CHAPTER 2 RELATED WORK

17 CHAPTER 2 RELATED WORK With the fast advances in inexpensive sensor technology and wireless communications, the design and development of large-scale WSN has become cost-effective and viable enough to attract the attention of several applications, such as health/environmental monitoring and battlefields surveillance. The main function of a wireless sensor network is to monitor a field and report data to the BS for further analysis and processing. However, the sensors suffer from several scarce resources, such as battery power, storage, CPU, and bandwidth, with energy being the most critical resource (Akyildiz et al 2002). Therefore, protocols designed for WSN, and particularly those for field coverage and data forwarding, should be as energyefficient as possible to enhance the lifetime of network. In addition to energy efficiency, coverage and connectivity are other two critical issues that need to be addressed in WSN. Hence, a major challenge of power management in WSNs is to achieve satisfactory QoS required by applications while minimizing the total energy consumption of the network so that the lifetime of network can be enhanced. Therefore, WSNs must satisfy both Sensing Coverage QoS and Network Connectivity QoS required by the applications simultaneously. The first section of this chapter presents different approaches to the energy conservation of WSNs. Survey on topology control protocols is given in the second section. Third section reviews different power aware routing protocols. Next section describes, related work in coverage, connectivity, and

18 sleep scheduling approaches for WSNs. Finally a survey on several clustering protocols is presented followed by the summary of this chapter. 2.1 ENERGY CONSERVATION SCHEMES: Energy Conservation Schemes Duty Cycling Power Aware Routing Clustering Protocols Topology Control Power Management Location Driven (Coverage Topology) Connectivity Driven (Connectivity Topology) Coverage Preserving Schemes Connectivity Maintenance Protocols Scheduling Algorithms Centralized Decentralized Figure 2.1 Categories of Energy Conservation Schemes Figure 2.1 shows the general categories of energy conservation schemes. The following gives an overview of each category: To reduce power consumption in WSN, the most effective way is by placing the radio transceiver in the low-power sleep mode whenever communication is not required. Ideally, the radio should be switched off as soon as there is no more data to send/receive, and should be resumed as soon as a new data becomes ready. This way nodes alternate between active and sleep periods depending on network activity. This behavior is usually referred

19 to as duty cycling, and duty cycle is defined as the fraction of time nodes are active during their lifetime (Anastasi et al 2009 and Wang et al 2006). Duty cycling can be achieved through two different methods. In the first method, it is possible to identify node redundancy and adaptively select only a minimum subset of nodes to remain active for maintaining connectivity. Nodes that are not temporarily needed for ensuring connectivity can go to sleep and save energy. Finding the optimal subset of nodes that guarantee connectivity is referred to as topology control. Therefore, the idea behind topology control is to reduce the spatial density of active nodes while maintaining desirable network connectivity and in turn increase the network lifetime. In the second method, active nodes that are selected by the topology control protocol need not maintain their radio continuously on. They can switch off the radio i.e., put it in the low-power sleep mode when there is no network activity, thus alternating between sleep and active states to reduce the idle listening time. Duty cycling operated on active nodes are referred to as power management. Therefore, topology control and power management are complementary techniques that implement duty cycling with different granularity. There are two issues that a topology control protocol must address: Density of sensor nodes in active mode Duration of activation of nodes Various factors can be used to decide which nodes are need to be activated/deactivated, and when. From this point of view, topology control protocols can be classified into two categories: Location driven: The decision about which node to turn on, and when, is based on the location of sensor nodes which is assumed to be known as Geographical Adaptive Fidelity (Xu et al 2001). Connectivity driven: Sensor nodes are dynamically

20 activated/deactivated in such way to ensure network connectivity (Cerpa et al 2002 and Chen et al 2002) or complete sensing coverage are fulfilled ( Koushanfar et al 2006). Topology control protocols can extend the network lifetime by a factor of 2-3 depending on the network redundancy with respect to a network with nodes always on ( Ganesan et al 2004 and Mainwaring et al 2002). However, many sensor network applications require a much longer network lifetime. To further increase network lifetime, topology control protocols must be combined with power management which introduces duty cycling even in active nodes (Yang et al 2004). An effective method for energy conservation in WSN is sleep/wakeup scheduling. For the sensor network to operate successfully, the active nodes must maintain both coverage and connectivity. This scheduling algorithm can be classified as centralized or decentralized localized and distributed. Centralized algorithms require global information of the whole sensor network. The centralized algorithms always provide nearly optimal solution since the algorithm has global view of the whole network. They have low-adaptability to the change of the network and are not suitable for largescalable network. Decentralized (localized and distributed) algorithms require only the local information to function and are run at large number of sensor nodes. Each sensor then makes its own decision of turning on or off. Thus, it is very adaptable to the dynamic and scalable nature of sensor networks. Power-aware routing aims at minimizing the per packet transmission power in multi-hop routing and enhance the system lifetime. As an active branch of energy conservation scheme in WSN, Clustering protocols mainly focus on energy consumption and network lifetime.

