Forwarding Behavior Research Articles

Computing Precise Control Interface Specifications

Verifying network programs is challenging because of how they divide labor: the control plane computes high level routes through the network and compiles them to device configurations, while the data plane uses these configurations to realize the desired forwarding behavior. In practice, the correctness of the data plane often assumes that the configurations generated by the control plane will satisfy complex specifications. Consequently, validation tools such as program verifiers, runtime monitors, fuzzers, and test-case generators must be aware of these control interface specifications (ci-specs) to avoid raising false alarms. In this paper, we propose the first algorithm for computing precise ci-specs for network data planes. Our specifications are designed to be efficiently monitorable —concretely, checking that a fixed configuration satisfies a ci-spec can be done in polynomial time. Our algorithm, based on modular program instrumentation, quantifier elimination, and a path-based analysis, is more expressive than prior work, and is applicable to practical network programs. We describe an implementation and show that ci-specs computed by our tool are useful for finding real bugs in real-world data plane programs.

Effects of sentiment quantity, dispersion, and dissimilarity on online review forwarding behavior: An empirical analysis

Online forwarding behavior, which involves users sharing information through URLs on social media platforms, has been extensively acknowledged as important to businesses, society, and individuals. Although previous research has discussed its antecedents from the sentiment perspective, most of them focus on the effect size, without differentiating the distinct effects of positive and negative sentiment. This study not only tests the association between positive (negative) sentiment and online forwarding but also examines how the aforementioned association varies from sentiment dispersion, measured by the variance of sentiment between individuals and groups (i.e., positive vs. negative sentiment dispersions); and sentiment dissimilarity, measured by the inconsistency between review content and title in terms of sentiment (i.e., positive vs. negative sentiment dissimilarities). Analysis of the data set collected from TripAdvisor yields the following findings: (1) positive sentiment negatively affects forwarding behavior, (2) negative sentiment positively affects forwarding behavior, (3) positive sentiment dispersion strengthens the negative effect of positive sentiment, but the moderation effect of positive sentiment dissimilarity is insignificant, and (4) negative sentiment dispersion/dissimilarity dampens the positive effect of negative sentiment. Findings extend our understanding of sentiment and online forwarding by highlighting the heterogeneous effects of positive and negative sentiments, thereby providing suggestions for forwarding function design.

Research on Secure Community Opportunity Network Based on Trust Model

With the innovation of wireless communication technology and the surge of data in mobile networks, traditional routing strategies need to be improved. Given the shortcomings of existing opportunistic routing strategies in transmission performance and security, this paper proposes a community opportunistic routing decision-making method based on the trust model. This algorithm calculates the node’s trust value through the node’s historical forwarding behavior and then calculates the node’s trust value based on the trust model. Thresholds and trust attenuation divide dynamic security communities. For message forwarding, nodes in the security community are prioritized as next-hop relay nodes, thus ensuring that message delivery is always in a safe and reliable environment. On this basis, better relay nodes are further selected for message forwarding based on the node centrality, remaining cache space, and remaining energy, effectively improving the message forwarding efficiency. Through node trust value and community cooperation, safe and efficient data transmission is achieved, thereby improving the transmission performance and security of the network. Through comparison of simulation and opportunistic network routing algorithms, compared with traditional methods, this strategy has the highest transmission success rate of 81% with slightly increased routing overhead, and this algorithm has the lowest average transmission delay.

