Redundant Packets Research Articles

A trust model for VANETs using malicious-aware multiple routing

Vehicular ad hoc networks (VANETs) enable multi-hop communication among vehicles, promoting information sharing and smarter collaborative driving. However, VANETs are facing several challenges due to the open wireless communication environment. Attackers may maliciously drop or alter packets so that the receiver cannot obtain correct information. In addition, the high mobility of vehicles may lead to link failures, consequently resulting in packet loss. In this paper, we propose a multipath-based trust model (MPTM), in which the reliability of packet transmission is guaranteed by data redundancy and the detection of potential attackers is achieved by trust evaluation. Specifically, we present a route discovery mechanism to find multiple routes that avoid potential attackers, which reduces the risk of attacks on redundant packets. The receivers identify correct information based on two factors including content consistency and route information. An attacker detection module is presented to evaluate the trustworthiness of vehicles involved in packet transmission and vehicles with trust value below a threshold are detected as attackers. We conducted extensive experiments using OMNeT++ simulation platform, considering various attack scenarios. Experiment results show that MPTM can reach 90% packet delivery ratio and effectively detect attackers in terms of 90% detection precision.

Research on intelligent lighting Bluetooth Mesh networking control based on AODV

This paper discusses the deficiencies of the flooding broadcast mechanism in Bluetooth Mesh networks and puts forth an enhanced routing protocol Olq-AODV. The limitations of traditional AODV routing protocols are particularly evident in smart light networks with high node density, where packet redundancy and link quality judgment are crucial. To this end, Olq-AODV introduces an optimized link quality judgment mechanism in the process of route discovery, considering delay, hop count, and cascade PRR, with the aim of improving the transmission efficiency and reliability of the network. The test results demonstrate that the Olq-AODV routing protocol outperforms the traditional flooding algorithm in terms of network transmission delay and packet delivery rate, and effectively improve the performance of Bluetooth Mesh networks.

Improved MPR Selection Algorithm-Based WS-OLSR Routing Protocol

Vehicle Ad Hoc Networks (VANETs) have become a viable technology to improve traffic flow and safety on the roads. Due to its effectiveness and scalability, the Wingsuit Search-based Optimised Link State Routing Protocol (WS-OLSR) is frequently used for data distribution in VANETs. However, the selection of MultiPoint Relays (MPRs) plays a pivotal role in WS-OLSR's performance. This paper presents an improved MPR selection algorithm tailored to WS-OLSR, designed to enhance the overall routing efficiency and reduce overhead. The analysis found that the current OLSR protocol has problems such as redundancy of HELLO and TC message packets or failure to update routing information in time, so a WSOLSR routing protocol based on improved-MPR selection algorithm was proposed. Firstly, factors such as node mobility and link changes are comprehensively considered to reflect network topology changes, and the broadcast cycle of node HELLO messages is controlled through topology changes. Secondly, a new MPR selection algorithm is proposed, considering link stability issues and nodes. Finally, evaluate its effectiveness in terms of packet delivery ratio, end-to-end delay, and control message overhead. Simulation results demonstrate the superior performance of our improved MR selection algorithm when compared to traditional approaches.

Efficiency of BIER Multicast in Large Networks

Bit Index Explicit Replication (BIER) has been introduced by the IETF to transport IP multicast (IPMC) traffic within a BIER domain. Its advantage over IPMC is improved scalability regarding the number of multicast groups. However, scaling BIER to large networks is a challenge. To that end, receivers of a BIER domain are assigned to smaller subdomains. To deliver an IPMC packet over a BIER domain, a copy is sent to any subdomain with a receiver for that packet. Consequently, some links may carry multiple copies of the same IPMC packet, which contradicts the multicast idea. In this paper, we propose and compare various algorithms to select subdomains for BIER in order to keep the overall BIER traffic low despite multiple packet copies. We apply them to investigate the traffic savings potential of IPMC and BIER relative to unicast under various conditions. We show that the traffic savings depend on network topology, network size, and the size of the multicast groups. Also the extra traffic caused by BIER depends on these factors. In spite of some redundant packets, BIER can efficiently reduce the overall traffic in most network topologies. Similarly to IPMC, BIER also avoids heavily loaded links. Finally, we demonstrate that BIER subdomains optimized for failure-free conditions do not cause extensive overload in case of single link failures.

