Mobility management associating all user equipments (UEs) to proper base stations (BSs) (also known as handover, HO) to achieve the designated performance optimization is one of the most crucial functions in mobile networks. Although effective mobility management has received considerable research attentions, existing schemes follow event-driving operations, in which HO decisions are made based on events of performance degradation that BSs/UEs passively suffer from or proactively foresee. However, the performance and decisions of these schemes are highly subject to the identified events, to lose generalization and better performance under unidentified events. To address this issue, we propose a foundation model (FM) based mobility management for the sixth generation (6G) mobile networks inherently supporting artificial intelligence (AI) computing, in which the FM generates the HO decisions for all UEs to maximize the overall throughput under the constraints of ping-pong rate and HO failure (HOF) rate by implicitly taking moving trajectories, traffic demands, channel conditions of all UEs and available resources of BSs into account. To this end, a hierarchical model structure composed of Long Short-Term Memory (LSTM) networks with multi-head attention (MHA) is adopted, which is trained by emulated datasets with augmentation. The performance evaluation results show that the proposed scheme outperforms the state-of-the-art schemes in terms of the average throughput over all UEs while satisfying the required ping-pong rate and HOF rate, and justify the robustness of the proposed FM under different network deployment scenarios.

With the rapid advancement of the Internet of Energy (IoE), peer-to-peer energy trading has become an important mechanism for decentralized energy balancing and distribution-network relief. However, prior works have focused mainly on single platforms or isolated communities, offering limited insight into coordinated operation across multiple communities within a unified IoE ecosystem. This work proposes an IoT-enabled hierarchical trading framework that manages both intra- and inter-community energy transactions through decentralized area platforms and a central coordinating platform. A mixed-integer programming model is formulated to maximize the daily profit of energy-trading operators by optimizing household charging and discharging decisions, supported by distributed renewable generation and battery storage. The framework incorporates a dynamic IoT-driven pricing mechanism that adapts buying and selling prices using real-time measurements from smart meters and local IoE communication networks, ensuring responsive and data-driven market operation under varying conditions. Because the optimization problem is NP-complete, a simplified harmony search (SHS) algorithm is adopted to efficiently generate high-quality solutions. Experiments across 20 system configurations show that SHS outperforms several widely used metaheuristic baselines in both solution quality and run-to-run stability. Results further indicate that larger communities yield higher profits, with the maximum observed when each community contains 25 households. Eliminating the central trading platform causes a drastic reduction in operator profit, confirming its essential role in enabling scalable coordination and equitable energy allocation across heterogeneous communities. These findings highlight the effectiveness of the proposed hierarchical IoE architecture and underscore the importance of centralized coordination in next-generation smart energy systems.

Pedestrian detection in agricultural environments remains an underexplored area in computer vision, particularly in the context of tea plantation management. This study proposes a vision-based personnel detection system customized for tea garden scenarios, aiming to enhance on-site monitoring and labor management. We adopted the YOLOv5 object detection framework and integrated it with the DeepSORT tracking algorithm to develop a real-time pedestrian recognition system. The model was initially trained on the MOT20 dataset and tested on video data collected from real-world tea plantations.Experimental results revealed that models trained on conventional urban pedestrian datasets struggle to maintain detection accuracy in tea garden environments due to occlusions by tea plants, varying lighting conditions, and target sizes. Recognition performance significantly decreased in steep-angle or top-down camera views, highlighting the need for optimized camera deployment and domain-adapted training. To address this, we conducted data augmentation and parameter tuning tailored to the tea plantation context, improving the model’s mean Average Precision (mAP) and tracking consistency. This work contributes a practical framework for customized-area personnel detection in agriculture and underscores the necessity of developing domain-specific datasets for better generalization. The findings provide valuable technical guidance for intelligent agricultural surveillance and pave the way for further research into vision-based monitoring in crop-specific environments.

As chronic diseases continue to rise globally, public health systems are under increasing pressure to find innovative and scalable care solutions. In response, this study introduces an AI-powered health advisory system that leverages generative AI techniques, specifically retrieval-augmented generation (RAG) and integrates seamlessly with LINE and the ChatGPT API. Designed to support individuals managing chronic conditions, the system provides real-time, personalized recommendations, including dietary guidance, medication reminders, and interpretations of health checkup results. Experimental evaluations show that the system achieves over$\mathbf{9 0 \%}$accuracy across key functions, underscoring its potential to enhance self-management and support preventive healthcare strategies.

In precision agriculture systems, camera networks serve as indispensable visual information acquisition devices. However, due to cost constraints and the complexity of field image information, coupled with environmental variability, image quality degradation often occurs, resulting in low-quality visual images. An effective solution for super-resolution (SR) is urgently needed. Traditional image super-resolution methods perform well under laboratory conditions, but their effectiveness is limited in real field environments due to the inability to accurately simulate actual degradation processes. To address this issue, a degradation framework tailored to field scenes is proposed, which involves precise estimation of image degradation features and the design of targeted degradation algorithms. Additionally, drawing inspiration from the powerful global dependency modeling capability of Transformers, an efficient hybrid architecture is designed that combines the local characteristics of convolutional neural networks with the global attention mechanism of Transformers, aiming to improve image reconstruction quality while reducing computational complexity. Experimental results demonstrate that the proposed method outperforms comparative methods, achieving better subjective and objective quality evaluations.

With promising benefits of splitting deep neural network (DNN) computation loads to the edge server, split computing has been a novel paradigm achieving high-quality artificial intelligence (AI) services for the energy-constrained user equipments (UEs). To satisfy the service demands of a large number of UEs, traditional edge-UE split computing evolves toward multitier split computing involving the edge and cloud servers with different capabilities, leading to a “complex” optimization involving communication and computation. To tackle this challenge, in this article, we propose a multitier deep reinforcement learning (DRL) decision-making scheme for distributed splitting point selection and computing resource allocation in the three-tier UE-edge-cloud split computing systems. With the proposed scheme, the high-dimensional optimization can be tackled by the UEs and an edge server with different control cycles through performing local decision-making tasks in a sequential manner. Based on the policies updated by the UEs and the edge server in successive stages, the overall performance of split computing can be continuously improved, which is justified through a theoretical convergence performance analysis. Comprehensive simulation studies show that the proposed multitier DRL decision-making scheme outperforms the conventional split computing schemes in terms of the overall latency, inference accuracy, and energy efficiency to practice multitier split computing.

