The Internet of Things (IoT) aims to transform the human society toward becoming intelligent, convenient, and efficient with potentially enormous economic and environmental benefits. Reliability is one of the main challenges that must be addressed to enable this revolutionized transformation. Based on the layered IoT architecture, this paper first identifies reliability challenges posed by specific enabling technologies of each layer. The paper then presents a systematic synthesis and review of IoT reliability related literature. Reliability models and solutions at four layers (perception, communication, support, and application) are reflected and classified. Despite the rich body of works performed, the IoT reliability research is still in its early stage. Challenging research problems and opportunities are then discussed in relation to current under-explored behaviors and future new aspects of evolving IoT system complexity and dynamics.

In the Internet of Things (IoT), various devices operate collaboratively in collecting data, relaying information to one another and processing information intelligently. Due to interactions and dependencies between the IoT devices, the malfunction of one device may trigger a cascade of unexpected and often undesired state changes of other devices, introducing or accelerating catastrophic cascading failures. Understanding causes of cascading failures and modeling their behavior and effects are crucial for guaranteeing the reliability of IoT systems and delivering the desired quality of service. This article systematically reviews cascading failures modeling and reliability analysis methodologies, as well as mitigation strategies for building resilience of IoT systems against cascading failures. The review covers diverse IoT applications, from smart grids to smart homes, from sensor networks to IoT cloud computing, from transportation networks to interdependent infrastructure networks. Opportunities and open research issues are also discussed in relation to restrictions of the current cascading failure models and methods, and potential new technologies and complexity of the constantly evolving IoT systems.

This paper proposes efficient methods to assess the reliability of phased-mission systems (PMS) considering both imperfect fault coverage (IPC), and common-cause failures (CCF). The IPC introduces multimode failures that must be considered in the accurate reliability analysis of PMS. Another difficulty in analysis is to allow for multiple CCF that can affect different subsets of system components, and which can occur s-dependently. Our methodology for resolving the above difficulties is to separate the consideration of both IPC and CCF from the combinatorics of the binary decision diagram-based solution, and adjust the input and output of the program to generate the reliability of PMS with IPC and CCF. According to the separation order, two equivalent approaches are developed. The applications and advantages of the approaches are illustrated through examples. PMS without IPC and/or CCF appear as special cases of the approaches

Due to rapid advancements on the Internet of Things (IoT), unmanned aerial vehicles (UAVs), also known as drones, are transforming numerous military and civil application areas. UAVs aim to enhance the production efficiency, ensure safety, and reduce risk, particularly protecting the human workforce in the case of harsh and dangerous environments. Due to the mission-critical, business-critical, or safety-critical nature of the UAV applications, it is pivotal that UAVs perform reliably to deliver the required service during the intended mission time. Therefore, reliability is one of the essential requirements for designing and operating UAVs. This article presents a critical review of UAV reliability literature in both theoretical and practical research, pinpointing failure causes and reliability challenges of UAV systems, classifying and reflecting on the reliability modeling, analysis, and design methods for UAV systems and key subsystems. Some open research problems and opportunities are also discussed to highlight potential new challenges for designing reliable and resilient UAVs and UAV-assisted IoT systems.

Many real-world critical systems, such as aircraft and human space flight systems, utilize mission aborts to enhance the survivability of the system. Specifically, the mission objectives of these systems can be aborted in cases where a certain malfunction condition is met, and a rescue or recovery procedure is then initiated for system survival. Traditional system reliability models typically cannot address the effects of mission aborts, and thus are not applicable to analyzing systems subject to mission abort requirements. In this paper, we first develop a numerical methodology to model and evaluate mission success probability and system survivability of 1-out-of-N warm standby systems subject to constant or adaptive mission abort policies. The system components are heterogeneous, characterized by different performances and different types of time-to-failure distributions. Based on the proposed evaluation method, we make another new contribution by formulating and solving the optimal mission abort problem, as well as a combined optimization problem that identifies the mission abort policy and component activation sequence maximizing mission success probability while achieving the desired level of system survivability. Efficiencies of constant and adaptive mission abort policies are compared through examples. Examples also demonstrate the tradeoff between system survivability and mission success probability due to the utilization of a mission abort policy. Such a tradeoff analysis can help identify optimal decisions on system mission abort and standby policies, promoting safe and reliable operation of warm standby systems.

Decision diagrams are graphical structures based on Shannon's decomposition. They have been extensively used for representing and manipulating logic functions in areas such as circuit verification, compact Markov chain representation, and symbolic model checking. However, their applicability in reliability modeling and analysis has only been recently studied. Moreover, the study had been mostly restricted to binary-state systems in which both the system and its components are either operational, or failed. Nevertheless, many practical systems are multi-state systems (MSS) in which both the system and its components may reside at multiple (more than two) performance levels (or states), varying from perfect operation to complete failure. This paper presents three forms of decision diagrams for the modeling and analysis of MSS: binary decision diagrams, logarithmically encoded binary decision diagrams, and multi-valued decision diagrams. We present both separated, and shared methods based on these decision diagrams. Comprehensive complexity analysis, and performance comparisons among these methods, are conducted with both mathematical, and empirical approaches.