Who are the best voluntary disseminators on social networking sites? An exploration based on panel data from twitter

PurposeUsers' voluntary forwarding behavior opens a new avenue for companies to promote their brands and products on social networking sites (SNS). However, research on voluntary information disseminators is limited. This paper aims to bring an in-depth understanding of voluntary disseminators by answering the following questions: (1) What is the underlying mechanism by which some users are more enthusiastic to voluntarily forward content of interest? (2) How to identify them? We propose a theoretical model based on the Elaboration-Likelihood Model (ELM) and examine three types of factors that moderate the effect of preference matching on individual forwarding behavior, including personal characteristics, tweet characteristics and sender–receiver relationships.Design/methodology/approachVia Twitter API, we randomly crawled 1967 Twitter users' data to validate the conceptual framework. Each user’s original tweets and retweeted tweets, profile data such as the number of followers and followees and verification status were obtained. The final corpus contains 163,554 data points composed of 1,634 valid twitterers' retweeting behavior. Tweets produced by these core users' followees were also crawled. These data points constitute an unbalanced panel data and we employ different models — fixed-effects, random-effects and pooled logit models — to test the moderation effects. The robustness test shows consistency among these different models.FindingsPreference matching significantly affects users' forwarding behavior, implying that SNS users are more likely to share contents that align with their preferences. In addition, we find that popular users with lots of followers, heavy SNS users who author tweets or forward other-sourced tweets more frequently and users who tend to produce longer original contents are more enthusiastic to disseminate contents of interest. Furthermore, interaction strength has a positive moderating effect on the relationship between preference matching and individuals' forwarding decisions, suggesting that users are more likely to disseminate content of interest when it comes from strong ties. However, the moderating effect of perceived affinity is significantly negative, indicating that an online community of individuals with many common friends is not an ideal place to engage individuals in sharing information.Originality/valueThis work brings about a deep understanding of users' voluntary forwarding behavior of content of interest. To the best of our knowledge, the current study is the first to examine (1) the underlying mechanism by which some users are more likely to voluntarily forward content of interest; and (2) how to identify these potential voluntary disseminators. By extending the ELM, we examine the moderating effect of tweet characteristics, sender–receiver relationships as well as personal characteristics. Our research findings provide practical guidelines for enterprises and government institutions to choose voluntary endorsers when trying to engage individuals in information dissemination on SNS.

GD3N: Adaptive clustering-based detection of selective forwarding attacks in WSNs under variable harsh environments

Wireless sensor networks (WSNs) are susceptible to numerous security threats due to their reliance on open environments and broadcast communication methods. Among these, the selective forwarding attack is notably challenging to detect. This difficulty arises from the ability of malicious nodes to imitate the behavior of normal nodes, and selectively drop data packets, which makes them virtually indistinguishable from normal ones, particularly under conditions of poor channel quality. To address this challenge with harsh environments, we introduce a novel methodology termed GD3N. This approach is underpinned by the design of a unique type of data point that encapsulates both short-term and long-term forwarding behaviors of nodes. It combines a refined version of the Gradient Diffusion Density-based Spatial Clustering of Applications with Noise (GD-DBSCAN) algorithm, with a novel Double-Parameter Neighbor Voting (DP-NV) method based on the data set. These innovations contribute to a significant enhancement in detection accuracy and a reduction in computational complexity when compared to traditional DBSCAN and NV methods. Simulation results show that our GD3N achieves a false detection rate (FDR) of less than 2%, a missed detection rate (MDR) of below 10%, and an overall detection accuracy rate (DAR) of over 95% across various testing scenarios.

Modified smell detection algorithm for optimal paths engineering in hybrid SDN

Optimal path engineering in hybrid software defined networking environments involves leveraging traditional networking and software defined capabilities to determine the most efficient paths for network traffic. In hybrid software defined networks, some parts of the network may be controlled by controllers, while others rely on traditional routing protocols. Leveraging the OpenFlow protocol, widely used in such deployments, to program flow entries in software defined switches is one approach to achieving optimal path engineering in hybrid software defined networks. By specifying specific rules and policies in the switches, we can control traffic forwarding behaviour and steer traffic along desired paths based on application requirements. In this work, the smell detection algorithm, a new variant of bio-inspired method, is adopted to explore its capability in routing. The new paradigm of smell detection agent, its architecture, protocols used, and the concept of hybrid defined network is also explained. A comprehensive experimental examination of within the controller, an algorithm based on smell detecting agents computes the most efficient route from where it began to the intended destination. Simulation results readily demonstrate the smell detection agent based technique's effective functioning in getting the optimum direction. In this work, bio-inspired algorithms outperformed conventional algorithms in terms of performance.

