Mobile Collection Research Articles

Sencar Based Load Balanced Clustering With Mobile Data Gathering In Wireless Sensor Networks

The wireless sensor networks consist of static sensors, which can be deployed in a wide environment for monitoring applications. While transmitting the data from source to static sink, the amount of energy consumption of the sensor node is high. This results in reduced lifetime of the network. Some of the WSN architectures have been proposed based on Mobile Elements such as three-layer framework is for mobile data collection, which includes the sensor layer, cluster head layer, and mobile collector layer (called SenCar layer). This framework employs distributed load balanced clustering and dual data uploading, it is referred to as LBC-DDU.In the sensor layer a distributed load balanced clustering algorithm is used for sensors to self-organize themselves into clusters. The cluster head layer use inter-cluster transmission range it is carefully chosen to guarantee the connectivity among the clusters. Multiple cluster heads within a cluster cooperate with each other to perform energy-saving in the inter-cluster communications. Through this transmissions cluster head information is send to the SenCar for its moving trajectory planning.This is done by utilizing multi-user multiple-input and multiple-output (MU-MIMO) technique. Then the results show each cluster has at most two cluster heads. LBC-DDU achieves higher energy saving per node and energy saving on cluster heads comparing with data collection through multi-hop relay to the static data sinks.

Priority based Multi SenCar Technique in Wireless Sensor Networks

Background/Objectives: In recent years Mobile WSN (Wireless Sensor Networks) has obtained more attention because it has an ability to allow economical and effective solutions in different fields. When designing the Mobile WSN, energy saving becomes an issue. So a network clustering technique is used to divide the network into small group of clusters and every cluster group is controlled and examined by one node called CH (Cluster Head). An effectual procedure for clustering and LEACH (Low Energy Adaptive Cluster Head Election) can forcefully decrease the consumption of energy and improve the lifetime of network. The existing WSN contains more sensor nodes that vary from one to another based on performance, computing power, initial energy, etc. According to decrease congestion and undesirable power, during the transmission of data long distance, stable number of transmit nodes are placed and selected as router to keep the connectivity. Then the Transmitted node collects the aggregated data using cluster head and moves it to base station. In this system, we are going to work the tradeoff between data assembly latency and power saving in gathering of mobile data by exploring an aggregation of local data and the SenCar moving length. A framework of three layers is suggested in WSN for mobile data group, which comprises layer of cluster head, layer of mobile collector and layer of Sencar. Methods: This framework deals with Dual Data Uploading and Load Balanced Clustering which is abbreviated as LBC-DDU. In emergency situations the signal priority is not mentioned in LBC-DDU. To achieve this problem, we suggested the Multi sensor techniques of priority Based in WSN (Wireless Sensor Networks). To assess the efficiency of the suggested priority scheme a large simulation are regulated. The proposed algorithm of shuffling allows the base station to identify the head of cluster and transmit node on the basis of residual energy, node density, base station distance and nodes overlapping ratio, etc. Findings and Improvements: We correlate the SenCar mobility in multi-hop routing to complete the advantages of double data uploading, which eventually shows data collection in priority based with both short time uploading of data and short path of moving.

Tropical forest tree positioning accuracy: A comparison of low cost GNSS-enabled devices

Today, faster than any previous communication technology, mobile technology such as smartphones, tablet phone and notebook has made a huge difference to the lives of a great number of people. Although one can collect data through a number of traditional channels, e.g. pen and paper and more advanced survey equipment, low cost smartphone and tablet phone are emerging as a good choice. To investigate the potential and performance of Global Navigation Satellite System (GNSS)-enabled smartphone and tablet phone in data collection, a study has been conducted at Forest Research Institute Malaysia (FR1M) Kepong, Kuala Lumpur. Geotrees mobile collector using a Sony Xperia C3 smartphone and Samsung Galaxy Tab 3 were used to geo-locate the attributes of tree species within the area of interest. Comparison was then made using a handheld GPS and a Total Station. The findings indicate that the horizontal positioning accuracy detected by the instruments used in this study were mostly accurate within 5 to 15m. The root mean square (RMS) errors were higher in the areas surrounded by larger canopy diameter trees. Based on the result, despite limited coverage of sky visibility, a GNSS-enabled smartphone and tablet phone was sufficiently adequate to augment the purpose of tree inventory for a certain area, and might suit to serve for low accuracy application.