Emergency message broadcasting scheme based on V2V and V2I

As an important part of the advanced Intelligent Traffic System (ITS) in the Internet of Vehicles (IoV), Vehicle to everything (V2X) communication technology provides reliable end-to-end connection and efficient packet transmission. Currently, the main challenge is design and implementation of the multi-hop broadcast protocol. In particular, the network topology is constantly changing because of the high-speed vehicle and diversified road structure, which troubles the selection of forward nodes. Therefore, we propose an Emergency Message Broadcast Protocol (EMBP). In this method, we select the optimal relay node according to the directionality and mobility, channel fading, and link connectivity and apply a suboptimal relay node selection mechanism to guarantee the reliability of broadcast. Simulation results show that EMBP is more efficient and stable than part classical protocols. Due to the complex communication environment of urban roads, to better improve the reliability of emergency message transmission, a broadcasting scheme Roadside Unit (RSU) Emergency Message Broadcast Protocol (REMBP), based on Vehicle to Vehicle (V2V) and Vehicle to Road (V2R) hybrid transmission of emergency messages is proposed on the basis of EMBP. Compared with EMBP, REMBP has better performance in terms of delivery rate, number of forwarding hops, end-to-end delay, average number of collisions, number of packet redundancy, and number of retransmissions.

A Continuous Object Tracking Scheme Based on Two-Stage Prediction in Industrial Internet of Things

Due to the poisonousness, explosiveness, and diffuseness of some continuous objects (e.g., toxic gas, nuclear radiation, industrial dust), continuous object tracking has a pivotal role in protecting the safety of the people, especially in hazardous industries. To improve production safety, the Industrial Internet of Things (IIoT) has become a promising technology for continuous object tracking. However, IIoT can hardly satisfy the requirements of both energy-efficiency and tracking accuracy due to diffusion characteristics, redundant packets, unnecessary awakened nodes, etc. To address these challenges, we propose a Two-stage Continuous Object predictive Tracking scheme based on a State Transition Model (TCOT-STM). First, the predictive tracking process of TCOT-STM is partitioned into two stages to determine wake-up regions where the future continuous objects are located. Considering the high diffusion speed in the tracking process, stage 1 tracking is designed by communication range calibration and global wake-up region establishing. To eliminate the redundant boundary nodes in the tracking process, stage 2 tracking is designed by inter-cluster gap eliminating, virtual node generating, and local wake-up region establishing. Then, a state transition model based on finite state machines is designed to awaken nodes selectively. Finally, with the state transition model and the wake-up regions determined by two-stage tracking, the potential boundary nodes are proactively awakened for predictive tracking. Simulation results demonstrate that the proposed TCOT-STM can reduce energy consumption and communication cost while improving tracking accuracy.

EdAR: An Experience-Driven Multipath Scheduler for Seamless Handoff in Mobile Networks

Multipath TCP (MPTCP) improves the bandwidth utilization in wireless network scenarios, since it can simultaneously utilize multiple interfaces for data transmission. However, with the fast growth of mobile devices and applications, link interruptions caused by handoffs still lead to drastic performance degradation in such scenarios. Typically, a series of packet losses on part of the links will block the transmission of the entire connection when handoff occurs. This paper proposes an Experience-driven Adaptive Redundant packet scheduler (EdAR) for MPTCP, aiming at achieving seamless handoffs in mobile networks. EdAR enables flexibly scheduling redundant packets with an experience-driven learning-based approach in the face of drastic network environment changes for multipath performance enhancement. To enable accurate learning and prediction, both the network environment and the best course of actions are jointly learned via a Deep Reinforcement Learning (DRL) agent, which we design with a hybrid structure to deal with the complexity of system states. Furthermore, both offline and online learning are utilized to allow the agent to adapt to different and changing network environments. Evaluation results show that EdAR outperforms the state-of-the-art MPTCP schedulers in most network scenarios. Specifically in mobile networks with frequent handoffs, EdAR brings 2× improvement in terms of the overall goodput.

