Multiple Source Nodes Research Articles

An Energy Conservation through an Adapted Probabilistic Scheduler for Timely Data Exchange in Local Mobile Cloud

Mobile Cloud Computing has emerged as a pivotal technology, enabling mobile devices to harness external resources for hosting applications and significantly reducing latency. Recent research introduces the concept of a 'local mobile cloud,' formed by proximate mobile devices, to offload complex real-time applications to nearby devices, which minimizes energy requirement, and communication latency. This research introduces a more efficient task scheduling algorithm that is based on probabilistic task scheduling technique. This moves computations from multiple source nodes to closer processing nodes. A simulation model for local mobile clouds using OMNET++, is used for assessing the performance of the task scheduling algorithm. Additionally, a comparative analysis of the task scheduler with alternative scheduling schemes was conducted to evaluate performance in terms of the energy consumption, and process completion time. The outcome of the study showed that the probabilistic task scheduling technique improved the computing time and further conserved the energy resource requirement.

Unidirectional links prefer local firing propagation in the neural network of C. elegans

Understanding the mechanisms of brain functions is currently a hot topic but most studies have been focused on the aspects of synchronization and various partial synchronization. We here study this problem from a new angle of signal propagation, where the local (global) propagation may be related to the status of health (mental disorder). We make a case study on the neural network of C. elegans by presenting a framework where one node is chosen as the source node to generate signals and all the other nodes are chosen as the target nodes. We interestingly find that the existence of the unidirectional links is the key element to generate the local firing propagation and the configuration of the unidirectional links will seriously influence the range of firing propagation. In details, our findings include: (i) The unidirectional links prefer the local firing propagation, while the bidirectional links prefer the global firing propagation. (ii) Multiple source nodes will enhance the firing propagation, where the range of firing propagation for choosing the multiple source nodes from different communities is slightly larger than that from the same community. And (iii) a resonance effect of the range of firing propagation on coupling strength is observed, i.e. there is an optimal coupling strength. Moreover, a brief theoretical analysis is provided to explain these results.

HCAR: A Hybrid-Coding-Aware Routing Protocol for Underwater Acoustic Sensor Networks

Because of the characteristics of the underwater acoustic sensor networks (UASNs), such as long propagation delay, narrow bandwidth, limited resources, and intermittent connectivity, the design of the routing protocol has always been the focus and difficulty in UASNs research. Most of the existing coding-based underwater routing protocols only use the error correction function of network coding to improve the reliability of data transmission, ignoring that the network coding can effectively optimize the performance of packet transmission. In this paper, we propose a hybrid coding-aware routing protocol (HCAR). HCAR introduces inter-flow network coding and designs a new routing framework that combines reactive routing with opportunistic routing. It exhaustively explores potential coding opportunities in the network and proactively creates coding opportunities during the entire routing process, which enables HCAR to take full advantage of the broadcast nature of the wireless medium, thereby effectively reducing the number of transmissions. In addition, we design a new encoding method that does not rely on opportunistic listening, and thus avoids introducing excessive delay and overhead. The simulation results show that HCAR can effectively optimize the routing problem of the networks with multiple source nodes and a single sink node, further reducing the transmission overhead while ensuring reliability. We also implemented the key mechanism of HCAR protocol to verify its feasibility through sea tests.

Age‐aware relay strategy with simultaneous wireless information and power transfer in remote monitoring systems

AbstractRemote monitoring systems has emerged to automate public services, where Internet of Things (IoT) devices are deployed to collect and relay streams of status updates to a remote control centre. The freshness of collected data is of critical importance to system performance, which can be quantified by the concept of age of information (AoI). To obtain timely perception of the monitored area, each sensor tends to sample status updates at the maximum frequency, which may lead to device drop‐outs due to limited battery capacity. Simultaneous wireless information and power transfer (SWIPT) is considered as a promising technology to prolong network lifetime. In this paper, a SWIPT‐enabled two‐hop relay network, where a relay node uses power splitting protocol to assist data transmissions from multiple source nodes to the destination is considered. The impact of power splitting coefficients, data arrival pattern and time‐varying channel conditions on the averaged age performance and expected lifetime of the system is theoretically analysed. Moreover, a lightweight relay strategy which selectively forward packets based on a reward function consisting of energy level at relay node and expected age reduction is developed. Simulation results show that the proposed strategy outperforms existing schemes in collecting status updates in a timely and energy‐efficient manner.