21 2.2 TOPOLOGY CONTROL ALGORITHMS The purpose of Topology Control protocols is to preserve network connectivity through reduced transmission power (Ababneh et al 2006). Most of the existing algorithms aim at maintaining network connectivity. In the literature Rodoplu et al (1999), a node chooses to relay through other nodes only when less power is used. Ramanathan et al (2000) proposed two centralized algorithms to minimize the maximal power used per node while maintaining the connectivity of the network. Two algorithms are proposed by Narayanaswamy et al (2002 ) and Kawadia et al (2003) to maintain network connectivity using the minimal transmission power. Li et al (2003) proposed LMST (Local Minimum Spanning Tree) a MST-based topology control scheme which preserves the network connectivity and has bounded node degree in a location based protocols. Li et al (2004) proposed a localized algorithm that builds a K-vertex connected topology based on the extended Kruskal s MST algorithm. Li et al (2003b) proposed a localized algorithm that preserves K-connectivity by having each node choose K closest neighbors in each of the p 6 cones. Hajiaghayi et al (2003) proposed three approximate algorithms (two centralized and one distributed) that build the K- connected topology. Santi (2005) proposed Topology Control (TC) is one of the most important techniques used in wireless ad hoc and sensor networks to reduce energy consumption and radio interference and extends the network operational time. The goal of this technique is to control the topology of the graph representing the communication links between network nodes with the purpose of maintaining some global graph property (e.g., connectivity), while reducing energy consumption and/or interference that are strictly related to the nodes' transmitting range. GAF is a location driven protocol that reduces energy consumption while keeping a constant level of routing fidelity. GAF is based on node

22 location information that can be provided by a Global Positioning System (GPS). GAF is independent from the routing protocol. It can be used with any existing routing protocol. It is able to conserve energy by identifying node redundancy, thus allowing the network lifetime to increase in proportion to node density (Xu et al 2001). All nodes within a cell are interchangeable from a routing perspective. This may result in an underutilization of radio coverage areas as nodes are forced to cover less than half the distance allowed by the radio range. In addition, GAF requires knowing the exact location of each node in the network, which might be expensive to achieve. This drawback is overcome by connectivity-driven protocols. Chen et al (2002) describes SPAN as a connectivity-driven protocol that adaptively elects coordinators of all nodes in the network. Coordinators stay awake continuously and perform multihop routing, while the other nodes stay in sleeping mode and periodically check if there is a need to wake up and become a coordinator. The protocol achieves the following four goals. First, it ensures that there is always sufficient number of coordinators so that every node is in the transmission range of at least one coordinator. Second, to spread energy consumption as uniformly as possible among network nodes, SPAN rotates the coordinators. Third, it tries to minimize the number of coordinators so that the network lifetime can be increased while avoiding performance degradation in terms of network capacity and message latency. Fourth, it elects coordinators in a decentralized way by using only local information. The SPAN election algorithm requires knowing neighbor and connectivity information to decide whether a node should become a coordinator or not. Such information is provided by the routing protocol. Therefore, SPAN depends on the routing protocol and requires modification in the routing lookup process. Hence, SPAN does not guarantee a connected topology. However, SPAN ensures energy efficiency and also improves communication latency.

23 Adaptive Self-Configuring sensor Network Topologies (ASCENT) is another connectivity-driven protocol that does not depend on the routing protocol and does not require modifying the routing state (Cerpa and Estrin 2004). In ASCENT a node decides whether to join the network or continues to sleep, based on the information about connectivity and packet loss that are determined locally by the node itself. The basic idea of ASCENT is that initially only some nodes are active, while all other nodes are passive, i.e., they listen to packets but do not transmit. In addition, it limits the packets loss due to collisions because the nodes density is regulated by the Neighbor Threshold value. The protocol has good scalability properties. However, energy saving does not increase proportionally with the node density because it depends on passive-sleep cycle and not on the number of active nodes. The Cone-Based Topology Control algorithm (CBTC) was proposed by Wattenhofer et al (2001) to ensure the network connectivity. An analysis of CBTC algorithm was presented in the literature Li et al (2001) and proves that 5 /6 is a tight upper bound on the cone degree for the algorithm to preserve connectivity. Also, the authors have presented three optimizations to the basic algorithm: the shrink-back operation, asymmetric edge removal, and pair wise edge removal, and proved that these improved performance while still preserving connectivity. The drawback of CBTC is that nodes should be equipped with multiple directional antennae, and this requires high cost for sensor nodes. Sparse Topology and Energy Management (STEM) trades off energy consumption in the monitoring state versus latency of switching back to the transfer state; the result is a significant increase of network lifetime (Schurgers et al 2002). STEM can be easily integrated into other topology control techniques such as GAF and SPAN for further energy savings. Compared to a network without topology control, a combination of GAF and

