A smart application in sensing is mainly powered by a two-stage process comprising sensing (collect data) and computing (process data). While the sensing stage is typically performed locally through a dedicated Internet of Things infrastructure, the computing stage may require a powerful infrastructure in the cloud. However, when connectivity is poor and low latency becomes a requirement — as in emergency response and disaster relief operations — edge computing and ad hoc cloud paradigms come in support to keep the computing stage locally. Being local network connectivity and data processing limited, it is vital to properly optimize how the computing workload will be consumed by the local ad hoc cloud. For this purpose, we present and evaluate the swarm-powered Optimized 3D Mapping Pipeline (OptiMaP) for emergency response 3D mapping missions, which is implemented as a collaborative embedded Robot Operating System (ROS) application integrating an ad hoc telecommunication middleware.We simulate — with Software-In-The-Loop — realistic 3D mapping missions comprising up to 5 drones and 363 images covering 0.293km2. We show how the completion times of mapping missions carried out in a typical centralized manner can be dramatically reduced by two versions of the OptiMaP framework powered, respectively, by a variable neighborhood search heuristic and a greedy method.

The popularity of drones is rapidly increasing across the different sectors of the economy. Aerial capabilities and relatively low costs make drones the perfect solution to improve the efficiency of operations that are typically carried out by humans. Besides automating field operations, drones acting de facto as a swarm can serve as an ad hoc cloud infrastructure built on top of computing and storage resources available across the swarm members and other elements. Even in the absence of Internet connectivity, this cloud can serve the workloads generated by the swarm members and the field agents. By considering the practical example of a swarm-powered 3-D reconstruction application on top of such cloud infrastructure, we present a new optimization problem for the efficient generation and execution of multinode computing workloads subject to data geolocation and clustering constraints. The objective is the minimization of the overall computing times, including both networking delays caused by the interdrone data transmission and computation delays. We prove that the problem is NP-hard and present two combinatorial formulations to model it. Computational results on the solution of the formulations show that one of them can be used to solve, within the configured time-limit, more than 50% of the considered real-world instances involving up to two hundred images and six drones.

The ability of TCP's congestion control scheme to adapt the rate of traffic flows and fairly use all the available resources is one of the Internet's pillars. So far, however, the elasticity of traffic has been disregarded in traffic engineering (TE) methodologies mainly because, only recently, the increase in access capacity has moved the bottlenecks from the access network to the operator network and hungry cloud-based applications have begun to use all the available bandwidth. We propose a new approach to TE with elastic demands which models the interaction between the network operator and the end-to-end congestion control scheme as a Stackelberg game. Given a set of elastic traffic demands only specified by their origin-destination pairs, the network operator chooses a set of routing paths (leader's problem) which, when coupled with the fair bandwidth allocation that the congestion control scheme would determine for the chosen routing (follower's problem), maximizes a network utility function. We present bilevel programming formulations for the above TE problem with two widely-adopted bandwidth allocation models, namely, max-min fairness and proportional fairness, and derive corresponding exact and approximate single-level mathematical programming reformulations. After discussing some key properties, we report on computational results obtained for different network topologies and instance sizes. Interestingly, even feasible solutions to our bilevel TE problems with large optimality gaps yield substantially higher network utility values than those obtained by solving a standard single-level TE problem and then fairly reallocating the bandwidth a posteriori.

Recent data confirm that the power consumption of the information and communications technologies (ICT) and of the Internet itself can no longer be ignored, considering the increasing pervasiveness and the importance of the sector on productivity and economic growth. Although the traffic load of communication networks varies greatly over time and rarely reaches capacity limits, its energy consumption is almost constant. Based on this observation, energy management strategies are being considered with the goal of minimizing the energy consumption, so that consumption becomes proportional to the traffic load either at the individual-device level or for the whole network. The focus of this paper is to minimize the energy consumption of the network through a management strategy that selectively switches off devices according to the traffic level. We consider a set of traffic scenarios and jointly optimize their energy consumption assuming a per-flow routing. We propose a traffic engineering mathematical programming formulation based on integer linear programming that includes constraints on the changes of the device states and routing paths to limit the impact on quality of service and the signaling overhead. We show a set of numerical results obtained using the energy consumption of real routers and study the impact of the different parameters and constraints on the optimal energy management strategy. We also present heuristic results to compare the optimal operational planning with online energy management operation .

We propose a multi-period model to minimize the energy consumption of IP networks while guaranteeing the satisfaction of all the traffic demands. Energy savings are achieved by putting into sleep mode cards and chassis. The multi-period optimization is constrained by inter-period limitations necessary to guarantee the stability of the networks. Both exact and heuristic solutions are proposed. Results show that up to 50% of the energy savings can be achieved for realistic test scenarios in networks operated with flow-based routing protocols (i.e. MPLS).

Internet energy consumption is rapidly becoming an issue due to the exponential traffic growth and the rapid expansion of communication infrastructures worldwide. In this paper we propose an off-line IP traffic engineering approach that allows to adapt the network energy consumption to different daily traffic scenarios (e.g., night, morning), by switching off and on (putting in sleeping mode and waking up) communication interfaces (links) and entire routers. We focus on routing domains where OSPF (Open Shortest Path First) protocol is adopted and we aim at optimizing energy consumption and network congestion by efficiently configuring the OSPF link weights. We present two heuristics, the Greedy Algorithm for Energy Saving (GA-ES) and the Two-stage Algorithm for Energy Saving (TA-ES). The computational results for three real network topologies show that it is possible to switch off up to 80% of the core nodes during low traffic periods (night hours), while moderately increasing the network congestion.