A novel disjoint set division algorithm for joint scheduling and routing in wireless sensor networks
|
|
- Winifred Parker
- 6 years ago
- Views:
Transcription
1 Wireless Netw (2015) 21: DOI /s A novel disjoint set division algorithm for joint scheduling and routing in wireless sensor networks Jie Tian Xiaoyuan Liang Tan Yan Mahesh Kumar Somashekar Guiling Wang Cesar Bandera Published online: 29 November 2014 Springer Science+Business Media New York 2014 Abstract High network connectivity and low energy consumption are two major challenges in wireless sensor networks (WSNs). It is even more challenging to achieve both at the same time. To tackle the problem, this paper proposes a novel disjoint Set Division (SEDO) algorithm for joint scheduling and routing in WSNs. We finely divide sensors into different disjoint sets with guaranteed connectivity based on their geographical locations to monitor the interested area. We propose a class of scheduling and routing algorithms, which sequentially schedule each disjoint set to be on and off and balance the energy consumption during packet transmission. Simulation results show that SEDO outperforms existing schemes with lower packet delivery latency and longer network lifetime. Keywords Wireless sensor networks Disjoint set division Joint scheduling and routing algorithm J. Tian (&) X. Liang T. Yan M. K. Somashekar G. Wang Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA jt66@njit.edu X. Liang xl367@njit.edu T. Yan ty7@njit.edu M. K. Somashekar mkv6@njit.edu G. Wang gwang@njit.edu C. Bandera School of Management, New Jersey Institute of Technology, Newark, NJ, USA bandera@njit.edu 1 Introduction Wireless sensor networks (WSNs) consist of a large number of wireless sensor nodes. Each node is powered by energy-constrained battery. A typical WSN application usually requires to deploy a large number of sensor nodes in a hostile environment, which makes replacing or recharging the battery very difficult. Once deployed, sensors are expected to keep working for months, which makes energy efficiency the essential requirement in WSNs. To improve energy efficiency, duty-cycle scheduling is widely applied to turn sensors off at most of the time, and only turn them on when they need to perform required duties (sensing, transmitting, etc.) [1 4]. When a sensor is off, it does not perform any task and consumes little energy, and thus its energy can be greatly preserved [5]. However, duty-cycle scheduling significantly affects the network connectivity [6], one of the most important network performance metrics. Off-duty sensors are unable to receive or forward messages. The smaller number of sensors are powered on at a time, the poorer potential connectivity the network has. A mis-configured scheduling can cause serious problems. For example, in a network monitoring forest fires, sensors in the area are required to report the sensed event in a timely fashion. It will be a disaster, if a fire is sensed and the event message can not be routed to the sink due to a broken link caused by off-duty sensors. Thus, a good scheduling scheme should improve energy efficiency under the constraint of network connectivity. This calls for a joint scheduling and routing mechanism that maintains network connectivity, meanwhile minimizes and balances the network energy consumption to prolong the network lifetime.
2 1444 Wireless Netw (2015) 21: To fulfill this requirement, in this paper, we design an algorithm to divide sensors into disjoint sets, which are scheduled to be on and off at different time slots. The set of sensors powered on conduct sensing and communication job, while other sets are sleeping and conserving energy. Our design has the following three objectives: (1) in each set, the connectivity from the sink to the sensors in network boundaries should be maintained, so that sensors can report the sensed events to the sink. (2) At each time slot, the amount of sensors that need to be powered on to maintain the network connectivity should be as small as possible. (3) The energy consumption in each set should be balanced. We propose a distributed disjoint Set Division (SEDO) algorithm for joint scheduling and routing in an area of interest. Given an area, SEDO first divides all the sensors in the area into a certain number of sets, such that each set maintains connectivity from a sink to area boundaries. The set division executes hop by hop from the sink to nodes in network boundaries, which assigns nodes to corresponding sets according to their geographical locations. To ensure the connectivity, during the set division, a node is only assigned to the set which contains nodes it can communicate with. To balance the energy consumption, we balance the number of nodes in each set. We schedule the disjoint sets to be on and off sequentially, such that only one set is on duty in every time slot. Whenever a node of an on-duty set needs to send message to the sink, a route is built within the set. A node always selects its neighbor with the highest amount of remaining energy as the next hop to balance energy consumption in packet forwarding. In this way, the network lifetime is prolonged. Simulation results show that SEDO outperforms previous works [7] and [8] with longer network lifetime and lower average packet delivery latency. The remainder of the paper is organized as follows. The system model and algorithm overview is presented in Sect. 2. The set division and scheduling is described in Sect. 3. A routing protocol is presented in Sect. 4. Section 5 shows the simulation results. Related work is discussed in Sect. 6. Finally, Sect. 7 concludes the paper. 2 Assumptions and scheme overview 2.1 Assumptions In the paper, we make following assumptions: The sensing area of a sensor node is a circle, which is modeled using an isotropic sensing model same as in [9 11]. Each sensor s communication range is at least one time more than its sensing range [5]. All the sensors have the same communication range and sensing range. We assume all the sensors are static and their locations are known, which can be obtained by GPS or various localization methods [12 14]. The network is loosely synchronized. Each node synchronizes locally with its neighboring nodes, which can be achieved by many mature techniques with low overhead [15 17]. There is a sink node in the network. Unlike regular sensor nodes, the sink has no energy constraint. Without loss of generality, we assume the sink is in the middle of a boundary of the area. 2.2 Scheme overview SEDO consists of three steps: (1) set division, (2) set scheduling, and (3) set routing. SEDO evenly divides all the sensors into a certain number of disjoint sets, schedules each of the sets to be on and off rotationally, and balances the routing energy cost in each on-duty set. In the beginning of set division, each sensor exchanges Hello messages to determine the minimum number of hops it takes to reach the sink. We also calculate the expected number of sets, k, for maintaining connectivity based on the sensors distribution in the network, based on which, we divide all the sensors in the network into k disjoint sets. The set division is executed in a distributed fashion, and it is conducted hop by hop from the sink to the network boundary. We select a few of sensors as Set Managers to manage the set assignment in each hop and select Set Managers for the next hop. In the first hop, the sink manages the set assignment, while the Set Managers are only responsible for selecting Set Managers for the next hop. The sink evenly assigns all the nodes within its communication range to k sets. After being assigned to a set, each sensor broadcasts its set information to its neighbors in the next hop. When the assignment in the first hop finishes, the assignment in the second hop starts. Starting from the second hop, the Set Managers are selected by the Set Managers in the previous hop. A Set Manager first collects its neighbors candidate sets, which are the sets that a node can be assigned to in order to maintain connectivity. Then, based on the candidate sets, the Set Manager calculates an assignment, such that each neighbor can be assigned to an appropriate set and the numbers of nodes in all the sets are balanced. After finishing the calculation, the Set Manager broadcasts the assignment to its neighbors, and randomly selects a neighbor to relay its role. Newly selected Set Managers continue the assignment process until all the nodes in the same hop are assigned to sets. The last Set Manager in this hop selects one neighbor in the next hop to continue the
3 Wireless Netw (2015) 21: assignment in the next hop. The whole procedure terminates if all the nodes in the network are assigned to sets. After set division, in set scheduling, each divided set is scheduled to be on and off rotationally, with only one set on duty at each time slot. In set routing, whenever a node in an on-duty set needs to send a message to the sink, a route is built from it to the sink within the set it belongs to. To build a route, the node first selects its neighbors in the previous hop from its current set. Then among all the neighbors, the nodes always choose the one with the highest amount of remaining energy as the next hop in the routing path. In such way, the energy consumption of all the node in packet forwarding is balanced. 3 Set division and scheduling Given a network, in this section we first calculate Minimum Hop Count (MHC) for each sensor to reach the sink, and the expected number of sets for the network. Based on the two parameters, we then design our set assignment protocol to divide all the nodes in the network into expected number of sets and assign every node to a set. After finishing the assignment, we schedule each set to be on and off sequentially to reduce energy consumption while at the same time maintain the network connectivity. 3.1 Initiation Calculating minimum hop count The MHC of a sensor is defined as the number of hops that the sensor takes to reach the sink through the shortest hop path. The calculation of the MHC can be done by simply broadcasting a Hello message from the sink to the entire network. Upon receiving the message, each sensor records the number of hops. A sensor selects the smallest hop number from all received messages as its MHC. Figure Hop 1 Sink Sensor shows an example. Node N 4 receives messages from the sink from two paths: sink! N 1! N 2! N 4 and sink! N 3! N 4. Since the second one has the smallest hop count, node N 4 sets its MHC to be 2. Similarly, node N 5 sets its MHC to be Calculating expected number of sets The expected number of sets is calculated based on the sensors distribution. The calculation contains two steps. The first step is to calculate N o, which is the optimal number of sensors to maintain connectivity for the entire network. It can be done according to [18] as follows: N o ¼ 2P area pffiffiffiffiffi 27 ; ð1þ rs 2 where P area is the size of the entire area and r s is the sensing range of each sensor. After N o is determined, the second step is to calculate k, the expected number of sets. We let N t represent the total number of sensors deployed in the network. Ideally, the optimal number of sets can be calculated as N t =N o. However, since the sensors are randomly distributed in the network, we need to further calculate the expected number of disjoint sets considering the randomness of deployment. Let c denote the expect number of sensors equal to one sensor in the optimal deployment. Then, the expected number of sets in the network is: N t k ¼ p q; ð2þ c N o where the operators of p and q mean the ceiling function. In the following, the value of c is discussed considering sensor s random distribution. Consider a network has an optimal deployment of sensors. When sensors are deployed randomly in such network, one single sensor must fall into the sensing area of one optimal position. Then the sensor has the largest overlapped sensing area with such optimal position. One example is shown in Fig. 2. Position O is the optimal position and sensor R is randomly deployed in the sensing area of position O. The overlapped sensing area is the shadowed area in Fig. 2. The sensor R must have the largest overlapped sensing area with position O comparing 4 Hop 2 Fig. 2 Overlapped sensing area 5 Hop 3 o α R Fig. 1 Calculation of the minimum hop count
