Energy Minimization In Networks Research Articles

Delay-sensitive energy-efficient routing scheme for the Wireless Sensor Network with path-constrained mobile sink

The use of mobile sinks (MSs) can address several challenges for traditional wireless sensor networks (WSNs), such as energy holes, meanwhile it also introduces some new issues. Especially, for the path constraint MS, the fixed trajectory and uncontrollable moving speed results in significant delay and even buffer overflows. Existing research mainly focused on delay-tolerant applications which are unsuitable for delay-sensitive data or adopted the greedy policy to collect data which is inefficient in energy. To minimize the energy consumption and guarantee delay requirement simultaneously, we propose a delay-sensitive and energy-efficient routing scheme. Firstly, to obtain the MS position, we propose a K-nearest neighbor-based prediction model, which can reduce the energy and delay overhead of the location update. Based on the prediction result, an efficient cluster routing scheme is designed. To ensure both search speed and the optimality of the cluster head (CH), we improve the Reed-Deer algorithm (RDA) as IRDA, and use the IRDA to find out CHs. To remove the redundancy of the CH, a removal method is proposed. After constructing clusters, a method is used to determine the next-hop of the CH by using the predication position of the MS, the received timestamp and the delay requirement of the data. A fault tolerance mechanism is supplied to deal the predication error and other unexpected situations. By applying IRDA and the prediction model, the proposed routing scheme avoids the frequent construction of new routes, reduces the hops of the end-to-end path, minimizes network energy consumption, and ensures timely data transmission. The comparison results indicates that the proposed protocol can effectively improve the network performance while guarantee delay requirement. Especially in realistic scenario, the proposed algorithm can effectively reduce energy consumption and the end-to-end delay and improve the packet delivery ratio.

Multi-Agent Safe Graph Reinforcement Learning for PV Inverters-Based Real-Time Decentralized Volt/Var Control in Zoned Distribution Networks

To realize real-time voltage/var control (VVC) in active distribution networks (ADNs), this paper proposes a new multi-agent safe graph reinforcement learning method to optimize reactive power output from PV inverters. The network is divided into several zones, and a decentralized framework is proposed for coordinated control of reactive power output in each zone to regulate voltage profiles and minimize network energy loss. The VVC problem is formulated as a multi-agent decentralized partially observable constrained Markov decision process. Each zone has a central control agent that embeds graph convolution networks (GCNs) in the policy network to improve the decision-making capability. The GCN extracts graph-structured features from the ADN topology, reflecting the relationship between VVC and grid topology, and can filter noise and impute missing data. The training process includes primal-dual policy optimization to rigorously satisfy voltage safety constraints. Simulations on a 141-bus distribution system demonstrate that the proposed method can effectively minimize network energy loss and reduce voltage deviations, even in the presence of noisy or incomplete input measurements.

Flat ball topology basics multi‐transceivers 100 km/2.5 Tbps gigabit‐ethernet PON architecture for energy efficiency in data centers

AbstractExisting electronic data sources desire high‐power requisites, in order to obtain a large bandwidth, minimum power dissipation and low latency in networks. In this instance, a gigabit ethernet passive optical network (GEPON) architecture based on a dynamic Flat Ball topology incorporating multi‐transceivers to minimize network energy consumption via passive devices along with bandwidth reconfiguration potential and low latency is reported in this paper. The proposed architecture connected with 200 nodes exhibits 26 as well as 40 dB total loss in uplink as well as downlink considerably, compared to Sirius and Sandwich tree. Also, Flat ball GEPON architecture permits 9 and 10 dBm energy saving in downlink as well as uplink, respectively, than conventional GEPON architecture at 1 ms delay. The proposed architecture with 50 Erlang load allows energy ratio of 39% in downlink and 37% in uplink, at 25 × 100 Gbps traffic rate for 55 nodes. Besides this, the faithful fiber range of 100 km considering fiber no‐linearities and impairments can be obtained successfully. The architecture can be bordered with 1:64 splitting ratio with 35 ms in downlink as well as 31 ms in uplink transmission delay at bit error rate of 10−3. Additionally, on comparing the proposed topology with existing architectures depict its superiority with minimal implementation cost, low‐power dissipation, less traffic delay as well as high scalability and high flexibility.

