Search in the Universe of Big Networks and Data

Searching in the Internet for some object characterised by its attributes in the form of data, such as a hotel in a certain city whose price is less than something, is one of our most common activities when we access the Web. We discuss this problem in a general setting, and compute the average amount of time and the energy it takes to find an object in an infinitely large search space. We consider the use of N search agents which act concurrently. Both the case where the search agent knows which way it needs to go to find the object, and the case where the search agent is perfectly ignorant and may even head away from the object being sought. We show that under mild conditions regarding the randomness of the search and the use of a time-out, the search agent will always find the object despite the fact that the search space is infinite. We obtain a formula for the average search time and the average energy expended by N search agents acting concurrently and independently of each other. We see that the time-out itself can be used to minimise the search time and the amount of energy that is consumed to find an object. An approximate formula is derived for the number of search agents that can help us guarantee that an object is found in a given time, and we discuss how the competition between search agents and other agents that try to hide the data object, can be used by opposing parties to guarantee their own success.Comment: IEEE Network Magazine - Special Issue on Networking for Big Data, July-August 201

Capacity Based Evacuation with Dynamic Exit Signs

Exit paths in buildings are designed to minimise evacuation time when the building is at full capacity. We present an evacuation support system which does this regardless of the number of evacuees. The core concept is to even-out congestion in the building by diverting evacuees to less-congested paths in order to make maximal usage of all accessible routes throughout the entire evacuation process. The system issues a set of flow-optimal routes using a capacity-constrained routing algorithm which anticipates evolutions in path metrics using the concept of "future capacity reservation". In order to direct evacuees in an intuitive manner whilst implementing the routing algorithm's scheme, we use dynamic exit signs, i.e. whose pointing direction can be controlled. To make this system practical and minimise reliance on sensors during the evacuation, we use an evacuee mobility model and make several assumptions on the characteristics of the evacuee flow. We validate this concept using simulations, and show how the underpinning assumptions may limit the system's performance, especially in low-headcount evacuations

G-Networks with Adders

Queueing networks are used to model the performance of the Internet, of manufacturing and job-shop systems, supply chains, and other networked systems in transportation or emergency management. Composed of service stations where customers receive service, and then move to another service station till they leave the network, queueing networks are based on probabilistic assumptions concerning service times and customer movement that represent the variability of system workloads. Subject to restrictive assumptions regarding external arrivals, Markovian movement of customers, and service time distributions, such networks can be solved efficiently with “product form solutions” that reduce the need for software simulators requiring lengthy computations. G-networks generalise these models to include the effect of “signals” that re-route customer traffic, or negative customers that reject service requests, and also have a convenient product form solution. This paper extends G-networks by including a new type of signal, that we call an “Adder”, which probabilistically changes the queue length at the service center that it visits, acting as a load regulator. We show that this generalisation of G-networks has a product form solution

Area-Based Results For Mine Detection

The cost and the closely related length of time spent in searching for mines or unexploded ordnance (UXO) may well be largely determined by the number of false alarms. False alarms can result in time consuming digging of soil or in additional multi-sensory tests in the minefield. In this paper, we consider two area-based methods for reducing false alarms. These are: a) the previously known `declaration\u27 technique and b) the new δ technique, which we introduce. We first derive expressions and lower bounds for false-alarm probabilities as a function of declaration area and discuss their impact on receiver operation characteristic (ROC) curves. Second, we exploit characteristics of the statistical distribution of sensory energy in the immediate neighborhood of targets and of false alarms from available calibrated data, to propose the δ technique, which significantly improves discrimination between targets and false alarms. The results are abundantly illustrated with statistical data and ROC curves using electromagnetic-induction sensor data made available through DARPA from measurements at various calibrated sites

Area-based results for mine detection

The cost and the closely related length of time spent in searching for mines or unexploded ordnance (UXO) may well be largely determined by the numher of false alarms. False alarms can result in time consuming digging of soil or in additional multisensory tests in the minefield. In this paper, we consider two area-based methods for reducing false alarms. These are: a) the previously known declaration\u27\u27 technique [8], [10] and b) the new delta technique, which we introduce. We first derive expressions and lower bounds for false-alarm probabilities as a function of declaration area and discuss their impact on receiver operation characteristic (ROC) curves. Second, we exploit characteristics of the statistical distribution of sensory energy in the immediate neighborhood of targets and of false alarms from available calibrated data, to propose the delta technique, which significantly improves discrimination between targets and false alarms. The results are abundantly illustrated with statistical data and ROC curves using electromagnetic-induction sensor data made available through DARPA [8] from measurements at various calibrated sites