Neural-Network-Assisted Packet Accelerators for Internet of Things Network Systems

Major device nodes within the internet of things (IoT) system collects and store information in bit forms of 0’s and 1’s regardless of its repetition. The nodes do not possess the capability of processing redundant data information except for outright rejection/replacement of packets of similar sizes. This becomes a research problem since high volumes of packet redundancy are prevalent owing to repetitive information. Many optimal solutions have been provided to reprocess redundant packets and to store them in edge server for accessibility by other connected IoT systems and networks servers. To do so, major IoT platforms implements tier-based network layers which primarily aid seamless communication among nodes. These network layers perform near-similar tasks of guaranteeing packet sensing and exchange although at often-higher energy requirement. To mitigate the energy concerns, packets are clustered and compressed, allowing exchange of essential information only. But the approach continues to present heavy packet-losses and/or redundancies. In this paper, two-tier layered network | where packet exchange is conducted at the top layer in order to lower energy consumption and promote system-reliability is investigated. All packets are first segregated into multiple clusters using the Voronoi cell-based correlation cluster formation (VC3F) technique. Cluster heads (CHs) are identified by their multi-path (M-Score) value, thus, assuming sole responsibility of redundant packet removal within each cluster. The redundant packets are then moved to the edge-tier layer using optimized multi-objective flower pollination (MO-FPO) routing, and finally processed using hybrid models of novel fast-fully connected neural network (F2CNN) accelerator and Lempel-Ziv-Welch (LZW) data compression. The F2CNN and LZW models are harmonized to further collectively explore potential benefits of both models. These benefits include the neural capability to work on the sensitivity level of the packets determined by the packet classification and validation approaches. The system is evaluated with detailed experimental investigations where higher system throughput, packet delivery ratio (PDR), end-to-end delay and system reliability are corroborated.

APAP: An adaptive packet-reproduction and active packet-loss data collection protocol for WSNs

In lossy, large-scale wireless sensor networks (WSNs), data collection protocols face the challenge of balancing expected data transmission reliability and limited resources of sensors. Packet redundancy strategies have been widely employed to guarantee transmission reliability. However, these schemes often result in energy waste, especially in sink-neared hotspot districts. We propose the APAP to balance the two competing interests in energy-constrained WSNs. APAP mainly consists of two parts: adaptive packet-reproduction routing and active packet-loss mechanism. In the first part, we design a compensation function to compensate for packet losses and employ it in the packet-reproduction routing. Through available network parameters, such a function offers an optimized number of redundant packets. In the second part, we devise a novel distributed packet-loss mechanism to detect and intercept redundant packets in the hotspot area. Hotspot nodes record the simple traffic information in their vicinity on a local micro-table, these nodes transform their mode based on the records to reach a basic consensus with one-hop neighbors, they then collaboratively intercept excess packets from outside, thus the injection traffic can be mitigated. Such an interception area has the potential to be expanded and prolonged as the redundancy becomes more severe. Our mechanism imposes low-complexity requirements on general devices. Moreover, thorough mathematical analyses are offered for the performance of APAP. Simulation results indicate that with high reliability, the maximum node energy consumption is reduced by 44.7% to 66.3% compared to conventional protocols in one round. Besides, the network lifetime is prolonged by 91.1% to 200%.