It has been challenging to find the optimal recipe parameters for semiconductor processes to find a balance between budgets and computing efficiency. Therefore, this study focuses on finding the optimal recipe parameters of a semiconductor process using an improves meta Bayesian optimization (MetaBO) method, which can be trained with extremely few samples and historical data so as to quickly find the optimal process parameter combinations for the product. Experimental results show that the improved MetaBO significantly improves overall quality and efficiency in both model training and new task evaluation.

As the birthplace of soybeans, China has become the world's largest importer and consumer of soybeans. The supply and demand imbalance of soybeans has become one of the significant food and oil security concerns in China.To solve this problem, this paper aims to study a soybean germination rate detection method based on image processing. By replacing manual detection of soybean germination rate and measuring soybean root length with machine-based approaches, basic image processing techniques including grayscale conversion, denoising, binarization, dilation, and erosion are applied to preprocess the images of soybean germination. An improved object detection algorithm is used to extract phenotypic features during soybean germination. The main focus of the study is twofold: (1) Measuring the seed area and the minimum bounding rectangle area of the image regions to determine the germination of seeds by comparing their ratio, thus obtaining the germination rate; (2) Using skeleton extraction and embryo-root separation methods to measure the changes in seed root length during germination, thereby selecting seeds with high vitality and improving the quality of soybean seeds.

To sustain ultra-reliable and low latency communication for the fifth generation (5G) networks, the latency of data forwarding over the core network is conventionally ignored. To significantly reduce the latency, a base station shall not permit to service a new session before the case of unacceptable latency taking place. To this end, the fundamental challenge turns out to proactively cognize that the requirements of reliability/latency are about to be violated. To address this challenge, in this article, a deep reinforcement learning based intelligent session management for the open radio access network is proposed to efficiently allocate the resources for the serving sessions and new sessions. The experimental testing results sufficiently show the practicability of our scheme for the 5G networks.

With continuous advances in novel ICT technologies (including Internet of Things (IoT), artificial intelligence (AI), cloud/fog/edge computing, mobile Internet, and smart terminals), smart cities have provided citizens a more convenient and smarter life with sustainable innovation, including client innovation, open innovation, public innovation, and cooperative innovation.A variety of value-added IoT applications for smart cities have been emerging, e.g., smart home, smart healthcare, smart transportation, smart energy management, smart retailing, smart grid, water system, smart building, smart factory, smart office, air pollution control, and so on.All the sensed information and facilities collaborate together to form economic and effective interaction, providing people with better working efficiency and life quality.Enhanced by ICT technologies, the public municipal services not only improve the government's performance, but also provide citizens with better life quality.Therefore, it has been an important indicator for every country in the world to improve city competitiveness through developing smart cities.The goal of this special issue is to publish both state-of-theart and predictive papers on recent advances in "Wireless Internet" selected from the 12th EAI International Wireless Internet Conference (WiCON 2019), which was held in Taichung (Taiwan) during November 26-27, 2019.After the event, an open call was published to encourage the contributions presented at WiCON 2019 to be extended and submitted to this special issue.After a rigorous review process, eleven high quality papers were selected for publication, which are briefly reviewed in the following.

Supporting ultra-reliable and low latency communication (URLLC) has been a mandatory function for the International Mobile Telecommunications 2020 (IMT-2020) systems and so as 3GPP New Radio (NR). Conventionally, methods for URLLC primarily focus on performance enhancement on the air interfaces, which ignore a fact that data transmissions through the core network (CN) and the backhaul data network (DN) may invoke considerable latency and such latency may not be addressed solely by a local base station (BS). In this case, before the event of unacceptable latency occur, a BS should not accept the request of a new session creation, so as not to violate the latency and reliability requirements of the existing serving sessions and the new session. For this purpose, the critical challenge lies in how to proactively detect/cognize that the latency/reliability requirement violation event is going to occur, which relies on an effective experience update and process. To tackle this challenge, we particularly note the feature of event prediction in machine learning (ML) methods through experience training, especially the capability of sequential decision making to interact with an unknown environment in reinforcement learning (RL). In this paper, an intelligent session management is therefore proposed. Based on the recent innovation of Open Radio Access Network (O-RAN) to sustain the proposed RL scheme for intelligent session management, an O-RAN based BS is able to effectively configure/admin the resources for each existing serving sessions and the new session. Our simulation results fully demonstrate the practicability of the proposed approach in supporting URLLC in O-RAN, to justify the potential of our approach in the design for 3GPP NR.

In large-scale agriculture, insufficient irrigation water may lead to overpumping of groundwater, increasing the risk of land subsidence. Growing dryland crops can effectively decrease the demand for irrigation water. However, the previous works on annual crop planning (ACP) focused on maximizing the profit through growing wetland crops and consuming much water. For sustainability, in this article, we propose a mathematical programming model for an ACP that allocates a land area for growing dryland and wetland crops to maximize the total profit and minimize the total irrigation water used for multiple cropping, under practical constraints. The simplified swarm optimization (SSO) improves the particle swarm optimization with four probabilities to determine the operations of updating solutions. We further propose dynamic SSO (DSSO) to solve the concerned ACP in which the four probabilities are adjusted dynamically according to the performance of the operations executed. Through simulation on a case study, the proposed DSSO demonstrates high performance over some classical approaches.

