MCGR-PB: A Multi-Player Cooperative Game Based Routing for Performance Balancing in Crowd Sensing Networks

As an emerging paradigm, Mobile CrowdSensing (MCS) network based fingerprint positioning technology can implement the site survey cheap and fast, and provide a more reliable location-based service for users. However, limited by the differences among individuals, the fingerprints collected from MCS network are troubled by fingerprint ineffectiveness or element missing problems. In order to ensure the availability of fingerprints, in this paper we propose a novel Low-rank and Sparse representation based Tensor Completion (LSTC) fingerprint recovery method to recover the ineffective fingerprints and the missing fingerprint elements accurately in MCS network. Specifically, the tensor low-rank characterization is used to exploit the global structure of the location fingerprints, and the tensor sparsity characterization is used to exploit the local structure of the location fingerprint elements. To promote the global optimal solution solving rapidly, a weight compensation scheme is proposed to fill the convex relaxation gap caused by the approximation of low-rank and sparsity. Meanwhile, to represent the local sparse characterization effectively, orthogonal dictionary learning is introduced and integrated into LSTC. The experimental results show that by using the tensor global and local structure information properly, the proposed LSTC can recover the fingerprint effectively, without jeopardizing the positioning accuracy.

Enhancement of wireless sensor network lifetime by deploying heterogeneous nodes

Effects of physical channel separation on application flows in a multi-radio multi-hop wireless mesh network: An experimental study on BilMesh testbed

Network performance metrics such as available bandwidth and latency are essential to achieve good Quality of Service (QoS) in multimedia streaming. There are unique requirements in network performance metrics for media applications, such as audio conferencing, video streaming, video conferencing, and high-definition (HD) video conferencing. In this paper, we focus on conference call type suggestion based on link quality prediction. The link's quality is classified based on the available bandwidth and latency between two network nodes. We have implemented and compared two of the most popular supervised learning based classification methods, i.e. logistic regression and support vector machine (SVM). We have compared the performance of both methods and their suitability to apply in link quality prediction. The experimental results show that SVM outperforms logistic regression for binary and multiclass classification in terms of accuracy.

This paper quantifies the impact of topological characteristics on the performance of single radio multichannel IEEE 802.11 mesh networks. Topological characteristics are the number of nodes per subnetwork, the hop count, the neighbor node density, the hidden nodes, the number of nodes in the neighborhood of the gateway, and the hidden nodes in the neighborhood of the gateway. Network performance metrics are throughput, fairness and delay. The data mining Support Vector Machine (SVM) model was used to extract the relationships between the network topology metrics and the network performance metrics based on data results obtained through ns-2 simulation of random networks. The results obtained can be used as a basis to design channel assignment algorithms or to aid the deployment and management of single radio wireless mesh networks.

A practical cross layer cooperative MAC framework for WSNs

Mobile crowdsensing networks have emerged to show elegant data collection capability in loosely cooperative network. However, in the sense of coverage quality, marginal works have considered the efficient (less participants) and effective (more coverage) designs for mobile crowdsensing network. We investigate the optimal coverage problem in distributed crowdsensing networks. In that, the sensing quality and the information delivery are jointly considered. Different from the conventional coverage problem, ours only select a subset of mobile users, so as to maximize the crowdsensing coverage with limited budget. We formulate our concerns as an optimal crowdsensing coverage problem, and prove its NP-completeness. In tackling this difficulty, we also prove the submodular property in our problem. Leveraging the favorable property in submodular optimization, we present the greedy algorithm with approximation ratio $O(\sqrt {k})$ , where $k$ is the number of selected users. Such that the information delivery and sensing coverage ratio could be guaranteed. Finally, we make extensive evaluations for the proposed scheme, with trace-driven tests. Evaluation results show that the proposed scheme could outperform the random selection by $2 \times $ with a random walk model, and over $3 \times $ with real trace data, in terms of crowdsensing coverage. Besides, the proposed scheme achieves near optimal solution comparing with the brute-force search results.

In 5G networks, time-series data will be omnipresent for the monitoring and management of network performance metrics. With the increase in the number of Internet of Things (IoT) devices, it is expected that the number of real-time time-series data streams will increase at a fast pace, making forecasting essential for the proactive successful management of the network. In this article, we discuss to use both linear and non-linear forecasting methods, including machine learning, deep learning, and neural networks to improve 5G networks’ management. For this purpose, we design and implement a real-time distributed forecasting framework, used to make simultaneous predictions of different network performance metrics, and with different learning algorithms. By using our framework, we compare the use of forecasting methods in two network scenarios, in a real vehicular network and in a 4G network, representing two different slices in a 5G network. We also integrate our framework in a 5G architecture. Using the best forecasting models assessed previously, we propose a dynamic threshold algorithm for multi-slice management, to ensure that the resources of each slice are updated according to the slices’ needs, while avoiding congestion and saving resources for other slices. The experimental results show that it is possible to forecast the slices’ needs and congestion probability, selecting the best forecasting approach or an ensemble of the best ones, and act accordingly in the network to optimize its management.

A GRASP-based network re-optimization strategy for improving RWA in multi-constrained optical transport infrastructures

Several active queue management (AQM) schemes have been proposed to address the bufferbloat issue, a long network delay and performance degradation issue caused by excess buffering of packets in network nodes. The solutions proposed up to now, however, satisfy only a subset of the key network performance metrics, i.e., end-to-end throughput, throughput fairness, and delay, in 802.11s-based multi-radio multi-channel wireless mesh networks (MRMC WMN). In this letter, we propose FD-AQM, a fairness-aware delay-controlled AQM to satisfy all three key network performance metrics simultaneously in the 802.11s-based MRMC WMN. The proposed FD-AQM achieves the goal by adopting a per-queue drop interval which is recalibrated according to the average round-trip time of flows, and a drop decision mechanism which considers average throughput as well as target delay. Through simulations, we show that FD-AQM is superior to other AQM schemes in terms of throughput and fairness.

