On Latency Reductions in Vehicle-to-Vehicle Networks by Random Linear Network Coding

In the Internet of vehicles(IoV),direct communication between vehicles,i.e.,vehicle-tovehicle(V2V)may have lower latency,compared to the schemes with help of Road Side Unit(RSU)or base station.In this paper,the scenario where the demands of a vehicle are satisfied by cooperative transmissions from those one in front is considered.Since the topology of the vehicle network is dynamic,random linear network coding is applied in such a multisource single-sink vehicle-to-vehicle network,where each vehicle is assumed to broadcast messages to others so that the intermediate vehicles between sources and sink can reduce the latency collaboratively.It is shown that the coding scheme can significantly reduce the time delay compared with the non-coding scheme even in the channels with high packet loss rate.In order to further optimize the coding scheme,one can increase the generation size,where the generation size means the number of raw data packets sent by the source node to the sink node in each round of communication.Under the premise of satisfying the coding validity,we can dynamically select the Galois field size according to the number of intermediate nodes.It is not surprised that the reduction in the Galois field size can further reduce the transmission latency.

Exact Decoding Probability of Random Linear Network Coding for Combinatorial Networks

Combinatorial networks are widely applied in many practical scenarios. In this paper, we compute the closed-form probability expressions of successful decoding at a sink and at all sinks in the multicast scenario, in which one source sends messages to k destinations through m relays using random linear network coding over a Galois field. The formulation at a （all） sink（s） represents the impact of major parameters, i.e., the size of field, the number of relays （and sinks） and provides theoretical groundings to numerical results in the literature. Such condition maps to the receivers＇ capability to decode the original information and its mathematical characterization is helpful to design the coding. In addition, numerical results show that, under a fixed exact decoding probability, the required field size can be minimized.

Nonlinear network coding based on multiplication and exponentiation in GF(2~m)

This article proposes a novel nonlinear network code in the GF（2^m） finite field. Different from previous linear network codes that linearly mix multiple input flows, the proposed nonlinear network code mixes input flows through both multiplication and exponentiation in the GF（2^m）. Three relevant rules for selecting discussed, and the relationship between the power parameter m proper parameters for the proposed nonlinear network code are and the coding coefficient K is explored. Further analysis shows that the proposed nonlinear network code is equivalent to a linear network code with deterministic coefficients.

Optimal chunk caching in network coding-based qualitative communication

Network processing in the current Internet is at the entirety of the data packet,which is problematic when encountering network congestion.The newly proposed Internet service named Qualitative Communication changes the network processing paradigm to an even finer granularity,namely chunk level,which obsoletes many existing networking policies and schemes,especially the caching algorithms and cache replacement policies that have been extensively explored in Web Caching,Content Delivery Networks(CDN)or Information-Centric Networks(ICN).This paper outlines all the new factors that are brought by random linear network coding-based Qualitative Communication and proves the importance and necessity of considering them.A novel metric is proposed by taking these new factors into consideration.An optimization problem is formulated to maximize the metric value of all retained chunks in the local storage of network nodes under the constraint of storage limit.A cache replacement scheme that obtains the optimal result in a recursive manner is proposed correspondingly.With the help of the introduced intelligent cache replacement algorithm,the performance evaluations show remarkably reduced end-to-end latency compared to the existing schemes in various network scenarios.

Fuzzy and IRLNC-based routing approach to improve data storage and system reliability in IoT

Internet of Things(IoT)based sensor network is largely utilized in various field for transmitting huge amount of data due to their ease and cheaper installation.While performing this entire process,there is a high possibility for data corruption in the mid of transmission.On the other hand,the network performance is also affected due to various attacks.To address these issues,an efficient algorithm that jointly offers improved data storage and reliable routing is proposed.Initially,after the deployment of sensor nodes,the election of the storage node is achieved based on a fuzzy expert system.Improved Random Linear Network Coding(IRLNC)is used to create an encoded packet.This encoded packet from the source and neighboring nodes is transmitted to the storage node.Finally,to transmit the encoded packet from the storage node to the destination shortest path is found using the Destination Sequenced Distance Vector(DSDV)algorithm.Experimental analysis of the proposed work is carried out by evaluating some of the statistical metrics.Average residual energy,packet delivery ratio,compression ratio and storage time achieved for the proposed work are 8.8%,0.92%,0.82%,and 69 s.Based on this analysis,it is revealed that better data storage system and system reliability is attained using this proposed work.