Familiarized QoS in Distributed Routing Protocol for Synthesis Based Wireless Networks

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c _{e-ISSN: 2348-6848, p- ISSN: 2348-795X Volume 2, Issue 12, December 2015}

International Journal of Research (IJR)

Available at http://internationaljournalofresearch.org

Familiarized QoS in Distributed Routing Protocol for

Synthesis Based Wireless Networks

1

_{M.N.BHASKAR &}

2

_B.ARAVIND

1

Associate Professor, Dept of CSE, Shri Shirdi Sai Institute of Science & Engineering, Affiliated to JNTUA, AP, India.

2

M.Tech Dept of CSE, Shri Shirdi Sai Institute of Science & Engineering, Affiliated to JNTUA, AP, India

Abstract —

Wireless Sensor Networks (WSNs) comprise of groups of tiny sensor nodes that are deployed for

collaborative missions such as environmental

monitoring, target tracking and surveillance. Due to the miniature size of the nodes, they are typically deployed in large numbers and communicate via

multiple hops through a wireless shared

communication channel. The successful

implementation of such networks is dependent on the enabling technologies (such as digital electronics and wireless communications), as well as the provisioning of Quality of Service (QoS) in the network. While there have been many efforts in QoS provisioning in traditional networks such as the Internet and Mobile Ad Hoc Networks (MANETs), these networks have very different characteristics from that of WSNs. Consequently, the QoS models and protocols that have been designed for the Internet and MANETs cannot be directly applied to WSNs. In this paper, we look at some of the existing QoS mechanisms in the networking literature, and the inherent characteristics of WSNs which make it challenging to provision for QoS in the network. We then identify some key performance metrics for WSN QoS and outline some mechanisms to achieve QoS in the sensor network.

Finally, we propose WISER – a framework to enhance

QoS in WIreless SEnsoR networks.

Index Terms — Wireless Sensor Networks; Quality

of Service; Network Lifetime; Coverage; Spatial Accuracy; Delay; Topology Management

I. INTRODUCTION

Wireless Sensor Networks (WSNs) are envisioned to be the next generation of networks which will form an integral part of man’s lives. The sensor nodes are usually small in size, with multi-modal sensing capabilities which allows them to collect raw data of

various physical parameters such as temperature, salinity, humidity, light intensity, pressure, sound, radiation, etc. They are equipped with wireless interfaces, enabling them to communicate with each other via multiple hops using Radio Frequency (RF) techniques. Due to the miniature size of these nodes, sensor networks can be densely deployed in a distributed manner in any terrain;

With advances in wireless communications, the applications of sensor networks are no longer limited to that of periodic monitoring of the environment. Wireless sensor networks can be used for a wide array

of applications spanning multiple domains –

healthcare, biometrics, home networking, military,

automotives, as well as construction and

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The successful implementation and deployment of such intelligent wireless sensor networks requires several enabling technologies, such as Micro-Electro-Mechanical Systems (MEMS), digital electronics and wireless communications [2], as well as the provisioning of Quality of Service (QoS) support to the applications that run on top of the network. While different applications may have specific QoS requirements, some of the more commonly used QoS metrics used to measure network performance are delay, throughput, bandwidth and efficiency of the protocol in use. There has been much effort invested into QoS support in the Internet and MANETs (Mobile Ad Hoc NETworks) within the last decade, leading to the proliferation of Internet QoS models such as Integrated Services (IntServ) [3] and Differentiated Services (DiffServ) [4], and MANET QoS models such as the Flexible QoS Model for MANETs (FQMM) [5] and integrated MANET QoS (iMAQ) model [6]. However, as these networks have fundamentally different characteristics from that of wireless sensor networks, the protocols and algorithms that are used to provide QoS in the Internet and MANETs cannot be directly applied to sensor networks. In addition, in such networks with higher resource availability and predictability, providing QoS beyond ―best effort‖ is already a challenge. It is therefore even more difficult to achieve QoS in wireless sensor networks, which have unpredictable and limited resources.