24 STEM can conserve more energy. However, these benefits come at the cost of increased setup latency, which depends on the number of hops in the multihop path, and the specific applications in use, which controls how much latency is allowed, and consequently how much energy consumption can be reduced. Another disadvantage is when STEM and GAF combined together; the routing protocol needs to address virtual nodes instead of real nodes (Ababneh 2010). XTC is an ad-hoc topology control algorithm presented by Wattenhofer and Zollinger (2004), which consists of three phases: Phase 1: Neighbor ordering: Phase 2: Neighbor order exchange: Phase 3: Edge selection: The XTC is a simple, local and fully distributed algorithm, since the algorithm is executed on each node in the network. Every node communicates with its neighbors not more than twice. XTC does not require node location and neighbor s position information. Also, XTC does not consider node failure and mobility, and fail to have reasonable spanner properties. But algorithm guarantees global connectedness using directional information. Minimum Energy Network Connectivity (MENC) problem was proposed by Cheng et al (2004), which is an NP-complete problem in ad hoc wireless networks. The problem consists of minimizing the transmission power of each sensor node in a wireless network, which results in minimizing the energy consumption of the network, while keeping its global connectivity at the same time. Two polynomial time approximation heuristics were provided to compute the power assignment of wireless nodes in both static and low mobility ad hoc wireless networks. Melodia et al (2005) determine optimal local topology knowledge for energy efficient geographic routing in ad hoc and sensor networks. They provide different localized forwarding scheme to demonstrate that only a limited local knowledge is needed to take energy efficient decisions.

25 An effective method for energy conservation is sleep scheduling. Ababneh and Selvakennedy (2007) and Ababneh (2010) proposed a topology control algorithm, called OTC (Optimized Topology Control Algorithm), for sensor networks. It uses two-hop neighborhood information to select a subset of nodes to be active among all nodes in the neighborhood. Each node in the network selects its own set of active neighbors from among its one-hop neighbors. This set is determined in such a way that it covers all two-hop neighbors. OTC achieves significant energy savings against (GAF, SPAN) others, while ensuring a load-balanced network which prolongs network life time. But, there is no proof that XTC has spanner properties on general weighted graph. Ma et al (2007) construct network with small number of coordinators while still maintain the topology connectivity. They assume that all wireless links in a sensor network are bidirectional since 802.15.4 MAC has an ACK mechanism for every frame. They consider their problem as minimal connected dominated set problem. Three topology algorithms have been proposed with different time complexity and power saving. Two heuristics are presented by Li et al (2008) for the minimum power topology control problem on general model, i.e., given a set of sensors in the Euclidean plane and a transmission power threshold for each directed pair of sensors; it finds a power assignment for each sensor to achieve a strong connectivity with minimum total transmission power. A distributed topology control algorithm, called ECTC (Energy efficient Topology Control Algorithm for Wireless Sensor Network) was presented by Ababneh et al (2009), which uses a clustering approach. It is built on the notion that when a region of a shared channel wireless sensor network has a sufficient density of nodes, significant energy saving is obtained by allowing redundant nodes to sleep. Using the two-hop

26 neighborhood information, certain nodes sequentially select a subset of nodes to be active among all nodes in the neighborhood, to ensure connectivity. Moreover, to ensure fairness, the role of active nodes is rotated periodically to ensure energy-balanced operations (Ababneh et al 2009). ECTC topology control algorithm that does not require location information a priori with the goal of minimizing the total energy spent in the network to communicate the information gathered by these sensor nodes to the BS. It was also found that there is a decreasing improvement on network lifetime, when more nodes are deployed within the same region (Ababneh 2010). A Link Quality Assured Topology Control (LQATC) algorithm for sensor network was investigated by He et al (2010), which is based on the received signal strength indicator. LQATC can guarantee the network connectivity, link symmetry, and reasonable degree of nodes. Furthermore, it improves network performance in terms of energy efficiency and average interference degree. An Energy Efficient Topology Maintenance Scheme (EETMS) was proposed by Yin et al (2011) to maintain the network connectivity and communication performance while reducing the maintenance overhead when nodes fail. In EETMS, the appropriate maintenance strategy is chosen according to different recovery modes. It was shown that EETMS can repair the network with small overhead. Most of the above works aim at maintaining connectivity of a network through reduced transmission power with the purpose of enhancing lifetime. However, network connectivity is not sufficient to provide a satisfactory communication performance when the links among nodes are lossy. Further, topology control only minimizes the transmission power of the network which does not consider idle power. Topology control protocols are

