Data collection point based mobile data gathering scheme with relay hop constraint

Krishna²,

2015 IEEE International Conference on Computational Intelligence &Amp; Communication Technology

2015

Recently, mobile collector or sink mobility has become an important research topic in WSNs. Data collector mobility can improve the performance of WSNs. Mobile sinks are mounted on some vehicles, animals or people moving randomly to collect information sensed by the sensor nodes where the data sink trajectories are changing or random. In the scenarios where the trajectories of the mobile collectors are fixed or predetermined, the efficient data collection problems are concerned to improve the performance of the network. Therefore, in this paper we propose an intelligent mobile data gathering scheme which dynamically changes its DGT (Data Gathering Tour) in between by utilizing information from NIT (Neighbor Information Table). Each CH (Cluster Head) learns about its neighbors whether they have data to send or not and thus creates a NIT. Based on the information in NIT at a CH, MDC (Mobile data Collector) decides its further course of action. Hence, the path travelled is dynamically pruned using this information at each cluster head. Simulation results show significant energy savings when this scheme is applied to various data collection scenarios in which different CHs are deployed as opposed to scenarios when no dynamic path pruning and data filtering is used.

Section: Related Workmentioning

confidence: 99%

An Efficient Mobile Data Collector Based Data Aggregation Scheme for Wireless Sensor Networks

Krishna²,

2015 IEEE International Conference on Computational Intelligence &Amp; Communication Technology

2015

“…The multi-hop data-gathering approach can be further divided into the hop count-free approach, such as [4,8,9,10,11,12,13,20], and the hop count restricted approach, such as [14,15,16]. …”

Section: Related Workmentioning

confidence: 99%

“…In [16], Chowdhury et al tried to make a better balance between energy consumption and data-gathering latency by adjusting the relationship between the relay hop count for sensor nodes and the route length of the mobile sink. They proposed a data collection points selection algorithm based on the standard shortest path tree.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

An Adaptive Clustering Approach Based on Minimum Travel Route Planning for Wireless Sensor Networks with a Mobile Sink

Tang

Yang

Zhu

et al. 2017

Sensors

In recent years, Wireless Sensor Networks with a Mobile Sink (WSN-MS) have been an active research topic due to the widespread use of mobile devices. However, how to get the balance between data delivery latency and energy consumption becomes a key issue of WSN-MS. In this paper, we study the clustering approach by jointly considering the Route planning for mobile sink and Clustering Problem (RCP) for static sensor nodes. We solve the RCP problem by using the minimum travel route clustering approach, which applies the minimum travel route of the mobile sink to guide the clustering process. We formulate the RCP problem as an Integer Non-Linear Programming (INLP) problem to shorten the travel route of the mobile sink under three constraints: the communication hops constraint, the travel route constraint and the loop avoidance constraint. We then propose an Imprecise Induction Algorithm (IIA) based on the property that the solution with a small hop count is more feasible than that with a large hop count. The IIA algorithm includes three processes: initializing travel route planning with a Traveling Salesman Problem (TSP) algorithm, transforming the cluster head to a cluster member and transforming the cluster member to a cluster head. Extensive experimental results show that the IIA algorithm could automatically adjust cluster heads according to the maximum hops parameter and plan a shorter travel route for the mobile sink. Compared with the Shortest Path Tree-based Data-Gathering Algorithm (SPT-DGA), the IIA algorithm has the characteristics of shorter route length, smaller cluster head count and faster convergence rate.

Turk J Elec Eng & Comp Sci

“…The second scheme is single hop data gathering (SHDG) [19], in which the ME traverses the deployment field and collects the data from all sensors with a single hop guarantee. The third scheme is the data collection point-based mobile data gathering scheme with relay hop constraint (DCP-MDGS-RHC) [20], which makes an adjustment between the relay hop count for local data aggregation and the moving tour length of the mobile collector. In this simulation, a general sensor network with N sensor nodes is distributed randomly over the Lm × Lm square field with the transmission range Tr for each sensor node.…”

Section: Performance Evaluationmentioning

confidence: 99%

An efficient hybrid data gathering algorithm based on multihop and mobile elements in WSNs

Ghaleb¹,

Subramaniam²,

Othman³

et al. 2017

Data gathering is a focal task in wireless sensor networks (WSNs) that expends most of the sensor nodes' energy. Two factors that are considered essential in data gathering are latency and power consumption. The multihop data gathering approach proves that latency is minimized due to the speed of forwarding data to the base station.However, this may lead to increased power consumption and increased possibility of an emerging hotspot area. In contrast, data gathering based on a mobile element (ME) proves that power consumption is minimized due to avoiding relay data in extreme schemes. However, this may increase the latency of data gathering due to the low velocity of the ME. In this article, an efficient hybrid data gathering algorithm called zonal data gathering based on multihop and ME in WSNs (ZDG-MME) is proposed. In ZDG-MME, intelligent data gathering is proposed, capturing the unique nature of nodes along with the node's position. In addition, it is able to forward the tailored data to the base station by segmenting the deployment field into two divisions. First, the inner division, which is the closest to the base station, reports the sensed data through multihop communications. Second, the outer division reports the data to certain nodes that locally buffer the data from affiliated sensors and await the ME for uploading. Furthermore, ZDG-MME analyzes the sensing area in a way to ensure balancing between latency and power consumption based on application requirements while avoiding the hotspot area. An extensive simulation clarifies the validity and effectiveness of the proposed approach in terms of ME tour length, data gathering latency, and total energy consumption.