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J. Inst. Eng. India Ser. B (January–March 2016) 97(1):91–96 DOI 10.1007/s40031-014-0160-6

REVIEW PAPER

Quality of Service Metrics in Wireless Sensor Networks: A Survey Itu Snigdh • Nisha Gupta

Received: 21 September 2012 / Accepted: 12 November 2014 / Published online: 16 December 2014 Ó The Institution of Engineers (India) 2014

Abstract Wireless ad hoc network is characterized by autonomous nodes communicating with each other by forming a multi hop radio network and maintaining connectivity in a decentralized manner. This paper presents a systematic approach to the interdependencies and the analogy of the various factors that affect and constrain the wireless sensor network. This article elaborates the quality of service parameters in terms of methods of deployment, coverage and connectivity which affect the lifetime of the network that have been addressed, till date by the different literatures. The analogy of the indispensable rudiments was discussed that are important factors to determine the varied quality of service achieved, yet have not been duly focused upon. Keywords WSN Coverage Lifetime Deployment Connectivity Energy conservation

Introduction The next generation of wireless communication systems has witnessed a need for the rapid deployment of independent mobile users. Today there are numerous commercial and industrial applications that require continuous monitoring and information collection pertaining to the I. Snigdh (&) Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India e-mail: [email protected]; [email protected] N. Gupta Department of Electronics and Communication Engineering, Birla Institute of Technology, Mesra, Ranchi 835215, Jharkhand, India

application they cater to. Use of wired sensors, however, may increase the cost of deployment and maintainability poses an issue. These typical scenarios confirm the importance of employing wireless sensor networks. Hence a wireless sensor network (WSN) is used typically in environments where running wires or cabling is cumbersome and a fast and easy to install and maintain network is required [1, 2]. A WSN consists of a number of sensors spread across a geographic area. Each sensor node has wireless communication capability and some level of intelligence for signal-processing and networking of data. A WSN, irrespective of the application categorization, can be broadly defined by the constraints laid by their coverage type and their connectivity requirements. The next issue that claims importance is the lifetime guarantee as a quality of service (QOS) parameter of WSN. These factors are, in turn, affected by the network topology and deployment as well as the energy constraints. The correlation between deployment, coverage and connectivity are the basic QOS for achieving energy efficiency and lifetime improvement. Hence reliability has been lately of much interest. The quality of the network depends on the answer on how to deploy and how much to deploy. Our article presents the QOS dependence on the existing strategies of deployment and coverage schemes and also the prospects of QOS assurance. The main aim of a wireless sensor network is to be able to deliver the required functionality with unattended operation for the longest possible time without sacrificing the major constraints. The requirement of self powered nodes forces the WSN to compromise the necessary QOS variable to application domains. QOS in ad hoc networks literatures have been elaborated as two different perspectives, namely, being application specific or network

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specific QOS [3]. The challenges for QOS support in WSN consider resource constraints, unbalanced traffic, data redundancy, network dynamics, energy balance, scalability and reliability, packet delivery performance, data delivery delay and reliable connections under failures. Also, QOS requirements of WSN has been discussed in context to the layers of the network model namely the distinct requirements for the application layer, network layer, transport and the MAC layer as well as extending the routing model of intra and inter-domain routing to support QOS [3]. However, the QOS is, in turn, affected by the network topology and coverage which determine the connectivity. Our article focuses on issues similar to these. Prior works include mathematical modeling of QOS route determination that enable a sensor to determine the optimal path to satisfy QOS constraints for multimedia sensor networks [4] or handling the QOS requirements due to the inherent qualities of health monitoring applications [5]. Data delivery delays, throughput and energy consumption are important QOS metrics that need to be handled by effective middleware design [6, 7]. On the contrary, WSN effectiveness is limited by its lifetime. Here it is shown that the deployment strategy and the corresponding coverage that effect the lifetime also needs due attention as the problem of delay, failure and reliability are related to and depend on them.

