Encouraging Cooperation in Participatory Sensing with Reputation and Game Analysis

Participatory sensing (PS) is an approach to distribute data collection, to analyze and interpret it. Identifying trusted and recommended participants with intention to have quality data to be analyzed is still a challenge because unlike in other domains, participatory sensing participants must fulfill the requirements of service provider, where participants are required to contribute quality data in a longer time frames. Many factors can influence the integrity of the information.One of major concerns in data contribution is the possibility of data truthfulness of being uncertain due to incompleteness, imprecision, vagueness, fragmentary. Consequently, it will cause the information to become unreliable to be analyzed. Detecting the uncertainty information is essentialto value the information. Therefore, the objective of this paper is two-fold. First, we give an overview of uncertainty information and the characteristics that suits participatory sensing system. Second, we outline how Case-based Reasoning approach can be implemented to tackling the uncertainty information in order to distinguish trusted and un-trusted participants.To address both objectives, this paper proposed uncertainty information detection approach based on information relevance using decision tree that integrate Case-based Reasoning, data mining, and information retrieval into our participatory sensing application, w8L0ss

Exploring the trust management mechanism in self-organizing complex network based on game theory

Owing to the characteristics of underwater communication, such as limited bandwidth, low transmission speed, and long delivery delay, it is significantly challenging to address trust management in the Internet of Underwater Things (IoUT). In the process of trust calculation, trust judgments between nodes may be conflicting based on the obtained diverse trust evidences. However, in existing studies, the analysis of trust-conflict adjudication is nonexhaustive and lacking in detail. Therefore, a controversy-adjudication method is proposed in this study to handle conflict recommendations, and a novel trust management mechanism is further investigated based on the controversy-adjudication method, including three phases: 1) trust calculation; 2) trust recommendation; and 3) trust evaluation. First, trust evidences, e.g., packet delivery ratio, end-to-end packet transmission latency, and residual energy, are collected to calculate trust for trustees. In addition, for the trustor without sufficient trust evidences, recommendations are required and an incentive mechanism is proposed based on the prisoner’s dilemma to encourage neighbors to participate in trust recommendation. Finally, trust values of trustees are obtained by executing trust evaluation based on the controversy-adjudication mechanism. Simulation results demonstrate that the proposed trust management mechanism outperforms existing related works in terms of accuracy and robustness against unreliable IoUT.

The Iterated Prisoner's Dilemma is a game-theoretical model which can be identified in many repeated real-world interactions between competing entities. The Tit for Tat strategy has been identified as a successful strategy which reinforces mutual cooperation, however, it is sensitive to environmental noise which disrupts continued cooperation between players to their detriment. This paper explores whether a population of Tit for Tat players may evolve specialised individual-based noise to counteract environmental noise. We have found that when the individual-based noise acts similarly to forgiveness it can counteract the environmental noise, although excessive forgiveness invites the evolution of exploitative individual-based noise, which is highly detrimental to the population when widespread.

Data mining discloses hidden, previously unknown, and potentially useful information from large amounts of data. As comparison to the traditional statistical and machine learning data analysis techniques, data mining emphasizes to provide a convenient and complete environment for the data analysis. Data mining has become a popular technology in analyzing complex data. Clustering is one of the data mining core techniques. In the field of data mining and data clustering, it is a highly desirable task to perform cluster analysis on large data sets with mixed numeric, categorical, ordinal, and ratio-scaled with binary and nominal values. However, most already available data merging and grouping through clustering algorithms are effective for the numeric data rather than the mixed data set. For this purpose, this paper makes efforts to present a new amalgamation algorithm for these mixed data sets by modifying the common cost function, trace of the within cluster dispersion matrix. The genetic algorithm (GA) is used to optimize the new cost function to obtain valid clustering result. We can compare and analyze that the GA-based clustering algorithm is feasible for the high-dimensional data sets with mixed data values that are obtained in real life results. Core Idea of Our Paper: By this paper, we try to describe a technique for estimating the cost function metrics from mixed numeric, categorical and other type databases by using an uncertain grade-of-membership clustering model with the efficiency of Genetic Algorithm. This technique can be applied to the problem of opportunity analysis for business decision-making. This general approach could be adapted to many other applications where a decision agent needs to assess the value of items from a set of opportunities with respect to a reference set representing its business. For processing numeric attributes, instead of generalizing them, a prototype may be developed for experiments with synthetic and real data sets, and comparison with those of the traditional approaches. The results confirmed the feasibility of the framework and the superiority of the extended techniques.