4 1446 Wireless Netw (2015) 21: to other optimal positions. The overlapped sensing area S o is calculated as: S o ¼ 2r 2 a r 2 p sinð2aþ; where 3 a p 2 : ð3þ Then we calculate the average overlapped sensing area of one sensor under the random distribution. Let S o denote the average overlapped sensing area. Then we have: S o ¼ R p 2 p3 2r 2 a r 2 sinð2aþda p 2 p 3 ¼ 5pr2 6 r2 24p : ð4þ Then we can obtain the average probability of one random sensor covering the sensing area of an optimal position: r2 S p ¼ o pr 2 ¼ p 2 : ð5þ According to [7], the number of sensor nodes required in a sensing area to provide a coverage intensity of at least t is: lnð1 tþ c ¼ p lnð1 pþ q: ð6þ By setting the coverage intensity t to be 95 %, we can obtain c ¼ 2. Thus, the expected number of sets in the network becomes: N t k ¼ p q ¼ p N t q; ð7þ c N o 2N o For a more strict scenario, the coverage intensity t can be set to 99 %, and thus c is 3. In this paper, we use c ¼ 2asa system parameter. 3.2 Set assignment Based on the MHC of each sensor and the expected number of sets for the entire network, in this section, we divide all the nodes in the network into the expected number of disjoint sets. Our goal is to make each set solely maintain the connectivity of the network, while keep the number of nodes in each set balanced. Given an area of interest, the sets are calculated hop by hop from the sink to boundaries of the area. In the first hop, the sink evenly assigns all the sensors within its communication range into k sets. Starting from the second hop, in each hop, Set Managers are selected to assign their neighbors to k sets based on the set information from the previous hop. After finishing the assignment in each hop, the Set Managers relay their roles to the nodes in the next hop to continue the set assignment. The process continues until all the nodes in the network are assigned to sets. After set assignment, there are k disjoint sets in the network in total. Nodes in each set are connected and ready to perform scheduling and routing in the network. The detailed protocol of each step is presented in the following sections In the first hop The set assignment is conducted by the sink in the first hop. The sink first sorts all its neighbors according to its distances to them, and then it sequentially assigns each node to one of the k sets. Suppose a sensor s distance to the sink is ranked at n th among the sorted distances, the sensor is assigned to the ½n mod k th set. If ½n mod k is 0, it is assigned to the k th set. The nodes assigned into set 1 become Set Managers of the first hop. Figure 3 shows an example of set assignment in the first hop. In Fig. 3, there are 8 nodes in the first hop, the expected number of sets k is 4, and D 1 [ D 2 [ D 3 [ D 3 [ D 4 [ D 5 [ D 6 [ D 7 [ D 8, where D i is the distance between node N i and the sink. The sink assigns all the sensors to k sets according to their distances. Thus, after set assignment, set 1 contains fnode N 1 ; node N 5 g, set 2 contains fnode N 2 ; node N 6 g, set 3 contains fnode N 3 ; node N 7 g, and set 4 contains fnode N 4 ; node N 8 g. After being assigned to a set, each node broadcasts its set information to its neighbors in the next hop. In the example, node N 1 and node N 5 are assigned to set 1, and thus they are the Set Managers of the first hop. The Set Manager in the first hop is only responsible for choosing Set Managers in the next hop. After set assignment, each Set Manager chooses one neighbor node in the next hop as Set Manager of that hop In the x th hop (x [ 1) Starting from the second hop, Set Managers in each hop are responsible for set assignment. By Eq. (7), every hop has k sets in total. The set that a node can be assigned to is the Candidate Set of the node. In each hop, before the assignment starts, no set has been created and every node in this hop receives the sets that its previous hop neighbors belong to. Thus, initially a node s candidate sets are the Fig. 3 Assigning hop-1 sensor nodes to set
5 Wireless Netw (2015) 21: sets that its previous hop neighbors are assigned to. For example, a node N 1 in hop x receives set information from its two neighbors in hop x 1, and the two nodes are assigned to set 1 and 2, respectively. Then set 1 and 2 are the candidate sets of N 1. Due to the uncontrollable deployment, nodes may have different number of previous hop neighbors. Thus, the initial number of candidate sets that each node has may vary a lot. Some nodes may have multiple candidate sets, while some only have one or do not have any. This will make that the number of nodes in each divided set differs much. To address this issue, when the set assignment starts, we first skip the nodes with one or zero candidate set, and assign the nodes that have multiple candidate sets. During the assignment, sets will be created, and a node will have more candidate sets, which are the sets that its current hop neighbors are assigned to. After a node is assigned to a set, such set will be added to all its neighbors candidate sets. By doing so, nodes will have more candidate sets, and we have more flexibilities to assign nodes and balance the number of nodes in each set. The detailed set calculation procedure is described as follows. Set calculation at a Set Manager: Upon being selected as a Set Manager, a node first obtains candidate sets of all its neighbors. Then, based on the number of each neighbor s candidate sets, the Set Manager categorizes its neighbors into two groups: (1) the nodes with one or zero candidate set and (2) the nodes with more than one candidate set. The Set Manager keeps two tables: neighbor information table and assignment table, as shown in Tables 1 and 2, respectively. The neighbor information table in a Set Manager includes information of all its neighbors. Inside the table, if a node is assigned to a set, then the value in the Assigned column becomes True. Otherwise, it is False. The assignment table records the assignment procedure by the Set Manager. The tables are updated during the set assignment. Table 1 Neighbor Information table To record more candidate sets for nodes, the Set Manager first assigns the nodes with more than one candidate set. Then it uses these nodes assigned sets to update the candidate sets in the neighbor information table, and continues the assignments. The detailed assignment procedure is described as follows: Step 1: Check the assignment table and select a set containing the least value in the column of Number of Current Member. If more than one set is selected, randomly choose one with the smallest Number of Candidate Nodes and label it as Id. Step 2: Filter out the nodes from neighbor information table, whose candidate sets don t include the selected set Id. Step 3: Divide the remaining nodes into different groups based on the number of candidate sets in each node and pick the group that has the smallest number of candidate sets as the first group. Step 4: Sort the nodes according to their candidate sets in the first group. A sorting algorithm is adopted to sort the candidate sets from the largest value of Number of Candidate Nodes to lowest value of Number of Candidate Nodes in the group and thus sort the nodes. One example is shown in Tables 3 and 4. The algorithm first sorts the candidate set 3, which has the largest value of Number of Candidate Nodes. Then node N 1 and N 3 are put before node N 2, since their candidate sets include set 3. Then the sort algorithm sorts the candidate set 1, which has the second largest value of Number of Candidate Nodes. Then node N 3 is put before node N 1, and the final sorting sequence is node N 3, node N 1 and node N 2. Step 5: Pick the first node in the first group and assign it to selected set Id. Update both tables. Go to Step 1 until all the neighbors are assigned to sets. Table 3 An example of neighbor information table 1 2, 3 False ðx 1 ; y 1 Þ x 2 1, 2 False ðx 2 ; y 2 Þ x 3 1, 3 False ðx 3 ; y 3 Þ x Table 4 An example of assignment table Table 2 Assignment table Set Number of candidate nodes Number of current members Set Number of candidate nodes Number of current members
6 1448 Wireless Netw (2015) 21: Algorithm1 Set Assignment Protocol for Nodes in Hop x (x>1) Notations: SCId i : candidate sets of node N i. SId i : assigned set of node N i. Req < Can Set >: a request message asking for a node s candidate sets. At all the nodes: (1) Upon receiving SId j from N j : SCId i = SCId i SId j ; (2) Upon being assigned to set s: SId i = s; Broadcast SId i ; At a Set Manager : n = Num of Neighbors ; Broadcast Req < Can Set >; while n = 0 do (1) Upon receiving SCId j from N j : Store it; n ; end while Execute Set Calculation ; Randomly assign a neighbor to be the Set Manager ; At a regular node: Set a timer. (1) Upon receiving Req < Can Set >: Reply with SCId i ; Cancel the timer; (2) Upon being selected as a Set Manager: Become a Set Manager ; Cancel the timer; (3) Upon timer firing: Become a Temporary Set Manager; Execute Set Calculation; During the set assignment, it is possible that some nodes cannot be reached by Set Managers and thus cannot be assigned to sets. We introduce Temporary Set Manager to solve the issue. Every node maintains a timer waiting for the assignment. If a node in hop x is not assigned to any set after timeout, it will broadcast a request message to vote itself as a Temporary Set Manager. If more than one node broadcasts the requests, the first node sending out the request will become the Temporary Set Manager, and the others will cancel their requests and wait for the assignment. Upon being a Temporary Set Manager, the node starts the set assignment. Unlike Set Manager, after finishing the assignment, the Temporary Set Manager does not relay the role to any node in the next hop. Assignment protocol: In hop x ðx [ 1Þ, upon receiving set information from a neighbor in hop x, a node adds the set to its candidate sets. Upon being assigned as a Set Manager, a node broadcasts requests asking for candidate sets of every neighbor in the current hop. Upon finishing collecting the candidate sets from all the neighbors, the Set Manager assigns each neighbor to a set according to Set Calculation. After finishing the set assignment, the Set Manager broadcasts assignment results to all its neighbors, and relays its role to a neighbor in the same hop to continue the assignment. Its neighbors also broadcast results after they receive the set assignment results. Upon receiving such result from a neighbor, a node adds the assigned set to its candidate sets. The newly selected Set Manager continues the assignment in hop x and relays its role after finishing the assignment. The process terminates when all the nodes in hop x are assigned to sets. Then, the last Set Manager in this hop randomly chooses one neighbor in the hop x þ 1 as Set Manager. The process continues until all the nodes in the networks are assigned to sets. The detailed protocol for each hop is illustrated in Algorithm 1.
