Multi-community command and control systems in law enforcement: An introductory planning guide

A set of planning guidelines for multi-community command and control systems in law enforcement is presented. Essential characteristics and applications of these systems are outlined. Requirements analysis, system concept design, implementation planning, and performance and cost modeling are described and demonstrated with numerous examples. Program management techniques and joint powers agreements for multicommunity programs are discussed in detail. A description of a typical multi-community computer-aided dispatch system is appended

Smartening the Environment using Wireless Sensor Networks in a Developing Country

The miniaturization process of various sensing devices has become a reality by enormous research and advancements accomplished in Micro Electro-Mechanical Systems (MEMS) and Very Large Scale Integration (VLSI) lithography. Regardless of such extensive efforts in optimizing the hardware, algorithm, and protocols for networking, there still remains a lot of scope to explore how these innovations can all be tied together to design Wireless Sensor Networks (WSN) for smartening the surrounding environment for some practical purposes. In this paper we explore the prospects of wireless sensor networks and propose a design level framework for developing a smart environment using WSNs, which could be beneficial for a developing country like Bangladesh. In connection to this, we also discuss the major aspects of wireless sensor networks.Comment: 5 page

Efficient Data Collection in Multimedia Vehicular Sensing Platforms

Vehicles provide an ideal platform for urban sensing applications, as they can be equipped with all kinds of sensing devices that can continuously monitor the environment around the travelling vehicle. In this work we are particularly concerned with the use of vehicles as building blocks of a multimedia mobile sensor system able to capture camera snapshots of the streets to support traffic monitoring and urban surveillance tasks. However, cameras are high data-rate sensors while wireless infrastructures used for vehicular communications may face performance constraints. Thus, data redundancy mitigation is of paramount importance in such systems. To address this issue in this paper we exploit sub-modular optimisation techniques to design efficient and robust data collection schemes for multimedia vehicular sensor networks. We also explore an alternative approach for data collection that operates on longer time scales and relies only on localised decisions rather than centralised computations. We use network simulations with realistic vehicular mobility patterns to verify the performance gains of our proposed schemes compared to a baseline solution that ignores data redundancy. Simulation results show that our data collection techniques can ensure a more accurate coverage of the road network while significantly reducing the amount of transferred data

SECMACE: Scalable and Robust Identity and Credential Management Infrastructure in Vehicular Communication Systems

Several years of academic and industrial research efforts have converged to a common understanding on fundamental security building blocks for the upcoming Vehicular Communication (VC) systems. There is a growing consensus towards deploying a special-purpose identity and credential management infrastructure, i.e., a Vehicular Public-Key Infrastructure (VPKI), enabling pseudonymous authentication, with standardization efforts towards that direction. In spite of the progress made by standardization bodies (IEEE 1609.2 and ETSI) and harmonization efforts (Car2Car Communication Consortium (C2C-CC)), significant questions remain unanswered towards deploying a VPKI. Deep understanding of the VPKI, a central building block of secure and privacy-preserving VC systems, is still lacking. This paper contributes to the closing of this gap. We present SECMACE, a VPKI system, which is compatible with the IEEE 1609.2 and ETSI standards specifications. We provide a detailed description of our state-of-the-art VPKI that improves upon existing proposals in terms of security and privacy protection, and efficiency. SECMACE facilitates multi-domain operations in the VC systems and enhances user privacy, notably preventing linking pseudonyms based on timing information and offering increased protection even against honest-but-curious VPKI entities. We propose multiple policies for the vehicle-VPKI interactions, based on which and two large-scale mobility trace datasets, we evaluate the full-blown implementation of SECMACE. With very little attention on the VPKI performance thus far, our results reveal that modest computing resources can support a large area of vehicles with very low delays and the most promising policy in terms of privacy protection can be supported with moderate overhead.Comment: 14 pages, 9 figures, 10 tables, IEEE Transactions on Intelligent Transportation System

4Sensing - decentralized processing for participatory sensing data

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática.Participatory sensing is a new application paradigm, stemming from both technical and social drives, which is currently gaining momentum as a research domain. It leverages the growing adoption of mobile phones equipped with sensors, such as camera, GPS and accelerometer, enabling users to collect and aggregate data, covering a wide area without incurring in the costs associated with a large-scale sensor network. Related research in participatory sensing usually proposes an architecture based on a centralized back-end. Centralized solutions raise a set of issues. On one side, there is the implications of having a centralized repository hosting privacy sensitive information. On the other side, this centralized model has financial costs that can discourage grassroots initiatives. This dissertation focuses on the data management aspects of a decentralized infrastructure for the support of participatory sensing applications, leveraging the body of work on participatory sensing and related areas, such as wireless and internet-wide sensor networks, peer-to-peer data management and stream processing. It proposes a framework covering a common set of data management requirements - from data acquisition, to processing, storage and querying - with the goal of lowering the barrier for the development and deployment of applications. Alternative architectural approaches - RTree, QTree and NTree - are proposed and evaluated experimentally in the context of a case-study application - SpeedSense - supporting the monitoring and prediction of traffic conditions, through the collection of speed and location samples in an urban setting, using GPS equipped mobile phones

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017

Middleware Technologies for Cloud of Things - a survey

The next wave of communication and applications rely on the new services provided by Internet of Things which is becoming an important aspect in human and machines future. The IoT services are a key solution for providing smart environments in homes, buildings and cities. In the era of a massive number of connected things and objects with a high grow rate, several challenges have been raised such as management, aggregation and storage for big produced data. In order to tackle some of these issues, cloud computing emerged to IoT as Cloud of Things (CoT) which provides virtually unlimited cloud services to enhance the large scale IoT platforms. There are several factors to be considered in design and implementation of a CoT platform. One of the most important and challenging problems is the heterogeneity of different objects. This problem can be addressed by deploying suitable "Middleware". Middleware sits between things and applications that make a reliable platform for communication among things with different interfaces, operating systems, and architectures. The main aim of this paper is to study the middleware technologies for CoT. Toward this end, we first present the main features and characteristics of middlewares. Next we study different architecture styles and service domains. Then we presents several middlewares that are suitable for CoT based platforms and lastly a list of current challenges and issues in design of CoT based middlewares is discussed.Comment: http://www.sciencedirect.com/science/article/pii/S2352864817301268, Digital Communications and Networks, Elsevier (2017

Time- and Computation-Efficient Data Localization at Vehicular Networks\u27 Edge

As Vehicular Networks rely increasingly on sensed data to enhance functionality and safety, efficient and distributed data analysis is needed to effectively leverage new technologies in real-world applications. Considering the tens of GBs per hour sensed by modern connected vehicles, traditional analysis, based on global data accumulation, can rapidly exhaust the capacity of the underlying network, becoming increasingly costly, slow, or even infeasible. Employing the edge processing paradigm, which aims at alleviating this drawback by leveraging vehicles\u27 computational power, we are the first to study how to localize, efficiently and distributively, relevant data in a vehicular fleet for analysis applications. This is achieved by appropriate methods to spread requests across the fleet, while efficiently balancing the time needed to identify relevant vehicles, and the computational overhead induced on the Vehicular Network. We evaluate our techniques using two large sets of real-world data in a realistic environment where vehicles join or leave the fleet during the distributed data localization process. As we show, our algorithms are both efficient and configurable, outperforming the baseline algorithms by up to a 40 7 speedup while reducing computational overhead by up to 3 7 , while providing good estimates for the fraction of vehicles with relevant data and fairly spreading the workload over the fleet. All code as well as detailed instructions are available at https://github.com/dcs-chalmers/dataloc_vn