In Internet-of-Things (IoT) edge inference systems, Softmax is a core component in multi-class classification heads and attention mechanisms. In resource-constrained IoT edge devices, conventional numerically stable implementations require multiple passes over the data, which increases memory band-width pressure and leads to hardly predictable variable processing latency. This paper presents Conditional Online Softmax, a control-flow-predictable algorithm that fuses reduction and normalization into a single pass to cut memory traffic while preserving numerical stability. We pair it with a branch-free vectorized kernel implemented using RISC-V Vector Extension (RVV) intrinsics, which better exploits instruction-level parallelism on low-core-count edge processors. On a standard RISC-V edge platform, Conditional Online Softmax achieves a 7% speedup over the numerically stable safe Softmax, and a 43% speedup over the original online Softmax. Furthermore, manual RVV vectorization delivers a 2:9× speedup compared to GNU Compiler Collection (GCC) auto-vectorization. The resulting design reduces cache misses and improves latency predictability, making it a drop-in replacement for Softmax in streaming and micro-batch pipelines commonly found in IoT gateways and sensor nodes. The implementation is portable across vector length (VLEN) and standard element width (SEW) settings and requires no specialized accelerators, which simplifies its deployment in practical IoT edge inference systems.

ABSTRACT Traditional imaging‐ and audio‐based sensing systems often face issues with environmental interference, privacy, and security. Wi‐Fi Channel State Information offers a non‐invasive alternative for human behaviour sensing but lacks precision in full‐body activity recognition. This study presents a sustainable edge computing system that integrates a 3D Convolutional Neural Network and a Bidirectional Gated Recurrent Unit (Bi‐GRU) with attention for real‐time human skeleton pose reconstruction. By aligning Wi‐Fi CSI with Kinect‐captured posture data, the system extracts spatial–temporal features to generate accurate 3D skeletal models. It accurately identifies trunk and posture by combining the precision of vision‐based recognition with the non‐invasive advantages of CSI‐based sensing. Leveraging edge computing enhances energy efficiency and reduces cloud transmission needs, making it suitable for sustainable healthcare, smart homes, and remote monitoring in resource‐limited settings.

Fine-grained visual classification (FGVC) in sustainable manufacturing faces challenges due to the diverse, complex, and highly similar objects in manufacturing environments. Traditional convolutional neural networks often require extensive annotations and high computational costs, limiting their effectiveness. This study introduces a fuzzy optimization feature fusion model (FOFFM) based on vision transformer, designed to enhance FGVC accuracy and efficiency. FOFFM addresses challenges, such as information loss during image-to-token mapping and high category similarity, by optimizing classification token capabilities and leveraging contrastive loss. By enhancing resource efficiency and reducing redundant computations, FOFFM contributes to lower energy consumption and operational costs, directly supporting sustainable manufacturing practices. Experimental results on the NABirds dataset demonstrate FOFFM's competitive performance with a streamlined, resource-efficient end-to-end training process. Unlike other methods, such as TransFG, FOFFM reduces computational complexity while maintaining robust accuracy, making it highly suitable for practical applications in sustainable manufacturing, particularly in optimizing resource utilization and minimizing environmental impact. This work provides valuable insights for manufacturing data analysis and contributes to advancing FGVC in sustainable manufacturing contexts.

While cloud computing services such as cloud storage are fairly mature, it remains challenging to design efficient and secure cryptographic schemes to facilitate fine-grained access control and achieve other features. For example, existing ciphertext-policy attribute-based encryption schemes do not generally have in-place limits on the number of access or access duration, which can consequently be exploited to perform economic denial of sustainability and other attacks. In addition, improving system efficiency can be challenging in large-scale operations. Thus, in this paper, we present a blockchain-based access control with k $k$ -times tamper resistance. Our proposed approach allows one to set and enforce quota/limits on the number of accesses allowed for each user, whose integrity is ensured using blockchain. In addition, our proposed approach also allows multiple attribute authorities to coexist and work together with a central authority to facilitate secret key distribution and improve system efficiency. We then evaluate the security and the efficiency of our proposed approach to demonstrate its utility.