7 Wireless Netw (2015) 21: An example Figure 4 shows an example of the set assignment. There are five nodes: N 0 ; N 1 ; N 2 ; N 3 and N 4. N 0 is the Set Manager in hop x 1, while N 1 ; N 2 ; N 3 and N 4 are the nodes in hop x and need to be assigned to different sets. The expected number of sets is k ¼ 3. Each node s candidate sets are listed in Fig. 4, where SCId i represents the candidate sets of node N i. We assume after finishing the set assignment for hop x 1; N 0 assigns its neighbor N 1 to be the Set Manager in hop x. The neighbor information table and assignment table of N 1 are shown in Tables 5 and 6, respectively. From Table 5 we can see that since N 4 only has one candidate set: set 3, N 4 will not be involved into the set selection until all the other nodes have been assigned to sets. Thus, we first consider N 1 ; N 2, and N 3 in the set selection. According to Step 1, since the numbers of current members in all sets are 0 and set 1 and 2 have the smallest number of candidate nodes in Table 6, we label set 1 as Id randomly. Then based on Step 2, N 3 is filtered out. According to Step 3, N 1 and N 2 are divided into two groups and N 1 is in the first group, since the number of candidate sets of N 1 is 2 and the number of candidate sets of N 2 is 3. Sorting in Step 4 is not needed. Then according to Step 5, N 1 is selected and assigned to set 1. After that, the Set Manager N 1 updates its neighbor information table and assignment table as shown in Tables 7 and 8, respectively. N 1 repeats the calculation until N 2 and N 3 are assigned to sets. After that, the neighbor information table and the assignment table are shown in Tables 9 and 10, respectively. The next assignment is to assign node N 4. N 1 first calculates distances from N 1 ; N 2, and N 3 to N 4 based on each node s position in the neighbor information table. If a node s distance to N 4 is less than the communication range, the assigned set of this node is added to the candidate sets of N 4. In this example, the distance between N 4 and N 1, and the distance between N 4 and N 3 are less than the communication range. Thus, both set 1 that N 1 is assigned to and set 2 that N 2 is assigned to are added to N 4 s candidate sets. The neighbor information table and assignment table are updated as shown in Tables 11 and 12, respectively. N 1 uses the same assignment algorithm to assign N 4. Then N 4 is assigned to set 1. Figure 5 shows the final results. The final neighbor information table and the assignment table are shown in Tables 13 and 14, respectively. After finishing assigning all the neighbors, N 1 randomly selects a node from N 2 ; N 3 and N 4 as the next Set Manager in the same hop to continue the assignment. From the example, it can be observed that each Set Manager balances the number of nodes in different disjoint sets locally and thus the number of nodes in different disjoint sets are balanced in the entire network. 3.4 Analysis In this section, we analyze the storage and computational complexity of SEDO Storage A Set Manager maintains two tables: neighbor information table and assignment table. We let m represent the upper bound of the number of neighbors that a node can have. Accordingly, there are m entries in the neighbor information table, and the size of each entry is at most k þ 6 bytes (k bytes for candidate sets information, 1 byte for node ID, 1 byte for assigned information and 4 bytes for position). In the assignment table, the number of entries is k, and the size of each entry is 3 bytes (1 byte for set ID, 1 byte for the number of candidate nodes, and 1 byte for the number of current members). So in total, the size of the two tables are mðk þ 6Þþ3k bytes. Since k and m are small numbers, the tables can definitely work well even in resource constraint sensors Computational complexity From the detailed assignment procedure illustrated in Sect , it takes OðkÞ to do Step 1, and OðmÞ to go through Steps 2 and 3. For the sorting algorithm in Step 4, OðmkÞ is required to sort m neighbors, each of which has at most k candidate sets. Thus, the computational complexity of the assignment algorithm for m neighbors assignment in a Set Manager is Oðmðk þ m þ mkþþ, which is dominated by Oðm 2 kþ. 3.5 Sets scheduling Fig. 4 Assigning hop-x sensor nodes to sets After all nodes are assigned to sets, we schedule every set to be on-duty and off-duty sequentially, such that only one set is on duty in every time slot. For example, suppose
8 1450 Wireless Netw (2015) 21: Table 5 Neighbor information table of N 1 : before assignment 0 1 True ðx 0 ; y 0 Þ x 1 1 1, 3 False ðx 1 ; y 1 Þ x 2 1, 2, 3 False ðx 2 ; y 2 Þ x 3 2, 3 False ðx 3 ; y 3 Þ x 4 3 False ðx 4 ; y 4 Þ x Table 11 Neighbor information table of N True ðx 0 ; y 0 Þ x True ðx 1 ; y 1 Þ x 2 3 True ðx 2 ; y 2 Þ x 3 2 True ðx 3 ; y 3 Þ x 4 1, 2, 3 False ðx 4 ; y 4 Þ x Table 6 Assignment table of N 1 : before assignment Set Number of candidate nodes Number of current members Table 12 Assignment table of N 1 Set Number of candidate nodes Number of current members Table 7 Neighbor information table of N True ðx 0 ; y 0 Þ x True ðx 1 ; y 1 Þ x 2 1, 2, 3 False ðx 2 ; y 2 Þ x 3 2, 3 False ðx 3 ; y 3 Þ x 4 3 False ðx 4 ; y 4 Þ x Fig. 5 Results of assigning hop-x sensor nodes to sets Table 8 Assignment table of N 1 Set Number of candidate nodes Number of current member Table 9 Neighbor information table of N True ðx 0 ; y 0 Þ x True ðx 1 ; y 1 Þ x 2 3 True ðx 2 ; y 2 Þ x 3 2 True ðx 3 ; y 3 Þ x 4 3 False ðx 4 ; y 4 Þ x Table 13 Neighbor information table of N True ðx 0 ; y 0 Þ x True ðx 1 ; y 1 Þ x 2 3 True ðx 2 ; y 2 Þ x 3 2 True ðx 3 ; y 3 Þ x 4 1 True ðx 4 ; y 4 Þ x Table 14 Assignment table of N 1 Set Number of candidate nodes Number of current member Table 10 Assignment table of N 1 Set Number of candidate nodes Number of current members there are k disjoint sets in the network, the scheduling sequence is set 1; set 2;...; set k; set 1; set 2 and so on. Each set works independently in the network. The sequential scheduling makes energy consumption even in all the sets.