Sencar Scheduling Algorithm based on Packet Lifetime in WSN’s

Objective: The main scope of this paper is to increase the network life-time and minimizing the data collecting delay in WSNs. Statistical Analysis: The simulation is developed by the simulator which contains libraries to design the communication of multimedia packets between sensor nodes in WSN's. Findings: A PLBC algorithm is proposed for sensors to coordinate with the SenCar's as per schedule to gather information from nodes within their residual lifetimes. In order to existing methods of clustering, our strategy generates a priority based data gathering from multiple rendezvous points instead of Cluster Heads (CHs) in a network to gather data based on lifetime of a node. At cluster head layer, multiple cluster heads in a network drives data to a polling point to reduce the delay. At SenCar layer mobile collector is armed with multiple antennas, which enables rendezvous point to update dual data simultaneously to mobile collector utilizing a MU-MIMO technique at each time. Applications/Improvements: The results of the simulation are obtained and plotted for latency and energy consumption, which can be used to consider the performance.

Challenges to Property Rate Administration in the Offinso South Municipality, Ghana

ABSTRACTDecentralized governments in Ghana have not been able to realize optimum benefits from their property tax potentials. The paper investigates the administration of property taxation in Ghana using the Offinso South Municipality (OSM) as a study area. Questionnaires were relied on to garner data from property owners in the municipality. It observed that low coverage and collection ratios, inadequate collection modes, and negative public perceptions have worked against the administration of property tax in the OSM. The paper suggests a complete overhaul of the property rate regime in the OSM if the buoyancy of property tax revenue is to be realized. It argues that the rating authority should purge the system of perceived corruptions. It suggests a revamp of the property rate collection means in order to slump rent seeking behavior of the mobile property rate collectors.

DaGCM: A Concurrent Data Uploading Framework for Mobile Data Gathering in Wireless Sensor Networks

Data uploading time constitutes a large portion of mobile data gathering time in wireless sensor networks. By equipping multiple antennas on the mobile collector, data uploading time can be greatly shortened. However, previous works only treated wireless link capacity as a constant and ignored power control on sensors, which would significantly deviate from the real wireless environments. To overcome this problem, in this paper we propose a new data gathering cost minimization framework for mobile data gathering in wireless sensor networks by considering dynamic wireless link capacity and power control jointly. Our new framework not only allows concurrent data uploading from sensors to the mobile collector, but also determines transmission power under elastic link capacities. We study the problem under constraints of flow conservation, energy consumption, elastic link capacity, transmission compatibility, and sojourn time. We employ the subgradient iteration algorithm to solve the minimization problem. We first relax the problem with Lagrangian dualization, then decompose the original problem into several subproblems, and present distributed algorithms to derive data rate, link flow and routing, power control, and transmission compatibility. For the mobile collector, we also propose a subalgorithm to determine sojourn time at different stopping locations. Finally, we provide extensive simulation results to demonstrate the convergence and robustness of proposed algorithms. The results reveal 20 percent shorter data collection latency on average with lower energy consumptions compared to previous works as well as lower data gathering cost and robustness in case of node failures.