27 based on a communication network graph; they do not address the issue of sensing redundancy. 2.3 POWER-AWARE ROUTING Power-aware routing aims at minimizing the per packet transmission power in multi-hop routing. Singh et al (1998) proposed five power-aware routing metrics to reduce energy consumption and enhance the system lifetime. The implementation of a minimum energy routing protocol based on Dynamic Source Routing (DSR) was discussed by Doshi et al (2002) and Doshi and Brown (2002). An online power-aware routing scheme was proposed by Li et al (2001a) to optimize system lifetime of WSNs. Chang and Tassiulas (2000) studied the problem of maximizing the lifetime of a network with known data rates. Chang et al (2000) formulated the problem of choosing routes and transmission power of each node to maximize the system lifetime as a linear programming problem and discussed two centralized algorithms. Sankar et al (2004) formulated maximum lifetime routing as a maximum concurrent flow problem and proposed a distributed algorithm. Dong et al (2005) studied the problem of minimum transmission energy routing in the presence of unreliable communication links. Two basic protocols: Minimum Transmission (MT) routing and Minimum Transmission Power (MTP) routing were presented by Woo et al (2003). MT was shown to be more reliable than the hop-count based routing scheme when given a lossy networks. A node in MT chooses the next hop node with the minimum expected number of transmissions to the BS. All communication links in the original MT protocol use the same transmission power. A node in MTP chooses the next hop node with minimum total expected transmission power to the BS.

28 Chipara et al (2006) proposed the Real time Power-Aware Routing (RPAR) protocol, which supports energy-efficient real-time communication in WSNs. RPAR achieves this by dynamically adapting transmission power and routing decisions based on packet deadlines. RPAR has several salient features. First, it improves the number of packets meeting their deadlines at low energy cost. Second, it has an efficient neighborhood manager that quickly discovers forwarding choices (pairs of a neighbor and a transmission power) that meet packet deadlines while introducing low communication and energy overhead. During high congestions RPAR retransmits the packet several times before the packet is successfully received which in turn increases the transmission power and energy wasted in active listening of nodes and also fails to find a route in the presence of holes in the network topology. Zimmerling et al (2008) introduced two routing schemes that efficiently utilize energy: Minimum Energy Relay Routing (MERR) and Adaptive MERR (AMERR) were introduced.these routing schemes take into account the channel characteristics, the radio component, and the distribution of the sensor nodes along a linear path modeled by a one-dimensional homogeneous Poisson process. Based on these parameters and assumptions, theoretical optimal lower bound of routing in a linear WSN is formed. AMERR achieves optimal performance, while MERR rapidly approaches optimal performance as the Poisson rate increases. MERR and AMERR are less complex and have better scalability. This routing protocol does not take fairness into account. The Dynamic Source Routing (DSR) allows nodes to dynamically discover a source route across multiple network hops to any destination in the network. To do this, each packet header contains the complete, ordered list of traversed nodes. If an intermediate node is not the destination or it does not

29 have any route to the destination in its route cache, it will initiate a route discovery process via request broadcast to its neighbors. If available, the complete route to the destination is found and returned to the initiator. Otherwise, the neighbor appends its address to the route record and re broadcast to its neighbors. When routes become invalid, DSR adapts by sending a route error packet to the source node, which stops using the route. For better reliability, DSR maintains multiple route entries in its routing table. The complete routing algorithm was described by Yang et al (2007) and Manjeshwar and Agrawal (2008). Ad hoc On-Demand Distance Vector Routing Protocol (AODV) is a routing protocol designed for wireless networks. AODV builds routes using a route request / route reply query cycle. When a source node desires a route to a destination for which it does not already have a route, it broadcasts a route request (RREQ) packet across the network. Nodes receiving this packet update their information for the source node and set up backward pointers to the source node in the route tables. In addition to the source node's IP address, current sequence number, and broadcast ID, the RREQ also contains the most recent sequence number for the destination of which the source node is aware. A node receiving the RREQ may send a route reply (RREP) if it is either the destination or if it has a route to the destination with corresponding sequence number greater than or equal to that contained in the RREQ. If this is the case, it unicast a RREP back to the source. Otherwise, it rebroadcasts the RREQ. The complete routing algorithm was described in the literature ( Notani 2008 and Perkins et al 2008). A novel real time Power Aware Two-Hop (PATH) based protocol was proposed by Rezayat et al (2010), that can improve real time performance in WSNs. PATH integrates the concept of using two-hop neighbor information with the power control mechanism to achieve better real-time

30 performance. This integration reduces the number of packet that cannot meet their delay requirement and so perform well. Also the reduction of packet dropping results in better energy consumption performance. An Energy Efficient, Power Aware Routing algorithm was presented by Ajina (2011), where energy efficiency scheme is integrated with power awareness parameters for routing of packets. This protocol significantly reduces the total number of route request packets, and it results in an increased packet delivery ratio, decreasing latency for the data packets, lower control overhead, and fewer collisions of packets, supporting reliability and decreasing power consumption. Each route request carries the cumulative cost, so very little bit overhead is increased to carry the cumulative cost. The major limitation of power-aware routing protocols is that it only minimizes the transmission power of nodes and ignores the power consumption in other radio states. As a result, it is only effective for the radio platforms with high transmission power or the networks with high workload where nodes operate in transmission state most of the time. 2.4 COVERAGE, CONNECTIVITY, AND SCHEDULING PROTOCOLS An effective method for energy conservation in WSN is sleep scheduling. For the sensor network to operate successfully, the active nodes must maintain both coverage and connectivity. Further, the network must be able to configure itself to any feasible degree of sensing coverage and network connectivity in order to support different applications and environments with diverse requirements. Number of configuration protocols for coverage and connectivity in WSN has been proposed with a goal to enhance the network lifetime. This

