configurable, have the capacity to collect, aggregate and finally send data to a BS [5, 6]. But sensor networks are severely energy constrained. Therefore it is desirable that the network protocols are designed to reduce the energy consumption in order to keep the network operating for long time [1]. Another crucial issue that is often neglected in design consideration is data delivery time but in most cases data from sensor network are time critical as in the case of battle field or medical or security monitoring system where it is important to receive the data with minimum delay [5, 7]. Data aggregation is another practical issue in WSN as it is well known that it requires far more energy to transmit one bit over wireless medium than to process it. The ratio of energy consumption for communication and computation is in the range of 1,000-10,000 as stated in [8]. So minimization of the amount and range of communication in maximum is required to lengthen the network lifetime. In case of periodic data collection application, as sensors are deployed densely, it may possible that the adjacent nodes have similar data so it is clearly useful to fuse this raw data into more meaningful information before transmitting to BS [9].

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Other issues that should also be taken care of include scalability, fault-tolerance, self-organization, node deployment etc [6, 10]. Here we propose COSEN, a hierarchical chain based protocol. Sensors are grouped into one higher level chain and several lower level chains. In every chain one sensor is elected as a chain-leader based on the residual energy and this node remains as a chainleader for an optimal number of rounds. One higher level leader is selected among all lower level leaders based on some measures at every round. All nodes in a lower level chain send messages to the lower level leader. Besides, all lower level leaders send the information to the higher level leader. The higher level leader is the node that transmits the information to the BS. After an optimal number of rounds, new group of chain leaders are selected. Due to multiple chains and hierarchical structure, COSEN requires much lower time and energy as compared to other protocols of WSN for data collection. The remainder of the paper is organized as follows: section 2 presents an overview of the related routing protocols. The network and radio models of our proposal are discussed in section 3. Section 4 describes the architecture of our proposal followed by a comparative analysis and simulation result in section 5. Finally, section 6 concludes the paper.

2. Related works Routing in sensor networks is different from the traditional wireless ad hoc networks due to the unique requirements of sensor networks [1]. Many new algorithms have been proposed addressing the application and architecture requirements. Most of the protocols can be classified as either data-centric or hierarchical or location-based. Data-centric routing protocols mainly utilize attribute-based query and involve naming of desired data. In hierarchical routing protocol the network is divided into clusters with one node acting as local cluster leader in each cluster. Data are collected and aggregated at each cluster head node before transmission to the BS. On the other hand Location-based routing protocols leverage the benefits of node’s position to route data towards desired location. Considering the energy awareness and time complexity for periodic data collection application, hierarchical routing protocols perform better than other solutions [11]. Among the hierarchical category LowEnergy Adaptive Clustering Hierarchy (LEACH) [9], Power Efficient Gathering in Sensor Information Systems (PEGASIS) [12], Threshold sensitive Energy Efficient sensor Network protocol (TEEN) [13], and

Base-Station Controlled Dynamic Clustering Protocol (BCDCP) [11] provide elegant solutions in the area of network layer. In LEACH, sensor nodes are organized into local clusters with one node in each cluster as cluster head. The cluster head receives data from all other sensors in the cluster, performs data aggregation, and transmits the aggregated data to the BS. LEACH uses rotation of the cluster head in order to evenly distribute the energy load. Thus LEACH is suitable for networks where every node has data to send at regular intervals. LEACH can achieve up to a factor of eight reductions in energy over conventional routing protocols such as direct transmission but still there are some scopes which if handled properly can save more energy. Firstly, the cluster setup overload that needs to be carried by the network at every round. Secondly, as data are transmitted directly from each cluster head node to the remote BS, there are many long distance transmissions in the network. And finally, due to random cluster setup and single hop communication between member node and cluster head, there can be situations where the distances between them are considerably long. PEGASIS is a near optimal chain-based protocol proposed for the same application environment as LEACH. A chain is formed including all nodes in the network using a greedy algorithm. Each node in PEGASIS identifies its closest neighbor by sending a power signal to its neighbor nodes and gradually reducing the power signal till it is heard by only one node. Each node then communicates only with its closest neighbor and takes turns, in a random fashion, to transmit data to the BS. This reduces the power required to transmit data per round. However, this achievement is faded by the excessive delay introduced by the single chain for the distant node. TEEN, a modification over LEACH, provides responses to drastic and sudden changes in the network. Therefore, it is referred to as reactive protocol and does not suit perfectly for periodic data collection scenario. BCDCP is also an improvement over LEACH where the energy expensive works such as cluster setup, routing path calculation etc. are carried by the BS which has no energy limitation. We provide a different solution without involving BS. Thus our proposal is completely self-organized and energy efficient with very limited delay. In our proposal we consider an energy and delay constrained periodical data collection environment such as battle field monitoring and our proposed protocol achieves almost equal energy efficiency as of PEGASIS but with much less delay.