Evaluating Latency in Fifth-Generation Vehicle-to-Everything Communications using Adaptive Neuro-Fuzzy Inference System Model

The fifth-generation new radio technology (5G NR) introduces improved functions to the air interface. In addition, the 5G NR non-standalone (NSA) will operate with long-term evolution, enabling vehicle-to-everything communications (V2X) for improved infotainment services. V2X includes four main classes of communications: vehicle-to-vehicle, vehicle-to-infrastructure, vehicle-to-pedestrian devices, and vehicle-to-network. However, the stringent transmission frequency, latency, and throughput requirements of infotainment applications constrain the transmitting packets of 5G-V2X-based NSA in highway scenarios. In this paper, the latency is improved by preventing the physical layer of gNodeB and the user equipment (UE) from sending redundant packets for service in a highway scenario. The proposed approach adopts an adaptive neuro-fuzzy inference system (ANFIS), a powerful modeling technique based on artificial neural networks ,and a fuzzy inference system. The performance of ANFIS is compared with that of the traditional 5G V2X NSA architecture in a simulation study using Voice over Internet Protocol (VoIP) traffic. The delays, throughputs, and packet losses of both architectures are determined in radio link control (RLC) and VoIP applications. The switch-modes, signal-to-interference-noise ratios (SINRs), hybrid automatic repeat request (HARQ) error rate, channel quality indicators (CQIs), served blocks, and transmission-state of gNodeB are computed for the two architectures for device-to-device (D2D), uplink (UL) and downlink (DL) traffic directions. The simulation results show comparable SINRs, CQIs, served blocks ,andswitchmodes in both scenarios, but the presented ANFIS model significantly outperforms the traditional architecture in delay by 66% in D2D, 29% in UL, 25% in DL, and packet loss by 21% in UL in RLC, the HARQ error rate by 9% in D2D, 30% in UL, 95% in DL, transmission-state in gNodeB by 29%, and the delay by 4% for UEs, and frame loss by 90%for UE in VoIP.

Investigation of Efficient Backup Tecniques To Reduce Late In Cloud Systems: A Modeling Study

From the formation stages of the information society to the present day, new developments have been experienced in many areas such as data formation, processing, storage and sharing of data in electronic environments. In this context, many new technological concepts have been included in our lives. When it comes to data storage environments, the concept of "Cloud Computing", which has been developing and increasing in popularity in recent years, comes to our minds. Cloud computing, with its known definition, is an internet-based technology service that provides access to the data stored on its servers at any time through internet access and allows users to benefit from the services offered by the system to the extent they want. While the advantages of cloud computing systems such as low cost, high performance, not requiring physical materials and being flexible can be mentioned, it is also emphasized that it brings many problems in terms of system. The most important of these problems; the lack of resources in cloud computing service providers, causing unpredictable delays in server response times. Based on all this information, this research is structured on the development of solution processes on these delays. In this sense, it is possible to consider three areas of cloud infrastructure; cloud computing, distributed storage and streaming communications. One of the first solutions that comes to mind is duplicating a task on multiple machines and waiting for the earliest copy to finish can reduce service latency; but intuitively, it costs additional computing resources and increases the queue load on servers. In this context, the effect of redundancy on the tail will be analyzed in the first part of the research. As a solution proposal, in this section, it will be discussed that service delays and queue load can be reduced by using the replication method, thus making the system more efficient. In a similar way, by requesting more than one copy of a file and waiting for any of them to arrive, the cloud storage requests will be accelerated for content download. In the second part of the research, generalization will be made from replication to coding and the (n, k) fork-join model will be studied to analyze the delay in accessing a storage system with (n, k) deletion code. This analysis will provide practical information that several users can access a content at the same time and provide faster service to the relevant users. Achieving low latency in streaming communication is even more difficult, as packets must be transmitted quickly and sequentially. Based on this structure, in the third part of the research, it is aimed to develop deletion codes to transmit redundant packet combinations and ensure smooth playback. Experimental modeling method was used as a research method in the study. In general, the aim of the research is to blend various mathematical tools from queuing, theory coding and renewal processes. It is foreseen that the techniques and insights to be developed with these dimensions of the research can be applied to other systems with stochastically changing components, and integrated studies can be made and contribute to the literature in this context.