Joint Trajectory Planning, Time and Power Allocation to Maximize Throughput in UAV Network

Due to the advantages of strong mobility, flexible deployment, and low cost, unmanned aerial vehicles (UAVs) are widely used in various industries. As a flying relay, UAVs can establish line-of-sight (LOS) links for different scenarios, effectively improving communication quality. In this paper, considering the limited energy budget of UAVs and the existence of multiple jammers, we introduce a simultaneous wireless information and power transfer (SWIPT) technology and study the problems of joint-trajectory planning, time, and power allocation to increase communication performance. Specifically, the network includes multiple UAVs, source nodes (SNs), destination nodes (DNs), and jammers. We assume that the UAVs need to communicate with DNs, the SNs use the SWIPT technology to transmit wireless energy and information to UAVs, and the jammers can interfere with the channel from UAVs to DNs. In this network, our target was to maximize the throughput of DNs by optimizing the UAV’s trajectory, time, and power allocation under the constraints of jammers and the actual motion of UAVs (including UAV energy budget, maximum speed, and anti-collision constraints). Since the formulated problem was non-convex and difficult to solve directly, we first decomposed the original problem into three subproblems. We then solved the subproblems by applying a successive convex optimization technology and a slack variables method. Finally, an efficient joint optimization algorithm was proposed to obtain a sub-optimal solution by using a block coordinate descent method. Simulation results indicated that the proposed algorithm has better performance than the four baseline schemes.

Computing Vector-Linear Functions on Diamond Network

Network function computation is investigated in the letter. In the model, a target function, of which the inputs are generated at multiple source nodes, is required to be computed with zero error at a sink node over a network. Toward this end, distributed coding by integrating communication and computation in networks is regarded as an efficient solution. We are interested in its fundamental computing capacity of importance in theory and applications. In the letter, we explicitly characterize the capacities of computing all the vector-linear functions over the diamond network. The diamond network has an important topology structure which not only is typical for many multi-terminal information-theoretic problems but also illustrates the combinatorial nature of the computing problem. By applying the computing capacities thus obtained, we solve the solvability problem of vector-linear functions over the diamond network. We determine all the solvable vector-linear functions and obtain an enhanced result that the remaining vector-linear functions are not only linearly non-solvable but also non-linearly non-solvable.

Whom to trust in a signed network? Optimal solution and two heuristic rules

Signed networks capture both positive and negative relationships in a social network. We assume that the network nodes are of two different types and, in agreement with social balance theory, link signs correlate with node types. Given a signed network and information on the type of some source nodes, we study the problem of judging the type of a target node. We first formulate a globally optimal Bayes solution to this problem. As this optimal solution is too complex to be used by humans, we proceed by introducing a heuristic based on the shortest paths between the source nodes and the target node. We prove that this heuristic is weakly better than a previously introduced heuristic based on random walks, and that it coincides with the optimal rule for star-like networks. With simulations, we assess the accuracy of the three type-judging rules and find that our heuristic is particularly accurate for networks with short distances and when there are multiple source nodes. Our work contributes to the active field of inference in complex networks and puts emphasis on methods that could be applied by humans facing network data.

Multi-source detection based on neighborhood entropy in social networks

The rapid development of social networking platforms has accelerated the spread of false information. Effective source location methods are essential to control the spread of false information. Most existing methods fail to make full use of the infection of neighborhood information in nodes, resulting in a poor source localization effect. In addition, most existing methods ignore the existence of multiple source nodes in the infected cluster and hard to identify the source nodes comprehensively. To solve these problems, we propose a new method about the multiple sources location with the neighborhood entropy. The method first defines the two kinds of entropy, i.e. infection adjacency entropy and infection intensity entropy, depending on whether neighbor nodes are infected or not. Then, the possibility of a node is evaluated by the neighborhood entropy. To locate the source nodes comprehensively, we propose a source location algorithm with the infected clusters. Other unrecognized source nodes in the infection cluster are identified by the cohesion of nodes, which can deal with the situation in the multiple source nodes in an infected cluster. We conduct experiments on various network topologies. Experimental results show that the two proposed algorithms outperform the existing methods.

Communication and trajectory design in UAV-enabled flying network

Thanks to the flexible deployment, mobility and low cost, unmanned aerial vehicle (UAV) will be widely used in civil and military scenarios. In this paper, a flying network in the sky is considered, where multiple aerial source nodes are equipped with sensing devices to explore the environment and transmit the collected information to the ground station for data fusion and processing. However, there exists distance restriction due to the signal attenuation. Therefore, a UAV is deployed as a flying relay to provide wireless connection. Firstly, we design a time-division multiple access scheme to satisfy the rate requirements. Secondly, to maximize the system throughput under the no-fly zones, flying speed and available energy constraints, we optimize the time, power and flying trajectory. Then we decompose it into three subproblems. To reduce computational complexity, we optimize the power allocation and flying trajectory by applying the Lagrange dual decomposition and the alternating direction method of multipliers, respectively. In conclusion, an overall algorithm is proposed to iteratively optimize these three variables. In the end, we obtain the solution and the simulation results show the effectiveness of the proposed algorithm.