In this paper, we first take a look at the various QoS mechanisms that exist today, and present an overview of the challenges and issues that exist in the deployment of wireless sensor networks. We then look at the how QoS can be achieved in these networks – which are characterized by multi-hop communications, dynamic environments and limited resources. The rest of this paper is organized as follows: The next section describes the related work and the motivation for QoS provisioning in WSNs. Section III details the multi-faceted challenges involved when considering QoS support in wireless sensor networks. Section IV presents some of the key performance metrics that can be used in WSN QoS and outlines some mechanisms which can be used to achieve QoS in the network. In Section V, we propose WISER, which is a framework

to enhance QoS in WIreless SEnsoR networks.

II. RELATED WORK AND MOTIVATION There is evidence of increasing research efforts in QoS provisioning in the networking literature; however, majority of the related work are targeted at cellular mobile telephony, wired Internet and MANETs. A few definitions for QoS in varying networks have also emerged in recent years – Crawley et al in [6] define QoS (in the Internet) as a set of service requirements to be met by the network, while transporting a flow while Nikaein et al in [8] propose that QoS (in a MANET) is the provision of ―a set of parameters in order to adapt the applications to the quality of the network while routing them through the network‖. Despite the slightly varying definitions that have been proposed for different types of networks, the QoS of any particular network can generally be considered to be its ability to deliver a guaranteed level of service to its users and/or applications. The service requirements can be specified in the form of performance metrics, which are typically computed in one of the three following ways: (i) concave (e.g. minimum bandwidth along each link); (ii) additive (e.g. total delay along a path); and (iii) multiplicative (e.g. packet delivery ratio along the entire route). Although some performance metrics such as throughput, delay, jitter (delay variance), bandwidth, packet delivery ratio (PDR), reliability, etc are more widely used than other metrics, each application has its own unique set of service parameters to be satisfied, while possibly compromising on other sets of metrics.

Loss-tolerant applications such as multimedia

applications are not adversely affected by occasional data loss, but are highly sensitive to delay and bandwidth. In contrast, other applications involving sensitive data integrity, such as electronic mail and banking transactions, require fully reliable data transfer, but may not require stringent delay constraints and can work with elastic bandwidth.

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regulates the permissible number of connections that can be allowed into

the system while still fulfilling the QoS requirements of the application – is required. Traffic classification involves the categorization of data packets into different levels of priority, or classes of service (CoS), based on the application requirements. These data packets are marked with their respective classes at the edge of the network, and preferential treatment is then given to the traffic classes with higher priority as they pass through each hop along the path. Traffic prioritization can be in the form of congestion management schemes such as scheduling [11], congestion avoidance or rate limiting. In general, data packets with lower priority are usually dropped at a higher rate than those with higher priority; this allows data flows with higher priority to have better QoS performance metrics such as reliability, end-to-end delay and throughput.

real-time service and controlled link sharing into the basic Internet architecture. IntServ is realized via a framework comprising of four components: (i) packet scheduler; (ii) admission control routine; (iii) classifier; and (iv) reservation protocol. Due to the large amount of state information that is required to be stored, IntServ is hardly scalable and therefore not suitable for use in distributed and autonomous systems like wireless sensor networks.

2) Differentiated Services (DiffServ): DiffServ

provides per-class service differentiation via traffic differentiation and prioritization [4]. Services and applications are classified in a simple and coarse method, thus avoiding the scalability problem that is inherent in IntServ. A traffic prioritization mechanism is used to classify network traffic into different classes of service, and preferential treatment is allocated to classes that are identified as having more stringent requirements. Per-flow state and other sophisticated

classifications, marking, policing and shaping

operations are pushed to the network edge. Each data packet is treated on an aggregate basis, which is also known as the Per-Hop-Behavior (PHB).

