Abstract: The monitoring of leaks in pipelines is an important issue to be addressed by researchers and the public. This is due the fact that they can have a great impact both economically and environmentally. In recent years, the effect of leakages of pipelines carrying oil, gas and nuclear fluids have posed a threat on humans as well as marine life. This paper provides a survey of recent methods of detecting pipeline leaks with special focus on software based methods. These methods include negative pressure wave, mass/volume balance, pressure point analysis, real time transient modeling, statistical methods as well as methods that employing digital signal processing. This paper also surveys some of the recent research attempts that focus on the employment of wireless sensor networks for leak detection and present research challenges that can be encountered in such environments.

Abstract: The analysis of multiple datasets on users’ behaviors opens interesting information fusion possibilities and, at the same time, creates a potential for re-identification and de-anonymization of users’ data. On the one hand, this kind of approaches can breach users’ privacy despite anonymization. On the other hand, combining different datasets is a key enabler for advanced context-awareness in that information from multiple sources can complement and enrich each other. In this work we analyze different anonymized mobility datasets in the direction of highlighting re-identification and information fusion possibilities. In particular we focus on call detail record (CDR) datasets released by mobile telecom operators and datasets comprising geo-localized messages released by social network sites. Results shows that: (1) in line with previous findings, few (about 4) data points are enough to uniquely pin point the majority (90 %) of the users, (2) more than 20 % of CDR users have a single social network user exhibiting a number of matching data points. We speculate that these two users might be the same person. (3) We derive an estimate of the probability of two users begin the same person given the number of data points they have in common, and estimate that for 3 % of the social network users we can find a CDR user very likely (>90 % probability) to be the same person.

Abstract: A simulation model was established, minimizing the makespan and stability value, to solve the dynamic scheduling of flexible job-shop problems, and an improved hybrid multi-phase quantum particle swarm algorithm is proposed. Firstly, a double chain structure coding method, including a machine allocation chain and a process chain, is proposed. Secondly, a dynamic periodic and event-driven scheduling strategy is proposed. Finally, the novel method is applied to the Brandimarte set and a dynamic simulation is performed. Comparing the results with the results of existing algorithms demonstrates the effectiveness of the proposed hybrid multi-phase quantum particle swarm optimization algorithm and strategy for solving the dynamic scheduling of flexible job-shop problems.

Abstract: In the last 20 years, the Kansei engineering system (KES) has employed a variety of mathematical models to overcome design problems in consumer products. However, the increasing globalization of consumer markets has made the acquisition of market knowledge more competitive than ever; therefore, a strong focus has recently been placed on developing the means to capture consumer affective responses and obtain comprehensive data related to preferences in product exterior features. Rough set theory (RST) is a rule-based knowledge acquisition method capable of targeting imprecise, non-linear human perceptions. Surprisingly, little research has been conducted into the development of KES combined with RST. Therefore, this study used a systemic approach to perform a visual design of a toothbrush by combining the Kansei engineering and RST for exploring the relationship between form and color during a product evaluation. We also provide a point-by-point comparison of KES with RST to provide a reference for the merging of these two techniques. These findings will be of considerable interest to marketing researchers, artists, product designers, and color scientists as well as manufacturers and research centers.

Abstract: Technological revolution in communication and embedded computing has led to the Internet of Things (IoT) where all objects are connected together to provide users with services. Nowadays, many third party service providers are providing a large number of IoT services. Suggesting suitable services to IoT users based on objects they own has not been tackled yet. In this paper, we investigate the possibilities of leveraging recommendation algorithms, especially graph-based, to IoT. We propose a graph-based model for IoT systems and conduct experiment in which analyze and explore correlations between performances of different algorithms. We show that the graph-based recommendation algorithm can be used to develop an effective recommender system for the IoT. Moreover, we show that some algorithms perform reasonably well and produce high quality results.

