High Power Nodes Research Articles

Enhancing energy utilization for high power node multicasting in wireless sensor networks

In multicasting packets of data from a node will be sent to a group of receiver nodes at the same time. Multicasting lowers transmission costs. Energy conservation is critical to a sensor network’s long-term viability. Sensor networks have limited and non-replenishable energy supplies, maximizing network lifetime is crucial in sensor nodes. As a result, clustering has become one of the popular methods for extending the lifetime of an entire system by integrating information at the cluster head. Cluster head (CH) selection is the important serving node in each cluster in the Wireless sensor networks (WSN). This paper introduces a High Power Node (HPN) multicasting approach which embeds a cluster of sink node data in packet headers to allow receiver for utilizing a approach for transferring multicast packet data via the shortest paths. The proposed Energy efficient multicasting cluster based routing (EEMCR) protocol utilized high power nodes, which shall play a critical role in minimal energy usage. The implementation findings demonstrate that, when compared with the previous methodologies, the suggested algorithm has enhanced in terms of packet delivery ratio (PDR), End to end delivery rate, efficiency and achieves low energy consumption. The proposed EEMCR obtain 95% efficiency. The results are then compared to other existing algorithms to determine the superiority of the proposed methodology.

Contextual Cluster-Based Glow-Worm Swarm Optimization (GSO) Coupled Wireless Sensor Networks for Smart Cities.

The cluster technique involves the creation of clusters and the selection of a cluster head (CH), which connects sensor nodes, known as cluster members (CM), to the CH. The CH receives data from the CM and collects data from sensor nodes, removing unnecessary data to conserve energy. It compresses the data and transmits them to base stations through multi-hop to reduce network load. Since CMs only communicate with their CH and have a limited range, they avoid redundant information. However, the CH's routing, compression, and aggregation functions consume power quickly compared to other protocols, like TPGF, LQEAR, MPRM, and P-LQCLR. To address energy usage in wireless sensor networks (WSNs), heterogeneous high-power nodes (HPN) are used to balance energy consumption. CHs close to the base station require effective algorithms for improvement. The cluster-based glow-worm optimization technique utilizes random clustering, distributed cluster leader selection, and link-based routing. The cluster head routes data to the next group leader, balancing energy utilization in the WSN. This algorithm reduces energy consumption through multi-hop communication, cluster construction, and cluster head election. The glow-worm optimization technique allows for faster convergence and improved multi-parameter selection. By combining these methods, a new routing scheme is proposed to extend the network's lifetime and balance energy in various environments. However, the proposed model consumes more energy than TPGF, and other protocols for packets with 0 or 1 retransmission count in a 260-node network. This is mainly due to the short INFO packets during the neighbor discovery period and the increased hop count of the proposed derived pathways. Herein, simulations are conducted to evaluate the technique's throughput and energy efficiency.

Dynamic Resource Orchestration for Service Capability Maximization in Fog-Enabled Connected Vehicle Networks

Technological advances in fog computing are precipitating an evolution in conventional vehicle networks to a new paradigm called fog-enabled connected vehicle networks (FCVNs). FCVNs provide communication efficiency for ensuring safe transportation through the massive Internet of vehicles. In FCVNs, massive vehicles tend to associate with roadside units and high power nodes, which act as fog nodes (FNs), when they have a good channel quality and/or popular contents. This circumstance may lead to a load imbalance among the FNs. This problem significantly decreases the resource utilization efficiency and service capability of the networks. In this article, we propose a dynamic resource orchestration (DRO) scheme to harmonize resource allocation for connected vehicles by migrating the offloaded services among FNs. A graph-theoretic approach is utilized to transform the FCVN into a directed graph model, where the maximum resource reduction obtained by service migrations is considered the weight of the link between every two FNs. Subsequently, the maximum weight matching solution is used to determine optimal pairs of FNs for migrating services to maximize network resource utilization. Our simulation results reveal that the proposed DRO scheme achieves significant improvements in terms of service capability, throughput, and resource utilization efficiency as compared with existing algorithms.

