Social-Media-based Lifelong Learning (SMLL) have come under the spotlight nowadays because they allow people to realise lifelong learning in their quotidian existence. However, the factors that determine SMLL have received little scholarly attention, hence our poor understanding in this regard. To remedy the deficiency, a research model was developed through Maslow’s need hierarchy theory to address the issue. The research findings show that (1) safety needs play an initial role that drives users gradually towards belongingness and love needs, self-esteem needs and self-actualisation needs; (2) self-actualisation needs serve as a sole pivotal role in users’ satisfaction with SMLL, which in turn affects their continuance intention towards SMLL. These findings reveal that social media not only allow people to safeguard their jobs by offering access to the latest knowledge but also help them fulfil their ambitions and realise their self-worth. This study also challenges the stereotypes about Maslow’s need hierarchy theory by showing that the levels of needs are not necessarily to be fulfilled in ascending order but can be satisfied simultaneously.

In this article we develop an intrusion detection attack-defense game for Internet of Things (IoT) systems for which autonomous IoT devices collaboratively solve a problem. We develop an analytical model to determine the conditions under which malicious nodes have no incentives to perform attack in the intrusion detection attack-defense game. We also develop a stochastic Petri net model to analyze the effect of attack-defense behaviors on system reliability, given a definition of system failure conditions as input. The performance evaluation results demonstrate that our intrusion detection system (IDS) attack-defense game design greatly improves system reliability over existing autonomous IoT systems without gaming design consideration when attacks are reckless and intensive.

We develop an analytical model to capture the interplay of attack-defense strategies of an autonomous distributed Internet of Things system (ADIoTS). Every node participates in intrusion detection of a target node of the same type, thus necessitating that every good node plays a set of defense strategies and every bad node plays a set of attack strategies for achieving their own goals. The end product is a methodology for identifying the best defense strategies to maximize the system lifetime.

In this paper, we propose a novel Hough circle-assisting deep-network inspection scheme (HCADIS), aiming at identifying defects in dense coffee beans. The proposed HCADIS plays a critical role in a camera-based defect removal system to collect defective bean positions for picking all defects off. The idea of the HCADIS is to mix intermediate data from a deep network and a feature engineering method call Hough circle transform for utilizing advantages of both methods in inspecting beans. The Hough circle transform is adopted because it performs quite stable and bean shapes are highly close to circles in nature. A set of core mechanisms are designed for collaboration between the deep network and the Hough circle transform for precisely and accurately inspecting defective beans. Finally, we implement a prototype of the HCADIS and conduct experiments for testing the proposed scheme. The test results reveal that the HCADIS indeed successfully inspect defects among dense beans with superior performance in various metrics. This work provides industrial participants useful experiences for creating deep-learning solutions to bean products in coffee industries.

We propose and analyze a 3-tier cloud-cloudlet-device hierarchical trust-based service management protocol called IoT-HiTrust for large-scale mobile cloud Internet of Things (IoT) systems. Our mobile cloud hierarchical service management protocol allows an IoT customer to report its service experiences and query its subjective service trust score toward an IoT service provider following a scalable report-and-query design. We conduct a formal scalability analysis along with an ns-3 simulation performance analysis demonstrating that IoT-HiTrust not only achieves scalability without compromising accuracy, convergence, and resiliency properties against malicious attacks but also outperforms contemporary distributed and centralized IoT trust management protocols. We test the feasibility by applying IoT-HiTrust to two case studies: 1) a smart city travel service composition and binding application and 2) an air pollution detection and response application. The results demonstrate that IoT-HiTrust outperforms contemporary distributed and centralized trust-based IoT service management protocols in selecting trustworthy nodes to maximize application performance, while achieving scalability.

We propose and analyze a novel location management scheme for mobile ad hoc networks (MANETs) called Dual-region Mobility Management (DrMoM). The basic design concept of DrMoM is to use local regions to complement existing location services in MANETs that assign home regions to mobile nodes and have mobile nodes in the home region of a mobile node serve as location servers for that node. DrMoM is based on the design notion of integrated mobility and service management for network cost minimization. Specifically, unlike existing location services that define the home region size statically at design time for all mobile users, DrMoM dynamically determines the optimal home region size and local region size per mobile user based on mobility and service characteristics of individual mobile nodes to minimize the overall network cost incurred by location management and data packet delivery. We develop a performance model to derive the optimal values of these two key design parameters under which the overall network cost incurred by DrMoM is minimized. Through a comparative performance study, we show that DrMoM outperforms a well-known scheme called SLURP based on static home regions as well as a region-based location management scheme called RUDLS which claims to outperform contemporary region-based location management schemes.

Patent deployment has become a competition strength for companies. The intelligence property can keep the competition advantage of a company from opponents through the patent deployment which can be visualized by the patent map technique. The patent map is an important strategic tool for establishing design strategies. Our past efforts studied the display techniques in design patent map, and the comparisons of design patents in United States and Taiwan. Of types of patents, design patents occupy a unique patent field, since design patents are not as definitive as other patent fields. Therefore, the construction of design patent map is extremely difficult. Current commercial patent map systems visualize the patents according to non-populace attributes. However, such patent map systems are insufficient for providing more objective results from populace to support more powerful evidences in law courts. A key component to support the patent map system adopting the populace opinions is a fast dissimilarity visualization engine which can traslate the dissimilarity of patents from the populace opinions to a patent map. This paper presents a GA-based dissimilarity visualization engine for the above mentioned purpose. We design a set of crossover and multion operations based on the observations could generate patent maps with better quality. Our primary results reveal that the GA-based dissimilarity visualization engine indeed speeds up around 50% than the traditional method. Hence, such the engine is quite suitable for impatient users on the internet platform.

