To ensure robustness in wireless networks, monitoring the network state, performance and functioning of the nodes and links is crucial, especially for critical applications. This paper targets Internet of Things (IoT) networks. In the IoT, devices (things) are vulnerable due to security risks from the Internet. Moreover, they are resource-constrained and connected via lossy links. This paper addresses the optimized scheduling of the monitoring role between the embedded devices in IoT networks. The objective is to minimize energy consumption and communication overhead of monitoring, for each node. Several subsets of the potential monitoring nodes are generated by solving a minimal vertex cover (VC) problem with constraint generation. Assuming periodical functioning, VCs are optimally assigned to time periods in order to distribute the monitoring role throughout the entire network. The assignment of VCs to periods is modeled as a multiobjective generalized assignment problem. To further optimize the energy consumption of the monitors, they are sequenced across time periods to minimize the state transitions of nodes. This part of the problem is modeled as a traveling salesman path problem. The proposed model is tested on randomly generated instances and the experimental results illustrate its effectiveness to optimize the scheduled monitoring for fault tolerance in IoT networks. Index Terms-Energy-efficient monitoring, generalized assignment problem (GAP), Internet of Things (IoT), robustness, scheduling, traveling salesman path (TSP) problem, vertex cover (VC). I. INTRODUCTION AND MOTIVATIONT HE Internet of Things (IoT) is a persistently growing network that seamlessly interconnects a tremendous number of heterogeneous, smart devices (things) with the Internet. The connection does not require human-to-human Manuscript
Critical Infrastructures (CIs) are resources that are essential for the performance of society, including its economy and its security. Large-scale disasters, whether natural or man-made, can have devastating primary (direct) effects on some CI and significant indirect effects (cascading effects) on other CIs, because CIs are interconnected and depend on each other's services. Recent work by Laugé et al. expressed the dependency values among CIs as dependency matrices for various durations of the primary CI failure. For better preparedness and mitigation of CI failures knowledge of the weak points in CI interdependencies is crucial. To this effect, we have developed a MATLAB code that identifies the forward paths and loops between pairs of CIs based on a simplified version of Laugé's matrices. The code calculates the parallel forward paths and loops dependencies to identify and quantify the amplification of cascading effects of any disruption that might hit one of the CIs included in the research. A main consequence, which has implications for expert assessment of dependencies between CIs, is that the cascading effects are not limited to the direct values expressed in the dependency matrices.
In the current era of digital world as well as globalization, the interconnectivity is growing at very swift rate. Now days, we are surrounded with number of gadgets, mobile devices, smartphones, wireless nodes and many other objects which are digitally connected in real time. Internet of Things (IoT) is one of the prominent domains in wireless networking which enable the link between the real world objects. With the implementation of IoT, the physical objects in real world can be connected with each other to share the information and communicate in real time with higher degree of performance as well as security. IoT works on the development and integration of smart objects which can be controlled using remote network infrastructure. This manuscript underlines the security and power aware programming in IoT for higher performance in Cooja.
This work is fulfilled in the context of the optimized monitoring of Internet of Things (IoT) networks. IoT networks are faulty; Things are resource-constrained in terms of energy and computational capabilities; they are also connected via lossy links. For IoT systems performing a critical mission, it is crucial to ensure connectivity, availability, and network reliability, which requires proactive network monitoring. The idea is to oversee the network state and functioning of the nodes and links; to ensure the early detection of faults and decrease node unreachability times. It is imperative to minimize the resulting monitoring energy consumption to allow the IoT network to perform its functions. Furthermore, to realize the integration of the monitoring mechanism with IoT services, this latter should work in tandem with the IoT standardized protocols, especially the IPv6 for Low-power Wireless Personal Area Networks (6LoWPAN) and the Routing Protocol for Low-power and lossy networks (RPL). In this paper, an optimized, proactive, passive, centralized monitoring system is proposed for IoT networks. The proposition ensures the optimal placement of monitoring nodes (monitors). Leveraging the graph built by RPL for routing (the DODAG), minimal sets of monitors are optimally placed to cover a given domain. The monitoring activity is optimally scheduled between several subsets of nodes to prolong longevity while minimizing the energy consumption for monitoring, communication, and state transitions. Our proposition provides the exact solution to the defined monitoring placement and scheduling problem via a Binary Integer Program. The model serves as a benchmark for the performance evaluation of contemporary models. Experimentation is designed using network instances of different topology. Results demonstrate the proposed model's effectiveness in realizing full monitoring coverage with minimum energy consumption and communication overhead and a balanced distributed monitoring role.
Due to the rapidly increasing of the mobile devices attached to the Internet. A lot of researches had been developed to manage and maximize the benefit of such integration. The innovations of this research are to build a mobile computing scheduling mechanism that considers the mobile clients as reseurces. Such mechanism will help in increasing the performance of the mobile computing environment, by distributing some of the functions of the access point among the available attached mobile devices. To this aim, the first step is how the mobile devices will be ranked and evaluated as a resource. The proposed mechanism is based on an investigating "Self ranking algorithm" which provides the greatest chance in achieving a proper solution depends on event based programming approach to start its execution in a pervasive computing environment. Using such mechanism will simplify5 the scheduling process by grouping the mobile devices according to their self ranking value and assign tasks to these groups, marimize the profit of the mobile devices integration with the already existed grid systems by using their computational power as an addition to the system overall power.
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