SAWR: Scheduling Algorithm for Wireless Sensor Networks with Rendezvous Nodes Amar Jukuntla
Keerthi Pendam
Arjun N
Assistant Professor, CSE Nalla Malla Reddy Engineering College Hyderabad, India
[email protected]
Assistant Professor, CSE Nalla Malla Reddy Engineering College Hyderabad, India
[email protected]
Assistant Professor, CSE Nalla Malla Reddy Engineering College Hyderabad, India
[email protected]
Abstract— A large class of Wireless Sensor Networks applications involved in monitoring environmental parameters in isolated urban areas covered with sensor nodes. Mobile sinks are mounted upon isolated urban areas with fixed trajectories to monitor environmental parameters. These MS provides ideal infrastructure for retrieving sensory data from isolated WSN fields. The existing approach uses the multi-hop technique for transfer of data from SNs lies within MS’s range and network traffic can be balanced among sensor nodes. When sensor nodes run out of energy, the resulting loss of network connectivity and decreasing network life time. In this paper, an ant colony optimization (ACO) scheduling algorithm is explored for maximizing the network life, it can be done in three phases. In phase one, finds the initial active sensor nodes with fully coverage constraints. In second phase, it uses successor sensor set, for activating the sensor nodes for reaching constraint. In third phase, sensory data will be forwarded to MS through rendezvous nodes. Keywords—ACO; Wireless Sensor Network; Network Life Time; Rendezvous Nodes;
I. INTRODUCTION In recent years there is a rapid growth in WSN applications, including battle field surveillance, security and disaster management, habitat monitoring, factory assessment, hazard detection in urban areas. Sensors in such applications must operate on limited power supplies like battery life time can be extended up to years. A fundamental challenge for these wireless sensor network with rendezvous nodes is to support with minimum network energy consumption. In WSN, information is collected by enabling the mobile sinks through rendezvous nodes. A mobile sink acts as a transporter that moves in the specified wireless sensor network and collects the data from sensor nodes from particular cluster through rendezvous nodes. These rendezvous node computes the information and forward to nearer mobile sinks through rendezvous nodes. Due to this wireless communication, energy will be exhausted, sensor nodes run out of energy this leads to limited network lifetime and no more data be transferred to the mobile sink through rendezvous nodes. One successful approach to draw out the network life time is to use scheduling activities for sensor nodes. In WSN, two
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operational modes are used for sensor nodes, i.e., active and passive modes. A sensor is designed to monitor environmental parameters for dedicated WSN. These monitoring task can be performed when sensor nodes are in active mode with large amount of power consumption. On the other hand, a sensor node can use low energy when it is in passive/sleep mode because it does not monitor the environmental parameters. The network life time can be prolonged by activating sensor nodes when it is necessary. But the main drawback of this method is, as sensor nodes will be scheduled into passive or active modes frequently, it may be harmful to circuits and reduce sensor nodes lifetime [9]. To better prolong network lifetime, this paper proposed a scheduling approach for WSN with rendezvous nodes (SAWR). This SAWR strategy divides sensor nodes into a first layer set and a successor set. The first layer sensor nodes are activated when network starts functioning, while other sensor nodes are scheduled into passive/sleep mode to conserve energy. These active sensor nodes are started transferring data to MS, when it is in the range of rendezvous node. Once active sensor runs out of energy, other passive sensor nodes (SN) are activated to satisfy the network requirements, while other sensors remain in active mode. SAWR fully utilizes the residual energy of SNs even though sensor nodes having different life times and does not require to change from one mode to another frequently. Data transformation can be done effectively. Based on this strategy, this paper uses an ant colony optimization based method, to search for global optimal solution. The main aim of this novel approach is to maintain network life time at a maximum, while identifying the minimum distance for forwarding packets from sensor nodes to rendezvous node in a network using a swarm intelligence based optimization technique called ACO. The rest of the paper explains about the ACO in WSN for prolonging the network life time along with RN nodes. II. PROPOSED SCHEDULING ALGORITHM FOR WSN Consider a set of sensors S = {s1, s2. . . sN} that are randomly deployed in a monitoring cluster. All sensor nodes have limited power supply and can monitor particular cluster
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area range R. In initial communication different nodes will be in different energy levels and life times. Let us, assume that the position of sensor nodes and life time of sensor nodes are known in advance. The scheduling algorithm works in following manner. Activate the sensor nodes in a cluster, and elect a cluster head [6]. Identify the active/passive sensor nodes and find the optimized path from active sensor nodes to cluster head [9]. Which leads to consumption of energy of passive sensor nodes. Once energy levels of the active sensor nodes runs out of energy, the passive/sleep mode sensor nodes get activated, selects a cluster head (CH), finds the optimized path to cluster head, monitors the environmental parameters and forwarded to the cluster head which in turn forwards into rendezvous nodes. This process continues until the network requirement satisfied. For computing network life time. First, monitor region is divided into Į small grid clusters. The grid cluster approximate is given by μ=