Overflow management with self-eliminations

We study admission control with packet redundancy, where large data items, called superpackets, exceed some Maximum Transmission Unit (MTU) and therefore are broken into several smaller packets. It is assumed that each superpacket is comprised of k packets, and that a superpacket is considered useful if at least (1−β)k of its packets arrive at the receiving end, for some redundancy parameter β∈[0,1). Our goal is to maximize the total profit of useful superpackets, under an overloaded network, where we are forced to drop packets.Our starting point is an algorithm, called priority, which is based on assigning a random priority to each superpacket. This algorithm was shown to perform well when β=0, with and without a buffer. However, the performance of priority deteriorates with the increase of β, since it delivers too many packets for high priority superpackets. To tackle this issue, we propose an online algorithm which introduces randomized self-elimination of packets, called pse. When there is no buffer, we show that the competitive ratio of pse is better than the competitive ratio of priority, for the case where (1−β)3⋅ρmax≤1, where ρmax is the maximal burst size-router capacity ratio. For real-world values (ρmax≤1.5), pse outperforms priority for β≥0.14. We also present simulation results, with a buffer, that demonstrate the behavior of pse in comparison with priority and tail-drop. It is shown that pse performs much better than priority when β is large. In fact, it is shown that pse behaves at least as good as both algorithms.

Adaptive link quality routing protocol for UASNs with double forwarding modes

Underwater Acoustic Sensor Networks (UASNs) has been used in various fields. However, UASNs is characterized by long delay, poor and time-varying link quality and unequal node distribution. Opportunistic routing (OR) uses broadcast forwarding to overcome poor link quality, but it increases delay compared to unicast forwarding, and most OR protocols cannot overcome routing voids. This paper proposes an Adaptive Link Quality Routing Protocol (ALQR) with double forwarding modes to solve these problems. This protocol innovatively selects the forwarding mode adaptively by link quality and uses distance vectors to construct candidate set to avoid routing voids and long detour. First, a new query algorithm is proposed to obtain the residual energy of neighbors and establish all distance vectors between sensor nodes and sink nodes, reducing the overhead of control packets. Distance vectors guide senders to determine the candidate set and avoid routing voids and long detours based on the shortest path principle. Second, an improved triangle metric is introduced to measure the accurate link quality. Based on this, a novel forwarding strategy combining unicast and broadcast is proposed. The unicast forwarding mode directedly select next hop based on route cost, which shortens the delay. Moreover, a new priority-based waiting-competition mechanism is proposed to determine the forwarding nodes in the broadcast forwarding mode, reducing redundant packets. The simulation results indicate that ALQR outperforms other routing protocols in terms of end-to-end delay and energy consumption when sacrificing little PDR. Specifically, delay is reduced by an average of 37.51% and 19.54% and energy consumption is reduced by an average of 35.01% and 16.41%, compared to DBR and DVOR.

Energy Proficient Reduced Coverage Set with Particle Swarm Optimization for Distributed Sensor Network

Retransmission avoidance is an essential need for any type of wireless communication. As retransmissions induce the unnecessary presence of redundant data in every accessible node. As storage capacity is symmetrical to the size of the memory, less storage capacity is experienced due to the restricted size of the respective node. In this proposed work, we have discussed the integration of the Energy Proficient Reduced Coverage Set with Particle Swarm Optimization (PSO). PSO is a metaheuristic global search enhancement technique that promotes the searching of the best nodes in the search space. PSO is integrated with a Reduced Coverage Set, to obtain an optimal path with only high-power transmitting nodes. Energy Proficient Reduced Coverage Set with PSO constructs a set of only best nodes based on the fitness solution, to cover the whole network. The proposed algorithm has experimented with a different number of nodes. Comparison has been made between original and improved algorithm shows that improved algorithm performs better than the existing by reducing the redundant packet transmissions by 18% ~ 40%, thereby increasing the network lifetime.