Design and Analysis of an Energy-Efficient Load Balancing and Bandwidth Aware Adaptive Multipath N-Channel Routing Approach in MANET

Mobile Ad Hoc Network (MANET) is a collection of spontaneous nodes that form a dynamic network without any centralized administration. Routing decisions for mobile communication is a challenging task because the continuous movement of nodes increases the routing overhead and energy consumption. Numerous studies have been conducted in the field of MANET to reduce the routing decision overhead and energy consumption. Their concepts are good for improving efficiency in terms of load balancing, congestion control, and energy consumption. This article provides new concepts that are able to give better outcomes. This paper, proposes an adaptive Multipath Multichannel (N-channel) Energy Efficient (MMEE) routing approach in which route selection strategies are dependent on predictive energy consumption per packet (calculated accustomed data delivery), available bandwidth, queue length, and channel utilization. Multipath provides multiple routes and balances network load, whereas multichannel reduces network collisions via a channel ideal assignment mechanism. In the multichannel mechanism, link bandwidth is divided into multiple sub-channels. Multiple source nodes access the channel bandwidth in a simultaneous manner that minimizes the network collision. The collaborative multipath multichannel mechanism provides more than one path between a single source or multiple sources to the destination without collision and congestion. The path selection is based on the MMEE routing approach. In the proposed MMEE, a load and bandwidth aware routing method selects the path, based on node energy and predicts their lifetime, which increases the network reliability. The result shows the comparative analysis between various multichannel medium access techniques such as MMAC, Parallel Rendezvous Multi Channel Medium Access Protocol (PRMMAC), Quality of Service Ad hoc On Demand Multipath Distance Vector (QoS-AOMDV), Q-learning based Multipath Routing (QMR), Topological Change Adaptive Ad hoc On-demand Multipath Distance Vector (TA-AOMDV), and the proposed MMEE method. The outcome concludes that the MMEE method performs better than other schemes. The proposed MMEE protocol provides better performance in terms of percentage of data received, throughput, and minimum energy utilization.

Network coding assisted reliable multi-source multicasting over a multi-hop wireless mesh network

This paper introduces a Network Coding (NC) Assisted Reliable Multi-Source Multicasting (NCRMM) technique for multi-hop wireless mesh networks. The NCRMM scheme decreases the average number of packet transmissions in a network, which in turn reduces resource expenditure and traffic congestion problems. Multicast Least Cost Anypath Routing (MLCAR) algorithm is considered in NCRMM to select the neighbouring nodes participating in efficient packet transmission from multiple source nodes to the corresponding destination sets. At each intermediate node, a Coding Window (CW) is defined which stores packets received at the node from the same and/or different sessions. In the proposed NCRMM algorithm, packets are judiciously combined using intra-session and inter-session NC to ensure efficient distribution of coded packets among the neighbouring nodes. This is done while ensuring that packets transmitted at each source node are reliably received at all nodes of the corresponding destination set. The results are obtained for NCRMM and compared with that of the respective MLCAR and NC-MLCAR approach through extensive simulations. It is observed that the cost of multicasting packets in the NCRMM scheme is significantly less than that of the existing MLCAR and NC-MLCAR schemes for both independent and correlated link scenarios. Results comparing the complexity of the proposed NCRMM scheme with other multicasting schemes are also presented.

Outage analysis of cognitive non‐orthogonal multiple access downlink system with secondary source selection in Nakagami‐m fading environment

SummaryCognitive inspired non‐orthogonal multiple access (Cognitive‐NOMA) technique allows multiple primary and secondary users to utilize the same time and frequency resources to explore the reliability and spectral efficient requirements of 5G communication networks. In this paper Cognitive‐NOMA downlink system with multiple secondary source (SS) nodes is proposed. An optimal SS node is selected based on the signal to interference plus noise ratio (SINR) requirements of both near and far primary users and the secondary user node. Each optimal SS node incorporates simultaneous wireless information and power transfer (SWIPT) and full‐duplex technique to harvest energy and transceiver operation. Analytical outage probability expressions of the proposed system are derived over Nakagami‐m fading environment with the assumption that SS transmission links are correlated. Numerical results infer that the outage performance of the proposed system depends on the number of SS nodes, self‐interference at the SS node, and the SINR constraint for the selection of optimal SS node.