9 Wireless Netw (2015) 21: The routing We divide sensors into different sets and schedule them rotationally, such that every formed set maintains connectivity of the network. When a node needs to communicate to the sink, a route needs to be established from the node to the sink within the set that the node resides. In this section, we propose a route selection scheme to establish route and balance the energy consumption in forwarding packets for all intermediate sensors. Our idea is to evenly utilize the energy of all the nodes so that no node will fail too early to decrease the network lifetime. When a sender in hop x needstosendamessagetothesinkas shown in Fig. 6, it starts to establish a route by first choosing its neighbors in the previous hop (hop x 1) as routing candidates. Then the sender selects a candidate that has the highest amount of remaining energy as the next hop in the route. After the sender selects a node in hop x 1 as the next hop in route, the sender forwards the packet to the node. Upon receiving the packet, this node continues the route selection process to select a node as its next hop. If a node cannot communicate with any node in its previous hop, it treats its neighbors in the current hop as the route candidates and forwards the message to the one with highest remaining energy. One example is shown in Fig. 7. Every time when a node forwards a packet, it updates its own remaining energy and all its neighbor nodes update the corresponding energy information. The route selection continues until the packet reaches the sink. In this way, each node rotationally selects its neighbors as the next transmission hop. The node with highest remaining energy at current time is always selected to forward packet as shown in Fig. 8, and the energy consumption in packet forwarding is balanced among all the nodes in the set. When more than one node wants to send data to the sink, the routing algorithm still performs effectively, since the route selection in each node is independent. If there are communication conflicts, they will be solved by lower layer network protocols, like MAC protocols. Moreover, the routing algorithm can be applied to other connectivity guaranteed set division and scheduling algorithm. 5 Performance evaluation SEDO was implemented in NS-2 simulator. We use the following three metrics to evaluate our scheme: Network lifetime, which is the duration from the time that the network starts to the time when there is a hop, in which all the sensors fail. 1 1 Failing of all the sensors in a hop causes network partition, and thus the network cannot operate after that time. Average sensing coverage of each set. Packet delivery latency, which is calculated by averaging all packet transmission delays in the network shown as below: delay ¼ P hmax i¼1 total packets 0 delay in hop i number of packets arrived in hop i h max ; where h max represents the maximum number of hops in the network. The entire simulation is divided into two phases: scheduling phase and routing phase. We compare SEDO with Liu s algorithm [7] and A1 algorithm [8], which are the schemes closest to our research. Liu s algorithm studies a joint scheduling to save energy with consideration of both sensing coverage and network connectivity. It creates multiple sets in the network and schedules them on and off sequentially, and has a time complexity of OðnÞ. A1 algorithm forms energy efficient connected dominating sets based on network topology to extend network lifetime, which also has a time complexity of OðnÞ. To be a fair comparison, all three algorithms divide the network into same number of sets. Moreover, in the routing evaluation, the proposed routing algorithm is implemented on A1 algorithm. In the simulation, sensor nodes are randomly deployed in a 100 m 100 m square field. The sink is placed at position ð50 m; 0Þ, which is the middle of bottom boundary of the field. The default setting of the simulation is shown in Table 15. All simulation results are the average of 100 experiments, which are on different initial deployments of sensors.?> 5.1 The scheduling phase In this section, the number of sensors varies from 150 to 350. Figure 9 shows the evaluation results of network lifetime. From the results, we can see that on average, the network lifetime under SEDO is 17.9 % longer than that under Liu s algorithm and 13.5 % longer than that under A1 algorithm on average. For example, when the number of nodes is 150, the network lifetime under SEDO is 1: s. It is 24 % longer than that under Liu s algorithm, which is 9: s, and 40 % longer than that under A1 algorithm, which is 8: s. Figure 10 is the average ratio of each set s sensing coverage to the whole interested area. From Fig. 10, we can see that although the sensing coverage provided by SEDO is 5 % lower than by Liu s algorithm, it can still cover more than 80 % of the area. The average ratios of SEDO and A1 algorithm are close. Figure 11 is the average ratio of each set s sensing coverage to the sensing coverage of all the sensor nodes. Similarly, SEDO achieves about
10 1452 Wireless Netw (2015) 21: Fig. 6 An example of routing selection Fig. 7 Extra routing selection Higher Energy at me t Higher Energy at me t x SEDO Liu s Algorithm A1 Fig. 8 Energy-based selection rule Table 15 Simulation setting Lifetime (Sec) Parameter Sensing range Communication range Packet size Communication bandwidth On-duty duration of each set Initial energy in each sensor Energy consumption rate: on-duty Energy consumption rate: off-duty Energy consumption rate: receiving data Energy consumption rate: sending data Value 10 m 20 m 64 kbyte 64 kbyte/s 10 s 20 mah 8 ma ma 10 ma 25 ma Number of Nodes Fig. 9 Network lifetime 85 % of the sensing coverage, which is 5 % less than Liu s algorithm and A1 algorithm. This is because Liu s algorithm needs to choose extra nodes to compensate the place that cannot be detected and A1 algorithm needs to choose extra nodes to maintain the connectivity, though such
11 Wireless Netw (2015) 21: Avg. Coverage / Area SEDO Liu s Algorithm A Number of Nodes Fig. 10 Ratio of the average coverage of a set to the whole area Calculated Packet Delay (Sec) SEDO Liu s Algorithm A Number of Nodes Fig. 12 Average packet delivery latency Avg. Coverage / Total Coverage SEDO Liu s Algorithm A Number of Nodes Fig. 11 Ratio of the average coverage of a set to the coverage of all the deployed sensors Lifetime (Sec) SEDO Liu s Algorithm A Probability of Packet Sending Nodes Fig. 13 Network lifetime under different transmission probability compensations do not increase the total sensing coverage much. 5.2 The routing phase Figure 12 shows the average packet delivery latency when the number of sensors varies from 150 to 350. From this figure, we can see that in most of the cases, SEDO achieves a lower delay than Liu s algorithm and A1 algorithm. This is because Liu s algorithm adds extra nodes to increase the sensing coverage, which increases the length of the path and the routing latency, and A1 algorithm doesn t consider reduce the number of hops in the set. From the results, it can be observed that on average, the average packet delay under SEDO is 0.2 s less than that under Liu s algorithm and 1 s less than that under A1 algorithm. Figure 13 shows the network lifetime under different transmission probability, when the number of sensors is 350. The transmission probability is the probability that a node needs to send a message to the sink in a time slot. From this figure we can see that the network lifetime under our scheme is 11 % longer than that under Liu s algorithm and 22 % longer than that under A1 algorithm averagely. This is because both Liu s algorithm and A1 algorithm need to select more nodes than ours, and thus consumes more energy. 6 Related work Recently, extensive research has been conducted to use duty-cycle scheduling to improve energy efficiency in wireless sensor networks. LEACH [19] and HEED [20] are proposed to cluster sensor nodes to prolong the network life time. For field surveillance, Gui and Mohapatra [5] develop two efficient sleep-awake schemes to minimize the power consumption. A TDMA sleep scheduling problem is studied in [1]. Lin et al. [21] present an adaptive multisensor scheduling scheme for collaborative target tracking in WSNs. A randomized scheduling algorithm is proposed
12 1454 Wireless Netw (2015) 21: in [22] via both analysis and simulations for improving network coverage and detection probability. Liu et al. [7] use random scheduling to achieve required sensing coverage and turn on extra sensor nodes to improve network connectivity. A1 algorithm proposed in [8] forms energy efficient connected dominating sets based on network topology to extend network lifetime. Ghidini and Das [2] propose a randomized duty cycling scheme based on Markov chains to make a tradeoff between connection delay and given energy efficiency. Tang and Yang [23] propose two sensing scheduling schemes to maximize the overall monitoring quality under resource constraints in. A robust multi-pipeline scheduling algorithm is proposed in [6] to achieve ultra low duty-cycle in sensor networks. Bagaa et al. [24] study data aggregation scheduling problem in wireless sensor networks to minimize data delivery latency, however, they do not optimize network lifetime. Previous works only concentrate on scheduling, while they either do not consider network connectivity or fail to balance energy consumption in routing, which are the two places our contribution lays in. 7 Conclusion In this paper, we study a joint scheduling and routing problem in wireless sensor networks. A novel disjoint Set Division algorithm, SEDO, is proposed, which conducts set division, set scheduling and set routing. In set division, sensors are divided into different sets with guaranteed connectivity based on the calculated hop information. In set scheduling, we schedule the sets to be on and off to save energy while at the same time maintain the connectivity. In set routing, a route selection strategy is proposed to balance energy consumption in packet forwarding. The simulation results show that SEDO outperforms existing scheme with lower packet delivery latency, high coverage and longer network lifetime. References 1. Ma, J., Lou, W., Wu, Y., Li, X.-Y., & Chen, G. (2009). Energy efficient tdma sleep scheduling in wireless sensor networks. In INFOCOM. 2. Ghidini, G., & Das, S. K. (2011). An energy-efficient markov chain-based randomized duty cycling scheme for wireless sensor networks. In 31st International conference on distributed computing systems. 3. Tian, J., Zhang, W., Wang, G., & Gao, X. (2014). 2D k-barrier duty-cycle scheduling for intruder detection in wireless sensor networks. Computer Communications, 43, Tian, J., Wang, G., Yan, T., & Zhang, W. (2014). Detect smart intruders in sensor networks by creating network dynamics. Computer Networks, 62, Gui, C., & Mohapatra, P. (2004). Power conservation and quality of surveillance in target tracking sensor networks. In MobiCom. 6. Cao, Y., Guo, S., & He, T. (2012). Robust multi-pipeline scheduling in low-duty-cycle wireless sensor networks. In INFOCOM. 7. Liu, C., Wu, K., Xiao, Y., & Sun, B. (2006). Random coverage with guaranteed connectivity: Joint scheduling for wireless sensor networks. IEEE Transactions on Parallel and Distributed Systems, 17(6), Rizvi, S., Qureshi, H. K., Khayam, S. A., Rakocevic, V., & Rajarajan, M. (2012). A1: An energy efficient topology control algorithm for connected area coverage in wireless sensor networks. Journal of Network and Computer Applications, 35(2), Wang, G., Cao, G., Porta, T. L., & Berman, P. (2007). Bidding protocols for deploying mobile sensors. IEEE Transaction on Mobile Computing, 6(5), Liu, B., Dousse, O., Wang, J., & Saipulla, A. (2008). Strong barrier coverage of wireless sensor networks. In MobiHoc. 11. Chen, A., Li, Z., Lai, T., & Liu, C. (2011). One-way barrier coverage with wireless sensors. In INFOCOM. 12. Chen, T., Yang, Z., Liu, Y., Guo, D., & Luo, X. (2011). A localizability-aided approach: Localization in non-localizable sensor and ad-hoc networks. In INFOCOM. 13. Liu, W., Wang, D., Jiang, H., Liu, W., & Wang, C. (2012). Approximate convex decomposition based localization in wireless sensor networks. In INFOCOM. 14. Huang, M., Chen, S., & Wang, Y. (2010). Minimum cost localization problem in wireless sensor networks. In SECON. 15. Yang, Z., Cai, L., Liu, Y., & Pan, J. (2012). Environment-aware clock skew estimation and synchronization for wireless sensor networks. In INFOCOM. 16. Zhong, Z., Chen, P., & He, T. (2011). On-demand time synchronization with predictable accuracy. In INFOCOM. 17. Chen, Y., Wang, Q., Chang, M., & Terzis, A. (2011). Ultra-low power time synchronization using passive radio receivers. In IPSN. 18. Slijepcevic, S., & Potkonjak, M. (2001). Power efficient organization of wireless sensor networks. In ICC. 19. Heinzelman, W., Chandrakasan, A., & Balakrishnan, H. (2002). An application-specific protocol architecture for wireless microsensor networks. IEEE Transaction on Wireless Communications, 1(4), Younis, O., & Fahmy, S. (2004). Distributed clustering in ad hoc sensor networks: A hybrid, energy-efficient approach. IEEE Transaction on Mobile Computing, 3(4), Lin, J., Xiao, W., Lewis, F., & Xie, L. (2009). Energy-efficient distributed adaptive multisensor scheduling for target tracking in wireless sensor networks. IEEE Transactions on Instrumentation and Measurement, 58(6), Xiao, Y., Chen, H., Wu, K., Sun, B., Zhang, Y., Sun, X., & Liu, C. (2009). Coverage and detection of a randomized scheduling algorithm in wireless sensor networks. IEEE Transactions on Computers, 59(4), Tang, S., & Yang, L. (2012). Morello: A quality-of-monitoring oriented sensing scheduling protocol in sensor networks. In The 31st annual IEEE international conference on computer communications: Mini-conference. 24. Bagaa, M., Derhab, A., Lasla, N., Ouadjaout, A., & Badache, N. (2012). Semi-structured and unstructured data aggregation scheduling in wireless sensor networks. In The 31st annual IEEE international conference on computer communications: Miniconference.