A wireless rechargeable sensor network was proposed to extend the lifetime of the wireless sensor network. In this article, a charger is combined together with a self-propelled vehicle to provide a more flexible result of charger deployment. The dynamic chargers path selection problem is defined and mapped into the traveling salesman problem. Four metaheuristic algorithms for Internet-of-Things (IoT) applications are designed, and the higher fitness value between the charging path and the number of dead IoT devices is achieved. However, metaheuristic approaches may spend more time on searching solutions so that many IoT devices overuse limited power and fail to be charged for a long time, leading to power exhaustion. In this article, the edge computing technique is applied to accelerate the obtainment of charging paths with the well-defined edge/centralized unit switching. Moreover, to assure the calculated path trustworthy and will not be tampered with, the blockchain technology is adopted. The proposed architecture maintains high-level information credibility while transmitting the information of charging paths within the cloud and edge. The simulation results showed that the proposed method is capable of achieving better charging efficiency and less deployment cost.

The vehicular ad hoc network (VANET) is a type of mobile wireless networks, where vehicles are allowed to broadcast a message to its neighbors and access data from other participants. However, how to guarantee the reliability of these broadcast messages and prevent malicious vehicles from accessing the private data of the VANETs is still an open problem to be solved. As a countermeasure, a fuzzy-based trustworthiness evaluation scheme for privilege management in VANETs is proposed in this article. In the proposed scheme, to ensure the result of trustworthiness is valid, mutual authentication with conditional anonymity between the evaluator and the vehicle to be evaluated is first employed. Then, based on the vehicle's behavioral big data, the trustworthiness of each vehicle is evaluated by utilizing the fuzzy theory. Note that the privilege of a vehicle and the reliability of the vehicle's messages are determined by its trustworthiness. Moreover, the mobility of vehicles is also considered in this article, since the location of a vehicle is not constant and the monitoring area of an road side unit is limited. The results of theoretical and experimental analyses demonstrate that the proposed scheme performs well in terms of security and efficiency.

For industrial applications of the artificial intelligence of things, mechanical control usually affects the overall product output and production schedule. Recently, more and more engineers have applied the deep reinforcement learning method to mechanical control to improve the company's profit. However, the problem of deep reinforcement learning training stage is that overfitting often occurs, which results in accidental control and increases the risk of overcontrol. In order to address this problem, in this article, an expected advantage learning method is proposed for moderating the maximum value of expectation-based deep reinforcement learning for industrial applications. With the tanh softmax policy of the softmax function, we replace the sigmod function with the tanh function as the softmax function activation value. It makes it so that the proposed expectation-based method can successfully decrease the value overfitting in cognitive computing. In the experimental results, the performance of the Deep Q Network algorithm, advantage learning algorithm, and propose expected advantage learning method were evaluated in every episodes with the four criteria: the total score, total step, average score, and highest score. Comparing with the AL algorithm, the total score of the proposed expected advantage learning method is increased by 6% in the same number of trainings. This shows that the action probability distribution of the proposed expected advantage learning method has better performance than the traditional soft-max strategy for the optimal setting control of industrial applications.

Cognitive computing over big data brings more development opportunities for enterprises and organizations in industrial informatics, and can make better decisions for them when they face data security challenges. To satisfy the requirement of real-time data storage in industrial Internet of Things (IoT), the remote unconstrained storage cloud is usually used to store the generated big data. However, the characteristic of semitrust of the cloud service provider determines that the data owners will worry about whether the data stored in cloud computing has been corrupted. In this article, a secure storage auditing is proposed, which supports efficient key updates and can be well used in cognitive industrial IoT environment. Moreover, the proposed basic auditing can be extended to support batch auditing that is suitable for multiple end devices to audit their data blocks simultaneously in practice. In addition, a hybrid data dynamics method is proposed, which employs a hash table to store the data blocks and uses a linked list to locate the operated data block. Compared with previous methods, the data block location time in the proposed data dynamics can be reduced by 40%. The security analysis results demonstrate that the proposed scheme can be proved to be correct, and is secure under computational differ-hellman (CDH) and discrete logarithm (DL) assumptions.