Abstract: The paper presents research on large-scale microscopic simulation of taxi services in Berlin and Barcelona based on floating car data collected by local taxi fleets. Firstly, Berlin’s and Barcelona’s taxi markets are shortly described and the demand and supply data obtained from FCD analysed. Secondly, the online taxi dispatching problem formulation for this specific case is given, followed by the definition of two real-time rule-based heuristics used to dispatch taxis dynamically within the simulation. Finally, the simulation setup in MATSim is described, and the results obtained with both heuristics are analysed and compared in terms of dispatching performance, proving the effectiveness of the second strategy at different demand and supply scales. This paper is an extended version of Maciejewski and Bischoff 2015, where only the Berlin case study was presented.

Abstract: The concept of cloud computing offers measurable computational or information resources as a service over the Internet. The major motivation behind the cloud setup is economic benefits, because it assures the reduction in expenditure for operational and infrastructural purposes. To transform it into a reality there are some impediments and hurdles which are required to be tackled, most profound of which are security, privacy and reliability issues. As the user data is revealed to the cloud, it departs the protection-sphere of the data owner. However, this brings partly new security and privacy concerns. This work focuses on these issues related to various cloud services and deployment models by spotlighting their major challenges. While the classical cryptography is an ancient discipline, modern cryptography, which has been mostly developed in the last few decades, is the subject of study which needs to be implemented so as to ensure strong security and privacy mechanisms in today’s real-world scenarios. The technological solutions, short and long term research goals of the cloud security will be described and addressed using various classical cryptographic mechanisms as well as modern ones. This work explores the new directions in cloud computing security, while highlighting the correct selection of these fundamental technologies from cryptographic point of view.

Abstract: A new anomaly detection model which is based on principal component analysis (PCA) is proposed in this paper. Our schema proposes a method to extract the user’s behavior and analyzes the features selected as representative of the user’s access. The PCA method is introduced to the anomaly detection model which adopts its improvements to make it more consistent with anomaly detection system design to describe the user’s behavior more completely and to improve the efficiency and stability of the algorithm. This paper also uses our scheme to the anomaly detection of the database system. Finally, the data sets from the internet are used to test the feasibility of this model. The experimental results show that our model can detect normal and abnormal user behavior precisely and effectively.

Abstract: Demand response provides utilities with a mechanism to share with end users the stochasticity resulting from the use of renewable sources. Pricing is accordingly used to reflect energy availability, to allocate such a limited resource to those loads that value it most. However, the strictly competitive mechanism can result in service interruption in presence of competing demand. To solve this issue we investigate on the use of forward contracts, i.e., service-level agreements priced to reflect the expectation of future supply and demand curves. Given the limited resources of microgrids, service availability is an opposite objective to the one of system reactivity. We firstly design policy-based brokers and identify then a learning broker based on artificial neural networks. We show the latter being progressively minimizing the reimbursement costs and maximizing the overall profit.

Abstract: Vehicular cloud computing is a technological paradigm shifting which takes advantage of cloud computing to provide vehicles with useful computing resources and services on the roads. The advancement in smart vehicles and information technologies motivate researchers and industries to pay attention to the combination of vehicular network with cloud computing in recent. In order to make the emerging vehicular cloud computing viable, security issues must be considered. Especially, privacy is one of the critical security issues in vehicular cloud services as well as vehicular communications since a third-party entity may be involved in cloud management and operations. In this paper, we propose an anonymous vehicle-to-infrastructure cloud access management system in which identity and location privacy of service requesting vehicles are prevented from not only a global eavesdropper but also any single system management entity. We devise pseudonymous service access tokens for vehicles and RSU-local revocation mechanism to reduce the size of revocation list containing revoked pseudonyms in the proposed system.

