Alexey Medvedev

Waste Management as an IoT-Enabled Service in Smart Cities

Intelligent Transportation Systems (ITS) enable new services within Smart Cities. Efficient Waste Collection is considered a fundamental service for Smart Cities. Internet of Things (IoT) can be applied both in ITS and Smart cities forming an advanced platform for novel applications. Surveillance systems can be used as an assistive technology for high Quality of Service (QoS) in waste collection. Specifically, IoT components: (i) RFIDs, (ii) sensors, (iii) cameras, and (iv) actuators are incorporated into ITS and surveillance systems for efficient waste collection. In this paper we propose an advanced Decision Support System (DSS) for efficient waste collection in Smart Cities. The system incorporates a model for data sharing between truck drivers on real time in order to perform waste collection and dynamic route optimization. The system handles the case of ineffective waste collection in inaccessible areas within the Smart City. Surveillance cameras are incorporated for capturing the problematic areas and provide evidence to the authorities. The waste collection system aims to provide high quality of service to the citizens of a Smart City.

Challenges and Opportunities of Waste Management in IoT-Enabled Smart Cities: A Survey

The new era of Web and Internet of Things (IoT) paradigm is being enabled by the proliferation of various devices like RFIDs, sensors, and actuators. Smart devices (devices having significant computational capabilities, transforming them to ‘smart things’) are embedded in the environment to monitor and collect ambient information. In a city, this leads to Smart City frameworks. Intelligent services could be offered on top of such information related to any aspect of humans’ activities. A typical example of services offered in the framework of Smart Cities is IoT-enabled waste management. Waste management involves not only the collection of the waste in the field but also the transport and disposal to the appropriate locations. In this paper, we present a comprehensive and thorough survey of ICT-enabled waste management models. Specifically, we focus on the adoption of smart devices as a key enabling technology in contemporary waste management. We report on the strengths and weaknesses of various models to reveal their characteristics. This survey sets up the basis for delivering new models in the domain as it reveals the needs for defining novel frameworks for waste management.

Robust waste collection exploiting cost efficiency of IoT potentiality in Smart Cities

Smart Cities constitute the future of civil habitation. Internet of Things (IoT) enable innovative services exploiting sensor data from sensors embedded in the city. Waste collection is treated as a potential IoT service which exploits robustness and cost efficiency of a heterogeneous fleet. In this paper we propose a dynamic routing algorithm which is robust and copes when a truck is overloaded or damaged and need replacement. We also incorporate a system model which assumes two kinds of trucks for waste collection, the Low Capacity Trucks (LCTs) and the High Capacity Trucks (HCTs). By incorporating HCTs we achieve reduction of the waste collection operational costs because route trips to the dumps are reduced due to high waste storage capacity of these trucks. Finally, the proposed models are evaluated on synthetic and real data from the city municipality of St. Petersburg, Russia. The models demonstrate consistency and correctness.

Top -- k Query Based Dynamic Scheduling for IoT-enabled Smart City Waste Collection

Smart Cities are being designed and built for comfortable human habitation. Among services that Smart Cities will offer is the environmentally-friendly waste/garbage collection and processing. In this paper, we motivate and propose an Internet of Things (IoT) enabled system architecture to achieve dynamic waste collection and delivery to processing plants or special garbage tips. In the past, waste collection was treated in a rather static manner using classical operations research approach. As proposed in this paper, nowadays, with the proliferation of sensors and actuators, as well as reliable and ubiquitous mobile communications, the Internet of Things (IoT) enables dynamic solutions aimed at optimizing the garbage truck fleet size, collection routes and prioritized waste pick-up. We propose a top -- k query based dynamic scheduling model to address the challenges of near real-time scheduling driven by sensor data streams. An Android app along with a user-friendly GUI is developed and presented in order to prove feasibility and evaluate a waste collection scenario using experimental data. Finally, the proposed models are evaluated on synthetic and real data from the city municipality of St. Petersburg, Russia. The models demonstrate consistency and correctness.