13 Wireless Netw (2015) 21: Jie Tian received the B.S. degree in Computer Science from Tianjin University, Tianjin, China, in 2005, and got his M.S. degree in Computer Science at Nankai University, Tianjin, China, in He is currently a Ph.D. candidate in Department of Computer Science at New Jersey Institute of Technology. His research includes wireless networks, ad hoc/sensor network and mobile computing. Xiaoyuan Liang is currently a Ph.D. student at Computer Science Department in New Jersey Institute of Technology since He received his B.S. degree from Harbin Institute of Technology in China. Tan Yan joined NEC Laboratories America in 2014, after completing his Ph.D. under the supervision of Dr. Guiling Wang, in Department of Computer Science, New Jersey Institute of Technology. He received M.S. from Department of Electrical & Computer Engineering, NJIT in 2009, and B.E. in 2007 from School of Information Science and Technology, Southeast University, Nanjing, China. His research includes Network Analytics, Time Series Mining, Mobile Ad Hoc Data Management and Dissemination, and Graph Theory. Mahesh Kumar Somashekar is currently working at Pivotal Software Inc. as a Software Engineer, and has been in the IT industry from He has mainly focused his career on networking, which involves Distributed Systems, Wireless Sensor Networks and Mobile Device Management. He has earned his Master s degree in Computer Science from New Jersey Institute of Technology in During the course of his professional and academic work, he has gained expertise on various technologies Hadoop & its eco-system, Java, C/C??, WSN, Spring, Hibernate, Solr, Web Services, PHP and Relational Database. He is a Sun Certified Java Programmer for Java 1.5 as well. Guiling Wang received the B.S. degree in software from Nankai University, Tianjin, China, and the Ph.D. degree in computer science and engineering with a minor in statistics from The Pennsylvania State University, State College, PA, USA, in She joined the New Jersey Institute of Technology, Newark, NJ, USA, in the fall of 2006 and was promoted to Associate Professor with tenure in June Cesar Bandera is assistant professor of entrepreneurship at the New Jersey Institute of Technology School of Management, and founding partner of wireless healthcare outreach firm Cell Podium. He has deployed mobile 3G multimedia campaigns for CDC, NIH, and EPA in the US and overseas. His research interests include point-of-care sensor networks, and his work in multimedia processing has yielded four patents. Bandera received his Ph.D. in Electrical and Computer Engineering from the University at Buffalo, NY.
Nodes Energy Conserving Algorithms to prevent Partitioning in Wireless Sensor Networks
IJCSNS International Journal of Computer Science and Network Security, VOL.17 No.9, September 2017 139 Nodes Energy Conserving Algorithms to prevent Partitioning in Wireless Sensor Networks MINA MAHDAVI
More informationIntra and Inter Cluster Synchronization Scheme for Cluster Based Sensor Network
Intra and Inter Cluster Synchronization Scheme for Cluster Based Sensor Network V. Shunmuga Sundari 1, N. Mymoon Zuviria 2 1 Student, 2 Asisstant Professor, Computer Science and Engineering, National College
More informationOptimization on TEEN routing protocol in cognitive wireless sensor network
Ge et al. EURASIP Journal on Wireless Communications and Networking (2018) 2018:27 DOI 10.1186/s13638-018-1039-z RESEARCH Optimization on TEEN routing protocol in cognitive wireless sensor network Yanhong
More informationConnected Point Coverage in Wireless Sensor Networks using Robust Spanning Trees
Connected Point Coverage in Wireless Sensor Networks using Robust Spanning Trees Pouya Ostovari Department of Computer and Information Siences Temple University Philadelphia, Pennsylvania, USA Email: ostovari@temple.edu
More informationMobile Sink to Track Multiple Targets in Wireless Visual Sensor Networks
Mobile Sink to Track Multiple Targets in Wireless Visual Sensor Networks William Shaw 1, Yifeng He 1, and Ivan Lee 1,2 1 Department of Electrical and Computer Engineering, Ryerson University, Toronto,
More informationHierarchical Energy Efficient Clustering Algorithm for WSN
Middle-East Journal of Scientific Research 23 (Sensing, Signal Processing and Security): 108-117, 2015 ISSN 1990-9233 IDOSI Publications, 2015 DOI: 10.5829/idosi.mejsr.2015.23.ssps.30 Hierarchical Energy
More informationNovel Cluster Based Routing Protocol in Wireless Sensor Networks
ISSN (Online): 1694-0784 ISSN (Print): 1694-0814 32 Novel Cluster Based Routing Protocol in Wireless Sensor Networks Bager Zarei 1, Mohammad Zeynali 2 and Vahid Majid Nezhad 3 1 Department of Computer
More informationAn Energy Efficient Data Dissemination Algorithm for Wireless Sensor Networks
, pp.135-140 http://dx.doi.org/10.14257/astl.2014.48.22 An Energy Efficient Data Dissemination Algorithm for Wireless Sensor Networks Jin Wang 1, Bo Tang 1, Zhongqi Zhang 1, Jian Shen 1, Jeong-Uk Kim 2
More informationViTAMin: A Virtual Backbone Tree Algorithm for Minimal Energy Consumption in Wireless Sensor Network Routing
ViTAMin: A Virtual Backbone Tree Algorithm for Minimal Energy Consumption in Wireless Sensor Network Routing Jaekwang Kim Department of Electrical and Computer Engineering, Sungkyunkwan University, Suwon,
More informationThe Impact of Clustering on the Average Path Length in Wireless Sensor Networks
The Impact of Clustering on the Average Path Length in Wireless Sensor Networks Azrina Abd Aziz Y. Ahmet Şekercioğlu Department of Electrical and Computer Systems Engineering, Monash University, Australia
More informationEnd-To-End Delay Optimization in Wireless Sensor Network (WSN)
Shweta K. Kanhere 1, Mahesh Goudar 2, Vijay M. Wadhai 3 1,2 Dept. of Electronics Engineering Maharashtra Academy of Engineering, Alandi (D), Pune, India 3 MITCOE Pune, India E-mail: shweta.kanhere@gmail.com,
More informationCROSS LAYER PROTOCOL (APTEEN) USING WSN FOR REAL TIME APPLICATION
CROSS LAYER PROTOCOL (APTEEN) USING WSN FOR REAL TIME APPLICATION V. A. Dahifale 1, N. Y. Siddiqui 2 PG Student, College of Engineering Kopargaon, Maharashtra, India 1 Assistant Professor, College of Engineering
More informationEnergy Optimized Routing Algorithm in Multi-sink Wireless Sensor Networks
Appl. Math. Inf. Sci. 8, No. 1L, 349-354 (2014) 349 Applied Mathematics & Information Sciences An International Journal http://dx.doi.org/10.12785/amis/081l44 Energy Optimized Routing Algorithm in Multi-sink
More informationSMITE: A Stochastic Compressive Data Collection. Sensor Networks
SMITE: A Stochastic Compressive Data Collection Protocol for Mobile Wireless Sensor Networks Longjiang Guo, Raheem Beyah, and Yingshu Li Department of Computer Science, Georgia State University, USA Data
More informationWSN Routing Protocols
WSN Routing Protocols 1 Routing Challenges and Design Issues in WSNs 2 Overview The design of routing protocols in WSNs is influenced by many challenging factors. These factors must be overcome before
More informationAN ENERGY EFFICIENT AND RELIABLE TWO TIER ROUTING PROTOCOL FOR TOPOLOGY CONTROL IN WIRELESS SENSOR NETWORKS
AN ENERGY EFFICIENT AND RELIABLE TWO TIER ROUTING PROTOCOL FOR TOPOLOGY CONTROL IN WIRELESS SENSOR NETWORKS Shivakumar A B 1, Rashmi K R 2, Ananda Babu J. 3 1,2 M.Tech (CSE) Scholar, 3 CSE, Assistant Professor,
More informationGeographical Routing Algorithms In Asynchronous Wireless Sensor Network
Geographical Routing Algorithms In Asynchronous Wireless Sensor Network Vaishali.S.K, N.G.Palan Electronics and telecommunication, Cummins College of engineering for women Karvenagar, Pune, India Abstract-
More informationAn Energy Efficient Coverage Method for Clustered Wireless Sensor Networks
An Energy Efficient Coverage Method for Clustered Wireless Sensor Networks J. Shanbehzadeh, M. Mehrani, A. Sarrafzadeh, and Z. Razaghi Abstract an important issue in WSN is the regional covering. A coverage
More informationA Low-Overhead Hybrid Routing Algorithm for ZigBee Networks. Zhi Ren, Lihua Tian, Jianling Cao, Jibi Li, Zilong Zhang
