Distributed Joint Cluster Formation and Resource Allocation Scheme for Cooperative Data Collection in Virtual MIMO-Based M2M Networks

We study the problem of admission control and resource (subcarriers, power and bit-loading) allocation in a heterogeneous OFDMA wireless network serving both QoS-constrained high-priority users and best-effort users. By clustering subcarriers, efficient algorithms for cluster and power allocation have been proposed. Our strategy maximizes the total network utility of the best-effort users while satisfying the QoS request for maximum number of high-priority users. We approximate best-effort user utility function as a piece-wise linear function and propose a linear programming based cluster allocation algorithm. The feasibility of the resource allocation problem depends on the number of HP users in the network. Since a large number of demanding HP users would render the resource allocation problem infeasible, a joint admission control and resource allocation scheme could be an efficient way of tackling both problems with less overhead. By incorporating admission control, we propose an efficient and optimal joint admission control and resource allocation algorithm.

An Adaptive Transmission Range Based Topology Control (ATRTC) scheme is proposed to reduce delay and improve reliability for data collection in delay and loss sensitive wireless sensor network. The core idea of the ATRTC scheme is to extend the transmission range to speed up data collection and improve the reliability of data collection. The main innovations of our work are as follows: (1) an adaptive transmission range adjustment method is proposed to improve data collection reliability and reduce data collection delay. The expansion of the transmission range will allow the data packet to be received by more receivers, thus improving the reliability of data transmission. On the other hand, by extending the transmission range, data packets can be transmitted to the sink with fewer hops. Thereby the delay of data collection is reduced and the reliability of data transmission is improved. Extending the transmission range will consume more energy. Fortunately, we found the imbalanced energy consumption of the network. There is a large amount of energy remains when the network died. ATRTC scheme proposed in this paper can make full use of the residual energy to extend the transmission range of nodes. Because of the expansion of transmission range, nodes in the network form multiple paths for data collection to the sink node. Therefore, the volume of data received and sent by the near‐sink nodes is reduced, the energy consumption of the near‐sink nodes is reduced, and the network lifetime is increased as well. (2) According to the analysis in this paper, compared with the CTPR scheme, the ATRTC scheme reduces the maximum energy consumption by 9%, increases the network lifetime by 10%, increases the data collection reliability by 7.3%, and reduces the network data collection time by 23%.

Virtual MIMO, also known as Multiuser-MIMO, has been widely investigated to improve data transmission rate of various wireless uplink systems. However, virtual MIMO transmission leads to performance degradation due to the multiuser interference. To obtain good trade-off between the system throughput and transmission performance, we investigate joint user grouping and resource allocation under the consideration of system throughput and average MSE performance in uplink SC-FDMA systems. Based on linear MIMO detection, we first develop MSE-oriented user grouping criterion for evaluation of transmission performance, then propose a dynamic user grouping and optimal resource allocation algorithm with elastic average MSE constraint. We evaluate the proposed joint resource allocation algorithms in LTE uplink scenarios and the results show that it achieves alterable trade-off between system throughput and average MSE.

To alleviate energy hole problem inherent in static Base station (BS) scheme and extend network work of battery-restricted wireless rechargable sensor networks, a joint mobile data col-lection and adaptive charging scheduling (MDC-ACS) scheme based on virtual grid is proposed in this paper, aiming to achieve whole network energy balance and high charging effi-ciency. In MDC-ACS scheme, network is firstly divided into several grids and several rendezvous are determined using geo-graphic information. Then, mobile data collector (MDC) moves in grids for data collection within the application delay and moving trajectory is guided by rendezvous. Due to the limited battery capacity of mobile wirelsee charging vehicle (WCV) and its energy consumption while cruising, an adaptive charging schedule scheme is proposed for maximizing recharging benefit. With extensive simulation, we demonstrate that MDC-ACS scheme can achieve better charging benefit and reduce average charging delay.

Distributed computing splits a large-scale job into multiple tasks and deals with them on clusters. Cluster resource allocation is the key point to restrict the efficiency of distributed computing platform. Hadoop is the current most popular open-source distributed platform. However, the existing scheduling strategies in Hadoop are kind of simple and cannot meet the needs such as sharing the cluster for multi-user, ensuring a concept of guaranteed capacity for each job, as well as providing good performance for interactive jobs. This paper researches the existing scheduling strategies, analyses the inadequacy and adds three new features in Hadoop which can raise the weight of job temporarily, grab cluster resources by higher-priority jobs and support the computing resources share among multi-user. Experiments show they can help in providing better performance for interactive jobs, as well as more fairly share of computing time among users.KeywordsHadoopDistributed SchedulingMapReduceFair Share