The next generation vehicular applications substantially shifting the paradigm of human activity have been projected to empower intelligent transportation systems. Targeting at supporting vehicle-to-everything connections, conventional mobile network architectures mandatorily requiring data routing through the core network, however, induce unacceptable costs both in end-to-end latency and backhaul resource consumption. The technical merit of moving computation and storage resources along with mobile vehicles consequently renders the mobile edge computing (MEC) a promising remedy to relieve the burden at the core network. To practice MEC, 3GPP has launched the normative works of a new paradigm known as the local area data network (LADN). Through performing in-network cache to store popular information at LADNs, a vehicle locating within the service area of an LADN is able to access particular location-based wireless application and information. Avoiding data routing through the core network, LADNs, however, encounter two critical challenges in downlink radio access to induce additional latency issues: 1) resource starvation at fronthaul links and 2) discrimination of quality-of-service requirements of vehicles with distinct capabilities. To tackle these challenges, through formulating the Lyapunov function, a stochastic optimization maximizing the utilization of fronthaul resources while stabilizing the queue (and thus latency) of each vehicle is proposed to address the resource starvation. Subsequently, a reinforcement learning-based multiarmed bandit algorithm is further proposed to achieve optimum harmonization of feedback-based and feedbackless transmissions, so as to strikes the tradeoff among energy efficiency, latency, and reliability. The performance evaluation results full demonstrate the effectiveness of the proposed design, to serve urgent needs in the deployment of LADNs.

The advanced computational capabilities of many resource constrained devices, such as smartphones have enabled various research areas including image retrieval from big data repositories for numerous Internet of Things (IoT) applications. The major challenges for image retrieval using smartphones in an IoT environment are the computational complexity and storage. To deal with big data in IoT environment for image retrieval, this paper proposes a light-weighted deep learning-based system for energy-constrained devices. The system first detects and crops face regions from an image using Viola-Jones algorithm with additional face and nonface classifier to eliminate the miss-detection problem. Second, the system uses convolutional layers of a cost effective pretrained CNN model with defined features to represent faces. Next, features of the big data repository are indexed to achieve a faster matching process for real-time retrieval. Finally, Euclidean distance is used to find similarity between query and repository images. For experimental evaluation, we created a local facial images dataset, including both single and group facial images. This dataset can be used by other researchers as a benchmark for comparison with other real-time facial image retrieval systems. The experimental results show that our proposed system outperforms other state-of-the-art feature extraction methods in terms of efficiency and retrieval for IoT-assisted energy-constrained platforms.

Manufacturing is involved with complex job shop scheduling problems (JSP). In smart factories, edge computing supports computing resources at the edge of production in a distributed way to reduce response time of making production decisions. However, most works on JSP did not consider edge computing. Therefore, this paper proposes a smart manufacturing factory framework based on edge computing, and further investigates the JSP under such a framework. With recent success of some AI applications, the deep Q network (DQN), which combines deep learning and reinforcement learning, has showed its great computing power to solve complex problems. Therefore, we adjust the DQN with an edge computing framework to solve the JSP. Different from the classical DQN with only one decision, this paper extends the DQN to address the decisions of multiple edge devices. Simulation results show that the proposed method performs better than the other methods using only one dispatching rule.

Vehicular cloud computing (VCC) system coordinates the vehicular cloud (consisting of vehicles' computing resources) and the remote cloud properly to provide in-time services to users. Although pervious works had established the models for resource allocation in the VCC system based on semi-Markov decision processes (SMDPs), few of them discussed heterogeneity of vehicles and influences of roadside units (RSUs). Heterogeneous vehicles made by different manufacturers may be equipped with different amount of computing resources; and furthermore, RSU can enhance the computing capability of VCC. Therefore, this paper creates an SMDP model for VCC resource allocation that additionally considers heterogeneous vehicles and RSUs, and proposes an approach for finding the optimal strategy of VCC resource allocation. The two additional features significantly elaborate the SMDP model, and demonstrate different results from the original model. Simulation shows that the resource allocation in the VCC system can be captured by the proposed model, which performs well in terms of long-term expected values (consisting of consumption costs of power and time), under various parameter settings.

Developing green energy to be applied in green cities has received much attention. The Internet of energy (IoE) effectively improves networking of distributed green energies through extending smart grids with bidirectional transmission of energy and distributed renewable energy facilities. Previous works on the IoE focused on decisions of IoE operators or optimization of the whole system. However, few considered optimal decisions of a single end user in the IoE. Therefore, this work creates a mixed-integer linear programming (MILP) model for a single end user that considers green energy generation, an energy storage, an electric vehicle, and an IoE-based energy trading platform to reduce energy waste. This model considers a complete system of charging control of multiple facilities of a single end user in the IoE, and allows the end user to purchase energy and sell green energy through the IoE, in which the energy prices of the electrical grid and the IoE platform are set by the power company and the energy market, respectively. Because MILP is NP complete and the proposed model involves a large number of variables and constraints, this paper further proposes a genetic algorithm for this problem, in which a repairing scheme is proposed to handle solution infeasibility of all constraints. By simulation, the proposed algorithm is verified to effectively reduce energy waste.

The papers in this special section examine the use of fog computing applications in industrial electronics. Due to the increased number of connected things in industrial applications, the growing volume and velocity of Internet of Things (IoTs) data exchange urge for more and more communication resources, leading to the bottleneck in terms of data processing, data latency, and traffic overhead. Fog computing emerges as an alternative for traditional cloud computing to support geographically distributed, latency-sensitive, and QoS-aware IoT applications while reducing the burden of data centers in traditional cloud computing. In particular, fog computing with the features (e.g., low latency, location awareness, and capacity of processing large number of nodes with wireless access) to support heterogeneity and real-time applications is an attractive solution to delay- and resource-constraint large-scale industrial applications. However, with the benefits of fog computing, the research challenges arise regarding fog computing for industrial applications.