Mistill: Distilling Distributed Network Protocols From Examples

Traffic Engineering (TE) mechanisms in data center networks make distributed forwarding decisions based on the global network state. Thus, new TE mechanisms require the design and implementation of effective information exchange and efficient decentralized algorithms to compute forwarding decisions, which is challenging and time-intensive. To automate and simplify this process, we propose Mistill. Mistill distills the forwarding behavior of TE policies from exemplary forwarding decisions into a Neural Network. Mistill learns (i) how to encode local state into update messages, (ii) which network devices must exchange updates, and (iii) how to map the exchanged updates into forwarding decisions. We demonstrate the abilities of Mistill by learning three TE policies, verifying their performance in simulations on synthetic and realworld traffic patterns, and by showing that the learned policies generalize to unseen traffic patterns. We implement Mistill as a proof-of-concept and show that Mistill reacts on average within 1.3 ms to changes in the network.

A Survey on Rerouting Techniques with P4 Programmable Data Plane Switches

Traditionally, the networking industry has been dominated by closed and proprietary hardware and software. Vendors have been controlling the network by hard-coding how packets should be processed and providing the network operators with a set of predefined protocols. Recently, the industry, operators, and the research community have started to pay special attention to data plane programmability, which allows the user to define the packet processing behavior. Allowing the network operators and programmers to define, deploy, and test new forwarding behaviors in a relatively short time paved the way for a significant wave of innovation and experimentation. With the emergence of programmable data planes, traffic rerouting has been used by the research community. Rerouting approaches are deployed to mitigate various network issues. Despite the considerable number of works that deploy innovative rerouting mechanisms using programmable switches, the literature lacks a comprehensive survey. To this end, this paper provides an in-depth overview, detailed analysis, and unique categorization of the recent programmable data plane-based rerouting approaches. The survey explains the need for rerouting by highlighting the promising results while dealing with link/node failures, load imbalance, and congestion. It then discusses the challenges and considerations and presents future perspectives and open research issues.

Research on Influencing Factors of Public Opinion Information Forwarding Behavior of Generation Z Group

[Purpose/Significance] With the continuous development of network technology, the ways of information dissemination become more and more diversified, and the threshold of information dissemination becomes lower and lower. This not only facilitates people's life, but also brings information fog, information polarization and other problems, causing great harm to the network environment. Through the research on the forwarding behavior of Z generation group's public opinion information, this study finds out the influencing factors, so as to help relevant departments improve the network environment and put forward new ideas for the management and control of online public opinion information. [Method/Process] In this study, through questionnaire survey and semi-structured interview, a sample survey was conducted on the "group in Hefei, and the grounded theory was analyzed with the help of Nvivo12 software. A comprehensive research model of influencing factors of public opinion information forwarding behavior of Generation Z group was constructed, and the characteristics of Generation Z group were analyzed. [Result/Conclusion] The research shows that information content mediates the attributes of information receivers. The intermediary role of consumers' internal state in the process of e-commerce anchor attribute influencing consumers' online purchase intention; The external environment plays a regulatory role in the transmission chain of public opinion information.

User behavior prediction model based on implicit links and multi-type rumor messages

Traditional prediction models of rumor forwarding are based solely on explicit network topology, and with no consideration for homogeneity and antagonism among multi-type rumor messages. To solve these problems, this study proposes a user behavior prediction model based on implicit links and multi-type rumor messages. First, because most existing studies are based on explicit network topology and ignore the influence of implicit links on information transmission, this study considers the interaction and similarity among users comprehensively and uses the K-dimension-tree algorithm to mine implicit links among non-friends, thereby improving the network topology. Second, given fuzziness and complexity of user forwarding behavior in multi-type rumor messages, considering the advantages of graph convolutional networks (GCNs) model in network representation, rumor information, user characteristics and network structure are fully represented with features. Finally, considering the high integration ability and adaptive ability of model fusion, a softmax layer is added to finalize the basic multi-classification, and then multiple GCN-based models are fused by a voting mechanism to realize the prediction of user forwarding behavior. Experiments show that the proposed model can effectively predict a user’s forwarding behavior under multi-type rumor topics, and the model has improved generalization ability.