This paper proposes a joint beamforming and resource allocation scheme to maximize the minimum capacity for wireless powered communication network (WPCN) designed for multi-user distributed massive multi-input multi-output (DM-MIMO) system with full-duplex radio remote heads (RRHs), which enable simultaneous uplink (UL) and downlink (DL) communications. The minimum capacity is maximized by a proposed algorithm based on the non-negative matrix theory. The simulation results have demonstrated that the achievable max-min capacity of the DM-MIMO is considerably higher compared to that of the CMMIMO system. It has also showed that the convergence speed of the proposed algorithm is so fast.

With the explosive increase of wireless traffic generated by Internet of Things (IoT), it is challenging for the ground communication infrastructures to achieve flexible and efficient data collection. In this paper, we propose an intelligent trajectory optimization and collision avoidance scheme for cooperative multi-UAV data collection in urban IoT environments, where a new Double Deep Q-Network (D3QN) is designed to maximize the total collected data by optimizing UAVs' flying speeds and dynamically adjusting the IoT-UAV associations. Simulations show that the proposed solution achieves the fastest convergence and the lowest collision rate with the average data collection rate of 91.15%, compared to baseline methods. Moreover, 4 UAVs can achieve the data collection rate of 95% compared to that of 50% in single UAV scenario.

In recent years, drone-based wireless data collection has emerged as a significant research hotspot in the Internet of Things (IoT) field. However, most existing studies primarily focus on dedicated drones, which incur high deployment costs. In contrast, reusing existing delivery drones provides a cost-effective alternative. This paper concerns the problem of scheduling delivery drones during their delivery to enable cost-efficient wireless data collection. Specifically, the problem aims to maximize the data collection amount while subject to the constraints of the drone’s battery capacity and package delivery deadlines. Nevertheless, solving this problem is challenging due to the involvement of three coupled variables: collection task allocation, collection time distribution, and flying path planning. To tackle this challenge, we propose MdSche, a multi-delivery drone scheduling scheme for wireless data collection. Specifically, we first design a time slicing-based data collection time discretization algorithm, which transforms the original problem into a Multi-Drone Scheduling Problem (MDSP) involving two variables. Then, an auxiliary graph construction method is employed to decompose MDSP into multiple independent single-drone scheduling subproblems. Finally, we construct a surrogate function to simplify each subproblem into a collection task allocation problem with only one variable. Theoretical analysis verifies that MdSche achieves an approximation ratio of 1/2(1 − (1/e)^1/4 ) in polynomial time. Extensive trace-based simulations show that, compared to five baseline methods, our approach improves the data collection amount by an average of 69.68%.

In the Internet of Vehicles, computation-intensive and latency-sensitive applications challenge vehicles’ computational capabilities and processing time. Vehicular Edge Computing(VEC), leveraging idle resource of nearby vehicles and Roadside Units (RSUs), addresses this by enabling collaborative task computing via Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) communications. This paper tackles the VEC system’s resource management for performance optimization, focusing on joint resource allocation and task offloading to minimize energy consumption while ensuring task completion. The formulated mixed-integer nonlinear programming problem is decomposed into sub-problems, and an Ant Colony System (ACS), Lagrange multipliers, and gradient methods-based scheme is proposed. Simulations show our scheme outperforms others in reducing task incomplete rate and energy consumption.

In this paper, we propose a joint resource allocation, routing, and connection admission control (CAC) scheme for uplink transmission in orthogonal frequency division multiple access (OFDMA) relay networks with cooperative relaying. For cooperative relaying, relay station can relay uplink data from mobile station (MS) to base station with cooperation of the MS using transmit diversity. Transmit diversity can be achieved by virtual MISO via distributed space---time coding. The proposed scheme jointly allocates OFDMA resources and selects path for each user with CAC to maximize the upink throughput of cooperative OFDMA relay networks. The basic OFDMA resource unit is considered as a resource element which is one subcarrier over one OFDMA symbol. An efficient multi-choice multi-dimensional knapsack (MMKP) algorithm is presented for the proposed scheme. The proposed MMKP algorithm provides a unified framework which is applicable to OFDMA networks with and without cooperative relaying. We evaluate the performance of the proposed scheme with and without cooperative relaying in a hilly terrain with heavy tree density by using OPNET-based simulation. We show that the cooperative relaying improve the uplink system throughput compared with non-cooperative relaying, and the proposed scheme outperforms the conventional link quality-based scheme in both cooperative and non-cooperative relay networks.