Coverage Coverage may be defined as a metric to measure qualitatively and quantitatively the amount and longevity of a sensor network in monitoring the region or point of interest. Research till date integrates coverage coupled either with the connectivity and communication for the optimization of lifetime or for energy conservation. It is important to provide connectivity and coverage at the same time, since a sensed data is of no use if it cannot be sent to the sink because of poor connectivity. After deployment, effective coverage and connectivity may be achieved through energy conserving techniques for a longer period of unattended operation: hence a longer lifetime. Depending on the objectives of the applications, coverage strategies can be roughly classified into three categories: area coverage, point coverage, and path coverage. Coverage terminology of WSN is shown in Fig. 1. The QOS factors like reliability, fault tolerance, delay are interrelated to the underlying coverage of the network. If the coverage is redundant, better strategies are available for a robust data delivery and hence reliability. Table 1 discusses the coverage approaches that are in the literature and can be used for analyzing the QOS parameters.

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J. Inst. Eng. India Ser. B (January–March 2016) 97(1):91–96

1-coverage Point coverage k-coverage Dense

Coverage types Area coverage Barrier/Path coverage

Sparse Redundant

Fig. 1 Coverage types

Open Issues The problem that challenges the very definition of effective network coverage is the occurrence of holes. The solution that exists is to relocate the closest redundant sensor to start with the healing but the selection of the victim is itself a complicated task. The other important issues to consider are minimizing total energy consumption, minimizing completion time of the overall movements via cascaded relocations of several sensor nodes, and minimizing average moving distances in cascaded relocations of several sensor nodes etc. Self-organizing techniques have also been proposed for enhancing the coverage of wireless sensor networks after initial random placement of sensor nodes. But they appear inappropriate in handling simultaneous relocations. One of the weak points is the possibility that more than one sensor node may move towards the same location. This problem has been tried to be resolved by inserting a delay time, usually different for each sensor. The problem with this approach is execution of the same algorithm by each individual sensor node in every possible blend, resulting in extra energy consumption. Multi-hop communication techniques have the ability to extend the coverage of the network range indefinitely. But for a given transmission range, multi-hop networking protocols increase the power consumption of the nodes, which may decrease the network lifetime. They also require a minimal node density, which may increase the deployment cost.

Deployment and Connectivity In general, deployment strategies of nodes require low cost and a high coverage quality. Deploying a high number of nodes densely requires careful handling of topology maintenance. Deployment can be considered in three phases namely; Pre deployment and deployment phase,


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Table 1 Existing strategies of coverage Method Art gallery problem [8]

Optimization criteria

Placement Mobility

Limitations

1-Coverage

Static

Yes

NP hard in 3-dimensional ROI

Dynamic

Yes

Inter-communication between nodes required for mobility

No coverage holes Voronoi diagram [9–12]

K-coverage

Useful for deterministic placement scheme

Non redundant coverage

It does not provide k-coverage for k [ 1 Redeployment difficult Communication failure with failure of nodes in the edge Delaunay triangulation [13, 14]

Extension of Vornoi

Static

Yes

Variants of the basic VORONOI method [15–18]

VOR-no coverage holes, K-coverage

1. Vector based algorithm (VEC)

MINIMAX-best coverage with fewest sensors

2. Vornoi based algorithm (VOC) 3. Min max algorithm

Cannot be constructed with localized algorithms Use of Distributed algorithms for their construction has been found to be inefficient

Determine which two sites are closest to each other by finding the shortest edge in the triangle Dynamic

Yes

VOR-constrained mobility MINIMAX-requires relocation of nodes VEC-provides the worst case coverage

VEC-coverage with minimum node relocation

4. Multiplicative weighted voronoi diagram (MWVD) Worst or best case coverage [19, 20] Path or barrier coverage

Dynamic

Yes

Employs Voronoi diagram to determine worst case or maximal breach path To find best case or maximal support path, Delaunay triangulation is used

Probabilistic sensing [21–23]

Distance based coverage capabilities

Dynamic

Yes

Centralized, do not scale well in large deployments Connectivity is lost with the failure of cluster head Redundancy of the network may be limited if the cluster heads do not spread