31 section reviews some of these protocols and presents summary of these protocols. 2.4.1 Coverage Preserving Protocols The issue of determining the required number of sensors to achieve full coverage of a desired region was addressed by Adlakha and Srivastava (2003). The minimum number of sensors needed to achieve k-coverage with high probability was presented by Kumar et al (2004) to be approximately the same, regardless of whether the sensors are deployed deterministically or randomly, if the sensors fail or sleep independently with equal probability. An optimal deployment pattern for achieving k-barrier coverage was established, efficient global algorithms for checking k-barrier coverage of a given region were developed, and it was shown that the non existence of localized algorithms for testing the existence of global barrier coverage (Kumar et al 2005). To address this limitation, localized algorithms were proposed by Chen et al (2007), so sensors can locally determine the existence of local barrier coverage. Moreover, optimal polynomial-time algorithms were proposed to solve the sleep-wake up problem for the barrier coverage model using sensors with equal and unequal lifetimes (kumar et al 2007). A directional sensors-based approach for network coverage was proposed by Ai and Abouzeid (2006), where the coverage region of a sensor depends on its location and orientation. The coverage problem in heterogeneous sensor networks was discussed by Lazos and Poovendran (2006a). They formulated the coverage problem as a set intersection problem and derived analytical expressions, which quantify the coverage achieved by stochastic coverage. An efficient distributed algorithm to optimally solve the best-coverage problem with the least energy consumption was discussed in the literature (Li et al 2003a). Optimal polynomial time worst and average

32 case algorithms for coverage calculation based on the Voronoi diagram and graph search algorithms were proposed in the literature (Megerian et al 2005 and Meguerdichian et al 2001). Huang et al (2005) proposed polynomial-time algorithms, in terms of the number of sensors. Surveys of a variety of approaches on energy-efficient coverage problems are discussed in the literature ( Cardei and Wu 2006, Ghosh and Das 2006). Wan and Yi (2006) investigated the issue on how the probability that a WSN can k-cover an area varies depending upon the sensor s sensing radius and the number of the sensors. The K-coverage eligibility (KE) algorithm was presented in (Xing et al 2005), which ensures that a monitored area is K-covered, but incurs a considerably high computation cost. Huang et al (2005) have attempted to reduce the computational cost of the K-coverage configuration. They proposed a K-perimeter-covered (KPC) algorithm to calculate the coverage degree of each sensor node by tracing the perimeter segments covered by its neighbors. Because this algorithm does not require the coverage to be considered within the sensing range of a node, the computational cost can be effectively reduced, but at the expense of reduced accuracy of determining the eligibility for each sensor node. Wueng and Hwang (2006) proposed an Efficient K-coverage Eligibility (EKE) algorithm that can correctly determine the eligibility of each sensor node with low cost. A distinct feature of the EKE algorithm is that the neighbors of each sensor node are classified into R neighbors and 2R neighbors. Instead of calculating the coverage degree of all intersection points within the sensing range of a node, the EKE algorithm only requires to focus on the candidate intersection points surrounding the lower coverage regions based on the characteristics of the R neighbors and 2R neighbors. Therefore, the computational cost of the EKE algorithm can be highly reduced. Because

33 the algorithm aims to determine the regions that exhibit a lower coverage degree and not the minimal coverage degree, its accuracy may be reduced. Wueng et al (2008) proposed an efficient and accurate K-coverage eligibility (AKCE) algorithm to ensure K-coverage with low computation cost. This algorithm accurately determines whether a sensor node is eligible to sleep or stay active within the sensing range of this node. Various Coverage Strategies for WSN have been reviewed in the literature (Aziz et al 2009). The strategies reviewed are categorized into three groups based on the approaches used, namely: force based, grid based or computational geometry based approach. The strategies studied are used during deployment phase where the coverage is calculated based on the placement of the sensors on the Region Of Interest (ROI). It was shown that triangular lattice pattern is optimal in terms of the number of sensors needed to provide coverage. The design considerations for coverage problems in WSN was reviewed by Fan and Jin (2010), and presented the existing solutions, and discussed the open problems in coverage of WSN. The existing solutions focus on the following consideration: evaluating and improving coverage performance of area and path coverage, while maintaining connectivity and maximizing the network lifetime. Although many schemes have been proposed and progress has been made in coverage problems of WSN, there are still many open research issues. An effective coverage scheme should be proposed to implement real applications but limited to theoretical study. Therefore, most existing centralized solutions need to be developed to include the distributed and localized algorithms. Most of the above work considers the coverage issue only. For the sensor network to operate successfully, the active nodes must maintain both coverage and connectivity.