Abstract: This research proposes a method for optimizing process performance; the method involves the use of multiple quality characteristics, fuzzy logic, and radial basis function neural networks (RBFNNs). In the method, each quality characteristic is transformed into a signal-to-noise ratio, and all the ratios are then provided as inputs to a fuzzy model to obtain a single comprehensive output measure (COM). The RBFNNs are used to generate a full factorial design. Finally, the average COM values are calculated for different factor levels, where for each factor, the level that maximizes the COM value is identified as the optimal level. Three case studies are presented for illustrating the method, and for all of them, the proposed method affords the largest total anticipated improvements in multiple quality responses compared with previously used methods, including the fuzzy, grey-Taguchi, Taguchi, and principal component analysis methods. The main advantages of the proposed method are its effectiveness in obtaining global optimal factor levels, its applicability and the requirement of less computational effort, and its efficiency in improving performance. In conclusion, the proposed method may enable practitioners optimize process performance by using multiple quality characteristics.

Abstract: Real time adaptation of marketing strategies and actions in smart commerce environments, such as shops and malls, is an open challenge with a tremendous impact for the survival of traditional retailers. A main issue of traditional retailers, in comparison with e-commerce shops, is that they usually rely on analysis of point-of-sales data after purchase and/or focus groups and self-reports where customers are asked about what they like or want. This techniques, even if solid grounded to marketing and consumer research, do not allow analysis of data and decision making in real time, i.e., when consumers are inside a shop. In this paper we present our results on the definition and validation of a decision support system for real time decision making on discount and promotion actions. The system makes decision on the basis of recognition and assessment of situations of interest for the consumers, modelled with heuristics related to behavioural economics results. We validated our solution in a virtual shop simulated with V-REP, demonstrating its capabilities to adapt with regards to the changes in the environments, in terms of sensors, people, products, and different situations.

Abstract: Computer networks have become so ubiquitous that the user can access various services by using network devices at anytime and anywhere. However, due to the open nature of the network, the security issue has become an important consideration in these network-based systems that cannot be ignored, especially in critical systems, such as life-critical system and financial system. User authentication scheme is the most used and effective mechanism for information security, and many user authentication schemes have been proposed by researchers. Recently, Shen et al. proposed a biometrics-based user authentication scheme for multi-server environments in critical systems. However, their scheme lacks the wrong password detection mechanism and is vulnerable to denial-of-service attack. Besides, they do not consider the user anonymity property, and may suffer from biometrics template lost attack because the biometrics template is directly stored in user’s smart card. In this paper, an enhanced biometrics-based user authentication scheme for multi-server environments in critical systems is presented by adopting the fuzzy extractor. The analysis shows that the proposed scheme not only removes the security weaknesses of previous schemes, but also keeps the computational efficiency.

Abstract: As the population ages, home computers with an Internet connection can provide the elderly with a new way to access information and services and manage Internet shopping tasks. One of the primary advantages of virtual environment (VE) technology for online shopping is its ability to provide a three-dimensional (3D) perspective to customers for a more realistic sense of the goods and the shopping environment. A sense of presence is one of the critical components required for an effective VE. However, side effects such as cybersickness may be caused by the display medium. When the quality of depth perception cues is poor, will the elderly’s experience of cybersickness influence their feeling of presence and performance of goods searching during exposure within a 3D virtual store with 3D displays? An experiment addressed associations among presence, cybersickness, and performance in a 3D virtual store with autostereoscopic, stereoscopic and monocular displays with good and poor depth perception cues in an elderly sample. The results showed that the virtual store with an autostereoscopic display with high-quality depth perception cues will produce good sense and realism in stereopsis to allow the elderly to experience presence within a virtual store. However, if the depth perception cues are poor, 3D displays, and especially stereoscopic displays, are not recommended; elderly users may lose interest in a 3D virtual store due to even more serious cybersickness than that experienced with a monocular display.