Different from the International Mobile Telecommunications Advanced (IMT-Advanced) system solely enhancing the transmission data rates regardless the variety of emerging wireless traffic, the IMT-2020 system supports enhanced mobile broadband (eMBB), massive machine-type communications (mMTC) and ultra-reliable and low latency communications (URLCC) to fully capture diverse wireless services in 2020. To satisfactorily gratify the scope of IMT-2020, 3GPP has launched the standardization activity of the fifth generation (5G) New Radio (NR) to deploy the first phase (Release 15) system in 2018 and the ready (Release 16) system in 2020. As eMBB is a legacy system from IMT- Advanced, URLLC jointly demanding low latency and high reliability, and mMTC emphasizes on high reliability may consequently induce significant impacts on the designs of NR air interface. On the advert of the conventional feedback based transmission in LTE/LTE-A designed for eMBB imposing potential inefficiency in the support of URLLC and mMTC, in this paper, we revisit the feedbackless transmission framework, and reveal a tradeoff between these two transmission frameworks. A multi-armed bandit (MAB) based reinforcement learning approach is therefore proposed to achieve the optimum harmonization of feedback and feedbackless transmissions. Our simulation results fully demonstrate the practicability of the proposed approach in supporting URLLC and mMTC, to justify the potential of our approach in the design of 5G NR.

Recently, a revolutionary effort to seek fundamental improvement of 802.11, known as IEEE 802.11ax, has been approved to deliver high efficiency wireless local area network (HEW) technologies for dense scenario. The de-facto random access mechanism of IEEE 802.11ax is uplink OFDMA-based random access (UL OFDMA RA) mechanism. In this paper, we provide a simple but accurate analytical model to study the system efficiency and delay performance of UL OFDMA RA under saturated condition, in the assumption of a finite number of stations and ideal channel conditions in a single-hop WLAN. Simulations are conducted to validate analytical results. In addition, we use this analytical model to identify the appropriate OFDMA-based random access parameter set (RAPS) that can maximize the system performance, a feature which is essential for system design and configuration.

Abstract Digital forensics‐related investigations have been a relative competitive research area by the computer specialists or law enforcement agency officers to confirm or refuse the assumptions with respect to the cybercrime scenes. WeChat is one of the most popular instant messaging application programs in contemporary age. While applying WeChat, there is precious information deposited in the computing devices that could be supportive evidences in a court of law. Hence, this research is focusing on the application of WeChat on the desktop PC under Windows 7 by means of the acquisition of volatile digital evidences in order to provide supporting information for some cybercrime cases. This paper provides generic investigation methods conducting forensic sound analysis on WeChat from the aspect of data volatility of the computing devices. By means of the proposed methodologies, the digital forensics specialists should be able to gather critical digital traces to confirm the previous actions of a WeChat user or even to reconstruct the crime scene to prove the suspect is guilty or innocent in a court of law. Copyright © 2017 John Wiley & Sons, Ltd.

Abstract In a vehicularad hocnetwork (VANET) environment, it is not easy to keep data secure and private. If the private data are stolen, the hacker might have the user identity and do some bad things. To provide secure transmitting, some data encryption mechanisms are necessary. In this paper, we proposed a group key management mechanism for data communication in VANETs. It is exploited on the basis of a decentralized architecture with the hierarchical key tree and cluster heads. The proposed mechanism not only provides a rekeying method in reducing the communication cost but also furnishes the forward security and backward security. Copyright © 2012 John Wiley & Sons, Ltd.

IEEE 802.11p Wireless Access in the Vehicular Environment (WAVE) has been serving as the de facto wireless protocol for a Vehicular Ad hoc Network (VANET) with the explosive growth of vehicular applications. These applications require guaranteed Quality-of-Service (QoS). However, the fundamental channel access mechanism of WAVE, Enhanced Distributed Channel Access (EDCA), is not able to provide guaranteed QoS due to the unpredictable random access. To remedy this problem, we propose a novel channel access scheme, called Earliest Deadline First based Carrier Sense Multiple Access (EDF-CSMA). EDF-CSMA based on EDCA dynamically adjusts the priority of real-time streaming to avoid collision and introduces an admission control policy according to time constraints to provide guaranteed QoS in multi-channel environments. An analytical model is carried out to study and compare the channel utilization of EDF-CSMA and QoS-aware Hybrid Coordination Function (HCF) controlled channel access (HCCA) method. The result shows that 60 percent of channel utilization is improved by EDF-CSMA. Additionally, real video-based simulations are conducted to evaluate the performance of EDF-CSMA and the existing EDCA method. The results show that EDF-CSMA reaches better QoS support than EDCA while maintaining efficient channel utilization.

In the light emitting diode (LED) manufacturing industry, the most expensive and crucial facilities are manufacturing machines. Condition-based maintenance (CBM) for crucial components of a manufacturing machine aims to forecast in advance the precise time when some aging component will be broken and replace it in time, to avoid performing abnormally to manufacture defect products. This study focuses on the CBM for a crucial component called particle filter of a pneumatic conveyor machine in the LED epitaxy plant. Conventional forecasting methods were based on the theory of statistics, which requests a large number of data samples and assumes some probability distribution. With advance of machine technology, however, the data samples of broken particle filters to be collected are very few, such that those conventional methods cannot be applied. As a result, this study proposes a novel hybrid grey forecasting and harmony search approach, in which grey forecasting was shown to perform well for small data samples. In the proposed method, operating conditions of particle filters are monitored and collected by industrial sensors. Then, those data are preprocessed by data filtering and clustering. Finally, a hybrid grey forecasting and harmony search approach is used to fit the curve of the aging condition of a particle filter. Numerical analysis of a real example in an LED epitaxy plant shows that the proposed method performs better than conventional methods.

In the age of multimedia big data, the popularity of mobile devices has been in an unprecedented growth, the speed of data increasing is faster than ever before, and Internet traffic is rapidly increasing, not only in volume but also in heterogeneity. Therefore, data processing and network overload have become two urgent problems. To address these problems, extensive papers have been published on image analysis using deep learning, but only a few works have exploited this approach for video analysis. In this article, a hybrid-stream model is proposed to solve these problems for video analysis. Functionality of this model covers Data Preprocessing, Data Classification, and Data-Load-Reduction Processing. Specifically, an improved Convolutional Neural Networks (CNN) classification algorithm is designed to evaluate the importance of each video frame and video clip to enhance classification precision. Then, a reliable keyframe extraction mechanism will recognize the importance of each frame or clip, and decide whether to abandon it automatically by a series of correlation operations. The model will reduce data load to a dynamic threshold changed by σ, control the input size of the video in mobile Internet, and thus reduce network overload. Through experimental simulations, we find that the size of processed video has been effectively reduced and the quality of experience (QoE) has not been lowered due to a suitably selected parameter η. The simulation also shows that the model has a steady performance and is powerful enough for continuously growing multimedia big data.