Modeling and Optimization of LoRa Networks under Multiple Constraints.

With the access of massive terminals of the Internet of Things (IoT), the low-power wide-area networks (LPWAN) applications represented by Long Range Radio (LoRa) will grow extensively in the future. The specific Long Range Wide Area Network (LoRaWAN) protocol within the LoRa network considers both low power consumption and long-range communication. It can optimize data transmission to achieve low communication latency, ensuring a responsive system and a favorable user experience. However, due to the limited resources in LoRa networks, if certain terminals have heavy traffic loads, it may result in unfair impacts on other terminals, leading to increased data transmission latency and disrupted operations for other terminals. Therefore, effectively optimizing resource allocation in LoRa networks has become a key issue in enhancing LoRa transmission performance. In this paper, a Mixed Integer Linear Programming (MILP) model is proposed to minimize network energy consumption under the maximization of user fairness as the optimization goal, which considers the constraints in the system to achieve adaptive resource allocation for spreading factor and transmission power. In addition, an efficient algorithm is proposed to solve this optimization problem by combining the Gurobi mathematical solver and heuristic genetic algorithm. The numerical results show that the proposed algorithm can significantly reduce the number of packet collisions, effectively minimize network energy consumption, as well as offering favorable fairness among terminals.

An Efficient Data Acquisition and Processing Scheme for Wireless Multimedia Sensor Networks.

This study studies the problem of efficient multimedia data acquisition and decreasing whole energy expenditure of wireless multimedia sensor networks and proposes a three-step multimedia data acquisition and wireless energy supplement strategy. Firstly, for network partition, this study proposes a network partition scheme based on vicinity likeness and distance of sensor nodes (VLD), which divides the whole sensor network into multiple regions. The physical links inside the region are dense and concentrated, while the link connections between regions are sparse. Disconnecting the connections between regions hardly affects the data transmission of sensor nodes. Secondly, this study proposes an efficient data acquisition and processing scheme for wireless multimedia sensor network ASS. Compared with other anchor selection schemes, this scheme has obvious performance advantages. Then, the problem of minimizing network energy expenditure is defined, and the optimal sensor node data perception rate and network link transmission rate of the optimization function are obtained by dual decomposition and sub-gradient method. Finally, in the case of a given network energy threshold, the performance of the overall strategy in this study is verified by comparing the amount of data collected by the base station.

Body Node Coordinator Placement Algorithm for WBAN Using Multi-Objective Swarm Optimization

A Body Node Coordinator (BNC) is responsible for coordinating and receiving the data transmissions of bio-sensor nodes within a wireless body area network (WBAN). Hence, it is necessary to place BNC at an optimal location in WBAN to minimize network energy consumption in data transmission. This paper presents a complete data routing strategy for WBAN that implements a multi-objective particle swarm optimization (MO-PSO) based BNC placement scheme along with a cluster routing protocol. The proposed scheme uses a three-variable particle structure where the initial two variables specify the BNC coordinates, and the third variable determines the cluster head node. A multi-objective fitness evaluation operator estimates the fitness of MO-PSO particles in terms of network energy consumption and average bit error rate (BER) observed during a single round of data transmission. The developed model evolves into an optimal BNC location that minimizes network energy consumption and average BER simultaneously. A reduced BER further brings about a significant increase in network throughput rate. Simulation results depict that the proposed scheme achieves 67.1% energy efficiency with a 94% network throughput rate. The proposed scheme meets the requirement of a short operational period apart from minimal involvement of sensor nodes.

