Distributed fault detection in smart spaces based on trust management

Application performance in a smart space is affected by faulty behaviours of nodes and communication networks. Detection of faults helps diagnosis of problems and maintenance can be done to restore performance, for example, by replacing or reconfiguring faulty parts. Fault detection methods in the literature are too complex for typical low-resource devices and they do not perform well in detecting intermittent faults. We propose a fully distributed fault detection method that relies on evaluating statements about trustworthiness of aggregated data from neighbors. Given one or more trust statements that describe a fault-free state, the trustor node determines for each observation coming from the trustee whether it is an outlier or not. Several fault types can be explored using different trust statements whose parameters are assessed differently. The trustor subsequently captures the observation history of the trustee node in only two evidence variables using evidence update rules that give more weight to recent observations. The proposed method detects not only permanent faults but also intermittent faults with high accuracy and low false alarm rate

Improving broadcast performance of radio duty-cycled Internet-of-Things devices

Asynchronous Radio Duty Cycling (ARDC) protocols can make embedded networked devices more energy efficient by keeping their radio off most of the time without a need for synchronization between devices. Some ARDC protocols can operate under 6LoWPAN adaptation layer in order to enable the vision of Internet-of-Things for battery operated devices. In this paper, we propose three different protocols which are modifications of the widely accepted ARDC protocol, ContikiMAC. The proposed solutions drastically improve energy efficiency and link layer delay for broadcast packets. Moreover, the proposed solutions are backward compatible with ContikiMAC and provide high reliability against frame reception errors. We present a detailed comparison with the legacy ContikiMAC and a standardized ARDC protocol, IEEE 802.15.4e Coordinated Sampled Listening (CSL), as well as the case of no duty cycling

Dependability analysis of asynchronous radio duty cycling protocols

Radio duty cycling (RDC) is a method for making resource constrained Internet-of-Things devices more energy efficient. RDC protocols achieve energy efficiency by keeping their radio off most of the time. Asynchronous RDC (ARDC) forms a subclass in which devices are not synchronized. ARDC behavior may lead to reliability and predictability problems for broadcast and unicast message transmissions in the MAC layer when the parameters of the protocol are not carefully selected, which we demonstrate by concrete examples. We investigate the dependability issues of generic ARDC protocols and provide analytical results for parameter settings that enhance dependability, while maintaining performance of the ARDC protocols in terms of energy efficiency and link delay. We also provide an analytical comparison of two widely accepted protocols, ContikiMAC and IEEE 802.15.4e Coordinated Sampled Listening

Trust-based neighbor unreachability detection for RPL

The IETF ROLL working group developed Routing Protocol for Low Power and Lossy Networks (RPL) for packet routing within Low power and Lossy Networks (LLNs) formed by resource constrained devices. RPL handles LLN issues such as unreachable parent and routing loops by triggering reconstruction of routing tree. For unreachable neighbor detection, RPL requires an external mechanism with very low communication overhead. This rules out the use of proactive mechanisms that employ keep-alive messages. Instead, RPL suggests to use IPv6 Neighbor Unreachability Detection (NUD), which is a reactive mechanism maintaining timers that trigger control messages. However, due to the use of fixed timeout intervals, NUD suffers from frequent premature timeouts and the communication overhead of NUD is still substantial. We propose a cross layer trust-based NUD mechanism, which adapts its timeout intervals based on network traffic without the need for explicit coordination among nodes. The proposed mechanism takes advantage of overhearing at the link layer and uses the statistics about packets that are not sent to the node itself, i.e. that would otherwise be discarded. Our evaluation shows that the proposed mechanism decreases the number of control messages significantly, while the packet delivery rate remains comparable to classical NUD

Improving failure prediction accuracy in smart environments

Smart Environments (SE) and Internet of Things (IoT) are two concepts that connect consumer electronics (CE) to each other and to the Internet domain. This enables various applications where CE devices work together to achieve goals of their users by communicating over a network. Application failures due to fluctuation of resources and environmental factors must be prevented, even though it is a challenge in such a distributed platform. Application adaptation is used extensively for this purpose. Since triggering of unnecessary adaptations affects application quality of service and user experience negatively, we propose a failure prediction method that makes a distinction between potential failures and outliers. The experiments show that the number of unnecessary adaptations is reduced significantly by the proposed method, while maintaining dependability

Improving failure prediction accuracy in smart environments

Smart Environments (SE) and Internet of Things (IoT) are two concepts that connect consumer electronics (CE) to each other and to the Internet domain. This enables various applications where CE devices work together to achieve goals of their users by communicating over a network. Application failures due to fluctuation of resources and environmental factors must be prevented, even though it is a challenge in such a distributed platform. Application adaptation is used extensively for this purpose. Since triggering of unnecessary adaptations affects application quality of service and user experience negatively, we propose a failure prediction method that makes a distinction between potential failures and outliers. The experiments show that the number of unnecessary adaptations is reduced significantly by the proposed method, while maintaining dependability