Disjoint Sets [24, 25]

Non redundant Complete coverage

Post-deployment phase, Redeployment of additional nodes phase. The deployment methods used are shown in Fig. 2. Deployment strategies may be classified under two classes. Class 1 is governed by the quality of metrics and is connectivity based. For realistic environments, non uniform event detection requirement is based on the regular and pseudo random methods according to the phenomenon characteristics and the target area. The focus is on event detection probabilities but the overall network connectivity is neglected. Class 2 is based solely on energy saving. It advocates deploying sensors on a grid or random topologies. Energy consumption is minimized by equilibrating the traffic load through the routing protocols or by dividing the networks into many subnets of sensors and scheduling the sleep and wake cycles among them [26].

Static

Yes

Needs sleep cycle scheduling for better performance Needs energy conservation techniques

Existing Methods Aim of the deployment methods is to achieve full coverage with lesser number of sensors. Deterministic methods employ both regular and planned approaches. These use deployment of static sensors to achieve full coverage. They also increase the sensing effectiveness of coverage with lesser number of sensors. Algorithms like ORRD [27] or robot deployment algorithms [28–30] claim full coverage taking into account the unpredicted obstacles in regular or irregular shapes with power conservation. Though full coverage and connectivity with redeployment to cover holes is achieved, it employs static sensors and hence cannot be a generalized solution for all WSN’s. Other similar enhanced algorithms focus on optimal coverage with energy conservation.

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Air dropping Random deployment

Random distribuon based Square

Grid based Deployment methods

Circular Hexagonal

total network lifetime. In many deployments it is not the average node lifetime that is important, but rather the minimum node lifetime. Lifetime calculation is based on the following constraints: 1. 2.

Regular Vornoi Computaonal geometry based

3. Delaunay triangulaon

Incremental deployment Planned

4. Probablisc based Virtual Force based

Fig. 2 Deployment methods

Random method of deployment is the simplest but is unbalanced. Moreover, it increases the hardware cost (GPS equipped nodes, localization algorithms) and creates coverage holes. The deployment is usually irregular (nonuniform density of nodes). Relay node deployment strategies for heterogeneous network uses random deployment strategies for both single and multi hop communication model. The associated problems are insufficient energy utilization and shortened lifetime. Lifetime oriented deployment and hybrid deployment study the tradeoff between the connectivity and lifetime extension in relay nodes deployment. It assumes that the network is organized as a hierarchical structure enhancing the scalability and data aggregation [14, 30]. Another deployment strategy is the quasi regular topology that tries to emulate the midway solution for both random and deterministic strategy. It tries to regularize topology to bring about an increase in the energy efficiency, depending on the requirements of the application so as to choose between the performance and the deployment cost [28].

Ratio of dead nodes over the total number of nodes. Based on complete coverage and connectivity. Loss of coverage or connectivity causes network failure. The total energy consumption of a sensor node keeping into account the worst case CPU, radio, sensor circuitry etc. Lifetime is the number of successful data gathering trips or cycles that are possible until connectivity or coverage is lost.

Table 2 shows the remedies for increase in lifetime used in the existing literature.

Open Issues The most significant factor in determining lifetime of a network is the energy supply or the radio power consumption. This power consumption can be reduced through decreasing the transmission output power or through decreasing the radio duty cycle. Both of these alternatives involve sacrificing other system metrics. Hence lifetime is compromised for continuous monitoring applications. When selected few sensors take the responsibility of connectivity, it is to ensure that minimality or fractional connectivity and fractional coverage for the network is satisfied. These requirements refer to both safety critical applications, where connectedness is mandatory or to a data collection application, where small percentage of connectedness can be tolerated. The major concern in such applications is the global clock synchronization among the sensor nodes to establish an effective coverage and hence the overall network lifetime.