Ergodic Secrecy Rate of Optimal Source Selection in a Multi-Source System With Unreliable Backhaul

The use of multiple source nodes with wireless backhaul is considered for secrecy enhancement through source node selection in future wireless networks. The ergodic secrecy rate (ESR) of optimal source node selection in the presence of multiple eavesdroppers over independent non-identically distributed (INID) Rayleigh fading channels is evaluated in closed-form. At high signal-to-noise ratio (SNR), the ESR is expressed as a simple weighted summation where each term relates to the contribution of an individual source and eavesdropper. An asymptotic analysis shows the effect of the system parameters and backhaul reliability on the performance. The proposed method can provide a generalized solution for the ESR of optimal transmit antenna selection in multi-antenna systems, optimal source node selection, and optimal relay selection with or without unreliable backhaul.

Robust Transceiver Design for Multi-Hop AF MIMO Relay Multicasting From Multiple Sources

In this article, the transceiver design optimization problem is investigated for multi-hop multicasting amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay systems, where multiple source nodes broadcast their message to multiple destination nodes via multiple serial relay nodes. Multiple antennas are installed at the sources, relays, and the destination nodes. In the transceiver design, we consider the mismatch between the true and the estimated channel state information (CSI), where the CSI mismatch follows the Gaussian-Kronecker model. A robust transceiver design algorithm is developed to jointly optimize the source, relay, and destination matrices to minimize the maximal weighted mean-squared error (WMSE) of the received message at all destination nodes. In particular, the WMSE is made statistically robust against the CSI mismatch by averaging through the distributions of the true CSI. Moreover, the WMSE decomposition is exploited to reduce the computational complexity of the transceiver optimization. Numerical simulations show a better performance of the proposed robust transceiver design against the channel mismatch.

A Novel Enhanced Quantum PSO for Optimal Network Configuration in Heterogeneous Industrial IoT

A novel enhanced quantum particle swarm optimization algorithm for IIoT deployments is proposed. It provides enhanced connectivity, reduced energy consumption, and optimized delay. We consider heterogeneous scenarios of network topologies for optimal path configuration by exploring and exploiting the hunts. It uses multiple inputs from heterogeneous IIoT into quantum and bio-inspired optimization techniques. The differential evolution operator and crossover operations are used for information interchange among the nodes to avoid trapping into local minima. The different topology scenarios are simulated to study the impact of $p$ -degrees of connectivity concerning objective functions’ evaluation and compared with existing techniques. The results demonstrate that our algorithm consumes a minimum of 30.3% lesser energy. Furthermore, it offers improved searching precision and convergence swiftness in the possible search space for $p$ -disjoint paths and reduces the delay by a minimum of 26.7%. Our algorithm also improves the throughput by a minimum of 29.87% since the quantum swarm inclines to generate additional diverse paths from multiple source nodes to the gateway.

Joint Data Collection and Resource Allocation for Distributed Machine Learning at the Edge

Under the paradigm of edge computing, the enormous data generated at the network edge can be processed locally. To make full utilization of these widely distributed data, we focus on an edge computing system that conducts distributed machine learning using gradient-descent based approaches. To ensure the system’s performance, there are two major challenges: how to collect data from multiple data source nodes for training jobs and how to allocate the limited resources on each edge server among these jobs. In this paper, we jointly consider the two challenges for distributed training (without service requirement), aiming to maximize the system throughput while ensuring the system’s quality of service (QoS). Specifically, we formulate the joint problem as a mixed-integer non-linear program, which is NP-hard, and propose an efficient approximation algorithm. Furthermore, we take service placement into consideration for diverse training jobs and propose an approximation algorithm. We also analyze that our proposed algorithm can achieve the constant bipartite approximation under many practical situations. We build a test-bed to evaluate the effectiveness of our proposed algorithm in a practical scenario. Extensive simulation results and testing results show that the proposed algorithms can improve the system throughput 56-69 percent compared with the conventional algorithms.

Energy-Efficiency Fairness of Interference Multi-Relay Networks for Multi-User Communications

We investigate a single-antenna multi-user multi-relay interference network, where multiple source nodes simultaneously communicate with their respective destination nodes via half-duplex decode-and-forward relays. To ensure fairness among users, we consider a power allocation strategy to maximize the worst-case energy efficiency (EE) of all users for a fixed relay assignment. The resulting optimization problem turns out to be non-convex. Different from those in the literature, our method here is an iterative algorithm where two geometric programs (GPs) are solved in each iteration, one producing an upper-bound to the solution of the original problem and the other providing a feasible lower-bound. Moreover, the upper-bound GP approaches the original problem asymptotically. Our algorithm also works for the problem arising in the non-interfering (orthogonal) transmission, which was previously solved as a fractional program. Numerical results reveal that non-orthogonal transmission outperforms orthogonal transmission in terms of the worst-case EE at low and medium signal-to-noise ratios.