DDGP: Distributed Data Gathering Protocol for vehicular networks

Vehicular Ad-Hoc Network (VANet) is an emerging research area, it offers a wide range of applications including safety, road traffic efficiency, and infotainment applications. Recently researchers are studying the possibility of making use of deployed VANet applications for data collection. In this case, vehicles are considered as mobile collectors that gather both real time and delay tolerant data and deliver them to interested entities. In this paper, we propose a novel Distributed Data Gathering Protocol (DDGP) for the collection of delay tolerant as well as real time data in both urban and highway environments. The main contribution of DDGP is a new medium access technique that enables vehicles to access the channel in a distributed way based on their location information. In addition, DDGP implements a new aggregation scheme, which deletes redundant, expired, and undesired data. We provide an analytical proof of correctness of DDGP, in addition to the performance evaluation through an extensive set of simulation experiments. Our results indicate that DDGP enhances the efficiency and the reliability of the data collection process by outperforming existing schemes in terms of several criteria such as delay and message overhead, aggregation ratio, and data retransmission rate.

Towards Distributed Optimal Movement Strategy for Data Gathering in Wireless Sensor Networks

In this paper, we address how to design a distributed movement strategy for mobile collectors, which can be either physical mobile agents or query/collector packets periodically launched by the sink, to achieve successful data gathering in wireless sensor networks. Formulating the problem as general random walks on a graph composed of sensor nodes, we analyze how much data can be successfully gathered in time under any Markovian random-walk movement strategies for mobile collectors moving over a graph (or network), while each sensor node is equipped with limited buffer space and data arrival rates are heterogeneous over different sensor nodes. In particular, from the analysis, we obtain the optimal movement strategy among a class of Markovian strategies so as to minimize the data loss rate over all sensor nodes, and explain how such an optimal movement strategy can be made to work in a distributed fashion. We demonstrate that our distributed optimal movement strategy can lead to about two times smaller loss rate than a standard random walk strategy under diverse scenarios. In particular, our strategy results in up to 70 percent cost savings for the deployment of multiple collectors to achieve the target data loss rate than the standard random walk strategy.

Network Cost Minimization for Mobile Data Gathering in Wireless Sensor Networks

Recent studies have shown that significant benefit can be achieved in wireless sensor networks (WSNs) by employing mobile collectors for data gathering via short-range communications. A typical scenario for such a scheme is that a mobile collector roams over the sensing field and pauses at some anchor points on its moving tour such that it can traverse the transmission range of all the sensors in the field and directly collect data from each sensor. In this paper, we study the performance optimization of such mobile data gathering by formulating it into a cost minimization problem constrained by channel capacity, required minimum data uploads from each sensor and bound of total sojourn time at all anchor points. In order to provide an efficient and distributed algorithm, we decompose this global optimization problem into two subproblems that can be solved by each sensor and the mobile collector, respectively. We show that such decomposition can be characterized as a pricing mechanism, in which each sensor independently adjusts its payment for the data uploading opportunity based on the shadow prices of different anchor points. Correspondingly, we give an efficient algorithm to jointly solve the two subproblems. Our theoretical analysis demonstrates that the proposed algorithm can achieve the optimal data control for each sensor and the optimal sojourn time allocation for the mobile collector, which minimizes the overall network cost. Finally, extensive simulation results further validate that our algorithm achieves lower cost than the compared data gathering strategy.

An energy-efficient path determination strategy for mobile data collectors in wireless sensor network

In wireless sensor networks, introduction of mobility has been considered to be a good strategy to greatly reduce the energy dissipation of the static sensor nodes. This task is achieved by considering the path in which the mobile data collectors move to collect data from the sensors. In this work a data gathering approach is proposed in which some mobile collectors visit only certain sojourn points (SPs) or data collection points in place of all sensor nodes. The mobile collectors start out on their journey after gathering information about the network from the sink, gather data from the sensors and transfer the data to the sink. To address this problem, an algorithm named Mobile Collector Path Planning (MCPP) is proposed. MCPP schema is validated via computer simulation considering both obstacle free and obstacle-resisting network and based on metrics like energy consumption by the static sensor nodes and network life time. The simulation results show a reduction of about 12% in energy consumption and 15% improvement in network lifetime as compared with existing algorithms.

