Data Collection Point Based Mobile Data Gathering ... - IEEE Xplore

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Scheme With Relay Hop Constraint. Srijit Chowdhury. Purabi Das School of Information Technology. Bengal Engineering and Science University. Shibpur, India.
Data Collection Point Based Mobile Data Gathering Scheme With Relay Hop Constraint Srijit Chowdhury

Chandan Giri

Purabi Das School of Information Technology Bengal Engineering and Science University Shibpur, India E-mail: [email protected]

Department of Information Technology Bengal Engineering and Science University Shibpur, India E-mail: [email protected]

Abstract— Various data gathering techniques using mobile collectors have been developed for energy saving in wireless sensor network. In the sensor field, a mobile collector may traverse within the transmission range of each sensor node to collect data directly from them without any relay. But as the velocity of a mobile collector is low, the data gathering latency may arises which is not desire for many time sensitive applications. To minimize data gathering latency, local data may be aggregated using multi hop transmissions and those collected data may be uploaded to the mobile collector. But, if the number of local relay hop is arbitrarily large then it may increase consumption of energy on packet relays and thus the overall efficiency of mobile data gathering may be reduced. Also, sensor has the limited buffer capacity to store data. So, the relay hop count should be constrained to a certain level to limit the energy consumption at sensors. In this work we have proposed a data collection point based approach that establishes a balance between the energy savings and data gathering latency, which makes an adjustment between the relay hop count for local data aggregation and the moving tour length of the mobile collector. Experimental results show the effectiveness of the proposed approach and provides better average relay hop count than other mobile data gathering schemes of same type.

applications. According to the empirical studies [7], [8], the packet relay speed in a WSN is about several hundred meters per second, which is much higher than the velocity at which the mobile collector moves. Hence, in general, the latency of multihop relay routing is much shorter than that of the mobile data gathering. From these observations, in our work we propose an algorithm called DCP-MDGS-RHC (Data collection point based mobile data gathering scheme with relay hop constraint) to show a deep relationship between the energy saving and the data gathering latency. The main contribution of our work is to show that the proposed method provides better solution in case of smaller as well as in a wide large sensor network compared to previous work. Rest of the paper is organized as follows. In section 2 we describe the related works on mobile relay based data gathering technique. Section 3 represents the DCP-MDGS-RHC problem. Section 4 presents the proposed algorithm with example. Section 5 shows the experimental results of our proposed algorithm by extensive and exhaustive simulations. Lastly in section 6, we draw conclusions. II.

Keywords— Wireless sensor networks; mobile data gathering; mobile collector; data collection point; multiple relay hop; tour length;

I.

INTRODUCTION

Experimental measurements have shown that generally data transmission in a wireless sensor network is very expensive in terms of energy consumption, while data processing consumes significantly less [1]. The energy cost of transmitting a single bit of information is approximately the same as that needed for processing a thousand operations in a typical sensor node. Several approaches like duty cycling, data-driven approaches, and mobility may be considered to reduce power consumption in wireless sensor networks [1]. Among them we consider the mobile relay based approach. In this approach, a large number of static sensor nodes distributed randomly in large area associated with a static data sink located at the centre of the field and a mobile collector (MC) is employed to collect data packets from each sensor nodes within a single hop data transmission. However, due to the low velocity of the mobile collector, it would incur long latency in data gathering, which may not meet the delay requirement of time-sensitive

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RELATED WORK

Based on the mobility pattern, MDG schemes can be divided into two broad categories: uncontrollable mobility and controllable mobility. In case of uncontrollable mobility [3], the MC either moves randomly or moves along a fixed track. This approach has generally high stability and reliability but it typically lacks the agility and cannot be adaptive to the sensor distribution and environmental dynamics. On the other hand using controllable mobility approach [4-7], MCs can freely move to any location in the field and its trajectory can be planned for specific purposes. The controllable mobility schemes can further be divided into three types as obtained from the literature a) The MC is controlled to traverse the transmission range of each sensor and collect data from them within single hop transmission [4]. This approach minimizes the cost of energy and balance energy consumption among different sensors without any multi hop relay, but it may results in long data gathering latency especially in a large-scale sensor network due to the low velocity of the MC. b) MCs gather data from the sensors in the vicinity via multi hop transmissions along its trajectory [5]. This approach

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can effectively minimize the moving tour of the mobile collector to a certain level; but as it does not impose any constraint on the relay hop count, energy consumption on packet relays may increase. As a result, network lifetime or certain level of energy efficiency cannot be guaranteed. c) This type of approach jointly considers data transmission patterns and moving tour planning [2][6-7]. M. Jhao and Y. Yang [2] proposed a polling based approach to pursue a tradeoff between the energy saving and data gathering latency, which achieves a balance between the relay hop count for local data aggregation and the moving tour length of the mobile collector. In our work we have considered the controllable mobility approach where we have tried to find out a minimal set of data collection points within a relay hop constraint so that the average relay hop of each sensor and the tour length of the mobile collector can be minimized to conserve the energy. III.

