Associative routing for wireless sensor networks

Abstract

Traditionally routing in computer networks has focused on finding paths along which data packets could be delivered to pre-identified destination nodes. Most existing routing protocols rely on the use of network addresses as unique node or group identifiers that are usually numeric and independent of any application semantics. The semantically-oblivious identification has forced network designers to incorporate resource/service discovery techniques at higher layers of the network stack, resulting in unnecessary overhead. While such overhead can be tolerated in high-speed wired networks, it significantly limits performance and network lifetime in wireless infrastructure-less networks with battery-powered resource-constrained devices like sensor networks. Moreover, sensor nodes are more naturally anonymous and therefore assigning unique identifiers to individual node limits network scalability and imposes significant overhead on resource management. In this paper, we propose associative routing as a class of routing protocols that enables dynamic semantically-rich descriptive identification of network resources and services. As such, associative routing presents a clear departure from most current network addressing schemes, eliminating the need for a separate phase of resource/service discovery. We hypothesize that since, in essence, resource discovery operates similarly to path discovery then both can be performed in a single phase, leading to significant reduction in traffic load and communication latency without any loss of generality. We also propose a framework for associative routing and present adaptive multi-criteria routing (AMCR) protocol as a realization of associative routing for sensor networks. AMCR exploits application-specific message semantics, represented as generic criteria, and adapts its operation according to observed traffic patterns. Analytical results demonstrate the effectiveness, efficiency, and scalability of AMCR.

Introduction

Recent advances in wireless communications have driven a myriad of research efforts aiming at designing methods and techniques for enabling ubiquitous service environments and cyber-physical systems. Wireless sensor networks (WSNs), gained research attention as a major enabler of such environments, resulting in a large number of proposed architectures and protocols for WSNs. WSNs are wireless ad hoc networks of heterogeneous, often limited capability, battery-powered, sensing and/or actuation devices. Long-lived shared WSNs are needed to enable ubiquitous service environments that are capable of providing in situ users with diverse services from multiple providers while preserving their changing QoS requirements. WSN platforms should provide secure dynamic task-based networking and in-network data storage as well as dynamic reconfiguration of tasks and network resources to adapt to changes in environment or requirements. WSNs started to transition from a subject of academic research to a technology that is deployed to serve real-life applications, however, the market of commercial WSN technologies and applications is still in its infancy.

Due to the very limited computational and communicational capabilities of individual sensor nodes, WSNs rely on the collaboration among large number of sensor nodes to perform desired functions and achieve its goals. It is very common to have a large number of sensors that are completely identical in hardware or play the same role. Also, the network resource management and maintenance functions becomes more costly as number of nodes in the networks increases if nodes are individually addressed. This makes the unique identification of individual sensor node both unnecessary and undesirable in large scale WSNs.

Routing is one of the most challenging problems in WSNs due to its infrastructure-less nature and unreliability of nodes. Although many routing protocols were proposed for WSNs, most of them still rely on unique node identifiers that applications are expected to provide. For applications to use such routing protocols, they need another resource discovery protocol that allows senders to query the network for the identifiers of the nodes they want to communicate with. This is currently the responsibility of the middle-ware. Such separation between resource or service discovery and route discovery results in unnecessary control traffic in the network that could drain the sensor batteries faster. For example, in AODV [1], broadcast is used to discover routes on demand. The same route control packets used to discover routes could serve a dual purpose by discovering resources too; saving the network from another broadcast storm.

The ability to efficiently and securely share WSN resource among multiple applications is another serious challenge that has been recently highlighted as a missing link in state-of-the-art WSN research in [2]. Applications like traffic monitoring, parking management, fire alarming, and security enforcement could share the same WSN resources to achieve multiple distinct goals in a more economic way. The evolution from single-purpose WSN deployments to more generic WSN platforms became inevitable for cost-effective production of WSN technologies.

The limited amount of non-renewable energy is the major challenge in many WSN applications that require long lifetimes. No matter how WSN technologies attempts to make the most efficient use of the node’s energy, such energy efficiency only delays the problem, but does not solve it. Long-lived shared WSNs are actually WSNs capable of running applications for longer than the lifetimes of individual nodes. Such WSNs should be capable of tolerating node death and quickly incorporate progressively deployed nodes to compensate for failed nodes. This makes the WSN infrastructure highly dynamic.

Changes in the network’s underlying resources, connectivity, mission, or QoS requirements necessitate the design of adaptable WSN architectures and protocols. The network should be able to change its structure and behavior at run-time in response to change in requirements, or environment. Such adaptation is essential for long-lived WSN applications that are likely to encounter changes in the underlying resources and/or mission during their long lifetime. This imposes additional challenges on routing protocols for this class of WSNs.

The contributions of this paper is twofold: first, we propose associative addressing for routing protocols in WSNs combining service/resource discovery with route discovery roles into a single coherent role. We show how such combination: (1) improves energy efficiency, (2) minimize communication latency, (3) enhances network scalability and simplifies management and maintenance operation through dynamic identification and addressing, (4) improves the network resilience by allowing transparent fail-over to nodes with similar attributes, (5) enables routing protocols to exploit application layer semantics, (6) supports on demand multi-cast by eliminating the need of joining multicast groups, and (7) allows routing protocol to preserve the anonymity and privacy of nodes when needed. Second, we present AMCR, a generic criteria-based routing protocol, exploiting associative addressing to allow destinations to be specified as a qualitative reference to node capabilities, administrative settings, and/or application published criteria. We show how AMCR: (1) enables sharing of resources among multiple heterogeneous applications and allows the network to adapt to changes in application behaviour, operating environment, and QoS requirements; (2) provides seamlessly routing of unicast/multicast/anycast/broadcast traffic; (3) employs a novel criteria indexing mechanism to optimize communication efficiency and adapts to the observed application’s semantics and load patterns; (4) preserves genericness by not imposing any restrictions on the application’s choice of desired communication patterns.

The remainder of this paper is organized as follows. Section 2 reviews the related work in literature. Section 3 discusses the associative routing approach. Section 4 presents the AMCR routing protocols and its analytic evaluation. Finally Section 5 is our conclusion.