Cloud RAN (C-RAN) where Base Band Units (BBUs) are collocated in a computing/processing center remotely away from their correspondent Remote Radio Heads (RRHs) for the efficient resource sharing is the prevailing RAN (Radio Access Network) design for next generation mobile networks. However, in C- RAN, the possible high latency from a RRH to the centralized Cloud BBU pool is not desirable for some latency critical applications. Thus, several local and smaller BBU pools are necessary to be deployed close to the RRHs to constrain the latency. This RAN architecture is so-call Fog RAN (F-RAN). A Mobile Edge Computing (MEC) center can be deployed beside or nearby a F-RAN BBU pool for timely processing. In this paper, we tackle the BBU resource allocation problem (a modified bin packing problem) between the set of RRHs and the set of BBU pools in F-RAN so that only a minimal number of BBU pools, i.e., the bins in a bin packing problem, will be turned on to serve all RRHs and so to save the energy consumption. Also, for the stability and fault tolerance, in addition to saving energy, the proposed algorithms also perform load balancing amid serving BBU pools. Our extensive simulation results show that the proposed scheme for energy efficient BBU resource allocation is able to achieve the goal of saving energy consumption and load balancing.

Artificial intelligence is one of the important technologies for industrial applications, but it needs a lot of computing resources and sensing data to support. Therefore, big data transmission is a challenge for current network architectures. In order to have high-performance computing requirements, this paper proposes an emerging network architecture that combines edge computing and cloud computing to reduce the transmission of useless data and solve bottleneck problems. Moreover, we define the resource allocation problem about multiple remote radio heads and multiple baseband unit pools in the cloud radio access network for fifth generation. The long short-term memory is used to predict dynamic throughput and genetic algorithm based resource allocation algorithm is used to optimize resource allocation. The simulation results represented that the proposed mechanism can achieve high resource utilization and reduce power consumption.

With the rapid development of Industrial Internet of Things, the category and quantity of industrial equipment will increase gradually. For centralized monitoring and management of numerous and multivariate equipment in the intelligent manufacturing process, the equipment categories shall be identified first. However, manual labeling of electrical equipment needs high costs. For the purpose of recognizing industrial equipment accurately in manufacturing systems, this study adopts the long short-term memory to analyze big data features and build a nonintrusive load monitoring system. Edge computing is used to implement parallel computing to improve the efficiency of equipment identification. Considering the practical popularity, the fairly priced low-frequency Smart Meter is used to collect the appliance data. According to the proposed optimal adjustment strategy of parameter model, the average random recognition rate can achieve 88% and the average recognition rate of the continuous data of a single electrical equipment can achieve 83.6%.

Nowadays, Software Defined Network (SDN) develops rapidly for its novel structure which separates the control plane and the data plane of network devices. Many researchers devoted themselves to the study of such a special network. However, some limitations restrict the development of SDN. On the one hand, the single controller in the conventional model bears all threats, and the corruption of it will result in network paralysis. On the other hand, the data will be increasing more in SDN switches in the data plane, while the storage space of these switches is limited. In order to solve the mentioned issues, we propose two corresponding protocols in this paper. Specifically, one is an anonymous protocol in the control plane, and the other is a verifiable outsourcing protocol in the data plane. The evaluation indicates that our protocol is correct, secure, and efficient.