Protection of Source and Sink in Wireless Sensor Networks

In wireless sensor networks messages are sent from source to sink, but it sometime becomes a risky communication way because of presence of eavesdropper. During transfer of packet or message from source to sink an eavesdropper may attack on the network and creates problem for the sender or receiver, as the eavesdropper may trace the path and detect the location of source or sink very easily. Because of this security is always remained a concern topic in wireless sensor networks. Different techniques introduced time to time for the protection of source and sink in wireless sensor networks. I n this paper we have also introduced a scheme for the location privacy. This technique especially effective when eavesdropper will try to locate the location of source or sink by using DREAM protocol. In this scheme deviate location information and false identity of sensor nodes is provided to the eavesdropper which can confuse the attacker and protects our source as well as sink. The nodes in these networks having identical capabilities and energy in a network is called homogeneous network (8). These types of networks can be again classified into flat and hierarchy topology. In the flat topology that the sensors close to the static sink consumes more energy than the sensors at the margin of the network. The drawbacks of flat topology can be overcome by using hierarchical topology i.e., clusters. In this, the group of nodes that forms the lower layer and the cluster heads at the higher layer (4). Cluster head, which collects data from the lower layers and then forwards it to the sink. Cluster head can acts as an aggregation point. Since the cluster head is collecting data from lower nodes, it consumes more energy than other nodes. So, the sensor nodes can be rotated dynamically to avoid the energy consumption. The Heterogeneous networks having small number of resource rich nodes and large number of resource limited basic nodes. The resource rich nodes are having powerful transceivers and batteries. The resource rich nodes can acts as cluster heads. The resource limited basic nodes having limited communication capabilities. Mobile Data Gathering is a technique that consists of one or more Mobile Collectors (MC's) (4). Mobile collector is a device equipped with powerful transceiver and high battery power. It gathers the data in short-range communications. MC roams over the sensing field to collects the data while moving or pause at some points on its moving path from the sensors. To attain the maximum energy saving, a mobile collector must travel the transmission range of each sensor node in the field. It helps the mobile collector to collect the data packets in a single hop. The path of the mobile collector in the sensing field may be random or planned. The mobility of the collector reduces the energy consumption in the

An Energy-Efficient and Relay Hop Bounded Mobile Data Gathering Algorithm in Wireless Sensor Networks

Recent studies reveal that great benefit can be achieved by employing mobile collectors to gather data in wireless sensor networks. Since the mobile collector can traverse the transmission range of each sensor, the energy of nodes may be saved near maximally. However, for directly receiving data packet from every node, the length of mobile collector route should be very long. Hence it may significantly increase the data gathering latency. To solve this problem, several algorithms have been proposed. One of them called BRH-MDG found that data gathering latency can be effectively shortened by performing proper local aggregation via multihop transmissions and then uploading the aggregated data to the mobile collector. But, the BRH-MDG algorithm did not carefully analyze and optimize the energy consumption of the entire network. In this paper, we propose a mathematical model for the energy consumption of the LNs and present a new algorithm called EEBRHM. The simulation results show that under the premise of bounded relay hop, compared with BRH-MDG, EEBRHM can prolong the networks lifetime by 730%.

Mobile Data Gathering with Load Balanced Clustering and Dual Data Uploading in Wireless Sensor Networks

In this paper, a three-layer framework is proposed for mobile data collection in wireless sensor networks, which includes the sensor layer, cluster head layer, and mobile collector (called SenCar) layer. The framework employs distributed load balanced clustering and dual data uploading, which is referred to as LBC-DDU. The objective is to achieve good scalability, long network lifetime and low data collection latency. At the sensor layer, a distributed load balanced clustering (LBC) algorithm is proposed for sensors to self-organize themselves into clusters. In contrast to existing clustering methods, our scheme generates multiple cluster heads in each cluster to balance the work load and facilitate dual data uploading. At the cluster head layer, the inter-cluster transmission range is carefully chosen to guarantee the connectivity among the clusters. Multiple cluster heads within a cluster cooperate with each other to perform energy-saving inter-cluster communications. Through inter-cluster transmissions, cluster head information is forwarded to SenCar for its moving trajectory planning. At the mobile collector layer, SenCar is equipped with two antennas, which enables two cluster heads to simultaneously upload data to SenCar in each time by utilizing multi-user multiple-input and multiple-output (MU-MIMO) technique. The trajectory planning for SenCar is optimized to fully utilize dual data uploading capability by properly selecting polling points in each cluster. By visiting each selected polling point, SenCar can efficiently gather data from cluster heads and transport the data to the static data sink. Extensive simulations are conducted to evaluate the effectiveness of the proposed LBC-DDU scheme. The results show that when each cluster has at most two cluster heads, LBC-DDU achieves over 50 percent energy saving per node and 60 percent energy saving on cluster heads comparing with data collection through multi-hop relay to the static data sink, and 20 percent shorter data collection time compared to traditional mobile data gathering.