One of the fundamental differences between the terrestrial radio frequency (RF) signal and underwater acoustic signal is long propagation delay. This paper is focused on a scheduling scheme for regional data collection in underwater acoustic sensor networks. Long propagation delay of the acoustic signal causes a serious problem for transmission scheduling. In this paper, we propose an efficient transmission scheduling algorithm using the property of long propagation delay for real-time data collection which is critical to tactical surveillance applications. We show through the simulation that the proposed scheme reduces total transmission time and increases the network efficiency significantly.

In recent years, energy-efficient data collection has evolved into the core problem in the resource-constrained Wireless Sensor Networks (WSNs). Different from existing data collection models in WSNs, we propose a collaborative data collection scheme based on optimal clustering to collect the sensed data in an energy-efficient and load-balanced manner. After dividing the data collection process into the intra-cluster data collection step and the inter-cluster data collection step, we model the optimal clustering problem as a separable convex optimization problem and solve it to obtain the analytical solutions of the optimal clustering size and the optimal data transmission radius. Then, we design a Cluster Heads (CHs)-linking algorithm based on the pseudo Hilbert curve to build a CH chain with the goal of collecting the compressed sensed data among CHs in an accumulative way. Furthermore, we also design a distributed cluster-constructing algorithm to construct the clusters around the virtual CHs in a distributed manner. The experimental results show that the proposed method not only reduces the total energy consumption and prolongs the network lifetime, but also effectively balances the distribution of energy consumption among CHs. By comparing it o the existing compression-based and non-compression-based data collection schemes, the average reductions of energy consumption are 17.9% and 67.9%, respectively. Furthermore, the average network lifetime extends no less than 20-times under the same comparison.

Sensor nodes equipped with various sensory devices can sense a wide range of information regarding human or things, thereby providing a foundation for Internet of Thing (IoT). Fast and energy-efficient data collection to the control center (CC) is of significance yet very challenging. To deal with this challenge, a low redundancy data collection (LRDC) scheme is proposed to reduce delay as well as energy consumption for monitoring network by using matrix completion technique. Due to the correlation of the location-dependent sensing data, some data without being collected can still be recovered by the matrix completion technology, thereby reducing the data amount for data collection and transmission, reducing the network energy consumption, and accelerating the process of data acquisition. Based on matrix completion technique, LRDC scheme can select only part of the nodes to sense data and transmit less data to CC. By doing so, the data collected by the network can be greatly reduced, which can effectively improve the network lifetime. In addition, LRDC scheme also proposes a method for quickly compensate sample data in cases of packet loss, whereby part of redundant data is sent in advance to the area closer to CC. If the data required for matrix completion is lost, these redundant data can be quickly obtained by CC, so the LRDC scheme has low delay characteristics. Simulation results demonstrate that LRDC scheme can achieve better performance than the traditional strategy, and it can reduce the maximum energy consumption of the network by 27.6–57.9% and reduce the delay by 0.7–17.9%.

With the recent advancements in nanotechnologies, Body Area Nanonetworks (BANNETs) are expected to be a promising solution for many critical biomedical applications. Due to the extremely small size of nanomachines, serious energy limitation becomes a challenging roadblock stunting the development of BANNETs. As an initial step towards this end, this paper focuses on the design of an energy-efficient data collection scheme in BANNETs. First, a sleep/wake-up mechanism is introduced to avoid the unnecessary energy consumption when no external request comes. Then, with a careful consideration of both node available energy and transmission energy consumption, we design a new node selection strategy to further reduce the energy consumption in the data collection process. Finally, we conduct extensive simulations for both the proposed data collection scheme and the benchmark greedy scheme to illustrate the energy efficiency of our scheme as well as to discuss the impacts of network parameters on network performance.

Aiming at the defects of large-scale large scale wireless sensor network data processing network traffic and high task latency, a data collection scheme of mobile node based on discrete elastic collision optimization algorithm and adaptive block compression sensing is proposed. Firstly, by analyzing the relationship between the network partitioning and the node deployment, an adaptive block compressed sensing data collection strategy is proposed to realize sensor node based on adaptive network block compressed sensing data collection. Designing mobile node data acquisition path planning strategy and multiple mobile nodes The collaborative computer system adopts the fitness value constraint transformation processing technology and the parallel discrete elastic collision optimization algorithm to achieve the purpose of balancing network node energy consumption and reducing data processing task delay. Finally, the simulation results show that the data collection scheme can effectively realize high-efficiency processing of large-scale sensor network data, and reduce network traffic and network task delay, and better balance network node energy consumption.