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This paper presents two novel mechanisms for the OLSR routing protocol, aiming to improve its energy performance in Mobile ah-hoc Networks. Routing protocols over MANET are an important issue and many proposals have been addressed to efficiently manage topology information, to offer network scalability and to prolong network lifetime. However, few papers consider a proactive protocol (like OLSR) to better manage the energy consumption. OLSR presents the advantage of finding a route between two nodes in the network in a very short time, thanks to its proactive scheme, but it can expend a lot of resources selecting the MultiPoint Relays (MPRs) and exchanging Topology Control information. We propose a modification in the MPR selection mechanism of OLSR protocol, based on the Willingness concept, in order to prolong the network lifetime without losses of performance (in terms of throughput, end-to-end delay or overhead). Additionally, we prove that the exclusion of the energy consumption due to the overhearing can extend the lifetime of the nodes without compromising the OLSR functioning at all. A comparison of an Energy-Efficient OLSR (EE-OLSR) and the classical OLSR protocol is performed, testing some different well-known energy aware metrics such as MTPR, CMMBCR and MDR. We notice how EE-OLSR outperforms classical OLSR, and MDR confirms to be the most performing metric to save battery energy in a dense mobile network with high traffic loads.

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In the last few years, we witnessed numerous episodes of terrorist attacks and menaces in public crowded places. The necessity of better surveillance in these places pushed the development of new automated solutions to spot and notify possible menaces as fast as possible. In this work, we propose a novel approach to create a decentralized architecture to manage patrolling drones and cameras exploiting lightweight protocols used in the internet of things (IoT) domain. Through the adoption of the mist computing paradigm it is possible to give to all the object of the smart ecosystem a cognitive intelligence to speed up the recognition and analysis tasks. Distributing the intelligence among all the objects of the surveillance ecosystem allows a faster recognition and reaction to possible warning situations. The recognition of unusual objects in certain areas, e.g., airports, train stations and bus stations, has been made using computer vision algorithms. The adoption of the IoT protocols in a hierarchical architecture provides high scalability allowing an easy and painless join of other smart objects. Also a study on the soft real-time feasibility has been conducted and is herein presented.