Lifetime Lifetime of a network is defined as the time to the first loss of coverage. But this definition does not take connectivity into account. Consequently, it should be extended to connectedness that is the ability to remain connected. Critical to any wireless sensor network deployment is the expected lifetime. The primary limiting factor for the lifetime of a sensor network is the energy supply. The strategies for achieving a longer sensor lifetime orient along maintaining the energy balance; either with effective deployment or negotiating between the amounts of energy required for the different tasks of sensors [2]. Each node must be designed to manage its local supply of energy in order to maximize

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The Analogy Prime design constraints in case of any general WSN can be summarized as Minimizing or Maximizing the energy or lifetime. But the commercial and academic outlooks differ. Other constraints are localization constraints, data centric routing, and topology control. The other factors that can improve the QOS like reliability and fault tolerance or delay are topology control and effective routing by selective forwarding and scheduling. Lastly, topology control and routing also depends to an extent on the deployment strategy and plays an important role in the energy


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Table 2 Existing remedies for increase in the lifetime Method

Achievements

Assumptions

Sensor relocation [31, 32]

Maintaining the energy balance using hexagonal grid positioning of CH’s

Location aware or computation-capable nodes Stationary ad hoc networks

Optimal density achieved through cell based strategies can extend the network lifetime Cover sets [33]

Division of sensor nodes into minimum no of cover sets 1-Coverage target constrained by the sensing capabilities of sensor nodes and switching between them to increase the network lifetime Optimizations using GA and other heuristic methods are possible for enhancement

Data gathering strategies [34, 35]

Dependence on source behavior, region of observation, User based deployment control base station location, no of nodes, available initial energy, path loss and radio energy parameters are evaluated in context to the total effective lifetime

Multiple moving base stations [36]

Provide improved network lifetime without sacrificing the data quality requirement of the application

By unequal clustering or scheduling the selection of cluster heads [37]

Prolonging lifetime by density iterative process that is Source diversity and layout of the network affect the guided by the assessment of the regions where higher deployment so different nodes have different service quality is needed and placing more nodes to reduce the requirements energy wastage

Optimizing the energy consumption [38, 39]

1. Minimise traffic load transmitting over the nodes

Subject to the following constraints

2. Maximise load balancing

1. Deployment cost

3. Minimize overhearing

2. Event detection probability

Exploits information concentrating on the data gathered in their immediate vicinity and requiring less precise knowledge about the data collected far away

4. Maximise delivery probability between the nodes

3. Energy consumption

Adaptive data propagation [40]

Sensor nodes deployed under coverage constraint

Heterogeneous WSN consisting of sensors and relays

Optimal routing [41]

Uses a query routing tree to be constructed for efficient In different scenarios like single and multi-hop networks usage of battery power for each node considers clustering, centralized versus distributed approach, static versus dynamic topologies and sink based versus ad hoc networks

Relay nodes deployed in a partially controlled way for maximized network lifetime

conservation. Metrics to minimize power that have been devised so far can be categorized as 1. 2.

Power aware—aim to minimize total power needed to route a message between two locations. Cost aware—use methods that extends a node’s battery lifetime [36].

Deployment may be accounted for sparse, dense or redundant coverage. Similar to the type of deployment strategy the coupled connectivity vary as fully connected, intermittent and sporadic connectivity. Both are in turn dependent on the type of application they service and have different restrictions on the effectiveness and amount of coverage. Researches show how various degrees of deviation from intended node placement affects the achievable coverage [27]. For positioning applications and environmental monitoring scenes where fault tolerance is stringent, K coverage is usually suggested as it includes power saving through node scheduling and is also applicable for covering

irregular regions [14]. In practical deployment scenarios, a fraction of the total energy budget is used up for the maintenance of the system and the connectivity of the network. The QoS parameters also require a system, which has been deployed with the inherent cost and be secure against the usual communication interferences, relocations of the node that require extra expenditure on the reconfiguration due to environmental conditions. The other requirement is that the network must be able to automatically perform a continual self-maintenance or generate error messages to maintain the connectivity for fault tolerance against the obvious discrepancies for a robust functioning lifetime.

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