% ఈ
(1)
Where % is number of grid clusters covered by at least one active sensor and Į number of grid clusters and μ=1 according to above equation, because entire region is covered with one active sensor node successfully. Otherwise, the network coverage is not satisfied. III. SCHEDULING APPROACH: IMPLEMENTATION OF ACO A. Solution Construction Behavior in ACO In this paper, a novel approach is proposed to use multiple construction graphs in ACO to guide the search. The artificial ant travels the first construction graph to search for a first layer set, and then it travels the second construction graph to find a series of successor cover sets [6]. Specifically, an artificial ant constructs a solution by the following two steps. This entire procedure is implemented in each cluster. Step 1: Finding initial sensor nodes layer set: In the first construction graph, O, Si and N respectively represents the origin point, the i-th sensor node and the number of sensors in a particular grid cluster. Pheromone is deposited on the vertexes, from origin vertex O and artificial ants started visiting vertexes one by one, and gradually finds initial sensor nodes layer set. Supporting the k-th ant is located at Si, the next vertex to be visited is chosen by ݔ݁ݐݎ݁ݒ݈ܾ݁݅ݏܽ݁ܨ݊݁݊݉ݎ݄݁ܲ݉ݑ݉݅ݔܽܯሺܵ ሻǡ ݂݅ ͳݍ൏ Ͳݍ ܲ݊݅ݐ݈ܿ݁ݏ݈ܽ݊݅ݐݎݎǡ ݁ݏ݅ݓݎ݄݁ݐ
ܲ =ቊσ
୦ୣ୰୭୫୭୬୰୴ୟ୪୳ୣ୵୧୲୦ୟ୰ୟ୫ୣ୲ୣ୰୧ୣୢ୦ୣ୳୰୧ୱ୲୧ୡ୴ୟ୪୳ୣĄ୨ ಈאూ౩ౘౢ౨౪ౙ౩ ୦ୣ୰୭୫୭୬ୣୟ୪୳ୣ୵୧୲୦୮ୟ୰ୟ୫ୣ୲ୣ୰୧ୣୢ୦୳୰ୣୱ୲୧ୡ୴ୟ୪୳ୣĄஐ
ǡ א
Ͳǡ
(4)
The ant selects sensor nodes according to above equations, until network coverage is satisfied and initial sensor nodes set layer is activated when network starts functioning. Step 2: Finding successor sensor node sets: once network start functioning, an active sensor runs out of energy, the second construction step is utilized for finding successor sensor to satisfy the coverage requirement. In construction graph, sensor node is represented with OSi, when it is runs out of energy, while Si represents the candidate successor sensor. The pheromone is deposited on the edges and artificial ant will move to vertex and chooses a successor sensor by above equations. Here pheromone is deposited on the edges, when the ant successfully finds a successor sensor node set, it will move to one vertex among os1, os2…osn, according to the shortest lifetime sensor in the newly found cover set. Then the ant continues to find a new successor cover set by the above methods [6]. This process repetitively until no successor cover set can be found. B. Algorithm Framework The ACO involves five steps for proposed algorithm in this paper, as illustrated as follows. Step 1: Initialization: Formation of Pheromones in a cluster.
ALGORITHM IN WSN
= ൜
The probability of returning value of S of proportional selection rule is computed as
(2)
Where feasible set of vertices F, pheromone value on Si can be computed on parameterized (ȕ) heuristic value ð can be computed as ð = no.of uncovered grid clusters that can be covered by Si (3)
This step initializes pheromones on the construction graph with in cluster. Formation of clusters in a WSN, borrows ideas from the algorithm of Chen [5] to build a cluster structure of unequal clusters. The formation of cluster and data forwarding will be explained in [5]. After cluster formation is done, finding the feasible path we borrowed a greedy algorithm. With the help of this greedy algorithm, finding the first layer of sensor nodes by selecting a sensor nodes with the largest heuristic value computed by (3). Once active sensor node runs out of energy, sleeping sensor nodes will be activated with largest heuristic value for monitoring environmental parameters within a cluster. This process is repeated until the full coverage constraint cannot be satisfied. Then the pheromone values are initialized as the pheromone value on vertex Si of first construction of initial sensor nodes set is equal to the pheromone value on edge between Origin sensor node OSi to Sj is equal to network life lime Nl. Step 2: Finding Shortest path with artificial nodes. In this step, each artificial ant finds a feasible path by the mechanism given in Section II. After end of this mechanism each construction process will be updated with pheromone value. Here when a sensor node runs out of energy say Si node, next sensor node Sj will be activated and pheromone