In order to monitor the stability of industrial systems, engineers installed diversified sensors in systems, and used communication devices to transfer the sensed data to the cloud platform for real-time monitoring and event detection. Furthermore, as industry demand for power grows, the scale and quantity of power systems gradually increase, and the original network data transmission architecture cannot bear such large-scale communication, especially the communication bandwidth tolerance isn't allowed for trusted industrial Internet of things. Therefore, this trusted transmission problem will be one of challenges of the industrial Internet of things. In the application of device load recognition, how to create power fingerprinting recognition sample data, reduce the cloud platform computation complexity and the transmission quantity of sensed data without losing detection accuracy are the subjects of this study. Therefore, this study proposes a resilient section selection mechanism of power fingerprinting applied to device load recognition, in order to determine the transmission time and select the power fingerprinting section to be resiliently transferred, and replace the cycle-fixed full power fingerprinting data transfer for trusted industrial Internet of things. According to the experimental results, in the case of multi-load, the power fingerprinting of the first 25% section have the maximum recognition of 87.5%.

With the growing popularity of mobile devices, a new type of peer-to-peer communication mode for mobile cloud computing has been introduced. By applying a variety of short-range wireless communication technologies to establish connections with nearby mobile devices, we can construct a mobile cloudlet in which each mobile device can either works as a computing service provider or a service requester. Although the paradigm of mobile cloudlet is cost-efficient in handling computation-intensive tasks, the understanding of its corresponding service mode from a theoretic perspective is still in its infancy. In this paper, we first propose a new mobile cloudlet-assisted service mode named Opportunistic task Scheduling over Co-located Clouds (OSCC), which achieves flexible cost-delay tradeoffs between conventional remote cloud service mode and mobile cloudlets service mode. Then, we perform detailed analytic studies for OSCC mode, and solve the energy minimization problem by compromising among remote cloud mode, mobile cloudlets mode and OSCC mode. We also conduct extensive simulations to verify the effectiveness of the proposed OSCC mode, and analyze its applicability. Moreover, experimental results show that when the ratio of data size after task execution over original data size associated with the task is smaller than 1 (i.e.,r<; 1) and the average meeting rate of two mobile devices λ is larger than 0:00014, our proposed OSCC mode outperforms existing service modes.

The constant rapid growth and development of modern medical technology has resulted in an ever-growing demand for higher quality health monitoring systems. This is especially true for the development of the Internet of Things: as the Internet of Things becomes more ubiquitous in dally life, so does the possibility of, and demand for, the ability to remotely monitor the health of patients at anytime, anywhere, using a wide variety of biometrie information. Thus e-Healthcare has become a significant trend in the medical field. In addition to monitoring a patient's health remotely, data can also be relayed to doctors or hospitals in real-time, in order to assist in correct medical decision-making. Hospitals have begun to implement this technology, and patients therefore have immediate access to required diagnoses and care. However, the use of a large number of remote medical sensors requires significant bandwidth, especially when relaying real-time information. Hospital networks thus become susceptible to problems arising from network congestion. This study proposes a load-balancing mechanism based on SDN-SFC for the optimization of hospital remote-monitoring network planning, which simultaneously eliminates the need for large amounts of hardware.

With the rapid development of cloud technology, many services have been transferred from local computers to the cloud-based platform, which decreases the amount of computation done on the former. The local computer could thus be developed in the direction of portability and power saving. Graphics processing, apart from providing user interfaces featuring diversified special effects, is also significant in terms of application programs and play interactions. It is exactly on the basis of the concept of graphics processing that cloud-support rendering is developed, which is aimed to improve the graphics efficiency in mobile devices, via the graphics processing units in the cloud-based platform. The cloud-based platform and the mobile devices are usually connected by the Internet; however, as remote rendering might call for greater network bandwidth, its efficiency will be compromised if the network bandwidth is not stable. Given this limitation, this paper sets out to propose a quality-of-service-aware resource allocation strategy for mobile 3D graphics rendering, which is a hybrid rendering technology combining the client-side graphics processing capabilities with the graphics processing units in the cloud-based platform. When network bandwidth is not stable, the technology is able to assess the current network bandwidth, and dynamically configure the rendered frames on the client side and cloud-based platforms. Even when the client side could not access the network, it would still be possible to carry out the drawing through the graphics processing units on the local computer. Three applications are tested in this research: the technology can increase the frame rate by an average of 44.99% when the bandwidth is 10% greater than the minimum limit, by an average of 44.57% when the bandwidth is less than the minimum limit, by an average of 30.86% when the bandwidth is 10% less than the minimum limit, and by an average of 33.74% when the bandwidth is not stable.