Energy-Efficient Device Activation, Rule Installation and Data Transmission in Software Defined DCNs

With the prosperity of cloud computing and video services, the demand for network resources has increased dramatically, leading to the remarkable growth in the amount of network energy consumption, a key factor restricting the development of data centers. Numerous existing works reduce network energy consumption by optimizing data transmission, but they ignore the energy consumption for data transmission preparation, such as activating devices and installing rules. In this paper, we jointly optimize device activation, rule installation and data transmission to minimize network energy consumption. Specifically, we first formulate the minimization problem of the energy consumption of device activation, rule installation, and data transmission. We then prove that it is NP-complete to get the optimal solution of the minimization problem, furthermore, we propose a heuristic algorithm to plan the path with minimum network energy consumption for each flow. The simulation results show that the energy consumption of our algorithm is close to the optimal solution solved by Gurobi, and our algorithm has lower complexity. Compared with the state-of-the-art algorithm, our algorithm always consumes less energy and has shorter flow completion time.

Distributed Optimization for Mobile Robots under Mobile Edge Computing Environment

Driven by the development of the Internet industry, mobile robots (MRs) technology has become increasingly mature and widely used in all walks of life. Since MRs are densely distributed in the network system, how to establish a reliable communication architecture to achieve good cooperation and resource sharing between MRs has become a research hotspot. In this respect, mobile edge computing (MEC) technology and millimeter wave (mmW) technology can provide powerful support. This paper proposes a mmW communication network architecture for distributed MRs in MEC environment. The mmW base station provides reliable communication services for MRs under the coverage of information cloud (IC). We design a joint resource and power allocation strategy aimed at minimizing network energy consumption. First, we use the Lyapunov optimization technique to transform the original infinite horizon Markov decision process (MDP) problem. Then, a semidistributed algorithm is introduced to solve the distributed optimization problem in the mmW network. By improving the autonomous decision-making ability of the mmW base station, the signaling overheads caused by information interaction are reduced, and information leakage is effectively avoided. Finally, the global optimal solution is obtained. Simulation results demonstrate the superiority of the proposed strategy.

Energy Saving Application of Variable Speed Auxiliary Pump Plus Hydro Turbine in Circulating Cooling Water System

The circulating cooling water system (CCWS) is a common industrial auxiliary system, and water pumps need to consume much energy to transport cooling water to distributed heat exchangers. Previous studies show that a pump network configuration with constant speed auxiliary pump plus hydro turbine (CSAP-T) plays a significant role in energy conservation. However, given the fluctuations in the production load and cooling water supply temperature, the flow demand of heat exchangers for cooling water varies. Under different working conditions, the CSAP-T scheme cannot supply the minimum cooling water flow required by all heat exchangers at the same time, thereby wasting energy. Therefore, this paper proposes a new fluid machinery network configuration called variable speed auxiliary pump plus hydro turbine (VSAP-T) and establishes a mathematical model of the total output power of the fluid machinery network in CCWS to minimize network energy consumption. To illustrate the effectiveness of the VSAP-T scheme, a real system is used as the research object, and both CSAP-T and VSAP-T schemes are used to optimize the system. When the working conditions are changed, the total output power of the system fluid machinery network can be conserved by 30% to 50%.

Field Computation in Media Exhibiting Hysteresis Using Hopfield Neural Networks

Assessment of local magnetization in objects exhibiting hysteresis is crucial to the accurate design and performance estimation for a wide range of electromagnetic devices. In the past, different efforts have been carried out to incorporate hysteresis models in field computation approaches. While those models varied in methodologies, they shared a common goal of offering an accurate and computationally efficient field computation tool. Recently, it was demonstrated that a two-dimensional vector hysteresis operator may be realized using a tri-node Hopfield neural network. The purpose of this paper is to offer a novel two-dimensional field computation approach in media exhibiting hysteresis that utilizes the aforementioned hysteresis operator. The approach is based on incorporating domain-to-domain interactions in the overall network energy formulation while employing typical Hopfield neural network energy minimization algorithms. Details of the proposed model, numerical simulations and comparisons with finite-element calculations are given in the paper.