Recently, IEEE 802 started a task group to investigate and deliver next generation WLAN technologies for the scenarios of dense networks with a large number of stations and access point. The proposal is specified as the IEEE 802.11ax amendment. Due to the significant network capacity increase achieved by 802.11ax, the term high-efficiency WLAN (HEW) is also used in reference to this new amendment. This paper summarizes the IEEE 802.11ax standardization activities in progress and presents an overview of the most important features proposed in the 802.11ax amendment. Expected features and challenges for 802.11ax in the design of physical layer (PHY) and media access control sub-layer (MAC), toward a new era of wireless LANs, are also discussed.

Social networks are merely a reflection of certain realities among people that have been identified. But in order for people or even computer systems (such as expert systems) to make sense of the social network, it needs to be analyzed with various methods so that the characteristics of the social network can be understood in a meaningful context. This is challenging not only due to the number of people that can be on social networks, but the changes in relationships between people on the social network over time. In this paper, we develop a method to help make sense of dynamic social networks. This is achieved by establishing a hierarchical community structure where each level represents a community partition at a specific granularity level. By organizing each level of the hierarchical community structure by granularity level, a person can essentially “zoom in” to view more detailed (smaller) communities and “zoom out” to view less detailed (larger) communities. Communities consisting of one or more subsets of people having relatively extensive links with other communities are identified and represented as overlapping community structures. Mechanisms are also in place to enable modifications to the social network to be dynamically updated on the hierarchical and overlapping community structure without recreating it in real time for every modification. The experimental results show that the genetic algorithm approach can effectively detect hierarchical and overlapping community structures.

ABSTRACT The vehicular ad hoc network (VANET) is a novel wireless network and suffers security threats as other networks. Information exchanged in the network might be stolen by malicious users, and some secure mechanisms are needed to provide the privacy. In VANET, the vehicles can be separated to different groups. When vehicles in the same group want to communicate with others, they can use the group key to protect the messages, and the privacy in the group communication is preserved. In this paper, we propose a group key management method with centralized key management scheme for VANET. In each group, there is a group leader, and the group leader will manage the members and the keys. By managing the group keys properly, the communication in the group can be secure. In the performance evaluation and analysis, our proposal provides better rekey mechanisms to related works and also achieves the forward secrecy and backward secrecy. Copyright © 2012 John Wiley & Sons, Ltd.

ABSTRACT Recently, mobile networks and radio frequency identification (RFID) technologies are both widely applied by many enterprises to develop their applications. The third‐generation (3G) mobile network technology is one of the popular mobile network technologies because service providers can serve users anytime and anywhere through 3G mobile network technology. A solution that combines RFID technology with 3G mobile network technology could identify users anytime and anywhere. However, RFID tags are limited in computation power and suffered from security threats, especially on mobile networks. An authentication with low‐cost RFID tags in Mobile Networks should be proposed in this paper. This paper designs a key agreement mechanism to achieve consistency, integration, legality, and safety. In order to design a low‐cost scheme, our authentication mechanism is built of simple XOR operations and one‐way hash function. Although the computation cost of our proposed mechanism is not the best, our proposed mechanism can achieve more security requirements for practical applications. Copyright © 2013 John Wiley & Sons, Ltd.

This study proposes an analytic localization algorithm by utilizing radical centers. Assume that a target node (blind node) can measure its distances to three or more anchor nodes (reference nodes). By picking three distance measurements to three anchor nodes, a radical center is computed. Through analytic formulation, the radical center is shown to be able to be treated as an estimation of the target node location. Since every three distance measurements generates one radical center, to further improve and fuse these estimations, effective filtering mechanisms are then proposed to filter out the improper estimations. Afterwards, the remaining radical centers are averaged, and the solution is the final estimation of the target node location. The location errors of the proposed method and the conventional MMSE (Minimum Mean Square Error method) are analytically compared. It is shown that the proposed algorithm outperforms the conventional MMSE method both in accuracy and efficiency. Extensive computer simulations were carried out and the results verify the advantage of the proposed location algorithm over the MMSE approach.

Topology construction, the initial step of the topology control, is an important technique for a wireless ad hoc network to be energy-efficient. In [4], we have proposed to use the relative neighborhood graph (RNG) to obtain an RNG-based topology in which the transmission ranges between wireless nodes are reduced. Then, among the RNG-based topology, we proposed a green clustering algorithm (GCA) to organize the wireless nodes into a clustered network topology. In this paper, we further analyze the energy consumption in exchanging data packets and cluster maintenance messages. Simulation results confirm that the proposed RGCA (i.e., combining the RNG and GCA) provides a way to construct an energy-efficient cluster topology for wireless ad hoc networks.

According to our studies, the current contention resolution algorithm adapted in wireless ad-hoc networks, binary exponential backoff scheme, does not function well in multi-hop environments due to its several performance issues and technical limitations. For example, unfair channel access, intensive collision, and throughput degradation are several widely known issues. Besides, BEB cannot support multimedia traffic since it does not include any priority mechanism. In this paper, we put forth a simple, fair channel access, priority provision, and well performed contention resolution algorithm for multi-hop wireless ad-hoc networks. Simulations are conducted to evaluate the performance scheme. As it turns out, the results show that the proposed algorithm can effectively alleviate the fairness problem and support multimedia traffic in multi-hop wireless ad-hoc networks.

ABSTRACT In the convergence network, the different technologies can be integrated to offer pervasive networking. Although it is able to provide better networking ability, the technical differentiation will make the management be more difficult. Especially when the security issues are considered, it is very necessary and important to develop some proper mechanisms for such a convergence environment. In this paper, we propose a secure authentication mechanism for third‐generation telecommunication (3G) and wireless local area network integrated networks. The proposed scheme is developed based on TS 33.234 and improves the network security with radio frequency identification tags. The analysis shows that the proposed scheme not only provides more functions but also satisfies more secure considerations for authentication in 3G network and wireless local area network convergence environment. Copyright © 2011 John Wiley & Sons, Ltd.