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value will be updated. From this we can’t get feasible solution. For finding best solution next updating process is important. Next updating process is done when all artificial ants completes their solution construction procedures. The main purpose of this updating is to find the best-so-far ant, so that subsequent ants can use the pheromone information to optimize the path with in a cluster and final pheromone value is updated. There is an iteration in this step, until meeting the termination condition. That is, whenever best solution is found, this step can be terminated. Step 3: RN node selection RN node selection largely determines network life time. RN nodes lie within range of travelling mobile sinks with respect to CH and the sensor field with respect to the sinks trajectory. According to ren..et al [7] each node is enrolled as a RN. The RN nodes should have capability to receive a relatively high amount of BEACON packets from MS and it must be in a fixed trajectory followed by Mobile Sink. Euclidean distance is used for finding the distance between SNs and MS. To keep track record of received BEACON packets, when a SN receives the i-th BEACON, it increases the BEACON packet counter by one, records the receipts time, signal strength and restarts the “Connection Released Timer”. SN also keeps track of first and last received BEACON packet. Due to these BEACON packet transmission in networks there may be a packet loss due to channel error or collision between packets and BEACON counter will be incremented by one. When “Connection Release Timer” expires the sensor node may assume that MS moves away from that location and the BEACON counter will be finalized. Herein, we assume that when sink moves, it return back to within the sensor node’s range in other traversal only. Then, each SN computed a value based on its residual energy, received signal strength of BEACON message and number of BEACON messages can be counted with respect to the average distance of SN from the mobile sink. Afterword’s RN message will be created to forward assign cluster head CH. This message contains node id, computational value, BEACON message received times (first and last). So highly computational valued node will be elected as a RN within the range of MS. This algorithm is executed by each SN within that clusters. The algorithm as follows Step 1: Step 2: Step 3: Step 4: Step 5:
First initialize all BEACON message counter bc=0, Time for initial message Ti=0, Time for last received message Tl=0; Wait until BEACON message received If BEACON message received BEACON message received update bc, Ti, Tl, and signal strength ss Connection Release Timer will be started
Step 6:
While ‘wait and receive BEACON messages until timer expires’ Step 7: If BEACON message received Update all receipt Ti, Tl, Ss and bc bc= bc+|ti-ti-1/total|;bc,r= bc,r +1; Tl= Ti Step 8: Restart ‘Connection Release Timer’ Step 9: End IF Step 10: End While Step 11: Comp_value= ್
1.
௦ σసభ ா௬̴ோ௦ௗ௨ ೞ +2ܾ +3 ்௧̴ா௬ ்௧̴ாைே௦
(5)
Step 12: SEND_RN_Message(comp_value, Ti,Tl,NODE_ID). Thus, each CH receives a RN_Messages from each SN with in a cluster and RN will be elected and based on RN_SELCTION_ALGORITHM et al..,[7]. RN will be located within the cluster and data will be forwarded to RN. Step 4: Storage of information (Data forwarding from one sensor node to another) In this step our SNs starts with the periodic recording of environmental parameters. The data accumulated at individual SNs are sent to local CH with time period. CHs perform data processing to remove spatial temporal data redundancy and local CHs are located two hops away. These local CHs forward filtered data towards to remote CH they are attached to. In intra cluster path, a second level of data filtering may apply. At finally, filtered data will be forwarded to the RN. If that cluster having multiple RNs, data not be equally distributed among them. Instead, CH delivers the data delivered by the most suitable RN, because of high computational value and it can accommodate its assigned data. Data_Distribution Algorithm is barrowed for data gathering and data forwarding from [4]. Step 5: Communication between Rn and Mobile Sink In last step, buffered data will be delivered from RN to MS, it occurs along with an immediately available link. A key requirement is used to determine the connection between the RN and MS. It starts communicating with MS when communication link is established. Whenever MS goes away from the RN it stops communicating with it. To address this issue, an acknowledge based protocol is used between these nodes. RN uses the POLL packets to detect the MS is within range or not. When broadcast packet receives an acknowledgement from MS, it realizes that MS is active and it is in connectivity state. After acknowledgement packed can be removed from the RN’s memory. More elaboration explanation about clustering protocols and data forwarding protocols can be found in. This approach prolongs the network life time and environmental parameters data transmission can be done effectively. This novel approach adopts scheduling algorithm from ACO for finding feasible solution by
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activating the sensor nodes when other nodes runs out of energy and information is transferred to MS through rendezvous nodes in a fixed trajectories. IV. CONCLUSION The scheduling algorithm for WSN with rendezvous nodes have significant impact on urban areas to monitor environmental parameters. In this paper, an optimized artificial ant colony based scheduling algorithm is used for prolonging network life time with full coverage constraints with rendezvous nodes. In initial phase, finds the active sensor nodes with fully coverage constraints. In second phase, successor sensor nodes are identified. In third phase, sensory data will be forwarded to mobile sink (MS) with in the range of rendezvous (rn) nodes through cluster head (CH). Network life time can be prolonged with the help of ACO scheduling algorithm. Future work is to extend the algorithm framework for routing protocols and information filtering and forwarding. REFERENCES [1]
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