Reliability Evaluation for Clustered WSNs under Malware Propagation.

We consider a clustered wireless sensor network (WSN) under epidemic-malware propagation conditions and solve the problem of how to evaluate its reliability so as to ensure efficient, continuous, and dependable transmission of sensed data from sensor nodes to the sink. Facing the contradiction between malware intention and continuous-time Markov chain (CTMC) randomness, we introduce a strategic game that can predict malware infection in order to model a successful infection as a CTMC state transition. Next, we devise a novel measure to compute the Mean Time to Failure (MTTF) of a sensor node, which represents the reliability of a sensor node continuously performing tasks such as sensing, transmitting, and fusing data. Since clustered WSNs can be regarded as parallel-serial-parallel systems, the reliability of a clustered WSN can be evaluated via classical reliability theory. Numerical results show the influence of parameters such as the true positive rate and the false positive rate on a sensor node's MTTF. Furthermore, we validate the method of reliability evaluation for a clustered WSN according to the number of sensor nodes in a cluster, the number of clusters in a route, and the number of routes in the WSN

The dynamics of complex systems. Studies and applications in computer science and biology

Our research has focused on the study of complex dynamics and on their use in both information security and bioinformatics. Our first work has been on chaotic discrete dynamical systems, and links have been established between these dynamics on the one hand, and either random or complex behaviors. Applications on information security are on the pseudorandom numbers generation, hash functions, informationhiding, and on security aspects on wireless sensor networks. On the bioinformatics level, we have applied our studies of complex systems to theevolution of genomes and to protein folding

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Security Mechanisms in Unattended Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consisting of a large number of sensor nodes are being deployed in potentially hostile environments for applications such as forest fire detection, battlefield surveillance, habitat monitoring, traffic management, etc. One common assumption in traditional WSNs is that a trusted third party, i.e., a sink, is assumed to be always available to collect sensed data in a real time or near real time fashion. Although many WSNs operate in such an on-site mode, there are WSN applications that do not fit into the real time data collection mode. For example, data collection in Unattended WSNs (UWSNs) relies on the periodical appearance of a mobile sink. As most existing security solutions developed for traditional WSNs rely on the presence of a trusted third party, it makes them not applicable to UWSNs directly. This motivates the research on security mechanisms for UWSNs. This dissertation contributes to security mechanisms in UWSNs from three important aspects, as, confidentiality and reliability, trust management, and capture resistance. The first aspect addresses data confidentiality and data reliability in UWSNs. We propose a data distribution scheme to provide forward secrecy, probabilistic backward secrecy and data reliability. Moreover, we demonstrate that backward secrecy of the historical data can be achieved through homomorphic encryption and key evolution. Furthermore, we propose a constrained optimization algorithm to further improve the above two data distribution schemes. The second study introduces trust management in UWSNs. We propose a set of efficient and robust trust management schemes for the case of UWSNs. The Advanced Scheme utilizes distributed trust data storage to provide trust data reliability and takes the advantages of both Geographic Hash Table (GHT) and Greedy Perimeter Stateless Routing (GPSR) to find storage nodes and to route trust data to them. In this way, it significantly reduces storage cost caused by distributed trust data storage and provides resilience to node compromise and node invalidation. The third study investigates how to detect a captured node and to resist node capture attack in UWSNs. We propose a node capture resistance and key refreshing scheme for UWSNs based on the Chinese remainder theorem. The scheme is able to provide forward secrecy, backward secrecy and collusion resistance for diminishing the effects of capture attacks

Systems and algorithms for wireless sensor networks based on animal and natural behavior

In last decade, there have been many research works about wireless sensor networks (WSNs) focused on improving the network performance as well as increasing the energy efficiency and communications effectiveness. Many of these new mechanisms have been implemented using the behaviors of certain animals, such as ants, bees, or schools of fish.These systems are called bioinspired systems and are used to improve aspects such as handling large-scale networks, provide dynamic nature, and avoid resource constraints, heterogeneity, unattended operation, or robustness, amongmanyothers.Therefore, thispaper aims to studybioinspired mechanisms in the field ofWSN, providing the concepts of these behavior patterns in which these new approaches are based. The paper will explain existing bioinspired systems in WSNs and analyze their impact on WSNs and their evolution. In addition, we will conduct a comprehensive review of recently proposed bioinspired systems, protocols, and mechanisms. 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Markov decision processes with applications in wireless sensor networks: A survey

Ministry of Education, Singapore under its Academic Research Funding Tier

Compilation of thesis abstracts, June 2007

NPS Class of June 2007This quarter’s Compilation of Abstracts summarizes cutting-edge, security-related research conducted by NPS students and presented as theses, dissertations, and capstone reports. Each expands knowledge in its field.http://archive.org/details/compilationofsis109452750