Current medium access control (MAC) protocols for wireless sensor networks (WSNs) do not support prioritized channel access and often suffer from varying degrees of starvation and fairness problems. That is, with a little bad luck, a sensor node might have to wait arbitrarily long to send its frame, and thus results in the invisibility of some sensed area. In this paper we introduce a simple, but yet well performing starvation free backoff scheme based on sensor MAC (S-MAC) protocol for WSNs. The proposed scheme allows a sensor node to adjust its backoff window size according to its re-transmission times. An analytical analysis is carried out to study the saturation throughput of proposed scheme under backlogged conditions. In addition to the theoretical analysis, simulations were also conducted to analyze the proposed scheme and the results show that our scheme can effectively alleviate the starvation and fairness problem and achieve better performance in high density WSNs.

Due to rapid advances in wireless network technologies, and the maturity of portable communication devices, ubiquitous wireless computing has become increasingly popular. One of the most important issues in ubiquitous wireless computing is the reduction of power consumption of the portable devices. In this study, we propose a traffic-aware power-saving protocol for IEEE 802.11 multi-hop ad hoc networks. In the proposed protocol, the mobile stations determine their sleep-awake patterns based on a quorum approach. In general, the length of the quorum cycle is an important factor in the sleep-awake ratio, and will determine the power consumption ratio. In light of this observation, the proposed protocol dynamically adjusts the length of the quorum cycle by considering the network utilization during its previous quorum cycle. Simulation results show that our proposed protocol can effectively reduce power consumption in mobile Ad Hoc networks.

This study achieves Quality-of-Service (QoS) management in heterogeneous networking using a distributed multi-agent scheme (DMAS) based on the concept of cooperation and the awareness algorithm. The proposed scheme is developed for supporting QoS management in a user-accepted and cost-effective fashion, which consists of a collection of problem-solving agents with three modules: the knowledge source, the in-cloud blackboard system, and the control engine built into the scheme. A set of problem-solving agents autonomously process local tasks and cooperatively interoperate via an in-cloud blackboard system to guarantee QoS. An awareness algorithm, called the Q-learning algorithm, calculates the exceptive rewards of a handoff to all access networks. These rewards are then used by these problem-solving agents to determine what to do. Through operations and cooperation among the active agents, a policy is selected and a user-accepted schedule that meets the specified QoS is generated. Compared with traditional QoS management mechanisms, the proposed DMAS scheme has a 36% lower packet loss ratio in video streaming applications and a 34% lower average delay in VoIP applications with only a minor sacrifice in system computational complexity.

Network Address Translation (NAT) technology is popularly applied to solve the depletion of IPv4 address. In general, it is an obstacle for the devices to communicate with the devices behind NAT directly based on the TCP/IP protocol. In this paper, we proposed a low cost transparent NAT traversal (LCTNT) method to present hole-punch technique for NAT by using exiting architecture without any modification. Our proposed method has the advantages of efficient IP address translation mechanism that is compatible with all existing application programs and TCP/IP protocol. Several experiments have been conducted to demonstrate the performance of our work.

In this paper we introduce an analytical model to analysis the saturation throughput of SSB algorithm, and then we use this analytical model to find out an optimal value of threshold for SSB algorithm that maximizes the network capacity based on the current channel status. Simulations are conducted to evaluate the performance of SSB protocol. The results confirm that the performance of SSB can be greatly improved by proper choice of the threshold according to the current channel status.

In this paper, an investigation toward mobile P2P architecture brings a new insight to realize future mobile cloud computing systems. Although mobile devices capacity in storing data and running computational process is very limited, the main problem for implementing mobile cloud computing environment is still motivated by the classic issue of information properties query process. Heterogeneous wireless networks (HWNs) architecture then exploited to solve this fundamental problem. The HWNs information properties dispersion model based on coverage area of mobile node aid to simplify the querying process. By HWN and mobile P2P integration a reliable mobile cloud computing service can perform services and resources exchange among peer nodes. In addition this integrated architecture gives the extension of mobile P2P into Network as a Service (NaaS) approach to resource sharing among mobile users.

In the sensor network, the system behaviours are usually presented according to the data monitored, but there are many malicious users who might attack the data. In order to make the sensor network applications work smoothly, it is important to provide the data transmission in highly secure. In this study, we will propose a secure, flexible and integrated conference key mechanism for the sensor networks. The proposal is developed by the elliptic curve cryptography and Lagrange interpolation. Compared to the other researches, the proposal provides lower computing cost, and improves some security problems. The security analysis shows that the proposed mechanism is able to defeat the security attacks, and provide the secure communication for the sensor network.

As Internet Protocol (IP) multicast allows the efficient use of network bandwidth for multipoint communication, it is expected to be an essential communication type for delivering multimedia services to mobile nodes (MNs). In this study, the authors address an issue of consumer multicast traffic support in a Proxy Mobile IPv6 (PMIPv6) environment wherein network-based mobility management is deployed for MNs. The recently standardised PMIPv6 multicast listener support provides options for deploying multicast listener functions in a PMIPv6 domain, whereas it does not address specific optimisations and efficiency improvements of multicast routing. The authors first review the PMIPv6 multicast listener support and point out the limitations of the current approach. Then, propose an improved multicast handover procedure that optimises multicastgroup management by utilising the context of consumer's MN running multicast applications. The authors develop analytical models to evaluate the proposed multicast handover procedure compared with the base one. From the conducted analysis, it is demonstrated that the proposed multicast handover procedure minimises the service interruption time and prevents the multicast packet loss during handovers. Furthermore, in terms of signalling cost consumption, the proposed multicast handover procedure operates on an equal basis with the base one.