Hummingbird: Dynamic Path Validation With Hidden Equal-Probability Sampling

Path validation has already been incrementally deployed in the Internet architecture. It secures packet forwarding by enabling end hosts to negotiate specific forwarding paths and enforcing on-path routers to prove their forwarding behaviors along these paths. Most existing path validation solutions target static paths, paying less attention to fully dynamic paths that support flexible routing. In this paper, we present Hummingbird as the first validation solution over fully dynamic paths. It features a hidden equal-probability sampling technique. Gaining efficiency via routers probabilistically sampling packets to validate, we craft the sampling probability such that each router validates a similar amount of packets given an unknown path length. We further hide the state of whether a packet has been sampled and validated using a lightweight, non-cryptographic scheme. This prevents attackers from differentiating and selectively mis-forwarding packets. We validate security and efficiency of Hummingbird through both theoretical proof and experimental evaluation.

ETD: An Efficient Time Delay Attack Detection Framework for UAV Networks

In recent years, Unmanned Aerial Vehicle (UAV) networks are widely used in both military and civilian scenarios. However, due to the distributed nature, they are also vulnerable to threats from adversaries. Time delay attack is a type of internal attack which maliciously delays the transmission of data packets and further causes great damage to UAV networks. Furthermore, it is easy to implement and difficult to detect due to the avoidance of packet modification and the unique characteristics of UAV networks. However, to the best of our knowledge, there is no research on time delay attack detection in UAV networks. In this paper, we propose an Efficient Time Delay Attack Detection Framework (ETD). First, we collect and select delay-related features from four different dimensions, namely delay, node, message and connection. Meanwhile, we utilize the pre-planned trajectory information to accurately calculate the real forwarding delay of nodes. Then, one-class classification is used to train the detection model, and the forwarding behaviors of all nodes can be evaluated, based on which their trust values can be obtained. Finally, the K-Means clustering method is used to distinguish malicious nodes from benign ones according to their trust values. Through extensive simulation, we demonstrate that ETD can achieve higher than 80% detection accuracy with less than 2.5% extra overhead in various settings of UAV networks and different routing protocols.

A Cultural Approach to Understanding Publics and their Information Behaviors during COVID-19: Self-Construal and Identity Salience

ABSTRACT Integrating the situational and cross-situational approaches to understanding publics, this study examined cultural antecedents (self-construal and political identity salience) to situational perceptions (problem recognition, involvement recognition, constraint recognition), situational motivation, and key information behavior regarding the COVID-19 pandemic. Results from an online survey (N = 556) showed that political identity salience and interdependent self-construal triggered publics’ situational perceptions, which in turn activated their situational motivation and information forwarding behaviors. The study contributed to public research through examining important cultural influences on value-laden and polarized issues and revealing additional nuances in communicative activeness.

Dynamic evaluation method on dissemination capability of microblog users based on topic segmentation

Different from the evaluation of the influence of nodes in social networks, user dissemination capability refers to the ability of users facilitating information spreading in the network. It is a hot issue in the field of information diffusion to evaluate users’ dissemination capability. Previous studies have rarely considered the impact of neighbor nodes or time decay on user dissemination capability. By constructing the user interaction network of Sina Microblog, we achieved a dynamic evaluation of user dissemination capability. Firstly, based on the thought of topic segmentation, the relationship strength between users under different topics was calculated respectively according to their historical interaction records. Secondly, we analyzed the effect of the subsequent forwarding behaviors of neighbor nodes and time decay on user dissemination capability, and proposed a dynamic evaluation method of user dissemination capability. Then, based on the modified PageRank algorithm, the ranking of user dissemination capability under different topics was calculated. Finally, experiments were conducted to verify the dynamic evaluation method on user dissemination capability. The results show that it can identify users with high dissemination capability under different topics, which might be helpful to intervene the information spreading.