PROBLEM DEFINITION

Mobile collector has the freedom to move to any location in the sensing field that provides an opportunity to plan an optimal tour. Our basic idea is to find a set of special nodes referred to as data collection points (DCP) in the network and determine the tour of the mobile collector by visiting each DCP in a specific sequence. With sensors properly affiliated with these DCPs, the relay routing for local data aggregation can be constrained within h hops. The value of h is set depending on the user-application that reflects how to balance between the energy saving and data gathering latency. For example, when the energy supply of sensors is not sufficient or the data gathering service is somewhat delay-tolerant, h is typically set to a small value. Each DCP temporarily buffers the data originated from its affiliated sensors. When the MC arrives, it sends request to each DCP for data uploading. Upon receiving the request, a DCP uploads buffered data packets to the MC in a single hop. The MC starts its tour from the static data sink, which is located generally at the centre of the sensing field. MCs collect data packets at the DCPs and then return the data to the data sink. Since the data sink is the starting and ending points of the data gathering tour, it can also be considered as a special DCP. We refer to this scheme as the data collection point based mobile data gathering with relay hop constraint. Fig. 1 illustrates the fact, where the sensors in the shadowed area will locally aggregate data packets to their affiliated DCP within two hops (i.e., h = 2) [2].

Fig. 1. Example of data collection point based mobile data gathering within relay hop constraint h=2.

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Hence the problem can be stated as follows: Find a minimal subset of sensors as the data collection points and a set of shortest paths that connect each sensor node to a data collection point within controllable relay hop constraint to minimize the average relay hop count and moving tour length of the mobile collector that visits all data collection points starting from the static data sink.

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Algorithm: DCP-MDGS-RHC G (V, E) where V is the set of sensor nodes in the sensor field and E is the set of edges/links among sensor nodes, relay hop constraint h, static data sink S. Set P of DCPs, a set of geometric trees { tp | p ∈ P }, the shortest moving path (Z) of the mobile collector visiting the DCPs in P and the data sink starting from the sink. Construct all SPTs (ST) for G started from the nearest node from the sink S (Let it be called as Root), so that they all together cover all the vertices in V. Select the Root as DCP and add it to P. Add all affiliated nodes of the Root to Tp. Set T' (V', E') = s where s ∈ ST For each path in T' starting from Root Find all h hop distant nodes and store them in TEMP. End For If any node in TEMP is leaf node Then Remove it. Update TEMP. End If Among all nodes in TEMP, If any 2 or more nodes have common parent and their children are within (h-1) hop (except Root) Then Select the parent as DCP. Remove those nodes that have common parent. Update TEMP. End If If TEMP has a node DCP (say PP') which has all the children within (h-1) hop Then Select the parent of PP' as DCP (if it is not already) Remove PP' from TEMP. Update TEMP. End If If TEMP has a node DCP (say PP"), such that its parent is a DCP with all the children within (h-1) hop except PP" Then Remove the parent DCP from TEMP. Update TEMP. End If Update P with TEMP. (a) Construct geometric tree tp with p ∈ TEMP as root and all its affiliated nodes. (b) TEMP is set to empty. (c) Remove T' from ST. If ST is not empty Go To Step 3. Else Go To Step 11. End If Find the shortest moving path Z of mobile collector starting from sink and covering all DCPs in P.

