Enablers for Smart Cities

Amal El Fallah Seghrouchni, Fuyuki Ishikawa, Laurent Hérault, Hideyuki Tokuda

• Publisher: Wiley-ISTE
• Year: 2016
• ISBN:
  9781119329992
• Edition: 1
• Topic: Physical Sciences
• Subtopic: Energy

Smart cities are a new vision for urban development. They integrate information and communication technology infrastructures – in the domains of artificial intelligence, distributed and cloud computing, and sensor networks – into a city, to facilitate quality of life for its citizens and sustainable growth. This book explores various concepts for the development of these new technologies (including agent-oriented programming, broadband infrastructures, wireless sensor networks, Internet-based networked applications, open data and open platforms), and how they can provide smart services and enablers in a range of public domains.

The most significant research, both established and emerging, is brought together to enable academics and practitioners to investigate the possibilities of smart cities, and to generate the knowledge and solutions required to develop and maintain them.

1
Shared Wireless Sensor Networks as Enablers for a Context Management System in Smart Cities

Wireless sensor networks (WSNs) are commonly used as a sensing infrastructure for smart city applications. A WSN is easy to use and can cover a wide area at low costs because of its wireless communication capability. The sensor nodes constituting a WSN are usually equipped with one or more sensor devices and can be used for different measurement purposes by reprogramming them. If WSNs could be shared by different smart city applications, they could be even more valuable enablers for smart cities. However, it is not easy to share WSNs. A shared WSN needs to support different kinds of measurement tasks at the same time and be able to accept new tasks at runtime. Even in a traditional closed WSN, its software should be carefully developed to satisfy certain quality requirements despite the severe resource constraints affecting the individual programmable sensor nodes (the sensor nodes of WSNs usually have quite limited resources, e.g. small batteries, low-spec CPU and narrow bandwidth). This issue is much harder to resolve in the case of a shared WSN. To satisfy the quality requirements of different applications, a WSN should be configured carefully according to specifications of the tasks, their quality requirements, and the environment, and should adapt its configuration in response to changes in the environment and the applications. A shared WSN should support various measurements, manage tasks at runtime and adapt to changes in the environment to reduce unnecessary consumption of resources. To develop such a shared WSN, we propose a middleware support for the network. In this chapter, we describe the architecture of our XAC middleware and the issues relevant to the shared WSN from the viewpoints of the task-description language, runtime task management and self-adaptation.

1.1. Introduction

In the smart cities of the future, many context-aware applications will support the citizen’s activities by proactively controlling the various devices used therein. Context-aware applications will recognize the current context of the city they are monitoring and actuate devices to amend their status. A key service in smart cities will be context management systems, which estimate context of cities and provide it to applications.

A context management system should be able to collect and update various types of content required by context-aware applications and should be able to be used easily in various environments. Here, a wireless sensor network (WSN) will be a key infrastructure in context management systems. A WSN is a wireless ad hoc network consisting of tiny computers equipped with sensors and wireless communication devices. It continuously records and produces data by measuring the environment via sensor nodes. It can produce one or more kinds of data, because its nodes are equipped with one or more kinds of sensors, and it can be programed to alter or switch tasks between the different sensor devices used for monitoring. Moreover, it can be easily used because it does not require any communication cables. Its nodes communicate with each other via wireless links and transmit the measured sensor data via multi-hop communications.

These features of the WSN are quite important for context management systems in smart cities. First, its “easy-to-deploy” feature is suitable for the smart cities. Sensor nodes are usually used in outdoor spaces, but it is not easy to connect sensor nodes using cables because of monetary and legal constraints. Second, the “reprogrammable” feature is suitable because a smart city usually hosts many applications that require different kinds of sensor data. A context management system should carefully balance the demands of these applications and the resource consumption of the sensor nodes. This can be realized by reprogramming a WSN. Therefore, the WSN is a key enabler for a context management system in a smart city.

A shared WSN for smart cities should:

1. 1) support various kinds of measurements;
2. 2) manage tasks at runtime;
3. 3) adapt to changes in the environment to reduce unnecessary resource consumption.

A shared WSN is used by many context-aware applications, which require different kinds of sensor data and different levels of accuracy. Therefore, it should be able to handle various measurements to produce one or more kinds of sensor data required by these applications with a level of accuracy. Moreover, applications using a WSN appear and disappear at runtime. Therefore, the WSN should be able to add or remove tasks at runtime without having to stop and start. Finally, a WSN should be able to adapt its behavior in response to changes in the environment. A WSN has severe resource limitations because each node in a WSN has CPU, memory, bandwidth and battery restrictions, and resources must be saved to increase the number of tasks that it can handle and to prolong its lifetime. Therefore, the WSN needs to automatically adapt to changes in the environment to reduce unnecessary resource consumption, that is to say without human intervention.

To develop such a shared WSN, middleware supports are needed. This chapter describes an example of middleware for a shared WSN, called XAC middleware. In addition, we discuss the research issues related to shared WSNs and the techniques used in XAC middleware.

1.2. Background

WSN software development is not easy because it requires programmers to have an in-depth knowledge of various fields, such as analysis of sensor data, distributed programing in wireless ad hoc networks and optimization of embedded systems. This section presents examples of types of WSN software to identify the issues concerning shared WSNs.

Figure 1.1. A smart environment

Let us first define our example– a smart environment is set up in an office building, where context-aware applications are introduced to optimize everyday business tasks. Consider the environment illustrated in Figure 1.1. Sensor nodes are used throughout rooms, corridors and stairwells to enable monitoring and to establish the current context of the building. Data sensed by the nodes are transferred via multi-hop communication to a central server (called the base station from here on).

Table 1.1 shows four scenarios, S1, S2, S3 and S4, envisioning context-aware applications in this example environment. In scenario S1, an application maintains the temperature levels in the conference rooms according to the preferences of the people in the room. The application in S2 determines the occupancy of conference rooms on the basis of the presence of people in the room and the reservation data of the room. Scenario S3 involves tracking applications that continuously monitor the current locations of staff inside the building, whereas the application in S4 detects suspicious intruders.

Table 1.1. Examples of context-aware application scenarios

Scenarios	Application name	Operational tasks	Environmental information	Accuracy requirement
S1	Temperature management	Adjust room temperature according to preferences of people in the room	Temperature in the room	Within 2°C of actual value
S2	Meeting-room management	Maintain occupancy of conference rooms based on their current occupancy and reservations	Presence of people in conference rooms	Determine correct room occupancy with 99% accuracy
S3	Staff-tracking management	Determines t...

Table of contents

• Cover
• Table of Contents
• Title
• Copyright
• Preface
• Introduction
• 1 Shared Wireless Sensor Networks as Enablers for a Context Management System in Smart Cities
• 2 Sensorizer: An Architecture for Regenerating Cyber-physical Data Streams from the Web
• 3 Smart Agent Foundations: From Planning to Spatio-temporal Guidance
• 4 A Multi-Agent Middleware for Deployment of Ambient Applications
• 5 ClouT: Cloud of Things for Empowering Citizen’s Clout in Smart Cities
• 6 sensiNact IoT Platform as a Service
• 7 Verification and Configuration of Smart Space Applications
• 8 SmartSantander: A Massive Self-Managed, Scalable and Interconnected IoT Deployment
• 9 Using Context-aware Multi-agent Systems for Robust Smart City Infrastructure
• 10 City of Santander
• 11 Fujisawa, Towards a Sustainable Smart City
• List of Authors
• Index
• End User License Agreement