34 2.4.2 Connectivity Maintenance Protocols This section discusses the various connectivity maintenance protocols and summarizes the limitations of the same. Approach for reducing idle listening power consumption of radios is to maintain communication connectivity composed of a small number of active nodes and schedule other nodes to sleep. ASCENT (Adaptive Self- Configuring sensor Networks Topologies) (Cerpa and Estrin 2004) and SPAN (Chen et al 2001) are two adaptive connectivity maintenance protocols in which each node assesses its local connectivity with neighbors and decides whether to join the communication backbone of the network. Adaptive Fidelity Energy-Conserving Algorithm (AFECA) (Xu et al 2000) is a routing scheme in which each node decides to be active or asleep based on application-level information as well as network density. Xu et al (2001) proposed a connectivity maintenance protocol called GAF that utilizes geographic information to ensure every node in the network is covered by the communication range of at least one active node. In the literature, the authors, Krohn et al (2006), present cooperative transmission to increase the connectivity, the disconnected parts of a WSN which had the disadvantage of the separation issue of multi-hop sensor networks. This approach gives better connectivity compared to multihop method with decreased number of nodes required in full coverage. Watfa (2008) provided algorithms to achieve connectivity in a sensor network. This paper first provides a simple multi hop connectivity algorithm and then when the network is disconnected, cooperative transmission techniques which can support especially topologies of clustered and partitioned networks that contain separated group of nodes was provided. This new communication principle can overcome connectivity problems in

35 sparse settings or heavily partitioned topologies. This algorithms increase the connectivity of a network while minimizing the energy consumption. Hefeeda and Ahmadi (2009) presented a simple Probabilistic Connectivity Model (PCMP). They have introduced the network packet delivery rate as a quantitative metric for communication quality. PCMP is a fairly general protocol that can employ different probabilistic as well as deterministic communication models, with minimal configuration. The advantages of PCMP are it achieves the target network delivery rates; PCMP is quite robust to several factors common in real environments such as node failures, drifts in node clocks, and errors in node locations, and Probabilistic communication models expose a tradeoff between packet delivery rates and number of activated nodes, which could be exploited by sensor network designers to optimize the number of deployed nodes. PCMP does not consider coverage and connectivity with degrees higher than one under probabilistic sensing and communication models In the literature (Anand et al 2012), the authors reviewed the major connectivity issues in WSNs, namely connectivity maintenance and monitoring, and surveyed various methods and protocols to monitor and also to maintain the connection. The connectivity maintenance protocol has the following limitations: As connectivity maintenance protocol only reduces the idle listening power of nodes, it is only effective when the idle power of the radio is high or the network workload is low. It is only suitable for the networks that are dense enough such that extraneous nodes can be turned off without impairing the network performance.

36 2.4.3 Integrated Coverage and Connectivity Schemes This section reviews the various Integrated Coverage and Connectivity schemes. Necessary and sufficient conditions for 1-covered, 1-connected wireless sensor grid network were given in the literature (Shakkottai et al 2005 and Shakkottai et al 2003). Also, a variety of algorithms have been proposed to maintain connectivity and coverage in large WSN (Shakkottai et al 2005 and Shakkottai et al 2003). The problem of coverage and connectivity in three-dimensional networks were studied in the literature (Alam and Haas 2006). In the literature (Ravelomanana 2004), several fundamental characteristics of randomly deployed WSN regarding communication and sensing range for connectivity and coverage in three-dimensional sensor networks were investigated. An optimal deployment strategy to achieve both full coverage and 2-connectivity regardless of the relationship between communication and sensing radii of the sensors was proposed by Bai et al (2006). The relationship between coverage and connectivity of WSN was studied and distributed protocols to guarantee both their coverage and connectivity were proposed by Huang et al (2007). A joint scheduling scheme based on a randomized algorithm for providing statistical sensing coverage and guaranteed network connectivity was presented by Liu et al (2006). A distributed algorithm to keep a small number of active sensors in a network regardless of the relationship between sensing and communication ranges was proposed by Zhang and Hou (2005). It was also proved that if the original network is connected and the identified active nodes can cover the same region as all the original nodes, then the network formed by the active nodes is connected when the communication range is at least twice the sensing range (Tian et al 2005).

37 In the literature (Gupta et al 2006), centralized and distributed algorithms for connected sensor cover were proposed so the network can selforganize its topology in response to a query and activate the necessary sensors to process the query. Datta et al (2005) proposed two self-stabilizing algorithms to the problem of minimal connected sensor cover. Zhou et al (2005) proposed a distributed and localized algorithm using the concept of the k th -order Voronoi diagram to provide fault tolerance and extend the network lifetime, while maintaining a required degree of coverage. Control and coordination algorithms were designed for a multi-vehicle network with limited sensing and communication capabilities (Cortes et al 2004). Also, adaptive, distributed, and asynchronous coverage algorithms were proposed for mobile sensing networks. Indeed, it was proved that mobility can be used to improve coverage in WSN (Liu et al 2005). The study on k-coverage and connectivity was given by Xing et al (2005) and it was proved that if the radius of the communication ranges of sensors is double the radius of their sensing ranges, the network is connected provided that sensing coverage is guaranteed. The network connectivity was also computed, based on whether the disconnected node is boundary or interior, and proposed a network configuration protocol based on the degree of coverage of the sensing application (Xing et al 2005). The k-coverage set and the k-connected coverage set problems were formalized in terms of linear programming and two non-global solutions were proposed for them (Yang et al 2006). A probabilistic Markov model was proposed to solve the problem of minimizing power consumption in each sensor while ensuring coverage and connectivity (Yener et al 2007). Sensing coverage and network connectivity are two of the most fundamental issues in WSNs, which have a great impact on the communication performance of WSNs. Classification of the coverage

