Biblio

In the multi-robot applications, the maintained and desired network may be destroyed by failed robots. The existing self-healing algorithms only handle with the case of single robot failure, however, multiple robot failures may cause several challenges, such as disconnected network and conflicts among repair paths. This paper presents a distributed self-healing algorithm based on 2-hop neighbor infomation to resolve the problems caused by multiple robot failures. Simulations and experiment show that the proposed algorithm manages to restore connectivity of the mobile robot network and improves the synchronization of the network globally, which validate the effectiveness of the proposed algorithm in resolving multiple robot failures.

NoCs are a well established research topic and several Implementations have been proposed for Self-healing. Self-healing refers to the ability of a system to detect faults or failures and fix them through healing or repairing. The main problems in current self-healing approaches are area overhead and scalability for complex structure since they are based on redundancy and spare blocks. Also, faulty router can isolate PE from other router nodes which can reduce the overall performance of the system. This paper presents a self-healing for a router to avoid denied fault PE function and isolation PE from other nodes. In the proposed design, the neighbor routers receive signal from a faulty router which keeps them to send the data packet which has only faulted router destination to a faulty router. Control unite turns on switches to connect four input ports to local ports successively to send coming packets to PE. The reliability of the proposed technique is studied and compared to conventional system with different failure rates. This approach is capable of healing 50% of the router. The area overhead is 14% for the proposed approach which is much lower compared to other approaches using redundancy.

Wireless sensor networks (WSNs) are one of the most rapidly developing information technologies and promise to have a variety of applications in Next Generation Networks (NGNs) including the IoT. In this paper, the focus will be on developing new methods for efficiently managing such large-scale networks composed of homogeneous wireless sensors/devices in urban environments such as homes, hospitals, stores and industrial compounds. Heterogeneous networks were proposed in a comparison with the homogeneous ones. The efficiency of these networks will depend on several optimization parameters such as the redundancy, as well as the percentages of coverage and energy saved. We tested the algorithm using different densities of sensors in the network and different values of tuning parameters for the optimization parameters. Obtained results show that our proposed algorithm performs better than the other greedy algorithm. Moreover, networks with more sensors maintain more redundancy and better percentage of coverage. However, it wastes more energy. The same method will be used for heterogeneous wireless sensors networks where devices have different characteristics and the network acts more efficient.

We present and explore a model of stateless and self-stabilizing distributed computation, inspired by real-world applications such as routing on today's Internet. Processors in our model do not have an internal state, but rather interact by repeatedly mapping incoming messages ("labels") to outgoing messages and output values. While seemingly too restrictive to be of interest, stateless computation encompasses both classical game-theoretic notions of strategic interaction and a broad range of practical applications (e.g., Internet protocols, circuits, diffusion of technologies in social networks). Our main technical contribution is a general impossibility result for stateless self-stabilization in our model, showing that even modest asynchrony (with wait times that are linear in the number of processors) can prevent a stateless protocol from reaching a stable global configuration. Furthermore, we present hardness results for verifying stateless self-stabilization. We also address several aspects of the computational power of stateless protocols. Most significantly, we show that short messages (of length that is logarithmic in the number of processors) yield substantial computational power, even on very poorly connected topologies.

In this paper we explore the opportunities, challenges and best practices around designing technologies for those affected by self-harm. Our work contributes to a growing HCI literature on mental health and wellbeing, as well as understandings of how to imbue appropriate value-sensitivity within the digital design process in these contexts. The first phase of our study was centred upon a hackathon during which teams of designers were asked to conceptualise and prototype digital products or services for those affected by self-harm. We discuss how value-sensitive actions and activities, including engagements with those with lived experiences of self-harm, were used to scaffold the conventional hackathon format in such a challenging context. Our approach was then extended through a series of critical engagements with clinicians and charity workers who provided appraisal of the prototypes and designs. Through analysis of these engagements we expose a number of design challenges for future HCI work that considers self-harm; moreover we offer insight into the role of stakeholder critiques in extending and rethinking hackathons as a design method in sensitive contexts.

The restoration of power distribution systems has a crucial role in the electric utility environment, taking into account both the pressure experienced by the operators that must choose the corrective actions to be followed in emergency restoration plans and the goals imposed by the regulatory agencies. In this sense, decision-aiding systems and self-healing networks may be good alternatives since they either perform an automated analysis of the situation, providing consistent and high-quality restoration plans, or even directly perform the restoration fast and automatically in both cases reducing the impacts caused by network disturbances. This work proposes a new restoration strategy which is novel in the sense it deals with the problem from the operator viewpoint, without simplifications that are used in most literature works. In this proposal, a permutation based genetic algorithm is employed to restore the maximum amount of loads, in real time, without depending on a priori knowledge of the location of the fault. To validate the proposed methodology two large real systems were tested: one with 2 substations, 5 feeders, 703 buses, and 132 switches, and; the other with 3 substations, 7 feeders, 21,633 buses, and 2,808 switches. These networks were tested considering situations of single and multiple failures. The results obtained were achieved with very low processing time (of the order of ten seconds), while compliance with all operational requirements was ensured.