Social Networking Service (SNS) emerges to be one of the most promising directions of web applications regarding the next generation of Internet technology evolutions. Substantively, innumerable global on-line community members share common interests with each other via the User Generated Content (UGC) platforms. Facebook is one of them and it facilitates the social networking participants to deliver the digital contents to authorized consumers or specific groups. As cybercrimes mushroom in recent years, more and more digital crime investigations have strong relations to Facebook. Unarguably, Facebook has been exploited via global perpetrators. Consequently, we spotlight on live data acquisition within the RAM of the desktop PC with emphasis on some distinct strings that could be found in order to reconstruct the previous Facebook session, which plays an extremely precious role for the associate digital forensics investigators to incubate additional thoughtful decisions concerning the discovery of breadcrumb digital evidences in this unparalleled cybercrime incidents era.

Abstract Windows Live Messenger (WLM) is one of the most prevalent ubiquitous Instant Messaging (IM) application programs being used by voluminous people worldwide to establish their cyber social lives. It dramatically changes the way of communication for human beings in the past decade in all aspects. However, WLM is like a sharp double‐edge sword, especially the exploited vulnerabilities concerning information security. Consequently, there is an urgent necessity for Digital Forensics (DF) to be strongly enforced within the organizations in order to specifically identify the real causes of the information breaches when utilize WLM. Few researches have formally incorporated the DF of WLM into generic guidelines for the associate personnel to follow. In this paper, we provide the system architecture of the experiment accompanied with our proposed Check Point (CP) methodology trying to disclose the possible digital evidences that could be explicitly collected and scientifically presented as probative evidences with respect to the persistently mushrooming information security incidents in the next generation wireless communication networks. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract Portable electronic communication devices can be used for many purposes and they are capable of integrating with ubiquitous computing (UC) infrastructures to carry on mobile multimedia communications. As those devices become prevalent, they incur potential network security threatening to organizations. Nowadays, the Skype is the most popular P2P VoIP application program, which is being used by millions of global users to place IP phone calls, transfer files, or communicate via instant messaging (IM). This phenomenon already generates imminent network security issues that are indispensable to digital forensics researchers or the law enforcement agencies worldwide. Cellular phones, smart phones, and personal digital assistants (PDAs) are the representative ones of those devices and there are some open sources or commercial software toolkits that can be utilized to proceed the forensics investigation concerning the electronic crimes in next generation communications (NGCs). A case review was conducted to illustrate the hidden digital trails within the PDA from the Registry of the Windows Mobile and volatile data in the RAM to discover the possible network security leakage scenarios that resulted in the vandalism of intangible digital assets of the organization. Copyright © 2010 John Wiley & Sons, Ltd.

Binary exponential backoff (BEB) algorithm, the defacto standard contention resolution algorithm providing collision avoidance over ad hoc wireless networks, has several technical limitations. For instance, high collision rate problem, unfair channel access, and throughput degradation are several widely known issues. In this paper, a self-adaptive backoff algorithm, namely slow start backoff (SSB) algorithm, is proposed and analyzed for ad hoc networks. Numerical results show that the proposed SSB algorithm outperforms the legacy BEB algorithm. In addition to the theoretical analysis, simulations were conducted to verify the analysis, showing that the proposed scheme works very well in ad hoc wireless networks.

In IEEE 802.11 standard, the use of multi-channel is one of the most important characteristic features contributing to the improvement of the overall network throughput. However, the channel utilization based on the use of multi-channel is relatively low because most medium access control (MAC) protocols were designed based primarily on the application of non-overlapping channels. This paper analyzes the channel utilization for the schemes using both non-overlapping and overlapping channels. Based on the analysis, we propose a scheme to exploit overlapping channels with collision avoidance mechanism. The proposed scheme makes use of the non-overlapping channels to increase the network throughput and it also takes into account the transmissions on different channels in order to maximize the throughput. Simulation results verify the effectiveness of the proposed scheme.

As wireless networks have been widely deployed for public mobile services, predicting the location of a mobile user in wireless networks became an interesting and challenging problem. If we can predict the next cell which the mobile users are going to correctly, the performance of wireless applications, such as call admission control, QoS and mobility management, can be improved as well. In this paper, we propose a mobility prediction algorithm based on dividing sensitive ranges. The division is in accordance with the cell transform probability. Then different prediction methods are applied according to the sensitivity of the range to gain high precision. Simulations are conducted to evaluate the performance of the proposed scheme. As it turns out, the simulation results show that the proposed scheme can accurately predict the location for mobile users even in the situation of lacking location history.

In a multi-agent system, there are many intelligent agents distributed in the network. To connect with these agents easily and arbitrarily, wireless ad hoc network is a candidate. Wireless ad hoc network is a distinct network environment with characteristics in self-organisation, dynamical network topologies and easy construction. To provide such networks with secure communication, many researchers tried different ways to create the session key(s). However, there are some drawbacks in session key generation procedure, and the robust session key could not be created. The authors propose a cluster-based secure communication mechanism in wireless ad hoc networks. The proposal applies the Diffie–Hellman key exchange protocol for the session key creation. It helps the clusterhead to reduce the communication overload, to avoid the time synchronisation problem in nodes authentication and to preserve the complete secrecy requirements.

In this paper, we propose a scalable transport scheme for delivering on-demand video streams over broadband wireless networks in next-generation network/IP multimedia subsystem (NGN/IMS) architecture. The proposed transport scheme makes use of fine-granular-scalability (FGS) encoded videos to accommodate high bandwidth variation of wireless networks. We formulate the bandwidth allocation to FGS-encoded streams as a resource allocation problem and develop a quality-aware bandwidth allocation scheme, called QABA. With QABA, the proposed transport scheme can dynamically adjust the bit rate allocated to different streams to maximize the overall perceptual quality when available network bandwidth varies with time. QABA is theoretically proven to be able to find the optimal bandwidth allocation for all on-demand streams. To validate the effectiveness of QABA, extensive trace-based simulations are performed.