38 problem from different angles was given by Zhu et al (2012), which describe the evaluation metrics of coverage control algorithms, analyze the relationship between sensing coverage and network connectivity. 2.4.4 Scheduling Sensors to Achieve Coverage/Connectivity The Sensors scheduling algorithms can be classified as centralized or decentralized localized and distributed. Centralized Algorithms require global information (such as sensor s sensing ranges, sensor s location, sensor s residual energy, etc.) of the whole sensor network. Besides, the algorithms are always executed at a powerful base station. The centralized algorithms always provide nearly optimal solution since the algorithm has global view of the whole network. However, its disadvantage is very slow speed for collecting the information through the network. They have low-adaptability to the change of the network and are not suitable for large-scalable network. Decentralized (localized and distributed) algorithms require only the local information to function and are run at large number of sensor nodes. Each sensor then makes its own decision of turning on or off. Thus, it is very adaptable to the dynamic and scalable nature of sensor networks. Obviously, the distributed and localized algorithms are preferred in wireless sensor network. Normally, the localized and distributed algorithms result in nondisjoint set covers. The following reviews are based on distributed algorithms. The main approaches of Ottawa Protocol (Tian et al 2002) and Coverage Configuration Protocol (CCP) (Wang et al 2003) are to find offduty eligibility rules for redundant nodes and then schedule the work status of these eligible nodes. The off-duty eligibility rule for a sensor to determine whether it is redundant is critical to such protocols. In the eligibility rule of

39 Ottawa protocol, node i is said to be eligible for turning off if the sum of the angles created by all of its neighboring nodes are larger than 2. However, this rule only takes the neighbors within a node s sensing area into account, bypassing the nodes outside the sensing area but still contributing to coverage sponsorship. Therefore, it is a sufficient but unnecessary condition, the Ottawa protocol can result in redundancy after turning off only a subset of eligible nodes. Hence, Ottawa protocol does not support configurable coverage degree. An extension to the Ottawa protocol was proposed as the Optimal Coverage-Preserving Scheme (OCoPS) in the literature ( Boukerche et al 2007), to provide more accurate coverage control. However, both Ottawa protocol and OCoPS only support 1-coverage only. Wang et al (2003) proposed a coverage and connectivity configuration protocol (CCP) which tries to maximize the number of sleeping nodes while maintaining k-coverage and k-connectivity. Here, k-coverage means each point in the monitoring area of the sensor network is sensed by at least k different nodes of the network. The authors prove that k-coverage implies k-connectivity and to decide k-coverage, a node only needs to check whether the intersection points inside its sensing area are k-covered. In CCP, a coverage-configurable off-duty rule is adopted to determine node eligibility. The CCP rule considers a node to be eligible if all the intersection points inside its sensing area are k-covered. An intersection point is defined as the intersection point of the sensing circles of two nodes or that of the sensing circle of one node with the boundary of the field. The CCP protocol performs better than the Ottawa protocol in coverage efficiency. However, CCP rule does not test the intersection points on a node s sensing circle. Therefore, blind points might be occurred in CCP. A probing based density control algorithm called PEAS is proposed by Ye et al (2003) to ensure prolonged network lifetime and sensing

40 coverage. Some functional nodes in PEAS continue working until they drain down the battery energy, which may cause reduced network connectivity. In order to balance energy consumption among the network, the ALUL (Average Linear Uncovered Length) protocol is presented by Gui and Mohapatra (2004). The design and analysis of novel protocols that can dynamically configure a network to achieve guaranteed degrees of sensing coverage and network connectivity was presented by Xing et al (2005). Authors first presented Coverage Configuration Protocol (CCP) that can provide different degrees of coverage requested by applications and also provided a geometric analysis of the relationship between coverage and connectivity. This analysis yields key insights for treating coverage and connectivity within a unified framework. The main aim of the CCP is to achieve the guaranteed degree of coverage and connectivity while achieving long life time of the sensor by maximizing the number of sleeping nodes. But the main limitation is, it cannot guarantee the connectivity when the communication range is less than twice that of the sensing range. To avoid this limitation, CCP can be integrated with SPAN protocol to provide both coverage and connectivity guarantees. But CCP should need accurate location information and neighborhood table. This is the limitation of CCP. Zhang and Hou (2005) proved that coverage implies connectivity if the ratio between the transmission range and sensing range is at least two. Depending on this, they proposed Optimal Geographic Density Control (OGDC) algorithm to maximize the number of sleeping sensor nodes while maintaining coverage. A sensor node is active only in the case it minimizes the overlapping area with the existing active sensor nodes and it covers an intersection point of two sensors. A sensor node decides this by using its own location and the location of other active nodes. OGDC first finds the position

