scholarly journals Verifiable Top-kQuery Processing in Tiered Mobile Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-13 ◽  
Author(s):  
Fangju Liu ◽  
Xingpo Ma ◽  
Junbin Liang ◽  
Mugang Lin
Keyword(s):  
Sensor Networks ◽  
Sensor Network ◽  
Network Model ◽  
Main Idea ◽  
Sensor Nodes ◽  
Mobile Sensor ◽  
Virtual Node ◽  
Static State ◽  
Mobile Sensor Network ◽  
Fine Grained

Verifiable top-kquery processing in tiered sensor networks, which refers to verifying the authenticity and the completeness of top-kquery results received by the network owner in tiered sensor networks, has received attention in very recent years. However, the existing solutions of this problem are only fit for static sensor network. In this paper, we try to solve the problem in a tiered mobile sensor network model, where not only static sensor nodes but also mobile sensor nodes existed. Based on the tiered mobile sensor network model, we propose a novel verifiable scheme named VTMSN for fine-grained top-kqueries. The main idea of VTMSN is as follows: it maps each of the positions where sensor nodes are in a static state to a virtual node and then establishes relationships among data items of each virtual node with their score orders, which are encrypted along with the scores of the data items and the time epochs using the distinct symmetric keys kept by each sensor node and the network owner. Both theory analysis and simulation results show the efficiency and the security of VTMSN.

Optimal Deployment Based on Tree in Multilayer Mobile Sensor Network

2014 ◽  
Vol 1044-1045 ◽  
pp. 1218-1221
Author(s):  
Xia Ling Zeng ◽  
Lin Zhang
Keyword(s):  
Sensor Network ◽  
Sensor Nodes ◽  
Information Collection ◽  
Network Coverage ◽  
Mobile Sensor ◽  
Optimization Scheme ◽  
Mobile Sensor Network ◽  
Coverage Ratio ◽  
Error Ratio ◽  
Optimal Deployment

In sensor networks, a reasonable distribution of sensor nodes is an important role for the improvement of sensor ability, information collection ability and network survival. For multilayer mobile sensor network, a tree-based deployment optimization scheme was proposed and better sensor coverage could be achieved by topology adjustment utilizing mobility of sensor node. Sink nodes complete mobile positioning of S nodes based on rectangle division method and bounding box algorithm, and achieve the distribution optimization of S nodes by establishing extending-tree of Sink node as the center. Results show that the location error ratio reduces with the increase of Sink nodes. Compared with initial random deployment network, the network coverage ratio after optimal deployment significantly enhances, which effectively improve the coverage sensor range of overall network.

scholarly journals Mobility Assisted Sensor Node Self-Deployment for Maximizing the Coverage of Wireless Sensor Networks using A Genetic Algorithm

2013 ◽  
Vol 10 (2) ◽  
pp. 33
Author(s):  
VV Juli ◽  
J Raja
Keyword(s):  
Genetic Algorithm ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Sensor Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Matlab Simulation ◽  
Mobile Sensor ◽  
Adequate Coverage ◽  
And Control

Wireless sensor networks extend the capability to monitor and control far-flung environments. However, sensor nodes must be deployed appropriately to reach an adequate coverage level for the successful acquisition of data. Modern sensing devices are able to move from one place to another for different purposes and constitute the mobile sensor network. This mobile sensor capability could be used to enhance the coverage of the sensor network. Since mobile sensor nodes have limited capabilities and power constraints, the algorithms which drive the sensors to optimal locations should extend the coverage. It should also reduce the power needed to move the sensors efficiently. In this paper, a genetic algorithm- (GA) based sensor deployment scheme is proposed to maximize network coverage, and the performance was studied with the random deployment using a Matlab simulation. 

scholarly journals Developing an Energy Efficient Ubiquitous Agriculture Mobile Sensor Network Based Threshold Built-In Mac Routing Protocol (TBMP)

2021 ◽  
Author(s):  
Dhaya R ◽  
Kanthavel R ◽  
Ahilan A
Keyword(s):  
Sensor Network ◽  
Routing Protocol ◽  
Energy Efficient ◽  
Agricultural Land ◽  
Path Loss ◽  
Climatic Conditions ◽  
Sensor Nodes ◽  
Mobile Sensor ◽  
Mobile Sensor Network ◽  
Smart Agriculture