HiCoDG: a hierarchical data-gathering scheme using cooperative multiple mobile elements.

In this paper, we study mobile element (ME)-based data-gathering schemes in wireless sensor networks. Due to the physical speed limits of mobile elements, the existing data-gathering schemes that use mobile elements can suffer from high data-gathering latency. In order to address this problem, this paper proposes a new hierarchical and cooperative data-gathering (HiCoDG) scheme that enables multiple mobile elements to cooperate with each other to collect and relay data. In HiCoDG, two types of mobile elements are used: the mobile collector (MC) and the mobile relay (MR). MCs collect data from sensors and forward them to the MR, which will deliver them to the sink. In this work, we also formulated an integer linear programming (ILP) optimization problem to find the optimal trajectories for MCs and the MR, such that the traveling distance of MEs is minimized. Two variants of HiCoDG, intermediate station (IS)-based and cooperative movement scheduling (CMS)-based, are proposed to facilitate cooperative data forwarding from MCs to the MR. An analytical model for estimating the average data-gathering latency in HiCoDG was also designed. Simulations were performed to compare the performance of the IS and CMS variants, as well as a multiple traveling salesman problem (mTSP)-based approach. The simulation results show that HiCoDG outperforms mTSP in terms of latency. The results also show that CMS can achieve the lowest latency with low energy consumption.

Joint Mobile Data Gathering and Energy Provisioning in Wireless Rechargeable Sensor Networks

The emerging wireless energy transfer technology enables charging sensor batteries in a wireless sensor network (WSN) and maintaining perpetual operation of the network. Recent breakthrough in this area has opened up a new dimension to the design of sensor network protocols. In the meanwhile, mobile data gathering has been considered as an efficient alternative to data relaying in WSNs. However, time variation of recharging rates in wireless rechargeable sensor networks imposes a great challenge in obtaining an optimal data gathering strategy. In this paper, we propose a framework of joint wireless energy replenishment and anchor-point based mobile data gathering (WerMDG) in WSNs by considering various sources of energy consumption and time-varying nature of energy replenishment. To that end, we first determine the anchor point selection strategy and the sequence to visit the anchor points. We then formulate the WerMDG problem into a network utility maximization problem which is constrained by flow, energy balance, link and battery capacity and the bounded sojourn time of the mobile collector. Furthermore, we present a distributed algorithm composed of cross-layer data control, scheduling and routing subalgorithms for each sensor node, and sojourn time allocation subalgorithm for the mobile collector at different anchor points. We also provide the convergence analysis of these subalgorithms. Finally, we implement the WerMDG algorithm in a distributed manner in the NS-2 simulator and give extensive numerical results to verify the convergence of the proposed algorithm and the impact of utility weight, link capacity and recharging rate on network performance.

A Robust Data Gathering Technique for Large Scaled Wireless Sensor Networks

In this paper we propose a data gathering scheme for large scaled wireless sensor networks using multiple mobile data collectors (MDCs) and the spatial division multiple access (SDMA) technique. We focus on minimizing the data gathering time by employing the SDMA technique, and minimizing the length of touring paths of MDCs by employing Voronoi diagrams. The proposed scheme facilitates all deployed sensors to experience uniform energy depletion by avoiding long-distance transmissions to the base station. This, in turns, helps to prolong the lifetime of the network, and the network remains connected for longer period of time and increases the robustness. Simulation results demonstrate that the proposed scheme greatly reduces the traversal distance of the data collectors compared to the grid based approximation algorithm, prolongs the network lifetime significantly compared to a network which has only a static data collector, or a network in which the mobile collectors only move along straight lines.

