Hence, as shown by the bold line in Fig 2 , if D requires D , DCC takes advantage of the fact that B has already received and cached D 12 prior to this transmission, and S need only send D 3 to B, whereupon B sends D to D to complete the transmission.
In this way, transmission time and collisions are reduced, resulting in increased throughput. It is an example based on Fig 1 of two-hop transmission from node S to node D via node B as a relay node based on content structured according to DCC. The process flow of the proposed model is illustrated in Fig 3. As discussed, high-likelihood content is determined according to the 80—20 law, and high-likelihood content is cached by nodes during the idle time of low traffic periods, preparing for transmission to receivers.
If the high-likelihood content required by a receiver has not been pre-fetched and cached, its request will be sent by the server. We first present a performance analysis of the proposed model. In our system, the total number of nodes participating in transmission is n, and the set of these nodes is represented as S n. The set S c represents the set of nodes contending for transmission. If a node in S c has a transmission task, it will send packets within a period. Otherwise, the transmission is failed. Thus, the probability of a packet being successfully transmitted for S c can be given by 4 where represents the probability that packet transmission has failed.
Therefore, the probability of successful transmission, herein defined as the PDR , can be expressed as 5 where P SD represents the probability of successful transmission without retransmission, and represent the probability of successful retransmission for nodes i and j , respectively, and P C represents the probability of a node contending for transmission.
In addition, we assume that the content requiring transmission is a Markov process. The throughput of the presented model is defined as 7 Here, is the stationary probability of the Markov process discussed above, and is the total transmission time of node i , which can be calculated as 8 where , , and are the successful transmission time, empty slot time, and contending time of node i , respectively.
The simulations were conducted using the NS The parameters are summarized in Table 1. Hence, we just define the values of back-off period for CDDC to show the difference of performance. Fig 4 shows the simulated throughput versus the number of nodes for the various transmission models considered. To include obvious high and low traffic periods in the simulations, we divided the simulation period into 2 N equal periods, and assigned M slots to each period, where N and M are integers. The main reason for this greater throughput is that coded caching helps the ContDM scheme to utilize high-likelihood content efficiently.
In addition, fewer nodes lead to a faster coded caching process. The reason is that the greater number of nodes in the CDCC model can provide more high-likelihood content, which can reduce transmission pressure and improve throughput.
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The reason for this much higher throughput is that the more highly contrasting high and low traffic periods allow for nearly all high-likelihood content to be cached in low traffic periods, which substantially reduces transmission pressure on servers during high traffic periods. The throughput of PTMAC obviously decreases with an increasing number of nodes because the collision avoidance scheme is influenced by transmission from all directions during high traffic periods. It means that CDDC is a better choice in burst transmission networks. In addition, we note that a larger value of w also provides a higher throughput.
OCA-MAC: A Cooperative TDMA-Based MAC Protocol for Vehicular Ad Hoc Networks
The 4 sub-contents scheme, as described in 9 , does not take full use of the sub-contents scheme, so when the number of nodes is small, the throughput is unsatisfactory. On the other hand, the 16 sub-contents scheme, as described in 10 , works worse in large scale nodes networks. Because too many contents transmitted by large numbers of nodes more easily lead to collisions in the transmission. That is why the throughput of 16 sub-contents decreases significantly when the number of nodes is more than PDR is a crucial evaluation of network performance, and also influences the throughput of networks.
We note that the PDR values of all five models decrease with an increasing number of nodes. Here, if content is unsuccessfully transmitted, the sender only needs to retransmit the missing sub-content rather than the entire content. The reasons for these substantially lower performances is that uncoded caching disturbs the asynchronous demand of networks and leads to a greater number of transmission collisions than CDCC, and centralized coded caching cannot handle with ContDM scheme efficiently in the network. The reason is that more nodes provide more popular contents caching, in this way, nodes can obtain contents more easily and reduce the traffic load and delay.
Meanwhile, when the number of nodes is less than , the advantage of popular contents caching cannot counteract the delay which is caused by computational work, so CDDC does not have a better performance on delay reducing than others. In the proposed model, the ContDM scheme can improve the transmission performance of a WANET by making full use of low traffic periods to reduce transmission pressure during high traffic periods.
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In addition, DCC can also reduce transmission congestion. Simulation results show the improvements on the transmission performance. These improvements are also of great benefit to practical WANET applications such as the IoT and vehicle networks, particularly for burst communication systems. In the future, we will focus on reducing energy consumption and developing applications of the proposed transmission model. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field.
Abstract Wireless ad hoc networks can experience extreme fluctuations in transmission traffic in the Internet of Things, which is widely used today. Introduction Nowadays, the Internet of Things IoT has greatly changed the way we live, work and study [ 1 ]. Transmission model description The overall transmission process includes two stages: placement stage and delivery stage.
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Download: PPT. Fig 3. Algorithm flow chart of the proposed transmission model. Analysis and simulations Analysis We first present a performance analysis of the proposed model. References 1. Ye Q, Zhuang W. Distributed and adaptive medium access control for Internet-of-Things-enabled mobile networks. View Article Google Scholar 2. Fairness in wireless networks: Issues, measures and challenges. View Article Google Scholar 3.
Throughput analysis of decentralized coded content caching in cellular networks. View Article Google Scholar 4. Jeon, S.
Cross-layer model design in wireless ad hoc networks for the Internet of Things
Caching in wireless multihop device-to-device networks. PloS one. A distributed multi-channel MAC protocol for ad hoc wireless networks. View Article Google Scholar 7. Jiang X, Du DH. View Article Google Scholar 8. Wang X, Li J. Network coding aware cooperative MAC protocol for wireless ad hoc networks.
Ad Hoc Networks
There has been considerable work on the performance evaluation of this protocol. To validate our analytical results, we have done extensive simulations, and it is observed that the simulation results match the analytical results very well. Volume 8 , Issue 1.
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SRN Models for Analysis of Multihop Wireless Ad Hoc Networks
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