The latest High Efficiency Video Coding (HEVC) standard could achieve the highest coding efficiency compared with the existing video coding standards. To improve the coding efficiency of the intra frame, a quad-tree-based variable block size coding structure that is flexible to adapt to various texture characteristics of images and up to 35 intra-prediction modes for each prediction unit (PU) is adopted in HEVC. However, the computational complexity is increased dramatically because all the possible combinations of the mode candidates are calculated in order to find the optimal rate distortion cost using the Lagrange multiplier. To alleviate the encoder computational load, this paper proposes an adaptive mode decision algorithm based on texture complexity and direction for HEVC intra prediction. First, an adaptive coding unit selection algorithm according to each depth levels' texture complexity is presented to filter out unnecessary coding block. Then, the original redundant mode candidates for each PU are reduced according to its texture direction. The simulation results show that the proposed algorithm could reduce around 56% encoding time on average while maintaining the encoding performance efficiently with only a 1.0% increase in BD-rate compared with the test model HM16 of HEVC.

In harsh and heterogeneous wireless environments, the communication reliability and latency of industrial wireless sensor networks (IWSNs) seriously suffer from both intra- and interinterference. This paper presents an interference resistant approach for IWSNs by utilizing cognitive radio techniques. To improve the interference detection performance while uploading data as little as possible, we present a new computationally efficient and effective belief function (BF) theory-based reliability-probability decision fusion rule for cooperative sensing. A factor called reliability degree is introduced to characterize the imprecision of sensor observations, and the basic belief assignments are constructed by combining this reliability degree and local detection performance. Unlike the inefficient existing BF-based fusion schemes, the proposed rule has an explicit form and it is equivalent to the well-known Chair-Vashney (CV) rule in high signal-to-noise ratio conditions. We applied the proposed rule in interference resistant IWSNs to detect and avoid interference. Both numerical results and tests results demonstrate that the proposed rule has significant improvement in detection performance, diversity gains, and throughput compared with existing BF fusion schemes and CV rule.

In this paper, the coverage problem of network planning in mobile multi-hop relay networks is defined on the basis of integer linear programming. In order to provide desired utilities and also meet deployment limitations for network planning, we propose a supergraph tree algorithm to place base stations and relay stations at the lowest cost position. Furthermore, another algorithm for avoiding the interference between base stations, which is called interference aware tree algorithm is also proposed. Both the proposed algorithms are formulated on the basis of a graph theoretic technique and analyzed in the simulation results. The results show that the supergraph tree algorithm provides the lowest construction cost with different network scenarios, and the interference aware tree algorithm provides the highest communication quality for mobile multi-hop relay infrastructure-based communication network planning. Copyright © 2013 John Wiley & Sons, Ltd.

As cloud computing and wireless body sensor network technologies become gradually developed, ubiquitous healthcare services prevent accidents instantly and effectively, as well as provides relevant information to reduce related processing time and cost. This study proposes a co-processing intermediary framework integrated cloud and wireless body sensor networks, which is mainly applied to fall detection and 3-D motion reconstruction. In this study, the main focuses includes distributed computing and resource allocation of processing sensing data over the computing architecture, network conditions and performance evaluation. Through this framework, the transmissions and computing time of sensing data are reduced to enhance overall performance for the services of fall events detection and 3-D motion reconstruction.