A Robust, Scalable, and Energy-Efficient Routing Strategy for UWSN Using a Novel Vector-Based Forwarding Routing Protocol

The Underwater Wireless Sensor Network (UWSN) is capable of supporting a wide range of low-data-rate acoustic sensor networks, as well as scalability and energy-efficient routing algorithms. Furthermore, because of the lower bandwidth and longer propagation delays, energy consumption is a major concern with underwater networks. Because radio transmissions are unstable in deep water, a UWSN often communicates via acoustic channels. UWSN features create constraints on data packet transmission and energy-efficient routing. The hardest challenge is creating an effective routing protocol for UWSNs that uses sensor node localization. There have been several routing algorithms reported for identifying nodes via a localization process. This paper proposes a novel vector-based forwarding (NVBF) and efficient depth-based routing (DBR) protocol to provide robust, scalable, and energy-efficient routing. Efficient DBR is a stochastic model that can survive even in acoustic channels with substantial transmission loss. An adjacent node table is created to decrease the energy consumption and end-to-end delay by limiting the adjacent node request generated every single time. The redundant packet transmission is reduced by creating a packet queue which also minimizes the energy. The NVBF algorithm uses a random waiting time to overcome the collision that occurs while sending the acknowledgment packets. Based on the previous sender’s depth and the depth of the own node, the DBR makes packet transmission decisions. The experiments are conducted using the NS3 simulator and the efficiency of the proposed approach is evaluated in terms of propagation delay, network lifetime, energy consumption, and delivery ratio. When compared to the existing techniques such as DBR, VBF, and energy-efficient DBR (EDBR), the proposed methodology offers improvement up to 40% and 30% in terms of propagation delay and delivery ratio.

A Reinforcement-Learning-Based Opportunistic Routing Protocol for Energy-Efficient and Void-Avoided UASNs

Underwater acoustic sensor networks (UASNs) have been used for various scenarios, such as marine exploration, and development of marine resources. However, due to the challenging underwater environment, UASNs suffers from high propagation delay, low transmission reliability and energy limitation, which pose significant obstacles to the delivery efficiency of data packets. What’s more, the sparse topology, the failure of node or link, and other factors can cause void holes, resulting in the considerable package retransmission and the low network reliability of UASNs. To this end, this paper proposed a reinforcement-learning-based opportunistic routing protocol (ROEVA) to reduce energy consumption as well as to improve the transmission reliability which also addresses the issue of void routing in underwater acoustic sensor networks. To seek optimal routing rules, a reward function based on reinforcement learning is proposed, where factors such as energy, delay, link quality, and depth information are all taken into account for appropriate routing decisions. Before forwarding the data packet, a two-hop availability checking function is defined, which can identify trap nodes and avoid routing holes. In addition, aiming to reduce packet redundancy and collisions, a waiting mechanism derived from opportunistic routing is proposed. According to the calculated Q-values, the waiting mechanism determines the priority list for packet forwarding. Evaluation results demonstrate that the proposed ROEVA protocol outperforms HHVBF, RCAR, QELAR and GEDAR in terms of energy efficiency, packet delivery ratio (PDR), average hop count, and end-to-end delay, which are analyzed by varying the number of nodes from 100 to 500.

An ICN-Based IPFS High-Availability Architecture

The Interplanetary File System (IPFS), a new type of P2P file system, enables people to obtain data from other peer nodes in a distributed system without the need to establish a connection with a distant server. However, IPFS suffers from low resolution efficiency and duplicate data delivery, resulting in poor system availability. The new Information-Centric Networking (ICN), on the other hand, applies the features of name resolution service and caching to achieve fast location and delivery of content. Therefore, there is a potential to optimize the availability of IPFS systems from the network layer. In this paper, we propose an ICN-based IPFS high-availability architecture, called IBIHA, which introduces enhanced nodes and information tables to manage data delivery based on the original IPFS network, and uses the algorithm of selecting high-impact nodes from the entitled network (PwRank) as the basis for deploying enhanced nodes in the network, thus achieving the effect of optimizing IPFS availability. The experimental results show that this architecture outperforms the IPFS network in terms of improving node resolution efficiency, reducing network redundant packets, and improving the rational utilization of network link resources.

