Coin Offering (ICO) is a fundraising method utilized by blockchain startups to raise capital by issuing and selling digital tokens to investors. ICOs have become widely popular for cryptocurrency fundraising, often generating millions of dollars, and surpassing traditional crowdfunding methods like Initial Public Offerings. However, ICO is a risky way of investing and raising capital due to the lack of regulations and standardisation. In this research, we delve into the impact of social media and sentiment analysis on the success of ICOs, employing various machine learning models and Large Language Models. Our analysis is based on data from over 1,000 ICOs gathered from diverse ICO information platforms, coupled with a corpus of 910,478 tweets associated with these ICOs. We extend our investigation to include other social media platforms such as BitcoinTalk, Telegram, Facebook, and Medium. Our analysis revealed that valuable insights regarding the success of ICOs can be derived by examining text sentiment and investigating metadata across these diverse social media channels.

Several challenges exist in disseminating multi-level secure (MLS) data in multi-domain environments. First, the security domains participating in data dissemination generally use different MLS labels and lattice structures. Second, when MLS data objects are transferred across multiple domains, there is a need for an agreed security policy that must be properly applied, and correctly enforced for the data objects. Moreover, the data sender may not be able to predetermine the data recipients located beyond its trust boundary. To address these challenges, we propose a new framework that enables secure dissemination and access of the data as intended by the owner. Our novel framework leverages simple public key infrastructure and active bundle, and allows domains to securely disseminate data without the need to repackage it for each domain.

In response to the dynamic and ever-evolving landscape of network attacks and cybersecurity, this study aims to enhance network security by identifying critical nodes and optimizing resource allocation within budget constraints. We introduce a novel approach leveraging node centrality scores from four widely-recognized centrality measures. Our unique contribution lies in converting these centrality metrics into actionable insights for identifying network attack probabilities, providing an unconventional yet effective method to bolster network robustness. Additionally, we propose a closed-form expression correlating network robustness with node-centric features, including importance scores and attack probabilities. At the core of our approach lies the development of a nonlinear optimization model that integrates predictive insights into node attack likelihood. Through this framework, we successfully determine an optimal resource allocation strategy, minimizing cyberattack risks on critical nodes while maximizing network robustness. Numerical results validate our approach, offering further insights into network dynamics and improved resilience against emerging cybersecurity threats.

In the realm of network analysis, the identification of critical nodes takes center stage due to their pivotal role in maintaining network functionality. These nodes wield immense importance, as their potential failure has the capacity to disrupt connectivity and pose threats to network security. This paper introduces an innovative approach to assess the vulnerability of these critical nodes by assessing their significance within the network structure. Through rigorous numerical analysis, our methodology not only demonstrates its effectiveness but also offers valuable insights into network dynamics. To enhance network robustness and, consequently, enhance network security, we formulate the network as a non-linear optimization problem. Our overarching objective is to determine the optimal security level, quantified as a resource allocation cost, for these critical nodes, ultimately aligning with our network security and robustness objectives.

Sentiment analysis is a highly valuable tool, particularly in the realm of social media, as it enables us to understand the public's opinions regarding specific products or topics.However, analyzing short and unstructured texts like tweets can present significant challenges.This paper explores conventional Machine Learning (ML) approaches like Naive Bayes, Logistic Regression, and Support Vector Machine to analyze sentiment and compares them against Bidirectional Encoder Representations from Transformer (BERT).Moreover, we suggest a new preprocessing technique for sentiment analysis to enhance the effectiveness of these methods.Our findings demonstrate noteworthy enhancements in the performance of conventional ML models.Interestingly, our study reveals that BERT outperforms all aforementioned models, yielding an accuracy of about 94%, though incurring a high computational cost.Additionally, Logistic Regression performs well with a 90.35% accuracy rate.With respect to feature extraction, we showcase that combining unigram and bigram words provides a more thorough comprehension of negation, as opposed to solely relying on unigrams.Finally, we propose an approach for managing emoticons and emojis that has proven to be useful in the fields of sentiment analysis and sarcasm interpretation.

Vehicular Networks enable vehicles to establish communication with both other vehicles and fixed road site units for the purpose of sharing critical safety and road traffic information. These networks contribute to the development of a self-aware, efficient, secure, and partially autonomous transportation system by facilitating the exchange and collection of Cooperative Awareness Messages (CAMs). However, vehicular networks are vulnerable to security attacks, where one or more vehicles may flood neighboring vehicles with malicious CAM messages. To effectively detect such attacks, an unsupervised online learning approach, such as our proposed modified version of the online K-means algorithm, can be employed. We evaluate this method using seven datasets, achieving nearly optimal detection performance. Our approach outperforms both the traditional online K-means algorithm and three other clustering methods, yielding higher accuracy and F1-scores.

