RSSI wireless signal is a reference information that is widely used in indoor positioning. However, due to the wireless multipath influence, the value of the received RSSI will have large fluctuations and cause large ...
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RSSI wireless signal is a reference information that is widely used in indoor positioning. However, due to the wireless multipath influence, the value of the received RSSI will have large fluctuations and cause large distance error when RSSI is fitted to distance. But experimental data showed that, being affected by the combined factors of the environment, the received RSSI feature vector which is formed by lots of RSSI values from different APs is a certain stability. Therefore, the paper proposed RSSI-based fingerprint feature vector algorithm which divides location area into grids, and mobile devices are localized through the similarity matching between the real-time RSSI feature vector and RSSI fingerprint database feature vectors. Test shows that the algorithm can achieve positioning accuracy up to 2-4 meters in a typical indoor environment.
Many time-sensitive applications impose high requirement on real-time response. There exist many algorithms and routing protocols for efficient data packet delivery. However, previous works set the same retransmission...
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Many time-sensitive applications impose high requirement on real-time response. There exist many algorithms and routing protocols for efficient data packet delivery. However, previous works set the same retransmission threshold for all the relay nodes along a delivery path. The method decreases the probability of a packet being transmitted through the delivery path within given deadline. In this paper, we focus on finding the optimal retransmission thresholds for the relay nodes, such that the summation of the probability of a packet being transmitted to the next relay node or destination node within the specified deadline is maximized. A distributed greedy algorithm that can be run on sensor node is proposed, which enables the sensor node to adaptively set the optimal retransmission threshold. To avoid dropping the packet forwarded to the destination within given deadline with high probability, we develop a packet dropped protocol based on probabilistic delay bound. Experimental results show that the proposed protocols have better performance.
The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a syst...
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The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.
Resource allocation is expected to be amost important factor especially for heterogeneous applications in wireless ad hoc networks. In this paper, a novel heterogeneous resource allocation algorithm (HRA) is presented...
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Resource allocation is expected to be amost important factor especially for heterogeneous applications in wireless ad hoc networks. In this paper, a novel heterogeneous resource allocation algorithm (HRA) is presented for ad hoc networks, supporting both elastic and inelastic traffic. First, by combining the first order Lagrangian method with pseudo utility, the original nonconvex problem is converted into a new convex one. Then, we successfully solve the heterogeneous problem with the dual-based decomposition approach. In addition, we integrate utility fairness into the resource allocation framework, which can adaptively manage the tradeoff between elastic and inelastic flows. Simulations show and prove that HRA converges fast and can achieve the global optimum starting from many different network conditions, such as elastic, inelastic, and hybrid scenario. With both considerations of flow rate and utility fairness, HRA improves the overall network utility and system throughput greatly.
In recent years, the use of wireless sensor networks has been increasing. Localization is a fundamental problem in wireless sensor networks (WSNs), since location information is essential for diverse applications such...
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In recent years, the use of wireless sensor networks has been increasing. Localization is a fundamental problem in wireless sensor networks (WSNs), since location information is essential for diverse applications such as tracking, quality network coverage, health, and energy efficiency. In this paper performance of localization algorithms such as range-free, range-based, and fuzzy-based decision is evaluated. We introduce a modification of an algorithm by providing weights to the correlation matrix to improve correctness. In all the cases the accuracy, precision, and computational complexity are evaluated as performance metrics. Location algorithms are evaluated using two scenarios, a first stage where all nodes are randomly distributed in a given area and a second scenario where four APs (access points) are placed on fixed positions and unknown nodes are randomly distributed within the sensing area. The received signal strength (RSS) is used to estimate the position of a node of interest. In the simulation results we show how our modified algorithm improves localization. On the other hand, we also have acceptable accuracy using distance-based algorithms, but they are more complex computationally.
This paper proposes a distributed localization algorithm which can be applied to an irregular three-dimensional wireless sensor network, considering the algorithm accuracy and complexity. The algorithm uses clusters t...