B. QoS Provisioning in MANETs

Mobile Ad Hoc Networks (MANETs) are a class of multi-hop networks with organizing and self-configuring properties. There is neither central administration nor fixed infrastructure in the network;

each node acts as both a host and a router to forward packets to other nodes in the network. The current Internet QoS models such as IntServ are unsuitable for use in MANETs because of the vast differences in characteristics between the two types of networks. Due to the mobility of the nodes in a MANET, as well as the inherent erratic behavior of the wireless channel, MANETs are characterized by dynamic topology,

complex route maintenance, temporal link

connectivity as well as unpredictable and varying resource availability [1][ 3]. Consequently, QoS provisioning in MANETs is a multi-faceted problem which requires the cooperation and integration of the various networking layers [1], viz. physical layer, Medium Access Control (MAC) layer, network layer, transport layer and application layer. The existing developments in MANET QoS can be broadly classified into QoS models, QoS resource reservation signaling, QoS routing and QoS MAC.

C. QoS Provisioning in WSNs

Wireless sensor networks have been envisioned for a wide range of applications, some of which may involve the collection of sensitive or critical data. For example, if underwater sensor nodes are thrown into the sea to monitor seismic activities and forewarn the possible occurrence of an earthquake or tsunami, the sensor network is not very useful if it is unable to inform the sink in time, on the impending natural disaster. Although delay is not a very crucial factor in sensor network applications such as periodic monitoring, shorter delays will nevertheless, be much desired over higher delays. Future WSNs may also be able to capture videos or snapshots of the physical environment and transmit these images (or videos) back to the sink for real-time data analysis. As such, there is a demand for QoS support in WSNs; however, existing Internet QoS and MANET QoS mechanisms are not directly applicable to WSNs due to the difference in the characteristics of such networks. In addition, there is currently no standardization in the networking community, on a framework and/or general guidelines on how QoS can be achieved in WSNs. This motivates the need for more research work and efforts in QoS provisioning in WSNs.

III.QOS IN WIRELESS SENSOR NETWORKS

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and elaborated on how these features may provide challenges when designing protocols for use in the network. In this section, we will look into the challenges of QoS provisioning in WSNs and some of the existing developments in QoS mechanisms for WSNs.

A. Difficulties of QoS provisioning in WSNs

The successful deployment of QoS in WSNs is a challenging task because it depends on both the inherent properties of the network, as well as the physical hardware constraints of the sensor nodes. According to I. F. Akyildiz et al in [2], the sensor network protocol stack comprises more than that of a simple multi-layered protocol stack – which already includes the physical layer,

data link layer, network layer, transport layer and application layer. It also involves three additional planes which are perpendicular to the multi-layer

stack; viz. task management plane, mobility

management plane and power management plane

B. QoS Performance Metrics in WSNs Unlike the

Internet and MANETs which can be used for a multitude of applications ranging from file transfer to multimedia applications, each WSN is usually deployed for a specific

application such as environmental monitoring or target tracking. In addition, each of these networks has their own unique characteristics and constraints; consequently, the QoS performance metrics in WSNs may differ significantly from those that are used in the Internet and MANETs. Some of the key performance metrics that should be considered in QoS provisioning in WSNs are as follows:

1) Energy efficiency: The energy limitation in

WSNs is one of the most challenging aspects involved when designing protocols and considering QoS support in the network, because it is directly related to the lifetime of the network. A sensor node that fails due to lack of energy is unable to sense the physical environment or communicate with its neighbours. This may lead to network partitions, which in turn affects the network lifetime.

The network lifetime has been defined in many ways in the literature. network lifetime as the

maximum time before any node in the network drains up its energy. Although this definition is commonly used, it does not portray an accurate overview of the network lifetime from the perspective of the application (or user). Due to the high density in which nodes are usually deployed, the sensed data is highly correlated; therefore, even if a particular node fails after expending all its energy, a neighboring node may still be able to perform the required functionalities of sensing and communication in that spatial location. As such, network lifetime cannot be considered based on the energy consumption of the nodes alone, but must also take into account the topological location of the nodes. We therefore adopt

the more generic definition of network lifetime as that proposed by Kumar et al in [], which is ―the time period during which the network continuously satisfies the application requirements‖, where application requirements may be specified in terms of coverage or delay and may vary depending on the specifications of each application.

