Accurate and Effective Data Collection with Minimum Energy Path Selection in Wireless Sensor Networks using Mobile Sinks (original) (raw)
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Due to the energy limitation in Wireless Sensor Networks (WSNs), most researches related to data collection in WSNs focus on how to collect the maximum amount of data from the network with minimizing the energy consumption as much as possible. Many types of research that are related to data collection are proposed to overcome this issue by using mobility with path constrained as Maximum Amount Shortest Path routing Protocol (MASP) and zone-based algorithms. Recently, Zone-based Energy-Aware Data Collection Protocol (ZEAL) and Enhanced ZEAL have been presented to reduce energy consumption and provide an acceptable data delivery rate. However, the time spent on data collection operations should be taken into account, especially concerning real-time systems, as time is the most critical factor for these systems' performance. In this paper, a routing protocol is proposed to improve the time needed for the data collection process considering less energy consumption. The presented pro...
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2008 IEEE International Conference on System of Systems Engineering, 2008
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Aeu-international Journal of Electronics and Communications, 2017
Mobile sink (MS) has drawn significant attention for solving hot spot problem (also known as energy hole problem) that results from multihop data collection using static sink in wireless sensor networks (WSNs). MS is regarded as a potential solution towards this problem as it significantly reduces energy consumption of the sensor nodes and thus enhances network lifetime. In this paper, we first propose an algorithm for designing efficient trajectory for MS, based on rendezvous points (RPs). We next propose another algorithm for the same problem which considers delay bound path formation of the MS. Both the algorithms use k-means clustering and a weight function by considering several network parameters for efficient selection of the RPs by ensuring the coverage of the entire network. We also propose an MS scheduling technique for effective data gathering. The effectiveness of the proposed algorithms is demonstrated through rigorous simulations and comparisons with some of the existing algorithms over several performance metrics.
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International Journal of Advanced Computer Science and Applications, 2017
Sensor nodes located in the vicinity of a static sink drain rapidly their batteries since they have to carry more traffic burden. This situation results in network partition, holes as well as data losses. To mitigate this issue, many research proposed the use of mobile sink in data collection as a potential solution. However, due to its speed, the mobile sink has very short communication time to pick up all data from the sensor nodes within the network, therefore the sink is forced to return back to gather the remaining data. In this paper, we propose a new data collection scheme that aims to decrease the latency and enlarge the staying time between the mobile sink and the meeting points that buffer data originated from the other sensor nodes. We have also handled the case of urgent data so that they can be delivered without any delay. Our proposed scheme is validated via extensive simulations using NS2 simulator. Our approach significantly decreases the latency and prolongs the contact time between the mobile sink and sensor nodes.
Energy-efficient Dynamic Mobile Sink Path Planning for Data Acquisition for Wireless Sensor Networks
International Journal of Advanced Trends in Computer Science and Engineering, 2021
Wireless sensor networks (WSNs) are widely used in various applications such as defense, forest fire, healthcare, structural health monitoring, etc., because of its flexibility, low cost and tiny. In WSNs, the sensor nodes are scattered over the target area to acquire the data from the environment and transmit it to the base station via single or multi-hop communication. Due to the sensor nodes' constrained battery, the sensor nodes near the base station are more involved in data transmissions. These relay nodes drain more energy and die soon, leading to a hotspot/energy-hole problem. Several algorithms have been proposed in the literature to address the hotspot problem using the mobile sink. However, most of the existing approaches are highly computational and also provide a static solution only. In this context, we proposed an energy-efficient dynamic mobile sink path construction with low computational complexity for data acquisition in WSNs. We use the minimum spanning tree-based clustering for selecting the data collection points and a computational geometry-based method to identify the visiting order of the data collection points by the mobile sink. Our proposed work is better than the existing approaches in terms of average energy consumption, network lifetime, fairness index, buffer utilization, etc.
International Journal of Modern Trends in Engineering and Research, 2014
In recent years there has been an increased focus on the use of sensor networks to sense and measure the environment. This leads to a wide variety of theoretical and practical issues on appropriate protocols for data sensing and transfer. Recent work shows sink mobility can improve the energy efficiency in wireless sensor networks (WSNs). However, data delivery latency often increases due to the speed limit of mobile sink. Most of them exploit mobility to address the problem of data collection in WSNs. The WSNs with MS (mobile Sink) and provide a comprehensive taxonomy of their architectures, based on the role of the MS. An overview of the data collection process in such a scenario, and identify the corresponding issues and challenges. A protocol named weighted rendezvous planning (WRP) which is a heuristic method that finds a near-optimal traveling tour that minimizes the energy consumption of sensor nodes. Focus on the path selection problem in delay-guaranteed sensor networks with a path-constrained mobile sink. Concentrate an efficient data collection scheme, which simultaneously improves the total amount of data and reduces the energy consumption. The optimal path is chosen to meet the requirement on delay as well as minimize the energy consumption of entire network. Predictable sink mobility is exploited to improve energy efficiency of sensor networks.