Data Transmission Control Method of Electrical Equipment of Automobile Based on the 5G Communication Technology

Conflicts emerge when the data transmission occurs between electrical equipment in an automobile. Hence, transmission delays appear as issues. For this reason, a method, called data transmission control, for electrical equipment of automobiles based on the 5G communication technology is proposed in this paper. Firstly, the wavelet feature decomposition was utilized to partition the frequency spectrum, and thus the statistical characteristics of electrical equipment of automobiles concerning the transmitted data were obtained. Then, the high-order approximate distribution method was adopted to construct a channel for a 5G network data transmission. Afterward, the control logic structure of the data transmission was built, and the problem, called the conflict of the data transmission, was alleviated through concurrent data collection and processing methods. On this basis, coding coefficients of a constructed global coding matrix were selected to encode and transmit source information. Also, the number of redundant data packets at each layer was adjusted. Finally, the data transmission control of the electrical equipment of the automobile was realized through the linear combination of the network nodes. The simulation results showed that the throughput of the proposed method was always higher than 7.7 MB/s, the bit error rate was around 0 when the signal-to-noise ratio was lower than 3, and the transmission delay was always below 0.5 s, which could provide a reference for the efficient and safe operation of automobiles.

Provisioning Intelligent Water Wave Optimization Approach for Underwater Acoustic Wireless Sensor Networks

In the Acoustics channel, it is incredibly challenging to offer data transfer for time-sourced applications in an energy-efficient manner due to higher error rate and propagation delay. Subsequently, conventional re-transmission over any failure generally initiates significantly larger end-to-end delay, and therefore it is not probable for time-based services. Moreover, standard techniques without any re-transmission consume enormous energy. This investigation proposes a novel multi-hop energy-aware transmission-based intelligent water wave optimization strategy. It ensures reduced end-to-end while attaining potential amongst overall energy efficiency end-to-end packet delay. It merges a naturally inspired meta-heuristic approach with multi-hop routing for data packets to reach the destination. The appropriate design of this Meta heuristic-based energy-aware scheme consumes lesser energy than the conventional one-hop transmission strategy without re-transmission. However, there is no hop-by-hop re-transmission facilitated. The proposed model shows only lesser delay than conventional methods with re-transmission. This work facilitates extensive work to carry out the proposed model performance with the MATLAB simulation environment. The results illustrate that the model is exceptionally energy-efficient with lesser packet delays. With 500 nodes, the packet delivery ratio of proposed model is 100%, average delay is reduced by 2%, total energy consumption is 8 J, average packet redundancy is 1.856, and idle energy is 6.9Mwh. The proposed model outperforms existing approaches like OSF, AOR, and DMR respectively.

An Adaptive Hierarchical Hybrid Multicast Based on Information-Centric Networking

Many information-centric services have emerged, such as IPTV and video conferencing. These services put a lot of demands on scalable multicast communication. However, traditional IP multicast has low adoption because of its poor scalability. Therefore, some stateless multicast methods were proposed, which encapsulate the destination’s information into the packet header without requiring routers to maintain the multicast forwarding state. However, stateless multicast also faces some problems, such as ingress router overload, high forwarding overhead, packet redundancy, etc. In addition, most multicast methods cannot optimize the multicast tree because the multicast flow is simply forwarded along the shortest path tree from the source to receivers. This paper proposes an Adaptive Hierarchical Hybrid Multicast (AHHM) based on Information-Centric Networking. To balance the forwarding states and forwarding overhead, AHHM is designed as a two-layer structure, in which the upper layer establishes a stateful main tree and the lower layer establishes several stateless sub trees. The router on the main tree is defined as the multicast join node (MJN), and AHHM uses the Name Resolution System to maintain the mapping between each multicast group name and corresponding MJNs. To optimize the multicast transmission path, we designed the minimum cost selection strategy for users to select the appropriate MJN to join. Simulation results show that compared with Source-Specific Multicast (SSM) and Bit Index Explicit Replication (BIER), AHHM can not only reduce the multicast forwarding states but also reduce the control overhead and link load.