A Comprehensive Survey on Radio Resource Management in 5G HetNets: Current Solutions, Future Trends and Open Issues

The 5G network technologies are intended to accommodate innovative services with a large influx of data traffic with lower energy consumption and increased quality of service and user quality of experience levels. In order to meet 5G expectations, heterogeneous networks (HetNets) have been introduced. They involve deployment of additional low power nodes within the coverage area of conventional high power nodes and their placement closer to user underlay HetNets. Due to the increased density of small-cell networks and radio access technologies, radio resource management (RRM) for potential 5G HetNets has emerged as a critical avenue. It plays a pivotal role in enhancing spectrum utilization, load balancing, and network energy efficiency. In this paper, we summarize the key challenges i.e. cross-tier interference, co-tier interference, and user association-resource-power allocation (UA-RA-PA) emerging in 5G HetNets and highlight their significance. In addition, we present a comprehensive survey of RRM schemes based on interference management (IM), UA-RA-PA and combined approaches (UA-RA-PA + IM). We introduce a taxonomy for individual (IM, UA-RA-PA) and combined approaches as a framework for systematically studying the existing schemes. These schemes are also qualitatively analyzed and compared to each other. Finally, challenges and opportunities for RRM in 5G are outlined, and design guidelines along with possible solutions for advanced mechanisms are presented.

A Reinforcement Learning Approach for Interference Management in Heterogeneous Wireless Networks

<span lang="EN-US">Due to the increased demand for scarce wireless bandwidth, it has become insufficient to serve the network user equipment using macrocell base stations only. Network densification through the addition of low power nodes (picocell) to conventional high power nodes addresses the bandwidth dearth issue, but unfortunately introduces unwanted interference into the network which causes a reduction in throughput. This paper developed a reinforcement learning model that assisted in coordinating interference in a heterogeneous network comprising macro-cell and pico-cell base stations. The learning mechanism was derived based on Q-learning, which consisted of agent, state, action, and reward. The base station was modeled as the agent, while the state represented the condition of the user equipment in terms of Signal to Interference Plus Noise Ratio. The action was represented by the transmission power level and the reward was given in terms of throughput. Simulation results showed that the proposed Q-learning scheme improved the performances of average user equipment throughput in the network. In particular, </span><span lang="EN-US">multi-agent systems with a normal learning rate increased the throughput of associated user equipment by a whooping 212.5% compared to a macrocell-only scheme.</span>

Fractional Frequency Reuse with Enhanced Scheduling Strategies

Heterogeneous networks are used to meet both capacity and coverage demands in a cellular network. As the frequencies are reused due to the limited spectrum availability, there exists co-channel interference (CCI) between the high power and low power nodes in such networks apart from the CCI between high power nodes. Inter cell interference coordination (ICIC) is a big challenge towards heterogeneous network deployments since users located at the cell range expansion (CRE) area are more vulnerable to stronger interference caused by the signals from the high power nodes. To enhance capacity, achieve good user experience and improve spectral efficiency interference management techniques like fractional frequency reuse (FFR) with enhanced scheduling strategies and ICIC using expectation maximization algorithm are proposed to mitigate the effects of the CCI. The resource allocation in FFR is done for the users in the cell center (full reuse) and CRE (partial reuse) regions based on the optimum post processing signal to interference plus noise ratio threshold with different schedulers in the two regions. It is shown that a particular combination shows the best throughput performance for the proposed enhanced FFR scheme.