In recent years, the major Smart Grid problem is that users are unable to determine the usage conditions of electronic appliances at home, hence, users, network service developers, and service providers are all hesitant to use or provide services. In order to provide specific power utilization information, effective recognition of the type of appliances is a topical subject. At present, most studies on appliance recognition focus on single appliance recognition, however, in most homes, users switch multiple appliances on and off at the same time. Thus, a database is required for multiple recognition features in order to achieve multiple appliance recognition. This study aims to explore methods to create samples for recognition, reduce the amount of computation, and make it applicable for the operation of embedded systems with a small amount of computation. This study creates a database mechanism, appliance recognition classification, and a waveform recognition method, in order to solve the large data volume problem in current appliance recognition systems.

This study proposes a non-invasive smart meter system that considers the power use habits of users unfamiliar with electric appliances, and can be used by inserting the smart meter into an electrical circuit. This study also creates a database mechanism, appliance recognition classification, and a waveform recognition method, in order to solve the large data volume problem in current appliance recognition systems. In comparison to other appliance recognition systems, the low-end embedded system chip used in this study has low power consumption, as well as high expandability and ease of use. This experiment is different from the research environments of other appliance recognition systems by considering parallel multi-appliance recognition and general users' habit of using power. This study will not make any assumption of power utilization in the experiment. The total system recognition rate is 84.42%, and the total recognition rate of a single electric appliance is 93.82%, proving the high feasibility of this study.

In recent years, under the concern of energy crisis, the government has actively cooperated with research institutions in developing smart meters. As the Internet of Things (IoT) and home energy management system become popular topics, electronic appliance recognition technology can help users identifying the electronic appliances being used, and further improving power usage habits. However, according to the power usage habits of home users, it is possible to simultaneously switch on and off electronic appliances. Therefore, this study discusses electronic appliance recognition in a parallel state, i.e. recognition of electronic appliances switched on and off simultaneously. This study also proposes a non-invasive smart meter system that considers the power usage habits of users unfamiliar with electronic appliances, which only requires inserting a smart meter into the electronic loop. Meanwhile, this study solves the problem of large data volume of the current electronic appliance recognition system by building a database mechanism, electronic appliance recognition classification, and waveform recognition. In comparison to other electronic appliance recognition systems, this study uses a low order embedded system chip to provide low power consumption, which have high expandability and convenience. Differing from previous studies, the experiment of this study considers electronic appliance recognition and the power usage habits of general users. The experimental results showed that the total recognition rate of a single electronic appliance can reach 96.14%, thus proving the feasibility of the proposed system.

In pervasive computing environment, due to the mobility feature of mobile terminals, the mobile service needs to dynamically adapt execution behavior to the changing computing environment as mobile user moves. However, previous researches mainly focused on deploying a service adaption module on mobile terminals or local central server to support the adaptive execution of mobile services, which brings huge overhead to mobile terminals or can hardly meet user's personalized requirements. Therefore, we propose a cloud based framework, called CAMSPF, which includes three parts: RMC (resource management cloud), AMSPC (adaptive mobile service provision cloud), and MSM (mobile service middleware). The CAMSPF deploys the service resources in RMC for realizing efficient resource management and provision, and constructs a PMSAA (private mobile service adaption agent) for each mobile user in AMSPC in order to efficiently support personalized adaptive execution of mobile service. In addition, the MCM is a lightweight software installed on mobile terminals by which CAMSPF can collect user's realtime context and monitor service request from mobile user. Our prototype implementation of CAMSPF verifies that the adaptive execution of mobile services can be performed more efficiently than other traditional approaches, with lower energy consumption on mobile terminals.

The Internet of Things (IoT) interconnects Internet and end device networks connected by identification information, which not only extends and explores the range of the Internet, but also constructs bridges of communication between realistic objects and the Internet. With increasing awareness about the energy crisis, the development of intelligent energy-saving systems has become a new trend in all circles. This work proposes an appliance-aware activity recognition mechanism for an IoT energy management system, and especially an intermediate management service layer for the conformability of current household appliances, which not only establishes communication services among various appliances, but also provides services for upper-layer application based on a service-oriented architecture. Further, the presented system has the capability to deduce human activities from appliances being used and the variation of their states, and provide energy management services. In other words, the system can identify home activities at present, and notify users what unused appliance is or turn them off automatically. According to experiment results, improving the usage behaviour of appliances by means of a remainder system improves the efficiency in saving household electricity.

