Bo-Chao Cheng

Schedulability Analysis for Hard Network Lifetime Wireless Sensor Networks With High Energy First Clustering

Network lifetime predictability is an essential system requirement for the type of wireless sensor network (WSN) used in safety-critical and highly-reliable applications. All sensor nodes in these time-critical WSNs should meet the lifetime constraint at any time instance, else it may cause severe consequences that involve economic losses, or even fatalities. In the literature, clustering sensors into groups is a popular strategy to maximize the network lifetime, but none of the clustering algorithms address the predictability issue for time-critical WSNs. In this paper, the High Energy First (HEF) clustering algorithm is chosen as a design reference model, which is proved in this paper to be an optimal clustering policy under certain ideal conditions. To address network lifetime predictability in practice, the network lifetime bounds and feasibility test for the HEF are developed via the worst case energy consumption analysis. The network simulator 2 (NS2) is used to verify the proposed network lifetime predictability model, and the results show that the derived bounds of the predictability provide accurate estimations of the system lifetime.

A packet marking with fair probability distribution function for minimizing the convergence time in wireless sensor networks

Wireless sensor networks (WSNs) contain a great number of nodes with sensing, processing, and wireless communicating capabilities. WSNs are expected to become the basic building blocks of the ubiquitous computing environments. However, inherited from its designed nature with limited resource constraints, WSNs exposed themselves to serious security threats. Their precious resources (e.g., low bandwidths and battery power) make a malicious node easy to launch the DoS flooding attacks by sending extra unnecessary packets. A DoS/DDoS attack may result in network disasters due to the energy exhaustion of the nodes along the attacking path. In the conventional IP network, edge sampling is a well known traceback algorithm to countermeasure DoS/DDoS attacks. Unfortunately, edge sampling is not effective enough for WSNs because it requires a lot of packets to reconstruct the attacking path, which may consume considerable energy and bandwidth. In addition, a shorter convergence time can reduce the failure rate of a traceback process due to mobility. This paper proposed an equality approach to deal with the traceback problem, called the edge sampling algorithm with probability distribution fairness (ESA-PDF), which reduces the convergence time of the conventional edge sampling algorithm. The salient features of the proposed ESA-PDF algorithm include: (1) able to produce faster convergence time, (2) capable of working as optimal in certain conditions, and (3) susceptible of integration with AODV routing protocol. Such a technique can provide a key answer required for advancing the state-of-the-art in DDoS mitigation and defenses in a realistic environment.

HMM machine learning and inference for Activities of Daily Living recognition

In a human-centric smart space, Activities of Daily Living (ADL) analysis can provide very useful information for elder care and long-term care services. ADL is defined as an assessment of a person’s functional status. Many recent researches concentrate on designing a good Context Aware Computing System to automate the actions necessarily triggered by ADL recognitions. Implementing a correct ADL recognition engine is a hard work, but will repay the system with lower inference errors and higher system dependability. A good ADL recognition engine is required to adjust its inference strategy based on the learning capability in order to avoid a high error rate, especially in real world inputs with a significant difference as compared to those in the training phase. In this paper, we proposed a powerful inference engine based on the Hidden Markov Model, called the Adaptive Learning Hidden Markov Model (ALHMM), which combines the Viterbi and Baum–Welch algorithms to enhance the accuracy and learning capability. The assessments of ALHMM are conducted on the Python platform and show the practical feasibility of Activity Recognition in residential homes. Such a technique can provide the key answer required for advancing the state-of-the-art in context-aware computing and applications in real life.

A Novel Probabilistic Matching Algorithm for Multi-Stage Attack Forecasts

Current intrusion detection systems (IDSs) can only discover single-step attacks but not complicated multi-stage attacks. Therefore, it is not only important, but also challenging for security managers to correlate security alerts with specific patterns to predict a multi-stage attack. In this paper, we propose Judge Evaluation of Attack intensioN (JEAN), which inspects the security alerts in the network and provides a probabilistic approach for the projection of the multi-stage attack by measuring the difference between the stored and the actual multi-stage attack session graphs (ASG). The experimental results show that JEAN is able to project possible attacks with more accuracy than Longest Common Subsequence (LCS) based approaches on DARPA 2000 and DARPA GCP (Grand Challenge Problem) specific attack scenario datasets.

Network lifetime bounds for hierarchical wireless sensor networks in the presence of energy constraints

In previous years, one popular problem that is constantly being researched into is how to prolong the lifetime of wireless sensor networks (WSNs). Many approaches to maximize network lifetime have been proposed and each approach provides different levels of energy savings and are efficient in their own aspects. However, these proposed algorithms are not suitable for use in a hard network lifetime environment where participating sensors should be working till the strict network lifetime requirement. The predictability of the network lifetime plays an important role in supporting guaranteed network lifetime services. This can be provided through the schedulability test that complements the online operations of safe and critical sensor network systems. In this paper, we focus on the study of the predictability of the network lifetime to enable the High Energy First clustering algorithm (HEF) to work in a hard lifetime environment and present a schedulability test to verify whether HEF can make the set of sensors schedulable in terms of N-of-N and K-of-N alive nodes.

