With the recent advancements in social network platform technology, an overwhelming amount of information is spreading rapidly. In this situation, it can become increasingly difficult to discern what information is false or true. If false information proliferates significantly, it can lead to undesirable outcomes. Hence, when we receive some information, we can pose the following two questions:$(i)$Is the information true?$(ii)$If not, who initially spread that information? The first problem is the rumor detection issue, while the second is the rumor source detection problem. A rumor-detection problem involves identifying and mitigating false or misleading information spread via various communication channels, particularly online platforms and social media. Rumors can range from harmless ones to deliberately misleading content aimed at deceiving or manipulating audiences. Detecting misinformation is crucial for maintaining the integrity of information ecosystems and preventing harmful effects such as the spread of false beliefs, polarization, and even societal harm. Therefore, it is very important to quickly distinguish such misinformation while simultaneously finding its source to block it from spreading on the network. However, most of the existing surveys have analyzed these two issues separately. In this work, we first survey the existing research on the rumor-detection and rumor source detection problems with joint detection approaches, simultaneously. This survey deals with these two issues together so that their relationship can be observed and it provides how the two problems are similar and different. The limitations arising from the rumor detection, rumor source detection, and their combination problems are also explained, and some challenges to be addressed in future works are presented.

In this paper, we study a problem of detecting the source of diffused information by querying individuals, given a sample snapshot of the information diffusion graph, where two queries are asked: (i) whether the respondent is the source or not, and (ii) if not, which neighbor spreads the information to the respondent. We consider the case when respondents may not always be truthful and some cost is taken for each query. Our goal is to quantify the necessary and sufficient budgets to achieve the detection probability 1- δ for any given 0 <; δ <; 1. To this end, we study two types of algorithms: adaptive and non-adaptive ones, each of which corresponds to whether we adaptively select the next respondents based on the answers of the previous respondents or not. We first provide the information theoretic lower bounds for the necessary budgets in both algorithm types. In terms of the sufficient budgets, we propose two practical estimation algorithms, each of non-adaptive and adaptive types, and for each algorithm, we quantitatively analyze the budget which ensures 1- δ detection accuracy. This theoretical analysis not only quantifies the budgets needed by practical estimation algorithms achieving a given target detection accuracy in finding the diffusion source, but also enables us to quantitatively characterize the amount of extra budget required in non-adaptive type of estimation, referred to as adaptivity gap. We validate our theoretical findings over synthetic and real-world social network topologies.

In this paper, we study a problem of detecting the source of diffused information by querying individuals, given a sample snapshot of the information diffusion graph, where two queries are asked: (i) whether the respondent is the source or not, and (ii) if not, which neighbor spreads the information to the respondent. We consider the case when respondents may not always be truthful and some cost is taken for each query. Our goal is to quantify the necessary and sufficient budgets to achieve the detection probability$1-\delta$for any given$0 < \delta < 1$. To this end, we study two types of algorithms: adaptive and non-adaptive ones, each of which corresponds to whether we adaptively select the next respondents based on the answers of the previous respondents or not. We first provide the information theoretic lower bounds for the necessary budgets in both algorithm types. In terms of the sufficient budgets, we propose two practical estimation algorithms, each of non-adaptive and adaptive types, and for each algorithm, we quantitatively analyze the budget which ensures$1-\delta$detection accuracy. This theoretical analysis not only quantifies the budgets needed by practical estimation algorithms achieving a given target detection accuracy in finding the diffusion source, but also enables us to quantitatively characterize the amount of extra budget required in non-adaptive type of estimation, refereed to as adaptivity gap. We validate our theoretical findings over synthetic and real-world social network topologies.

Social networks are the major routes for most individuals to exchange their opinions about new products, social trends and political issues via their interactions. It is often of significant importance to figure out who initially diffuses the information, i.e., finding a rumor source or a trend setter. It is known that such a task is highly challenging and the source detection probability cannot be beyond 31% for regular trees, if we just estimate the source from a given diffusion snapshot. In practice, finding the source often entails the process of querying that asks “Are you the rumor source?” or “Who tells you the rumor?” that would increase the chance of detecting the source. In this paper, we consider two kinds of querying: (a) simple batch querying and (b) interactive querying with direction under the assumption that queriees can be untruthful with some probability. We propose estimation algorithms for those queries, and quantify their detection performance and the amount of extra budget due to untruthfulness, analytically showing that querying significantly improves the detection performance. We perform extensive simulations to validate our theoretical findings over synthetic and real-world social network topologies.