Data Gathering Mechanisms with Multiple Mobile Collectors in Wireless Sensor Network

Energy consumption becomes a primary concern in a Wireless Sensor Network. It have emerged as an effective solution for a wide range of applications. Introduced a new data-gathering mechanism for large- scale wireless sensor network by introducing mobility into the network. An M-Collector(mobile data collector) starts the data-gathering tour periodically from the static data sink, polls each sensor while traversing its transmission range. It directly collects data from the sensor in single-hop communications, and finally transports the data to the static sink. We propose a data-gathering algorithm where multiple mobile collectors traverse through several shorter sub tours concurrently to satisfy the distance/time constraints. To pursue high energy saving at sensor nodes, a mobile collector should traverse the transmission range of each sensor in the field such that each data packet can be directly transmitted to the mobile collector without any relay. Also it minimize the delay caused by the Mobile Collector to connect with the base station.

Non-Uniform Cluster-Based Mobile Data Collector Routing Protocol in Wireless Sensor Networks

One fundamental task of wireless sensor networks (WSNs) is to save sensor power, and try to prolong the lifetime of WSNs. Traditional routing protocols are almost based on homogeneous WSNs, all sensors are use single hop or multi-hop to transmit data to base station, which lead to overload sensors next to base station, thus appear energy hole and made WSN outwork. In this paper, we use a new novel mobile collectors mobile strategy method to prolong network lifetime, and reduce hot-spot effect around base station. We propose the routing method can effectively balance the network sensors energy depletion, more save energy and longer whole network lifetime compare to LEACH.

Clustering-Based Data Gathering in Wireless Sensor Network with Mobile Collector

Study has shown that clustering in Wireless Sensor Networks can provide well scalability and robustness. Recently, mobile collector (MC) is exploited to achieve energy saving and reduced latency. In this paper, we propose a mobile data gathering protocol using clustering and controlled mobility techniques. First, we formalize the clustering based approach and its requirement, and then propose a heuristic algorithm aimed at minimizing the number of clusters. After the cluster heads (CHs) are determined, protocol produces MC’s tour along which MC gathers cashed data in CHs in sequence. Simulation analysis shows that the number of CHs is less than other similar schemes and the tour length is shortened, accordingly.

Energy-efficient clustering in lossy wireless sensor networks

Recent experimental studies have revealed that a large percentage of wireless links are lossy and unreliable for data delivery in wireless sensor networks (WSNs). Such findings raise new challenges for the design of clustering algorithms in WSNs in terms of data reliability and energy efficiency. In this paper, we propose distributed clustering algorithms for lossy WSNs with a mobile collector, where the mobile collector moves close to each cluster head to receive data directly and then uploads collected data to the base station. We first consider constructing one-hop clusters in lossy WSNs where all cluster members are within the direct communication range of their cluster heads. We formulate the problem into an integer program, aiming at maximizing the network lifetime, which is defined as the number of rounds of data collection until the first node dies. We then prove that the problem is NP-hard. After that, we propose a metric-based distributed clustering algorithm to solve the problem. We adopt a metric called selection weight for each sensor node that indicates both link qualities around the node and its capability of being a cluster head. We further extend the algorithm to multi-hop clustering to achieve better scalability. We have found out that the performance of the one-hop clustering algorithm in small WSNs is very close to the optimal results obtained by mathematical tools. We have conducted extensive simulations for large WSNs and the results demonstrate that the proposed clustering algorithms can significantly improve the data reception ratio, reduce the total energy consumption in the network and prolong network lifetime compared to a typical distributed clustering algorithm, HEED, that does not consider lossy links.