Introducing an artificial neural network energy minimization multi-scale drag scheme for fluidized particles

Particles under fluidization conditions tend to clog and aggregate, and form meso–scale structures that significantly affect gas-solid transport phenomena. In the last decade, resolution of multi–scale particle structures has been attained by using advanced sub-grid models, such as the Energy Minimization Multi-Scale (EMMS) scheme. The current work aims to develop an ANN (Artificial Neural Network) to better resolve the effect of such structures. The ANN is developed, trained and validated using data generated by a custom-built FORTRAN code that solves the EMMS equations for a wide variety of gas-particle mixture properties (1 < dp* < 10). The model is tested in the simulation of a pilot-scale CFB carbonator. The difference in the predictions of the CFD model incorporating the ANN-EMMS drag scheme compared to the conventional EMMS drag scheme is 11.29% in terms of pressure drop (dP/dz) in average and less than 1% in terms of CO2 concentration at the exit of the reactor.

Virtualization Enabled Multi-Point Cooperation with Convergence of Communication, Caching, and Computing

NFV can effectively improve the flexibility of resource allocation and save energy consumption in networks. With NFV, the servers in communication networks can be virtualized as different types of VMs, including SIVMs and CIVMs. To deal with the ever-increasing traffic, in this article, we study the cooperation of servers at different locations. This can efficiently and flexibly utilize the communication, caching, and computing resources to minimize network energy consumption with QoS guarantee. Specifically, we first investigate the impacts of different types of VMs on network performance. Then, we propose a joint allocation of different types of VMs in different servers, and discuss the tradeoffs between SIVMs and CIVMs. Case studies are also provided to verify the feasibility of VM based multi-point cooperation. Finally, potential research directions are presented to shed light on the future study of VM cooperation.

Mobility-Aware Energy Optimization in Hosts Selection for Computation Offloading in Multi-Access Edge Computing

Multi-access edge computing (MEC) has been proposed as an approach capable of addressing latency and bandwidth issues in application computation offloading to extend the capabilities beyond the computational and storage limitations of mobile devices. However, there is a critical challenge in MEC to maintain the service continuity between the offloaded user application that is running on the MEC host and the mobile device when a user is moving from radio node to radio node. Furthermore, energy consumption of application computation offloading is an important consideration for MEC service providers in terms of operational costs. Therefore, we formulate the MEC host selection and user application migration problem as a shortest path problem of network energy minimization. We simulate the problem in a hierarchical MEC network deployment environment. We also propose the metric, computational intensity (CI), that can be used by MEC service providers to address the MEC host selection problem. Our results show that with the increment of CI, the selection of MEC hosts tends to move toward level 3 (central deployment) due to energy efficiency and then return to the deployment at level 1 (radio node level) due to latency constraint of the user application. We show that with high accuracy in predicting the user mobility and the available resources in the MEC network, latency- and mobility-aware MEC host selection and user application migration can be pre-calculated to improve response time and energy efficiency.

EETC: Energy Efficient Tree-Clustering in Delay Constrained Wireless Sensor Network

Joint employment of multi-hop data forwarding and mobile data-collector is a popular technique for efficient data collection in energy constraint and delay sensitive wireless sensor networks (WSNs). Existing tree-based data forwarding methods take the joint advantages of clustering and multi-hop data forwarding. However, the performances of all these approaches hardly meet the desired level of efficiency and thus, finding an efficient tree-clustering method to save network energy and extending the network lifetime is still a relevant issue in WSN. In this work, we study the problem of multi-hop data forwarding and propose a novel tree-clustering scheme named energy efficient tree clustering (EETC) which minimizes network energy consumption and extend the network lifetime while maintaining a pre-bound tour delay of the mobile sink. EETC uses a heuristic clustering algorithm named Optimal Generation of Clusters (OGENCL) in the clustering phase. In the proposed method, the number of relay hops between a cluster member node and the CH has been restricted to balance the network load. For further balancing the network load, we use an upper bound on the cluster size. The OGENCL problem is formulated as a Mixed Integer Linear Programming (MILP) Problem. Extensive simulations have been performed and compared with existing works to show the effectiveness of the proposed scheme for network load balance, energy consumption and network lifetime.