A Low-Overhead Hybrid Routing Algorithm for ZigBee Networks Zhi Ren, Lihua Tian, Jianling Cao, Jibi Li, Zilong Zhang Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts
More informationAd Hoc Networks. WA-MAC: A weather adaptive MAC protocol in survivability-heterogeneous wireless sensor networks
Ad Hoc Networks 67 (2017) 40 52 Contents lists available at ScienceDirect Ad Hoc Networks journal homepage: www.elsevier.com/locate/adhoc WA-MAC: A weather adaptive MAC protocol in survivability-heterogeneous
More informationAn Energy Efficiency Routing Algorithm of Wireless Sensor Network Based on Round Model. Zhang Ying-Hui
Joint International Mechanical, Electronic and Information Technology Conference (JIMET 2015) An Energy Efficiency Routing Algorithm of Wireless Sensor Network Based on Round Model Zhang Ying-Hui Software
More informationEnergy Aware Node Placement Algorithm for Wireless Sensor Network
Advance in Electronic and Electric Engineering. ISSN 2231-1297, Volume 4, Number 6 (2014), pp. 541-548 Research India Publications http://www.ripublication.com/aeee.htm Energy Aware Node Placement Algorithm
More informationEnhancement of Hierarchy Cluster-Tree Routing for Wireless Sensor Network
Enhancement of Hierarchy Cluster-Tree Routing for Wireless Sensor Network Xuxing Ding Tel: 86-553-388-3560 E-mail: dxx200@163.com Fangfang Xie Tel: 86-553-388-3560 E-mail: fangtinglei@yahoo.com.cn Qing
More informationEnergy Efficient Clustering Protocol for Wireless Sensor Network
Energy Efficient Clustering Protocol for Wireless Sensor Network Shraddha Agrawal #1, Rajeev Pandey #2, Mahesh Motwani #3 # Department of Computer Science and Engineering UIT RGPV, Bhopal, India 1 45shraddha@gmail.com
More informationEvaluation of Cartesian-based Routing Metrics for Wireless Sensor Networks
Evaluation of Cartesian-based Routing Metrics for Wireless Sensor Networks Ayad Salhieh Department of Electrical and Computer Engineering Wayne State University Detroit, MI 48202 ai4874@wayne.edu Loren
More informationAn Efficient Data-Centric Routing Approach for Wireless Sensor Networks using Edrina
An Efficient Data-Centric Routing Approach for Wireless Sensor Networks using Edrina Rajasekaran 1, Rashmi 2 1 Asst. Professor, Department of Electronics and Communication, St. Joseph College of Engineering,
More informationIMPROVING WIRELESS SENSOR NETWORK LIFESPAN THROUGH ENERGY EFFICIENT ALGORITHMS
IMPROVING WIRELESS SENSOR NETWORK LIFESPAN THROUGH ENERGY EFFICIENT ALGORITHMS 1 M.KARPAGAM, 2 DR.N.NAGARAJAN, 3 K.VIJAIPRIYA 1 Department of ECE, Assistant Professor, SKCET, Coimbatore, TamilNadu, India
More informationA Fault-recovery Routing Approach for Loop-based Clustering WSN
A Fault-recovery Routing Approach for Loop-based Clustering WSN Ming Xu 1, Shengdong Zhang 1, Jiannong Cao 2, Xiaoxing Guo 3 ( 1 School of Computer, National Univ. of Defense Technology, Changsha, China)
More informationAn Efficient Bandwidth Estimation Schemes used in Wireless Mesh Networks
An Efficient Bandwidth Estimation Schemes used in Wireless Mesh Networks First Author A.Sandeep Kumar Narasaraopeta Engineering College, Andhra Pradesh, India. Second Author Dr S.N.Tirumala Rao (Ph.d)
More informationMinimum Overlapping Layers and Its Variant for Prolonging Network Lifetime in PMRC-based Wireless Sensor Networks
Minimum Overlapping Layers and Its Variant for Prolonging Network Lifetime in PMRC-based Wireless Sensor Networks Qiaoqin Li 12, Mei Yang 1, Hongyan Wang 1, Yingtao Jiang 1, Jiazhi Zeng 2 1 Department
More informationMaximizing the Lifetime of Clustered Wireless Sensor Network VIA Cooperative Communication
Vol., Issue.3, May-June 0 pp--7 ISSN: - Maximizing the Lifetime of Clustered Wireless Sensor Network VIA Cooperative Communication J. Divakaran, S. ilango sambasivan Pg student, Sri Shakthi Institute of
More informationROUTING ALGORITHMS Part 2: Data centric and hierarchical protocols
ROUTING ALGORITHMS Part 2: Data centric and hierarchical protocols 1 Negative Reinforcement Time out Explicitly degrade the path by re-sending interest with lower data rate. Source Gradient New Data Path
More informationA PROPOSAL FOR IMPROVE THE LIFE- TIME OF WIRELESS SENSOR NETWORK
A PROPOSAL FOR IMPROVE THE LIFE- TIME OF WIRELESS SENSOR NETWORK ABSTRACT Tran Cong Hung1 and Nguyen Hong Quan2 1Post & Telecommunications Institute of Technology, Vietnam 2University of Science, Ho Chi
More informationMultichannel MAC for Energy Efficient Home Area Networks
1st International Workshop on GReen Optimized Wireless Networks (GROWN'13) Multichannel MAC for Energy Efficient Home Area Networks Kok Keong Chai, Shihab Jimaa, Yun Li, Yue Chen, and Siying Wang Abstract
More informationReliable and Energy Efficient Protocol for Wireless Sensor Network
Reliable and Energy Efficient Protocol for Wireless Sensor Network Hafiyya.R.M 1, Fathima Anwar 2 P.G. Student, Department of Computer Engineering, M.E.A Engineering College, Perinthalmanna, Kerala, India
More informationEnergy-Efficient Communication Protocol for Wireless Micro-sensor Networks
Energy-Efficient Communication Protocol for Wireless Micro-sensor Networks Paper by: Wendi Rabiner Heinzelman, Anantha Chandrakasan, and Hari Balakrishnan Outline Brief Introduction on Wireless Sensor
More informationIMPROVING THE DATA COLLECTION RATE IN WIRELESS SENSOR NETWORKS BY USING THE MOBILE RELAYS
IMPROVING THE DATA COLLECTION RATE IN WIRELESS SENSOR NETWORKS BY USING THE MOBILE RELAYS 1 K MADHURI, 2 J.KRISHNA, 3 C.SIVABALAJI II M.Tech CSE, AITS, Asst Professor CSE, AITS, Asst Professor CSE, NIST
More informationOn Distributed Algorithms for Maximizing the Network Lifetime in Wireless Sensor Networks
On Distributed Algorithms for Maximizing the Network Lifetime in Wireless Sensor Networks Akshaye Dhawan Georgia State University Atlanta, Ga 30303 akshaye@cs.gsu.edu Abstract A key challenge in Wireless
More informationROAL: A Randomly Ordered Activation and Layering Protocol for Ensuring K-Coverage in Wireless Sensor Networks
ROAL: A Randomly Ordered Activation and Layering Protocol for Ensuring K-Coverage in Wireless Sensor Networks Hogil Kim and Eun Jung Kim Department of Computer Science Texas A&M University College Station,
More informationOutline. Introduction. Outline. Introduction (Cont.) Introduction (Cont.)
An Energy-Efficient Distributed Algorithm for Minimum-Latency Aggregation Scheduling in Wireless Sensor Networks Yingshu Li, Longjiang Guo, and Sushil K. Prasad Department of Computer Science, Georgia
More informationPower Aware Cluster Based Routing (PACBR) Protocol for Wireless Sensor Network
Power Aware Cluster Based Routing (PACBR) Protocol for Wireless Sensor Network Ayan Kumar Das 1, Rituparna Chaki 2, and Atreyee Biswas 3 1 Department of Information Technology, Calcutta Institute of Engineering
More informationEEEM: An Energy-Efficient Emulsion Mechanism for Wireless Sensor Networks
EEEM: An Energy-Efficient Emulsion Mechanism for Wireless Sensor Networks M.Sudha 1, J.Sundararajan 2, M.Maheswari 3 Assistant Professor, ECE, Paavai Engineering College, Namakkal, Tamilnadu, India 1 Principal,
More informationEnergy Conservation through Sleep Scheduling in Wireless Sensor Network 1. Sneha M. Patil, Archana B. Kanwade 2
Energy Conservation through Sleep Scheduling in Wireless Sensor Network 1. Sneha M. Patil, Archana B. Kanwade 2 1 Student Department of Electronics & Telecommunication, SITS, Savitribai Phule Pune University,
More informationPrianka.P 1, Thenral 2
An Efficient Routing Protocol design and Optimizing Sensor Coverage Area in Wireless Sensor Networks Prianka.P 1, Thenral 2 Department of Electronics Communication and Engineering, Ganadipathy Tulsi s
More informationIJREAT International Journal of Research in Engineering & Advanced Technology, Volume 1, Issue 2, April-May, 2013 ISSN:
Fast Data Collection with Reduced Interference and Increased Life Time in Wireless Sensor Networks Jayachandran.J 1 and Ramalakshmi.R 2 1 M.Tech Network Engineering, Kalasalingam University, Krishnan koil.