Recently, the problem of detecting the rumor source in a social network has been much studied, where it has been shown that the detection probability cannot be beyond 31% even for regular trees. In this paper, we study the impact of an anti-rumor on the rumor source detection. We first show a negative result: the anti-rumor's diffusion does not increase the detection probability under Maximum-Likelihood-Estimator (MLE) when the number of infected nodes are sufficiently large by passive diffusion that the anti-rumor starts to be spread by a special node, called the protector, after is reached by the rumor. We next consider the case when the distance between the rumor source and the protector follows a certain type of distribution, but its parameter is hidden. Then, we propose the following learning algorithm: a) learn the distance distribution parameters under MLE, and b) detect the rumor source under Maximum-A-Posterior-Estimator (MAPE) based on the learnt parameters. We provide an analytic characterization of the rumor source detection probability for regular trees under the proposed algorithm, where MAPE outperforms MLE by up to 50% for 3-regular trees and by up to 63% when the degree of the regular tree becomes large. We demonstrate our theoretical findings through numerical results, and further present the simulation results for general topologies (e.g., Facebook and US power grid networks) even without knowledge of the distance distribution, showing that under a simple protector placement algorithm, MAPE produces the detection probability much larger than that by MLE.

While the current Internet has a host-based TCP/IP architecture, the vast majority of Internet usage is attributed to the content retrieval and distribution. This mismatch has proliferated content delivery network (CDN) technologies and P2P file-sharing systems (e.g., BitTorrent); however, the inefficiency of content delivery is not fundamentally solved, and there are business/operation and content copyright/availability issues, respectively. Also, in the research community, there have been efforts to redesign the current architecture from a content-centric perspective by introducing the route-by-name paradigm and in-network caching, which however entails many issues like the routing scalability and processing overhead. We propose a lookup-based communication framework for content-oriented networking (LOCON), whose main components are locator lookup, coordinated caching, and parallel transmissions. LOCON aims to accommodate incremental deployment, support legacy end-hosts, and provide business incentives between network operators and content publishers. To carry out the experiments, we build a networking testbed on top of Amazon data centers around the world. LOCON performs better than other lookup-by-name networking technologies and CCN (i.e., a route-by-name scheme) in terms of content delivery metrics.

BitTorrent has achieved a great success in the field of peer-to-peer (P2P) file sharing. Although BitTorrent allows peers to share files efficiently and scalably, it shows inefficiency when a client participates in multiple torrents where each of them concurrently competing for the limited link bandwidth. In this paper, we propose a new bandwidth allocation algorithm, which greedily increases the bandwidth consumption for downloading, to reduce file transfer time considering the current download/upload status. To compensate overall performance degradation resulting from our greedy allocation, we suggest modifying the choking algorithm of BitTorrent to consider the ratio of seeders and leechers in each torrent. Through comprehensive experiments, we validate the performance gain of the proposed scheme over original BitTorrent in a mix of WiFi and Ethernet testbed and large scale public torrents.

While the Internet was designed with host-oriented networking applications, recent Internet statistics show that content-oriented traffic has become more and more dominant. Even though content-oriented networking, which tries to resolve this discordance, has received increasing attention, there have been few comprehensive and quantitative studies on how to realize a content-oriented networking architecture. In this paper, we focus on the design alternatives of the content-oriented networking architecture and evaluate their performance: (i) how to locate contents, (ii) how to cache contents, and (iii) how to deliver contents. There are two major infrastructure alternatives in substantiating these mechanisms: a tree and a distributed hash table (DHT).We carry out comprehensive simulation experiments to compare these alternatives in terms of content transfer latency, cache effectiveness, and failure resilience.

IEEE 802.11 WLAN has been widely accepted throughout the world. However, it has large overhead due to idle backoff slots and frequent collisions depending on the number of nodes. In this paper, we propose a novel medium access control scheme, scheduling-based coordination function (SCF). SCF polls the next station to be serviced by piggybacking. The selection of the next station is based on self-clocked fair queueing (SCFQ) scheduling in a distributed manner. Due to polling, SCF does not suffer from collisions and is efficient owing to the SCFQ scheme. SCF improves the system throughput up to 63.2% compared to IEEE 802.11 DCF. Comprehensive simulation is performed to compare SCF with DCF, PCF, and fast collision resolution (FCR).

In wireless sensor networks (WSNs), reducing energy consumption of resource constrained sensor nodes is one of the most important issues. In this paper, we propose a task aware (TA) MAC protocol, which improves energy efficiency and throughput by introducing a channel access scheme depending on traffic load in WSNs. The amount of traffic load of a sensor node can be estimated by its task activity, where a task is an operation that the sensor node performs based on the schedule set by data dissemination procedures in advance. In addition, the sensor node collects neighbor nodes' task activities and determines its channel access probability using the collected information. Consequently, the sensor node can choose a more suitable channel access probability which is adaptive to its traffic load as well as neighbor's traffic load. We carry out performance analysis using a p-persistent MAC protocol. The results reveal that the TA-MAC protocol exhibits less collisions than the normal p-persistent MAC protocol and thus it achieves energy efficient operations. Also, it can been seen that the TA-MAC protocol improves system throughput compared with other protocols