Programmable Deterministic Zero-Copy DMA Mechanism for FPGA Accelerator

With the expansion of network scales, the B/S architecture of monolithic applications is gradually being replaced by microservices. The unbundling of services has led to exponential growth in the size of APIs. When handling massive microservice requests, the commercial NIC shows limitations in three aspects: deterministic, programmability, and data copy. To ensure that each microservice node handles requests efficiently, flexibly, and precisely, this paper proposes a programmable deterministic multi-queue FPGA Accelerator. The Accelerator relies on the instantiated 1000 queues and the queue management unit to extend the rule-based RSS algorithm for the serverless-friendly programmability of packet distribution. A PTP hardware clock is added to collaborate with the queue management unit to control the deterministic delivery. To improve the sending and receiving efficiency of network node data, a driver adapted to the FPGA accelerator is designed to realize zero-copy. Experiments conducted on a 100 Gbps FPGA show that the Accelerator can support the multi-queue transmission with various packet sizes, define the forwarding behavior, and almost approach the line rate on an 8-core FPGA device. In addition, it can forward packets with low latency close to that of the current state-of-the-art ovs-DPDK. This Accelerator overcomes, to some extent, the limitations of commercial NICs when oriented to microservice architectures.

The impact factors of social media users' forwarding behavior of COVID-19 vaccine topic: Based on empirical analysis of Chinese Weibo users.

IntroductionSocial media, an essential source of public access to information regarding the COVID-19 vaccines, has a significant effect on the transmission of information regarding the COVID-19 vaccines and helps the public gain correct insights into the effectiveness and safety of the COVID-19 vaccines. The forwarding behavior of social media users on posts concerned with COVID-19 vaccine topics can rapidly disseminate vaccine information in a short period, which has a significant effect on transmission and helps the public access relevant information. However, the factors of social media users' forwarding posts are still uncertain thus far. In this paper, we investigated the factors of the forwarding COVID-19 vaccines Weibo posts on Chinese social media and verified the correlation between social network characteristics, Weibo textual sentiment characteristics, and post forwarding.MethodsThis paper used data mining, machine learning, sentiment analysis, social network analysis, and regression analysis. Using “新冠疫苗 (COVID-19 vaccine)” as the keyword, we used data mining to crawl 121,834 Weibo posts on Sina Weibo from 1 January 2021 to 31 May 2021. Weibo posts not closely correlated with the topic of the COVID-19 vaccines were filtered out using machine learning. In the end, 3,158 posts were used for data analysis. The proportions of positive sentiment and negative sentiment in the textual of Weibo posts were calculated through sentiment analysis. On that basis, the sentiment characteristics of Weibo posts were determined. The social network characteristics of information transmission on the COVID-19 vaccine topic were determined through social network analysis. The correlation between social network characteristics, sentiment characteristics of the text, and the forwarding volume of posts was verified through regression analysis.ResultsThe results suggest that there was a significant positive correlation between the degree of posting users in the social network structure and the amount of forwarding. The relationship between the closeness centrality and the forwarding volume was significantly positive. The betweenness centrality was significantly positively correlated with the forwarding volume. There was no significant relationship between the number of posts containing more positive sentiments and the forwarding volume of posts. There was a significant positive correlation between the number of Weibo posts containing more negative sentiments and the forwarding volume.ConclusionAccording to the characteristics of users, COVID-19 vaccine posts from opinion leaders, “gatekeepers,” and users with high-closeness centrality are more likely to be reposted. Users with these characteristics should be valued for their important role in disseminating information about COVID-19 vaccines. In addition, the sentiment contained in the Weibo post is an important factor influencing the public to forward vaccine posts. Special attention should be paid to the negative sentimental tendency contained in this post on Weibo to mitigate the negative impact of the information epidemic and improve the transmission effect of COVID-19 vaccine information.