More informationAN EFFICIENT MAC PROTOCOL BASED ON HYBRID SUPERFRAME FOR WIRELESS SENSOR NETWORKS
AN EFFICIENT MAC PROTOCOL BASED ON HYBRID SUPERFRAME FOR WIRELESS SENSOR NETWORKS Ge Ma and Dongyu Qiu Department of Electrical and Computer Engineering Concordia University, Montreal, QC, Canada tina0702@gmail.com,
More informationLocation Based Energy-Efficient Reliable Routing Protocol for Wireless Sensor Networks
Location Based Energy-Efficient Reliable Routing Protocol for Wireless Sensor Networks RAFE ALASEM 1, AHMED REDA 2 AND MAHMUD MANSOUR 3 (1) Computer Science Department Imam Muhammad ibn Saud Islamic University
More informationA Simple Sink Mobility Support Algorithm for Routing Protocols in Wireless Sensor Networks
A Simple Mobility Support Algorithm for Routing Protocols in Wireless Sensor Networks Chun-Su Park, You-Sun Kim, Kwang-Wook Lee, Seung-Kyun Kim, and Sung-Jea Ko Department of Electronics Engineering, Korea
More informationENERGY EFFICIENT MULTIPATH ROUTING FOR MOBILE AD HOC NETWORKS
ENERGY EFFICIENT MULTIPATH ROUTING FOR MOBILE AD HOC NETWORKS May Cho Aye and Aye Moe Aung Faculty of Information and Communication Technology, University of Technology (Yatanarpon Cyber City), Pyin Oo
More informationPerformance of a Novel Energy-Efficient and Energy Awareness Scheme for Long-Lifetime Wireless Sensor Networks
Sensors and Materials, Vol. 27, No. 8 (2015) 697 708 MYU Tokyo S & M 1106 Performance of a Novel Energy-Efficient and Energy Awareness Scheme for Long-Lifetime Wireless Sensor Networks Tan-Hsu Tan 1, Neng-Chung
More informationQoS Challenges and QoS-Aware MAC Protocols in Wireless Sensor Networks
QoS Challenges and QoS-Aware MAC Protocols in Wireless Sensor Networks S. Shiney Lillia PG Student, Department of Computer Science and Engineering, National Institute of Technology Puducherry, Puducherry,
More informationAn Energy-Efficient Data-Dissemination Protocol in Wireless Sensor Networks
An Energy-Efficient Data-Dissemination Protocol in Wireless Sensor Networks Zehua Zhou Xiaojing Xiang State University of New York at Buffalo Buffalo, NY, USA {zzhou5, xxiang}@cse.buffalo.edu Xin Wang
More information(EBHCR) Energy Balancing and Hierarchical Clustering Based Routing algorithm for Wireless Sensor Networks
Australian Journal of Basic and Applied Sciences, 5(9): 1376-1380, 2011 ISSN 1991-8178 (EBHCR) Energy Balancing and Hierarchical Clustering Based Routing algorithm for Wireless Sensor Networks 1 Roghaiyeh
More informationTarget Tracking in Wireless Sensor Network
International Journal of Information & Computation Technology. ISSN 0974-2239 Volume 4, Number 6 (2014), pp. 643-648 International Research Publications House http://www. irphouse.com Target Tracking in
More informationIntegrated Routing and Query Processing in Wireless Sensor Networks
Integrated Routing and Query Processing in Wireless Sensor Networks T.Krishnakumar Lecturer, Nandha Engineering College, Erode krishnakumarbtech@gmail.com ABSTRACT Wireless Sensor Networks are considered
More informationDistributed Data Aggregation Scheduling in Wireless Sensor Networks
Distributed Data Aggregation Scheduling in Wireless Sensor Networks Bo Yu, Jianzhong Li, School of Computer Science and Technology, Harbin Institute of Technology, China Email: bo yu@hit.edu.cn, lijzh@hit.edu.cn
More informationAnalysis of Cluster-Based Energy-Dynamic Routing Protocols in WSN
Analysis of Cluster-Based Energy-Dynamic Routing Protocols in WSN Mr. V. Narsing Rao 1, Dr.K.Bhargavi 2 1,2 Asst. Professor in CSE Dept., Sphoorthy Engineering College, Hyderabad Abstract- Wireless Sensor
More informationAn Energy-Efficient Hierarchical Routing for Wireless Sensor Networks
Volume 2 Issue 9, 213, ISSN-2319-756 (Online) An Energy-Efficient Hierarchical Routing for Wireless Sensor Networks Nishi Sharma Rajasthan Technical University Kota, India Abstract: The popularity of Wireless
More informationEfficient Clustering Routing Algorithm Based on Opportunistic Routing
Int. J. Communications, Network and System Sciences, 2016, 9, 198-208 Published Online May 2016 in SciRes. http://www.scirp.org/journal/ijcns http://dx.doi.org/10.4236/ijcns.2016.95019 Efficient Clustering
More informationAn Enhanced General Self-Organized Tree-Based Energy- Balance Routing Protocol (EGSTEB) for Wireless Sensor Network
www.ijecs.in International Journal Of Engineering And Computer Science ISSN: 239-7242 Volume 4 Issue 8 Aug 205, Page No. 3640-3643 An Enhanced General Self-Organized Tree-Based Energy- Balance Routing
More informationAn Energy-efficient Distributed Self-organized Clustering Based Splitting and Merging in Wireless Sensor Networks
RESEARCH ARTICLE OPEN ACCESS An Energy-efficient Distributed Self-organized Clustering Based Splitting and Merging in Wireless Sensor Networks Mrs.J.Monisha, PG scholar, Mrs.M.MuthuSelvi, Assistant Professor,
More informationTime Synchronization in Wireless Sensor Networks: CCTS
Time Synchronization in Wireless Sensor Networks: CCTS 1 Nerin Thomas, 2 Smita C Thomas 1, 2 M.G University, Mount Zion College of Engineering, Pathanamthitta, India Abstract: A time synchronization algorithm
More informationMobile Agent Driven Time Synchronized Energy Efficient WSN
Mobile Agent Driven Time Synchronized Energy Efficient WSN Sharanu 1, Padmapriya Patil 2 1 M.Tech, Department of Electronics and Communication Engineering, Poojya Doddappa Appa College of Engineering,
More informationDelay Performance of Multi-hop Wireless Sensor Networks With Mobile Sinks
Delay Performance of Multi-hop Wireless Sensor Networks With Mobile Sinks Aswathy M.V & Sreekantha Kumar V.P CSE Dept, Anna University, KCG College of Technology, Karappakkam,Chennai E-mail : aswathy.mv1@gmail.com,
More informationModified Low Energy Adaptive Clustering Hierarchy for Heterogeneous Wireless Sensor Network
Modified Low Energy Adaptive Clustering Hierarchy for Heterogeneous Wireless Sensor Network C.Divya1, N.Krishnan2, A.Petchiammal3 Center for Information Technology and Engineering Manonmaniam Sundaranar
More informationAn Adaptive Self-Organization Protocol for Wireless Sensor Networks
An Adaptive Self-Organization Protocol for Wireless Sensor Networks Kil-Woong Jang 1 and Byung-Soon Kim 2 1 Dept. of Mathematical and Information Science, Korea Maritime University 1 YeongDo-Gu Dongsam-Dong,
More informationA Reduce Identical Composite Event Transmission Algorithm for Wireless Sensor Networks
Appl. Math. Inf. Sci. 6 No. 2S pp. 713S-719S (2012) Applied Mathematics & Information Sciences An International Journal @ 2012 NSP Natural Sciences Publishing Cor. A Reduce Identical Composite Event Transmission
More informationInternational Journal of Scientific & Engineering Research, Volume 4, Issue 7, July ISSN
International Journal of Scientific & Engineering Research, Volume 4, Issue 7, July-2013 1472 DYNAMIC AND EFFICIENT TREE-BASED DATA COLLECTION IN WIRELESS SENSOR NETWORKS S.Thivyan Ravi Kumar, B.Maheswari,
More informationCONCLUSIONS AND SCOPE FOR FUTURE WORK
Introduction CONCLUSIONS AND SCOPE FOR FUTURE WORK 7.1 Conclusions... 154 7.2 Scope for Future Work... 157 7 1 Chapter 7 150 Department of Computer Science Conclusion and scope for future work In this
More informationAN EFFICIENT MAC PROTOCOL FOR SUPPORTING QOS IN WIRELESS SENSOR NETWORKS
AN EFFICIENT MAC PROTOCOL FOR SUPPORTING QOS IN WIRELESS SENSOR NETWORKS YINGHUI QIU School of Electrical and Electronic Engineering, North China Electric Power University, Beijing, 102206, China ABSTRACT
More information6367(Print), ISSN (Online) Volume 4, Issue 2, March April (2013), IAEME & TECHNOLOGY (IJCET)
INTERNATIONAL International Journal of Computer JOURNAL Engineering OF COMPUTER and Technology ENGINEERING (IJCET), ISSN 0976- & TECHNOLOGY (IJCET) ISSN 0976 6367(Print) ISSN 0976 6375(Online) Volume 4,
More informationCACHING IN WIRELESS SENSOR NETWORKS BASED ON GRIDS
International Journal of Wireless Communications and Networking 3(1), 2011, pp. 7-13 CACHING IN WIRELESS SENSOR NETWORKS BASED ON GRIDS Sudhanshu Pant 1, Naveen Chauhan 2 and Brij Bihari Dubey 3 Department
More informationEvent Driven Routing Protocols For Wireless Sensor Networks
Event Driven Routing Protocols For Wireless Sensor Networks Sherif Moussa 1, Ghada Abdel Halim 2, Salah Abdel-Mageid 2 1 Faculty of Engineering, Canadian University Dubai, Dubai, UAE. 2 Faculty of Engineering,