Manufacturers usually face customers with different service requirements. The challenge they face is how to determine which customers to serve when the inventory supply is limited. We consider the problem of a manufacturer serving two types of customers: those with long term commitment, divided into several backorder classes; and those without commitment consisting of a single lost sales class. We treat the problem within a continuous time integrated production and inventory control framework. We propose a two-step strategy to fully characterize the optimal policy. In a first step, we use the concept of L-natural convexity to partially describe the optimal policy. Based on these results, in a second step, we reformulate the problem and fully characterize the optimal policy. We show that the latter is characterized by state-dependent multidimensional thresholds. Due to the computational complexity of the problem, we propose three heuristic policies: The first uses linear thresholds that mimic the optimal policy. These thresholds are computed through a decomposition of the original problem into a series of single-lost sales, single-backorder class problems. The second heuristic treats all demand classes equally. The third heuristic uses static thresholds to control production and inventory rationing among demand classes. Extensive numerical results show that, for problems with one lost sales and two or three backorder classes, the first heuristic outperforms the other two with a cost deviation less than 1.25%, from the optimal. Furthermore, computing the thresholds of this heuristic is orders of magnitude faster than computing the optimal policy.

Assemble-to-order (ATO) strategies are common to many industries. Despite their popularity, ATO systems remain challenging to analyze. We consider a general-product structure ATO problem modeled as an infinite horizon Markov decision process. As the optimal policy of such a system is computationally intractable, we develop a heuristic policy that is based on a decomposition of the original system, into a series of two-component ATO subsystems. We show that our decomposition heuristic policy (DHP) possesses many properties similar to those encountered in special-product structure ATO systems. Extensive numerical experiments show that the DHP is very efficient. In particular, we show that the DHP requires less than 10(-5) the time required to obtain the optimal policy, with an average percentage cost gap less than 4% for systems with up to 5 components and 6 products. We also show that the DHP outperforms the state aggregation heuristic of Nadar et al. (2018), in terms of cost and computational effort. We further develop an information relaxation-based lower bound on the performance of the optimal policy. We show that such a bound is very efficient with an average percentage gap not exceeding 0.5% for systems with up to 5 components and 6 products. Using this lower bound, we further show that the average suboptimality gap of the DHP is within 9% for two special-product structure ATO systems, with up to 9 components and 10 products. Using a sophisticated computing platform, we believe the DHP can handle systems with a large number of components and products. (C) 2020 Elsevier B.V. All rights reserved.

Overloaded network devices are becoming an increasing problem especially in resource limited networks with the continuous and rapid increase of wireless devices and the huge volume of data generated. Admission and routing control policy at a network device can be used to balance the goals of maximizing throughput and ensuring sufficient resources for high priority flows. In this paper we formulate the admission and routing control problem of two types of flows where one has a higher priority than the other as a Markov decision problem. We characterize the optimal admission and routing policy, and show that it is a state-dependent threshold type policy. Furthermore, we conduct extensive numerical experiments to gain more insight into the behavior of the optimal policy under different systems' parameters. While dynamic programming can be used to solve such problems, the large size of the state space makes it untractable and too resource intensive to run on wireless devices. Therefore, we propose a fast heuristic that exploits the structure of the optimal policy. We empirically show that the heuristic performs very well with an average reward deviation of 1.4% from the optimal while being orders of magnitude faster than the optimal policy. We further generalize the heuristic for the general case of a system with n (n>2) types of flows.

We consider a manufacturer selling a product through two markets: long-term and walk-in. The walk-in demand is dynamically priced in response to market conditions while the long-term demand is priced ahead of time according to contractual agreements. Demand for the product fluctuates due to varying state-of-the-world conditions evolving according to a finite-state Markov chain. Units are produced ahead of demand, with exponentially distributed production times. If orders cannot be fulfilled immediately, they are either backordered, in the case of the long-term demand, or lost, in the case of the walk-in demand. The objective of the manufacturer is to coordinate pricing, production scheduling and inventory allocation, in order to maximize the expected profit. Due to the different characteristics of the two markets, the long-term market is quoted a single price while the walk-in market is dynamically priced. We formulate the problem as a Markov decision process and characterize the structure of the optimal policy. We show that the optimal policy, in addition to specifying the optimal walk-in market sales price, is characterized by two state-dependent thresholds: one specifies how to allocate inventory among the two markets and the second specifies how to schedule production. We also study the special cases of static and state-of-the-world dependent walk-in market pricing strategies as well as the case of a spot market where the price is exogenously set. Finally, we conduct numerical experiments to show how the optimal policy is affected by system parameter changes.