An Energy Efficient Resource Allocation Scheme Based on Cloud-Computing in H-CRAN

Compared with the cloud radio access network (C-RAN), heterogeneous C-RAN with the high-power node entity which separates the control and broadcast functionalities from the baseband processing unit (BBU) pool. It makes the user access, resource allocation, load balancing more flexible, which also makes the intralayer interference and interlayer interference more complex. Therefore, the heavy inverse operations for dense matrices and the complicated power allocation algorithms in beamforming perform large floating-point calculations per second in BBU pool. As frequency resources grow scarcer, green communication with high energy efficiency (EE) and low carbon emissions has raised significant concerns. In this paper, we focus on the EE advantages achieved by selectively cooperative transmission and associated power consumption model. A joint channel matrix sparseness and normalized water-filling resource allocation algorithm is proposed and formulated to improve EE at different user density through mathematical derivation. By reducing the computation complexity of cooperative transmission, the proposed scheme decreases the digital baseband power consumption, which is more adaptable for tidal phenomenon. Simulation results show that the proposed algorithm can effectively reduce the energy consumption of baseband and improve the EE of the system.

A New Algorithm for High Power Node Multicasting in Wireless Sensor Networks

Multicast routing protocols normally depend upon the standard establishment of a multicast tree that needs maintenance of state information by individual nodes. In the case of dynamic networks with burst traffic, between the bursts of data, a long period of silence is expected. The multicast state preservation appends a huge amount of transmission and memory overload. This is no longer suitable for any applications. Therefore, this paper proposes a stateless high power node multicasting method utilizing a group of sink node data, which are implanted in packet headers, to facilitate recipients to fix the finest technique to send data packets in the multicast traffic. In order to eliminate expensive state information of the neighbor table, the HPN multicast protocol utilizes the awareness of the exact positions of sensor nodes. This protocol will be preferably appropriate for multicasting in active networks. The simulation results confirm that HPN multicast affords elevated success rates and little delay exclusive of the trouble of state information.

Delay Guaranteed Network Association for Mobile Machines in Heterogeneous Cloud Radio Access Network

In the heterogeneous cloud radio access network (H-CRAN), which consists of multiple access points (APs) providing smaller coverage and a high power node (HPN) providing ubiquitous coverage, the mobile machines can connect to multiple APs and HPN by coordinated multi-point transmission (CoMP) concurrently to achieve ultra-reliable and low-latency communication. However, the current network association (or priorly known as handovers), which only focuses on switching between two base stations, may not be an efficient scheme in H-CRAN. In this paper, we innovate a proactive network association mechanism by taking CoMP into consideration under the H-CRAN architecture. We consider two scenarios under the H-CRAN architecture: with and without the assistance of HPN in the network. By regarding APs/HPN in H-CRAN as resources that allocated to mobile machines, a novel proactive network association concept is proposed, and then generalized from one-to-one to multiple-to-multiple case. With the assistance of Lyapunov optimization theory , effective bandwidth, and capacity theory , we can prove that this proactive network association scheme can guarantee that the queueing delay performance and the delay violation probability can be both smaller than a corresponding upper bound. That is, both low-latency and ultra-reliable communication can be guaranteed. We also conduct experiments by using real trace from taxis movement data to verify the analytical results. Our results suggest the guidelines to design the proactive network association scheme in H-CRAN.

Joint power allocation and relay selection strategy for 5G network: a step towards green communication

Green communication has emerged as the most important concept for the next generation networks. Along with improved data rate and capacity, the upcoming 5G networks aim at improving energy efficiency without compromising on the user experience. In this paper, we have used amplify and forward relays in the heterogeneous network topology consisting of low power and high power nodes. A three layered system model for power optimization is discussed using a relay selection strategy for power optimization with the aim to improve energy efficiency of the network. Further, we have used Hidden Markov Model for training and maintaining of base station, relay and SCA with the aim of probabilistic power allocation to client nodes in order to solve the power optimization problem. We have also used adaptive modulation schemes for lowering the power consumption of the network to meet our goal of green communication for the next generation network.