A biased random-key genetic algorithm (BRKGA) is a general search procedure for finding optimal or near-optimal solutions to hard combinatorial optimization problems. It is derived from the random-key genetic algorithm of Bean (1994), differing in the way solutions are combined to produce offspring. BRKGAs have three key features that specialize genetic algorithms: A fixed chromosome encoding using a vector of N random keys or alleles over the real interval [0, 1), where the value of N depends on the instance of the optimization problem; A well-defined evolutionary process adopting parameterized uniform crossover to generate offspring and thus evolve the population; The introduction of new chromosomes called mutants in place of the mutation operator usually found in evolutionary algorithms. Such features simplify and standardize the procedure with a set of self-contained tasks from which only one is problem-dependent: that of decoding a chromosome, i.e. using, the keys to construct a solution to the underlying optimization problem, from which the objective function value or fitness can be computed. BRKGAs have the additional characteristic that, under a weak assumption, crossover always produces feasible offspring and, therefore, a repair or healing procedure to recover feasibility is not required in a BRKGA. In this tutorial we review the basic components of a BRKGA and introduce an Application Programming Interface (API) for quick implementations of BRKGA heuristics. We then apply the framework to a number of hard combinatorial optimization problems, including 2-D and 3-D packing problems, network design problems, routing problems, scheduling problems, and data mining. We conclude with a brief review of other domains where BRKGA heuristics have been applied.

Automated program repair is of great promise for future programming environments. It is also of obvious importance for patching vulnerabilities in software, or for building self-healing systems for critical infra-structure. Traditional program repair techniques tend to lift the fix from elsewhere in the program via syntax based approaches. In this talk, I will mention how the search problems in program repair can be solved by semantic analysis techniques. Here semantic analysis methods are not only used to guide the search, but also for extracting formal specifications from tests. I will conclude with positioning of the syntax based and semantic based methods for vulnerability patching, future generation programming, and self-healing systems.

Preventive and reactive security measures can only partially mitigate the damage caused by modern ransomware attacks. Indeed, the remarkable amount of illicit profit and the cyber-criminals' increasing interest in ransomware schemes suggest that a fair number of users are actually paying the ransoms. Unfortunately, pure-detection approaches (e.g., based on analysis sandboxes or pipelines) are not sufficient nowadays, because often we do not have the luxury of being able to isolate a sample to analyze, and when this happens it is already too late for several users! We believe that a forward-looking solution is to equip modern operating systems with practical self-healing capabilities against this serious threat. Towards such a vision, we propose ShieldFS, an add-on driver that makes the Windows native filesystem immune to ransomware attacks. For each running process, ShieldFS dynamically toggles a protection layer that acts as a copy-on-write mechanism, according to the outcome of its detection component. Internally, ShieldFS monitors the low-level filesystem activity to update a set of adaptive models that profile the system activity over time. Whenever one or more processes violate these models, their operations are deemed malicious and the side effects on the filesystem are transparently rolled back. We designed ShieldFS after an analysis of billions of low-level, I/O filesystem requests generated by thousands of benign applications, which we collected from clean machines in use by real users for about one month. This is the first measurement on the filesystem activity of a large set of benign applications in real working conditions. We evaluated ShieldFS in real-world working conditions on real, personal machines, against samples from state of the art ransomware families. ShieldFS was able to detect the malicious activity at runtime and transparently recover all the original files. Although the models can be tuned to fit various filesystem usage profiles, our results show that our initial tuning yields high accuracy even on unseen samples and variants.

Existing compact routing schemes, e.g., Thorup and Zwick [SPAA 2001] and Chechik [PODC 2013], often have no means to tolerate failures, once the system has been setup and started. This paper presents, to our knowledge, the first self-healing compact routing scheme. Besides, our schemes are developed for low memory nodes, i.e., nodes need only O(log2 n) memory, and are thus, compact schemes. We introduce two algorithms of independent interest: The first is CompactFT, a novel compact version (using only O(log n) local memory) of the self-healing algorithm Forgiving Tree of Hayes et al. [PODC 2008]. The second algorithm (CompactFTZ) combines CompactFT with Thorup-Zwick's tree-based compact routing scheme [SPAA 2001] to produce a fully compact self-healing routing scheme. In the self-healing model, the adversary deletes nodes one at a time with the affected nodes self-healing locally by adding few edges. CompactFT recovers from each attack in only O(1) time and Δ messages, with only +3 degree increase and O(logΔ) graph diameter increase, over any sequence of deletions (Δ is the initial maximum degree). Additionally, CompactFTZ guarantees delivery of a packet sent from sender s as long as the receiver t has not been deleted, with only an additional O(y logΔ) latency, where y is the number of nodes that have been deleted on the path between s and t. If t has been deleted, s gets informed and the packet removed from the network.

In the context of service-oriented applications, the self-healing property provides reliable execution in order to support failures and assist automatic recovery techniques. This paper presents a knowledge-based approach for self-healing Composite Service (CS) applications. A CS is an application composed by a set of services interacting each other and invoked on the Web. Our approach is supported by Service Agents, which are in charge of the CS fault-tolerance execution control, making decisions about the selection of recovery and proactive strategies. Service Agents decisions are based on the information they have about the whole application, about themselves, and about what it is expected and what it is really happening at run-time. Hence, application knowledge for decision making comprises off-line precomputed global and local information, user QoS preferences, and propagated actual run-time information. Our approach is evaluated experimentally using a case study.

The popularity of Cloud computing has considerably increased during the last years. The increase of Cloud users and their interactions with the Cloud infrastructure raise the risk of resources faults. Such a problem can lead to a bad reputation of the Cloud environment which slows down the evolution of this technology. To address this issue, the dynamic and the complex architecture of the Cloud should be taken into account. Indeed, this architecture requires that resources protection and healing must be transparent and without external intervention. Unlike previous work, we suggest integrating the fundamental aspects of autonomic computing in the Cloud to deal with the self-healing of Cloud resources. Starting from the high degree of match between autonomic computing systems and multiagent systems, we propose to take advantage from the autonomous behaviour of agent technology to create an intelligent Cloud that supports autonomic aspects. Our proposed solution is a multi-agent system which interacts with the Cloud infrastructure to analyze the resources state and execute Checkpoint/Replication strategy or migration technique to solve the problem of failed resources.