Adaptive policies for perimeter surveillance problems

We consider the problem of sequentially choosing observation regions along a line, with an aim of maximising the detection of events of interest. Such a problem may arise when monitoring the movements of endangered or migratory species, detecting crossings of a border, policing activities at sea, and in many other settings. In each case, the key operational challenge is to learn an allocation of surveillance resources which maximises successful detection of events of interest. We present a combinatorial multi-armed bandit model with Poisson rewards and a novel filtered feedback mechanism - arising from the failure to detect certain intrusions - where reward distributions are dependent on the actions selected. Our solution method is an upper confidence bound approach and we derive upper and lower bounds on its expected performance. We prove that the gap between these bounds is of constant order, and demonstrate empirically that our approach is more reliable in simulated problems than competing algorithms

Drones and the Fourth Amendment: Redefining Expectations of Privacy

Drones have gained notoriety as a weapon against foreign terrorist targets; yet, they have also recently made headlines as an instrument for domestic surveillance. With their sophisticated capabilities and continuously decreasing costs, it is not surprising that drones have attracted numerous consumers—most notably, law enforcement. Courts will likely soon have to decipher the limits on the government’s use of drones under the Fourth Amendment. But it is unclear where, or even whether, drones would fall under the current jurisprudence. Because of their diverse and sophisticated designs and capabilities, drones might be able to maneuver through the Fourth Amendment’s doctrinal loopholes. This Note advocates analyzing drones under an adapted approach to the reasonable-expectation-of-privacy test in Katz v. United States. Courts should focus more on the test’s oft-neglected first prong—whether a person exhibited a subjective expectation of privacy—and analyze what information falls within the scope of that expectation, excluding information knowingly exposed to the plain view of the public. This analysis also considers instances when, although a subjective expectation exists, it may be impossible or implausible to reasonably exhibit that expectation, a dilemma especially relevant to an analysis of drones. Courts that adopt the recommended analysis would have a coherent and comprehensible approach to factually dynamic cases challenging the constitutionality of drone surveillance. Until then, the constitutional uncertainties of these cases will likely linger

Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey

Wireless sensor networks (WSNs) consist of autonomous and resource-limited devices. The devices cooperate to monitor one or more physical phenomena within an area of interest. WSNs operate as stochastic systems because of randomness in the monitored environments. For long service time and low maintenance cost, WSNs require adaptive and robust methods to address data exchange, topology formulation, resource and power optimization, sensing coverage and object detection, and security challenges. In these problems, sensor nodes are to make optimized decisions from a set of accessible strategies to achieve design goals. This survey reviews numerous applications of the Markov decision process (MDP) framework, a powerful decision-making tool to develop adaptive algorithms and protocols for WSNs. Furthermore, various solution methods are discussed and compared to serve as a guide for using MDPs in WSNs

Streaming multimedia over WMSNs: an online multipath routing protocol

Routing is a challenge to Wireless Multimedia Sensor Networks (WMSNs) for supporting multimedia applications due to nodes' energy constraints and computational capabilities, and the ways sensor nodes obtain forwarding information. In this paper, we propose an online multipath routing protocol that uses nodes' positions to make forwarding decisions at each hop. Real-time decisions are made without any need to have the entire network topology knowledge. The protocol achieves load-balancing and minimises nodes' energy consumption by utilizing: (a) smart greedy forwarding scheme for selecting next hop, and (b) walking back forwarding scheme to bypass network holes. Performance comparisons of the proposed protocol (schemes) are made with TPGF and GPSR. The results show that our schemes: (a) maximise the overall network lifespan by not draining energy from some specific nodes, (b) provide QoS delivery for video streams by using best nodes along the route, and (c) scale better in high density WMSN

A Model for Perimeter-Defense Problems with Heterogeneous Teams

We develop a model of the multi-agent perimeter-defense game to calculate how an adaptive defense should be organized. This model is inspired by the human immune system and captures settings such as heterogeneous teams, limited resource allocations, partial observability of the attacking side, and decentralization. An optimal defense, that minimizes the harm under constraints of the energy spent to maintain a large and diverse repertoire, must maintain coverage of the perimeter from a diverse attacker population. The model characterizes how a defense might take advantage of its ability to respond strongly to attackers of the same type but weakly to attackers of diverse types to minimize the number of diverse defenders and while reducing harm. We first study the model from a steady-state perimeter-defense perspective and then extend it to mobile defenders and evolving attacker distributions. The optimal defender distribution is supported on a discrete set and similarly a Kalman filter obtaining local information is able to track a discrete, sometimes unknown, attacker distribution. Simulation experiments are performed to study the efficacy of the model under different constraints.Comment: 8 pages, 6 figure

Tracking people in crowds by a part matching approach

The major difficulty in human tracking is the problem raised by challenging occlusions where the target person is repeatedly and extensively occluded by either the background or another moving object. These types of occlusions may cause significant changes in the person's shape, appearance or motion, thus making the data association problem extremely difficult to solve. Unlike most of the existing methods for human tracking that handle occlusions by data association of the complete human body, in this paper we propose a method that tracks people under challenging spatial occlusions based on body part tracking. The human model we propose consists of five body parts with six degrees of freedom and each part is represented by a rich set of features. The tracking is solved using a layered data association approach, direct comparison between features (feature layer) and subsequently matching between parts of the same bodies (part layer) lead to a final decision for the global match (global layer). Experimental results have confirmed the effectiveness of the proposed method. © 2008 IEEE