Fake online reviews in e-commerce significantly affect online consumers, merchants, and, as a result, market efficiency. Despite scholarly efforts to examine fake reviews, there still lacks a survey that can systematically analyze and summarize its antecedents and consequences. This study proposes an antecedent–consequence–intervention conceptual framework to develop an initial research agenda for investigating fake reviews. Based on a review of the extant literature on this issue, we identify 20 future research questions and suggest 18 propositions. Notably, research on fake reviews is often limited by lack of high-quality datasets. To alleviate this problem, we comprehensively compile and summarize the existing fake reviews-related public datasets. We conclude by presenting the theoretical and practical implications of the current research.

Fake online reviews: Literature review, synthesis, and directions for future research

Fog computing at industrial level, architecture, latency, energy, and security: A review

‘Sentiment’ literally means ‘Emotions’. Sentiment analysis, synonymous to opinion mining, is a type of data mining that refers to the analy-sis of data obtained from microblogging sites, social media updates, online news reports, user reviews etc., in order to study the sentiments of the people towards an event, organization, product, brand, person etc. In this work, sentiment classification is done into multiple classes. The proposed methodology based on KNN classification algorithm shows an improvement over one of the existing methodologies which is based on SVM classification algorithm. The data used for analysis has been taken from Twitter, this being the most popular microblogging site. The source data has been extracted from Twitter using Python’s Tweepy. N-Gram modeling technique has been used for feature extraction and the supervised machine learning algorithm k-nearest neighbor has been used for sentiment classification. The performance of proposed and existing techniques is compared in terms of accuracy, precision and recall. It is analyzed and concluded that the proposed technique performs better in terms of all the standard evaluation parameters. 

KNN classifier based approach for multi-class sentiment analysis of twitter data

Internet of Things has been growing, due to which the number of user requests on fog computing layer has also increased. Fog works in a real-time environment and offers from connected devices need to be processed immediately. With the increase in users requests on fog layer, virtual machines (VMs) at fog layer become overloaded. Load balancing mechanism can distribute load among all the VMs in equal proportion. It has become a necessity in the fog layer to equally, and equitably distribute all the workload among the existing VMs in the segment. Till now, many load balancing techniques have been proposed for fog computing. An empirical study of existing methods in load balancing have been conducted, and taxonomy has been presented in a hierarchical form. Besides, the article contains the year-wise comprehensive review and summary of research articles published in the area of load balancing from 2013 to 2020. Furthermore, article also contains our proposed fog computing architecture to resolve load balancing problem. It also covers current issues and challenges that can be resolved in future research works. The paper concludes by providing future directions.

A systematic study of load balancing approaches in the fog computing environment

The wide-spread Internet of Things (IoT) utilization in almost every scope of our life made it possible to automate daily life tasks with no human intervention. This promising technology has immense potential for making life much easier and open new opportunities for newly developed applications to emerge. However, meeting the diverse Quality of Service (QoS) demands of different applications remains a formidable topic due to diverse traffic patterns, unpredictable network traffic, and resource-limited nature of IoT devices. In this context, application-tailored QoS provisioning mechanisms have been the primary focus of academic research. This paper presents a literature review on QoS techniques developed in academia for IoT applications and investigates current research trends. Background knowledge on IoT, QoS metrics, and critical enabling technologies will be given beforehand, delving into the literature review. According to the comparison presented in this work, the commonly considered QoS metrics are Latency, Reliability, Throughput, and Network Usage. The reviewed studies considered the metrics that fit their provisioning solutions.

/pdf/iot-provisioning-qos-based-on-cloud-and-fog-computing-2bhw4ilujx.pdf

IoT Provisioning QoS based on Cloud and Fog Computing

Focus and research on fog computing environment is increasing in recent days. Orchestrating the resources in the fog computing environment and distributing the task with the help of simple load balancing algorithms improve the task processing in fog environment. Fog resources include end users resources, networking resources, and cloud resources as well, in which networking resources take the central control over the fog nodes at particular location. These control nodes attempt to reduce the burden of cloud as well as improve the task processing efficiency by distributing the load across the fog nodes evenly on controlling the fog nodes based on availability of the nodes. The objective of this work is to evenly distribute the load across the available fog nodes and reduce the response time of the task processing. The results have been verified using cloud analyst tool in which the proposed approach is compared with round-robin algorithm in terms of response time. The results show that the response time has improved substantially in the proposed approach.

