A Proposed Bi-layer Crime Prevention Framework Using Big Data Analytics

The future of science and technology sounds very promising. The need to adopt new technologies while navigating towards industry 4.0 has changed the perceptions of law enforcement agency to contend against criminal minds. It is sad but true that the conventional crime prevention system followed by government agencies is not effective for long-term implications. With advanced technologies that constantly generate and exchange data, big data analytics can be applied to predict and prevent crime from happening. However, dealing with the overwhelming amount of complex and heterogeneous crime-related data is never an easy task. Additionally, there are many data analytical techniques and each of them has its own strengths and weaknesses. In order to identify the most efficient techniques, recent literature is reviewed to spotlight the trend as well as to shed light on the research gaps and challenges in various areas. The areas include crime data collection and preprocessing, crime data analysis, crime prediction and crime prevention. These techniques are further analyzed by considering the advantages and disadvantages which then provides insight to propose a bi-layer crime prevention framework. The first layer intends to support the law enforcement agency’s daily operation while the second layer serves as a countermeasure for first layer. Both layers aim to reduce the crime rate by involving law enforcement agency through the utilization of various big data sources and techniques effectively. The proposed crime prevention framework will progressively collect data to deter criminal behavior for city’s environmental design. Ultimately, a safe and secure city is molded in the near future

Developing an online cooperative police patrol routing strategy

A cooperative routing strategy for daily operations is necessary to maintain the effects of hotspot policing and to reduce crime and disorder. Existing robot patrol routing strategies are not suitable, as they omit the peculiarities and challenges of daily police patrol including minimising the average time lag between two consecutive visits to hotspots, as well as coordinating multiple patrollers and imparting unpredictability to patrol routes. In this research, we propose a set of guidelines for patrol routing strategies to meet the challenges of police patrol. Following these guidelines, we develop an innovative heuristic-based and Bayesian-inspired real-time strategy for cooperative routing police patrols. Using two real-world cases and a benchmark patrol strategy, an online agent-based simulation has been implemented to testify the efficiency, flexibility, scalability, unpredictability, and robustness of the proposed strategy and the usability of the proposed guidelines

Analysing the police patrol routing problem : a review

Police patrol is a complex process. While on patrol, police officers must balance many intersecting responsibilities. Most notably, police must proactively patrol and prevent offenders from committing crimes but must also reactively respond to real-time incidents. Efficient patrol strategies are crucial to manage scarce police resources and minimize emergency response times. The objective of this review paper is to discuss solution methods that can be used to solve the so-called police patrol routing problem (PPRP). The starting point of the review is the existing literature on the dynamic vehicle routing problem (DVRP). A keyword search resulted in 30 articles that focus on the DVRP with a link to police. Although the articles refer to policing, there is no specific focus on the PPRP; hence, there is a knowledge gap. A diversity of approaches is put forward ranging from more convenient solution methods such as a (hybrid) Genetic Algorithm (GA), linear programming and routing policies, to more complex Markov Decision Processes and Online Stochastic Combinatorial Optimization. Given the objectives, characteristics, advantages and limitations, the (hybrid) GA, routing policies and local search seem the most valuable solution methods for solving the PPRP

Communication Aware Mobile Robot Teams

The type of scenarios that could benefit from a team of robots that are able to self configure into an ad-hoc multi-hop mobile communication network while completing a task in an unknown environment, range from search and rescue in a partially collapsed building to providing a security perimeter around a region of interest. In this thesis, we present a hybrid system that enables a team of robots to maintain a prescribed end-to-end data rate while moving through a complex unknown environment, in a distributed manner, to complete a specific task. This is achieved by a systematic decomposition of the real-time situational awareness problem into subproblems that can be efficiently solved by distributed optimization. The validity of this approach is demonstrated through multiple simulations and experiments in which the a team of robots is able to accurately map an unknown environment and then transition to complete a traditional situational awareness task. We also present MCTP, a lightweight communication protocol that is specifically designed for use in ad-hoc multi-hop wireless networks composed of low-cost low-power transceivers. This protocol leverages the spatial diversity found in mobile robot teams as well as recently developed robust routing systems designed to minimize the variance of the end-to-end communication link. The combination of the hybrid system and MCTP results in a system that is able to complete a task, with minimal global coordination, while providing near loss-less communication over an ad-hoc multi-hop network created by the members of the team in unknown environments

Robotic Searching for Stationary, Unknown and Transient Radio Sources

Searching for objects in physical space is one of the most important tasks for humans. Mobile sensor networks can be great tools for the task. Transient targets refer to a class of objects which are not identifiable unless momentary sensing and signaling conditions are satisfied. The transient property is often introduced by target attributes, privacy concerns, environment constraints, and sensing limitations. Transient target localization problems are challenging because the transient property is often coupled with factors such as sensing range limits, various coverage functions, constrained mobility, signal correspondence, limited number of searchers, and a vast searching region. To tackle these challenge tasks, we gradually increase complexity of the transient target localization problem such as Single Robot Single Target (SRST), Multiple Robots Single Target (MRST), Single Robot Multiple Targets (SRMT) and Multiple Robots Multiple Targets (MRMT). We propose the expected searching time (EST) as a primary metric to assess the searching ability of a single robot and the spatiotemporal probability occupancy grid (SPOG) method that captures transient characteristics of multiple targets and tracks the spatiotemporal posterior probability distribution of the target transmissions. Besides, we introduce a team of multiple robots and develop a sensor fusion model using the signal strength ratio from the paired robots in centralized and decentralized manners. We have implemented and validated the algorithms under a hardware-driven simulation and physical experiments

Emergent Behavior Development and Control in Multi-Agent Systems

Emergence in natural systems is the development of complex behaviors that result from the aggregation of simple agent-to-agent and agent-to-environment interactions. Emergence research intersects with many disciplines such as physics, biology, and ecology and provides a theoretical framework for investigating how order appears to spontaneously arise in complex adaptive systems. In biological systems, emergent behaviors allow simple agents to collectively accomplish multiple tasks in highly dynamic environments; ensuring system survival. These systems all display similar properties: self-organized hierarchies, robustness, adaptability, and decentralized task execution. However, current algorithmic approaches merely present theoretical models without showing how these models actually create hierarchical, emergent systems. To fill this research gap, this dissertation presents an algorithm based on entropy and speciation - defined as morphological or physiological differences in a population - that results in hierarchical emergent phenomena in multi-agent systems. Results show that speciation creates system hierarchies composed of goal-aligned entities, i.e. niches. As niche actions aggregate into more complex behaviors, more levels emerge within the system hierarchy, eventually resulting in a system that can meet multiple tasks and is robust to environmental changes. Speciation provides a powerful tool for creating goal-aligned, decentralized systems that are inherently robust and adaptable, meeting the scalability demands of current, multi-agent system design. Results in base defense, k-n assignment, division of labor and resource competition experiments, show that speciated populations create hierarchical self-organized systems, meet multiple tasks and are more robust to environmental change than non-speciated populations