Secure Mobile Sink Node location in Wireless Sensor Network using Dynamic Routing Protocol

The important device in the Wireless Sensor Network (WSN) is the Sink Node (SN). That is used to store, collect and analyze data from every sensor node in the network. Thus the main role of SN in WSN makes it a big target for traffic analysis attack. Therefore, securing the SN position is a substantial issue. This study presents Security for Mobile Sink Node location using Dynamic Routing Protocol called (SMSNDRP), in order to increase complexity for adversary trying to discover mobile SN location. In addition to that, it minimizes network energy consumption. The proposed protocol which is applied on WSN framework consists of 50 nodes with static and mobile SN. The results havw shown in each round a dynamic change in the route to reach mobile SN, besides prolong the network lifetime in compare with static SN.

DIPS: Dual-Interface Dual-Pipeline Scheduling for Energy-Efficient Multihop Communications in IoT

The future Internet of Things (IoT) will enable Internet connectivity for a vast amount of battery-powered devices, which usually need to communicate with each other or to some remote gateways through multihop communications. Although ZigBee has become a widely used communication technology in IoT, Wi-Fi, on the other hand, has its unique advantages such as high throughput and native IP compatibility, despite its potentially higher energy consumption. With the development of IoT, more and more IoT devices are equipped with multiple radio interfaces, such as both Wi-Fi and ZigBee. Inspired by this, we propose a dual-interface dual-pipeline scheduling (DIPS) scheme, which leverages an activation pipeline mainly constructed by low-power ZigBee interfaces to wake up a data pipeline constructed by high-power Wi-Fi interfaces on demand, toward enabling multihop data delivery in IoT. The objective is to minimize network energy consumption while satisfying certain end-to-end delay requirements. Extensive simulations and prototype-based experiments have been conducted. The results show that the energy consumption of DIPS is 96.5% and 92.8% lower than that of the IEEE 802.11’s standard power saving scheme and a state-of-the-art pipeline-based scheme in moderate traffic scenarios, respectively.

The Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP) Method for Solving Wireless Sensor Networks Optimization Problems in Smart Grid Applications

Prior studies in Wireless Sensor Network (WSN) optimization mostly concentrate on maximizing network coverage and minimizing network energy consumption. However, there are other factors that could affect the WSN Quality of Service (QoS). In this paper, four objective functions that affect WSN QoS, namely end-to-end delay, end-to-end latency, network throughput and energy efficiency are studied. Optimal value of packet payload size that is able to minimize the end-to-end delay and end-to-end latency, while also maximizing the network throughput and energy efficiency is sought. To do this, a smart grid application case study together with a WSN QoS model is used to find the optimal value of the packet payload size. Our proposed method, named Multi-Objective Optimization Algorithm Based on Sperm Fertilization Procedure (MOSFP), along with other three state-of-the-art multi-objective optimization algorithms known as OMOPSO, NSGA-II and SPEA2, are utilized in this study. Different packet payload sizes are supplied to the algorithms and their optimal value is derived. From the experiments, the knee point and the intersection point of all the obtained Pareto fronts for all the algorithms show that the optimal packet payload size that manages the trade-offs between the four objective functions is equal to 45 bytes. The results also show that the performance of our proposed MOSFP method is highly competitive and found to have the best average value compared to the other three algorithms. Furthermore, the overall performance of MOSFP on four objective functions outperformed OMOPSO, NSGA-II and SPEA2 by 3%, 6% and 51%, respectively.