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This paper proposes a distributed localization algorithm which can be applied to an irregular three-dimensional wireless sensor network, considering the algorithm accuracy and complexity. The algorithm uses clusters to eliminate the multihop distance errors. An anchor node position optimization scheme is proposed to maximize the uniformity of each subnetwork. The proposed algorithm also employs a new three-dimensional coordinate transformation algorithm, which helps to reduce the errors introduced by coordinate integration between clusters and improves the localization precision. The simulation and performance analysis results show that the localization accuracy of the D3D-MDS algorithm increases by 49.1% compared with 3D-DV-HOP and 38.6% compared with 3D-MDS-MAP. This distributed localization scheme also demonstrates a low computational complexity compared with other centralized localization algorithms.
A novel collision resolution algorithm for wireless sensor networks is formally analysed via probabilistic model checking. The algorithm called 2CS-WSN is specifically designed to be used during the contention phase o...
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A novel collision resolution algorithm for wireless sensor networks is formally analysed via probabilistic model checking. The algorithm called 2CS-WSN is specifically designed to be used during the contention phase of IEEE 802.15.4. Discrete time Markov chains (DTMCs) have been proposed as modelling formalism and the well-known probabilistic symbolic model checker PRISMis used to check some correctness properties and different operating modes and, furthermore, to collect some performance measures. Thus, all the benefits of formal verification and simulation are gathered. These correctness properties as well as practical and relevant scenarios for the real world have agreed with the algorithm designers.
Recent years have witnessed a growing interest in using Wi-Fi received signal strength for indoor fingerprint-based positioning. However, previous study about this problem has primarily faced two main challenges. One ...
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Recent years have witnessed a growing interest in using Wi-Fi received signal strength for indoor fingerprint-based positioning. However, previous study about this problem has primarily faced two main challenges. One is that positioning fingerprint feature using received signal strength is unstable due to heterogeneous devices and dynamic environment status, which will greatly degrade the positioning accuracy. Another is that some improved positioning fingerprint features will suffer the curse of dimensionality in online positioning. In this paper, we designed a novel positioning fingerprint feature using the segment similarity of Wi-Fi access points, considering both the received signal strength value and the Wi-Fi access point. Based on this designed fingerprint feature, we proposed a two-stage positioning algorithm for indoor fingerprint-based positioning. Experiment results indicate that our proposed positioning methodology can not only achieve better positioning performance but also consume less positioning time compared to three baseline methods.
Based on multiobjective particle swarm optimization, a localization algorithm named multiobjective particle swarm optimization localization algorithm (MOPSOLA) is proposed to solve the multiobjective optimization loca...
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Based on multiobjective particle swarm optimization, a localization algorithm named multiobjective particle swarm optimization localization algorithm (MOPSOLA) is proposed to solve the multiobjective optimization localization issues in wireless sensor networks. The multiobjective functions consist of the space distance constraint and the geometric topology constraint. The optimal solution is found by multiobjective particle swarm optimization algorithm. Dynamic method is adopted to maintain the archive in order to limit the size of archive, and the global optimum is obtained according to the proportion of selection. The simulation results show considerable improvements in terms of localization accuracy and convergence rate while keeping a limited archive size by a method using the global optimal selection operator and dynamically maintaining the archive.
Data aggregation algorithm aims to reduce the redundant information by gathering the sensed data, save energy, and prolong the lifetime of the network. However, the data aggregation technology will increase the networ...
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Data aggregation algorithm aims to reduce the redundant information by gathering the sensed data, save energy, and prolong the lifetime of the network. However, the data aggregation technology will increase the network transmission delay of wireless sensor networks. Minimum-latency aggregation scheduling is designed to minimize the number of scheduled time slots to perform an aggregation. In this paper, we present an Adaptive Aggregation Scheduling Algorithm based on the Grid Partition ( AASA-GP) in large-scale wireless sensor networks. By dividing the network into grids based on the geographical information, we allocate the channels according to the grid coordinates. Nodes with the same grid coordinates use the same channel and the adjacent grids use the different channels, so we can effectively avoid the wireless media transmission interference, increase the parallel transfer rate, and reduce the aggregation latency. Our extensive evaluation results demonstrate the superiority of the AASA-GP. For small-scale networks, the resultant latency is comparable with the best practice, and it is more suitable for large-scale wireless sensor networks.
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