Efficient Load Balancing Algorithm for Task Preprocessing in Fog Computing Environment

In general, Wireless communication is defined as sharing of information between one or more systems through wireless links. Wireless networks can be categorized into two different modes as infrastructure based and infrastructure less. Infrastructure based mode is the most common use wireless mode for the end user loop. Infrastructure fewer modes also called as the Ad hoc mode relies on wireless communications without any fixed infrastructure. Infrastructure based networks are characterized by their use of access points (AP), or base stations. The most known example of infrastructure based wireless network is GSM and more recently, Wi-Fi. Ad Hoc networks introduce a new way of communication. An ad hoc network is a collection of wireless mobile nodes that dynamically functions as a network without the use of any existing infrastructure and centralized administration. Mobile Ad-hoc networking (MANET), an emerging field of wireless networking is an autonomous system of wireless mobile hosts, connected by wireless links that dynamically create a temporary network and establish an infrastructure less network. The topology of the network may change frequently and unpredictably. Multicast routing has been widely applied in mobile ad hoc networks (MANETs), to support different group oriented applications like video conferencing, interactions with Special interest groups etc., efficiently. This paper presents the comparative analysis of two multicast routing protocols, PUMA and ODMRP.

Analysis of Multicast Routing Protocols: Puma and Odmrp

Social network platforms have gained enormous growth in present days. Users are more attracted towards social network sites such as Twitter, Facebook, micro blogs and YouTube to express their opinion and views and share the knowledge. With the tremendous increase in technological growth and with the diversity of products, online e-commerce servicing sites are becoming competitor's for each other to increase their proliferation in business offering to post positive product spam reviews. Users generally express their opinion based on different sentiment orientations, ratings and the features of the product. This tends to create ambiguity at the customers end in arriving at a decision based on the criticism, building fake opinion on the products. Opinion spam detection provides scope in building opinion from the reviews of various networking sites. A novel framework using meta-heuristic and k-means clustering approach is proposed for identifying opinion spam detection using flower pollination, grey wolf and moth flame. Amazon automotive product dataset is chosen for analysis and it is observed that grey wolf algorithm performs better than flower pollination and moth flame optimisation algorithms in terms of improved convergence speed, mean, standard deviation, variance and elapsed run time.

Online social networking services and spam detection approaches in opinion mining - a review

Secured communication in ad hoc wireless networks is primarily important, because the communication signals are openly available as they propagate through air and are more susceptible to attacks ranging from passive eavesdropping to active interfering. The lack of any central coordination and shared wireless medium makes them more vulnerable to attacks than wired networks. Nodes act both as hosts and routers and are interconnected by Multi- hop communication path for forwarding and receiving packets to/from other nodes. The objective of this paper is to propose a key exchange and encryption mechanism that aims to use the MAC address as an additional parameter as the message specific key[to encrypt]and forward data among the nodes. The nodes are organized in spanning tree fashion, as they avoid forming cycles and exchange of key occurs only with authenticated neighbors in ad hoc networks, where nodes join or leave the network dynamically.

Secure Key Exchange and Encryption Mechanism for Group Communication in Wireless Ad Hoc Networks

An independent, self-governing group of mobile wireless hosts communicate through wireless links by forming a temporary network (Mobile Ad-hoc Network) in a dynamic manner without any fixed and centralized infrastructure. Since the nodes in MANET are not stationary, same path may not be taken always for routing between the sender and the receiver(s). So, routing in such situation is a complicated task. In addition, the resources of the wireless nodes are limited. With the intention of saving the resources of the nodes in the network, multicasting can be used instead of multiple unicast data transmissions whenever a node needs to send same data in parallel to several destinations. The group- oriented services are the primary ones that support today's need and trend of communication. In recent years, such services are provided with the help of MANETs. The residual energy of the battery of a mobile node is finite (before recharging is done), which is a constraint while developing multicast routing protocols. Genetic Algorithm (GA) presents an improved solution for the multi-constrained multicast routing problem. By choosing proper fitness function and values for metrics such as initial population size, crossover and mutation that closely relates to the chosen scenario, the genetic algorithm optimizes the routes in terms of selected metrics. This paper focuses to provide a survey and analyses the categories of multicast routing protocols. In addition, this also covers the details of the application of genetic

/pdf/survey-of-genetic-based-approach-for-multicast-routing-in-89z9h8i16l.pdf

S. Sumathy

Papers

Efficient Load Balancing Algorithm for Task Preprocessing in Fog Computing Environment

Analysis of Multicast Routing Protocols: Puma and Odmrp

Online social networking services and spam detection approaches in opinion mining - a review

Secure Key Exchange and Encryption Mechanism for Group Communication in Wireless Ad Hoc Networks

Survey of genetic based approach for multicast routing in manet