Wireless Sensor and Robot Networks, pp. 249-265 (2014) No AccessChapter 10: Mobile Wireless Sensor Networks: Challenges and Business ApplicationsEssia Hamouda and John GerdesEssia HamoudaCalifornia State University Fullerton, USA and John GerdesUniversity of South Carolina, Integrated Information Technology, USAhttps://doi.org/10.1142/9789814551342_0010Cited by:1 Previous AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: Wireless sensor networks (WSNs) have emerged as an effective solution for a wide range of business applications. Most of the traditional WSN architectures consist of stationary nodes that are densely deployed over a sensing area. Recently, several WSN architectures deploying mobile devices have been proposed to exploit node mobility, and address problems of data transmission and collection in WSNs. In this chapter, we first define mobile wireless sensor networks (MWSNs). Then, we present an overview of the major challenges encountered in data transmission in MWSNs. Finally, we focus on the importance of MWSNs in business applications. We identify recent business applications where mobile wireless sensors have become important along with the challenges they are facing. FiguresReferencesRelatedDetailsCited By 1Self‐stabilising hybrid connectivity control protocol for WSNsGokou Hervé Fabrice Diédié, Boko Aka and Michel Babri1 February 2019 | IET Wireless Sensor Systems, Vol. 9, No. 1 Wireless Sensor and Robot NetworksMetrics History PDF download

In this paper, we consider an assemble-to-order manufacturing system producing a single end product, assembled from n components, and serving an after sales market for individual components. Components are produced in a make-to-stock fashion, one unit at a time, on independent production facilities. Production times are exponentially distributed with finite production rates. The components are stocked ahead of demand and therefore incur a holding cost rate per unit. Demand for the end product as well as for the individual components occurs continuously over time according to independent Poisson streams. In order to characterize the optimal production and inventory rationing policies, we formulate such a problem using a Markov decision process framework. In particular, we show that the optimal component production policy is a state-dependent base-stock policy. We also show that the optimal component inventory rationing policy is a rationing policy with state-dependent rationing levels. Recognizing that such a policy is generally not only difficult to obtain numerically but also is difficult to implement in practice, we propose three heuristic policies that are easier to implement in practice. We show that two of these heuristics are highly efficient compared to the optimal policy. In particular, we show that one of the two heuristics strikes a balance between high efficiency and computational effort and thus can be used as an effective substitute of the optimal policy.

L'introduction d'actionneurs capables de se deplacer sur ordre dans les reseaux de capteurs a permis l'emergence d'un nouveau genre de protocoles de routage. Ceux-ci tirent parti de cette nouvelle possibilite de relocaliser les elements du reseau pour adapter dynamiquement sa topologie au trafic. Ils vont ainsi faire se deplacer physiquement les nœuds au fur et a mesure du routage afin d'optimiser le cout des transmissions radio. Toutefois, dans les reseaux de capteurs, il y a souvent plusieurs nœuds geographiquement proches pour reporter un meme evenement a la station de base. Les messages routes empruntent alors differents chemins qui sont physiquement proches, et certains nœuds appartiennent a plusieurs d'entre eux. Ces derniers vont alors sans cesse devoir se relocaliser sur les differents chemins et donc mourir prematurement. En reponse a ce probleme, nous proposons PAMAL, le premier protocole de routage qui optimise la topologie reseau et sait tirer avantage des intersections des chemins de routage de maniere completement locale. PAMAL va ainsi provoquer la fusion des chemins de routage qui se croisent, et ce de plus en plus pres des sources au cours et du temps. Les resultats de simulations montrent que ce comportement associe a un mecanisme d'agregation permet d'ameliorer la duree de vie du reseau de 37 %.

We consider a path-scheduling problem at a resource constrained node A that transmits two types of flows to a given destination through alternate paths. Type-1 flow is assumed to have a higher priority than type-2 flow thus, it is never rejected upon arrival. Type-2 flow, on the other hand, may be denied admission to the queue. Once accepted to the system, a packet joins queue 1 and is guaranteed service independent of its type. Instead of being rejected from service, packets have the option to be served at a slower server behind a second queue (queue 2) at node A. The slow server is intended mostly to serve low priority packets, therefore, type-1 packets are charged a switching cost in the event they are sent to queue 2. Transmitted packets receive a reward depending on which queue they were served at. The reward represents the resources saved for making that decision. A good path-scheduling policy at node A can reduce resource consumption at node A, extend the life of the efficient path, maximizes the service of both flows and guarantees the service of at least the high priority flow to the full extent. We propose and solve the path-scheduling problem for node A, which maximizes the average reward of successfully transmitting flows to a given sink, by dynamically assigning packets to one of the queues based on the packet type, the instantaneous queue lengths and the average reward for the associated path. We formulated the path-scheduling problem as a Markov decision process and show that the optimal policy is threshold-type.