Abstract Smart agriculture has been a promising model with the intention of supervising farms by means of contemporary wireless technologies to enhance the quantity and quality of yield at the same time as minimizing the individual labor requirement. In addition the effective utilization of the Sensors as communication components that is the key one to monitor and manage soil, water, light, humidity, temperature. A Mobile Ad-hoc sensor node comprises sensors to gather real time environment from the agricultural land with the wireless communication technology and process the data before sharing information with other nodes in the network. On the other hand, the challenges have been enormously high path loss and lack of communication range under the environment when passing through soil, sand, water and other climatic conditions. As Wireless Sensor Networks (WSNs) has self-organized and adhoc wireless capability to monitor physical or environmental conditions, it can be used effectively in smart agriculture. As sensor nodes have been limited itself by means of power to be in active mode always, the design of such energy efficient Agriculture WSN is a paramount issue. Hence it has been planned to utilize the WSN as well as Ubiquitous technology for the smart agriculture with energy efficiency. With the purpose of build up a model, a Ubiquitous agriculture Mobile Sensor Network based Threshold built-in MAC Routing protocol (TBMP) has been proposed to make it fit for minimal resource utilization by comparing with the existing protocols IMR and PTSR. In addition, the testing will be done to monitor changes in environmental surroundings in the agricultural land smartly in order to obtain maximum usage of Ubiquitous concept by applying existing and proposed protocols.

scholarly journals Sharp extremal bounds on information diffusion capacities of mobile sensor networks

2018 ◽  
Author(s):  
Jesse Geneson
Keyword(s):  
Sensor Networks ◽  
Sensor Network ◽  
Information Diffusion ◽  
Mobile Sensor Networks ◽  
Proof Technique ◽  
Mobile Sensor ◽  
Diffusion Capacity ◽  
Mobile Sensor Network ◽  
Combinatorial Model ◽  
Algorithm Proof

In this paper, we prove that the maximum possible information diffusion capacity of a mobile sensor network with $n$ agents is equal to $1-\frac{n-2}{n^2}$, and the minimum possible capacity is equal to $\frac{2n+2}{3n}$, assuming that each pair of agents communicates exactly once. Our upper bound shows that the collector-distributor construction of (Gu et al, 2018) attains the maximum possible capacity of any mobile sensor network where agents are limited to communicating exactly once. For mobile sensor networks where agents can communicate any number of times and there are a total of $m$ communications, we prove that the maximum possible capacity is equal to $1-\frac{\binom{n-1}{2}}{m n}$ for $m \geq n$ and we also prove that the minimum possible capacity is equal to $\frac{2}{n}$. We also discuss an error in the proof from (Gu et al, 2018) that the capacity in the random combinatorial model approaches $1$ with high probability as the size of the network increases. However, we show that their gossip algorithm proof technique works for a less restricted version of the model.

scholarly journals Study of Node Distribution and Density Optimization in Mobile Sensor Network 3D Space

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Musong Gu ◽  
Chaobang Gao ◽  
Jingjing Lyu ◽  
Wenjie Fan ◽  
Lei You
Keyword(s):  
Sensor Network ◽  
Network Performance ◽  
Sensor Nodes ◽  
Mobile Sensor ◽  
Chemical Reaction Optimization ◽  
Mobile Sensor Network ◽  
3D Space ◽  
Reaction Optimization ◽  
Node Distribution ◽  
Information Collecting

Mobile sensor network is applied in information collection in emergencies. As the mobile sensor network in real environment is widely deployed with different height and the redundancy of the sensor node needs to be as low as possible, therefore, it is necessary to effectively deploy mobile sensor nodes in the 3D space to have reasonable layout and optimized density. To this end, we established the optimization model of mobile sensor network deployment and solved the model with chemical reaction optimization (CRO). The experimental results have shown that compared with traditional particle swarm optimization (PSO), CRO algorithm can achieve reasonable deployment more rapidly and enhance the network performance evaluation value effectively. The reasonable deployment of mobile sensor network node is very significant to information collecting, postperiod decision-making, and rapid rescuing work in emergencies.

Research on Multilevel Mobile Sensor Network Control Based on Probability Coverage Model

2014 ◽  
Vol 602-605 ◽  
pp. 1214-1218
Author(s):  
Xia Ling Zeng ◽  
Lin Zhang
Keyword(s):  
Energy Saving ◽  
Sensor Network ◽  
Network Model ◽  
Control Program ◽  
Transmission Efficiency ◽  
Network Control ◽  
Mobile Sensor ◽  
Mobile Sensor Network ◽  
Coverage Model ◽  
Saving Effect

The control of mobile coverage model is the focus research of wireless sensor network. If a node can balance the network load according to the network needs, which can greatly reduce the consumption of the network, improve the network transmission efficiency and prolong the network life cycle. Accordingly, this paper uses probability coverage principle to improve multilevel mobile sensor network model, which improves the mobile sensor network coverage and enhances the energy-saving effect of the network nodes. In order to verify the effectiveness and reliability of the multilevel mobile sensor network model, this paper uses MATLAB simulation platform to test the performance of network, and design the control program of node region division and moving path for mobile sensor network, which realizes the node automatic classification and moving. Finally, this paper utilizes the Plot curve of MATLAB to obtain the coverage rate of multilevel mobile sensor network and the curve of energy saving changing with the number of nodes. It provides a theoretical reference for the research on mobile sensor network.

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