In the library, recognizing the activity of the reader can better uncover the reading habit of the reader and make book management more convenient. In this study, we present the design and implementation of a reading activity recognition approach based on passive RFID tags. By collecting and analyzing the phase profiling distribution feature, our approach can trace the reader's trajectory, recognize which book is picked up, and detect the book misplacement. We give a detailed analysis of the factors that can affect phase profiling in theory and combine these factors with relevant activities. The proposed approach recognizes the activities based on the amplitude of the variation of phase profiling, so that the activities can be inferred in real time through the phase monitoring of tags. We then implement our approach with off-the-shelf RFID equipment, and the experiments show that our approach can achieve high accuracy and efficiency in activity recognition in a real-world situation. We conclude our work and further discuss the necessity of a personalized book recommendation system in future libraries.

At present, vast numbers of information resources are available on the Internet. However, one emerging issue is how to search and exploit these information resources in an efficient and flexible manner with high scalability. In this study, we focused our attention on the design of a distributed hash table (DHT)-based search system that supports efficient scalable multi-attribute queries of information resources in a distributed manner. Our proposed system, named D-AVTree, is built on top of a ring-based DHT, which partitions a one-dimensional key space across nodes in the system. It utilizes a descriptive naming scheme, which names each resource using an attribute-value (AV) tree, and the resource names are mapped to d-bit keys in order to distribute the resource information to responsible nodes based on a DHT routing algorithm. Our mapping scheme maps each AV branch of a resource name to a d-bit key where AV branches that share a subsequence of AV pairs are mapped to a continuous portion of the key space. Therefore, our mapping scheme ensures that the number of resources distributed to a node is small and it facilitates efficient multi-attribute queries by querying only a small number of nodes. Further, the scheme has good compatibility with key-based load balancing algorithms for DHT-based networks. Our system can achieve both efficiency and a good degree of load balancing even when the distribution of AV pairs in the resource names is skewed. Our simulation results demonstrated the efficiency of our solution in terms of the distribution cost, query hit ratio, and the degree of load balancing compared with conventional approaches.

In a real-time system, tasks are required to be completed before their deadlines. Under normal workload conditions, a scheduler with a proper scheduling policy can make all the tasks meet their deadlines. However, in practical environment, system workload may vary widely. Once system workload becomes too heavy, so that there does not exist a feasible schedule can make all the tasks meet their deadlines, we say the system is overloaded under which some tasks will miss their deadlines. To alleviate the degrees of system performance degradation caused by the missed deadline tasks, the design of scheduling is crucial. Many design objectives can be considered. In this paper, we first focus on maximizing the total number of tasks that can be completed before their deadlines. A scheduling method based on satisfiability modulo theories (SMT) is proposed. In the method, the problem of scheduling is treated as a satisfiability problem. The key work is to formalize the satisfiability problem using first-order language. After the formalization, a SMT solver (e.g., Z3, Yices) is employed to solve this satisfiability problem. An optimal schedule can be generated based on the solution model returned by the SMT solver. The correctness of this method and the optimality of the generated schedule can be verified in a straightforward manner. The time efficiency of the proposed method is demonstrated through various simulations. Moreover, in the proposed SMT-based scheduling method, we define the scheduling constraints as system constraints and target constraints. This means if we want to design scheduling to achieve other objectives, only the target constraints need to be modified. To demonstrate this advantage, we adapt the SMT-based scheduling method to other design objectives: maximizing effective processor utilization and maximizing obtained values of completed tasks. Only very little changes are needed in the adaption procedure, which means the proposed SMT-based scheduling method is flexible and sufficiently general.

In mobile and wireless networks, controlling data delivery latency is one of open problems due to the stochastic nature of wireless channels, which are inherently unreliable. This paper explores how the current best-effort throughput-oriented wireless services might evolve into latency-sensitive enablers of new mobile applications such as remote three-dimensional (3D) graphical rendering for interactive virtual/augmented-reality overlay. Assuming that the signal propagation delay and achievable throughput meet the standard latency requirements of the user application, we examine the idea of trading excess/federated bandwidth for the elimination of non-negligible delay of data re-ordering, caused by temporal transmission failures and buffer overflows. The general system design is based on (i) spatially diverse data delivery over multiple paths with uncorrelated outage likelihoods; and (ii) forward packet-loss protection (FPP), creating encoding redundancy for proactive recovery of intolerably delayed data without end-to-end retransmissions. Analysis and evaluation are based on traces of real life traffic, which is measured in live carrier-grade long term evolution (LTE) networks and campus WiFi networks, due to no such system/environment yet to verify the importance of spatial diversity and encoding redundancy. Analysis and evaluation reveal the seriousness of the latency problem and that the proposed FPP with spatial diversity and encoding redundancy can minimize the delay of re-ordering. Moreover, a novel FPP effectiveness coefficient is proposed to explicitly represent the effectiveness of EPP implementation.