Secrecy-Optimized Resource Allocation for Device-to-Device Communication Underlaying Heterogeneous Networks

Device-to-device (D2D) communications have recently attracted much attention for the potential capability to improve spectral efficiency (SE) underlaying the existing heterogeneous networks (HetNets). Due to no sophisticated control, D2D-worked user equipments (DUEs) themselves cannot resist eavesdropping or security attacks. It is urgent to maximize the secure capacity for both cellular users and DUEs. This paper formulates the radio resource-allocation problem to maximize the secure capacity of DUEs for D2D communication underlaying HetNets, which consist of high-power nodes (HPNs) and low-power nodes (LPNs). The optimization objective function with transmit bit rate and power constraints, which is nonconvex and hard to directly derive, is first transformed into a matrix form. Then, the equivalent convex form of the optimization problem is derived according to Perron–Frobenius theory. A heuristic iterative algorithm based on the proximal theory is proposed to solve this equivalent convex problem through evaluating the proximal operator of the Lagrange function. Numerical results show that the proposed radio resource-allocation solution significantly improves the secure capacity with a fast convergence speed.

Clustering in Wireless Sensor Networks- A Survey

Increased demand of Wireless Sensor Networks (WSN) in various applications has made it a hot research area. Several challenges imposed which include energy conservation, scalability, limited network resources etc. with energy conservation being the most important. Clustering improves the energy efficiency by making high power nodes as cluster heads (CHs) which reduces the chance of energy depletion of nodes. Scalability, fault tolerance, data aggregation, energy efficiency are some of the main objectives of clustering. This paper discusses various challenges associated with clustering and different methods or techniques developed to overcome these challenges. Various clustering approaches have been summarized and few prominent Quality of service (QoS) based clustering routing protocols for WSN have been identified. Comparison of these approaches and protocols is discussed based on some parameters.

Identification of multi-spreader users in social networks for viral marketing

Identifying high spreading power nodes is an interesting problem in social networks. Finding super spreader nodes becomes an arduous task when the nodes appear in large numbers, and the number of existing links becomes enormous among them. One of the methods that is used for identifying the nodes is to rank them based on k-shell decomposition. Nevertheless, one of the disadvantages of this method is that it assigns the same rank to the nodes of a shell. Another disadvantage of this method is that only one indicator is fairly used to rank the nodes. k-Shell is an approach that is used for ranking separate spreaders, yet it does not have enough efficiency when a group of nodes with maximum spreading needs to be selected; therefore, this method, alone, does not have enough efficiency. Accordingly, in this study a hybrid method is presented to identify the super spreaders based on k-shell measure. Afterwards, a suitable method is presented to select a group of superior nodes in order to maximize the spread of influence. Experimental results on seven complex networks show that our proposed methods outperforms other well-known measures and represents comparatively more accurate performance in identifying the super spreader nodes.

FISH: Finite Projective Plane Based Interference Cancellation for Slotted ALOHA

Due to the large number of light weight, low power devices in the future Internet, an efficient distributed multiple access protocol that can support a small fixed number of machines with continuous traffic along with a large variable number of devices with sporadic traffic is needed. The traffic in the future Internet has fundamentally different requirements: first, the accessing devices are of low power, which limit the transmission range; secondly, the delay is very stringent; thirdly, the number of active devices is time varying. In this paper, we propose a finite projective plane (FPP) based multiple access protocol (FISH) to meet such requirements. A low power node can transmit the replicas of a data packet in multiple time slots to achieve the same range of transmission as a high power node at one time. The initialization time slot of a frame allows for the estimation of the number of active nodes and the adjustment of the size of the frame to maximize the throughput. The interference cancellation and conflict resolution scheme of the FISH protocol is based on the theory of FPP. By using the property that the union of the sets of the FPP sharing one common point is a complete set, we are able to extract each of the individual user signals. One distinguishing feature of the FISH protocol is the amplification of the signal-to-noise ratio (SNR), which can either extend the transmission range or improve the bit error rate without sacrificing the throughput. In the performance evaluation, we demonstrate that the throughput is identical to the Slotted Aloha, while the SNR is magnified m times, where m is the order of the FPP. The proposed FISH protocol is of great use in the machine-to-machine communications as well as traditional cellular and satellite systems.

