Biblio

The standard routing technique that was developed for satisfying low power IoT application needs is RPL which is a protocol in compliance with 6LoWPAN specification. RPL was created for addressing the issues and challenges of constrained and lossy network routing. However, RPL does not accomplish efficiency with respect to power and reliability altogether which are definitely needed in IoT applications. RPL runs on routing metrics and objective function which determines the optimal path in routing. This paper focuses on contributing a comprehensive survey on the improved objective functions proposed by several researchers for RPL. In addition, the paper concentrates on highlighting the strengths and shortcomings of the different approaches in designing the objective function. The approaches built on Fuzzy logic are found to be more efficient and the relevant works related to these are compared. Furthermore, we present the insights drawn from the survey and summarize the challenges which can be effectively utilized for future works.

The article deals with the mathematical model for information security controls optimization and evaluation of the information security systems effectiveness. Distinctive features of APT attacks are given. The generalized efficiency criterion in which both the requirements of the return of security investment maximization and the return on attack minimization are simultaneously met. The generalized reduced gradient method for solving the optimization of the objective function based on formulated efficiency criterion is proposed.

The penalization of the objective function due to energy deficits is a key element for determining the operational policy of hydroelectric reservoirs. Its definition impacts not only operations, but also system expansion. Brazil historically defined these penalties with basis on a proxy of the economic deficit cost, a value in \$/MWh obtained with aid of the Input-Output Matrix. We propose an approach where these penalties are obtained in order to minimize the operation cost and cost of rationing of the system, considering a criterion of security of supply. A case study with data from the Brazilian System illustrates its application.

We introduce a novel distributed derivative-free optimization framework that is resilient to stragglers. The proposed method employs coded search directions at which the objective function is evaluated, and a decoding step to find the next iterate. Our framework can be seen as an extension of evolution strategies and structured exploration methods where structured search directions were utilized. As an application, we consider black-box adversarial attacks on deep convolutional neural networks. Our numerical experiments demonstrate a significant improvement in the computation times.

A significant segment of the Internet of Things (IoT) is the resource constrained Low Power and Lossy Networks (LLNs). The communication protocol used in LLNs is 6LOWPAN (IPv6 over Low-power Wireless Personal Area Network) which makes use of RPL (IPv6 Routing Protocol over Low power and Lossy network) as its routing protocol. In recent times, several security breaches in IoT networks occurred by targeting routers to instigate various DDoS (Distributed Denial of Service) attacks. Hence, routing security has become an important problem in securing the IoT environment. Though RPL meets all the routing requirements of LLNs, it is important to perform a holistic security assessment of RPL as it is susceptible to many security attacks. An important attribute of RPL is its rank property. The rank property defines the placement of sensor nodes in the RPL DODAG (Destination Oriented Directed Acyclic Graphs) based on an Objective Function. Examples of Objective Functions include Expected Transmission Count, Packet Delivery Rate etc. Rank property assists in routing path optimization, reducing control overhead and maintaining a loop free topology through rank based data path validation. In this paper, we investigate the vulnerabilities of the rank property of RPL by constructing an Attack Graph. For the construction of the Attack Graph we analyzed all the possible threats associated with rank property. Through our investigation we found that violation of protocols related to rank property results in several RPL attacks causing topological sub-optimization, topological isolation, resource consumption and traffic disruption. Routing security essentially comprises mechanisms to ensure correct implementation of the routing protocol. In this paper, we also present some observations which can be used to devise mechanisms to prevent the exploitation of the vulnerabilities of the rank property.

This paper formulates a power system related optimal control problem, motivated by potential cyber-attacks on grid control systems, and ensuing defensive response to such attacks. The problem is formulated as a standard nonlinear program in the GAMS optimization environment, with system dynamics discretized over a short time horizon providing constraint equations, which are then treated via waveform relaxation. Selection of objective function and additional decision variables is explored first for identifying grid vulnerability to cyber-attacks that act by modifying feedback control system parameters. The resulting decisions for the attacker are then fixed, and the optimization problem is modified with a new objective function and decision variables, to explore a defender's possible response to such attacks.

Existing methods for multi-objective optimization usually provide only an approximation of a Pareto front, and there is little theoretical guarantee of finding the real Pareto front. This paper is concerned with the possibility of fully determining the true Pareto front for those continuous multi-objective optimization problems for which there are a finite number of local optima in terms of each single objective function and there is an effective method to find all such local optima. To this end, some generalized theoretical conditions are firstly given to guarantee a complete cover of the actual Pareto front for both discrete and continuous problems. Then based on such conditions, an effective search procedure inspired by the rising sea level phenomenon is proposed particularly for continuous problems of the concerned class. Even for general continuous problems to which not all local optima are available, the new method may still work well to approximate the true Pareto front. The good practicability of the proposed method is especially underpinned by multi-optima evolutionary algorithms. The advantages of the proposed method in terms of both solution quality and computational efficiency are illustrated by the simulation results.