A Novel Trust Adaptability Approach for Secure Data Transmissions using Enhanced Collaborative Nodes Trustworthiness in Mobile Ad-hoc Networks

Objectives: To find an efficient security routing model based on trust adaptability by considering various transmission parameters that influence the node’s behavior. Methods: Enhanced Collaborative Trust Based Approach (ECTBA) was applied to isolate the malicious nodes from routing by computing their enhanced collaborative trust value based on the node’s behavior using transmission parameters. Parameters that influence the node’s behavior like the number of data packets and control packets forwarded, dropped, or misrouted by the node are quantified to compute direct trust value and neighbor reputation. Node’s Enhanced Collaborative trust value was generated by the combination of direct and neighbor observations. Findings: The proposed strategy is compared with several cases like the Direct Trust Based Approach (DTBA), where routing involves trustworthy nodes categorized based on only direct trust, existing methods like Belief-dependent trust evolution method(BETM), Novel extended trust-dependent method (NETM) where routing is done with nodes that are categorized as trustworthy depending on direct and indirect observations and simple AODV routing performed with all the possible random nodes without any trust detection. The performance parameters of the proposed ECTBA exhibit a success rate of 10.2% in false positives detection (FPD),network throughput of 438.12 Kbps,and packet delivery ratio (PDR) of 92.3%.This method proves to be a better method when compared with the traditional trust-based security methods(BETM and NETM) in terms of efficiency. Novelty: This research suggested a novel and fine-tuned method for quantifying a node’s trustworthiness and for secure routing that coupled the direct and indirect observations into enhanced collaborative trustbased on the node’s behavior by considering the transmission parameters. The present work highlights the combination of data packets forwarding behavior and control packets forwarding behavior in computing enhanced collaborative trust to decide the involvement of intermediate trustworthy nodes’ which is not tried before but this study does. Keywords: Dynamic Topology; Direct Trust; Neighbor Trust; Secure Routing; Enhanced Collaborative Trust

Prediction of the Forwarding Volume of Campus Microblog Public Opinion Emergencies Using Neural Network

With the continuous expansion of colleges and universities, the proportion of college students among netizens has increased rapidly. They have become an important component of Chinese netizens and the main audience of Weibo. It is easier for online public opinion to form in some social media platforms than in others. We explain places for political conversation in Chinese cyberspace in terms of interaction, which results in various types of political discussion, based on research on authoritarian deliberation. The crisis of college network public opinion caused by the improper use of Weibo by college students occurs frequently. College network public opinion has become an important factor affecting the development and stability of colleges and universities. Social network rumor forwarding behavior refers to whether users forward specific rumors. Also, there is no surety about the truthfulness of the rumor. Taking Weibo as an example, this paper proposes a neural network-based model for predicting the forwarding volume of public opinion emergencies on campus Weibo. It solves the problem of low prediction accuracy of traditional SVM and other models. The data-driven experimental findings suggest that the technique described in this study can increase the accuracy of predicting forwarding volume in unexpected campus public opinion events.

WPFD: Active User-Side Detection of Evil Twins

The bothersome evil twin problem has an active user-side remedy in the form of the Wireless Packet Forwarding Detector (WPFD). The evil twin issue can lead to further security problems, including man-in-the-middle (MITM) attacks. Open public Wi-Fi connections have provided potential answers to this issue, although they often need more data that people either cannot get or are too pricey for regular users. The solution that we created does not require these standards. It allows users’ notebooks to be used to check for evil twins. We have succeeded in developing a user-side detection system that can successfully identify the presence of an evil twin. The packet forwarding behavior generated by the evil twin and the TCP/IP (Transmission Control Protocol/Internet Protocol) protocol are both used by the WPFD. It can identify evil twins without a hitch when we utilize accessible Wi-Fi settings in public spaces or IoT smart homes with unencrypted WLANs (Wireless Local Area Network). However, neither additional data nor a wireless network administrator’s assistance is needed. We compare our work to various publications on popular Rogue Access Points (APs) or IoT (Internet of Things) smart homes. The WPFD does not require any extra setup to install on the host of any end user. According to experimental findings, the WPFD true positive and true negative rates are 100% even when Received Signal Strength Index (RSSI) is 45%.

