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Mobile Data Gathering with Bounded Relay in Wireless Sensor Networks IEEE PROJECTS 2018




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Sensing data gathering is an important and fundamental issue in the Internet of Things (IoT). However, for battery-powered sensors, energy depletion is unavoidable. Using mobile sinks to collect sensing data by one-hop transmission is an effective way to prolong the lifetime of wireless sensor networks but will inevitably cause an excessive long delay time of data gathering. In order to reduce the delay time of mobile data gathering, it is necessary to incorporate multi-hop transmission into mobile data gathering. In this paper, a new mobile data gathering algorithm with multi-hop transmission is proposed to reduce the delay time of data gathering. The proposed algorithm is called the Bounded Relay Combine-TSP-Reduce (BR-CTR). The BR-CTR algorithm visits the convergence area of sensors’ communication ranges to reduce the number of visiting points. The BR-CTR algorithm is integrated with a path adjustment mechanism, which can further shorten the planned traveling path effectively. In performance evaluation, we compare the BR-CTR algorithm not only with the existing mobile data gathering algorithms with one-hop transmission but also with the existing mobile data gathering algorithms with multi-hop transmission in terms of the length of traveling path, delay time, network lifetime and buffer size requirement. Experimental results indicate that the proposed algorithm has high performance on all the above-mentioned indices.




Choice of visiting points

We know that reducing the number of visiting points is helpful for reducing the length of traveling path. Hence, we choose the communication overlap areas formed by more sensors to be the visiting areas for the mobile sink and use the TSP algorithm to plan the traveling path based on these visiting areas.

However, through the clique search algorithm, we can obtain only the coverage level of communication of each area in WSNs and have no knowledge of the actual location of these areas. Besides, for the TSP algorithm, the inputs are points, not areas. Hence, we have to find an appropriate representative point of each visiting area.


Planning of local data gathering

In order to shorten data gathering latency, it is necessary to incorporate multi-hop transmission into mobile data gathering.In the proposed algorithm, we use sensors within a radius rc of each visiting point to assist in local data gathering. In other words, it is necessary to plan the relay routing paths (Definition 1) for sensors outside the radius rc of each visiting point.

In planning local data gathering, we start with areas with the highest coverage level of communication. This method allows the mobile sink to have more sensors that can assist in relaying data for sensors outside the visiting area when it moves to a visiting point (i.e. the energy consumption can be more balanced).


Planning and adjustment of the traveling path

After determining the representative points and planning the relay routing path for local data gathering, we will use the TSP algorithm [16]to plan an optimal traveling path based on these representative points (or use the approximation algorithm for TSP to find a near optimal traveling path [18]). Because the traveling path is an optimal path planned based on the “representative points of overlapping areas” instead of the “overlapping areas”, the traveling path can be further shortened. We will adjust the locations of the representative points (note: the visiting points remain within the overlap area after adjustment) to reduce the length of the traveling path.

Our method of adjusting the representative points is to adjust the line formed by the visiting point and the points before and after it. This method is inspired by Pythagorean theorem. Given a triangle, if the bottom leg is fixed, the shorter the height, the shorter the sum of all its sides. Based on this concept, we find the new representative point in each overlap area to reduce the length of the traveling path. We will elaborate how to adjust a representative point of an overlap area


Existing Sytem

In IoT applications, sensors report their sensing data to the sink periodically. Generally, sensors usually report their sensing data to a fixed sink through multi-hop transmission in Wireless Sensor Networks (WSNs). Mostresearch of this problem focuses on how to plan optimal routing paths for sensors to report their sensing data to the fixed sink . However, if sensing data are transmitted by pure multi-hop transmission, sensors have to perform their monitoring task and relay data for other sensors. The data relay operation is disadvantageous to battery-powered sensors (i.e. the network lifetime will be shortened). Especially for sensors located near the sink, the amount of data to relay will be very huge. Therefore, previous research has proposed methods that use mobile sinks by one-hop transmission to collect sensing data in the network.

These methods help to avoid the problem of unbalanced energy consumption caused by multi-hop transmission. The velocity of mobile sinks is far slower than that of wireless transmission. Using mobile sinks to collect sensing data from all sensors by one-hop transmission may cause the problem of long data gathering latency. Therefore, most research of this type of data gathering problem discusses how to plan a shorter traveling path for mobile sinks. The main feature of data gathering by mobile sinks is that with the assistance of the mobile sinks, sensors do not consume energy for relaying data. Therefore, sensor energy can be used solely for the surveillance task


Proposed System

1) To alleviate the problem of unbalanced energy consumption caused by multi-hop transmission: By limiting the number of sensors that each sensor in the visiting areas can assist and the number of relay hop counts of multi-hop transmission for sensors outside the visiting areas, we can effectively alleviate the problem of unbalanced energy consumption caused by multi-hop transmission. 2) To avoid the data loss problem arising from a buffer overflow: Taking into consideration the buffer size, we place a limit on the number of sensors to assist. This constraint can avoid buffer overflows that occur during sensors' data relay operation.

3) To reduce the delay time of data gathering by shortening the traveling path: Visiting the convergence area of communication ranges of sensors can reduce the number of visiting points. Reducing the number of visiting points is helpful for reducing the length of the traveling path. Moreover, the proposed path adjustment mechanism can further shorten the traveling path.


Conclusion




In this paper, we propose the BR-CTR algorithm to solve the mobile data gathering problem with bounded relay consideration. The BR-CTR incorporates multi-hop transmission into mobile data gathering to reduce the delay time of data gathering. In the choice of visiting areas, the BR-CTR algorithm visits the overlap areas of communication ranges of “more sensors” first. In local data gathering, the BR-CTR algorithm sets a limit on the number of sensors to assist for sensors within each visiting area (i.e. the maximum number of sensors for which each sensor within the visiting area can help relay the sensing data). Besides, it also constrains the number of relay hop counts of multi -hop transmission for sensors outside the visiting areas. In planning and adjustment of the traveling path, the BR-CTR algorithm finds the representative point (i.e. center of mass) in each visiting area and then uses the TSP algorithm to plan the traveling path based on all the representative points.

Finally, it uses the proposed path adjustment mechanism and relay routing path adjustment mechanism to further shorten the traveling path and avoid unnecessary forwarding of data. Through simulation experiments, we confirm that the proposed BR-CTR algorithm outperforms the existing mobile data gathering algorithms with one-hop transmission for WSNs (TSP , COM , CSS and STC ) in terms of the length of traveling path and the delay time of data gathering. Moreover, we also confirm that the proposed BR-CTR algorithm outperforms the existing mobile data gathering algorithms with multi-hop transmission for WSNs (SPT-DGA) in terms of the length of traveling path, the delay time of data gathering, network lifetime and requirement of buffer size.


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