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IV. PROPOSED ALGORITHM In this section, we have proposed a heuristic algorithm that finds the minimal subset of sensors as the DCPs. The algorithm does not need to run more than once for a specific sensor network unless the network topology changes or the relay hop constraint changes. In order to find optimal DCP locations among sensors, relay routing paths and the tour of the mobile collector should be jointly considered. The tour of the mobile collector can be minimized in two ways: First, the sensors selected as the DCPs are compactly distributed and close to the data sink. Second, the number of the DCPs is the minimum under the relay hop constraint. Based on these criteria, we have proposed an algorithm, named DCP-MDGSRHC algorithm. The basic idea is to iteratively find DCPs among the sensors which are nearer to the root. Also, each DCP is linked to as many as possible sensors within the relay hop constraint in order to minimize the total number of DCPs as well as to minimize the average relay hop count within the relay hop constraint. A. Explanation of Algorithm Assuming that after applying any standard shortest path tree algorithm on a network graph consists of 25 wireless sensor nodes, the tree structure of all the nodes generated as shown in Fig. 2(a). Red colored node (S) is the static data sink. Node 1 is the nearest node to the sink. According to step 2 of the algorithm, the nearest node of the sink is selected as Root and it is added to the list TEMP as DCP. According to step 3 of the algorithm, all the selected h hop (h=2) nodes are to be added to the list TEMP as follows. At iteration 1 in step 3, nodes 9, 25, 23 and 11 are selected as DCP [see Fig. 2(b)]. Similarly, at iteration 2, nodes 15 and 5 are selected [see Fig. 2(c)], at iteration 3, node 2 is selected [see Fig. 2(d)] and at iteration 4, node 13 and 22 are selected as DCP [see Fig. 2(e)]. So, now in the list TEMP, there are ten numbers of temporarily selected data collection points. Among them, 5, 9 and 23 are leaf nodes. According to step 4 of the algorithm these nodes are removed from the list TEMP (see Fig. 2(f)), because our target is to find DCPs with as many number as affiliated nodes to these DCPs and thus minimizing the number of DCPs. Now, two DCPs 13 and 22 in TEMP has the common parent (node 18), so according to the step 5 these nodes are removed from the TEMP and their parent node 18 is selected as DCP to maximize the number of affiliated nodes to the data collection points. (See Fig. 2(g)). Now, two DCPs 2 and 11 have all the children node as leaves. So, according to step 6, those DCPs are removed from the list TEMP and their parent nodes (21 and 3 respectively) are selected to the list TP as new data collection points. (see Fig. 2(h)). The DCP node 25 in TEMP is the parent node of DCP node 3 and it has all the children as leaf except the child node 3, so according to the step 7, the DCP 25 is removed from the list TEMP and affiliated to the DCP node 3 with all its children (see Fig. 2(i)). Finally, in Fig. 2(j), according to the step 11, the shortest moving path of the mobile collector visiting all the finally selected DCPs starting from the static sink node is shown by green solid line.

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EXPERIMENTAL RESULTS

The proposed algorithm is verified by doing simulation with Desktop Computer having - Intel icore 3 processor, 2 GB RAM and LINUX Operating System. Algorithm is implemented in C programming language. The performance of the proposed DCP-MDGS-RHC algorithm is compared with the other existing algorithms for a small sensor network. Proposed algorithm is also simulated for large networks. We restrict the relay hop h=2, because, a sensor network is expected to achieve a certain level of energy efficiency. For example, if each transmission costs one unit of energy and the energy efficiency of 33% (0.33 energy

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unit/packet) is expected, then each packet should be forwarded from its source sensor to the static data sink in no more than three hops on average, i.e., each data packet should be relayed to its DCP within two hops. Another reason is that, the DCPs need to buffer the locally aggregated data before uploading or transfer data to the mobile collector when it arrives. Hence it is not desirable to associate too many sensors with a DCP due to buffer constraint on the sensor nodes. For example, consider a sensor network with an average node degree of 3. If a sensor is selected as a DCP and the local relaying is constrained within 2 hops, there will be up to 10 sensors affiliated with this DCP including itself. Therefore, due to the the limited buffer capacity of the DCPs relay hop should be controllable according to the size of the network as well as according to the energy and buffer capacity of the sensor nodes. The following sections discussed about the simulation results obtained by our proposed algorithm. A. Experimental results in smaller network This section discusses about the performance of the proposed algorithm for a small sensor network having 30 sensors, which are non-uniformly distributed over a 70m X 70m square area. The results are compared with other mobile data gathering schemes like SPT-DGA[2] and PB-PSA[2]. It is assumed that the static data sink is located at the center of the area and the relay hop parameter is set to 2. We have applied the nearest neighbour (NN) algorithm [9] to calculate the moving tour length; the mobile collector starts moving from the data sink and visits the nearest unvisited DCP and continues the visit before finally returning to the sink. For each performance point we made simulations of experiments 500 times and calculated the average of results found. Simulation results are represented in Table 1. Column 1 shows the different parameters based on which we have compared our results with the previous works. TABLE I.

PERFORMANCE COMPARISON IN SMALL NETWORK WHERE L X L=70M X 70M, N=30, H=2 SPT-DGA [2]

