Task allocation in group of nodes in the IoT: A consensus approach

The realization of the Internet of Things (IoT) paradigm relies on the implementation of systems of cooperative intelligent objects with key interoperability capabilities. In order for objects to dynamically cooperate to IoT applications' execution, they need to make their resources available in a flexible way. However, available resources such as electrical energy, memory, processing, and object capability to perform a given task, are often limited. Therefore, resource allocation that ensures the fulfilment of network requirements is a critical challenge. In this paper, we propose a distributed optimization protocol based on consensus algorithm, to solve the problem of resource allocation and management in IoT heterogeneous networks. The proposed protocol is robust against links or nodes failures, so it's adaptive in dynamic scenarios where the network topology changes in runtime. We consider an IoT scenario where nodes involved in the same IoT task need to adjust their task frequency and buffer occupancy. We demonstrate that, using the proposed protocol, the network converges to a solution where resources are homogeneously allocated among nodes. Performance evaluation of experiments in simulation mode and in real scenarios show that the algorithm converges with a percentage error of about±5% with respect to the optimal allocation obtainable with a centralized approach

Newton-Raphson Consensus for Distributed Convex Optimization

We address the problem of distributed uncon- strained convex optimization under separability assumptions, i.e., the framework where each agent of a network is endowed with a local private multidimensional convex cost, is subject to communication constraints, and wants to collaborate to compute the minimizer of the sum of the local costs. We propose a design methodology that combines average consensus algorithms and separation of time-scales ideas. This strategy is proved, under suitable hypotheses, to be globally convergent to the true minimizer. Intuitively, the procedure lets the agents distributedly compute and sequentially update an approximated Newton- Raphson direction by means of suitable average consensus ratios. We show with numerical simulations that the speed of convergence of this strategy is comparable with alternative optimization strategies such as the Alternating Direction Method of Multipliers. Finally, we propose some alternative strategies which trade-off communication and computational requirements with convergence speed.Comment: 18 pages, preprint with proof

On the Impact of Wireless Jamming on the Distributed Secondary Microgrid Control

The secondary control in direct current microgrids (MGs) is used to restore the voltage deviations caused by the primary droop control, where the latter is implemented locally in each distributed generator and reacts to load variations. Numerous recent works propose to implement the secondary control in a distributed fashion, relying on a communication system to achieve consensus among MG units. This paper shows that, if the system is not designed to cope with adversary communication impairments, then a malicious attacker can apply a simple jamming of a few units of the MG and thus compromise the secondary MG control. Compared to other denial-of-service attacks that are oriented against the tertiary control, such as economic dispatch, the attack on the secondary control presented here can be more severe, as it disrupts the basic functionality of the MG