Abstract: The primary objective of this research work is to develop a novel benchmark database of digitized and calibrated, cervical cells obtained from slides of Papanicolaou smear test, which is done for screening of cervical cancer. This database can serve as a potential tool for designing, developing, training, testing and validating various artificial intelligence based systems for prognosis of cervical cancer by characterization and classification of Papanicolaou smear images. The database can also be used by other researchers for comparative analysis of working efficiencies of various machine learning and image processing algorithms. The database can be obtained by sending a request to the corresponding author or can be downloaded from http://digitalpapsmeardb.in/ . Besides developing a rich machine learning database we have also presented a novel artificial intelligence based hybrid ensemble technique for efficient screening of cervical cancer by automated analysis of Papanicolaou smear images. The correct and timely diagnosis of cervical cancer is one of the major problems in the medical world. From the literature it has been found that different pattern recognition techniques can help them to improve in this domain. Papanicolaou smear (also referred to as Pap smear) is a microscopic examination of samples of human cells scraped from the lower, narrow part of the uterus, called cervix. A sample of cells after being stained by using Papanicolaou method is analyzed under microscope for the presence of any unusual developments indicating any precancerous and potentially precancerous developments. Abnormal findings, if observed are subjected to further precise diagnostic subroutines. Examining the cell images for abnormalities in the cervix provides grounds for provision of prompt action and thus reducing incidence and deaths from cervical cancer. It is the most popular technique used for screening of cervical cancer. Pap smear test, if done with a regular screening programs and proper follow-up, can reduce cervical cancer mortality by up to 80 % (Arbyn et al. Ann Oncol 21:48–458, 2010). The contribution of this paper is that we have created a rich machine learning database of quantitatively profiled and calibrated cervical cells obtained from Pap-smear test slides. The database so created consists of data of about 200 clinical cases (8091 cervical cells), which have been obtained from multiple health care centers, so as to ensure diversity in data. The Pap-smear slides were processed using a multi-headed digital microscope and images of cervical cells were obtained, which were then passed through various data preprocessing subroutines. After preprocessing the cells were morphologically profiled and scaled to obtain separate quantitative measurements of various features of cytoplasm and nucleus respectively. The cells in the database were carefully classified in different corresponding classes according to the latest 2001-Bethesda system of classification, by multiple cytotechnicians and histopathologists. In addition to this, we have also pioneered to apply a novel hybrid ensemble system to this database in order to evaluate the effectiveness of both novel database and novel hybrid ensemble technique to screen cervical cancer by categorization of Pap smear data. The paper also presents a comparative analysis of multiple artificial intelligence based classification algorithms for prognosis of cervical cancer. For evaluating the effectiveness and correctness of the digital database prepared in this work, authors implemented this database for training, testing and validating fifteen different artificial intelligence based machine learning algorithms. All the algorithms trained with this database presented commendable efficiency in screening of cervical cancer. For two-class problem all the algorithms trained with the digital database showed the efficiencies in the range of about 93 to 95 % while as in case of multi class problem algorithms expressed the efficiencies in the range of about 69 to 78 %. The results indicate that the novel digital database prepared in this work can be efficiently used for developing new machine learning based techniques for automated screening of cervical cancer. The results also indicate that hybrid ensemble technique is an efficient method for classification of pap-smear images and hence can be effectively used for diagnosis of cervical cancer. Among all the algorithms implemented, the hybrid ensemble approach outperformed and expressed an efficiency of about 98 % for 2-class problem and about 86 % for 7-class problem. The results when compared with the all the standalone classifiers were significantly better for both two-class and multi-class problems.

