Congestion Control for 6LoWPAN Wireless Sensor Networks: Toward the Internet of Things

The internet of things (IoT) is considered to be the next big challenge for the Internet research community. Recently, the IoT has drawn significant research attention [1]. The IoT will comprise of billions of communicating devices, which extend the borders of the cyber world with physical entities and virtual components [2, 3]. These things, such as wireless sensor nodes, radio-frequency identification (RFID) tags and near-field communication (NFC) devices, are connected to the Internet with the ability to sense status and use real-time data. Also, they access historical data and developed algorithms, possibly triggering devices. This is leading to very powerful smart environments, e.g. building, health care, etc. [1, 4].

This chapter presents a comprehensive literature review on congestion control for WSNs and 6LoWPAN networks. \(\bullet \) It gives a review of performance metrics, operating systems and simulators used to evaluate and test proposed congestion control mechanisms as well as explaining which operating systems and simulators support the 6LoWPAN protocol stack.

This chapter presents a comprehensive congestion analysis for 6LoWPAN network through analytical modelling, simulations and testbed results. Congestion occurs when multiple sensor nodes start to send packets concurrently at high data rate or when a node relays many flows across the network. Thus, link collision on the wireless channel and packet overflow at buffer nodes occur in the network [1]. Recently, a few papers have been presented to address congestion in 6LoWPAN networks [2‐5], but none considered congestion assessment and analysis. In [6], Hull et al. did a testbed experiment in a traditional WSN protocol stack with TinyOS where B-MAC and the single destination DSDV (Destination Sequenced Distance Vector) routing protocol are used. In this chapter, experiments in 6LoWPAN wireless sensor networks using the 6LoWPAN protocol stack and Contiki OS are considered.

It is known that existing protocols and the architecture of the Internet are inefficient for WSNs. Recently, the IETF has developed a set of IP-based protocols for 6LoWPAN networks through the 6LoWPAN and ROLL working groups [1]. One of the main protocols is RPL [2] which is expected to be the standard routing protocol for 6LoWPAN networks [3]. Many metrics have been proposed to be used with RPL that can be divided into link and node metrics, e.g. hop count, expected transmission count (ETX), node energy, latency, link quality and throughput [4].

WSNs connected to the Internet through 6LoWPAN have wide applications in industrial, automation, health care, military, environment, logistics, etc. An estimate by Bell Labs suggests that from 50 to 100 billion things are expected to be connected to the Internet by 2020 [1], and the number of the wireless sensor devices will account for a majority of these. Generally, the applications can be categorized into four types: event-based, continuous, query-based and hybrid applications based on the data delivery method [2, 3]. In the hybrid application type, the first three categories are combined into hybrid application, i.e. sensor nodes send packets in response to an event (event based) and at the same time send packets periodically (continuous) as well as send a reply to a sink query (query based). This type of application will be common in the future as WSNs are integrated with the Internet to form the IoT [4]. In the IoT applications, the sensor nodes host many different application types simultaneously (event based, continuous and query based) with varied requirements. Some of them are real-time applications, where the application data is time-critical and delay-constrained, while others are non-real-time applications. Some applications send very important data and losing this data is not permitted, e.g. medical applications and fire detection applications. This brings new challenges to the congestion control algorithms and mechanisms designed to be aware of application priorities as well as node priorities. However, according to our best knowledge, none of the existing congestion control literature in WSNs and 6LoWPAN networks supports awareness of both node priorities and application priorities. To address this, later we define a ‘priority cost function’ to support node priority awareness and distinguish between high-priority nodes and low-priority nodes.

In general, two main methods are used to solve and alleviate congestion in WSNs and 6LoWPAN networks: rate adaptation (traffic control) and traffic engineering, i.e. selection of an alternate non-congested path (resource control) to forward packets to destination nodes [1, 2]. In traffic control, the sending rate of the source node is reduced to a specific value such that the number of injected packets into the network is reduced and therefore, congestion is alleviated. However, for time-critical and delay-constrained application (e.g. medical applications and fire detection applications), reducing the data rate is not desirable and impractical. In the resource control method, packets are forwarded to destination node through alternative non-congested paths without adjusting the sending rate. However, sometimes non-congested paths are not available and therefore, congestion cannot be avoided. Thus, it is very important to combine the above two strategies into a hybrid scheme and utilize the positive aspects of using both traffic control and resource control. In such case, the resource control strategy is firstly used for searching non-congested paths. If they are not available, then the sending rate is reduced by applying the traffic control strategy. To the best of our knowledge, no existing congestion control mechanism in 6LoWPAN networks combines both strategies to solve the congestion problem.

In this section, a summary of the research findings of the thesis is presented. This thesis presents a concrete, solid and logically ordered work on congestion control for 6LoWPAN networks as a step toward successful implementation of the IoT and supporting the IoT application requirements.