Node Failure Detection And Fault Management In Mobile Wireless Networks With Persistent Connectivity

We adopt a probabilistic strategy and propose two hub disappointment recognition plots that deliberately consolidate limited observing, area estimation and hub cooperation. Broad reproduction brings about both associated and disengaged systems show that our plans accomplish high disappointment identification rates (near an upper bound) and low false positive rates, and cause low correspondence overhead. Contrasted with methodologies that utilization concentrated checking, our approach has up to 80% lower correspondence overhead, and just somewhat bring down recognition rates and marginally higher false positive rates. Also, our approach has the favorable position that it is relevant to both associated and detached systems while brought together observing is just material to associated systems

Predictive intelligence to the edge through approximate collaborative context reasoning

We focus on Internet of Things (IoT) environments where a network of sensing and computing devices are responsible to locally process contextual data, reason and collaboratively infer the appearance of a specific phenomenon (event). Pushing processing and knowledge inference to the edge of the IoT network allows the complexity of the event reasoning process to be distributed into many manageable pieces and to be physically located at the source of the contextual information. This enables a huge amount of rich data streams to be processed in real time that would be prohibitively complex and costly to deliver on a traditional centralized Cloud system. We propose a lightweight, energy-efficient, distributed, adaptive, multiple-context perspective event reasoning model under uncertainty on each IoT device (sensor/actuator). Each device senses and processes context data and infers events based on different local context perspectives: (i) expert knowledge on event representation, (ii) outliers inference, and (iii) deviation from locally predicted context. Such novel approximate reasoning paradigm is achieved through a contextualized, collaborative belief-driven clustering process, where clusters of devices are formed according to their belief on the presence of events. Our distributed and federated intelligence model efficiently identifies any localized abnormality on the contextual data in light of event reasoning through aggregating local degrees of belief, updates, and adjusts its knowledge to contextual data outliers and novelty detection. We provide comprehensive experimental and comparison assessment of our model over real contextual data with other localized and centralized event detection models and show the benefits stemmed from its adoption by achieving up to three orders of magnitude less energy consumption and high quality of inference

Routing based Roles Assignment for Monitoring 6LowPAN Networks

International audienceIn this work, we present a novel approach to assign monitoring roles in constrained, low power and lossy networks using available local information provided by the routing layer. The resulting monitoring architecture is adaptive taking benefit from the reactivity of the routing protocol when dynamic changes occur due to connectivity or nodes movement. The simulation results reveal that our assignment approach is more efficient, less aggressive and less resources consuming than its competitors

Data fusion and type-2 fuzzy inference in contextual data stream monitoring

Data stream monitoring provides the basis for building intelligent context-aware applications over contextual data streams. A number of wireless sensors could be spread in a specific area and monitor contextual parameters for identifying phenomena e.g., fire or flood. A back-end system receives measurements and derives decisions for possible abnormalities related to negative effects. We propose a mechanism, which based on multivariate sensors data streams, provides real-time identification of phenomena. The proposed framework performs contextual information fusion over consensus theory for the efficient measurements aggregation while time-series prediction is adopted to result future insights on the aggregated values. The unanimous fused and predicted pieces of context are fed into a Type-2 fuzzy inference system to derive highly accurate identification of events. The Type-2 inference process offers reasoning capabilities under the uncertainty of the phenomena identification. We provide comprehensive experimental evaluation over real contextual data and report on the advantages and disadvantages of the proposed mechanism. Our mechanism is further compared with Type-1 fuzzy inference and other mechanisms to demonstrate its false alarms minimization capability

Creation and maintenance of a communication tree in wireless sensor networks

A local reconfiguration algorithm (INP) for reliable routing in wireless sensor networks that consist of many static (fixed) energy-constrained nodes is introduced in the dissertation. For routing around crash fault nodes, a communication tree structure connecting sensor nodes to the base station (sink or root) is dynamically reconfigured during information dissemination. Unlike other location based routing approaches, INP does not take any support from a high costing system that gives position information such as GPS. For reconfigurations, INP uses only local relational information in the tree structure among nearby nodes by collaboration between the nodes that does not need global maintenance, so that INP is energy efficient and it scales to large sensor networks. The performance of the algorithm is compared to the single path with repair routing scheme (SWR) that uses a global metric and the modified GRAdient broadcast scheme (GRAB-F) that uses interleaving multiple paths by computation and by simulations. The comparisons demonstrate that using local relative information is mostly enough for reconfigurations, and it consumes less energy and mostly better delivery rates than other algorithms especially in dense environments. For the control observer to know the network health status, two new diagnosis algorithms (Repre and Local) that deal with crash faults for wireless sensor networks are also introduced in the dissertation. The control observer knows not only the static faults found by periodic testing but also the dynamic faults found by a path reconfiguration algorithm like INP that is invoked from evidence during information dissemination. With based on this information, the control observer properly treats the network without lateness. Local algorithm is introduced for providing scalability to reduce communication energy consumption when the network size grows. The performance of these algorithms is computationally compared with other crash faults identification algorithm (WSNDiag). The comparisons demonstrate that maintaining the communication tree with local reconfigurations in Repre and Local needs less energy than making a tree per each diagnosis procedure in WSNDiag. They also demonstrate that providing scalability in Local needs less energy than other approaches