Abstract: Today, privacy is a key concept. It is also one which is rapidly evolving with technological advances, and there is no consensus on a single definition for it. In fact, the concept of privacy has been defined in many different ways, ranging from the “right to be left alone” to being a “commodity” that can be bought and sold. In the same time, powerful Ambient Intelligence (AmI) systems are being developed, that deploy context-aware, personalised, adaptive and anticipatory services. In such systems personal data is vastly collected, stored, and distributed, making privacy preservation a critical issue. The human-centred focus of AmI systems has prompted the introduction of new kinds of technologies, e.g. Privacy Enhancing Technologies (PET), and methodologies, e.g. Privacy by Design (PbD), whereby privacy concerns are included in the design of the system. One particular application field, where privacy preservation is of critical importance is Ambient Assisted Living (AAL). Emerging from the continuous increase of the ageing population, AAL focuses on intelligent systems of assistance for a better, healthier and safer life in their living environment. In this paper, we first build on our previous work, in which we introduced a new tripartite categorisation of privacy as a right, an enabler, and a commodity. Second, we highlight the specific privacy issues raised in AAL. Third, we review and discuss current approaches for privacy preservation. Finally, drawing on lessons learned from AAL, we provide insights on the challenges and opportunities that lie ahead. Part of our methodology is a statistical analysis performed on the IEEE publications database. We illustrate our work with AAL scenarios elaborated in cooperation with the city of Luxembourg.

Abstract: Nowadays, transfer learning is one of the main research areas in machine learning that is helpful for labeling the data with low cost. In this paper, we propose a novel bagging-based ensemble transfer learning (BETL). The BETL framework includes three operations: Initiate, Update, and Integrate. In the Initiate operation, we use bootstrap sampling to divide the source data into many subsets, and add the labeled data from the target domain into these subsets separately so that the source data and the target data arrive at a reasonable ratio, then we learn as many initial classifiers as the elements of an ensemble. In the Update operation, we utilize the initial classifiers and an updateable classifier to repeatedly label the data that hasn’t been labeled yet in the target domain, and then, add the newly labeled data into the target domain to renew the updateable classifier. In the Integrate operation, we integrate the updated classifiers from each iteration into a pool to predict the labels of the test data via the majority vote strategy. In order to demonstrate the effectiveness of our method in the classification process, we conduct experiments on UCI data set, real world data set, and text data set. The results show that our method can effectively label the unlabeled data in the target domain, which greatly enhances the performance of target domain.

Abstract: Providing Web services from the mobile cloud is a current research topic. The mobile cloud provides the computing resources and infrastructure to support the seamless provision of Web services in a lightweight manner. Security has become a major concern with the emergence of mobile cloud Web services. In this paper, we investigate the security aspects of a system for complex mobile Web service provisioning. We characterize the security requirements of the individual components and present a security framework to provide authentication and confidentiality between clients and mobile hosts. Our solution is based on the use of existing security protocols between clients and the mobile hosts as well as a key management protocol between the individual mobile hosts implementing an out-of-band key exchange that is simple in practice, flexible and secure. We examine the performance of this approach by evaluating a prototype implementation of our security framework.

Abstract: Thanks to their spatially distributed sensors, cyber-physical system (CPS) applications are currently collecting large amounts of heterogeneous data. When it comes to allowing several decision-makers to collaboratively plan their actions, these applications need appropriate tools for an efficient storage, analysis, and visualization of the available data. Spatial data warehouses (SDWs) have proven their efficiency in carrying out these operations. However, because of the increasing volumes of data, the commonly used spatial extract-transform-load (SETL) process generally fails to update the SDW within acceptable timeframes. In order to solve this problem, we propose to perform the SETL tasks in a distributed, parallel manner by means of a grid of computing resources. In addition to being the unique solution that uses grid computing for the SETL process of SDWs, our solution makes use of cloud computing techniques to shorten the spatial data processing time and reduce resource consumption. To meet our goals, we propose a multi-agent-based solution to adequately schedule and balance the processing activities over the grid while allowing a joint use of real-time and archive data for personalized reporting and visualization of services envisioned to the decision-makers who are using the same CPS application.

Abstract: Semantic Web is an effective technology for intelligent Internet of Things (IoT), where Web Services are commonly used to describe the entity functions in the transaction process. However, as a specification of information processing, conventional Web Services cannot fully meet the needs of execution and control of the transactions on IoT. To facilitate describing entities involved in the transactions on IoT, by extending OWL-S, we propose a specification model of dynamic services for IoT entities. In our model, information of entity status is issued in real-time by the extended structure and is released to the requesters as dynamic services. In this way, the transactions on IoT can be constructed and executed intelligently as needed. Finally, the experimental results demonstrated the effectiveness of the proposed model of dynamic entity services for IoT.