More informationMaximum Coverage Range based Sensor Node Selection Approach to Optimize in WSN
Maximum Coverage Range based Sensor Node Selection Approach to Optimize in WSN Rinku Sharma 1, Dr. Rakesh Joon 2 1 Post Graduate Scholar, 2 Assistant Professor, Department of Electronics and Communication
More informationOPTIMIZED LEHE: A MODIFIED DATA GATHERING MODEL FOR WIRELESS SENSOR NETWORK
ISSN: 0976-3104 SPECIAL ISSUE: Emerging Technologies in Networking and Security (ETNS) Arasu et al. ARTICLE OPEN ACCESS OPTIMIZED LEHE: A MODIFIED DATA GATHERING MODEL FOR WIRELESS SENSOR NETWORK S. Senthil
More informationA METHOD FOR DETECTING FALSE POSITIVE AND FALSE NEGATIVE ATTACKS USING SIMULATION MODELS IN STATISTICAL EN- ROUTE FILTERING BASED WSNS
A METHOD FOR DETECTING FALSE POSITIVE AND FALSE NEGATIVE ATTACKS USING SIMULATION MODELS IN STATISTICAL EN- ROUTE FILTERING BASED WSNS Su Man Nam 1 and Tae Ho Cho 2 1 College of Information and Communication
More informationEfficient Cluster Based Data Collection Using Mobile Data Collector for Wireless Sensor Network
ISSN (e): 2250 3005 Volume, 06 Issue, 06 June 2016 International Journal of Computational Engineering Research (IJCER) Efficient Cluster Based Data Collection Using Mobile Data Collector for Wireless Sensor
More informationEnergy Enhanced Base Station Controlled Dynamic Clustering Protocol for Wireless Sensor Networks
Journal of Advances in Computer Networks, Vol. 1, No. 1, March 213 Energy Enhanced Base Station Controlled Dynamic Clustering Protocol for Wireless Sensor Networks K. Padmanabhan and P. Kamalakkannan consume
More informationAll Rights Reserved 2017 IJARCET
END-TO-END DELAY WITH MARKOVIAN QUEUING BASED OPTIMUM ROUTE ALLOCATION FOR MANETs S. Sudha, Research Scholar Mrs. V.S.LAVANYA M.Sc(IT)., M.C.A., M.Phil., Assistant Professor, Department of Computer Science,
More informationMultiHop Routing for Delay Minimization in WSN
MultiHop Routing for Delay Minimization in WSN Sandeep Chaurasia, Saima Khan, Sudesh Gupta Abstract Wireless sensor network, consists of sensor nodes in capacity of hundred or thousand, which deployed
More informationInternational Journal of Research in Advent Technology Available Online at:
HETEROGENEOUS CLUSTER BASED ROUTING PROTOCOLS IN WIRELESS SENSOR NETWORK- A SURVEY Padmavati 1, T.C. Aseri 2 1 2 CSE Dept 1 2 PEC University of Technology 1 padmavati@pec.ac.in, trilokchand@pec.ac.in ABSTARCT:
More informationA Scheme of Multi-path Adaptive Load Balancing in MANETs
4th International Conference on Machinery, Materials and Computing Technology (ICMMCT 2016) A Scheme of Multi-path Adaptive Load Balancing in MANETs Yang Tao1,a, Guochi Lin2,b * 1,2 School of Communication
More informationDelay Analysis of ML-MAC Algorithm For Wireless Sensor Networks
Delay Analysis of ML-MAC Algorithm For Wireless Sensor Networks Madhusmita Nandi School of Electronics Engineering, KIIT University Bhubaneswar-751024, Odisha, India ABSTRACT The present work is to evaluate
More informationA Clustering approach for reducing the Energy usage of Nodes in Wireless Sensor Networks using Central Control Clustering Algorithm
A Clustering approach for reducing the Energy usage of Nodes in Wireless Sensor Networks using Central Control Clustering Algorithm Virendra P. Yadav 1, Prof. Kemal U. Koche 2 1 Research Scholar, Department
More informationEnergy Efficient Routing of Wireless Sensor Networks Using Virtual Backbone and life time Maximization of Nodes
Energy Efficient Routing of Wireless Sensor Networks Using Virtual Backbone and life time Maximization of Nodes Umesh B.N 1, Dr G Vasanth 2 and Dr Siddaraju 3 1 Research Scholar, 2 Professor & Head, Dept
More informationCOMPARISON OF ENERGY EFFICIENT DATA TRANSMISSION APPROACHES FOR FLAT WIRELESS SENSOR NETWORKS
COMPARISON OF ENERGY EFFICIENT DATA TRANSMISSION APPROACHES FOR FLAT WIRELESS SENSOR NETWORKS Saraswati Mishra 1 and Prabhjot Kaur 2 Department of Electrical, Electronics and Communication Engineering,
More informationAdapting Distance Based Clustering Concept to a Heterogeneous Network
International Journal of Computer Theory and Engineering, Vol. 7, No. 3, June 215 Adapting Distance Based Clustering Concept to a Heterogeneous Network N. Laloo, M. Z. A. A. Aungnoo, and M. S. Sunhaloo
More informationData gathering using mobile agents for reducing traffic in dense mobile wireless sensor networks
Mobile Information Systems 9 (23) 295 34 295 DOI.3233/MIS-364 IOS Press Data gathering using mobile agents for reducing traffic in dense mobile wireless sensor networks Keisuke Goto, Yuya Sasaki, Takahiro
More informationTime Slot Assignment Algorithms for Reducing Upstream Latency in IEEE j Networks
Time Slot Assignment Algorithms for Reducing Upstream Latency in IEEE 802.16j Networks Shimpei Tanaka Graduate School of Information Science and Technology Osaka University, Japan sinpei-t@ist.osaka-u.ac.jp
More informationResearch on Heterogeneous Communication Network for Power Distribution Automation
3rd International Conference on Material, Mechanical and Manufacturing Engineering (IC3ME 2015) Research on Heterogeneous Communication Network for Power Distribution Automation Qiang YU 1,a*, Hui HUANG
More informationAmeliorate Threshold Distributed Energy Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks
Vol. 5, No. 5, 214 Ameliorate Threshold Distributed Energy Efficient Clustering Algorithm for Heterogeneous Wireless Sensor Networks MOSTAFA BAGHOURI SAAD CHAKKOR ABDERRAHMANE HAJRAOUI Abstract Ameliorating
More informationIMPACT OF LEADER SELECTION STRATEGIES ON THE PEGASIS DATA GATHERING PROTOCOL FOR WIRELESS SENSOR NETWORKS
IMPACT OF LEADER SELECTION STRATEGIES ON THE PEGASIS DATA GATHERING PROTOCOL FOR WIRELESS SENSOR NETWORKS Indu Shukla, Natarajan Meghanathan Jackson State University, Jackson MS, USA indu.shukla@jsums.edu,
More informationEnergy Efficient Tracking of Land-Based Targets Using Wireless Sensor Networks
Energy Efficient Tracking of Land-Based Targets Using Wireless Sensor Networks Ali Berrached Le Phan University of Houston-Downtown One Main Street S705, Houston, Texas 77002 (713)221-8639 Berracheda@uhd.edu
More informationAn Improved Chain-based Hierarchical Routing Protocol for Wireless Sensor Networks
An Improved Chain-based Hierarchical Routing Protocol for Wireless Sensor Networks Samah Alnajdi, Fuad Bajaber Department of Information Technology Faculty of Computing and Information Technology King
More informationMitigating Hot Spot Problems in Wireless Sensor Networks Using Tier-Based Quantification Algorithm
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 16, No 1 Sofia 2016 Print ISSN: 1311-9702; Online ISSN: 1314-4081 DOI: 10.1515/cait-2016-0005 Mitigating Hot Spot Problems
More informationSensor Deployment, Self- Organization, And Localization. Model of Sensor Nodes. Model of Sensor Nodes. WiSe
Sensor Deployment, Self- Organization, And Localization Material taken from Sensor Network Operations by Shashi Phoa, Thomas La Porta and Christopher Griffin, John Wiley, 2007 5/20/2008 WiSeLab@WMU; www.cs.wmich.edu/wise
More informationNew Data Clustering Algorithm (NDCA)
Vol. 7, No. 5, 216 New Data Clustering Algorithm () Abdullah Abdulkarem Mohammed Al-Matari Information Technology Department, Faculty of Computers and Information, Cairo University, Cairo, Egypt Prof.
More informationKeywords Clustering, Sensor Nodes, Residual Energy, Wireless Sensor Networks, Zones
Zone-Based Clustering Protocol for Heterogeneous Wireless Sensor Networks S Taruna*, Sakshi Shringi** *(Department of Computer Science, Banasthali University, India) ** (Department of Information Technology,
More informationAdaptive Opportunistic Routing Protocol for Energy Harvesting Wireless Sensor Networks
Adaptive Opportunistic Routing Protocol for Energy Harvesting Wireless Sensor Networks Zhi Ang Eu and Hwee-Pink Tan Institute for Infocomm Research, Singapore Email: {zaeu,hptan}@ir.a-star.edu.sg Abstract
More informationSPATIAL CORRELATION BASED CLUSTERING ALGORITHM FOR RANDOM AND UNIFORM TOPOLOGY IN WSNs
SPATIAL CORRELATION BASED CLUSTERING ALGORITHM FOR RANDOM AND UNIFORM TOPOLOGY IN WSNs Bhavana H.T 1, Jayanthi K Murthy 2 1 M.Tech Scholar, Dept. of ECE, BMS College of Engineering, Bangalore 2 Associate
More information