System for Malicious Node Detection in IPv6-based Wireless Sensor Networks

U posljednje vrijeme javlja se trend implementacije IPv6 protokola u bežične senzorske mreže (BSM) kao posljedica težnje ka njihovoj integraciji sa drugim vrstama mreža temeljenih na IP protokolu. Ova disertacija bavi se sigurnosnim aspektima ovih IPv6- temeljenih BSM. Nakon kraćeg pregleda koncepta BSM detaljnije se razrađuje postupak implementacije IPv6 protokola u BSM. Potom slijedi detaljna analiza sigurnosnih prijetnji i napada prisutnih u IPv6-temeljenim BSM. Za neke od njih dane su i moguće protumjere. Nadalje, dan je prijedlog novog modularnog sigurnosnog okvira za IPv6 temeljene BSM. Objašnjeni su struktura i funkcije njegovih modula, te su dane preporuke za njihovu implementaciju. Također, dano je i rješenje distribuiranog adaptivnog sustava za otkrivanje zlonamjernih čvorova u IPv6-temeljenim BSM. Sustav se temelji na distribuiranim algoritmima i postupku kolektivnog odlučivanja. Predloženi sustav uvodi inovativni koncept procjene vjerojatnosti zlonamjernog ponašanja senzorskih čvorova. Sustav je implementiran i testiran kroz više različitih scenarija u tri različite mrežne topologije. U konačnici, provedena analiza pokazala je da je predloženi sustav energetski učinkovit i da pokazuje dobru sposobnost detekcije zlonamjernih čvorova.Recently occures the trend of implementation of the IPv6 protocol into wireless sensor networks (WSN) as a consequence of tendency of their integration with other types of IPbased networks. This thesis deals with the security aspects of these IPv6-based WSN. After short review of the WSN concept, the implementation process of the IPv6 protocol into WSN is elaborated in more details. Afterwards, there is a detailed analysis of security threats and attacks which are present in IPv6-based WSN. For some of them possible countermeasures are given. Furthermore, the proposal of the novel and modular security framework for IPv6- based WSN is given. The structure and the functions of its modules are explained, and recommendations for their implementation are given. Also, the solution of adaptive distributed system for malicious node detection in IPv6-based WSN is given. The system is based on distributed algorithms and collective decision-making process. Proposed system introduces innovative concept of probability estimation for malicious behavior of sensor nodes. The system is implemented and tested through several different scenarios in three different network topologies. Finally, performed analysis showed that proposed system is energy efficient and has good capability for detection of malicious nodes

System for Malicious Node Detection in IPv6-based Wireless Sensor Networks

U posljednje vrijeme javlja se trend implementacije IPv6 protokola u bežične senzorske mreže (BSM) kao posljedica težnje ka njihovoj integraciji sa drugim vrstama mreža temeljenih na IP protokolu. Ova disertacija bavi se sigurnosnim aspektima ovih IPv6- temeljenih BSM. Nakon kraćeg pregleda koncepta BSM detaljnije se razrađuje postupak implementacije IPv6 protokola u BSM. Potom slijedi detaljna analiza sigurnosnih prijetnji i napada prisutnih u IPv6-temeljenim BSM. Za neke od njih dane su i moguće protumjere. Nadalje, dan je prijedlog novog modularnog sigurnosnog okvira za IPv6 temeljene BSM. Objašnjeni su struktura i funkcije njegovih modula, te su dane preporuke za njihovu implementaciju. Također, dano je i rješenje distribuiranog adaptivnog sustava za otkrivanje zlonamjernih čvorova u IPv6-temeljenim BSM. Sustav se temelji na distribuiranim algoritmima i postupku kolektivnog odlučivanja. Predloženi sustav uvodi inovativni koncept procjene vjerojatnosti zlonamjernog ponašanja senzorskih čvorova. Sustav je implementiran i testiran kroz više različitih scenarija u tri različite mrežne topologije. U konačnici, provedena analiza pokazala je da je predloženi sustav energetski učinkovit i da pokazuje dobru sposobnost detekcije zlonamjernih čvorova.Recently occures the trend of implementation of the IPv6 protocol into wireless sensor networks (WSN) as a consequence of tendency of their integration with other types of IPbased networks. This thesis deals with the security aspects of these IPv6-based WSN. After short review of the WSN concept, the implementation process of the IPv6 protocol into WSN is elaborated in more details. Afterwards, there is a detailed analysis of security threats and attacks which are present in IPv6-based WSN. For some of them possible countermeasures are given. Furthermore, the proposal of the novel and modular security framework for IPv6- based WSN is given. The structure and the functions of its modules are explained, and recommendations for their implementation are given. Also, the solution of adaptive distributed system for malicious node detection in IPv6-based WSN is given. The system is based on distributed algorithms and collective decision-making process. Proposed system introduces innovative concept of probability estimation for malicious behavior of sensor nodes. The system is implemented and tested through several different scenarios in three different network topologies. Finally, performed analysis showed that proposed system is energy efficient and has good capability for detection of malicious nodes