Multiple Connectivity and Spectrum Access Utilisation in Heterogeneous Small Cell Networks

In the context of heterogeneous and small cell networks, users will have the possibility to connect to multiple radio access (RA) carriers that will be available by a dense deployment of RA infrastructure consisting of high-power and low-power access nodes. Determining which RAs a user should be associated with and select from, for its downlink transmissions, depends on the long-term and short-term data rates that these RAs may offer to the user. In this study the multi-RA association and utilisation is decomposed into a multi-RA to user association problem that assigns multiple RAs to users, and a multi-RA selection problem that determines which of the assigned RAs should be used at any time for the user transmissions. As a solution to the first problem, we propose a distributed dual-based spectrum access scheme (DSA) that considers multi-connectivity, whilst, the second problem is solved by means of a heuristic multi-RA selection scheme that utilise different multi-radio transmit diversity (MRTD) schemes while taking into account different inter-cell interference coordination (ICIC) schemes. Our two-step approach is evaluated by means of simulations which demonstrate cell-edge user throughput performance improvements that exceed 100 % when the multi-connectivity DSA is employed. Further significant user rate and energy efficiency improvements up to 69 and 38 % respectively can be achieved when MRTD is combined with ICIC.

Coverage and Rate Analysis in Heterogeneous Cloud Radio Access Networks With Device-to-Device Communication

The implementation of heterogeneous cloud radio access network (H-CRAN) architecture is faced with practical challenges, such as the capacity and time-delay limitations of the fronthaul links. This paper considers the use of device-to-device (D2D) communication to offload the remote radio heads (RRHs) located in the coverage region of high-power nodes (HPNs). We propose an H-CRAN with non-uniformly deployed D2D communication, in which D2D links are only utilized outside a specified distance from any HPN. Based on the analytical framework provided in this paper, the coverage and the average ergodic rate of a typical user equipment (UE) are characterized. Through defining the exclusion area appropriately, the proposed non-uniform D2D deployment can achieve performance improvement compared with uniform D2D deployment. In addition, to account for the capacity constraint of fronthaul, we characterize the average traffic delivery latency experience by a typical UE when served by RRHs as a quality-of-service metric. Our results show that for a lower fronthaul capacity regime, the proposed non-uniform D2D deployment achieves lower average traffic delivery latency compared with both the uniform D2D deployment and the pure H-CRAN scenarios.

ECBK: Enhanced Cluster Based Key Management Scheme for Achieving Quality of Service

Wireless sensor networks consist of many small nodes with distributing devices to monitor conditions at different locations. Usually wireless sensor nodes are sprinkled in a sensor field grouping limited areas. This paper highlights the Enhanced Cluster Based Key management (ECBK) protocol to achieve secure data delivery based on clustering mechanism. This protocol gives more importance to Cluster Coordinator node, which is used to coordinate the members and provide protective communication among the sensor nodes to enhance reliability. In Enhanced Cluster Based Key management two types of nodes are deployed. The high power nodes form clusters with surrounding nodes to enable the routing process without interference. This paper introduces ECBK protocol that balances the load among the clusters, achieves high throughput, end to end delay will be reduced, routing overhead also reduced and also it prolongs the network lifetime. Simulation results show that the presence of high transmission nodes reduces the delay, load balance, routing overhead, and enhances the throughput increased by 45% compared to other similar methods.