41 where each active node should locate if a full coverage is achieved. Then OGDC selects the nodes closest to these positions as active node set and put all the other nodes into sleep to conserve energy. OGDC ignores boundary positions; hence, the desired coverage degree is not achieved In the literature (Gao et al 2006), the Sensor Scheduling for k- Coverage (SSC) problem was investigated, which requires to efficiently schedule the sensors, so that the monitored area can be k-covered throughout the whole network lifetime with the purpose of enhancing network lifetime. The SSC problem is NP-hard and they proposed two heuristic algorithms under different scenarios. In addition, they developed a guideline for users to better design a sensor deployment plan to save energy by using a density control scheme. Yener et al (2007) considers a multi-hop sensor network and addresses the problem of minimizing power consumption in each sensor node locally while ensuring two global properties: (i) communication connectivity, and (ii) sensing coverage. A sensor node saves energy by suspending its sensing and communication activities according to a Markovian stochastic process. It was shown that a power level to induce a coverage radius w(n)/n is sufficient for connectivity provided that w(n). Authors present a Markov model and its solution for steady state distributions to determine the operation of a single node. Given the steady state probabilities, they have constructed a non-linear optimization problem to minimize the power consumption. An energy-efficient scheduling protocol for k-covered WSNs was proposed by Ammari and Das (2009). The design of a geographic forwarding protocol for duty-cycled k-covered WSNs with data aggregation was discussed. This paper addresses the joint k-coverage and geographic forwarding protocol for WSN.

42 Bulut and Korpeoglu (2011) proposed distributed and energy efficient sleep scheduling and routing scheme that can be used to extend the lifetime of a sensor network while maintaining a user defined coverage and connectivity. This scheme can activate and deactivate the three basic units of a sensor node (sensing, processing, and communication units) independently. This paper also provides a probabilistic method to calculate how much the sensing area of a node is covered by other active nodes in its neighborhood. The method is utilized by the proposed scheduling and routing scheme to reduce the control message overhead while deciding the next modes (fullactive, semi-active, inactive/sleeping) of sensor nodes. This sleep scheduling and routing scheme can significantly increase the network lifetime while keeping the message complexity low and preserving both connectivity and coverage. The protocol can work to maintain a desired partial coverage only. Sahoo and Sheu (2011) proposed potential connectivity and coverage maintenance algorithms for the WSN that let the sensors work alternatively by identifying the redundant sensing regions in the post deployed scenarios and maintain the network with limited mobility. Decision of mobility of the nodes among immediate neighbors of a dead node is totally autonomous and distributed, and it is made to maintain the network without disturbing the existing coverage and connectivity. Average mobility distance and energy consumption of the nodes are limited to maintain the coverage and connectivity of this approach. Moreover, the proposed algorithms work for the communication range (R c ) of a node that is equal to sensing range (R s ) or for R c <2R s. In duty-cycled WSN for event monitoring, existing approaches are mainly concentrated on energy-efficient scheduling of sensor nodes to guarantee the coverage performance, ignoring another crucial issue of connectivity. The connectivity problem is extremely challenging in the duty-

43 cycled WSNs due to the fact that the link connections between nodes are transient and unstable. He et al (2012) proposed a new kind of network, called partitioned synchronous network, to address the coverage and connectivity problem at the same time. Synchronous network has a better connectivity performance than that of asynchronous network, while coverage performances of the two types of networks are similar. In this section, a variety of approaches that compute the sensor spatial density that is needed to guarantee some degree of coverage of a field was described. However, all the schemes bound for the sensor spatial density are asymptotic and depend on the geometry of the field and its size. The sensor spatial density to achieve coverage should depend only on the sensing range of the sensors and the degree of coverage required by the application. Hence, new approaches are required to provide more accurate bounds on the sensor spatial density. Furthermore, different approaches for coverage, connectivity, duty-cycling, and data forwarding in WSN were presented. However, due to their dependency, a few protocols that combine some of them have been proposed. Hence, more energy efficient protocol need to be proposed in which coverage, connectivity, scheduling, are jointly considered. 2.5 CLUSTERING ALGORITHMS FOR WSN Clustering is needed in WSNs because of its network scalability, energy-saving attributes and network topology stabilities. However, there also exists some disadvantages associated with individual clustering scheme, such as additional overheads during cluster-head (CH) selection, assignment and cluster construction process. Most of the clustering protocols in WSNs mainly focus on energy consumption and network lifetime. They do not support adaptive multi-level clustering (Amis et al 2000, Kawadia et al 2003, Karenos et al 2008 and Changjiang et al 2011).