In this paper we model and analyze a cross-layer integrated mobility and service management scheme called DMAPwSR in Mobile IPv6 environments with the goal to minimize the overall mobility and service management cost for serving mobile users with diverse mobility and service characteristics. The basic idea of DMAPwSR is that each mobile node (MN) can utilize its application-layer knowledge to choose smart routers to be its dynamic mobility anchor points (DMAPs) to balance the cost associated with mobility services vs. packet delivery services. These smart routers are just access routers for MIPv6 systems except that they are capable of processing binding messages from the MN and storing the current location of the MN in the routing table for forwarding service packets destined to the MN. The MN's DMAP changes dynamically as the MN roams across the MIPv6 network. Furthermore the DMAP service area also changes dynamically reflecting the MN's mobility and service behaviors dynamically. Unlike previous mobility management protocols such as HMIPv6 that focus only on mobility management, DMAPwSR considers integrated mobility and service management. We develop an analytical model based on stochastic Petri nets to analyze DMAPwSR and compare its performance against MIPv6 and HMIPv6. We validate analytical solutions obtained through extensive simulation including sensitivity analysis of simulation results with respect to the network coverage model, the MN's residence time distribution and the DMAP service area definition.

We propose and analyze a regional registration based mobile multicast service management scheme which we call URRMoM for efficiently supporting mobile multicast in Mobile IP networks. URRMoM extends the design concept of Remote Subscription (RS) by using mobile multicast agents (MMAs), each covering a number of MHs in its service area through tunneling multicast packets received from the multicast source to them. URRMoM adopts a user-central design allowing each mobile host to determine its optimal MMA service area dynamically depending on its service and mobility characteristics. We develop an analytical model to describe the behavior of a MH operating under URRMoM and derive the optimal service area of the MMA based on the MH’s service and mobility characteristics to minimize the total network traffic generated due to multicast packet delivery and multicast tree maintenance. We demonstrate the benefit of our proposed regional registration based mobile multicast service management scheme over existing schemes including RS and RBMoM.

We address reconfigurable algorithms for managing replicated data with strict consistency requirements, that is, whenever the user performs an update operation, the update is applied to all reachable copies as part of the update protocol. A key issue of designing such algorithms is to determine how often one should detect and react to failure conditions so that reorganization operations can be performed at the appropriate time to improve the availability of replicated data. We use dynamic voting as a case study to illustrate how often such failure detection and reconfiguration activities should be performed so as to maximize the data availability. We show that there exists an optimal period at which the failure detection and reconfiguration activities should be performed to optimize the system availability. Stochastic Petri nets (SPNs) are used as a tool to facilitate our analysis.

Voting is a simple and yet effective way of managing replicated data in distributed systems. In this paper we analyze its response time behavior. We investigate a technique for obtaining the access time distribution for requests that access replicated data maintained by the distributed system. The technique is based on Petri net modeling and can be used to estimate the reliability of real-time applications which must access replicate data with a deadline requirement.

Dynamic voting is considered a promising technique for achieving high availability in distributed systems with data replication. To date, stochastic analysis of dynamic voting algorithms is restricted to either site or link Markov models, but not both, possibly because of the difficulty in specifying the state-space which grows exponentially as the number of sites increases. Furthermore, to reduce the state-space, the assumption of "frequent updates" was normally made, which results in an overestimation of the availability. In this paper, we develop a Petri net model that considers both site and link failures and also relaxes the modeling assumption of frequent updates. We test our Petri net model on ring and star network topologies to analyze if availability under dynamic voting can be seriously degraded if updates are not frequent under various site and link failure/repair situations. Finally, We use the Petri net developed in the paper to determine the maximum achievable improvement in availability when null updates are introduced to augment regular updates to keep the status of availability up-to-date.

Pessimistic control algorithms for replicated data permit only one partition to perform update operations at any time so as to ensure mutual exclusion of the replicated data object. Existing availability modelling and analyses of pessimistic control algorithms for replicated data management are constrained to either site-failure-only or link-failure-only models, but not both, because of the large state space which needs to be considered. Moreover, the assumption of having an independent repairman for each link and each site has been made to reduce the complexity of analysis. In this paper, we remove these restrictions with the help of stochastic Petric nets. In addition to including both site and link failures/repairs events in our analysis, we investigate the effect of repair dependency which occurs when many sites and links may have to share the same repairman due to repair constraints. Four repairman models are examined in the paper: (a) independent repairman with one repairman assigned to each link and each node; (b) dependent repairman with FIFO servicing discipline; (c) dependent repairman with linear-order servicing discipline; and (d) dependent repairman with best-first servicing discipline. Using dynamic voting as a case study, we compare and contrast the resulting availabilities due to the use of these four different repairman models and give a physical interpretation of the differences. We show that ignoring concurrent site and link failures/repairs events or repair dependency can very unrealistically overestimate the availability of replicated data.