Hybrid classification engine for cardiac arrhythmia cloud service in elderly healthcare management

The self-regulation ability of the elderly is largely degenerated with the age increases, and the elderly often expose to great potential hazards of heart disorders. In practice, the electrocardiography (ECG) is one of the well-known non-invasive procedures used as records of heart rhythms and diagnosis of unusual heart diseases. In this paper, we propose a healthcare management system, named CardiaGuard, which is specialized in monitoring and analysis the heart disorder events for the elderly. The CardiaGuard cloud service is an expert system designed based on the hybrid classifier implemented using support vector machine (SVM) and random tree (RT) classification algorithm. We conduct a comprehensive performance evaluation which shows the proposed hybrid classification engine are able to detect six types of cardiac disorders with higher accuracy rate than the SVM-based classifier alone. CardiaGuard poses a great solution to enhance the quality of good clinical practice on the healthcare management for the elderly in cardiology. Close relation between the 25th beat before the point and the 50th after it.Alarm service can trigger the instant response.Personal ECG records may help easily catch the features for personal body.General ECG database is an assistant of other new arrhythmias.Hybrid classifier offers feasible and flexible arrhythmia identification.

An objective-oriented service model for VoIP overlay networks over DiffServ/MPLS networks

Bandwidth provisioning and QoS mapping are key issues to support multimedia services such as VoIP in the emerging network technologies. The classification feature enables MPLS (Multi-protocol Label Switch) to support differentiated types of services (DiffServ) with needed QoS. The differentiated service model provides a variety of mechanisms to achieve different objectives (such as call level or packet level satisfaction). To design a good service model, which can balance the call level and packet level QoS performances, is a challenging task. In this paper, we propose two objective-oriented service models for the VoIP services over the DiffServ/MPLS networks. They can be modeled as the continuous time Markov chains (CTMC) and the performance are assessed in details. The salient point for the proposed service models is to solve the myth of the trade-off between the service quality (users concern) and the system revenue (system providers concern) - involving how to meet each users SLA requirements while maximizing system revenue. The analytical results in this paper can provide useful information to both user and service provider for signing a cost-effective contract which provides a better trade-off between cost and QoS.

Smart Home Sensor Networks Pose Goal-Driven Solutions to Wireless Vacuum Systems

Home sensor nodes are devices embedded in home appliances and are designed to sense environments, to process collected information, to perform a specific task, and to cooperate with other units. The advances of VLSI technologies and wireless sensor networks (WSN) turn the inspirational idea of intelligent home appliances into reality. In this paper, we focus on a category of home appliances, Smart Home Vacuum (SHV), where mobility and battery are critical design concerns. The emphasis of this paper is on the design of SHV system and the development of a goal driven task planning (GDTP) engine which can be implemented in the wireless vacuum systems to maximize the network lifetime as well as the cleaning efficiency. Unlike LEACH, proposed GDTP engine is a goal-driven approach to select the cluster heads to satisfy the design goals. Simulations are conducted by the network simulator (ns-2) and the experiment results indicate that GDTP performs better than other algorithms in terms of the network lifetime and the cleaning area coverage.

FBT: an efficient traceback scheme in hierarchical wireless sensor network

With limited resource constraints, wireless sensor networks (WSNs) pose unique technical challenges: WSNs are vulnerable to DoS/DDoS attacks that can easily exhaust rare available resources to prevent execution of their expected functions. Reconstructing the attacking path and locating the attacking source are challenging tasks in the traceback research areas. In conventional IP networks, probabilistic packet marking (PPM) schemes are among the widely used traceback algorithms. However, due to their high convergence times, conventional PPM schemes are not enough for the need for quick and accurate traceback in WSNs. Because marking probability assignment schemes have considerable influence on convergence time and performance, we place a particular emphasis on how to improve both easiness and efficiency for the marking probability assignment of the PPM. In this paper, we propose a novel traceback scheme, called fishbone traceback (FBT), which can be deployed in hierarchical WSN environments. FBT is designed based on the two-layer labelling technique and a smart marking probability distribution function (MPDF). The use of two-layer FBT labels is to derive the main branch (‘fish spine’) of the attacking path quickly, while the use of MPDF can greatly reduce the convergence time by integrating with a priori information of hierarchical WSN topology. The FBT path reconstruction procedure is able to rebuild the spine path (for inter-cluster traceback) via cluster head marking packets. It also reforms the details of the micro fishbone path (for intra-cluster traceback) on-demand within a cluster. Both numerical analysis and simulation results show that our solution has better performance in terms of shorter traceback convergence time. In particular, the proposed FBT also includes many salient features (such as the enhanced robustness of the traceback algorithm in case of multi-attack and reusable spine path), which enable FBT to be a practical solution to the traceback problem in hierarchical WSNs. Copyright

Context-Aware Gateway for Ubiquitous SIP-Based Services in Smart Homes

The smart home concept brings innovation and convenience to our every days life style at home. Without context awareness, smart home applications cannot offer services that can adapt to users¿ dynamic situations; they also fail to give preferential treatment for users¿ various preferences. Such design paradigm forces human to live in the world of machines rather than a human oriented world. The use of SIP-protocol allows Smart Home devices and services to be connected compatibly and effectively. In this paper, we propose an adaptive SIP Context-Aware Gateway (SCAG) for the ubiquitous SIP-based services. With the SCAG solution, the home owner is able to use preferred devices for communication and to post his/her context information to home appliances or to people and civil society communities on an ¿as needed¿ basis.