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3. Network and Radio models In our proposal we consider the following network model assumptions: x The BS is located far from the sensor network and fixed. x All nodes are homogeneous and energy constrained. x Data are collected periodically from the network and delay critical. For the sake of uniformity COSEN uses the same radio model as used in LEACH and PEGASIS. The energy consumed in transmitter amplifier for transmission is Eamp=100pJ/bit/m2 for a decent signal to noise ratio (SNR). In addition energies required in running transmitter and receiver electronics are equal and given by Eelec=Etx-elec=Erx_elec=50nJ/bit. Thus for free space model, the total transmission cost for a k-bit message to transmit to a distance d is given by the Equation 1.

Etx (k , d )

Eelec * k  Eamp * k * d 2 (1)

The energy consumption in the receiver is given by Equation 2. Erx (k ) Eelec * k (2) The medium assumed to be symmetric such that the energy required for transmitting a message from node A to B and from node B to A are same at a fixed SNR. We can say from Equation 1, energy dissipation is certainly dominated by the long distance transmission. Moreover, the energy cost for data aggregation is considered as 5nJ/bit/message [9]. The radio speed is considered as 1Mbps [14]. It is further assumed that information processing time in a node is 5 – 10 milliseconds [14].

4. COSEN: Network

Chain

Oriented

Sensor

COSEN operates in two phases - chain formation phase followed by data transmission phase. In the chain formation phase, chains of different levels are formed and in data transmission phase, information is transmitted along with the designated paths. One higher level chain and several lower level chains are formed with the deployed sensors. In each chain, one node is elected as a leader. In every kind of chains, the chain-leader is elected based on some criteria or measures. Lower level leader nodes are responsible to collect information from lower level chains and send the information towards higher level leader. Higher level leader sends the information to BS. In the following sub-sections we discuss the phases of

COSEN in details. We consider that sensor nodes are capable of dynamic power adjustment. Therefore nodes can adjust the amplifier electronics to adjust/accommodate for any required distance.

4.1 Chain Formation Phase Sensor nodes are deployed randomly in the target field. COSEN forms several lower level chains including all the sensors. Each chain is of fixed length. Let us call this length CL. Simulation results show that a chain containing around 15-20% of the sensors gives the optimal results. Due to the space limitation we do not provide the details of the simulation in this paper. The simulation results are described at length in [15]. In our proposal we consider each chain contains 20% of the sensor nodes. For a N-node network where each chain contains CL nodes the number of chains is N/CL (if N mod CL = 0) or N/CL+1 (if N mod CL  0). Chain formation starts from the node at the furthest position from BS using a greedy algorithm. A node in a chain selects the nearest live node that is not already inserted into any other chain and adds it to the chain. If the chain length exceeds CL, new chain formation starts. This way chain formation continues until all the live nodes are grouped into chains. Positions of the nodes may be obtained by methods based on triangulation [10,16,17], where nodes approximate their positions using radio strengths from a few known points. Certainly this extra negotiation consumes extra energy but, as this process takes place once at the beginning of network setup, it is negligible. The chain formation algorithm is given by the flowchart depicted in Fig. 1. We consider that the chain formation takes place whenever every 20% nodes of the initial deployed sensors die. This is due to the optimal length of chain and for efficient distribution of energy dissipation. After fixing the chains, next target is to identify the leader node in a chain. Unlike PEGASIS, where leaders are chosen randomly in every round, COSEN selects leaders for every chain based on the remaining energy in each sensor of the chain. In addition, COSEN does not change leaders in every round but after n number of rounds. Simulation results show that if n = N/CL (or N/CL+1), COSEN performs better [15]. The benefits of using a slight larger duration between leaders selections rather than selecting leaders in every round are i) less communication overhead ii) reduction of time required for selecting leaders in every round and iii) maximize the utilization of higher level chain. Once the leaders are selected, a higher level leader is selected among the leaders using a greedy algorithm. The higher level leader is the only node that sends

