Keywords

1 Introduction

Urban environments, such as cities, are challenged by the pace of urbanization, which has reached exponential growth, with a growth rate of almost 150.000 people per day, either due to migration or births. Between 2011 and 2050, the world’s urban population is projected to rise by 72%. For example, the population will grow from 3.6 billion to 6.3 billion and the population share in urban areas from 52% in 2011 to 67% in 2050. Due to climate changes and other environmental pressures, cities are increasingly required to become “smart” and take substantial measures to meet stringent targets imposed by commitments and legal obligations [1].

Today, the monitoring of most water quality parameters (e.g. color, turbidity, ion concentration or pH) still involves taking separate samples from the water body or effluent stream, and transporting these to a laboratory for analysis and the costs of sample collection often place severe restrictions on the number of samples that can be taken and therefore on the amount of information that can be obtained about the system [2].

The challenge today is primarily to implement appropriate solutions efficiently, rather than only focusing on new technology development. Smart cities cannot be developed through a patchwork approach, but by the step-by-step adoption of incremental improvements [3].

Smart cities are developed by introducing smart systems, working for the benefit of both residents and the environment. Urban infrastructures will need to better meet the challenges of city environments: energy and water scarcity, pollution and emissions, traffic congestion, crime prevention, waste disposal, and safety risks from ageing infrastructures [4]. The increased mobility of our societies has created intense competition between cities for investment, talent, and jobs. Continuous monitoring has been used extensively for industrial process control for many years.

Cloud computing and Internet of Things (IoT) are two popular ICT paradigms that have come to the attention of the research community over the last years. The cloud computing paradigm realizes and promotes the delivery of hardware and software resources over the Internet, according to an on-demand utility based model [5].

So that, Cloud computing and IoT are therefore two critical technologies for realizing the ubiquitous communications vision. The cloud can provide large-scale and long-lived storage and processing resources for the personalized ubiquitous applications delivered through the IoT as well as important backend resources [6].

SNMP is a standard protocol that is defined on application layer in TCP/IP to manage and monitor networked devices. The devices that are managed by SNMP keep their information in a form of a tree called MIB (Management Information Base), and the management progresses in the way that remote manager refers the value of the MIB node of interesting device [7].

The rest of the paper is organized as follows: Sect. 2 describes some of the existent SNMP tools, Sect. 3 presents the monitoring system architecture and functionalities. Section 4 concludes the paper.

2 Related Work

SNMP traces are typically captured using tcp-dump and stored in pcap format. Hence, generic tools, which can process raw pcap files, can be used to split and merge the raw traffic traces. In order to analyze the traces, we have analyzed additional tools, which use the intermediate formats described above. Since traces may become quite large, tools should be relatively efficient and fast [8].

2.1 Conversion Tool (snmpdump)

A new tool called snmpdump accomplishes the conversion of raw pcap traces into intermediate formats and it reads raw pcap files as input and produces traces in XML or CSV format as output. Since the XML format retains all information, it is also supported as an input format and this allows using snmpdump as a filter. In addition, snmpdump accepts CSV format as input even though this format does not retain all information [9].

The data provided by the generic XML parser, which automatically checks well-formedness, is used to generate an in memory representation. Although not surprising, it should be noted that parsing raw pcap files is significantly faster and hence pcap remains a good choice for processing large trace files if no filtering or anonymization is needed [10].

The filter module of snmpdump is responsible to filter out message fields that should be suppressed, for instance because specific sensitive data must be removed. The message fields that should be suppressed are selected using a regular expression and the suppression essentially changes the attributes of the selected message fields.

An SNMP message flow is defined as all messages between a source and destination address pair which belong to a command generator (CG)/command responder (CR) relationship or a notification originator (NO)/notification receiver (NR) relationship [11].

The implementation of the flow identification module re quires to deal with reordered messages and to associate responses (and reports) to prior requests since responses do not indicate whether they are sent in response to a notification or a data retrieval operation [12].

The anonymization module is responsible for anonymizing message fields it makes use of a reusable anonymization library called 1libanon. The library provides anonymization functions for standard data types such as signed/unsigned integers and octet strings as well as specific functions for MAC addresses or IP addresses. A more detailed description of prefix- and lexicographic-order-preserving IP address anonymization can be found in [13].

2.2 Analysis Tools

Analysis tools usually read the intermediate format produced by snmpdump in the first stage and extract meaningful statistics in a second processing stage.

A Java 5.0 based statistics generator takes an intermediate file as input, parses it sequentially and handles internally each extracted packet to a set of statistics generators, which are responsible for maintaining counters and other data structures.

Currently the program is capable of generating basic protocol statistics, relations between managers and agents and statistics about error responses.

In addition to the Java program, a collection of Perl scripts has been developed (part of the snmpdump distribution) which analyze intermediate files in CSV format. Besides the generation of basic statistics, the scripts are able to analyze SNMP walks (sequences of GetNext or GetBulk operations) in order to produce data about how applications retrieve management information and which strategies are used to implement tables. Additional tools can perform object name (OID) conversion and aggregation on base statistics by reading the MIB identifiers lists that can be created using the smidurmp MIB compiler.