Centralized Energy-Efficient Multiuser Multiantenna Relaying in Next-Generation Radio Access Networks

This paper addresses the design of a multiuser relaying subnetwork within a cloud radio access network (C-RAN) from an energy-efficient perspective. In the relaying subnetwork, multiple source–destination pairs communicate with the assistance of multiple remote radio heads (RRHs) connected to the baseband unit pool. Exploiting the flexible centralized processing structure of C-RAN, where RRHs can be adaptively activated/deactivated, we formulate the problem as a quality-of-service (QoS) based network energy minimization problem via joint RRH selection and relaying matrix optimization. Since the resultant optimization problem is nonconvex and mathematically challenging, we propose an iterative solution based on the concept of the re-weighted $l_1$ norm, along with a block-coordinate descent type algorithm. The active RRHs are then determined in a single attempt by thresholding a group sparsity pattern associated with the set of all RRH relaying matrices. To circumvent a potentially undesirable condition, where the selected subset of RRHs fails to simultaneously satisfy all the destination users’ QoS levels, we conceive a UE admission control mechanism for overcoming the associated infeasibility problem. Our simulation results demonstrate the explicit benefits of the proposed design approach, which results in a significantly lower energy consumption of the relaying subnetwork than conventional cooperative relaying.

Application driven inverse type constraint satisfaction problems

Mathematical science in tight integration with the natural and life sciences provide sophisticated models and algorithms of knowledge acquisition. Overall rapid development of nowadays science and technologies is a direct consequence of such interdisciplinary developments. The same time, there exist particular research domains, e.g., the biological research domain, that appears as a semi-exact discipline (except of its genomics part). This is due to insufficient level of readiness of mathematical, physical and chemical sciences, being in power to provide the proper interpretation of the real size biological interrelations. Enrichment of mathematics to this new sophisticated level may be a long lasting but unavoidable process. Current series of studies is driven by a number of applied problems that are focused around the unique integrative technique of getting knowledge from fragmented data and descriptions. A large number of links connect these ideas to the problems of: artificial intelligence, intelligent information management, high performance computation, and other research domains. Our objective is in provision of integrated solutions of inverse type combinatorial problems that will help to sustain a set of applied problems. The base set of applied problems we consider involves the radiation therapy planning, the wireless sensor network integrated-connectivity-coverage protocols with its decentralized management, and the network tomography scenario devoted to the issue of energy minimization in networks. As we will see the necessary mathematical technique of solving these problems is focused around the model description by the sets of constraints and relations, by integration of the partial knowledge about these models. Two scenarios have been considered. One is based on the use of projections and their interpretation, and the second is based on the local neighbourhood analysis (generic projections). Given projections and/or neighbourhoods (in inverse manner), it is to analyse the consistency issue; to know the properties of the set of solutions; and to reconstruct one or all solutions of these problems. In mathematical level the problem is related to the well-known open problems such as the Berge’s hypothesis about the simple hypergraphic degree sequences. The technique to be used in investigations involves Boolean domain studies and the n-cube geometry, Lagrangean relaxation together with integer linear programming, Minkowski geometry, Voronoi diagrams, and the Constraint satisfaction mathematics. Prototype solutions and demonstrations to the mentioned applied problems will be provided.

Delay Aware Resource Management for Grid Energy Savings in Green Cellular Base Stations With Hybrid Power Supplies

Base stations equipped with resources to harvest renewable energy are not only environment-friendly but can also reduce the grid energy consumed, thus bringing cost savings for the cellular network operators. Intelligent management of the harvested energy can further increase the cost savings. Such management of energy savings has to be carefully coupled with managing the quality of service so as to ensure customer satisfaction. In such a process, there is a trade-off between the energy drawn from grid and the quality of service. Unlike prior studies which mainly focus on network energy minimization, this paper proposes a framework for jointly managing the grid energy savings and the quality of service (in terms of the network latency), which is achieved by downlink power control and user association reconfiguration. We use a real BS deployment scenario from London, U.K., to show the performance of our proposed framework and compare it against existing benchmarks. We show that the proposed framework can lead to around 60% grid energy savings as well as better network latency performance than the traditionally used scheme.