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information to the BS. For the higher level leader selection the criteria COSEN considers are i) distance from BS ii) Energy remains in the node and iii) Not selected as higher level leader for the last N/CL (or N/CL+1) rounds. COSEN tries to ensure that nodes closer to BS take turn to transmit frequently than the nodes those are far from the BS. In this way COSEN can use the energy of the network optimally. Every 20% of initial sensors’ death, COSEN reconstructs the chains; select the leaders and the higher level leader for the optimal use of energy of the network.

4.2 Data Collection and Transmission Phase After the formation of the chain and selection of leaders, sensors start data collection operation. This should be noted that chain formation phase does not precede data collection phase always. It precedes data collection phase whenever it is necessary to reconstruct new chains.

We assume that sensors always have data to send to the BS so data is aggregated at each node level before transmission. The similar token passing mechanism is adopted for the initiation of the data transmission as in PEGASIS. As shown in Fig. 2, at the beginning of a round, the leader node n3 transmits a token toward the end nodes of the chain. Each end node in a chain starts by transmitting to the next node. The node in the next position receives the data and fuses this data with its own and transmits it to the next node. This is how data propagate from the furthest node in the chain to the chain-leader. Every leader then transmits the information to the next leader in the higher level chain using the same fashion. Whenever higher level leader gets all the information, it transmits the information to the BS after data fusion.

Figure 2. Token passing approach

5. Comparative Analysis and Simulation Results

Figure 1. Chain formation algorithm

In LEACH, 5% of the total nodes of a sensor network act as cluster heads. Thus for a 100-node network there are at least five long distance transmissions from five cluster head nodes to BS. But if we increase the size of the network to 10000 nodes, there are 500 long distance transmissions to the BS. Also the cost of dynamic cluster setup has to be incurred by the network at every round. In addition, LEACH utilizes TDMA scheduling in order to gather information from the cluster-member nodes to the cluster head. Delay in one round can be estimated like the following: there are approximately 20 nodes per cluster for a 100-node network. If t unit of time is required for one node to transmit information to the cluster head; with a TDMA schedule for 19 nodes requires approximately 19t unit of time to collect data from all the nodes in a cluster and from five cluster heads to BS transmission require extra 4t unit of time delay before transmission from the last head. In total approximately 23t units of delay may occur. In case of PEGASIS, all the nodes in the network form a chain using greedy algorithm. During each round, one node among the chain takes turn to collect and transmit data to the BS. So the number of long

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distance transmission reduces to minimum but at the same time introduces an excessive delay for distant nodes in the chain to transmit to the BS. If we assume it needs approximately same unit time delay t to transmit from one node to the next node, then for Nnode network, if the leader is the end node in the chain, other end node needs (N-1)t unit of delay to reach the leader node. So far for a 100-node network, the delay is 99t. But for a 10000 node network, the delay can be 9999t unit which is considerably high. In our protocol we consider a network of N=100 nodes so there is 5 chains each contains CL=20 nodes. Therefore, in extreme case, in order to reach the furthest node in a chain there is always a same delay of (CL-1)t i.e. 19t units of delay. There is some additional delay in the higher level chain. For a 100-node network there are five leader nodes. Therefore extra (N/CL-1)t i.e. 4t unit delay occurs in the worst case. In total there can be (N/CL-1)t + (CL-1)t unit of delay. That is much less as compared of PEGASIS while achieving approximately the same energy savings. Table 1 shows a comparison for between LEACH, PEGASIS and COSEN in one round of data transmission for a 100-node network. It is clear from Table 1 that COSEN outperforms LEACH by avoiding the overload caused by dynamic cluster setup and minimizing the number of long distance transmissions. At the same time it causes much less delay to deliver information to the BS from distant nodes as compared to PEGASIS. In our simulation, we considered 100 nodes placed randomly in a place of 50 meter × 50 meter. We use Cartesian coordinates to locate the sensors. The BS is located at (25, 150). In the simulation we mainly compared COSEN with PEGASIS. Fig. 3 shows the comparison of energy consumption. It is shown that after several hundreds of round the amount of energy consumed are approximately same. But the good point for COSEN is that, it spends energy in totally distributed way such that the network can operate higher number of rounds before the first sensor dies. COSEN lifetime pattern is depicted in Fig. 4. Whereas the first node dies for PEGASIS at 350 rounds, the first node dies at around 450 rounds for COSEN. The ultimate improvement of COSEN from PEGASIS is that, the delay is much lower in COSEN. Time requirement comparisons are depicted in Fig. 5.