Impact of Anode Doping on Avalanche in Vertical GaN Pin Diodes with Hybrid Edge Termination Design

Introduction: Large area GaN power devices are seldom reported to avalanche and the theoretical studies of their edge termination still struggle to match experimental results especially for planar structures. We report for the first time a punch through avalanche on 1.2 kV vertical GaN diode with optimized hybrid edge termination design to fit the device structure. Furthermore, we provide a systematic study that pairs the Sentaurus TCAD simulation to the reverse characteristics of three different anode designs with three different remaining dose in the anode extension region. Device Fabrication: PiN diodes with three different anode doping were fabricated on a non-homogenous GaN substrate. 5×1017, 1×1018, and 2×1019 cm-3 Mg-doped layer were grown on a MOCVD lightly doped layer of 8um with doping concentration of the order of 1-2×1016 cm-3. The Edge termination process starts with etching a trench 1µm depth and about 140µm from anode edge, followed by isolation nitrogen implant using a box profile with three different energies and total depth of ~650 nm. Finally, the junction termination extension and guard rings (JTE/GR) hybrid is implemented by nitrogen implant to achieve a full planar device structure with final depth of 300 nm. Figure 1 depicts the device structure. The same JTE/GR hybrid implant depth into three different anode doping which results in different residual charge in the extension region thickness that is expected to impact the field management. Experimental Result: Three devices with the different anode doping were tested under identical forward and reverse biases conditions. The three devices display similar forward behavior with ideality factor ranging 2<n<2.2 as shown in Figure 2. Their reverse characteristics however, are varying proportional to the anode doping. The highest breakdown voltage and the sharpest breakdown characteristics was displayed by anode doping of 1×1018 cm-3. The other two anode doping devices fail short to achieve the expected breakdown voltage and both devices also exhibit higher leakage than the 1×1018 cm-3.. All devices are designed to withhold 1.2 kV, thus 2×1019 and 5×1017 cm-3 anode are ~ 500 V away from the targeted value. Considering the fact that these three wafers have the same drift region thickness and doping, and were processed together through the edge termination therefore, the drastic difference in the breakdown voltage can be only explained by the remaining dose in the extension region. To further understand the edge termination efficacy in these devices an avalanche test was conducted, reverse sweep was applied at 25,100,150, &200 °C for all three devices. Figure 3 depicts the avalanche results. Both devices with 1×1019 and 5×1017 are struggling to avalanche due to the high leakage and inconsistent trend with temperature. The PiN diode with 1×1018 cm-3 avalanched with an increase of breakdown voltage 10 V for every 50 °C. This trend of the breakdown with temperature is possible due to the increase in leakage current, consequently a temperature coefficient α= 3.1×10-4 K-1 was calculated using equation: . To better understand this difference a simulation was conducted using Sentaurus TCAD. The model is designed to observe the behavioral trends thus the absolute numbers in the model do not capture the devices non-idealities. The 2D distribution of the electric field is displayed in Figure 4 for all 3 doing levels. Conclusion: The reverse characteristic and TCAD simulation indicate an improvement in the field management with the decrease in the anode doping to 1×1018 cm-3 which subsequently means less charges in the edge termination region than the standard 2×1019 doping level . These results show that the anode dose is a function of anode doping level, implant depth and the edge termination region thickness. To optimize edge termination design one needs to manage the dose in the termination region which is a function of anode doping level, implant depth and the edge termination region thickness. Figure 1