3 Proposed Remote Telemetry Systems in Urban Environments

The proposed system is based on an underwater sensor network which is connected to a cloud platform by means of a reconfigurable wireless transceiver.

The sensor network integrates several low cost sensors that can measure different parameters such as water level, flow, temperature and pressure. The measured parameters will be transmitted through an operational communication node, which should be able to ensure a reliable communication with timing and variation delay constraints.

An Underwater Sensor Network (UWSN) offers a unified vision, providing a real time visualization of the underwater events. All this information is available in a cloud platform responsible for the collection of environmental data. The platform provides an interface that users can access anywhere via Internet. We present the proposed network architecture for a UWSN composed by a low power sensor, a transceiver and an access point that provides the collected data to a cloud platform.

Figure 1 illustrates the initial architecture of the system, remote monitoring installations, data concentration unit (gateway) and the Central Server. A special applications server will be utilized for tasks eventually situated outside the possibilities of the Central Server. Communication between the mentioned system parts as well as towards the users goes through the Internet, so that everything is easily and in any possible way relocatable and reconfigurable.

Fig. 1.
figure 1

Monitoring system for water infrastructure in urban environment.

Full size image

The main functional specifications of the proposed monitoring system for water infrastructure monitoring in urban environments are:

  • The water and underwater sensor is wireless connected to the transceiver, which is powered by a local battery and solar panel, thus providing a long period of working autonomy for the system.

  • Telemetry application transmits data through GSM-GPRS and Internet, or in cases of no GSM coverage through the UHF band.

  • A solar panel shall be used to power the station and the sensors.

  • A server for a central database shall be hosted on a cloud platform which delivers elastic resources (CPU, storage, memory, etc.) and equipped with software focused mainly on data presentation in various forms, entirely available to users and also on other special tasks (data mining, etc.).

  • In case of floods, people that may be affected, could be informed via an application installed on their mobile phone, or in case of earthquake, people who remained trapped under rubble can be detected by means of a signal emitted by their phone. Also in case of weather alert codes peoples who are in the affected zone could receive an alert on their Smartphone.

  • The identification and recording the presence of pollution in water by sending qualitative data or spatial data (e.g. GPS coordinates) about a specific incident, by both authorities or people who has a mobile phone with internet connectivity.

  • For other possible scenarios: If there is a disaster warning made by the Government, the system will take the warning into account, increase the monitoring (collecting more data, which implies increased resource usage) and notify the users.

When an SNMP query is sent from an SNMP manager which is listed in the Permitted Manager’s list but the community name in the SNMP query doesn’t match the agent’s community name configured on the security tab of the SNMP agent, like when the community name is misspelled (it is case sensitive).

When both of the above conditions are true in a given situation. An agent traps all the trap destinations of all the communities, provided these community names are configured in the Security tab of an agent.

During the implementation process of the proposed system we analyzed IoT/cloud computing interoperability [14], including security and the possibility of using several protocols and standards, such as NETCONF, CPE WAN Management Protocol, OMA (Open Mobile Alliance) and ZigBee.

The NETCONF [15] protocol introduces a simple mechanism which can provide the possibility to manage different devices, retrieve configuration data information and upload and manipulate new configuration data. The RPC-based mechanism used by NETCONF facilitates the communication between clients (script or application running as part of a network manager) and servers (network devices).

The security aspect is always a challenge, because the limited battery life and computing resources of the sensor does not allow the running of standard encryption algorithms. Additionally, even though lightweight encryption algorithms have been proposed, the different hardware architectures of the sensors are hardening the efficient implementation of these algorithms. Work, therefore, has been put in place to build a unifying middleware for enabling security in IoT [16].

TR-069 [17] describes the CPE WAN Management Protocol, which can be used for the communication between a CPE and an ACS server (Auto-Configuration Server). The CPE protocol introduces a mechanism that encompasses the secure auto-configuration of a CPE and other control functions into a common framework.

ZigBee is a wireless networking standard aimed to remote control applications, having the main advantage that it is suitable for operation in harsh radio environments or isolated locations. Also, ZigBee represents a very low-cost and low-power consumption wireless communications standard [18].

After several tests, it has been found that the most appropriate protocol for the implementation of the proposed system is SNMP.

The generic prototype architecture of the proposed system can be seen in Fig. 2.

Fig. 2.
figure 2

Generic prototype architecture.

Full size image

The OGC standard was proposed for data exchange between the urban IoT prototype and a central IoT server for urban environments.

4 Conclusions

SNMP is a standard protocol for management of network devices. In this paper we have presented a remote telemetry system for urban environments which is using SNMP traps for monitoring events. The system is based on an underwater sensor network which is connected to a cloud platform by means of a reconfigurable wireless transceiver. The sensors must be able to perform self-configuration and calibration, and also they have to adapt to these environment conditions. As future work we envision to monitor other urban infrastructures.