Abstract: Machine learning based network traffic classification is a critical technique for network management, and has attracted much attention. Recently, most of the researchers focus on achieving high flow classification accuracy (FCA). However the amount of “mice” flows is more than that of “elephant” flows in the Internet, these classifiers hence are more suitable for “mice” flows, but have low byte classification accuracy (BCA). To address this issue, the notion of byte misclassification is firstly explored. According to the exploration that most misclassified bytes belong to the minority class, a novel method of network traffic classification is proposed by combining the data re-sampling and ensemble learning algorithms. To enhance the classification accuracy of the minority class, the data re-sampling algorithm is employed to increase the number of minority class flows. The data re-sampling however will change the data distribution and degrade the generalization of a classifier. A boosting-style ensemble learning algorithm with the consideration of ensemble diversity hence is employed to improve the generalization. The experiments conducted on the real-world traffic datasets show that the proposed method achieves over 90 % BCA and 96 % FCA on average, and improves about 7.15 % BCA by comparing with the existing methods.

Abstract: Cich, G (reprint author), Hasselt Univ, Transportat Res Inst IMOB, Wetenschapspk 5 Bus 6, Diepenbeek, Belgium glenn.cich@uhasselt.be; luk.knapen@uhasselt.be; tom.bellemans@uhasselt.be; davy.janssens@uhasselt.be; geert.wets@uhasselt.be

Abstract: Vehicular ad-hoc network (VANET) is an emerging technology which can offer a wide variety of promising applications, such as safety-related and infotainment applications. However, VANET also raises important security and privacy concerns that must be properly addressed for widespread deployment. In this paper, we first proposed a provable secure pseudonym-based cryptosystem with a trusted authority, including a pseudonym-based encryption scheme, a pseudonym-based multi-receiver encryption scheme, a pseudonym-based signature scheme, and a pseudonym-based one-pass key establishment protocol. We then presented a secure and efficient data access scheme for VANET based on cooperative caching technology and the proposed pseudonym-based cryptosystem. The efficiency of data access was greatly improved by allowing the sharing and coordination of cached data among multiple vehicles, and the anonymity of vehicles, data confidentiality, integrity and non-repudiation were guaranteed by employing the proposed pseudonym-based cryptosystem. Simulation results have shown that the proposed data access scheme is suitable to the VANET environment.

Abstract: Recent research on large data processing have been actively carried out in the name of cloud computing. Video surveillance system must handle larger amounts of data in real time. Video surveillance systems in a cloud computing environment constantly need to handle larger amounts of data in order to recognize and track an object. The system requires a technique which can handle larger amounts of data in order to recognize and track an object by extracting the feature of the object. However, most object tracking approaches based on feature matching have a problem, showing high computational complexity and/or weak robustness in various environments. This paper proposes a robust object recognition and tracking method, which uses an advanced feature matching for use on real time environment. Our algorithm recognizes an object using invariant features, and reduces the dimension of a feature descriptor to deal with the problems. The experimental result shows that our method is faster and more robust than the traditional methods, as well as the proposed method that can detect and track a moving object accurately in various environments.

Abstract: Due to limited resources of actors, third-party auditing has vital significance for ambient intelligence systems, which ensures the integrity of data stored in the information manager. However, Current public key based third-party auditing schemes are costly, while simple MAC based third-party auditing schemes can’t fulfill two security requirements, namely the privacy-preserving and the integrity-against-TPA requirements. Taking both security and efficiency into account, this paper proposes a novel simple MAC based third-party auditing scheme, which is much more efficient than current public key based schemes, while still fulfills the above two security requirements. Therefore, it is feasible to be deployed in ambient intelligence systems.

