Remote monitoring cost minimization for an unreliable sensor network with guaranteed network throughput

AbstractIn this paper we consider a link-unreliable remote monitoring scenario where the monitoring center is geographically located far away from the region of the deployed sensor network, and sensing data by the sensors in the network will be transferred to the remote monitoring center through a third party telecommunication service. A cost associated with this service will be incurred, which will be determined by the number of gateways employed and the cumulative volume of data successfully received within a specified monitoring period. For this scenario, we first formulate a novel constrained optimization problem with an objective to minimize the service cost while a pre-defined network throughput is guaranteed. We refer to this problem as the throughput guaranteed service cost minimization problem and prove that it is NP-complete. We then propose a heuristic for it. The key ingredients of the heuristic include identifying gateways and finding an energy-efficient forest of routing trees rooted at the gateways. We also perform theoretical analysis on the solution obtained. Finally, we conduct experiments by simulations to evaluate the performance of the proposed algorithm. Experimental results demonstrate the proposed algorithm outperforms other algorithms in terms of both the service cost and the network lifetime

An Adaptive Fault-Tolerant Communication Scheme for Body Sensor Networks

A high degree of reliability for critical data transmission is required in body sensor networks (BSNs). However, BSNs are usually vulnerable to channel impairments due to body fading effect and RF interference, which may potentially cause data transmission to be unreliable. In this paper, an adaptive and flexible fault-tolerant communication scheme for BSNs, namely AFTCS, is proposed. AFTCS adopts a channel bandwidth reservation strategy to provide reliable data transmission when channel impairments occur. In order to fulfill the reliability requirements of critical sensors, fault-tolerant priority and queue are employed to adaptively adjust the channel bandwidth allocation. Simulation results show that AFTCS can alleviate the effect of channel impairments, while yielding lower packet loss rate and latency for critical sensors at runtime.Comment: 10 figures, 19 page

Approximation Algorithms for Capacitated Minimum Forest Problems in Wireless sensor Networks with a Mobile Sink

To deploy a wireless sensor network for the purpose of large-scale monitoring, in this paper, we propose a heterogeneous and hierarchical wireless sensor network architecture. The architecture consists of sensor nodes, gateway nodes, and mobile sinks. Th

Efficient Virtual Network Embedding Via Exploring Periodic Resource Demands

Cloud computing built on virtualization technologies promises provisioning elastic computing and communication resources to enterprise users. To share cloud resources efficiently, embedding virtual networks of different users to a distributed cloud consisting of multiple data centers (a substrate network) poses great challenges. Motivated by the fact that most enterprise virtual networks usually operate on long-term basics and have the characteristics of periodic resource demands, in this paper we study the virtual network embedding problem by embedding as many virtual networks as possible to a substrate network such that the revenue of the service provider of the substrate network is maximized, while meeting various Service Level Agreements (SLAs) between enterprise users and the cloud service provider. For this problem, we propose an efficient embedding algorithm by exploring periodic resource demands of virtual networks, and employing a novel embedding metric that models the workloads on both substrate nodes and communication links if the periodic resource demands of virtual networks are given; otherwise, we propose a prediction model to predict the periodic resource demands of these virtual networks based on their historic resource demands. We also evaluate the performance of the proposed algorithms by experimental simulation. Experimental results demonstrate that the proposed algorithms outperform existing algorithms, improving the revenue from 10% to 31%

Online unicasting and multicasting in software-defined networks

Software-Defined Networking (SDN) has emerged as the paradigm of the next-generation networking through separating the control plane from the data plane. In a software-defined network, the forwarding table at each switch node usually is implemented by expensive and power-hungry Ternary Content Addressable Memory (TCAM) that only has limited numbers of entries. In addition, the bandwidth capacity at each link is limited as well. Provisioning quality services to users by admitting their requests subject to such critical network resource constraints is a fundamental problem, and very little attention has been paid. In this paper, we study online unicasting and multicasting in SDNs with an objective of maximizing the network throughput under network resource constraints, for which we first propose a novel cost model to accurately capture the usages of network resources at switch nodes and links. We then devise two online algorithms with competitive ratios O(log n) and O(Kϵlog n) for online unicasting and multicasting, respectively, where n is the network size, K is the maximum number of destinations in any multicast request, and ϵ is a constant with 0 < ϵ ≤ 1. We finally evaluate the proposed algorithms empirically through simulations. The simulation results demonstrate that the proposed algorithms are very promising

Spatiotemporal Graph Neural Network based Mask Reconstruction for Video Object Segmentation

This paper addresses the task of segmenting class-agnostic objects in semi-supervised setting. Although previous detection based methods achieve relatively good performance, these approaches extract the best proposal by a greedy strategy, which may lose the local patch details outside the chosen candidate. In this paper, we propose a novel spatiotemporal graph neural network (STG-Net) to reconstruct more accurate masks for video object segmentation, which captures the local contexts by utilizing all proposals. In the spatial graph, we treat object proposals of a frame as nodes and represent their correlations with an edge weight strategy for mask context aggregation. To capture temporal information from previous frames, we use a memory network to refine the mask of current frame by retrieving historic masks in a temporal graph. The joint use of both local patch details and temporal relationships allow us to better address the challenges such as object occlusion and missing. Without online learning and fine-tuning, our STG-Net achieves state-of-the-art performance on four large benchmarks (DAVIS, YouTube-VOS, SegTrack-v2, and YouTube-Objects), demonstrating the effectiveness of the proposed approach.Comment: Accepted by AAAI 202

Minimizing the Deployment Cost of UAVs for Delay-Sensitive Data Collection in IoT Networks

In this paper, we study the deployment of Unmanned Aerial Vehicles (UAVs) to collect data from IoT devices, by finding a data collection tour for each UAV. To ensure the \u27freshness\u27 of the collected data, the total time spent in the tour of each UAV that consists of the UAV flying time and data collection time must be no greater than a given delay B, e.g., 20 minutes. In this paper, we consider a problem of deploying the minimum number of UAVs and finding their data collection tours, subject to the constraint that the total time spent in each tour of any UAV is no greater than B. Specifically, we study two variants of the problem: one is that a UAV needs to fly to the location of each IoT device to collect its data; the other is that a UAV is able to collect the data of an IoT device if the Euclidean distance between them is no greater than the wireless transmission range of the IoT device. For the first variant of the problem, we propose a novel 4-approximation algorithm, which improves the best approximation ratio 4 4/7 for it so far. For the second variant, we devise the very first constant factor approximation algorithm. We also evaluate the performance of the proposed algorithms via extensive experiment simulations. Experimental results show that the numbers of UAVs deployed by the proposed algorithms are from 11% to 19% less than those by existing algorithms on average

Evaluation on Transfer Efficiency at Integrated Transport Terminals through Multilevel Grey Evaluation

AbstractTransfer efficiency in integrated transportation terminal is greatly important for both passengers and operational companies. In this paper, we proposed various criteria and a hierarchy index system to evaluate the performance of the transfer condition inside Beijing South Railway Station. To make the assessment more scientific, we assign weightings to each of them by integrated weighting method. Then we use an evaluation method, Multi-level Grey Evaluation, to calculate the performance indexes of different transfer modes in the station and further we compare the ranking results of transfer efficiency of different transfer modes