In typical mobile wireless sensor networks, flows sent from collecting sensors to a sink could traverse inefficient resource expensive paths and experience arbitrary delays. This is particularly problematic in event-based sensor network where flows are of great importance. In this paper, we are interested in energy-aware routing algorithms that explicitly take advantage of node mobility to improve energy consumption of computed paths. Mobility is a two-sword edge however. Moving a node may render the network disconnected and results in early termination of information delivery. To mitigate these problems, we propose a family of routing algorithm called Connectivity preservation Mobile routing protocols for actuator and sensor NETworks CoMNet, that uses local information and modifies the network topology to support resource efficient transmissions. Our extensive simulations show that CoMNet has high energetic performance improvement compared to existing routing algorithms. More importantly, we show that CoMNet guarantees network connectivity and efficient resource consumption.

In a Radio-Frequency IDentification network, while several readers are placed close together to improve coverage and consequently read rate, reader-reader collision problems happen frequently and inevitably. High probability of collision not only impairs the benefit of multi-reader deployment, but also results in misreadings in moving RFID tags. In order to eliminate or reduce reader collisions, we propose an Adaptive Color based Reader Anti-collision Scheduling algorithm (ACoRAS) for 13.56 MHz RFID technology where every reader is assigned a set of colors that allows it to read tags during a specific time slot within a time frame. Only the reader holding a color (token) can read at a time. Due to application constraints, the number of available colors should be limited, a perfect coloring scheme is not always feasible. ACoRAS tries to assign colors in such a way that overlapping areas at a given time are reduced. To the best of our knowledge ACoRAS is the first reader anti-collision algorithm which considers, within its design, both application and hardware requirements in reading tags. We show, through extensive simulations, that ACoRAS outperforms several anti-collision methods and detects more than 99% of mobile tags while fitting application requirements.

In mobile wireless sensor networks, flows sent from data collecting sensors to a sink could traverse inefficient resource expensive paths. Such paths may have several negative effects such as devices battery depletion that may cause the network to be disconnected and packets to experience arbitrary delays. This is particularly problematic in eventbased sensor networks (deployed in disaster recovery missions) where flows are of great importance. In this paper, we use node mobility to improve energy consumption of computed paths. Mobility is a two-sword edge, however. Moving a node may render the network disconnected and useless. We propose CoMNet (Connectivity preservation Mobile routing protocol for actuator and sensor NETworks), a localized mechanism that modifies the network topology to support resource efficient transmissions. To the best of our knowledge, CoMNet is the first georouting algorithm which considers controlled mobility to improve routing energy consumption while ensuring network connectivity. CoMNet is based on (i) a cost to progress metric which optimizes both sending and moving costs, (ii) the use of a connected dominating set to maintain network connectivity. CoMNet is general enough to be applied to various networks (actuator, sensor). Our simulations show that CoMNet guarantees network connectivity and is effective in achieving high delivery rates and substantial energy savings compared to traditional approaches.

We propose, end-to-end (EtE), a novel EtE localized routing protocol for wireless sensor networks that is energy-efficient and guarantees delivery. To forward a packet, a node s in graph G computes the cost of the energy weighted shortest path (SP) between s and each of its neighbors in the forward direction towards the destination which minimizes the ratio of the cost of the SP to the progress (reduction in distance towards the destination). It then sends the message to the first node on the SP from s to x: say node x′. Node x′ restarts the same greedy routing process until the destination is reached or an obstacle is encountered and the routing fails. To recover from the latter scenario, local minima trap, our algorithm invokes an energy-aware Face routing that guarantees delivery. Our work is the first to optimize energy consumption of Face routing. It works as follows. First, it builds a connected dominating set from graph G, second it computes its Gabriel graph to obtain the planar graph G′. Face routing is invoked and applied to G′ only to determine which edges to follow in the recovery process. On each edge, greedy routing is applied. This two-phase (greedy–Face) EtE routing process reiterates until the final destination is reached. Simulation results show that EtE outperforms several existing geographical routing on energy consumption metric and delivery rate. Moreover, we prove that the computed path length and the total energy of the path are constant factors of the optimal for dense networks.