With the increase in commercial electronic equipment and its complicated control interfaces, how to design an effective and user-friendly control interface has become a topic for many researchers. This research introduces two-directional communication of an interactive graphical user interface on a universal remote control (URC). It is different from current URCs where users must often spend huge amounts of time setting the command codes and encoding each device. With the increase in the number of appliances that the controller needs to manage and the complicated and numerous control buttons, using such controllers often causes difficulties for users. This research employs a cross-platform with integration theories, so when a user wants to connect an appliance, both the appliance end and the controller end will build a two-directional connection through pairing over Radio Frequency for Consumer Electronics (RF4CE). After connection, the system will automatically set the communication protocol between the controller and the device. The appliance will automatically transmit its current state and service in the form of bundles to the controller, then the controller will project it onto an LCD screen. The controller can also show the number of appliances connected to the current position of the user, allowing the user to use one controller to control all home appliances with ease, achieving a simplified and instinctive control interface to build the integrated control environment for commercial appliances.

Cloud multimedia services provide an efficient, flexible, and scalable data processing method and offer a solution for the user demands of high quality and diversified multimedia. As intelligent mobile phones and wireless networks become more and more popular, network services for users are no longer limited to the home. Multimedia information can be obtained easily using mobile devices, allowing users to enjoy ubiquitous network services. Considering the limited bandwidth available for mobile streaming and different device requirements, this study presented a network and device-aware Quality of Service (QoS) approach that provides multimedia data suitable for a terminal unit environment via interactive mobile streaming services, further considering the overall network environment and adjusting the interactive transmission frequency and the dynamic multimedia transcoding, to avoid the waste of bandwidth and terminal power. Finally, this study realized a prototype of this architecture to validate the feasibility of the proposed method. According to the experiment, this method could provide efficient self-adaptive multimedia streaming services for varying bandwidth environments.

When the mobile station (MS) is far away from the base station (BS) or terrain and buildings interfere with transmission, the data rate decreases with the increased distance from the BS, and the coverage range of the BS will be shortened to only a few kilometers. The IEEE Working Group has proposed the 802.16j relay technology. This technology can be used to forward data for a BS while overcoming the signal strength attenuation. Since the cost of constructing the relay station is low, the MS will have to scan more than one signal when entering the Mobile Multi-Hop Relay (MMR) network. In this paper, we introduce a novel path selection mechanism that exploits the remaining slot and signal strength indicator (SSI). Since the signal strength will affect the Modulation and Coding Scheme (MCS) combination, we propose to use SSI integrated with the MCS, taking the hop count into account. We demonstrate that our proposed scheme is capable of producing an optimal path selection.

SUMMARY In two‐tier high‐performance networks (HPNs), some facilities are constructed to form a powerful supercomputing environment, and to alleviate server load. Then, the applications are provided by them in co‐operated, parallel and distributed manners. A proper way to select facilities is crucial to the performance of two‐tier HPNs The problem of selecting facilities can be regarded as a kind of the facility location problem, which is to determine an optimal subset of facilities that will be open to serve users. The traditional facility location problem aims to minimize the incurred costs between the users/servers and their assigned facilities. In two‐tier HPNs, the incurred costs can be regarded as the transmission costs, e.g. transmission latency, bandwidth overhead. We observe that most of the packets are transmitted among the facilities for application servicing and framework maintaining. In this paper, we address the problem of selecting facilities in two‐tier HPNs by minimizing the transmission costs from servers to users by passing through the selected facilities. Our problem is different from the traditional facility location problem, which only considers the transmission costs between the users/servers and their assigned facilities. In our problem, the transmission costs between the selected facilities are further considered. The problem is formulated as a 0/1 integer non‐linear programming (0/1 INLP) and 0/1 integer linear programming (0/1 ILP). Further, a simple heuristic algorithm is proposed for obtaining a feasible solution when the network sizes increase, since solving INLPs and ILPs for large‐scale problems takes long time. Copyright © 2010 John Wiley & Sons, Ltd.