With the rapid development of Internet of Things (IoT) technology, the number of nodes in wireless sensor networks (WSNs) is explosively increasing, and the scale of network is increased gradually. Traditional single-layer non-clustering network is no longer suitable for current WSNs, which results in high maintenance cost and fast deterioration of network performance. By analyzing the impact of existing static and dynamic clustering schemes on network performance, it is concluded that additional factors need to be considered to improve the overall performance of the network, such as residual energy of nodes, number of neighbor nodes and load balancing. Therefore, an adaptive multi-layer clustering networking strategy based on capability weights is proposed. Based on the real-time changes of each cluster density, node load and residual energy, the node capacity weights are updated dynamically according to the actual network performance, then the cluster heads are renewed adaptively. By comparing the performance metrics in the experiments, proposed strategy can effectively reduce the load of key nodes such as cluster head, and improves the network performance metrics such as average transmission delay, average transmission hops and load balancing.

A geo-temporal mobility prediction method for cooperative time-validity-constrained content delivery in opportunistic networks

Το αντικείμενο της διατριβής αυτής είναι η μελέτη της διαστρωματικής (cross-layer) προσέγγισης ανάπτυξης ασύρματων δικτύων κατανεμημένης λειτουργίας με απαιτήσεις επικοινωνίας πραγματικού χρόνου και περιορισμένους διαθέσιμους πόρους. Επιπλέον βασικό στόχο αποτελεί και η σχεδίαση, πρόταση αντίστοιχης αρχιτεκτονικής η οποία στοχεύει στη βέλτιστη διαχείριση διαθεσίμων δικτυακών πόρων σε καταστάσεις συμφόρησης του δικτύου και κατά συνέπεια στην βελτίωση της απόδοσης αυτού. Μέσα από μελέτες στα πλαίσια της διατριβής αναδεικνύεται η σημασία του φαινομένου συμφόρησης ως κύριου παράγοντα σπατάλης δικτυακών πόρων καθώς και οδήγησης του δικτύου σε κατάσταση κορεσμού με αρνητική επίπτωση σε όλες τις παραμέτρους απόδοσης του δικτύου. Στόχος, λοιπόν, της προτεινόμενης επικοινωνιακής αρχιτεκτονικής είναι η αποφυγή του φαινομένου συμφόρησης έτσι ώστε το δίκτυο να οδηγείται δυναμικά σε ένα σταθερό σημείο απόδοσης (όσο αυτό είναι δυνατό) το οποίο θα επιτρέπει στο δίκτυο να αποδίδει όσο το δυνατόν καλύτερα αποφεύγοντας τη ίδια στιγμή άσκοπη σπατάλη πόρων. Βασική παράμετρος στη σχεδίαση αποτελεί η συμβατότητα τόσο ως προς σημαντικό εύρος διαθέσιμων πρωτοκόλλων σε διάφορα επίπεδα, όσο και ως προς τη δυνατότητα συνύπαρξης κόμβων που ενσωματώνουν την προτεινόμενη αρχιτεκτονική με κόμβους χωρίς αυτήν. Η προτεινόμενη αρχιτεκτονική υλοποιήθηκε στα πλαίσια γνωστού και αξιόπιστου δικτυακού εξομοίωση. Αξιολόγηση της υλοποίησης αυτής μέσα από μεγάλο αριθμό πειραμάτων έδειξε επίτευξη του στόχου καθώς το φαινόμενο της συμφόρησης αντιμετωπίστηκε σε όλες τις περιπτώσεις με σημαντικά οφέλη στην απόδοση του δικτύου και διαχείριση των πόρων. Επιπλέον, λόγω του μεγάλου βαθμού παραμετροποίησης αποτελεί ιδανική βάση για μελλοντικές προεκτάσεις.

SummaryDelay tolerant network (DTN) has attracted significant attention of the researchers due to its capability of delivering the message even in the situation where network connectivity is irregular. Irregular networks result in non‐delivery of data to user at right time, and the same is not possible with traditional algorithms due to requirement of end‐to‐end connectivity between nodes. DTN uses story–carry–forward technique to deliver a message in the environment of disconnection. Hence, for message transmission from source node to destination node, a relay node or forwarder node is required. The efficiency of the network is mainly dependent on the selection of forwarder node. Hence, forwarder node selection is very crucial in DTNs. The objective of this paper is to propose an efficient relay node selection technique integrated with fuzzy logic, based on four properties of node, namely, number of encounters, residual energy, buffer capacity, and speed so that performance metrics of the network can be improve. Proposed method uses fuzzy logic to decide which node should be selected as relay node in multi hop strategy. The study of simulation results indicate that proposed algorithm improves performance of the network with regards to delivery ratio, delay, hop count, and throughput.

We categorized VPN’s impact in to two different aspects; The affecting aspect, and the affected aspect. The affecting aspect of the impact encompasses factors such as security, algorithms, hardware, and software. Whereas the affected aspect is of the likes of network performance. While VPNs have managed to integrate security, one of the affecting aspect of the impact on one hand, on the other hand, VPNs should be regarded as a potential threat to network performance. In this study, for affordability purpose, we choose to use NS-2 simulated test-bed to shed light on the VPN’s performance impact in a network. Considering the most common network performance metrics, throughput and delay; we assessed these performance metrics by means of average and percentage changes theories. The findings emphasize quantitative impact on the TCP/IP throughput than on its counterpart UDP/IP. We finally developed an analytical equation to model this VPN’s performance impact.