Energy-Efficient Resource Assignment and Power Allocation in Heterogeneous Cloud Radio Access Networks

Taking full advantages of both heterogeneous networks (HetNets) and cloud access radio access networks (CRANs), heterogeneous cloud radio access networks (H-CRANs) are presented to enhance both the spectral and energy efficiencies, where remote radio heads (RRHs) are mainly used to provide high data rates for users with high quality of service (QoS) requirements, while the high power node (HPN) is deployed to guarantee the seamless coverage and serve users with low QoS requirements. To mitigate the inter-tier interference and improve EE performances in H-CRANs, characterizing user association with RRH/HPN is considered in this paper, and the traditional soft fractional frequency reuse (S-FFR) is enhanced. Based on the RRH/HPN association constraint and the enhanced S-FFR, an energy-efficient optimization problem with the resource assignment and power allocation for the orthogonal frequency division multiple access (OFDMA) based H-CRANs is formulated as a non-convex objective function. To deal with the non-convexity, an equivalent convex feasibility problem is reformulated, and closedform expressions for the energy-efficient resource allocation solution to jointly allocate the resource block and transmit power are derived by the Lagrange dual decomposition method. Simulation results confirm that the H-CRAN architecture and the corresponding resource allocation solution can enhance the energy efficiency significantly.

Coordinated multi-point transmission for relaxation of self-backhauling bottlenecks in heterogeneous networks

The heterogeneous deployment of high-power macro cells and low-power nodes (LPNs) is now widely acknowledged as an essential requirement towards meeting the continued demand for mobile data capacity. The selection of the optimum backhaul solution for the LPNs obliges operators to consider not only the capacity of the backhaul but also other key factors so as to fully leverage the benefits provided by LPNs: the cost of the backhauling may limit the density of LPN deployments and the backhaul configuration requirements impact on the flexibility of LPN deployment. To that end, self-backhauling of LPNs via the existing macro radio access network (RAN) provides an attractive solution, particularly for deployment scenarios that are very cost-sensitive and/or require high flexibility. However, use of self-backhauling usually makes backhaul as a bottleneck due to the a) limited bandwidth allocated for legacy macro RAN, b) the need to share resources with macro user equipment (UE), and c) the high-intercell interference particularly in the macro cell edge. In this paper, we provide an overview of self-backhauled LPNs and investigate possible performance enhancements through the use of coordinated multi-point (CoMP) transmission to relax the downlink backhaul capacity bottleneck for self-backhauled LPNs. To that end, we carry out analytical studies for a practical limited-feedback CoMP technique and numerically verify the derived capacity outage expressions. Furthermore, we implement a simulation study for an exemplary heterogeneous network deployment in a realistic radio propagation environment. The results of the studies demonstrate that significant spectral efficiency and throughput gains for the LPN backhaul are achievable through the use of selected CoMP technique under realizable feedback overhead, even under feedback bit error. The achieved relaxation in the backhaul bottleneck is observed providing improved performance for the UEs served by the LPNs. Furthermore, more resources will be available for macro UEs leading to overall performance gains compared to the case without CoMP.

Recent Advances in Underlay Heterogeneous Networks: Interference Control, Resource Allocation, and Self-Organization

By deploying additional low power nodes (LPNs) within the coverage area of traditional high power nodes (HPNs) and bringing them closer to users, underlay heterogeneous networks (HetNets) can significantly boost the overall spectral efficiency (SE) and energy efficiency (EE) through a full spatial resource reuse. Considering that the severe intra-tier interference among dense LPNs and inter-tier interference between LPNs and HPNs are challenging the successful rollout and commercial operations of underlay HetNets, a great emphasis is given towards advanced techniques that take interference control, radio resource allocation, and self-organization into account to enhance both SE and EE in this paper. The interference control techniques presented in this paper are classified as the spatial interference coordination at the transmitter and the interference cancelation at the receiver. For the radio resource allocation, the multi-dimensional optimization, cross-layer optimization, and cooperative radio resource management are comprehensively summarized. The self-configuration, self-optimization, and self-healing techniques for the self-organized underlay HetNets are surveyed. Furthermore, this paper outlines the potential open issues for underlay HetNets to improve SE and EE when combining with energy harvesting and cloud computing.