Abstract: Most research, in the area of target detection and tracking in wireless sensor networks (WSN), is focused on a single or multiple targets tracking. However, limited research is aimed at tracking and detection of continuous objects such as forest fires, biochemical materials and mudflows, etc. These continuous objects pose new challenges due to their nature and characteristics of changing in size and shape, shrinking and expanding, splitting into multiple objects, or merging of multiple objects into one object. Continuous objects tracking and detection require extensive communication, which consumes a considerable amount of network energy. To this end, this paper proposes a new algorithm named Continuous Object Detection and Tracking (CODAT). This paper also introduces a new data structure for reporting data. This new data structure reduces the communication cost of the overall algorithm without compromising the accuracy for reconstructing the boundary of a continuous object at the base station. A concept for differentiating between the holes in the phenomenon and overall phenomenon changes at the base station level is also introduced which provides additional information to the user as an added improvement while maintaining the high accuracy and efficiency. To demonstrate the feasibility and efficiency of this algorithm, it is implemented and compared its results with two known algorithms, including Continuous Boundary Monitoring (COBOM) and Detection and Monitoring for Continuous Objects (DEMOCO). The simulation results show that CODAT outperforms COBOM and DEMOCO with dense WSNs.

Abstract: During the last decade, in parallel with the rapid growth of mobile communications and devices, location-based social networks have met a tremendous growth with the acceptance of the public being constantly increasing. Users have access to a plethora of venues and points of interest, while they are able to share their visits to various locations along with comments and ratings about their experience (a process which is often referred to as “check-ins”). Location recommendations based on users’ needs have been a subject of interest for many researchers, while location prediction schemes have been developed in order to provide user’s possible future locations. In this paper, we present a novel method for predicting a user’s location based on machine learning techniques. In addition, following the incremental trend towards data accumulation in social networks, we introduce a clustering based prediction method in order to enhance the recommender system. For the prediction process we propose a probabilistic neural network and confirm its superior performance against two other types of neural networks, while for the clustering process we use a K-means clustering algorithm. The dataset we used was based on input from a well-known location-based social network. Prediction results can be used in order to make appropriate suggestions for venues or points of interests to users, based on their interests and social connections.

Abstract: The progressive installation of renewable energy sources requires the coordination of energy consuming devices. At consumer level, this coordination can be done by a home energy management system (HEMS). Interoperability issues need to be solved among smart appliances as well as between smart and non-smart, i.e., legacy devices. We expect current standardization efforts to soon provide technologies to design smart appliances in order to cope with the current interoperability issues. Nevertheless, common electrical devices affect energy consumption significantly and therefore deserve consideration within energy management applications. This paper discusses the integration of smart and legacy devices into a generic system architecture and, subsequently, elaborates the requirements and components which are necessary to realize such an architecture including an application of load detection for the identification of running loads and their integration into existing HEM systems. We assess the feasibility of such an approach with a case study based on a measurement campaign on real households. We show how the information of detected appliances can be extracted in order to create device profiles allowing for their integration and management within a HEMS.

Abstract: In order to overcome the shortcomings of existing wireless temperature-humidity monitoring systems, such as high-energy consumption, poor man–machine interaction, differential interference and no reverse real-time control etc., a novel real-time temperature-humidity monitoring system based on short distance wireless microwave communication is designed. In addition, some intelligent algorithms are briefly introduced in wireless communication for frequency assignment, improving energy efficiency and other resource allocation problems. This system uses the chip STC89C52 as the control chip, chip SHT11 as the data acquisition module, chip nRF905 as wireless transceiver module and chip MAX232 as the serial interface between the upper and the lower computer. The system requirements, system design, correlative hardware circuits, control flow chart and software design are depicted in details. This system can realize data acquisition, processing, transmission, control and display of temperature and humidity information. The system functions verification and performance test results show that this system has strong interference, low energy consumption and high precision. This system has great valuable, which has been applied in industrial and agricultural fields, such as greenhouse cultivation and laboratory storage.