Abstract CBR (constant bit rate) traffic is expected to be an important traffic source in wireless networks. Such sources usually have stringent jitter or delay requirements and in many cases they should be delivered exactly as they were generated. In this paper, we propose a strictly guaranteed QoS (quality‐of‐service) provisioning CAC (call admission control) scheme with a polling‐based scheduling policy for CBR traffic in IEEE 802.11e wireless LANs. Under such a scheme, the proposed transmit‐permission policy for HCCA (HCF controlled channel access) method can forecast the maximum suffered delay for each packet and derive sufficient conditions so that all the CBR sources satisfy their time constraints to provide deterministic QoS guarantees. A simple analytical model is carried out to estimate the average queueing delay of the proposed scheme. In addition to theoretical analysis, simulations are conducted to validate its promising performance. Our simulation results show that the proposed scheme maintains a high throughput with respect to the whole range of system load. Copyright © 2009 John Wiley & Sons, Ltd.

One of the major challenges in transmitting real time video data in broadband wireless access (BWA) systems is that the bit rate of the video traffic varies from time to time, and hence the bandwidth utilization is low. To cope with the difficulties, we propose an enhanced uplink scheduling algorithm for variable bit rate video (VBR) traffic transmission in IEEE 802.16 BWA systems. In the proposed algorithm, the base station (BS) assigns uplink bandwidth to the video user by considering the traffic state transitions. The uplink bandwidth is divided into several intervals and each interval represents a traffic state. Only when the traffic state is changed, the bandwidth request process is incurred. Also, by using two reserved bits in the generic MAC header of IEEE 802.16 BWA systems as piggyback bits, the information of video traffic state transition can be sent to the BS without extra overhead. Simulations conducted with QualNet show that the proposed algorithm provides better performance in that the bandwidth waste ratio of our proposed algorithm is less than that of the conventional algorithms.

In this paper, we proposed a backoff mechanism to improve the performance of initial ranging for the IEEE 802.16 standard. In the proposed scheme, the size of contention window in the initial ranging is combined with exponential and linear backoff mechanisms, and it is adjusted based on the current network environments. In addition, a threshold in the proposed scheme is set to find out the current network situation. If the contention window size is smaller than the threshold, the contention window size is tuned exponentially in response to a light network traffic load. On the other hand, when the contention window size is larger than the threshold, the contention window size is tuned linearly in response to a congested scenario. The numerical simulation results show that the proposed scheme can achieve a better performance in terms of collision rate, average access delay, and system throughput in both light and congested environments.

Abstract IEEE 802.11 protocol supports adaptive rate mechanism, which selects the transmission rate according to the condition of the wireless channel, to enhance the system performance. Thus, research of multi‐rate IEEE 802.11 medium access control (MAC) performance has become one of the hot research topics. In this paper, we study the performance of multi‐rate IEEE 802.11 MAC over a Gaussian channel. An accurate analytical model is presented to compute the system saturation throughput. We validate our model in both single‐rate and multi‐rate networks through various simulations. The results show that our model is accurate and channel error has a significant impact on system performance. In addition, our numerical results show that the performance of single‐rate IEEE 802.11 DCF with basic access method is better than that with RTS/CTS mechanism in a high‐rate and high‐load network and vice versa. In a multi‐rate network, the performance of IEEE 802.11 DCF with RTS/CTS mechanism is better than that with basic access method in a congested and error‐prone wireless environment. Copyright © 2008 John Wiley & Sons, Ltd.

According to the latest version of the IEEE 802.11 standard, the backoff parameters of its collision avoidance mechanism are far from optimal, especially in a heavy load or error-prone WLAN environment. This strategy has a high collision probability and channel utilization is degraded in bursty arrivals or congested scenarios. Besides, the standard backoff mechanism may treat noise corruption as packet collisions. In this paper, we identify the relationship between backoff parameters, contention level, and channel BER in order to propose a simple, but yet well-performing distributed algorithm that allows a station to dynamically adjust its contention window size based on turn-around-time measurement of channel status. In addition to theoretical analysis, simulations are conducted to evaluate its performance. The proposed scheme works very well in providing a substantial performance improvement in heavy loaded and error-prone WLAN environments.

IEEE 802.11, the standard of wireless local area networks (WLANs), allows the coexistence of asynchronous and time-bounded traffic using the distributed coordination function (DCF) and point coordination function (PCF) modes of operations, respectively. In spite of its increasing popularity in real-world applications, the protocol suffers from the lack of any priority and access control policy to cope with various types of multimedia traffic, as well as user mobility. To expand support for applications with quality-of-service (QoS) requirements, the 802.11E task group was formed to enhance the original IEEE 802.11 medium access control (MAC) protocol. However, the problem of choosing the right set of MAC parameters and QoS mechanism to provide predictable QoS in IEEE 802.11 networks remains unsolved. In this paper, we propose a polling with nonpreemptive priority-based access control scheme for the IEEE 802.11 protocol. Under such a scheme, modifying the DCF access method in the contention period supports multiple levels of priorities such that user handoff calls can be supported in wireless LANs. The proposed transmit-permission policy and adaptive bandwidth allocation scheme derive sufficient conditions such that all the time-bounded traffic sources satisfy their time constraints to provide various QoS guarantees in the contention free period, while maintaining efficient bandwidth utilization at the same time. In addition, our proposed scheme is provably optimal for voice traffic in that it gives minimum average waiting time for voice packets. In addition to theoretical analysis, simulations are conducted to evaluate the performance of the proposed scheme. As it turns out, our design indeed provides a good performance in the IEEE 802.11 WLAN's environment, and can be easily incorporated into the hybrid coordination function (HCF) access scheme in the IEEE 802.11e standard.

The DQRUMA (distributed-queueing request update multiple access) protocol has been considered as an access protocol for the BAHAMA (broadband ad hoc wireless ATM local area network). However, it cannot support the service discipline of integrated multimedia traffic since it does not include any priority and access control policy. In this paper, we propose a nonpreemptive priority-based access control scheme for the DQRUMA protocol. Under such a scheme, modifying the CSMA/CA protocol in the contention period supports many levels of priorities such that user mobility (handoff) can be supported in BAHAMA. Besides, the proposed transmit-permission policy and adaptive bandwidth allocation scheme provide various QoS (quality-of-service) guarantees while maintaining high bandwidth utilization. Simulations show that it provides a good performance in ad hoc wireless ATM LAN environments.