Figure 3. Energy consumption comparison

Figure 4. Lifetime of COSEN and PEGASIS

Figure 5 (i). Time required for single round

6. Conclusions In this paper we propose a protocol for information collection in an energy and time constraint sensor network. For designing the protocol we consider how

Figure 5 (ii). Time required for multiple rounds Figure 5. Time requirements of COSEN and PEGASIS

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Table 1. Comparative analysis among LEACH, PEGASIS and COSEN (single round for 100-nodes) (Considering worst case) Parameters Number of Transmission (Long Distance)

LEACH 5

PEGASIS 1

COSEN 1

Number of Transmission (Short Distance)

95

99

99

Number of Reception (Short Distance)

95

99

99

Unit Delay

23

99

23

Dynamic cluster setup overload

Others

to form chains such that energy consumption reduces. Also we consider about the delay of the network. Our protocol shows better performance than both LEACH and PEGASIS in terms of energy consumption and network delay. Moreover, we find in the simulation that our protocol takes higher number of rounds than that of PEGASIS before the first sensor dies. Furthermore, COSEN is simple and easy to implement. Here for the sake of simplicity we describe a two-layer hierarchical chain based protocol. But the protocol can be extended to multiple layers hierarchical chain based protocol. In our future work we want to include other issues such as MAC layer transmission conflicts, sensor sleep/wake cycles etc.

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Excessive delay

Journal of Communication Systems, special issue on QoS support and service differentiation in wireless networks, Vol. 17(6), 2004, pp. 663-687. [8] Zhao F et al., ''Collaborative signal and information processing: an information-directed approach'', Proceedings of the IEEE, 91, No 8, Aug 2003, pp 1199—1209. [9] W.R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy-efficient Communication Protocols for Wireless Microsensor Networks”, Proceedings of the 33rd Hawaii International Conference on System Sciences, Jan. 2000. [10] J.N. Al-Karaki and A.E. Kamal, “Routing Techniques in Wireless Sensor Networks: A Survey”, in IEEE Wireless Communications, vol. 11, no. 6, Dec 2004. [11] S.D. Muruganathan, D.C.F. Ma, R.I. Bhasin, and A.O. Fapojuwo, ”A centralized energy-efficient routing protocol for wireless sensor networks”, IEEE Radio Communications Magazine, Mar 2005, pp. 8-13. [12] S. Lindsay and C. Raghavendra, “PEGASIS: PowerEfficient Gathering in Sensor Information Systems”, international Conf. on Communications, 2001. [13] A. Manjeshwar and D.P. Agrawal, “TEEN: A Routing Protocol for Enhanced Efficiency in Wireless Sensor Networks”, 1st Intl. Workshop on Parallel and Distributed Computing, Apr 2001. [14] J. Kulik , W. Heinzelman , H. Balakrishnan, “Negotiation-based protocols for disseminating information in wireless sensor networks”, Wireless Networks, v.8 n.2/3, Mar. 2002, , pp.169-185. [15] N. Tabassum. Q.E.K. Mamun, and Y. Urano, “An Energy-Aware Protocol for Periodical Data Collection in Wireless Sensor Networks”, submitted in the First IEEE Intl. Conf on Wireless Broadband and Ultra Wideband Communications, Sydney, Australia, Mar 1316, 2006. [16] N. Bulusu, J. Heidemann, and D. Estrin, “GPS-less Low Cost Out Door Localization for Very Small Devices”, IEEE Personal Communication, Oct 2000, pp. 28-34. [17] R. Min et al., "Low Power Wireless Sensor Networks", Proceedings of International Conference on VLSI Design, Bangalore, India, January 2001, pp. 205-210.

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No cluster setup overload Reasonable delay