Trust management is fundamental to identify malicious, selfish, and compromised nodes which have been authenticated. Many trust management mechanisms are proposed to provide effective security solution for wire and wireless networks, even some of them have become one of the most known in this field. But in fact, trust management in wireless sensor networks still remains a challenging field. Traditional trust models developed for wireless sensor networks are not well suited for sensor networks because the trust evaluation process isn’t related to certain application. In this paper, we propose a self-adaptive trust management scheme, which can adaptively adjust the trust evaluation process to cope with application requirement changes of trust management. The simulation results show that our scheme has less rate of inauthentic downloads of nodes, energy consumption and message overheads as compared to the current state-of-the-art trust management schemes, and it is more suitable for large-scale sensor networks.

Data mining and knowledge discovery aim at finding useful information from typically massive collections of data, and then extracting useful knowledge from the information. To date a large number of approaches have been proposed to find useful information and discover useful knowledge; for example, decision trees, Bayesian belief networks, evidence theory, rough set theory, fuzzy set theory, kNN (k-nearest-neighborhood) classifier, neural networks, and support vector machines. However, these approaches are based on a specific data type. In the real world, an intelligent system often encounters mixed data types, incomplete information (missing values), and imprecise information (fuzzy conditions). In the UCI (University of California – Irvine) Machine Learning Repository, it can be seen that there are many real world data sets with missing values and mixed data types. It is a challenge to enable machine learning or data mining approaches to deal with mixed data types (Ching, 1995; Coppock, 2003) because there are difficulties in finding a measure of similarity between objects with mixed data type attributes. The problem with mixed data types is a long-standing issue faced in data mining. The emerging techniques targeted at this issue can be classified into three classes as follows: (1) Symbolic data mining approaches plus different discretizers (e.g., Dougherty et al., 1995; Wu, 1996; Kurgan et al., 2004; Diday, 2004; Darmont et al., 2006; Wu et al., 2007) for transformation from continuous data to symbolic data; (2) Numerical data mining approaches plus transformation from symbolic data to numerical data (e.g.,, Kasabov, 2003; Darmont et al., 2006; Hadzic et al., 2007); (3) Hybrid of symbolic data mining approaches and numerical data mining approaches (e.g.,, Tung, 2002; Kasabov, 2003; Leng et al., 2005; Wu et al., 2006). Since hybrid approaches have the potential to exploit the advantages from both symbolic data mining and numerical data mining approaches, this chapter, after discassing the merits and shortcomings of current approaches, focuses on applying Self-Organizing Computing Network Model to construct a hybrid system to solve the problems of knowledge discovery from databases with a diversity of data types. Future trends for data mining on mixed type data are then discussed. Finally a conclusion is presented.

The rapid development and increasing popularity of mobile devices have facilitated the development of computation‐intensive applications and services, but their constrained compute, storage and battery capabilities have also created the requirements for optimized resource provision and service orchestration. Emerging mobile edge computing (MEC) and next‐generation networks (6G) can meet these requirements of mobile devices by migrating complex computing tasks and large volumes of data to edge servers with acceptable latency, thus effectively enhancing the quality of experience (QoE) of mobile users. Intelligent Vehicular Collaboration, as an intelligent mobile application or service in intelligent connected vehicle (ICV) applications, enables precise and effective driving decisions based on a large amount of real‐time traffic information statistics and intelligent analysis of vehicle status to improve the user driving experience and the efficiency of intelligent transportation systems(ITS). However, the openness and dynamics of wireless communications and the high frequency of information interaction of MEC‐enabled intelligent vehicular collaboration services in the 6G era make them highly vulnerable to cyber attacks. How to achieve dynamically trustworthy, secure, and intelligent vehicular collaboration services in the presence of malicious node access and cyber attacks in the 6G MEC‐enabled vehicular network is a significant challenge. In this paper, we propose a novel blockchain‐enabled trust management mechanism to address this challenge. First, we design a spatiotemporal‐correlated message credibility assessment method and a dynamic mutual trust evaluation method to obtain accurate ratings for messages and mobile nodes respectively. Based on the above methods, we propose an improved blockchain‐based intelligent trust management mechanism that enables transparent, tamper‐evident, and trustworthy secure intelligent vehicular collaboration services while maintaining stable and efficient system performance. Simulation results show that our suggested solutions can efficiently evaluate the trust level of ICVs and achieve efficient and secure intelligent vehicular collaboration with acceptable communication and computation overheads.