Using body sensor networks (BodyNets) to monitoring the human health is increasingly emerging as a dominant application framework for the evolving sensor network technology. In this paper, we explore how to promote such network work in cold region. Different to the ordinary region, sensors not only need to collect sensory data of human characteristic but also accurately monitor the surround environment of the target human. Specially, the monitoring objectives are affected by the combined effects of many parameters, such as the movement of target human and uncertainty of nature. These parameters are featured in vague and many-to-many association etc, which make the monitoring process in high complexity. We analyze the requirement of Bodynets during the process of monitoring health condition, and design an adaptable system model. Moreover, we use distributed Bayesian estimation to eliminate the inaccuracy of sensory information with the consideration of time and spatial distribution effects on monitoring process. The experiment result show that our design can efficiently guarantee the information accuracy of monitoring objective.

Parallel structures may be used to increase a system processing speed in case of large amount of data or highly complex calculations. Dynamic Voltage and Frequency Scaling (DVFS) may be used for simpler calculations in order to decrease the system voltage or frequency and achieve lower power consumption. Combining these two mechanisms may lead to higher efficiency and lower power consumption. In this paper, we introduce a parallel decoding process with Digital Signal Processing (DSP) for power efficiency in a heterogeneous multi-core embedded system. We describe a parallel low-power design on the system level. Under the condition of preserving the original decoding process, we manage the size of the system's multimedia buffer by considering the spontaneous streaming transfer and tuning the decoding process scheduling time by using the DVFS system in order to decrease the multimedia data dependency and achieve a multi-core embedded system with accurate and low-power detection mechanism.

Multimedia video sharing has been developed rapidly over the past years. P2P multimedia sharing mechanisms for P2P network such as PPLive, PPStream, Joost, have been used popularly. However, if Content Server and Client in the home network have to transmit via P2P sharing, P2P network must be adopted, thus it is unable to increase the network transmission speed through this intranet connection. Although there are DLNA, HAVi, and Jini protocols in the home network to share multimedia files, it cannot access P2P network due to the limitation of home network framework. Therefore, this paper extends the DLNA-based multimedia sharing system to P2P network on OSGi frameworks, so that users can access multimedia resource on P2P Network via DLNA, and P2P network users can apply P2P network mechanism in OSGI bundle to access shared DLNA multimedia resource in the home network.

This study designs and implements a cross-platform, cross-domain P2P multimedia sharing system in the Taiwan Advance Research and Education Network. The system allows users to easily access the multimedia resources of the entire network from any network node, and all multimedia providers can share local multimedia resources from any location on the entire network. Moreover, this is a Peer-to-Peer based transmission architecture model, thus, users can easily maintain the network, and enhance the service quality and efficiency of the entire network through relevant mechanisms.

In a traditional intercom system that only allows place-to-place communication in a house, it is necessary to dial the extension number of a specific family member, or dial each extension number individually via the intercom broadcasting, in order to reach the desired person. Additional master stations controlled the intercom system are required, and it is load to maintain the stations for general users. In order to solve these problems, we propose the digital living network alliance (DLNA) compatible family intercom system (DFIS): the architecture to support user mobility. This architecture makes it possible to quickly reach a family member without the knowledge of the extension number that caters to a fixed location. In other words, it will be not necessary to dial the extension number of the location of the desired person. We introduce call control and phone handling under the subject of DFIS architecture and demonstrate that the proposed DLNA family intercom device and DLNA family intercom adaptor can work well.