PODPAC: open-source Python software for enabling harmonized, plug-and-play processing of disparate earth observation data sets and seamless transition onto the serverless cloud by earth scientists

In this paper, we present the Pipeline for Observational Data Processing, Analysis, and Collaboration (PODPAC) software. PODPAC is an open-source Python library designed to enable widespread exploitation of NASA earth science data by enabling multi-scale and multi-windowed access, exploration, and integration of available earth science datasets to support analysis and analytics; automatic accounting for geospatial data formats, projections, and resolutions; simplified implementation and parallelization of geospatial data processing routines; standardized sharing of data and algorithms; and seamless transition of algorithms and data products from local development to distributed, serverless processing on commercial cloud computing environments. We describe the key elements of PODPAC’s architecture, including Nodes for unified encapsulation of disparate scientific data sources; Algorithms for plug-and-play processing and harmonization of multiple data source Nodes; and Lambda functions for serverless execution and sharing of new data products via the cloud. We provide an overview of our open-source code implementation and testing process for development and deployment of PODPAC. We describe our interactive, JupyterLab-based end-user documentation including quick-start examples and detailed use case studies. We conclude with examples of PODPAC’s application to: encapsulate data sources available on Amazon Web Services (AWS) Open Data repository; harmonize processing of multiple earth science data sets for downscaling of NASA Soil Moisture Active Passive (SMAP) soil moisture data; and deploy a serverless SMAP-based drought monitoring application for use access from mobile devices. We postulate that PODPAC will also be an effective tool for wrangling and standardizing massive earth science data sets for use in model training for machine learning applications.

A Reinforcement Learning Framework for Optimizing Age of Information in RF-Powered Communication Systems

In this paper, we study a real-time monitoring system in which multiple source nodes are responsible for sending update packets to a common destination node in order to maintain the freshness of information at the destination. Since it may not always be feasible to replace or recharge batteries in all source nodes, we consider that the nodes are powered through wireless energy transfer (WET) by the destination. For this system setup, we investigate the optimal online sampling policy (referred to as the age-optimal policy ) that jointly optimizes WET and scheduling of update packet transmissions with the objective of minimizing the long-term average weighted sum of Age of Information (AoI) values for different physical processes (observed by the source nodes) at the destination node, referred to as the sum-AoI . To solve this optimization problem, we first model this setup as an average cost Markov decision process (MDP) with finite state and action spaces. Due to the extreme curse of dimensionality in the state space of the formulated MDP, classical reinforcement learning algorithms are no longer applicable to our problem even for reasonable-scale settings. Motivated by this, we propose a deep reinforcement learning (DRL) algorithm that can learn the age-optimal policy in a computationally-efficient manner. We further characterize the structural properties of the age-optimal policy analytically, and demonstrate that it has a threshold-based structure with respect to the AoI values for different processes. We extend our analysis to characterize the structural properties of the policy that maximizes average throughput for our system setup, referred to as the throughput-optimal policy . Afterwards, we analytically demonstrate that the structures of the age-optimal and throughput-optimal policies are different. We also numerically demonstrate these structures as well as the impact of system design parameters on the optimal achievable average weighted sum-AoI.

Adaptive-SMC Based Output Impedance Shaping in DC Microgrids Affected by Inverter Loads

Inverter connected single-phase AC loads cause second-order oscillations in source currents and DC bus voltage. These oscillations degrade the efficiency and reliability of the dc microgrid. In this paper, an adaptive sliding mode control based output impedance shaping (ASMC-OIS) methodology is proposed for voltage regulation, proportional load sharing, and second-order ripple management in a dc microgrid. By using the proposed control method, the magnitude of the output impedance of the source interfacing converter is increased at 2 $f_{ac}$ programmably, which results in the reduction of second-order ripple currents propagating through the converter. Instead, it is propagated to the dc-link capacitor or towards the nodes which consists of some ripple absorption active or passive filter. This leads to an increase in the energy density of the ripple filters. The dynamic consensus-based secondary control is incorporated to ensure proportional load current sharing. A graph theoretical analysis is presented to analyze per unit load sharing among all the nodes. Stability of the proposed controller is analyzed considering multiple source nodes using Lyapunov's approach. A dc microgrid consisting of parallel-connected dc-dc boost converters, dc load, and inverter load is simulated to verify the proposed control strategy. The proposed ASMC-OIS methodology is validated through experimentation.