Ranking systems such as those in product review sites and recommender systems usually use ratings to rank favorite items based on both their quality and popularity. Since higher ranked items are more likely selected and yield more revenues for their owners, providers of unpopular and low quality items have strong incentives to strategically manipulate their ranking. This paper analyzes the adversary cost for manipulating these rankings in a variety of scenarios. Particularly, we analyze and compare the adversarial cost to attack ranking systems that use various trust measures to detect and eliminate malicious ratings to systems that use no such a trust management mechanism. We provide theoretical results showing the relation between the capability of the trust mechanism in detecting malicious ratings and the minimal adversarial cost for successfully changing the ranking. Furthermore, we study the impact of sharing trust information between ranking systems to the adversarial cost. It is proved that sharing information between two ranking systems on common user identities and malicious behaviors detected can increase considerably the minimal adversarial cost to successfully attack the two systems under certain assumptions. The numerical evaluation of our results shows that the estimated adversary cost for manipulating the item ranking can be made significant when proper trust mechanisms are employed or combined.

Football, which is a popular world-wide sport, has become one of the most practiced sports but also, with more study cases. Scouting and game analysis that is currently made has offered the possibility to improve the competition and increase the performance levels within a team. Taking this into account it emerged the term Scouting. The objective of this study is to streamline the Scouting process in Football, through Data Mining (DM) techniques and following the Cross Industry Standard Process for Data Mining (CRIPS-DM) methodology. The goal of DM was to develop and evaluate predictive models capable of forecasting a score of a football player’s performance. Based on this target, 2808 classification models and 936 regression models were developed and evaluated. For the classification, the maximum accuracy percentage was centered at 94% for the Forward player position, while for the regression the minimum error value was 0.07 for the Forward position. The results obtained allow to streamline the Scouting process in Football thus enhancing the sporting advantage.

Summary form only given. We present a distributed infrastructure for mixed data and text mining. Our approach is based on extending the discovery net infrastructure, a grid-computing environment for knowledge discovery, to allow end users to construct complex distributed text and data mining workflows. We describe our architecture, data model and visual programming approach and present a number of mixed data text mining examples over biological data.

A recommendation management defense mechanism based on trust model in underwater acoustic sensor networks

Cooperative-centrality enabled investigations on edge-based trustworthy framework for cloud focused internet of things

Many Internet of Things (IoT) networks comprise tiny devices with limited processing power and tight energy restrictions. These limitations make it difficult to use established security mechanisms protecting the devices against malicious attacks. This holds particularly for Intrusion Detection Systems (IDS) that help to detect various net-based attacks but often demand significant network and computing resources. In this article, we design and evaluate some IDS mechanisms for IoT Networks that are suited to small devices. They use a trust management mechanism that allows devices to manage reputation information about their neighbors. This mechanism makes it possible to single out maliciously behaving units in a processing and energy-friendly way. The approach is explained in the context of the healthcare domain.

The Vehicular Named Data Networking (V-NDN) improves the speed of message acquisition between vehicles and reduces network overhead by using a in-network caching mechanism. The vehicles in V-NDN have the capability of built-in caching, in other words, they can cache contents passing by and provide content services for users. However, malicious nodes in V-NDN might apply fake messages for malicious purposes, which is one of the major risks of network security. In this paper, we build a trust management mechanism based on blockchain to solve the above problems. In the proposed mechanism, vehicles first judge the credibility of the received message based on the vehicle reputation value and the feature of the message itself. Then the vehicle reputation value is updated according to the message credibility. Finally, the blockchain is used to realize the consensus of the message credibility and the vehicle reputation value. We conduct experiments on the simulation platform and simulation results show that the proposed mechanism can effectively improve the accuracy of message credibility judgment and malicious vehicles detection, thereby improving the security of the V-NDN.