Citywatcher: Annotating and Searching Video Data Streams for Smart Cities Applications

Digital pervasive video cameras can be abundantly found everywhere these days and their numbers grow continuously. Modern cities have large networks of surveillance cameras including CCTV, street crossings and the like. Sometimes authorities need a video-recording of some road accident (or of some other event) to understand what happened and identify a driver who may have been at fault. In this paper we discuss the challenges of annotating and retrieving video data streams from vehicle-mounted surveillance cameras. We also propose and evaluate the CityWatcher application – an Android application for recording video streams, annotating them with location, timestamp and additional context in order to make them discoverable and available to authorized Internet of Things applications

Data Ingestion and Storage Performance of IoT Platforms: Study of OpenIoT

Internet of Things is a very active research area with great commercialisation potential. The number of IoT platforms is already exceeding 300 and still growing. However, performance evaluation and benchmarking of IoT platforms are still in their infancy. As a step towards developing a performance benchmarking approach for IoT platforms, this paper analyses and compares a number of popular IoT platforms from data ingestion and storage capability perspectives. In order to test the proposed approach, we use the widely used open source IoT platform, OpenIoT. The results of the experiments and the lessons learnt are presented and discussed. While having a great research promise and pioneering contribution to semantic interoperability of IoT silos, the experimental results indicate OpenIoT platform needs more development effort to be ready for any substantial deployment in commercial IoT applications.

Reporting Road Problems in Smart Cities Using OpenIoT Framework

Video streaming from cameras, closed-circuit television (CCTV), smartphones and Internet-connected objects (ICO) largely contributes to big data traffic on the Internet. Video streaming provides enormous amount of useful information for delivery of efficient and effective services in smart cities. Modern cities have large networks of surveillance cameras including CCTV, street crossings and the like. In this paper we discuss the challenges of annotating and retrieving video data streams from vehicle-mounted surveillance cameras. We also propose and evaluate the CityWatcher application – an Android application for recording video streams, annotating them with location, timestamp and additional context in order to make them discoverable and available to authorized Internet of Things applications. One of such applications is based on crowdsourced alerts to city authorities about road problems, like potholes, cracks, traffic accidents. These alerts are driver-initiated and are rewarded through an incentive mechanism. OpenIoT platform is used for infrastructure and development support.

Ontology-Based Voice Annotation of Data Streams in Vehicles

With proliferation of the Internet of Things, annotation and generation of metadata describing data streams produced by sensors becomes even more urgent and important. This article proposes a method of annotating data streams with voice and extracting semantics from data. The strengths and weaknesses of existing voice recognition systems are discussed and it is argued that ontologies should play important role in making annotations meaningful and useful for various services and applications, including annotating road conditions and traffic situations. The architecture and implementation of the proposed system is discussed and demonstrated.

Storing and Indexing IoT Context for Smart City Applications

IoT system interoperability, data fusion, data discovery and access control for providing Context-as-a-Service as well as tools for building context-aware smart city applications are all significant research challenges for IoT-enabled smart cities. These middleware platforms have to cope with potentially big data generated from millions of devices in large cities. The amount of context, metadata, annotations in IoT ecosystems equals and may even exceed the amount of raw data. This paper discusses the challenges of context storage, retrieval and indexing for smart city applications. We analyse, compare and categorise existing approaches, tools and technologies relevant to the identified challenges. The paper proposes a conceptual architecture of a hybrid context storage and indexing mechanism that enables and supports the Context Spaces theory based representation of context for large-scale smart city applications. We illustrate the proposed approach using solid waste management system with adaptive on-demand garbage collection from IoT-enabled garbage bins.

SWM-PnR: Ontology-Based Context-Driven Knowledge Representation for IoT-Enabled Waste Management

Using knowledge-based and semantic technologies in IoT is a very active research and promising area. This paper proposes a method of ontology-based context-driven knowledge representation for IoT-enabled hard waste management as part of a wider international project that aims at building IoT ecosystems for smart cities. The paper presents the development of the waste management ontology, rules, and proposes a multistage data processing method that allows extracting knowledge about specific nontrivial situations on its basis. The paper describes implementation of the proposed system as a web application, where the content types are based on ontology, and data processing occurs according to the proposed algorithm. Benefits of the proposed knowledge-based system are discussed and demonstrated. The proposed approach will significantly improve monitoring and management of waste collection, route planning, and problem reporting.