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average relay hop count, maximum number of affiliated sensors to a DCP, average number of affiliated sensors to a DCP as shown in Table 1. It is observed from Table 1 that the proposed DCP-MDGS-RHC algorithm provides shortest tour length of MC and minimum number of selected DCPs among all the previous works. It is also seen that maximum number of affiliated sensors and thus average number of affiliated sensors are high relative to other solutions and thus average relay hop is relatively higher than other methods. These observations further reveal the intrinsic trade-off between the tour length and the relay hop count. Since DCP-MDGS-RHC algorithm gives priority to reduce the number of DCPs within controlled h-hop neighbour, it results in the smallest number of DCPs compared to others. This will also lead to the most average affiliated sensors for a DCP for a given number of sensors. However, due to the non uniform distribution of sensors in the sensor field DCP-MDGS-RHC algorithm results more number of affiliated sensors to a DCP and thus produces higher average relay hop count. B. Experimental results for larger network The connectivity of a sensor network depends on two main factors - deployment density (number of nodes clustered within a specific area) and the transmission range of sensors. We have measured the average tour length and average relay hop count for all the combinations of transmission ranges varying from 10m to 50m, and the number of nodes varying from 100 to 500. Assuming that the sensors are non-uniformly deployed in a 200m by 200m field, the mobile collector starts each tour from the sink point which is the centre point of the field considered and returns to the same point after each tour. A very sparsely deployed network in large area where the transmission ranges is relatively short (less than 10m), it is very possible that the network is totally disconnected. In this case, the mobile collector may need to visit every sensor and the implementation of algorithm is immaterial in this case. We have compared the tour length and average relay hop count in larger networks considering the relay hop bound h=2. From the results shown in the Figures 3 and 4, we have observed two important facts. First, for the networks with the same size, as the transmission range go longer, we obtain the shorter average tour length the mobile collector needs to travel as more transmission range causes more sensor nodes to be affiliated to a DCP and thus reduces the total number of DCPs. The average relay hop count keeps increasing as more transmission range causes more sensor nodes to be affiliated to a DCP. Second, for a specific transmission range, both the relative tour length and average relay hop count increases as the network size increases. It is also observed from Fig. 3(b) and 4(b) that the proposed algorithm gives the lower average relay hop count compared to SPT-DGA[2] and PB-PSA[2] algorithms and thus it has achieved relatively higher tour length than others as shown in Fig. 3(a) and 4(a) respectively.

The results of different solutions contain the number of selected DCPs, moving tour length of the mobile collector,

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Fig. 3. Performance of DCP-MDGS-RHC algorithm for tour length and average relay hop count as a function of transmission range (Rs).

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Fig. 4: Performance of DCP-MDGS-RHC algorithm for tour length and average relay hop count as a function of number of nodes deployed in a fixed area.

In Fig.5, we’ve plotted tour length and average relay hop count as a function of side length L which is varying from 100m to 500 m and also compare the performance of the proposed algorithm with other schemes like SPT-DGA [2], PBPSA [2], SHDG [7] and CME [5]. We fixed the number of sensors to 400 and transmission range to 30 m. In single-hop data gathering (SHDG), a mobile collector reaches at some points selected from a set of predefined positions to collect data from each sensor within a single hop. In the controlled mobile element scheme (CME), a mobile collector moves along some parallel straight tracks and collects data from the sensors with multi hop relays. From Fig. 5(a), it is observed that as L increases, the tour length of all the schemes are increasing. The

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reason is that the sensors will be more sparsely distributed with the increase of L and thus the mobile collector needs to travel much longer path and it has to the collect data from more DCPs. On the other hand in Fig. 5(b) we see that the average relay hop counts are decreasing for all schemes except CME which is continuously rising. The reason is that in case of CME scheme as the sensors are more sparsely distributed with the increase of L, multi hop relay also increases. From Fig. 5(b), it is observed that our algorithm shows comparatively lower average relay hop count than that of SPT-DGA and PB-PSA and thus produces slightly higher tour length compared to SPTDGA [2] and PB-PSA[2].

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Fig. 5: Performance comparison of DCP-MDGS-RHC algorithm for tour length and average relay hop count as a function of L.

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Fig. 6: Performance comparison of DCP-MDGS-RHC algorithm for tour length and average relay hop count as a function of h.

Fig. 6 shows the performance of the proposed algorithm as a function of controllable relay hop constraint (h). We set value of N, L and Rs to 200, 200 m and 30 m respectively. It is observed from Fig. 6 that as h is increasing the tour lengths are decreasing and the average relay hop counts are increasing in all schemes. We see that our proposed algorithm shows smaller average relay hop count compared to others as shown in Fig. 6(b) and thus results higher tour length than that of others. VI.

CONCLUSION

In this work it is assumed that each sensor is only be able to communicate with its neighbours. It is seen that a balance

between mobile tour and restricted multi hop relay will be an efficient energy conservation technique for data gathering in WSN. To prove the relevance of our algorithm we have shown simulation results in case of small network and compared the results with other existing algorithms. We have also compared our solution for a larger network with other solutions related to mobile data gathering techniques to prove the relevance of our solution. Our proposed algorithm can be implemented dynamically i.e., if the network structure is changed due to some adverse conditions like hardware failure or any kind of communication failure of some DCP nodes then the algorithm is require to execute again and it finds the new set of DCPs and the new tour path of the mobile collector.

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