2) Coverage: The diversity of sensor network

applications has led to a range of interpretations of sensor network coverage [2]. Despite the discrepancies in the definitions, the main objective of coverage is somewhat consistent – to ensure that each physical region in the space of interest in within the sensing (and/or communication) range of at least one sensor node. The coverage of the sensor network is closely correlated with the denseness of the node deployment. A sparse network results in a sparse coverage, whereby the space of interest is partially covered by sensors. Dense networks result in dense coverage, whereby the space of interest is (almost) fully covered by the sensors. In very dense networks which have redundant coverage [1], the space of interest is covered by multiple sensors, resulting in high spatial correlation in the data that is observed by nodes which have geographical proximity. As a QoS metric, we define the coverage of a sensor network as the ratio of the space that is covered by the sensor nodes to the total space of interest.

3) Spatial accuracy: Wireless sensor nodes are

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communications, is able to make logical deductions of the observations only if it has spatial (or locality) information of each data that is collected. For wireless sensor networks that are deployed outdoors, Global Positioning System (GPS) can be used to provide the location of each node, which will attach this information to its sensed data before forwarding it to the sink. Unfortunately, GPS is expensive and cannot be used indoors; therefore localization techniques such as those proposed in [1] have to be used as an alternative to obtain the (relative) locations of the nodes and provide spatial accuracy of the sensed data. The type of localization technique being employed determines the granularity of the location information can be fine (high spatial accuracy) or coarse (low spatial accuracy). However, as high spatial accuracy usually results in higher overheads than low spatial accuracy, the appropriate level of spatial accuracy should also be based on the application requirements of the network.

4) Temporal accuracy: As like spatial accuracy,

temporal accuracy is required in wireless sensor networks to ascertain the time period during which an event occurs. In wired and centralized networks such as the Internet, it is possible to achieve high temporal accuracy because of the high propagation speed of the communication medium. However, wireless sensor networks typically communicate in a distributed, multi-hop manner, via a shared communication channel. In terrestrial networks, the wireless links are prone to high bit error rates (BER), resulting in high link instability. In underwater networks where sensor nodes utilize acoustic waves instead of radio waves for communication, the speed of propagation is five orders of magnitude slower than that in terrestrial networks [3]. Such factors like link instability and slow propagation speed makes it difficult to achieve time synchronization in the network – thus leading to low temporal accuracy. An accurate time synchronization protocol in the network, such as that proposed in [3], is hence necessary to achieve high temporal accuracy.

5) Delay: Sensor network applications can be

classified into two domains – periodic applications such as environmental monitoring, or event-driven applications such as target tracking. Event-driven applications tend to have stricter delay constraints; the sink must be able to receive notification that a particular event has occurred in a particular region of the network within a short time period after the

occurrence so that it can react appropriately. However, as demonstrated by the authors in [35], stringent delay requirements can severely deteriorate the network lifetime. Henceforth, tradeoffs are involved when designing protocols for use in sensor networks.

IV.CONCLUSION AND FUTURE WORK

While many diverse applications have been envisioned for use in wireless sensor networks, there are still many issues that need to be worked on before Quality of Service can be supported in these networks. In this paper, we have looked at the existing mechanisms to provide QoS in different networks and also examined some of the constraints in WSNs which makes it difficult to provision for QoS. We have also

proposed WISER – a framework which aims to

enhance QoS in WIreless SEnsoR Networks. WISER

is made up of a few different network components; however, it is not quintessential for the wireless sensor network to implement all the modules in the framework. Only certain components in the framework can be implemented, depending on the specific application requirements. In addition, as sensor networks are typically application-specific, we have

attempted to make WISER as generic as possible by

not putting constraints on the different protocols that have to be used for each component.

As part of future work, we will verify the

correctness and completeness of WISER in

provisioning for QoS in wireless sensor networks. Furthermore, we will also evaluate the efficiency of

WISER as a framework to provide QoS support in the

network, by considering the QoS performance metrics that we have identified earlier on, viz. coverage, temporal accuracy, spatial accuracy, etc

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