The Open Automation and Control Systems Journal, 2016
Several recent studies have demonstrated the benefits of using the Wireless Sensor Network (WSN) technology in largescale monitoring applications, such as planetary exploration and battlefield surveillance. Sensor nodes generate continuous stream of data, which must be processed and delivered to end users in a timely manner. This is a very challenging task due to constraints in sensor node's hardware resources. Mobile Unmanned Ground Vehicles (UGV) has been put forward as a solution to increase network lifetime and to improve system's Quality of Service (QoS). UGV are mobile devices that can move closer to data sources to reduce the bridging distance to the sink. They gather and process sensory data before they transmit it over a long-range communication technology. In large-scale monitored physical environments, the deployment of multiple-UGV is essential to deliver consistent QoS across different parts of the network. However, data sink mobility causes intermittent connectivity and high reconnection overhead, which may introduce considerable data delivery delay. Consequently, frequent network reconfigurations in multiple data sink networks must be managed in an effective way. In this paper, we contribute an algorithm to allow nodes to choose between multiple available UGVs, with the primary objective of reducing the network reconfiguration and signalling overhead. This is realised by assigning each node to the mobile sink that offers the longest connectivity time. The proposed algorithm takes into account the UGV's mobility parameters, including its movement direction and velocity, to achieve longer connectivity period. Experimental results show that the proposed algorithm can reduce end-to-end delay and improve packet delivery ratio, while maintaining low sink discovery and handover overhead. When compared to its best rivals in the literature, the proposed approach improves the packet delivery ratio by up to 22%, end-to-end delay by up to 28%, energy consumption by up to 58%, and doubles the network lifetime.
AN ENERGY EFFICIENT MOBILE SINK PATH SELECTION TO IMPROVE THE LIFE TIME FOR WIRELESS SENSOR NETWORKS
IAEME PUBLICATION, 2019
Using a mobile sink to reduce the energy consumption of nodes and to prevent the formation of energy holes in wireless sensor networks(WSNs).Benefits are dependent on the path taken by the mobile sink, particularly in delay sensitive applications ,as all sensed data must be collected within a given time constraint. An approach proposed to address this challenge is to form a hybrid moving pattern in which a mobile sink node only visits rendezvous points (RPs), as opposed to all nodes. Sensor nodes that are not RPs forward their sensed data via multi-hopping to the nearest RP node. Fundamental problem becomes computing a tour that visits all the RPs within a given delay bound. Identifying the optimal tour, however is an NP hard problem .To address this problem Weighted Rendezvous Planning (WRP) method is proposed whereby each sensor node is assigned a weight corresponding to its hop distance from the tour and the number of data packets that it forwards to the closest RP.WRP enables a mobile sink to retrieve all sensed data within a given deadline while conserving the energy expenditure of sensor nodes. More specifically, WRP reduces energy consumption and also increases the network lifetime as compared with existing methods.
Connectivity-Based Data Gathering with Path-Constrained Mobile Sink in Wireless Sensor Networks
Wireless Sensor Network, 2014
The design of an effective and robust data gathering algorithm is crucial to the overall performance of wireless sensor networks (WSN). However, using traditional routing algorithms for data gathering is energy-inefficient for sensor nodes with limited power resources and multi-hop communication protocols. Data gathering with mobile sinks provided an effective solution to this problem. The major drawback of this approach is the time and path constraints of the mobile sink, which limit the mobile sink to collect data from all sensor nodes and, then, data routing is still required for these unreachable parts by the mobile sink. This paper presents a new data gathering algorithm called Connectivity-Based Data Collection (CBDC). The CBDC algorithm utilizes the connectivity between sensor nodes so as to determine the trajectory of the mobile sink whilst satisfying its path constraint and minimizing the number of multi-hop communications. The presented results show that CBDC, in comparison with the LEACH-C algorithm, prolongs the network life time at different connectivity levels of sensor networks, varying number of sensor nodes and at different path constraints of the mobile sink.