Devanagari is the most widely used script in India. Here, a method is introduced for recognizing Devanagari characters using Neural network. The proposed method reduces the number of output unit necessary for a conventional neural network where the classification is based on a winner take all basis. An automatic coding procedure for representing the output layer of the network and a different method for the final classification is also proposed. Along with the automatic coding procedure, a heuristic method for representing the output units by exploiting the structural information of Devanagari character is also demonstrated. Besides, it has been shown by random representation of the output layer that the representation effects the generalization/performance of the network. The proposed automatic representation gave the recognition rate of 98.09% for 44 categories.

IEEE 802.15.4 based devices are a key component for mobile and pervasive computing. However, their small dimensions and reduced resources, together with the intrinsic properties of wireless communication, make it difficult to evaluate such networked systems through real-world trials. In this paper we present an emulation testbed intended for the evaluation of IEEE 802.15.4 networked systems. The testbed builds on the generic framework of the wireless network testbed QOMB, and adds IEEE 802.15.4 network, processor and sensing emulation functionality. We validated the testbed through a series of experiments carried out both through real-world trials in a smart home environment, and through emulation experiments on our testbed. Our results show that one can accurately, and in real time, execute IEEE 802.15.4 network applications on our testbed in an emulated environment that reproduces closely the real scenario.

Activity recognition has recently been playing an important role in several research domains, especially within the healthcare system. It is important for physicians to know what their patients do in daily life. Nevertheless, existing research work has failed to adequately identify human activity because of the variety of human lifestyles. To address this shortcoming, we propose the high performance activity recognition framework by introducing a new user context and activity location in the activity log (AL2). In this paper, the user's context is comprised by context-aware infrastructure and human posture. We propose a context sensor network to collect information from the surrounding home environment. We also propose a range-based algorithm to classify human posture for combination with the traditional user's context. For recognition process, ontology-based activity recognition (OBAR) is developed. The ontology concept is the main approach that uses to define the semantic information and model human activity in OBAR. We also introduce a new activity log ontology, called AL2 for investigating activities that occur at the user's location at that time. Through experimental studies, the results reveal that the proposed context-aware activity recognition engine architecture can achieve an average accuracy of 96.60%.

Given the worldwide proliferation of cyberattacks, it is imperative that cybersecurity education and training are addressed in a timely manner. These activities typically require trainees to do hands-on practice in order to consolidate and improve their skills, for which purpose training environments called cyber ranges are used. In this paper we present an open-source system named CyRIS (Cyber Range Instantiation System) that supports this endeavor by fully automating the training environment setup, thus making it possible for any organization to conduct more numerous and variate training activities. CyRIS uses a text-based representation in YAML format to describe the characteristics of the training environment, including both environment setup and security content generation. Based on this description, CyRIS automatically creates the corresponding cyber range instances on a computer and network infrastructure, for simultaneous use by multiple trainees. We have evaluated CyRIS in various realistic scenarios, and our results demonstrate its suitability for cybersecurity education and training, both in terms of features and performance, including for large-scale training sessions with hundreds of participants.

Feedback data loss can severely degrade overall system performance. In addition, it can affect the control and computation of the Cyber-physical Systems (CPS). CPS hold enormous potential for a wide range of emerging applications that include different data traffic patterns. These data traffic patterns have wide varieties of diversities. To recover various traffic patterns we need to know the nature of their underlying property. In this paper, we propose a data recovery framework for different traffic patterns of CPS, which comprises data pre-processing step. In the proposed framework, we designed a Data Pattern Analyzer to classify the different patterns and built a model based on the pattern as a data pre-processing step. Inside the framework, we propose a data recovery scheme, called Efficient Temporal and Spatial Data Recovery (ETSDR) algorithm to recover the incomplete feedback for CPS to maintain real time control. In this algorithm, we utilize the temporal model based on the traffic pattern and consider the spatial correlation of the nearest neighbor sensors. Numerical results reveal that the proposed ETSDR outperforms both the weighted prediction (WP) and the exponentially weighted moving average (EWMA) algorithms regardless of the increment percentage of missing data in terms of the root mean square error, the mean absolute error, and the integral of absolute error.